ICAR Wiki - User contributions [en]

Section 24: Recording resilience in sheep and goats

2025-10-10T12:03:33Z

Aashish: /* Introduction and scope */

=== Introduction and scope ===

==== Introduction ====
The present guidelines aim at addressing resilience traits in small ruminants, as well as the description of the environment.

These recommendations are mainly based on a work achieved in the SMARTER H2020 project (n° 772787) whose objective was to promote harmonisation and international cooperation on breeding processes in small ruminant, especially those concerning the selection of efficiency and resilience. In this project, case studies of across country genetic evaluation, implemented as a proof of concept, have highlighted the importance of analysing traits that have been collected and/or calculated on a same way across country. Therefore, it appears fundamental that novel traits, such as resilience-related traits, which are not still widely routinely recorded on-farm for selection purposes, be recorded identically, or at least in the most similar way as possible. For that purpose, recommendations must be proposed, for countries or breeding organisations that would like to start to record efficiency or resilience traits, or that would like to set up an across-country genetic evaluation on these traits. The more similar the traits, the higher the genetic correlation across country (at same level of connection across country).

In addition, as resilience may be considered as basically related to the environmental challenges such as nutritional, disease or climatic challenges, the documentation of the environment is also described. Tackling the record of the environment is a novelty in selection of small ruminant.

The recommendations issued in a deliverable of the SMARTER project have been basically written by the partners of the project working on tasks dedicated to the different resilience-related traits and as well by the Sheep, Goat and Camelid ICAR Working Group. The Working Group was indeed involved, as partner for some of the members, as stakeholders for some other, and through ICAR who was a partner itself. Therefore, these guidelines are the fruit of a close cooperation between many academic and non-academic co-authors. Materials were also collected from results obtained in other projects (e.g. H2020 iSAGE, POCTEFA ARDI).

The recommendations, even though they target to suggest people measuring and calculating the traits the same way, are more informative than normative. The different ways to measure and calculate the traits are presented, without imposing one way, yet while suggesting some general features. Five sub-sections of recommendations were written: health and disease, survival of foetus and young, behaviour, lifetime resilience, record of the environment. All sub-sections are written with the same template and are consistent by themselves.

All the recommendations are based on the current state of the art. However, they are meant to evolve with new results and new research, and they are meant to be enhanced, consolidated, enriched. It is possible to add a new trait, a new proxy, a new sub-section. In brief, the recommendations must keep alive to stick to the evolving state of the art. This implies that the consortium that produced these recommendations, in some way, continue to contribute. ICAR, with its working group dedicated to sheep and goat, is the relevant organisation to collect and integrate the different novelties and contributions.

===== Scope =====
The SMARTER recommendations cover the following fields, shown in the figure 1.
[[File:SMARTER recommendations.jpg|center|thumb|600x600px|Figure 1. Fields covered by the SMARTER recommendations ]]
The resilience-related traits are: health and disease (with a focus on resistance to parasites, to footrot, and to mastitis), survival foetus and young, behaviour traits (with a focus on behavioural reactivity towards conspecifics or humans, maternal reactivity, behaviour at grazing), lifetime resilience.

The record of the environment covers the meteorological data and the diet. The record of the rations was studied in the on-farm protocols of SMARTER-WP1, especially in France. The record of the meteorological data benefited from works carried out in the H2020 iSAGE and POCTEFA ARDI projects, some of the SMARTER partners being committed in those projects.

The recommendations are conceived to be evolutive. Amendments can be brought in the next years, especially when the recommendations will turn into ICAR guidelines, either to strengthen results or include new insights, or to add new sub-sections or new traits. For example: (i) in the record of the environment, sensor data may be included; (ii) new disease whose resistance has a genetic component.

==== Definition of resilience ====
In these guidelines, we use the following definition of the resilience.

Resilience is defined as the ability of an animal/system to either maintain or revert quickly to high production and health status when exposed to a diversity of challenges, with a focus on nutritional and/or health challenges. Resilience is therefore the trajectory that captures the deviation from, and recovery to, the unchallenged state. Direct indicators of health and welfare will address gastro-intestinal parasitism, lameness (footrot) and mastitis, the most economically important endemic diseases of small ruminants. Indirect indicators of health and welfare of economic importance for breeders are lamb and foetal survival, functional longevity, maternal and lamb behaviour, and neonatal vigour..

==== Recording of resilience ====
The resilience-related traits that are described below for sheep and goats are:

* [[Section 24: Recording resilience in sheep and goats#Guidelines on recording health and disease in sheep and goats|health and disease (Chapter 2);]]
* [[Section 24: Recording resilience in sheep and goats#Guidelines on recording lifetime resilience in sheep and goats|lifetime resilience (Chapter 3);]]
* [[Section 24: Recording resilience in sheep and goats#Guidelines on survival recording of foetus and young in sheep and goats|survival of foetus and young (Chapter4);]]
* [[Section 24: Recording resilience in sheep and goats#Guidelines on recording behavioural traits in sheep and goats|behavioural traits (Chapter 5).]]

==== Recording of the environment ====
The record of the environment in sheep and goats is described below in the [[Section 24: Recording resilience in sheep and goats#Guidelines on recording behavioural traits in sheep and goats|Chapter 6]] of these guidelines

==== Acknowledgements ====
We gratefully acknowledge the contributions to these guidelines on recording resilience-related traits and the environment in sheep and goat by all the people working in the ICAR working group on sheep, goat, camelids and/or participating to the SMARTER project:

The different documents giving the recommendations of each sub-sections list in their own acknowledgements the persons involved in the writing of the guidelines.

This work received funding from the European Unions’ Horizon 2020 Research & Innovation program under grant agreement N°772787—SMARTER.

== Guidelines on recording health and disease in sheep and goats ==
'''Change summary'''
{| class="wikitable"
|'''Date of change'''
|'''Nature of Change'''
|-
|October 2024
|First version
|-
|December 2024
|Tracked change revisions by MS
|}

=== Introduction and scope ===
==== Introduction ====
Livestock diseases cause significant economic losses due reduced productivity, failing to express the genetic potential of animals, treatment costs, and consequently the culling of animals. Therefore, health and resistance to disease are keys factors for increasing resilience in farm animals in general and in small ruminants in particular. Among the challenges that sheep and goats must face, the infectious challenges are among the most important. They lead to losses of production and difficulties of reproduction. They also generate an increase in the consumption of chemical input. Beyond actual extra cost that may hamper the sustainability of the farms, but also of the breeding programs, there is a risk for the environment and the occurrence of resistance to drugs.

In most cases, an integrated approach is the more beneficial and efficient, mixing the different leverages. Among them, the control of the challenges by the host through its genetic resistance has shown its efficiency for some disease (resistance to scrapie, resistance to mastitis in dairy species) or is promising (resistance to parasites, resistance to footrot).

These guidelines on health and disease phenotypes are dedicated to any kind of health and disease resistance indicators. However, to start, we focus on the traits studied in SMARTER, which are the resistance to parasites and the resistance to footrot and mastitis in meat sheep and dairy sheep and goats.

==== Scope ====
This section on recording health and disease in sheep and goats starts following the task achieved in SMARTER and includes the following three sub-sections:

* Resistance to parasites
* Resistance to mastitis
* Resistance to footrot
=== Resistance to parasites ===

==== Definition, terminology, rationale ====
Resistance may be defined as the host’s ability to limit its parasite load (Råberg et al., 2007). The resistance to parasites described here corresponds to the resistance to gastro-intestinal nematodes (GIN). They are one of the main constraints for grazing sheep. They cause substantial economic losses due to lower production levels, the costs of anthelmintic treatments and the mortality of severely affected sheep. GIN control strategies mainly rely on treatment with anthelmintics. In many regions of the world, studies have reported the development of GIN resistance to most anthelmintic molecules due to their extensive use. Additionally, the possible presence of drug residues in animal products and the negative impact of these molecules on the micro and macro fauna of the soil are of concern. Therefore, sustainable GIN control may be a priority with schemes that do not only rely on anthelmintics but include complementary strategies such as nutritional supplementation with tannins and/or proteins, pasture management, and genetic selection of resistant animals. This latter strategy relies on the existence of genetic variation of host resistance to GIN both between and within breeds.

The faecal egg count (FEC), which is the number of parasite eggs per gram of faeces, is the most commonly used indicator to assess this resistance to GIN. In many countries, the selection for parasite resistance is based on FEC measures in natural infestation conditions under natural grazing conditions. As FEC measurements in sheep and goats are extremely costly and laborious, and because response to artificial challenges is highly correlated to response to natural infestation, it is therefore possible to implement a protocol of experimental infestation, as it is the case in France.

Beside FEC, different phenotypes can be used to measure resistance to GINs such as packed cell volume (PCV), FAffa MAlan CHArt (FAMACHA©) score, DAG score, immunological traits, and blood bepsinogen dosing (Shaw et al., 2012; Bishop, 2012; Bell et al., 2019; Sabatini et al., 2023).

==== Data recording ====

===== Indicators of parasite resistance or resilience =====

====== Faecal Egg Count ======
Faecal Egg Count (FEC) is the main indicator that measures the egg excretion intensity. It measures the number of parasite eggs per gram of faeces. This trait is related to the resistance of the animal (ability to limit the installation, the development and the prolificacy of the nematode inside the digestive tract (especially the abomasum). FEC is determined for each sample using a modified MacMaster technique (Whitlock, 1948 or Raynaud, 1970) with a sensitivity of 100 or 15 eggs per gram, respectively. The measure may be done in natural or in experimental infestation. FEC can be applied to one species (for example ''Haemonchus contortus'' (''Hc'')) or several species (including ''Hc'', ''Teladorsagia circumcincta'', ''Trichostrongylus colubriformis'', etc).

The distribution of the FEC has an asymmetric distribution (some high value, many low or medium value). A transformation must be applied to process a genetic analysis. The most frequent transformations are a root (fourth, third or square root) or a log transformation.

====== Packed Cell Volume ======
Packed Cell Volume (PCV) - Blood samples were collected in EDTA coated tubes and PCV values were determined individually by centrifugation in microhematocrit tubes with a relative centrifugal force of 9500 for 10 min.

PCV can be exploited as a single value of more relevantly as a gain/loss of PCV between two points. Variation of PCV is a relevant indicator of the resilience of the sheep / goat.

====== FAMACHA score ======
FAMACHA® score – As the anaemia provoked by some hematophagous parasites is at some stage visible on the mucosa (especially ocular mucosa), a scale of grading, based on the colour of the ocular mucosa, ranging from 1 (dark red mucosa) to 5 (white mucosa) has been built. This score was developed in South Africa to facilitate the clinical identification of anaemic sheep infected with H. contortus (Van Wyk and Bath, 2002).

As drawbacks, the FAMACHA® score does not allow to detect the non-hematophagous parasites and it appears quite belatedly: a FAMACHA® score over 3 concerns animals with a PCV below 20%. The method is not specific, anaemia being possibly caused by other reason than ''Haemonchus contortus''. It is however interesting to detect the anaemia. FAMACHA® score is related to the resilience of the sheep / goat.

====== DAG score ======
DAG score is an indicator for assessing dagginess, which measures faecal soiling in sheep. DAG score uses a 5-point or 6-point scoring scale ranging from 0 (no dags) to 5 or 6 (very daggy). Dag score scale shows the degree or extent of faecal contamination of the fleece.

The key is to be consistent when scoring a mob of sheep and for these sheep to have been run under similar conditions. Faecal contamination should not be confused with urine stain in ewe lambs and hoggets.

====== Immunological traits ======
Immunological and physiological profiles may be linked to phenotypes of resistance to parasites (strongyles). These new immunological and physiological profiles are blood lymphocytes cytokine production and serum levels of nematode parasite-specific Immunoglobulin A (IgA) that are produced upon whole blood stimulation. In SMARTER experiment in SRUC, blood was stimulated with pokeweed mitogen (a lectin that non-specifically activates lymphocytes irrespectively of their antigen specificity), and Teladorsagia circumcincta (T-ci) larval antigen to activate parasite-specific T lymphocytes.

Adaptive immune response may be determined by quantifying:

* cytokines interferon-gamma (IFN-γ), which relate to T-helper type 1 (Th1),
* interleukin IL-4, which relates to T-helper type 2 (Th2) and
* interleukin IL-10, which relate to regulatory T cell (Treg) responses.

Each immune trait displays a significant genetic variation (heritabilities ranging from 0.14 to 0.77). Heritability of IgA is moderate (0.41). Correlations with FEC are rather weak, from 0 to 0.27 but not significantly different from 0.

====== Blood Pepsinogen dosing ======
Blood pepsinogen is an indicator of the integrity of the gastric mucosa. The determination of serum pepsinogen is therefore a proxy in the diagnosis of abomasal strongylosis of ruminants (pepsinogen in blood is caused by an increase in the permeability of the abomasum mucosa due to presence of nematodes). There is a correlation between the concentration of pepsinogen in the blood and the number of worms harboured by the host.

===== Natural infestation =====

====== General considerations ======
Measurements (FEC or other proxies) are mainly undertaken in natural infestation under natural grazing conditions. In natural condition of infestation, frequency and amounts of yearly samplings have to be assessed according to the climate and epidemiological conditions and breeds. Local knowledge is essential for adjusting protocols to each country, as the level of infestation is strongly influenced by seasonality and the grazing system.

Several countries (e.g. Australia, New Zealand, and Uruguay), have incorporated the genetic evaluation of FEC at various ages into their national evaluation systems. In any case, in order to have data useful for the genetic evaluation, a representative sample of sheep in the flock involved in the selection scheme has to be periodically monitored to decide whether to sample the whole flock, i.e. when the number of infected animals and the level of infestation are considered sufficient to appreciate individual variability, individual FEC can be measured on the whole flock.

Further data related to environmental factors affecting the level of infestation should be recorded to be included in the genetic model for estimating the breeding values:

* Farm management mainly grazing system
* Birth type
* Sex
* Age of dam
* Parity
* Lambing date
* Sampling date
* Frequency, date, and molecule of anthelmintic administration

Additionally, stool cultures can be performed from the faecal samples taken (one per management group).

====== Description of the protocol and the measures (Uruguayan protocol) ======
At weaning, lambs undergo anthelmintic treatment, and their treatment efficacy is checked 8-14 days later through the analysis of FEC samples from 20 randomly selected lambs to confirm the absence of egg excretion. Subsequently, FEC is monitored every 15 days by collecting samples (based on epidemiological conditions) from 10-15% of lambs in each management group. The first individual FEC sampling is conducted when the FEC arithmetic mean exceeds 500 with no more than 20% samples exhibiting zero FEC. At this point, the lambs undergo anthelmintic treatment again, and their treatment efficacy is evaluated after 8-14 days. If the FEC mean remains above 500, a second individual sampling is conducted. Throughout the protocol, faecal egg counts (FEC1 and FEC2) are measured at the end of the first and second natural infestations. Generally, with some variations based on the breed, these samplings correspond to lambs at 9 and 11 months of age, respectively.

Currently, to simplify the protocol, only one sampling is conducted, and the control begins on a fixed date (early autumn) when the most significant parasite, H. contortus, prevails. Along with the FEC records (FEC1 and FEC2), other records, such as body weight, FAMACHA®, and body condition score, can also be taken.

===== Experimental infestation (French protocol) =====
As mentioned above, FEC measurements on sheep in commercial flocks are extremely costly and laborious. It has been shown that sheep that are selected on the basis of their response to artificial challenges respond similarly when exposed to natural infestation, and a high positive genetic correlation was estimated between FEC recorded under artificial or natural infestation. Moreover, it has been shown that selection of rams for parasite resistance after artificial challenges allows to improve the resistance of their female offspring for parasite infestation in natural conditions. Thus, an alternative approach may be to select rams gathered for AI progeny-testing or performance-testing by artificially infecting them with standardized doses of larvae.

In most cases, resistance to GIN is assessed in natural infestation conditions at grazing. However, the intensity of natural infestation in grazing animals depends on climatic conditions and may vary from season to season leading to over- or under-estimation of the genetic parameters of resistance. In France, sheep breeds are selected collectively on breeding stations and the strategy is to take advantage of this organization to implement the GIN control selection by phenotyping rams after experimental infestation. There are two main advantages. Firstly, a large diffusion of the genetic progress is expected via these rams, which are the future elite males. Secondly, the experimental infestation performed in control stations allow to evaluate these rams in homogeneous conditions (standardization of doses of infestation, farming conditions, climatic conditions, etc) during the ram evaluation period. Previous studies (Gruner et al., 2004) estimated high genetic correlations between resistances to experimental and natural infestation, between infestation by different parasite species (''Haemonchus contortus'' and ''Trichostrongylus colubriformis'') and between resistance in adult sheep and lambs. Moreover, recent works have shown that the genetic correlation between the resistance of rams in experimental conditions and the resistance of pregnant or milking ewes in natural conditions of GIN infestation are high.

====== Description of the protocol and the measures ======
An original protocol for phenotyping resistance to gastro-intestinal parasitism has been conceived and developed in France, targeted to rams (or bucks) gathered in a breeding centre or station, or an AI centre (Jacquiet et al., 2015; Aguerre et al., 2018). Males bred indoors, supposed to be naïve, are artificially infected twice with L3 larvae of a given strain of ''Haemonchus contortus'' susceptible to anthelminthic. Males are subjected to a first infestation (after a coprological examination be performed to confirm that no eggs were excreted before the artificial infestation) with a given dose of L3 larvae (D0). At D30, the males are phenotyped (FEC30 and possibly PCV30) and treated with an anthelminthic. After a 15-day recovery period, the rams are challenged again with a given dose of L3 larvae of Haemonchus contortus. At that time (D45), the efficacy of anthelmintic treatment is ensured in each male. Thirty days after (D75) the second challenge, the males are phenotyped (FEC30 and possibly PCV30) and treated again. The protocol lasts 2 and a half months. During the protocol, the measures carried out are as follows:

* faecal egg counts (FEC30 and FEC75) at the end of the first and second infestation (from faecal sample).
* packed cell volumes PCV0, PCV30, PCV45 and PCV75 at the start and the end of both infestation (from blood sample).

====== Calculation of variables ======
The FEC30 and FEC75 are used per se. Variations of PCV are calculated:

* PCV_loss_inf1 = PCV0-PCV30 (or ratio PCV30/PCV0)
* PCV_loss_inf2 = PCV45-PCV75 (or ratio PCV75/PCV45)
* PCV_recovery = PCV45-PCV0

where PCV_loss_inf1 and PCV_loss_inf2 represent the loss of PCV after each infestation, while PCV_recovery represents the males’ capacity to recover after the first infestation.

PCV variations might be interpreted as an indicator of resilience of the animal, i.e. its ability to maintain its blood parameters despite the parasitical challenge.

==== Use for genetic analysis / genetic evaluation ====

===== Model for genetic analysis =====
The genetic analysis of experimentally infected animals that are raised indoors may include:

* Fixed effects: contemporary group (mob x doses of larvae), age of animals (eg. 1 year, 2 years, 3years, 4 years and older)
* Random additive effect of the animals
* Residual effect

The genetic analysis of naturally infected animals that are raised outdoors may include:

* Fixed effects: they obviously will depend of the type of animals (females in lactation vs lambs/kids). They should include flock/herd, year x season (e.g. spring, summer, autumn, winter), anthelmintic treatments (e.g. eprinomectin, ivermectin, moxidectin …) in interaction with the number of days between the date of treatment and the sampling date (e.g. less than 70 days, between 70 and 100 days, more than 100 days). For females in lactation: age and/or parity, litter size before lactation (single or multiple new-born lambs). For lambs or kids: age of the dam, type of birth or rearing, and age at the time of the records, expressed in day.
* Random additive effect of the animals
* Random permanent environment effect if repeated measures (e.g. for FEC 1 & 2)
* Residual effect

===== Genetic parameters =====
Pooled estimates of heritability to resistance to gastrointestinal parasites gained by meta-analysis (Mucha et al., 2022) in dairy goats and sheep and meat sheep are shown in Tables 1 and 2, while Table 3 shows the heritabilities estimated for the experimentally infected rams. In addition, we mention a paper from Casu et al (2022) in which a heritability of 0.21 for FEC was found in a 20 year follow-up study in an experimental flock in Sardinia, Italy.

{| class="wikitable"
| colspan="7" |'''''Table 1. Pooled estimates of heritability of resistance to gastrointestinal parasites from meta- analysis in dairy goats and sheep.'''''
|-
|'''Trait1'''
|'''Species'''
|'''Pooled h2(±SE)'''
|'''Min2 h2'''
|'''Max3 h2'''
|'''N obs'''
|'''N studies'''
|-
| rowspan="2" |FEC
|Goats
|0.07 ± 0.01
|0.04
|0.15
|8
|2
|-
|Sheep
|0.14 ± 0.04
|0.09
|0.35
|6
|3
|}
1Trait: FEC – faecal egg count

2Minimum h2 from individual studies included in meta-analysis

3Maximum h2 from individual studies included in meta-analysis

{| class="wikitable"
| colspan="6" |'''''Table 2. Pooled estimates of heritability of resistance to gastrointestinal parasites from meta- analysis in meat sheep (Mucha et al., 2022).'''''
|-
|'''Trait1'''
|'''Pooled h2 (±SE)'''
|'''Min2 h2'''
|'''Max3 h2'''
|'''N obs'''
|'''N studies'''
|-
|DAG
|0.30±0.06
|0.06
|0.63
|37
|15
|-
|FCons
|0.14±0.02
|0.03
|0.27
|13
|5
|-
|NBW4
|0.10±0.02
|0.00
|0.54
|11
|3
|-
|Par-Ab
|0.18±0.07
|0.05
|0.29
|6
|3
|-
|Par-Ig
|0.36±0.06
|0.13
|0.67
|24
|8
|-
|FEC
|0.29±0.03
|0.00
|0.82
|118
|32
|-
|HC
|0.32±0.14
|0.08
|0.56
|5
|2
|}
1Trait: DAG – dagginess, FCons – faecal consistency, NBW – number of worms, Par-Ab – parasitism anitbodies, Par-Ig – parasitism immunoglobulin, FEC –faecal egg count, HC - Haematocrit

2Minimum h2 from individual studies included in meta-analysis

3Maximum h2 from individual studies included in meta-analysis

4Pooled heritability obtained from a simple random effects model as the three level meta-analysis model did not converge

{| class="wikitable"
| colspan="2" |'''''Table 3. Estimates of heritability of resistance to gastrointestinal parasites from meta-analysis in dairy sheep in experimental infestations (Aguerre et al., 2018)'''''
|-
|'''Trait1'''
|'''h2'''
|-
|Root FEC_inf1
|0.14±0.04
|-
|RootFEC_inf2
|0.35±0.08
|-
|PCV_loss_inf1
|0.24±0.05
|-
|PCV_loss_inf2
|0.18±0.06
|-
|PCV-recovery
|0.16±0.06
|}

=== Resistance to mastitis ===

==== Definition, terminology, rationale ====
In small ruminants, mastitis mainly consists in subclinical infections caused by coagulase- negative staphylococci, which is much more frequent than clinical mastitis (Bergonier et al., 2003). Under these conditions, somatic cell count (SCC) is an accurate, indirect measure to predict mammary gland infection. Therefore, SCC could be used as an indirect selection criterion for mastitis resistance as is widely done in dairy cattle. Moreover, selection for mastitis resistance in dairy sheep and goats could mainly focus on selection against subclinical mastitis using SCC, considering the low incidence of clinical cases in these species (<5%), compared to dairy cattle for which clinical cases occur frequently (Bergonier et al., 2003).

Clinical mastitis is not recorded in dairy small ruminants, mainly because of its low incidence and because SCC is a relevant and simple indicator of intra-mammary infections. Work completed in France has developed two lines of ewes (experimental farm INRAE-La Fage) and goat (experimental farm INRAE-Bourges), a high line generated from sires with unfavourable EBVs for somatic cells and a low line generated from sires with favourable EBVs for somatic cells. For both sheep (Rupp et al., 2009) and goats (Rupp et al., 2019), the low line has the lowest SCC, the lowest incidence of clinical mastitis and the lowest incidence of chronic mastitis (abscesses or unbalanced udder) and subclinical mastitis (assessed by milk bacteriology).

Even though SCC is the established indicator for use in animal breeding, the use of the California Milk test (CMT) is a very good indicator of SCC for monitoring udder health in flock/herd management in dairy and meat-producing small ruminants.

==== Data recording ====

===== Somatic Cell Count (SCC) =====
Large scale somatic cell counting relies on the application of routine methods, such as fluoro- opto-electronic counting. The somatic cell counter must be properly calibrated against a reference and laboratories must frequently verify the calibration settings are still correct.

The design for recording SCC will depend upon the objective. For flock/herd management related to high bulk SCC, the whole flock/herd should be sampled and analysed to identify the animals with the highest SCC. For genetic purpose, simplified designs might be implemented.

In dairy species, somatic cell counting is achieved within the milk recording design and the sampling design, as for milk components such as fat and protein content. As in small ruminants, most of the designs are simplified ones compared to the A4 method (all daily milkings recorded, once a month) (see [[Section 16 – Dairy Sheep and Goats|ICAR Guidelines Section 16: dairy sheep and goats]]), SCC are quite often available at one out of the two daily milkings. In this case, use of SCC must be handled accordingly.

As for milk composition, with the aim of simplifying and decreasing further the cost of recording, it is possible/recommended to measure SCC on only a part of the flock/herd (first parity or first two parities). It is also possible to go further in the simplification of the design, for example by sampling only a part of the lactation within a part-lactation sampling as proposed in the [[Section 16 – Dairy Sheep and Goats|section 16 of the ICAR Guidelines]]. The genetic parameters of test-day and lactation mean for Somatic Cell Score (SCS - log-transformed SCC) show that the records of the middle of the lactation appear as the most representative of the whole lactation. Two to four individual samples per female and per lactation, collected monthly in the middle part of the lactation are highly correlated (around 0.98) with SCS determined from samples collected over the complete lactation (A4 method) but are hardly less heritable compared with the A4 homologous traits (negligible loss of precision for SCS) (Astruc and Barillet, 2004). The balance between cost and genetic efficiency, depending on the genetic correlations close to 1, is clearly in favour of the part-lactation sampling compared to A4 testing.

===== California Mastitis Test (CMT) =====
The California mastitis test is an animal-side diagnostic test that provides an estimate of the level of infection within a mammary gland. A sample of milk (~3ml) from each udder half is combined with an equal volume of reagent in a CMT paddle and mixed for 15 to 20 seconds. The reaction is scored based on the level of thickening of the mixture from zero (no thickening) consistent with no, or low, levels of infection, to four (gel formation with elevated surface) indicating high levels of infection.

A previous study (McLaren et al., 2018) has demonstrated the strong correlation between CMT score and SCC from samples collected from pedigree meat sheep in the UK.

==== Calculation of traits ====
Test-day SCC must be transformed to Somatic Cell Score (SCS) by the logarithmic transformation of Ali and Shook (1980) to achieve normality of distribution.

Example: SCS = log2+(SCC/100,000)+ 3

The table 4 gives correspondence between SCC and SCS

{| class="wikitable"
| colspan="2" |'''''Table 4. Correspondence between somatic cell score and somatic cell count'''''
|-
|'''Somatic Cell Count (SCC)'''
|'''Somatic Cell Score (SCS)'''
|-
|12,500
|0
|-
|25,000
|1
|-
|50,000
|2
|-
|100,000
|3
|-
|200,000
|4
|-
|400,000
|5
|-
|800,000
|6
|-
|1,600,000
|7
|}
SCS can be adjusted for days-in-milk (DIM). In this case, the adjustment procedure must be defined from a study based on healthy ewes/goats with enough number of test-days over the lactation. Then a lactation SCS (LSCS) may be calculated (case of lactation model in genetic evaluation).

LSCS can be computed as the weighted arithmetic mean of test-day SCS (adjusted or not for DIM). Weights are either 1 (equivalent to no weight) or r2, where r is the correlation between one measure and the mean of all other records.

==== Use for genetic analysis / genetic evaluation ====

===== Genetic model =====
The genetic model might include the following fixed effects:

* Flock x year (x parity)
* Month of lambing/kidding
* Age at lambing/kidding
* Number of lambs/kids born

===== Genetic parameters =====
Pooled estimates of heritability of somatic cell score gained by meta-analysis (Mucha et al., 2022) in dairy goats and sheep and meat sheep are shown in Table 5.

{| class="wikitable"
| colspan="7" |'''''Table 5. Pooled estimates of heritability of somatic cell score from meta-analysis in dairy goats and sheep (Mucha et al., 2022)'''''
|-
|'''Trait1'''
|'''Species'''
|'''Pooled h2 (± SE)'''
|'''Min2 h2'''
|'''Max3 h2'''
|'''N obs'''
|'''N studies'''
|-
|SCS
|Goats

Sheep
|0.21±0.01

0.13±0.02
|0.19

0.03
|0.24

0.27
|5

29
|3

22
|}
1Trait: SCS – somatic cell score

2Minimum h2 from individual studies included in meta-analysis

3Maximum h2 from individual studies included in meta-analysis

{| class="wikitable"
| colspan="6" |'''''Table 6. Pooled estimates of genetic correlations (rg) between resilience (SCS, FEC) and efficiency (MY, FC, PC) traits from meta-analysis in dairy goats (Mucha et al., 2022)'''''
|-
|'''Traits1'''
|'''Pooled rg (± SE)'''
|'''Min2 rg'''
|'''Max3 rg'''
|'''N obs'''
|'''N studies'''
|-
|SCS & MY
|0.35±0.31ns
|0.00
|0.59
|3
|3
|-
|SCS & FC4
| -0.19±0.01
| -0.20
| -0.18
|3
|2
|-
|SCS & PC
| -0.06±0.05ns
| -0.13
|0.00
|3
|2
|-
|FEC & MY
|0.17±0.35ns
| -0.21
|0.63
|4
|2
|}
1Traits: SCS – somatic cell score, FEC – faecal egg count, MY – milk yield, FC – fat content, PC – protein content

2Minimum rg from individual studies included in meta-analysis.

3Mmaximum rg from individual studies included in meta-analysis.

nspooled correlations obtained from a simple random effects model as the three level meta-analysis model did not converge

{| class="wikitable"
| colspan="6" |'''''Table 7. Pooled estimates of genetic correlations between resilience (SCS) and efficiency (MY, FY, PY, FC, PC) traits from meta-analysis in dairy sheep (Mucha et al., 2022)'''''
|-
|Traits1
|Pooled rg (± SE)
|Min2 rg
|Max3 rg
|N obs
|N studies
|-
|SCS & MY
| -0.05±0.10ns
| -0.30
|0.23
|16
|11
|-
|SCS & FC
|0.04±0.05ns
| -0.16
|0.16
|8
|8
|-
|SCS & PC
|0.12±0.03
|0.02
|0.24
|12
|9
|-
|SCS & FY
|0.11±0.15ns
| -0.04
|0.31
|4
|4
|-
|SCS & PY
|0.17±0.10ns
|0.06
|0.31
|4
|4
|}
1Traits: SCS – somatic cell score, MY – milk yield, FY – fat yield, PY – protein yield, FC – fat content, PC – protein content

2Minimum rg from individual studies included in meta-analysis

3Maximum rg from individual studies included in meta-analysis

nsPooled correlations obtained from a simple random effects model as the three level meta-analysis model did not converge

ns – Pooled estimate did not differ significantly from zero

{| class="wikitable"
| colspan="5" |'''''Table 8. Estimates of heritability of somatic cell score, clinical mastitis and CMT in meat and dairy and meat sheep (source Oget et al., 2019)'''''
|-
|'''Sheep'''
|'''Breed'''
|'''Trait1'''
|'''Heritability (±SE)'''
|'''Reference'''
|-
|Dairy
|Chios
|CMT
|0.12±0.06
|Banos et al., 2017
|-
|Meat
|Belclare, Charollais, Suffolk, Texel,

Vendeen breeds
|CM
|0.04±0.03

|O’Brien et al., 2017
|-
|Meat
|Texel
|SCS
|0.11±0.04
|McLaren et al., 2018
|-
|Meat
|Texel
|CMT
|0.08-0.09±0.04
|McLaren et al., 2018
|-
|Meat
|Texel
|CMT
|0.07
|Kaseja et al., 2023 submitted paper (SMARTER, D2.3)

|}
1CMT - California mastitis test, CM - Clinical mastitis (examination and palpation of the udder), SCS – Somatic Cell Score

=== Resistance to footrot ===

==== Definition, terminology, rationale ====
Footrot is caused by ''Dichelobacter nodosus'' and is a major cause of lameness in sheep. The disease is highly contagious and endemic in many countries that causes pain and welfare issues in affected animals. In addition to the direct impacts on time and veterinary / medicine costs, the disease has further, indirect, impacts through reducing fertility and milk supply.

The presence of footrot is assessed by inspection of the hooves of lame animals.

==== Data recording ====

===== Scoring methods =====
Each hoof is assessed individually and scored based on the five-point scale (used in UK): clean, unaffected hoof (score 0), mild inter-digital inflammation (score 1), inter-digital necrosis (score 2), under-running of the sole of the hoof (score 3) and fully under-run to the abaxial wall of the hoof (score 4) (Conington et al., 2008).

The sum of scores is calculated by adding all four scores (for each hoof), hence the animal can obtain the phenotype in a range from zero to 16.

In France, where footrot is usually not recorded, a simplified scoring system has been developed using a scale (0 normal and severity of lesions scored from 1 to 3) adapted from the Victorian Farmers Federation and Coopers Animal Health.

Additionally, the health of feet is assessed in France and the UK for other important hoof lesions including white line degeneration, contagious ovine digital dermatitis, horn growth, presence of abscess, granuloma, interdigital hyperplasia, and panaritium).

==== Calculation of traits ====
Sum of scores are log-transformed in order to normalise the data using the formula ln(Sum of scores + 1). The addition of one prevents to logarithm the value of sum of scores equal to zero. Each animal can obtain transformed score ranging between zero and 2.83.

==== Use for genetic analysis / genetic evaluation ====

===== Genetic model =====
The genetic model might include the following fixed effects:

* Age of the dam
* Scorer (if more than one)
* Vaccine status (if some animals treated with the vaccination against ovine foot-rot)
* Flock or Flock x Year interaction

===== Genetic parameters =====
The estimated heritability for UK meat sheep (Table 9) varies between 0.12 (Nieuwhof et al., 2008). to 0.23 (Kaseja et al., 2023, unpublished results)
Table 9. Estimates of heritability of resistance to footrot in meat sheep breeds.

{| class="wikitable"
| colspan="4" |'''''Table 9. Estimates of heritability of resistance to footrot in meat sheep breeds.'''''
|-
|'''Breed'''
|'''Trait1'''
|'''Heritability (SE)'''
|'''Reference'''
|-
|Texel
|RF
|0.12(0.02)
|Kaseja et al, 2023 in press
|-
|Scottish Blackface
|CM
|0.19 to 0.23
|Kaseja et al., 2023 in press.
|-
|Scottish lambs
|SCS
|0.12
|Nieuwhof et al., 2008
|-
|Texel
|CMT
|0.18
|Mucha et al., 2015
|}
1RF - Resistance to footrot, CM - Clinical mastitis (examination and palpation of the udder), SCS – Somatic Cell Score, CMT - California mastitis test

=== Acknowledgements ===
We gratefully acknowledge the contributions to small ruminant health and disease guideline by the following people:

* Joanne Conington, SRUC, the UK
* Jean-Michel Astruc, IDELE, France
* Rachel Rupp, INRAE, France
* Beat Bapst, Qualitas AG, Switzerland
* Donagh Berry, TEAGASC, Ireland
* Beatriz Carracelas, INIA, Uruguay
* Antonello Carta, Agris Sardegna, Italy
* Gabriel Ciappesoni, INIA, Uruguay
* Arnaud Delpeuch, IDELE, France
* Frédéric Douhart, INRAE, France
* Karolina Kaseja, SRUC, the UK
* Ed Smith, The British Texel Sheep Society, the UK
* Flavie Tortereau, INRAE, France
* Stefen Werne, FiBL, Switzerland
* Marija Špehar, Centre for Livestock Breeding Zagreb, Croatia

This work received funding from the European Unions’ Horizon 2020 Research & Innovation program under grant agreement N°772787—SMARTER.

This work also used deliverable from the Eurosheep project (Horizon 2020 under agreement N° 863056).

=== References ===
Aguerre, S., Jacquiet, P., Brodier, H., Bournazel, J.P., Grisez, C., Prévot, F., Michot, L., Fidelle, F., Astruc, J.M., Moreno, C.R. (2018). Resistance to gastrointestinal nematodes in dairy sheep: Genetic variability and relevance of artificial infection of nucleus rams to select for resistant ewes on farms. Vet. Parasitol. 256:16-23. <nowiki>https://doi.org/10.1016/j.vetpar.2018.04.004</nowiki>

Ali, A., Shook, G. (1980). An optimum transformation for somatic cell concentration in milk. J. Dairy Sci. 63:487-490.

Astruc J.M., Barillet F. (2004). Current challenge for milk recording in dairy sheep and goats: the simplification of milk sampling design for chemical composition and somatic cell counts of milk. Proceedings of the 34th ICAR session, Sousse, Tunisia, 31 May-3 June 2004.

Banos, G., Bramis, G., Bush, S.J., Clark, E.L., McCulloch, M.E.B., Smith, J., Schulze, G., Arsenos, G., Hume, D.A., Psifidi, A. (2017). The genomic architecture of mastitis resistance in dairy sheep. BMC Genom. 18 624.

Bell, A., McNally, J., Smith, D.V., Rahman, A., Hunt, P., Kotze, A.C., Dominik, S., Ingham, A. (2019). Quantification of Differences in Resistance to Gastrointestinal Nematode Infections in Sheep Using a Multivariate Blood Parameter. Vet. Parasitol. 270:31–39.

Bishop, S.C. (2012). Possibilities to Breed for Resistance to Nematode Parasite Infections in Small Ruminants in Tropical Production Systems. Animal., 6:741–747.

Bergonier, D., Crémoux, R. de, Rupp, R., Lagriffoul, G., Berthelot, X. (2003). Mastitis of dairy small ruminants. Vet. Res. 34:689–716.

Casu, S., Usai, M.G., Sechi, T. et al. Association analysis and functional annotation of imputed sequence data within genomic regions influencing resistance to gastro-intestinal parasites detected by an LDLA approach in a nucleus flock of Sarda dairy sheep. Genet Sel Evol 54, 2 (2022). <nowiki>https://doi.org/10.1186/s12711-021-00690-7</nowiki>

Conington, J., Hosie, B., Nieuwhof, G.J., Bishop, S.C., Bünger, L. (2008). Breeding for resistance to footrot- the use of hoof lesion scoring to quantify footrot in sheep. Vet. Res. Commun. 32(8):583-9. <nowiki>https://doi.org/10.1007/s11259-008-9062-x</nowiki>

Gruner, L., Bouix, J., Brunel, J.C. (2004). High genetic correlation between resistance to Haemonchus contortus and to Trichostrongylus colubriformis in INRA 401 sheep. Vet. Parasitol. 119:51–58.

Jacquiet, P., Salle, G., Grisez, C., Prevot, F., Lienard, E., Astruc, J.M, Francois, D., Moreno, C. (2015). Selection of sheep for resistance to gastro-intestinal nematodes in France: where are we and where are we going? 25th International Conference of the WAAVP, Liverpool, UK, 2015, 16-20 August

McLaren, A., Kaseja, K., Yates, J., Mucha, S., Lambe, N.R., Conington, J.(2018). New mastitis phenotypes suitable for genomic selection in meat sheep and their genetic relationships with udder conformation and lamb live weights. Animal. 12(12):2470-2479. doi: 10.1017/S1751731118000393.

Mucha, S., Bunger, L., Conington, J. (2015). Genome-wide association study of footrot in Texel sheep. Genetics Selection Evolution, 47 (1), pp.35. DOI 10.1186/s12711-015-0119-3

Mucha, S., Tortereau, F., Doeschl-Wilson, A., Rupp R., Conington, J. (2022). Animal Board Invited Review: Meta-analysis of genetic parameters for resilience and efficiency traits in goats and sheep. Animal. 16(3):100456. <nowiki>https://doi.org/10.1016/j.animal.2022.100456</nowiki>.

Nieuwhof, G.J., Conington, J., Bünger, L., Haresign, W., Bishop, S.C. (2008). Genetic and phenotypic aspects of resistance to footrot in sheep of different breeds and ages. Animal. 2(9):1289-1296. <nowiki>https://doi.org/10.1017/S1751731108002577</nowiki>

O’Brien, A.C., McHugh, N., Wall, E., Pabiou, T., McDermott, K., Randles, S., Fair, S., Berry, D.P. (2017). Genetic parameters for lameness, mastitis and dagginess in a multi-breed sheep population. Animal 11, 911–919. DOI: 10.1017/S1751731116002445

Oget, C., Tosser-Klopp, G., Rupp, R. (2019). Genetic and genomic studies in ovine mastitis. Small Ruminant Research 176, 55-64. <nowiki>https://doi.org/10.1016/j.smallrumres.2019.05.011</nowiki>.

Råberg, L, Sim, D., Read, A.F. (2007). ‘Disentangling genetic variation for resistance and tolerance to infectious diseases in animals’, Science. 318(5851), 812-814 DOI: 10.1126/science.1148526

Raynaud J.P. (1970). Etude de l’efficacité d’une technique de coproscopie quantitative pour le diagnostic de routine et le controle des infestations parasitaires des bovins, ovins, equines et porcins. Ann. Parasitol. 45: 321–342

Rupp, R., Bergonier, D., Dion, S., Hygonenq, M.C., Aurel, M.R., Robert-Granié, C., Foucras, G. (2009). Response to somatic cell count-based selection for mastitis resistance in a divergent selection experiment in sheep. J. Dairy Sci. 92, 1203–1219.

Rupp, R., Huau, C., Caillat, H., Fassier, T, Bouvier, F., Pampouille, E., Clément, V., Palhière, I., Larroque, H., Tosser-Klopp, G., Jacquiet, P., Rainard, P. (2019). Divergent selection on milk somatic cell count in goats improves udder health and milk quality with no effect on nematode resistance. J Dairy Sci. 102(6):5242-5253. doi: 10.3168/jds.2018-15664.

Sabatini, G.A., de Almeida Borges, F., Claerebout, E. et al. (2023). Practical guide to the diagnostics of ruminant gastrointestinal nematodes, liver fluke and lungworm infection: interpretation and usability of results. Parasit. Vectors. 16, 58. <nowiki>https://doi.org/10.1186/s13071-023-05680-w</nowiki>

Shaw, R.J., Morris, C.A., Wheeler, M., Tate, M., Sutherland, I.A. (2012). Salivary IgA: A Suitable Measure of Immunity to Gastrointestinal Nematodes in Sheep. Vet. Parasitol. 186, 109–117

Van Wyk, J.A., Bath, G.F. (2002). The FAMACHA© system for managing haemonchosis in sheep and goats by clinically identifying individual animals for treatment. Vet. Res. 33:509–529.

Whitlock, H.V. (1948). Some modifications of the McMaster helminth egg counting technique and apparatus. J. Coun. Sci. Ind. Res. 21:177.

=== Annexes ===
[[File:Annex 1 Famacha.jpg|center|thumb|600x600px|Picture of FAMACHA score (source FiBL – Qualitas)]]

[[File:Annex 2 Farmacha 2.jpg|center|thumb|600x600px|Picture of FAMACHA score (source FiBL – Qualitas)]][[File:Annex 3 Uruguayan protocol of natural infestation.jpg|center|thumb|800x800px|Uruguayan protocol of natural infestation for recording the resistance to gastrointestinal parasites]]
[[File:Annex 4 French protocol for phenotyping the resistance.jpg|center|thumb|600x600px|French protocol for phenotyping the resistance to gastrointestinal parasites]]

== Guidelines on recording lifetime resilience in sheep and goats ==
'''Change Summary'''
{| class="wikitable"
|'''Date of change'''
|'''Nature of Change'''
|-
|October 2024
|First version
|-
|November 26th 2024
|Comments made by JC
|-
|December 23rd 2024
|Comments made by MS
|}

=== Introduction and scope ===

==== Introduction ====
Lifetime resilience is often tackled through longevity and aspects of productive longevity. Longevity is a trait to quantify productive lifespan of livestock, and for increasing durability and profitability of farms. In dairy ruminants, longevity definitions include: (i) true longevity (all culling reasons, including milk productivity); and (ii) functional longevity (all culling reasons, except voluntary productivity, such as milk productivity or growth). Functional longevity (corrected for production level – milk, growth) reflects the animals’ accumulated ability to overcome health and nutritional challenges. It is an indirect global approach to quantify adaptive capacity to various production environments. Different indicators may be calculated. One indicator is the length of productive life which is computed as the time interval (in days) between first lambing/kidding and culling. Longevity is linked with various predictors, such as fertility, udder health and conformation, resistance to disease, body condition score changes across ewe/doe lifetime. These predictors may be used in breeding program to get an earlier breeding value of longevity and may help to manage and monitor lifetime resilience at the farmer level.

==== Scope ====
The scope of these guidelines is to define approaches for the definition of longevity as well as the traits that can be calculated, and the downstream analyses that can be set up (including the use of early predictors to enhance longevity in the evaluation process).

To propose a grid for setting up an observation of the culling causes.

=== Longevity ===

==== Definition, terminology, rationale ====
The notion of longevity can cover several meanings. Longevity can be understood as the true longevity, i.e. the ability of the animal to live as long as possible, whatever its production level and its functional characteristics. Animal longevity also depends on the replacement rate which is often a choice of the breeders. Animals may be culled due to production level such as milk production or growth or fat/muscle depth, leading to ’voluntary’ culling (i.e. an animal is culled because we '''want''' to do it). In contrast, ‘involuntary culling’ is defined as an animal having to leave the flock or herd due to illness / accident/ functional disability etc (i.e. they are culled because we '''have''' to do it)

Involuntary culling may be due to:

* Udder health problem (clinical, subclinical, chronic mastitis).
* Lack of resistance to disease such as parasites.
* Problem of footrot.
* Unfavourable shape of the udder (lack of adaptation to machine milking or to suckling).
* Unfavourable general conformation.
* Undesired behaviour (temperament in the milking parlour).
* Infertility or any problem of reproduction.
* Problem of feet or legs, lameness.
* Lack or excess of body tissue mobilisation.

any other undesirable aspect associated with the animal’s inability to produce. Voluntary culling may be due to:

* Low productivity,
* Management decision to cull for age,
* Management decision to cull for a specific coat colour / other phenotype that does not meet the type desired,
* Farmer doesn’t like the animal,
* Economic reason to reduce the number of breeding animals in the flock/herd.

Even if some of these reasons for culling may be considered per se in the selection process by phenotyping and evaluating related traits (for example resistance to mastitis, resistance to gastro- intestinal parasite, fertility, udder morphology), it is often not possible to account for all of them. If properly modelled, functional longevity may be considered as a global and composite approach, allowing to assess the sustainability of the population in selection and of the practiced selection.

For this, different traits may be considered, quite often they are relatively easy to compute with data usually already existing in the genetic database (ex. length of productive life, which can be calculated as the culling date minus the date of the first lambing). There is no additional recording to set up. The difficulties in handling functional longevity are related to the modelling of the trait, given that the trait is fully known when the animal is culled. When not yet culled, the model to set up are quite complex. An example of this was reported by Brotherstone et al. (1997) for dairy cattle and Conington et al. (2004) for hill sheep, whereby live animals’ EBVs for longevity are based on their probability of survival at a given age combined with actual cull dates of relatives that became breeding females in the flock.

Even though there is no need to identify/know the cause of culling, the knowledge of the cause of culling might be a relevant observation of the hierarchy of the culling cause, which may lead to put an emphasis on some specific issue. For example, if we observe an increase in some culling causes (let’s say parasitism) this should lead to a deliberate selection programme to breed more resistant animals to parasites.

One drawback of the functional longevity trait is its lack of precocity. As stated above, it is necessary to have the date of culling or to have accumulated enough lactation to compute the trait. And an appropriate model (e.g. survival analysis) can only partially disentangle this difficulty. It is possible to address this issue by running a multi trait genetic evaluation model combining the longevity trait and some other proxy traits (such as udder morphology, udder health, etc). The use of Genomic Selection is a complementary way to generate early prediction of genetic merit for longevity, provided there is good accuracy of the EBVs of animals in the associated reference population.

==== Data recording ====

===== Longevity traits =====
The table 1 presents some criteria commonly used in small ruminants to measure longevity. Here, the criteria deal with true longevity, the only one measurable in herd/flocks. Functional longevity will be estimated later, at the statistical analysis step. Table 1 also shows the data required for calculating the longevity criteria. For example, the length of productive life is referred to as the difference between the time a female enters the breeding flock/herd and the date she exits it due to being culled or dying. It is important to notice that the culling date, which is rarely recorded by the farmers, can be replaced by the date of the last event registered for the animal (for example, date of the last performance recording, or of the last reproduction event).

{| class="wikitable"
| colspan="3" |'''Table 1. Definition of some commonly used longevity criteria.'''
|-
|'''Longevity criteria'''
|'''Raw data required'''
|'''Calculation'''
|-
|Length of total lifespan (LTL)
|Birth date (BD)
Culling or death date (CD)
|LTL= CD - BD
in days (or months or years)
|-
|Length of productive life (LPL)
|First lambing/kidding date (FKD)
Culling or death date (CD)
|LPL = CD – FKD
in days (or months or years)
|-
|Total number of days in production (NDL)
|Days in milk per lactation (DIM)
or
Lambing/kidding date + dry off date for each lactation
|NDL = ∑ DIM
|-
|Number of lactations (NLACT)
|Each lambing/kidding event (KE)
|NLACT = ∑ KE
|-
|Number of lambs or kids during lifetime (NLAMB)
|Prolificacy at each lambing/kidding (PR). This may or may not include no. lambs born dead + no. lambs born alive
|NLAMB = ∑ PR
|}
The length of total lifespan can be estimated easily, with only two variables usually registered by farmers. The difference with the length of productive life is that it considers the period when animals had the first lambing/kidding as well as the lambing/kidding interval. If the age at the first lambing/kidding and the lambing/kidding interval are similar between animals, the length of total lifespan will be very close to the length of productive life.

The total number of days in production only covers the “useful” life of the females because it doesn’t include the unproductive periods (such as dry off or large lambing/kidding interval after reproduction failure), compared to length of productive life. But the number of variables necessary to compute it is larger.

For the total number of lambs or kids during a lifetime, it is necessary to include all live-born lambs/kids only or those reared to weaning, if these data are routinely recorded.

==== Calculation of traits ====
The last column of Table 1 indicates how to calculate the different longevity criteria, from the raw variables.

The length of total lifespan and the length of productive life are estimated as differences in days between two dates: i) the culling date and ii) the birth date or the first lambing/kidding date, respectively. The total number of days in production corresponds to the sum of the days in milk of each lactation of the female. For the last two criteria (number of lactations or number of lambs/kids), the estimation corresponds to cumulative performance across lifetime.

Instead of waiting for the end of the animal's life to calculate the longevity criterion (which is sometimes long), one solution deals with limiting the animal career to a maximum number of years or lactations. For example, the length of productive life can be calculated only on the first 6 lactations. Subsequently, the length of productive life will be defined as the total number of days between the first lambing/kidding and the end of the 6th lactation. In the same way, the total number of lambs/kids can be estimated at a fixed age, 8 years old for example.

==== Use for genetic analysis / genetic evaluation. ====

===== Models =====
The genetic ability for longevity is evaluated via the functional longevity, i.e. the true longevity corrected for production traits. Functional longevity is defined at this step, by integrating the level of production as fixed effect in the analysis of longevity criteria described in Table 1.

Different methods are used for the genetic evaluation of longevity traits.

The first method is based on linear models. The main advantage of these models is their ease of implementation because they are used for most of the traits under selection. But they have different drawbacks regarding longevity:

* they do not fit well longevity because longevity indicators do not follow a normal distribution
* they consider only animals that have finished their productive life (unless separate predictors are used). This has two consequences: the longevity data are skewed if living animals are ignored; the breeding value is available lately in the life of the animals. This is notably the case for males for whom most of their offspring must be culled to be evaluated.
* they are not able to include time-dependant variables (e.g. parity, lactation stage). Time dependant variables are useful to take into account the changes in breeding conditions that occur during the life of the animal, and thus to better model longevity data.

The second method is based on proportional hazard model or survival analysis. This type of model counterbalances all the drawbacks of linear models and thus, are the best ones to estimate breeding values for functional longevity. Nevertheless, they are complicated to implement in a routine genetic evaluation process, and a few software exist for genetic survival analyses such as Survival kit, (Ducrocq et al, 2005). However, an evaluation based on an animal model is not feasible in large dataset, leading to use sire-maternal grand-sire models or sire models. Under this assumption, ewes/does EBVs are not available (Ducrocq, 2001).

A third method, less widespread, considers the first three lactations as separate traits in a multiple trait animal linear model. Each lactation is assigned to 1 (instead of 0) once the female reaches the next lactation.

===== Factors of variation =====
The main factors of variation of longevity data are:

* herd/flock
* year
* kidding/lambing season
* birth season
* age at first lambing/kidding
* breed
* herd/flock size and herd/flock size variation
* lactation stage, parity (if survival analysis model)
* number of lambs/kids born and reared (for meat sheep and goats)
* within herd/flock production level: this factor of variation is essential to integrate to estimate the functional longevity. Usually, it is the within herd/flock level of production (and not the absolute level of production) that is considered because it explains the decision of the breeder to cull the animal.

===== Heritabilities of functional longevity =====
Heritabilities range between 5% and 17% (Sasaki, 2013, Castañeda-Bustos et al., 2014, Geddes et al., 2017, Palhière et al (2018), Buisson et al (2022), Pineda-Quiroga & Ugarte, 2022) indicating that this trait has a low to moderate genetic background. This might be due to the composite signification of longevity, which represents a synthesis of various abilities (see § on predictors).

However, the genetic variation coefficients are moderate suggesting that a genetic variability may be exploited to set up a selection programme.

===== Genetic correlations =====
The genetic correlations between functional longevity and other traits are:

* close to 0 for milk production traits. This results from the model, in which longevity is corrected for level of production,
* from 0 to 0.40 for udder type traits (Castañeda-Bustos et al., 2014). The rear udder attachment and the udder floor position are the most correlated to functional longevity,
* from 0.20 to 0.50 for general conformation,
* from 0.01 to 0.15 for reproduction traits (kidding interval, age at first kidding, artificial insemination fertility),
* from -0.15 and -0.40 for somatic cell counts.

===== EBVs and reliabilities =====
For dairy animals, because of the low accuracy of breeding values, only males (and especially artificial insemination males) evaluated from the longevity data of their daughters, have EBVs that can be used for selection. A minimum number of daughters culled per sire is required to reach a sufficient accuracy. The consequence is that the AI males get their first longevity EBV quite late in their life. Survival analysis models, because they consider censored data (living daughters), enable better accuracy and thus, an earlier EBV for AI males.

Other strategies are possible to increase the accuracy of functional longevity EBVs:

* introduce genomic information in the genetic evaluation
* use a multiple trait model, including both functional longevity and other traits considered as predictors of longevity listed below.

Given the low heritability of survival traits, the fact that it is expressed late in life (at death or culling), the trait becomes accurate enough when sufficient information on culling or reproduction/lactation is available. It is necessary to enhance direct evaluations by indirect information coming from early predictors. Some relevant predictors are listed below:

* Morphological traits, such as general conformation or udder morphology (especially in dairy species),
* Reproduction traits (fertility, lambing/kidding interval, age at first lambing/kidding, pregnancy scan results, …),
* Udder health, and particularly milk somatic cell count,
* Resistance to disease such as resistance to parasites or to footrot,
* Traits related to feet and legs, such as lameness or twisted or bowed legs, closed or opened hocks,
* Serum immunoglobulin concentration in the early life (Ithurbide et al, 2022a),
* Maturity (dairy species) that can be defined as the ability to maintain a good level of production over the parities, independently of the level of production on the whole lifetime (equivalent of a persistency, but over the lactations and not over the test-days) (Arnal et al, 2022),
* Milk metabolites (Ithurbide et al, 2022b)
* Body tissue mobilisation (McLaren et al., 2023). It was demonstrated that ewe tissue mobilisation was genetically associated with ewe fertility and productive longevity (such as pregnancy scan result, foetal loss from scan to lambing, lamb loss from lambing to weaning, number of lambs weaned). It is made possible by collecting body condition score (BCS) data throughout the reproductive cycle (e.g. pre-mating, pregnancy scan, pre lambing, mid lactation, weaning) and calculating gain or loss of BCS between physiological stage.

These predictors are linked to longevity traits. An unfavourable udder shape, reproduction disorders, a susceptibility to a given disease or a low maturity may lead to involuntary culling and therefore a low longevity of the animal. Few genetic correlations have been published but correlations between EBVs show favourable correlations between these predictors and longevity.

Longevity traits, once evaluated, either in linear or survival analysis model, may be combined with the longevity traits in a multi-trait evaluation, to incorporate the information from early predictors.

A full multiple trait evaluation is not feasible in large datasets. Therefore, approximate strategies must be used, such as considering records adjusted for all non-genetic effects in linear models (yield deviation or daughter yield deviation, other type of pseudo records), or sub-indices incorporating traits that are linked together e.g. pulling together data on footrot, mastitis and parasite resistance could be considered together in a ‘health’ sub-index.

==== Culling causes ====
Even though the knowledge of the causes of culling is not necessary to generate a phenotype of longevity and an EBV of functional longevity, the knowledge of the causes of culling, through an observation based on a sufficient panel of flocks/herds, and repeated each year, may give relevant information on the hierarchy and the evolution of the culling causes. It may also enable better understanding of the strategies of culling by farmers leading to better modelling of functional longevity.

Culling causes may be collected with different levels of precision, from a general group of causes to a precise cause, through intermediate information.

In sheep as in goat, the following group of culling causes may be collected:

* Udder health (mastitis)
* Udder morphology
* Production ability
* Respiratory disorders
* Reproduction disorders
* Digestive disorders
* Nervous disorders
* Musculoskeletal disorders
* Skin disorders
* Conformation
* General condition
* Age
* Behaviour
* Accident
* Other ailments (e.g. sudden death, brucellosis, intoxication, fever …)
* Voluntary culling

Each group may be completed with sub-group or precise cause. Below are two examples, first for udder health (table 2), second for reproduction disorders (table 3).

{| class="wikitable"
| colspan="3" |'''Table 2. Detailed categorisation of udder health culling causes.'''
|-
|'''Group'''
|'''Sub-group'''
|'''Specific cause'''
|-
| rowspan="21" |Udder health (mastitis)
| rowspan="2" |Gangrenous mastitis
|Gangrenous mastitis
|-
|Brief mastitis
|-
| rowspan="7" |Characteristic symptoms
|Mastitis
|-
|Clinical mastitis
|-
|Mastitis during suckling
|-
|Coliform mastitis
|-
|Listeria mastitis
|-
|Mastitis before lambing/kidding
|-
|Agalactia mastitis
|-
|Functional symptoms
|Blood in the milk
|-
| rowspan="6" |Chronic mastitis, palpation
|induration of the udder
|-
|Bumps in the udder
|-
|Nodules
|-
|Mammary abcess
|-
|Saggy udder
|-
|Visna mastitis
|-
| rowspan="2" |Unbalanced udder
|Milk in one side
|-
|Unbalanced udder
|-
| rowspan="2" |Subclinical
|Subclinical mastitis
|-
|Somatic cell count (SCC) and California mastitis test– CMT
|-
|Other
|Other
|}

{| class="wikitable"
| colspan="3" |'''Table 3. Detailed categorisation of reproduction disorders culling causes'''
|-
|'''Group'''
|'''Sub-group'''
|'''Specific cause'''
|-
| rowspan="28" |Reproduction disorders
| rowspan="3" |Fecundity
|Open + infertile
|-
|Lately fertile, out of season
|-
|Ram infertile
|-
| rowspan="6" |Gestation
|Abortion
|-
|Vagina or rectal prolapse
|-
|Pregnancy toxaemia
|-
|Difficult gestation
|-
|Early abortion
|-
|Late abortion
|-
| rowspan="8" |Lambing/kidding
|Difficult lambing/kidding
|-
|Caesarean
|-
|Uterus inversion
|-
|Infection during lambing/kidding
|-
|Vagina or rectal prolapse
|-
|non deliverance
|-
|Acute metritis
|-
|Chronic metritis
|-
| rowspan="4" |Miscellaneous
|Reproduction disorders
|-
|Vaginal sponge infection
|-
|Hermaphrodite
|-
|Various
|-
| rowspan="4" |Male: testicles
|1 testicle
|-
|Small testicles
|-
|Abscess
|-
|Contagious epididymitis
|-
| rowspan="3" |Male: penis
|Urinary gravel
|-
|Wound
|-
|Phimosis
|}

=== Acknowledgements ===
We gratefully acknowledge the contributions to these lifetime resilience guidelines by the following people:

* Joanne Conington, SRUC, the UK
* Marija Špehar, Centre for Livestock Breeding Zagreb, Croatia
* Isabelle Palhière, INRAE, France
* Jean-Michel Astruc, IDELE, France
* Carolina Pineda Quiroga, NEIKER, France

This work received funding from the European Unions’ Horizon 2020 Research & Innovation program under grant agreement N°772787—SMARTER.

=== References ===
Arnal M., Palhiere I., Clément V. (2022). Maturity, a new indicator to improve longevity of Saanen dairy goats in France. Proceedings of 12th World Congress on Genetics Applied to Livestock Production (WCGALP), Jul 2022, Rotterdam, Netherlands. doi:10.3920/978-90-8686-940-4_738.

Brotherstone, S., Veerkamp, R. F. and Hill, W. G. (1997). Genetic parameters for a simple predictor of the lifespan of Holstein-Friesian dairy cattle and its relationship to production. Animal Science 65: 31-37.

Buisson D., J.M. Astruc, L. Doutre, I. Palhière. Toward a genetic evaluation for functional longevity in French dairy sheep breeds. Proc 12th WCGALP, 2022

Castañeda-Bustos, V. J., Montaldo, H. H., Torres-Hernández, G., Pérez-Elizalde, S., Valencia-Posadas, M., Hernández-Mendo, O., & Shepard, L. (2014). Estimation of genetic parameters for productive life, reproduction, and milk-production traits in US dairy goats. Journal of Dairy Science, 97(4), 2462-2473.

Castañeda-Bustos, V. J., Montaldo, H. H., Valencia-Posadas, M., Shepard, L., Pérez-Elizalde, S., Hernández-Mendo, O., & Torres-Hernández, G. (2017). Linear and nonlinear genetic relationships between type traits and productive life in US dairy goats. Journal of Dairy Science, 100(2), 1232-1245.

Conington, J., Bishop, S. C., Grundy, B., Waterhouse, A., & Simm, G. (2001). Multi-trait selection indexes for sustainable UK hill sheep production. Animal Science, 73(3), 413-423.

Conington, J., Bishop, S.C., Waterhouse, A. and Simm, G. (2004). A bio-economic approach to derive economic values for pasture-based sheep genetic improvement programmes. Journal of Animal Science 82: 1290-1304. <nowiki>https://doi.org/10.2527/2004.8251290x</nowiki>

Ducrocq, V. (2001). A Two-Step Procedure to get Animal Model Solutions in Weibull Survival Models Used for Genetic Evaluations on Length of Productive Life. Interbull Bulletin, vol.27, pp.147-152

Ducrocq, V. (2005). An Improved model for the French genetic evaluation of dairy bulls on length of productive life of their daughters. Animal Science, 80(3), 249-256.

Geddes, L., Desire, S., Mucha, S., Coffey, M., Mrode, R. and Conington, J. (2018). Genetic parameters for longevity traits in UK dairy goats. IN: Proceedings of the World Congress on Genetics Applied to Livestock Production, Volume Species - Caprine: 547.

Ithurbide, M., Huau, C., Palhière, I., Fassier, T., Friggens, N. C., & Rupp, R. (2022a). Selection on functional longevity in a commercial population of dairy goats translates into significant differences in longevity in a common farm environment. Journal of Dairy Science, 105(5), 4289-4300.

Ithurbide, M., Wang, H., Huau, C., Palhière, I., Fassier, T., Pires, J. & Rupp, R. (2022b). Milk metabolite profiles in goats selected for longevity support link between resource allocation and resilience. In Proceedings of 12th World Congress on Genetics Applied to Livestock Production (WCGALP) pp. 276-279

McLaren A, Lambe, N R and Conington J. (2023). Genetic associations of ewe body condition score and lamb rearing performance in extensively managed sheep. 105336. Livestock Science September 2023 <nowiki>https://doi.org/10.1016/j.livsci.2023.105336</nowiki>

Palhière I., C. Oget, R. Rupp, Functional longevity is heritable and controlled by a major gene in French dairy goats, 11th WCGALP, Auckland, Nouvelle-Zelande, 11-16 février 2018

Pineda-Quiroga, C., Ugarte, E. (2022). An approach to functional longevity in Latxa dairy sheep. Livestock Science 263, 105003

Sasaki, O, (2013), Estimation of genetic parameters far longevity traits in dairy cattle: A review with focus o n the characteristics of analytical models, Animai Science Journal, 84(6), 449-460,

SMARTER Deliverable 2,2 - "New breeding goals far lifetime resilience far materna!sheep breeding programmes"

== Guidelines on survival recording of foetus and young in sheep and goats ==
'''Change Summary'''
{| class="wikitable"
|'''Date of change'''
|'''Nature of Change'''
|-
|October 2024
|First version
|-
|December 2024
|Tracked change revisions by MS
|}

=== Introduction and scope ===

==== Introduction ====
Foetal and young survival are parameters linked to neonatal vigour scores, maternal and young behaviours, stress responses, immunity transfer and traits related to dam fertility and longevity. Minimising mortality, either in utero (e.g., embryo/foetus) or pre-weaning, are crucial to profitable small ruminant production systems. Survival depends on an interaction between the environment and behaviour of both, the ewe and the lamb. Ewes must give birth without complications and provide reliable source of colostrum along with mothering environment. Lamb must adapt to the extra-uterine environment, thermoregulate and be able to stand and suckle in a reasonably short period after birth (Brien et al., 2014; Plush et al., 2016). Despite this, pre- weaning survival in many species is far from ideal (Binns et al., 2002; Yapi et al., 1990, Chaarani et al., 1991, Green and Morgan, 1993, Nash et al., 1996). This can be particularly worse in small ruminant production systems which are typically more extensive and therefore prevailing weather conditions can be an additional stressor as well as predators. Moreover, the poly-ovulatory nature of species such as sheep and goats also predisposes such species to greater foetal and pre-weaned young losses (Scales et al., 1986).

Litter size can be determined using trans-abdominal ultrasonography of the uterine horns at ideally 40-70 days post-fertilisation. Good accuracy in determining foetal number has been reported from trans-abdominal ultrasonography (Taverne et al., 1985). The number of young eventually born can then be used to assess foetal loss since the time of scanning. At birth, young survival is usually based on dead or not in the first 24 h post-birth while stillborn individuals or those dead within 24 hours are usually defined as failed to survive. Young survival can also be considered as different age group categories until weaning – for example from 1 day to 7 days of age. Young animals (i.e., < 7 days) are greatest at risk of mortality (Binns et al., 2002) and tend to die of exposure to hypothermia, starvation, septicaemia, or repercussions from trauma suffered at birth.

==== Scope ====
The aim of the present section is to define approaches for the definition of foetal and lamb survival as well as the data editing and downstream analyses (including statistical models).

=== Definition, terminology, rationale ===
A plethora of different definitions exist depending on whether defined at the level of the individual (i.e., binary trait) or that of the litter. A non-exhaustive list is given below.

'''Foetal survival (at an individual level):'''

* Can be defined as a binary trait of 0 (died between scanning and birth) or 1 (survived between scanning and birth). A dummy ID for the dead foetus would need to be constructed but the parentage would still potentially be known (especially if generated from AI).

'''Foetal survival (at a litter level):'''

* Whether or not some foetal mortality has occurred defined as a binary trait (i.e., the number of individuals born is less than the number scanned in utero)
* Number of individual foetuses scanned alive (along with gestational age)
* Number of foetuses scanned minus the number that were born (dead or alive) – this is a measure of foetal mortality as opposed to survival and assumes stillborn young are considered in the definition of a young survival trait. It is a count trait
* The number of young born divided by the number of foetuses scanned (this is mortality rate figure but per little with a penalty on losses for smaller litter sizes).

'''Young survival (at an individual level):'''

* Can be defined as a binary trait of 0 (dead within 24 hours of birth) or 1 (alive after 24 hours of birth). The dead animal would need to receive an ID and can, of course, be genotyped to verify parentage (but also used for downstream genomic analyses discussed later).

'''Young survival (at a litter level):'''

* Number of lambs born alive (NLBA)
* Number of lambs dead within 24 hours of birth
* Number of lambs dead within 24 hours of birth divided by the total number of lambs born

=== Recording survival of foetuses and young in small ruminant ===
In all instances, accurate data is crucial. Data should be collected on the animal/dam itself (dead or alive) but also potential confounding effects that could be considered for inclusion in the statistical model as fixed effects. Examples include contemporary group (e.g., flock-date of scanning, flock-year-season of birth (for each NLB separately), ewe parity, litter size). Ideally also all individuals should be genotyped. Because the heritability of foetal or young animal mortality in small ruminants is relatively low (<0.1; Safari et al., 2005; Brien et al., 2014), a large number of records are required to achieve accurate genetic/genomic evaluations. Care should also be taken when interpreting the scoring (and the following genetic evaluations), some jurisdictions may record mortality rather than survival or may record mortality but propose genetic evaluations as survival (i.e., positive value is favourable).

==== Pregnancy scanning records ====
Ideally scanning should be undertaken 40 to 70 days post-fertilisation. This may be possible to (easily) achieve where extensive AI has been used but, otherwise, should ideally be 30 days after the last female has been marked as been served by natural mating. Skilled operators should be able to determine the number of foetuses from 30 to 100 days of gestation; usually only one operator will scan a flock on a given day so will be confounded with flock-date of scanning contemporary group. If AI is solely used or if single sire mated, then the parentage of the foetus should be known; if mob mated or single sire mated at AI, then superfecundation could cause a discrepancy in recorded sire.

==== Young survival ====
Young survival can be defined at birth, ideally as a binary trait as to whether the animal was born stillborn or died within 24 hours (survival = 0) or was still alive 24 hours after birth (survival = 1). If information is also available on the reason for death (i.e., autopsy results) then, where sufficient data exists for any one ailment, it could be analysed separately as separate traits. This could be particularly important for generating separate genetic evaluations for the main diseases thereby not only possibly increasing the heritability through more accurate data, but also provide genetic evaluations specific to individual ailments which could enable more selection pressure on these traits in situations where they are more impactful. Ideally a genotype of the dead animal should be generated. Any obvious external defects should be noted.

==== Ancillary information ====
Having ancillary information coinciding with an event is useful for several reasons:

* For helping data editing (e.g., comparing actual birth date to expected birth date based on recorded service information)
* For adjustment in the statistical model (e.g., dam parity)
* Understanding the risk factors associated with survival
* Enabling more precise estimates of correlations with other performance traits by having information on multiple features from the same animal
* Adjusting for possible selection in multi-trait genetic evaluation models

Possible ancillary information can be divided into those associated with 1) the past of prevailing environmental conditions, 2) the dam (or sire), or 3) the individual. Examples include:

1. Environment:

* Weather related factors (rainfall, temperature, wind including direction)
* Flock
* Date of scanning or date of birth

2. Dam

* Parity
* Age
* Breed
* Genotype
* Litter size
* Mating type (i.e., AI versus natural)
* Body condition score (change) and live-weight (change)
* Mothering ability
* Colostrum quality and yield

3. Individual

* Days since service (for foetal survival trait)
* Birthing difficulty
* Birth weight
* Gender
* Genotype
* Sire
* Autopsy results if possible

=== Use for genetic analysis / genetic evaluation ===

==== Data editing and statistical modelling ====
In order to estimate contemporary group effects well, the larger the contemporary group, the better the group estimates. Therefore, imposing a minimum contemporary group size prior to data analysis should be considered as should good genetic connectedness with other contemporary groups. Genetic connectedness can be an issue with small ruminant populations in particular, especially where natural mating prevails.

===== Data editing =====
'''Foetal survival''' ''-'' Each flock-scanning date can be firstly investigated at a macro level to measure ultrasound quality control. Simple cross-references between the number of females with scanning data versus those presented as well as the ID numbers of both is useful to ensure all data were properly recorded. High foetal mortality rates could simply be indicative of high foetal loss (e.g., abortions due to causes like chlamydial and toxoplasma) as well as poor operator competence – assessing the rate for individual operators across flocks (and time) could be useful to assess operator proficiency. A high proportion of litters where the number of young born (dead or alive) exceeds that recorded at scanning suggests a poor accuracy of recording. It should be considered to discard the data from that date but also to investigate the operator in more detail across other flocks, and irrespective, the scanning results from that litter at least should be discarded. The proportion of scanned litters with >3 detected foetuses should also be calculated; depending on the expected prolificacy of the animals (e.g., breed), then the appropriate editing of either the individual data points or the date in its entirety should be assessed.

'''Young mortality''' ''-'' A high incidence of young mortality per contemporary group could simply be a consequence of some underlying issue (e.g., predation, disease) or indeed a high fecundity rate; a low incidence of young could be indicative of a good stock person. Therefore, it can be difficult to distinguish between high and low quality data. Using guaranteed high quality and reliable data, it is possible to estimate the expected distribution of the incidence of young animal mortality for different population strata such as flock size, ewe age, breed, litter size. Using these distributions, the probability that the mean mortality for a contemporary group fits this distribution can be estimated and a decision made as to whether or not to include the data in the downstream analyses.

===== Statistical modelling =====
Lamb survival is a complex trait influenced by direct genetic, maternal genetic, and environmental effects. Due to discrete expression of phenotype (dead or alive: 0 or 1) it is described as a threshold trait (Falconer, 1989) that violates the assumption of normality, and therefore linear models are theoretically not appropriate for the analysis. However, examples from the literature analysed survival data and reported that linear models were marginally more accurate at predicting missing phenotypes than were logit-transformed alternatives and are convenient for interpretation on the observed scale (Matos et al., 2000; Everett-Hincks et al., 2014; Cloete et al. 2009; Vanderick et al., 2015;).

Random effects considered in the statistical model are direct and maternal genetic effects and maternal permanent environment across parities. A litter permanent environmental effect should also be considered as a random effect where the trait is that of the individual (and not the ewe). Traditionally, relationships were accounted for though the pedigree data, however this can often now be supplemented with genome-wide genotype information to generate a H matrix (i.e., combines genomic and ancestry information). Whether the estimation of these additional covariance components improve the fit to the data can be deduced by a likelihood ratio test but ideally a metric such as the AIC or BIC to account for the increased complexity of the model.

The choice of environmental factors included in the model will depend on the population being studied and considers the following fixed effects:

* Contemporary group (e.g., flock-date of scanning for foetal survival and flock-year-season of birth or flock-year-season-birth rank of birth)
* Lamb gender (may not be possible for foetal survival trait)
* Dam parity
* Mating type (i.e., AI versus natural)
* Dam age nested within parity
* Day of gestation (for foetal survival) if available or defined as a categorical variable
* Litter size (at scanning or birth) or birth type (single and multiple)
* Heterosis and recombination loss of the dam and foetus/young
* Inbreeding coefficient of the dam and foetus/young
* Age of the sire
* Breed composition of the dam and foetus/young

Adjusting for the effects such as dystocia or birth weight, may not be appropriate in the statistical model for young survival as they are likely to be genetically correlated with survival and thus may remove some of the true genetic variance – nonetheless, the eventual decision will be based on the genetic evaluation system employed and how the economic value on the traits within the overall breeding objectives are constructed.

==== Genomic association analyses ====
Where genotypes are available, then a genome-wide association study (or candidate gene study) can be undertaken (Esmaeili-Fard et al., 2021). Although it is not possible to have the genotype of the aborted foetus, it could still be possible to undertake a genomic analysis especially by focusing on the genotype/haplotype of the living animals versus the expectation based on the genotype/haplotype of the parents (Ben Braiek et al., 2021).

=== Acknowledgements ===
We gratefully acknowledge the contributions to these recording of survival of foetus and young guidelines by the following people:

* Donagh Berry, TEAGASC, Ireland
* Joanne Conington, SRUC, the UK
* Maxime Ben Braiek, INRAE, France
* Arnaud Delpeuch, IDELE, France
* Marija Špehar, Centre for Livestock Breeding Zagreb, Croatia

This work received funding from the European Unions’ Horizon 2020 Research & Innovation program under grant agreement N°772787—SMARTER.

=== References ===
Ben Braiek, M., Fabre, S., Hozé, C., et al. (2021). Identification of homozygous haplotypes carrying putative recessive lethal mutations that compromise fertility traits in French Lacaune dairy sheep. Genet. Sel. Evol. 53:41. <nowiki>https://doi.org/10.1186/s12711-021-00634-1</nowiki>

Binns, S.H., I.J.Cox, S. Rizvi, L.E.Green. (2002). Risk factors for lamb mortality on UK sheep farms. Prev.Vet. Med.. 52:287-303.

Brien, F.D., Cloete, S.W.P., Fogarty, N.M., Greeff, J.C., Hebart, M.L., Hiendleder, S., Hocking Edwards, J.E., Kelly, J.M., Kind, K.L., Kleeman, D.O., Plush, K.L., Miller, D.R (2014). A review of genetic and epigenetic factors affecting lamb survival. Anim. Prod. Sci. 54:667–693.

Chaarani, B., Robinson, R.A., Johnson, D.W. (1991). Lamb mortality in Meknes Province (Morocco). Prev. Vet. Med. 10:283-298.

Cloete, S.W.P., Misztal, I., Olivier, J.J. (2009). Genetic parameters and trends for lamb survival and birth weight in a Merino flock divergently selected for multiple rearing ability. J. Anim. Sci. 87:2196–2208. doi:10.2527/jas.2008-1065.

Esmaeili-Fard, S.M., Gholizadeh, M., Hafezian, S.H., Abdollahi-Arpanahi, R. (2021) Genes and Pathways Affecting Sheep Productivity Traits: Genetic Parameters, Genome-Wide Association Mapping, and Pathway Enrichment Analysis. Front. Genet. 12:710613. doi:10.3389/fgene.2021.710613.

Everett-Hincks, J.M., Mathias-Davis, H.C,, Greer, G.J., Auvray, B.A., Dodds, K.G. (2014). Genetic parameters for lamb birth weight, survival and deathrisk traits. J. Anim. Sci. 92:2885–2895. doi:10.2527/jas.2013-7176.

Falconer, D.S. (1989). Introduction to Quantitative Genetics.’ (Longmans Green/John Wiley & Sons: Harlow, Essex, UK).

Green, L.E., Morgan, K.L. (1993). Mortality in early born, housed lambs in south-west England. Prev. Vet. Med. 17:251-261.

Matos, C.A.P., Thomas, D.L., Young, L.D., Gianola, D. (2000). Genetic analyses of lamb survival in Rambouillet and Finnsheep flocks by linear and threshold models. Anim. Sci. 71:227–234. doi:10.1017/S1357729800055053.

Nash, M.L., Hungerford, L.L., Nash, T.G., Zinn, G.M. (1996). Risk factors for perinatal and postnatal mortality in lambs. Vet. Rec. 139:64-67.

Plush, K.J., Brien, F.D., Hebart, M.L., Hynd, P.I. (2016). Thermogenesis and physiological maturity in neonatal lambs: a unifying concept in lamb survival. Anim. Prod. Sci. 56:736–745. <nowiki>https://doi.org/10.1071/AN15099</nowiki>.

Safari, E, Atkins, K.D., Fogarty, N.M., Gilmour, A.R (2005). Analysis of lamb survival in Australian Merino. Proceedings of the Association for the Advancement of Animal Breeding and Genetics. 16:28–31.

Scales, G. H., Burton R. N., Moss, R. A. (1986). Lamb mortality, birthweight, and nutrition in late pregnancy. N. Z. J. Agric. Res. 29:1.

Taverne, M.A.M. Lavoir, M.C., van Oord R., van der Weyden, G.C. (1985) Accuracy of pregnancy diagnosis and prediction of foetal numbers in sheep with linear‐array real‐time ultrasound scanning. Vet. Q. 7:(4)256-263, DOI: 10.1080/01652176.1985.9693997.

Vanderick, S., Auvray, B., Newman, S.A., Dodds, K.G., Gengler, N., EverettHincks, J.M. (2015). Derivation of a new lamb survival trait for the New Zealand sheep industry. J. Anim. Sci. 93:3765–3772. doi:10.2527/jas.2015-9058.

Yapi, C.V., Boylan, W.J., Robinson, R.A. (1990). Factors associated with causes of preweaning lamb mortality. Prev. Vet. Med., 10:145-152.

The technical references (papers cited or used) are documented in each piece of recommendations.

== Guidelines on recording behavioural traits in sheep and goats ==
{| class="wikitable"
|'''Date of change'''
|'''Nature of Change'''
|-
|October 2024
|First version
|-
|November 2024
|Tracked change revisions by JC
|-
|December 2024
|Tracked change revisions by MS
|}

=== Introduction and scope ===

==== Introduction ====
Genetic selection including behavioural traits could be an advantageous strategy for improving robustness and welfare of farm animals in various farming conditions by minimizing unsuitable responses to changes in their social and physical environment, limiting an excessive fear of humans and improving sociability (Mignon-Grasteau et al., 2005). Farm animals are social and gregarious, and relational behaviours are essential for ensuring social cohesion, social facilitation, offspring survival and docility toward humans. Breed differences and genetic variation within breed have been reported in lambs for early social behaviours and found to be heritable, and associated with some QTL, suggesting such behaviours could be selected early (Boissy et al., 2005; Beausoleil et al., 2012; Hazard et al., 2014; Cloete et al., 2020). In addition, such early social reactivity of lambs towards conspecifics or humans was identified as a robust trait and that selection for early social reactivity of lambs towards conspecifics or humans is feasible (Hazard et al., 2016; 2022).

The behaviour of both ewes and lambs, and their interaction at lambing, have been widely described. Such behaviour is important for the survival of the offspring, especially in extensive farming conditions as reviewed by Dwyer et al. (2014). Moreover, it has been shown that primiparous ewes are more prone to abandon their lambs due to their lack of maternal experience (Dwyer, 2008) and that lamb survival at birth is lowly heritable (Brien et al., 2014). Taken together these factors could hinder the development of extensive farming systems. Genetic selection on maternal attachment traits could therefore be advantageous to improve offspring survival and growth, and reduce labour, as suggested by Mignon-Grasteau et al. (2005). Genetic variations in maternal behaviour between breeds of sheep have been well documented (for review see: Dwyer, 2008; von Borstel et al., 2011) while little was known about within-breed genetic variability and even less about maternal reactivity traits. We hypothesized that maternal attachment to the litter has a genetic component in sheep, and we recently reported that as expected the maternal reactivity at lambing is a heritable trait (Hazard et al., 2020;2021).

Grazing behaviour is also important for animals raised in extensive production systems because it can support adaptability to changing environments. In particular, small ruminants reared in semi-extensive systems face many environmental and welfare challenges that are difficult to quantify. The evidence in the literature suggests that there are differences in grazing behaviour between and within breeds of sheep (Simm et al., 1996; Brand, 2000). The notion is that natural selection combined with subjective artificial selection have led to some animals being more adaptive to extensive conditions. In this regard, genetic variation may exist for key grazing behaviour traits (Simm et al., 1996; Dwyer et al., 2005), but relevant literature is scarce. During the SMARTER H2020 project, a study was performed on grazing behaviour of the indigenous Boutsko Greek mountainous sheep breed, which is reared semi-extensively. The results showed that duration of grazing and speed are heritable traits (Vouraki et al., 2025).

==== Acronyms used in these guidelines ====

* AT Arena Test
* CT Corridor Test
* GPS Global Positioning System
* LS Lambing Site
* PCA Principal Component Analysis

==== Scope ====
The aim of the present report is i) to define the behavioural traits of interest, ii) to describe approaches for behavioural measurements, iii) to describe their use for genetic analysis and evaluation.

To-date, the present guidelines describe 3 groups of traits related to behaviour:

* Behavioural reactivity towards conspecifics or humans
* Maternal reactivity
* Behaviour at grazing

Kid/lamb vigour is a relevant behavioural trait, but this trait is tackled within the section “foetus and young survival in sheep and goats” of the guidelines.

Most of the work undertaken on behaviour concerned sheep. This has been particularly the case in SMARTER. Most of the recommendations might be applied to goats as well. Nevertheless, we will use the ovine terms in the guidelines below.
[[File:Section_24-1_Three_groups_of_traits_related_to_behaviour_guidelines.jpg|center|thumb|600x600px|Three groups of traits related to behaviour guidelines]]

=== Behavioural reactivity towards conspecifics or humans ===

==== Definition, terminology, rationale ====

===== Behavioural reactivity towards conspecifics (i.e. sociability): =====
It is the social motivation of the lambs to join their conspecifics in response to social isolation with or without presence of a motionless human. Expression of higher levels of a panel of behaviours, including vocalisations and locomotion, is hypothesised as an active way to maintain social link with conspecifics.

==== Behavioural reactivity towards humans (i.e. docility): ====
It is the reactivity of isolated lambs to a walking human. Higher flight distance between the lamb and a human indicates a lower docility toward a human.

Behavioural reactivity towards conspecifics and humans are measured in standardised behavioural tests (arena and corridor tests, described below).

Higher sociability and/or docility towards humans may improve adaptation of sheep to harsh environments through social facilitation (i.e. transmission of feeding preferences…), social cohesion (i.e. transhumance…) and reactivity to handling. Consequently, improving such behavioural traits may improve welfare, production, and labour of shepherd.

==== Data recording ====

===== Behavioural tests =====
The test described below have been implemented in France. It must be considered as a possible test, as others can be described later and enrich these guidelines.

Lambs must be individually exposed just after weaning (i.e. approximately 10 days after weaning) to two behavioural tests. The delay between weaning and behavioural tests must be sufficient for the change of social preferences of lambs for their dam to conspecifics.

The arena test (AT) consists of two successive phases evaluating 1) reactivity to social isolation (AT1), 2) the motivation of the lamb towards conspecifics in presence of a motionless human (AT2). The arena test is performed indoors. The arena test pen consists in an unfamiliar enclosure virtually divided into 7 zones as described in detail by Ligout ''et al''. (2011) (Figure 1). On one side of the enclosure (i.e. at the opposite of the entrance), a grid separates the tested lamb from another smaller pen containing 3 or 4 conspecifics. The first phase of the test (arena test phase 1, AT1) starts once the tested animal joins its flock-mates located behind a grid at the opposite side of the arena (time duration for joining: lower than 15 sec). No behavioural recording is performed during the joining. At this time, an opaque panel is pulled down (from the outside of the pen) between the flock-mates and the tested lamb to prevent visual contact. After one minute the phase 1 stops and the panel is pulled up so the lamb can see its flock-mates again. Once the lamb has returned near to its flock-mates, or after 1 minute if the lamb did not do so, a non-familiar human slowly enters the arena through a door located near the pen of the flock-mates and stood 20 cm in front of the grid separating the arena from the lamb’s flock-mates. The second phase (arena test phase 2, AT2) starts once the human is in place and lasts for a further 1 minute.
[[File:Experimental_setup_of_the_arena_test_for_estimating_the_social_reactivity_of_lambs.jpg|center|thumb|600x600px|Figure 1. Experimental setup of the arena test for estimating the social reactivity of lambs. At the beginning of the test, animals can join their flock mates placed behind a grid barrier (social attraction, phase 0) and then were individually exposed to the social isolation (phase 1), and to the social attraction in presence of a motionless human (phase 2). (Adapted from Ligout et al., 2011)]]
The corridor test (CT) consists of two successive phases evaluating 1) reactivity to social isolation (CT1) and 2) reactivity to an approaching human (CT2). The test pen consists in a closed, wide rectangular circuit and has been described in detail by Boissy ''et al''. (Boissy et al., 2005) (Figure 2). The first phase (corridor test phase 1, CT1) starts when the lamb enters the testing pen and lasts for 30 seconds. After that time a non-familiar human enters the testing pen and the second phase (corridor test phase 2, CT2) starts and lasts 1 minute. During this phase, the human walks at a regular speed through the corridor (the corridor is divided into 6 virtual zones and one zone is crossed every 5 seconds) until two complete tours has been achieved.

===== Behavioural traits =====
Several behaviours are measured during behavioural tests: vocalisations (i.e. frequency of high- pitched bleats), locomotion (i.e. number of virtual zones crossed), the proximity score (i.e. weighting of time spent in virtual zones, a high score indicated a high duration spent close to conspecifics and a human).

An investigator counts the lamb’s vocalisations directly during the tests, from outside the pen using a laptop: number of times the animal bleats with an open mouth (high bleats, AT1/2- HBLEAT, CT1-HBLEAT). Locomotor activity is assessed by measuring the number of virtual zones crossed during arena test phases 1 and 2 (AT1/2-LOCOM) and corridor test phase 1 (CT1- LOCOM). This behaviour can be assessed using video recording or using infrared cells regularly positioned along the AT to detect displacement. The proximity to flock-mates and the human during AT2 is calculated by weighting of time spent in virtual zones (i.e. a high score indicated a high duration spent close to conspecifics and a human).

During CT2, every five seconds throughout this phase, an investigator records with a laptop the zones in which the human and the animal are located. In addition, the walking human records with a stopwatch the total duration during which the head of the lamb is visible. The mean flight distance (DIST) separating the human and the lamb (i.e. knowing the length of each virtual zone) and the time during which the human sees the lamb (SEEN) is measured in CT2.

==== Calculation of traits ====
Deviations from normality of row data must be tested using relevant statistical tests (e.g. the Kolmogorov–Smirnov test). Several raw measures must be transformed in order to minimise major deviations from the normal distribution. Square root transformation is applied to AT1/2- HBLEAT, CT1-HBLEAT. A multivariate analysis may be performed to take into account the multidimensional aspect of behavioural responses. Results of principal component analysis (PCA) indicate that the main principal components is structured mainly with similar behaviour (i.e. higher weight of similar behaviours for the different tests on the same component). Consequently, three synthetic variables may be constructed using PCA. Each PCA is performed for a set of similar behavioural variables across the behavioural tests. The first component of each PCA, explaining the largest part of total variance, is defined as a synthetic variable. Two synthetic variables are specific to the reactivity to social isolation: high bleats (HBLEAT, using AT1/2-HBLEAT and CT1- HBLEAT), locomotion (LOCOM, using AT1/2-LOCOM and CT1-LOCOM). One synthetic variable is specific to the reactivity to an approaching human: the tolerance to being approached when the lamb is free to flee (HUMAPPRO, using CT2-DIST and CT2-SEEN).

==== Use for genetic analysis and genetic evaluation ====
Genetic analyses and genetic evaluation can be performed on single traits and synthetic variables. Genetic analyses (estimation of (co)variance components and prediction of breeding values) for quantitative behavioural traits may be implemented with a mixed model methodology in animal model. Random effects should include:

* a direct additive genetic effect of the animal (i.e. lamb),
* a maternal permanent environment effect (i.e dam), that describes lamb phenotypic variation caused by the environment of the ewe
* a litter permanent environment effect, that accounts for phenotypic variation caused by the environment of the litter of the lamb being tested.

All relevant fixed effects and interactions should be included in the model. Factors that could be considered include:

* a combination of the litter size at lambing and the number of lambs suckled with their dam
* sex, age, live weight of the lamb,
* dam parity and/or age of dam nested withing parity if needed contemporary group (e.g., depending on the data collection: flock-year-season, grazing location…)

=== Maternal reactivity ===

==== Definition, terminology, rationale ====
* Behavioural reactivity at lambing (i.e. maternal reactivity). It is the social motivation or attachment of the ewe for the litter expressed in response to an approaching human, or the withdrawal of the litter with or without presence of a human. Expression of higher levels of a panel of behaviours, including maternal behaviour scores, vocalisations and locomotion, is hypothesised as an active way to maintain social link with lambs.

Maternal reactivity is measured in standardised behavioural tests (a scoring test outdoors, an arena test indoors, described in the controlled test below) or a maternal behaviour score (MBS) designed for use in extensive sheep systems as described by O’Connor ''et al'' (1985), the genetic basis of which was reported by Lambe et al., 2001 for Scottish Blackface sheep.

Higher maternal reactivity may improve adaptation of sheep to harsh environments through a higher behavioural autonomy at lambing and a reducing dependency to the support provided by shepherds. Consequently, improving such behavioural traits may improve welfare, production, and labour of the shepherd.

==== Data recording ====

===== Behavioural tests =====
The controlled test described below have been implemented in France. It must be considered as a possible test, as others can be described later and enrich these guidelines. Ewes are individually exposed to two behavioural tests: a scoring test performed just after lambing, outside at the lambing site, and then an arena test performed indoor, one day after lambing. The second test is performed after the bonding period needed to establish the social link between ewes and lambs and which occurs generally within the first twelve hours after lambing (Keller et al, 2003).

Scoring test at lambing site: Maternal reactivity is assessed outside at the lambing site approximately 2 hours after lambing, only on ewes that lambed during daylight when the shepherd approaches the lambing ewes to catch lambs for weighing and identification. Scoring at lambing is not performed in the following situations: if the location of the lambing site does not readily facilitate the testing procedure, if there are perturbations of scoring due to interference by other ewes, for sanitary reasons that could affect behaviours (including difficult lambing, death of all lambs of a litter). Measurement of maternal reactivity at the lambing site (LS) consists of two successive phases: (1) when the shepherd approaches the lambs; and (2) the capture and displacement of the lambs by the shepherd. In the first phase (LS1), the shepherd stands approximately 15 meters away from the lambing spot and approaches the ewes and the lambs at a regular speed (1 m/s). In the second phase (LS2), the shepherd catches all the lambs at the same time and moves away from the lambing spot in the same direction as that of the approach, stopping at the starting point where he places the lambs back on the ground and then moves 15 meters away to allow the ewe to restore contact with her lambs. This second phase of the test is not applied to ewes that flee at the approach of the shepherd and do not return within 60 seconds after the end of LS1.

Arena test: After lambing, all the ewes and lambs (both day and night births) are transferred to a shelter close to the place of lambing and penned individually for few hours. They are then moved to a collective pen until the next day when they are tested in the arena test (24h ± 6h after lambing). The arena test (AT) is performed indoors and adapted from the original test developed by Boissy and colleagues (2005) to investigate social attachment in sheep (Ligout et al., 2011). In the present study, the test consists of three successive phases evaluating the ewe’s 1) attraction to her litter, 2) reactivity to social separation from her litter, and 3) reactivity to a conflict between social attraction to her litter and avoidance of a motionless human. The test pen consists of an unfamiliar enclosure virtually divided into 7 zones (zone 7 being the zone nearest to the litter). On one side of the enclosure, a grid separates the tested ewe from another smaller pen containing her lamb(s). The first phase of the test (AT1) starts when the tested ewe enters the arena and lasts for 30 s. Then, a remotely controlled opaque panel is pulled down in front of the grid to prevent visual contact between the tested ewe and her lambs. The second phase (AT2), during which the tested ewe is separated from her lambs, lasts 1 min. Finally, the panel is raised so the tested ewe can see her lamb(s) again. Once the ewe has returned near to her lamb(s), a non-familiar shepherd slowly enters the arena through a door located near the grid separating the arena from the litter and stands 20 cm in front of the grid. The third phase of the test (AT3) starts once the shepherd is in place and lasts for 1 min.

===== Behavioural traits =====
Scoring test at lambing site: A scoring system, close to those defined by O’Connor et al. (1985), and further validated for hill sheep by Lambe ''et al.'' (2001) for use in animal breeding programmes to enable many animals to be scored relatively quickly and easily in extensive sheep systems. The simple scoring system measures maternal reactivity described for each of the two phases described above. In LS1, a maternal behaviour score (LS1-MBS) is recorded on a 5-point scale as follows: 1 - ewe flees and does not return to the lambs within 60 s; 2 - ewe retreats (i.e., at least 2-3 m) but comes back to her lambs within 60 s; 3 - ewe retreats with at least one lamb and comes back; 4 - ewe retreats and returns repeatedly; 5 - ewe stays close to the lambing spot. In LS2, a second maternal behaviour score (LS2-MBS) is recorded on a 4-point scale as follows: 1 - ewe flees; 2 - ewe stays close to the lambing spot, 3 - ewe follows but from a distance (i.e., 1 to 2 m), 4- ewe follows, staying close to the shepherd (i.e., less than 1 m).

'''Arena test''': Locomotor activity and localisation are analysed from the video footage or infrared cells (as described above). Locomotor activity is assessed by measuring the number of zones crossed during the 3 phases (AT1/2/3-LOCOM). The time spent in each zone is recorded. The ewe’s proximity to the litter and/or the human during phases 1 and 3 (AT1/3-PROX) is calculated using the following formula:
[[File:Arena_test_formula.jpg|left|thumb|410x410px]]

Two types of vocalisations are recorded manually during the test with an electronic device: number of high-pitched bleats are recorded when the animal bleats with an open mouth (AT1/2/3-HBLEAT) and number of low-pitched bleats are recorded when the animal bleats with a closed mouth (AT1/2/3-LBLEAT).

==== Calculation of traits ====
Logarithmic transformation is applied to AT1/2/3-LBLEAT to minimise major deviations from the normal distribution. All other elementary variables described above are directly used for genetic analyses.

==== Use for genetic analysis / genetic evaluation ====
The (co)variance components for quantitative behavioural traits can be estimated by restricted maximum likelihood (REML) methodology applied in an animal model. The (co)variance components for categorical behaviours can be estimated by MCMC and Gibbs sampling methods using a threshold model (Gilmour et al., 2009).

Assuming that all ewes are measured every year, the analyses assume a repeatability model with behaviour measured across productive cycles considered to be the same trait with a constant variance. Random effects typically include a direct additive and permanent environmental genetic effects of the animal (i.e., ewe).

All relevant fixed effects and interactions should be included in the model. Factors that could be considered can include:

* The litter size at lambing.
* Dam parity or age or age of the dam nested within parity (if significant).
* Contemporary group (e.g., depending on the data collection: flock-year-season effect…).

=== Behaviour at grazing ===

==== Definition, terminology, rationale ====
Grazing behaviour is a complex combination of various movements and activities of animals in different spatial-temporal scales (Andriamandroso et al, 2016). Indicative traits related to grazing behaviour include:

* Duration of grazing
* Distance walked
* Speed
* Altitude difference
* Elevation gain/loss
* Energy expenditure at grazing

A better understanding of the phenotypic and genetic background of grazing behaviour traits could help towards the development of appropriate breeding programmes to increase adaptation to extensive rearing conditions. However, recording of such traits is challenging. The use of new technologies such as global positioning systems (GPS) could help towards efficiently monitoring grazing behaviour (Homburger et al., 2014; Feldt and Schlecht, 2016).

==== Data recording ====
The following guidelines for recording grazing behaviour traits of sheep are based on a study implemented in Greece (Vouraki et al., 2025). Specifically, in the latter study, grazing behaviour of Boutsko sheep reared semi-extensively in mountainous regions was monitored using GPS technology. Moreover, phenotypic and genetic parameters for key grazing behaviour traits were estimated. These guidelines could be enriched in the future based on other relevant studies.

Monitoring of sheep grazing behaviour is performed using appropriate GPS devices attached on designated collars (Figure 3). Rotational monitoring of animals can be applied to reduce the number of devices needed. Selected GPS devices should be of low weight in order to be accepted by the animals without any obvious irritation. Batteries with extended life should be used to provide sufficient energy for GPS tracking for as many as possible consecutive days. In the aforementioned study, “Tractive GPS” devices (Tractive, Pasching, Austria) were used that weighed 28 grams. GPS tracking of each animal was performed for 4-10 days at 2-60 minutes intervals; number of tracking days and intervals were based on available signal and animal movement.

GPS generated data of each animal for the total tracking period are exported in .gpx format. In the case of “Tractive GPS”, the location history function of MyTractive web app ([https://my.tractive.com/#/ <nowiki>https://my.tractive.com/#/</nowiki>)] is used to export recorded data. Then, the exported files are split by date using a designated software such as GPSBabel (version 1.8.0). For each animal, daily routes and corresponding GPS data can be visualized and extracted using appropriate software such as Viking GPS data editor and analyser (version 2.0).

Recorded grazing behaviour traits via these devices include duration of daily grazing (min), distance (km), speed (km/hour), minimum and maximum altitude, and total elevation gain. Other useful metrics including number and average distance between tracking points, tracking duration and route followed by the animals should also be extracted to be used in ensuing analyses.
[[File:Figure_3._GPS.jpg|center|thumb|600x600px|Figure 3. GPS device attached on designated collar.]]

==== Calculation of traits ====
Based on minimum and maximum altitude, altitude difference is calculated. Moreover, energy expenditure for walking can be estimated using the following formula of AFRC (Alderman and Cottrill, 1993):

EE= (0.0026×HD+0.028×VD)×BW

where:

EE = energy expenditure for walking (MJ);

HD = horizontal distance (km, calculated as the difference between distance and elevation gain); VD = vertical distance (km, corresponding to elevation gain);

BW = body weight (kg).

Quality control of GPS generated phenotypes is necessary to sense-check the data for extreme values and errors. Specifically, limits are set for minimum and maximum altitudes to reflect the real altitude of the region being studied. Tracking points beyond these limits are then removed from the corresponding .gpx files and data are re-calculated. Moreover, daily records for which GPS tracking of animals had stopped before returning to their shed, must be excluded. Finally, if needed, grazing behaviour traits should be logarithmically transformed to ensure normality of distribution prior to analyses.

==== Use for genetic analysis / genetic evaluation ====
(Co)variance components of grazing behaviour phenotypes and relevant breeding values (EBVs) can be estimated by restricted maximum likelihood methodology applied to an animal mixed model that can include the following random and fixed effects:

Random effects: additive genetic effect and permanent environmental effect of the animal

The relevant fixed effects may include:

* Farm
* Number of GPS tracking points
* Tracking duration
* Distance between tracking points
* Climatic parameters (e.g. temperature-humidity index)
* Sampling time

It may also be desirable to include social grouping (if known), as this can also affect individual animal behaviours.

=== Acknowledgements ===
We gratefully acknowledge the contributions to these recording of behaviour guidelines by the following people:

* Dominique Hazard, INRAE, France
* Angeliki Argyriadou, University of Thessaloniki, Greece
* Georgios Arsenos, University of Thessaloniki, Greece
* Alain Boissy, INRAE, France
* Vasileia Fotiadou, University of Thessaloniki, Greece
* Sotiria Vouraki, University of Thessaloniki, Greece
* Joanne Conington, SRUC, the UK
* Marija Špehar, Centre for Livestock Breeding Zagreb, Croatia

This work received funding from the European Unions’ Horizon 2020 Research & Innovation program under grant agreement N°772787—SMARTER.

=== References ===
Alderman, G., Cottrill, B. Energy and Protein Requirements of Ruminants. In An Advisory Manual Prepared by the AFRC Technical Committee on Responses to Nutrients; CAB International: Wallingford, UK, 1993.

Andriamandroso, A., J. Bindelle, B. Mercatoris, F. Lebeau (2016). A review on the use of sensors to monitor cattle jaw movements and behavior when grazing. Biotechnologie, Agronomie, Société et Environnement, 20.

Beausoleil, N. J., D. Blache, K. J. Stafford, D. J. Mellor, and A. D. L. Noble. (2012). Selection for temperament in sheep: Domain-general and context-specific traits. Appl. Anim. Behav. Sci. 139:74–85.

Boissy, A., Bouix, J., Orgeur, P., Poindron, P., Bibe, B., & Le Neindre, P. (2005). Genetic analysis of emotional reactivity in sheep: effects of the genotypes of the lambs and of their dams. ''Genetics Selection'' ''Evolution, 37'', 381-401. doi:10.1051/gse:2005007

Brand, T. S. (2000). Grazing behaviour and diet selection by Dorper sheep. Small Ruminant Research, 36(2), 147-158.

Brien, F. D., Cloete, S. W. P., Fogarty, N. M., Greeff, J. C., Hebart, M. L., Hiendleder, S., . . . Miller, D. R. (2014). A review of the genetic and epigenetic factors affecting lamb survival. Animal Production Science, 54, 667-693. doi:10.1071/an13140

Cloete, S. W. P., Burger, M., Scholtz, A. J., Cloete, J. J. E., Kruger, A. C. M., & Dzama, K. (2020). Arena behaviour of Merino weaners is heritable and affected by divergent selection for number of lambs weaned per ewe mated. Applied Animal Behaviour Science, 233. doi:10.1016/j.applanim.2020.105152

Dwyer, C. M., Lawrence, A. B. (2005). A review of the behavioural and physiological adaptations of hill and lowland breeds of sheep that favour lamb survival. Applied animal behaviour science, 92(3), 235-260.

Dwyer, C. M. (2008). Genetic and physiological determinants of maternal behavior and lamb survival: Implications for low-input sheep management. Journal of Animal Science, 86, E246-E258. doi:10.2527/jas.2007-0404

Dwyer, C. M. (2014). Maternal behaviour and lamb survival: from neuroendocrinology to practical application. animal, 8, 102-112. doi:doi:10.1017/S1751731113001614

Feldt, T., Schlecht, E. (2016). Analysis of GPS trajectories to assess spatio-temporal differences in grazing patterns and land use preferences of domestic livestock in southwestern Madagascar. Pastoralism, 6(1), 1-17.

Gilmour, A. R., Gogel, B. J., Cullis, B. R., & Thompson, R. (2009). ASReml User Guide Release 3.0 VSN International Ltd, Hemel Hempstead, HP1 1ES, UK. www.vsni.co.uk.

Hazard, D., Moreno, C., Foulquié, D., Delval, E., François, D., Bouix, J., Boissy, A. (2014). Identification of QTLs for behavioral reactivity to social separation and humans in sheep using the OvineSNP50 BeadChip. ''BMC Genomics, 15'', 778. doi:10.1186/1471-2164-15-778

Hazard, D., Bouix, J., Chassier, M., Delval, E., Foulquie, D., Fassier, T., Boissy, A. (2016). Genotype by environment interactions for behavioral reactivity in sheep. ''Journal of Animal Science, 94'', 1459-1471. doi:10.2527/jas2015-0277

Hazard, D., Macé, T., Kempeneers, A., Delval, E., Foulquié, D., Bouix, J., & Boissy, A. (2020). Genetic parameters estimates for ewes’ behavioural reactivity towards their litter after lambing. ''Journal of Animal Breeding and Genetics, n/a''. doi:10.1111/jbg.12474

Hazard, D., Kempeneers, A., Delval, E., Bouix, J., Foulquie, D., & Boissy, A. (2021). Maternal reactivity of ewes at lambing is genetically linked to their behavioural reactivity in an arena test. Journal of Animal Breeding and Genetics, 139, 193-203. doi:10.1111/jbg.12656

Hazard, D., E. Delval, S. Douls, C. Durand, G. Bonnafe, D. Foulquié, D. Marcon, C. Allain, S. Parisot, A. Boissy (2022). Divergent genetic selections for social attractiveness or tolerance toward humans in sheep. WCGALP 2022

Homburger, H., Schneider, M. K., Hilfiker, S., Lüscher, A. (2014). Inferring behavioral states of grazing livestock from high-frequency position data alone. ''PLoS One'', ''9''(12), e114522.

Keller, M., Meurisse, M., Poindron, P., Nowak, R., Ferreira, G., Shayit, M., & Levy, F. (2003). Maternal experience influences the establishment of visual/auditory, but not olfactory recognition of the newborn lamb by ewes at parturition. Developmental Psychobiology, 43, 167-176. doi:10.1002/dev.10130

Lambe, N R; Conington, J; Bishop, S C; Waterhouse, A; Simm, G (2001). A Genetic Analysis of maternal behaviour score in Scottish Blackface sheep. Animal Science 72: p415-425. Doi:10.1017/s1357729800055922.

Ligout, S., Foulquie, D., Sebe, F., Bouix, J., & Boissy, A. (2011). Assessment of sociability in farm animals: the use of arena test in lambs. Applied Animal Behaviour Science, 135, 57-62. doi:10.1016/j.applanim.2011.09.004

Mignon-Grasteau, S., Boissy, A., Bouix, J., Faure, J.-M., Fisher, A. D., Hinch, G. N., . . . Beaumont, C. (2005). Genetics of adaptation and domestication in livestock. ''Livestock Production Science, 93'', 3-14. doi:10.1016/j.livprodsci.2004.11.001

O’Connor, C. E., Jay, N. P., Nicol, A. M., & Beatson, P. R. (1985). Ewe maternal behaviour score and lamb survival. Proceedings of the New Zealand Society of Animal Production, 45 159–162.

O’Connor, C.E., Lawrence, A. B. and Wood-Gush, D. G. M. (1992). Influence of litter size and parity on maternal behaviour at parturition in Scottish Blackface sheep. Applied Animal Behaviour Science 33: 345–355. <nowiki>https://doi.org/10.1016/S0168-1591(05)80071-1</nowiki>

Simm, G., Conington, J., Bishop, S. C., Dwyer, C. M., Pattinson, S. (1996). Genetic selection for extensive conditions. Applied Animal Behaviour Science, 49(1), 47-59.

SMARTER deliverable D2.4. New prototype and report for industry on GPS-generated phenotypes for behavioural adaptation to extensive grazing systems; artificial rearing adaptation phenotypes; lamb vigour scores linked to lamb survival; new foetal and neonatal survival phenotypes (in preparation).

von Borstel, U. K., Moors, E., Schichowski, C., & Gauly, M. (2011). Breed differences in maternal behaviour in relation to lamb (Ovis orientalis aries) productivity. Livestock Science, 137, 42-48. doi:10.1016/j.livsci.2010.09.028

Vouraki, S., Papanikolopoulou, V., Argyriadou, A., Priskas S., Banos, G., Arsenos, G. (2025). Phenotypic and genetic parameters of grazing behaviour of semi-extensively reared Boutsko sheep. Applied Animal Behaviour Science, vol. 282, Jan 2025, 106473. <nowiki>https://doi.org/10.1016/j.applanim.2024.106473</nowiki>

== Guidelines on recording the environment in sheep and goats ==
'''Change Summary'''
{| class="wikitable"
|'''Date of change'''
|'''Nature of Change'''
|-
|October 2024
|First version
|}

=== Introduction and scope ===

==== Introduction ====
In the genetic evaluation process, the genetic model includes environmental effects (generally fixed effects, in some cases random effects) to correct the phenotypes from these effects, not related to the genetic value of the animal. These environmental effects that affects the expression of the genotypes depend on the traits and the method of phenotyping, the environment itself (flock/herd, year, parity, season of lambing, number of born or reared lambs/kids, scorer, gender of the lamb/kid, management of mob groups, etc). The quality of the record of the environment is important to correct relevantly the performance of the animal.

Some other environmental effects that are usually included in a general flock/year or management mob group effect could be identified, such as the feeding effect or the climate effect. By including these effects in the genetic model, we could get less biased and more precise EBVs, especially when these effects are individualised or are period-specific (feeding might depend on such and such groups of animals, climate might influence the performance of such and such test- day). Moreover, the more precise knowledge of environmental effect might be valorised for flock/herd management and extension services towards farmers.

Moreover, feeding can be considered as an environmental effect, but as well be constitutive of a performance. This is typically the case for feed efficiency where the quantity and the quality of the diets allows to calculate the phenotype.

Likewise, with the climatic change, breeding for animals more resistant or more resilient to higher temperatures (especially thermal stress) becomes a selection objective per se (example of heat tolerance). In this context, the conditions of temperatures (or temperature/humidity combination) not only might be an environmental factor, but be part of the phenotype.

Other environmental effects can be described and should enrich this document in the future.

==== Scope ====
This document focuses on those data that are worth recording the precise the environment or to calculate novel traits of interest.

Following SMARTER work, the document will describe the record of the diet ([[Section 24: Recording resilience in sheep and goats#Recording the diet|Chapter 6]]) and the record of meteorological data ([[Section 24: Recording resilience in sheep and goats#Meteorological data|Chapter 6]])

Further factors might be described later, letting this document open to new section in the future, including:

* Recording the diet in small ruminant
* Recording meteorological data
* Other environmental records

=== Recording the diet ===

==== Definition, terminology, rationale ====
Recording the diet consists in collecting data on the quantity and quality of a ration that an animal, a group of animals of a flock/herd consumes at a given period.

The characterisation of the ration, in terms of energy and protein depends upon the countries. For example, the French INRAE Feeding System for Ruminants (Nozière et al., 2018) is different from the British one (AFRC, 1993). This is the reason for which we will describe in this section general recommendations, that can be applied, translated to the domestic feeding system used

Breeding for more efficient animals is more and more important for economic reason (the feeding resources are costly, might be rare in years with climatic excess such as heat or drought) and for environmental reasons (feed/food competition, emission of green-house gases). Feed efficiency is a trait of high interest in this context. Even though it is deceptive to calculate gold standard efficiency trait in private farm, the knowledge of diets in those farms should help to correctly manage the proxies that are promoted in SMARTER. Diet could also be used as a corrective factor in evaluation models in the future. In addition, it might be a support to better understand the herd/flock effect and its variation across year, and therefore give more acute and relevant advice to the farmers.

It is difficult and time-consuming to collect the data for establishing the diet in the flock/herds. The diet is collective in most of the situations (the same amount of forage is given to all animal because the forage is not given individually). When the concentrate is given through Automated Concentrate Feeder (ACF) in the milking parlour, the individualisation is not at the animal scale but at a limited number of groups scale. That’s why we suggest recommendations that must be adapted to each situation.

The aim is to tend to the better possible estimation of the forage ingestion, given that the direct measurement is impossible in commercial farms. Proxies are studied to get indirect measurement of the intake, but they are not validated so far (Near Infra Red Spectra technique). As soon as validated results are available, these recommendations will be updated.

==== Data recording ====

===== When to record the diet =====
The diet may be recorded at relevant period of the physiological status of the animals in the flock/herd. It is possible to take advantage of the visit of a technician to record the ration (for example when performance recording such as at each (or some of the) test-day when milk recording, or at weighing visit in meat sheep performance recording.

Below are examples of relevant physiological status:

* At mating (or before the mating and after the mating)
* End of gestation (in the month preceding the lambing/kidding)
* After lambing/kidding
* At weaning or just after weaning (peak of production in dairy animals)
* Dairy animals: at each test-day or at some of the test-day

In case of ACF (Automatic Concentrate Feeder), it is possible to record the distribution of concentrate more frequently.

It may be useful to establish the requirements of animals (on average) at each point of diet record. The requirements must concern the energy (in the unit usually used in the country) and the protein (in the unit usually used in the country).

===== How to record the diet =====
'''Individual diet'''
* This can be obtained through ACF for concentrate, mainly in the milking parlour.
* Intake of forage cannot be collected individually but can be predicted through the intake capacity system, such as the one proposed by INRAE (Nozière et al., 2018).

'''Collective diet (at the flock/herd scale or at the mob scale)'''

* Forage (hay, or haylage): some bales of each preservation technic can be weighed once a year with a dry matter (DM) measurement for haylage (it can substantially vary). For hay, DM can be estimated at 85%. Afterward, we can just record how many bales of a given quality (several cutting stages are preserved and not given at random) are distributed per flock per time unit. For silages, it is more complicated, but based on the same procedure, we can weigh one distribution (assuming that it will be constant over time) and simultaneously measured DM. In both situations, if refusals cannot be measured, they must be sufficient for assuming an ad libitum distribution. When the feeding system used in the country can predict the DM intake through the intake capacity of the animal and the quality of the feed, individual diet can be estimated.
* Grazing: for dairy sheep grazing within a short duration per day or the full day, intake can be estimated through ad hoc system. As an example, the new French INRATion feeding software (INRATion V5®) proposes such estimation based on grazing duration, biomass availability and quality.

===== Defining the constitution of the diet =====

====== Precise the type of distribution of the ration ======

* collective ration
* individual ration (concentrate when ACF)
* pasture

====== Categories of feedstuff ======

* Hay
* Partially or fully fermented fodder and fodder preserved by silaging or wrapping:
** Silage
** Wrapped bales

* Pasture
* Straw
* Green feeding
* Dehydrated alfalfa
* Pulp (dehydrated beet pulp, citrus pulp, etc)
* Cake (soybean, rapeseed or sunflower seed)
* Cereals grain (wheat, barley, maize, etc)
* Complete commercial concentrate
* Other by-products of agro-food industry (cereal brans, brewer’s grains, hulls etc.)

====== Species ======
For each category, specify the species (rye grass, alfalfa, clover, maize, wheat, barley, etc), physiological stage or age of regrowth, and harvest conditions (cutting length of the forage and added preservative or not for silages, conditions of hay making drying in the field or mechanically dried).

===== Characterizing the diet =====

====== Quantity ======
Quantity distributed, refused, consumed. Check that these amounts are regularly distributed, refused and consumed because it can markedly influence the animal performance specifically for dairy animals at test day.

The quantity of each feedstuff may be expressed in kg dry matter for forage, in kg gross matter for concentrate. However, final diet for requirement calculation must be expressed as DM.

====== Requirements ======
Requirements for the main categories of animals: it depends on the physiological status (maintenance, production, growing, pregnancy)

Average requirement coverage ratio (energy and nitrogen). For example, the requirement coverage ratio in French dairy sheep is roughly 115% for energy and about 125% for nitrogen of the requirements of the average ewe. That allows covering the requirements of about 85-90% of the flock. Difference between energy and nitrogen is assumed to be covered through the body reserve mobilisation.

====== Quality characterization ======
The feedstuffs and the ration must be characterized at least in terms of

* Energy
* Protein (or nitrogen)

In case of commercial concentrate, data written on the label are used.

Energy and protein can be expressed in the current unit used in the country. For example, in France, energy is expressed in UFL which is equal to 1.7 Mcal Net energy (Nozière et al., 2018).

It may also be expressed in the international unit, which can be Mcal or MJ.

==== Use for genetic analysis / genetic evaluation ====
'''Diet as part of a phenotype'''

Calculation of feed efficiency phenotypes: see recommendations on feed efficiency.

'''Diet as part of a factor in the evaluation model'''

In most of situations it is impossible in small ruminants to establish individual consumption, for practical reason. The collective effect of the diet is explained in the flock/year effect. The intermediate situation should be when ACF allows to identify several groups within the flock/herd, at a specific test-day or visit. It is possible in this case to put in the model a mob effect grouping animals being given the same amount of concentrate. This should result in a more precise calculation of the breeding value of the animal. Nevertheless, this approach has so far not be used to our knowledge.

=== Meteorological data ===

==== Definition, terminology, rationale ====
Meteorological conditions may affect the environment effect on the traits of interest. Even though they may be absorbed in a flock effect at the scale of the year or at the scale of a given test-day, it is relevant to be able to quantify the effect of such and such meteorological parameter (and especially the heat stress) ot the zootechnical traits. The global warming and the higher temperature in which the animals are bred emphasises this interest. It is possible to better assess the comfort zone of the populations, that means the meteorological conditions in which the zootechnical traits are not affected. It is also possible to identify animals better adapted to an increase in temperatures or able to be resilient to a wide range of temperatures, that means to maintain their productive ability. In this case, meteorological data, combined with a production trait (growth, milk production, milk composition) or fertility trait, are used as a resilience characterisation by assessing the ability of the animals to recover their production following meteorological challenges.

Meteorological data are mostly temperature, humidity, precipitations, wind speed and radiations. An issue in small ruminants is to select for adapted animals to new environmental challenges, without artificializing their environment of breeding. Mainly because the economic and societal constraints are such as breeding animals outdoors on pasture is desired and breeding indoors inartificialized environment may be costly in terms of energy.

==== Data recording ====

===== Meteorological data from weather station =====
The aim is to affect outdoors meteorological data to a farm. This can be obtained by assigning to the farm the meteorological data of the closest or more relevant weather stations, using the geographical coordinates of both the farm and the weather station.

The following data may be used:

* Temperature (minimum, maximum, average)
* Relative humidity (amount of moisture in air compared to the maximum amount of moisture it can have at a specific temperature). Expressed in %.
* Specific humidity (ratio of water vapor mass to the total mass of air and water vapor.
* Wind speed
* Precipitations and precipitation type
* Solar radiation
* Atmospheric radiation
* Evapotranspiration

Different index accounting for weather factors have been proposed. One of the most popular is the Temperature Humidity Index (THI) which may be calculated to get a single value representing the combined effects of air temperature and humidity associated with the level of thermal stress.

Different formulas of THI are proposed in the literature. Below is an example of formula proposed by Finocchiaro (et al., 2005):

THI = T − [0.55 × (1 − RH)/100] × (T − 14.4)

where T is the mean daily in °C and RH is the mean relative humidity expressed in percent. Quite often, the parameter used in the analysis model is the temperature of the THI (mainly because temperature and relative humidity are the most available parameters).

Let us also mention the Heat Load Index, referred to as the 'HLI', which is an index that brings together all the weather factors into one number to allow easy interpretation of the cooling capacity of the environment.

The assignation of meteorological data to a farm depends on the countries and on the availability of weather data.

In some countries, the territory may be cut out in a grid, each cell of the grid being considered to have the same meteorological parameters because they are close to the same weather station of reference. As an example, this is the case in France with a grid named SAFRAN cutting the territory into 9892 cells of 64 square kilometres each [8 km by 8 km] (Annex 1). This grid was used, thanks to specific permission from Meteo France, to affect each farm of a given project (by using its GPS coordinate) to a single cell of the grid and thus get relevant meteorological parameters.

The meteorological spatialised data are collected from weather station, on which specific interpolation are applied to present these data on the SAFRAN grid.

The meteorological data key period to consider must be thought according to the production system associated to the breed, type of traits measured and analysed. For example, for milk production (milk recording), we may consider the 3 days preceding the test-day. For semen production, we may consider the meteorological data either at the day of the semen collection, or during the spermatogenesis, which is around 50 days before the semen collection. For the insemination itself (which is in case of fresh semen the same day as semen production), we may consider climate data either the very day of the insemination operation or during a week preceding it.

===== Environmental data from sensor in the farm =====
Temperature and humidity may also be collected on site, thanks to sensors situated on-farm, for example in the sheep pen or the stable.

The number of sensors may depend upon the situation and configuration of each building, the goal being to be representative of the pen. In the practical situations of the SMARTER project, 2 to 3 sensors were set in the pen where animals are indoors at a height of 2 meters above the ground, so that they are protected from the animals. If the pen is already equipped by sensors, it is possible to retrieve the data from the existing sensors. The sensors must cover all the relevant groups of animals (primiparous, multiparous, etc), even if they are in different buildings. Measures might be collected several times a day, for example once an hour, to get a precise evaluation of the daily temperature and hygrometry. To relevantly collect the atmosphere of the building, the sensors must be set in a place free from too much air flow or too much sunshine. It is important to regularly check the batteries to avoid loss of data.

==== Use for genetic analysis / genetic evaluation ====
Effect of meteorological parameters (eg. temperature or THI) may be estimated on zootechnical traits, using different types of linear models.

The parameter may be considered as a categorical variable (each degree of the parameter being defined as a different class). Or it may be considered in a linear regression on degrees of the parameter.

Reaction norms model, using Legendre polynomial for example, may be used to assess populational losses of the zootechnical trait due to high or low temperature and/or humidity.

Two types of analysis can be made:

* a populational analysis (populational response to the effect of temperature or THI). It gives the comfort rage of each population and how much the loss is with lower or higher temperature or THI.
* an analysis of the genetic components using a random regression model. It permits to estimates genetic parameters of traits according to the temperature or THI and to calculate EBVs of animals at different temperatures or THI levels. Such EBVs allow to identify less vulnerable animals along a range of climate values, so as to identify and select the most robust animals.

=== Other environmental record ===
To be completed (or not) when necessary

=== Acknowledgements ===
We gratefully acknowledge the contributions to these environment documentation guidelines by the following people:

* Jean-Michel Astruc, IDELE, France
* Antonello Carta, Agris, Italy
* Philippe Hassoun, INRAE, France,
* Gilles Lagriffoul, IDELE, France
* Carolina Pineda Quiroga, NEIKER, Spain
* Eva Ugarte, NEIKER, Spain

This work received funding from the European Unions’ Horizon 2020 Research & Innovation program under grant agreement N°772787—SMARTER.

=== References ===
Finocchiaro R, van Kaam JB, Portolano B, Misztal I. Effect of heat stress on production of Mediterranean dairy sheep. J Dairy Sci. 2005 May;88(5):1855-64. doi: 10.3168/jds.S0022-0302(05)72860-5. PMID:15829679.

Nozière, P., Sauvant, D., Delaby, L. 2018. INRA Feeding System for Ruminants. Wageningen Academic Publishers, 640 p., 2018, 978-90-8686-292-4. ⟨10.3920/978-90-8686-292-4⟩. ⟨hal-02791719⟩

AFRC (Agricultural and Food Research Council). 1993. Energy and protein requirements of ruminants. CAB International, Wallingford.

=== Annexes ===
[[File:SAFRAN_grid_from_Meteo_France.jpg|center|thumb|600x600px|SAFRAN grid from Meteo France in the case of France]]

Section 24: Recording resilience in sheep and goats

2025-10-10T12:02:56Z

Aashish: /* Introduction and scope */

=== Introduction and scope ===
<div class="mw-collapsible mw-collapsed">
<div>
==== Introduction ====
The present guidelines aim at addressing resilience traits in small ruminants, as well as the description of the environment.

These recommendations are mainly based on a work achieved in the SMARTER H2020 project (n° 772787) whose objective was to promote harmonisation and international cooperation on breeding processes in small ruminant, especially those concerning the selection of efficiency and resilience. In this project, case studies of across country genetic evaluation, implemented as a proof of concept, have highlighted the importance of analysing traits that have been collected and/or calculated on a same way across country. Therefore, it appears fundamental that novel traits, such as resilience-related traits, which are not still widely routinely recorded on-farm for selection purposes, be recorded identically, or at least in the most similar way as possible. For that purpose, recommendations must be proposed, for countries or breeding organisations that would like to start to record efficiency or resilience traits, or that would like to set up an across-country genetic evaluation on these traits. The more similar the traits, the higher the genetic correlation across country (at same level of connection across country).

In addition, as resilience may be considered as basically related to the environmental challenges such as nutritional, disease or climatic challenges, the documentation of the environment is also described. Tackling the record of the environment is a novelty in selection of small ruminant.

The recommendations issued in a deliverable of the SMARTER project have been basically written by the partners of the project working on tasks dedicated to the different resilience-related traits and as well by the Sheep, Goat and Camelid ICAR Working Group. The Working Group was indeed involved, as partner for some of the members, as stakeholders for some other, and through ICAR who was a partner itself. Therefore, these guidelines are the fruit of a close cooperation between many academic and non-academic co-authors. Materials were also collected from results obtained in other projects (e.g. H2020 iSAGE, POCTEFA ARDI).

The recommendations, even though they target to suggest people measuring and calculating the traits the same way, are more informative than normative. The different ways to measure and calculate the traits are presented, without imposing one way, yet while suggesting some general features. Five sub-sections of recommendations were written: health and disease, survival of foetus and young, behaviour, lifetime resilience, record of the environment. All sub-sections are written with the same template and are consistent by themselves.

All the recommendations are based on the current state of the art. However, they are meant to evolve with new results and new research, and they are meant to be enhanced, consolidated, enriched. It is possible to add a new trait, a new proxy, a new sub-section. In brief, the recommendations must keep alive to stick to the evolving state of the art. This implies that the consortium that produced these recommendations, in some way, continue to contribute. ICAR, with its working group dedicated to sheep and goat, is the relevant organisation to collect and integrate the different novelties and contributions.
</div>
</div>

===== Scope =====
The SMARTER recommendations cover the following fields, shown in the figure 1.
[[File:SMARTER recommendations.jpg|center|thumb|600x600px|Figure 1. Fields covered by the SMARTER recommendations ]]
The resilience-related traits are: health and disease (with a focus on resistance to parasites, to footrot, and to mastitis), survival foetus and young, behaviour traits (with a focus on behavioural reactivity towards conspecifics or humans, maternal reactivity, behaviour at grazing), lifetime resilience.

The record of the environment covers the meteorological data and the diet. The record of the rations was studied in the on-farm protocols of SMARTER-WP1, especially in France. The record of the meteorological data benefited from works carried out in the H2020 iSAGE and POCTEFA ARDI projects, some of the SMARTER partners being committed in those projects.

The recommendations are conceived to be evolutive. Amendments can be brought in the next years, especially when the recommendations will turn into ICAR guidelines, either to strengthen results or include new insights, or to add new sub-sections or new traits. For example: (i) in the record of the environment, sensor data may be included; (ii) new disease whose resistance has a genetic component.

==== Definition of resilience ====
In these guidelines, we use the following definition of the resilience.

Resilience is defined as the ability of an animal/system to either maintain or revert quickly to high production and health status when exposed to a diversity of challenges, with a focus on nutritional and/or health challenges. Resilience is therefore the trajectory that captures the deviation from, and recovery to, the unchallenged state. Direct indicators of health and welfare will address gastro-intestinal parasitism, lameness (footrot) and mastitis, the most economically important endemic diseases of small ruminants. Indirect indicators of health and welfare of economic importance for breeders are lamb and foetal survival, functional longevity, maternal and lamb behaviour, and neonatal vigour..

==== Recording of resilience ====
The resilience-related traits that are described below for sheep and goats are:

* [[Section 24: Recording resilience in sheep and goats#Guidelines on recording health and disease in sheep and goats|health and disease (Chapter 2);]]
* [[Section 24: Recording resilience in sheep and goats#Guidelines on recording lifetime resilience in sheep and goats|lifetime resilience (Chapter 3);]]
* [[Section 24: Recording resilience in sheep and goats#Guidelines on survival recording of foetus and young in sheep and goats|survival of foetus and young (Chapter4);]]
* [[Section 24: Recording resilience in sheep and goats#Guidelines on recording behavioural traits in sheep and goats|behavioural traits (Chapter 5).]]

==== Recording of the environment ====
The record of the environment in sheep and goats is described below in the [[Section 24: Recording resilience in sheep and goats#Guidelines on recording behavioural traits in sheep and goats|Chapter 6]] of these guidelines

==== Acknowledgements ====
We gratefully acknowledge the contributions to these guidelines on recording resilience-related traits and the environment in sheep and goat by all the people working in the ICAR working group on sheep, goat, camelids and/or participating to the SMARTER project:

The different documents giving the recommendations of each sub-sections list in their own acknowledgements the persons involved in the writing of the guidelines.

This work received funding from the European Unions’ Horizon 2020 Research & Innovation program under grant agreement N°772787—SMARTER.

== Guidelines on recording health and disease in sheep and goats ==
'''Change summary'''
{| class="wikitable"
|'''Date of change'''
|'''Nature of Change'''
|-
|October 2024
|First version
|-
|December 2024
|Tracked change revisions by MS
|}

=== Introduction and scope ===
==== Introduction ====
Livestock diseases cause significant economic losses due reduced productivity, failing to express the genetic potential of animals, treatment costs, and consequently the culling of animals. Therefore, health and resistance to disease are keys factors for increasing resilience in farm animals in general and in small ruminants in particular. Among the challenges that sheep and goats must face, the infectious challenges are among the most important. They lead to losses of production and difficulties of reproduction. They also generate an increase in the consumption of chemical input. Beyond actual extra cost that may hamper the sustainability of the farms, but also of the breeding programs, there is a risk for the environment and the occurrence of resistance to drugs.

In most cases, an integrated approach is the more beneficial and efficient, mixing the different leverages. Among them, the control of the challenges by the host through its genetic resistance has shown its efficiency for some disease (resistance to scrapie, resistance to mastitis in dairy species) or is promising (resistance to parasites, resistance to footrot).

These guidelines on health and disease phenotypes are dedicated to any kind of health and disease resistance indicators. However, to start, we focus on the traits studied in SMARTER, which are the resistance to parasites and the resistance to footrot and mastitis in meat sheep and dairy sheep and goats.

==== Scope ====
This section on recording health and disease in sheep and goats starts following the task achieved in SMARTER and includes the following three sub-sections:

* Resistance to parasites
* Resistance to mastitis
* Resistance to footrot
=== Resistance to parasites ===

==== Definition, terminology, rationale ====
Resistance may be defined as the host’s ability to limit its parasite load (Råberg et al., 2007). The resistance to parasites described here corresponds to the resistance to gastro-intestinal nematodes (GIN). They are one of the main constraints for grazing sheep. They cause substantial economic losses due to lower production levels, the costs of anthelmintic treatments and the mortality of severely affected sheep. GIN control strategies mainly rely on treatment with anthelmintics. In many regions of the world, studies have reported the development of GIN resistance to most anthelmintic molecules due to their extensive use. Additionally, the possible presence of drug residues in animal products and the negative impact of these molecules on the micro and macro fauna of the soil are of concern. Therefore, sustainable GIN control may be a priority with schemes that do not only rely on anthelmintics but include complementary strategies such as nutritional supplementation with tannins and/or proteins, pasture management, and genetic selection of resistant animals. This latter strategy relies on the existence of genetic variation of host resistance to GIN both between and within breeds.

The faecal egg count (FEC), which is the number of parasite eggs per gram of faeces, is the most commonly used indicator to assess this resistance to GIN. In many countries, the selection for parasite resistance is based on FEC measures in natural infestation conditions under natural grazing conditions. As FEC measurements in sheep and goats are extremely costly and laborious, and because response to artificial challenges is highly correlated to response to natural infestation, it is therefore possible to implement a protocol of experimental infestation, as it is the case in France.

Beside FEC, different phenotypes can be used to measure resistance to GINs such as packed cell volume (PCV), FAffa MAlan CHArt (FAMACHA©) score, DAG score, immunological traits, and blood bepsinogen dosing (Shaw et al., 2012; Bishop, 2012; Bell et al., 2019; Sabatini et al., 2023).

==== Data recording ====

===== Indicators of parasite resistance or resilience =====

====== Faecal Egg Count ======
Faecal Egg Count (FEC) is the main indicator that measures the egg excretion intensity. It measures the number of parasite eggs per gram of faeces. This trait is related to the resistance of the animal (ability to limit the installation, the development and the prolificacy of the nematode inside the digestive tract (especially the abomasum). FEC is determined for each sample using a modified MacMaster technique (Whitlock, 1948 or Raynaud, 1970) with a sensitivity of 100 or 15 eggs per gram, respectively. The measure may be done in natural or in experimental infestation. FEC can be applied to one species (for example ''Haemonchus contortus'' (''Hc'')) or several species (including ''Hc'', ''Teladorsagia circumcincta'', ''Trichostrongylus colubriformis'', etc).

The distribution of the FEC has an asymmetric distribution (some high value, many low or medium value). A transformation must be applied to process a genetic analysis. The most frequent transformations are a root (fourth, third or square root) or a log transformation.

====== Packed Cell Volume ======
Packed Cell Volume (PCV) - Blood samples were collected in EDTA coated tubes and PCV values were determined individually by centrifugation in microhematocrit tubes with a relative centrifugal force of 9500 for 10 min.

PCV can be exploited as a single value of more relevantly as a gain/loss of PCV between two points. Variation of PCV is a relevant indicator of the resilience of the sheep / goat.

====== FAMACHA score ======
FAMACHA® score – As the anaemia provoked by some hematophagous parasites is at some stage visible on the mucosa (especially ocular mucosa), a scale of grading, based on the colour of the ocular mucosa, ranging from 1 (dark red mucosa) to 5 (white mucosa) has been built. This score was developed in South Africa to facilitate the clinical identification of anaemic sheep infected with H. contortus (Van Wyk and Bath, 2002).

As drawbacks, the FAMACHA® score does not allow to detect the non-hematophagous parasites and it appears quite belatedly: a FAMACHA® score over 3 concerns animals with a PCV below 20%. The method is not specific, anaemia being possibly caused by other reason than ''Haemonchus contortus''. It is however interesting to detect the anaemia. FAMACHA® score is related to the resilience of the sheep / goat.

====== DAG score ======
DAG score is an indicator for assessing dagginess, which measures faecal soiling in sheep. DAG score uses a 5-point or 6-point scoring scale ranging from 0 (no dags) to 5 or 6 (very daggy). Dag score scale shows the degree or extent of faecal contamination of the fleece.

The key is to be consistent when scoring a mob of sheep and for these sheep to have been run under similar conditions. Faecal contamination should not be confused with urine stain in ewe lambs and hoggets.

====== Immunological traits ======
Immunological and physiological profiles may be linked to phenotypes of resistance to parasites (strongyles). These new immunological and physiological profiles are blood lymphocytes cytokine production and serum levels of nematode parasite-specific Immunoglobulin A (IgA) that are produced upon whole blood stimulation. In SMARTER experiment in SRUC, blood was stimulated with pokeweed mitogen (a lectin that non-specifically activates lymphocytes irrespectively of their antigen specificity), and Teladorsagia circumcincta (T-ci) larval antigen to activate parasite-specific T lymphocytes.

Adaptive immune response may be determined by quantifying:

* cytokines interferon-gamma (IFN-γ), which relate to T-helper type 1 (Th1),
* interleukin IL-4, which relates to T-helper type 2 (Th2) and
* interleukin IL-10, which relate to regulatory T cell (Treg) responses.

Each immune trait displays a significant genetic variation (heritabilities ranging from 0.14 to 0.77). Heritability of IgA is moderate (0.41). Correlations with FEC are rather weak, from 0 to 0.27 but not significantly different from 0.

====== Blood Pepsinogen dosing ======
Blood pepsinogen is an indicator of the integrity of the gastric mucosa. The determination of serum pepsinogen is therefore a proxy in the diagnosis of abomasal strongylosis of ruminants (pepsinogen in blood is caused by an increase in the permeability of the abomasum mucosa due to presence of nematodes). There is a correlation between the concentration of pepsinogen in the blood and the number of worms harboured by the host.

===== Natural infestation =====

====== General considerations ======
Measurements (FEC or other proxies) are mainly undertaken in natural infestation under natural grazing conditions. In natural condition of infestation, frequency and amounts of yearly samplings have to be assessed according to the climate and epidemiological conditions and breeds. Local knowledge is essential for adjusting protocols to each country, as the level of infestation is strongly influenced by seasonality and the grazing system.

Several countries (e.g. Australia, New Zealand, and Uruguay), have incorporated the genetic evaluation of FEC at various ages into their national evaluation systems. In any case, in order to have data useful for the genetic evaluation, a representative sample of sheep in the flock involved in the selection scheme has to be periodically monitored to decide whether to sample the whole flock, i.e. when the number of infected animals and the level of infestation are considered sufficient to appreciate individual variability, individual FEC can be measured on the whole flock.

Further data related to environmental factors affecting the level of infestation should be recorded to be included in the genetic model for estimating the breeding values:

* Farm management mainly grazing system
* Birth type
* Sex
* Age of dam
* Parity
* Lambing date
* Sampling date
* Frequency, date, and molecule of anthelmintic administration

Additionally, stool cultures can be performed from the faecal samples taken (one per management group).

====== Description of the protocol and the measures (Uruguayan protocol) ======
At weaning, lambs undergo anthelmintic treatment, and their treatment efficacy is checked 8-14 days later through the analysis of FEC samples from 20 randomly selected lambs to confirm the absence of egg excretion. Subsequently, FEC is monitored every 15 days by collecting samples (based on epidemiological conditions) from 10-15% of lambs in each management group. The first individual FEC sampling is conducted when the FEC arithmetic mean exceeds 500 with no more than 20% samples exhibiting zero FEC. At this point, the lambs undergo anthelmintic treatment again, and their treatment efficacy is evaluated after 8-14 days. If the FEC mean remains above 500, a second individual sampling is conducted. Throughout the protocol, faecal egg counts (FEC1 and FEC2) are measured at the end of the first and second natural infestations. Generally, with some variations based on the breed, these samplings correspond to lambs at 9 and 11 months of age, respectively.

Currently, to simplify the protocol, only one sampling is conducted, and the control begins on a fixed date (early autumn) when the most significant parasite, H. contortus, prevails. Along with the FEC records (FEC1 and FEC2), other records, such as body weight, FAMACHA®, and body condition score, can also be taken.

===== Experimental infestation (French protocol) =====
As mentioned above, FEC measurements on sheep in commercial flocks are extremely costly and laborious. It has been shown that sheep that are selected on the basis of their response to artificial challenges respond similarly when exposed to natural infestation, and a high positive genetic correlation was estimated between FEC recorded under artificial or natural infestation. Moreover, it has been shown that selection of rams for parasite resistance after artificial challenges allows to improve the resistance of their female offspring for parasite infestation in natural conditions. Thus, an alternative approach may be to select rams gathered for AI progeny-testing or performance-testing by artificially infecting them with standardized doses of larvae.

In most cases, resistance to GIN is assessed in natural infestation conditions at grazing. However, the intensity of natural infestation in grazing animals depends on climatic conditions and may vary from season to season leading to over- or under-estimation of the genetic parameters of resistance. In France, sheep breeds are selected collectively on breeding stations and the strategy is to take advantage of this organization to implement the GIN control selection by phenotyping rams after experimental infestation. There are two main advantages. Firstly, a large diffusion of the genetic progress is expected via these rams, which are the future elite males. Secondly, the experimental infestation performed in control stations allow to evaluate these rams in homogeneous conditions (standardization of doses of infestation, farming conditions, climatic conditions, etc) during the ram evaluation period. Previous studies (Gruner et al., 2004) estimated high genetic correlations between resistances to experimental and natural infestation, between infestation by different parasite species (''Haemonchus contortus'' and ''Trichostrongylus colubriformis'') and between resistance in adult sheep and lambs. Moreover, recent works have shown that the genetic correlation between the resistance of rams in experimental conditions and the resistance of pregnant or milking ewes in natural conditions of GIN infestation are high.

====== Description of the protocol and the measures ======
An original protocol for phenotyping resistance to gastro-intestinal parasitism has been conceived and developed in France, targeted to rams (or bucks) gathered in a breeding centre or station, or an AI centre (Jacquiet et al., 2015; Aguerre et al., 2018). Males bred indoors, supposed to be naïve, are artificially infected twice with L3 larvae of a given strain of ''Haemonchus contortus'' susceptible to anthelminthic. Males are subjected to a first infestation (after a coprological examination be performed to confirm that no eggs were excreted before the artificial infestation) with a given dose of L3 larvae (D0). At D30, the males are phenotyped (FEC30 and possibly PCV30) and treated with an anthelminthic. After a 15-day recovery period, the rams are challenged again with a given dose of L3 larvae of Haemonchus contortus. At that time (D45), the efficacy of anthelmintic treatment is ensured in each male. Thirty days after (D75) the second challenge, the males are phenotyped (FEC30 and possibly PCV30) and treated again. The protocol lasts 2 and a half months. During the protocol, the measures carried out are as follows:

* faecal egg counts (FEC30 and FEC75) at the end of the first and second infestation (from faecal sample).
* packed cell volumes PCV0, PCV30, PCV45 and PCV75 at the start and the end of both infestation (from blood sample).

====== Calculation of variables ======
The FEC30 and FEC75 are used per se. Variations of PCV are calculated:

* PCV_loss_inf1 = PCV0-PCV30 (or ratio PCV30/PCV0)
* PCV_loss_inf2 = PCV45-PCV75 (or ratio PCV75/PCV45)
* PCV_recovery = PCV45-PCV0

where PCV_loss_inf1 and PCV_loss_inf2 represent the loss of PCV after each infestation, while PCV_recovery represents the males’ capacity to recover after the first infestation.

PCV variations might be interpreted as an indicator of resilience of the animal, i.e. its ability to maintain its blood parameters despite the parasitical challenge.

==== Use for genetic analysis / genetic evaluation ====

===== Model for genetic analysis =====
The genetic analysis of experimentally infected animals that are raised indoors may include:

* Fixed effects: contemporary group (mob x doses of larvae), age of animals (eg. 1 year, 2 years, 3years, 4 years and older)
* Random additive effect of the animals
* Residual effect

The genetic analysis of naturally infected animals that are raised outdoors may include:

* Fixed effects: they obviously will depend of the type of animals (females in lactation vs lambs/kids). They should include flock/herd, year x season (e.g. spring, summer, autumn, winter), anthelmintic treatments (e.g. eprinomectin, ivermectin, moxidectin …) in interaction with the number of days between the date of treatment and the sampling date (e.g. less than 70 days, between 70 and 100 days, more than 100 days). For females in lactation: age and/or parity, litter size before lactation (single or multiple new-born lambs). For lambs or kids: age of the dam, type of birth or rearing, and age at the time of the records, expressed in day.
* Random additive effect of the animals
* Random permanent environment effect if repeated measures (e.g. for FEC 1 & 2)
* Residual effect

===== Genetic parameters =====
Pooled estimates of heritability to resistance to gastrointestinal parasites gained by meta-analysis (Mucha et al., 2022) in dairy goats and sheep and meat sheep are shown in Tables 1 and 2, while Table 3 shows the heritabilities estimated for the experimentally infected rams. In addition, we mention a paper from Casu et al (2022) in which a heritability of 0.21 for FEC was found in a 20 year follow-up study in an experimental flock in Sardinia, Italy.

{| class="wikitable"
| colspan="7" |'''''Table 1. Pooled estimates of heritability of resistance to gastrointestinal parasites from meta- analysis in dairy goats and sheep.'''''
|-
|'''Trait1'''
|'''Species'''
|'''Pooled h2(±SE)'''
|'''Min2 h2'''
|'''Max3 h2'''
|'''N obs'''
|'''N studies'''
|-
| rowspan="2" |FEC
|Goats
|0.07 ± 0.01
|0.04
|0.15
|8
|2
|-
|Sheep
|0.14 ± 0.04
|0.09
|0.35
|6
|3
|}
1Trait: FEC – faecal egg count

2Minimum h2 from individual studies included in meta-analysis

3Maximum h2 from individual studies included in meta-analysis

{| class="wikitable"
| colspan="6" |'''''Table 2. Pooled estimates of heritability of resistance to gastrointestinal parasites from meta- analysis in meat sheep (Mucha et al., 2022).'''''
|-
|'''Trait1'''
|'''Pooled h2 (±SE)'''
|'''Min2 h2'''
|'''Max3 h2'''
|'''N obs'''
|'''N studies'''
|-
|DAG
|0.30±0.06
|0.06
|0.63
|37
|15
|-
|FCons
|0.14±0.02
|0.03
|0.27
|13
|5
|-
|NBW4
|0.10±0.02
|0.00
|0.54
|11
|3
|-
|Par-Ab
|0.18±0.07
|0.05
|0.29
|6
|3
|-
|Par-Ig
|0.36±0.06
|0.13
|0.67
|24
|8
|-
|FEC
|0.29±0.03
|0.00
|0.82
|118
|32
|-
|HC
|0.32±0.14
|0.08
|0.56
|5
|2
|}
1Trait: DAG – dagginess, FCons – faecal consistency, NBW – number of worms, Par-Ab – parasitism anitbodies, Par-Ig – parasitism immunoglobulin, FEC –faecal egg count, HC - Haematocrit

2Minimum h2 from individual studies included in meta-analysis

3Maximum h2 from individual studies included in meta-analysis

4Pooled heritability obtained from a simple random effects model as the three level meta-analysis model did not converge

{| class="wikitable"
| colspan="2" |'''''Table 3. Estimates of heritability of resistance to gastrointestinal parasites from meta-analysis in dairy sheep in experimental infestations (Aguerre et al., 2018)'''''
|-
|'''Trait1'''
|'''h2'''
|-
|Root FEC_inf1
|0.14±0.04
|-
|RootFEC_inf2
|0.35±0.08
|-
|PCV_loss_inf1
|0.24±0.05
|-
|PCV_loss_inf2
|0.18±0.06
|-
|PCV-recovery
|0.16±0.06
|}

=== Resistance to mastitis ===

==== Definition, terminology, rationale ====
In small ruminants, mastitis mainly consists in subclinical infections caused by coagulase- negative staphylococci, which is much more frequent than clinical mastitis (Bergonier et al., 2003). Under these conditions, somatic cell count (SCC) is an accurate, indirect measure to predict mammary gland infection. Therefore, SCC could be used as an indirect selection criterion for mastitis resistance as is widely done in dairy cattle. Moreover, selection for mastitis resistance in dairy sheep and goats could mainly focus on selection against subclinical mastitis using SCC, considering the low incidence of clinical cases in these species (<5%), compared to dairy cattle for which clinical cases occur frequently (Bergonier et al., 2003).

Clinical mastitis is not recorded in dairy small ruminants, mainly because of its low incidence and because SCC is a relevant and simple indicator of intra-mammary infections. Work completed in France has developed two lines of ewes (experimental farm INRAE-La Fage) and goat (experimental farm INRAE-Bourges), a high line generated from sires with unfavourable EBVs for somatic cells and a low line generated from sires with favourable EBVs for somatic cells. For both sheep (Rupp et al., 2009) and goats (Rupp et al., 2019), the low line has the lowest SCC, the lowest incidence of clinical mastitis and the lowest incidence of chronic mastitis (abscesses or unbalanced udder) and subclinical mastitis (assessed by milk bacteriology).

Even though SCC is the established indicator for use in animal breeding, the use of the California Milk test (CMT) is a very good indicator of SCC for monitoring udder health in flock/herd management in dairy and meat-producing small ruminants.

==== Data recording ====

===== Somatic Cell Count (SCC) =====
Large scale somatic cell counting relies on the application of routine methods, such as fluoro- opto-electronic counting. The somatic cell counter must be properly calibrated against a reference and laboratories must frequently verify the calibration settings are still correct.

The design for recording SCC will depend upon the objective. For flock/herd management related to high bulk SCC, the whole flock/herd should be sampled and analysed to identify the animals with the highest SCC. For genetic purpose, simplified designs might be implemented.

In dairy species, somatic cell counting is achieved within the milk recording design and the sampling design, as for milk components such as fat and protein content. As in small ruminants, most of the designs are simplified ones compared to the A4 method (all daily milkings recorded, once a month) (see [[Section 16 – Dairy Sheep and Goats|ICAR Guidelines Section 16: dairy sheep and goats]]), SCC are quite often available at one out of the two daily milkings. In this case, use of SCC must be handled accordingly.

As for milk composition, with the aim of simplifying and decreasing further the cost of recording, it is possible/recommended to measure SCC on only a part of the flock/herd (first parity or first two parities). It is also possible to go further in the simplification of the design, for example by sampling only a part of the lactation within a part-lactation sampling as proposed in the [[Section 16 – Dairy Sheep and Goats|section 16 of the ICAR Guidelines]]. The genetic parameters of test-day and lactation mean for Somatic Cell Score (SCS - log-transformed SCC) show that the records of the middle of the lactation appear as the most representative of the whole lactation. Two to four individual samples per female and per lactation, collected monthly in the middle part of the lactation are highly correlated (around 0.98) with SCS determined from samples collected over the complete lactation (A4 method) but are hardly less heritable compared with the A4 homologous traits (negligible loss of precision for SCS) (Astruc and Barillet, 2004). The balance between cost and genetic efficiency, depending on the genetic correlations close to 1, is clearly in favour of the part-lactation sampling compared to A4 testing.

===== California Mastitis Test (CMT) =====
The California mastitis test is an animal-side diagnostic test that provides an estimate of the level of infection within a mammary gland. A sample of milk (~3ml) from each udder half is combined with an equal volume of reagent in a CMT paddle and mixed for 15 to 20 seconds. The reaction is scored based on the level of thickening of the mixture from zero (no thickening) consistent with no, or low, levels of infection, to four (gel formation with elevated surface) indicating high levels of infection.

A previous study (McLaren et al., 2018) has demonstrated the strong correlation between CMT score and SCC from samples collected from pedigree meat sheep in the UK.

==== Calculation of traits ====
Test-day SCC must be transformed to Somatic Cell Score (SCS) by the logarithmic transformation of Ali and Shook (1980) to achieve normality of distribution.

Example: SCS = log2+(SCC/100,000)+ 3

The table 4 gives correspondence between SCC and SCS

{| class="wikitable"
| colspan="2" |'''''Table 4. Correspondence between somatic cell score and somatic cell count'''''
|-
|'''Somatic Cell Count (SCC)'''
|'''Somatic Cell Score (SCS)'''
|-
|12,500
|0
|-
|25,000
|1
|-
|50,000
|2
|-
|100,000
|3
|-
|200,000
|4
|-
|400,000
|5
|-
|800,000
|6
|-
|1,600,000
|7
|}
SCS can be adjusted for days-in-milk (DIM). In this case, the adjustment procedure must be defined from a study based on healthy ewes/goats with enough number of test-days over the lactation. Then a lactation SCS (LSCS) may be calculated (case of lactation model in genetic evaluation).

LSCS can be computed as the weighted arithmetic mean of test-day SCS (adjusted or not for DIM). Weights are either 1 (equivalent to no weight) or r2, where r is the correlation between one measure and the mean of all other records.

==== Use for genetic analysis / genetic evaluation ====

===== Genetic model =====
The genetic model might include the following fixed effects:

* Flock x year (x parity)
* Month of lambing/kidding
* Age at lambing/kidding
* Number of lambs/kids born

===== Genetic parameters =====
Pooled estimates of heritability of somatic cell score gained by meta-analysis (Mucha et al., 2022) in dairy goats and sheep and meat sheep are shown in Table 5.

{| class="wikitable"
| colspan="7" |'''''Table 5. Pooled estimates of heritability of somatic cell score from meta-analysis in dairy goats and sheep (Mucha et al., 2022)'''''
|-
|'''Trait1'''
|'''Species'''
|'''Pooled h2 (± SE)'''
|'''Min2 h2'''
|'''Max3 h2'''
|'''N obs'''
|'''N studies'''
|-
|SCS
|Goats

Sheep
|0.21±0.01

0.13±0.02
|0.19

0.03
|0.24

0.27
|5

29
|3

22
|}
1Trait: SCS – somatic cell score

2Minimum h2 from individual studies included in meta-analysis

3Maximum h2 from individual studies included in meta-analysis

{| class="wikitable"
| colspan="6" |'''''Table 6. Pooled estimates of genetic correlations (rg) between resilience (SCS, FEC) and efficiency (MY, FC, PC) traits from meta-analysis in dairy goats (Mucha et al., 2022)'''''
|-
|'''Traits1'''
|'''Pooled rg (± SE)'''
|'''Min2 rg'''
|'''Max3 rg'''
|'''N obs'''
|'''N studies'''
|-
|SCS & MY
|0.35±0.31ns
|0.00
|0.59
|3
|3
|-
|SCS & FC4
| -0.19±0.01
| -0.20
| -0.18
|3
|2
|-
|SCS & PC
| -0.06±0.05ns
| -0.13
|0.00
|3
|2
|-
|FEC & MY
|0.17±0.35ns
| -0.21
|0.63
|4
|2
|}
1Traits: SCS – somatic cell score, FEC – faecal egg count, MY – milk yield, FC – fat content, PC – protein content

2Minimum rg from individual studies included in meta-analysis.

3Mmaximum rg from individual studies included in meta-analysis.

nspooled correlations obtained from a simple random effects model as the three level meta-analysis model did not converge

{| class="wikitable"
| colspan="6" |'''''Table 7. Pooled estimates of genetic correlations between resilience (SCS) and efficiency (MY, FY, PY, FC, PC) traits from meta-analysis in dairy sheep (Mucha et al., 2022)'''''
|-
|Traits1
|Pooled rg (± SE)
|Min2 rg
|Max3 rg
|N obs
|N studies
|-
|SCS & MY
| -0.05±0.10ns
| -0.30
|0.23
|16
|11
|-
|SCS & FC
|0.04±0.05ns
| -0.16
|0.16
|8
|8
|-
|SCS & PC
|0.12±0.03
|0.02
|0.24
|12
|9
|-
|SCS & FY
|0.11±0.15ns
| -0.04
|0.31
|4
|4
|-
|SCS & PY
|0.17±0.10ns
|0.06
|0.31
|4
|4
|}
1Traits: SCS – somatic cell score, MY – milk yield, FY – fat yield, PY – protein yield, FC – fat content, PC – protein content

2Minimum rg from individual studies included in meta-analysis

3Maximum rg from individual studies included in meta-analysis

nsPooled correlations obtained from a simple random effects model as the three level meta-analysis model did not converge

ns – Pooled estimate did not differ significantly from zero

{| class="wikitable"
| colspan="5" |'''''Table 8. Estimates of heritability of somatic cell score, clinical mastitis and CMT in meat and dairy and meat sheep (source Oget et al., 2019)'''''
|-
|'''Sheep'''
|'''Breed'''
|'''Trait1'''
|'''Heritability (±SE)'''
|'''Reference'''
|-
|Dairy
|Chios
|CMT
|0.12±0.06
|Banos et al., 2017
|-
|Meat
|Belclare, Charollais, Suffolk, Texel,

Vendeen breeds
|CM
|0.04±0.03

|O’Brien et al., 2017
|-
|Meat
|Texel
|SCS
|0.11±0.04
|McLaren et al., 2018
|-
|Meat
|Texel
|CMT
|0.08-0.09±0.04
|McLaren et al., 2018
|-
|Meat
|Texel
|CMT
|0.07
|Kaseja et al., 2023 submitted paper (SMARTER, D2.3)

|}
1CMT - California mastitis test, CM - Clinical mastitis (examination and palpation of the udder), SCS – Somatic Cell Score

=== Resistance to footrot ===

==== Definition, terminology, rationale ====
Footrot is caused by ''Dichelobacter nodosus'' and is a major cause of lameness in sheep. The disease is highly contagious and endemic in many countries that causes pain and welfare issues in affected animals. In addition to the direct impacts on time and veterinary / medicine costs, the disease has further, indirect, impacts through reducing fertility and milk supply.

The presence of footrot is assessed by inspection of the hooves of lame animals.

==== Data recording ====

===== Scoring methods =====
Each hoof is assessed individually and scored based on the five-point scale (used in UK): clean, unaffected hoof (score 0), mild inter-digital inflammation (score 1), inter-digital necrosis (score 2), under-running of the sole of the hoof (score 3) and fully under-run to the abaxial wall of the hoof (score 4) (Conington et al., 2008).

The sum of scores is calculated by adding all four scores (for each hoof), hence the animal can obtain the phenotype in a range from zero to 16.

In France, where footrot is usually not recorded, a simplified scoring system has been developed using a scale (0 normal and severity of lesions scored from 1 to 3) adapted from the Victorian Farmers Federation and Coopers Animal Health.

Additionally, the health of feet is assessed in France and the UK for other important hoof lesions including white line degeneration, contagious ovine digital dermatitis, horn growth, presence of abscess, granuloma, interdigital hyperplasia, and panaritium).

==== Calculation of traits ====
Sum of scores are log-transformed in order to normalise the data using the formula ln(Sum of scores + 1). The addition of one prevents to logarithm the value of sum of scores equal to zero. Each animal can obtain transformed score ranging between zero and 2.83.

==== Use for genetic analysis / genetic evaluation ====

===== Genetic model =====
The genetic model might include the following fixed effects:

* Age of the dam
* Scorer (if more than one)
* Vaccine status (if some animals treated with the vaccination against ovine foot-rot)
* Flock or Flock x Year interaction

===== Genetic parameters =====
The estimated heritability for UK meat sheep (Table 9) varies between 0.12 (Nieuwhof et al., 2008). to 0.23 (Kaseja et al., 2023, unpublished results)
Table 9. Estimates of heritability of resistance to footrot in meat sheep breeds.

{| class="wikitable"
| colspan="4" |'''''Table 9. Estimates of heritability of resistance to footrot in meat sheep breeds.'''''
|-
|'''Breed'''
|'''Trait1'''
|'''Heritability (SE)'''
|'''Reference'''
|-
|Texel
|RF
|0.12(0.02)
|Kaseja et al, 2023 in press
|-
|Scottish Blackface
|CM
|0.19 to 0.23
|Kaseja et al., 2023 in press.
|-
|Scottish lambs
|SCS
|0.12
|Nieuwhof et al., 2008
|-
|Texel
|CMT
|0.18
|Mucha et al., 2015
|}
1RF - Resistance to footrot, CM - Clinical mastitis (examination and palpation of the udder), SCS – Somatic Cell Score, CMT - California mastitis test

=== Acknowledgements ===
We gratefully acknowledge the contributions to small ruminant health and disease guideline by the following people:

* Joanne Conington, SRUC, the UK
* Jean-Michel Astruc, IDELE, France
* Rachel Rupp, INRAE, France
* Beat Bapst, Qualitas AG, Switzerland
* Donagh Berry, TEAGASC, Ireland
* Beatriz Carracelas, INIA, Uruguay
* Antonello Carta, Agris Sardegna, Italy
* Gabriel Ciappesoni, INIA, Uruguay
* Arnaud Delpeuch, IDELE, France
* Frédéric Douhart, INRAE, France
* Karolina Kaseja, SRUC, the UK
* Ed Smith, The British Texel Sheep Society, the UK
* Flavie Tortereau, INRAE, France
* Stefen Werne, FiBL, Switzerland
* Marija Špehar, Centre for Livestock Breeding Zagreb, Croatia

This work received funding from the European Unions’ Horizon 2020 Research & Innovation program under grant agreement N°772787—SMARTER.

This work also used deliverable from the Eurosheep project (Horizon 2020 under agreement N° 863056).

=== References ===
Aguerre, S., Jacquiet, P., Brodier, H., Bournazel, J.P., Grisez, C., Prévot, F., Michot, L., Fidelle, F., Astruc, J.M., Moreno, C.R. (2018). Resistance to gastrointestinal nematodes in dairy sheep: Genetic variability and relevance of artificial infection of nucleus rams to select for resistant ewes on farms. Vet. Parasitol. 256:16-23. <nowiki>https://doi.org/10.1016/j.vetpar.2018.04.004</nowiki>

Ali, A., Shook, G. (1980). An optimum transformation for somatic cell concentration in milk. J. Dairy Sci. 63:487-490.

Astruc J.M., Barillet F. (2004). Current challenge for milk recording in dairy sheep and goats: the simplification of milk sampling design for chemical composition and somatic cell counts of milk. Proceedings of the 34th ICAR session, Sousse, Tunisia, 31 May-3 June 2004.

Banos, G., Bramis, G., Bush, S.J., Clark, E.L., McCulloch, M.E.B., Smith, J., Schulze, G., Arsenos, G., Hume, D.A., Psifidi, A. (2017). The genomic architecture of mastitis resistance in dairy sheep. BMC Genom. 18 624.

Bell, A., McNally, J., Smith, D.V., Rahman, A., Hunt, P., Kotze, A.C., Dominik, S., Ingham, A. (2019). Quantification of Differences in Resistance to Gastrointestinal Nematode Infections in Sheep Using a Multivariate Blood Parameter. Vet. Parasitol. 270:31–39.

Bishop, S.C. (2012). Possibilities to Breed for Resistance to Nematode Parasite Infections in Small Ruminants in Tropical Production Systems. Animal., 6:741–747.

Bergonier, D., Crémoux, R. de, Rupp, R., Lagriffoul, G., Berthelot, X. (2003). Mastitis of dairy small ruminants. Vet. Res. 34:689–716.

Casu, S., Usai, M.G., Sechi, T. et al. Association analysis and functional annotation of imputed sequence data within genomic regions influencing resistance to gastro-intestinal parasites detected by an LDLA approach in a nucleus flock of Sarda dairy sheep. Genet Sel Evol 54, 2 (2022). <nowiki>https://doi.org/10.1186/s12711-021-00690-7</nowiki>

Conington, J., Hosie, B., Nieuwhof, G.J., Bishop, S.C., Bünger, L. (2008). Breeding for resistance to footrot- the use of hoof lesion scoring to quantify footrot in sheep. Vet. Res. Commun. 32(8):583-9. <nowiki>https://doi.org/10.1007/s11259-008-9062-x</nowiki>

Gruner, L., Bouix, J., Brunel, J.C. (2004). High genetic correlation between resistance to Haemonchus contortus and to Trichostrongylus colubriformis in INRA 401 sheep. Vet. Parasitol. 119:51–58.

Jacquiet, P., Salle, G., Grisez, C., Prevot, F., Lienard, E., Astruc, J.M, Francois, D., Moreno, C. (2015). Selection of sheep for resistance to gastro-intestinal nematodes in France: where are we and where are we going? 25th International Conference of the WAAVP, Liverpool, UK, 2015, 16-20 August

McLaren, A., Kaseja, K., Yates, J., Mucha, S., Lambe, N.R., Conington, J.(2018). New mastitis phenotypes suitable for genomic selection in meat sheep and their genetic relationships with udder conformation and lamb live weights. Animal. 12(12):2470-2479. doi: 10.1017/S1751731118000393.

Mucha, S., Bunger, L., Conington, J. (2015). Genome-wide association study of footrot in Texel sheep. Genetics Selection Evolution, 47 (1), pp.35. DOI 10.1186/s12711-015-0119-3

Mucha, S., Tortereau, F., Doeschl-Wilson, A., Rupp R., Conington, J. (2022). Animal Board Invited Review: Meta-analysis of genetic parameters for resilience and efficiency traits in goats and sheep. Animal. 16(3):100456. <nowiki>https://doi.org/10.1016/j.animal.2022.100456</nowiki>.

Nieuwhof, G.J., Conington, J., Bünger, L., Haresign, W., Bishop, S.C. (2008). Genetic and phenotypic aspects of resistance to footrot in sheep of different breeds and ages. Animal. 2(9):1289-1296. <nowiki>https://doi.org/10.1017/S1751731108002577</nowiki>

O’Brien, A.C., McHugh, N., Wall, E., Pabiou, T., McDermott, K., Randles, S., Fair, S., Berry, D.P. (2017). Genetic parameters for lameness, mastitis and dagginess in a multi-breed sheep population. Animal 11, 911–919. DOI: 10.1017/S1751731116002445

Oget, C., Tosser-Klopp, G., Rupp, R. (2019). Genetic and genomic studies in ovine mastitis. Small Ruminant Research 176, 55-64. <nowiki>https://doi.org/10.1016/j.smallrumres.2019.05.011</nowiki>.

Råberg, L, Sim, D., Read, A.F. (2007). ‘Disentangling genetic variation for resistance and tolerance to infectious diseases in animals’, Science. 318(5851), 812-814 DOI: 10.1126/science.1148526

Raynaud J.P. (1970). Etude de l’efficacité d’une technique de coproscopie quantitative pour le diagnostic de routine et le controle des infestations parasitaires des bovins, ovins, equines et porcins. Ann. Parasitol. 45: 321–342

Rupp, R., Bergonier, D., Dion, S., Hygonenq, M.C., Aurel, M.R., Robert-Granié, C., Foucras, G. (2009). Response to somatic cell count-based selection for mastitis resistance in a divergent selection experiment in sheep. J. Dairy Sci. 92, 1203–1219.

Rupp, R., Huau, C., Caillat, H., Fassier, T, Bouvier, F., Pampouille, E., Clément, V., Palhière, I., Larroque, H., Tosser-Klopp, G., Jacquiet, P., Rainard, P. (2019). Divergent selection on milk somatic cell count in goats improves udder health and milk quality with no effect on nematode resistance. J Dairy Sci. 102(6):5242-5253. doi: 10.3168/jds.2018-15664.

Sabatini, G.A., de Almeida Borges, F., Claerebout, E. et al. (2023). Practical guide to the diagnostics of ruminant gastrointestinal nematodes, liver fluke and lungworm infection: interpretation and usability of results. Parasit. Vectors. 16, 58. <nowiki>https://doi.org/10.1186/s13071-023-05680-w</nowiki>

Shaw, R.J., Morris, C.A., Wheeler, M., Tate, M., Sutherland, I.A. (2012). Salivary IgA: A Suitable Measure of Immunity to Gastrointestinal Nematodes in Sheep. Vet. Parasitol. 186, 109–117

Van Wyk, J.A., Bath, G.F. (2002). The FAMACHA© system for managing haemonchosis in sheep and goats by clinically identifying individual animals for treatment. Vet. Res. 33:509–529.

Whitlock, H.V. (1948). Some modifications of the McMaster helminth egg counting technique and apparatus. J. Coun. Sci. Ind. Res. 21:177.

=== Annexes ===
[[File:Annex 1 Famacha.jpg|center|thumb|600x600px|Picture of FAMACHA score (source FiBL – Qualitas)]]

[[File:Annex 2 Farmacha 2.jpg|center|thumb|600x600px|Picture of FAMACHA score (source FiBL – Qualitas)]][[File:Annex 3 Uruguayan protocol of natural infestation.jpg|center|thumb|800x800px|Uruguayan protocol of natural infestation for recording the resistance to gastrointestinal parasites]]
[[File:Annex 4 French protocol for phenotyping the resistance.jpg|center|thumb|600x600px|French protocol for phenotyping the resistance to gastrointestinal parasites]]

== Guidelines on recording lifetime resilience in sheep and goats ==
'''Change Summary'''
{| class="wikitable"
|'''Date of change'''
|'''Nature of Change'''
|-
|October 2024
|First version
|-
|November 26th 2024
|Comments made by JC
|-
|December 23rd 2024
|Comments made by MS
|}

=== Introduction and scope ===

==== Introduction ====
Lifetime resilience is often tackled through longevity and aspects of productive longevity. Longevity is a trait to quantify productive lifespan of livestock, and for increasing durability and profitability of farms. In dairy ruminants, longevity definitions include: (i) true longevity (all culling reasons, including milk productivity); and (ii) functional longevity (all culling reasons, except voluntary productivity, such as milk productivity or growth). Functional longevity (corrected for production level – milk, growth) reflects the animals’ accumulated ability to overcome health and nutritional challenges. It is an indirect global approach to quantify adaptive capacity to various production environments. Different indicators may be calculated. One indicator is the length of productive life which is computed as the time interval (in days) between first lambing/kidding and culling. Longevity is linked with various predictors, such as fertility, udder health and conformation, resistance to disease, body condition score changes across ewe/doe lifetime. These predictors may be used in breeding program to get an earlier breeding value of longevity and may help to manage and monitor lifetime resilience at the farmer level.

==== Scope ====
The scope of these guidelines is to define approaches for the definition of longevity as well as the traits that can be calculated, and the downstream analyses that can be set up (including the use of early predictors to enhance longevity in the evaluation process).

To propose a grid for setting up an observation of the culling causes.

=== Longevity ===

==== Definition, terminology, rationale ====
The notion of longevity can cover several meanings. Longevity can be understood as the true longevity, i.e. the ability of the animal to live as long as possible, whatever its production level and its functional characteristics. Animal longevity also depends on the replacement rate which is often a choice of the breeders. Animals may be culled due to production level such as milk production or growth or fat/muscle depth, leading to ’voluntary’ culling (i.e. an animal is culled because we '''want''' to do it). In contrast, ‘involuntary culling’ is defined as an animal having to leave the flock or herd due to illness / accident/ functional disability etc (i.e. they are culled because we '''have''' to do it)

Involuntary culling may be due to:

* Udder health problem (clinical, subclinical, chronic mastitis).
* Lack of resistance to disease such as parasites.
* Problem of footrot.
* Unfavourable shape of the udder (lack of adaptation to machine milking or to suckling).
* Unfavourable general conformation.
* Undesired behaviour (temperament in the milking parlour).
* Infertility or any problem of reproduction.
* Problem of feet or legs, lameness.
* Lack or excess of body tissue mobilisation.

any other undesirable aspect associated with the animal’s inability to produce. Voluntary culling may be due to:

* Low productivity,
* Management decision to cull for age,
* Management decision to cull for a specific coat colour / other phenotype that does not meet the type desired,
* Farmer doesn’t like the animal,
* Economic reason to reduce the number of breeding animals in the flock/herd.

Even if some of these reasons for culling may be considered per se in the selection process by phenotyping and evaluating related traits (for example resistance to mastitis, resistance to gastro- intestinal parasite, fertility, udder morphology), it is often not possible to account for all of them. If properly modelled, functional longevity may be considered as a global and composite approach, allowing to assess the sustainability of the population in selection and of the practiced selection.

For this, different traits may be considered, quite often they are relatively easy to compute with data usually already existing in the genetic database (ex. length of productive life, which can be calculated as the culling date minus the date of the first lambing). There is no additional recording to set up. The difficulties in handling functional longevity are related to the modelling of the trait, given that the trait is fully known when the animal is culled. When not yet culled, the model to set up are quite complex. An example of this was reported by Brotherstone et al. (1997) for dairy cattle and Conington et al. (2004) for hill sheep, whereby live animals’ EBVs for longevity are based on their probability of survival at a given age combined with actual cull dates of relatives that became breeding females in the flock.

Even though there is no need to identify/know the cause of culling, the knowledge of the cause of culling might be a relevant observation of the hierarchy of the culling cause, which may lead to put an emphasis on some specific issue. For example, if we observe an increase in some culling causes (let’s say parasitism) this should lead to a deliberate selection programme to breed more resistant animals to parasites.

One drawback of the functional longevity trait is its lack of precocity. As stated above, it is necessary to have the date of culling or to have accumulated enough lactation to compute the trait. And an appropriate model (e.g. survival analysis) can only partially disentangle this difficulty. It is possible to address this issue by running a multi trait genetic evaluation model combining the longevity trait and some other proxy traits (such as udder morphology, udder health, etc). The use of Genomic Selection is a complementary way to generate early prediction of genetic merit for longevity, provided there is good accuracy of the EBVs of animals in the associated reference population.

==== Data recording ====

===== Longevity traits =====
The table 1 presents some criteria commonly used in small ruminants to measure longevity. Here, the criteria deal with true longevity, the only one measurable in herd/flocks. Functional longevity will be estimated later, at the statistical analysis step. Table 1 also shows the data required for calculating the longevity criteria. For example, the length of productive life is referred to as the difference between the time a female enters the breeding flock/herd and the date she exits it due to being culled or dying. It is important to notice that the culling date, which is rarely recorded by the farmers, can be replaced by the date of the last event registered for the animal (for example, date of the last performance recording, or of the last reproduction event).

{| class="wikitable"
| colspan="3" |'''Table 1. Definition of some commonly used longevity criteria.'''
|-
|'''Longevity criteria'''
|'''Raw data required'''
|'''Calculation'''
|-
|Length of total lifespan (LTL)
|Birth date (BD)
Culling or death date (CD)
|LTL= CD - BD
in days (or months or years)
|-
|Length of productive life (LPL)
|First lambing/kidding date (FKD)
Culling or death date (CD)
|LPL = CD – FKD
in days (or months or years)
|-
|Total number of days in production (NDL)
|Days in milk per lactation (DIM)
or
Lambing/kidding date + dry off date for each lactation
|NDL = ∑ DIM
|-
|Number of lactations (NLACT)
|Each lambing/kidding event (KE)
|NLACT = ∑ KE
|-
|Number of lambs or kids during lifetime (NLAMB)
|Prolificacy at each lambing/kidding (PR). This may or may not include no. lambs born dead + no. lambs born alive
|NLAMB = ∑ PR
|}
The length of total lifespan can be estimated easily, with only two variables usually registered by farmers. The difference with the length of productive life is that it considers the period when animals had the first lambing/kidding as well as the lambing/kidding interval. If the age at the first lambing/kidding and the lambing/kidding interval are similar between animals, the length of total lifespan will be very close to the length of productive life.

The total number of days in production only covers the “useful” life of the females because it doesn’t include the unproductive periods (such as dry off or large lambing/kidding interval after reproduction failure), compared to length of productive life. But the number of variables necessary to compute it is larger.

For the total number of lambs or kids during a lifetime, it is necessary to include all live-born lambs/kids only or those reared to weaning, if these data are routinely recorded.

==== Calculation of traits ====
The last column of Table 1 indicates how to calculate the different longevity criteria, from the raw variables.

The length of total lifespan and the length of productive life are estimated as differences in days between two dates: i) the culling date and ii) the birth date or the first lambing/kidding date, respectively. The total number of days in production corresponds to the sum of the days in milk of each lactation of the female. For the last two criteria (number of lactations or number of lambs/kids), the estimation corresponds to cumulative performance across lifetime.

Instead of waiting for the end of the animal's life to calculate the longevity criterion (which is sometimes long), one solution deals with limiting the animal career to a maximum number of years or lactations. For example, the length of productive life can be calculated only on the first 6 lactations. Subsequently, the length of productive life will be defined as the total number of days between the first lambing/kidding and the end of the 6th lactation. In the same way, the total number of lambs/kids can be estimated at a fixed age, 8 years old for example.

==== Use for genetic analysis / genetic evaluation. ====

===== Models =====
The genetic ability for longevity is evaluated via the functional longevity, i.e. the true longevity corrected for production traits. Functional longevity is defined at this step, by integrating the level of production as fixed effect in the analysis of longevity criteria described in Table 1.

Different methods are used for the genetic evaluation of longevity traits.

The first method is based on linear models. The main advantage of these models is their ease of implementation because they are used for most of the traits under selection. But they have different drawbacks regarding longevity:

* they do not fit well longevity because longevity indicators do not follow a normal distribution
* they consider only animals that have finished their productive life (unless separate predictors are used). This has two consequences: the longevity data are skewed if living animals are ignored; the breeding value is available lately in the life of the animals. This is notably the case for males for whom most of their offspring must be culled to be evaluated.
* they are not able to include time-dependant variables (e.g. parity, lactation stage). Time dependant variables are useful to take into account the changes in breeding conditions that occur during the life of the animal, and thus to better model longevity data.

The second method is based on proportional hazard model or survival analysis. This type of model counterbalances all the drawbacks of linear models and thus, are the best ones to estimate breeding values for functional longevity. Nevertheless, they are complicated to implement in a routine genetic evaluation process, and a few software exist for genetic survival analyses such as Survival kit, (Ducrocq et al, 2005). However, an evaluation based on an animal model is not feasible in large dataset, leading to use sire-maternal grand-sire models or sire models. Under this assumption, ewes/does EBVs are not available (Ducrocq, 2001).

A third method, less widespread, considers the first three lactations as separate traits in a multiple trait animal linear model. Each lactation is assigned to 1 (instead of 0) once the female reaches the next lactation.

===== Factors of variation =====
The main factors of variation of longevity data are:

* herd/flock
* year
* kidding/lambing season
* birth season
* age at first lambing/kidding
* breed
* herd/flock size and herd/flock size variation
* lactation stage, parity (if survival analysis model)
* number of lambs/kids born and reared (for meat sheep and goats)
* within herd/flock production level: this factor of variation is essential to integrate to estimate the functional longevity. Usually, it is the within herd/flock level of production (and not the absolute level of production) that is considered because it explains the decision of the breeder to cull the animal.

===== Heritabilities of functional longevity =====
Heritabilities range between 5% and 17% (Sasaki, 2013, Castañeda-Bustos et al., 2014, Geddes et al., 2017, Palhière et al (2018), Buisson et al (2022), Pineda-Quiroga & Ugarte, 2022) indicating that this trait has a low to moderate genetic background. This might be due to the composite signification of longevity, which represents a synthesis of various abilities (see § on predictors).

However, the genetic variation coefficients are moderate suggesting that a genetic variability may be exploited to set up a selection programme.

===== Genetic correlations =====
The genetic correlations between functional longevity and other traits are:

* close to 0 for milk production traits. This results from the model, in which longevity is corrected for level of production,
* from 0 to 0.40 for udder type traits (Castañeda-Bustos et al., 2014). The rear udder attachment and the udder floor position are the most correlated to functional longevity,
* from 0.20 to 0.50 for general conformation,
* from 0.01 to 0.15 for reproduction traits (kidding interval, age at first kidding, artificial insemination fertility),
* from -0.15 and -0.40 for somatic cell counts.

===== EBVs and reliabilities =====
For dairy animals, because of the low accuracy of breeding values, only males (and especially artificial insemination males) evaluated from the longevity data of their daughters, have EBVs that can be used for selection. A minimum number of daughters culled per sire is required to reach a sufficient accuracy. The consequence is that the AI males get their first longevity EBV quite late in their life. Survival analysis models, because they consider censored data (living daughters), enable better accuracy and thus, an earlier EBV for AI males.

Other strategies are possible to increase the accuracy of functional longevity EBVs:

* introduce genomic information in the genetic evaluation
* use a multiple trait model, including both functional longevity and other traits considered as predictors of longevity listed below.

Given the low heritability of survival traits, the fact that it is expressed late in life (at death or culling), the trait becomes accurate enough when sufficient information on culling or reproduction/lactation is available. It is necessary to enhance direct evaluations by indirect information coming from early predictors. Some relevant predictors are listed below:

* Morphological traits, such as general conformation or udder morphology (especially in dairy species),
* Reproduction traits (fertility, lambing/kidding interval, age at first lambing/kidding, pregnancy scan results, …),
* Udder health, and particularly milk somatic cell count,
* Resistance to disease such as resistance to parasites or to footrot,
* Traits related to feet and legs, such as lameness or twisted or bowed legs, closed or opened hocks,
* Serum immunoglobulin concentration in the early life (Ithurbide et al, 2022a),
* Maturity (dairy species) that can be defined as the ability to maintain a good level of production over the parities, independently of the level of production on the whole lifetime (equivalent of a persistency, but over the lactations and not over the test-days) (Arnal et al, 2022),
* Milk metabolites (Ithurbide et al, 2022b)
* Body tissue mobilisation (McLaren et al., 2023). It was demonstrated that ewe tissue mobilisation was genetically associated with ewe fertility and productive longevity (such as pregnancy scan result, foetal loss from scan to lambing, lamb loss from lambing to weaning, number of lambs weaned). It is made possible by collecting body condition score (BCS) data throughout the reproductive cycle (e.g. pre-mating, pregnancy scan, pre lambing, mid lactation, weaning) and calculating gain or loss of BCS between physiological stage.

These predictors are linked to longevity traits. An unfavourable udder shape, reproduction disorders, a susceptibility to a given disease or a low maturity may lead to involuntary culling and therefore a low longevity of the animal. Few genetic correlations have been published but correlations between EBVs show favourable correlations between these predictors and longevity.

Longevity traits, once evaluated, either in linear or survival analysis model, may be combined with the longevity traits in a multi-trait evaluation, to incorporate the information from early predictors.

A full multiple trait evaluation is not feasible in large datasets. Therefore, approximate strategies must be used, such as considering records adjusted for all non-genetic effects in linear models (yield deviation or daughter yield deviation, other type of pseudo records), or sub-indices incorporating traits that are linked together e.g. pulling together data on footrot, mastitis and parasite resistance could be considered together in a ‘health’ sub-index.

==== Culling causes ====
Even though the knowledge of the causes of culling is not necessary to generate a phenotype of longevity and an EBV of functional longevity, the knowledge of the causes of culling, through an observation based on a sufficient panel of flocks/herds, and repeated each year, may give relevant information on the hierarchy and the evolution of the culling causes. It may also enable better understanding of the strategies of culling by farmers leading to better modelling of functional longevity.

Culling causes may be collected with different levels of precision, from a general group of causes to a precise cause, through intermediate information.

In sheep as in goat, the following group of culling causes may be collected:

* Udder health (mastitis)
* Udder morphology
* Production ability
* Respiratory disorders
* Reproduction disorders
* Digestive disorders
* Nervous disorders
* Musculoskeletal disorders
* Skin disorders
* Conformation
* General condition
* Age
* Behaviour
* Accident
* Other ailments (e.g. sudden death, brucellosis, intoxication, fever …)
* Voluntary culling

Each group may be completed with sub-group or precise cause. Below are two examples, first for udder health (table 2), second for reproduction disorders (table 3).

{| class="wikitable"
| colspan="3" |'''Table 2. Detailed categorisation of udder health culling causes.'''
|-
|'''Group'''
|'''Sub-group'''
|'''Specific cause'''
|-
| rowspan="21" |Udder health (mastitis)
| rowspan="2" |Gangrenous mastitis
|Gangrenous mastitis
|-
|Brief mastitis
|-
| rowspan="7" |Characteristic symptoms
|Mastitis
|-
|Clinical mastitis
|-
|Mastitis during suckling
|-
|Coliform mastitis
|-
|Listeria mastitis
|-
|Mastitis before lambing/kidding
|-
|Agalactia mastitis
|-
|Functional symptoms
|Blood in the milk
|-
| rowspan="6" |Chronic mastitis, palpation
|induration of the udder
|-
|Bumps in the udder
|-
|Nodules
|-
|Mammary abcess
|-
|Saggy udder
|-
|Visna mastitis
|-
| rowspan="2" |Unbalanced udder
|Milk in one side
|-
|Unbalanced udder
|-
| rowspan="2" |Subclinical
|Subclinical mastitis
|-
|Somatic cell count (SCC) and California mastitis test– CMT
|-
|Other
|Other
|}

{| class="wikitable"
| colspan="3" |'''Table 3. Detailed categorisation of reproduction disorders culling causes'''
|-
|'''Group'''
|'''Sub-group'''
|'''Specific cause'''
|-
| rowspan="28" |Reproduction disorders
| rowspan="3" |Fecundity
|Open + infertile
|-
|Lately fertile, out of season
|-
|Ram infertile
|-
| rowspan="6" |Gestation
|Abortion
|-
|Vagina or rectal prolapse
|-
|Pregnancy toxaemia
|-
|Difficult gestation
|-
|Early abortion
|-
|Late abortion
|-
| rowspan="8" |Lambing/kidding
|Difficult lambing/kidding
|-
|Caesarean
|-
|Uterus inversion
|-
|Infection during lambing/kidding
|-
|Vagina or rectal prolapse
|-
|non deliverance
|-
|Acute metritis
|-
|Chronic metritis
|-
| rowspan="4" |Miscellaneous
|Reproduction disorders
|-
|Vaginal sponge infection
|-
|Hermaphrodite
|-
|Various
|-
| rowspan="4" |Male: testicles
|1 testicle
|-
|Small testicles
|-
|Abscess
|-
|Contagious epididymitis
|-
| rowspan="3" |Male: penis
|Urinary gravel
|-
|Wound
|-
|Phimosis
|}

=== Acknowledgements ===
We gratefully acknowledge the contributions to these lifetime resilience guidelines by the following people:

* Joanne Conington, SRUC, the UK
* Marija Špehar, Centre for Livestock Breeding Zagreb, Croatia
* Isabelle Palhière, INRAE, France
* Jean-Michel Astruc, IDELE, France
* Carolina Pineda Quiroga, NEIKER, France

This work received funding from the European Unions’ Horizon 2020 Research & Innovation program under grant agreement N°772787—SMARTER.

=== References ===
Arnal M., Palhiere I., Clément V. (2022). Maturity, a new indicator to improve longevity of Saanen dairy goats in France. Proceedings of 12th World Congress on Genetics Applied to Livestock Production (WCGALP), Jul 2022, Rotterdam, Netherlands. doi:10.3920/978-90-8686-940-4_738.

Brotherstone, S., Veerkamp, R. F. and Hill, W. G. (1997). Genetic parameters for a simple predictor of the lifespan of Holstein-Friesian dairy cattle and its relationship to production. Animal Science 65: 31-37.

Buisson D., J.M. Astruc, L. Doutre, I. Palhière. Toward a genetic evaluation for functional longevity in French dairy sheep breeds. Proc 12th WCGALP, 2022

Castañeda-Bustos, V. J., Montaldo, H. H., Torres-Hernández, G., Pérez-Elizalde, S., Valencia-Posadas, M., Hernández-Mendo, O., & Shepard, L. (2014). Estimation of genetic parameters for productive life, reproduction, and milk-production traits in US dairy goats. Journal of Dairy Science, 97(4), 2462-2473.

Castañeda-Bustos, V. J., Montaldo, H. H., Valencia-Posadas, M., Shepard, L., Pérez-Elizalde, S., Hernández-Mendo, O., & Torres-Hernández, G. (2017). Linear and nonlinear genetic relationships between type traits and productive life in US dairy goats. Journal of Dairy Science, 100(2), 1232-1245.

Conington, J., Bishop, S. C., Grundy, B., Waterhouse, A., & Simm, G. (2001). Multi-trait selection indexes for sustainable UK hill sheep production. Animal Science, 73(3), 413-423.

Conington, J., Bishop, S.C., Waterhouse, A. and Simm, G. (2004). A bio-economic approach to derive economic values for pasture-based sheep genetic improvement programmes. Journal of Animal Science 82: 1290-1304. <nowiki>https://doi.org/10.2527/2004.8251290x</nowiki>

Ducrocq, V. (2001). A Two-Step Procedure to get Animal Model Solutions in Weibull Survival Models Used for Genetic Evaluations on Length of Productive Life. Interbull Bulletin, vol.27, pp.147-152

Ducrocq, V. (2005). An Improved model for the French genetic evaluation of dairy bulls on length of productive life of their daughters. Animal Science, 80(3), 249-256.

Geddes, L., Desire, S., Mucha, S., Coffey, M., Mrode, R. and Conington, J. (2018). Genetic parameters for longevity traits in UK dairy goats. IN: Proceedings of the World Congress on Genetics Applied to Livestock Production, Volume Species - Caprine: 547.

Ithurbide, M., Huau, C., Palhière, I., Fassier, T., Friggens, N. C., & Rupp, R. (2022a). Selection on functional longevity in a commercial population of dairy goats translates into significant differences in longevity in a common farm environment. Journal of Dairy Science, 105(5), 4289-4300.

Ithurbide, M., Wang, H., Huau, C., Palhière, I., Fassier, T., Pires, J. & Rupp, R. (2022b). Milk metabolite profiles in goats selected for longevity support link between resource allocation and resilience. In Proceedings of 12th World Congress on Genetics Applied to Livestock Production (WCGALP) pp. 276-279

McLaren A, Lambe, N R and Conington J. (2023). Genetic associations of ewe body condition score and lamb rearing performance in extensively managed sheep. 105336. Livestock Science September 2023 <nowiki>https://doi.org/10.1016/j.livsci.2023.105336</nowiki>

Palhière I., C. Oget, R. Rupp, Functional longevity is heritable and controlled by a major gene in French dairy goats, 11th WCGALP, Auckland, Nouvelle-Zelande, 11-16 février 2018

Pineda-Quiroga, C., Ugarte, E. (2022). An approach to functional longevity in Latxa dairy sheep. Livestock Science 263, 105003

Sasaki, O, (2013), Estimation of genetic parameters far longevity traits in dairy cattle: A review with focus o n the characteristics of analytical models, Animai Science Journal, 84(6), 449-460,

SMARTER Deliverable 2,2 - "New breeding goals far lifetime resilience far materna!sheep breeding programmes"

== Guidelines on survival recording of foetus and young in sheep and goats ==
'''Change Summary'''
{| class="wikitable"
|'''Date of change'''
|'''Nature of Change'''
|-
|October 2024
|First version
|-
|December 2024
|Tracked change revisions by MS
|}

=== Introduction and scope ===

==== Introduction ====
Foetal and young survival are parameters linked to neonatal vigour scores, maternal and young behaviours, stress responses, immunity transfer and traits related to dam fertility and longevity. Minimising mortality, either in utero (e.g., embryo/foetus) or pre-weaning, are crucial to profitable small ruminant production systems. Survival depends on an interaction between the environment and behaviour of both, the ewe and the lamb. Ewes must give birth without complications and provide reliable source of colostrum along with mothering environment. Lamb must adapt to the extra-uterine environment, thermoregulate and be able to stand and suckle in a reasonably short period after birth (Brien et al., 2014; Plush et al., 2016). Despite this, pre- weaning survival in many species is far from ideal (Binns et al., 2002; Yapi et al., 1990, Chaarani et al., 1991, Green and Morgan, 1993, Nash et al., 1996). This can be particularly worse in small ruminant production systems which are typically more extensive and therefore prevailing weather conditions can be an additional stressor as well as predators. Moreover, the poly-ovulatory nature of species such as sheep and goats also predisposes such species to greater foetal and pre-weaned young losses (Scales et al., 1986).

Litter size can be determined using trans-abdominal ultrasonography of the uterine horns at ideally 40-70 days post-fertilisation. Good accuracy in determining foetal number has been reported from trans-abdominal ultrasonography (Taverne et al., 1985). The number of young eventually born can then be used to assess foetal loss since the time of scanning. At birth, young survival is usually based on dead or not in the first 24 h post-birth while stillborn individuals or those dead within 24 hours are usually defined as failed to survive. Young survival can also be considered as different age group categories until weaning – for example from 1 day to 7 days of age. Young animals (i.e., < 7 days) are greatest at risk of mortality (Binns et al., 2002) and tend to die of exposure to hypothermia, starvation, septicaemia, or repercussions from trauma suffered at birth.

==== Scope ====
The aim of the present section is to define approaches for the definition of foetal and lamb survival as well as the data editing and downstream analyses (including statistical models).

=== Definition, terminology, rationale ===
A plethora of different definitions exist depending on whether defined at the level of the individual (i.e., binary trait) or that of the litter. A non-exhaustive list is given below.

'''Foetal survival (at an individual level):'''

* Can be defined as a binary trait of 0 (died between scanning and birth) or 1 (survived between scanning and birth). A dummy ID for the dead foetus would need to be constructed but the parentage would still potentially be known (especially if generated from AI).

'''Foetal survival (at a litter level):'''

* Whether or not some foetal mortality has occurred defined as a binary trait (i.e., the number of individuals born is less than the number scanned in utero)
* Number of individual foetuses scanned alive (along with gestational age)
* Number of foetuses scanned minus the number that were born (dead or alive) – this is a measure of foetal mortality as opposed to survival and assumes stillborn young are considered in the definition of a young survival trait. It is a count trait
* The number of young born divided by the number of foetuses scanned (this is mortality rate figure but per little with a penalty on losses for smaller litter sizes).

'''Young survival (at an individual level):'''

* Can be defined as a binary trait of 0 (dead within 24 hours of birth) or 1 (alive after 24 hours of birth). The dead animal would need to receive an ID and can, of course, be genotyped to verify parentage (but also used for downstream genomic analyses discussed later).

'''Young survival (at a litter level):'''

* Number of lambs born alive (NLBA)
* Number of lambs dead within 24 hours of birth
* Number of lambs dead within 24 hours of birth divided by the total number of lambs born

=== Recording survival of foetuses and young in small ruminant ===
In all instances, accurate data is crucial. Data should be collected on the animal/dam itself (dead or alive) but also potential confounding effects that could be considered for inclusion in the statistical model as fixed effects. Examples include contemporary group (e.g., flock-date of scanning, flock-year-season of birth (for each NLB separately), ewe parity, litter size). Ideally also all individuals should be genotyped. Because the heritability of foetal or young animal mortality in small ruminants is relatively low (<0.1; Safari et al., 2005; Brien et al., 2014), a large number of records are required to achieve accurate genetic/genomic evaluations. Care should also be taken when interpreting the scoring (and the following genetic evaluations), some jurisdictions may record mortality rather than survival or may record mortality but propose genetic evaluations as survival (i.e., positive value is favourable).

==== Pregnancy scanning records ====
Ideally scanning should be undertaken 40 to 70 days post-fertilisation. This may be possible to (easily) achieve where extensive AI has been used but, otherwise, should ideally be 30 days after the last female has been marked as been served by natural mating. Skilled operators should be able to determine the number of foetuses from 30 to 100 days of gestation; usually only one operator will scan a flock on a given day so will be confounded with flock-date of scanning contemporary group. If AI is solely used or if single sire mated, then the parentage of the foetus should be known; if mob mated or single sire mated at AI, then superfecundation could cause a discrepancy in recorded sire.

==== Young survival ====
Young survival can be defined at birth, ideally as a binary trait as to whether the animal was born stillborn or died within 24 hours (survival = 0) or was still alive 24 hours after birth (survival = 1). If information is also available on the reason for death (i.e., autopsy results) then, where sufficient data exists for any one ailment, it could be analysed separately as separate traits. This could be particularly important for generating separate genetic evaluations for the main diseases thereby not only possibly increasing the heritability through more accurate data, but also provide genetic evaluations specific to individual ailments which could enable more selection pressure on these traits in situations where they are more impactful. Ideally a genotype of the dead animal should be generated. Any obvious external defects should be noted.

==== Ancillary information ====
Having ancillary information coinciding with an event is useful for several reasons:

* For helping data editing (e.g., comparing actual birth date to expected birth date based on recorded service information)
* For adjustment in the statistical model (e.g., dam parity)
* Understanding the risk factors associated with survival
* Enabling more precise estimates of correlations with other performance traits by having information on multiple features from the same animal
* Adjusting for possible selection in multi-trait genetic evaluation models

Possible ancillary information can be divided into those associated with 1) the past of prevailing environmental conditions, 2) the dam (or sire), or 3) the individual. Examples include:

1. Environment:

* Weather related factors (rainfall, temperature, wind including direction)
* Flock
* Date of scanning or date of birth

2. Dam

* Parity
* Age
* Breed
* Genotype
* Litter size
* Mating type (i.e., AI versus natural)
* Body condition score (change) and live-weight (change)
* Mothering ability
* Colostrum quality and yield

3. Individual

* Days since service (for foetal survival trait)
* Birthing difficulty
* Birth weight
* Gender
* Genotype
* Sire
* Autopsy results if possible

=== Use for genetic analysis / genetic evaluation ===

==== Data editing and statistical modelling ====
In order to estimate contemporary group effects well, the larger the contemporary group, the better the group estimates. Therefore, imposing a minimum contemporary group size prior to data analysis should be considered as should good genetic connectedness with other contemporary groups. Genetic connectedness can be an issue with small ruminant populations in particular, especially where natural mating prevails.

===== Data editing =====
'''Foetal survival''' ''-'' Each flock-scanning date can be firstly investigated at a macro level to measure ultrasound quality control. Simple cross-references between the number of females with scanning data versus those presented as well as the ID numbers of both is useful to ensure all data were properly recorded. High foetal mortality rates could simply be indicative of high foetal loss (e.g., abortions due to causes like chlamydial and toxoplasma) as well as poor operator competence – assessing the rate for individual operators across flocks (and time) could be useful to assess operator proficiency. A high proportion of litters where the number of young born (dead or alive) exceeds that recorded at scanning suggests a poor accuracy of recording. It should be considered to discard the data from that date but also to investigate the operator in more detail across other flocks, and irrespective, the scanning results from that litter at least should be discarded. The proportion of scanned litters with >3 detected foetuses should also be calculated; depending on the expected prolificacy of the animals (e.g., breed), then the appropriate editing of either the individual data points or the date in its entirety should be assessed.

'''Young mortality''' ''-'' A high incidence of young mortality per contemporary group could simply be a consequence of some underlying issue (e.g., predation, disease) or indeed a high fecundity rate; a low incidence of young could be indicative of a good stock person. Therefore, it can be difficult to distinguish between high and low quality data. Using guaranteed high quality and reliable data, it is possible to estimate the expected distribution of the incidence of young animal mortality for different population strata such as flock size, ewe age, breed, litter size. Using these distributions, the probability that the mean mortality for a contemporary group fits this distribution can be estimated and a decision made as to whether or not to include the data in the downstream analyses.

===== Statistical modelling =====
Lamb survival is a complex trait influenced by direct genetic, maternal genetic, and environmental effects. Due to discrete expression of phenotype (dead or alive: 0 or 1) it is described as a threshold trait (Falconer, 1989) that violates the assumption of normality, and therefore linear models are theoretically not appropriate for the analysis. However, examples from the literature analysed survival data and reported that linear models were marginally more accurate at predicting missing phenotypes than were logit-transformed alternatives and are convenient for interpretation on the observed scale (Matos et al., 2000; Everett-Hincks et al., 2014; Cloete et al. 2009; Vanderick et al., 2015;).

Random effects considered in the statistical model are direct and maternal genetic effects and maternal permanent environment across parities. A litter permanent environmental effect should also be considered as a random effect where the trait is that of the individual (and not the ewe). Traditionally, relationships were accounted for though the pedigree data, however this can often now be supplemented with genome-wide genotype information to generate a H matrix (i.e., combines genomic and ancestry information). Whether the estimation of these additional covariance components improve the fit to the data can be deduced by a likelihood ratio test but ideally a metric such as the AIC or BIC to account for the increased complexity of the model.

The choice of environmental factors included in the model will depend on the population being studied and considers the following fixed effects:

* Contemporary group (e.g., flock-date of scanning for foetal survival and flock-year-season of birth or flock-year-season-birth rank of birth)
* Lamb gender (may not be possible for foetal survival trait)
* Dam parity
* Mating type (i.e., AI versus natural)
* Dam age nested within parity
* Day of gestation (for foetal survival) if available or defined as a categorical variable
* Litter size (at scanning or birth) or birth type (single and multiple)
* Heterosis and recombination loss of the dam and foetus/young
* Inbreeding coefficient of the dam and foetus/young
* Age of the sire
* Breed composition of the dam and foetus/young

Adjusting for the effects such as dystocia or birth weight, may not be appropriate in the statistical model for young survival as they are likely to be genetically correlated with survival and thus may remove some of the true genetic variance – nonetheless, the eventual decision will be based on the genetic evaluation system employed and how the economic value on the traits within the overall breeding objectives are constructed.

==== Genomic association analyses ====
Where genotypes are available, then a genome-wide association study (or candidate gene study) can be undertaken (Esmaeili-Fard et al., 2021). Although it is not possible to have the genotype of the aborted foetus, it could still be possible to undertake a genomic analysis especially by focusing on the genotype/haplotype of the living animals versus the expectation based on the genotype/haplotype of the parents (Ben Braiek et al., 2021).

=== Acknowledgements ===
We gratefully acknowledge the contributions to these recording of survival of foetus and young guidelines by the following people:

* Donagh Berry, TEAGASC, Ireland
* Joanne Conington, SRUC, the UK
* Maxime Ben Braiek, INRAE, France
* Arnaud Delpeuch, IDELE, France
* Marija Špehar, Centre for Livestock Breeding Zagreb, Croatia

This work received funding from the European Unions’ Horizon 2020 Research & Innovation program under grant agreement N°772787—SMARTER.

=== References ===
Ben Braiek, M., Fabre, S., Hozé, C., et al. (2021). Identification of homozygous haplotypes carrying putative recessive lethal mutations that compromise fertility traits in French Lacaune dairy sheep. Genet. Sel. Evol. 53:41. <nowiki>https://doi.org/10.1186/s12711-021-00634-1</nowiki>

Binns, S.H., I.J.Cox, S. Rizvi, L.E.Green. (2002). Risk factors for lamb mortality on UK sheep farms. Prev.Vet. Med.. 52:287-303.

Brien, F.D., Cloete, S.W.P., Fogarty, N.M., Greeff, J.C., Hebart, M.L., Hiendleder, S., Hocking Edwards, J.E., Kelly, J.M., Kind, K.L., Kleeman, D.O., Plush, K.L., Miller, D.R (2014). A review of genetic and epigenetic factors affecting lamb survival. Anim. Prod. Sci. 54:667–693.

Chaarani, B., Robinson, R.A., Johnson, D.W. (1991). Lamb mortality in Meknes Province (Morocco). Prev. Vet. Med. 10:283-298.

Cloete, S.W.P., Misztal, I., Olivier, J.J. (2009). Genetic parameters and trends for lamb survival and birth weight in a Merino flock divergently selected for multiple rearing ability. J. Anim. Sci. 87:2196–2208. doi:10.2527/jas.2008-1065.

Esmaeili-Fard, S.M., Gholizadeh, M., Hafezian, S.H., Abdollahi-Arpanahi, R. (2021) Genes and Pathways Affecting Sheep Productivity Traits: Genetic Parameters, Genome-Wide Association Mapping, and Pathway Enrichment Analysis. Front. Genet. 12:710613. doi:10.3389/fgene.2021.710613.

Everett-Hincks, J.M., Mathias-Davis, H.C,, Greer, G.J., Auvray, B.A., Dodds, K.G. (2014). Genetic parameters for lamb birth weight, survival and deathrisk traits. J. Anim. Sci. 92:2885–2895. doi:10.2527/jas.2013-7176.

Falconer, D.S. (1989). Introduction to Quantitative Genetics.’ (Longmans Green/John Wiley & Sons: Harlow, Essex, UK).

Green, L.E., Morgan, K.L. (1993). Mortality in early born, housed lambs in south-west England. Prev. Vet. Med. 17:251-261.

Matos, C.A.P., Thomas, D.L., Young, L.D., Gianola, D. (2000). Genetic analyses of lamb survival in Rambouillet and Finnsheep flocks by linear and threshold models. Anim. Sci. 71:227–234. doi:10.1017/S1357729800055053.

Nash, M.L., Hungerford, L.L., Nash, T.G., Zinn, G.M. (1996). Risk factors for perinatal and postnatal mortality in lambs. Vet. Rec. 139:64-67.

Plush, K.J., Brien, F.D., Hebart, M.L., Hynd, P.I. (2016). Thermogenesis and physiological maturity in neonatal lambs: a unifying concept in lamb survival. Anim. Prod. Sci. 56:736–745. <nowiki>https://doi.org/10.1071/AN15099</nowiki>.

Safari, E, Atkins, K.D., Fogarty, N.M., Gilmour, A.R (2005). Analysis of lamb survival in Australian Merino. Proceedings of the Association for the Advancement of Animal Breeding and Genetics. 16:28–31.

Scales, G. H., Burton R. N., Moss, R. A. (1986). Lamb mortality, birthweight, and nutrition in late pregnancy. N. Z. J. Agric. Res. 29:1.

Taverne, M.A.M. Lavoir, M.C., van Oord R., van der Weyden, G.C. (1985) Accuracy of pregnancy diagnosis and prediction of foetal numbers in sheep with linear‐array real‐time ultrasound scanning. Vet. Q. 7:(4)256-263, DOI: 10.1080/01652176.1985.9693997.

Vanderick, S., Auvray, B., Newman, S.A., Dodds, K.G., Gengler, N., EverettHincks, J.M. (2015). Derivation of a new lamb survival trait for the New Zealand sheep industry. J. Anim. Sci. 93:3765–3772. doi:10.2527/jas.2015-9058.

Yapi, C.V., Boylan, W.J., Robinson, R.A. (1990). Factors associated with causes of preweaning lamb mortality. Prev. Vet. Med., 10:145-152.

The technical references (papers cited or used) are documented in each piece of recommendations.

== Guidelines on recording behavioural traits in sheep and goats ==
{| class="wikitable"
|'''Date of change'''
|'''Nature of Change'''
|-
|October 2024
|First version
|-
|November 2024
|Tracked change revisions by JC
|-
|December 2024
|Tracked change revisions by MS
|}

=== Introduction and scope ===

==== Introduction ====
Genetic selection including behavioural traits could be an advantageous strategy for improving robustness and welfare of farm animals in various farming conditions by minimizing unsuitable responses to changes in their social and physical environment, limiting an excessive fear of humans and improving sociability (Mignon-Grasteau et al., 2005). Farm animals are social and gregarious, and relational behaviours are essential for ensuring social cohesion, social facilitation, offspring survival and docility toward humans. Breed differences and genetic variation within breed have been reported in lambs for early social behaviours and found to be heritable, and associated with some QTL, suggesting such behaviours could be selected early (Boissy et al., 2005; Beausoleil et al., 2012; Hazard et al., 2014; Cloete et al., 2020). In addition, such early social reactivity of lambs towards conspecifics or humans was identified as a robust trait and that selection for early social reactivity of lambs towards conspecifics or humans is feasible (Hazard et al., 2016; 2022).

The behaviour of both ewes and lambs, and their interaction at lambing, have been widely described. Such behaviour is important for the survival of the offspring, especially in extensive farming conditions as reviewed by Dwyer et al. (2014). Moreover, it has been shown that primiparous ewes are more prone to abandon their lambs due to their lack of maternal experience (Dwyer, 2008) and that lamb survival at birth is lowly heritable (Brien et al., 2014). Taken together these factors could hinder the development of extensive farming systems. Genetic selection on maternal attachment traits could therefore be advantageous to improve offspring survival and growth, and reduce labour, as suggested by Mignon-Grasteau et al. (2005). Genetic variations in maternal behaviour between breeds of sheep have been well documented (for review see: Dwyer, 2008; von Borstel et al., 2011) while little was known about within-breed genetic variability and even less about maternal reactivity traits. We hypothesized that maternal attachment to the litter has a genetic component in sheep, and we recently reported that as expected the maternal reactivity at lambing is a heritable trait (Hazard et al., 2020;2021).

Grazing behaviour is also important for animals raised in extensive production systems because it can support adaptability to changing environments. In particular, small ruminants reared in semi-extensive systems face many environmental and welfare challenges that are difficult to quantify. The evidence in the literature suggests that there are differences in grazing behaviour between and within breeds of sheep (Simm et al., 1996; Brand, 2000). The notion is that natural selection combined with subjective artificial selection have led to some animals being more adaptive to extensive conditions. In this regard, genetic variation may exist for key grazing behaviour traits (Simm et al., 1996; Dwyer et al., 2005), but relevant literature is scarce. During the SMARTER H2020 project, a study was performed on grazing behaviour of the indigenous Boutsko Greek mountainous sheep breed, which is reared semi-extensively. The results showed that duration of grazing and speed are heritable traits (Vouraki et al., 2025).

==== Acronyms used in these guidelines ====

* AT Arena Test
* CT Corridor Test
* GPS Global Positioning System
* LS Lambing Site
* PCA Principal Component Analysis

==== Scope ====
The aim of the present report is i) to define the behavioural traits of interest, ii) to describe approaches for behavioural measurements, iii) to describe their use for genetic analysis and evaluation.

To-date, the present guidelines describe 3 groups of traits related to behaviour:

* Behavioural reactivity towards conspecifics or humans
* Maternal reactivity
* Behaviour at grazing

Kid/lamb vigour is a relevant behavioural trait, but this trait is tackled within the section “foetus and young survival in sheep and goats” of the guidelines.

Most of the work undertaken on behaviour concerned sheep. This has been particularly the case in SMARTER. Most of the recommendations might be applied to goats as well. Nevertheless, we will use the ovine terms in the guidelines below.
[[File:Section_24-1_Three_groups_of_traits_related_to_behaviour_guidelines.jpg|center|thumb|600x600px|Three groups of traits related to behaviour guidelines]]

=== Behavioural reactivity towards conspecifics or humans ===

==== Definition, terminology, rationale ====

===== Behavioural reactivity towards conspecifics (i.e. sociability): =====
It is the social motivation of the lambs to join their conspecifics in response to social isolation with or without presence of a motionless human. Expression of higher levels of a panel of behaviours, including vocalisations and locomotion, is hypothesised as an active way to maintain social link with conspecifics.

==== Behavioural reactivity towards humans (i.e. docility): ====
It is the reactivity of isolated lambs to a walking human. Higher flight distance between the lamb and a human indicates a lower docility toward a human.

Behavioural reactivity towards conspecifics and humans are measured in standardised behavioural tests (arena and corridor tests, described below).

Higher sociability and/or docility towards humans may improve adaptation of sheep to harsh environments through social facilitation (i.e. transmission of feeding preferences…), social cohesion (i.e. transhumance…) and reactivity to handling. Consequently, improving such behavioural traits may improve welfare, production, and labour of shepherd.

==== Data recording ====

===== Behavioural tests =====
The test described below have been implemented in France. It must be considered as a possible test, as others can be described later and enrich these guidelines.

Lambs must be individually exposed just after weaning (i.e. approximately 10 days after weaning) to two behavioural tests. The delay between weaning and behavioural tests must be sufficient for the change of social preferences of lambs for their dam to conspecifics.

The arena test (AT) consists of two successive phases evaluating 1) reactivity to social isolation (AT1), 2) the motivation of the lamb towards conspecifics in presence of a motionless human (AT2). The arena test is performed indoors. The arena test pen consists in an unfamiliar enclosure virtually divided into 7 zones as described in detail by Ligout ''et al''. (2011) (Figure 1). On one side of the enclosure (i.e. at the opposite of the entrance), a grid separates the tested lamb from another smaller pen containing 3 or 4 conspecifics. The first phase of the test (arena test phase 1, AT1) starts once the tested animal joins its flock-mates located behind a grid at the opposite side of the arena (time duration for joining: lower than 15 sec). No behavioural recording is performed during the joining. At this time, an opaque panel is pulled down (from the outside of the pen) between the flock-mates and the tested lamb to prevent visual contact. After one minute the phase 1 stops and the panel is pulled up so the lamb can see its flock-mates again. Once the lamb has returned near to its flock-mates, or after 1 minute if the lamb did not do so, a non-familiar human slowly enters the arena through a door located near the pen of the flock-mates and stood 20 cm in front of the grid separating the arena from the lamb’s flock-mates. The second phase (arena test phase 2, AT2) starts once the human is in place and lasts for a further 1 minute.
[[File:Experimental_setup_of_the_arena_test_for_estimating_the_social_reactivity_of_lambs.jpg|center|thumb|600x600px|Figure 1. Experimental setup of the arena test for estimating the social reactivity of lambs. At the beginning of the test, animals can join their flock mates placed behind a grid barrier (social attraction, phase 0) and then were individually exposed to the social isolation (phase 1), and to the social attraction in presence of a motionless human (phase 2). (Adapted from Ligout et al., 2011)]]
The corridor test (CT) consists of two successive phases evaluating 1) reactivity to social isolation (CT1) and 2) reactivity to an approaching human (CT2). The test pen consists in a closed, wide rectangular circuit and has been described in detail by Boissy ''et al''. (Boissy et al., 2005) (Figure 2). The first phase (corridor test phase 1, CT1) starts when the lamb enters the testing pen and lasts for 30 seconds. After that time a non-familiar human enters the testing pen and the second phase (corridor test phase 2, CT2) starts and lasts 1 minute. During this phase, the human walks at a regular speed through the corridor (the corridor is divided into 6 virtual zones and one zone is crossed every 5 seconds) until two complete tours has been achieved.

===== Behavioural traits =====
Several behaviours are measured during behavioural tests: vocalisations (i.e. frequency of high- pitched bleats), locomotion (i.e. number of virtual zones crossed), the proximity score (i.e. weighting of time spent in virtual zones, a high score indicated a high duration spent close to conspecifics and a human).

An investigator counts the lamb’s vocalisations directly during the tests, from outside the pen using a laptop: number of times the animal bleats with an open mouth (high bleats, AT1/2- HBLEAT, CT1-HBLEAT). Locomotor activity is assessed by measuring the number of virtual zones crossed during arena test phases 1 and 2 (AT1/2-LOCOM) and corridor test phase 1 (CT1- LOCOM). This behaviour can be assessed using video recording or using infrared cells regularly positioned along the AT to detect displacement. The proximity to flock-mates and the human during AT2 is calculated by weighting of time spent in virtual zones (i.e. a high score indicated a high duration spent close to conspecifics and a human).

During CT2, every five seconds throughout this phase, an investigator records with a laptop the zones in which the human and the animal are located. In addition, the walking human records with a stopwatch the total duration during which the head of the lamb is visible. The mean flight distance (DIST) separating the human and the lamb (i.e. knowing the length of each virtual zone) and the time during which the human sees the lamb (SEEN) is measured in CT2.

==== Calculation of traits ====
Deviations from normality of row data must be tested using relevant statistical tests (e.g. the Kolmogorov–Smirnov test). Several raw measures must be transformed in order to minimise major deviations from the normal distribution. Square root transformation is applied to AT1/2- HBLEAT, CT1-HBLEAT. A multivariate analysis may be performed to take into account the multidimensional aspect of behavioural responses. Results of principal component analysis (PCA) indicate that the main principal components is structured mainly with similar behaviour (i.e. higher weight of similar behaviours for the different tests on the same component). Consequently, three synthetic variables may be constructed using PCA. Each PCA is performed for a set of similar behavioural variables across the behavioural tests. The first component of each PCA, explaining the largest part of total variance, is defined as a synthetic variable. Two synthetic variables are specific to the reactivity to social isolation: high bleats (HBLEAT, using AT1/2-HBLEAT and CT1- HBLEAT), locomotion (LOCOM, using AT1/2-LOCOM and CT1-LOCOM). One synthetic variable is specific to the reactivity to an approaching human: the tolerance to being approached when the lamb is free to flee (HUMAPPRO, using CT2-DIST and CT2-SEEN).

==== Use for genetic analysis and genetic evaluation ====
Genetic analyses and genetic evaluation can be performed on single traits and synthetic variables. Genetic analyses (estimation of (co)variance components and prediction of breeding values) for quantitative behavioural traits may be implemented with a mixed model methodology in animal model. Random effects should include:

* a direct additive genetic effect of the animal (i.e. lamb),
* a maternal permanent environment effect (i.e dam), that describes lamb phenotypic variation caused by the environment of the ewe
* a litter permanent environment effect, that accounts for phenotypic variation caused by the environment of the litter of the lamb being tested.

All relevant fixed effects and interactions should be included in the model. Factors that could be considered include:

* a combination of the litter size at lambing and the number of lambs suckled with their dam
* sex, age, live weight of the lamb,
* dam parity and/or age of dam nested withing parity if needed contemporary group (e.g., depending on the data collection: flock-year-season, grazing location…)

=== Maternal reactivity ===

==== Definition, terminology, rationale ====
* Behavioural reactivity at lambing (i.e. maternal reactivity). It is the social motivation or attachment of the ewe for the litter expressed in response to an approaching human, or the withdrawal of the litter with or without presence of a human. Expression of higher levels of a panel of behaviours, including maternal behaviour scores, vocalisations and locomotion, is hypothesised as an active way to maintain social link with lambs.

Maternal reactivity is measured in standardised behavioural tests (a scoring test outdoors, an arena test indoors, described in the controlled test below) or a maternal behaviour score (MBS) designed for use in extensive sheep systems as described by O’Connor ''et al'' (1985), the genetic basis of which was reported by Lambe et al., 2001 for Scottish Blackface sheep.

Higher maternal reactivity may improve adaptation of sheep to harsh environments through a higher behavioural autonomy at lambing and a reducing dependency to the support provided by shepherds. Consequently, improving such behavioural traits may improve welfare, production, and labour of the shepherd.

==== Data recording ====

===== Behavioural tests =====
The controlled test described below have been implemented in France. It must be considered as a possible test, as others can be described later and enrich these guidelines. Ewes are individually exposed to two behavioural tests: a scoring test performed just after lambing, outside at the lambing site, and then an arena test performed indoor, one day after lambing. The second test is performed after the bonding period needed to establish the social link between ewes and lambs and which occurs generally within the first twelve hours after lambing (Keller et al, 2003).

Scoring test at lambing site: Maternal reactivity is assessed outside at the lambing site approximately 2 hours after lambing, only on ewes that lambed during daylight when the shepherd approaches the lambing ewes to catch lambs for weighing and identification. Scoring at lambing is not performed in the following situations: if the location of the lambing site does not readily facilitate the testing procedure, if there are perturbations of scoring due to interference by other ewes, for sanitary reasons that could affect behaviours (including difficult lambing, death of all lambs of a litter). Measurement of maternal reactivity at the lambing site (LS) consists of two successive phases: (1) when the shepherd approaches the lambs; and (2) the capture and displacement of the lambs by the shepherd. In the first phase (LS1), the shepherd stands approximately 15 meters away from the lambing spot and approaches the ewes and the lambs at a regular speed (1 m/s). In the second phase (LS2), the shepherd catches all the lambs at the same time and moves away from the lambing spot in the same direction as that of the approach, stopping at the starting point where he places the lambs back on the ground and then moves 15 meters away to allow the ewe to restore contact with her lambs. This second phase of the test is not applied to ewes that flee at the approach of the shepherd and do not return within 60 seconds after the end of LS1.

Arena test: After lambing, all the ewes and lambs (both day and night births) are transferred to a shelter close to the place of lambing and penned individually for few hours. They are then moved to a collective pen until the next day when they are tested in the arena test (24h ± 6h after lambing). The arena test (AT) is performed indoors and adapted from the original test developed by Boissy and colleagues (2005) to investigate social attachment in sheep (Ligout et al., 2011). In the present study, the test consists of three successive phases evaluating the ewe’s 1) attraction to her litter, 2) reactivity to social separation from her litter, and 3) reactivity to a conflict between social attraction to her litter and avoidance of a motionless human. The test pen consists of an unfamiliar enclosure virtually divided into 7 zones (zone 7 being the zone nearest to the litter). On one side of the enclosure, a grid separates the tested ewe from another smaller pen containing her lamb(s). The first phase of the test (AT1) starts when the tested ewe enters the arena and lasts for 30 s. Then, a remotely controlled opaque panel is pulled down in front of the grid to prevent visual contact between the tested ewe and her lambs. The second phase (AT2), during which the tested ewe is separated from her lambs, lasts 1 min. Finally, the panel is raised so the tested ewe can see her lamb(s) again. Once the ewe has returned near to her lamb(s), a non-familiar shepherd slowly enters the arena through a door located near the grid separating the arena from the litter and stands 20 cm in front of the grid. The third phase of the test (AT3) starts once the shepherd is in place and lasts for 1 min.

===== Behavioural traits =====
Scoring test at lambing site: A scoring system, close to those defined by O’Connor et al. (1985), and further validated for hill sheep by Lambe ''et al.'' (2001) for use in animal breeding programmes to enable many animals to be scored relatively quickly and easily in extensive sheep systems. The simple scoring system measures maternal reactivity described for each of the two phases described above. In LS1, a maternal behaviour score (LS1-MBS) is recorded on a 5-point scale as follows: 1 - ewe flees and does not return to the lambs within 60 s; 2 - ewe retreats (i.e., at least 2-3 m) but comes back to her lambs within 60 s; 3 - ewe retreats with at least one lamb and comes back; 4 - ewe retreats and returns repeatedly; 5 - ewe stays close to the lambing spot. In LS2, a second maternal behaviour score (LS2-MBS) is recorded on a 4-point scale as follows: 1 - ewe flees; 2 - ewe stays close to the lambing spot, 3 - ewe follows but from a distance (i.e., 1 to 2 m), 4- ewe follows, staying close to the shepherd (i.e., less than 1 m).

'''Arena test''': Locomotor activity and localisation are analysed from the video footage or infrared cells (as described above). Locomotor activity is assessed by measuring the number of zones crossed during the 3 phases (AT1/2/3-LOCOM). The time spent in each zone is recorded. The ewe’s proximity to the litter and/or the human during phases 1 and 3 (AT1/3-PROX) is calculated using the following formula:
[[File:Arena_test_formula.jpg|left|thumb|410x410px]]

Two types of vocalisations are recorded manually during the test with an electronic device: number of high-pitched bleats are recorded when the animal bleats with an open mouth (AT1/2/3-HBLEAT) and number of low-pitched bleats are recorded when the animal bleats with a closed mouth (AT1/2/3-LBLEAT).

==== Calculation of traits ====
Logarithmic transformation is applied to AT1/2/3-LBLEAT to minimise major deviations from the normal distribution. All other elementary variables described above are directly used for genetic analyses.

==== Use for genetic analysis / genetic evaluation ====
The (co)variance components for quantitative behavioural traits can be estimated by restricted maximum likelihood (REML) methodology applied in an animal model. The (co)variance components for categorical behaviours can be estimated by MCMC and Gibbs sampling methods using a threshold model (Gilmour et al., 2009).

Assuming that all ewes are measured every year, the analyses assume a repeatability model with behaviour measured across productive cycles considered to be the same trait with a constant variance. Random effects typically include a direct additive and permanent environmental genetic effects of the animal (i.e., ewe).

All relevant fixed effects and interactions should be included in the model. Factors that could be considered can include:

* The litter size at lambing.
* Dam parity or age or age of the dam nested within parity (if significant).
* Contemporary group (e.g., depending on the data collection: flock-year-season effect…).

=== Behaviour at grazing ===

==== Definition, terminology, rationale ====
Grazing behaviour is a complex combination of various movements and activities of animals in different spatial-temporal scales (Andriamandroso et al, 2016). Indicative traits related to grazing behaviour include:

* Duration of grazing
* Distance walked
* Speed
* Altitude difference
* Elevation gain/loss
* Energy expenditure at grazing

A better understanding of the phenotypic and genetic background of grazing behaviour traits could help towards the development of appropriate breeding programmes to increase adaptation to extensive rearing conditions. However, recording of such traits is challenging. The use of new technologies such as global positioning systems (GPS) could help towards efficiently monitoring grazing behaviour (Homburger et al., 2014; Feldt and Schlecht, 2016).

==== Data recording ====
The following guidelines for recording grazing behaviour traits of sheep are based on a study implemented in Greece (Vouraki et al., 2025). Specifically, in the latter study, grazing behaviour of Boutsko sheep reared semi-extensively in mountainous regions was monitored using GPS technology. Moreover, phenotypic and genetic parameters for key grazing behaviour traits were estimated. These guidelines could be enriched in the future based on other relevant studies.

Monitoring of sheep grazing behaviour is performed using appropriate GPS devices attached on designated collars (Figure 3). Rotational monitoring of animals can be applied to reduce the number of devices needed. Selected GPS devices should be of low weight in order to be accepted by the animals without any obvious irritation. Batteries with extended life should be used to provide sufficient energy for GPS tracking for as many as possible consecutive days. In the aforementioned study, “Tractive GPS” devices (Tractive, Pasching, Austria) were used that weighed 28 grams. GPS tracking of each animal was performed for 4-10 days at 2-60 minutes intervals; number of tracking days and intervals were based on available signal and animal movement.

GPS generated data of each animal for the total tracking period are exported in .gpx format. In the case of “Tractive GPS”, the location history function of MyTractive web app ([https://my.tractive.com/#/ <nowiki>https://my.tractive.com/#/</nowiki>)] is used to export recorded data. Then, the exported files are split by date using a designated software such as GPSBabel (version 1.8.0). For each animal, daily routes and corresponding GPS data can be visualized and extracted using appropriate software such as Viking GPS data editor and analyser (version 2.0).

Recorded grazing behaviour traits via these devices include duration of daily grazing (min), distance (km), speed (km/hour), minimum and maximum altitude, and total elevation gain. Other useful metrics including number and average distance between tracking points, tracking duration and route followed by the animals should also be extracted to be used in ensuing analyses.
[[File:Figure_3._GPS.jpg|center|thumb|600x600px|Figure 3. GPS device attached on designated collar.]]

==== Calculation of traits ====
Based on minimum and maximum altitude, altitude difference is calculated. Moreover, energy expenditure for walking can be estimated using the following formula of AFRC (Alderman and Cottrill, 1993):

EE= (0.0026×HD+0.028×VD)×BW

where:

EE = energy expenditure for walking (MJ);

HD = horizontal distance (km, calculated as the difference between distance and elevation gain); VD = vertical distance (km, corresponding to elevation gain);

BW = body weight (kg).

Quality control of GPS generated phenotypes is necessary to sense-check the data for extreme values and errors. Specifically, limits are set for minimum and maximum altitudes to reflect the real altitude of the region being studied. Tracking points beyond these limits are then removed from the corresponding .gpx files and data are re-calculated. Moreover, daily records for which GPS tracking of animals had stopped before returning to their shed, must be excluded. Finally, if needed, grazing behaviour traits should be logarithmically transformed to ensure normality of distribution prior to analyses.

==== Use for genetic analysis / genetic evaluation ====
(Co)variance components of grazing behaviour phenotypes and relevant breeding values (EBVs) can be estimated by restricted maximum likelihood methodology applied to an animal mixed model that can include the following random and fixed effects:

Random effects: additive genetic effect and permanent environmental effect of the animal

The relevant fixed effects may include:

* Farm
* Number of GPS tracking points
* Tracking duration
* Distance between tracking points
* Climatic parameters (e.g. temperature-humidity index)
* Sampling time

It may also be desirable to include social grouping (if known), as this can also affect individual animal behaviours.

=== Acknowledgements ===
We gratefully acknowledge the contributions to these recording of behaviour guidelines by the following people:

* Dominique Hazard, INRAE, France
* Angeliki Argyriadou, University of Thessaloniki, Greece
* Georgios Arsenos, University of Thessaloniki, Greece
* Alain Boissy, INRAE, France
* Vasileia Fotiadou, University of Thessaloniki, Greece
* Sotiria Vouraki, University of Thessaloniki, Greece
* Joanne Conington, SRUC, the UK
* Marija Špehar, Centre for Livestock Breeding Zagreb, Croatia

This work received funding from the European Unions’ Horizon 2020 Research & Innovation program under grant agreement N°772787—SMARTER.

=== References ===
Alderman, G., Cottrill, B. Energy and Protein Requirements of Ruminants. In An Advisory Manual Prepared by the AFRC Technical Committee on Responses to Nutrients; CAB International: Wallingford, UK, 1993.

Andriamandroso, A., J. Bindelle, B. Mercatoris, F. Lebeau (2016). A review on the use of sensors to monitor cattle jaw movements and behavior when grazing. Biotechnologie, Agronomie, Société et Environnement, 20.

Beausoleil, N. J., D. Blache, K. J. Stafford, D. J. Mellor, and A. D. L. Noble. (2012). Selection for temperament in sheep: Domain-general and context-specific traits. Appl. Anim. Behav. Sci. 139:74–85.

Boissy, A., Bouix, J., Orgeur, P., Poindron, P., Bibe, B., & Le Neindre, P. (2005). Genetic analysis of emotional reactivity in sheep: effects of the genotypes of the lambs and of their dams. ''Genetics Selection'' ''Evolution, 37'', 381-401. doi:10.1051/gse:2005007

Brand, T. S. (2000). Grazing behaviour and diet selection by Dorper sheep. Small Ruminant Research, 36(2), 147-158.

Brien, F. D., Cloete, S. W. P., Fogarty, N. M., Greeff, J. C., Hebart, M. L., Hiendleder, S., . . . Miller, D. R. (2014). A review of the genetic and epigenetic factors affecting lamb survival. Animal Production Science, 54, 667-693. doi:10.1071/an13140

Cloete, S. W. P., Burger, M., Scholtz, A. J., Cloete, J. J. E., Kruger, A. C. M., & Dzama, K. (2020). Arena behaviour of Merino weaners is heritable and affected by divergent selection for number of lambs weaned per ewe mated. Applied Animal Behaviour Science, 233. doi:10.1016/j.applanim.2020.105152

Dwyer, C. M., Lawrence, A. B. (2005). A review of the behavioural and physiological adaptations of hill and lowland breeds of sheep that favour lamb survival. Applied animal behaviour science, 92(3), 235-260.

Dwyer, C. M. (2008). Genetic and physiological determinants of maternal behavior and lamb survival: Implications for low-input sheep management. Journal of Animal Science, 86, E246-E258. doi:10.2527/jas.2007-0404

Dwyer, C. M. (2014). Maternal behaviour and lamb survival: from neuroendocrinology to practical application. animal, 8, 102-112. doi:doi:10.1017/S1751731113001614

Feldt, T., Schlecht, E. (2016). Analysis of GPS trajectories to assess spatio-temporal differences in grazing patterns and land use preferences of domestic livestock in southwestern Madagascar. Pastoralism, 6(1), 1-17.

Gilmour, A. R., Gogel, B. J., Cullis, B. R., & Thompson, R. (2009). ASReml User Guide Release 3.0 VSN International Ltd, Hemel Hempstead, HP1 1ES, UK. www.vsni.co.uk.

Hazard, D., Moreno, C., Foulquié, D., Delval, E., François, D., Bouix, J., Boissy, A. (2014). Identification of QTLs for behavioral reactivity to social separation and humans in sheep using the OvineSNP50 BeadChip. ''BMC Genomics, 15'', 778. doi:10.1186/1471-2164-15-778

Hazard, D., Bouix, J., Chassier, M., Delval, E., Foulquie, D., Fassier, T., Boissy, A. (2016). Genotype by environment interactions for behavioral reactivity in sheep. ''Journal of Animal Science, 94'', 1459-1471. doi:10.2527/jas2015-0277

Hazard, D., Macé, T., Kempeneers, A., Delval, E., Foulquié, D., Bouix, J., & Boissy, A. (2020). Genetic parameters estimates for ewes’ behavioural reactivity towards their litter after lambing. ''Journal of Animal Breeding and Genetics, n/a''. doi:10.1111/jbg.12474

Hazard, D., Kempeneers, A., Delval, E., Bouix, J., Foulquie, D., & Boissy, A. (2021). Maternal reactivity of ewes at lambing is genetically linked to their behavioural reactivity in an arena test. Journal of Animal Breeding and Genetics, 139, 193-203. doi:10.1111/jbg.12656

Hazard, D., E. Delval, S. Douls, C. Durand, G. Bonnafe, D. Foulquié, D. Marcon, C. Allain, S. Parisot, A. Boissy (2022). Divergent genetic selections for social attractiveness or tolerance toward humans in sheep. WCGALP 2022

Homburger, H., Schneider, M. K., Hilfiker, S., Lüscher, A. (2014). Inferring behavioral states of grazing livestock from high-frequency position data alone. ''PLoS One'', ''9''(12), e114522.

Keller, M., Meurisse, M., Poindron, P., Nowak, R., Ferreira, G., Shayit, M., & Levy, F. (2003). Maternal experience influences the establishment of visual/auditory, but not olfactory recognition of the newborn lamb by ewes at parturition. Developmental Psychobiology, 43, 167-176. doi:10.1002/dev.10130

Lambe, N R; Conington, J; Bishop, S C; Waterhouse, A; Simm, G (2001). A Genetic Analysis of maternal behaviour score in Scottish Blackface sheep. Animal Science 72: p415-425. Doi:10.1017/s1357729800055922.

Ligout, S., Foulquie, D., Sebe, F., Bouix, J., & Boissy, A. (2011). Assessment of sociability in farm animals: the use of arena test in lambs. Applied Animal Behaviour Science, 135, 57-62. doi:10.1016/j.applanim.2011.09.004

Mignon-Grasteau, S., Boissy, A., Bouix, J., Faure, J.-M., Fisher, A. D., Hinch, G. N., . . . Beaumont, C. (2005). Genetics of adaptation and domestication in livestock. ''Livestock Production Science, 93'', 3-14. doi:10.1016/j.livprodsci.2004.11.001

O’Connor, C. E., Jay, N. P., Nicol, A. M., & Beatson, P. R. (1985). Ewe maternal behaviour score and lamb survival. Proceedings of the New Zealand Society of Animal Production, 45 159–162.

O’Connor, C.E., Lawrence, A. B. and Wood-Gush, D. G. M. (1992). Influence of litter size and parity on maternal behaviour at parturition in Scottish Blackface sheep. Applied Animal Behaviour Science 33: 345–355. <nowiki>https://doi.org/10.1016/S0168-1591(05)80071-1</nowiki>

Simm, G., Conington, J., Bishop, S. C., Dwyer, C. M., Pattinson, S. (1996). Genetic selection for extensive conditions. Applied Animal Behaviour Science, 49(1), 47-59.

SMARTER deliverable D2.4. New prototype and report for industry on GPS-generated phenotypes for behavioural adaptation to extensive grazing systems; artificial rearing adaptation phenotypes; lamb vigour scores linked to lamb survival; new foetal and neonatal survival phenotypes (in preparation).

von Borstel, U. K., Moors, E., Schichowski, C., & Gauly, M. (2011). Breed differences in maternal behaviour in relation to lamb (Ovis orientalis aries) productivity. Livestock Science, 137, 42-48. doi:10.1016/j.livsci.2010.09.028

Vouraki, S., Papanikolopoulou, V., Argyriadou, A., Priskas S., Banos, G., Arsenos, G. (2025). Phenotypic and genetic parameters of grazing behaviour of semi-extensively reared Boutsko sheep. Applied Animal Behaviour Science, vol. 282, Jan 2025, 106473. <nowiki>https://doi.org/10.1016/j.applanim.2024.106473</nowiki>

== Guidelines on recording the environment in sheep and goats ==
'''Change Summary'''
{| class="wikitable"
|'''Date of change'''
|'''Nature of Change'''
|-
|October 2024
|First version
|}

=== Introduction and scope ===

==== Introduction ====
In the genetic evaluation process, the genetic model includes environmental effects (generally fixed effects, in some cases random effects) to correct the phenotypes from these effects, not related to the genetic value of the animal. These environmental effects that affects the expression of the genotypes depend on the traits and the method of phenotyping, the environment itself (flock/herd, year, parity, season of lambing, number of born or reared lambs/kids, scorer, gender of the lamb/kid, management of mob groups, etc). The quality of the record of the environment is important to correct relevantly the performance of the animal.

Some other environmental effects that are usually included in a general flock/year or management mob group effect could be identified, such as the feeding effect or the climate effect. By including these effects in the genetic model, we could get less biased and more precise EBVs, especially when these effects are individualised or are period-specific (feeding might depend on such and such groups of animals, climate might influence the performance of such and such test- day). Moreover, the more precise knowledge of environmental effect might be valorised for flock/herd management and extension services towards farmers.

Moreover, feeding can be considered as an environmental effect, but as well be constitutive of a performance. This is typically the case for feed efficiency where the quantity and the quality of the diets allows to calculate the phenotype.

Likewise, with the climatic change, breeding for animals more resistant or more resilient to higher temperatures (especially thermal stress) becomes a selection objective per se (example of heat tolerance). In this context, the conditions of temperatures (or temperature/humidity combination) not only might be an environmental factor, but be part of the phenotype.

Other environmental effects can be described and should enrich this document in the future.

==== Scope ====
This document focuses on those data that are worth recording the precise the environment or to calculate novel traits of interest.

Following SMARTER work, the document will describe the record of the diet ([[Section 24: Recording resilience in sheep and goats#Recording the diet|Chapter 6]]) and the record of meteorological data ([[Section 24: Recording resilience in sheep and goats#Meteorological data|Chapter 6]])

Further factors might be described later, letting this document open to new section in the future, including:

* Recording the diet in small ruminant
* Recording meteorological data
* Other environmental records

=== Recording the diet ===

==== Definition, terminology, rationale ====
Recording the diet consists in collecting data on the quantity and quality of a ration that an animal, a group of animals of a flock/herd consumes at a given period.

The characterisation of the ration, in terms of energy and protein depends upon the countries. For example, the French INRAE Feeding System for Ruminants (Nozière et al., 2018) is different from the British one (AFRC, 1993). This is the reason for which we will describe in this section general recommendations, that can be applied, translated to the domestic feeding system used

Breeding for more efficient animals is more and more important for economic reason (the feeding resources are costly, might be rare in years with climatic excess such as heat or drought) and for environmental reasons (feed/food competition, emission of green-house gases). Feed efficiency is a trait of high interest in this context. Even though it is deceptive to calculate gold standard efficiency trait in private farm, the knowledge of diets in those farms should help to correctly manage the proxies that are promoted in SMARTER. Diet could also be used as a corrective factor in evaluation models in the future. In addition, it might be a support to better understand the herd/flock effect and its variation across year, and therefore give more acute and relevant advice to the farmers.

It is difficult and time-consuming to collect the data for establishing the diet in the flock/herds. The diet is collective in most of the situations (the same amount of forage is given to all animal because the forage is not given individually). When the concentrate is given through Automated Concentrate Feeder (ACF) in the milking parlour, the individualisation is not at the animal scale but at a limited number of groups scale. That’s why we suggest recommendations that must be adapted to each situation.

The aim is to tend to the better possible estimation of the forage ingestion, given that the direct measurement is impossible in commercial farms. Proxies are studied to get indirect measurement of the intake, but they are not validated so far (Near Infra Red Spectra technique). As soon as validated results are available, these recommendations will be updated.

==== Data recording ====

===== When to record the diet =====
The diet may be recorded at relevant period of the physiological status of the animals in the flock/herd. It is possible to take advantage of the visit of a technician to record the ration (for example when performance recording such as at each (or some of the) test-day when milk recording, or at weighing visit in meat sheep performance recording.

Below are examples of relevant physiological status:

* At mating (or before the mating and after the mating)
* End of gestation (in the month preceding the lambing/kidding)
* After lambing/kidding
* At weaning or just after weaning (peak of production in dairy animals)
* Dairy animals: at each test-day or at some of the test-day

In case of ACF (Automatic Concentrate Feeder), it is possible to record the distribution of concentrate more frequently.

It may be useful to establish the requirements of animals (on average) at each point of diet record. The requirements must concern the energy (in the unit usually used in the country) and the protein (in the unit usually used in the country).

===== How to record the diet =====
'''Individual diet'''
* This can be obtained through ACF for concentrate, mainly in the milking parlour.
* Intake of forage cannot be collected individually but can be predicted through the intake capacity system, such as the one proposed by INRAE (Nozière et al., 2018).

'''Collective diet (at the flock/herd scale or at the mob scale)'''

* Forage (hay, or haylage): some bales of each preservation technic can be weighed once a year with a dry matter (DM) measurement for haylage (it can substantially vary). For hay, DM can be estimated at 85%. Afterward, we can just record how many bales of a given quality (several cutting stages are preserved and not given at random) are distributed per flock per time unit. For silages, it is more complicated, but based on the same procedure, we can weigh one distribution (assuming that it will be constant over time) and simultaneously measured DM. In both situations, if refusals cannot be measured, they must be sufficient for assuming an ad libitum distribution. When the feeding system used in the country can predict the DM intake through the intake capacity of the animal and the quality of the feed, individual diet can be estimated.
* Grazing: for dairy sheep grazing within a short duration per day or the full day, intake can be estimated through ad hoc system. As an example, the new French INRATion feeding software (INRATion V5®) proposes such estimation based on grazing duration, biomass availability and quality.

===== Defining the constitution of the diet =====

====== Precise the type of distribution of the ration ======

* collective ration
* individual ration (concentrate when ACF)
* pasture

====== Categories of feedstuff ======

* Hay
* Partially or fully fermented fodder and fodder preserved by silaging or wrapping:
** Silage
** Wrapped bales

* Pasture
* Straw
* Green feeding
* Dehydrated alfalfa
* Pulp (dehydrated beet pulp, citrus pulp, etc)
* Cake (soybean, rapeseed or sunflower seed)
* Cereals grain (wheat, barley, maize, etc)
* Complete commercial concentrate
* Other by-products of agro-food industry (cereal brans, brewer’s grains, hulls etc.)

====== Species ======
For each category, specify the species (rye grass, alfalfa, clover, maize, wheat, barley, etc), physiological stage or age of regrowth, and harvest conditions (cutting length of the forage and added preservative or not for silages, conditions of hay making drying in the field or mechanically dried).

===== Characterizing the diet =====

====== Quantity ======
Quantity distributed, refused, consumed. Check that these amounts are regularly distributed, refused and consumed because it can markedly influence the animal performance specifically for dairy animals at test day.

The quantity of each feedstuff may be expressed in kg dry matter for forage, in kg gross matter for concentrate. However, final diet for requirement calculation must be expressed as DM.

====== Requirements ======
Requirements for the main categories of animals: it depends on the physiological status (maintenance, production, growing, pregnancy)

Average requirement coverage ratio (energy and nitrogen). For example, the requirement coverage ratio in French dairy sheep is roughly 115% for energy and about 125% for nitrogen of the requirements of the average ewe. That allows covering the requirements of about 85-90% of the flock. Difference between energy and nitrogen is assumed to be covered through the body reserve mobilisation.

====== Quality characterization ======
The feedstuffs and the ration must be characterized at least in terms of

* Energy
* Protein (or nitrogen)

In case of commercial concentrate, data written on the label are used.

Energy and protein can be expressed in the current unit used in the country. For example, in France, energy is expressed in UFL which is equal to 1.7 Mcal Net energy (Nozière et al., 2018).

It may also be expressed in the international unit, which can be Mcal or MJ.

==== Use for genetic analysis / genetic evaluation ====
'''Diet as part of a phenotype'''

Calculation of feed efficiency phenotypes: see recommendations on feed efficiency.

'''Diet as part of a factor in the evaluation model'''

In most of situations it is impossible in small ruminants to establish individual consumption, for practical reason. The collective effect of the diet is explained in the flock/year effect. The intermediate situation should be when ACF allows to identify several groups within the flock/herd, at a specific test-day or visit. It is possible in this case to put in the model a mob effect grouping animals being given the same amount of concentrate. This should result in a more precise calculation of the breeding value of the animal. Nevertheless, this approach has so far not be used to our knowledge.

=== Meteorological data ===

==== Definition, terminology, rationale ====
Meteorological conditions may affect the environment effect on the traits of interest. Even though they may be absorbed in a flock effect at the scale of the year or at the scale of a given test-day, it is relevant to be able to quantify the effect of such and such meteorological parameter (and especially the heat stress) ot the zootechnical traits. The global warming and the higher temperature in which the animals are bred emphasises this interest. It is possible to better assess the comfort zone of the populations, that means the meteorological conditions in which the zootechnical traits are not affected. It is also possible to identify animals better adapted to an increase in temperatures or able to be resilient to a wide range of temperatures, that means to maintain their productive ability. In this case, meteorological data, combined with a production trait (growth, milk production, milk composition) or fertility trait, are used as a resilience characterisation by assessing the ability of the animals to recover their production following meteorological challenges.

Meteorological data are mostly temperature, humidity, precipitations, wind speed and radiations. An issue in small ruminants is to select for adapted animals to new environmental challenges, without artificializing their environment of breeding. Mainly because the economic and societal constraints are such as breeding animals outdoors on pasture is desired and breeding indoors inartificialized environment may be costly in terms of energy.

==== Data recording ====

===== Meteorological data from weather station =====
The aim is to affect outdoors meteorological data to a farm. This can be obtained by assigning to the farm the meteorological data of the closest or more relevant weather stations, using the geographical coordinates of both the farm and the weather station.

The following data may be used:

* Temperature (minimum, maximum, average)
* Relative humidity (amount of moisture in air compared to the maximum amount of moisture it can have at a specific temperature). Expressed in %.
* Specific humidity (ratio of water vapor mass to the total mass of air and water vapor.
* Wind speed
* Precipitations and precipitation type
* Solar radiation
* Atmospheric radiation
* Evapotranspiration

Different index accounting for weather factors have been proposed. One of the most popular is the Temperature Humidity Index (THI) which may be calculated to get a single value representing the combined effects of air temperature and humidity associated with the level of thermal stress.

Different formulas of THI are proposed in the literature. Below is an example of formula proposed by Finocchiaro (et al., 2005):

THI = T − [0.55 × (1 − RH)/100] × (T − 14.4)

where T is the mean daily in °C and RH is the mean relative humidity expressed in percent. Quite often, the parameter used in the analysis model is the temperature of the THI (mainly because temperature and relative humidity are the most available parameters).

Let us also mention the Heat Load Index, referred to as the 'HLI', which is an index that brings together all the weather factors into one number to allow easy interpretation of the cooling capacity of the environment.

The assignation of meteorological data to a farm depends on the countries and on the availability of weather data.

In some countries, the territory may be cut out in a grid, each cell of the grid being considered to have the same meteorological parameters because they are close to the same weather station of reference. As an example, this is the case in France with a grid named SAFRAN cutting the territory into 9892 cells of 64 square kilometres each [8 km by 8 km] (Annex 1). This grid was used, thanks to specific permission from Meteo France, to affect each farm of a given project (by using its GPS coordinate) to a single cell of the grid and thus get relevant meteorological parameters.

The meteorological spatialised data are collected from weather station, on which specific interpolation are applied to present these data on the SAFRAN grid.

The meteorological data key period to consider must be thought according to the production system associated to the breed, type of traits measured and analysed. For example, for milk production (milk recording), we may consider the 3 days preceding the test-day. For semen production, we may consider the meteorological data either at the day of the semen collection, or during the spermatogenesis, which is around 50 days before the semen collection. For the insemination itself (which is in case of fresh semen the same day as semen production), we may consider climate data either the very day of the insemination operation or during a week preceding it.

===== Environmental data from sensor in the farm =====
Temperature and humidity may also be collected on site, thanks to sensors situated on-farm, for example in the sheep pen or the stable.

The number of sensors may depend upon the situation and configuration of each building, the goal being to be representative of the pen. In the practical situations of the SMARTER project, 2 to 3 sensors were set in the pen where animals are indoors at a height of 2 meters above the ground, so that they are protected from the animals. If the pen is already equipped by sensors, it is possible to retrieve the data from the existing sensors. The sensors must cover all the relevant groups of animals (primiparous, multiparous, etc), even if they are in different buildings. Measures might be collected several times a day, for example once an hour, to get a precise evaluation of the daily temperature and hygrometry. To relevantly collect the atmosphere of the building, the sensors must be set in a place free from too much air flow or too much sunshine. It is important to regularly check the batteries to avoid loss of data.

==== Use for genetic analysis / genetic evaluation ====
Effect of meteorological parameters (eg. temperature or THI) may be estimated on zootechnical traits, using different types of linear models.

The parameter may be considered as a categorical variable (each degree of the parameter being defined as a different class). Or it may be considered in a linear regression on degrees of the parameter.

Reaction norms model, using Legendre polynomial for example, may be used to assess populational losses of the zootechnical trait due to high or low temperature and/or humidity.

Two types of analysis can be made:

* a populational analysis (populational response to the effect of temperature or THI). It gives the comfort rage of each population and how much the loss is with lower or higher temperature or THI.
* an analysis of the genetic components using a random regression model. It permits to estimates genetic parameters of traits according to the temperature or THI and to calculate EBVs of animals at different temperatures or THI levels. Such EBVs allow to identify less vulnerable animals along a range of climate values, so as to identify and select the most robust animals.

=== Other environmental record ===
To be completed (or not) when necessary

=== Acknowledgements ===
We gratefully acknowledge the contributions to these environment documentation guidelines by the following people:

* Jean-Michel Astruc, IDELE, France
* Antonello Carta, Agris, Italy
* Philippe Hassoun, INRAE, France,
* Gilles Lagriffoul, IDELE, France
* Carolina Pineda Quiroga, NEIKER, Spain
* Eva Ugarte, NEIKER, Spain

This work received funding from the European Unions’ Horizon 2020 Research & Innovation program under grant agreement N°772787—SMARTER.

=== References ===
Finocchiaro R, van Kaam JB, Portolano B, Misztal I. Effect of heat stress on production of Mediterranean dairy sheep. J Dairy Sci. 2005 May;88(5):1855-64. doi: 10.3168/jds.S0022-0302(05)72860-5. PMID:15829679.

Nozière, P., Sauvant, D., Delaby, L. 2018. INRA Feeding System for Ruminants. Wageningen Academic Publishers, 640 p., 2018, 978-90-8686-292-4. ⟨10.3920/978-90-8686-292-4⟩. ⟨hal-02791719⟩

AFRC (Agricultural and Food Research Council). 1993. Energy and protein requirements of ruminants. CAB International, Wallingford.

=== Annexes ===
[[File:SAFRAN_grid_from_Meteo_France.jpg|center|thumb|600x600px|SAFRAN grid from Meteo France in the case of France]]

Section 24: Recording resilience in sheep and goats

2025-10-10T12:02:29Z

Aashish: /* Introduction and scope */

=== Introduction and scope ===
<div class="mw-collapsible mw-collapsed">
<div>
==== Introduction ====
The present guidelines aim at addressing resilience traits in small ruminants, as well as the description of the environment.

These recommendations are mainly based on a work achieved in the SMARTER H2020 project (n° 772787) whose objective was to promote harmonisation and international cooperation on breeding processes in small ruminant, especially those concerning the selection of efficiency and resilience. In this project, case studies of across country genetic evaluation, implemented as a proof of concept, have highlighted the importance of analysing traits that have been collected and/or calculated on a same way across country. Therefore, it appears fundamental that novel traits, such as resilience-related traits, which are not still widely routinely recorded on-farm for selection purposes, be recorded identically, or at least in the most similar way as possible. For that purpose, recommendations must be proposed, for countries or breeding organisations that would like to start to record efficiency or resilience traits, or that would like to set up an across-country genetic evaluation on these traits. The more similar the traits, the higher the genetic correlation across country (at same level of connection across country).

In addition, as resilience may be considered as basically related to the environmental challenges such as nutritional, disease or climatic challenges, the documentation of the environment is also described. Tackling the record of the environment is a novelty in selection of small ruminant.

The recommendations issued in a deliverable of the SMARTER project have been basically written by the partners of the project working on tasks dedicated to the different resilience-related traits and as well by the Sheep, Goat and Camelid ICAR Working Group. The Working Group was indeed involved, as partner for some of the members, as stakeholders for some other, and through ICAR who was a partner itself. Therefore, these guidelines are the fruit of a close cooperation between many academic and non-academic co-authors. Materials were also collected from results obtained in other projects (e.g. H2020 iSAGE, POCTEFA ARDI).

The recommendations, even though they target to suggest people measuring and calculating the traits the same way, are more informative than normative. The different ways to measure and calculate the traits are presented, without imposing one way, yet while suggesting some general features. Five sub-sections of recommendations were written: health and disease, survival of foetus and young, behaviour, lifetime resilience, record of the environment. All sub-sections are written with the same template and are consistent by themselves.

All the recommendations are based on the current state of the art. However, they are meant to evolve with new results and new research, and they are meant to be enhanced, consolidated, enriched. It is possible to add a new trait, a new proxy, a new sub-section. In brief, the recommendations must keep alive to stick to the evolving state of the art. This implies that the consortium that produced these recommendations, in some way, continue to contribute. ICAR, with its working group dedicated to sheep and goat, is the relevant organisation to collect and integrate the different novelties and contributions.

===== Scope =====
The SMARTER recommendations cover the following fields, shown in the figure 1.
[[File:SMARTER recommendations.jpg|center|thumb|600x600px|Figure 1. Fields covered by the SMARTER recommendations ]]
The resilience-related traits are: health and disease (with a focus on resistance to parasites, to footrot, and to mastitis), survival foetus and young, behaviour traits (with a focus on behavioural reactivity towards conspecifics or humans, maternal reactivity, behaviour at grazing), lifetime resilience.

The record of the environment covers the meteorological data and the diet. The record of the rations was studied in the on-farm protocols of SMARTER-WP1, especially in France. The record of the meteorological data benefited from works carried out in the H2020 iSAGE and POCTEFA ARDI projects, some of the SMARTER partners being committed in those projects.

The recommendations are conceived to be evolutive. Amendments can be brought in the next years, especially when the recommendations will turn into ICAR guidelines, either to strengthen results or include new insights, or to add new sub-sections or new traits. For example: (i) in the record of the environment, sensor data may be included; (ii) new disease whose resistance has a genetic component.

==== Definition of resilience ====
In these guidelines, we use the following definition of the resilience.

Resilience is defined as the ability of an animal/system to either maintain or revert quickly to high production and health status when exposed to a diversity of challenges, with a focus on nutritional and/or health challenges. Resilience is therefore the trajectory that captures the deviation from, and recovery to, the unchallenged state. Direct indicators of health and welfare will address gastro-intestinal parasitism, lameness (footrot) and mastitis, the most economically important endemic diseases of small ruminants. Indirect indicators of health and welfare of economic importance for breeders are lamb and foetal survival, functional longevity, maternal and lamb behaviour, and neonatal vigour..

==== Recording of resilience ====
The resilience-related traits that are described below for sheep and goats are:

* [[Section 24: Recording resilience in sheep and goats#Guidelines on recording health and disease in sheep and goats|health and disease (Chapter 2);]]
* [[Section 24: Recording resilience in sheep and goats#Guidelines on recording lifetime resilience in sheep and goats|lifetime resilience (Chapter 3);]]
* [[Section 24: Recording resilience in sheep and goats#Guidelines on survival recording of foetus and young in sheep and goats|survival of foetus and young (Chapter4);]]
* [[Section 24: Recording resilience in sheep and goats#Guidelines on recording behavioural traits in sheep and goats|behavioural traits (Chapter 5).]]

==== Recording of the environment ====
The record of the environment in sheep and goats is described below in the [[Section 24: Recording resilience in sheep and goats#Guidelines on recording behavioural traits in sheep and goats|Chapter 6]] of these guidelines

==== Acknowledgements ====
We gratefully acknowledge the contributions to these guidelines on recording resilience-related traits and the environment in sheep and goat by all the people working in the ICAR working group on sheep, goat, camelids and/or participating to the SMARTER project:

The different documents giving the recommendations of each sub-sections list in their own acknowledgements the persons involved in the writing of the guidelines.

This work received funding from the European Unions’ Horizon 2020 Research & Innovation program under grant agreement N°772787—SMARTER.

== Guidelines on recording health and disease in sheep and goats ==
'''Change summary'''
{| class="wikitable"
|'''Date of change'''
|'''Nature of Change'''
|-
|October 2024
|First version
|-
|December 2024
|Tracked change revisions by MS
|}

=== Introduction and scope ===
==== Introduction ====
Livestock diseases cause significant economic losses due reduced productivity, failing to express the genetic potential of animals, treatment costs, and consequently the culling of animals. Therefore, health and resistance to disease are keys factors for increasing resilience in farm animals in general and in small ruminants in particular. Among the challenges that sheep and goats must face, the infectious challenges are among the most important. They lead to losses of production and difficulties of reproduction. They also generate an increase in the consumption of chemical input. Beyond actual extra cost that may hamper the sustainability of the farms, but also of the breeding programs, there is a risk for the environment and the occurrence of resistance to drugs.

In most cases, an integrated approach is the more beneficial and efficient, mixing the different leverages. Among them, the control of the challenges by the host through its genetic resistance has shown its efficiency for some disease (resistance to scrapie, resistance to mastitis in dairy species) or is promising (resistance to parasites, resistance to footrot).

These guidelines on health and disease phenotypes are dedicated to any kind of health and disease resistance indicators. However, to start, we focus on the traits studied in SMARTER, which are the resistance to parasites and the resistance to footrot and mastitis in meat sheep and dairy sheep and goats.

==== Scope ====
This section on recording health and disease in sheep and goats starts following the task achieved in SMARTER and includes the following three sub-sections:

* Resistance to parasites
* Resistance to mastitis
* Resistance to footrot
=== Resistance to parasites ===

==== Definition, terminology, rationale ====
Resistance may be defined as the host’s ability to limit its parasite load (Råberg et al., 2007). The resistance to parasites described here corresponds to the resistance to gastro-intestinal nematodes (GIN). They are one of the main constraints for grazing sheep. They cause substantial economic losses due to lower production levels, the costs of anthelmintic treatments and the mortality of severely affected sheep. GIN control strategies mainly rely on treatment with anthelmintics. In many regions of the world, studies have reported the development of GIN resistance to most anthelmintic molecules due to their extensive use. Additionally, the possible presence of drug residues in animal products and the negative impact of these molecules on the micro and macro fauna of the soil are of concern. Therefore, sustainable GIN control may be a priority with schemes that do not only rely on anthelmintics but include complementary strategies such as nutritional supplementation with tannins and/or proteins, pasture management, and genetic selection of resistant animals. This latter strategy relies on the existence of genetic variation of host resistance to GIN both between and within breeds.

The faecal egg count (FEC), which is the number of parasite eggs per gram of faeces, is the most commonly used indicator to assess this resistance to GIN. In many countries, the selection for parasite resistance is based on FEC measures in natural infestation conditions under natural grazing conditions. As FEC measurements in sheep and goats are extremely costly and laborious, and because response to artificial challenges is highly correlated to response to natural infestation, it is therefore possible to implement a protocol of experimental infestation, as it is the case in France.

Beside FEC, different phenotypes can be used to measure resistance to GINs such as packed cell volume (PCV), FAffa MAlan CHArt (FAMACHA©) score, DAG score, immunological traits, and blood bepsinogen dosing (Shaw et al., 2012; Bishop, 2012; Bell et al., 2019; Sabatini et al., 2023).

==== Data recording ====

===== Indicators of parasite resistance or resilience =====

====== Faecal Egg Count ======
Faecal Egg Count (FEC) is the main indicator that measures the egg excretion intensity. It measures the number of parasite eggs per gram of faeces. This trait is related to the resistance of the animal (ability to limit the installation, the development and the prolificacy of the nematode inside the digestive tract (especially the abomasum). FEC is determined for each sample using a modified MacMaster technique (Whitlock, 1948 or Raynaud, 1970) with a sensitivity of 100 or 15 eggs per gram, respectively. The measure may be done in natural or in experimental infestation. FEC can be applied to one species (for example ''Haemonchus contortus'' (''Hc'')) or several species (including ''Hc'', ''Teladorsagia circumcincta'', ''Trichostrongylus colubriformis'', etc).

The distribution of the FEC has an asymmetric distribution (some high value, many low or medium value). A transformation must be applied to process a genetic analysis. The most frequent transformations are a root (fourth, third or square root) or a log transformation.

====== Packed Cell Volume ======
Packed Cell Volume (PCV) - Blood samples were collected in EDTA coated tubes and PCV values were determined individually by centrifugation in microhematocrit tubes with a relative centrifugal force of 9500 for 10 min.

PCV can be exploited as a single value of more relevantly as a gain/loss of PCV between two points. Variation of PCV is a relevant indicator of the resilience of the sheep / goat.

====== FAMACHA score ======
FAMACHA® score – As the anaemia provoked by some hematophagous parasites is at some stage visible on the mucosa (especially ocular mucosa), a scale of grading, based on the colour of the ocular mucosa, ranging from 1 (dark red mucosa) to 5 (white mucosa) has been built. This score was developed in South Africa to facilitate the clinical identification of anaemic sheep infected with H. contortus (Van Wyk and Bath, 2002).

As drawbacks, the FAMACHA® score does not allow to detect the non-hematophagous parasites and it appears quite belatedly: a FAMACHA® score over 3 concerns animals with a PCV below 20%. The method is not specific, anaemia being possibly caused by other reason than ''Haemonchus contortus''. It is however interesting to detect the anaemia. FAMACHA® score is related to the resilience of the sheep / goat.

====== DAG score ======
DAG score is an indicator for assessing dagginess, which measures faecal soiling in sheep. DAG score uses a 5-point or 6-point scoring scale ranging from 0 (no dags) to 5 or 6 (very daggy). Dag score scale shows the degree or extent of faecal contamination of the fleece.

The key is to be consistent when scoring a mob of sheep and for these sheep to have been run under similar conditions. Faecal contamination should not be confused with urine stain in ewe lambs and hoggets.

====== Immunological traits ======
Immunological and physiological profiles may be linked to phenotypes of resistance to parasites (strongyles). These new immunological and physiological profiles are blood lymphocytes cytokine production and serum levels of nematode parasite-specific Immunoglobulin A (IgA) that are produced upon whole blood stimulation. In SMARTER experiment in SRUC, blood was stimulated with pokeweed mitogen (a lectin that non-specifically activates lymphocytes irrespectively of their antigen specificity), and Teladorsagia circumcincta (T-ci) larval antigen to activate parasite-specific T lymphocytes.

Adaptive immune response may be determined by quantifying:

* cytokines interferon-gamma (IFN-γ), which relate to T-helper type 1 (Th1),
* interleukin IL-4, which relates to T-helper type 2 (Th2) and
* interleukin IL-10, which relate to regulatory T cell (Treg) responses.

Each immune trait displays a significant genetic variation (heritabilities ranging from 0.14 to 0.77). Heritability of IgA is moderate (0.41). Correlations with FEC are rather weak, from 0 to 0.27 but not significantly different from 0.

====== Blood Pepsinogen dosing ======
Blood pepsinogen is an indicator of the integrity of the gastric mucosa. The determination of serum pepsinogen is therefore a proxy in the diagnosis of abomasal strongylosis of ruminants (pepsinogen in blood is caused by an increase in the permeability of the abomasum mucosa due to presence of nematodes). There is a correlation between the concentration of pepsinogen in the blood and the number of worms harboured by the host.

===== Natural infestation =====

====== General considerations ======
Measurements (FEC or other proxies) are mainly undertaken in natural infestation under natural grazing conditions. In natural condition of infestation, frequency and amounts of yearly samplings have to be assessed according to the climate and epidemiological conditions and breeds. Local knowledge is essential for adjusting protocols to each country, as the level of infestation is strongly influenced by seasonality and the grazing system.

Several countries (e.g. Australia, New Zealand, and Uruguay), have incorporated the genetic evaluation of FEC at various ages into their national evaluation systems. In any case, in order to have data useful for the genetic evaluation, a representative sample of sheep in the flock involved in the selection scheme has to be periodically monitored to decide whether to sample the whole flock, i.e. when the number of infected animals and the level of infestation are considered sufficient to appreciate individual variability, individual FEC can be measured on the whole flock.

Further data related to environmental factors affecting the level of infestation should be recorded to be included in the genetic model for estimating the breeding values:

* Farm management mainly grazing system
* Birth type
* Sex
* Age of dam
* Parity
* Lambing date
* Sampling date
* Frequency, date, and molecule of anthelmintic administration

Additionally, stool cultures can be performed from the faecal samples taken (one per management group).

====== Description of the protocol and the measures (Uruguayan protocol) ======
At weaning, lambs undergo anthelmintic treatment, and their treatment efficacy is checked 8-14 days later through the analysis of FEC samples from 20 randomly selected lambs to confirm the absence of egg excretion. Subsequently, FEC is monitored every 15 days by collecting samples (based on epidemiological conditions) from 10-15% of lambs in each management group. The first individual FEC sampling is conducted when the FEC arithmetic mean exceeds 500 with no more than 20% samples exhibiting zero FEC. At this point, the lambs undergo anthelmintic treatment again, and their treatment efficacy is evaluated after 8-14 days. If the FEC mean remains above 500, a second individual sampling is conducted. Throughout the protocol, faecal egg counts (FEC1 and FEC2) are measured at the end of the first and second natural infestations. Generally, with some variations based on the breed, these samplings correspond to lambs at 9 and 11 months of age, respectively.

Currently, to simplify the protocol, only one sampling is conducted, and the control begins on a fixed date (early autumn) when the most significant parasite, H. contortus, prevails. Along with the FEC records (FEC1 and FEC2), other records, such as body weight, FAMACHA®, and body condition score, can also be taken.

===== Experimental infestation (French protocol) =====
As mentioned above, FEC measurements on sheep in commercial flocks are extremely costly and laborious. It has been shown that sheep that are selected on the basis of their response to artificial challenges respond similarly when exposed to natural infestation, and a high positive genetic correlation was estimated between FEC recorded under artificial or natural infestation. Moreover, it has been shown that selection of rams for parasite resistance after artificial challenges allows to improve the resistance of their female offspring for parasite infestation in natural conditions. Thus, an alternative approach may be to select rams gathered for AI progeny-testing or performance-testing by artificially infecting them with standardized doses of larvae.

In most cases, resistance to GIN is assessed in natural infestation conditions at grazing. However, the intensity of natural infestation in grazing animals depends on climatic conditions and may vary from season to season leading to over- or under-estimation of the genetic parameters of resistance. In France, sheep breeds are selected collectively on breeding stations and the strategy is to take advantage of this organization to implement the GIN control selection by phenotyping rams after experimental infestation. There are two main advantages. Firstly, a large diffusion of the genetic progress is expected via these rams, which are the future elite males. Secondly, the experimental infestation performed in control stations allow to evaluate these rams in homogeneous conditions (standardization of doses of infestation, farming conditions, climatic conditions, etc) during the ram evaluation period. Previous studies (Gruner et al., 2004) estimated high genetic correlations between resistances to experimental and natural infestation, between infestation by different parasite species (''Haemonchus contortus'' and ''Trichostrongylus colubriformis'') and between resistance in adult sheep and lambs. Moreover, recent works have shown that the genetic correlation between the resistance of rams in experimental conditions and the resistance of pregnant or milking ewes in natural conditions of GIN infestation are high.

====== Description of the protocol and the measures ======
An original protocol for phenotyping resistance to gastro-intestinal parasitism has been conceived and developed in France, targeted to rams (or bucks) gathered in a breeding centre or station, or an AI centre (Jacquiet et al., 2015; Aguerre et al., 2018). Males bred indoors, supposed to be naïve, are artificially infected twice with L3 larvae of a given strain of ''Haemonchus contortus'' susceptible to anthelminthic. Males are subjected to a first infestation (after a coprological examination be performed to confirm that no eggs were excreted before the artificial infestation) with a given dose of L3 larvae (D0). At D30, the males are phenotyped (FEC30 and possibly PCV30) and treated with an anthelminthic. After a 15-day recovery period, the rams are challenged again with a given dose of L3 larvae of Haemonchus contortus. At that time (D45), the efficacy of anthelmintic treatment is ensured in each male. Thirty days after (D75) the second challenge, the males are phenotyped (FEC30 and possibly PCV30) and treated again. The protocol lasts 2 and a half months. During the protocol, the measures carried out are as follows:

* faecal egg counts (FEC30 and FEC75) at the end of the first and second infestation (from faecal sample).
* packed cell volumes PCV0, PCV30, PCV45 and PCV75 at the start and the end of both infestation (from blood sample).

====== Calculation of variables ======
The FEC30 and FEC75 are used per se. Variations of PCV are calculated:

* PCV_loss_inf1 = PCV0-PCV30 (or ratio PCV30/PCV0)
* PCV_loss_inf2 = PCV45-PCV75 (or ratio PCV75/PCV45)
* PCV_recovery = PCV45-PCV0

where PCV_loss_inf1 and PCV_loss_inf2 represent the loss of PCV after each infestation, while PCV_recovery represents the males’ capacity to recover after the first infestation.

PCV variations might be interpreted as an indicator of resilience of the animal, i.e. its ability to maintain its blood parameters despite the parasitical challenge.

==== Use for genetic analysis / genetic evaluation ====

===== Model for genetic analysis =====
The genetic analysis of experimentally infected animals that are raised indoors may include:

* Fixed effects: contemporary group (mob x doses of larvae), age of animals (eg. 1 year, 2 years, 3years, 4 years and older)
* Random additive effect of the animals
* Residual effect

The genetic analysis of naturally infected animals that are raised outdoors may include:

* Fixed effects: they obviously will depend of the type of animals (females in lactation vs lambs/kids). They should include flock/herd, year x season (e.g. spring, summer, autumn, winter), anthelmintic treatments (e.g. eprinomectin, ivermectin, moxidectin …) in interaction with the number of days between the date of treatment and the sampling date (e.g. less than 70 days, between 70 and 100 days, more than 100 days). For females in lactation: age and/or parity, litter size before lactation (single or multiple new-born lambs). For lambs or kids: age of the dam, type of birth or rearing, and age at the time of the records, expressed in day.
* Random additive effect of the animals
* Random permanent environment effect if repeated measures (e.g. for FEC 1 & 2)
* Residual effect

===== Genetic parameters =====
Pooled estimates of heritability to resistance to gastrointestinal parasites gained by meta-analysis (Mucha et al., 2022) in dairy goats and sheep and meat sheep are shown in Tables 1 and 2, while Table 3 shows the heritabilities estimated for the experimentally infected rams. In addition, we mention a paper from Casu et al (2022) in which a heritability of 0.21 for FEC was found in a 20 year follow-up study in an experimental flock in Sardinia, Italy.

{| class="wikitable"
| colspan="7" |'''''Table 1. Pooled estimates of heritability of resistance to gastrointestinal parasites from meta- analysis in dairy goats and sheep.'''''
|-
|'''Trait1'''
|'''Species'''
|'''Pooled h2(±SE)'''
|'''Min2 h2'''
|'''Max3 h2'''
|'''N obs'''
|'''N studies'''
|-
| rowspan="2" |FEC
|Goats
|0.07 ± 0.01
|0.04
|0.15
|8
|2
|-
|Sheep
|0.14 ± 0.04
|0.09
|0.35
|6
|3
|}
1Trait: FEC – faecal egg count

2Minimum h2 from individual studies included in meta-analysis

3Maximum h2 from individual studies included in meta-analysis

{| class="wikitable"
| colspan="6" |'''''Table 2. Pooled estimates of heritability of resistance to gastrointestinal parasites from meta- analysis in meat sheep (Mucha et al., 2022).'''''
|-
|'''Trait1'''
|'''Pooled h2 (±SE)'''
|'''Min2 h2'''
|'''Max3 h2'''
|'''N obs'''
|'''N studies'''
|-
|DAG
|0.30±0.06
|0.06
|0.63
|37
|15
|-
|FCons
|0.14±0.02
|0.03
|0.27
|13
|5
|-
|NBW4
|0.10±0.02
|0.00
|0.54
|11
|3
|-
|Par-Ab
|0.18±0.07
|0.05
|0.29
|6
|3
|-
|Par-Ig
|0.36±0.06
|0.13
|0.67
|24
|8
|-
|FEC
|0.29±0.03
|0.00
|0.82
|118
|32
|-
|HC
|0.32±0.14
|0.08
|0.56
|5
|2
|}
1Trait: DAG – dagginess, FCons – faecal consistency, NBW – number of worms, Par-Ab – parasitism anitbodies, Par-Ig – parasitism immunoglobulin, FEC –faecal egg count, HC - Haematocrit

2Minimum h2 from individual studies included in meta-analysis

3Maximum h2 from individual studies included in meta-analysis

4Pooled heritability obtained from a simple random effects model as the three level meta-analysis model did not converge

{| class="wikitable"
| colspan="2" |'''''Table 3. Estimates of heritability of resistance to gastrointestinal parasites from meta-analysis in dairy sheep in experimental infestations (Aguerre et al., 2018)'''''
|-
|'''Trait1'''
|'''h2'''
|-
|Root FEC_inf1
|0.14±0.04
|-
|RootFEC_inf2
|0.35±0.08
|-
|PCV_loss_inf1
|0.24±0.05
|-
|PCV_loss_inf2
|0.18±0.06
|-
|PCV-recovery
|0.16±0.06
|}

=== Resistance to mastitis ===

==== Definition, terminology, rationale ====
In small ruminants, mastitis mainly consists in subclinical infections caused by coagulase- negative staphylococci, which is much more frequent than clinical mastitis (Bergonier et al., 2003). Under these conditions, somatic cell count (SCC) is an accurate, indirect measure to predict mammary gland infection. Therefore, SCC could be used as an indirect selection criterion for mastitis resistance as is widely done in dairy cattle. Moreover, selection for mastitis resistance in dairy sheep and goats could mainly focus on selection against subclinical mastitis using SCC, considering the low incidence of clinical cases in these species (<5%), compared to dairy cattle for which clinical cases occur frequently (Bergonier et al., 2003).

Clinical mastitis is not recorded in dairy small ruminants, mainly because of its low incidence and because SCC is a relevant and simple indicator of intra-mammary infections. Work completed in France has developed two lines of ewes (experimental farm INRAE-La Fage) and goat (experimental farm INRAE-Bourges), a high line generated from sires with unfavourable EBVs for somatic cells and a low line generated from sires with favourable EBVs for somatic cells. For both sheep (Rupp et al., 2009) and goats (Rupp et al., 2019), the low line has the lowest SCC, the lowest incidence of clinical mastitis and the lowest incidence of chronic mastitis (abscesses or unbalanced udder) and subclinical mastitis (assessed by milk bacteriology).

Even though SCC is the established indicator for use in animal breeding, the use of the California Milk test (CMT) is a very good indicator of SCC for monitoring udder health in flock/herd management in dairy and meat-producing small ruminants.

==== Data recording ====

===== Somatic Cell Count (SCC) =====
Large scale somatic cell counting relies on the application of routine methods, such as fluoro- opto-electronic counting. The somatic cell counter must be properly calibrated against a reference and laboratories must frequently verify the calibration settings are still correct.

The design for recording SCC will depend upon the objective. For flock/herd management related to high bulk SCC, the whole flock/herd should be sampled and analysed to identify the animals with the highest SCC. For genetic purpose, simplified designs might be implemented.

In dairy species, somatic cell counting is achieved within the milk recording design and the sampling design, as for milk components such as fat and protein content. As in small ruminants, most of the designs are simplified ones compared to the A4 method (all daily milkings recorded, once a month) (see [[Section 16 – Dairy Sheep and Goats|ICAR Guidelines Section 16: dairy sheep and goats]]), SCC are quite often available at one out of the two daily milkings. In this case, use of SCC must be handled accordingly.

As for milk composition, with the aim of simplifying and decreasing further the cost of recording, it is possible/recommended to measure SCC on only a part of the flock/herd (first parity or first two parities). It is also possible to go further in the simplification of the design, for example by sampling only a part of the lactation within a part-lactation sampling as proposed in the [[Section 16 – Dairy Sheep and Goats|section 16 of the ICAR Guidelines]]. The genetic parameters of test-day and lactation mean for Somatic Cell Score (SCS - log-transformed SCC) show that the records of the middle of the lactation appear as the most representative of the whole lactation. Two to four individual samples per female and per lactation, collected monthly in the middle part of the lactation are highly correlated (around 0.98) with SCS determined from samples collected over the complete lactation (A4 method) but are hardly less heritable compared with the A4 homologous traits (negligible loss of precision for SCS) (Astruc and Barillet, 2004). The balance between cost and genetic efficiency, depending on the genetic correlations close to 1, is clearly in favour of the part-lactation sampling compared to A4 testing.

===== California Mastitis Test (CMT) =====
The California mastitis test is an animal-side diagnostic test that provides an estimate of the level of infection within a mammary gland. A sample of milk (~3ml) from each udder half is combined with an equal volume of reagent in a CMT paddle and mixed for 15 to 20 seconds. The reaction is scored based on the level of thickening of the mixture from zero (no thickening) consistent with no, or low, levels of infection, to four (gel formation with elevated surface) indicating high levels of infection.

A previous study (McLaren et al., 2018) has demonstrated the strong correlation between CMT score and SCC from samples collected from pedigree meat sheep in the UK.

==== Calculation of traits ====
Test-day SCC must be transformed to Somatic Cell Score (SCS) by the logarithmic transformation of Ali and Shook (1980) to achieve normality of distribution.

Example: SCS = log2+(SCC/100,000)+ 3

The table 4 gives correspondence between SCC and SCS

{| class="wikitable"
| colspan="2" |'''''Table 4. Correspondence between somatic cell score and somatic cell count'''''
|-
|'''Somatic Cell Count (SCC)'''
|'''Somatic Cell Score (SCS)'''
|-
|12,500
|0
|-
|25,000
|1
|-
|50,000
|2
|-
|100,000
|3
|-
|200,000
|4
|-
|400,000
|5
|-
|800,000
|6
|-
|1,600,000
|7
|}
SCS can be adjusted for days-in-milk (DIM). In this case, the adjustment procedure must be defined from a study based on healthy ewes/goats with enough number of test-days over the lactation. Then a lactation SCS (LSCS) may be calculated (case of lactation model in genetic evaluation).

LSCS can be computed as the weighted arithmetic mean of test-day SCS (adjusted or not for DIM). Weights are either 1 (equivalent to no weight) or r2, where r is the correlation between one measure and the mean of all other records.

==== Use for genetic analysis / genetic evaluation ====

===== Genetic model =====
The genetic model might include the following fixed effects:

* Flock x year (x parity)
* Month of lambing/kidding
* Age at lambing/kidding
* Number of lambs/kids born

===== Genetic parameters =====
Pooled estimates of heritability of somatic cell score gained by meta-analysis (Mucha et al., 2022) in dairy goats and sheep and meat sheep are shown in Table 5.

{| class="wikitable"
| colspan="7" |'''''Table 5. Pooled estimates of heritability of somatic cell score from meta-analysis in dairy goats and sheep (Mucha et al., 2022)'''''
|-
|'''Trait1'''
|'''Species'''
|'''Pooled h2 (± SE)'''
|'''Min2 h2'''
|'''Max3 h2'''
|'''N obs'''
|'''N studies'''
|-
|SCS
|Goats

Sheep
|0.21±0.01

0.13±0.02
|0.19

0.03
|0.24

0.27
|5

29
|3

22
|}
1Trait: SCS – somatic cell score

2Minimum h2 from individual studies included in meta-analysis

3Maximum h2 from individual studies included in meta-analysis

{| class="wikitable"
| colspan="6" |'''''Table 6. Pooled estimates of genetic correlations (rg) between resilience (SCS, FEC) and efficiency (MY, FC, PC) traits from meta-analysis in dairy goats (Mucha et al., 2022)'''''
|-
|'''Traits1'''
|'''Pooled rg (± SE)'''
|'''Min2 rg'''
|'''Max3 rg'''
|'''N obs'''
|'''N studies'''
|-
|SCS & MY
|0.35±0.31ns
|0.00
|0.59
|3
|3
|-
|SCS & FC4
| -0.19±0.01
| -0.20
| -0.18
|3
|2
|-
|SCS & PC
| -0.06±0.05ns
| -0.13
|0.00
|3
|2
|-
|FEC & MY
|0.17±0.35ns
| -0.21
|0.63
|4
|2
|}
1Traits: SCS – somatic cell score, FEC – faecal egg count, MY – milk yield, FC – fat content, PC – protein content

2Minimum rg from individual studies included in meta-analysis.

3Mmaximum rg from individual studies included in meta-analysis.

nspooled correlations obtained from a simple random effects model as the three level meta-analysis model did not converge

{| class="wikitable"
| colspan="6" |'''''Table 7. Pooled estimates of genetic correlations between resilience (SCS) and efficiency (MY, FY, PY, FC, PC) traits from meta-analysis in dairy sheep (Mucha et al., 2022)'''''
|-
|Traits1
|Pooled rg (± SE)
|Min2 rg
|Max3 rg
|N obs
|N studies
|-
|SCS & MY
| -0.05±0.10ns
| -0.30
|0.23
|16
|11
|-
|SCS & FC
|0.04±0.05ns
| -0.16
|0.16
|8
|8
|-
|SCS & PC
|0.12±0.03
|0.02
|0.24
|12
|9
|-
|SCS & FY
|0.11±0.15ns
| -0.04
|0.31
|4
|4
|-
|SCS & PY
|0.17±0.10ns
|0.06
|0.31
|4
|4
|}
1Traits: SCS – somatic cell score, MY – milk yield, FY – fat yield, PY – protein yield, FC – fat content, PC – protein content

2Minimum rg from individual studies included in meta-analysis

3Maximum rg from individual studies included in meta-analysis

nsPooled correlations obtained from a simple random effects model as the three level meta-analysis model did not converge

ns – Pooled estimate did not differ significantly from zero

{| class="wikitable"
| colspan="5" |'''''Table 8. Estimates of heritability of somatic cell score, clinical mastitis and CMT in meat and dairy and meat sheep (source Oget et al., 2019)'''''
|-
|'''Sheep'''
|'''Breed'''
|'''Trait1'''
|'''Heritability (±SE)'''
|'''Reference'''
|-
|Dairy
|Chios
|CMT
|0.12±0.06
|Banos et al., 2017
|-
|Meat
|Belclare, Charollais, Suffolk, Texel,

Vendeen breeds
|CM
|0.04±0.03

|O’Brien et al., 2017
|-
|Meat
|Texel
|SCS
|0.11±0.04
|McLaren et al., 2018
|-
|Meat
|Texel
|CMT
|0.08-0.09±0.04
|McLaren et al., 2018
|-
|Meat
|Texel
|CMT
|0.07
|Kaseja et al., 2023 submitted paper (SMARTER, D2.3)

|}
1CMT - California mastitis test, CM - Clinical mastitis (examination and palpation of the udder), SCS – Somatic Cell Score

=== Resistance to footrot ===

==== Definition, terminology, rationale ====
Footrot is caused by ''Dichelobacter nodosus'' and is a major cause of lameness in sheep. The disease is highly contagious and endemic in many countries that causes pain and welfare issues in affected animals. In addition to the direct impacts on time and veterinary / medicine costs, the disease has further, indirect, impacts through reducing fertility and milk supply.

The presence of footrot is assessed by inspection of the hooves of lame animals.

==== Data recording ====

===== Scoring methods =====
Each hoof is assessed individually and scored based on the five-point scale (used in UK): clean, unaffected hoof (score 0), mild inter-digital inflammation (score 1), inter-digital necrosis (score 2), under-running of the sole of the hoof (score 3) and fully under-run to the abaxial wall of the hoof (score 4) (Conington et al., 2008).

The sum of scores is calculated by adding all four scores (for each hoof), hence the animal can obtain the phenotype in a range from zero to 16.

In France, where footrot is usually not recorded, a simplified scoring system has been developed using a scale (0 normal and severity of lesions scored from 1 to 3) adapted from the Victorian Farmers Federation and Coopers Animal Health.

Additionally, the health of feet is assessed in France and the UK for other important hoof lesions including white line degeneration, contagious ovine digital dermatitis, horn growth, presence of abscess, granuloma, interdigital hyperplasia, and panaritium).

==== Calculation of traits ====
Sum of scores are log-transformed in order to normalise the data using the formula ln(Sum of scores + 1). The addition of one prevents to logarithm the value of sum of scores equal to zero. Each animal can obtain transformed score ranging between zero and 2.83.

==== Use for genetic analysis / genetic evaluation ====

===== Genetic model =====
The genetic model might include the following fixed effects:

* Age of the dam
* Scorer (if more than one)
* Vaccine status (if some animals treated with the vaccination against ovine foot-rot)
* Flock or Flock x Year interaction

===== Genetic parameters =====
The estimated heritability for UK meat sheep (Table 9) varies between 0.12 (Nieuwhof et al., 2008). to 0.23 (Kaseja et al., 2023, unpublished results)
Table 9. Estimates of heritability of resistance to footrot in meat sheep breeds.

{| class="wikitable"
| colspan="4" |'''''Table 9. Estimates of heritability of resistance to footrot in meat sheep breeds.'''''
|-
|'''Breed'''
|'''Trait1'''
|'''Heritability (SE)'''
|'''Reference'''
|-
|Texel
|RF
|0.12(0.02)
|Kaseja et al, 2023 in press
|-
|Scottish Blackface
|CM
|0.19 to 0.23
|Kaseja et al., 2023 in press.
|-
|Scottish lambs
|SCS
|0.12
|Nieuwhof et al., 2008
|-
|Texel
|CMT
|0.18
|Mucha et al., 2015
|}
1RF - Resistance to footrot, CM - Clinical mastitis (examination and palpation of the udder), SCS – Somatic Cell Score, CMT - California mastitis test

=== Acknowledgements ===
We gratefully acknowledge the contributions to small ruminant health and disease guideline by the following people:

* Joanne Conington, SRUC, the UK
* Jean-Michel Astruc, IDELE, France
* Rachel Rupp, INRAE, France
* Beat Bapst, Qualitas AG, Switzerland
* Donagh Berry, TEAGASC, Ireland
* Beatriz Carracelas, INIA, Uruguay
* Antonello Carta, Agris Sardegna, Italy
* Gabriel Ciappesoni, INIA, Uruguay
* Arnaud Delpeuch, IDELE, France
* Frédéric Douhart, INRAE, France
* Karolina Kaseja, SRUC, the UK
* Ed Smith, The British Texel Sheep Society, the UK
* Flavie Tortereau, INRAE, France
* Stefen Werne, FiBL, Switzerland
* Marija Špehar, Centre for Livestock Breeding Zagreb, Croatia

This work received funding from the European Unions’ Horizon 2020 Research & Innovation program under grant agreement N°772787—SMARTER.

This work also used deliverable from the Eurosheep project (Horizon 2020 under agreement N° 863056).

=== References ===
Aguerre, S., Jacquiet, P., Brodier, H., Bournazel, J.P., Grisez, C., Prévot, F., Michot, L., Fidelle, F., Astruc, J.M., Moreno, C.R. (2018). Resistance to gastrointestinal nematodes in dairy sheep: Genetic variability and relevance of artificial infection of nucleus rams to select for resistant ewes on farms. Vet. Parasitol. 256:16-23. <nowiki>https://doi.org/10.1016/j.vetpar.2018.04.004</nowiki>

Ali, A., Shook, G. (1980). An optimum transformation for somatic cell concentration in milk. J. Dairy Sci. 63:487-490.

Astruc J.M., Barillet F. (2004). Current challenge for milk recording in dairy sheep and goats: the simplification of milk sampling design for chemical composition and somatic cell counts of milk. Proceedings of the 34th ICAR session, Sousse, Tunisia, 31 May-3 June 2004.

Banos, G., Bramis, G., Bush, S.J., Clark, E.L., McCulloch, M.E.B., Smith, J., Schulze, G., Arsenos, G., Hume, D.A., Psifidi, A. (2017). The genomic architecture of mastitis resistance in dairy sheep. BMC Genom. 18 624.

Bell, A., McNally, J., Smith, D.V., Rahman, A., Hunt, P., Kotze, A.C., Dominik, S., Ingham, A. (2019). Quantification of Differences in Resistance to Gastrointestinal Nematode Infections in Sheep Using a Multivariate Blood Parameter. Vet. Parasitol. 270:31–39.

Bishop, S.C. (2012). Possibilities to Breed for Resistance to Nematode Parasite Infections in Small Ruminants in Tropical Production Systems. Animal., 6:741–747.

Bergonier, D., Crémoux, R. de, Rupp, R., Lagriffoul, G., Berthelot, X. (2003). Mastitis of dairy small ruminants. Vet. Res. 34:689–716.

Casu, S., Usai, M.G., Sechi, T. et al. Association analysis and functional annotation of imputed sequence data within genomic regions influencing resistance to gastro-intestinal parasites detected by an LDLA approach in a nucleus flock of Sarda dairy sheep. Genet Sel Evol 54, 2 (2022). <nowiki>https://doi.org/10.1186/s12711-021-00690-7</nowiki>

Conington, J., Hosie, B., Nieuwhof, G.J., Bishop, S.C., Bünger, L. (2008). Breeding for resistance to footrot- the use of hoof lesion scoring to quantify footrot in sheep. Vet. Res. Commun. 32(8):583-9. <nowiki>https://doi.org/10.1007/s11259-008-9062-x</nowiki>

Gruner, L., Bouix, J., Brunel, J.C. (2004). High genetic correlation between resistance to Haemonchus contortus and to Trichostrongylus colubriformis in INRA 401 sheep. Vet. Parasitol. 119:51–58.

Jacquiet, P., Salle, G., Grisez, C., Prevot, F., Lienard, E., Astruc, J.M, Francois, D., Moreno, C. (2015). Selection of sheep for resistance to gastro-intestinal nematodes in France: where are we and where are we going? 25th International Conference of the WAAVP, Liverpool, UK, 2015, 16-20 August

McLaren, A., Kaseja, K., Yates, J., Mucha, S., Lambe, N.R., Conington, J.(2018). New mastitis phenotypes suitable for genomic selection in meat sheep and their genetic relationships with udder conformation and lamb live weights. Animal. 12(12):2470-2479. doi: 10.1017/S1751731118000393.

Mucha, S., Bunger, L., Conington, J. (2015). Genome-wide association study of footrot in Texel sheep. Genetics Selection Evolution, 47 (1), pp.35. DOI 10.1186/s12711-015-0119-3

Mucha, S., Tortereau, F., Doeschl-Wilson, A., Rupp R., Conington, J. (2022). Animal Board Invited Review: Meta-analysis of genetic parameters for resilience and efficiency traits in goats and sheep. Animal. 16(3):100456. <nowiki>https://doi.org/10.1016/j.animal.2022.100456</nowiki>.

Nieuwhof, G.J., Conington, J., Bünger, L., Haresign, W., Bishop, S.C. (2008). Genetic and phenotypic aspects of resistance to footrot in sheep of different breeds and ages. Animal. 2(9):1289-1296. <nowiki>https://doi.org/10.1017/S1751731108002577</nowiki>

O’Brien, A.C., McHugh, N., Wall, E., Pabiou, T., McDermott, K., Randles, S., Fair, S., Berry, D.P. (2017). Genetic parameters for lameness, mastitis and dagginess in a multi-breed sheep population. Animal 11, 911–919. DOI: 10.1017/S1751731116002445

Oget, C., Tosser-Klopp, G., Rupp, R. (2019). Genetic and genomic studies in ovine mastitis. Small Ruminant Research 176, 55-64. <nowiki>https://doi.org/10.1016/j.smallrumres.2019.05.011</nowiki>.

Råberg, L, Sim, D., Read, A.F. (2007). ‘Disentangling genetic variation for resistance and tolerance to infectious diseases in animals’, Science. 318(5851), 812-814 DOI: 10.1126/science.1148526

Raynaud J.P. (1970). Etude de l’efficacité d’une technique de coproscopie quantitative pour le diagnostic de routine et le controle des infestations parasitaires des bovins, ovins, equines et porcins. Ann. Parasitol. 45: 321–342

Rupp, R., Bergonier, D., Dion, S., Hygonenq, M.C., Aurel, M.R., Robert-Granié, C., Foucras, G. (2009). Response to somatic cell count-based selection for mastitis resistance in a divergent selection experiment in sheep. J. Dairy Sci. 92, 1203–1219.

Rupp, R., Huau, C., Caillat, H., Fassier, T, Bouvier, F., Pampouille, E., Clément, V., Palhière, I., Larroque, H., Tosser-Klopp, G., Jacquiet, P., Rainard, P. (2019). Divergent selection on milk somatic cell count in goats improves udder health and milk quality with no effect on nematode resistance. J Dairy Sci. 102(6):5242-5253. doi: 10.3168/jds.2018-15664.

Sabatini, G.A., de Almeida Borges, F., Claerebout, E. et al. (2023). Practical guide to the diagnostics of ruminant gastrointestinal nematodes, liver fluke and lungworm infection: interpretation and usability of results. Parasit. Vectors. 16, 58. <nowiki>https://doi.org/10.1186/s13071-023-05680-w</nowiki>

Shaw, R.J., Morris, C.A., Wheeler, M., Tate, M., Sutherland, I.A. (2012). Salivary IgA: A Suitable Measure of Immunity to Gastrointestinal Nematodes in Sheep. Vet. Parasitol. 186, 109–117

Van Wyk, J.A., Bath, G.F. (2002). The FAMACHA© system for managing haemonchosis in sheep and goats by clinically identifying individual animals for treatment. Vet. Res. 33:509–529.

Whitlock, H.V. (1948). Some modifications of the McMaster helminth egg counting technique and apparatus. J. Coun. Sci. Ind. Res. 21:177.

=== Annexes ===
[[File:Annex 1 Famacha.jpg|center|thumb|600x600px|Picture of FAMACHA score (source FiBL – Qualitas)]]

[[File:Annex 2 Farmacha 2.jpg|center|thumb|600x600px|Picture of FAMACHA score (source FiBL – Qualitas)]][[File:Annex 3 Uruguayan protocol of natural infestation.jpg|center|thumb|800x800px|Uruguayan protocol of natural infestation for recording the resistance to gastrointestinal parasites]]
[[File:Annex 4 French protocol for phenotyping the resistance.jpg|center|thumb|600x600px|French protocol for phenotyping the resistance to gastrointestinal parasites]]

== Guidelines on recording lifetime resilience in sheep and goats ==
'''Change Summary'''
{| class="wikitable"
|'''Date of change'''
|'''Nature of Change'''
|-
|October 2024
|First version
|-
|November 26th 2024
|Comments made by JC
|-
|December 23rd 2024
|Comments made by MS
|}

=== Introduction and scope ===

==== Introduction ====
Lifetime resilience is often tackled through longevity and aspects of productive longevity. Longevity is a trait to quantify productive lifespan of livestock, and for increasing durability and profitability of farms. In dairy ruminants, longevity definitions include: (i) true longevity (all culling reasons, including milk productivity); and (ii) functional longevity (all culling reasons, except voluntary productivity, such as milk productivity or growth). Functional longevity (corrected for production level – milk, growth) reflects the animals’ accumulated ability to overcome health and nutritional challenges. It is an indirect global approach to quantify adaptive capacity to various production environments. Different indicators may be calculated. One indicator is the length of productive life which is computed as the time interval (in days) between first lambing/kidding and culling. Longevity is linked with various predictors, such as fertility, udder health and conformation, resistance to disease, body condition score changes across ewe/doe lifetime. These predictors may be used in breeding program to get an earlier breeding value of longevity and may help to manage and monitor lifetime resilience at the farmer level.

==== Scope ====
The scope of these guidelines is to define approaches for the definition of longevity as well as the traits that can be calculated, and the downstream analyses that can be set up (including the use of early predictors to enhance longevity in the evaluation process).

To propose a grid for setting up an observation of the culling causes.

=== Longevity ===

==== Definition, terminology, rationale ====
The notion of longevity can cover several meanings. Longevity can be understood as the true longevity, i.e. the ability of the animal to live as long as possible, whatever its production level and its functional characteristics. Animal longevity also depends on the replacement rate which is often a choice of the breeders. Animals may be culled due to production level such as milk production or growth or fat/muscle depth, leading to ’voluntary’ culling (i.e. an animal is culled because we '''want''' to do it). In contrast, ‘involuntary culling’ is defined as an animal having to leave the flock or herd due to illness / accident/ functional disability etc (i.e. they are culled because we '''have''' to do it)

Involuntary culling may be due to:

* Udder health problem (clinical, subclinical, chronic mastitis).
* Lack of resistance to disease such as parasites.
* Problem of footrot.
* Unfavourable shape of the udder (lack of adaptation to machine milking or to suckling).
* Unfavourable general conformation.
* Undesired behaviour (temperament in the milking parlour).
* Infertility or any problem of reproduction.
* Problem of feet or legs, lameness.
* Lack or excess of body tissue mobilisation.

any other undesirable aspect associated with the animal’s inability to produce. Voluntary culling may be due to:

* Low productivity,
* Management decision to cull for age,
* Management decision to cull for a specific coat colour / other phenotype that does not meet the type desired,
* Farmer doesn’t like the animal,
* Economic reason to reduce the number of breeding animals in the flock/herd.

Even if some of these reasons for culling may be considered per se in the selection process by phenotyping and evaluating related traits (for example resistance to mastitis, resistance to gastro- intestinal parasite, fertility, udder morphology), it is often not possible to account for all of them. If properly modelled, functional longevity may be considered as a global and composite approach, allowing to assess the sustainability of the population in selection and of the practiced selection.

For this, different traits may be considered, quite often they are relatively easy to compute with data usually already existing in the genetic database (ex. length of productive life, which can be calculated as the culling date minus the date of the first lambing). There is no additional recording to set up. The difficulties in handling functional longevity are related to the modelling of the trait, given that the trait is fully known when the animal is culled. When not yet culled, the model to set up are quite complex. An example of this was reported by Brotherstone et al. (1997) for dairy cattle and Conington et al. (2004) for hill sheep, whereby live animals’ EBVs for longevity are based on their probability of survival at a given age combined with actual cull dates of relatives that became breeding females in the flock.

Even though there is no need to identify/know the cause of culling, the knowledge of the cause of culling might be a relevant observation of the hierarchy of the culling cause, which may lead to put an emphasis on some specific issue. For example, if we observe an increase in some culling causes (let’s say parasitism) this should lead to a deliberate selection programme to breed more resistant animals to parasites.

One drawback of the functional longevity trait is its lack of precocity. As stated above, it is necessary to have the date of culling or to have accumulated enough lactation to compute the trait. And an appropriate model (e.g. survival analysis) can only partially disentangle this difficulty. It is possible to address this issue by running a multi trait genetic evaluation model combining the longevity trait and some other proxy traits (such as udder morphology, udder health, etc). The use of Genomic Selection is a complementary way to generate early prediction of genetic merit for longevity, provided there is good accuracy of the EBVs of animals in the associated reference population.

==== Data recording ====

===== Longevity traits =====
The table 1 presents some criteria commonly used in small ruminants to measure longevity. Here, the criteria deal with true longevity, the only one measurable in herd/flocks. Functional longevity will be estimated later, at the statistical analysis step. Table 1 also shows the data required for calculating the longevity criteria. For example, the length of productive life is referred to as the difference between the time a female enters the breeding flock/herd and the date she exits it due to being culled or dying. It is important to notice that the culling date, which is rarely recorded by the farmers, can be replaced by the date of the last event registered for the animal (for example, date of the last performance recording, or of the last reproduction event).

{| class="wikitable"
| colspan="3" |'''Table 1. Definition of some commonly used longevity criteria.'''
|-
|'''Longevity criteria'''
|'''Raw data required'''
|'''Calculation'''
|-
|Length of total lifespan (LTL)
|Birth date (BD)
Culling or death date (CD)
|LTL= CD - BD
in days (or months or years)
|-
|Length of productive life (LPL)
|First lambing/kidding date (FKD)
Culling or death date (CD)
|LPL = CD – FKD
in days (or months or years)
|-
|Total number of days in production (NDL)
|Days in milk per lactation (DIM)
or
Lambing/kidding date + dry off date for each lactation
|NDL = ∑ DIM
|-
|Number of lactations (NLACT)
|Each lambing/kidding event (KE)
|NLACT = ∑ KE
|-
|Number of lambs or kids during lifetime (NLAMB)
|Prolificacy at each lambing/kidding (PR). This may or may not include no. lambs born dead + no. lambs born alive
|NLAMB = ∑ PR
|}
The length of total lifespan can be estimated easily, with only two variables usually registered by farmers. The difference with the length of productive life is that it considers the period when animals had the first lambing/kidding as well as the lambing/kidding interval. If the age at the first lambing/kidding and the lambing/kidding interval are similar between animals, the length of total lifespan will be very close to the length of productive life.

The total number of days in production only covers the “useful” life of the females because it doesn’t include the unproductive periods (such as dry off or large lambing/kidding interval after reproduction failure), compared to length of productive life. But the number of variables necessary to compute it is larger.

For the total number of lambs or kids during a lifetime, it is necessary to include all live-born lambs/kids only or those reared to weaning, if these data are routinely recorded.

==== Calculation of traits ====
The last column of Table 1 indicates how to calculate the different longevity criteria, from the raw variables.

The length of total lifespan and the length of productive life are estimated as differences in days between two dates: i) the culling date and ii) the birth date or the first lambing/kidding date, respectively. The total number of days in production corresponds to the sum of the days in milk of each lactation of the female. For the last two criteria (number of lactations or number of lambs/kids), the estimation corresponds to cumulative performance across lifetime.

Instead of waiting for the end of the animal's life to calculate the longevity criterion (which is sometimes long), one solution deals with limiting the animal career to a maximum number of years or lactations. For example, the length of productive life can be calculated only on the first 6 lactations. Subsequently, the length of productive life will be defined as the total number of days between the first lambing/kidding and the end of the 6th lactation. In the same way, the total number of lambs/kids can be estimated at a fixed age, 8 years old for example.

==== Use for genetic analysis / genetic evaluation. ====

===== Models =====
The genetic ability for longevity is evaluated via the functional longevity, i.e. the true longevity corrected for production traits. Functional longevity is defined at this step, by integrating the level of production as fixed effect in the analysis of longevity criteria described in Table 1.

Different methods are used for the genetic evaluation of longevity traits.

The first method is based on linear models. The main advantage of these models is their ease of implementation because they are used for most of the traits under selection. But they have different drawbacks regarding longevity:

* they do not fit well longevity because longevity indicators do not follow a normal distribution
* they consider only animals that have finished their productive life (unless separate predictors are used). This has two consequences: the longevity data are skewed if living animals are ignored; the breeding value is available lately in the life of the animals. This is notably the case for males for whom most of their offspring must be culled to be evaluated.
* they are not able to include time-dependant variables (e.g. parity, lactation stage). Time dependant variables are useful to take into account the changes in breeding conditions that occur during the life of the animal, and thus to better model longevity data.

The second method is based on proportional hazard model or survival analysis. This type of model counterbalances all the drawbacks of linear models and thus, are the best ones to estimate breeding values for functional longevity. Nevertheless, they are complicated to implement in a routine genetic evaluation process, and a few software exist for genetic survival analyses such as Survival kit, (Ducrocq et al, 2005). However, an evaluation based on an animal model is not feasible in large dataset, leading to use sire-maternal grand-sire models or sire models. Under this assumption, ewes/does EBVs are not available (Ducrocq, 2001).

A third method, less widespread, considers the first three lactations as separate traits in a multiple trait animal linear model. Each lactation is assigned to 1 (instead of 0) once the female reaches the next lactation.

===== Factors of variation =====
The main factors of variation of longevity data are:

* herd/flock
* year
* kidding/lambing season
* birth season
* age at first lambing/kidding
* breed
* herd/flock size and herd/flock size variation
* lactation stage, parity (if survival analysis model)
* number of lambs/kids born and reared (for meat sheep and goats)
* within herd/flock production level: this factor of variation is essential to integrate to estimate the functional longevity. Usually, it is the within herd/flock level of production (and not the absolute level of production) that is considered because it explains the decision of the breeder to cull the animal.

===== Heritabilities of functional longevity =====
Heritabilities range between 5% and 17% (Sasaki, 2013, Castañeda-Bustos et al., 2014, Geddes et al., 2017, Palhière et al (2018), Buisson et al (2022), Pineda-Quiroga & Ugarte, 2022) indicating that this trait has a low to moderate genetic background. This might be due to the composite signification of longevity, which represents a synthesis of various abilities (see § on predictors).

However, the genetic variation coefficients are moderate suggesting that a genetic variability may be exploited to set up a selection programme.

===== Genetic correlations =====
The genetic correlations between functional longevity and other traits are:

* close to 0 for milk production traits. This results from the model, in which longevity is corrected for level of production,
* from 0 to 0.40 for udder type traits (Castañeda-Bustos et al., 2014). The rear udder attachment and the udder floor position are the most correlated to functional longevity,
* from 0.20 to 0.50 for general conformation,
* from 0.01 to 0.15 for reproduction traits (kidding interval, age at first kidding, artificial insemination fertility),
* from -0.15 and -0.40 for somatic cell counts.

===== EBVs and reliabilities =====
For dairy animals, because of the low accuracy of breeding values, only males (and especially artificial insemination males) evaluated from the longevity data of their daughters, have EBVs that can be used for selection. A minimum number of daughters culled per sire is required to reach a sufficient accuracy. The consequence is that the AI males get their first longevity EBV quite late in their life. Survival analysis models, because they consider censored data (living daughters), enable better accuracy and thus, an earlier EBV for AI males.

Other strategies are possible to increase the accuracy of functional longevity EBVs:

* introduce genomic information in the genetic evaluation
* use a multiple trait model, including both functional longevity and other traits considered as predictors of longevity listed below.

Given the low heritability of survival traits, the fact that it is expressed late in life (at death or culling), the trait becomes accurate enough when sufficient information on culling or reproduction/lactation is available. It is necessary to enhance direct evaluations by indirect information coming from early predictors. Some relevant predictors are listed below:

* Morphological traits, such as general conformation or udder morphology (especially in dairy species),
* Reproduction traits (fertility, lambing/kidding interval, age at first lambing/kidding, pregnancy scan results, …),
* Udder health, and particularly milk somatic cell count,
* Resistance to disease such as resistance to parasites or to footrot,
* Traits related to feet and legs, such as lameness or twisted or bowed legs, closed or opened hocks,
* Serum immunoglobulin concentration in the early life (Ithurbide et al, 2022a),
* Maturity (dairy species) that can be defined as the ability to maintain a good level of production over the parities, independently of the level of production on the whole lifetime (equivalent of a persistency, but over the lactations and not over the test-days) (Arnal et al, 2022),
* Milk metabolites (Ithurbide et al, 2022b)
* Body tissue mobilisation (McLaren et al., 2023). It was demonstrated that ewe tissue mobilisation was genetically associated with ewe fertility and productive longevity (such as pregnancy scan result, foetal loss from scan to lambing, lamb loss from lambing to weaning, number of lambs weaned). It is made possible by collecting body condition score (BCS) data throughout the reproductive cycle (e.g. pre-mating, pregnancy scan, pre lambing, mid lactation, weaning) and calculating gain or loss of BCS between physiological stage.

These predictors are linked to longevity traits. An unfavourable udder shape, reproduction disorders, a susceptibility to a given disease or a low maturity may lead to involuntary culling and therefore a low longevity of the animal. Few genetic correlations have been published but correlations between EBVs show favourable correlations between these predictors and longevity.

Longevity traits, once evaluated, either in linear or survival analysis model, may be combined with the longevity traits in a multi-trait evaluation, to incorporate the information from early predictors.

A full multiple trait evaluation is not feasible in large datasets. Therefore, approximate strategies must be used, such as considering records adjusted for all non-genetic effects in linear models (yield deviation or daughter yield deviation, other type of pseudo records), or sub-indices incorporating traits that are linked together e.g. pulling together data on footrot, mastitis and parasite resistance could be considered together in a ‘health’ sub-index.

==== Culling causes ====
Even though the knowledge of the causes of culling is not necessary to generate a phenotype of longevity and an EBV of functional longevity, the knowledge of the causes of culling, through an observation based on a sufficient panel of flocks/herds, and repeated each year, may give relevant information on the hierarchy and the evolution of the culling causes. It may also enable better understanding of the strategies of culling by farmers leading to better modelling of functional longevity.

Culling causes may be collected with different levels of precision, from a general group of causes to a precise cause, through intermediate information.

In sheep as in goat, the following group of culling causes may be collected:

* Udder health (mastitis)
* Udder morphology
* Production ability
* Respiratory disorders
* Reproduction disorders
* Digestive disorders
* Nervous disorders
* Musculoskeletal disorders
* Skin disorders
* Conformation
* General condition
* Age
* Behaviour
* Accident
* Other ailments (e.g. sudden death, brucellosis, intoxication, fever …)
* Voluntary culling

Each group may be completed with sub-group or precise cause. Below are two examples, first for udder health (table 2), second for reproduction disorders (table 3).

{| class="wikitable"
| colspan="3" |'''Table 2. Detailed categorisation of udder health culling causes.'''
|-
|'''Group'''
|'''Sub-group'''
|'''Specific cause'''
|-
| rowspan="21" |Udder health (mastitis)
| rowspan="2" |Gangrenous mastitis
|Gangrenous mastitis
|-
|Brief mastitis
|-
| rowspan="7" |Characteristic symptoms
|Mastitis
|-
|Clinical mastitis
|-
|Mastitis during suckling
|-
|Coliform mastitis
|-
|Listeria mastitis
|-
|Mastitis before lambing/kidding
|-
|Agalactia mastitis
|-
|Functional symptoms
|Blood in the milk
|-
| rowspan="6" |Chronic mastitis, palpation
|induration of the udder
|-
|Bumps in the udder
|-
|Nodules
|-
|Mammary abcess
|-
|Saggy udder
|-
|Visna mastitis
|-
| rowspan="2" |Unbalanced udder
|Milk in one side
|-
|Unbalanced udder
|-
| rowspan="2" |Subclinical
|Subclinical mastitis
|-
|Somatic cell count (SCC) and California mastitis test– CMT
|-
|Other
|Other
|}

{| class="wikitable"
| colspan="3" |'''Table 3. Detailed categorisation of reproduction disorders culling causes'''
|-
|'''Group'''
|'''Sub-group'''
|'''Specific cause'''
|-
| rowspan="28" |Reproduction disorders
| rowspan="3" |Fecundity
|Open + infertile
|-
|Lately fertile, out of season
|-
|Ram infertile
|-
| rowspan="6" |Gestation
|Abortion
|-
|Vagina or rectal prolapse
|-
|Pregnancy toxaemia
|-
|Difficult gestation
|-
|Early abortion
|-
|Late abortion
|-
| rowspan="8" |Lambing/kidding
|Difficult lambing/kidding
|-
|Caesarean
|-
|Uterus inversion
|-
|Infection during lambing/kidding
|-
|Vagina or rectal prolapse
|-
|non deliverance
|-
|Acute metritis
|-
|Chronic metritis
|-
| rowspan="4" |Miscellaneous
|Reproduction disorders
|-
|Vaginal sponge infection
|-
|Hermaphrodite
|-
|Various
|-
| rowspan="4" |Male: testicles
|1 testicle
|-
|Small testicles
|-
|Abscess
|-
|Contagious epididymitis
|-
| rowspan="3" |Male: penis
|Urinary gravel
|-
|Wound
|-
|Phimosis
|}

=== Acknowledgements ===
We gratefully acknowledge the contributions to these lifetime resilience guidelines by the following people:

* Joanne Conington, SRUC, the UK
* Marija Špehar, Centre for Livestock Breeding Zagreb, Croatia
* Isabelle Palhière, INRAE, France
* Jean-Michel Astruc, IDELE, France
* Carolina Pineda Quiroga, NEIKER, France

This work received funding from the European Unions’ Horizon 2020 Research & Innovation program under grant agreement N°772787—SMARTER.

=== References ===
Arnal M., Palhiere I., Clément V. (2022). Maturity, a new indicator to improve longevity of Saanen dairy goats in France. Proceedings of 12th World Congress on Genetics Applied to Livestock Production (WCGALP), Jul 2022, Rotterdam, Netherlands. doi:10.3920/978-90-8686-940-4_738.

Brotherstone, S., Veerkamp, R. F. and Hill, W. G. (1997). Genetic parameters for a simple predictor of the lifespan of Holstein-Friesian dairy cattle and its relationship to production. Animal Science 65: 31-37.

Buisson D., J.M. Astruc, L. Doutre, I. Palhière. Toward a genetic evaluation for functional longevity in French dairy sheep breeds. Proc 12th WCGALP, 2022

Castañeda-Bustos, V. J., Montaldo, H. H., Torres-Hernández, G., Pérez-Elizalde, S., Valencia-Posadas, M., Hernández-Mendo, O., & Shepard, L. (2014). Estimation of genetic parameters for productive life, reproduction, and milk-production traits in US dairy goats. Journal of Dairy Science, 97(4), 2462-2473.

Castañeda-Bustos, V. J., Montaldo, H. H., Valencia-Posadas, M., Shepard, L., Pérez-Elizalde, S., Hernández-Mendo, O., & Torres-Hernández, G. (2017). Linear and nonlinear genetic relationships between type traits and productive life in US dairy goats. Journal of Dairy Science, 100(2), 1232-1245.

Conington, J., Bishop, S. C., Grundy, B., Waterhouse, A., & Simm, G. (2001). Multi-trait selection indexes for sustainable UK hill sheep production. Animal Science, 73(3), 413-423.

Conington, J., Bishop, S.C., Waterhouse, A. and Simm, G. (2004). A bio-economic approach to derive economic values for pasture-based sheep genetic improvement programmes. Journal of Animal Science 82: 1290-1304. <nowiki>https://doi.org/10.2527/2004.8251290x</nowiki>

Ducrocq, V. (2001). A Two-Step Procedure to get Animal Model Solutions in Weibull Survival Models Used for Genetic Evaluations on Length of Productive Life. Interbull Bulletin, vol.27, pp.147-152

Ducrocq, V. (2005). An Improved model for the French genetic evaluation of dairy bulls on length of productive life of their daughters. Animal Science, 80(3), 249-256.

Geddes, L., Desire, S., Mucha, S., Coffey, M., Mrode, R. and Conington, J. (2018). Genetic parameters for longevity traits in UK dairy goats. IN: Proceedings of the World Congress on Genetics Applied to Livestock Production, Volume Species - Caprine: 547.

Ithurbide, M., Huau, C., Palhière, I., Fassier, T., Friggens, N. C., & Rupp, R. (2022a). Selection on functional longevity in a commercial population of dairy goats translates into significant differences in longevity in a common farm environment. Journal of Dairy Science, 105(5), 4289-4300.

Ithurbide, M., Wang, H., Huau, C., Palhière, I., Fassier, T., Pires, J. & Rupp, R. (2022b). Milk metabolite profiles in goats selected for longevity support link between resource allocation and resilience. In Proceedings of 12th World Congress on Genetics Applied to Livestock Production (WCGALP) pp. 276-279

McLaren A, Lambe, N R and Conington J. (2023). Genetic associations of ewe body condition score and lamb rearing performance in extensively managed sheep. 105336. Livestock Science September 2023 <nowiki>https://doi.org/10.1016/j.livsci.2023.105336</nowiki>

Palhière I., C. Oget, R. Rupp, Functional longevity is heritable and controlled by a major gene in French dairy goats, 11th WCGALP, Auckland, Nouvelle-Zelande, 11-16 février 2018

Pineda-Quiroga, C., Ugarte, E. (2022). An approach to functional longevity in Latxa dairy sheep. Livestock Science 263, 105003

Sasaki, O, (2013), Estimation of genetic parameters far longevity traits in dairy cattle: A review with focus o n the characteristics of analytical models, Animai Science Journal, 84(6), 449-460,

SMARTER Deliverable 2,2 - "New breeding goals far lifetime resilience far materna!sheep breeding programmes"

== Guidelines on survival recording of foetus and young in sheep and goats ==
'''Change Summary'''
{| class="wikitable"
|'''Date of change'''
|'''Nature of Change'''
|-
|October 2024
|First version
|-
|December 2024
|Tracked change revisions by MS
|}

=== Introduction and scope ===

==== Introduction ====
Foetal and young survival are parameters linked to neonatal vigour scores, maternal and young behaviours, stress responses, immunity transfer and traits related to dam fertility and longevity. Minimising mortality, either in utero (e.g., embryo/foetus) or pre-weaning, are crucial to profitable small ruminant production systems. Survival depends on an interaction between the environment and behaviour of both, the ewe and the lamb. Ewes must give birth without complications and provide reliable source of colostrum along with mothering environment. Lamb must adapt to the extra-uterine environment, thermoregulate and be able to stand and suckle in a reasonably short period after birth (Brien et al., 2014; Plush et al., 2016). Despite this, pre- weaning survival in many species is far from ideal (Binns et al., 2002; Yapi et al., 1990, Chaarani et al., 1991, Green and Morgan, 1993, Nash et al., 1996). This can be particularly worse in small ruminant production systems which are typically more extensive and therefore prevailing weather conditions can be an additional stressor as well as predators. Moreover, the poly-ovulatory nature of species such as sheep and goats also predisposes such species to greater foetal and pre-weaned young losses (Scales et al., 1986).

Litter size can be determined using trans-abdominal ultrasonography of the uterine horns at ideally 40-70 days post-fertilisation. Good accuracy in determining foetal number has been reported from trans-abdominal ultrasonography (Taverne et al., 1985). The number of young eventually born can then be used to assess foetal loss since the time of scanning. At birth, young survival is usually based on dead or not in the first 24 h post-birth while stillborn individuals or those dead within 24 hours are usually defined as failed to survive. Young survival can also be considered as different age group categories until weaning – for example from 1 day to 7 days of age. Young animals (i.e., < 7 days) are greatest at risk of mortality (Binns et al., 2002) and tend to die of exposure to hypothermia, starvation, septicaemia, or repercussions from trauma suffered at birth.

==== Scope ====
The aim of the present section is to define approaches for the definition of foetal and lamb survival as well as the data editing and downstream analyses (including statistical models).

=== Definition, terminology, rationale ===
A plethora of different definitions exist depending on whether defined at the level of the individual (i.e., binary trait) or that of the litter. A non-exhaustive list is given below.

'''Foetal survival (at an individual level):'''

* Can be defined as a binary trait of 0 (died between scanning and birth) or 1 (survived between scanning and birth). A dummy ID for the dead foetus would need to be constructed but the parentage would still potentially be known (especially if generated from AI).

'''Foetal survival (at a litter level):'''

* Whether or not some foetal mortality has occurred defined as a binary trait (i.e., the number of individuals born is less than the number scanned in utero)
* Number of individual foetuses scanned alive (along with gestational age)
* Number of foetuses scanned minus the number that were born (dead or alive) – this is a measure of foetal mortality as opposed to survival and assumes stillborn young are considered in the definition of a young survival trait. It is a count trait
* The number of young born divided by the number of foetuses scanned (this is mortality rate figure but per little with a penalty on losses for smaller litter sizes).

'''Young survival (at an individual level):'''

* Can be defined as a binary trait of 0 (dead within 24 hours of birth) or 1 (alive after 24 hours of birth). The dead animal would need to receive an ID and can, of course, be genotyped to verify parentage (but also used for downstream genomic analyses discussed later).

'''Young survival (at a litter level):'''

* Number of lambs born alive (NLBA)
* Number of lambs dead within 24 hours of birth
* Number of lambs dead within 24 hours of birth divided by the total number of lambs born

=== Recording survival of foetuses and young in small ruminant ===
In all instances, accurate data is crucial. Data should be collected on the animal/dam itself (dead or alive) but also potential confounding effects that could be considered for inclusion in the statistical model as fixed effects. Examples include contemporary group (e.g., flock-date of scanning, flock-year-season of birth (for each NLB separately), ewe parity, litter size). Ideally also all individuals should be genotyped. Because the heritability of foetal or young animal mortality in small ruminants is relatively low (<0.1; Safari et al., 2005; Brien et al., 2014), a large number of records are required to achieve accurate genetic/genomic evaluations. Care should also be taken when interpreting the scoring (and the following genetic evaluations), some jurisdictions may record mortality rather than survival or may record mortality but propose genetic evaluations as survival (i.e., positive value is favourable).

==== Pregnancy scanning records ====
Ideally scanning should be undertaken 40 to 70 days post-fertilisation. This may be possible to (easily) achieve where extensive AI has been used but, otherwise, should ideally be 30 days after the last female has been marked as been served by natural mating. Skilled operators should be able to determine the number of foetuses from 30 to 100 days of gestation; usually only one operator will scan a flock on a given day so will be confounded with flock-date of scanning contemporary group. If AI is solely used or if single sire mated, then the parentage of the foetus should be known; if mob mated or single sire mated at AI, then superfecundation could cause a discrepancy in recorded sire.

==== Young survival ====
Young survival can be defined at birth, ideally as a binary trait as to whether the animal was born stillborn or died within 24 hours (survival = 0) or was still alive 24 hours after birth (survival = 1). If information is also available on the reason for death (i.e., autopsy results) then, where sufficient data exists for any one ailment, it could be analysed separately as separate traits. This could be particularly important for generating separate genetic evaluations for the main diseases thereby not only possibly increasing the heritability through more accurate data, but also provide genetic evaluations specific to individual ailments which could enable more selection pressure on these traits in situations where they are more impactful. Ideally a genotype of the dead animal should be generated. Any obvious external defects should be noted.

==== Ancillary information ====
Having ancillary information coinciding with an event is useful for several reasons:

* For helping data editing (e.g., comparing actual birth date to expected birth date based on recorded service information)
* For adjustment in the statistical model (e.g., dam parity)
* Understanding the risk factors associated with survival
* Enabling more precise estimates of correlations with other performance traits by having information on multiple features from the same animal
* Adjusting for possible selection in multi-trait genetic evaluation models

Possible ancillary information can be divided into those associated with 1) the past of prevailing environmental conditions, 2) the dam (or sire), or 3) the individual. Examples include:

1. Environment:

* Weather related factors (rainfall, temperature, wind including direction)
* Flock
* Date of scanning or date of birth

2. Dam

* Parity
* Age
* Breed
* Genotype
* Litter size
* Mating type (i.e., AI versus natural)
* Body condition score (change) and live-weight (change)
* Mothering ability
* Colostrum quality and yield

3. Individual

* Days since service (for foetal survival trait)
* Birthing difficulty
* Birth weight
* Gender
* Genotype
* Sire
* Autopsy results if possible

=== Use for genetic analysis / genetic evaluation ===

==== Data editing and statistical modelling ====
In order to estimate contemporary group effects well, the larger the contemporary group, the better the group estimates. Therefore, imposing a minimum contemporary group size prior to data analysis should be considered as should good genetic connectedness with other contemporary groups. Genetic connectedness can be an issue with small ruminant populations in particular, especially where natural mating prevails.

===== Data editing =====
'''Foetal survival''' ''-'' Each flock-scanning date can be firstly investigated at a macro level to measure ultrasound quality control. Simple cross-references between the number of females with scanning data versus those presented as well as the ID numbers of both is useful to ensure all data were properly recorded. High foetal mortality rates could simply be indicative of high foetal loss (e.g., abortions due to causes like chlamydial and toxoplasma) as well as poor operator competence – assessing the rate for individual operators across flocks (and time) could be useful to assess operator proficiency. A high proportion of litters where the number of young born (dead or alive) exceeds that recorded at scanning suggests a poor accuracy of recording. It should be considered to discard the data from that date but also to investigate the operator in more detail across other flocks, and irrespective, the scanning results from that litter at least should be discarded. The proportion of scanned litters with >3 detected foetuses should also be calculated; depending on the expected prolificacy of the animals (e.g., breed), then the appropriate editing of either the individual data points or the date in its entirety should be assessed.

'''Young mortality''' ''-'' A high incidence of young mortality per contemporary group could simply be a consequence of some underlying issue (e.g., predation, disease) or indeed a high fecundity rate; a low incidence of young could be indicative of a good stock person. Therefore, it can be difficult to distinguish between high and low quality data. Using guaranteed high quality and reliable data, it is possible to estimate the expected distribution of the incidence of young animal mortality for different population strata such as flock size, ewe age, breed, litter size. Using these distributions, the probability that the mean mortality for a contemporary group fits this distribution can be estimated and a decision made as to whether or not to include the data in the downstream analyses.

===== Statistical modelling =====
Lamb survival is a complex trait influenced by direct genetic, maternal genetic, and environmental effects. Due to discrete expression of phenotype (dead or alive: 0 or 1) it is described as a threshold trait (Falconer, 1989) that violates the assumption of normality, and therefore linear models are theoretically not appropriate for the analysis. However, examples from the literature analysed survival data and reported that linear models were marginally more accurate at predicting missing phenotypes than were logit-transformed alternatives and are convenient for interpretation on the observed scale (Matos et al., 2000; Everett-Hincks et al., 2014; Cloete et al. 2009; Vanderick et al., 2015;).

Random effects considered in the statistical model are direct and maternal genetic effects and maternal permanent environment across parities. A litter permanent environmental effect should also be considered as a random effect where the trait is that of the individual (and not the ewe). Traditionally, relationships were accounted for though the pedigree data, however this can often now be supplemented with genome-wide genotype information to generate a H matrix (i.e., combines genomic and ancestry information). Whether the estimation of these additional covariance components improve the fit to the data can be deduced by a likelihood ratio test but ideally a metric such as the AIC or BIC to account for the increased complexity of the model.

The choice of environmental factors included in the model will depend on the population being studied and considers the following fixed effects:

* Contemporary group (e.g., flock-date of scanning for foetal survival and flock-year-season of birth or flock-year-season-birth rank of birth)
* Lamb gender (may not be possible for foetal survival trait)
* Dam parity
* Mating type (i.e., AI versus natural)
* Dam age nested within parity
* Day of gestation (for foetal survival) if available or defined as a categorical variable
* Litter size (at scanning or birth) or birth type (single and multiple)
* Heterosis and recombination loss of the dam and foetus/young
* Inbreeding coefficient of the dam and foetus/young
* Age of the sire
* Breed composition of the dam and foetus/young

Adjusting for the effects such as dystocia or birth weight, may not be appropriate in the statistical model for young survival as they are likely to be genetically correlated with survival and thus may remove some of the true genetic variance – nonetheless, the eventual decision will be based on the genetic evaluation system employed and how the economic value on the traits within the overall breeding objectives are constructed.

==== Genomic association analyses ====
Where genotypes are available, then a genome-wide association study (or candidate gene study) can be undertaken (Esmaeili-Fard et al., 2021). Although it is not possible to have the genotype of the aborted foetus, it could still be possible to undertake a genomic analysis especially by focusing on the genotype/haplotype of the living animals versus the expectation based on the genotype/haplotype of the parents (Ben Braiek et al., 2021).

=== Acknowledgements ===
We gratefully acknowledge the contributions to these recording of survival of foetus and young guidelines by the following people:

* Donagh Berry, TEAGASC, Ireland
* Joanne Conington, SRUC, the UK
* Maxime Ben Braiek, INRAE, France
* Arnaud Delpeuch, IDELE, France
* Marija Špehar, Centre for Livestock Breeding Zagreb, Croatia

This work received funding from the European Unions’ Horizon 2020 Research & Innovation program under grant agreement N°772787—SMARTER.

=== References ===
Ben Braiek, M., Fabre, S., Hozé, C., et al. (2021). Identification of homozygous haplotypes carrying putative recessive lethal mutations that compromise fertility traits in French Lacaune dairy sheep. Genet. Sel. Evol. 53:41. <nowiki>https://doi.org/10.1186/s12711-021-00634-1</nowiki>

Binns, S.H., I.J.Cox, S. Rizvi, L.E.Green. (2002). Risk factors for lamb mortality on UK sheep farms. Prev.Vet. Med.. 52:287-303.

Brien, F.D., Cloete, S.W.P., Fogarty, N.M., Greeff, J.C., Hebart, M.L., Hiendleder, S., Hocking Edwards, J.E., Kelly, J.M., Kind, K.L., Kleeman, D.O., Plush, K.L., Miller, D.R (2014). A review of genetic and epigenetic factors affecting lamb survival. Anim. Prod. Sci. 54:667–693.

Chaarani, B., Robinson, R.A., Johnson, D.W. (1991). Lamb mortality in Meknes Province (Morocco). Prev. Vet. Med. 10:283-298.

Cloete, S.W.P., Misztal, I., Olivier, J.J. (2009). Genetic parameters and trends for lamb survival and birth weight in a Merino flock divergently selected for multiple rearing ability. J. Anim. Sci. 87:2196–2208. doi:10.2527/jas.2008-1065.

Esmaeili-Fard, S.M., Gholizadeh, M., Hafezian, S.H., Abdollahi-Arpanahi, R. (2021) Genes and Pathways Affecting Sheep Productivity Traits: Genetic Parameters, Genome-Wide Association Mapping, and Pathway Enrichment Analysis. Front. Genet. 12:710613. doi:10.3389/fgene.2021.710613.

Everett-Hincks, J.M., Mathias-Davis, H.C,, Greer, G.J., Auvray, B.A., Dodds, K.G. (2014). Genetic parameters for lamb birth weight, survival and deathrisk traits. J. Anim. Sci. 92:2885–2895. doi:10.2527/jas.2013-7176.

Falconer, D.S. (1989). Introduction to Quantitative Genetics.’ (Longmans Green/John Wiley & Sons: Harlow, Essex, UK).

Green, L.E., Morgan, K.L. (1993). Mortality in early born, housed lambs in south-west England. Prev. Vet. Med. 17:251-261.

Matos, C.A.P., Thomas, D.L., Young, L.D., Gianola, D. (2000). Genetic analyses of lamb survival in Rambouillet and Finnsheep flocks by linear and threshold models. Anim. Sci. 71:227–234. doi:10.1017/S1357729800055053.

Nash, M.L., Hungerford, L.L., Nash, T.G., Zinn, G.M. (1996). Risk factors for perinatal and postnatal mortality in lambs. Vet. Rec. 139:64-67.

Plush, K.J., Brien, F.D., Hebart, M.L., Hynd, P.I. (2016). Thermogenesis and physiological maturity in neonatal lambs: a unifying concept in lamb survival. Anim. Prod. Sci. 56:736–745. <nowiki>https://doi.org/10.1071/AN15099</nowiki>.

Safari, E, Atkins, K.D., Fogarty, N.M., Gilmour, A.R (2005). Analysis of lamb survival in Australian Merino. Proceedings of the Association for the Advancement of Animal Breeding and Genetics. 16:28–31.

Scales, G. H., Burton R. N., Moss, R. A. (1986). Lamb mortality, birthweight, and nutrition in late pregnancy. N. Z. J. Agric. Res. 29:1.

Taverne, M.A.M. Lavoir, M.C., van Oord R., van der Weyden, G.C. (1985) Accuracy of pregnancy diagnosis and prediction of foetal numbers in sheep with linear‐array real‐time ultrasound scanning. Vet. Q. 7:(4)256-263, DOI: 10.1080/01652176.1985.9693997.

Vanderick, S., Auvray, B., Newman, S.A., Dodds, K.G., Gengler, N., EverettHincks, J.M. (2015). Derivation of a new lamb survival trait for the New Zealand sheep industry. J. Anim. Sci. 93:3765–3772. doi:10.2527/jas.2015-9058.

Yapi, C.V., Boylan, W.J., Robinson, R.A. (1990). Factors associated with causes of preweaning lamb mortality. Prev. Vet. Med., 10:145-152.

The technical references (papers cited or used) are documented in each piece of recommendations.

== Guidelines on recording behavioural traits in sheep and goats ==
{| class="wikitable"
|'''Date of change'''
|'''Nature of Change'''
|-
|October 2024
|First version
|-
|November 2024
|Tracked change revisions by JC
|-
|December 2024
|Tracked change revisions by MS
|}

=== Introduction and scope ===

==== Introduction ====
Genetic selection including behavioural traits could be an advantageous strategy for improving robustness and welfare of farm animals in various farming conditions by minimizing unsuitable responses to changes in their social and physical environment, limiting an excessive fear of humans and improving sociability (Mignon-Grasteau et al., 2005). Farm animals are social and gregarious, and relational behaviours are essential for ensuring social cohesion, social facilitation, offspring survival and docility toward humans. Breed differences and genetic variation within breed have been reported in lambs for early social behaviours and found to be heritable, and associated with some QTL, suggesting such behaviours could be selected early (Boissy et al., 2005; Beausoleil et al., 2012; Hazard et al., 2014; Cloete et al., 2020). In addition, such early social reactivity of lambs towards conspecifics or humans was identified as a robust trait and that selection for early social reactivity of lambs towards conspecifics or humans is feasible (Hazard et al., 2016; 2022).

The behaviour of both ewes and lambs, and their interaction at lambing, have been widely described. Such behaviour is important for the survival of the offspring, especially in extensive farming conditions as reviewed by Dwyer et al. (2014). Moreover, it has been shown that primiparous ewes are more prone to abandon their lambs due to their lack of maternal experience (Dwyer, 2008) and that lamb survival at birth is lowly heritable (Brien et al., 2014). Taken together these factors could hinder the development of extensive farming systems. Genetic selection on maternal attachment traits could therefore be advantageous to improve offspring survival and growth, and reduce labour, as suggested by Mignon-Grasteau et al. (2005). Genetic variations in maternal behaviour between breeds of sheep have been well documented (for review see: Dwyer, 2008; von Borstel et al., 2011) while little was known about within-breed genetic variability and even less about maternal reactivity traits. We hypothesized that maternal attachment to the litter has a genetic component in sheep, and we recently reported that as expected the maternal reactivity at lambing is a heritable trait (Hazard et al., 2020;2021).

Grazing behaviour is also important for animals raised in extensive production systems because it can support adaptability to changing environments. In particular, small ruminants reared in semi-extensive systems face many environmental and welfare challenges that are difficult to quantify. The evidence in the literature suggests that there are differences in grazing behaviour between and within breeds of sheep (Simm et al., 1996; Brand, 2000). The notion is that natural selection combined with subjective artificial selection have led to some animals being more adaptive to extensive conditions. In this regard, genetic variation may exist for key grazing behaviour traits (Simm et al., 1996; Dwyer et al., 2005), but relevant literature is scarce. During the SMARTER H2020 project, a study was performed on grazing behaviour of the indigenous Boutsko Greek mountainous sheep breed, which is reared semi-extensively. The results showed that duration of grazing and speed are heritable traits (Vouraki et al., 2025).

==== Acronyms used in these guidelines ====

* AT Arena Test
* CT Corridor Test
* GPS Global Positioning System
* LS Lambing Site
* PCA Principal Component Analysis

==== Scope ====
The aim of the present report is i) to define the behavioural traits of interest, ii) to describe approaches for behavioural measurements, iii) to describe their use for genetic analysis and evaluation.

To-date, the present guidelines describe 3 groups of traits related to behaviour:

* Behavioural reactivity towards conspecifics or humans
* Maternal reactivity
* Behaviour at grazing

Kid/lamb vigour is a relevant behavioural trait, but this trait is tackled within the section “foetus and young survival in sheep and goats” of the guidelines.

Most of the work undertaken on behaviour concerned sheep. This has been particularly the case in SMARTER. Most of the recommendations might be applied to goats as well. Nevertheless, we will use the ovine terms in the guidelines below.
[[File:Section_24-1_Three_groups_of_traits_related_to_behaviour_guidelines.jpg|center|thumb|600x600px|Three groups of traits related to behaviour guidelines]]

=== Behavioural reactivity towards conspecifics or humans ===

==== Definition, terminology, rationale ====

===== Behavioural reactivity towards conspecifics (i.e. sociability): =====
It is the social motivation of the lambs to join their conspecifics in response to social isolation with or without presence of a motionless human. Expression of higher levels of a panel of behaviours, including vocalisations and locomotion, is hypothesised as an active way to maintain social link with conspecifics.

==== Behavioural reactivity towards humans (i.e. docility): ====
It is the reactivity of isolated lambs to a walking human. Higher flight distance between the lamb and a human indicates a lower docility toward a human.

Behavioural reactivity towards conspecifics and humans are measured in standardised behavioural tests (arena and corridor tests, described below).

Higher sociability and/or docility towards humans may improve adaptation of sheep to harsh environments through social facilitation (i.e. transmission of feeding preferences…), social cohesion (i.e. transhumance…) and reactivity to handling. Consequently, improving such behavioural traits may improve welfare, production, and labour of shepherd.

==== Data recording ====

===== Behavioural tests =====
The test described below have been implemented in France. It must be considered as a possible test, as others can be described later and enrich these guidelines.

Lambs must be individually exposed just after weaning (i.e. approximately 10 days after weaning) to two behavioural tests. The delay between weaning and behavioural tests must be sufficient for the change of social preferences of lambs for their dam to conspecifics.

The arena test (AT) consists of two successive phases evaluating 1) reactivity to social isolation (AT1), 2) the motivation of the lamb towards conspecifics in presence of a motionless human (AT2). The arena test is performed indoors. The arena test pen consists in an unfamiliar enclosure virtually divided into 7 zones as described in detail by Ligout ''et al''. (2011) (Figure 1). On one side of the enclosure (i.e. at the opposite of the entrance), a grid separates the tested lamb from another smaller pen containing 3 or 4 conspecifics. The first phase of the test (arena test phase 1, AT1) starts once the tested animal joins its flock-mates located behind a grid at the opposite side of the arena (time duration for joining: lower than 15 sec). No behavioural recording is performed during the joining. At this time, an opaque panel is pulled down (from the outside of the pen) between the flock-mates and the tested lamb to prevent visual contact. After one minute the phase 1 stops and the panel is pulled up so the lamb can see its flock-mates again. Once the lamb has returned near to its flock-mates, or after 1 minute if the lamb did not do so, a non-familiar human slowly enters the arena through a door located near the pen of the flock-mates and stood 20 cm in front of the grid separating the arena from the lamb’s flock-mates. The second phase (arena test phase 2, AT2) starts once the human is in place and lasts for a further 1 minute.
[[File:Experimental_setup_of_the_arena_test_for_estimating_the_social_reactivity_of_lambs.jpg|center|thumb|600x600px|Figure 1. Experimental setup of the arena test for estimating the social reactivity of lambs. At the beginning of the test, animals can join their flock mates placed behind a grid barrier (social attraction, phase 0) and then were individually exposed to the social isolation (phase 1), and to the social attraction in presence of a motionless human (phase 2). (Adapted from Ligout et al., 2011)]]
The corridor test (CT) consists of two successive phases evaluating 1) reactivity to social isolation (CT1) and 2) reactivity to an approaching human (CT2). The test pen consists in a closed, wide rectangular circuit and has been described in detail by Boissy ''et al''. (Boissy et al., 2005) (Figure 2). The first phase (corridor test phase 1, CT1) starts when the lamb enters the testing pen and lasts for 30 seconds. After that time a non-familiar human enters the testing pen and the second phase (corridor test phase 2, CT2) starts and lasts 1 minute. During this phase, the human walks at a regular speed through the corridor (the corridor is divided into 6 virtual zones and one zone is crossed every 5 seconds) until two complete tours has been achieved.

===== Behavioural traits =====
Several behaviours are measured during behavioural tests: vocalisations (i.e. frequency of high- pitched bleats), locomotion (i.e. number of virtual zones crossed), the proximity score (i.e. weighting of time spent in virtual zones, a high score indicated a high duration spent close to conspecifics and a human).

An investigator counts the lamb’s vocalisations directly during the tests, from outside the pen using a laptop: number of times the animal bleats with an open mouth (high bleats, AT1/2- HBLEAT, CT1-HBLEAT). Locomotor activity is assessed by measuring the number of virtual zones crossed during arena test phases 1 and 2 (AT1/2-LOCOM) and corridor test phase 1 (CT1- LOCOM). This behaviour can be assessed using video recording or using infrared cells regularly positioned along the AT to detect displacement. The proximity to flock-mates and the human during AT2 is calculated by weighting of time spent in virtual zones (i.e. a high score indicated a high duration spent close to conspecifics and a human).

During CT2, every five seconds throughout this phase, an investigator records with a laptop the zones in which the human and the animal are located. In addition, the walking human records with a stopwatch the total duration during which the head of the lamb is visible. The mean flight distance (DIST) separating the human and the lamb (i.e. knowing the length of each virtual zone) and the time during which the human sees the lamb (SEEN) is measured in CT2.

==== Calculation of traits ====
Deviations from normality of row data must be tested using relevant statistical tests (e.g. the Kolmogorov–Smirnov test). Several raw measures must be transformed in order to minimise major deviations from the normal distribution. Square root transformation is applied to AT1/2- HBLEAT, CT1-HBLEAT. A multivariate analysis may be performed to take into account the multidimensional aspect of behavioural responses. Results of principal component analysis (PCA) indicate that the main principal components is structured mainly with similar behaviour (i.e. higher weight of similar behaviours for the different tests on the same component). Consequently, three synthetic variables may be constructed using PCA. Each PCA is performed for a set of similar behavioural variables across the behavioural tests. The first component of each PCA, explaining the largest part of total variance, is defined as a synthetic variable. Two synthetic variables are specific to the reactivity to social isolation: high bleats (HBLEAT, using AT1/2-HBLEAT and CT1- HBLEAT), locomotion (LOCOM, using AT1/2-LOCOM and CT1-LOCOM). One synthetic variable is specific to the reactivity to an approaching human: the tolerance to being approached when the lamb is free to flee (HUMAPPRO, using CT2-DIST and CT2-SEEN).

==== Use for genetic analysis and genetic evaluation ====
Genetic analyses and genetic evaluation can be performed on single traits and synthetic variables. Genetic analyses (estimation of (co)variance components and prediction of breeding values) for quantitative behavioural traits may be implemented with a mixed model methodology in animal model. Random effects should include:

* a direct additive genetic effect of the animal (i.e. lamb),
* a maternal permanent environment effect (i.e dam), that describes lamb phenotypic variation caused by the environment of the ewe
* a litter permanent environment effect, that accounts for phenotypic variation caused by the environment of the litter of the lamb being tested.

All relevant fixed effects and interactions should be included in the model. Factors that could be considered include:

* a combination of the litter size at lambing and the number of lambs suckled with their dam
* sex, age, live weight of the lamb,
* dam parity and/or age of dam nested withing parity if needed contemporary group (e.g., depending on the data collection: flock-year-season, grazing location…)

=== Maternal reactivity ===

==== Definition, terminology, rationale ====
* Behavioural reactivity at lambing (i.e. maternal reactivity). It is the social motivation or attachment of the ewe for the litter expressed in response to an approaching human, or the withdrawal of the litter with or without presence of a human. Expression of higher levels of a panel of behaviours, including maternal behaviour scores, vocalisations and locomotion, is hypothesised as an active way to maintain social link with lambs.

Maternal reactivity is measured in standardised behavioural tests (a scoring test outdoors, an arena test indoors, described in the controlled test below) or a maternal behaviour score (MBS) designed for use in extensive sheep systems as described by O’Connor ''et al'' (1985), the genetic basis of which was reported by Lambe et al., 2001 for Scottish Blackface sheep.

Higher maternal reactivity may improve adaptation of sheep to harsh environments through a higher behavioural autonomy at lambing and a reducing dependency to the support provided by shepherds. Consequently, improving such behavioural traits may improve welfare, production, and labour of the shepherd.

==== Data recording ====

===== Behavioural tests =====
The controlled test described below have been implemented in France. It must be considered as a possible test, as others can be described later and enrich these guidelines. Ewes are individually exposed to two behavioural tests: a scoring test performed just after lambing, outside at the lambing site, and then an arena test performed indoor, one day after lambing. The second test is performed after the bonding period needed to establish the social link between ewes and lambs and which occurs generally within the first twelve hours after lambing (Keller et al, 2003).

Scoring test at lambing site: Maternal reactivity is assessed outside at the lambing site approximately 2 hours after lambing, only on ewes that lambed during daylight when the shepherd approaches the lambing ewes to catch lambs for weighing and identification. Scoring at lambing is not performed in the following situations: if the location of the lambing site does not readily facilitate the testing procedure, if there are perturbations of scoring due to interference by other ewes, for sanitary reasons that could affect behaviours (including difficult lambing, death of all lambs of a litter). Measurement of maternal reactivity at the lambing site (LS) consists of two successive phases: (1) when the shepherd approaches the lambs; and (2) the capture and displacement of the lambs by the shepherd. In the first phase (LS1), the shepherd stands approximately 15 meters away from the lambing spot and approaches the ewes and the lambs at a regular speed (1 m/s). In the second phase (LS2), the shepherd catches all the lambs at the same time and moves away from the lambing spot in the same direction as that of the approach, stopping at the starting point where he places the lambs back on the ground and then moves 15 meters away to allow the ewe to restore contact with her lambs. This second phase of the test is not applied to ewes that flee at the approach of the shepherd and do not return within 60 seconds after the end of LS1.

Arena test: After lambing, all the ewes and lambs (both day and night births) are transferred to a shelter close to the place of lambing and penned individually for few hours. They are then moved to a collective pen until the next day when they are tested in the arena test (24h ± 6h after lambing). The arena test (AT) is performed indoors and adapted from the original test developed by Boissy and colleagues (2005) to investigate social attachment in sheep (Ligout et al., 2011). In the present study, the test consists of three successive phases evaluating the ewe’s 1) attraction to her litter, 2) reactivity to social separation from her litter, and 3) reactivity to a conflict between social attraction to her litter and avoidance of a motionless human. The test pen consists of an unfamiliar enclosure virtually divided into 7 zones (zone 7 being the zone nearest to the litter). On one side of the enclosure, a grid separates the tested ewe from another smaller pen containing her lamb(s). The first phase of the test (AT1) starts when the tested ewe enters the arena and lasts for 30 s. Then, a remotely controlled opaque panel is pulled down in front of the grid to prevent visual contact between the tested ewe and her lambs. The second phase (AT2), during which the tested ewe is separated from her lambs, lasts 1 min. Finally, the panel is raised so the tested ewe can see her lamb(s) again. Once the ewe has returned near to her lamb(s), a non-familiar shepherd slowly enters the arena through a door located near the grid separating the arena from the litter and stands 20 cm in front of the grid. The third phase of the test (AT3) starts once the shepherd is in place and lasts for 1 min.

===== Behavioural traits =====
Scoring test at lambing site: A scoring system, close to those defined by O’Connor et al. (1985), and further validated for hill sheep by Lambe ''et al.'' (2001) for use in animal breeding programmes to enable many animals to be scored relatively quickly and easily in extensive sheep systems. The simple scoring system measures maternal reactivity described for each of the two phases described above. In LS1, a maternal behaviour score (LS1-MBS) is recorded on a 5-point scale as follows: 1 - ewe flees and does not return to the lambs within 60 s; 2 - ewe retreats (i.e., at least 2-3 m) but comes back to her lambs within 60 s; 3 - ewe retreats with at least one lamb and comes back; 4 - ewe retreats and returns repeatedly; 5 - ewe stays close to the lambing spot. In LS2, a second maternal behaviour score (LS2-MBS) is recorded on a 4-point scale as follows: 1 - ewe flees; 2 - ewe stays close to the lambing spot, 3 - ewe follows but from a distance (i.e., 1 to 2 m), 4- ewe follows, staying close to the shepherd (i.e., less than 1 m).

'''Arena test''': Locomotor activity and localisation are analysed from the video footage or infrared cells (as described above). Locomotor activity is assessed by measuring the number of zones crossed during the 3 phases (AT1/2/3-LOCOM). The time spent in each zone is recorded. The ewe’s proximity to the litter and/or the human during phases 1 and 3 (AT1/3-PROX) is calculated using the following formula:
[[File:Arena_test_formula.jpg|left|thumb|410x410px]]

Two types of vocalisations are recorded manually during the test with an electronic device: number of high-pitched bleats are recorded when the animal bleats with an open mouth (AT1/2/3-HBLEAT) and number of low-pitched bleats are recorded when the animal bleats with a closed mouth (AT1/2/3-LBLEAT).

==== Calculation of traits ====
Logarithmic transformation is applied to AT1/2/3-LBLEAT to minimise major deviations from the normal distribution. All other elementary variables described above are directly used for genetic analyses.

==== Use for genetic analysis / genetic evaluation ====
The (co)variance components for quantitative behavioural traits can be estimated by restricted maximum likelihood (REML) methodology applied in an animal model. The (co)variance components for categorical behaviours can be estimated by MCMC and Gibbs sampling methods using a threshold model (Gilmour et al., 2009).

Assuming that all ewes are measured every year, the analyses assume a repeatability model with behaviour measured across productive cycles considered to be the same trait with a constant variance. Random effects typically include a direct additive and permanent environmental genetic effects of the animal (i.e., ewe).

All relevant fixed effects and interactions should be included in the model. Factors that could be considered can include:

* The litter size at lambing.
* Dam parity or age or age of the dam nested within parity (if significant).
* Contemporary group (e.g., depending on the data collection: flock-year-season effect…).

=== Behaviour at grazing ===

==== Definition, terminology, rationale ====
Grazing behaviour is a complex combination of various movements and activities of animals in different spatial-temporal scales (Andriamandroso et al, 2016). Indicative traits related to grazing behaviour include:

* Duration of grazing
* Distance walked
* Speed
* Altitude difference
* Elevation gain/loss
* Energy expenditure at grazing

A better understanding of the phenotypic and genetic background of grazing behaviour traits could help towards the development of appropriate breeding programmes to increase adaptation to extensive rearing conditions. However, recording of such traits is challenging. The use of new technologies such as global positioning systems (GPS) could help towards efficiently monitoring grazing behaviour (Homburger et al., 2014; Feldt and Schlecht, 2016).

==== Data recording ====
The following guidelines for recording grazing behaviour traits of sheep are based on a study implemented in Greece (Vouraki et al., 2025). Specifically, in the latter study, grazing behaviour of Boutsko sheep reared semi-extensively in mountainous regions was monitored using GPS technology. Moreover, phenotypic and genetic parameters for key grazing behaviour traits were estimated. These guidelines could be enriched in the future based on other relevant studies.

Monitoring of sheep grazing behaviour is performed using appropriate GPS devices attached on designated collars (Figure 3). Rotational monitoring of animals can be applied to reduce the number of devices needed. Selected GPS devices should be of low weight in order to be accepted by the animals without any obvious irritation. Batteries with extended life should be used to provide sufficient energy for GPS tracking for as many as possible consecutive days. In the aforementioned study, “Tractive GPS” devices (Tractive, Pasching, Austria) were used that weighed 28 grams. GPS tracking of each animal was performed for 4-10 days at 2-60 minutes intervals; number of tracking days and intervals were based on available signal and animal movement.

GPS generated data of each animal for the total tracking period are exported in .gpx format. In the case of “Tractive GPS”, the location history function of MyTractive web app ([https://my.tractive.com/#/ <nowiki>https://my.tractive.com/#/</nowiki>)] is used to export recorded data. Then, the exported files are split by date using a designated software such as GPSBabel (version 1.8.0). For each animal, daily routes and corresponding GPS data can be visualized and extracted using appropriate software such as Viking GPS data editor and analyser (version 2.0).

Recorded grazing behaviour traits via these devices include duration of daily grazing (min), distance (km), speed (km/hour), minimum and maximum altitude, and total elevation gain. Other useful metrics including number and average distance between tracking points, tracking duration and route followed by the animals should also be extracted to be used in ensuing analyses.
[[File:Figure_3._GPS.jpg|center|thumb|600x600px|Figure 3. GPS device attached on designated collar.]]

==== Calculation of traits ====
Based on minimum and maximum altitude, altitude difference is calculated. Moreover, energy expenditure for walking can be estimated using the following formula of AFRC (Alderman and Cottrill, 1993):

EE= (0.0026×HD+0.028×VD)×BW

where:

EE = energy expenditure for walking (MJ);

HD = horizontal distance (km, calculated as the difference between distance and elevation gain); VD = vertical distance (km, corresponding to elevation gain);

BW = body weight (kg).

Quality control of GPS generated phenotypes is necessary to sense-check the data for extreme values and errors. Specifically, limits are set for minimum and maximum altitudes to reflect the real altitude of the region being studied. Tracking points beyond these limits are then removed from the corresponding .gpx files and data are re-calculated. Moreover, daily records for which GPS tracking of animals had stopped before returning to their shed, must be excluded. Finally, if needed, grazing behaviour traits should be logarithmically transformed to ensure normality of distribution prior to analyses.

==== Use for genetic analysis / genetic evaluation ====
(Co)variance components of grazing behaviour phenotypes and relevant breeding values (EBVs) can be estimated by restricted maximum likelihood methodology applied to an animal mixed model that can include the following random and fixed effects:

Random effects: additive genetic effect and permanent environmental effect of the animal

The relevant fixed effects may include:

* Farm
* Number of GPS tracking points
* Tracking duration
* Distance between tracking points
* Climatic parameters (e.g. temperature-humidity index)
* Sampling time

It may also be desirable to include social grouping (if known), as this can also affect individual animal behaviours.

=== Acknowledgements ===
We gratefully acknowledge the contributions to these recording of behaviour guidelines by the following people:

* Dominique Hazard, INRAE, France
* Angeliki Argyriadou, University of Thessaloniki, Greece
* Georgios Arsenos, University of Thessaloniki, Greece
* Alain Boissy, INRAE, France
* Vasileia Fotiadou, University of Thessaloniki, Greece
* Sotiria Vouraki, University of Thessaloniki, Greece
* Joanne Conington, SRUC, the UK
* Marija Špehar, Centre for Livestock Breeding Zagreb, Croatia

This work received funding from the European Unions’ Horizon 2020 Research & Innovation program under grant agreement N°772787—SMARTER.

=== References ===
Alderman, G., Cottrill, B. Energy and Protein Requirements of Ruminants. In An Advisory Manual Prepared by the AFRC Technical Committee on Responses to Nutrients; CAB International: Wallingford, UK, 1993.

Andriamandroso, A., J. Bindelle, B. Mercatoris, F. Lebeau (2016). A review on the use of sensors to monitor cattle jaw movements and behavior when grazing. Biotechnologie, Agronomie, Société et Environnement, 20.

Beausoleil, N. J., D. Blache, K. J. Stafford, D. J. Mellor, and A. D. L. Noble. (2012). Selection for temperament in sheep: Domain-general and context-specific traits. Appl. Anim. Behav. Sci. 139:74–85.

Boissy, A., Bouix, J., Orgeur, P., Poindron, P., Bibe, B., & Le Neindre, P. (2005). Genetic analysis of emotional reactivity in sheep: effects of the genotypes of the lambs and of their dams. ''Genetics Selection'' ''Evolution, 37'', 381-401. doi:10.1051/gse:2005007

Brand, T. S. (2000). Grazing behaviour and diet selection by Dorper sheep. Small Ruminant Research, 36(2), 147-158.

Brien, F. D., Cloete, S. W. P., Fogarty, N. M., Greeff, J. C., Hebart, M. L., Hiendleder, S., . . . Miller, D. R. (2014). A review of the genetic and epigenetic factors affecting lamb survival. Animal Production Science, 54, 667-693. doi:10.1071/an13140

Cloete, S. W. P., Burger, M., Scholtz, A. J., Cloete, J. J. E., Kruger, A. C. M., & Dzama, K. (2020). Arena behaviour of Merino weaners is heritable and affected by divergent selection for number of lambs weaned per ewe mated. Applied Animal Behaviour Science, 233. doi:10.1016/j.applanim.2020.105152

Dwyer, C. M., Lawrence, A. B. (2005). A review of the behavioural and physiological adaptations of hill and lowland breeds of sheep that favour lamb survival. Applied animal behaviour science, 92(3), 235-260.

Dwyer, C. M. (2008). Genetic and physiological determinants of maternal behavior and lamb survival: Implications for low-input sheep management. Journal of Animal Science, 86, E246-E258. doi:10.2527/jas.2007-0404

Dwyer, C. M. (2014). Maternal behaviour and lamb survival: from neuroendocrinology to practical application. animal, 8, 102-112. doi:doi:10.1017/S1751731113001614

Feldt, T., Schlecht, E. (2016). Analysis of GPS trajectories to assess spatio-temporal differences in grazing patterns and land use preferences of domestic livestock in southwestern Madagascar. Pastoralism, 6(1), 1-17.

Gilmour, A. R., Gogel, B. J., Cullis, B. R., & Thompson, R. (2009). ASReml User Guide Release 3.0 VSN International Ltd, Hemel Hempstead, HP1 1ES, UK. www.vsni.co.uk.

Hazard, D., Moreno, C., Foulquié, D., Delval, E., François, D., Bouix, J., Boissy, A. (2014). Identification of QTLs for behavioral reactivity to social separation and humans in sheep using the OvineSNP50 BeadChip. ''BMC Genomics, 15'', 778. doi:10.1186/1471-2164-15-778

Hazard, D., Bouix, J., Chassier, M., Delval, E., Foulquie, D., Fassier, T., Boissy, A. (2016). Genotype by environment interactions for behavioral reactivity in sheep. ''Journal of Animal Science, 94'', 1459-1471. doi:10.2527/jas2015-0277

Hazard, D., Macé, T., Kempeneers, A., Delval, E., Foulquié, D., Bouix, J., & Boissy, A. (2020). Genetic parameters estimates for ewes’ behavioural reactivity towards their litter after lambing. ''Journal of Animal Breeding and Genetics, n/a''. doi:10.1111/jbg.12474

Hazard, D., Kempeneers, A., Delval, E., Bouix, J., Foulquie, D., & Boissy, A. (2021). Maternal reactivity of ewes at lambing is genetically linked to their behavioural reactivity in an arena test. Journal of Animal Breeding and Genetics, 139, 193-203. doi:10.1111/jbg.12656

Hazard, D., E. Delval, S. Douls, C. Durand, G. Bonnafe, D. Foulquié, D. Marcon, C. Allain, S. Parisot, A. Boissy (2022). Divergent genetic selections for social attractiveness or tolerance toward humans in sheep. WCGALP 2022

Homburger, H., Schneider, M. K., Hilfiker, S., Lüscher, A. (2014). Inferring behavioral states of grazing livestock from high-frequency position data alone. ''PLoS One'', ''9''(12), e114522.

Keller, M., Meurisse, M., Poindron, P., Nowak, R., Ferreira, G., Shayit, M., & Levy, F. (2003). Maternal experience influences the establishment of visual/auditory, but not olfactory recognition of the newborn lamb by ewes at parturition. Developmental Psychobiology, 43, 167-176. doi:10.1002/dev.10130

Lambe, N R; Conington, J; Bishop, S C; Waterhouse, A; Simm, G (2001). A Genetic Analysis of maternal behaviour score in Scottish Blackface sheep. Animal Science 72: p415-425. Doi:10.1017/s1357729800055922.

Ligout, S., Foulquie, D., Sebe, F., Bouix, J., & Boissy, A. (2011). Assessment of sociability in farm animals: the use of arena test in lambs. Applied Animal Behaviour Science, 135, 57-62. doi:10.1016/j.applanim.2011.09.004

Mignon-Grasteau, S., Boissy, A., Bouix, J., Faure, J.-M., Fisher, A. D., Hinch, G. N., . . . Beaumont, C. (2005). Genetics of adaptation and domestication in livestock. ''Livestock Production Science, 93'', 3-14. doi:10.1016/j.livprodsci.2004.11.001

O’Connor, C. E., Jay, N. P., Nicol, A. M., & Beatson, P. R. (1985). Ewe maternal behaviour score and lamb survival. Proceedings of the New Zealand Society of Animal Production, 45 159–162.

O’Connor, C.E., Lawrence, A. B. and Wood-Gush, D. G. M. (1992). Influence of litter size and parity on maternal behaviour at parturition in Scottish Blackface sheep. Applied Animal Behaviour Science 33: 345–355. <nowiki>https://doi.org/10.1016/S0168-1591(05)80071-1</nowiki>

Simm, G., Conington, J., Bishop, S. C., Dwyer, C. M., Pattinson, S. (1996). Genetic selection for extensive conditions. Applied Animal Behaviour Science, 49(1), 47-59.

SMARTER deliverable D2.4. New prototype and report for industry on GPS-generated phenotypes for behavioural adaptation to extensive grazing systems; artificial rearing adaptation phenotypes; lamb vigour scores linked to lamb survival; new foetal and neonatal survival phenotypes (in preparation).

von Borstel, U. K., Moors, E., Schichowski, C., & Gauly, M. (2011). Breed differences in maternal behaviour in relation to lamb (Ovis orientalis aries) productivity. Livestock Science, 137, 42-48. doi:10.1016/j.livsci.2010.09.028

Vouraki, S., Papanikolopoulou, V., Argyriadou, A., Priskas S., Banos, G., Arsenos, G. (2025). Phenotypic and genetic parameters of grazing behaviour of semi-extensively reared Boutsko sheep. Applied Animal Behaviour Science, vol. 282, Jan 2025, 106473. <nowiki>https://doi.org/10.1016/j.applanim.2024.106473</nowiki>

== Guidelines on recording the environment in sheep and goats ==
'''Change Summary'''
{| class="wikitable"
|'''Date of change'''
|'''Nature of Change'''
|-
|October 2024
|First version
|}

=== Introduction and scope ===

==== Introduction ====
In the genetic evaluation process, the genetic model includes environmental effects (generally fixed effects, in some cases random effects) to correct the phenotypes from these effects, not related to the genetic value of the animal. These environmental effects that affects the expression of the genotypes depend on the traits and the method of phenotyping, the environment itself (flock/herd, year, parity, season of lambing, number of born or reared lambs/kids, scorer, gender of the lamb/kid, management of mob groups, etc). The quality of the record of the environment is important to correct relevantly the performance of the animal.

Some other environmental effects that are usually included in a general flock/year or management mob group effect could be identified, such as the feeding effect or the climate effect. By including these effects in the genetic model, we could get less biased and more precise EBVs, especially when these effects are individualised or are period-specific (feeding might depend on such and such groups of animals, climate might influence the performance of such and such test- day). Moreover, the more precise knowledge of environmental effect might be valorised for flock/herd management and extension services towards farmers.

Moreover, feeding can be considered as an environmental effect, but as well be constitutive of a performance. This is typically the case for feed efficiency where the quantity and the quality of the diets allows to calculate the phenotype.

Likewise, with the climatic change, breeding for animals more resistant or more resilient to higher temperatures (especially thermal stress) becomes a selection objective per se (example of heat tolerance). In this context, the conditions of temperatures (or temperature/humidity combination) not only might be an environmental factor, but be part of the phenotype.

Other environmental effects can be described and should enrich this document in the future.

==== Scope ====
This document focuses on those data that are worth recording the precise the environment or to calculate novel traits of interest.

Following SMARTER work, the document will describe the record of the diet ([[Section 24: Recording resilience in sheep and goats#Recording the diet|Chapter 6]]) and the record of meteorological data ([[Section 24: Recording resilience in sheep and goats#Meteorological data|Chapter 6]])

Further factors might be described later, letting this document open to new section in the future, including:

* Recording the diet in small ruminant
* Recording meteorological data
* Other environmental records

=== Recording the diet ===

==== Definition, terminology, rationale ====
Recording the diet consists in collecting data on the quantity and quality of a ration that an animal, a group of animals of a flock/herd consumes at a given period.

The characterisation of the ration, in terms of energy and protein depends upon the countries. For example, the French INRAE Feeding System for Ruminants (Nozière et al., 2018) is different from the British one (AFRC, 1993). This is the reason for which we will describe in this section general recommendations, that can be applied, translated to the domestic feeding system used

Breeding for more efficient animals is more and more important for economic reason (the feeding resources are costly, might be rare in years with climatic excess such as heat or drought) and for environmental reasons (feed/food competition, emission of green-house gases). Feed efficiency is a trait of high interest in this context. Even though it is deceptive to calculate gold standard efficiency trait in private farm, the knowledge of diets in those farms should help to correctly manage the proxies that are promoted in SMARTER. Diet could also be used as a corrective factor in evaluation models in the future. In addition, it might be a support to better understand the herd/flock effect and its variation across year, and therefore give more acute and relevant advice to the farmers.

It is difficult and time-consuming to collect the data for establishing the diet in the flock/herds. The diet is collective in most of the situations (the same amount of forage is given to all animal because the forage is not given individually). When the concentrate is given through Automated Concentrate Feeder (ACF) in the milking parlour, the individualisation is not at the animal scale but at a limited number of groups scale. That’s why we suggest recommendations that must be adapted to each situation.

The aim is to tend to the better possible estimation of the forage ingestion, given that the direct measurement is impossible in commercial farms. Proxies are studied to get indirect measurement of the intake, but they are not validated so far (Near Infra Red Spectra technique). As soon as validated results are available, these recommendations will be updated.

==== Data recording ====

===== When to record the diet =====
The diet may be recorded at relevant period of the physiological status of the animals in the flock/herd. It is possible to take advantage of the visit of a technician to record the ration (for example when performance recording such as at each (or some of the) test-day when milk recording, or at weighing visit in meat sheep performance recording.

Below are examples of relevant physiological status:

* At mating (or before the mating and after the mating)
* End of gestation (in the month preceding the lambing/kidding)
* After lambing/kidding
* At weaning or just after weaning (peak of production in dairy animals)
* Dairy animals: at each test-day or at some of the test-day

In case of ACF (Automatic Concentrate Feeder), it is possible to record the distribution of concentrate more frequently.

It may be useful to establish the requirements of animals (on average) at each point of diet record. The requirements must concern the energy (in the unit usually used in the country) and the protein (in the unit usually used in the country).

===== How to record the diet =====
'''Individual diet'''
* This can be obtained through ACF for concentrate, mainly in the milking parlour.
* Intake of forage cannot be collected individually but can be predicted through the intake capacity system, such as the one proposed by INRAE (Nozière et al., 2018).

'''Collective diet (at the flock/herd scale or at the mob scale)'''

* Forage (hay, or haylage): some bales of each preservation technic can be weighed once a year with a dry matter (DM) measurement for haylage (it can substantially vary). For hay, DM can be estimated at 85%. Afterward, we can just record how many bales of a given quality (several cutting stages are preserved and not given at random) are distributed per flock per time unit. For silages, it is more complicated, but based on the same procedure, we can weigh one distribution (assuming that it will be constant over time) and simultaneously measured DM. In both situations, if refusals cannot be measured, they must be sufficient for assuming an ad libitum distribution. When the feeding system used in the country can predict the DM intake through the intake capacity of the animal and the quality of the feed, individual diet can be estimated.
* Grazing: for dairy sheep grazing within a short duration per day or the full day, intake can be estimated through ad hoc system. As an example, the new French INRATion feeding software (INRATion V5®) proposes such estimation based on grazing duration, biomass availability and quality.

===== Defining the constitution of the diet =====

====== Precise the type of distribution of the ration ======

* collective ration
* individual ration (concentrate when ACF)
* pasture

====== Categories of feedstuff ======

* Hay
* Partially or fully fermented fodder and fodder preserved by silaging or wrapping:
** Silage
** Wrapped bales

* Pasture
* Straw
* Green feeding
* Dehydrated alfalfa
* Pulp (dehydrated beet pulp, citrus pulp, etc)
* Cake (soybean, rapeseed or sunflower seed)
* Cereals grain (wheat, barley, maize, etc)
* Complete commercial concentrate
* Other by-products of agro-food industry (cereal brans, brewer’s grains, hulls etc.)

====== Species ======
For each category, specify the species (rye grass, alfalfa, clover, maize, wheat, barley, etc), physiological stage or age of regrowth, and harvest conditions (cutting length of the forage and added preservative or not for silages, conditions of hay making drying in the field or mechanically dried).

===== Characterizing the diet =====

====== Quantity ======
Quantity distributed, refused, consumed. Check that these amounts are regularly distributed, refused and consumed because it can markedly influence the animal performance specifically for dairy animals at test day.

The quantity of each feedstuff may be expressed in kg dry matter for forage, in kg gross matter for concentrate. However, final diet for requirement calculation must be expressed as DM.

====== Requirements ======
Requirements for the main categories of animals: it depends on the physiological status (maintenance, production, growing, pregnancy)

Average requirement coverage ratio (energy and nitrogen). For example, the requirement coverage ratio in French dairy sheep is roughly 115% for energy and about 125% for nitrogen of the requirements of the average ewe. That allows covering the requirements of about 85-90% of the flock. Difference between energy and nitrogen is assumed to be covered through the body reserve mobilisation.

====== Quality characterization ======
The feedstuffs and the ration must be characterized at least in terms of

* Energy
* Protein (or nitrogen)

In case of commercial concentrate, data written on the label are used.

Energy and protein can be expressed in the current unit used in the country. For example, in France, energy is expressed in UFL which is equal to 1.7 Mcal Net energy (Nozière et al., 2018).

It may also be expressed in the international unit, which can be Mcal or MJ.

==== Use for genetic analysis / genetic evaluation ====
'''Diet as part of a phenotype'''

Calculation of feed efficiency phenotypes: see recommendations on feed efficiency.

'''Diet as part of a factor in the evaluation model'''

In most of situations it is impossible in small ruminants to establish individual consumption, for practical reason. The collective effect of the diet is explained in the flock/year effect. The intermediate situation should be when ACF allows to identify several groups within the flock/herd, at a specific test-day or visit. It is possible in this case to put in the model a mob effect grouping animals being given the same amount of concentrate. This should result in a more precise calculation of the breeding value of the animal. Nevertheless, this approach has so far not be used to our knowledge.

=== Meteorological data ===

==== Definition, terminology, rationale ====
Meteorological conditions may affect the environment effect on the traits of interest. Even though they may be absorbed in a flock effect at the scale of the year or at the scale of a given test-day, it is relevant to be able to quantify the effect of such and such meteorological parameter (and especially the heat stress) ot the zootechnical traits. The global warming and the higher temperature in which the animals are bred emphasises this interest. It is possible to better assess the comfort zone of the populations, that means the meteorological conditions in which the zootechnical traits are not affected. It is also possible to identify animals better adapted to an increase in temperatures or able to be resilient to a wide range of temperatures, that means to maintain their productive ability. In this case, meteorological data, combined with a production trait (growth, milk production, milk composition) or fertility trait, are used as a resilience characterisation by assessing the ability of the animals to recover their production following meteorological challenges.

Meteorological data are mostly temperature, humidity, precipitations, wind speed and radiations. An issue in small ruminants is to select for adapted animals to new environmental challenges, without artificializing their environment of breeding. Mainly because the economic and societal constraints are such as breeding animals outdoors on pasture is desired and breeding indoors inartificialized environment may be costly in terms of energy.

==== Data recording ====

===== Meteorological data from weather station =====
The aim is to affect outdoors meteorological data to a farm. This can be obtained by assigning to the farm the meteorological data of the closest or more relevant weather stations, using the geographical coordinates of both the farm and the weather station.

The following data may be used:

* Temperature (minimum, maximum, average)
* Relative humidity (amount of moisture in air compared to the maximum amount of moisture it can have at a specific temperature). Expressed in %.
* Specific humidity (ratio of water vapor mass to the total mass of air and water vapor.
* Wind speed
* Precipitations and precipitation type
* Solar radiation
* Atmospheric radiation
* Evapotranspiration

Different index accounting for weather factors have been proposed. One of the most popular is the Temperature Humidity Index (THI) which may be calculated to get a single value representing the combined effects of air temperature and humidity associated with the level of thermal stress.

Different formulas of THI are proposed in the literature. Below is an example of formula proposed by Finocchiaro (et al., 2005):

THI = T − [0.55 × (1 − RH)/100] × (T − 14.4)

where T is the mean daily in °C and RH is the mean relative humidity expressed in percent. Quite often, the parameter used in the analysis model is the temperature of the THI (mainly because temperature and relative humidity are the most available parameters).

Let us also mention the Heat Load Index, referred to as the 'HLI', which is an index that brings together all the weather factors into one number to allow easy interpretation of the cooling capacity of the environment.

The assignation of meteorological data to a farm depends on the countries and on the availability of weather data.

In some countries, the territory may be cut out in a grid, each cell of the grid being considered to have the same meteorological parameters because they are close to the same weather station of reference. As an example, this is the case in France with a grid named SAFRAN cutting the territory into 9892 cells of 64 square kilometres each [8 km by 8 km] (Annex 1). This grid was used, thanks to specific permission from Meteo France, to affect each farm of a given project (by using its GPS coordinate) to a single cell of the grid and thus get relevant meteorological parameters.

The meteorological spatialised data are collected from weather station, on which specific interpolation are applied to present these data on the SAFRAN grid.

The meteorological data key period to consider must be thought according to the production system associated to the breed, type of traits measured and analysed. For example, for milk production (milk recording), we may consider the 3 days preceding the test-day. For semen production, we may consider the meteorological data either at the day of the semen collection, or during the spermatogenesis, which is around 50 days before the semen collection. For the insemination itself (which is in case of fresh semen the same day as semen production), we may consider climate data either the very day of the insemination operation or during a week preceding it.

===== Environmental data from sensor in the farm =====
Temperature and humidity may also be collected on site, thanks to sensors situated on-farm, for example in the sheep pen or the stable.

The number of sensors may depend upon the situation and configuration of each building, the goal being to be representative of the pen. In the practical situations of the SMARTER project, 2 to 3 sensors were set in the pen where animals are indoors at a height of 2 meters above the ground, so that they are protected from the animals. If the pen is already equipped by sensors, it is possible to retrieve the data from the existing sensors. The sensors must cover all the relevant groups of animals (primiparous, multiparous, etc), even if they are in different buildings. Measures might be collected several times a day, for example once an hour, to get a precise evaluation of the daily temperature and hygrometry. To relevantly collect the atmosphere of the building, the sensors must be set in a place free from too much air flow or too much sunshine. It is important to regularly check the batteries to avoid loss of data.

==== Use for genetic analysis / genetic evaluation ====
Effect of meteorological parameters (eg. temperature or THI) may be estimated on zootechnical traits, using different types of linear models.

The parameter may be considered as a categorical variable (each degree of the parameter being defined as a different class). Or it may be considered in a linear regression on degrees of the parameter.

Reaction norms model, using Legendre polynomial for example, may be used to assess populational losses of the zootechnical trait due to high or low temperature and/or humidity.

Two types of analysis can be made:

* a populational analysis (populational response to the effect of temperature or THI). It gives the comfort rage of each population and how much the loss is with lower or higher temperature or THI.
* an analysis of the genetic components using a random regression model. It permits to estimates genetic parameters of traits according to the temperature or THI and to calculate EBVs of animals at different temperatures or THI levels. Such EBVs allow to identify less vulnerable animals along a range of climate values, so as to identify and select the most robust animals.

=== Other environmental record ===
To be completed (or not) when necessary

=== Acknowledgements ===
We gratefully acknowledge the contributions to these environment documentation guidelines by the following people:

* Jean-Michel Astruc, IDELE, France
* Antonello Carta, Agris, Italy
* Philippe Hassoun, INRAE, France,
* Gilles Lagriffoul, IDELE, France
* Carolina Pineda Quiroga, NEIKER, Spain
* Eva Ugarte, NEIKER, Spain

This work received funding from the European Unions’ Horizon 2020 Research & Innovation program under grant agreement N°772787—SMARTER.

=== References ===
Finocchiaro R, van Kaam JB, Portolano B, Misztal I. Effect of heat stress on production of Mediterranean dairy sheep. J Dairy Sci. 2005 May;88(5):1855-64. doi: 10.3168/jds.S0022-0302(05)72860-5. PMID:15829679.

Nozière, P., Sauvant, D., Delaby, L. 2018. INRA Feeding System for Ruminants. Wageningen Academic Publishers, 640 p., 2018, 978-90-8686-292-4. ⟨10.3920/978-90-8686-292-4⟩. ⟨hal-02791719⟩

AFRC (Agricultural and Food Research Council). 1993. Energy and protein requirements of ruminants. CAB International, Wallingford.

=== Annexes ===
[[File:SAFRAN_grid_from_Meteo_France.jpg|center|thumb|600x600px|SAFRAN grid from Meteo France in the case of France]]

Section 24: Recording resilience in sheep and goats

2025-10-10T12:01:39Z

Aashish: /* Introduction and scope */

=== Introduction and scope ===
<div class="mw-collapsible mw-collapsed">
<div>
==== Introduction ====
The present guidelines aim at addressing resilience traits in small ruminants, as well as the description of the environment.

These recommendations are mainly based on a work achieved in the SMARTER H2020 project (n° 772787) whose objective was to promote harmonisation and international cooperation on breeding processes in small ruminant, especially those concerning the selection of efficiency and resilience. In this project, case studies of across country genetic evaluation, implemented as a proof of concept, have highlighted the importance of analysing traits that have been collected and/or calculated on a same way across country. Therefore, it appears fundamental that novel traits, such as resilience-related traits, which are not still widely routinely recorded on-farm for selection purposes, be recorded identically, or at least in the most similar way as possible. For that purpose, recommendations must be proposed, for countries or breeding organisations that would like to start to record efficiency or resilience traits, or that would like to set up an across-country genetic evaluation on these traits. The more similar the traits, the higher the genetic correlation across country (at same level of connection across country).

In addition, as resilience may be considered as basically related to the environmental challenges such as nutritional, disease or climatic challenges, the documentation of the environment is also described. Tackling the record of the environment is a novelty in selection of small ruminant.

The recommendations issued in a deliverable of the SMARTER project have been basically written by the partners of the project working on tasks dedicated to the different resilience-related traits and as well by the Sheep, Goat and Camelid ICAR Working Group. The Working Group was indeed involved, as partner for some of the members, as stakeholders for some other, and through ICAR who was a partner itself. Therefore, these guidelines are the fruit of a close cooperation between many academic and non-academic co-authors. Materials were also collected from results obtained in other projects (e.g. H2020 iSAGE, POCTEFA ARDI).

The recommendations, even though they target to suggest people measuring and calculating the traits the same way, are more informative than normative. The different ways to measure and calculate the traits are presented, without imposing one way, yet while suggesting some general features. Five sub-sections of recommendations were written: health and disease, survival of foetus and young, behaviour, lifetime resilience, record of the environment. All sub-sections are written with the same template and are consistent by themselves.

All the recommendations are based on the current state of the art. However, they are meant to evolve with new results and new research, and they are meant to be enhanced, consolidated, enriched. It is possible to add a new trait, a new proxy, a new sub-section. In brief, the recommendations must keep alive to stick to the evolving state of the art. This implies that the consortium that produced these recommendations, in some way, continue to contribute. ICAR, with its working group dedicated to sheep and goat, is the relevant organisation to collect and integrate the different novelties and contributions.
</div>

===== Scope =====
The SMARTER recommendations cover the following fields, shown in the figure 1.
[[File:SMARTER recommendations.jpg|center|thumb|600x600px|Figure 1. Fields covered by the SMARTER recommendations ]]
The resilience-related traits are: health and disease (with a focus on resistance to parasites, to footrot, and to mastitis), survival foetus and young, behaviour traits (with a focus on behavioural reactivity towards conspecifics or humans, maternal reactivity, behaviour at grazing), lifetime resilience.

The record of the environment covers the meteorological data and the diet. The record of the rations was studied in the on-farm protocols of SMARTER-WP1, especially in France. The record of the meteorological data benefited from works carried out in the H2020 iSAGE and POCTEFA ARDI projects, some of the SMARTER partners being committed in those projects.

The recommendations are conceived to be evolutive. Amendments can be brought in the next years, especially when the recommendations will turn into ICAR guidelines, either to strengthen results or include new insights, or to add new sub-sections or new traits. For example: (i) in the record of the environment, sensor data may be included; (ii) new disease whose resistance has a genetic component.

==== Definition of resilience ====
In these guidelines, we use the following definition of the resilience.

Resilience is defined as the ability of an animal/system to either maintain or revert quickly to high production and health status when exposed to a diversity of challenges, with a focus on nutritional and/or health challenges. Resilience is therefore the trajectory that captures the deviation from, and recovery to, the unchallenged state. Direct indicators of health and welfare will address gastro-intestinal parasitism, lameness (footrot) and mastitis, the most economically important endemic diseases of small ruminants. Indirect indicators of health and welfare of economic importance for breeders are lamb and foetal survival, functional longevity, maternal and lamb behaviour, and neonatal vigour..

==== Recording of resilience ====
The resilience-related traits that are described below for sheep and goats are:

* [[Section 24: Recording resilience in sheep and goats#Guidelines on recording health and disease in sheep and goats|health and disease (Chapter 2);]]
* [[Section 24: Recording resilience in sheep and goats#Guidelines on recording lifetime resilience in sheep and goats|lifetime resilience (Chapter 3);]]
* [[Section 24: Recording resilience in sheep and goats#Guidelines on survival recording of foetus and young in sheep and goats|survival of foetus and young (Chapter4);]]
* [[Section 24: Recording resilience in sheep and goats#Guidelines on recording behavioural traits in sheep and goats|behavioural traits (Chapter 5).]]

==== Recording of the environment ====
The record of the environment in sheep and goats is described below in the [[Section 24: Recording resilience in sheep and goats#Guidelines on recording behavioural traits in sheep and goats|Chapter 6]] of these guidelines

==== Acknowledgements ====
We gratefully acknowledge the contributions to these guidelines on recording resilience-related traits and the environment in sheep and goat by all the people working in the ICAR working group on sheep, goat, camelids and/or participating to the SMARTER project:

The different documents giving the recommendations of each sub-sections list in their own acknowledgements the persons involved in the writing of the guidelines.

This work received funding from the European Unions’ Horizon 2020 Research & Innovation program under grant agreement N°772787—SMARTER.

== Guidelines on recording health and disease in sheep and goats ==
'''Change summary'''
{| class="wikitable"
|'''Date of change'''
|'''Nature of Change'''
|-
|October 2024
|First version
|-
|December 2024
|Tracked change revisions by MS
|}

=== Introduction and scope ===
==== Introduction ====
Livestock diseases cause significant economic losses due reduced productivity, failing to express the genetic potential of animals, treatment costs, and consequently the culling of animals. Therefore, health and resistance to disease are keys factors for increasing resilience in farm animals in general and in small ruminants in particular. Among the challenges that sheep and goats must face, the infectious challenges are among the most important. They lead to losses of production and difficulties of reproduction. They also generate an increase in the consumption of chemical input. Beyond actual extra cost that may hamper the sustainability of the farms, but also of the breeding programs, there is a risk for the environment and the occurrence of resistance to drugs.

In most cases, an integrated approach is the more beneficial and efficient, mixing the different leverages. Among them, the control of the challenges by the host through its genetic resistance has shown its efficiency for some disease (resistance to scrapie, resistance to mastitis in dairy species) or is promising (resistance to parasites, resistance to footrot).

These guidelines on health and disease phenotypes are dedicated to any kind of health and disease resistance indicators. However, to start, we focus on the traits studied in SMARTER, which are the resistance to parasites and the resistance to footrot and mastitis in meat sheep and dairy sheep and goats.

==== Scope ====
This section on recording health and disease in sheep and goats starts following the task achieved in SMARTER and includes the following three sub-sections:

* Resistance to parasites
* Resistance to mastitis
* Resistance to footrot
=== Resistance to parasites ===

==== Definition, terminology, rationale ====
Resistance may be defined as the host’s ability to limit its parasite load (Råberg et al., 2007). The resistance to parasites described here corresponds to the resistance to gastro-intestinal nematodes (GIN). They are one of the main constraints for grazing sheep. They cause substantial economic losses due to lower production levels, the costs of anthelmintic treatments and the mortality of severely affected sheep. GIN control strategies mainly rely on treatment with anthelmintics. In many regions of the world, studies have reported the development of GIN resistance to most anthelmintic molecules due to their extensive use. Additionally, the possible presence of drug residues in animal products and the negative impact of these molecules on the micro and macro fauna of the soil are of concern. Therefore, sustainable GIN control may be a priority with schemes that do not only rely on anthelmintics but include complementary strategies such as nutritional supplementation with tannins and/or proteins, pasture management, and genetic selection of resistant animals. This latter strategy relies on the existence of genetic variation of host resistance to GIN both between and within breeds.

The faecal egg count (FEC), which is the number of parasite eggs per gram of faeces, is the most commonly used indicator to assess this resistance to GIN. In many countries, the selection for parasite resistance is based on FEC measures in natural infestation conditions under natural grazing conditions. As FEC measurements in sheep and goats are extremely costly and laborious, and because response to artificial challenges is highly correlated to response to natural infestation, it is therefore possible to implement a protocol of experimental infestation, as it is the case in France.

Beside FEC, different phenotypes can be used to measure resistance to GINs such as packed cell volume (PCV), FAffa MAlan CHArt (FAMACHA©) score, DAG score, immunological traits, and blood bepsinogen dosing (Shaw et al., 2012; Bishop, 2012; Bell et al., 2019; Sabatini et al., 2023).

==== Data recording ====

===== Indicators of parasite resistance or resilience =====

====== Faecal Egg Count ======
Faecal Egg Count (FEC) is the main indicator that measures the egg excretion intensity. It measures the number of parasite eggs per gram of faeces. This trait is related to the resistance of the animal (ability to limit the installation, the development and the prolificacy of the nematode inside the digestive tract (especially the abomasum). FEC is determined for each sample using a modified MacMaster technique (Whitlock, 1948 or Raynaud, 1970) with a sensitivity of 100 or 15 eggs per gram, respectively. The measure may be done in natural or in experimental infestation. FEC can be applied to one species (for example ''Haemonchus contortus'' (''Hc'')) or several species (including ''Hc'', ''Teladorsagia circumcincta'', ''Trichostrongylus colubriformis'', etc).

The distribution of the FEC has an asymmetric distribution (some high value, many low or medium value). A transformation must be applied to process a genetic analysis. The most frequent transformations are a root (fourth, third or square root) or a log transformation.

====== Packed Cell Volume ======
Packed Cell Volume (PCV) - Blood samples were collected in EDTA coated tubes and PCV values were determined individually by centrifugation in microhematocrit tubes with a relative centrifugal force of 9500 for 10 min.

PCV can be exploited as a single value of more relevantly as a gain/loss of PCV between two points. Variation of PCV is a relevant indicator of the resilience of the sheep / goat.

====== FAMACHA score ======
FAMACHA® score – As the anaemia provoked by some hematophagous parasites is at some stage visible on the mucosa (especially ocular mucosa), a scale of grading, based on the colour of the ocular mucosa, ranging from 1 (dark red mucosa) to 5 (white mucosa) has been built. This score was developed in South Africa to facilitate the clinical identification of anaemic sheep infected with H. contortus (Van Wyk and Bath, 2002).

As drawbacks, the FAMACHA® score does not allow to detect the non-hematophagous parasites and it appears quite belatedly: a FAMACHA® score over 3 concerns animals with a PCV below 20%. The method is not specific, anaemia being possibly caused by other reason than ''Haemonchus contortus''. It is however interesting to detect the anaemia. FAMACHA® score is related to the resilience of the sheep / goat.

====== DAG score ======
DAG score is an indicator for assessing dagginess, which measures faecal soiling in sheep. DAG score uses a 5-point or 6-point scoring scale ranging from 0 (no dags) to 5 or 6 (very daggy). Dag score scale shows the degree or extent of faecal contamination of the fleece.

The key is to be consistent when scoring a mob of sheep and for these sheep to have been run under similar conditions. Faecal contamination should not be confused with urine stain in ewe lambs and hoggets.

====== Immunological traits ======
Immunological and physiological profiles may be linked to phenotypes of resistance to parasites (strongyles). These new immunological and physiological profiles are blood lymphocytes cytokine production and serum levels of nematode parasite-specific Immunoglobulin A (IgA) that are produced upon whole blood stimulation. In SMARTER experiment in SRUC, blood was stimulated with pokeweed mitogen (a lectin that non-specifically activates lymphocytes irrespectively of their antigen specificity), and Teladorsagia circumcincta (T-ci) larval antigen to activate parasite-specific T lymphocytes.

Adaptive immune response may be determined by quantifying:

* cytokines interferon-gamma (IFN-γ), which relate to T-helper type 1 (Th1),
* interleukin IL-4, which relates to T-helper type 2 (Th2) and
* interleukin IL-10, which relate to regulatory T cell (Treg) responses.

Each immune trait displays a significant genetic variation (heritabilities ranging from 0.14 to 0.77). Heritability of IgA is moderate (0.41). Correlations with FEC are rather weak, from 0 to 0.27 but not significantly different from 0.

====== Blood Pepsinogen dosing ======
Blood pepsinogen is an indicator of the integrity of the gastric mucosa. The determination of serum pepsinogen is therefore a proxy in the diagnosis of abomasal strongylosis of ruminants (pepsinogen in blood is caused by an increase in the permeability of the abomasum mucosa due to presence of nematodes). There is a correlation between the concentration of pepsinogen in the blood and the number of worms harboured by the host.

===== Natural infestation =====

====== General considerations ======
Measurements (FEC or other proxies) are mainly undertaken in natural infestation under natural grazing conditions. In natural condition of infestation, frequency and amounts of yearly samplings have to be assessed according to the climate and epidemiological conditions and breeds. Local knowledge is essential for adjusting protocols to each country, as the level of infestation is strongly influenced by seasonality and the grazing system.

Several countries (e.g. Australia, New Zealand, and Uruguay), have incorporated the genetic evaluation of FEC at various ages into their national evaluation systems. In any case, in order to have data useful for the genetic evaluation, a representative sample of sheep in the flock involved in the selection scheme has to be periodically monitored to decide whether to sample the whole flock, i.e. when the number of infected animals and the level of infestation are considered sufficient to appreciate individual variability, individual FEC can be measured on the whole flock.

Further data related to environmental factors affecting the level of infestation should be recorded to be included in the genetic model for estimating the breeding values:

* Farm management mainly grazing system
* Birth type
* Sex
* Age of dam
* Parity
* Lambing date
* Sampling date
* Frequency, date, and molecule of anthelmintic administration

Additionally, stool cultures can be performed from the faecal samples taken (one per management group).

====== Description of the protocol and the measures (Uruguayan protocol) ======
At weaning, lambs undergo anthelmintic treatment, and their treatment efficacy is checked 8-14 days later through the analysis of FEC samples from 20 randomly selected lambs to confirm the absence of egg excretion. Subsequently, FEC is monitored every 15 days by collecting samples (based on epidemiological conditions) from 10-15% of lambs in each management group. The first individual FEC sampling is conducted when the FEC arithmetic mean exceeds 500 with no more than 20% samples exhibiting zero FEC. At this point, the lambs undergo anthelmintic treatment again, and their treatment efficacy is evaluated after 8-14 days. If the FEC mean remains above 500, a second individual sampling is conducted. Throughout the protocol, faecal egg counts (FEC1 and FEC2) are measured at the end of the first and second natural infestations. Generally, with some variations based on the breed, these samplings correspond to lambs at 9 and 11 months of age, respectively.

Currently, to simplify the protocol, only one sampling is conducted, and the control begins on a fixed date (early autumn) when the most significant parasite, H. contortus, prevails. Along with the FEC records (FEC1 and FEC2), other records, such as body weight, FAMACHA®, and body condition score, can also be taken.

===== Experimental infestation (French protocol) =====
As mentioned above, FEC measurements on sheep in commercial flocks are extremely costly and laborious. It has been shown that sheep that are selected on the basis of their response to artificial challenges respond similarly when exposed to natural infestation, and a high positive genetic correlation was estimated between FEC recorded under artificial or natural infestation. Moreover, it has been shown that selection of rams for parasite resistance after artificial challenges allows to improve the resistance of their female offspring for parasite infestation in natural conditions. Thus, an alternative approach may be to select rams gathered for AI progeny-testing or performance-testing by artificially infecting them with standardized doses of larvae.

In most cases, resistance to GIN is assessed in natural infestation conditions at grazing. However, the intensity of natural infestation in grazing animals depends on climatic conditions and may vary from season to season leading to over- or under-estimation of the genetic parameters of resistance. In France, sheep breeds are selected collectively on breeding stations and the strategy is to take advantage of this organization to implement the GIN control selection by phenotyping rams after experimental infestation. There are two main advantages. Firstly, a large diffusion of the genetic progress is expected via these rams, which are the future elite males. Secondly, the experimental infestation performed in control stations allow to evaluate these rams in homogeneous conditions (standardization of doses of infestation, farming conditions, climatic conditions, etc) during the ram evaluation period. Previous studies (Gruner et al., 2004) estimated high genetic correlations between resistances to experimental and natural infestation, between infestation by different parasite species (''Haemonchus contortus'' and ''Trichostrongylus colubriformis'') and between resistance in adult sheep and lambs. Moreover, recent works have shown that the genetic correlation between the resistance of rams in experimental conditions and the resistance of pregnant or milking ewes in natural conditions of GIN infestation are high.

====== Description of the protocol and the measures ======
An original protocol for phenotyping resistance to gastro-intestinal parasitism has been conceived and developed in France, targeted to rams (or bucks) gathered in a breeding centre or station, or an AI centre (Jacquiet et al., 2015; Aguerre et al., 2018). Males bred indoors, supposed to be naïve, are artificially infected twice with L3 larvae of a given strain of ''Haemonchus contortus'' susceptible to anthelminthic. Males are subjected to a first infestation (after a coprological examination be performed to confirm that no eggs were excreted before the artificial infestation) with a given dose of L3 larvae (D0). At D30, the males are phenotyped (FEC30 and possibly PCV30) and treated with an anthelminthic. After a 15-day recovery period, the rams are challenged again with a given dose of L3 larvae of Haemonchus contortus. At that time (D45), the efficacy of anthelmintic treatment is ensured in each male. Thirty days after (D75) the second challenge, the males are phenotyped (FEC30 and possibly PCV30) and treated again. The protocol lasts 2 and a half months. During the protocol, the measures carried out are as follows:

* faecal egg counts (FEC30 and FEC75) at the end of the first and second infestation (from faecal sample).
* packed cell volumes PCV0, PCV30, PCV45 and PCV75 at the start and the end of both infestation (from blood sample).

====== Calculation of variables ======
The FEC30 and FEC75 are used per se. Variations of PCV are calculated:

* PCV_loss_inf1 = PCV0-PCV30 (or ratio PCV30/PCV0)
* PCV_loss_inf2 = PCV45-PCV75 (or ratio PCV75/PCV45)
* PCV_recovery = PCV45-PCV0

where PCV_loss_inf1 and PCV_loss_inf2 represent the loss of PCV after each infestation, while PCV_recovery represents the males’ capacity to recover after the first infestation.

PCV variations might be interpreted as an indicator of resilience of the animal, i.e. its ability to maintain its blood parameters despite the parasitical challenge.

==== Use for genetic analysis / genetic evaluation ====

===== Model for genetic analysis =====
The genetic analysis of experimentally infected animals that are raised indoors may include:

* Fixed effects: contemporary group (mob x doses of larvae), age of animals (eg. 1 year, 2 years, 3years, 4 years and older)
* Random additive effect of the animals
* Residual effect

The genetic analysis of naturally infected animals that are raised outdoors may include:

* Fixed effects: they obviously will depend of the type of animals (females in lactation vs lambs/kids). They should include flock/herd, year x season (e.g. spring, summer, autumn, winter), anthelmintic treatments (e.g. eprinomectin, ivermectin, moxidectin …) in interaction with the number of days between the date of treatment and the sampling date (e.g. less than 70 days, between 70 and 100 days, more than 100 days). For females in lactation: age and/or parity, litter size before lactation (single or multiple new-born lambs). For lambs or kids: age of the dam, type of birth or rearing, and age at the time of the records, expressed in day.
* Random additive effect of the animals
* Random permanent environment effect if repeated measures (e.g. for FEC 1 & 2)
* Residual effect

===== Genetic parameters =====
Pooled estimates of heritability to resistance to gastrointestinal parasites gained by meta-analysis (Mucha et al., 2022) in dairy goats and sheep and meat sheep are shown in Tables 1 and 2, while Table 3 shows the heritabilities estimated for the experimentally infected rams. In addition, we mention a paper from Casu et al (2022) in which a heritability of 0.21 for FEC was found in a 20 year follow-up study in an experimental flock in Sardinia, Italy.

{| class="wikitable"
| colspan="7" |'''''Table 1. Pooled estimates of heritability of resistance to gastrointestinal parasites from meta- analysis in dairy goats and sheep.'''''
|-
|'''Trait1'''
|'''Species'''
|'''Pooled h2(±SE)'''
|'''Min2 h2'''
|'''Max3 h2'''
|'''N obs'''
|'''N studies'''
|-
| rowspan="2" |FEC
|Goats
|0.07 ± 0.01
|0.04
|0.15
|8
|2
|-
|Sheep
|0.14 ± 0.04
|0.09
|0.35
|6
|3
|}
1Trait: FEC – faecal egg count

2Minimum h2 from individual studies included in meta-analysis

3Maximum h2 from individual studies included in meta-analysis

{| class="wikitable"
| colspan="6" |'''''Table 2. Pooled estimates of heritability of resistance to gastrointestinal parasites from meta- analysis in meat sheep (Mucha et al., 2022).'''''
|-
|'''Trait1'''
|'''Pooled h2 (±SE)'''
|'''Min2 h2'''
|'''Max3 h2'''
|'''N obs'''
|'''N studies'''
|-
|DAG
|0.30±0.06
|0.06
|0.63
|37
|15
|-
|FCons
|0.14±0.02
|0.03
|0.27
|13
|5
|-
|NBW4
|0.10±0.02
|0.00
|0.54
|11
|3
|-
|Par-Ab
|0.18±0.07
|0.05
|0.29
|6
|3
|-
|Par-Ig
|0.36±0.06
|0.13
|0.67
|24
|8
|-
|FEC
|0.29±0.03
|0.00
|0.82
|118
|32
|-
|HC
|0.32±0.14
|0.08
|0.56
|5
|2
|}
1Trait: DAG – dagginess, FCons – faecal consistency, NBW – number of worms, Par-Ab – parasitism anitbodies, Par-Ig – parasitism immunoglobulin, FEC –faecal egg count, HC - Haematocrit

2Minimum h2 from individual studies included in meta-analysis

3Maximum h2 from individual studies included in meta-analysis

4Pooled heritability obtained from a simple random effects model as the three level meta-analysis model did not converge

{| class="wikitable"
| colspan="2" |'''''Table 3. Estimates of heritability of resistance to gastrointestinal parasites from meta-analysis in dairy sheep in experimental infestations (Aguerre et al., 2018)'''''
|-
|'''Trait1'''
|'''h2'''
|-
|Root FEC_inf1
|0.14±0.04
|-
|RootFEC_inf2
|0.35±0.08
|-
|PCV_loss_inf1
|0.24±0.05
|-
|PCV_loss_inf2
|0.18±0.06
|-
|PCV-recovery
|0.16±0.06
|}

=== Resistance to mastitis ===

==== Definition, terminology, rationale ====
In small ruminants, mastitis mainly consists in subclinical infections caused by coagulase- negative staphylococci, which is much more frequent than clinical mastitis (Bergonier et al., 2003). Under these conditions, somatic cell count (SCC) is an accurate, indirect measure to predict mammary gland infection. Therefore, SCC could be used as an indirect selection criterion for mastitis resistance as is widely done in dairy cattle. Moreover, selection for mastitis resistance in dairy sheep and goats could mainly focus on selection against subclinical mastitis using SCC, considering the low incidence of clinical cases in these species (<5%), compared to dairy cattle for which clinical cases occur frequently (Bergonier et al., 2003).

Clinical mastitis is not recorded in dairy small ruminants, mainly because of its low incidence and because SCC is a relevant and simple indicator of intra-mammary infections. Work completed in France has developed two lines of ewes (experimental farm INRAE-La Fage) and goat (experimental farm INRAE-Bourges), a high line generated from sires with unfavourable EBVs for somatic cells and a low line generated from sires with favourable EBVs for somatic cells. For both sheep (Rupp et al., 2009) and goats (Rupp et al., 2019), the low line has the lowest SCC, the lowest incidence of clinical mastitis and the lowest incidence of chronic mastitis (abscesses or unbalanced udder) and subclinical mastitis (assessed by milk bacteriology).

Even though SCC is the established indicator for use in animal breeding, the use of the California Milk test (CMT) is a very good indicator of SCC for monitoring udder health in flock/herd management in dairy and meat-producing small ruminants.

==== Data recording ====

===== Somatic Cell Count (SCC) =====
Large scale somatic cell counting relies on the application of routine methods, such as fluoro- opto-electronic counting. The somatic cell counter must be properly calibrated against a reference and laboratories must frequently verify the calibration settings are still correct.

The design for recording SCC will depend upon the objective. For flock/herd management related to high bulk SCC, the whole flock/herd should be sampled and analysed to identify the animals with the highest SCC. For genetic purpose, simplified designs might be implemented.

In dairy species, somatic cell counting is achieved within the milk recording design and the sampling design, as for milk components such as fat and protein content. As in small ruminants, most of the designs are simplified ones compared to the A4 method (all daily milkings recorded, once a month) (see [[Section 16 – Dairy Sheep and Goats|ICAR Guidelines Section 16: dairy sheep and goats]]), SCC are quite often available at one out of the two daily milkings. In this case, use of SCC must be handled accordingly.

As for milk composition, with the aim of simplifying and decreasing further the cost of recording, it is possible/recommended to measure SCC on only a part of the flock/herd (first parity or first two parities). It is also possible to go further in the simplification of the design, for example by sampling only a part of the lactation within a part-lactation sampling as proposed in the [[Section 16 – Dairy Sheep and Goats|section 16 of the ICAR Guidelines]]. The genetic parameters of test-day and lactation mean for Somatic Cell Score (SCS - log-transformed SCC) show that the records of the middle of the lactation appear as the most representative of the whole lactation. Two to four individual samples per female and per lactation, collected monthly in the middle part of the lactation are highly correlated (around 0.98) with SCS determined from samples collected over the complete lactation (A4 method) but are hardly less heritable compared with the A4 homologous traits (negligible loss of precision for SCS) (Astruc and Barillet, 2004). The balance between cost and genetic efficiency, depending on the genetic correlations close to 1, is clearly in favour of the part-lactation sampling compared to A4 testing.

===== California Mastitis Test (CMT) =====
The California mastitis test is an animal-side diagnostic test that provides an estimate of the level of infection within a mammary gland. A sample of milk (~3ml) from each udder half is combined with an equal volume of reagent in a CMT paddle and mixed for 15 to 20 seconds. The reaction is scored based on the level of thickening of the mixture from zero (no thickening) consistent with no, or low, levels of infection, to four (gel formation with elevated surface) indicating high levels of infection.

A previous study (McLaren et al., 2018) has demonstrated the strong correlation between CMT score and SCC from samples collected from pedigree meat sheep in the UK.

==== Calculation of traits ====
Test-day SCC must be transformed to Somatic Cell Score (SCS) by the logarithmic transformation of Ali and Shook (1980) to achieve normality of distribution.

Example: SCS = log2+(SCC/100,000)+ 3

The table 4 gives correspondence between SCC and SCS

{| class="wikitable"
| colspan="2" |'''''Table 4. Correspondence between somatic cell score and somatic cell count'''''
|-
|'''Somatic Cell Count (SCC)'''
|'''Somatic Cell Score (SCS)'''
|-
|12,500
|0
|-
|25,000
|1
|-
|50,000
|2
|-
|100,000
|3
|-
|200,000
|4
|-
|400,000
|5
|-
|800,000
|6
|-
|1,600,000
|7
|}
SCS can be adjusted for days-in-milk (DIM). In this case, the adjustment procedure must be defined from a study based on healthy ewes/goats with enough number of test-days over the lactation. Then a lactation SCS (LSCS) may be calculated (case of lactation model in genetic evaluation).

LSCS can be computed as the weighted arithmetic mean of test-day SCS (adjusted or not for DIM). Weights are either 1 (equivalent to no weight) or r2, where r is the correlation between one measure and the mean of all other records.

==== Use for genetic analysis / genetic evaluation ====

===== Genetic model =====
The genetic model might include the following fixed effects:

* Flock x year (x parity)
* Month of lambing/kidding
* Age at lambing/kidding
* Number of lambs/kids born

===== Genetic parameters =====
Pooled estimates of heritability of somatic cell score gained by meta-analysis (Mucha et al., 2022) in dairy goats and sheep and meat sheep are shown in Table 5.

{| class="wikitable"
| colspan="7" |'''''Table 5. Pooled estimates of heritability of somatic cell score from meta-analysis in dairy goats and sheep (Mucha et al., 2022)'''''
|-
|'''Trait1'''
|'''Species'''
|'''Pooled h2 (± SE)'''
|'''Min2 h2'''
|'''Max3 h2'''
|'''N obs'''
|'''N studies'''
|-
|SCS
|Goats

Sheep
|0.21±0.01

0.13±0.02
|0.19

0.03
|0.24

0.27
|5

29
|3

22
|}
1Trait: SCS – somatic cell score

2Minimum h2 from individual studies included in meta-analysis

3Maximum h2 from individual studies included in meta-analysis

{| class="wikitable"
| colspan="6" |'''''Table 6. Pooled estimates of genetic correlations (rg) between resilience (SCS, FEC) and efficiency (MY, FC, PC) traits from meta-analysis in dairy goats (Mucha et al., 2022)'''''
|-
|'''Traits1'''
|'''Pooled rg (± SE)'''
|'''Min2 rg'''
|'''Max3 rg'''
|'''N obs'''
|'''N studies'''
|-
|SCS & MY
|0.35±0.31ns
|0.00
|0.59
|3
|3
|-
|SCS & FC4
| -0.19±0.01
| -0.20
| -0.18
|3
|2
|-
|SCS & PC
| -0.06±0.05ns
| -0.13
|0.00
|3
|2
|-
|FEC & MY
|0.17±0.35ns
| -0.21
|0.63
|4
|2
|}
1Traits: SCS – somatic cell score, FEC – faecal egg count, MY – milk yield, FC – fat content, PC – protein content

2Minimum rg from individual studies included in meta-analysis.

3Mmaximum rg from individual studies included in meta-analysis.

nspooled correlations obtained from a simple random effects model as the three level meta-analysis model did not converge

{| class="wikitable"
| colspan="6" |'''''Table 7. Pooled estimates of genetic correlations between resilience (SCS) and efficiency (MY, FY, PY, FC, PC) traits from meta-analysis in dairy sheep (Mucha et al., 2022)'''''
|-
|Traits1
|Pooled rg (± SE)
|Min2 rg
|Max3 rg
|N obs
|N studies
|-
|SCS & MY
| -0.05±0.10ns
| -0.30
|0.23
|16
|11
|-
|SCS & FC
|0.04±0.05ns
| -0.16
|0.16
|8
|8
|-
|SCS & PC
|0.12±0.03
|0.02
|0.24
|12
|9
|-
|SCS & FY
|0.11±0.15ns
| -0.04
|0.31
|4
|4
|-
|SCS & PY
|0.17±0.10ns
|0.06
|0.31
|4
|4
|}
1Traits: SCS – somatic cell score, MY – milk yield, FY – fat yield, PY – protein yield, FC – fat content, PC – protein content

2Minimum rg from individual studies included in meta-analysis

3Maximum rg from individual studies included in meta-analysis

nsPooled correlations obtained from a simple random effects model as the three level meta-analysis model did not converge

ns – Pooled estimate did not differ significantly from zero

{| class="wikitable"
| colspan="5" |'''''Table 8. Estimates of heritability of somatic cell score, clinical mastitis and CMT in meat and dairy and meat sheep (source Oget et al., 2019)'''''
|-
|'''Sheep'''
|'''Breed'''
|'''Trait1'''
|'''Heritability (±SE)'''
|'''Reference'''
|-
|Dairy
|Chios
|CMT
|0.12±0.06
|Banos et al., 2017
|-
|Meat
|Belclare, Charollais, Suffolk, Texel,

Vendeen breeds
|CM
|0.04±0.03

|O’Brien et al., 2017
|-
|Meat
|Texel
|SCS
|0.11±0.04
|McLaren et al., 2018
|-
|Meat
|Texel
|CMT
|0.08-0.09±0.04
|McLaren et al., 2018
|-
|Meat
|Texel
|CMT
|0.07
|Kaseja et al., 2023 submitted paper (SMARTER, D2.3)

|}
1CMT - California mastitis test, CM - Clinical mastitis (examination and palpation of the udder), SCS – Somatic Cell Score

=== Resistance to footrot ===

==== Definition, terminology, rationale ====
Footrot is caused by ''Dichelobacter nodosus'' and is a major cause of lameness in sheep. The disease is highly contagious and endemic in many countries that causes pain and welfare issues in affected animals. In addition to the direct impacts on time and veterinary / medicine costs, the disease has further, indirect, impacts through reducing fertility and milk supply.

The presence of footrot is assessed by inspection of the hooves of lame animals.

==== Data recording ====

===== Scoring methods =====
Each hoof is assessed individually and scored based on the five-point scale (used in UK): clean, unaffected hoof (score 0), mild inter-digital inflammation (score 1), inter-digital necrosis (score 2), under-running of the sole of the hoof (score 3) and fully under-run to the abaxial wall of the hoof (score 4) (Conington et al., 2008).

The sum of scores is calculated by adding all four scores (for each hoof), hence the animal can obtain the phenotype in a range from zero to 16.

In France, where footrot is usually not recorded, a simplified scoring system has been developed using a scale (0 normal and severity of lesions scored from 1 to 3) adapted from the Victorian Farmers Federation and Coopers Animal Health.

Additionally, the health of feet is assessed in France and the UK for other important hoof lesions including white line degeneration, contagious ovine digital dermatitis, horn growth, presence of abscess, granuloma, interdigital hyperplasia, and panaritium).

==== Calculation of traits ====
Sum of scores are log-transformed in order to normalise the data using the formula ln(Sum of scores + 1). The addition of one prevents to logarithm the value of sum of scores equal to zero. Each animal can obtain transformed score ranging between zero and 2.83.

==== Use for genetic analysis / genetic evaluation ====

===== Genetic model =====
The genetic model might include the following fixed effects:

* Age of the dam
* Scorer (if more than one)
* Vaccine status (if some animals treated with the vaccination against ovine foot-rot)
* Flock or Flock x Year interaction

===== Genetic parameters =====
The estimated heritability for UK meat sheep (Table 9) varies between 0.12 (Nieuwhof et al., 2008). to 0.23 (Kaseja et al., 2023, unpublished results)
Table 9. Estimates of heritability of resistance to footrot in meat sheep breeds.

{| class="wikitable"
| colspan="4" |'''''Table 9. Estimates of heritability of resistance to footrot in meat sheep breeds.'''''
|-
|'''Breed'''
|'''Trait1'''
|'''Heritability (SE)'''
|'''Reference'''
|-
|Texel
|RF
|0.12(0.02)
|Kaseja et al, 2023 in press
|-
|Scottish Blackface
|CM
|0.19 to 0.23
|Kaseja et al., 2023 in press.
|-
|Scottish lambs
|SCS
|0.12
|Nieuwhof et al., 2008
|-
|Texel
|CMT
|0.18
|Mucha et al., 2015
|}
1RF - Resistance to footrot, CM - Clinical mastitis (examination and palpation of the udder), SCS – Somatic Cell Score, CMT - California mastitis test

=== Acknowledgements ===
We gratefully acknowledge the contributions to small ruminant health and disease guideline by the following people:

* Joanne Conington, SRUC, the UK
* Jean-Michel Astruc, IDELE, France
* Rachel Rupp, INRAE, France
* Beat Bapst, Qualitas AG, Switzerland
* Donagh Berry, TEAGASC, Ireland
* Beatriz Carracelas, INIA, Uruguay
* Antonello Carta, Agris Sardegna, Italy
* Gabriel Ciappesoni, INIA, Uruguay
* Arnaud Delpeuch, IDELE, France
* Frédéric Douhart, INRAE, France
* Karolina Kaseja, SRUC, the UK
* Ed Smith, The British Texel Sheep Society, the UK
* Flavie Tortereau, INRAE, France
* Stefen Werne, FiBL, Switzerland
* Marija Špehar, Centre for Livestock Breeding Zagreb, Croatia

This work received funding from the European Unions’ Horizon 2020 Research & Innovation program under grant agreement N°772787—SMARTER.

This work also used deliverable from the Eurosheep project (Horizon 2020 under agreement N° 863056).

=== References ===
Aguerre, S., Jacquiet, P., Brodier, H., Bournazel, J.P., Grisez, C., Prévot, F., Michot, L., Fidelle, F., Astruc, J.M., Moreno, C.R. (2018). Resistance to gastrointestinal nematodes in dairy sheep: Genetic variability and relevance of artificial infection of nucleus rams to select for resistant ewes on farms. Vet. Parasitol. 256:16-23. <nowiki>https://doi.org/10.1016/j.vetpar.2018.04.004</nowiki>

Ali, A., Shook, G. (1980). An optimum transformation for somatic cell concentration in milk. J. Dairy Sci. 63:487-490.

Astruc J.M., Barillet F. (2004). Current challenge for milk recording in dairy sheep and goats: the simplification of milk sampling design for chemical composition and somatic cell counts of milk. Proceedings of the 34th ICAR session, Sousse, Tunisia, 31 May-3 June 2004.

Banos, G., Bramis, G., Bush, S.J., Clark, E.L., McCulloch, M.E.B., Smith, J., Schulze, G., Arsenos, G., Hume, D.A., Psifidi, A. (2017). The genomic architecture of mastitis resistance in dairy sheep. BMC Genom. 18 624.

Bell, A., McNally, J., Smith, D.V., Rahman, A., Hunt, P., Kotze, A.C., Dominik, S., Ingham, A. (2019). Quantification of Differences in Resistance to Gastrointestinal Nematode Infections in Sheep Using a Multivariate Blood Parameter. Vet. Parasitol. 270:31–39.

Bishop, S.C. (2012). Possibilities to Breed for Resistance to Nematode Parasite Infections in Small Ruminants in Tropical Production Systems. Animal., 6:741–747.

Bergonier, D., Crémoux, R. de, Rupp, R., Lagriffoul, G., Berthelot, X. (2003). Mastitis of dairy small ruminants. Vet. Res. 34:689–716.

Casu, S., Usai, M.G., Sechi, T. et al. Association analysis and functional annotation of imputed sequence data within genomic regions influencing resistance to gastro-intestinal parasites detected by an LDLA approach in a nucleus flock of Sarda dairy sheep. Genet Sel Evol 54, 2 (2022). <nowiki>https://doi.org/10.1186/s12711-021-00690-7</nowiki>

Conington, J., Hosie, B., Nieuwhof, G.J., Bishop, S.C., Bünger, L. (2008). Breeding for resistance to footrot- the use of hoof lesion scoring to quantify footrot in sheep. Vet. Res. Commun. 32(8):583-9. <nowiki>https://doi.org/10.1007/s11259-008-9062-x</nowiki>

Gruner, L., Bouix, J., Brunel, J.C. (2004). High genetic correlation between resistance to Haemonchus contortus and to Trichostrongylus colubriformis in INRA 401 sheep. Vet. Parasitol. 119:51–58.

Jacquiet, P., Salle, G., Grisez, C., Prevot, F., Lienard, E., Astruc, J.M, Francois, D., Moreno, C. (2015). Selection of sheep for resistance to gastro-intestinal nematodes in France: where are we and where are we going? 25th International Conference of the WAAVP, Liverpool, UK, 2015, 16-20 August

McLaren, A., Kaseja, K., Yates, J., Mucha, S., Lambe, N.R., Conington, J.(2018). New mastitis phenotypes suitable for genomic selection in meat sheep and their genetic relationships with udder conformation and lamb live weights. Animal. 12(12):2470-2479. doi: 10.1017/S1751731118000393.

Mucha, S., Bunger, L., Conington, J. (2015). Genome-wide association study of footrot in Texel sheep. Genetics Selection Evolution, 47 (1), pp.35. DOI 10.1186/s12711-015-0119-3

Mucha, S., Tortereau, F., Doeschl-Wilson, A., Rupp R., Conington, J. (2022). Animal Board Invited Review: Meta-analysis of genetic parameters for resilience and efficiency traits in goats and sheep. Animal. 16(3):100456. <nowiki>https://doi.org/10.1016/j.animal.2022.100456</nowiki>.

Nieuwhof, G.J., Conington, J., Bünger, L., Haresign, W., Bishop, S.C. (2008). Genetic and phenotypic aspects of resistance to footrot in sheep of different breeds and ages. Animal. 2(9):1289-1296. <nowiki>https://doi.org/10.1017/S1751731108002577</nowiki>

O’Brien, A.C., McHugh, N., Wall, E., Pabiou, T., McDermott, K., Randles, S., Fair, S., Berry, D.P. (2017). Genetic parameters for lameness, mastitis and dagginess in a multi-breed sheep population. Animal 11, 911–919. DOI: 10.1017/S1751731116002445

Oget, C., Tosser-Klopp, G., Rupp, R. (2019). Genetic and genomic studies in ovine mastitis. Small Ruminant Research 176, 55-64. <nowiki>https://doi.org/10.1016/j.smallrumres.2019.05.011</nowiki>.

Råberg, L, Sim, D., Read, A.F. (2007). ‘Disentangling genetic variation for resistance and tolerance to infectious diseases in animals’, Science. 318(5851), 812-814 DOI: 10.1126/science.1148526

Raynaud J.P. (1970). Etude de l’efficacité d’une technique de coproscopie quantitative pour le diagnostic de routine et le controle des infestations parasitaires des bovins, ovins, equines et porcins. Ann. Parasitol. 45: 321–342

Rupp, R., Bergonier, D., Dion, S., Hygonenq, M.C., Aurel, M.R., Robert-Granié, C., Foucras, G. (2009). Response to somatic cell count-based selection for mastitis resistance in a divergent selection experiment in sheep. J. Dairy Sci. 92, 1203–1219.

Rupp, R., Huau, C., Caillat, H., Fassier, T, Bouvier, F., Pampouille, E., Clément, V., Palhière, I., Larroque, H., Tosser-Klopp, G., Jacquiet, P., Rainard, P. (2019). Divergent selection on milk somatic cell count in goats improves udder health and milk quality with no effect on nematode resistance. J Dairy Sci. 102(6):5242-5253. doi: 10.3168/jds.2018-15664.

Sabatini, G.A., de Almeida Borges, F., Claerebout, E. et al. (2023). Practical guide to the diagnostics of ruminant gastrointestinal nematodes, liver fluke and lungworm infection: interpretation and usability of results. Parasit. Vectors. 16, 58. <nowiki>https://doi.org/10.1186/s13071-023-05680-w</nowiki>

Shaw, R.J., Morris, C.A., Wheeler, M., Tate, M., Sutherland, I.A. (2012). Salivary IgA: A Suitable Measure of Immunity to Gastrointestinal Nematodes in Sheep. Vet. Parasitol. 186, 109–117

Van Wyk, J.A., Bath, G.F. (2002). The FAMACHA© system for managing haemonchosis in sheep and goats by clinically identifying individual animals for treatment. Vet. Res. 33:509–529.

Whitlock, H.V. (1948). Some modifications of the McMaster helminth egg counting technique and apparatus. J. Coun. Sci. Ind. Res. 21:177.

=== Annexes ===
[[File:Annex 1 Famacha.jpg|center|thumb|600x600px|Picture of FAMACHA score (source FiBL – Qualitas)]]

[[File:Annex 2 Farmacha 2.jpg|center|thumb|600x600px|Picture of FAMACHA score (source FiBL – Qualitas)]][[File:Annex 3 Uruguayan protocol of natural infestation.jpg|center|thumb|800x800px|Uruguayan protocol of natural infestation for recording the resistance to gastrointestinal parasites]]
[[File:Annex 4 French protocol for phenotyping the resistance.jpg|center|thumb|600x600px|French protocol for phenotyping the resistance to gastrointestinal parasites]]

== Guidelines on recording lifetime resilience in sheep and goats ==
'''Change Summary'''
{| class="wikitable"
|'''Date of change'''
|'''Nature of Change'''
|-
|October 2024
|First version
|-
|November 26th 2024
|Comments made by JC
|-
|December 23rd 2024
|Comments made by MS
|}

=== Introduction and scope ===

==== Introduction ====
Lifetime resilience is often tackled through longevity and aspects of productive longevity. Longevity is a trait to quantify productive lifespan of livestock, and for increasing durability and profitability of farms. In dairy ruminants, longevity definitions include: (i) true longevity (all culling reasons, including milk productivity); and (ii) functional longevity (all culling reasons, except voluntary productivity, such as milk productivity or growth). Functional longevity (corrected for production level – milk, growth) reflects the animals’ accumulated ability to overcome health and nutritional challenges. It is an indirect global approach to quantify adaptive capacity to various production environments. Different indicators may be calculated. One indicator is the length of productive life which is computed as the time interval (in days) between first lambing/kidding and culling. Longevity is linked with various predictors, such as fertility, udder health and conformation, resistance to disease, body condition score changes across ewe/doe lifetime. These predictors may be used in breeding program to get an earlier breeding value of longevity and may help to manage and monitor lifetime resilience at the farmer level.

==== Scope ====
The scope of these guidelines is to define approaches for the definition of longevity as well as the traits that can be calculated, and the downstream analyses that can be set up (including the use of early predictors to enhance longevity in the evaluation process).

To propose a grid for setting up an observation of the culling causes.

=== Longevity ===

==== Definition, terminology, rationale ====
The notion of longevity can cover several meanings. Longevity can be understood as the true longevity, i.e. the ability of the animal to live as long as possible, whatever its production level and its functional characteristics. Animal longevity also depends on the replacement rate which is often a choice of the breeders. Animals may be culled due to production level such as milk production or growth or fat/muscle depth, leading to ’voluntary’ culling (i.e. an animal is culled because we '''want''' to do it). In contrast, ‘involuntary culling’ is defined as an animal having to leave the flock or herd due to illness / accident/ functional disability etc (i.e. they are culled because we '''have''' to do it)

Involuntary culling may be due to:

* Udder health problem (clinical, subclinical, chronic mastitis).
* Lack of resistance to disease such as parasites.
* Problem of footrot.
* Unfavourable shape of the udder (lack of adaptation to machine milking or to suckling).
* Unfavourable general conformation.
* Undesired behaviour (temperament in the milking parlour).
* Infertility or any problem of reproduction.
* Problem of feet or legs, lameness.
* Lack or excess of body tissue mobilisation.

any other undesirable aspect associated with the animal’s inability to produce. Voluntary culling may be due to:

* Low productivity,
* Management decision to cull for age,
* Management decision to cull for a specific coat colour / other phenotype that does not meet the type desired,
* Farmer doesn’t like the animal,
* Economic reason to reduce the number of breeding animals in the flock/herd.

Even if some of these reasons for culling may be considered per se in the selection process by phenotyping and evaluating related traits (for example resistance to mastitis, resistance to gastro- intestinal parasite, fertility, udder morphology), it is often not possible to account for all of them. If properly modelled, functional longevity may be considered as a global and composite approach, allowing to assess the sustainability of the population in selection and of the practiced selection.

For this, different traits may be considered, quite often they are relatively easy to compute with data usually already existing in the genetic database (ex. length of productive life, which can be calculated as the culling date minus the date of the first lambing). There is no additional recording to set up. The difficulties in handling functional longevity are related to the modelling of the trait, given that the trait is fully known when the animal is culled. When not yet culled, the model to set up are quite complex. An example of this was reported by Brotherstone et al. (1997) for dairy cattle and Conington et al. (2004) for hill sheep, whereby live animals’ EBVs for longevity are based on their probability of survival at a given age combined with actual cull dates of relatives that became breeding females in the flock.

Even though there is no need to identify/know the cause of culling, the knowledge of the cause of culling might be a relevant observation of the hierarchy of the culling cause, which may lead to put an emphasis on some specific issue. For example, if we observe an increase in some culling causes (let’s say parasitism) this should lead to a deliberate selection programme to breed more resistant animals to parasites.

One drawback of the functional longevity trait is its lack of precocity. As stated above, it is necessary to have the date of culling or to have accumulated enough lactation to compute the trait. And an appropriate model (e.g. survival analysis) can only partially disentangle this difficulty. It is possible to address this issue by running a multi trait genetic evaluation model combining the longevity trait and some other proxy traits (such as udder morphology, udder health, etc). The use of Genomic Selection is a complementary way to generate early prediction of genetic merit for longevity, provided there is good accuracy of the EBVs of animals in the associated reference population.

==== Data recording ====

===== Longevity traits =====
The table 1 presents some criteria commonly used in small ruminants to measure longevity. Here, the criteria deal with true longevity, the only one measurable in herd/flocks. Functional longevity will be estimated later, at the statistical analysis step. Table 1 also shows the data required for calculating the longevity criteria. For example, the length of productive life is referred to as the difference between the time a female enters the breeding flock/herd and the date she exits it due to being culled or dying. It is important to notice that the culling date, which is rarely recorded by the farmers, can be replaced by the date of the last event registered for the animal (for example, date of the last performance recording, or of the last reproduction event).

{| class="wikitable"
| colspan="3" |'''Table 1. Definition of some commonly used longevity criteria.'''
|-
|'''Longevity criteria'''
|'''Raw data required'''
|'''Calculation'''
|-
|Length of total lifespan (LTL)
|Birth date (BD)
Culling or death date (CD)
|LTL= CD - BD
in days (or months or years)
|-
|Length of productive life (LPL)
|First lambing/kidding date (FKD)
Culling or death date (CD)
|LPL = CD – FKD
in days (or months or years)
|-
|Total number of days in production (NDL)
|Days in milk per lactation (DIM)
or
Lambing/kidding date + dry off date for each lactation
|NDL = ∑ DIM
|-
|Number of lactations (NLACT)
|Each lambing/kidding event (KE)
|NLACT = ∑ KE
|-
|Number of lambs or kids during lifetime (NLAMB)
|Prolificacy at each lambing/kidding (PR). This may or may not include no. lambs born dead + no. lambs born alive
|NLAMB = ∑ PR
|}
The length of total lifespan can be estimated easily, with only two variables usually registered by farmers. The difference with the length of productive life is that it considers the period when animals had the first lambing/kidding as well as the lambing/kidding interval. If the age at the first lambing/kidding and the lambing/kidding interval are similar between animals, the length of total lifespan will be very close to the length of productive life.

The total number of days in production only covers the “useful” life of the females because it doesn’t include the unproductive periods (such as dry off or large lambing/kidding interval after reproduction failure), compared to length of productive life. But the number of variables necessary to compute it is larger.

For the total number of lambs or kids during a lifetime, it is necessary to include all live-born lambs/kids only or those reared to weaning, if these data are routinely recorded.

==== Calculation of traits ====
The last column of Table 1 indicates how to calculate the different longevity criteria, from the raw variables.

The length of total lifespan and the length of productive life are estimated as differences in days between two dates: i) the culling date and ii) the birth date or the first lambing/kidding date, respectively. The total number of days in production corresponds to the sum of the days in milk of each lactation of the female. For the last two criteria (number of lactations or number of lambs/kids), the estimation corresponds to cumulative performance across lifetime.

Instead of waiting for the end of the animal's life to calculate the longevity criterion (which is sometimes long), one solution deals with limiting the animal career to a maximum number of years or lactations. For example, the length of productive life can be calculated only on the first 6 lactations. Subsequently, the length of productive life will be defined as the total number of days between the first lambing/kidding and the end of the 6th lactation. In the same way, the total number of lambs/kids can be estimated at a fixed age, 8 years old for example.

==== Use for genetic analysis / genetic evaluation. ====

===== Models =====
The genetic ability for longevity is evaluated via the functional longevity, i.e. the true longevity corrected for production traits. Functional longevity is defined at this step, by integrating the level of production as fixed effect in the analysis of longevity criteria described in Table 1.

Different methods are used for the genetic evaluation of longevity traits.

The first method is based on linear models. The main advantage of these models is their ease of implementation because they are used for most of the traits under selection. But they have different drawbacks regarding longevity:

* they do not fit well longevity because longevity indicators do not follow a normal distribution
* they consider only animals that have finished their productive life (unless separate predictors are used). This has two consequences: the longevity data are skewed if living animals are ignored; the breeding value is available lately in the life of the animals. This is notably the case for males for whom most of their offspring must be culled to be evaluated.
* they are not able to include time-dependant variables (e.g. parity, lactation stage). Time dependant variables are useful to take into account the changes in breeding conditions that occur during the life of the animal, and thus to better model longevity data.

The second method is based on proportional hazard model or survival analysis. This type of model counterbalances all the drawbacks of linear models and thus, are the best ones to estimate breeding values for functional longevity. Nevertheless, they are complicated to implement in a routine genetic evaluation process, and a few software exist for genetic survival analyses such as Survival kit, (Ducrocq et al, 2005). However, an evaluation based on an animal model is not feasible in large dataset, leading to use sire-maternal grand-sire models or sire models. Under this assumption, ewes/does EBVs are not available (Ducrocq, 2001).

A third method, less widespread, considers the first three lactations as separate traits in a multiple trait animal linear model. Each lactation is assigned to 1 (instead of 0) once the female reaches the next lactation.

===== Factors of variation =====
The main factors of variation of longevity data are:

* herd/flock
* year
* kidding/lambing season
* birth season
* age at first lambing/kidding
* breed
* herd/flock size and herd/flock size variation
* lactation stage, parity (if survival analysis model)
* number of lambs/kids born and reared (for meat sheep and goats)
* within herd/flock production level: this factor of variation is essential to integrate to estimate the functional longevity. Usually, it is the within herd/flock level of production (and not the absolute level of production) that is considered because it explains the decision of the breeder to cull the animal.

===== Heritabilities of functional longevity =====
Heritabilities range between 5% and 17% (Sasaki, 2013, Castañeda-Bustos et al., 2014, Geddes et al., 2017, Palhière et al (2018), Buisson et al (2022), Pineda-Quiroga & Ugarte, 2022) indicating that this trait has a low to moderate genetic background. This might be due to the composite signification of longevity, which represents a synthesis of various abilities (see § on predictors).

However, the genetic variation coefficients are moderate suggesting that a genetic variability may be exploited to set up a selection programme.

===== Genetic correlations =====
The genetic correlations between functional longevity and other traits are:

* close to 0 for milk production traits. This results from the model, in which longevity is corrected for level of production,
* from 0 to 0.40 for udder type traits (Castañeda-Bustos et al., 2014). The rear udder attachment and the udder floor position are the most correlated to functional longevity,
* from 0.20 to 0.50 for general conformation,
* from 0.01 to 0.15 for reproduction traits (kidding interval, age at first kidding, artificial insemination fertility),
* from -0.15 and -0.40 for somatic cell counts.

===== EBVs and reliabilities =====
For dairy animals, because of the low accuracy of breeding values, only males (and especially artificial insemination males) evaluated from the longevity data of their daughters, have EBVs that can be used for selection. A minimum number of daughters culled per sire is required to reach a sufficient accuracy. The consequence is that the AI males get their first longevity EBV quite late in their life. Survival analysis models, because they consider censored data (living daughters), enable better accuracy and thus, an earlier EBV for AI males.

Other strategies are possible to increase the accuracy of functional longevity EBVs:

* introduce genomic information in the genetic evaluation
* use a multiple trait model, including both functional longevity and other traits considered as predictors of longevity listed below.

Given the low heritability of survival traits, the fact that it is expressed late in life (at death or culling), the trait becomes accurate enough when sufficient information on culling or reproduction/lactation is available. It is necessary to enhance direct evaluations by indirect information coming from early predictors. Some relevant predictors are listed below:

* Morphological traits, such as general conformation or udder morphology (especially in dairy species),
* Reproduction traits (fertility, lambing/kidding interval, age at first lambing/kidding, pregnancy scan results, …),
* Udder health, and particularly milk somatic cell count,
* Resistance to disease such as resistance to parasites or to footrot,
* Traits related to feet and legs, such as lameness or twisted or bowed legs, closed or opened hocks,
* Serum immunoglobulin concentration in the early life (Ithurbide et al, 2022a),
* Maturity (dairy species) that can be defined as the ability to maintain a good level of production over the parities, independently of the level of production on the whole lifetime (equivalent of a persistency, but over the lactations and not over the test-days) (Arnal et al, 2022),
* Milk metabolites (Ithurbide et al, 2022b)
* Body tissue mobilisation (McLaren et al., 2023). It was demonstrated that ewe tissue mobilisation was genetically associated with ewe fertility and productive longevity (such as pregnancy scan result, foetal loss from scan to lambing, lamb loss from lambing to weaning, number of lambs weaned). It is made possible by collecting body condition score (BCS) data throughout the reproductive cycle (e.g. pre-mating, pregnancy scan, pre lambing, mid lactation, weaning) and calculating gain or loss of BCS between physiological stage.

These predictors are linked to longevity traits. An unfavourable udder shape, reproduction disorders, a susceptibility to a given disease or a low maturity may lead to involuntary culling and therefore a low longevity of the animal. Few genetic correlations have been published but correlations between EBVs show favourable correlations between these predictors and longevity.

Longevity traits, once evaluated, either in linear or survival analysis model, may be combined with the longevity traits in a multi-trait evaluation, to incorporate the information from early predictors.

A full multiple trait evaluation is not feasible in large datasets. Therefore, approximate strategies must be used, such as considering records adjusted for all non-genetic effects in linear models (yield deviation or daughter yield deviation, other type of pseudo records), or sub-indices incorporating traits that are linked together e.g. pulling together data on footrot, mastitis and parasite resistance could be considered together in a ‘health’ sub-index.

==== Culling causes ====
Even though the knowledge of the causes of culling is not necessary to generate a phenotype of longevity and an EBV of functional longevity, the knowledge of the causes of culling, through an observation based on a sufficient panel of flocks/herds, and repeated each year, may give relevant information on the hierarchy and the evolution of the culling causes. It may also enable better understanding of the strategies of culling by farmers leading to better modelling of functional longevity.

Culling causes may be collected with different levels of precision, from a general group of causes to a precise cause, through intermediate information.

In sheep as in goat, the following group of culling causes may be collected:

* Udder health (mastitis)
* Udder morphology
* Production ability
* Respiratory disorders
* Reproduction disorders
* Digestive disorders
* Nervous disorders
* Musculoskeletal disorders
* Skin disorders
* Conformation
* General condition
* Age
* Behaviour
* Accident
* Other ailments (e.g. sudden death, brucellosis, intoxication, fever …)
* Voluntary culling

Each group may be completed with sub-group or precise cause. Below are two examples, first for udder health (table 2), second for reproduction disorders (table 3).

{| class="wikitable"
| colspan="3" |'''Table 2. Detailed categorisation of udder health culling causes.'''
|-
|'''Group'''
|'''Sub-group'''
|'''Specific cause'''
|-
| rowspan="21" |Udder health (mastitis)
| rowspan="2" |Gangrenous mastitis
|Gangrenous mastitis
|-
|Brief mastitis
|-
| rowspan="7" |Characteristic symptoms
|Mastitis
|-
|Clinical mastitis
|-
|Mastitis during suckling
|-
|Coliform mastitis
|-
|Listeria mastitis
|-
|Mastitis before lambing/kidding
|-
|Agalactia mastitis
|-
|Functional symptoms
|Blood in the milk
|-
| rowspan="6" |Chronic mastitis, palpation
|induration of the udder
|-
|Bumps in the udder
|-
|Nodules
|-
|Mammary abcess
|-
|Saggy udder
|-
|Visna mastitis
|-
| rowspan="2" |Unbalanced udder
|Milk in one side
|-
|Unbalanced udder
|-
| rowspan="2" |Subclinical
|Subclinical mastitis
|-
|Somatic cell count (SCC) and California mastitis test– CMT
|-
|Other
|Other
|}

{| class="wikitable"
| colspan="3" |'''Table 3. Detailed categorisation of reproduction disorders culling causes'''
|-
|'''Group'''
|'''Sub-group'''
|'''Specific cause'''
|-
| rowspan="28" |Reproduction disorders
| rowspan="3" |Fecundity
|Open + infertile
|-
|Lately fertile, out of season
|-
|Ram infertile
|-
| rowspan="6" |Gestation
|Abortion
|-
|Vagina or rectal prolapse
|-
|Pregnancy toxaemia
|-
|Difficult gestation
|-
|Early abortion
|-
|Late abortion
|-
| rowspan="8" |Lambing/kidding
|Difficult lambing/kidding
|-
|Caesarean
|-
|Uterus inversion
|-
|Infection during lambing/kidding
|-
|Vagina or rectal prolapse
|-
|non deliverance
|-
|Acute metritis
|-
|Chronic metritis
|-
| rowspan="4" |Miscellaneous
|Reproduction disorders
|-
|Vaginal sponge infection
|-
|Hermaphrodite
|-
|Various
|-
| rowspan="4" |Male: testicles
|1 testicle
|-
|Small testicles
|-
|Abscess
|-
|Contagious epididymitis
|-
| rowspan="3" |Male: penis
|Urinary gravel
|-
|Wound
|-
|Phimosis
|}

=== Acknowledgements ===
We gratefully acknowledge the contributions to these lifetime resilience guidelines by the following people:

* Joanne Conington, SRUC, the UK
* Marija Špehar, Centre for Livestock Breeding Zagreb, Croatia
* Isabelle Palhière, INRAE, France
* Jean-Michel Astruc, IDELE, France
* Carolina Pineda Quiroga, NEIKER, France

This work received funding from the European Unions’ Horizon 2020 Research & Innovation program under grant agreement N°772787—SMARTER.

=== References ===
Arnal M., Palhiere I., Clément V. (2022). Maturity, a new indicator to improve longevity of Saanen dairy goats in France. Proceedings of 12th World Congress on Genetics Applied to Livestock Production (WCGALP), Jul 2022, Rotterdam, Netherlands. doi:10.3920/978-90-8686-940-4_738.

Brotherstone, S., Veerkamp, R. F. and Hill, W. G. (1997). Genetic parameters for a simple predictor of the lifespan of Holstein-Friesian dairy cattle and its relationship to production. Animal Science 65: 31-37.

Buisson D., J.M. Astruc, L. Doutre, I. Palhière. Toward a genetic evaluation for functional longevity in French dairy sheep breeds. Proc 12th WCGALP, 2022

Castañeda-Bustos, V. J., Montaldo, H. H., Torres-Hernández, G., Pérez-Elizalde, S., Valencia-Posadas, M., Hernández-Mendo, O., & Shepard, L. (2014). Estimation of genetic parameters for productive life, reproduction, and milk-production traits in US dairy goats. Journal of Dairy Science, 97(4), 2462-2473.

Castañeda-Bustos, V. J., Montaldo, H. H., Valencia-Posadas, M., Shepard, L., Pérez-Elizalde, S., Hernández-Mendo, O., & Torres-Hernández, G. (2017). Linear and nonlinear genetic relationships between type traits and productive life in US dairy goats. Journal of Dairy Science, 100(2), 1232-1245.

Conington, J., Bishop, S. C., Grundy, B., Waterhouse, A., & Simm, G. (2001). Multi-trait selection indexes for sustainable UK hill sheep production. Animal Science, 73(3), 413-423.

Conington, J., Bishop, S.C., Waterhouse, A. and Simm, G. (2004). A bio-economic approach to derive economic values for pasture-based sheep genetic improvement programmes. Journal of Animal Science 82: 1290-1304. <nowiki>https://doi.org/10.2527/2004.8251290x</nowiki>

Ducrocq, V. (2001). A Two-Step Procedure to get Animal Model Solutions in Weibull Survival Models Used for Genetic Evaluations on Length of Productive Life. Interbull Bulletin, vol.27, pp.147-152

Ducrocq, V. (2005). An Improved model for the French genetic evaluation of dairy bulls on length of productive life of their daughters. Animal Science, 80(3), 249-256.

Geddes, L., Desire, S., Mucha, S., Coffey, M., Mrode, R. and Conington, J. (2018). Genetic parameters for longevity traits in UK dairy goats. IN: Proceedings of the World Congress on Genetics Applied to Livestock Production, Volume Species - Caprine: 547.

Ithurbide, M., Huau, C., Palhière, I., Fassier, T., Friggens, N. C., & Rupp, R. (2022a). Selection on functional longevity in a commercial population of dairy goats translates into significant differences in longevity in a common farm environment. Journal of Dairy Science, 105(5), 4289-4300.

Ithurbide, M., Wang, H., Huau, C., Palhière, I., Fassier, T., Pires, J. & Rupp, R. (2022b). Milk metabolite profiles in goats selected for longevity support link between resource allocation and resilience. In Proceedings of 12th World Congress on Genetics Applied to Livestock Production (WCGALP) pp. 276-279

McLaren A, Lambe, N R and Conington J. (2023). Genetic associations of ewe body condition score and lamb rearing performance in extensively managed sheep. 105336. Livestock Science September 2023 <nowiki>https://doi.org/10.1016/j.livsci.2023.105336</nowiki>

Palhière I., C. Oget, R. Rupp, Functional longevity is heritable and controlled by a major gene in French dairy goats, 11th WCGALP, Auckland, Nouvelle-Zelande, 11-16 février 2018

Pineda-Quiroga, C., Ugarte, E. (2022). An approach to functional longevity in Latxa dairy sheep. Livestock Science 263, 105003

Sasaki, O, (2013), Estimation of genetic parameters far longevity traits in dairy cattle: A review with focus o n the characteristics of analytical models, Animai Science Journal, 84(6), 449-460,

SMARTER Deliverable 2,2 - "New breeding goals far lifetime resilience far materna!sheep breeding programmes"

== Guidelines on survival recording of foetus and young in sheep and goats ==
'''Change Summary'''
{| class="wikitable"
|'''Date of change'''
|'''Nature of Change'''
|-
|October 2024
|First version
|-
|December 2024
|Tracked change revisions by MS
|}

=== Introduction and scope ===

==== Introduction ====
Foetal and young survival are parameters linked to neonatal vigour scores, maternal and young behaviours, stress responses, immunity transfer and traits related to dam fertility and longevity. Minimising mortality, either in utero (e.g., embryo/foetus) or pre-weaning, are crucial to profitable small ruminant production systems. Survival depends on an interaction between the environment and behaviour of both, the ewe and the lamb. Ewes must give birth without complications and provide reliable source of colostrum along with mothering environment. Lamb must adapt to the extra-uterine environment, thermoregulate and be able to stand and suckle in a reasonably short period after birth (Brien et al., 2014; Plush et al., 2016). Despite this, pre- weaning survival in many species is far from ideal (Binns et al., 2002; Yapi et al., 1990, Chaarani et al., 1991, Green and Morgan, 1993, Nash et al., 1996). This can be particularly worse in small ruminant production systems which are typically more extensive and therefore prevailing weather conditions can be an additional stressor as well as predators. Moreover, the poly-ovulatory nature of species such as sheep and goats also predisposes such species to greater foetal and pre-weaned young losses (Scales et al., 1986).

Litter size can be determined using trans-abdominal ultrasonography of the uterine horns at ideally 40-70 days post-fertilisation. Good accuracy in determining foetal number has been reported from trans-abdominal ultrasonography (Taverne et al., 1985). The number of young eventually born can then be used to assess foetal loss since the time of scanning. At birth, young survival is usually based on dead or not in the first 24 h post-birth while stillborn individuals or those dead within 24 hours are usually defined as failed to survive. Young survival can also be considered as different age group categories until weaning – for example from 1 day to 7 days of age. Young animals (i.e., < 7 days) are greatest at risk of mortality (Binns et al., 2002) and tend to die of exposure to hypothermia, starvation, septicaemia, or repercussions from trauma suffered at birth.

==== Scope ====
The aim of the present section is to define approaches for the definition of foetal and lamb survival as well as the data editing and downstream analyses (including statistical models).

=== Definition, terminology, rationale ===
A plethora of different definitions exist depending on whether defined at the level of the individual (i.e., binary trait) or that of the litter. A non-exhaustive list is given below.

'''Foetal survival (at an individual level):'''

* Can be defined as a binary trait of 0 (died between scanning and birth) or 1 (survived between scanning and birth). A dummy ID for the dead foetus would need to be constructed but the parentage would still potentially be known (especially if generated from AI).

'''Foetal survival (at a litter level):'''

* Whether or not some foetal mortality has occurred defined as a binary trait (i.e., the number of individuals born is less than the number scanned in utero)
* Number of individual foetuses scanned alive (along with gestational age)
* Number of foetuses scanned minus the number that were born (dead or alive) – this is a measure of foetal mortality as opposed to survival and assumes stillborn young are considered in the definition of a young survival trait. It is a count trait
* The number of young born divided by the number of foetuses scanned (this is mortality rate figure but per little with a penalty on losses for smaller litter sizes).

'''Young survival (at an individual level):'''

* Can be defined as a binary trait of 0 (dead within 24 hours of birth) or 1 (alive after 24 hours of birth). The dead animal would need to receive an ID and can, of course, be genotyped to verify parentage (but also used for downstream genomic analyses discussed later).

'''Young survival (at a litter level):'''

* Number of lambs born alive (NLBA)
* Number of lambs dead within 24 hours of birth
* Number of lambs dead within 24 hours of birth divided by the total number of lambs born

=== Recording survival of foetuses and young in small ruminant ===
In all instances, accurate data is crucial. Data should be collected on the animal/dam itself (dead or alive) but also potential confounding effects that could be considered for inclusion in the statistical model as fixed effects. Examples include contemporary group (e.g., flock-date of scanning, flock-year-season of birth (for each NLB separately), ewe parity, litter size). Ideally also all individuals should be genotyped. Because the heritability of foetal or young animal mortality in small ruminants is relatively low (<0.1; Safari et al., 2005; Brien et al., 2014), a large number of records are required to achieve accurate genetic/genomic evaluations. Care should also be taken when interpreting the scoring (and the following genetic evaluations), some jurisdictions may record mortality rather than survival or may record mortality but propose genetic evaluations as survival (i.e., positive value is favourable).

==== Pregnancy scanning records ====
Ideally scanning should be undertaken 40 to 70 days post-fertilisation. This may be possible to (easily) achieve where extensive AI has been used but, otherwise, should ideally be 30 days after the last female has been marked as been served by natural mating. Skilled operators should be able to determine the number of foetuses from 30 to 100 days of gestation; usually only one operator will scan a flock on a given day so will be confounded with flock-date of scanning contemporary group. If AI is solely used or if single sire mated, then the parentage of the foetus should be known; if mob mated or single sire mated at AI, then superfecundation could cause a discrepancy in recorded sire.

==== Young survival ====
Young survival can be defined at birth, ideally as a binary trait as to whether the animal was born stillborn or died within 24 hours (survival = 0) or was still alive 24 hours after birth (survival = 1). If information is also available on the reason for death (i.e., autopsy results) then, where sufficient data exists for any one ailment, it could be analysed separately as separate traits. This could be particularly important for generating separate genetic evaluations for the main diseases thereby not only possibly increasing the heritability through more accurate data, but also provide genetic evaluations specific to individual ailments which could enable more selection pressure on these traits in situations where they are more impactful. Ideally a genotype of the dead animal should be generated. Any obvious external defects should be noted.

==== Ancillary information ====
Having ancillary information coinciding with an event is useful for several reasons:

* For helping data editing (e.g., comparing actual birth date to expected birth date based on recorded service information)
* For adjustment in the statistical model (e.g., dam parity)
* Understanding the risk factors associated with survival
* Enabling more precise estimates of correlations with other performance traits by having information on multiple features from the same animal
* Adjusting for possible selection in multi-trait genetic evaluation models

Possible ancillary information can be divided into those associated with 1) the past of prevailing environmental conditions, 2) the dam (or sire), or 3) the individual. Examples include:

1. Environment:

* Weather related factors (rainfall, temperature, wind including direction)
* Flock
* Date of scanning or date of birth

2. Dam

* Parity
* Age
* Breed
* Genotype
* Litter size
* Mating type (i.e., AI versus natural)
* Body condition score (change) and live-weight (change)
* Mothering ability
* Colostrum quality and yield

3. Individual

* Days since service (for foetal survival trait)
* Birthing difficulty
* Birth weight
* Gender
* Genotype
* Sire
* Autopsy results if possible

=== Use for genetic analysis / genetic evaluation ===

==== Data editing and statistical modelling ====
In order to estimate contemporary group effects well, the larger the contemporary group, the better the group estimates. Therefore, imposing a minimum contemporary group size prior to data analysis should be considered as should good genetic connectedness with other contemporary groups. Genetic connectedness can be an issue with small ruminant populations in particular, especially where natural mating prevails.

===== Data editing =====
'''Foetal survival''' ''-'' Each flock-scanning date can be firstly investigated at a macro level to measure ultrasound quality control. Simple cross-references between the number of females with scanning data versus those presented as well as the ID numbers of both is useful to ensure all data were properly recorded. High foetal mortality rates could simply be indicative of high foetal loss (e.g., abortions due to causes like chlamydial and toxoplasma) as well as poor operator competence – assessing the rate for individual operators across flocks (and time) could be useful to assess operator proficiency. A high proportion of litters where the number of young born (dead or alive) exceeds that recorded at scanning suggests a poor accuracy of recording. It should be considered to discard the data from that date but also to investigate the operator in more detail across other flocks, and irrespective, the scanning results from that litter at least should be discarded. The proportion of scanned litters with >3 detected foetuses should also be calculated; depending on the expected prolificacy of the animals (e.g., breed), then the appropriate editing of either the individual data points or the date in its entirety should be assessed.

'''Young mortality''' ''-'' A high incidence of young mortality per contemporary group could simply be a consequence of some underlying issue (e.g., predation, disease) or indeed a high fecundity rate; a low incidence of young could be indicative of a good stock person. Therefore, it can be difficult to distinguish between high and low quality data. Using guaranteed high quality and reliable data, it is possible to estimate the expected distribution of the incidence of young animal mortality for different population strata such as flock size, ewe age, breed, litter size. Using these distributions, the probability that the mean mortality for a contemporary group fits this distribution can be estimated and a decision made as to whether or not to include the data in the downstream analyses.

===== Statistical modelling =====
Lamb survival is a complex trait influenced by direct genetic, maternal genetic, and environmental effects. Due to discrete expression of phenotype (dead or alive: 0 or 1) it is described as a threshold trait (Falconer, 1989) that violates the assumption of normality, and therefore linear models are theoretically not appropriate for the analysis. However, examples from the literature analysed survival data and reported that linear models were marginally more accurate at predicting missing phenotypes than were logit-transformed alternatives and are convenient for interpretation on the observed scale (Matos et al., 2000; Everett-Hincks et al., 2014; Cloete et al. 2009; Vanderick et al., 2015;).

Random effects considered in the statistical model are direct and maternal genetic effects and maternal permanent environment across parities. A litter permanent environmental effect should also be considered as a random effect where the trait is that of the individual (and not the ewe). Traditionally, relationships were accounted for though the pedigree data, however this can often now be supplemented with genome-wide genotype information to generate a H matrix (i.e., combines genomic and ancestry information). Whether the estimation of these additional covariance components improve the fit to the data can be deduced by a likelihood ratio test but ideally a metric such as the AIC or BIC to account for the increased complexity of the model.

The choice of environmental factors included in the model will depend on the population being studied and considers the following fixed effects:

* Contemporary group (e.g., flock-date of scanning for foetal survival and flock-year-season of birth or flock-year-season-birth rank of birth)
* Lamb gender (may not be possible for foetal survival trait)
* Dam parity
* Mating type (i.e., AI versus natural)
* Dam age nested within parity
* Day of gestation (for foetal survival) if available or defined as a categorical variable
* Litter size (at scanning or birth) or birth type (single and multiple)
* Heterosis and recombination loss of the dam and foetus/young
* Inbreeding coefficient of the dam and foetus/young
* Age of the sire
* Breed composition of the dam and foetus/young

Adjusting for the effects such as dystocia or birth weight, may not be appropriate in the statistical model for young survival as they are likely to be genetically correlated with survival and thus may remove some of the true genetic variance – nonetheless, the eventual decision will be based on the genetic evaluation system employed and how the economic value on the traits within the overall breeding objectives are constructed.

==== Genomic association analyses ====
Where genotypes are available, then a genome-wide association study (or candidate gene study) can be undertaken (Esmaeili-Fard et al., 2021). Although it is not possible to have the genotype of the aborted foetus, it could still be possible to undertake a genomic analysis especially by focusing on the genotype/haplotype of the living animals versus the expectation based on the genotype/haplotype of the parents (Ben Braiek et al., 2021).

=== Acknowledgements ===
We gratefully acknowledge the contributions to these recording of survival of foetus and young guidelines by the following people:

* Donagh Berry, TEAGASC, Ireland
* Joanne Conington, SRUC, the UK
* Maxime Ben Braiek, INRAE, France
* Arnaud Delpeuch, IDELE, France
* Marija Špehar, Centre for Livestock Breeding Zagreb, Croatia

This work received funding from the European Unions’ Horizon 2020 Research & Innovation program under grant agreement N°772787—SMARTER.

=== References ===
Ben Braiek, M., Fabre, S., Hozé, C., et al. (2021). Identification of homozygous haplotypes carrying putative recessive lethal mutations that compromise fertility traits in French Lacaune dairy sheep. Genet. Sel. Evol. 53:41. <nowiki>https://doi.org/10.1186/s12711-021-00634-1</nowiki>

Binns, S.H., I.J.Cox, S. Rizvi, L.E.Green. (2002). Risk factors for lamb mortality on UK sheep farms. Prev.Vet. Med.. 52:287-303.

Brien, F.D., Cloete, S.W.P., Fogarty, N.M., Greeff, J.C., Hebart, M.L., Hiendleder, S., Hocking Edwards, J.E., Kelly, J.M., Kind, K.L., Kleeman, D.O., Plush, K.L., Miller, D.R (2014). A review of genetic and epigenetic factors affecting lamb survival. Anim. Prod. Sci. 54:667–693.

Chaarani, B., Robinson, R.A., Johnson, D.W. (1991). Lamb mortality in Meknes Province (Morocco). Prev. Vet. Med. 10:283-298.

Cloete, S.W.P., Misztal, I., Olivier, J.J. (2009). Genetic parameters and trends for lamb survival and birth weight in a Merino flock divergently selected for multiple rearing ability. J. Anim. Sci. 87:2196–2208. doi:10.2527/jas.2008-1065.

Esmaeili-Fard, S.M., Gholizadeh, M., Hafezian, S.H., Abdollahi-Arpanahi, R. (2021) Genes and Pathways Affecting Sheep Productivity Traits: Genetic Parameters, Genome-Wide Association Mapping, and Pathway Enrichment Analysis. Front. Genet. 12:710613. doi:10.3389/fgene.2021.710613.

Everett-Hincks, J.M., Mathias-Davis, H.C,, Greer, G.J., Auvray, B.A., Dodds, K.G. (2014). Genetic parameters for lamb birth weight, survival and deathrisk traits. J. Anim. Sci. 92:2885–2895. doi:10.2527/jas.2013-7176.

Falconer, D.S. (1989). Introduction to Quantitative Genetics.’ (Longmans Green/John Wiley & Sons: Harlow, Essex, UK).

Green, L.E., Morgan, K.L. (1993). Mortality in early born, housed lambs in south-west England. Prev. Vet. Med. 17:251-261.

Matos, C.A.P., Thomas, D.L., Young, L.D., Gianola, D. (2000). Genetic analyses of lamb survival in Rambouillet and Finnsheep flocks by linear and threshold models. Anim. Sci. 71:227–234. doi:10.1017/S1357729800055053.

Nash, M.L., Hungerford, L.L., Nash, T.G., Zinn, G.M. (1996). Risk factors for perinatal and postnatal mortality in lambs. Vet. Rec. 139:64-67.

Plush, K.J., Brien, F.D., Hebart, M.L., Hynd, P.I. (2016). Thermogenesis and physiological maturity in neonatal lambs: a unifying concept in lamb survival. Anim. Prod. Sci. 56:736–745. <nowiki>https://doi.org/10.1071/AN15099</nowiki>.

Safari, E, Atkins, K.D., Fogarty, N.M., Gilmour, A.R (2005). Analysis of lamb survival in Australian Merino. Proceedings of the Association for the Advancement of Animal Breeding and Genetics. 16:28–31.

Scales, G. H., Burton R. N., Moss, R. A. (1986). Lamb mortality, birthweight, and nutrition in late pregnancy. N. Z. J. Agric. Res. 29:1.

Taverne, M.A.M. Lavoir, M.C., van Oord R., van der Weyden, G.C. (1985) Accuracy of pregnancy diagnosis and prediction of foetal numbers in sheep with linear‐array real‐time ultrasound scanning. Vet. Q. 7:(4)256-263, DOI: 10.1080/01652176.1985.9693997.

Vanderick, S., Auvray, B., Newman, S.A., Dodds, K.G., Gengler, N., EverettHincks, J.M. (2015). Derivation of a new lamb survival trait for the New Zealand sheep industry. J. Anim. Sci. 93:3765–3772. doi:10.2527/jas.2015-9058.

Yapi, C.V., Boylan, W.J., Robinson, R.A. (1990). Factors associated with causes of preweaning lamb mortality. Prev. Vet. Med., 10:145-152.

The technical references (papers cited or used) are documented in each piece of recommendations.

== Guidelines on recording behavioural traits in sheep and goats ==
{| class="wikitable"
|'''Date of change'''
|'''Nature of Change'''
|-
|October 2024
|First version
|-
|November 2024
|Tracked change revisions by JC
|-
|December 2024
|Tracked change revisions by MS
|}

=== Introduction and scope ===

==== Introduction ====
Genetic selection including behavioural traits could be an advantageous strategy for improving robustness and welfare of farm animals in various farming conditions by minimizing unsuitable responses to changes in their social and physical environment, limiting an excessive fear of humans and improving sociability (Mignon-Grasteau et al., 2005). Farm animals are social and gregarious, and relational behaviours are essential for ensuring social cohesion, social facilitation, offspring survival and docility toward humans. Breed differences and genetic variation within breed have been reported in lambs for early social behaviours and found to be heritable, and associated with some QTL, suggesting such behaviours could be selected early (Boissy et al., 2005; Beausoleil et al., 2012; Hazard et al., 2014; Cloete et al., 2020). In addition, such early social reactivity of lambs towards conspecifics or humans was identified as a robust trait and that selection for early social reactivity of lambs towards conspecifics or humans is feasible (Hazard et al., 2016; 2022).

The behaviour of both ewes and lambs, and their interaction at lambing, have been widely described. Such behaviour is important for the survival of the offspring, especially in extensive farming conditions as reviewed by Dwyer et al. (2014). Moreover, it has been shown that primiparous ewes are more prone to abandon their lambs due to their lack of maternal experience (Dwyer, 2008) and that lamb survival at birth is lowly heritable (Brien et al., 2014). Taken together these factors could hinder the development of extensive farming systems. Genetic selection on maternal attachment traits could therefore be advantageous to improve offspring survival and growth, and reduce labour, as suggested by Mignon-Grasteau et al. (2005). Genetic variations in maternal behaviour between breeds of sheep have been well documented (for review see: Dwyer, 2008; von Borstel et al., 2011) while little was known about within-breed genetic variability and even less about maternal reactivity traits. We hypothesized that maternal attachment to the litter has a genetic component in sheep, and we recently reported that as expected the maternal reactivity at lambing is a heritable trait (Hazard et al., 2020;2021).

Grazing behaviour is also important for animals raised in extensive production systems because it can support adaptability to changing environments. In particular, small ruminants reared in semi-extensive systems face many environmental and welfare challenges that are difficult to quantify. The evidence in the literature suggests that there are differences in grazing behaviour between and within breeds of sheep (Simm et al., 1996; Brand, 2000). The notion is that natural selection combined with subjective artificial selection have led to some animals being more adaptive to extensive conditions. In this regard, genetic variation may exist for key grazing behaviour traits (Simm et al., 1996; Dwyer et al., 2005), but relevant literature is scarce. During the SMARTER H2020 project, a study was performed on grazing behaviour of the indigenous Boutsko Greek mountainous sheep breed, which is reared semi-extensively. The results showed that duration of grazing and speed are heritable traits (Vouraki et al., 2025).

==== Acronyms used in these guidelines ====

* AT Arena Test
* CT Corridor Test
* GPS Global Positioning System
* LS Lambing Site
* PCA Principal Component Analysis

==== Scope ====
The aim of the present report is i) to define the behavioural traits of interest, ii) to describe approaches for behavioural measurements, iii) to describe their use for genetic analysis and evaluation.

To-date, the present guidelines describe 3 groups of traits related to behaviour:

* Behavioural reactivity towards conspecifics or humans
* Maternal reactivity
* Behaviour at grazing

Kid/lamb vigour is a relevant behavioural trait, but this trait is tackled within the section “foetus and young survival in sheep and goats” of the guidelines.

Most of the work undertaken on behaviour concerned sheep. This has been particularly the case in SMARTER. Most of the recommendations might be applied to goats as well. Nevertheless, we will use the ovine terms in the guidelines below.
[[File:Section_24-1_Three_groups_of_traits_related_to_behaviour_guidelines.jpg|center|thumb|600x600px|Three groups of traits related to behaviour guidelines]]

=== Behavioural reactivity towards conspecifics or humans ===

==== Definition, terminology, rationale ====

===== Behavioural reactivity towards conspecifics (i.e. sociability): =====
It is the social motivation of the lambs to join their conspecifics in response to social isolation with or without presence of a motionless human. Expression of higher levels of a panel of behaviours, including vocalisations and locomotion, is hypothesised as an active way to maintain social link with conspecifics.

==== Behavioural reactivity towards humans (i.e. docility): ====
It is the reactivity of isolated lambs to a walking human. Higher flight distance between the lamb and a human indicates a lower docility toward a human.

Behavioural reactivity towards conspecifics and humans are measured in standardised behavioural tests (arena and corridor tests, described below).

Higher sociability and/or docility towards humans may improve adaptation of sheep to harsh environments through social facilitation (i.e. transmission of feeding preferences…), social cohesion (i.e. transhumance…) and reactivity to handling. Consequently, improving such behavioural traits may improve welfare, production, and labour of shepherd.

==== Data recording ====

===== Behavioural tests =====
The test described below have been implemented in France. It must be considered as a possible test, as others can be described later and enrich these guidelines.

Lambs must be individually exposed just after weaning (i.e. approximately 10 days after weaning) to two behavioural tests. The delay between weaning and behavioural tests must be sufficient for the change of social preferences of lambs for their dam to conspecifics.

The arena test (AT) consists of two successive phases evaluating 1) reactivity to social isolation (AT1), 2) the motivation of the lamb towards conspecifics in presence of a motionless human (AT2). The arena test is performed indoors. The arena test pen consists in an unfamiliar enclosure virtually divided into 7 zones as described in detail by Ligout ''et al''. (2011) (Figure 1). On one side of the enclosure (i.e. at the opposite of the entrance), a grid separates the tested lamb from another smaller pen containing 3 or 4 conspecifics. The first phase of the test (arena test phase 1, AT1) starts once the tested animal joins its flock-mates located behind a grid at the opposite side of the arena (time duration for joining: lower than 15 sec). No behavioural recording is performed during the joining. At this time, an opaque panel is pulled down (from the outside of the pen) between the flock-mates and the tested lamb to prevent visual contact. After one minute the phase 1 stops and the panel is pulled up so the lamb can see its flock-mates again. Once the lamb has returned near to its flock-mates, or after 1 minute if the lamb did not do so, a non-familiar human slowly enters the arena through a door located near the pen of the flock-mates and stood 20 cm in front of the grid separating the arena from the lamb’s flock-mates. The second phase (arena test phase 2, AT2) starts once the human is in place and lasts for a further 1 minute.
[[File:Experimental_setup_of_the_arena_test_for_estimating_the_social_reactivity_of_lambs.jpg|center|thumb|600x600px|Figure 1. Experimental setup of the arena test for estimating the social reactivity of lambs. At the beginning of the test, animals can join their flock mates placed behind a grid barrier (social attraction, phase 0) and then were individually exposed to the social isolation (phase 1), and to the social attraction in presence of a motionless human (phase 2). (Adapted from Ligout et al., 2011)]]
The corridor test (CT) consists of two successive phases evaluating 1) reactivity to social isolation (CT1) and 2) reactivity to an approaching human (CT2). The test pen consists in a closed, wide rectangular circuit and has been described in detail by Boissy ''et al''. (Boissy et al., 2005) (Figure 2). The first phase (corridor test phase 1, CT1) starts when the lamb enters the testing pen and lasts for 30 seconds. After that time a non-familiar human enters the testing pen and the second phase (corridor test phase 2, CT2) starts and lasts 1 minute. During this phase, the human walks at a regular speed through the corridor (the corridor is divided into 6 virtual zones and one zone is crossed every 5 seconds) until two complete tours has been achieved.

===== Behavioural traits =====
Several behaviours are measured during behavioural tests: vocalisations (i.e. frequency of high- pitched bleats), locomotion (i.e. number of virtual zones crossed), the proximity score (i.e. weighting of time spent in virtual zones, a high score indicated a high duration spent close to conspecifics and a human).

An investigator counts the lamb’s vocalisations directly during the tests, from outside the pen using a laptop: number of times the animal bleats with an open mouth (high bleats, AT1/2- HBLEAT, CT1-HBLEAT). Locomotor activity is assessed by measuring the number of virtual zones crossed during arena test phases 1 and 2 (AT1/2-LOCOM) and corridor test phase 1 (CT1- LOCOM). This behaviour can be assessed using video recording or using infrared cells regularly positioned along the AT to detect displacement. The proximity to flock-mates and the human during AT2 is calculated by weighting of time spent in virtual zones (i.e. a high score indicated a high duration spent close to conspecifics and a human).

During CT2, every five seconds throughout this phase, an investigator records with a laptop the zones in which the human and the animal are located. In addition, the walking human records with a stopwatch the total duration during which the head of the lamb is visible. The mean flight distance (DIST) separating the human and the lamb (i.e. knowing the length of each virtual zone) and the time during which the human sees the lamb (SEEN) is measured in CT2.

==== Calculation of traits ====
Deviations from normality of row data must be tested using relevant statistical tests (e.g. the Kolmogorov–Smirnov test). Several raw measures must be transformed in order to minimise major deviations from the normal distribution. Square root transformation is applied to AT1/2- HBLEAT, CT1-HBLEAT. A multivariate analysis may be performed to take into account the multidimensional aspect of behavioural responses. Results of principal component analysis (PCA) indicate that the main principal components is structured mainly with similar behaviour (i.e. higher weight of similar behaviours for the different tests on the same component). Consequently, three synthetic variables may be constructed using PCA. Each PCA is performed for a set of similar behavioural variables across the behavioural tests. The first component of each PCA, explaining the largest part of total variance, is defined as a synthetic variable. Two synthetic variables are specific to the reactivity to social isolation: high bleats (HBLEAT, using AT1/2-HBLEAT and CT1- HBLEAT), locomotion (LOCOM, using AT1/2-LOCOM and CT1-LOCOM). One synthetic variable is specific to the reactivity to an approaching human: the tolerance to being approached when the lamb is free to flee (HUMAPPRO, using CT2-DIST and CT2-SEEN).

==== Use for genetic analysis and genetic evaluation ====
Genetic analyses and genetic evaluation can be performed on single traits and synthetic variables. Genetic analyses (estimation of (co)variance components and prediction of breeding values) for quantitative behavioural traits may be implemented with a mixed model methodology in animal model. Random effects should include:

* a direct additive genetic effect of the animal (i.e. lamb),
* a maternal permanent environment effect (i.e dam), that describes lamb phenotypic variation caused by the environment of the ewe
* a litter permanent environment effect, that accounts for phenotypic variation caused by the environment of the litter of the lamb being tested.

All relevant fixed effects and interactions should be included in the model. Factors that could be considered include:

* a combination of the litter size at lambing and the number of lambs suckled with their dam
* sex, age, live weight of the lamb,
* dam parity and/or age of dam nested withing parity if needed contemporary group (e.g., depending on the data collection: flock-year-season, grazing location…)

=== Maternal reactivity ===

==== Definition, terminology, rationale ====
* Behavioural reactivity at lambing (i.e. maternal reactivity). It is the social motivation or attachment of the ewe for the litter expressed in response to an approaching human, or the withdrawal of the litter with or without presence of a human. Expression of higher levels of a panel of behaviours, including maternal behaviour scores, vocalisations and locomotion, is hypothesised as an active way to maintain social link with lambs.

Maternal reactivity is measured in standardised behavioural tests (a scoring test outdoors, an arena test indoors, described in the controlled test below) or a maternal behaviour score (MBS) designed for use in extensive sheep systems as described by O’Connor ''et al'' (1985), the genetic basis of which was reported by Lambe et al., 2001 for Scottish Blackface sheep.

Higher maternal reactivity may improve adaptation of sheep to harsh environments through a higher behavioural autonomy at lambing and a reducing dependency to the support provided by shepherds. Consequently, improving such behavioural traits may improve welfare, production, and labour of the shepherd.

==== Data recording ====

===== Behavioural tests =====
The controlled test described below have been implemented in France. It must be considered as a possible test, as others can be described later and enrich these guidelines. Ewes are individually exposed to two behavioural tests: a scoring test performed just after lambing, outside at the lambing site, and then an arena test performed indoor, one day after lambing. The second test is performed after the bonding period needed to establish the social link between ewes and lambs and which occurs generally within the first twelve hours after lambing (Keller et al, 2003).

Scoring test at lambing site: Maternal reactivity is assessed outside at the lambing site approximately 2 hours after lambing, only on ewes that lambed during daylight when the shepherd approaches the lambing ewes to catch lambs for weighing and identification. Scoring at lambing is not performed in the following situations: if the location of the lambing site does not readily facilitate the testing procedure, if there are perturbations of scoring due to interference by other ewes, for sanitary reasons that could affect behaviours (including difficult lambing, death of all lambs of a litter). Measurement of maternal reactivity at the lambing site (LS) consists of two successive phases: (1) when the shepherd approaches the lambs; and (2) the capture and displacement of the lambs by the shepherd. In the first phase (LS1), the shepherd stands approximately 15 meters away from the lambing spot and approaches the ewes and the lambs at a regular speed (1 m/s). In the second phase (LS2), the shepherd catches all the lambs at the same time and moves away from the lambing spot in the same direction as that of the approach, stopping at the starting point where he places the lambs back on the ground and then moves 15 meters away to allow the ewe to restore contact with her lambs. This second phase of the test is not applied to ewes that flee at the approach of the shepherd and do not return within 60 seconds after the end of LS1.

Arena test: After lambing, all the ewes and lambs (both day and night births) are transferred to a shelter close to the place of lambing and penned individually for few hours. They are then moved to a collective pen until the next day when they are tested in the arena test (24h ± 6h after lambing). The arena test (AT) is performed indoors and adapted from the original test developed by Boissy and colleagues (2005) to investigate social attachment in sheep (Ligout et al., 2011). In the present study, the test consists of three successive phases evaluating the ewe’s 1) attraction to her litter, 2) reactivity to social separation from her litter, and 3) reactivity to a conflict between social attraction to her litter and avoidance of a motionless human. The test pen consists of an unfamiliar enclosure virtually divided into 7 zones (zone 7 being the zone nearest to the litter). On one side of the enclosure, a grid separates the tested ewe from another smaller pen containing her lamb(s). The first phase of the test (AT1) starts when the tested ewe enters the arena and lasts for 30 s. Then, a remotely controlled opaque panel is pulled down in front of the grid to prevent visual contact between the tested ewe and her lambs. The second phase (AT2), during which the tested ewe is separated from her lambs, lasts 1 min. Finally, the panel is raised so the tested ewe can see her lamb(s) again. Once the ewe has returned near to her lamb(s), a non-familiar shepherd slowly enters the arena through a door located near the grid separating the arena from the litter and stands 20 cm in front of the grid. The third phase of the test (AT3) starts once the shepherd is in place and lasts for 1 min.

===== Behavioural traits =====
Scoring test at lambing site: A scoring system, close to those defined by O’Connor et al. (1985), and further validated for hill sheep by Lambe ''et al.'' (2001) for use in animal breeding programmes to enable many animals to be scored relatively quickly and easily in extensive sheep systems. The simple scoring system measures maternal reactivity described for each of the two phases described above. In LS1, a maternal behaviour score (LS1-MBS) is recorded on a 5-point scale as follows: 1 - ewe flees and does not return to the lambs within 60 s; 2 - ewe retreats (i.e., at least 2-3 m) but comes back to her lambs within 60 s; 3 - ewe retreats with at least one lamb and comes back; 4 - ewe retreats and returns repeatedly; 5 - ewe stays close to the lambing spot. In LS2, a second maternal behaviour score (LS2-MBS) is recorded on a 4-point scale as follows: 1 - ewe flees; 2 - ewe stays close to the lambing spot, 3 - ewe follows but from a distance (i.e., 1 to 2 m), 4- ewe follows, staying close to the shepherd (i.e., less than 1 m).

'''Arena test''': Locomotor activity and localisation are analysed from the video footage or infrared cells (as described above). Locomotor activity is assessed by measuring the number of zones crossed during the 3 phases (AT1/2/3-LOCOM). The time spent in each zone is recorded. The ewe’s proximity to the litter and/or the human during phases 1 and 3 (AT1/3-PROX) is calculated using the following formula:
[[File:Arena_test_formula.jpg|left|thumb|410x410px]]

Two types of vocalisations are recorded manually during the test with an electronic device: number of high-pitched bleats are recorded when the animal bleats with an open mouth (AT1/2/3-HBLEAT) and number of low-pitched bleats are recorded when the animal bleats with a closed mouth (AT1/2/3-LBLEAT).

==== Calculation of traits ====
Logarithmic transformation is applied to AT1/2/3-LBLEAT to minimise major deviations from the normal distribution. All other elementary variables described above are directly used for genetic analyses.

==== Use for genetic analysis / genetic evaluation ====
The (co)variance components for quantitative behavioural traits can be estimated by restricted maximum likelihood (REML) methodology applied in an animal model. The (co)variance components for categorical behaviours can be estimated by MCMC and Gibbs sampling methods using a threshold model (Gilmour et al., 2009).

Assuming that all ewes are measured every year, the analyses assume a repeatability model with behaviour measured across productive cycles considered to be the same trait with a constant variance. Random effects typically include a direct additive and permanent environmental genetic effects of the animal (i.e., ewe).

All relevant fixed effects and interactions should be included in the model. Factors that could be considered can include:

* The litter size at lambing.
* Dam parity or age or age of the dam nested within parity (if significant).
* Contemporary group (e.g., depending on the data collection: flock-year-season effect…).

=== Behaviour at grazing ===

==== Definition, terminology, rationale ====
Grazing behaviour is a complex combination of various movements and activities of animals in different spatial-temporal scales (Andriamandroso et al, 2016). Indicative traits related to grazing behaviour include:

* Duration of grazing
* Distance walked
* Speed
* Altitude difference
* Elevation gain/loss
* Energy expenditure at grazing

A better understanding of the phenotypic and genetic background of grazing behaviour traits could help towards the development of appropriate breeding programmes to increase adaptation to extensive rearing conditions. However, recording of such traits is challenging. The use of new technologies such as global positioning systems (GPS) could help towards efficiently monitoring grazing behaviour (Homburger et al., 2014; Feldt and Schlecht, 2016).

==== Data recording ====
The following guidelines for recording grazing behaviour traits of sheep are based on a study implemented in Greece (Vouraki et al., 2025). Specifically, in the latter study, grazing behaviour of Boutsko sheep reared semi-extensively in mountainous regions was monitored using GPS technology. Moreover, phenotypic and genetic parameters for key grazing behaviour traits were estimated. These guidelines could be enriched in the future based on other relevant studies.

Monitoring of sheep grazing behaviour is performed using appropriate GPS devices attached on designated collars (Figure 3). Rotational monitoring of animals can be applied to reduce the number of devices needed. Selected GPS devices should be of low weight in order to be accepted by the animals without any obvious irritation. Batteries with extended life should be used to provide sufficient energy for GPS tracking for as many as possible consecutive days. In the aforementioned study, “Tractive GPS” devices (Tractive, Pasching, Austria) were used that weighed 28 grams. GPS tracking of each animal was performed for 4-10 days at 2-60 minutes intervals; number of tracking days and intervals were based on available signal and animal movement.

GPS generated data of each animal for the total tracking period are exported in .gpx format. In the case of “Tractive GPS”, the location history function of MyTractive web app ([https://my.tractive.com/#/ <nowiki>https://my.tractive.com/#/</nowiki>)] is used to export recorded data. Then, the exported files are split by date using a designated software such as GPSBabel (version 1.8.0). For each animal, daily routes and corresponding GPS data can be visualized and extracted using appropriate software such as Viking GPS data editor and analyser (version 2.0).

Recorded grazing behaviour traits via these devices include duration of daily grazing (min), distance (km), speed (km/hour), minimum and maximum altitude, and total elevation gain. Other useful metrics including number and average distance between tracking points, tracking duration and route followed by the animals should also be extracted to be used in ensuing analyses.
[[File:Figure_3._GPS.jpg|center|thumb|600x600px|Figure 3. GPS device attached on designated collar.]]

==== Calculation of traits ====
Based on minimum and maximum altitude, altitude difference is calculated. Moreover, energy expenditure for walking can be estimated using the following formula of AFRC (Alderman and Cottrill, 1993):

EE= (0.0026×HD+0.028×VD)×BW

where:

EE = energy expenditure for walking (MJ);

HD = horizontal distance (km, calculated as the difference between distance and elevation gain); VD = vertical distance (km, corresponding to elevation gain);

BW = body weight (kg).

Quality control of GPS generated phenotypes is necessary to sense-check the data for extreme values and errors. Specifically, limits are set for minimum and maximum altitudes to reflect the real altitude of the region being studied. Tracking points beyond these limits are then removed from the corresponding .gpx files and data are re-calculated. Moreover, daily records for which GPS tracking of animals had stopped before returning to their shed, must be excluded. Finally, if needed, grazing behaviour traits should be logarithmically transformed to ensure normality of distribution prior to analyses.

==== Use for genetic analysis / genetic evaluation ====
(Co)variance components of grazing behaviour phenotypes and relevant breeding values (EBVs) can be estimated by restricted maximum likelihood methodology applied to an animal mixed model that can include the following random and fixed effects:

Random effects: additive genetic effect and permanent environmental effect of the animal

The relevant fixed effects may include:

* Farm
* Number of GPS tracking points
* Tracking duration
* Distance between tracking points
* Climatic parameters (e.g. temperature-humidity index)
* Sampling time

It may also be desirable to include social grouping (if known), as this can also affect individual animal behaviours.

=== Acknowledgements ===
We gratefully acknowledge the contributions to these recording of behaviour guidelines by the following people:

* Dominique Hazard, INRAE, France
* Angeliki Argyriadou, University of Thessaloniki, Greece
* Georgios Arsenos, University of Thessaloniki, Greece
* Alain Boissy, INRAE, France
* Vasileia Fotiadou, University of Thessaloniki, Greece
* Sotiria Vouraki, University of Thessaloniki, Greece
* Joanne Conington, SRUC, the UK
* Marija Špehar, Centre for Livestock Breeding Zagreb, Croatia

This work received funding from the European Unions’ Horizon 2020 Research & Innovation program under grant agreement N°772787—SMARTER.

=== References ===
Alderman, G., Cottrill, B. Energy and Protein Requirements of Ruminants. In An Advisory Manual Prepared by the AFRC Technical Committee on Responses to Nutrients; CAB International: Wallingford, UK, 1993.

Andriamandroso, A., J. Bindelle, B. Mercatoris, F. Lebeau (2016). A review on the use of sensors to monitor cattle jaw movements and behavior when grazing. Biotechnologie, Agronomie, Société et Environnement, 20.

Beausoleil, N. J., D. Blache, K. J. Stafford, D. J. Mellor, and A. D. L. Noble. (2012). Selection for temperament in sheep: Domain-general and context-specific traits. Appl. Anim. Behav. Sci. 139:74–85.

Boissy, A., Bouix, J., Orgeur, P., Poindron, P., Bibe, B., & Le Neindre, P. (2005). Genetic analysis of emotional reactivity in sheep: effects of the genotypes of the lambs and of their dams. ''Genetics Selection'' ''Evolution, 37'', 381-401. doi:10.1051/gse:2005007

Brand, T. S. (2000). Grazing behaviour and diet selection by Dorper sheep. Small Ruminant Research, 36(2), 147-158.

Brien, F. D., Cloete, S. W. P., Fogarty, N. M., Greeff, J. C., Hebart, M. L., Hiendleder, S., . . . Miller, D. R. (2014). A review of the genetic and epigenetic factors affecting lamb survival. Animal Production Science, 54, 667-693. doi:10.1071/an13140

Cloete, S. W. P., Burger, M., Scholtz, A. J., Cloete, J. J. E., Kruger, A. C. M., & Dzama, K. (2020). Arena behaviour of Merino weaners is heritable and affected by divergent selection for number of lambs weaned per ewe mated. Applied Animal Behaviour Science, 233. doi:10.1016/j.applanim.2020.105152

Dwyer, C. M., Lawrence, A. B. (2005). A review of the behavioural and physiological adaptations of hill and lowland breeds of sheep that favour lamb survival. Applied animal behaviour science, 92(3), 235-260.

Dwyer, C. M. (2008). Genetic and physiological determinants of maternal behavior and lamb survival: Implications for low-input sheep management. Journal of Animal Science, 86, E246-E258. doi:10.2527/jas.2007-0404

Dwyer, C. M. (2014). Maternal behaviour and lamb survival: from neuroendocrinology to practical application. animal, 8, 102-112. doi:doi:10.1017/S1751731113001614

Feldt, T., Schlecht, E. (2016). Analysis of GPS trajectories to assess spatio-temporal differences in grazing patterns and land use preferences of domestic livestock in southwestern Madagascar. Pastoralism, 6(1), 1-17.

Gilmour, A. R., Gogel, B. J., Cullis, B. R., & Thompson, R. (2009). ASReml User Guide Release 3.0 VSN International Ltd, Hemel Hempstead, HP1 1ES, UK. www.vsni.co.uk.

Hazard, D., Moreno, C., Foulquié, D., Delval, E., François, D., Bouix, J., Boissy, A. (2014). Identification of QTLs for behavioral reactivity to social separation and humans in sheep using the OvineSNP50 BeadChip. ''BMC Genomics, 15'', 778. doi:10.1186/1471-2164-15-778

Hazard, D., Bouix, J., Chassier, M., Delval, E., Foulquie, D., Fassier, T., Boissy, A. (2016). Genotype by environment interactions for behavioral reactivity in sheep. ''Journal of Animal Science, 94'', 1459-1471. doi:10.2527/jas2015-0277

Hazard, D., Macé, T., Kempeneers, A., Delval, E., Foulquié, D., Bouix, J., & Boissy, A. (2020). Genetic parameters estimates for ewes’ behavioural reactivity towards their litter after lambing. ''Journal of Animal Breeding and Genetics, n/a''. doi:10.1111/jbg.12474

Hazard, D., Kempeneers, A., Delval, E., Bouix, J., Foulquie, D., & Boissy, A. (2021). Maternal reactivity of ewes at lambing is genetically linked to their behavioural reactivity in an arena test. Journal of Animal Breeding and Genetics, 139, 193-203. doi:10.1111/jbg.12656

Hazard, D., E. Delval, S. Douls, C. Durand, G. Bonnafe, D. Foulquié, D. Marcon, C. Allain, S. Parisot, A. Boissy (2022). Divergent genetic selections for social attractiveness or tolerance toward humans in sheep. WCGALP 2022

Homburger, H., Schneider, M. K., Hilfiker, S., Lüscher, A. (2014). Inferring behavioral states of grazing livestock from high-frequency position data alone. ''PLoS One'', ''9''(12), e114522.

Keller, M., Meurisse, M., Poindron, P., Nowak, R., Ferreira, G., Shayit, M., & Levy, F. (2003). Maternal experience influences the establishment of visual/auditory, but not olfactory recognition of the newborn lamb by ewes at parturition. Developmental Psychobiology, 43, 167-176. doi:10.1002/dev.10130

Lambe, N R; Conington, J; Bishop, S C; Waterhouse, A; Simm, G (2001). A Genetic Analysis of maternal behaviour score in Scottish Blackface sheep. Animal Science 72: p415-425. Doi:10.1017/s1357729800055922.

Ligout, S., Foulquie, D., Sebe, F., Bouix, J., & Boissy, A. (2011). Assessment of sociability in farm animals: the use of arena test in lambs. Applied Animal Behaviour Science, 135, 57-62. doi:10.1016/j.applanim.2011.09.004

Mignon-Grasteau, S., Boissy, A., Bouix, J., Faure, J.-M., Fisher, A. D., Hinch, G. N., . . . Beaumont, C. (2005). Genetics of adaptation and domestication in livestock. ''Livestock Production Science, 93'', 3-14. doi:10.1016/j.livprodsci.2004.11.001

O’Connor, C. E., Jay, N. P., Nicol, A. M., & Beatson, P. R. (1985). Ewe maternal behaviour score and lamb survival. Proceedings of the New Zealand Society of Animal Production, 45 159–162.

O’Connor, C.E., Lawrence, A. B. and Wood-Gush, D. G. M. (1992). Influence of litter size and parity on maternal behaviour at parturition in Scottish Blackface sheep. Applied Animal Behaviour Science 33: 345–355. <nowiki>https://doi.org/10.1016/S0168-1591(05)80071-1</nowiki>

Simm, G., Conington, J., Bishop, S. C., Dwyer, C. M., Pattinson, S. (1996). Genetic selection for extensive conditions. Applied Animal Behaviour Science, 49(1), 47-59.

SMARTER deliverable D2.4. New prototype and report for industry on GPS-generated phenotypes for behavioural adaptation to extensive grazing systems; artificial rearing adaptation phenotypes; lamb vigour scores linked to lamb survival; new foetal and neonatal survival phenotypes (in preparation).

von Borstel, U. K., Moors, E., Schichowski, C., & Gauly, M. (2011). Breed differences in maternal behaviour in relation to lamb (Ovis orientalis aries) productivity. Livestock Science, 137, 42-48. doi:10.1016/j.livsci.2010.09.028

Vouraki, S., Papanikolopoulou, V., Argyriadou, A., Priskas S., Banos, G., Arsenos, G. (2025). Phenotypic and genetic parameters of grazing behaviour of semi-extensively reared Boutsko sheep. Applied Animal Behaviour Science, vol. 282, Jan 2025, 106473. <nowiki>https://doi.org/10.1016/j.applanim.2024.106473</nowiki>

== Guidelines on recording the environment in sheep and goats ==
'''Change Summary'''
{| class="wikitable"
|'''Date of change'''
|'''Nature of Change'''
|-
|October 2024
|First version
|}

=== Introduction and scope ===

==== Introduction ====
In the genetic evaluation process, the genetic model includes environmental effects (generally fixed effects, in some cases random effects) to correct the phenotypes from these effects, not related to the genetic value of the animal. These environmental effects that affects the expression of the genotypes depend on the traits and the method of phenotyping, the environment itself (flock/herd, year, parity, season of lambing, number of born or reared lambs/kids, scorer, gender of the lamb/kid, management of mob groups, etc). The quality of the record of the environment is important to correct relevantly the performance of the animal.

Some other environmental effects that are usually included in a general flock/year or management mob group effect could be identified, such as the feeding effect or the climate effect. By including these effects in the genetic model, we could get less biased and more precise EBVs, especially when these effects are individualised or are period-specific (feeding might depend on such and such groups of animals, climate might influence the performance of such and such test- day). Moreover, the more precise knowledge of environmental effect might be valorised for flock/herd management and extension services towards farmers.

Moreover, feeding can be considered as an environmental effect, but as well be constitutive of a performance. This is typically the case for feed efficiency where the quantity and the quality of the diets allows to calculate the phenotype.

Likewise, with the climatic change, breeding for animals more resistant or more resilient to higher temperatures (especially thermal stress) becomes a selection objective per se (example of heat tolerance). In this context, the conditions of temperatures (or temperature/humidity combination) not only might be an environmental factor, but be part of the phenotype.

Other environmental effects can be described and should enrich this document in the future.

==== Scope ====
This document focuses on those data that are worth recording the precise the environment or to calculate novel traits of interest.

Following SMARTER work, the document will describe the record of the diet ([[Section 24: Recording resilience in sheep and goats#Recording the diet|Chapter 6]]) and the record of meteorological data ([[Section 24: Recording resilience in sheep and goats#Meteorological data|Chapter 6]])

Further factors might be described later, letting this document open to new section in the future, including:

* Recording the diet in small ruminant
* Recording meteorological data
* Other environmental records

=== Recording the diet ===

==== Definition, terminology, rationale ====
Recording the diet consists in collecting data on the quantity and quality of a ration that an animal, a group of animals of a flock/herd consumes at a given period.

The characterisation of the ration, in terms of energy and protein depends upon the countries. For example, the French INRAE Feeding System for Ruminants (Nozière et al., 2018) is different from the British one (AFRC, 1993). This is the reason for which we will describe in this section general recommendations, that can be applied, translated to the domestic feeding system used

Breeding for more efficient animals is more and more important for economic reason (the feeding resources are costly, might be rare in years with climatic excess such as heat or drought) and for environmental reasons (feed/food competition, emission of green-house gases). Feed efficiency is a trait of high interest in this context. Even though it is deceptive to calculate gold standard efficiency trait in private farm, the knowledge of diets in those farms should help to correctly manage the proxies that are promoted in SMARTER. Diet could also be used as a corrective factor in evaluation models in the future. In addition, it might be a support to better understand the herd/flock effect and its variation across year, and therefore give more acute and relevant advice to the farmers.

It is difficult and time-consuming to collect the data for establishing the diet in the flock/herds. The diet is collective in most of the situations (the same amount of forage is given to all animal because the forage is not given individually). When the concentrate is given through Automated Concentrate Feeder (ACF) in the milking parlour, the individualisation is not at the animal scale but at a limited number of groups scale. That’s why we suggest recommendations that must be adapted to each situation.

The aim is to tend to the better possible estimation of the forage ingestion, given that the direct measurement is impossible in commercial farms. Proxies are studied to get indirect measurement of the intake, but they are not validated so far (Near Infra Red Spectra technique). As soon as validated results are available, these recommendations will be updated.

==== Data recording ====

===== When to record the diet =====
The diet may be recorded at relevant period of the physiological status of the animals in the flock/herd. It is possible to take advantage of the visit of a technician to record the ration (for example when performance recording such as at each (or some of the) test-day when milk recording, or at weighing visit in meat sheep performance recording.

Below are examples of relevant physiological status:

* At mating (or before the mating and after the mating)
* End of gestation (in the month preceding the lambing/kidding)
* After lambing/kidding
* At weaning or just after weaning (peak of production in dairy animals)
* Dairy animals: at each test-day or at some of the test-day

In case of ACF (Automatic Concentrate Feeder), it is possible to record the distribution of concentrate more frequently.

It may be useful to establish the requirements of animals (on average) at each point of diet record. The requirements must concern the energy (in the unit usually used in the country) and the protein (in the unit usually used in the country).

===== How to record the diet =====
'''Individual diet'''
* This can be obtained through ACF for concentrate, mainly in the milking parlour.
* Intake of forage cannot be collected individually but can be predicted through the intake capacity system, such as the one proposed by INRAE (Nozière et al., 2018).

'''Collective diet (at the flock/herd scale or at the mob scale)'''

* Forage (hay, or haylage): some bales of each preservation technic can be weighed once a year with a dry matter (DM) measurement for haylage (it can substantially vary). For hay, DM can be estimated at 85%. Afterward, we can just record how many bales of a given quality (several cutting stages are preserved and not given at random) are distributed per flock per time unit. For silages, it is more complicated, but based on the same procedure, we can weigh one distribution (assuming that it will be constant over time) and simultaneously measured DM. In both situations, if refusals cannot be measured, they must be sufficient for assuming an ad libitum distribution. When the feeding system used in the country can predict the DM intake through the intake capacity of the animal and the quality of the feed, individual diet can be estimated.
* Grazing: for dairy sheep grazing within a short duration per day or the full day, intake can be estimated through ad hoc system. As an example, the new French INRATion feeding software (INRATion V5®) proposes such estimation based on grazing duration, biomass availability and quality.

===== Defining the constitution of the diet =====

====== Precise the type of distribution of the ration ======

* collective ration
* individual ration (concentrate when ACF)
* pasture

====== Categories of feedstuff ======

* Hay
* Partially or fully fermented fodder and fodder preserved by silaging or wrapping:
** Silage
** Wrapped bales

* Pasture
* Straw
* Green feeding
* Dehydrated alfalfa
* Pulp (dehydrated beet pulp, citrus pulp, etc)
* Cake (soybean, rapeseed or sunflower seed)
* Cereals grain (wheat, barley, maize, etc)
* Complete commercial concentrate
* Other by-products of agro-food industry (cereal brans, brewer’s grains, hulls etc.)

====== Species ======
For each category, specify the species (rye grass, alfalfa, clover, maize, wheat, barley, etc), physiological stage or age of regrowth, and harvest conditions (cutting length of the forage and added preservative or not for silages, conditions of hay making drying in the field or mechanically dried).

===== Characterizing the diet =====

====== Quantity ======
Quantity distributed, refused, consumed. Check that these amounts are regularly distributed, refused and consumed because it can markedly influence the animal performance specifically for dairy animals at test day.

The quantity of each feedstuff may be expressed in kg dry matter for forage, in kg gross matter for concentrate. However, final diet for requirement calculation must be expressed as DM.

====== Requirements ======
Requirements for the main categories of animals: it depends on the physiological status (maintenance, production, growing, pregnancy)

Average requirement coverage ratio (energy and nitrogen). For example, the requirement coverage ratio in French dairy sheep is roughly 115% for energy and about 125% for nitrogen of the requirements of the average ewe. That allows covering the requirements of about 85-90% of the flock. Difference between energy and nitrogen is assumed to be covered through the body reserve mobilisation.

====== Quality characterization ======
The feedstuffs and the ration must be characterized at least in terms of

* Energy
* Protein (or nitrogen)

In case of commercial concentrate, data written on the label are used.

Energy and protein can be expressed in the current unit used in the country. For example, in France, energy is expressed in UFL which is equal to 1.7 Mcal Net energy (Nozière et al., 2018).

It may also be expressed in the international unit, which can be Mcal or MJ.

==== Use for genetic analysis / genetic evaluation ====
'''Diet as part of a phenotype'''

Calculation of feed efficiency phenotypes: see recommendations on feed efficiency.

'''Diet as part of a factor in the evaluation model'''

In most of situations it is impossible in small ruminants to establish individual consumption, for practical reason. The collective effect of the diet is explained in the flock/year effect. The intermediate situation should be when ACF allows to identify several groups within the flock/herd, at a specific test-day or visit. It is possible in this case to put in the model a mob effect grouping animals being given the same amount of concentrate. This should result in a more precise calculation of the breeding value of the animal. Nevertheless, this approach has so far not be used to our knowledge.

=== Meteorological data ===

==== Definition, terminology, rationale ====
Meteorological conditions may affect the environment effect on the traits of interest. Even though they may be absorbed in a flock effect at the scale of the year or at the scale of a given test-day, it is relevant to be able to quantify the effect of such and such meteorological parameter (and especially the heat stress) ot the zootechnical traits. The global warming and the higher temperature in which the animals are bred emphasises this interest. It is possible to better assess the comfort zone of the populations, that means the meteorological conditions in which the zootechnical traits are not affected. It is also possible to identify animals better adapted to an increase in temperatures or able to be resilient to a wide range of temperatures, that means to maintain their productive ability. In this case, meteorological data, combined with a production trait (growth, milk production, milk composition) or fertility trait, are used as a resilience characterisation by assessing the ability of the animals to recover their production following meteorological challenges.

Meteorological data are mostly temperature, humidity, precipitations, wind speed and radiations. An issue in small ruminants is to select for adapted animals to new environmental challenges, without artificializing their environment of breeding. Mainly because the economic and societal constraints are such as breeding animals outdoors on pasture is desired and breeding indoors inartificialized environment may be costly in terms of energy.

==== Data recording ====

===== Meteorological data from weather station =====
The aim is to affect outdoors meteorological data to a farm. This can be obtained by assigning to the farm the meteorological data of the closest or more relevant weather stations, using the geographical coordinates of both the farm and the weather station.

The following data may be used:

* Temperature (minimum, maximum, average)
* Relative humidity (amount of moisture in air compared to the maximum amount of moisture it can have at a specific temperature). Expressed in %.
* Specific humidity (ratio of water vapor mass to the total mass of air and water vapor.
* Wind speed
* Precipitations and precipitation type
* Solar radiation
* Atmospheric radiation
* Evapotranspiration

Different index accounting for weather factors have been proposed. One of the most popular is the Temperature Humidity Index (THI) which may be calculated to get a single value representing the combined effects of air temperature and humidity associated with the level of thermal stress.

Different formulas of THI are proposed in the literature. Below is an example of formula proposed by Finocchiaro (et al., 2005):

THI = T − [0.55 × (1 − RH)/100] × (T − 14.4)

where T is the mean daily in °C and RH is the mean relative humidity expressed in percent. Quite often, the parameter used in the analysis model is the temperature of the THI (mainly because temperature and relative humidity are the most available parameters).

Let us also mention the Heat Load Index, referred to as the 'HLI', which is an index that brings together all the weather factors into one number to allow easy interpretation of the cooling capacity of the environment.

The assignation of meteorological data to a farm depends on the countries and on the availability of weather data.

In some countries, the territory may be cut out in a grid, each cell of the grid being considered to have the same meteorological parameters because they are close to the same weather station of reference. As an example, this is the case in France with a grid named SAFRAN cutting the territory into 9892 cells of 64 square kilometres each [8 km by 8 km] (Annex 1). This grid was used, thanks to specific permission from Meteo France, to affect each farm of a given project (by using its GPS coordinate) to a single cell of the grid and thus get relevant meteorological parameters.

The meteorological spatialised data are collected from weather station, on which specific interpolation are applied to present these data on the SAFRAN grid.

The meteorological data key period to consider must be thought according to the production system associated to the breed, type of traits measured and analysed. For example, for milk production (milk recording), we may consider the 3 days preceding the test-day. For semen production, we may consider the meteorological data either at the day of the semen collection, or during the spermatogenesis, which is around 50 days before the semen collection. For the insemination itself (which is in case of fresh semen the same day as semen production), we may consider climate data either the very day of the insemination operation or during a week preceding it.

===== Environmental data from sensor in the farm =====
Temperature and humidity may also be collected on site, thanks to sensors situated on-farm, for example in the sheep pen or the stable.

The number of sensors may depend upon the situation and configuration of each building, the goal being to be representative of the pen. In the practical situations of the SMARTER project, 2 to 3 sensors were set in the pen where animals are indoors at a height of 2 meters above the ground, so that they are protected from the animals. If the pen is already equipped by sensors, it is possible to retrieve the data from the existing sensors. The sensors must cover all the relevant groups of animals (primiparous, multiparous, etc), even if they are in different buildings. Measures might be collected several times a day, for example once an hour, to get a precise evaluation of the daily temperature and hygrometry. To relevantly collect the atmosphere of the building, the sensors must be set in a place free from too much air flow or too much sunshine. It is important to regularly check the batteries to avoid loss of data.

==== Use for genetic analysis / genetic evaluation ====
Effect of meteorological parameters (eg. temperature or THI) may be estimated on zootechnical traits, using different types of linear models.

The parameter may be considered as a categorical variable (each degree of the parameter being defined as a different class). Or it may be considered in a linear regression on degrees of the parameter.

Reaction norms model, using Legendre polynomial for example, may be used to assess populational losses of the zootechnical trait due to high or low temperature and/or humidity.

Two types of analysis can be made:

* a populational analysis (populational response to the effect of temperature or THI). It gives the comfort rage of each population and how much the loss is with lower or higher temperature or THI.
* an analysis of the genetic components using a random regression model. It permits to estimates genetic parameters of traits according to the temperature or THI and to calculate EBVs of animals at different temperatures or THI levels. Such EBVs allow to identify less vulnerable animals along a range of climate values, so as to identify and select the most robust animals.

=== Other environmental record ===
To be completed (or not) when necessary

=== Acknowledgements ===
We gratefully acknowledge the contributions to these environment documentation guidelines by the following people:

* Jean-Michel Astruc, IDELE, France
* Antonello Carta, Agris, Italy
* Philippe Hassoun, INRAE, France,
* Gilles Lagriffoul, IDELE, France
* Carolina Pineda Quiroga, NEIKER, Spain
* Eva Ugarte, NEIKER, Spain

This work received funding from the European Unions’ Horizon 2020 Research & Innovation program under grant agreement N°772787—SMARTER.

=== References ===
Finocchiaro R, van Kaam JB, Portolano B, Misztal I. Effect of heat stress on production of Mediterranean dairy sheep. J Dairy Sci. 2005 May;88(5):1855-64. doi: 10.3168/jds.S0022-0302(05)72860-5. PMID:15829679.

Nozière, P., Sauvant, D., Delaby, L. 2018. INRA Feeding System for Ruminants. Wageningen Academic Publishers, 640 p., 2018, 978-90-8686-292-4. ⟨10.3920/978-90-8686-292-4⟩. ⟨hal-02791719⟩

AFRC (Agricultural and Food Research Council). 1993. Energy and protein requirements of ruminants. CAB International, Wallingford.

=== Annexes ===
[[File:SAFRAN_grid_from_Meteo_France.jpg|center|thumb|600x600px|SAFRAN grid from Meteo France in the case of France]]

Glossary: A

2025-07-03T12:11:12Z

Aashish:

[[Category: Glossary]]
<div style="column-count:3">
<blockquote>
:[[Glossary: ABRI | ABRI]]
:[[Glossary: Accumulated_yield | Accumulated yield]]
:[[Glossary: Accuracy | Accuracy]]
:[[Glossary: Acid_detergent_fibre | Acid detergent fibre]]
:[[Glossary: ADED | ADED]]
:[[Glossary: ADIS | ADIS]]
:[[Glossary: AGBU | AGBU]]
:[[Glossary: Age_at_first_calving | Age at first calving]]
:[[Glossary: Age_at_puberty | Age at puberty]]
:[[Glossary: Age_of_heifer | Age of heifer]]
:[[Glossary: Alternative_method | Alternative method]]
:[[Glossary: Animal_identification_confirmation | Animal identification confirmation]]
:[[Glossary: Applicant | Applicant]]
:[[Glossary: Asymmetric_claws | Asymmetric claws]]
:[[Glossary: Audit | Audit]]
:[[Glossary: Auditor | Auditor]]
:[[Glossary: Automatic_Milking_System | Automatic Milking System]]
:[[Glossary: Average_daily_weight_gain | Average daily weight gain]]
:[[Glossary: Average_yield | Average yield]]
:[[Glossary: Axial_horn_fissure | Axial horn fissure]]

Glossary: A

2025-07-03T12:09:44Z

Aashish:

Glossary: C

2025-03-25T11:07:17Z

Aashish:

[[Category: Glossary]]
<div style="column-count:3">
<blockquote>
:[[Glossary: Calf_Mortality | Calf Mortality]]
:[[Glossary: Calibration | Calibration]]
:[[Glossary: Calving_Interval | Calving Interval]]
:[[Glossary: Calving_rate | Calving rate]]
:[[Glossary: Carcass_length | Carcass length]]
:[[Glossary: Carcass_weight_(cold) | Carcass weight (cold)]]
:[[Glossary: Carry_Over | Carry Over]]
:[[Glossary: Cause_of_death | Cause of death]]
:[[Glossary: Certification | Certification]]
:[[Glossary: CH4_intensity | CH4 intensity]]
:[[Glossary: CH4_production_(liters_or_gr/day) | CH4 production (liters or gr/day)]]
:[[Glossary: Chronic_cow_and_persistent_lesion | Chronic cow and persistent lesion]]
:[[Glossary: Claw_Trimming_Data | Claw Trimming Data]]
:[[Glossary: Clinical_mastitis | Clinical mastitis]]
:[[Glossary: Code_of_Practice | Code of Practice]]
:[[Glossary: Coefficient_of_variation | Coefficient of variation]]
:[[Glossary: Collection | Collection]]
:[[Glossary: Collection_sequence_for_a_given_location_and_a_given_day | Collection sequence for a given location and a given day]]
:[[Glossary: Competent_Authority | Competent Authority]]
:[[Glossary: Computer_Assisted_Semen_Analysis | Computer Assisted Semen Analysis]]
:[[Glossary: Concave_dorsal_wall | Concave dorsal wall]]
:[[Glossary: Conception | Conception]]
:[[Glossary: Conception_rate | Conception rate]]
:[[Glossary: Conception_rate_of_bull | Conception rate of bull]]
:[[Glossary: Conception_rate_of_herd | Conception rate of herd]]
:[[Glossary: Conformation_score | Conformation score]]
:[[Glossary: Consultative_review | Consultative review]]
:[[Glossary: Conventional_ear_tags | Conventional ear tags]]
:[[Glossary: CoQ | CoQ]]
:[[Glossary: Corkscrew_claw | Corkscrew claw]]
:[[Glossary: Corns | Corns]]
:[[Glossary: Cows_at_risk | Cows at risk]]
:[[Glossary: CRC | CRC]]
:

Glossary: C

2025-03-25T11:06:07Z

Aashish:

Glossary: A

2025-03-25T10:55:42Z

Aashish:

Glossary: A

2025-03-25T10:55:09Z

Aashish:

Glossary: A

2025-03-25T10:54:03Z

Aashish:

Category:Glossary A

2025-03-25T10:50:53Z

Aashish: Blanked the page

Glossary: A

2025-03-25T10:50:18Z

Aashish:

Glossary: A

2025-03-25T10:49:53Z

Aashish:

[[Category: Glossary]]
<div style="column-count:3">
<blockquote>
:[[Glossary: ABRI | ABRI]]
:[[Glossary: Accumulated_yield | Accumulated yield]]
:[[Glossary: Accuracy | Accuracy]]
:[[Glossary: Acid_detergent_fibre | Acid detergent fibre]]
:[[Glossary: ADED | ADED]]
:[[Glossary: ADIS | ADIS]]
:[[Glossary: AGBU | AGBU]]
:[[Glossary: Age_at_first_calving | Age at first calving]]
:[[Glossary: Age_at_puberty | Age at puberty]]
:[[Glossary: Age_of_heifer | Age of heifer]]
:[[Glossary: Alternative_method | Alternative method]]
:[[Glossary: Animal_identification_confirmation | Animal identification confirmation]]
:[[Glossary: Applicant | Applicant]]
:[[Glossary: Asymmetric_claws | Asymmetric claws]] AZ
:[[Glossary: Audit | Audit]]
:[[Glossary: Auditor | Auditor]]
:[[Glossary: Automatic_Milking_System | Automatic Milking System]]
:[[Glossary: Average_daily_weight_gain | Average daily weight gain]]
:[[Glossary: Average_yield | Average yield]]
:[[Glossary: Axial_horn_fissure | Axial horn fissure]]

Category:Glossary A

2025-03-25T10:49:17Z

Aashish:

Glossary: Az

Category:Glossary A

2025-03-25T10:48:19Z

Aashish: Blanked the page

Category:Glossary A

2025-03-25T10:47:36Z

Aashish: Created page with "Az"

Section 04 – DNA Technology

2025-01-31T10:07:23Z

Aashish: /* Renewal of accreditation */

== Molecular Genetics ==
<div class="mw-collapsible mw-collapsed">
<div>
=== Introduction ===
Advances in molecular biology, especially genomics, provide a new set of information to be incorporated into the animal industry. On one hand, the use of molecular information may contribute to the enhancement of consumers' trust in the ability to monitor and control the animal production chain. On the other hand, molecular information will greatly contribute to the achievement of genetic improvement for animal traits through the use of genomic breeding values, marker assisted selection, gene introgression, heterosis prediction, pedigree validation/prediction, and genetic defect carrier status. In most cases, advantages of using molecular information via genomic evaluations, comes from improved accuracy of animal breeding values, shortened generation intervals, and increased intensity of selection. Even with these advancements there is still a need for research and development in the search for associations between genetic markers and traits of interest, especially as new traits are included in national evaluation indexes. In addition to that, even with the current incorporation of genomic information into national selection schemes, an understanding of gene action, gene interactions, and differential gene expression to avoid negative collateral effects is needed. Cooperation between animal industries and research is required for a successful and beneficial search for genetic information in commercial livestock populations.

=== Genetic Markers ===
Genetic markers are the fundamental molecular tools for genomics, even as the type of marker used has changed. The first genetic marker associations in livestock were reported using blood typing in the 1960s, the technology then moved to microsatellites (MS) in the 1990s and more recently to the use of Single Nucleotide Polymorphism (SNP). SNP and MS are polymorphic DNA sequences (alleles) at a specific locus of a particular chromosome. While blood typing has been an ICAR approved method of parentage verification currently there are few, if any, commercial labs still offering this testing. For this reason, ICAR no longer recommends blood typing as the basis for carrying out parentage analysis in livestock species where MS or SNP technology is widely available.

==== Microsatellites ====
These are segments of DNA containing tandem repeats of simple motifs usually dimers or trimers. These segments are located throughout the genome and normally in non-coding regions. Over time, these regions are subject to the addition or subtraction of tandem repeats, which means that each microsatellite can have multiple unique alleles. Microsatellites are commonly used in many livestock species for parentage validation.

==== Single Nucleotide Polymorphism (SNP) ====
SNP are the most common type of genetic variation: each SNP represents a variation in a single nucleotide. There are millions of SNP located throughout the genome of every livestock species. For genomics the most informative SNP traditionally are either located in (a) coding regions where different alleles change the structure or function of the encoded protein, or (b) at non-coding regions that are involved in the regulatory function of the gene. For genomic breeding values, SNP that are located in other regions of the genome are also informative as they could be in linkage disequilibrium with alleles that do cause a phenotype change.

One of the big advantages of SNP is their deployment on SNP arrays with a strong parallel processing capacity whereby thousands or hundreds of thousands of SNP can be screened together in a cost-effective and efficient manner across a large number of animals. Currently, the largest livestock genotyping labs can process hundreds of thousands of animals yearly on such arrays. The availability of these large SNP panels is therefore bolstering the search for mutations underlying genetic variation for simple and complex traits. It is also revolutionizing the speed at which trait associated genes or gene regions are being discovered as well as the adoption rate of genomic selection strategies. SNP genotypes have become the international standard for the basis of parentage analysis and ICAR recommends this approach over the use of microsatellites wherever possible due to the improved accuracy and the ease of comparing results between genotyping laboratories.

=== Current and Potential Uses of DNA Technologies ===

==== Parentage verification and parental assignment authentication ====
Prior to the emergence of SNP genotyping, parentage verification was the main commercial use of genetic markers. Traditionally, parentage testing was based on the exclusion of relationship (i.e.: sire or dam) when an animal has a genotype inconsistent to a putative relationship. New trends in animal production systems are tending to encourage animal production in larger numbers per farm in response to environmental and production related constraints. In these large settings, multiple animals could be bred or give birth on the same day, which can result in more pedigree recording errors. As the cost of the analysis decreases and the number of genetic markers available increases, breed societies are now able to build up pedigree records using genetic markers to predict the pedigree of calves born in a herd at a given time. This normally requires a prior knowledge of candidate sires and dams for a calf when lower number (<200) of markers are used, but with enough SNP the correct parents can be predicted without prior knowledge being available as long as the parent is also genotyped. The probability of assignment to a correct pair of animals will depend on the number of markers used, number of alleles per loci, the minor allele frequency in the population, the number of parents, and the number of possible matings. The International Society of Animal Genetics (www.isag.us) has species-specific panels recommended of microsatellite and SNP markers for this purpose, which can be accessed via a link such as provided in ''[https://www.icar.org/Guidelines/04-DNA-Technology-App-1-Cattle-SNP-ISAG-core-additional-panel-2013.xlsx Appendix 1]. Link to SNP markers recommended by ISAG for parentage verification''. For cattle, ICAR has developed a set of parentage SNP, ICAR554, which incorporates the ISAG recommended panel and other highly informative SNP. This panel allows for highly accurate parentage validation and discovery while not allowing for accurate imputation to a higher density. Therefore, the ICAR554 panel can be shared among countries and competitors for parentage analysis without fear of others being able to use them to predict genomic breeding values. ICAR and the Interbull Centre collaborate in offering an international genotype exchange service, referred to as GenoEx, which is described further in Chapter 5 specifically for the exchange of SNP genotypes for the purposes of parentage analysis.

==== Traceability and authentication of animal products offered to consumers ====
Due to multiple crises, including BSE outbreaks to ground beef containing horsemeat, and with increased consumer interest in where their food comes from the traceability of meat products is of greater concern to the industry. Traceability is based on the availability of a verification and control system that monitors all relevant details throughout the entire livestock production chain. Since an individual’s genetic sequence is unique and does not change, its DNA remains constant from ‘conception to consumption’. Therefore, use of genetic markers allows one to match the DNA of an individual at birth to the final product.

Genetic markers for the authentication of animal products for labels of quality related to geographic location and labels of quality related to specific breeds or their crosses are/or will be very useful. However, this requires the establishment of molecular standards or allele frequencies for each breed within a species. A lot of information is coming from studies of genetic diversity among breeds. Genomic regions subject to intense selection in each population are of particular interest. With a large enough set of SNP and genotyped purebred reference animals it is also possible to predict the most likely breed composition of individuals.

==== Molecular genetic information for marker-assisted selection schemes ====
Quantitative traits are generally assumed to be controlled by a large number of genes. However, individual genes sometimes account for a significant amount of variation of the trait. Such is the case for the Myostatin gene and double muscling in beef cattle, the DGAT1 gene and milk components in dairy cattle, or the Booroola fecundity gene and ovulation rate in sheep. Since the genotype of an animal does not change during its lifetime, use of DNA information through the identification of markers linked to QTL with effects on production traits or the identification of a gene itself together with the causative variant is of great interest. Nevertheless, with complex traits there is a growing need of having a sufficiently large marker set to incorporate molecular information for selection decisions. Including genomic information as a selection criterion is of special interest for traits that are difficult and costly to measure and/or are measured late in life. By 2022, >177,000 cattle, >34,000 swine, >16,000 chicken and >4,000 sheep QTL have been identified that are associated with economically important traits such as health, carcass, milk, fertility, and body conformation. The AnimalQTLdb database housed at the [https://www.animalgenome.org/ National Animal Genome Research Program] contains up to date information on cattle, chicken, horse, pig, trout, and sheep QTL data assembled from published data.

Recording schemes have been collecting information for decades on the most common production traits measured in domestic livestock. There is an ever-increasing volume of information becoming available, but for some traits like meat quality, disease resistance and feed efficiency, those records are very expensive to measure, difficult to obtain, or are performed late in the animal’s life. Because of these challenges information for such traits is commonly collected on a reduced number of animals in any given population.

For these challenging, but economically important traits, genetic markers and genomic selection offer significant opportunities for trait selection where it was not economically feasible before. In general, genetic markers and genomics will play an important role for important traits regardless of the livestock species. Genomics can also allow us to increase selection intensities since we can predict genomic breeding values on a large number of animals and thus have more candidates for selection.

==== Disease resistance and genetic defects ====
Another group of traits with a high potential for the use of molecular data and genomics are those linked to resistance, resilience, and susceptibility to diseases. There are a number of multi-factorial or complex diseases that are the result of the interaction between an animal’s genome and environmental components. Disease resistance traits are among the most difficult to include in genetic improvement programs because they require good field measurement of the disease status of the animals and a systematic control of management or environmental conditions that allow for the identification of the environmental influence on the health status of the animal. Infectious diseases depend very much upon environmental factors such as the degree of exposure to the pathogen agent. Thus, if exposure is low, animals will show little variation. Part of the phenotypic differences for resistance may be differences in the degree of challenge. Therefore, if genes or genetic markers linked to resistance are correctly identified, resistant animals will be able to be selected on the base of their genomic information. For many diseases, identification of genes associated with resistance will require experimental conditions to be used. Genetic analysis to identify heterozygous carriers of genetic diseases caused by single, recessive genes are currently in use. Examples in dairy cattle include complex vertebral malformation (CVM), brachyspina (BY), cholesterol deficiency (CD) and several genes, gene regions or haplotypes causing embryo loss or stillbirth in different dairy breeds. In 2022, [https://www.omia.org/home/ OMIA (Online Mendelian Inheritance in Animals),] listed >1000 traits or genetic defects in livestock with a known causative mutation (cattle: 186, pig: 58, chicken: 56, sheep: 49, horse: 48, goat:17). Including these causative allele or associated haplotypes in a breeding program will allow producers to minimize their risk from genetic defects while maximizing genetic progress from beneficial traits.

=== Technical Aspects ===

==== DNA collection ====
Systematic collection of DNA is recommended in several livestock populations. DNA may be obtained from any nuclear cell in the body. Protocols for DNA extraction are now available for blood (white cells), semen, saliva (epithelial cells), hair follicles, muscle, skin, organs (such as liver, spleen etc.). Red blood cells may also be used for poultry as they retain the nuclear body while most other species do not. Small amounts of tissue material are required for routine DNA analysis. However, if there are multiple future uses of an individual’s DNA (whole genome sequencing, traceability, causative allele validations, …), then DNA storage costs, extraction costs, quality, and quantity obtained by different protocols will have to be carefully examined and optimized. Common collection methods include hair follicles, tissue samples (often ear punch) in an enclosed container, blood spots on filter paper, and nasal swabs.

==== Data organization ====
A centralised database may be organised in respect to the main uses of the genetic information:

* Parent verification, assignment, and/or discovery
* Traceability of meat products
* Breed identification or breed diversity
* Qualitative and quantitative traits 

Database tables may contain:

* Animal identification to link to all other information on the animal and its relatives.
* Number of genetic markers: n
* Standard name of each marker i (for i= 1, n)
* Accession number for marker such as the dbSNP ID
* Alleles for marker i
* Genomic location of marker i
* Effect of non-reference allele on the protein
* Phenotypic effect of the allele
* Association with other traits

==== Parentage accuracy ====
While use of microsatellite and SNP markers are both ICAR accredited methods of parentage verification they do not have the same power of parentage accuracy. Briefly the order of accuracy for ISAG and ICAR approved parentage marker panels are:

Microsatellites << small SNP panels (100 or less) < large SNP panels (500 or more)

This order is based on both genotyping accuracy and total genomic information. Comparing the genomic marker error rate in cattle microsatellites have a 1-5% error rate (Baruch and Weller, 2008<ref>Baruch, E., and J. I. Weller. 2008. 'Estimation of the number of SNP genetic markers required for parentage verification', ''Animal Genetics'', 39: 474-79.</ref>) while the SNP error rate is <0.1% (Cooper, Wiggans, and VanRaden 2013<ref>Cooper, T. A., G. R. Wiggans, and P. M. VanRaden. 2013. 'Short communication: relationship of call rate and accuracy of single nucleotide polymorphism genotypes in dairy cattle', ''Journal of dairy science'', 96: 3336-9.</ref>). As 2-3 SNP provide the same parentage exclusion accuracy as 1 microsatellite marker (Vignal et al. 2002<ref>Vignal, A., D. Milan, M. SanCristobal, and A. Eggen. 2002. 'A review on SNP and other types of molecular markers and their use in animal genetics', ''Genet Sel Evol'', 34: 275-305.

1.4.4 Genomic quality control checks</ref>), the 100 and 200 ISAG parentage SNP panels are more accurate than the 12 ISAG parentage microsatellite markers. In the same manner parentage panels of over 500 SNP (McClure et al. 2018<ref>McClure, M. C., J. McCarthy, P. Flynn, J. C. McClure, E. Dair, D. K. O'Connell, and J. F. Kearney. 2018. 'SNP Data Quality Control in a National Beef and Dairy Cattle System and Highly Accurate SNP Based Parentage Verification and Identification', ''Front Genet'', 9: 84.</ref>), such as the ICAR554, are recommended for parentage prediction which requires an even higher level of accuracy.

==== Genomic quality control checks ====
One of the most important parts of a large genomic database is to ensure that a genotype associated with an individual animal truly belongs to that animal. Most large livestock genomic databases deal with SNP data only and the quality of SNP genotyping data is of paramount importance (Wu at al., Evaluation of genotyping concordance for commercial bovine SNP arrays using quality-assurance samples, Animal Genetics, 50: 367-371, 2019). This section, therefore, focuses on quality control for that genomic data type. Both sample and SNP quality control measures are needed, and it is encouraged to develop a system for them early.

For those working with genotype data there are two main concerns. First, is ensuring that the genotype data itself is of high quality and can be trusted. Second, is ensuring that the genotype truly belongs to the individual listed. The recommended quality control checks below will work for any livestock species. The basic checks can be performed with minimal information about the individual, while some of the advanced checks require data that not everyone will have, such as historic animal location.

===== ''Basic genotype quality control checks for SNP-based genotype data'' =====
Genotype:
<ol style="list-style-type:number">
<li> Exclude SNP that have a genotype call rate below 90% when analyzed in your population. Using 500 or more animals to determine the SNP call rate is recommended. Chromosome Y SNP should have their call rate determined only in males for this filter.</li>
<li> Invalidate the individual’s genotype if its overall call rate is below <90%. For SNP-based genotypes, such as those from Illumina or Affymetrix chips, the accuracy of called genotypes is questionable when the individual’s overall call rate is <90%.</li>
<li> Check to see that the animal has all three genotype classes (i.e.: AA, AB and BB) in its full genotype file. If any genotype class is missing or has a frequency below 20% then invalidate the full genotype.</li>
<li> Check to ensure that there are no unexpected alleles in the genotype file. For example, genotypes in AB format should not have T, G, 0, 1, 2 or 9. If present, then invalidate the full genotype file.</li>

</ol>

Parentage:
<ol style="list-style-type:number">
<li> Parent (Sire or Dam) validation. If using 200 or less SNP, a listed sire will validate if <1% of the offspring-parent genotypes are in conflict. A conflicting genotype is where the offspring and listed parent have opposite homozygous genotypes.</li>
<li> Mating validation after parent validation. For all parentage validation SNP where the animal is heterozygous, if for >1% of those SNP the sire and dam are homozygous for the same allele then the listed mating is invalidated. This could represent a case where the offspring and one of the parents were mislabeled with the other’s identification (so the offspring’s genotype belongs to the sire or dam and vice versa). Under such cases, it is recommended to resample the DNA and regenotype, potentially with a panel that includes more SNP.</li>

</ol>

===== ''Advanced quality control checks for SNP-based genotype data'' =====
Animal:
<ol style="list-style-type:number">

<li> Parentage discovery. Using SNP data to predict who an animal’s likely parent is can be very useful, but steps must be taken to ensure a very high probability that the prediction is accurate. The following are recommended:</li>

# Using 500 or more SNP that have a minor allele frequency (MAF) above 20% and call rates above 90%. It is advised to calculate the MAF across your full population. Predicted parents should have <1% conflict rate with the animal.
# Sex check. Make sure that you have a process established to ensure that only males are predicted as the sire and only females as the dam.
# Date of Birth check. If you do not include a check that the predicted parent is older than the animal than the predicted individual could actually be an offspring of the animal.
# Age gap. Cattle normally reach sexual maturity at 11-12 months of age, but this can be as young as 8-9 months, and even younger if in-vitro fertilization is a technology used within the population. Under normal circumstances, a minimum of 17 months between the birth dates of the animal and its predicted parent is recommended to ensure that the predicted parent could have been sexually mature at the time of the breeding.
# Grey zone SNP conflicts. The majority of animals will have <0.5% or >1.5% conflicting genotypes with the individual when parentage discovery is conducted. Those with <0.5% pass the prediction and those with >1.5% fail. Most failed animals will have >8% conflict rates. For those animals who have between 0.5 and 1.5% conflicting SNP when a set parentage panel is used (i.e.: the ICAR554 SNP list), it is advised that the conflict rate from all available SNP be used between the two individuals and if the percent conflicting is <1% they validate as the parent, but if >1% they fail.

<li> Genetic Relationship Matrix (GRM). If an animal’s true parent is not genotyped, then it cannot be directly predicted or validated. The genetic relationship between closely related animals can be used to suggest a potential, non-genotyped, parent. It is recommended that 7,000 or more SNP be used to calculate the GRM. GRM results DO NOT validate a relationship, but only suggest. Caution should be used as GRM values can be inflated for inbred individuals. Full-sibs and parent-child should have GRM values around 50%, while half-sibs would be around 25%. The range for each group can vary 5-10% from the expected value.</li>

<li> Sex prediction. How to perform a sex prediction depends on the type and number of SNP an animal has from the X and Y chromosomes. While not every commercial chip includes chromosome Y SNP, they typically contain chromosome X markers.</li>

# Pseudo autosomal region (PAR) SNP. As both the X and Y chromosomes contain the PAR, SNP from this region should be excluded from sex prediction. If the PAR position boundaries are not published for your species they can be roughly determined by analyzing the chromosome X SNP in known males and females and identifying the region where the MAF in males for a continuous set of SNP is >1%. Non-PAR regions of chromosome X will have SNP with average MAF of <1% in males and >>1% in females.
# Chromosome X predicted. Use non-PAR SNP to determine the animal’s chromosome X heterozygosity rate (number of heterozygous chromosome X SNP / total number of chromosome X SNP). If the average heterozygosity rate is <5%, the predicted sex is male, and if >15% its female. If the rate is between 5 and 15% then the predicted sex is unknown.
# Chromosome Y predicted. Using chromosome Y SNP to predict sex is logically simpler but many commercial chips do not contain them. Say you have 7 chromosome Y SNP with high call rates in males, it is recommended using the following logic. Male is predicted when 6-7 of the Y SNP are present; female is predicted when <1 SNP is present and ambiguous sex is predicted when 2-5 Y SNP are present.
# Ambiguous sex prediction. If one set of sex chromosome SNP returns an ambiguous sex prediction and the other doesn’t it is recommended using the latter as the predicted sex. If both SNP sets are ambiguous, the animal could have Turner syndrome (X0), or Klinefelter’s syndrome (XXY), in this case it is recommended returning an ambiguous predicted sex.
# If the predicted sex from the chromosome X and Y analysis disagree, it is recommended returning an ambiguous predicted sex. This could also indicate a possible Klinefelter syndrome (XXY) animal.
# Sex selected AI semen straws. Sex prediction should not be carried out on DNA obtained from sex selected AI semen straws.

<li> Offspring Quality control. The genotyped and listed offspring of an animal can be used to identify potential cases where the animal’s genotype actually belongs to another animal. These should be used as flags to indicate a potential investigation, but it is recommended to temporarily invalidate the animal’s genotype until cleared. Advised thresholds for those flags are:</li>
# AI sire: If >80% of genotyped offspring fail if >10 offspring are genotyped.
# Stock/herd bull: If >80% of genotyped offspring fail if >5 offspring are genotyped.
# Dam: If 100% of genotyped offspring fail if 2 offspring are genotyped, else if >5 offspring are genotyped then use >80%.

<li> Duplicate genotype. The only case where two or more animals should share the exact same genotype is if they are identical twins or clones. Checking to see if >1 animal has the same genotypes is a useful quality control check. It is recommended using your parentage SNP set for initial screening and for any pair that have >99% identical genotypes and then using all available SNP to see if >99% of the genotypes match.</li>

</ol>

For standardization purposes with respect to the nomenclature of genes or loci, a web site is available at: https://www.genenames.org/about/guidelines#genenames and markers at: [https://hgvs-nomenclature.org/versions/21.0/ <nowiki>http://www.HGVS.org/varnomen</nowiki>.]

</div>
</div>

== ICAR Services Related to DNA Technology ==
ICAR offers three services that are related to the use of DNA all of which are linked to parentage analysis in one form or another, as shown in Figure 1. ICAR DNA services., and describing them in more detail in the other sections.
[[File:ICAR DNA service.png|thumb|Figure 1. ICAR DNA services.|center|415x415px]]

== ICAR Accreditation of Laboratories Providing DNA Genotyping Services ==
<div class="mw-collapsible mw-collapsed">
<div>
=== Introduction ===
Considering the need for high quality standards in all uses of molecular data, ICAR has for several years offered an accreditation service based on defined minimum requirements for laboratories providing DNA genotyping services. The basic requirements of this accreditation include proof of the minimum internal management quality assurance standards and a Rank 1 result from participation in the most recent biennial international ring test developed and offered by the International Society for Animal Genetics (ISAG).

In addition, such laboratories generally have been analyzing the resulting genotypes to carry out microsatellite- and/or SNP-based parentage analysis services including either parentage verification or animal identification confirmation. This ICAR accreditation service has previously been used for recognizing the genotyping laboratory as an accredited organization to provide parentage analysis functions without specifically testing the technical accuracy of doing so. Effective 2021, the SNP-based parentage analysis accreditation service for DNA Data Interpretation Centres has replaced the previous laboratory accreditation for SNP-based parentage verification. In the future, a similar technical process for the accreditation of microsatellite-based parentage analysis may be introduced by ICAR but until such time, the existing process for the accreditation of genotyping laboratories will remain in effect.

The following guidelines for accreditation are provided for microsatellite- and SNP-based genotyping in cattle. Minimum requirements for additional species and other DNA tests may be defined in the future.

=== Scope ===
These guidelines are for the accreditation, by ICAR, of genotyping laboratories that analyze biological samples from cattle using microsatellite- and or SNP-based genotyping, which may be subsequently used for various levels of parentage analysis, genotype imputation, estimation of genomic breeding values and other activities related to genomic selection strategies. This accreditation process also includes parentage verification based on microsatellites since ICAR has not established this service as part of the portfolio of possible accreditations for DNA data interpretation centres. For genotyping laboratories that would like to receive ICAR accreditation for SNP-based parentage verification, they must now apply separately to ICAR for its parallel service of parentage analysis accreditation for DNA data interpretation centres, as described in section 4.

=== ICAR Guidelines for Accreditation of Genotyping Laboratories ===
The accreditation process comprises the following steps:

* Application for accreditation
* Payment of relevant fee
* Review of application
* Granting of accreditation

==== Application for accreditation ====
Laboratories requesting accreditation only for microsatellite- based genotyping and parentage verification must apply by downloading and completing the appropriate form as provided in [https://www.icar.org/wp-content/uploads/2022/05/Annex-II-Application-Form-for-STR-Accreditation.pdf ''Appendix 2. Application form for microsatellite-based parentage testing in cattle''.] Laboratories seeking ICAR accreditation involving SNP-based genotyping must apply by downloading and completing the appropriate form as provided in [https://www.icar.org/wp-content/uploads/2022/05/Annex-V-Application-Form-for-SNP-Accreditation.pdf ''Appendix 3. Application form for SNP-based genotyping required for parentage analysis in cattle.''] Laboratories that have previously received ICAR accreditation for either service may re-apply prior to the expiry of any such accreditation using a shortened renewal form available on the ICAR web site. All application forms must be emailed to the ICAR secretariat at dna@icar.org and be filled out accurately and completely including the necessary documentation as required.

==== Payment of relevant fee ====
Along with the completed application form, the applicant must also provide full payment of the relevant fee as established by ICAR and given in [https://www.icar.org/index.php/certifications/certification-and-accreditation-of-dna-genetic-laboratories/guidelines-for-str-and-snp-based-parentage-testing-in-cattle/ ''Appendix 4. ICAR DNA Laboratory Accreditation Service fees''.]

==== Review of application ====
The application will be evaluated by a committee of experts appointed by ICAR that will either:

* Approve the application
* Request additional information, or
* Reject the application

In the case of rejection, the laboratory may make a new submission as part of the ICAR annual call for applications for any subsequent year after the failed application.

==== Granting of accreditation ====
Accreditation will be given for a period of two calendar years with an expiry date of December 31st of the second year after receiving ICAR accreditation as a laboratory providing DNA genotyping services.

==== Renewal of accreditation ====
In advance of the expiry date of any existing ICAR accreditation, normally during the same year of the December 31st expiry date, a laboratory can apply for renewal of their accreditation by submitting an application as described in section 3.3.1 above and successfully completing the other steps outlined in this section 3.

==== Laboratory accreditation ====
Effective the 2022 ICAR call for accreditation of genotyping laboratories, ISO17025 accreditation, or an equivalent accreditation for ensuring quality internal management systems, is a mandatory requirement for SNP-based accreditation. In addition, effective the 2022 call for accreditation of genotyping laboratories for microsatellite (STR)-based accreditation, ISO9001 certification will no longer be acceptable and only ISO17025, or an equivalent accreditation, will be an acceptable level of accreditation to ensure quality internal management systems. During the year of application for ICAR accreditation as a laboratory providing DNA genotyping services, the applicant must provide proof of ISO17025 accreditation with an expiry date of October 31st of the following calendar year, or later.

==== Participation and performance in ring test ====
On a biennial basis, initiated during even years (i.e.: 2022, 20246, etc…) and discussed at its biennial conference in odd years (2023, 2025, etc.), ISAG conducts an international ring (comparison) test of laboratories for both microsatellite- and SNP-based genotyping. The participation in ISAG and performance within these ring tests must be disclosed, and certificates provided to ICAR, when available. Applicants must also sign a release allowing ISAG to directly disclose their ring test results to ICAR. Participation in the most recent ISAG ring test is a minimum requirement to qualify for ICAR accreditation. For the ISAG microsatellite ring test, lab genotyping performance for the official set of 12 ISAG microsatellites must be disclosed. The committee of experts will decide performance thresholds for each ring test with due consideration for the structure of the ring test and the average performance of laboratories in the ring test that year. Only those laboratories achieving Rank 1 status in the most recent biennial ISAG ring test shall automatically qualify to receive ICAR accreditation as a genotyping laboratory. Laboratories achieving a Rank 2 status in the most recent ISAG ring test may qualify to receive ICAR accreditation, at the discretion of the committee of experts, but must provide evidence of Rank 1 status for previous ISAG ring tests as well as documentation outlining the cause of the Rank 2 result and any associated actions to mitigate similar outcomes in future ISAG ring tests. Laboratories achieving a status lower than Rank 2 in the most recent ISAG ring test do not qualify for ICAR accreditation as a laboratory providing DNA genotyping services.

==== Microsatellite markers ====
The names of all microsatellites typed on all animals (marker set I) and of the additional ones assayed in the case of unresolved parentage (marker set II) must be declared, as well as the number of animals typed in at least the last two years. The minimum requirement for international exchange is the complete set of 12 official ISAG microsatellite markers. To ensure sufficient experience within the lab, analysis of 500 animals per year is set as minimum requirement for microsatellite parentage verification certification.

''[https://www.icar.org/wp-content/uploads/2018/05/03-Annex-III-ISAG-microsatellites.pdf Appendix 5. ISAG recommended microsatellites for parentage verification in cattle]'' contains the list of microsatellite markers recommended by ISAG and the method for calculating 1 parent and 2 parent exclusion probabilities. The rules for microsatellite-based parentage verification in cattle are described in ''[https://www.icar.org/wp-content/uploads/2018/05/01-Annex-I-guidelines-microsats-STRs.pdf Appendix 6. Rules for microsatellite-based parentage testing in cattle]''. Exclusion probability (PE; 2 parents and 1 parent) of each marker and of the complete marker sets must be calculated and provided in the application. The type of population and number of animals (minimum 150) used for computations are to be described. ICAR recommends using Holstein as a reference group when possible. The ICAR committee of experts will evaluate that an appropriate PE is reached for accreditation, on the basis of the population analyzed.

==== SNP markers ====
ICAR accreditation of SNP-based parentage verification is based on the full set of 200 SNP previously recommended by ISAG. The name of all SNP genotyped on all animals (marker set I, including the 100 “Core” SNP) and of the additional markers assayed in the case of unresolved parentage (marker set II, including the 100 “Additional” SNP) must be declared, as well as the number of animals SNP genotyped in at least the last two years. ICAR recommends using the full set of 200 SNP for parentage verification of all animals genotyped (''see [https://www.icar.org/Guidelines/04-DNA-Technology-App7-SNP-list-for-parentage-verification.pdf Appendix 7. List of approved SNP for parentage verification in]'' [https://www.icar.org/Guidelines/04-DNA-Technology-App7-SNP-list-for-parentage-verification.pdf cattle]). ICAR may, however, based on scientific evidence, identify specific problematic SNP that must be excluded for parentage analysis, as described in the ICAR documentation related to the accreditation of DNA data interpretation centres outlined in section 4.

==== Marker nomenclature ====
Nomenclature of markers must be described. ISAG nomenclature is required for the official ISAG 12 marker set as well as for the ISAG SNP marker set.
</div>
</div>

== Accreditation of Organisations Performing SNP-Based Parentage Analysis ==
<div class="mw-collapsible mw-collapsed">
<div>
=== Introduction ===
With the advent of SNP genotyping, the function of DNA genotyping as a laboratory activity can be separated from the functions of performing parentage verification and parentage discovery. Consequently, ICAR has established a separate accreditation for applying the results of SNP-based genotyping, which may be undertaken by laboratories, breed association societies, genetic evaluation centres and any other organization involved in parentage verification and/or the data processing of SNP genotypes.

Parentage verification and discovery are concerned with using the results that are delivered by the laboratories from DNA genotyping and require SNP genotypes for the animal itself, its recorded parents and other possible parents in the case of parentage discovery. Organizations undertaking this function may be service providers between laboratories that ICAR has accredited for microsatellite- and or SNP-based DNA genotyping and end users that may include breed societies, breeding companies, breeders and commercial farmers.

Service providers could use different laboratories for different breeds and/or species. Considering the importance of animal identification and parentage verification in animal recording, ICAR has decided to define the minimum requirements for using the results of DNA genotyping, and other information, for the purpose of:

# Parentage verification
# Parentage discovery, and
# Animal identification confirmation

The purpose of these guidelines is to provide a basis for the accreditation of processes used by organizations that use SNP genotypes in cattle. Minimum requirements for additional species and other DNA analyses may be defined in the future.

=== Scope ===
These guidelines are for the accreditation, by ICAR, of organizations that use the results of SNP-based tests for parentage analysis in cattle, which includes parentage verification, parentage discovery, and/or animal identification confirmation.

=== Accreditation of Organizations Performing Parentage Analysis ===
The ICAR accreditation process comprises the following steps:

* Application for accreditation
* Payment of relevant fee
* Review of application
* Technical processing of test data files
* Granting of accreditation

==== Application ====
Organizations carrying out SNP-based parentage analysis and requesting ICAR accreditation as a DNA Data Interpretation Centre must apply by downloading and completing the appropriate form included below as [https://www.icar.org/wp-content/uploads/2016/10/6-Annex-V-Application-Form-forICAR-Accreditation-of-DNA-Centres.pdf ''Appendix 8. Application form for organizations seeking ICAR parentage analysis accreditation for DNA data interpretation centres''.] This form must be filled out accurately and completely, providing necessary documentation as required, and submitted to ICAR with payment of the appropriate fee.

==== Review of application ====
The application will first be reviewed internally by ICAR for its completeness and additional details may be requested as needed. ICAR administration will also confirm receipt of the applicable fee.

==== Technical processing of test files ====
The applicant organization will receive a set of data files from ICAR through the Interbull Centre, for processing using its existing procedures for carrying out the level of parentage analysis for which the applicant is seeking ICAR accreditation as a DNA Data Interpretation Centre. A detailed description of this step is described in [https://www.icar.org/index.php/certifications/certification-and-accreditation-of-dna-genetic-laboratories/two-new-dna-based-services/dna-data-interpretation-centres/ ''Appendix 9. Applicant's Guide for ICAR Parentage Analysis Accreditation for DNA Data Interpretation Centres.''] In order for the applicant to be successful in obtaining the requested ICAR accreditation, it's procedures for conducting parentage analysis must exactly follow [https://www.icar.org/Documents/GenoEx/ICAR%20Guidelines%20for%20Parentage%20Verification%20and%20Parentage%20Discovery%20based%20on%20SNP.pdf ''Appendix 10. ICAR Guidelines for Parentage Verification and Parentage Discovery Based on SNP Genotypes''.] The list of SNP to be used for either parentage verification (N=200) or parentage discovery (N=554) are available in [https://www.icar.org/Guidelines/04-DNA-Technology-App-11-SNP-list-for-parentage-verification-or-discovery.pdf ''Appendix 11. List of SNP to be used for either parentage verification or parentage discovery''.] Once the applicant has completed its internal parentage analysis procedures based on the accreditation test files it received, it must send a data file of results back to the Interbull Centre. A maximum time period for 90 calendar days will be allowed for the applicant to submit acceptable files of results back to the Interbull Centre.

==== Granting of accreditation ====
Once the Interbull Centre receives the file of parentage analysis results from the applicant, it will complete the technical review and determine if the applicant has successfully completed the accreditation or not. The Interbull Centre shall inform ICAR of the results and ICAR shall issue a formal notification to the applicant. In the event the applicant was not successful in receiving ICAR accreditation, the applicant may initiate a new request for accreditation by completing and submitting the appropriate forms and providing payment of the applicable fee, as outlined above.

==== Renewal of accreditation ====
In advance of the expiry date of any existing ICAR accreditation, which coincides with the two-year anniversary date of the current accreditation, an applicant can apply for renewal of their accreditation by submitting an application as described in section 4.3.1 above and successfully completing the other required steps outlined in this section 4.
</div>
</div>

== Genotype Exchange Service – GenoEx-PSE ==
Effective 2018, ICAR has made available a genotype exchange service for parentage analysis, GenoEx-PSE, offered through the Interbull Centre. The main goal of this service is to facilitate the international exchange of SNP genotypes such that approved service users can carry out parentage analysis services at a national level in an efficient manner. The GenoEx-PSE database system and user interface has been developed to allow for the exchange of SNP genotypes for either parentage verification or parentage discovery based on the list of SNP provided in ''Appendix 11. List of SNP to be used for either parentage verification or parentage discovery''.

In order for an organization to qualify as a service user for GenoEx-PSE, it must first receive ICAR accreditation as a DNA data interpretation centre. The level of such ICAR accreditation (i.e.: for SNP-based parentage verification alone or for both SNP-based parentage verification and discovery) shall determine the highest level of SNP that may be exchanged via the GenoEx-PSE service. For details associated with this ICAR service, refer to the GenoEx-PSE web site at [https://genoex.org/ www.GenoEx.org.]

== Appendix list ==
<div class="mw-collapsible mw-collapsed">
<div>
=== Appendix 1. Link to SNP markers recommended by ISAG for parentage verification ===
https://www.icar.org/Guidelines/04-DNA-Technology-App-1-Cattle-SNP-ISAG-core-additional-panel-2013.xlsx

=== Appendix 2. Application form for microsatellite-based parentage testing in cattle ===
Please refer [https://www.icar.org/wp-content/uploads/2022/05/Annex-II-Application-Form-for-STR-Accreditation.pdf here] on the ICAR website for the Form for ICAR laboratory accreditation for STR Microsatellite-based Parentage Testing in Cattle.

=== Appendix 3. Application form for SNP-based genotyping required for parentage analysis in cattle ===
Please refer [https://www.icar.org/wp-content/uploads/2022/05/Annex-V-Application-Form-for-SNP-Accreditation.pdf here] on the ICAR website for the Form for ICAR laboratory accreditation for SNP-based genotyping required for Parentage Analysis in Cattle.

=== Appendix 4. ICAR DNA Laboratory Accreditation Service fees ===
Please refer [https://www.icar.org/index.php/certifications/certification-and-accreditation-of-dna-genetic-laboratories/guidelines-for-str-and-snp-based-parentage-testing-in-cattle/ here] on the ICAR website for DNA testing accreditation services fees.

=== Appendix 5. ISAG recommended microsatellites for parentage verification in cattle ===
Please refer [https://www.icar.org/wp-content/uploads/2018/05/03-Annex-III-ISAG-microsatellites.pdf here] on the ICAR website for the list of ISAG recommended microsatellites for parentage verification in cattle.

=== Appendix 6. Rules for microsatellite-based parentage testing in cattle ===
Please refer [https://www.icar.org/wp-content/uploads/2018/05/01-Annex-I-guidelines-microsats-STRs.pdf here] on the ICAR website for the rules for microsatellite-based parentage verification in cattle.

=== Appendix 7. List of approved SNP for parentage verification in cattle ===
Please refer [https://www.icar.org/Guidelines/04-DNA-Technology-App7-SNP-list-for-parentage-verification.pdf here] on the ICAR website for the ICAR approved list of 200 SNP for parentage verification in cattle.

=== Appendix 8. Application form for organizations seeking ICAR parentage analysis accreditation for DNA data interpretation centres ===
Please refer [https://www.icar.org/wp-content/uploads/2016/10/6-Annex-V-Application-Form-forICAR-Accreditation-of-DNA-Centres.pdf here] on the ICAR website for the application form for organizations seeking ICAR accreditation status as a DNA data interpretation centre.

=== Appendix 9. Applicant's Guide for ICAR Parentage Analysis Accreditation for DNA Data Interpretation Centres ===
Please refer [https://www.icar.org/index.php/certifications/certification-and-accreditation-of-dna-genetic-laboratories/two-new-dna-based-services/dna-data-interpretation-centres/ here] on the ICAR website for the Applicant's Guide for ICAR Parentage Analysis Accreditation for DNA Data Interpretation Centres.

=== Appendix 10. ICAR Guidelines for Parentage Verification and Parentage Discovery Based on SNP Genotypes ===
Please refer [https://www.icar.org/Documents/GenoEx/ICAR%20Guidelines%20for%20Parentage%20Verification%20and%20Parentage%20Discovery%20based%20on%20SNP.pdf here] on the ICAR website for the ICAR Guidelines for Parentage Verification and Parentage Discovery Based on SNP Genotypes.

=== Appendix 11. List of SNP to be used for either parentage verification or parentage discovery ===
Please refer [https://www.icar.org/Guidelines/04-DNA-Technology-App-11-SNP-list-for-parentage-verification-or-discovery.pdf here] on the ICAR website for the list of SNP to be used for either parentage verification or parentage discovery.
</div>
</div>

Section 04 – DNA Technology

2025-01-31T10:06:40Z

Aashish: /* Renewal of accreditation */

== Molecular Genetics ==
<div class="mw-collapsible mw-collapsed">
<div>
=== Introduction ===
Advances in molecular biology, especially genomics, provide a new set of information to be incorporated into the animal industry. On one hand, the use of molecular information may contribute to the enhancement of consumers' trust in the ability to monitor and control the animal production chain. On the other hand, molecular information will greatly contribute to the achievement of genetic improvement for animal traits through the use of genomic breeding values, marker assisted selection, gene introgression, heterosis prediction, pedigree validation/prediction, and genetic defect carrier status. In most cases, advantages of using molecular information via genomic evaluations, comes from improved accuracy of animal breeding values, shortened generation intervals, and increased intensity of selection. Even with these advancements there is still a need for research and development in the search for associations between genetic markers and traits of interest, especially as new traits are included in national evaluation indexes. In addition to that, even with the current incorporation of genomic information into national selection schemes, an understanding of gene action, gene interactions, and differential gene expression to avoid negative collateral effects is needed. Cooperation between animal industries and research is required for a successful and beneficial search for genetic information in commercial livestock populations.

=== Genetic Markers ===
Genetic markers are the fundamental molecular tools for genomics, even as the type of marker used has changed. The first genetic marker associations in livestock were reported using blood typing in the 1960s, the technology then moved to microsatellites (MS) in the 1990s and more recently to the use of Single Nucleotide Polymorphism (SNP). SNP and MS are polymorphic DNA sequences (alleles) at a specific locus of a particular chromosome. While blood typing has been an ICAR approved method of parentage verification currently there are few, if any, commercial labs still offering this testing. For this reason, ICAR no longer recommends blood typing as the basis for carrying out parentage analysis in livestock species where MS or SNP technology is widely available.

==== Microsatellites ====
These are segments of DNA containing tandem repeats of simple motifs usually dimers or trimers. These segments are located throughout the genome and normally in non-coding regions. Over time, these regions are subject to the addition or subtraction of tandem repeats, which means that each microsatellite can have multiple unique alleles. Microsatellites are commonly used in many livestock species for parentage validation.

==== Single Nucleotide Polymorphism (SNP) ====
SNP are the most common type of genetic variation: each SNP represents a variation in a single nucleotide. There are millions of SNP located throughout the genome of every livestock species. For genomics the most informative SNP traditionally are either located in (a) coding regions where different alleles change the structure or function of the encoded protein, or (b) at non-coding regions that are involved in the regulatory function of the gene. For genomic breeding values, SNP that are located in other regions of the genome are also informative as they could be in linkage disequilibrium with alleles that do cause a phenotype change.

One of the big advantages of SNP is their deployment on SNP arrays with a strong parallel processing capacity whereby thousands or hundreds of thousands of SNP can be screened together in a cost-effective and efficient manner across a large number of animals. Currently, the largest livestock genotyping labs can process hundreds of thousands of animals yearly on such arrays. The availability of these large SNP panels is therefore bolstering the search for mutations underlying genetic variation for simple and complex traits. It is also revolutionizing the speed at which trait associated genes or gene regions are being discovered as well as the adoption rate of genomic selection strategies. SNP genotypes have become the international standard for the basis of parentage analysis and ICAR recommends this approach over the use of microsatellites wherever possible due to the improved accuracy and the ease of comparing results between genotyping laboratories.

=== Current and Potential Uses of DNA Technologies ===

==== Parentage verification and parental assignment authentication ====
Prior to the emergence of SNP genotyping, parentage verification was the main commercial use of genetic markers. Traditionally, parentage testing was based on the exclusion of relationship (i.e.: sire or dam) when an animal has a genotype inconsistent to a putative relationship. New trends in animal production systems are tending to encourage animal production in larger numbers per farm in response to environmental and production related constraints. In these large settings, multiple animals could be bred or give birth on the same day, which can result in more pedigree recording errors. As the cost of the analysis decreases and the number of genetic markers available increases, breed societies are now able to build up pedigree records using genetic markers to predict the pedigree of calves born in a herd at a given time. This normally requires a prior knowledge of candidate sires and dams for a calf when lower number (<200) of markers are used, but with enough SNP the correct parents can be predicted without prior knowledge being available as long as the parent is also genotyped. The probability of assignment to a correct pair of animals will depend on the number of markers used, number of alleles per loci, the minor allele frequency in the population, the number of parents, and the number of possible matings. The International Society of Animal Genetics (www.isag.us) has species-specific panels recommended of microsatellite and SNP markers for this purpose, which can be accessed via a link such as provided in ''[https://www.icar.org/Guidelines/04-DNA-Technology-App-1-Cattle-SNP-ISAG-core-additional-panel-2013.xlsx Appendix 1]. Link to SNP markers recommended by ISAG for parentage verification''. For cattle, ICAR has developed a set of parentage SNP, ICAR554, which incorporates the ISAG recommended panel and other highly informative SNP. This panel allows for highly accurate parentage validation and discovery while not allowing for accurate imputation to a higher density. Therefore, the ICAR554 panel can be shared among countries and competitors for parentage analysis without fear of others being able to use them to predict genomic breeding values. ICAR and the Interbull Centre collaborate in offering an international genotype exchange service, referred to as GenoEx, which is described further in Chapter 5 specifically for the exchange of SNP genotypes for the purposes of parentage analysis.

==== Traceability and authentication of animal products offered to consumers ====
Due to multiple crises, including BSE outbreaks to ground beef containing horsemeat, and with increased consumer interest in where their food comes from the traceability of meat products is of greater concern to the industry. Traceability is based on the availability of a verification and control system that monitors all relevant details throughout the entire livestock production chain. Since an individual’s genetic sequence is unique and does not change, its DNA remains constant from ‘conception to consumption’. Therefore, use of genetic markers allows one to match the DNA of an individual at birth to the final product.

Genetic markers for the authentication of animal products for labels of quality related to geographic location and labels of quality related to specific breeds or their crosses are/or will be very useful. However, this requires the establishment of molecular standards or allele frequencies for each breed within a species. A lot of information is coming from studies of genetic diversity among breeds. Genomic regions subject to intense selection in each population are of particular interest. With a large enough set of SNP and genotyped purebred reference animals it is also possible to predict the most likely breed composition of individuals.

==== Molecular genetic information for marker-assisted selection schemes ====
Quantitative traits are generally assumed to be controlled by a large number of genes. However, individual genes sometimes account for a significant amount of variation of the trait. Such is the case for the Myostatin gene and double muscling in beef cattle, the DGAT1 gene and milk components in dairy cattle, or the Booroola fecundity gene and ovulation rate in sheep. Since the genotype of an animal does not change during its lifetime, use of DNA information through the identification of markers linked to QTL with effects on production traits or the identification of a gene itself together with the causative variant is of great interest. Nevertheless, with complex traits there is a growing need of having a sufficiently large marker set to incorporate molecular information for selection decisions. Including genomic information as a selection criterion is of special interest for traits that are difficult and costly to measure and/or are measured late in life. By 2022, >177,000 cattle, >34,000 swine, >16,000 chicken and >4,000 sheep QTL have been identified that are associated with economically important traits such as health, carcass, milk, fertility, and body conformation. The AnimalQTLdb database housed at the [https://www.animalgenome.org/ National Animal Genome Research Program] contains up to date information on cattle, chicken, horse, pig, trout, and sheep QTL data assembled from published data.

Recording schemes have been collecting information for decades on the most common production traits measured in domestic livestock. There is an ever-increasing volume of information becoming available, but for some traits like meat quality, disease resistance and feed efficiency, those records are very expensive to measure, difficult to obtain, or are performed late in the animal’s life. Because of these challenges information for such traits is commonly collected on a reduced number of animals in any given population.

For these challenging, but economically important traits, genetic markers and genomic selection offer significant opportunities for trait selection where it was not economically feasible before. In general, genetic markers and genomics will play an important role for important traits regardless of the livestock species. Genomics can also allow us to increase selection intensities since we can predict genomic breeding values on a large number of animals and thus have more candidates for selection.

==== Disease resistance and genetic defects ====
Another group of traits with a high potential for the use of molecular data and genomics are those linked to resistance, resilience, and susceptibility to diseases. There are a number of multi-factorial or complex diseases that are the result of the interaction between an animal’s genome and environmental components. Disease resistance traits are among the most difficult to include in genetic improvement programs because they require good field measurement of the disease status of the animals and a systematic control of management or environmental conditions that allow for the identification of the environmental influence on the health status of the animal. Infectious diseases depend very much upon environmental factors such as the degree of exposure to the pathogen agent. Thus, if exposure is low, animals will show little variation. Part of the phenotypic differences for resistance may be differences in the degree of challenge. Therefore, if genes or genetic markers linked to resistance are correctly identified, resistant animals will be able to be selected on the base of their genomic information. For many diseases, identification of genes associated with resistance will require experimental conditions to be used. Genetic analysis to identify heterozygous carriers of genetic diseases caused by single, recessive genes are currently in use. Examples in dairy cattle include complex vertebral malformation (CVM), brachyspina (BY), cholesterol deficiency (CD) and several genes, gene regions or haplotypes causing embryo loss or stillbirth in different dairy breeds. In 2022, [https://www.omia.org/home/ OMIA (Online Mendelian Inheritance in Animals),] listed >1000 traits or genetic defects in livestock with a known causative mutation (cattle: 186, pig: 58, chicken: 56, sheep: 49, horse: 48, goat:17). Including these causative allele or associated haplotypes in a breeding program will allow producers to minimize their risk from genetic defects while maximizing genetic progress from beneficial traits.

=== Technical Aspects ===

==== DNA collection ====
Systematic collection of DNA is recommended in several livestock populations. DNA may be obtained from any nuclear cell in the body. Protocols for DNA extraction are now available for blood (white cells), semen, saliva (epithelial cells), hair follicles, muscle, skin, organs (such as liver, spleen etc.). Red blood cells may also be used for poultry as they retain the nuclear body while most other species do not. Small amounts of tissue material are required for routine DNA analysis. However, if there are multiple future uses of an individual’s DNA (whole genome sequencing, traceability, causative allele validations, …), then DNA storage costs, extraction costs, quality, and quantity obtained by different protocols will have to be carefully examined and optimized. Common collection methods include hair follicles, tissue samples (often ear punch) in an enclosed container, blood spots on filter paper, and nasal swabs.

==== Data organization ====
A centralised database may be organised in respect to the main uses of the genetic information:

* Parent verification, assignment, and/or discovery
* Traceability of meat products
* Breed identification or breed diversity
* Qualitative and quantitative traits 

Database tables may contain:

* Animal identification to link to all other information on the animal and its relatives.
* Number of genetic markers: n
* Standard name of each marker i (for i= 1, n)
* Accession number for marker such as the dbSNP ID
* Alleles for marker i
* Genomic location of marker i
* Effect of non-reference allele on the protein
* Phenotypic effect of the allele
* Association with other traits

==== Parentage accuracy ====
While use of microsatellite and SNP markers are both ICAR accredited methods of parentage verification they do not have the same power of parentage accuracy. Briefly the order of accuracy for ISAG and ICAR approved parentage marker panels are:

Microsatellites << small SNP panels (100 or less) < large SNP panels (500 or more)

This order is based on both genotyping accuracy and total genomic information. Comparing the genomic marker error rate in cattle microsatellites have a 1-5% error rate (Baruch and Weller, 2008<ref>Baruch, E., and J. I. Weller. 2008. 'Estimation of the number of SNP genetic markers required for parentage verification', ''Animal Genetics'', 39: 474-79.</ref>) while the SNP error rate is <0.1% (Cooper, Wiggans, and VanRaden 2013<ref>Cooper, T. A., G. R. Wiggans, and P. M. VanRaden. 2013. 'Short communication: relationship of call rate and accuracy of single nucleotide polymorphism genotypes in dairy cattle', ''Journal of dairy science'', 96: 3336-9.</ref>). As 2-3 SNP provide the same parentage exclusion accuracy as 1 microsatellite marker (Vignal et al. 2002<ref>Vignal, A., D. Milan, M. SanCristobal, and A. Eggen. 2002. 'A review on SNP and other types of molecular markers and their use in animal genetics', ''Genet Sel Evol'', 34: 275-305.

1.4.4 Genomic quality control checks</ref>), the 100 and 200 ISAG parentage SNP panels are more accurate than the 12 ISAG parentage microsatellite markers. In the same manner parentage panels of over 500 SNP (McClure et al. 2018<ref>McClure, M. C., J. McCarthy, P. Flynn, J. C. McClure, E. Dair, D. K. O'Connell, and J. F. Kearney. 2018. 'SNP Data Quality Control in a National Beef and Dairy Cattle System and Highly Accurate SNP Based Parentage Verification and Identification', ''Front Genet'', 9: 84.</ref>), such as the ICAR554, are recommended for parentage prediction which requires an even higher level of accuracy.

==== Genomic quality control checks ====
One of the most important parts of a large genomic database is to ensure that a genotype associated with an individual animal truly belongs to that animal. Most large livestock genomic databases deal with SNP data only and the quality of SNP genotyping data is of paramount importance (Wu at al., Evaluation of genotyping concordance for commercial bovine SNP arrays using quality-assurance samples, Animal Genetics, 50: 367-371, 2019). This section, therefore, focuses on quality control for that genomic data type. Both sample and SNP quality control measures are needed, and it is encouraged to develop a system for them early.

For those working with genotype data there are two main concerns. First, is ensuring that the genotype data itself is of high quality and can be trusted. Second, is ensuring that the genotype truly belongs to the individual listed. The recommended quality control checks below will work for any livestock species. The basic checks can be performed with minimal information about the individual, while some of the advanced checks require data that not everyone will have, such as historic animal location.

===== ''Basic genotype quality control checks for SNP-based genotype data'' =====
Genotype:
<ol style="list-style-type:number">
<li> Exclude SNP that have a genotype call rate below 90% when analyzed in your population. Using 500 or more animals to determine the SNP call rate is recommended. Chromosome Y SNP should have their call rate determined only in males for this filter.</li>
<li> Invalidate the individual’s genotype if its overall call rate is below <90%. For SNP-based genotypes, such as those from Illumina or Affymetrix chips, the accuracy of called genotypes is questionable when the individual’s overall call rate is <90%.</li>
<li> Check to see that the animal has all three genotype classes (i.e.: AA, AB and BB) in its full genotype file. If any genotype class is missing or has a frequency below 20% then invalidate the full genotype.</li>
<li> Check to ensure that there are no unexpected alleles in the genotype file. For example, genotypes in AB format should not have T, G, 0, 1, 2 or 9. If present, then invalidate the full genotype file.</li>

</ol>

Parentage:
<ol style="list-style-type:number">
<li> Parent (Sire or Dam) validation. If using 200 or less SNP, a listed sire will validate if <1% of the offspring-parent genotypes are in conflict. A conflicting genotype is where the offspring and listed parent have opposite homozygous genotypes.</li>
<li> Mating validation after parent validation. For all parentage validation SNP where the animal is heterozygous, if for >1% of those SNP the sire and dam are homozygous for the same allele then the listed mating is invalidated. This could represent a case where the offspring and one of the parents were mislabeled with the other’s identification (so the offspring’s genotype belongs to the sire or dam and vice versa). Under such cases, it is recommended to resample the DNA and regenotype, potentially with a panel that includes more SNP.</li>

</ol>

===== ''Advanced quality control checks for SNP-based genotype data'' =====
Animal:
<ol style="list-style-type:number">

<li> Parentage discovery. Using SNP data to predict who an animal’s likely parent is can be very useful, but steps must be taken to ensure a very high probability that the prediction is accurate. The following are recommended:</li>

# Using 500 or more SNP that have a minor allele frequency (MAF) above 20% and call rates above 90%. It is advised to calculate the MAF across your full population. Predicted parents should have <1% conflict rate with the animal.
# Sex check. Make sure that you have a process established to ensure that only males are predicted as the sire and only females as the dam.
# Date of Birth check. If you do not include a check that the predicted parent is older than the animal than the predicted individual could actually be an offspring of the animal.
# Age gap. Cattle normally reach sexual maturity at 11-12 months of age, but this can be as young as 8-9 months, and even younger if in-vitro fertilization is a technology used within the population. Under normal circumstances, a minimum of 17 months between the birth dates of the animal and its predicted parent is recommended to ensure that the predicted parent could have been sexually mature at the time of the breeding.
# Grey zone SNP conflicts. The majority of animals will have <0.5% or >1.5% conflicting genotypes with the individual when parentage discovery is conducted. Those with <0.5% pass the prediction and those with >1.5% fail. Most failed animals will have >8% conflict rates. For those animals who have between 0.5 and 1.5% conflicting SNP when a set parentage panel is used (i.e.: the ICAR554 SNP list), it is advised that the conflict rate from all available SNP be used between the two individuals and if the percent conflicting is <1% they validate as the parent, but if >1% they fail.

<li> Genetic Relationship Matrix (GRM). If an animal’s true parent is not genotyped, then it cannot be directly predicted or validated. The genetic relationship between closely related animals can be used to suggest a potential, non-genotyped, parent. It is recommended that 7,000 or more SNP be used to calculate the GRM. GRM results DO NOT validate a relationship, but only suggest. Caution should be used as GRM values can be inflated for inbred individuals. Full-sibs and parent-child should have GRM values around 50%, while half-sibs would be around 25%. The range for each group can vary 5-10% from the expected value.</li>

<li> Sex prediction. How to perform a sex prediction depends on the type and number of SNP an animal has from the X and Y chromosomes. While not every commercial chip includes chromosome Y SNP, they typically contain chromosome X markers.</li>

# Pseudo autosomal region (PAR) SNP. As both the X and Y chromosomes contain the PAR, SNP from this region should be excluded from sex prediction. If the PAR position boundaries are not published for your species they can be roughly determined by analyzing the chromosome X SNP in known males and females and identifying the region where the MAF in males for a continuous set of SNP is >1%. Non-PAR regions of chromosome X will have SNP with average MAF of <1% in males and >>1% in females.
# Chromosome X predicted. Use non-PAR SNP to determine the animal’s chromosome X heterozygosity rate (number of heterozygous chromosome X SNP / total number of chromosome X SNP). If the average heterozygosity rate is <5%, the predicted sex is male, and if >15% its female. If the rate is between 5 and 15% then the predicted sex is unknown.
# Chromosome Y predicted. Using chromosome Y SNP to predict sex is logically simpler but many commercial chips do not contain them. Say you have 7 chromosome Y SNP with high call rates in males, it is recommended using the following logic. Male is predicted when 6-7 of the Y SNP are present; female is predicted when <1 SNP is present and ambiguous sex is predicted when 2-5 Y SNP are present.
# Ambiguous sex prediction. If one set of sex chromosome SNP returns an ambiguous sex prediction and the other doesn’t it is recommended using the latter as the predicted sex. If both SNP sets are ambiguous, the animal could have Turner syndrome (X0), or Klinefelter’s syndrome (XXY), in this case it is recommended returning an ambiguous predicted sex.
# If the predicted sex from the chromosome X and Y analysis disagree, it is recommended returning an ambiguous predicted sex. This could also indicate a possible Klinefelter syndrome (XXY) animal.
# Sex selected AI semen straws. Sex prediction should not be carried out on DNA obtained from sex selected AI semen straws.

<li> Offspring Quality control. The genotyped and listed offspring of an animal can be used to identify potential cases where the animal’s genotype actually belongs to another animal. These should be used as flags to indicate a potential investigation, but it is recommended to temporarily invalidate the animal’s genotype until cleared. Advised thresholds for those flags are:</li>
# AI sire: If >80% of genotyped offspring fail if >10 offspring are genotyped.
# Stock/herd bull: If >80% of genotyped offspring fail if >5 offspring are genotyped.
# Dam: If 100% of genotyped offspring fail if 2 offspring are genotyped, else if >5 offspring are genotyped then use >80%.

<li> Duplicate genotype. The only case where two or more animals should share the exact same genotype is if they are identical twins or clones. Checking to see if >1 animal has the same genotypes is a useful quality control check. It is recommended using your parentage SNP set for initial screening and for any pair that have >99% identical genotypes and then using all available SNP to see if >99% of the genotypes match.</li>

</ol>

For standardization purposes with respect to the nomenclature of genes or loci, a web site is available at: https://www.genenames.org/about/guidelines#genenames and markers at: [https://hgvs-nomenclature.org/versions/21.0/ <nowiki>http://www.HGVS.org/varnomen</nowiki>.]

</div>
</div>

== ICAR Services Related to DNA Technology ==
ICAR offers three services that are related to the use of DNA all of which are linked to parentage analysis in one form or another, as shown in Figure 1. ICAR DNA services., and describing them in more detail in the other sections.
[[File:ICAR DNA service.png|thumb|Figure 1. ICAR DNA services.|center|415x415px]]

== ICAR Accreditation of Laboratories Providing DNA Genotyping Services ==
<div class="mw-collapsible mw-collapsed">
<div>
=== Introduction ===
Considering the need for high quality standards in all uses of molecular data, ICAR has for several years offered an accreditation service based on defined minimum requirements for laboratories providing DNA genotyping services. The basic requirements of this accreditation include proof of the minimum internal management quality assurance standards and a Rank 1 result from participation in the most recent biennial international ring test developed and offered by the International Society for Animal Genetics (ISAG).

In addition, such laboratories generally have been analyzing the resulting genotypes to carry out microsatellite- and/or SNP-based parentage analysis services including either parentage verification or animal identification confirmation. This ICAR accreditation service has previously been used for recognizing the genotyping laboratory as an accredited organization to provide parentage analysis functions without specifically testing the technical accuracy of doing so. Effective 2021, the SNP-based parentage analysis accreditation service for DNA Data Interpretation Centres has replaced the previous laboratory accreditation for SNP-based parentage verification. In the future, a similar technical process for the accreditation of microsatellite-based parentage analysis may be introduced by ICAR but until such time, the existing process for the accreditation of genotyping laboratories will remain in effect.

The following guidelines for accreditation are provided for microsatellite- and SNP-based genotyping in cattle. Minimum requirements for additional species and other DNA tests may be defined in the future.

=== Scope ===
These guidelines are for the accreditation, by ICAR, of genotyping laboratories that analyze biological samples from cattle using microsatellite- and or SNP-based genotyping, which may be subsequently used for various levels of parentage analysis, genotype imputation, estimation of genomic breeding values and other activities related to genomic selection strategies. This accreditation process also includes parentage verification based on microsatellites since ICAR has not established this service as part of the portfolio of possible accreditations for DNA data interpretation centres. For genotyping laboratories that would like to receive ICAR accreditation for SNP-based parentage verification, they must now apply separately to ICAR for its parallel service of parentage analysis accreditation for DNA data interpretation centres, as described in section 4.

=== ICAR Guidelines for Accreditation of Genotyping Laboratories ===
The accreditation process comprises the following steps:

* Application for accreditation
* Payment of relevant fee
* Review of application
* Granting of accreditation

==== Application for accreditation ====
Laboratories requesting accreditation only for microsatellite- based genotyping and parentage verification must apply by downloading and completing the appropriate form as provided in [https://www.icar.org/wp-content/uploads/2022/05/Annex-II-Application-Form-for-STR-Accreditation.pdf ''Appendix 2. Application form for microsatellite-based parentage testing in cattle''.] Laboratories seeking ICAR accreditation involving SNP-based genotyping must apply by downloading and completing the appropriate form as provided in [https://www.icar.org/wp-content/uploads/2022/05/Annex-V-Application-Form-for-SNP-Accreditation.pdf ''Appendix 3. Application form for SNP-based genotyping required for parentage analysis in cattle.''] Laboratories that have previously received ICAR accreditation for either service may re-apply prior to the expiry of any such accreditation using a shortened renewal form available on the ICAR web site. All application forms must be emailed to the ICAR secretariat at dna@icar.org and be filled out accurately and completely including the necessary documentation as required.

==== Payment of relevant fee ====
Along with the completed application form, the applicant must also provide full payment of the relevant fee as established by ICAR and given in [https://www.icar.org/index.php/certifications/certification-and-accreditation-of-dna-genetic-laboratories/guidelines-for-str-and-snp-based-parentage-testing-in-cattle/ ''Appendix 4. ICAR DNA Laboratory Accreditation Service fees''.]

==== Review of application ====
The application will be evaluated by a committee of experts appointed by ICAR that will either:

* Approve the application
* Request additional information, or
* Reject the application

In the case of rejection, the laboratory may make a new submission as part of the ICAR annual call for applications for any subsequent year after the failed application.

==== Granting of accreditation ====
Accreditation will be given for a period of two calendar years with an expiry date of December 31st of the second year after receiving ICAR accreditation as a laboratory providing DNA genotyping services.

==== Renewal of accreditation ====
In advance of the expiry date of any existing [[Preamble#Scope|ICAR accreditation]], normally during the same year of the December 31st expiry date, a laboratory can apply for renewal of their accreditation by submitting an application as described in section 3.3.1 above and successfully completing the other steps outlined in this section 3.

==== Laboratory accreditation ====
Effective the 2022 ICAR call for accreditation of genotyping laboratories, ISO17025 accreditation, or an equivalent accreditation for ensuring quality internal management systems, is a mandatory requirement for SNP-based accreditation. In addition, effective the 2022 call for accreditation of genotyping laboratories for microsatellite (STR)-based accreditation, ISO9001 certification will no longer be acceptable and only ISO17025, or an equivalent accreditation, will be an acceptable level of accreditation to ensure quality internal management systems. During the year of application for ICAR accreditation as a laboratory providing DNA genotyping services, the applicant must provide proof of ISO17025 accreditation with an expiry date of October 31st of the following calendar year, or later.

==== Participation and performance in ring test ====
On a biennial basis, initiated during even years (i.e.: 2022, 20246, etc…) and discussed at its biennial conference in odd years (2023, 2025, etc.), ISAG conducts an international ring (comparison) test of laboratories for both microsatellite- and SNP-based genotyping. The participation in ISAG and performance within these ring tests must be disclosed, and certificates provided to ICAR, when available. Applicants must also sign a release allowing ISAG to directly disclose their ring test results to ICAR. Participation in the most recent ISAG ring test is a minimum requirement to qualify for ICAR accreditation. For the ISAG microsatellite ring test, lab genotyping performance for the official set of 12 ISAG microsatellites must be disclosed. The committee of experts will decide performance thresholds for each ring test with due consideration for the structure of the ring test and the average performance of laboratories in the ring test that year. Only those laboratories achieving Rank 1 status in the most recent biennial ISAG ring test shall automatically qualify to receive ICAR accreditation as a genotyping laboratory. Laboratories achieving a Rank 2 status in the most recent ISAG ring test may qualify to receive ICAR accreditation, at the discretion of the committee of experts, but must provide evidence of Rank 1 status for previous ISAG ring tests as well as documentation outlining the cause of the Rank 2 result and any associated actions to mitigate similar outcomes in future ISAG ring tests. Laboratories achieving a status lower than Rank 2 in the most recent ISAG ring test do not qualify for ICAR accreditation as a laboratory providing DNA genotyping services.

==== Microsatellite markers ====
The names of all microsatellites typed on all animals (marker set I) and of the additional ones assayed in the case of unresolved parentage (marker set II) must be declared, as well as the number of animals typed in at least the last two years. The minimum requirement for international exchange is the complete set of 12 official ISAG microsatellite markers. To ensure sufficient experience within the lab, analysis of 500 animals per year is set as minimum requirement for microsatellite parentage verification certification.

''[https://www.icar.org/wp-content/uploads/2018/05/03-Annex-III-ISAG-microsatellites.pdf Appendix 5. ISAG recommended microsatellites for parentage verification in cattle]'' contains the list of microsatellite markers recommended by ISAG and the method for calculating 1 parent and 2 parent exclusion probabilities. The rules for microsatellite-based parentage verification in cattle are described in ''[https://www.icar.org/wp-content/uploads/2018/05/01-Annex-I-guidelines-microsats-STRs.pdf Appendix 6. Rules for microsatellite-based parentage testing in cattle]''. Exclusion probability (PE; 2 parents and 1 parent) of each marker and of the complete marker sets must be calculated and provided in the application. The type of population and number of animals (minimum 150) used for computations are to be described. ICAR recommends using Holstein as a reference group when possible. The ICAR committee of experts will evaluate that an appropriate PE is reached for accreditation, on the basis of the population analyzed.

==== SNP markers ====
ICAR accreditation of SNP-based parentage verification is based on the full set of 200 SNP previously recommended by ISAG. The name of all SNP genotyped on all animals (marker set I, including the 100 “Core” SNP) and of the additional markers assayed in the case of unresolved parentage (marker set II, including the 100 “Additional” SNP) must be declared, as well as the number of animals SNP genotyped in at least the last two years. ICAR recommends using the full set of 200 SNP for parentage verification of all animals genotyped (''see [https://www.icar.org/Guidelines/04-DNA-Technology-App7-SNP-list-for-parentage-verification.pdf Appendix 7. List of approved SNP for parentage verification in]'' [https://www.icar.org/Guidelines/04-DNA-Technology-App7-SNP-list-for-parentage-verification.pdf cattle]). ICAR may, however, based on scientific evidence, identify specific problematic SNP that must be excluded for parentage analysis, as described in the ICAR documentation related to the accreditation of DNA data interpretation centres outlined in section 4.

==== Marker nomenclature ====
Nomenclature of markers must be described. ISAG nomenclature is required for the official ISAG 12 marker set as well as for the ISAG SNP marker set.
</div>
</div>

== Accreditation of Organisations Performing SNP-Based Parentage Analysis ==
<div class="mw-collapsible mw-collapsed">
<div>
=== Introduction ===
With the advent of SNP genotyping, the function of DNA genotyping as a laboratory activity can be separated from the functions of performing parentage verification and parentage discovery. Consequently, ICAR has established a separate accreditation for applying the results of SNP-based genotyping, which may be undertaken by laboratories, breed association societies, genetic evaluation centres and any other organization involved in parentage verification and/or the data processing of SNP genotypes.

Parentage verification and discovery are concerned with using the results that are delivered by the laboratories from DNA genotyping and require SNP genotypes for the animal itself, its recorded parents and other possible parents in the case of parentage discovery. Organizations undertaking this function may be service providers between laboratories that ICAR has accredited for microsatellite- and or SNP-based DNA genotyping and end users that may include breed societies, breeding companies, breeders and commercial farmers.

Service providers could use different laboratories for different breeds and/or species. Considering the importance of animal identification and parentage verification in animal recording, ICAR has decided to define the minimum requirements for using the results of DNA genotyping, and other information, for the purpose of:

# Parentage verification
# Parentage discovery, and
# Animal identification confirmation

The purpose of these guidelines is to provide a basis for the accreditation of processes used by organizations that use SNP genotypes in cattle. Minimum requirements for additional species and other DNA analyses may be defined in the future.

=== Scope ===
These guidelines are for the accreditation, by ICAR, of organizations that use the results of SNP-based tests for parentage analysis in cattle, which includes parentage verification, parentage discovery, and/or animal identification confirmation.

=== Accreditation of Organizations Performing Parentage Analysis ===
The ICAR accreditation process comprises the following steps:

* Application for accreditation
* Payment of relevant fee
* Review of application
* Technical processing of test data files
* Granting of accreditation

==== Application ====
Organizations carrying out SNP-based parentage analysis and requesting ICAR accreditation as a DNA Data Interpretation Centre must apply by downloading and completing the appropriate form included below as [https://www.icar.org/wp-content/uploads/2016/10/6-Annex-V-Application-Form-forICAR-Accreditation-of-DNA-Centres.pdf ''Appendix 8. Application form for organizations seeking ICAR parentage analysis accreditation for DNA data interpretation centres''.] This form must be filled out accurately and completely, providing necessary documentation as required, and submitted to ICAR with payment of the appropriate fee.

==== Review of application ====
The application will first be reviewed internally by ICAR for its completeness and additional details may be requested as needed. ICAR administration will also confirm receipt of the applicable fee.

==== Technical processing of test files ====
The applicant organization will receive a set of data files from ICAR through the Interbull Centre, for processing using its existing procedures for carrying out the level of parentage analysis for which the applicant is seeking ICAR accreditation as a DNA Data Interpretation Centre. A detailed description of this step is described in [https://www.icar.org/index.php/certifications/certification-and-accreditation-of-dna-genetic-laboratories/two-new-dna-based-services/dna-data-interpretation-centres/ ''Appendix 9. Applicant's Guide for ICAR Parentage Analysis Accreditation for DNA Data Interpretation Centres.''] In order for the applicant to be successful in obtaining the requested ICAR accreditation, it's procedures for conducting parentage analysis must exactly follow [https://www.icar.org/Documents/GenoEx/ICAR%20Guidelines%20for%20Parentage%20Verification%20and%20Parentage%20Discovery%20based%20on%20SNP.pdf ''Appendix 10. ICAR Guidelines for Parentage Verification and Parentage Discovery Based on SNP Genotypes''.] The list of SNP to be used for either parentage verification (N=200) or parentage discovery (N=554) are available in [https://www.icar.org/Guidelines/04-DNA-Technology-App-11-SNP-list-for-parentage-verification-or-discovery.pdf ''Appendix 11. List of SNP to be used for either parentage verification or parentage discovery''.] Once the applicant has completed its internal parentage analysis procedures based on the accreditation test files it received, it must send a data file of results back to the Interbull Centre. A maximum time period for 90 calendar days will be allowed for the applicant to submit acceptable files of results back to the Interbull Centre.

==== Granting of accreditation ====
Once the Interbull Centre receives the file of parentage analysis results from the applicant, it will complete the technical review and determine if the applicant has successfully completed the accreditation or not. The Interbull Centre shall inform ICAR of the results and ICAR shall issue a formal notification to the applicant. In the event the applicant was not successful in receiving ICAR accreditation, the applicant may initiate a new request for accreditation by completing and submitting the appropriate forms and providing payment of the applicable fee, as outlined above.

==== Renewal of accreditation ====
In advance of the expiry date of any existing ICAR accreditation, which coincides with the two-year anniversary date of the current accreditation, an applicant can apply for renewal of their accreditation by submitting an application as described in section 4.3.1 above and successfully completing the other required steps outlined in this section 4.
</div>
</div>

== Genotype Exchange Service – GenoEx-PSE ==
Effective 2018, ICAR has made available a genotype exchange service for parentage analysis, GenoEx-PSE, offered through the Interbull Centre. The main goal of this service is to facilitate the international exchange of SNP genotypes such that approved service users can carry out parentage analysis services at a national level in an efficient manner. The GenoEx-PSE database system and user interface has been developed to allow for the exchange of SNP genotypes for either parentage verification or parentage discovery based on the list of SNP provided in ''Appendix 11. List of SNP to be used for either parentage verification or parentage discovery''.

In order for an organization to qualify as a service user for GenoEx-PSE, it must first receive ICAR accreditation as a DNA data interpretation centre. The level of such ICAR accreditation (i.e.: for SNP-based parentage verification alone or for both SNP-based parentage verification and discovery) shall determine the highest level of SNP that may be exchanged via the GenoEx-PSE service. For details associated with this ICAR service, refer to the GenoEx-PSE web site at [https://genoex.org/ www.GenoEx.org.]

== Appendix list ==
<div class="mw-collapsible mw-collapsed">
<div>
=== Appendix 1. Link to SNP markers recommended by ISAG for parentage verification ===
https://www.icar.org/Guidelines/04-DNA-Technology-App-1-Cattle-SNP-ISAG-core-additional-panel-2013.xlsx

=== Appendix 2. Application form for microsatellite-based parentage testing in cattle ===
Please refer [https://www.icar.org/wp-content/uploads/2022/05/Annex-II-Application-Form-for-STR-Accreditation.pdf here] on the ICAR website for the Form for ICAR laboratory accreditation for STR Microsatellite-based Parentage Testing in Cattle.

=== Appendix 3. Application form for SNP-based genotyping required for parentage analysis in cattle ===
Please refer [https://www.icar.org/wp-content/uploads/2022/05/Annex-V-Application-Form-for-SNP-Accreditation.pdf here] on the ICAR website for the Form for ICAR laboratory accreditation for SNP-based genotyping required for Parentage Analysis in Cattle.

=== Appendix 4. ICAR DNA Laboratory Accreditation Service fees ===
Please refer [https://www.icar.org/index.php/certifications/certification-and-accreditation-of-dna-genetic-laboratories/guidelines-for-str-and-snp-based-parentage-testing-in-cattle/ here] on the ICAR website for DNA testing accreditation services fees.

=== Appendix 5. ISAG recommended microsatellites for parentage verification in cattle ===
Please refer [https://www.icar.org/wp-content/uploads/2018/05/03-Annex-III-ISAG-microsatellites.pdf here] on the ICAR website for the list of ISAG recommended microsatellites for parentage verification in cattle.

=== Appendix 6. Rules for microsatellite-based parentage testing in cattle ===
Please refer [https://www.icar.org/wp-content/uploads/2018/05/01-Annex-I-guidelines-microsats-STRs.pdf here] on the ICAR website for the rules for microsatellite-based parentage verification in cattle.

=== Appendix 7. List of approved SNP for parentage verification in cattle ===
Please refer [https://www.icar.org/Guidelines/04-DNA-Technology-App7-SNP-list-for-parentage-verification.pdf here] on the ICAR website for the ICAR approved list of 200 SNP for parentage verification in cattle.

=== Appendix 8. Application form for organizations seeking ICAR parentage analysis accreditation for DNA data interpretation centres ===
Please refer [https://www.icar.org/wp-content/uploads/2016/10/6-Annex-V-Application-Form-forICAR-Accreditation-of-DNA-Centres.pdf here] on the ICAR website for the application form for organizations seeking ICAR accreditation status as a DNA data interpretation centre.

=== Appendix 9. Applicant's Guide for ICAR Parentage Analysis Accreditation for DNA Data Interpretation Centres ===
Please refer [https://www.icar.org/index.php/certifications/certification-and-accreditation-of-dna-genetic-laboratories/two-new-dna-based-services/dna-data-interpretation-centres/ here] on the ICAR website for the Applicant's Guide for ICAR Parentage Analysis Accreditation for DNA Data Interpretation Centres.

=== Appendix 10. ICAR Guidelines for Parentage Verification and Parentage Discovery Based on SNP Genotypes ===
Please refer [https://www.icar.org/Documents/GenoEx/ICAR%20Guidelines%20for%20Parentage%20Verification%20and%20Parentage%20Discovery%20based%20on%20SNP.pdf here] on the ICAR website for the ICAR Guidelines for Parentage Verification and Parentage Discovery Based on SNP Genotypes.

=== Appendix 11. List of SNP to be used for either parentage verification or parentage discovery ===
Please refer [https://www.icar.org/Guidelines/04-DNA-Technology-App-11-SNP-list-for-parentage-verification-or-discovery.pdf here] on the ICAR website for the list of SNP to be used for either parentage verification or parentage discovery.
</div>
</div>

Section 04 – DNA Technology

2025-01-31T10:04:25Z

Aashish: /* Renewal of accreditation */

== Molecular Genetics ==
<div class="mw-collapsible mw-collapsed">
<div>
=== Introduction ===
Advances in molecular biology, especially genomics, provide a new set of information to be incorporated into the animal industry. On one hand, the use of molecular information may contribute to the enhancement of consumers' trust in the ability to monitor and control the animal production chain. On the other hand, molecular information will greatly contribute to the achievement of genetic improvement for animal traits through the use of genomic breeding values, marker assisted selection, gene introgression, heterosis prediction, pedigree validation/prediction, and genetic defect carrier status. In most cases, advantages of using molecular information via genomic evaluations, comes from improved accuracy of animal breeding values, shortened generation intervals, and increased intensity of selection. Even with these advancements there is still a need for research and development in the search for associations between genetic markers and traits of interest, especially as new traits are included in national evaluation indexes. In addition to that, even with the current incorporation of genomic information into national selection schemes, an understanding of gene action, gene interactions, and differential gene expression to avoid negative collateral effects is needed. Cooperation between animal industries and research is required for a successful and beneficial search for genetic information in commercial livestock populations.

=== Genetic Markers ===
Genetic markers are the fundamental molecular tools for genomics, even as the type of marker used has changed. The first genetic marker associations in livestock were reported using blood typing in the 1960s, the technology then moved to microsatellites (MS) in the 1990s and more recently to the use of Single Nucleotide Polymorphism (SNP). SNP and MS are polymorphic DNA sequences (alleles) at a specific locus of a particular chromosome. While blood typing has been an ICAR approved method of parentage verification currently there are few, if any, commercial labs still offering this testing. For this reason, ICAR no longer recommends blood typing as the basis for carrying out parentage analysis in livestock species where MS or SNP technology is widely available.

==== Microsatellites ====
These are segments of DNA containing tandem repeats of simple motifs usually dimers or trimers. These segments are located throughout the genome and normally in non-coding regions. Over time, these regions are subject to the addition or subtraction of tandem repeats, which means that each microsatellite can have multiple unique alleles. Microsatellites are commonly used in many livestock species for parentage validation.

==== Single Nucleotide Polymorphism (SNP) ====
SNP are the most common type of genetic variation: each SNP represents a variation in a single nucleotide. There are millions of SNP located throughout the genome of every livestock species. For genomics the most informative SNP traditionally are either located in (a) coding regions where different alleles change the structure or function of the encoded protein, or (b) at non-coding regions that are involved in the regulatory function of the gene. For genomic breeding values, SNP that are located in other regions of the genome are also informative as they could be in linkage disequilibrium with alleles that do cause a phenotype change.

One of the big advantages of SNP is their deployment on SNP arrays with a strong parallel processing capacity whereby thousands or hundreds of thousands of SNP can be screened together in a cost-effective and efficient manner across a large number of animals. Currently, the largest livestock genotyping labs can process hundreds of thousands of animals yearly on such arrays. The availability of these large SNP panels is therefore bolstering the search for mutations underlying genetic variation for simple and complex traits. It is also revolutionizing the speed at which trait associated genes or gene regions are being discovered as well as the adoption rate of genomic selection strategies. SNP genotypes have become the international standard for the basis of parentage analysis and ICAR recommends this approach over the use of microsatellites wherever possible due to the improved accuracy and the ease of comparing results between genotyping laboratories.

=== Current and Potential Uses of DNA Technologies ===

==== Parentage verification and parental assignment authentication ====
Prior to the emergence of SNP genotyping, parentage verification was the main commercial use of genetic markers. Traditionally, parentage testing was based on the exclusion of relationship (i.e.: sire or dam) when an animal has a genotype inconsistent to a putative relationship. New trends in animal production systems are tending to encourage animal production in larger numbers per farm in response to environmental and production related constraints. In these large settings, multiple animals could be bred or give birth on the same day, which can result in more pedigree recording errors. As the cost of the analysis decreases and the number of genetic markers available increases, breed societies are now able to build up pedigree records using genetic markers to predict the pedigree of calves born in a herd at a given time. This normally requires a prior knowledge of candidate sires and dams for a calf when lower number (<200) of markers are used, but with enough SNP the correct parents can be predicted without prior knowledge being available as long as the parent is also genotyped. The probability of assignment to a correct pair of animals will depend on the number of markers used, number of alleles per loci, the minor allele frequency in the population, the number of parents, and the number of possible matings. The International Society of Animal Genetics (www.isag.us) has species-specific panels recommended of microsatellite and SNP markers for this purpose, which can be accessed via a link such as provided in ''[https://www.icar.org/Guidelines/04-DNA-Technology-App-1-Cattle-SNP-ISAG-core-additional-panel-2013.xlsx Appendix 1]. Link to SNP markers recommended by ISAG for parentage verification''. For cattle, ICAR has developed a set of parentage SNP, ICAR554, which incorporates the ISAG recommended panel and other highly informative SNP. This panel allows for highly accurate parentage validation and discovery while not allowing for accurate imputation to a higher density. Therefore, the ICAR554 panel can be shared among countries and competitors for parentage analysis without fear of others being able to use them to predict genomic breeding values. ICAR and the Interbull Centre collaborate in offering an international genotype exchange service, referred to as GenoEx, which is described further in Chapter 5 specifically for the exchange of SNP genotypes for the purposes of parentage analysis.

==== Traceability and authentication of animal products offered to consumers ====
Due to multiple crises, including BSE outbreaks to ground beef containing horsemeat, and with increased consumer interest in where their food comes from the traceability of meat products is of greater concern to the industry. Traceability is based on the availability of a verification and control system that monitors all relevant details throughout the entire livestock production chain. Since an individual’s genetic sequence is unique and does not change, its DNA remains constant from ‘conception to consumption’. Therefore, use of genetic markers allows one to match the DNA of an individual at birth to the final product.

Genetic markers for the authentication of animal products for labels of quality related to geographic location and labels of quality related to specific breeds or their crosses are/or will be very useful. However, this requires the establishment of molecular standards or allele frequencies for each breed within a species. A lot of information is coming from studies of genetic diversity among breeds. Genomic regions subject to intense selection in each population are of particular interest. With a large enough set of SNP and genotyped purebred reference animals it is also possible to predict the most likely breed composition of individuals.

==== Molecular genetic information for marker-assisted selection schemes ====
Quantitative traits are generally assumed to be controlled by a large number of genes. However, individual genes sometimes account for a significant amount of variation of the trait. Such is the case for the Myostatin gene and double muscling in beef cattle, the DGAT1 gene and milk components in dairy cattle, or the Booroola fecundity gene and ovulation rate in sheep. Since the genotype of an animal does not change during its lifetime, use of DNA information through the identification of markers linked to QTL with effects on production traits or the identification of a gene itself together with the causative variant is of great interest. Nevertheless, with complex traits there is a growing need of having a sufficiently large marker set to incorporate molecular information for selection decisions. Including genomic information as a selection criterion is of special interest for traits that are difficult and costly to measure and/or are measured late in life. By 2022, >177,000 cattle, >34,000 swine, >16,000 chicken and >4,000 sheep QTL have been identified that are associated with economically important traits such as health, carcass, milk, fertility, and body conformation. The AnimalQTLdb database housed at the [https://www.animalgenome.org/ National Animal Genome Research Program] contains up to date information on cattle, chicken, horse, pig, trout, and sheep QTL data assembled from published data.

Recording schemes have been collecting information for decades on the most common production traits measured in domestic livestock. There is an ever-increasing volume of information becoming available, but for some traits like meat quality, disease resistance and feed efficiency, those records are very expensive to measure, difficult to obtain, or are performed late in the animal’s life. Because of these challenges information for such traits is commonly collected on a reduced number of animals in any given population.

For these challenging, but economically important traits, genetic markers and genomic selection offer significant opportunities for trait selection where it was not economically feasible before. In general, genetic markers and genomics will play an important role for important traits regardless of the livestock species. Genomics can also allow us to increase selection intensities since we can predict genomic breeding values on a large number of animals and thus have more candidates for selection.

==== Disease resistance and genetic defects ====
Another group of traits with a high potential for the use of molecular data and genomics are those linked to resistance, resilience, and susceptibility to diseases. There are a number of multi-factorial or complex diseases that are the result of the interaction between an animal’s genome and environmental components. Disease resistance traits are among the most difficult to include in genetic improvement programs because they require good field measurement of the disease status of the animals and a systematic control of management or environmental conditions that allow for the identification of the environmental influence on the health status of the animal. Infectious diseases depend very much upon environmental factors such as the degree of exposure to the pathogen agent. Thus, if exposure is low, animals will show little variation. Part of the phenotypic differences for resistance may be differences in the degree of challenge. Therefore, if genes or genetic markers linked to resistance are correctly identified, resistant animals will be able to be selected on the base of their genomic information. For many diseases, identification of genes associated with resistance will require experimental conditions to be used. Genetic analysis to identify heterozygous carriers of genetic diseases caused by single, recessive genes are currently in use. Examples in dairy cattle include complex vertebral malformation (CVM), brachyspina (BY), cholesterol deficiency (CD) and several genes, gene regions or haplotypes causing embryo loss or stillbirth in different dairy breeds. In 2022, [https://www.omia.org/home/ OMIA (Online Mendelian Inheritance in Animals),] listed >1000 traits or genetic defects in livestock with a known causative mutation (cattle: 186, pig: 58, chicken: 56, sheep: 49, horse: 48, goat:17). Including these causative allele or associated haplotypes in a breeding program will allow producers to minimize their risk from genetic defects while maximizing genetic progress from beneficial traits.

=== Technical Aspects ===

==== DNA collection ====
Systematic collection of DNA is recommended in several livestock populations. DNA may be obtained from any nuclear cell in the body. Protocols for DNA extraction are now available for blood (white cells), semen, saliva (epithelial cells), hair follicles, muscle, skin, organs (such as liver, spleen etc.). Red blood cells may also be used for poultry as they retain the nuclear body while most other species do not. Small amounts of tissue material are required for routine DNA analysis. However, if there are multiple future uses of an individual’s DNA (whole genome sequencing, traceability, causative allele validations, …), then DNA storage costs, extraction costs, quality, and quantity obtained by different protocols will have to be carefully examined and optimized. Common collection methods include hair follicles, tissue samples (often ear punch) in an enclosed container, blood spots on filter paper, and nasal swabs.

==== Data organization ====
A centralised database may be organised in respect to the main uses of the genetic information:

* Parent verification, assignment, and/or discovery
* Traceability of meat products
* Breed identification or breed diversity
* Qualitative and quantitative traits 

Database tables may contain:

* Animal identification to link to all other information on the animal and its relatives.
* Number of genetic markers: n
* Standard name of each marker i (for i= 1, n)
* Accession number for marker such as the dbSNP ID
* Alleles for marker i
* Genomic location of marker i
* Effect of non-reference allele on the protein
* Phenotypic effect of the allele
* Association with other traits

==== Parentage accuracy ====
While use of microsatellite and SNP markers are both ICAR accredited methods of parentage verification they do not have the same power of parentage accuracy. Briefly the order of accuracy for ISAG and ICAR approved parentage marker panels are:

Microsatellites << small SNP panels (100 or less) < large SNP panels (500 or more)

This order is based on both genotyping accuracy and total genomic information. Comparing the genomic marker error rate in cattle microsatellites have a 1-5% error rate (Baruch and Weller, 2008<ref>Baruch, E., and J. I. Weller. 2008. 'Estimation of the number of SNP genetic markers required for parentage verification', ''Animal Genetics'', 39: 474-79.</ref>) while the SNP error rate is <0.1% (Cooper, Wiggans, and VanRaden 2013<ref>Cooper, T. A., G. R. Wiggans, and P. M. VanRaden. 2013. 'Short communication: relationship of call rate and accuracy of single nucleotide polymorphism genotypes in dairy cattle', ''Journal of dairy science'', 96: 3336-9.</ref>). As 2-3 SNP provide the same parentage exclusion accuracy as 1 microsatellite marker (Vignal et al. 2002<ref>Vignal, A., D. Milan, M. SanCristobal, and A. Eggen. 2002. 'A review on SNP and other types of molecular markers and their use in animal genetics', ''Genet Sel Evol'', 34: 275-305.

1.4.4 Genomic quality control checks</ref>), the 100 and 200 ISAG parentage SNP panels are more accurate than the 12 ISAG parentage microsatellite markers. In the same manner parentage panels of over 500 SNP (McClure et al. 2018<ref>McClure, M. C., J. McCarthy, P. Flynn, J. C. McClure, E. Dair, D. K. O'Connell, and J. F. Kearney. 2018. 'SNP Data Quality Control in a National Beef and Dairy Cattle System and Highly Accurate SNP Based Parentage Verification and Identification', ''Front Genet'', 9: 84.</ref>), such as the ICAR554, are recommended for parentage prediction which requires an even higher level of accuracy.

==== Genomic quality control checks ====
One of the most important parts of a large genomic database is to ensure that a genotype associated with an individual animal truly belongs to that animal. Most large livestock genomic databases deal with SNP data only and the quality of SNP genotyping data is of paramount importance (Wu at al., Evaluation of genotyping concordance for commercial bovine SNP arrays using quality-assurance samples, Animal Genetics, 50: 367-371, 2019). This section, therefore, focuses on quality control for that genomic data type. Both sample and SNP quality control measures are needed, and it is encouraged to develop a system for them early.

For those working with genotype data there are two main concerns. First, is ensuring that the genotype data itself is of high quality and can be trusted. Second, is ensuring that the genotype truly belongs to the individual listed. The recommended quality control checks below will work for any livestock species. The basic checks can be performed with minimal information about the individual, while some of the advanced checks require data that not everyone will have, such as historic animal location.

===== ''Basic genotype quality control checks for SNP-based genotype data'' =====
Genotype:
<ol style="list-style-type:number">
<li> Exclude SNP that have a genotype call rate below 90% when analyzed in your population. Using 500 or more animals to determine the SNP call rate is recommended. Chromosome Y SNP should have their call rate determined only in males for this filter.</li>
<li> Invalidate the individual’s genotype if its overall call rate is below <90%. For SNP-based genotypes, such as those from Illumina or Affymetrix chips, the accuracy of called genotypes is questionable when the individual’s overall call rate is <90%.</li>
<li> Check to see that the animal has all three genotype classes (i.e.: AA, AB and BB) in its full genotype file. If any genotype class is missing or has a frequency below 20% then invalidate the full genotype.</li>
<li> Check to ensure that there are no unexpected alleles in the genotype file. For example, genotypes in AB format should not have T, G, 0, 1, 2 or 9. If present, then invalidate the full genotype file.</li>

</ol>

Parentage:
<ol style="list-style-type:number">
<li> Parent (Sire or Dam) validation. If using 200 or less SNP, a listed sire will validate if <1% of the offspring-parent genotypes are in conflict. A conflicting genotype is where the offspring and listed parent have opposite homozygous genotypes.</li>
<li> Mating validation after parent validation. For all parentage validation SNP where the animal is heterozygous, if for >1% of those SNP the sire and dam are homozygous for the same allele then the listed mating is invalidated. This could represent a case where the offspring and one of the parents were mislabeled with the other’s identification (so the offspring’s genotype belongs to the sire or dam and vice versa). Under such cases, it is recommended to resample the DNA and regenotype, potentially with a panel that includes more SNP.</li>

</ol>

===== ''Advanced quality control checks for SNP-based genotype data'' =====
Animal:
<ol style="list-style-type:number">

<li> Parentage discovery. Using SNP data to predict who an animal’s likely parent is can be very useful, but steps must be taken to ensure a very high probability that the prediction is accurate. The following are recommended:</li>

# Using 500 or more SNP that have a minor allele frequency (MAF) above 20% and call rates above 90%. It is advised to calculate the MAF across your full population. Predicted parents should have <1% conflict rate with the animal.
# Sex check. Make sure that you have a process established to ensure that only males are predicted as the sire and only females as the dam.
# Date of Birth check. If you do not include a check that the predicted parent is older than the animal than the predicted individual could actually be an offspring of the animal.
# Age gap. Cattle normally reach sexual maturity at 11-12 months of age, but this can be as young as 8-9 months, and even younger if in-vitro fertilization is a technology used within the population. Under normal circumstances, a minimum of 17 months between the birth dates of the animal and its predicted parent is recommended to ensure that the predicted parent could have been sexually mature at the time of the breeding.
# Grey zone SNP conflicts. The majority of animals will have <0.5% or >1.5% conflicting genotypes with the individual when parentage discovery is conducted. Those with <0.5% pass the prediction and those with >1.5% fail. Most failed animals will have >8% conflict rates. For those animals who have between 0.5 and 1.5% conflicting SNP when a set parentage panel is used (i.e.: the ICAR554 SNP list), it is advised that the conflict rate from all available SNP be used between the two individuals and if the percent conflicting is <1% they validate as the parent, but if >1% they fail.

<li> Genetic Relationship Matrix (GRM). If an animal’s true parent is not genotyped, then it cannot be directly predicted or validated. The genetic relationship between closely related animals can be used to suggest a potential, non-genotyped, parent. It is recommended that 7,000 or more SNP be used to calculate the GRM. GRM results DO NOT validate a relationship, but only suggest. Caution should be used as GRM values can be inflated for inbred individuals. Full-sibs and parent-child should have GRM values around 50%, while half-sibs would be around 25%. The range for each group can vary 5-10% from the expected value.</li>

<li> Sex prediction. How to perform a sex prediction depends on the type and number of SNP an animal has from the X and Y chromosomes. While not every commercial chip includes chromosome Y SNP, they typically contain chromosome X markers.</li>

# Pseudo autosomal region (PAR) SNP. As both the X and Y chromosomes contain the PAR, SNP from this region should be excluded from sex prediction. If the PAR position boundaries are not published for your species they can be roughly determined by analyzing the chromosome X SNP in known males and females and identifying the region where the MAF in males for a continuous set of SNP is >1%. Non-PAR regions of chromosome X will have SNP with average MAF of <1% in males and >>1% in females.
# Chromosome X predicted. Use non-PAR SNP to determine the animal’s chromosome X heterozygosity rate (number of heterozygous chromosome X SNP / total number of chromosome X SNP). If the average heterozygosity rate is <5%, the predicted sex is male, and if >15% its female. If the rate is between 5 and 15% then the predicted sex is unknown.
# Chromosome Y predicted. Using chromosome Y SNP to predict sex is logically simpler but many commercial chips do not contain them. Say you have 7 chromosome Y SNP with high call rates in males, it is recommended using the following logic. Male is predicted when 6-7 of the Y SNP are present; female is predicted when <1 SNP is present and ambiguous sex is predicted when 2-5 Y SNP are present.
# Ambiguous sex prediction. If one set of sex chromosome SNP returns an ambiguous sex prediction and the other doesn’t it is recommended using the latter as the predicted sex. If both SNP sets are ambiguous, the animal could have Turner syndrome (X0), or Klinefelter’s syndrome (XXY), in this case it is recommended returning an ambiguous predicted sex.
# If the predicted sex from the chromosome X and Y analysis disagree, it is recommended returning an ambiguous predicted sex. This could also indicate a possible Klinefelter syndrome (XXY) animal.
# Sex selected AI semen straws. Sex prediction should not be carried out on DNA obtained from sex selected AI semen straws.

<li> Offspring Quality control. The genotyped and listed offspring of an animal can be used to identify potential cases where the animal’s genotype actually belongs to another animal. These should be used as flags to indicate a potential investigation, but it is recommended to temporarily invalidate the animal’s genotype until cleared. Advised thresholds for those flags are:</li>
# AI sire: If >80% of genotyped offspring fail if >10 offspring are genotyped.
# Stock/herd bull: If >80% of genotyped offspring fail if >5 offspring are genotyped.
# Dam: If 100% of genotyped offspring fail if 2 offspring are genotyped, else if >5 offspring are genotyped then use >80%.

<li> Duplicate genotype. The only case where two or more animals should share the exact same genotype is if they are identical twins or clones. Checking to see if >1 animal has the same genotypes is a useful quality control check. It is recommended using your parentage SNP set for initial screening and for any pair that have >99% identical genotypes and then using all available SNP to see if >99% of the genotypes match.</li>

</ol>

For standardization purposes with respect to the nomenclature of genes or loci, a web site is available at: https://www.genenames.org/about/guidelines#genenames and markers at: [https://hgvs-nomenclature.org/versions/21.0/ <nowiki>http://www.HGVS.org/varnomen</nowiki>.]

</div>
</div>

== ICAR Services Related to DNA Technology ==
ICAR offers three services that are related to the use of DNA all of which are linked to parentage analysis in one form or another, as shown in Figure 1. ICAR DNA services., and describing them in more detail in the other sections.
[[File:ICAR DNA service.png|thumb|Figure 1. ICAR DNA services.|center|415x415px]]

== ICAR Accreditation of Laboratories Providing DNA Genotyping Services ==
<div class="mw-collapsible mw-collapsed">
<div>
=== Introduction ===
Considering the need for high quality standards in all uses of molecular data, ICAR has for several years offered an accreditation service based on defined minimum requirements for laboratories providing DNA genotyping services. The basic requirements of this accreditation include proof of the minimum internal management quality assurance standards and a Rank 1 result from participation in the most recent biennial international ring test developed and offered by the International Society for Animal Genetics (ISAG).

In addition, such laboratories generally have been analyzing the resulting genotypes to carry out microsatellite- and/or SNP-based parentage analysis services including either parentage verification or animal identification confirmation. This ICAR accreditation service has previously been used for recognizing the genotyping laboratory as an accredited organization to provide parentage analysis functions without specifically testing the technical accuracy of doing so. Effective 2021, the SNP-based parentage analysis accreditation service for DNA Data Interpretation Centres has replaced the previous laboratory accreditation for SNP-based parentage verification. In the future, a similar technical process for the accreditation of microsatellite-based parentage analysis may be introduced by ICAR but until such time, the existing process for the accreditation of genotyping laboratories will remain in effect.

The following guidelines for accreditation are provided for microsatellite- and SNP-based genotyping in cattle. Minimum requirements for additional species and other DNA tests may be defined in the future.

=== Scope ===
These guidelines are for the accreditation, by ICAR, of genotyping laboratories that analyze biological samples from cattle using microsatellite- and or SNP-based genotyping, which may be subsequently used for various levels of parentage analysis, genotype imputation, estimation of genomic breeding values and other activities related to genomic selection strategies. This accreditation process also includes parentage verification based on microsatellites since ICAR has not established this service as part of the portfolio of possible accreditations for DNA data interpretation centres. For genotyping laboratories that would like to receive ICAR accreditation for SNP-based parentage verification, they must now apply separately to ICAR for its parallel service of parentage analysis accreditation for DNA data interpretation centres, as described in section 4.

=== ICAR Guidelines for Accreditation of Genotyping Laboratories ===
The accreditation process comprises the following steps:

* Application for accreditation
* Payment of relevant fee
* Review of application
* Granting of accreditation

==== Application for accreditation ====
Laboratories requesting accreditation only for microsatellite- based genotyping and parentage verification must apply by downloading and completing the appropriate form as provided in [https://www.icar.org/wp-content/uploads/2022/05/Annex-II-Application-Form-for-STR-Accreditation.pdf ''Appendix 2. Application form for microsatellite-based parentage testing in cattle''.] Laboratories seeking ICAR accreditation involving SNP-based genotyping must apply by downloading and completing the appropriate form as provided in [https://www.icar.org/wp-content/uploads/2022/05/Annex-V-Application-Form-for-SNP-Accreditation.pdf ''Appendix 3. Application form for SNP-based genotyping required for parentage analysis in cattle.''] Laboratories that have previously received ICAR accreditation for either service may re-apply prior to the expiry of any such accreditation using a shortened renewal form available on the ICAR web site. All application forms must be emailed to the ICAR secretariat at dna@icar.org and be filled out accurately and completely including the necessary documentation as required.

==== Payment of relevant fee ====
Along with the completed application form, the applicant must also provide full payment of the relevant fee as established by ICAR and given in [https://www.icar.org/index.php/certifications/certification-and-accreditation-of-dna-genetic-laboratories/guidelines-for-str-and-snp-based-parentage-testing-in-cattle/ ''Appendix 4. ICAR DNA Laboratory Accreditation Service fees''.]

==== Review of application ====
The application will be evaluated by a committee of experts appointed by ICAR that will either:

* Approve the application
* Request additional information, or
* Reject the application

In the case of rejection, the laboratory may make a new submission as part of the ICAR annual call for applications for any subsequent year after the failed application.

==== Granting of accreditation ====
Accreditation will be given for a period of two calendar years with an expiry date of December 31st of the second year after receiving ICAR accreditation as a laboratory providing DNA genotyping services.

==== Renewal of accreditation ====
In advance of the expiry date of any existing [[#Application_for_accreditation|ICAR accreditation]], normally during the same year of the December 31st expiry date, a laboratory can apply for renewal of their accreditation by submitting an application as described in section 3.3.1 above and successfully completing the other steps outlined in this section 3.

==== Laboratory accreditation ====
Effective the 2022 ICAR call for accreditation of genotyping laboratories, ISO17025 accreditation, or an equivalent accreditation for ensuring quality internal management systems, is a mandatory requirement for SNP-based accreditation. In addition, effective the 2022 call for accreditation of genotyping laboratories for microsatellite (STR)-based accreditation, ISO9001 certification will no longer be acceptable and only ISO17025, or an equivalent accreditation, will be an acceptable level of accreditation to ensure quality internal management systems. During the year of application for ICAR accreditation as a laboratory providing DNA genotyping services, the applicant must provide proof of ISO17025 accreditation with an expiry date of October 31st of the following calendar year, or later.

==== Participation and performance in ring test ====
On a biennial basis, initiated during even years (i.e.: 2022, 20246, etc…) and discussed at its biennial conference in odd years (2023, 2025, etc.), ISAG conducts an international ring (comparison) test of laboratories for both microsatellite- and SNP-based genotyping. The participation in ISAG and performance within these ring tests must be disclosed, and certificates provided to ICAR, when available. Applicants must also sign a release allowing ISAG to directly disclose their ring test results to ICAR. Participation in the most recent ISAG ring test is a minimum requirement to qualify for ICAR accreditation. For the ISAG microsatellite ring test, lab genotyping performance for the official set of 12 ISAG microsatellites must be disclosed. The committee of experts will decide performance thresholds for each ring test with due consideration for the structure of the ring test and the average performance of laboratories in the ring test that year. Only those laboratories achieving Rank 1 status in the most recent biennial ISAG ring test shall automatically qualify to receive ICAR accreditation as a genotyping laboratory. Laboratories achieving a Rank 2 status in the most recent ISAG ring test may qualify to receive ICAR accreditation, at the discretion of the committee of experts, but must provide evidence of Rank 1 status for previous ISAG ring tests as well as documentation outlining the cause of the Rank 2 result and any associated actions to mitigate similar outcomes in future ISAG ring tests. Laboratories achieving a status lower than Rank 2 in the most recent ISAG ring test do not qualify for ICAR accreditation as a laboratory providing DNA genotyping services.

==== Microsatellite markers ====
The names of all microsatellites typed on all animals (marker set I) and of the additional ones assayed in the case of unresolved parentage (marker set II) must be declared, as well as the number of animals typed in at least the last two years. The minimum requirement for international exchange is the complete set of 12 official ISAG microsatellite markers. To ensure sufficient experience within the lab, analysis of 500 animals per year is set as minimum requirement for microsatellite parentage verification certification.

''[https://www.icar.org/wp-content/uploads/2018/05/03-Annex-III-ISAG-microsatellites.pdf Appendix 5. ISAG recommended microsatellites for parentage verification in cattle]'' contains the list of microsatellite markers recommended by ISAG and the method for calculating 1 parent and 2 parent exclusion probabilities. The rules for microsatellite-based parentage verification in cattle are described in ''[https://www.icar.org/wp-content/uploads/2018/05/01-Annex-I-guidelines-microsats-STRs.pdf Appendix 6. Rules for microsatellite-based parentage testing in cattle]''. Exclusion probability (PE; 2 parents and 1 parent) of each marker and of the complete marker sets must be calculated and provided in the application. The type of population and number of animals (minimum 150) used for computations are to be described. ICAR recommends using Holstein as a reference group when possible. The ICAR committee of experts will evaluate that an appropriate PE is reached for accreditation, on the basis of the population analyzed.

==== SNP markers ====
ICAR accreditation of SNP-based parentage verification is based on the full set of 200 SNP previously recommended by ISAG. The name of all SNP genotyped on all animals (marker set I, including the 100 “Core” SNP) and of the additional markers assayed in the case of unresolved parentage (marker set II, including the 100 “Additional” SNP) must be declared, as well as the number of animals SNP genotyped in at least the last two years. ICAR recommends using the full set of 200 SNP for parentage verification of all animals genotyped (''see [https://www.icar.org/Guidelines/04-DNA-Technology-App7-SNP-list-for-parentage-verification.pdf Appendix 7. List of approved SNP for parentage verification in]'' [https://www.icar.org/Guidelines/04-DNA-Technology-App7-SNP-list-for-parentage-verification.pdf cattle]). ICAR may, however, based on scientific evidence, identify specific problematic SNP that must be excluded for parentage analysis, as described in the ICAR documentation related to the accreditation of DNA data interpretation centres outlined in section 4.

==== Marker nomenclature ====
Nomenclature of markers must be described. ISAG nomenclature is required for the official ISAG 12 marker set as well as for the ISAG SNP marker set.
</div>
</div>

== Accreditation of Organisations Performing SNP-Based Parentage Analysis ==
<div class="mw-collapsible mw-collapsed">
<div>
=== Introduction ===
With the advent of SNP genotyping, the function of DNA genotyping as a laboratory activity can be separated from the functions of performing parentage verification and parentage discovery. Consequently, ICAR has established a separate accreditation for applying the results of SNP-based genotyping, which may be undertaken by laboratories, breed association societies, genetic evaluation centres and any other organization involved in parentage verification and/or the data processing of SNP genotypes.

Parentage verification and discovery are concerned with using the results that are delivered by the laboratories from DNA genotyping and require SNP genotypes for the animal itself, its recorded parents and other possible parents in the case of parentage discovery. Organizations undertaking this function may be service providers between laboratories that ICAR has accredited for microsatellite- and or SNP-based DNA genotyping and end users that may include breed societies, breeding companies, breeders and commercial farmers.

Service providers could use different laboratories for different breeds and/or species. Considering the importance of animal identification and parentage verification in animal recording, ICAR has decided to define the minimum requirements for using the results of DNA genotyping, and other information, for the purpose of:

# Parentage verification
# Parentage discovery, and
# Animal identification confirmation

The purpose of these guidelines is to provide a basis for the accreditation of processes used by organizations that use SNP genotypes in cattle. Minimum requirements for additional species and other DNA analyses may be defined in the future.

=== Scope ===
These guidelines are for the accreditation, by ICAR, of organizations that use the results of SNP-based tests for parentage analysis in cattle, which includes parentage verification, parentage discovery, and/or animal identification confirmation.

=== Accreditation of Organizations Performing Parentage Analysis ===
The ICAR accreditation process comprises the following steps:

* Application for accreditation
* Payment of relevant fee
* Review of application
* Technical processing of test data files
* Granting of accreditation

==== Application ====
Organizations carrying out SNP-based parentage analysis and requesting ICAR accreditation as a DNA Data Interpretation Centre must apply by downloading and completing the appropriate form included below as [https://www.icar.org/wp-content/uploads/2016/10/6-Annex-V-Application-Form-forICAR-Accreditation-of-DNA-Centres.pdf ''Appendix 8. Application form for organizations seeking ICAR parentage analysis accreditation for DNA data interpretation centres''.] This form must be filled out accurately and completely, providing necessary documentation as required, and submitted to ICAR with payment of the appropriate fee.

==== Review of application ====
The application will first be reviewed internally by ICAR for its completeness and additional details may be requested as needed. ICAR administration will also confirm receipt of the applicable fee.

==== Technical processing of test files ====
The applicant organization will receive a set of data files from ICAR through the Interbull Centre, for processing using its existing procedures for carrying out the level of parentage analysis for which the applicant is seeking ICAR accreditation as a DNA Data Interpretation Centre. A detailed description of this step is described in [https://www.icar.org/index.php/certifications/certification-and-accreditation-of-dna-genetic-laboratories/two-new-dna-based-services/dna-data-interpretation-centres/ ''Appendix 9. Applicant's Guide for ICAR Parentage Analysis Accreditation for DNA Data Interpretation Centres.''] In order for the applicant to be successful in obtaining the requested ICAR accreditation, it's procedures for conducting parentage analysis must exactly follow [https://www.icar.org/Documents/GenoEx/ICAR%20Guidelines%20for%20Parentage%20Verification%20and%20Parentage%20Discovery%20based%20on%20SNP.pdf ''Appendix 10. ICAR Guidelines for Parentage Verification and Parentage Discovery Based on SNP Genotypes''.] The list of SNP to be used for either parentage verification (N=200) or parentage discovery (N=554) are available in [https://www.icar.org/Guidelines/04-DNA-Technology-App-11-SNP-list-for-parentage-verification-or-discovery.pdf ''Appendix 11. List of SNP to be used for either parentage verification or parentage discovery''.] Once the applicant has completed its internal parentage analysis procedures based on the accreditation test files it received, it must send a data file of results back to the Interbull Centre. A maximum time period for 90 calendar days will be allowed for the applicant to submit acceptable files of results back to the Interbull Centre.

==== Granting of accreditation ====
Once the Interbull Centre receives the file of parentage analysis results from the applicant, it will complete the technical review and determine if the applicant has successfully completed the accreditation or not. The Interbull Centre shall inform ICAR of the results and ICAR shall issue a formal notification to the applicant. In the event the applicant was not successful in receiving ICAR accreditation, the applicant may initiate a new request for accreditation by completing and submitting the appropriate forms and providing payment of the applicable fee, as outlined above.

==== Renewal of accreditation ====
In advance of the expiry date of any existing ICAR accreditation, which coincides with the two-year anniversary date of the current accreditation, an applicant can apply for renewal of their accreditation by submitting an application as described in section 4.3.1 above and successfully completing the other required steps outlined in this section 4.
</div>
</div>

== Genotype Exchange Service – GenoEx-PSE ==
Effective 2018, ICAR has made available a genotype exchange service for parentage analysis, GenoEx-PSE, offered through the Interbull Centre. The main goal of this service is to facilitate the international exchange of SNP genotypes such that approved service users can carry out parentage analysis services at a national level in an efficient manner. The GenoEx-PSE database system and user interface has been developed to allow for the exchange of SNP genotypes for either parentage verification or parentage discovery based on the list of SNP provided in ''Appendix 11. List of SNP to be used for either parentage verification or parentage discovery''.

In order for an organization to qualify as a service user for GenoEx-PSE, it must first receive ICAR accreditation as a DNA data interpretation centre. The level of such ICAR accreditation (i.e.: for SNP-based parentage verification alone or for both SNP-based parentage verification and discovery) shall determine the highest level of SNP that may be exchanged via the GenoEx-PSE service. For details associated with this ICAR service, refer to the GenoEx-PSE web site at [https://genoex.org/ www.GenoEx.org.]

== Appendix list ==
<div class="mw-collapsible mw-collapsed">
<div>
=== Appendix 1. Link to SNP markers recommended by ISAG for parentage verification ===
https://www.icar.org/Guidelines/04-DNA-Technology-App-1-Cattle-SNP-ISAG-core-additional-panel-2013.xlsx

=== Appendix 2. Application form for microsatellite-based parentage testing in cattle ===
Please refer [https://www.icar.org/wp-content/uploads/2022/05/Annex-II-Application-Form-for-STR-Accreditation.pdf here] on the ICAR website for the Form for ICAR laboratory accreditation for STR Microsatellite-based Parentage Testing in Cattle.

=== Appendix 3. Application form for SNP-based genotyping required for parentage analysis in cattle ===
Please refer [https://www.icar.org/wp-content/uploads/2022/05/Annex-V-Application-Form-for-SNP-Accreditation.pdf here] on the ICAR website for the Form for ICAR laboratory accreditation for SNP-based genotyping required for Parentage Analysis in Cattle.

=== Appendix 4. ICAR DNA Laboratory Accreditation Service fees ===
Please refer [https://www.icar.org/index.php/certifications/certification-and-accreditation-of-dna-genetic-laboratories/guidelines-for-str-and-snp-based-parentage-testing-in-cattle/ here] on the ICAR website for DNA testing accreditation services fees.

=== Appendix 5. ISAG recommended microsatellites for parentage verification in cattle ===
Please refer [https://www.icar.org/wp-content/uploads/2018/05/03-Annex-III-ISAG-microsatellites.pdf here] on the ICAR website for the list of ISAG recommended microsatellites for parentage verification in cattle.

=== Appendix 6. Rules for microsatellite-based parentage testing in cattle ===
Please refer [https://www.icar.org/wp-content/uploads/2018/05/01-Annex-I-guidelines-microsats-STRs.pdf here] on the ICAR website for the rules for microsatellite-based parentage verification in cattle.

=== Appendix 7. List of approved SNP for parentage verification in cattle ===
Please refer [https://www.icar.org/Guidelines/04-DNA-Technology-App7-SNP-list-for-parentage-verification.pdf here] on the ICAR website for the ICAR approved list of 200 SNP for parentage verification in cattle.

=== Appendix 8. Application form for organizations seeking ICAR parentage analysis accreditation for DNA data interpretation centres ===
Please refer [https://www.icar.org/wp-content/uploads/2016/10/6-Annex-V-Application-Form-forICAR-Accreditation-of-DNA-Centres.pdf here] on the ICAR website for the application form for organizations seeking ICAR accreditation status as a DNA data interpretation centre.

=== Appendix 9. Applicant's Guide for ICAR Parentage Analysis Accreditation for DNA Data Interpretation Centres ===
Please refer [https://www.icar.org/index.php/certifications/certification-and-accreditation-of-dna-genetic-laboratories/two-new-dna-based-services/dna-data-interpretation-centres/ here] on the ICAR website for the Applicant's Guide for ICAR Parentage Analysis Accreditation for DNA Data Interpretation Centres.

=== Appendix 10. ICAR Guidelines for Parentage Verification and Parentage Discovery Based on SNP Genotypes ===
Please refer [https://www.icar.org/Documents/GenoEx/ICAR%20Guidelines%20for%20Parentage%20Verification%20and%20Parentage%20Discovery%20based%20on%20SNP.pdf here] on the ICAR website for the ICAR Guidelines for Parentage Verification and Parentage Discovery Based on SNP Genotypes.

=== Appendix 11. List of SNP to be used for either parentage verification or parentage discovery ===
Please refer [https://www.icar.org/Guidelines/04-DNA-Technology-App-11-SNP-list-for-parentage-verification-or-discovery.pdf here] on the ICAR website for the list of SNP to be used for either parentage verification or parentage discovery.
</div>
</div>

Section 04 – DNA Technology

2025-01-31T10:01:43Z

Aashish: /* Payment of relevant fee */

Section 04 – DNA Technology

2025-01-31T09:59:39Z

Aashish: /* Payment of relevant fee */

== Molecular Genetics ==
<div class="mw-collapsible mw-collapsed">
<div>
=== Introduction ===
Advances in molecular biology, especially genomics, provide a new set of information to be incorporated into the animal industry. On one hand, the use of molecular information may contribute to the enhancement of consumers' trust in the ability to monitor and control the animal production chain. On the other hand, molecular information will greatly contribute to the achievement of genetic improvement for animal traits through the use of genomic breeding values, marker assisted selection, gene introgression, heterosis prediction, pedigree validation/prediction, and genetic defect carrier status. In most cases, advantages of using molecular information via genomic evaluations, comes from improved accuracy of animal breeding values, shortened generation intervals, and increased intensity of selection. Even with these advancements there is still a need for research and development in the search for associations between genetic markers and traits of interest, especially as new traits are included in national evaluation indexes. In addition to that, even with the current incorporation of genomic information into national selection schemes, an understanding of gene action, gene interactions, and differential gene expression to avoid negative collateral effects is needed. Cooperation between animal industries and research is required for a successful and beneficial search for genetic information in commercial livestock populations.

=== Genetic Markers ===
Genetic markers are the fundamental molecular tools for genomics, even as the type of marker used has changed. The first genetic marker associations in livestock were reported using blood typing in the 1960s, the technology then moved to microsatellites (MS) in the 1990s and more recently to the use of Single Nucleotide Polymorphism (SNP). SNP and MS are polymorphic DNA sequences (alleles) at a specific locus of a particular chromosome. While blood typing has been an ICAR approved method of parentage verification currently there are few, if any, commercial labs still offering this testing. For this reason, ICAR no longer recommends blood typing as the basis for carrying out parentage analysis in livestock species where MS or SNP technology is widely available.

==== Microsatellites ====
These are segments of DNA containing tandem repeats of simple motifs usually dimers or trimers. These segments are located throughout the genome and normally in non-coding regions. Over time, these regions are subject to the addition or subtraction of tandem repeats, which means that each microsatellite can have multiple unique alleles. Microsatellites are commonly used in many livestock species for parentage validation.

==== Single Nucleotide Polymorphism (SNP) ====
SNP are the most common type of genetic variation: each SNP represents a variation in a single nucleotide. There are millions of SNP located throughout the genome of every livestock species. For genomics the most informative SNP traditionally are either located in (a) coding regions where different alleles change the structure or function of the encoded protein, or (b) at non-coding regions that are involved in the regulatory function of the gene. For genomic breeding values, SNP that are located in other regions of the genome are also informative as they could be in linkage disequilibrium with alleles that do cause a phenotype change.

One of the big advantages of SNP is their deployment on SNP arrays with a strong parallel processing capacity whereby thousands or hundreds of thousands of SNP can be screened together in a cost-effective and efficient manner across a large number of animals. Currently, the largest livestock genotyping labs can process hundreds of thousands of animals yearly on such arrays. The availability of these large SNP panels is therefore bolstering the search for mutations underlying genetic variation for simple and complex traits. It is also revolutionizing the speed at which trait associated genes or gene regions are being discovered as well as the adoption rate of genomic selection strategies. SNP genotypes have become the international standard for the basis of parentage analysis and ICAR recommends this approach over the use of microsatellites wherever possible due to the improved accuracy and the ease of comparing results between genotyping laboratories.

=== Current and Potential Uses of DNA Technologies ===

==== Parentage verification and parental assignment authentication ====
Prior to the emergence of SNP genotyping, parentage verification was the main commercial use of genetic markers. Traditionally, parentage testing was based on the exclusion of relationship (i.e.: sire or dam) when an animal has a genotype inconsistent to a putative relationship. New trends in animal production systems are tending to encourage animal production in larger numbers per farm in response to environmental and production related constraints. In these large settings, multiple animals could be bred or give birth on the same day, which can result in more pedigree recording errors. As the cost of the analysis decreases and the number of genetic markers available increases, breed societies are now able to build up pedigree records using genetic markers to predict the pedigree of calves born in a herd at a given time. This normally requires a prior knowledge of candidate sires and dams for a calf when lower number (<200) of markers are used, but with enough SNP the correct parents can be predicted without prior knowledge being available as long as the parent is also genotyped. The probability of assignment to a correct pair of animals will depend on the number of markers used, number of alleles per loci, the minor allele frequency in the population, the number of parents, and the number of possible matings. The International Society of Animal Genetics (www.isag.us) has species-specific panels recommended of microsatellite and SNP markers for this purpose, which can be accessed via a link such as provided in ''[https://www.icar.org/Guidelines/04-DNA-Technology-App-1-Cattle-SNP-ISAG-core-additional-panel-2013.xlsx Appendix 1]. Link to SNP markers recommended by ISAG for parentage verification''. For cattle, ICAR has developed a set of parentage SNP, ICAR554, which incorporates the ISAG recommended panel and other highly informative SNP. This panel allows for highly accurate parentage validation and discovery while not allowing for accurate imputation to a higher density. Therefore, the ICAR554 panel can be shared among countries and competitors for parentage analysis without fear of others being able to use them to predict genomic breeding values. ICAR and the Interbull Centre collaborate in offering an international genotype exchange service, referred to as GenoEx, which is described further in Chapter 5 specifically for the exchange of SNP genotypes for the purposes of parentage analysis.

==== Traceability and authentication of animal products offered to consumers ====
Due to multiple crises, including BSE outbreaks to ground beef containing horsemeat, and with increased consumer interest in where their food comes from the traceability of meat products is of greater concern to the industry. Traceability is based on the availability of a verification and control system that monitors all relevant details throughout the entire livestock production chain. Since an individual’s genetic sequence is unique and does not change, its DNA remains constant from ‘conception to consumption’. Therefore, use of genetic markers allows one to match the DNA of an individual at birth to the final product.

Genetic markers for the authentication of animal products for labels of quality related to geographic location and labels of quality related to specific breeds or their crosses are/or will be very useful. However, this requires the establishment of molecular standards or allele frequencies for each breed within a species. A lot of information is coming from studies of genetic diversity among breeds. Genomic regions subject to intense selection in each population are of particular interest. With a large enough set of SNP and genotyped purebred reference animals it is also possible to predict the most likely breed composition of individuals.

==== Molecular genetic information for marker-assisted selection schemes ====
Quantitative traits are generally assumed to be controlled by a large number of genes. However, individual genes sometimes account for a significant amount of variation of the trait. Such is the case for the Myostatin gene and double muscling in beef cattle, the DGAT1 gene and milk components in dairy cattle, or the Booroola fecundity gene and ovulation rate in sheep. Since the genotype of an animal does not change during its lifetime, use of DNA information through the identification of markers linked to QTL with effects on production traits or the identification of a gene itself together with the causative variant is of great interest. Nevertheless, with complex traits there is a growing need of having a sufficiently large marker set to incorporate molecular information for selection decisions. Including genomic information as a selection criterion is of special interest for traits that are difficult and costly to measure and/or are measured late in life. By 2022, >177,000 cattle, >34,000 swine, >16,000 chicken and >4,000 sheep QTL have been identified that are associated with economically important traits such as health, carcass, milk, fertility, and body conformation. The AnimalQTLdb database housed at the [https://www.animalgenome.org/ National Animal Genome Research Program] contains up to date information on cattle, chicken, horse, pig, trout, and sheep QTL data assembled from published data.

Recording schemes have been collecting information for decades on the most common production traits measured in domestic livestock. There is an ever-increasing volume of information becoming available, but for some traits like meat quality, disease resistance and feed efficiency, those records are very expensive to measure, difficult to obtain, or are performed late in the animal’s life. Because of these challenges information for such traits is commonly collected on a reduced number of animals in any given population.

For these challenging, but economically important traits, genetic markers and genomic selection offer significant opportunities for trait selection where it was not economically feasible before. In general, genetic markers and genomics will play an important role for important traits regardless of the livestock species. Genomics can also allow us to increase selection intensities since we can predict genomic breeding values on a large number of animals and thus have more candidates for selection.

==== Disease resistance and genetic defects ====
Another group of traits with a high potential for the use of molecular data and genomics are those linked to resistance, resilience, and susceptibility to diseases. There are a number of multi-factorial or complex diseases that are the result of the interaction between an animal’s genome and environmental components. Disease resistance traits are among the most difficult to include in genetic improvement programs because they require good field measurement of the disease status of the animals and a systematic control of management or environmental conditions that allow for the identification of the environmental influence on the health status of the animal. Infectious diseases depend very much upon environmental factors such as the degree of exposure to the pathogen agent. Thus, if exposure is low, animals will show little variation. Part of the phenotypic differences for resistance may be differences in the degree of challenge. Therefore, if genes or genetic markers linked to resistance are correctly identified, resistant animals will be able to be selected on the base of their genomic information. For many diseases, identification of genes associated with resistance will require experimental conditions to be used. Genetic analysis to identify heterozygous carriers of genetic diseases caused by single, recessive genes are currently in use. Examples in dairy cattle include complex vertebral malformation (CVM), brachyspina (BY), cholesterol deficiency (CD) and several genes, gene regions or haplotypes causing embryo loss or stillbirth in different dairy breeds. In 2022, [https://www.omia.org/home/ OMIA (Online Mendelian Inheritance in Animals),] listed >1000 traits or genetic defects in livestock with a known causative mutation (cattle: 186, pig: 58, chicken: 56, sheep: 49, horse: 48, goat:17). Including these causative allele or associated haplotypes in a breeding program will allow producers to minimize their risk from genetic defects while maximizing genetic progress from beneficial traits.

=== Technical Aspects ===

==== DNA collection ====
Systematic collection of DNA is recommended in several livestock populations. DNA may be obtained from any nuclear cell in the body. Protocols for DNA extraction are now available for blood (white cells), semen, saliva (epithelial cells), hair follicles, muscle, skin, organs (such as liver, spleen etc.). Red blood cells may also be used for poultry as they retain the nuclear body while most other species do not. Small amounts of tissue material are required for routine DNA analysis. However, if there are multiple future uses of an individual’s DNA (whole genome sequencing, traceability, causative allele validations, …), then DNA storage costs, extraction costs, quality, and quantity obtained by different protocols will have to be carefully examined and optimized. Common collection methods include hair follicles, tissue samples (often ear punch) in an enclosed container, blood spots on filter paper, and nasal swabs.

==== Data organization ====
A centralised database may be organised in respect to the main uses of the genetic information:

* Parent verification, assignment, and/or discovery
* Traceability of meat products
* Breed identification or breed diversity
* Qualitative and quantitative traits 

Database tables may contain:

* Animal identification to link to all other information on the animal and its relatives.
* Number of genetic markers: n
* Standard name of each marker i (for i= 1, n)
* Accession number for marker such as the dbSNP ID
* Alleles for marker i
* Genomic location of marker i
* Effect of non-reference allele on the protein
* Phenotypic effect of the allele
* Association with other traits

==== Parentage accuracy ====
While use of microsatellite and SNP markers are both ICAR accredited methods of parentage verification they do not have the same power of parentage accuracy. Briefly the order of accuracy for ISAG and ICAR approved parentage marker panels are:

Microsatellites << small SNP panels (100 or less) < large SNP panels (500 or more)

This order is based on both genotyping accuracy and total genomic information. Comparing the genomic marker error rate in cattle microsatellites have a 1-5% error rate (Baruch and Weller, 2008<ref>Baruch, E., and J. I. Weller. 2008. 'Estimation of the number of SNP genetic markers required for parentage verification', ''Animal Genetics'', 39: 474-79.</ref>) while the SNP error rate is <0.1% (Cooper, Wiggans, and VanRaden 2013<ref>Cooper, T. A., G. R. Wiggans, and P. M. VanRaden. 2013. 'Short communication: relationship of call rate and accuracy of single nucleotide polymorphism genotypes in dairy cattle', ''Journal of dairy science'', 96: 3336-9.</ref>). As 2-3 SNP provide the same parentage exclusion accuracy as 1 microsatellite marker (Vignal et al. 2002<ref>Vignal, A., D. Milan, M. SanCristobal, and A. Eggen. 2002. 'A review on SNP and other types of molecular markers and their use in animal genetics', ''Genet Sel Evol'', 34: 275-305.

1.4.4 Genomic quality control checks</ref>), the 100 and 200 ISAG parentage SNP panels are more accurate than the 12 ISAG parentage microsatellite markers. In the same manner parentage panels of over 500 SNP (McClure et al. 2018<ref>McClure, M. C., J. McCarthy, P. Flynn, J. C. McClure, E. Dair, D. K. O'Connell, and J. F. Kearney. 2018. 'SNP Data Quality Control in a National Beef and Dairy Cattle System and Highly Accurate SNP Based Parentage Verification and Identification', ''Front Genet'', 9: 84.</ref>), such as the ICAR554, are recommended for parentage prediction which requires an even higher level of accuracy.

==== Genomic quality control checks ====
One of the most important parts of a large genomic database is to ensure that a genotype associated with an individual animal truly belongs to that animal. Most large livestock genomic databases deal with SNP data only and the quality of SNP genotyping data is of paramount importance (Wu at al., Evaluation of genotyping concordance for commercial bovine SNP arrays using quality-assurance samples, Animal Genetics, 50: 367-371, 2019). This section, therefore, focuses on quality control for that genomic data type. Both sample and SNP quality control measures are needed, and it is encouraged to develop a system for them early.

For those working with genotype data there are two main concerns. First, is ensuring that the genotype data itself is of high quality and can be trusted. Second, is ensuring that the genotype truly belongs to the individual listed. The recommended quality control checks below will work for any livestock species. The basic checks can be performed with minimal information about the individual, while some of the advanced checks require data that not everyone will have, such as historic animal location.

===== ''Basic genotype quality control checks for SNP-based genotype data'' =====
Genotype:
<ol style="list-style-type:number">
<li> Exclude SNP that have a genotype call rate below 90% when analyzed in your population. Using 500 or more animals to determine the SNP call rate is recommended. Chromosome Y SNP should have their call rate determined only in males for this filter.</li>
<li> Invalidate the individual’s genotype if its overall call rate is below <90%. For SNP-based genotypes, such as those from Illumina or Affymetrix chips, the accuracy of called genotypes is questionable when the individual’s overall call rate is <90%.</li>
<li> Check to see that the animal has all three genotype classes (i.e.: AA, AB and BB) in its full genotype file. If any genotype class is missing or has a frequency below 20% then invalidate the full genotype.</li>
<li> Check to ensure that there are no unexpected alleles in the genotype file. For example, genotypes in AB format should not have T, G, 0, 1, 2 or 9. If present, then invalidate the full genotype file.</li>

</ol>

Parentage:
<ol style="list-style-type:number">
<li> Parent (Sire or Dam) validation. If using 200 or less SNP, a listed sire will validate if <1% of the offspring-parent genotypes are in conflict. A conflicting genotype is where the offspring and listed parent have opposite homozygous genotypes.</li>
<li> Mating validation after parent validation. For all parentage validation SNP where the animal is heterozygous, if for >1% of those SNP the sire and dam are homozygous for the same allele then the listed mating is invalidated. This could represent a case where the offspring and one of the parents were mislabeled with the other’s identification (so the offspring’s genotype belongs to the sire or dam and vice versa). Under such cases, it is recommended to resample the DNA and regenotype, potentially with a panel that includes more SNP.</li>

</ol>

===== ''Advanced quality control checks for SNP-based genotype data'' =====
Animal:
<ol style="list-style-type:number">

<li> Parentage discovery. Using SNP data to predict who an animal’s likely parent is can be very useful, but steps must be taken to ensure a very high probability that the prediction is accurate. The following are recommended:</li>

# Using 500 or more SNP that have a minor allele frequency (MAF) above 20% and call rates above 90%. It is advised to calculate the MAF across your full population. Predicted parents should have <1% conflict rate with the animal.
# Sex check. Make sure that you have a process established to ensure that only males are predicted as the sire and only females as the dam.
# Date of Birth check. If you do not include a check that the predicted parent is older than the animal than the predicted individual could actually be an offspring of the animal.
# Age gap. Cattle normally reach sexual maturity at 11-12 months of age, but this can be as young as 8-9 months, and even younger if in-vitro fertilization is a technology used within the population. Under normal circumstances, a minimum of 17 months between the birth dates of the animal and its predicted parent is recommended to ensure that the predicted parent could have been sexually mature at the time of the breeding.
# Grey zone SNP conflicts. The majority of animals will have <0.5% or >1.5% conflicting genotypes with the individual when parentage discovery is conducted. Those with <0.5% pass the prediction and those with >1.5% fail. Most failed animals will have >8% conflict rates. For those animals who have between 0.5 and 1.5% conflicting SNP when a set parentage panel is used (i.e.: the ICAR554 SNP list), it is advised that the conflict rate from all available SNP be used between the two individuals and if the percent conflicting is <1% they validate as the parent, but if >1% they fail.

<li> Genetic Relationship Matrix (GRM). If an animal’s true parent is not genotyped, then it cannot be directly predicted or validated. The genetic relationship between closely related animals can be used to suggest a potential, non-genotyped, parent. It is recommended that 7,000 or more SNP be used to calculate the GRM. GRM results DO NOT validate a relationship, but only suggest. Caution should be used as GRM values can be inflated for inbred individuals. Full-sibs and parent-child should have GRM values around 50%, while half-sibs would be around 25%. The range for each group can vary 5-10% from the expected value.</li>

<li> Sex prediction. How to perform a sex prediction depends on the type and number of SNP an animal has from the X and Y chromosomes. While not every commercial chip includes chromosome Y SNP, they typically contain chromosome X markers.</li>

# Pseudo autosomal region (PAR) SNP. As both the X and Y chromosomes contain the PAR, SNP from this region should be excluded from sex prediction. If the PAR position boundaries are not published for your species they can be roughly determined by analyzing the chromosome X SNP in known males and females and identifying the region where the MAF in males for a continuous set of SNP is >1%. Non-PAR regions of chromosome X will have SNP with average MAF of <1% in males and >>1% in females.
# Chromosome X predicted. Use non-PAR SNP to determine the animal’s chromosome X heterozygosity rate (number of heterozygous chromosome X SNP / total number of chromosome X SNP). If the average heterozygosity rate is <5%, the predicted sex is male, and if >15% its female. If the rate is between 5 and 15% then the predicted sex is unknown.
# Chromosome Y predicted. Using chromosome Y SNP to predict sex is logically simpler but many commercial chips do not contain them. Say you have 7 chromosome Y SNP with high call rates in males, it is recommended using the following logic. Male is predicted when 6-7 of the Y SNP are present; female is predicted when <1 SNP is present and ambiguous sex is predicted when 2-5 Y SNP are present.
# Ambiguous sex prediction. If one set of sex chromosome SNP returns an ambiguous sex prediction and the other doesn’t it is recommended using the latter as the predicted sex. If both SNP sets are ambiguous, the animal could have Turner syndrome (X0), or Klinefelter’s syndrome (XXY), in this case it is recommended returning an ambiguous predicted sex.
# If the predicted sex from the chromosome X and Y analysis disagree, it is recommended returning an ambiguous predicted sex. This could also indicate a possible Klinefelter syndrome (XXY) animal.
# Sex selected AI semen straws. Sex prediction should not be carried out on DNA obtained from sex selected AI semen straws.

<li> Offspring Quality control. The genotyped and listed offspring of an animal can be used to identify potential cases where the animal’s genotype actually belongs to another animal. These should be used as flags to indicate a potential investigation, but it is recommended to temporarily invalidate the animal’s genotype until cleared. Advised thresholds for those flags are:</li>
# AI sire: If >80% of genotyped offspring fail if >10 offspring are genotyped.
# Stock/herd bull: If >80% of genotyped offspring fail if >5 offspring are genotyped.
# Dam: If 100% of genotyped offspring fail if 2 offspring are genotyped, else if >5 offspring are genotyped then use >80%.

<li> Duplicate genotype. The only case where two or more animals should share the exact same genotype is if they are identical twins or clones. Checking to see if >1 animal has the same genotypes is a useful quality control check. It is recommended using your parentage SNP set for initial screening and for any pair that have >99% identical genotypes and then using all available SNP to see if >99% of the genotypes match.</li>

</ol>

For standardization purposes with respect to the nomenclature of genes or loci, a web site is available at: https://www.genenames.org/about/guidelines#genenames and markers at: [https://hgvs-nomenclature.org/versions/21.0/ <nowiki>http://www.HGVS.org/varnomen</nowiki>.]

</div>
</div>

== ICAR Services Related to DNA Technology ==
ICAR offers three services that are related to the use of DNA all of which are linked to parentage analysis in one form or another, as shown in Figure 1. ICAR DNA services., and describing them in more detail in the other sections.
[[File:ICAR DNA service.png|thumb|Figure 1. ICAR DNA services.|center|415x415px]]

== ICAR Accreditation of Laboratories Providing DNA Genotyping Services ==
<div class="mw-collapsible mw-collapsed">
<div>
=== Introduction ===
Considering the need for high quality standards in all uses of molecular data, ICAR has for several years offered an accreditation service based on defined minimum requirements for laboratories providing DNA genotyping services. The basic requirements of this accreditation include proof of the minimum internal management quality assurance standards and a Rank 1 result from participation in the most recent biennial international ring test developed and offered by the International Society for Animal Genetics (ISAG).

In addition, such laboratories generally have been analyzing the resulting genotypes to carry out microsatellite- and/or SNP-based parentage analysis services including either parentage verification or animal identification confirmation. This ICAR accreditation service has previously been used for recognizing the genotyping laboratory as an accredited organization to provide parentage analysis functions without specifically testing the technical accuracy of doing so. Effective 2021, the SNP-based parentage analysis accreditation service for DNA Data Interpretation Centres has replaced the previous laboratory accreditation for SNP-based parentage verification. In the future, a similar technical process for the accreditation of microsatellite-based parentage analysis may be introduced by ICAR but until such time, the existing process for the accreditation of genotyping laboratories will remain in effect.

The following guidelines for accreditation are provided for microsatellite- and SNP-based genotyping in cattle. Minimum requirements for additional species and other DNA tests may be defined in the future.

=== Scope ===
These guidelines are for the accreditation, by ICAR, of genotyping laboratories that analyze biological samples from cattle using microsatellite- and or SNP-based genotyping, which may be subsequently used for various levels of parentage analysis, genotype imputation, estimation of genomic breeding values and other activities related to genomic selection strategies. This accreditation process also includes parentage verification based on microsatellites since ICAR has not established this service as part of the portfolio of possible accreditations for DNA data interpretation centres. For genotyping laboratories that would like to receive ICAR accreditation for SNP-based parentage verification, they must now apply separately to ICAR for its parallel service of parentage analysis accreditation for DNA data interpretation centres, as described in section 4.

=== ICAR Guidelines for Accreditation of Genotyping Laboratories ===
The accreditation process comprises the following steps:

* Application for accreditation
* Payment of relevant fee
* Review of application
* Granting of accreditation

==== Application for accreditation ====
Laboratories requesting accreditation only for microsatellite- based genotyping and parentage verification must apply by downloading and completing the appropriate form as provided in [https://www.icar.org/wp-content/uploads/2022/05/Annex-II-Application-Form-for-STR-Accreditation.pdf ''Appendix 2. Application form for microsatellite-based parentage testing in cattle''.] Laboratories seeking ICAR accreditation involving SNP-based genotyping must apply by downloading and completing the appropriate form as provided in [https://www.icar.org/wp-content/uploads/2022/05/Annex-V-Application-Form-for-SNP-Accreditation.pdf ''Appendix 3. Application form for SNP-based genotyping required for parentage analysis in cattle.''] Laboratories that have previously received ICAR accreditation for either service may re-apply prior to the expiry of any such accreditation using a shortened renewal form available on the ICAR web site. All application forms must be emailed to the ICAR secretariat at dna@icar.org and be filled out accurately and completely including the necessary documentation as required.

==== Payment of relevant fee ====
Along with the completed application form, the applicant must also provide full payment of the relevant fee as established by ICAR and given in [https://www.icar.org/index.php/certifications/certification-and-accreditation-of-dna-genetic-laboratories/guidelines-for-str-and-snp-based-parentage-testing-in-cattle/ ''Appendix 4. ICAR DNA Laboratory Accreditation Service fees''.] [[#Application|refer]]

==== Review of application ====
The application will be evaluated by a committee of experts appointed by ICAR that will either:

* Approve the application
* Request additional information, or
* Reject the application

In the case of rejection, the laboratory may make a new submission as part of the ICAR annual call for applications for any subsequent year after the failed application.

==== Granting of accreditation ====
Accreditation will be given for a period of two calendar years with an expiry date of December 31st of the second year after receiving ICAR accreditation as a laboratory providing DNA genotyping services.

==== Renewal of accreditation ====
In advance of the expiry date of any existing ICAR accreditation, normally during the same year of the December 31st expiry date, a laboratory can apply for renewal of their accreditation by submitting an application as described in section 3.3.1 above and successfully completing the other steps outlined in this section 3.

==== Laboratory accreditation ====
Effective the 2022 ICAR call for accreditation of genotyping laboratories, ISO17025 accreditation, or an equivalent accreditation for ensuring quality internal management systems, is a mandatory requirement for SNP-based accreditation. In addition, effective the 2022 call for accreditation of genotyping laboratories for microsatellite (STR)-based accreditation, ISO9001 certification will no longer be acceptable and only ISO17025, or an equivalent accreditation, will be an acceptable level of accreditation to ensure quality internal management systems. During the year of application for ICAR accreditation as a laboratory providing DNA genotyping services, the applicant must provide proof of ISO17025 accreditation with an expiry date of October 31st of the following calendar year, or later.

==== Participation and performance in ring test ====
On a biennial basis, initiated during even years (i.e.: 2022, 20246, etc…) and discussed at its biennial conference in odd years (2023, 2025, etc.), ISAG conducts an international ring (comparison) test of laboratories for both microsatellite- and SNP-based genotyping. The participation in ISAG and performance within these ring tests must be disclosed, and certificates provided to ICAR, when available. Applicants must also sign a release allowing ISAG to directly disclose their ring test results to ICAR. Participation in the most recent ISAG ring test is a minimum requirement to qualify for ICAR accreditation. For the ISAG microsatellite ring test, lab genotyping performance for the official set of 12 ISAG microsatellites must be disclosed. The committee of experts will decide performance thresholds for each ring test with due consideration for the structure of the ring test and the average performance of laboratories in the ring test that year. Only those laboratories achieving Rank 1 status in the most recent biennial ISAG ring test shall automatically qualify to receive ICAR accreditation as a genotyping laboratory. Laboratories achieving a Rank 2 status in the most recent ISAG ring test may qualify to receive ICAR accreditation, at the discretion of the committee of experts, but must provide evidence of Rank 1 status for previous ISAG ring tests as well as documentation outlining the cause of the Rank 2 result and any associated actions to mitigate similar outcomes in future ISAG ring tests. Laboratories achieving a status lower than Rank 2 in the most recent ISAG ring test do not qualify for ICAR accreditation as a laboratory providing DNA genotyping services.

==== Microsatellite markers ====
The names of all microsatellites typed on all animals (marker set I) and of the additional ones assayed in the case of unresolved parentage (marker set II) must be declared, as well as the number of animals typed in at least the last two years. The minimum requirement for international exchange is the complete set of 12 official ISAG microsatellite markers. To ensure sufficient experience within the lab, analysis of 500 animals per year is set as minimum requirement for microsatellite parentage verification certification.

''[https://www.icar.org/wp-content/uploads/2018/05/03-Annex-III-ISAG-microsatellites.pdf Appendix 5. ISAG recommended microsatellites for parentage verification in cattle]'' contains the list of microsatellite markers recommended by ISAG and the method for calculating 1 parent and 2 parent exclusion probabilities. The rules for microsatellite-based parentage verification in cattle are described in ''[https://www.icar.org/wp-content/uploads/2018/05/01-Annex-I-guidelines-microsats-STRs.pdf Appendix 6. Rules for microsatellite-based parentage testing in cattle]''. Exclusion probability (PE; 2 parents and 1 parent) of each marker and of the complete marker sets must be calculated and provided in the application. The type of population and number of animals (minimum 150) used for computations are to be described. ICAR recommends using Holstein as a reference group when possible. The ICAR committee of experts will evaluate that an appropriate PE is reached for accreditation, on the basis of the population analyzed.

==== SNP markers ====
ICAR accreditation of SNP-based parentage verification is based on the full set of 200 SNP previously recommended by ISAG. The name of all SNP genotyped on all animals (marker set I, including the 100 “Core” SNP) and of the additional markers assayed in the case of unresolved parentage (marker set II, including the 100 “Additional” SNP) must be declared, as well as the number of animals SNP genotyped in at least the last two years. ICAR recommends using the full set of 200 SNP for parentage verification of all animals genotyped (''see [https://www.icar.org/Guidelines/04-DNA-Technology-App7-SNP-list-for-parentage-verification.pdf Appendix 7. List of approved SNP for parentage verification in]'' [https://www.icar.org/Guidelines/04-DNA-Technology-App7-SNP-list-for-parentage-verification.pdf cattle]). ICAR may, however, based on scientific evidence, identify specific problematic SNP that must be excluded for parentage analysis, as described in the ICAR documentation related to the accreditation of DNA data interpretation centres outlined in section 4.

==== Marker nomenclature ====
Nomenclature of markers must be described. ISAG nomenclature is required for the official ISAG 12 marker set as well as for the ISAG SNP marker set.
</div>
</div>

== Accreditation of Organisations Performing SNP-Based Parentage Analysis ==
<div class="mw-collapsible mw-collapsed">
<div>
=== Introduction ===
With the advent of SNP genotyping, the function of DNA genotyping as a laboratory activity can be separated from the functions of performing parentage verification and parentage discovery. Consequently, ICAR has established a separate accreditation for applying the results of SNP-based genotyping, which may be undertaken by laboratories, breed association societies, genetic evaluation centres and any other organization involved in parentage verification and/or the data processing of SNP genotypes.

Parentage verification and discovery are concerned with using the results that are delivered by the laboratories from DNA genotyping and require SNP genotypes for the animal itself, its recorded parents and other possible parents in the case of parentage discovery. Organizations undertaking this function may be service providers between laboratories that ICAR has accredited for microsatellite- and or SNP-based DNA genotyping and end users that may include breed societies, breeding companies, breeders and commercial farmers.

Service providers could use different laboratories for different breeds and/or species. Considering the importance of animal identification and parentage verification in animal recording, ICAR has decided to define the minimum requirements for using the results of DNA genotyping, and other information, for the purpose of:

# Parentage verification
# Parentage discovery, and
# Animal identification confirmation

The purpose of these guidelines is to provide a basis for the accreditation of processes used by organizations that use SNP genotypes in cattle. Minimum requirements for additional species and other DNA analyses may be defined in the future.

=== Scope ===
These guidelines are for the accreditation, by ICAR, of organizations that use the results of SNP-based tests for parentage analysis in cattle, which includes parentage verification, parentage discovery, and/or animal identification confirmation.

=== Accreditation of Organizations Performing Parentage Analysis ===
The ICAR accreditation process comprises the following steps:

* Application for accreditation
* Payment of relevant fee
* Review of application
* Technical processing of test data files
* Granting of accreditation

==== Application ====
Organizations carrying out SNP-based parentage analysis and requesting ICAR accreditation as a DNA Data Interpretation Centre must apply by downloading and completing the appropriate form included below as [https://www.icar.org/wp-content/uploads/2016/10/6-Annex-V-Application-Form-forICAR-Accreditation-of-DNA-Centres.pdf ''Appendix 8. Application form for organizations seeking ICAR parentage analysis accreditation for DNA data interpretation centres''.] This form must be filled out accurately and completely, providing necessary documentation as required, and submitted to ICAR with payment of the appropriate fee.

==== Review of application ====
The application will first be reviewed internally by ICAR for its completeness and additional details may be requested as needed. ICAR administration will also confirm receipt of the applicable fee.

==== Technical processing of test files ====
The applicant organization will receive a set of data files from ICAR through the Interbull Centre, for processing using its existing procedures for carrying out the level of parentage analysis for which the applicant is seeking ICAR accreditation as a DNA Data Interpretation Centre. A detailed description of this step is described in [https://www.icar.org/index.php/certifications/certification-and-accreditation-of-dna-genetic-laboratories/two-new-dna-based-services/dna-data-interpretation-centres/ ''Appendix 9. Applicant's Guide for ICAR Parentage Analysis Accreditation for DNA Data Interpretation Centres.''] In order for the applicant to be successful in obtaining the requested ICAR accreditation, it's procedures for conducting parentage analysis must exactly follow [https://www.icar.org/Documents/GenoEx/ICAR%20Guidelines%20for%20Parentage%20Verification%20and%20Parentage%20Discovery%20based%20on%20SNP.pdf ''Appendix 10. ICAR Guidelines for Parentage Verification and Parentage Discovery Based on SNP Genotypes''.] The list of SNP to be used for either parentage verification (N=200) or parentage discovery (N=554) are available in [https://www.icar.org/Guidelines/04-DNA-Technology-App-11-SNP-list-for-parentage-verification-or-discovery.pdf ''Appendix 11. List of SNP to be used for either parentage verification or parentage discovery''.] Once the applicant has completed its internal parentage analysis procedures based on the accreditation test files it received, it must send a data file of results back to the Interbull Centre. A maximum time period for 90 calendar days will be allowed for the applicant to submit acceptable files of results back to the Interbull Centre.

==== Granting of accreditation ====
Once the Interbull Centre receives the file of parentage analysis results from the applicant, it will complete the technical review and determine if the applicant has successfully completed the accreditation or not. The Interbull Centre shall inform ICAR of the results and ICAR shall issue a formal notification to the applicant. In the event the applicant was not successful in receiving ICAR accreditation, the applicant may initiate a new request for accreditation by completing and submitting the appropriate forms and providing payment of the applicable fee, as outlined above.

==== Renewal of accreditation ====
In advance of the expiry date of any existing ICAR accreditation, which coincides with the two-year anniversary date of the current accreditation, an applicant can apply for renewal of their accreditation by submitting an application as described in section 4.3.1 above and successfully completing the other required steps outlined in this section 4.
</div>
</div>

== Genotype Exchange Service – GenoEx-PSE ==
Effective 2018, ICAR has made available a genotype exchange service for parentage analysis, GenoEx-PSE, offered through the Interbull Centre. The main goal of this service is to facilitate the international exchange of SNP genotypes such that approved service users can carry out parentage analysis services at a national level in an efficient manner. The GenoEx-PSE database system and user interface has been developed to allow for the exchange of SNP genotypes for either parentage verification or parentage discovery based on the list of SNP provided in ''Appendix 11. List of SNP to be used for either parentage verification or parentage discovery''.

In order for an organization to qualify as a service user for GenoEx-PSE, it must first receive ICAR accreditation as a DNA data interpretation centre. The level of such ICAR accreditation (i.e.: for SNP-based parentage verification alone or for both SNP-based parentage verification and discovery) shall determine the highest level of SNP that may be exchanged via the GenoEx-PSE service. For details associated with this ICAR service, refer to the GenoEx-PSE web site at [https://genoex.org/ www.GenoEx.org.]

== Appendix list ==
<div class="mw-collapsible mw-collapsed">
<div>
=== Appendix 1. Link to SNP markers recommended by ISAG for parentage verification ===
https://www.icar.org/Guidelines/04-DNA-Technology-App-1-Cattle-SNP-ISAG-core-additional-panel-2013.xlsx

=== Appendix 2. Application form for microsatellite-based parentage testing in cattle ===
Please refer [https://www.icar.org/wp-content/uploads/2022/05/Annex-II-Application-Form-for-STR-Accreditation.pdf here] on the ICAR website for the Form for ICAR laboratory accreditation for STR Microsatellite-based Parentage Testing in Cattle.

=== Appendix 3. Application form for SNP-based genotyping required for parentage analysis in cattle ===
Please refer [https://www.icar.org/wp-content/uploads/2022/05/Annex-V-Application-Form-for-SNP-Accreditation.pdf here] on the ICAR website for the Form for ICAR laboratory accreditation for SNP-based genotyping required for Parentage Analysis in Cattle.

=== Appendix 4. ICAR DNA Laboratory Accreditation Service fees ===
Please refer [https://www.icar.org/index.php/certifications/certification-and-accreditation-of-dna-genetic-laboratories/guidelines-for-str-and-snp-based-parentage-testing-in-cattle/ here] on the ICAR website for DNA testing accreditation services fees.

=== Appendix 5. ISAG recommended microsatellites for parentage verification in cattle ===
Please refer [https://www.icar.org/wp-content/uploads/2018/05/03-Annex-III-ISAG-microsatellites.pdf here] on the ICAR website for the list of ISAG recommended microsatellites for parentage verification in cattle.

=== Appendix 6. Rules for microsatellite-based parentage testing in cattle ===
Please refer [https://www.icar.org/wp-content/uploads/2018/05/01-Annex-I-guidelines-microsats-STRs.pdf here] on the ICAR website for the rules for microsatellite-based parentage verification in cattle.

=== Appendix 7. List of approved SNP for parentage verification in cattle ===
Please refer [https://www.icar.org/Guidelines/04-DNA-Technology-App7-SNP-list-for-parentage-verification.pdf here] on the ICAR website for the ICAR approved list of 200 SNP for parentage verification in cattle.

=== Appendix 8. Application form for organizations seeking ICAR parentage analysis accreditation for DNA data interpretation centres ===
Please refer [https://www.icar.org/wp-content/uploads/2016/10/6-Annex-V-Application-Form-forICAR-Accreditation-of-DNA-Centres.pdf here] on the ICAR website for the application form for organizations seeking ICAR accreditation status as a DNA data interpretation centre.

=== Appendix 9. Applicant's Guide for ICAR Parentage Analysis Accreditation for DNA Data Interpretation Centres ===
Please refer [https://www.icar.org/index.php/certifications/certification-and-accreditation-of-dna-genetic-laboratories/two-new-dna-based-services/dna-data-interpretation-centres/ here] on the ICAR website for the Applicant's Guide for ICAR Parentage Analysis Accreditation for DNA Data Interpretation Centres.

=== Appendix 10. ICAR Guidelines for Parentage Verification and Parentage Discovery Based on SNP Genotypes ===
Please refer [https://www.icar.org/Documents/GenoEx/ICAR%20Guidelines%20for%20Parentage%20Verification%20and%20Parentage%20Discovery%20based%20on%20SNP.pdf here] on the ICAR website for the ICAR Guidelines for Parentage Verification and Parentage Discovery Based on SNP Genotypes.

=== Appendix 11. List of SNP to be used for either parentage verification or parentage discovery ===
Please refer [https://www.icar.org/Guidelines/04-DNA-Technology-App-11-SNP-list-for-parentage-verification-or-discovery.pdf here] on the ICAR website for the list of SNP to be used for either parentage verification or parentage discovery.
</div>
</div>

Section 04 – DNA Technology

2025-01-31T09:59:00Z

Aashish: /* Payment of relevant fee */

== Molecular Genetics ==
<div class="mw-collapsible mw-collapsed">
<div>
=== Introduction ===
Advances in molecular biology, especially genomics, provide a new set of information to be incorporated into the animal industry. On one hand, the use of molecular information may contribute to the enhancement of consumers' trust in the ability to monitor and control the animal production chain. On the other hand, molecular information will greatly contribute to the achievement of genetic improvement for animal traits through the use of genomic breeding values, marker assisted selection, gene introgression, heterosis prediction, pedigree validation/prediction, and genetic defect carrier status. In most cases, advantages of using molecular information via genomic evaluations, comes from improved accuracy of animal breeding values, shortened generation intervals, and increased intensity of selection. Even with these advancements there is still a need for research and development in the search for associations between genetic markers and traits of interest, especially as new traits are included in national evaluation indexes. In addition to that, even with the current incorporation of genomic information into national selection schemes, an understanding of gene action, gene interactions, and differential gene expression to avoid negative collateral effects is needed. Cooperation between animal industries and research is required for a successful and beneficial search for genetic information in commercial livestock populations.

=== Genetic Markers ===
Genetic markers are the fundamental molecular tools for genomics, even as the type of marker used has changed. The first genetic marker associations in livestock were reported using blood typing in the 1960s, the technology then moved to microsatellites (MS) in the 1990s and more recently to the use of Single Nucleotide Polymorphism (SNP). SNP and MS are polymorphic DNA sequences (alleles) at a specific locus of a particular chromosome. While blood typing has been an ICAR approved method of parentage verification currently there are few, if any, commercial labs still offering this testing. For this reason, ICAR no longer recommends blood typing as the basis for carrying out parentage analysis in livestock species where MS or SNP technology is widely available.

==== Microsatellites ====
These are segments of DNA containing tandem repeats of simple motifs usually dimers or trimers. These segments are located throughout the genome and normally in non-coding regions. Over time, these regions are subject to the addition or subtraction of tandem repeats, which means that each microsatellite can have multiple unique alleles. Microsatellites are commonly used in many livestock species for parentage validation.

==== Single Nucleotide Polymorphism (SNP) ====
SNP are the most common type of genetic variation: each SNP represents a variation in a single nucleotide. There are millions of SNP located throughout the genome of every livestock species. For genomics the most informative SNP traditionally are either located in (a) coding regions where different alleles change the structure or function of the encoded protein, or (b) at non-coding regions that are involved in the regulatory function of the gene. For genomic breeding values, SNP that are located in other regions of the genome are also informative as they could be in linkage disequilibrium with alleles that do cause a phenotype change.

One of the big advantages of SNP is their deployment on SNP arrays with a strong parallel processing capacity whereby thousands or hundreds of thousands of SNP can be screened together in a cost-effective and efficient manner across a large number of animals. Currently, the largest livestock genotyping labs can process hundreds of thousands of animals yearly on such arrays. The availability of these large SNP panels is therefore bolstering the search for mutations underlying genetic variation for simple and complex traits. It is also revolutionizing the speed at which trait associated genes or gene regions are being discovered as well as the adoption rate of genomic selection strategies. SNP genotypes have become the international standard for the basis of parentage analysis and ICAR recommends this approach over the use of microsatellites wherever possible due to the improved accuracy and the ease of comparing results between genotyping laboratories.

=== Current and Potential Uses of DNA Technologies ===

==== Parentage verification and parental assignment authentication ====
Prior to the emergence of SNP genotyping, parentage verification was the main commercial use of genetic markers. Traditionally, parentage testing was based on the exclusion of relationship (i.e.: sire or dam) when an animal has a genotype inconsistent to a putative relationship. New trends in animal production systems are tending to encourage animal production in larger numbers per farm in response to environmental and production related constraints. In these large settings, multiple animals could be bred or give birth on the same day, which can result in more pedigree recording errors. As the cost of the analysis decreases and the number of genetic markers available increases, breed societies are now able to build up pedigree records using genetic markers to predict the pedigree of calves born in a herd at a given time. This normally requires a prior knowledge of candidate sires and dams for a calf when lower number (<200) of markers are used, but with enough SNP the correct parents can be predicted without prior knowledge being available as long as the parent is also genotyped. The probability of assignment to a correct pair of animals will depend on the number of markers used, number of alleles per loci, the minor allele frequency in the population, the number of parents, and the number of possible matings. The International Society of Animal Genetics (www.isag.us) has species-specific panels recommended of microsatellite and SNP markers for this purpose, which can be accessed via a link such as provided in ''[https://www.icar.org/Guidelines/04-DNA-Technology-App-1-Cattle-SNP-ISAG-core-additional-panel-2013.xlsx Appendix 1]. Link to SNP markers recommended by ISAG for parentage verification''. For cattle, ICAR has developed a set of parentage SNP, ICAR554, which incorporates the ISAG recommended panel and other highly informative SNP. This panel allows for highly accurate parentage validation and discovery while not allowing for accurate imputation to a higher density. Therefore, the ICAR554 panel can be shared among countries and competitors for parentage analysis without fear of others being able to use them to predict genomic breeding values. ICAR and the Interbull Centre collaborate in offering an international genotype exchange service, referred to as GenoEx, which is described further in Chapter 5 specifically for the exchange of SNP genotypes for the purposes of parentage analysis.

==== Traceability and authentication of animal products offered to consumers ====
Due to multiple crises, including BSE outbreaks to ground beef containing horsemeat, and with increased consumer interest in where their food comes from the traceability of meat products is of greater concern to the industry. Traceability is based on the availability of a verification and control system that monitors all relevant details throughout the entire livestock production chain. Since an individual’s genetic sequence is unique and does not change, its DNA remains constant from ‘conception to consumption’. Therefore, use of genetic markers allows one to match the DNA of an individual at birth to the final product.

Genetic markers for the authentication of animal products for labels of quality related to geographic location and labels of quality related to specific breeds or their crosses are/or will be very useful. However, this requires the establishment of molecular standards or allele frequencies for each breed within a species. A lot of information is coming from studies of genetic diversity among breeds. Genomic regions subject to intense selection in each population are of particular interest. With a large enough set of SNP and genotyped purebred reference animals it is also possible to predict the most likely breed composition of individuals.

==== Molecular genetic information for marker-assisted selection schemes ====
Quantitative traits are generally assumed to be controlled by a large number of genes. However, individual genes sometimes account for a significant amount of variation of the trait. Such is the case for the Myostatin gene and double muscling in beef cattle, the DGAT1 gene and milk components in dairy cattle, or the Booroola fecundity gene and ovulation rate in sheep. Since the genotype of an animal does not change during its lifetime, use of DNA information through the identification of markers linked to QTL with effects on production traits or the identification of a gene itself together with the causative variant is of great interest. Nevertheless, with complex traits there is a growing need of having a sufficiently large marker set to incorporate molecular information for selection decisions. Including genomic information as a selection criterion is of special interest for traits that are difficult and costly to measure and/or are measured late in life. By 2022, >177,000 cattle, >34,000 swine, >16,000 chicken and >4,000 sheep QTL have been identified that are associated with economically important traits such as health, carcass, milk, fertility, and body conformation. The AnimalQTLdb database housed at the [https://www.animalgenome.org/ National Animal Genome Research Program] contains up to date information on cattle, chicken, horse, pig, trout, and sheep QTL data assembled from published data.

Recording schemes have been collecting information for decades on the most common production traits measured in domestic livestock. There is an ever-increasing volume of information becoming available, but for some traits like meat quality, disease resistance and feed efficiency, those records are very expensive to measure, difficult to obtain, or are performed late in the animal’s life. Because of these challenges information for such traits is commonly collected on a reduced number of animals in any given population.

For these challenging, but economically important traits, genetic markers and genomic selection offer significant opportunities for trait selection where it was not economically feasible before. In general, genetic markers and genomics will play an important role for important traits regardless of the livestock species. Genomics can also allow us to increase selection intensities since we can predict genomic breeding values on a large number of animals and thus have more candidates for selection.

==== Disease resistance and genetic defects ====
Another group of traits with a high potential for the use of molecular data and genomics are those linked to resistance, resilience, and susceptibility to diseases. There are a number of multi-factorial or complex diseases that are the result of the interaction between an animal’s genome and environmental components. Disease resistance traits are among the most difficult to include in genetic improvement programs because they require good field measurement of the disease status of the animals and a systematic control of management or environmental conditions that allow for the identification of the environmental influence on the health status of the animal. Infectious diseases depend very much upon environmental factors such as the degree of exposure to the pathogen agent. Thus, if exposure is low, animals will show little variation. Part of the phenotypic differences for resistance may be differences in the degree of challenge. Therefore, if genes or genetic markers linked to resistance are correctly identified, resistant animals will be able to be selected on the base of their genomic information. For many diseases, identification of genes associated with resistance will require experimental conditions to be used. Genetic analysis to identify heterozygous carriers of genetic diseases caused by single, recessive genes are currently in use. Examples in dairy cattle include complex vertebral malformation (CVM), brachyspina (BY), cholesterol deficiency (CD) and several genes, gene regions or haplotypes causing embryo loss or stillbirth in different dairy breeds. In 2022, [https://www.omia.org/home/ OMIA (Online Mendelian Inheritance in Animals),] listed >1000 traits or genetic defects in livestock with a known causative mutation (cattle: 186, pig: 58, chicken: 56, sheep: 49, horse: 48, goat:17). Including these causative allele or associated haplotypes in a breeding program will allow producers to minimize their risk from genetic defects while maximizing genetic progress from beneficial traits.

=== Technical Aspects ===

==== DNA collection ====
Systematic collection of DNA is recommended in several livestock populations. DNA may be obtained from any nuclear cell in the body. Protocols for DNA extraction are now available for blood (white cells), semen, saliva (epithelial cells), hair follicles, muscle, skin, organs (such as liver, spleen etc.). Red blood cells may also be used for poultry as they retain the nuclear body while most other species do not. Small amounts of tissue material are required for routine DNA analysis. However, if there are multiple future uses of an individual’s DNA (whole genome sequencing, traceability, causative allele validations, …), then DNA storage costs, extraction costs, quality, and quantity obtained by different protocols will have to be carefully examined and optimized. Common collection methods include hair follicles, tissue samples (often ear punch) in an enclosed container, blood spots on filter paper, and nasal swabs.

==== Data organization ====
A centralised database may be organised in respect to the main uses of the genetic information:

* Parent verification, assignment, and/or discovery
* Traceability of meat products
* Breed identification or breed diversity
* Qualitative and quantitative traits 

Database tables may contain:

* Animal identification to link to all other information on the animal and its relatives.
* Number of genetic markers: n
* Standard name of each marker i (for i= 1, n)
* Accession number for marker such as the dbSNP ID
* Alleles for marker i
* Genomic location of marker i
* Effect of non-reference allele on the protein
* Phenotypic effect of the allele
* Association with other traits

==== Parentage accuracy ====
While use of microsatellite and SNP markers are both ICAR accredited methods of parentage verification they do not have the same power of parentage accuracy. Briefly the order of accuracy for ISAG and ICAR approved parentage marker panels are:

Microsatellites << small SNP panels (100 or less) < large SNP panels (500 or more)

This order is based on both genotyping accuracy and total genomic information. Comparing the genomic marker error rate in cattle microsatellites have a 1-5% error rate (Baruch and Weller, 2008<ref>Baruch, E., and J. I. Weller. 2008. 'Estimation of the number of SNP genetic markers required for parentage verification', ''Animal Genetics'', 39: 474-79.</ref>) while the SNP error rate is <0.1% (Cooper, Wiggans, and VanRaden 2013<ref>Cooper, T. A., G. R. Wiggans, and P. M. VanRaden. 2013. 'Short communication: relationship of call rate and accuracy of single nucleotide polymorphism genotypes in dairy cattle', ''Journal of dairy science'', 96: 3336-9.</ref>). As 2-3 SNP provide the same parentage exclusion accuracy as 1 microsatellite marker (Vignal et al. 2002<ref>Vignal, A., D. Milan, M. SanCristobal, and A. Eggen. 2002. 'A review on SNP and other types of molecular markers and their use in animal genetics', ''Genet Sel Evol'', 34: 275-305.

1.4.4 Genomic quality control checks</ref>), the 100 and 200 ISAG parentage SNP panels are more accurate than the 12 ISAG parentage microsatellite markers. In the same manner parentage panels of over 500 SNP (McClure et al. 2018<ref>McClure, M. C., J. McCarthy, P. Flynn, J. C. McClure, E. Dair, D. K. O'Connell, and J. F. Kearney. 2018. 'SNP Data Quality Control in a National Beef and Dairy Cattle System and Highly Accurate SNP Based Parentage Verification and Identification', ''Front Genet'', 9: 84.</ref>), such as the ICAR554, are recommended for parentage prediction which requires an even higher level of accuracy.

==== Genomic quality control checks ====
One of the most important parts of a large genomic database is to ensure that a genotype associated with an individual animal truly belongs to that animal. Most large livestock genomic databases deal with SNP data only and the quality of SNP genotyping data is of paramount importance (Wu at al., Evaluation of genotyping concordance for commercial bovine SNP arrays using quality-assurance samples, Animal Genetics, 50: 367-371, 2019). This section, therefore, focuses on quality control for that genomic data type. Both sample and SNP quality control measures are needed, and it is encouraged to develop a system for them early.

For those working with genotype data there are two main concerns. First, is ensuring that the genotype data itself is of high quality and can be trusted. Second, is ensuring that the genotype truly belongs to the individual listed. The recommended quality control checks below will work for any livestock species. The basic checks can be performed with minimal information about the individual, while some of the advanced checks require data that not everyone will have, such as historic animal location.

===== ''Basic genotype quality control checks for SNP-based genotype data'' =====
Genotype:
<ol style="list-style-type:number">
<li> Exclude SNP that have a genotype call rate below 90% when analyzed in your population. Using 500 or more animals to determine the SNP call rate is recommended. Chromosome Y SNP should have their call rate determined only in males for this filter.</li>
<li> Invalidate the individual’s genotype if its overall call rate is below <90%. For SNP-based genotypes, such as those from Illumina or Affymetrix chips, the accuracy of called genotypes is questionable when the individual’s overall call rate is <90%.</li>
<li> Check to see that the animal has all three genotype classes (i.e.: AA, AB and BB) in its full genotype file. If any genotype class is missing or has a frequency below 20% then invalidate the full genotype.</li>
<li> Check to ensure that there are no unexpected alleles in the genotype file. For example, genotypes in AB format should not have T, G, 0, 1, 2 or 9. If present, then invalidate the full genotype file.</li>

</ol>

Parentage:
<ol style="list-style-type:number">
<li> Parent (Sire or Dam) validation. If using 200 or less SNP, a listed sire will validate if <1% of the offspring-parent genotypes are in conflict. A conflicting genotype is where the offspring and listed parent have opposite homozygous genotypes.</li>
<li> Mating validation after parent validation. For all parentage validation SNP where the animal is heterozygous, if for >1% of those SNP the sire and dam are homozygous for the same allele then the listed mating is invalidated. This could represent a case where the offspring and one of the parents were mislabeled with the other’s identification (so the offspring’s genotype belongs to the sire or dam and vice versa). Under such cases, it is recommended to resample the DNA and regenotype, potentially with a panel that includes more SNP.</li>

</ol>

===== ''Advanced quality control checks for SNP-based genotype data'' =====
Animal:
<ol style="list-style-type:number">

<li> Parentage discovery. Using SNP data to predict who an animal’s likely parent is can be very useful, but steps must be taken to ensure a very high probability that the prediction is accurate. The following are recommended:</li>

# Using 500 or more SNP that have a minor allele frequency (MAF) above 20% and call rates above 90%. It is advised to calculate the MAF across your full population. Predicted parents should have <1% conflict rate with the animal.
# Sex check. Make sure that you have a process established to ensure that only males are predicted as the sire and only females as the dam.
# Date of Birth check. If you do not include a check that the predicted parent is older than the animal than the predicted individual could actually be an offspring of the animal.
# Age gap. Cattle normally reach sexual maturity at 11-12 months of age, but this can be as young as 8-9 months, and even younger if in-vitro fertilization is a technology used within the population. Under normal circumstances, a minimum of 17 months between the birth dates of the animal and its predicted parent is recommended to ensure that the predicted parent could have been sexually mature at the time of the breeding.
# Grey zone SNP conflicts. The majority of animals will have <0.5% or >1.5% conflicting genotypes with the individual when parentage discovery is conducted. Those with <0.5% pass the prediction and those with >1.5% fail. Most failed animals will have >8% conflict rates. For those animals who have between 0.5 and 1.5% conflicting SNP when a set parentage panel is used (i.e.: the ICAR554 SNP list), it is advised that the conflict rate from all available SNP be used between the two individuals and if the percent conflicting is <1% they validate as the parent, but if >1% they fail.

<li> Genetic Relationship Matrix (GRM). If an animal’s true parent is not genotyped, then it cannot be directly predicted or validated. The genetic relationship between closely related animals can be used to suggest a potential, non-genotyped, parent. It is recommended that 7,000 or more SNP be used to calculate the GRM. GRM results DO NOT validate a relationship, but only suggest. Caution should be used as GRM values can be inflated for inbred individuals. Full-sibs and parent-child should have GRM values around 50%, while half-sibs would be around 25%. The range for each group can vary 5-10% from the expected value.</li>

<li> Sex prediction. How to perform a sex prediction depends on the type and number of SNP an animal has from the X and Y chromosomes. While not every commercial chip includes chromosome Y SNP, they typically contain chromosome X markers.</li>

# Pseudo autosomal region (PAR) SNP. As both the X and Y chromosomes contain the PAR, SNP from this region should be excluded from sex prediction. If the PAR position boundaries are not published for your species they can be roughly determined by analyzing the chromosome X SNP in known males and females and identifying the region where the MAF in males for a continuous set of SNP is >1%. Non-PAR regions of chromosome X will have SNP with average MAF of <1% in males and >>1% in females.
# Chromosome X predicted. Use non-PAR SNP to determine the animal’s chromosome X heterozygosity rate (number of heterozygous chromosome X SNP / total number of chromosome X SNP). If the average heterozygosity rate is <5%, the predicted sex is male, and if >15% its female. If the rate is between 5 and 15% then the predicted sex is unknown.
# Chromosome Y predicted. Using chromosome Y SNP to predict sex is logically simpler but many commercial chips do not contain them. Say you have 7 chromosome Y SNP with high call rates in males, it is recommended using the following logic. Male is predicted when 6-7 of the Y SNP are present; female is predicted when <1 SNP is present and ambiguous sex is predicted when 2-5 Y SNP are present.
# Ambiguous sex prediction. If one set of sex chromosome SNP returns an ambiguous sex prediction and the other doesn’t it is recommended using the latter as the predicted sex. If both SNP sets are ambiguous, the animal could have Turner syndrome (X0), or Klinefelter’s syndrome (XXY), in this case it is recommended returning an ambiguous predicted sex.
# If the predicted sex from the chromosome X and Y analysis disagree, it is recommended returning an ambiguous predicted sex. This could also indicate a possible Klinefelter syndrome (XXY) animal.
# Sex selected AI semen straws. Sex prediction should not be carried out on DNA obtained from sex selected AI semen straws.

<li> Offspring Quality control. The genotyped and listed offspring of an animal can be used to identify potential cases where the animal’s genotype actually belongs to another animal. These should be used as flags to indicate a potential investigation, but it is recommended to temporarily invalidate the animal’s genotype until cleared. Advised thresholds for those flags are:</li>
# AI sire: If >80% of genotyped offspring fail if >10 offspring are genotyped.
# Stock/herd bull: If >80% of genotyped offspring fail if >5 offspring are genotyped.
# Dam: If 100% of genotyped offspring fail if 2 offspring are genotyped, else if >5 offspring are genotyped then use >80%.

<li> Duplicate genotype. The only case where two or more animals should share the exact same genotype is if they are identical twins or clones. Checking to see if >1 animal has the same genotypes is a useful quality control check. It is recommended using your parentage SNP set for initial screening and for any pair that have >99% identical genotypes and then using all available SNP to see if >99% of the genotypes match.</li>

</ol>

For standardization purposes with respect to the nomenclature of genes or loci, a web site is available at: https://www.genenames.org/about/guidelines#genenames and markers at: [https://hgvs-nomenclature.org/versions/21.0/ <nowiki>http://www.HGVS.org/varnomen</nowiki>.]

</div>
</div>

== ICAR Services Related to DNA Technology ==
ICAR offers three services that are related to the use of DNA all of which are linked to parentage analysis in one form or another, as shown in Figure 1. ICAR DNA services., and describing them in more detail in the other sections.
[[File:ICAR DNA service.png|thumb|Figure 1. ICAR DNA services.|center|415x415px]]

== ICAR Accreditation of Laboratories Providing DNA Genotyping Services ==
<div class="mw-collapsible mw-collapsed">
<div>
=== Introduction ===
Considering the need for high quality standards in all uses of molecular data, ICAR has for several years offered an accreditation service based on defined minimum requirements for laboratories providing DNA genotyping services. The basic requirements of this accreditation include proof of the minimum internal management quality assurance standards and a Rank 1 result from participation in the most recent biennial international ring test developed and offered by the International Society for Animal Genetics (ISAG).

In addition, such laboratories generally have been analyzing the resulting genotypes to carry out microsatellite- and/or SNP-based parentage analysis services including either parentage verification or animal identification confirmation. This ICAR accreditation service has previously been used for recognizing the genotyping laboratory as an accredited organization to provide parentage analysis functions without specifically testing the technical accuracy of doing so. Effective 2021, the SNP-based parentage analysis accreditation service for DNA Data Interpretation Centres has replaced the previous laboratory accreditation for SNP-based parentage verification. In the future, a similar technical process for the accreditation of microsatellite-based parentage analysis may be introduced by ICAR but until such time, the existing process for the accreditation of genotyping laboratories will remain in effect.

The following guidelines for accreditation are provided for microsatellite- and SNP-based genotyping in cattle. Minimum requirements for additional species and other DNA tests may be defined in the future.

=== Scope ===
These guidelines are for the accreditation, by ICAR, of genotyping laboratories that analyze biological samples from cattle using microsatellite- and or SNP-based genotyping, which may be subsequently used for various levels of parentage analysis, genotype imputation, estimation of genomic breeding values and other activities related to genomic selection strategies. This accreditation process also includes parentage verification based on microsatellites since ICAR has not established this service as part of the portfolio of possible accreditations for DNA data interpretation centres. For genotyping laboratories that would like to receive ICAR accreditation for SNP-based parentage verification, they must now apply separately to ICAR for its parallel service of parentage analysis accreditation for DNA data interpretation centres, as described in section 4.

=== ICAR Guidelines for Accreditation of Genotyping Laboratories ===
The accreditation process comprises the following steps:

* Application for accreditation
* Payment of relevant fee
* Review of application
* Granting of accreditation

==== Application for accreditation ====
Laboratories requesting accreditation only for microsatellite- based genotyping and parentage verification must apply by downloading and completing the appropriate form as provided in [https://www.icar.org/wp-content/uploads/2022/05/Annex-II-Application-Form-for-STR-Accreditation.pdf ''Appendix 2. Application form for microsatellite-based parentage testing in cattle''.] Laboratories seeking ICAR accreditation involving SNP-based genotyping must apply by downloading and completing the appropriate form as provided in [https://www.icar.org/wp-content/uploads/2022/05/Annex-V-Application-Form-for-SNP-Accreditation.pdf ''Appendix 3. Application form for SNP-based genotyping required for parentage analysis in cattle.''] Laboratories that have previously received ICAR accreditation for either service may re-apply prior to the expiry of any such accreditation using a shortened renewal form available on the ICAR web site. All application forms must be emailed to the ICAR secretariat at dna@icar.org and be filled out accurately and completely including the necessary documentation as required.

==== Payment of relevant fee ====
Along with the completed application form, the applicant must also provide full payment of the relevant fee as established by ICAR and given in [https://www.icar.org/index.php/certifications/certification-and-accreditation-of-dna-genetic-laboratories/guidelines-for-str-and-snp-based-parentage-testing-in-cattle/ ''Appendix 4. ICAR DNA Laboratory Accreditation Service fees''.] [[#Microsatellite_markers|refer]]

==== Review of application ====
The application will be evaluated by a committee of experts appointed by ICAR that will either:

* Approve the application
* Request additional information, or
* Reject the application

In the case of rejection, the laboratory may make a new submission as part of the ICAR annual call for applications for any subsequent year after the failed application.

==== Granting of accreditation ====
Accreditation will be given for a period of two calendar years with an expiry date of December 31st of the second year after receiving ICAR accreditation as a laboratory providing DNA genotyping services.

==== Renewal of accreditation ====
In advance of the expiry date of any existing ICAR accreditation, normally during the same year of the December 31st expiry date, a laboratory can apply for renewal of their accreditation by submitting an application as described in section 3.3.1 above and successfully completing the other steps outlined in this section 3.

==== Laboratory accreditation ====
Effective the 2022 ICAR call for accreditation of genotyping laboratories, ISO17025 accreditation, or an equivalent accreditation for ensuring quality internal management systems, is a mandatory requirement for SNP-based accreditation. In addition, effective the 2022 call for accreditation of genotyping laboratories for microsatellite (STR)-based accreditation, ISO9001 certification will no longer be acceptable and only ISO17025, or an equivalent accreditation, will be an acceptable level of accreditation to ensure quality internal management systems. During the year of application for ICAR accreditation as a laboratory providing DNA genotyping services, the applicant must provide proof of ISO17025 accreditation with an expiry date of October 31st of the following calendar year, or later.

==== Participation and performance in ring test ====
On a biennial basis, initiated during even years (i.e.: 2022, 20246, etc…) and discussed at its biennial conference in odd years (2023, 2025, etc.), ISAG conducts an international ring (comparison) test of laboratories for both microsatellite- and SNP-based genotyping. The participation in ISAG and performance within these ring tests must be disclosed, and certificates provided to ICAR, when available. Applicants must also sign a release allowing ISAG to directly disclose their ring test results to ICAR. Participation in the most recent ISAG ring test is a minimum requirement to qualify for ICAR accreditation. For the ISAG microsatellite ring test, lab genotyping performance for the official set of 12 ISAG microsatellites must be disclosed. The committee of experts will decide performance thresholds for each ring test with due consideration for the structure of the ring test and the average performance of laboratories in the ring test that year. Only those laboratories achieving Rank 1 status in the most recent biennial ISAG ring test shall automatically qualify to receive ICAR accreditation as a genotyping laboratory. Laboratories achieving a Rank 2 status in the most recent ISAG ring test may qualify to receive ICAR accreditation, at the discretion of the committee of experts, but must provide evidence of Rank 1 status for previous ISAG ring tests as well as documentation outlining the cause of the Rank 2 result and any associated actions to mitigate similar outcomes in future ISAG ring tests. Laboratories achieving a status lower than Rank 2 in the most recent ISAG ring test do not qualify for ICAR accreditation as a laboratory providing DNA genotyping services.

==== Microsatellite markers ====
The names of all microsatellites typed on all animals (marker set I) and of the additional ones assayed in the case of unresolved parentage (marker set II) must be declared, as well as the number of animals typed in at least the last two years. The minimum requirement for international exchange is the complete set of 12 official ISAG microsatellite markers. To ensure sufficient experience within the lab, analysis of 500 animals per year is set as minimum requirement for microsatellite parentage verification certification.

''[https://www.icar.org/wp-content/uploads/2018/05/03-Annex-III-ISAG-microsatellites.pdf Appendix 5. ISAG recommended microsatellites for parentage verification in cattle]'' contains the list of microsatellite markers recommended by ISAG and the method for calculating 1 parent and 2 parent exclusion probabilities. The rules for microsatellite-based parentage verification in cattle are described in ''[https://www.icar.org/wp-content/uploads/2018/05/01-Annex-I-guidelines-microsats-STRs.pdf Appendix 6. Rules for microsatellite-based parentage testing in cattle]''. Exclusion probability (PE; 2 parents and 1 parent) of each marker and of the complete marker sets must be calculated and provided in the application. The type of population and number of animals (minimum 150) used for computations are to be described. ICAR recommends using Holstein as a reference group when possible. The ICAR committee of experts will evaluate that an appropriate PE is reached for accreditation, on the basis of the population analyzed.

==== SNP markers ====
ICAR accreditation of SNP-based parentage verification is based on the full set of 200 SNP previously recommended by ISAG. The name of all SNP genotyped on all animals (marker set I, including the 100 “Core” SNP) and of the additional markers assayed in the case of unresolved parentage (marker set II, including the 100 “Additional” SNP) must be declared, as well as the number of animals SNP genotyped in at least the last two years. ICAR recommends using the full set of 200 SNP for parentage verification of all animals genotyped (''see [https://www.icar.org/Guidelines/04-DNA-Technology-App7-SNP-list-for-parentage-verification.pdf Appendix 7. List of approved SNP for parentage verification in]'' [https://www.icar.org/Guidelines/04-DNA-Technology-App7-SNP-list-for-parentage-verification.pdf cattle]). ICAR may, however, based on scientific evidence, identify specific problematic SNP that must be excluded for parentage analysis, as described in the ICAR documentation related to the accreditation of DNA data interpretation centres outlined in section 4.

==== Marker nomenclature ====
Nomenclature of markers must be described. ISAG nomenclature is required for the official ISAG 12 marker set as well as for the ISAG SNP marker set.
</div>
</div>

== Accreditation of Organisations Performing SNP-Based Parentage Analysis ==
<div class="mw-collapsible mw-collapsed">
<div>
=== Introduction ===
With the advent of SNP genotyping, the function of DNA genotyping as a laboratory activity can be separated from the functions of performing parentage verification and parentage discovery. Consequently, ICAR has established a separate accreditation for applying the results of SNP-based genotyping, which may be undertaken by laboratories, breed association societies, genetic evaluation centres and any other organization involved in parentage verification and/or the data processing of SNP genotypes.

Parentage verification and discovery are concerned with using the results that are delivered by the laboratories from DNA genotyping and require SNP genotypes for the animal itself, its recorded parents and other possible parents in the case of parentage discovery. Organizations undertaking this function may be service providers between laboratories that ICAR has accredited for microsatellite- and or SNP-based DNA genotyping and end users that may include breed societies, breeding companies, breeders and commercial farmers.

Service providers could use different laboratories for different breeds and/or species. Considering the importance of animal identification and parentage verification in animal recording, ICAR has decided to define the minimum requirements for using the results of DNA genotyping, and other information, for the purpose of:

# Parentage verification
# Parentage discovery, and
# Animal identification confirmation

The purpose of these guidelines is to provide a basis for the accreditation of processes used by organizations that use SNP genotypes in cattle. Minimum requirements for additional species and other DNA analyses may be defined in the future.

=== Scope ===
These guidelines are for the accreditation, by ICAR, of organizations that use the results of SNP-based tests for parentage analysis in cattle, which includes parentage verification, parentage discovery, and/or animal identification confirmation.

=== Accreditation of Organizations Performing Parentage Analysis ===
The ICAR accreditation process comprises the following steps:

* Application for accreditation
* Payment of relevant fee
* Review of application
* Technical processing of test data files
* Granting of accreditation

==== Application ====
Organizations carrying out SNP-based parentage analysis and requesting ICAR accreditation as a DNA Data Interpretation Centre must apply by downloading and completing the appropriate form included below as [https://www.icar.org/wp-content/uploads/2016/10/6-Annex-V-Application-Form-forICAR-Accreditation-of-DNA-Centres.pdf ''Appendix 8. Application form for organizations seeking ICAR parentage analysis accreditation for DNA data interpretation centres''.] This form must be filled out accurately and completely, providing necessary documentation as required, and submitted to ICAR with payment of the appropriate fee.

==== Review of application ====
The application will first be reviewed internally by ICAR for its completeness and additional details may be requested as needed. ICAR administration will also confirm receipt of the applicable fee.

==== Technical processing of test files ====
The applicant organization will receive a set of data files from ICAR through the Interbull Centre, for processing using its existing procedures for carrying out the level of parentage analysis for which the applicant is seeking ICAR accreditation as a DNA Data Interpretation Centre. A detailed description of this step is described in [https://www.icar.org/index.php/certifications/certification-and-accreditation-of-dna-genetic-laboratories/two-new-dna-based-services/dna-data-interpretation-centres/ ''Appendix 9. Applicant's Guide for ICAR Parentage Analysis Accreditation for DNA Data Interpretation Centres.''] In order for the applicant to be successful in obtaining the requested ICAR accreditation, it's procedures for conducting parentage analysis must exactly follow [https://www.icar.org/Documents/GenoEx/ICAR%20Guidelines%20for%20Parentage%20Verification%20and%20Parentage%20Discovery%20based%20on%20SNP.pdf ''Appendix 10. ICAR Guidelines for Parentage Verification and Parentage Discovery Based on SNP Genotypes''.] The list of SNP to be used for either parentage verification (N=200) or parentage discovery (N=554) are available in [https://www.icar.org/Guidelines/04-DNA-Technology-App-11-SNP-list-for-parentage-verification-or-discovery.pdf ''Appendix 11. List of SNP to be used for either parentage verification or parentage discovery''.] Once the applicant has completed its internal parentage analysis procedures based on the accreditation test files it received, it must send a data file of results back to the Interbull Centre. A maximum time period for 90 calendar days will be allowed for the applicant to submit acceptable files of results back to the Interbull Centre.

==== Granting of accreditation ====
Once the Interbull Centre receives the file of parentage analysis results from the applicant, it will complete the technical review and determine if the applicant has successfully completed the accreditation or not. The Interbull Centre shall inform ICAR of the results and ICAR shall issue a formal notification to the applicant. In the event the applicant was not successful in receiving ICAR accreditation, the applicant may initiate a new request for accreditation by completing and submitting the appropriate forms and providing payment of the applicable fee, as outlined above.

==== Renewal of accreditation ====
In advance of the expiry date of any existing ICAR accreditation, which coincides with the two-year anniversary date of the current accreditation, an applicant can apply for renewal of their accreditation by submitting an application as described in section 4.3.1 above and successfully completing the other required steps outlined in this section 4.
</div>
</div>

== Genotype Exchange Service – GenoEx-PSE ==
Effective 2018, ICAR has made available a genotype exchange service for parentage analysis, GenoEx-PSE, offered through the Interbull Centre. The main goal of this service is to facilitate the international exchange of SNP genotypes such that approved service users can carry out parentage analysis services at a national level in an efficient manner. The GenoEx-PSE database system and user interface has been developed to allow for the exchange of SNP genotypes for either parentage verification or parentage discovery based on the list of SNP provided in ''Appendix 11. List of SNP to be used for either parentage verification or parentage discovery''.

In order for an organization to qualify as a service user for GenoEx-PSE, it must first receive ICAR accreditation as a DNA data interpretation centre. The level of such ICAR accreditation (i.e.: for SNP-based parentage verification alone or for both SNP-based parentage verification and discovery) shall determine the highest level of SNP that may be exchanged via the GenoEx-PSE service. For details associated with this ICAR service, refer to the GenoEx-PSE web site at [https://genoex.org/ www.GenoEx.org.]

== Appendix list ==
<div class="mw-collapsible mw-collapsed">
<div>
=== Appendix 1. Link to SNP markers recommended by ISAG for parentage verification ===
https://www.icar.org/Guidelines/04-DNA-Technology-App-1-Cattle-SNP-ISAG-core-additional-panel-2013.xlsx

=== Appendix 2. Application form for microsatellite-based parentage testing in cattle ===
Please refer [https://www.icar.org/wp-content/uploads/2022/05/Annex-II-Application-Form-for-STR-Accreditation.pdf here] on the ICAR website for the Form for ICAR laboratory accreditation for STR Microsatellite-based Parentage Testing in Cattle.

=== Appendix 3. Application form for SNP-based genotyping required for parentage analysis in cattle ===
Please refer [https://www.icar.org/wp-content/uploads/2022/05/Annex-V-Application-Form-for-SNP-Accreditation.pdf here] on the ICAR website for the Form for ICAR laboratory accreditation for SNP-based genotyping required for Parentage Analysis in Cattle.

=== Appendix 4. ICAR DNA Laboratory Accreditation Service fees ===
Please refer [https://www.icar.org/index.php/certifications/certification-and-accreditation-of-dna-genetic-laboratories/guidelines-for-str-and-snp-based-parentage-testing-in-cattle/ here] on the ICAR website for DNA testing accreditation services fees.

=== Appendix 5. ISAG recommended microsatellites for parentage verification in cattle ===
Please refer [https://www.icar.org/wp-content/uploads/2018/05/03-Annex-III-ISAG-microsatellites.pdf here] on the ICAR website for the list of ISAG recommended microsatellites for parentage verification in cattle.

=== Appendix 6. Rules for microsatellite-based parentage testing in cattle ===
Please refer [https://www.icar.org/wp-content/uploads/2018/05/01-Annex-I-guidelines-microsats-STRs.pdf here] on the ICAR website for the rules for microsatellite-based parentage verification in cattle.

=== Appendix 7. List of approved SNP for parentage verification in cattle ===
Please refer [https://www.icar.org/Guidelines/04-DNA-Technology-App7-SNP-list-for-parentage-verification.pdf here] on the ICAR website for the ICAR approved list of 200 SNP for parentage verification in cattle.

=== Appendix 8. Application form for organizations seeking ICAR parentage analysis accreditation for DNA data interpretation centres ===
Please refer [https://www.icar.org/wp-content/uploads/2016/10/6-Annex-V-Application-Form-forICAR-Accreditation-of-DNA-Centres.pdf here] on the ICAR website for the application form for organizations seeking ICAR accreditation status as a DNA data interpretation centre.

=== Appendix 9. Applicant's Guide for ICAR Parentage Analysis Accreditation for DNA Data Interpretation Centres ===
Please refer [https://www.icar.org/index.php/certifications/certification-and-accreditation-of-dna-genetic-laboratories/two-new-dna-based-services/dna-data-interpretation-centres/ here] on the ICAR website for the Applicant's Guide for ICAR Parentage Analysis Accreditation for DNA Data Interpretation Centres.

=== Appendix 10. ICAR Guidelines for Parentage Verification and Parentage Discovery Based on SNP Genotypes ===
Please refer [https://www.icar.org/Documents/GenoEx/ICAR%20Guidelines%20for%20Parentage%20Verification%20and%20Parentage%20Discovery%20based%20on%20SNP.pdf here] on the ICAR website for the ICAR Guidelines for Parentage Verification and Parentage Discovery Based on SNP Genotypes.

=== Appendix 11. List of SNP to be used for either parentage verification or parentage discovery ===
Please refer [https://www.icar.org/Guidelines/04-DNA-Technology-App-11-SNP-list-for-parentage-verification-or-discovery.pdf here] on the ICAR website for the list of SNP to be used for either parentage verification or parentage discovery.
</div>
</div>

Section 04 – DNA Technology

2025-01-31T09:58:16Z

Aashish: /* Payment of relevant fee */

== Molecular Genetics ==
<div class="mw-collapsible mw-collapsed">
<div>
=== Introduction ===
Advances in molecular biology, especially genomics, provide a new set of information to be incorporated into the animal industry. On one hand, the use of molecular information may contribute to the enhancement of consumers' trust in the ability to monitor and control the animal production chain. On the other hand, molecular information will greatly contribute to the achievement of genetic improvement for animal traits through the use of genomic breeding values, marker assisted selection, gene introgression, heterosis prediction, pedigree validation/prediction, and genetic defect carrier status. In most cases, advantages of using molecular information via genomic evaluations, comes from improved accuracy of animal breeding values, shortened generation intervals, and increased intensity of selection. Even with these advancements there is still a need for research and development in the search for associations between genetic markers and traits of interest, especially as new traits are included in national evaluation indexes. In addition to that, even with the current incorporation of genomic information into national selection schemes, an understanding of gene action, gene interactions, and differential gene expression to avoid negative collateral effects is needed. Cooperation between animal industries and research is required for a successful and beneficial search for genetic information in commercial livestock populations.

=== Genetic Markers ===
Genetic markers are the fundamental molecular tools for genomics, even as the type of marker used has changed. The first genetic marker associations in livestock were reported using blood typing in the 1960s, the technology then moved to microsatellites (MS) in the 1990s and more recently to the use of Single Nucleotide Polymorphism (SNP). SNP and MS are polymorphic DNA sequences (alleles) at a specific locus of a particular chromosome. While blood typing has been an ICAR approved method of parentage verification currently there are few, if any, commercial labs still offering this testing. For this reason, ICAR no longer recommends blood typing as the basis for carrying out parentage analysis in livestock species where MS or SNP technology is widely available.

==== Microsatellites ====
These are segments of DNA containing tandem repeats of simple motifs usually dimers or trimers. These segments are located throughout the genome and normally in non-coding regions. Over time, these regions are subject to the addition or subtraction of tandem repeats, which means that each microsatellite can have multiple unique alleles. Microsatellites are commonly used in many livestock species for parentage validation.

==== Single Nucleotide Polymorphism (SNP) ====
SNP are the most common type of genetic variation: each SNP represents a variation in a single nucleotide. There are millions of SNP located throughout the genome of every livestock species. For genomics the most informative SNP traditionally are either located in (a) coding regions where different alleles change the structure or function of the encoded protein, or (b) at non-coding regions that are involved in the regulatory function of the gene. For genomic breeding values, SNP that are located in other regions of the genome are also informative as they could be in linkage disequilibrium with alleles that do cause a phenotype change.

One of the big advantages of SNP is their deployment on SNP arrays with a strong parallel processing capacity whereby thousands or hundreds of thousands of SNP can be screened together in a cost-effective and efficient manner across a large number of animals. Currently, the largest livestock genotyping labs can process hundreds of thousands of animals yearly on such arrays. The availability of these large SNP panels is therefore bolstering the search for mutations underlying genetic variation for simple and complex traits. It is also revolutionizing the speed at which trait associated genes or gene regions are being discovered as well as the adoption rate of genomic selection strategies. SNP genotypes have become the international standard for the basis of parentage analysis and ICAR recommends this approach over the use of microsatellites wherever possible due to the improved accuracy and the ease of comparing results between genotyping laboratories.

=== Current and Potential Uses of DNA Technologies ===

==== Parentage verification and parental assignment authentication ====
Prior to the emergence of SNP genotyping, parentage verification was the main commercial use of genetic markers. Traditionally, parentage testing was based on the exclusion of relationship (i.e.: sire or dam) when an animal has a genotype inconsistent to a putative relationship. New trends in animal production systems are tending to encourage animal production in larger numbers per farm in response to environmental and production related constraints. In these large settings, multiple animals could be bred or give birth on the same day, which can result in more pedigree recording errors. As the cost of the analysis decreases and the number of genetic markers available increases, breed societies are now able to build up pedigree records using genetic markers to predict the pedigree of calves born in a herd at a given time. This normally requires a prior knowledge of candidate sires and dams for a calf when lower number (<200) of markers are used, but with enough SNP the correct parents can be predicted without prior knowledge being available as long as the parent is also genotyped. The probability of assignment to a correct pair of animals will depend on the number of markers used, number of alleles per loci, the minor allele frequency in the population, the number of parents, and the number of possible matings. The International Society of Animal Genetics (www.isag.us) has species-specific panels recommended of microsatellite and SNP markers for this purpose, which can be accessed via a link such as provided in ''[https://www.icar.org/Guidelines/04-DNA-Technology-App-1-Cattle-SNP-ISAG-core-additional-panel-2013.xlsx Appendix 1]. Link to SNP markers recommended by ISAG for parentage verification''. For cattle, ICAR has developed a set of parentage SNP, ICAR554, which incorporates the ISAG recommended panel and other highly informative SNP. This panel allows for highly accurate parentage validation and discovery while not allowing for accurate imputation to a higher density. Therefore, the ICAR554 panel can be shared among countries and competitors for parentage analysis without fear of others being able to use them to predict genomic breeding values. ICAR and the Interbull Centre collaborate in offering an international genotype exchange service, referred to as GenoEx, which is described further in Chapter 5 specifically for the exchange of SNP genotypes for the purposes of parentage analysis.

==== Traceability and authentication of animal products offered to consumers ====
Due to multiple crises, including BSE outbreaks to ground beef containing horsemeat, and with increased consumer interest in where their food comes from the traceability of meat products is of greater concern to the industry. Traceability is based on the availability of a verification and control system that monitors all relevant details throughout the entire livestock production chain. Since an individual’s genetic sequence is unique and does not change, its DNA remains constant from ‘conception to consumption’. Therefore, use of genetic markers allows one to match the DNA of an individual at birth to the final product.

Genetic markers for the authentication of animal products for labels of quality related to geographic location and labels of quality related to specific breeds or their crosses are/or will be very useful. However, this requires the establishment of molecular standards or allele frequencies for each breed within a species. A lot of information is coming from studies of genetic diversity among breeds. Genomic regions subject to intense selection in each population are of particular interest. With a large enough set of SNP and genotyped purebred reference animals it is also possible to predict the most likely breed composition of individuals.

==== Molecular genetic information for marker-assisted selection schemes ====
Quantitative traits are generally assumed to be controlled by a large number of genes. However, individual genes sometimes account for a significant amount of variation of the trait. Such is the case for the Myostatin gene and double muscling in beef cattle, the DGAT1 gene and milk components in dairy cattle, or the Booroola fecundity gene and ovulation rate in sheep. Since the genotype of an animal does not change during its lifetime, use of DNA information through the identification of markers linked to QTL with effects on production traits or the identification of a gene itself together with the causative variant is of great interest. Nevertheless, with complex traits there is a growing need of having a sufficiently large marker set to incorporate molecular information for selection decisions. Including genomic information as a selection criterion is of special interest for traits that are difficult and costly to measure and/or are measured late in life. By 2022, >177,000 cattle, >34,000 swine, >16,000 chicken and >4,000 sheep QTL have been identified that are associated with economically important traits such as health, carcass, milk, fertility, and body conformation. The AnimalQTLdb database housed at the [https://www.animalgenome.org/ National Animal Genome Research Program] contains up to date information on cattle, chicken, horse, pig, trout, and sheep QTL data assembled from published data.

Recording schemes have been collecting information for decades on the most common production traits measured in domestic livestock. There is an ever-increasing volume of information becoming available, but for some traits like meat quality, disease resistance and feed efficiency, those records are very expensive to measure, difficult to obtain, or are performed late in the animal’s life. Because of these challenges information for such traits is commonly collected on a reduced number of animals in any given population.

For these challenging, but economically important traits, genetic markers and genomic selection offer significant opportunities for trait selection where it was not economically feasible before. In general, genetic markers and genomics will play an important role for important traits regardless of the livestock species. Genomics can also allow us to increase selection intensities since we can predict genomic breeding values on a large number of animals and thus have more candidates for selection.

==== Disease resistance and genetic defects ====
Another group of traits with a high potential for the use of molecular data and genomics are those linked to resistance, resilience, and susceptibility to diseases. There are a number of multi-factorial or complex diseases that are the result of the interaction between an animal’s genome and environmental components. Disease resistance traits are among the most difficult to include in genetic improvement programs because they require good field measurement of the disease status of the animals and a systematic control of management or environmental conditions that allow for the identification of the environmental influence on the health status of the animal. Infectious diseases depend very much upon environmental factors such as the degree of exposure to the pathogen agent. Thus, if exposure is low, animals will show little variation. Part of the phenotypic differences for resistance may be differences in the degree of challenge. Therefore, if genes or genetic markers linked to resistance are correctly identified, resistant animals will be able to be selected on the base of their genomic information. For many diseases, identification of genes associated with resistance will require experimental conditions to be used. Genetic analysis to identify heterozygous carriers of genetic diseases caused by single, recessive genes are currently in use. Examples in dairy cattle include complex vertebral malformation (CVM), brachyspina (BY), cholesterol deficiency (CD) and several genes, gene regions or haplotypes causing embryo loss or stillbirth in different dairy breeds. In 2022, [https://www.omia.org/home/ OMIA (Online Mendelian Inheritance in Animals),] listed >1000 traits or genetic defects in livestock with a known causative mutation (cattle: 186, pig: 58, chicken: 56, sheep: 49, horse: 48, goat:17). Including these causative allele or associated haplotypes in a breeding program will allow producers to minimize their risk from genetic defects while maximizing genetic progress from beneficial traits.

=== Technical Aspects ===

==== DNA collection ====
Systematic collection of DNA is recommended in several livestock populations. DNA may be obtained from any nuclear cell in the body. Protocols for DNA extraction are now available for blood (white cells), semen, saliva (epithelial cells), hair follicles, muscle, skin, organs (such as liver, spleen etc.). Red blood cells may also be used for poultry as they retain the nuclear body while most other species do not. Small amounts of tissue material are required for routine DNA analysis. However, if there are multiple future uses of an individual’s DNA (whole genome sequencing, traceability, causative allele validations, …), then DNA storage costs, extraction costs, quality, and quantity obtained by different protocols will have to be carefully examined and optimized. Common collection methods include hair follicles, tissue samples (often ear punch) in an enclosed container, blood spots on filter paper, and nasal swabs.

==== Data organization ====
A centralised database may be organised in respect to the main uses of the genetic information:

* Parent verification, assignment, and/or discovery
* Traceability of meat products
* Breed identification or breed diversity
* Qualitative and quantitative traits 

Database tables may contain:

* Animal identification to link to all other information on the animal and its relatives.
* Number of genetic markers: n
* Standard name of each marker i (for i= 1, n)
* Accession number for marker such as the dbSNP ID
* Alleles for marker i
* Genomic location of marker i
* Effect of non-reference allele on the protein
* Phenotypic effect of the allele
* Association with other traits

==== Parentage accuracy ====
While use of microsatellite and SNP markers are both ICAR accredited methods of parentage verification they do not have the same power of parentage accuracy. Briefly the order of accuracy for ISAG and ICAR approved parentage marker panels are:

Microsatellites << small SNP panels (100 or less) < large SNP panels (500 or more)

This order is based on both genotyping accuracy and total genomic information. Comparing the genomic marker error rate in cattle microsatellites have a 1-5% error rate (Baruch and Weller, 2008<ref>Baruch, E., and J. I. Weller. 2008. 'Estimation of the number of SNP genetic markers required for parentage verification', ''Animal Genetics'', 39: 474-79.</ref>) while the SNP error rate is <0.1% (Cooper, Wiggans, and VanRaden 2013<ref>Cooper, T. A., G. R. Wiggans, and P. M. VanRaden. 2013. 'Short communication: relationship of call rate and accuracy of single nucleotide polymorphism genotypes in dairy cattle', ''Journal of dairy science'', 96: 3336-9.</ref>). As 2-3 SNP provide the same parentage exclusion accuracy as 1 microsatellite marker (Vignal et al. 2002<ref>Vignal, A., D. Milan, M. SanCristobal, and A. Eggen. 2002. 'A review on SNP and other types of molecular markers and their use in animal genetics', ''Genet Sel Evol'', 34: 275-305.

1.4.4 Genomic quality control checks</ref>), the 100 and 200 ISAG parentage SNP panels are more accurate than the 12 ISAG parentage microsatellite markers. In the same manner parentage panels of over 500 SNP (McClure et al. 2018<ref>McClure, M. C., J. McCarthy, P. Flynn, J. C. McClure, E. Dair, D. K. O'Connell, and J. F. Kearney. 2018. 'SNP Data Quality Control in a National Beef and Dairy Cattle System and Highly Accurate SNP Based Parentage Verification and Identification', ''Front Genet'', 9: 84.</ref>), such as the ICAR554, are recommended for parentage prediction which requires an even higher level of accuracy.

==== Genomic quality control checks ====
One of the most important parts of a large genomic database is to ensure that a genotype associated with an individual animal truly belongs to that animal. Most large livestock genomic databases deal with SNP data only and the quality of SNP genotyping data is of paramount importance (Wu at al., Evaluation of genotyping concordance for commercial bovine SNP arrays using quality-assurance samples, Animal Genetics, 50: 367-371, 2019). This section, therefore, focuses on quality control for that genomic data type. Both sample and SNP quality control measures are needed, and it is encouraged to develop a system for them early.

For those working with genotype data there are two main concerns. First, is ensuring that the genotype data itself is of high quality and can be trusted. Second, is ensuring that the genotype truly belongs to the individual listed. The recommended quality control checks below will work for any livestock species. The basic checks can be performed with minimal information about the individual, while some of the advanced checks require data that not everyone will have, such as historic animal location.

===== ''Basic genotype quality control checks for SNP-based genotype data'' =====
Genotype:
<ol style="list-style-type:number">
<li> Exclude SNP that have a genotype call rate below 90% when analyzed in your population. Using 500 or more animals to determine the SNP call rate is recommended. Chromosome Y SNP should have their call rate determined only in males for this filter.</li>
<li> Invalidate the individual’s genotype if its overall call rate is below <90%. For SNP-based genotypes, such as those from Illumina or Affymetrix chips, the accuracy of called genotypes is questionable when the individual’s overall call rate is <90%.</li>
<li> Check to see that the animal has all three genotype classes (i.e.: AA, AB and BB) in its full genotype file. If any genotype class is missing or has a frequency below 20% then invalidate the full genotype.</li>
<li> Check to ensure that there are no unexpected alleles in the genotype file. For example, genotypes in AB format should not have T, G, 0, 1, 2 or 9. If present, then invalidate the full genotype file.</li>

</ol>

Parentage:
<ol style="list-style-type:number">
<li> Parent (Sire or Dam) validation. If using 200 or less SNP, a listed sire will validate if <1% of the offspring-parent genotypes are in conflict. A conflicting genotype is where the offspring and listed parent have opposite homozygous genotypes.</li>
<li> Mating validation after parent validation. For all parentage validation SNP where the animal is heterozygous, if for >1% of those SNP the sire and dam are homozygous for the same allele then the listed mating is invalidated. This could represent a case where the offspring and one of the parents were mislabeled with the other’s identification (so the offspring’s genotype belongs to the sire or dam and vice versa). Under such cases, it is recommended to resample the DNA and regenotype, potentially with a panel that includes more SNP.</li>

</ol>

===== ''Advanced quality control checks for SNP-based genotype data'' =====
Animal:
<ol style="list-style-type:number">

<li> Parentage discovery. Using SNP data to predict who an animal’s likely parent is can be very useful, but steps must be taken to ensure a very high probability that the prediction is accurate. The following are recommended:</li>

# Using 500 or more SNP that have a minor allele frequency (MAF) above 20% and call rates above 90%. It is advised to calculate the MAF across your full population. Predicted parents should have <1% conflict rate with the animal.
# Sex check. Make sure that you have a process established to ensure that only males are predicted as the sire and only females as the dam.
# Date of Birth check. If you do not include a check that the predicted parent is older than the animal than the predicted individual could actually be an offspring of the animal.
# Age gap. Cattle normally reach sexual maturity at 11-12 months of age, but this can be as young as 8-9 months, and even younger if in-vitro fertilization is a technology used within the population. Under normal circumstances, a minimum of 17 months between the birth dates of the animal and its predicted parent is recommended to ensure that the predicted parent could have been sexually mature at the time of the breeding.
# Grey zone SNP conflicts. The majority of animals will have <0.5% or >1.5% conflicting genotypes with the individual when parentage discovery is conducted. Those with <0.5% pass the prediction and those with >1.5% fail. Most failed animals will have >8% conflict rates. For those animals who have between 0.5 and 1.5% conflicting SNP when a set parentage panel is used (i.e.: the ICAR554 SNP list), it is advised that the conflict rate from all available SNP be used between the two individuals and if the percent conflicting is <1% they validate as the parent, but if >1% they fail.

<li> Genetic Relationship Matrix (GRM). If an animal’s true parent is not genotyped, then it cannot be directly predicted or validated. The genetic relationship between closely related animals can be used to suggest a potential, non-genotyped, parent. It is recommended that 7,000 or more SNP be used to calculate the GRM. GRM results DO NOT validate a relationship, but only suggest. Caution should be used as GRM values can be inflated for inbred individuals. Full-sibs and parent-child should have GRM values around 50%, while half-sibs would be around 25%. The range for each group can vary 5-10% from the expected value.</li>

<li> Sex prediction. How to perform a sex prediction depends on the type and number of SNP an animal has from the X and Y chromosomes. While not every commercial chip includes chromosome Y SNP, they typically contain chromosome X markers.</li>

# Pseudo autosomal region (PAR) SNP. As both the X and Y chromosomes contain the PAR, SNP from this region should be excluded from sex prediction. If the PAR position boundaries are not published for your species they can be roughly determined by analyzing the chromosome X SNP in known males and females and identifying the region where the MAF in males for a continuous set of SNP is >1%. Non-PAR regions of chromosome X will have SNP with average MAF of <1% in males and >>1% in females.
# Chromosome X predicted. Use non-PAR SNP to determine the animal’s chromosome X heterozygosity rate (number of heterozygous chromosome X SNP / total number of chromosome X SNP). If the average heterozygosity rate is <5%, the predicted sex is male, and if >15% its female. If the rate is between 5 and 15% then the predicted sex is unknown.
# Chromosome Y predicted. Using chromosome Y SNP to predict sex is logically simpler but many commercial chips do not contain them. Say you have 7 chromosome Y SNP with high call rates in males, it is recommended using the following logic. Male is predicted when 6-7 of the Y SNP are present; female is predicted when <1 SNP is present and ambiguous sex is predicted when 2-5 Y SNP are present.
# Ambiguous sex prediction. If one set of sex chromosome SNP returns an ambiguous sex prediction and the other doesn’t it is recommended using the latter as the predicted sex. If both SNP sets are ambiguous, the animal could have Turner syndrome (X0), or Klinefelter’s syndrome (XXY), in this case it is recommended returning an ambiguous predicted sex.
# If the predicted sex from the chromosome X and Y analysis disagree, it is recommended returning an ambiguous predicted sex. This could also indicate a possible Klinefelter syndrome (XXY) animal.
# Sex selected AI semen straws. Sex prediction should not be carried out on DNA obtained from sex selected AI semen straws.

<li> Offspring Quality control. The genotyped and listed offspring of an animal can be used to identify potential cases where the animal’s genotype actually belongs to another animal. These should be used as flags to indicate a potential investigation, but it is recommended to temporarily invalidate the animal’s genotype until cleared. Advised thresholds for those flags are:</li>
# AI sire: If >80% of genotyped offspring fail if >10 offspring are genotyped.
# Stock/herd bull: If >80% of genotyped offspring fail if >5 offspring are genotyped.
# Dam: If 100% of genotyped offspring fail if 2 offspring are genotyped, else if >5 offspring are genotyped then use >80%.

<li> Duplicate genotype. The only case where two or more animals should share the exact same genotype is if they are identical twins or clones. Checking to see if >1 animal has the same genotypes is a useful quality control check. It is recommended using your parentage SNP set for initial screening and for any pair that have >99% identical genotypes and then using all available SNP to see if >99% of the genotypes match.</li>

</ol>

For standardization purposes with respect to the nomenclature of genes or loci, a web site is available at: https://www.genenames.org/about/guidelines#genenames and markers at: [https://hgvs-nomenclature.org/versions/21.0/ <nowiki>http://www.HGVS.org/varnomen</nowiki>.]

</div>
</div>

== ICAR Services Related to DNA Technology ==
ICAR offers three services that are related to the use of DNA all of which are linked to parentage analysis in one form or another, as shown in Figure 1. ICAR DNA services., and describing them in more detail in the other sections.
[[File:ICAR DNA service.png|thumb|Figure 1. ICAR DNA services.|center|415x415px]]

== ICAR Accreditation of Laboratories Providing DNA Genotyping Services ==
<div class="mw-collapsible mw-collapsed">
<div>
=== Introduction ===
Considering the need for high quality standards in all uses of molecular data, ICAR has for several years offered an accreditation service based on defined minimum requirements for laboratories providing DNA genotyping services. The basic requirements of this accreditation include proof of the minimum internal management quality assurance standards and a Rank 1 result from participation in the most recent biennial international ring test developed and offered by the International Society for Animal Genetics (ISAG).

In addition, such laboratories generally have been analyzing the resulting genotypes to carry out microsatellite- and/or SNP-based parentage analysis services including either parentage verification or animal identification confirmation. This ICAR accreditation service has previously been used for recognizing the genotyping laboratory as an accredited organization to provide parentage analysis functions without specifically testing the technical accuracy of doing so. Effective 2021, the SNP-based parentage analysis accreditation service for DNA Data Interpretation Centres has replaced the previous laboratory accreditation for SNP-based parentage verification. In the future, a similar technical process for the accreditation of microsatellite-based parentage analysis may be introduced by ICAR but until such time, the existing process for the accreditation of genotyping laboratories will remain in effect.

The following guidelines for accreditation are provided for microsatellite- and SNP-based genotyping in cattle. Minimum requirements for additional species and other DNA tests may be defined in the future.

=== Scope ===
These guidelines are for the accreditation, by ICAR, of genotyping laboratories that analyze biological samples from cattle using microsatellite- and or SNP-based genotyping, which may be subsequently used for various levels of parentage analysis, genotype imputation, estimation of genomic breeding values and other activities related to genomic selection strategies. This accreditation process also includes parentage verification based on microsatellites since ICAR has not established this service as part of the portfolio of possible accreditations for DNA data interpretation centres. For genotyping laboratories that would like to receive ICAR accreditation for SNP-based parentage verification, they must now apply separately to ICAR for its parallel service of parentage analysis accreditation for DNA data interpretation centres, as described in section 4.

=== ICAR Guidelines for Accreditation of Genotyping Laboratories ===
The accreditation process comprises the following steps:

* Application for accreditation
* Payment of relevant fee
* Review of application
* Granting of accreditation

==== Application for accreditation ====
Laboratories requesting accreditation only for microsatellite- based genotyping and parentage verification must apply by downloading and completing the appropriate form as provided in [https://www.icar.org/wp-content/uploads/2022/05/Annex-II-Application-Form-for-STR-Accreditation.pdf ''Appendix 2. Application form for microsatellite-based parentage testing in cattle''.] Laboratories seeking ICAR accreditation involving SNP-based genotyping must apply by downloading and completing the appropriate form as provided in [https://www.icar.org/wp-content/uploads/2022/05/Annex-V-Application-Form-for-SNP-Accreditation.pdf ''Appendix 3. Application form for SNP-based genotyping required for parentage analysis in cattle.''] Laboratories that have previously received ICAR accreditation for either service may re-apply prior to the expiry of any such accreditation using a shortened renewal form available on the ICAR web site. All application forms must be emailed to the ICAR secretariat at dna@icar.org and be filled out accurately and completely including the necessary documentation as required.

==== Payment of relevant fee ====
Along with the completed application form, the applicant must also provide full payment of the relevant fee as established by ICAR and given in [https://www.icar.org/index.php/certifications/certification-and-accreditation-of-dna-genetic-laboratories/guidelines-for-str-and-snp-based-parentage-testing-in-cattle/ ''Appendix 4. ICAR DNA Laboratory Accreditation Service fees''.] [[#Microsatellites_Markers|refer]]

==== Review of application ====
The application will be evaluated by a committee of experts appointed by ICAR that will either:

* Approve the application
* Request additional information, or
* Reject the application

In the case of rejection, the laboratory may make a new submission as part of the ICAR annual call for applications for any subsequent year after the failed application.

==== Granting of accreditation ====
Accreditation will be given for a period of two calendar years with an expiry date of December 31st of the second year after receiving ICAR accreditation as a laboratory providing DNA genotyping services.

==== Renewal of accreditation ====
In advance of the expiry date of any existing ICAR accreditation, normally during the same year of the December 31st expiry date, a laboratory can apply for renewal of their accreditation by submitting an application as described in section 3.3.1 above and successfully completing the other steps outlined in this section 3.

==== Laboratory accreditation ====
Effective the 2022 ICAR call for accreditation of genotyping laboratories, ISO17025 accreditation, or an equivalent accreditation for ensuring quality internal management systems, is a mandatory requirement for SNP-based accreditation. In addition, effective the 2022 call for accreditation of genotyping laboratories for microsatellite (STR)-based accreditation, ISO9001 certification will no longer be acceptable and only ISO17025, or an equivalent accreditation, will be an acceptable level of accreditation to ensure quality internal management systems. During the year of application for ICAR accreditation as a laboratory providing DNA genotyping services, the applicant must provide proof of ISO17025 accreditation with an expiry date of October 31st of the following calendar year, or later.

==== Participation and performance in ring test ====
On a biennial basis, initiated during even years (i.e.: 2022, 20246, etc…) and discussed at its biennial conference in odd years (2023, 2025, etc.), ISAG conducts an international ring (comparison) test of laboratories for both microsatellite- and SNP-based genotyping. The participation in ISAG and performance within these ring tests must be disclosed, and certificates provided to ICAR, when available. Applicants must also sign a release allowing ISAG to directly disclose their ring test results to ICAR. Participation in the most recent ISAG ring test is a minimum requirement to qualify for ICAR accreditation. For the ISAG microsatellite ring test, lab genotyping performance for the official set of 12 ISAG microsatellites must be disclosed. The committee of experts will decide performance thresholds for each ring test with due consideration for the structure of the ring test and the average performance of laboratories in the ring test that year. Only those laboratories achieving Rank 1 status in the most recent biennial ISAG ring test shall automatically qualify to receive ICAR accreditation as a genotyping laboratory. Laboratories achieving a Rank 2 status in the most recent ISAG ring test may qualify to receive ICAR accreditation, at the discretion of the committee of experts, but must provide evidence of Rank 1 status for previous ISAG ring tests as well as documentation outlining the cause of the Rank 2 result and any associated actions to mitigate similar outcomes in future ISAG ring tests. Laboratories achieving a status lower than Rank 2 in the most recent ISAG ring test do not qualify for ICAR accreditation as a laboratory providing DNA genotyping services.

==== Microsatellite markers ====
The names of all microsatellites typed on all animals (marker set I) and of the additional ones assayed in the case of unresolved parentage (marker set II) must be declared, as well as the number of animals typed in at least the last two years. The minimum requirement for international exchange is the complete set of 12 official ISAG microsatellite markers. To ensure sufficient experience within the lab, analysis of 500 animals per year is set as minimum requirement for microsatellite parentage verification certification.

''[https://www.icar.org/wp-content/uploads/2018/05/03-Annex-III-ISAG-microsatellites.pdf Appendix 5. ISAG recommended microsatellites for parentage verification in cattle]'' contains the list of microsatellite markers recommended by ISAG and the method for calculating 1 parent and 2 parent exclusion probabilities. The rules for microsatellite-based parentage verification in cattle are described in ''[https://www.icar.org/wp-content/uploads/2018/05/01-Annex-I-guidelines-microsats-STRs.pdf Appendix 6. Rules for microsatellite-based parentage testing in cattle]''. Exclusion probability (PE; 2 parents and 1 parent) of each marker and of the complete marker sets must be calculated and provided in the application. The type of population and number of animals (minimum 150) used for computations are to be described. ICAR recommends using Holstein as a reference group when possible. The ICAR committee of experts will evaluate that an appropriate PE is reached for accreditation, on the basis of the population analyzed.

==== SNP markers ====
ICAR accreditation of SNP-based parentage verification is based on the full set of 200 SNP previously recommended by ISAG. The name of all SNP genotyped on all animals (marker set I, including the 100 “Core” SNP) and of the additional markers assayed in the case of unresolved parentage (marker set II, including the 100 “Additional” SNP) must be declared, as well as the number of animals SNP genotyped in at least the last two years. ICAR recommends using the full set of 200 SNP for parentage verification of all animals genotyped (''see [https://www.icar.org/Guidelines/04-DNA-Technology-App7-SNP-list-for-parentage-verification.pdf Appendix 7. List of approved SNP for parentage verification in]'' [https://www.icar.org/Guidelines/04-DNA-Technology-App7-SNP-list-for-parentage-verification.pdf cattle]). ICAR may, however, based on scientific evidence, identify specific problematic SNP that must be excluded for parentage analysis, as described in the ICAR documentation related to the accreditation of DNA data interpretation centres outlined in section 4.

==== Marker nomenclature ====
Nomenclature of markers must be described. ISAG nomenclature is required for the official ISAG 12 marker set as well as for the ISAG SNP marker set.
</div>
</div>

== Accreditation of Organisations Performing SNP-Based Parentage Analysis ==
<div class="mw-collapsible mw-collapsed">
<div>
=== Introduction ===
With the advent of SNP genotyping, the function of DNA genotyping as a laboratory activity can be separated from the functions of performing parentage verification and parentage discovery. Consequently, ICAR has established a separate accreditation for applying the results of SNP-based genotyping, which may be undertaken by laboratories, breed association societies, genetic evaluation centres and any other organization involved in parentage verification and/or the data processing of SNP genotypes.

Parentage verification and discovery are concerned with using the results that are delivered by the laboratories from DNA genotyping and require SNP genotypes for the animal itself, its recorded parents and other possible parents in the case of parentage discovery. Organizations undertaking this function may be service providers between laboratories that ICAR has accredited for microsatellite- and or SNP-based DNA genotyping and end users that may include breed societies, breeding companies, breeders and commercial farmers.

Service providers could use different laboratories for different breeds and/or species. Considering the importance of animal identification and parentage verification in animal recording, ICAR has decided to define the minimum requirements for using the results of DNA genotyping, and other information, for the purpose of:

# Parentage verification
# Parentage discovery, and
# Animal identification confirmation

The purpose of these guidelines is to provide a basis for the accreditation of processes used by organizations that use SNP genotypes in cattle. Minimum requirements for additional species and other DNA analyses may be defined in the future.

=== Scope ===
These guidelines are for the accreditation, by ICAR, of organizations that use the results of SNP-based tests for parentage analysis in cattle, which includes parentage verification, parentage discovery, and/or animal identification confirmation.

=== Accreditation of Organizations Performing Parentage Analysis ===
The ICAR accreditation process comprises the following steps:

* Application for accreditation
* Payment of relevant fee
* Review of application
* Technical processing of test data files
* Granting of accreditation

==== Application ====
Organizations carrying out SNP-based parentage analysis and requesting ICAR accreditation as a DNA Data Interpretation Centre must apply by downloading and completing the appropriate form included below as [https://www.icar.org/wp-content/uploads/2016/10/6-Annex-V-Application-Form-forICAR-Accreditation-of-DNA-Centres.pdf ''Appendix 8. Application form for organizations seeking ICAR parentage analysis accreditation for DNA data interpretation centres''.] This form must be filled out accurately and completely, providing necessary documentation as required, and submitted to ICAR with payment of the appropriate fee.

==== Review of application ====
The application will first be reviewed internally by ICAR for its completeness and additional details may be requested as needed. ICAR administration will also confirm receipt of the applicable fee.

==== Technical processing of test files ====
The applicant organization will receive a set of data files from ICAR through the Interbull Centre, for processing using its existing procedures for carrying out the level of parentage analysis for which the applicant is seeking ICAR accreditation as a DNA Data Interpretation Centre. A detailed description of this step is described in [https://www.icar.org/index.php/certifications/certification-and-accreditation-of-dna-genetic-laboratories/two-new-dna-based-services/dna-data-interpretation-centres/ ''Appendix 9. Applicant's Guide for ICAR Parentage Analysis Accreditation for DNA Data Interpretation Centres.''] In order for the applicant to be successful in obtaining the requested ICAR accreditation, it's procedures for conducting parentage analysis must exactly follow [https://www.icar.org/Documents/GenoEx/ICAR%20Guidelines%20for%20Parentage%20Verification%20and%20Parentage%20Discovery%20based%20on%20SNP.pdf ''Appendix 10. ICAR Guidelines for Parentage Verification and Parentage Discovery Based on SNP Genotypes''.] The list of SNP to be used for either parentage verification (N=200) or parentage discovery (N=554) are available in [https://www.icar.org/Guidelines/04-DNA-Technology-App-11-SNP-list-for-parentage-verification-or-discovery.pdf ''Appendix 11. List of SNP to be used for either parentage verification or parentage discovery''.] Once the applicant has completed its internal parentage analysis procedures based on the accreditation test files it received, it must send a data file of results back to the Interbull Centre. A maximum time period for 90 calendar days will be allowed for the applicant to submit acceptable files of results back to the Interbull Centre.

==== Granting of accreditation ====
Once the Interbull Centre receives the file of parentage analysis results from the applicant, it will complete the technical review and determine if the applicant has successfully completed the accreditation or not. The Interbull Centre shall inform ICAR of the results and ICAR shall issue a formal notification to the applicant. In the event the applicant was not successful in receiving ICAR accreditation, the applicant may initiate a new request for accreditation by completing and submitting the appropriate forms and providing payment of the applicable fee, as outlined above.

==== Renewal of accreditation ====
In advance of the expiry date of any existing ICAR accreditation, which coincides with the two-year anniversary date of the current accreditation, an applicant can apply for renewal of their accreditation by submitting an application as described in section 4.3.1 above and successfully completing the other required steps outlined in this section 4.
</div>
</div>

== Genotype Exchange Service – GenoEx-PSE ==
Effective 2018, ICAR has made available a genotype exchange service for parentage analysis, GenoEx-PSE, offered through the Interbull Centre. The main goal of this service is to facilitate the international exchange of SNP genotypes such that approved service users can carry out parentage analysis services at a national level in an efficient manner. The GenoEx-PSE database system and user interface has been developed to allow for the exchange of SNP genotypes for either parentage verification or parentage discovery based on the list of SNP provided in ''Appendix 11. List of SNP to be used for either parentage verification or parentage discovery''.

In order for an organization to qualify as a service user for GenoEx-PSE, it must first receive ICAR accreditation as a DNA data interpretation centre. The level of such ICAR accreditation (i.e.: for SNP-based parentage verification alone or for both SNP-based parentage verification and discovery) shall determine the highest level of SNP that may be exchanged via the GenoEx-PSE service. For details associated with this ICAR service, refer to the GenoEx-PSE web site at [https://genoex.org/ www.GenoEx.org.]

== Appendix list ==
<div class="mw-collapsible mw-collapsed">
<div>
=== Appendix 1. Link to SNP markers recommended by ISAG for parentage verification ===
https://www.icar.org/Guidelines/04-DNA-Technology-App-1-Cattle-SNP-ISAG-core-additional-panel-2013.xlsx

=== Appendix 2. Application form for microsatellite-based parentage testing in cattle ===
Please refer [https://www.icar.org/wp-content/uploads/2022/05/Annex-II-Application-Form-for-STR-Accreditation.pdf here] on the ICAR website for the Form for ICAR laboratory accreditation for STR Microsatellite-based Parentage Testing in Cattle.

=== Appendix 3. Application form for SNP-based genotyping required for parentage analysis in cattle ===
Please refer [https://www.icar.org/wp-content/uploads/2022/05/Annex-V-Application-Form-for-SNP-Accreditation.pdf here] on the ICAR website for the Form for ICAR laboratory accreditation for SNP-based genotyping required for Parentage Analysis in Cattle.

=== Appendix 4. ICAR DNA Laboratory Accreditation Service fees ===
Please refer [https://www.icar.org/index.php/certifications/certification-and-accreditation-of-dna-genetic-laboratories/guidelines-for-str-and-snp-based-parentage-testing-in-cattle/ here] on the ICAR website for DNA testing accreditation services fees.

=== Appendix 5. ISAG recommended microsatellites for parentage verification in cattle ===
Please refer [https://www.icar.org/wp-content/uploads/2018/05/03-Annex-III-ISAG-microsatellites.pdf here] on the ICAR website for the list of ISAG recommended microsatellites for parentage verification in cattle.

=== Appendix 6. Rules for microsatellite-based parentage testing in cattle ===
Please refer [https://www.icar.org/wp-content/uploads/2018/05/01-Annex-I-guidelines-microsats-STRs.pdf here] on the ICAR website for the rules for microsatellite-based parentage verification in cattle.

=== Appendix 7. List of approved SNP for parentage verification in cattle ===
Please refer [https://www.icar.org/Guidelines/04-DNA-Technology-App7-SNP-list-for-parentage-verification.pdf here] on the ICAR website for the ICAR approved list of 200 SNP for parentage verification in cattle.

=== Appendix 8. Application form for organizations seeking ICAR parentage analysis accreditation for DNA data interpretation centres ===
Please refer [https://www.icar.org/wp-content/uploads/2016/10/6-Annex-V-Application-Form-forICAR-Accreditation-of-DNA-Centres.pdf here] on the ICAR website for the application form for organizations seeking ICAR accreditation status as a DNA data interpretation centre.

=== Appendix 9. Applicant's Guide for ICAR Parentage Analysis Accreditation for DNA Data Interpretation Centres ===
Please refer [https://www.icar.org/index.php/certifications/certification-and-accreditation-of-dna-genetic-laboratories/two-new-dna-based-services/dna-data-interpretation-centres/ here] on the ICAR website for the Applicant's Guide for ICAR Parentage Analysis Accreditation for DNA Data Interpretation Centres.

=== Appendix 10. ICAR Guidelines for Parentage Verification and Parentage Discovery Based on SNP Genotypes ===
Please refer [https://www.icar.org/Documents/GenoEx/ICAR%20Guidelines%20for%20Parentage%20Verification%20and%20Parentage%20Discovery%20based%20on%20SNP.pdf here] on the ICAR website for the ICAR Guidelines for Parentage Verification and Parentage Discovery Based on SNP Genotypes.

=== Appendix 11. List of SNP to be used for either parentage verification or parentage discovery ===
Please refer [https://www.icar.org/Guidelines/04-DNA-Technology-App-11-SNP-list-for-parentage-verification-or-discovery.pdf here] on the ICAR website for the list of SNP to be used for either parentage verification or parentage discovery.
</div>
</div>

Section 04 – DNA Technology

2025-01-31T08:56:14Z

Aashish: /* Payment of relevant fee */

Section 04 – DNA Technology

2025-01-31T08:55:55Z

Aashish: /* Payment of relevant fee */

== Molecular Genetics ==
<div class="mw-collapsible mw-collapsed">
<div>
=== Introduction ===
Advances in molecular biology, especially genomics, provide a new set of information to be incorporated into the animal industry. On one hand, the use of molecular information may contribute to the enhancement of consumers' trust in the ability to monitor and control the animal production chain. On the other hand, molecular information will greatly contribute to the achievement of genetic improvement for animal traits through the use of genomic breeding values, marker assisted selection, gene introgression, heterosis prediction, pedigree validation/prediction, and genetic defect carrier status. In most cases, advantages of using molecular information via genomic evaluations, comes from improved accuracy of animal breeding values, shortened generation intervals, and increased intensity of selection. Even with these advancements there is still a need for research and development in the search for associations between genetic markers and traits of interest, especially as new traits are included in national evaluation indexes. In addition to that, even with the current incorporation of genomic information into national selection schemes, an understanding of gene action, gene interactions, and differential gene expression to avoid negative collateral effects is needed. Cooperation between animal industries and research is required for a successful and beneficial search for genetic information in commercial livestock populations.

=== Genetic Markers ===
Genetic markers are the fundamental molecular tools for genomics, even as the type of marker used has changed. The first genetic marker associations in livestock were reported using blood typing in the 1960s, the technology then moved to microsatellites (MS) in the 1990s and more recently to the use of Single Nucleotide Polymorphism (SNP). SNP and MS are polymorphic DNA sequences (alleles) at a specific locus of a particular chromosome. While blood typing has been an ICAR approved method of parentage verification currently there are few, if any, commercial labs still offering this testing. For this reason, ICAR no longer recommends blood typing as the basis for carrying out parentage analysis in livestock species where MS or SNP technology is widely available.

==== Microsatellites ====
These are segments of DNA containing tandem repeats of simple motifs usually dimers or trimers. These segments are located throughout the genome and normally in non-coding regions. Over time, these regions are subject to the addition or subtraction of tandem repeats, which means that each microsatellite can have multiple unique alleles. Microsatellites are commonly used in many livestock species for parentage validation.

==== Single Nucleotide Polymorphism (SNP) ====
SNP are the most common type of genetic variation: each SNP represents a variation in a single nucleotide. There are millions of SNP located throughout the genome of every livestock species. For genomics the most informative SNP traditionally are either located in (a) coding regions where different alleles change the structure or function of the encoded protein, or (b) at non-coding regions that are involved in the regulatory function of the gene. For genomic breeding values, SNP that are located in other regions of the genome are also informative as they could be in linkage disequilibrium with alleles that do cause a phenotype change.

One of the big advantages of SNP is their deployment on SNP arrays with a strong parallel processing capacity whereby thousands or hundreds of thousands of SNP can be screened together in a cost-effective and efficient manner across a large number of animals. Currently, the largest livestock genotyping labs can process hundreds of thousands of animals yearly on such arrays. The availability of these large SNP panels is therefore bolstering the search for mutations underlying genetic variation for simple and complex traits. It is also revolutionizing the speed at which trait associated genes or gene regions are being discovered as well as the adoption rate of genomic selection strategies. SNP genotypes have become the international standard for the basis of parentage analysis and ICAR recommends this approach over the use of microsatellites wherever possible due to the improved accuracy and the ease of comparing results between genotyping laboratories.

=== Current and Potential Uses of DNA Technologies ===

==== Parentage verification and parental assignment authentication ====
Prior to the emergence of SNP genotyping, parentage verification was the main commercial use of genetic markers. Traditionally, parentage testing was based on the exclusion of relationship (i.e.: sire or dam) when an animal has a genotype inconsistent to a putative relationship. New trends in animal production systems are tending to encourage animal production in larger numbers per farm in response to environmental and production related constraints. In these large settings, multiple animals could be bred or give birth on the same day, which can result in more pedigree recording errors. As the cost of the analysis decreases and the number of genetic markers available increases, breed societies are now able to build up pedigree records using genetic markers to predict the pedigree of calves born in a herd at a given time. This normally requires a prior knowledge of candidate sires and dams for a calf when lower number (<200) of markers are used, but with enough SNP the correct parents can be predicted without prior knowledge being available as long as the parent is also genotyped. The probability of assignment to a correct pair of animals will depend on the number of markers used, number of alleles per loci, the minor allele frequency in the population, the number of parents, and the number of possible matings. The International Society of Animal Genetics (www.isag.us) has species-specific panels recommended of microsatellite and SNP markers for this purpose, which can be accessed via a link such as provided in ''[https://www.icar.org/Guidelines/04-DNA-Technology-App-1-Cattle-SNP-ISAG-core-additional-panel-2013.xlsx Appendix 1]. Link to SNP markers recommended by ISAG for parentage verification''. For cattle, ICAR has developed a set of parentage SNP, ICAR554, which incorporates the ISAG recommended panel and other highly informative SNP. This panel allows for highly accurate parentage validation and discovery while not allowing for accurate imputation to a higher density. Therefore, the ICAR554 panel can be shared among countries and competitors for parentage analysis without fear of others being able to use them to predict genomic breeding values. ICAR and the Interbull Centre collaborate in offering an international genotype exchange service, referred to as GenoEx, which is described further in Chapter 5 specifically for the exchange of SNP genotypes for the purposes of parentage analysis.

==== Traceability and authentication of animal products offered to consumers ====
Due to multiple crises, including BSE outbreaks to ground beef containing horsemeat, and with increased consumer interest in where their food comes from the traceability of meat products is of greater concern to the industry. Traceability is based on the availability of a verification and control system that monitors all relevant details throughout the entire livestock production chain. Since an individual’s genetic sequence is unique and does not change, its DNA remains constant from ‘conception to consumption’. Therefore, use of genetic markers allows one to match the DNA of an individual at birth to the final product.

Genetic markers for the authentication of animal products for labels of quality related to geographic location and labels of quality related to specific breeds or their crosses are/or will be very useful. However, this requires the establishment of molecular standards or allele frequencies for each breed within a species. A lot of information is coming from studies of genetic diversity among breeds. Genomic regions subject to intense selection in each population are of particular interest. With a large enough set of SNP and genotyped purebred reference animals it is also possible to predict the most likely breed composition of individuals.

==== Molecular genetic information for marker-assisted selection schemes ====
Quantitative traits are generally assumed to be controlled by a large number of genes. However, individual genes sometimes account for a significant amount of variation of the trait. Such is the case for the Myostatin gene and double muscling in beef cattle, the DGAT1 gene and milk components in dairy cattle, or the Booroola fecundity gene and ovulation rate in sheep. Since the genotype of an animal does not change during its lifetime, use of DNA information through the identification of markers linked to QTL with effects on production traits or the identification of a gene itself together with the causative variant is of great interest. Nevertheless, with complex traits there is a growing need of having a sufficiently large marker set to incorporate molecular information for selection decisions. Including genomic information as a selection criterion is of special interest for traits that are difficult and costly to measure and/or are measured late in life. By 2022, >177,000 cattle, >34,000 swine, >16,000 chicken and >4,000 sheep QTL have been identified that are associated with economically important traits such as health, carcass, milk, fertility, and body conformation. The AnimalQTLdb database housed at the [https://www.animalgenome.org/ National Animal Genome Research Program] contains up to date information on cattle, chicken, horse, pig, trout, and sheep QTL data assembled from published data.

Recording schemes have been collecting information for decades on the most common production traits measured in domestic livestock. There is an ever-increasing volume of information becoming available, but for some traits like meat quality, disease resistance and feed efficiency, those records are very expensive to measure, difficult to obtain, or are performed late in the animal’s life. Because of these challenges information for such traits is commonly collected on a reduced number of animals in any given population.

For these challenging, but economically important traits, genetic markers and genomic selection offer significant opportunities for trait selection where it was not economically feasible before. In general, genetic markers and genomics will play an important role for important traits regardless of the livestock species. Genomics can also allow us to increase selection intensities since we can predict genomic breeding values on a large number of animals and thus have more candidates for selection.

==== Disease resistance and genetic defects ====
Another group of traits with a high potential for the use of molecular data and genomics are those linked to resistance, resilience, and susceptibility to diseases. There are a number of multi-factorial or complex diseases that are the result of the interaction between an animal’s genome and environmental components. Disease resistance traits are among the most difficult to include in genetic improvement programs because they require good field measurement of the disease status of the animals and a systematic control of management or environmental conditions that allow for the identification of the environmental influence on the health status of the animal. Infectious diseases depend very much upon environmental factors such as the degree of exposure to the pathogen agent. Thus, if exposure is low, animals will show little variation. Part of the phenotypic differences for resistance may be differences in the degree of challenge. Therefore, if genes or genetic markers linked to resistance are correctly identified, resistant animals will be able to be selected on the base of their genomic information. For many diseases, identification of genes associated with resistance will require experimental conditions to be used. Genetic analysis to identify heterozygous carriers of genetic diseases caused by single, recessive genes are currently in use. Examples in dairy cattle include complex vertebral malformation (CVM), brachyspina (BY), cholesterol deficiency (CD) and several genes, gene regions or haplotypes causing embryo loss or stillbirth in different dairy breeds. In 2022, [https://www.omia.org/home/ OMIA (Online Mendelian Inheritance in Animals),] listed >1000 traits or genetic defects in livestock with a known causative mutation (cattle: 186, pig: 58, chicken: 56, sheep: 49, horse: 48, goat:17). Including these causative allele or associated haplotypes in a breeding program will allow producers to minimize their risk from genetic defects while maximizing genetic progress from beneficial traits.

=== Technical Aspects ===

==== DNA collection ====
Systematic collection of DNA is recommended in several livestock populations. DNA may be obtained from any nuclear cell in the body. Protocols for DNA extraction are now available for blood (white cells), semen, saliva (epithelial cells), hair follicles, muscle, skin, organs (such as liver, spleen etc.). Red blood cells may also be used for poultry as they retain the nuclear body while most other species do not. Small amounts of tissue material are required for routine DNA analysis. However, if there are multiple future uses of an individual’s DNA (whole genome sequencing, traceability, causative allele validations, …), then DNA storage costs, extraction costs, quality, and quantity obtained by different protocols will have to be carefully examined and optimized. Common collection methods include hair follicles, tissue samples (often ear punch) in an enclosed container, blood spots on filter paper, and nasal swabs.

==== Data organization ====
A centralised database may be organised in respect to the main uses of the genetic information:

* Parent verification, assignment, and/or discovery
* Traceability of meat products
* Breed identification or breed diversity
* Qualitative and quantitative traits 

Database tables may contain:

* Animal identification to link to all other information on the animal and its relatives.
* Number of genetic markers: n
* Standard name of each marker i (for i= 1, n)
* Accession number for marker such as the dbSNP ID
* Alleles for marker i
* Genomic location of marker i
* Effect of non-reference allele on the protein
* Phenotypic effect of the allele
* Association with other traits

==== Parentage accuracy ====
While use of microsatellite and SNP markers are both ICAR accredited methods of parentage verification they do not have the same power of parentage accuracy. Briefly the order of accuracy for ISAG and ICAR approved parentage marker panels are:

Microsatellites << small SNP panels (100 or less) < large SNP panels (500 or more)

This order is based on both genotyping accuracy and total genomic information. Comparing the genomic marker error rate in cattle microsatellites have a 1-5% error rate (Baruch and Weller, 2008<ref>Baruch, E., and J. I. Weller. 2008. 'Estimation of the number of SNP genetic markers required for parentage verification', ''Animal Genetics'', 39: 474-79.</ref>) while the SNP error rate is <0.1% (Cooper, Wiggans, and VanRaden 2013<ref>Cooper, T. A., G. R. Wiggans, and P. M. VanRaden. 2013. 'Short communication: relationship of call rate and accuracy of single nucleotide polymorphism genotypes in dairy cattle', ''Journal of dairy science'', 96: 3336-9.</ref>). As 2-3 SNP provide the same parentage exclusion accuracy as 1 microsatellite marker (Vignal et al. 2002<ref>Vignal, A., D. Milan, M. SanCristobal, and A. Eggen. 2002. 'A review on SNP and other types of molecular markers and their use in animal genetics', ''Genet Sel Evol'', 34: 275-305.

1.4.4 Genomic quality control checks</ref>), the 100 and 200 ISAG parentage SNP panels are more accurate than the 12 ISAG parentage microsatellite markers. In the same manner parentage panels of over 500 SNP (McClure et al. 2018<ref>McClure, M. C., J. McCarthy, P. Flynn, J. C. McClure, E. Dair, D. K. O'Connell, and J. F. Kearney. 2018. 'SNP Data Quality Control in a National Beef and Dairy Cattle System and Highly Accurate SNP Based Parentage Verification and Identification', ''Front Genet'', 9: 84.</ref>), such as the ICAR554, are recommended for parentage prediction which requires an even higher level of accuracy.

==== Genomic quality control checks ====
One of the most important parts of a large genomic database is to ensure that a genotype associated with an individual animal truly belongs to that animal. Most large livestock genomic databases deal with SNP data only and the quality of SNP genotyping data is of paramount importance (Wu at al., Evaluation of genotyping concordance for commercial bovine SNP arrays using quality-assurance samples, Animal Genetics, 50: 367-371, 2019). This section, therefore, focuses on quality control for that genomic data type. Both sample and SNP quality control measures are needed, and it is encouraged to develop a system for them early.

For those working with genotype data there are two main concerns. First, is ensuring that the genotype data itself is of high quality and can be trusted. Second, is ensuring that the genotype truly belongs to the individual listed. The recommended quality control checks below will work for any livestock species. The basic checks can be performed with minimal information about the individual, while some of the advanced checks require data that not everyone will have, such as historic animal location.

===== ''Basic genotype quality control checks for SNP-based genotype data'' =====
Genotype:
<ol style="list-style-type:number">
<li> Exclude SNP that have a genotype call rate below 90% when analyzed in your population. Using 500 or more animals to determine the SNP call rate is recommended. Chromosome Y SNP should have their call rate determined only in males for this filter.</li>
<li> Invalidate the individual’s genotype if its overall call rate is below <90%. For SNP-based genotypes, such as those from Illumina or Affymetrix chips, the accuracy of called genotypes is questionable when the individual’s overall call rate is <90%.</li>
<li> Check to see that the animal has all three genotype classes (i.e.: AA, AB and BB) in its full genotype file. If any genotype class is missing or has a frequency below 20% then invalidate the full genotype.</li>
<li> Check to ensure that there are no unexpected alleles in the genotype file. For example, genotypes in AB format should not have T, G, 0, 1, 2 or 9. If present, then invalidate the full genotype file.</li>

</ol>

Parentage:
<ol style="list-style-type:number">
<li> Parent (Sire or Dam) validation. If using 200 or less SNP, a listed sire will validate if <1% of the offspring-parent genotypes are in conflict. A conflicting genotype is where the offspring and listed parent have opposite homozygous genotypes.</li>
<li> Mating validation after parent validation. For all parentage validation SNP where the animal is heterozygous, if for >1% of those SNP the sire and dam are homozygous for the same allele then the listed mating is invalidated. This could represent a case where the offspring and one of the parents were mislabeled with the other’s identification (so the offspring’s genotype belongs to the sire or dam and vice versa). Under such cases, it is recommended to resample the DNA and regenotype, potentially with a panel that includes more SNP.</li>

</ol>

===== ''Advanced quality control checks for SNP-based genotype data'' =====
Animal:
<ol style="list-style-type:number">

<li> Parentage discovery. Using SNP data to predict who an animal’s likely parent is can be very useful, but steps must be taken to ensure a very high probability that the prediction is accurate. The following are recommended:</li>

# Using 500 or more SNP that have a minor allele frequency (MAF) above 20% and call rates above 90%. It is advised to calculate the MAF across your full population. Predicted parents should have <1% conflict rate with the animal.
# Sex check. Make sure that you have a process established to ensure that only males are predicted as the sire and only females as the dam.
# Date of Birth check. If you do not include a check that the predicted parent is older than the animal than the predicted individual could actually be an offspring of the animal.
# Age gap. Cattle normally reach sexual maturity at 11-12 months of age, but this can be as young as 8-9 months, and even younger if in-vitro fertilization is a technology used within the population. Under normal circumstances, a minimum of 17 months between the birth dates of the animal and its predicted parent is recommended to ensure that the predicted parent could have been sexually mature at the time of the breeding.
# Grey zone SNP conflicts. The majority of animals will have <0.5% or >1.5% conflicting genotypes with the individual when parentage discovery is conducted. Those with <0.5% pass the prediction and those with >1.5% fail. Most failed animals will have >8% conflict rates. For those animals who have between 0.5 and 1.5% conflicting SNP when a set parentage panel is used (i.e.: the ICAR554 SNP list), it is advised that the conflict rate from all available SNP be used between the two individuals and if the percent conflicting is <1% they validate as the parent, but if >1% they fail.

<li> Genetic Relationship Matrix (GRM). If an animal’s true parent is not genotyped, then it cannot be directly predicted or validated. The genetic relationship between closely related animals can be used to suggest a potential, non-genotyped, parent. It is recommended that 7,000 or more SNP be used to calculate the GRM. GRM results DO NOT validate a relationship, but only suggest. Caution should be used as GRM values can be inflated for inbred individuals. Full-sibs and parent-child should have GRM values around 50%, while half-sibs would be around 25%. The range for each group can vary 5-10% from the expected value.</li>

<li> Sex prediction. How to perform a sex prediction depends on the type and number of SNP an animal has from the X and Y chromosomes. While not every commercial chip includes chromosome Y SNP, they typically contain chromosome X markers.</li>

# Pseudo autosomal region (PAR) SNP. As both the X and Y chromosomes contain the PAR, SNP from this region should be excluded from sex prediction. If the PAR position boundaries are not published for your species they can be roughly determined by analyzing the chromosome X SNP in known males and females and identifying the region where the MAF in males for a continuous set of SNP is >1%. Non-PAR regions of chromosome X will have SNP with average MAF of <1% in males and >>1% in females.
# Chromosome X predicted. Use non-PAR SNP to determine the animal’s chromosome X heterozygosity rate (number of heterozygous chromosome X SNP / total number of chromosome X SNP). If the average heterozygosity rate is <5%, the predicted sex is male, and if >15% its female. If the rate is between 5 and 15% then the predicted sex is unknown.
# Chromosome Y predicted. Using chromosome Y SNP to predict sex is logically simpler but many commercial chips do not contain them. Say you have 7 chromosome Y SNP with high call rates in males, it is recommended using the following logic. Male is predicted when 6-7 of the Y SNP are present; female is predicted when <1 SNP is present and ambiguous sex is predicted when 2-5 Y SNP are present.
# Ambiguous sex prediction. If one set of sex chromosome SNP returns an ambiguous sex prediction and the other doesn’t it is recommended using the latter as the predicted sex. If both SNP sets are ambiguous, the animal could have Turner syndrome (X0), or Klinefelter’s syndrome (XXY), in this case it is recommended returning an ambiguous predicted sex.
# If the predicted sex from the chromosome X and Y analysis disagree, it is recommended returning an ambiguous predicted sex. This could also indicate a possible Klinefelter syndrome (XXY) animal.
# Sex selected AI semen straws. Sex prediction should not be carried out on DNA obtained from sex selected AI semen straws.

<li> Offspring Quality control. The genotyped and listed offspring of an animal can be used to identify potential cases where the animal’s genotype actually belongs to another animal. These should be used as flags to indicate a potential investigation, but it is recommended to temporarily invalidate the animal’s genotype until cleared. Advised thresholds for those flags are:</li>
# AI sire: If >80% of genotyped offspring fail if >10 offspring are genotyped.
# Stock/herd bull: If >80% of genotyped offspring fail if >5 offspring are genotyped.
# Dam: If 100% of genotyped offspring fail if 2 offspring are genotyped, else if >5 offspring are genotyped then use >80%.

<li> Duplicate genotype. The only case where two or more animals should share the exact same genotype is if they are identical twins or clones. Checking to see if >1 animal has the same genotypes is a useful quality control check. It is recommended using your parentage SNP set for initial screening and for any pair that have >99% identical genotypes and then using all available SNP to see if >99% of the genotypes match.</li>

</ol>

For standardization purposes with respect to the nomenclature of genes or loci, a web site is available at: https://www.genenames.org/about/guidelines#genenames and markers at: [https://hgvs-nomenclature.org/versions/21.0/ <nowiki>http://www.HGVS.org/varnomen</nowiki>.]

</div>
</div>

== ICAR Services Related to DNA Technology ==
ICAR offers three services that are related to the use of DNA all of which are linked to parentage analysis in one form or another, as shown in Figure 1. ICAR DNA services., and describing them in more detail in the other sections.
[[File:ICAR DNA service.png|thumb|Figure 1. ICAR DNA services.|center|415x415px]]

== ICAR Accreditation of Laboratories Providing DNA Genotyping Services ==
<div class="mw-collapsible mw-collapsed">
<div>
=== Introduction ===
Considering the need for high quality standards in all uses of molecular data, ICAR has for several years offered an accreditation service based on defined minimum requirements for laboratories providing DNA genotyping services. The basic requirements of this accreditation include proof of the minimum internal management quality assurance standards and a Rank 1 result from participation in the most recent biennial international ring test developed and offered by the International Society for Animal Genetics (ISAG).

In addition, such laboratories generally have been analyzing the resulting genotypes to carry out microsatellite- and/or SNP-based parentage analysis services including either parentage verification or animal identification confirmation. This ICAR accreditation service has previously been used for recognizing the genotyping laboratory as an accredited organization to provide parentage analysis functions without specifically testing the technical accuracy of doing so. Effective 2021, the SNP-based parentage analysis accreditation service for DNA Data Interpretation Centres has replaced the previous laboratory accreditation for SNP-based parentage verification. In the future, a similar technical process for the accreditation of microsatellite-based parentage analysis may be introduced by ICAR but until such time, the existing process for the accreditation of genotyping laboratories will remain in effect.

The following guidelines for accreditation are provided for microsatellite- and SNP-based genotyping in cattle. Minimum requirements for additional species and other DNA tests may be defined in the future.

=== Scope ===
These guidelines are for the accreditation, by ICAR, of genotyping laboratories that analyze biological samples from cattle using microsatellite- and or SNP-based genotyping, which may be subsequently used for various levels of parentage analysis, genotype imputation, estimation of genomic breeding values and other activities related to genomic selection strategies. This accreditation process also includes parentage verification based on microsatellites since ICAR has not established this service as part of the portfolio of possible accreditations for DNA data interpretation centres. For genotyping laboratories that would like to receive ICAR accreditation for SNP-based parentage verification, they must now apply separately to ICAR for its parallel service of parentage analysis accreditation for DNA data interpretation centres, as described in section 4.

=== ICAR Guidelines for Accreditation of Genotyping Laboratories ===
The accreditation process comprises the following steps:

* Application for accreditation
* Payment of relevant fee
* Review of application
* Granting of accreditation

==== Application for accreditation ====
Laboratories requesting accreditation only for microsatellite- based genotyping and parentage verification must apply by downloading and completing the appropriate form as provided in [https://www.icar.org/wp-content/uploads/2022/05/Annex-II-Application-Form-for-STR-Accreditation.pdf ''Appendix 2. Application form for microsatellite-based parentage testing in cattle''.] Laboratories seeking ICAR accreditation involving SNP-based genotyping must apply by downloading and completing the appropriate form as provided in [https://www.icar.org/wp-content/uploads/2022/05/Annex-V-Application-Form-for-SNP-Accreditation.pdf ''Appendix 3. Application form for SNP-based genotyping required for parentage analysis in cattle.''] Laboratories that have previously received ICAR accreditation for either service may re-apply prior to the expiry of any such accreditation using a shortened renewal form available on the ICAR web site. All application forms must be emailed to the ICAR secretariat at dna@icar.org and be filled out accurately and completely including the necessary documentation as required.

==== Payment of relevant fee ====
Along with the completed application form, the applicant must also provide full payment of the relevant fee as established by ICAR and given in [https://www.icar.org/index.php/certifications/certification-and-accreditation-of-dna-genetic-laboratories/guidelines-for-str-and-snp-based-parentage-testing-in-cattle/ ''Appendix 4. ICAR DNA Laboratory Accreditation Service fees''.] refer to [[Preamble#Scope | hey]]

==== Review of application ====
The application will be evaluated by a committee of experts appointed by ICAR that will either:

* Approve the application
* Request additional information, or
* Reject the application

In the case of rejection, the laboratory may make a new submission as part of the ICAR annual call for applications for any subsequent year after the failed application.

==== Granting of accreditation ====
Accreditation will be given for a period of two calendar years with an expiry date of December 31st of the second year after receiving ICAR accreditation as a laboratory providing DNA genotyping services.

==== Renewal of accreditation ====
In advance of the expiry date of any existing ICAR accreditation, normally during the same year of the December 31st expiry date, a laboratory can apply for renewal of their accreditation by submitting an application as described in section 3.3.1 above and successfully completing the other steps outlined in this section 3.

==== Laboratory accreditation ====
Effective the 2022 ICAR call for accreditation of genotyping laboratories, ISO17025 accreditation, or an equivalent accreditation for ensuring quality internal management systems, is a mandatory requirement for SNP-based accreditation. In addition, effective the 2022 call for accreditation of genotyping laboratories for microsatellite (STR)-based accreditation, ISO9001 certification will no longer be acceptable and only ISO17025, or an equivalent accreditation, will be an acceptable level of accreditation to ensure quality internal management systems. During the year of application for ICAR accreditation as a laboratory providing DNA genotyping services, the applicant must provide proof of ISO17025 accreditation with an expiry date of October 31st of the following calendar year, or later.

==== Participation and performance in ring test ====
On a biennial basis, initiated during even years (i.e.: 2022, 20246, etc…) and discussed at its biennial conference in odd years (2023, 2025, etc.), ISAG conducts an international ring (comparison) test of laboratories for both microsatellite- and SNP-based genotyping. The participation in ISAG and performance within these ring tests must be disclosed, and certificates provided to ICAR, when available. Applicants must also sign a release allowing ISAG to directly disclose their ring test results to ICAR. Participation in the most recent ISAG ring test is a minimum requirement to qualify for ICAR accreditation. For the ISAG microsatellite ring test, lab genotyping performance for the official set of 12 ISAG microsatellites must be disclosed. The committee of experts will decide performance thresholds for each ring test with due consideration for the structure of the ring test and the average performance of laboratories in the ring test that year. Only those laboratories achieving Rank 1 status in the most recent biennial ISAG ring test shall automatically qualify to receive ICAR accreditation as a genotyping laboratory. Laboratories achieving a Rank 2 status in the most recent ISAG ring test may qualify to receive ICAR accreditation, at the discretion of the committee of experts, but must provide evidence of Rank 1 status for previous ISAG ring tests as well as documentation outlining the cause of the Rank 2 result and any associated actions to mitigate similar outcomes in future ISAG ring tests. Laboratories achieving a status lower than Rank 2 in the most recent ISAG ring test do not qualify for ICAR accreditation as a laboratory providing DNA genotyping services.

==== Microsatellite markers ====
The names of all microsatellites typed on all animals (marker set I) and of the additional ones assayed in the case of unresolved parentage (marker set II) must be declared, as well as the number of animals typed in at least the last two years. The minimum requirement for international exchange is the complete set of 12 official ISAG microsatellite markers. To ensure sufficient experience within the lab, analysis of 500 animals per year is set as minimum requirement for microsatellite parentage verification certification.

''[https://www.icar.org/wp-content/uploads/2018/05/03-Annex-III-ISAG-microsatellites.pdf Appendix 5. ISAG recommended microsatellites for parentage verification in cattle]'' contains the list of microsatellite markers recommended by ISAG and the method for calculating 1 parent and 2 parent exclusion probabilities. The rules for microsatellite-based parentage verification in cattle are described in ''[https://www.icar.org/wp-content/uploads/2018/05/01-Annex-I-guidelines-microsats-STRs.pdf Appendix 6. Rules for microsatellite-based parentage testing in cattle]''. Exclusion probability (PE; 2 parents and 1 parent) of each marker and of the complete marker sets must be calculated and provided in the application. The type of population and number of animals (minimum 150) used for computations are to be described. ICAR recommends using Holstein as a reference group when possible. The ICAR committee of experts will evaluate that an appropriate PE is reached for accreditation, on the basis of the population analyzed.

==== SNP markers ====
ICAR accreditation of SNP-based parentage verification is based on the full set of 200 SNP previously recommended by ISAG. The name of all SNP genotyped on all animals (marker set I, including the 100 “Core” SNP) and of the additional markers assayed in the case of unresolved parentage (marker set II, including the 100 “Additional” SNP) must be declared, as well as the number of animals SNP genotyped in at least the last two years. ICAR recommends using the full set of 200 SNP for parentage verification of all animals genotyped (''see [https://www.icar.org/Guidelines/04-DNA-Technology-App7-SNP-list-for-parentage-verification.pdf Appendix 7. List of approved SNP for parentage verification in]'' [https://www.icar.org/Guidelines/04-DNA-Technology-App7-SNP-list-for-parentage-verification.pdf cattle]). ICAR may, however, based on scientific evidence, identify specific problematic SNP that must be excluded for parentage analysis, as described in the ICAR documentation related to the accreditation of DNA data interpretation centres outlined in section 4.

==== Marker nomenclature ====
Nomenclature of markers must be described. ISAG nomenclature is required for the official ISAG 12 marker set as well as for the ISAG SNP marker set.
</div>
</div>

== Accreditation of Organisations Performing SNP-Based Parentage Analysis ==
<div class="mw-collapsible mw-collapsed">
<div>
=== Introduction ===
With the advent of SNP genotyping, the function of DNA genotyping as a laboratory activity can be separated from the functions of performing parentage verification and parentage discovery. Consequently, ICAR has established a separate accreditation for applying the results of SNP-based genotyping, which may be undertaken by laboratories, breed association societies, genetic evaluation centres and any other organization involved in parentage verification and/or the data processing of SNP genotypes.

Parentage verification and discovery are concerned with using the results that are delivered by the laboratories from DNA genotyping and require SNP genotypes for the animal itself, its recorded parents and other possible parents in the case of parentage discovery. Organizations undertaking this function may be service providers between laboratories that ICAR has accredited for microsatellite- and or SNP-based DNA genotyping and end users that may include breed societies, breeding companies, breeders and commercial farmers.

Service providers could use different laboratories for different breeds and/or species. Considering the importance of animal identification and parentage verification in animal recording, ICAR has decided to define the minimum requirements for using the results of DNA genotyping, and other information, for the purpose of:

# Parentage verification
# Parentage discovery, and
# Animal identification confirmation

The purpose of these guidelines is to provide a basis for the accreditation of processes used by organizations that use SNP genotypes in cattle. Minimum requirements for additional species and other DNA analyses may be defined in the future.

=== Scope ===
These guidelines are for the accreditation, by ICAR, of organizations that use the results of SNP-based tests for parentage analysis in cattle, which includes parentage verification, parentage discovery, and/or animal identification confirmation.

=== Accreditation of Organizations Performing Parentage Analysis ===
The ICAR accreditation process comprises the following steps:

* Application for accreditation
* Payment of relevant fee
* Review of application
* Technical processing of test data files
* Granting of accreditation

==== Application ====
Organizations carrying out SNP-based parentage analysis and requesting ICAR accreditation as a DNA Data Interpretation Centre must apply by downloading and completing the appropriate form included below as [https://www.icar.org/wp-content/uploads/2016/10/6-Annex-V-Application-Form-forICAR-Accreditation-of-DNA-Centres.pdf ''Appendix 8. Application form for organizations seeking ICAR parentage analysis accreditation for DNA data interpretation centres''.] This form must be filled out accurately and completely, providing necessary documentation as required, and submitted to ICAR with payment of the appropriate fee.

==== Review of application ====
The application will first be reviewed internally by ICAR for its completeness and additional details may be requested as needed. ICAR administration will also confirm receipt of the applicable fee.

==== Technical processing of test files ====
The applicant organization will receive a set of data files from ICAR through the Interbull Centre, for processing using its existing procedures for carrying out the level of parentage analysis for which the applicant is seeking ICAR accreditation as a DNA Data Interpretation Centre. A detailed description of this step is described in [https://www.icar.org/index.php/certifications/certification-and-accreditation-of-dna-genetic-laboratories/two-new-dna-based-services/dna-data-interpretation-centres/ ''Appendix 9. Applicant's Guide for ICAR Parentage Analysis Accreditation for DNA Data Interpretation Centres.''] In order for the applicant to be successful in obtaining the requested ICAR accreditation, it's procedures for conducting parentage analysis must exactly follow [https://www.icar.org/Documents/GenoEx/ICAR%20Guidelines%20for%20Parentage%20Verification%20and%20Parentage%20Discovery%20based%20on%20SNP.pdf ''Appendix 10. ICAR Guidelines for Parentage Verification and Parentage Discovery Based on SNP Genotypes''.] The list of SNP to be used for either parentage verification (N=200) or parentage discovery (N=554) are available in [https://www.icar.org/Guidelines/04-DNA-Technology-App-11-SNP-list-for-parentage-verification-or-discovery.pdf ''Appendix 11. List of SNP to be used for either parentage verification or parentage discovery''.] Once the applicant has completed its internal parentage analysis procedures based on the accreditation test files it received, it must send a data file of results back to the Interbull Centre. A maximum time period for 90 calendar days will be allowed for the applicant to submit acceptable files of results back to the Interbull Centre.

==== Granting of accreditation ====
Once the Interbull Centre receives the file of parentage analysis results from the applicant, it will complete the technical review and determine if the applicant has successfully completed the accreditation or not. The Interbull Centre shall inform ICAR of the results and ICAR shall issue a formal notification to the applicant. In the event the applicant was not successful in receiving ICAR accreditation, the applicant may initiate a new request for accreditation by completing and submitting the appropriate forms and providing payment of the applicable fee, as outlined above.

==== Renewal of accreditation ====
In advance of the expiry date of any existing ICAR accreditation, which coincides with the two-year anniversary date of the current accreditation, an applicant can apply for renewal of their accreditation by submitting an application as described in section 4.3.1 above and successfully completing the other required steps outlined in this section 4.
</div>
</div>

== Genotype Exchange Service – GenoEx-PSE ==
Effective 2018, ICAR has made available a genotype exchange service for parentage analysis, GenoEx-PSE, offered through the Interbull Centre. The main goal of this service is to facilitate the international exchange of SNP genotypes such that approved service users can carry out parentage analysis services at a national level in an efficient manner. The GenoEx-PSE database system and user interface has been developed to allow for the exchange of SNP genotypes for either parentage verification or parentage discovery based on the list of SNP provided in ''Appendix 11. List of SNP to be used for either parentage verification or parentage discovery''.

In order for an organization to qualify as a service user for GenoEx-PSE, it must first receive ICAR accreditation as a DNA data interpretation centre. The level of such ICAR accreditation (i.e.: for SNP-based parentage verification alone or for both SNP-based parentage verification and discovery) shall determine the highest level of SNP that may be exchanged via the GenoEx-PSE service. For details associated with this ICAR service, refer to the GenoEx-PSE web site at [https://genoex.org/ www.GenoEx.org.]

== Appendix list ==
<div class="mw-collapsible mw-collapsed">
<div>
=== Appendix 1. Link to SNP markers recommended by ISAG for parentage verification ===
https://www.icar.org/Guidelines/04-DNA-Technology-App-1-Cattle-SNP-ISAG-core-additional-panel-2013.xlsx

=== Appendix 2. Application form for microsatellite-based parentage testing in cattle ===
Please refer [https://www.icar.org/wp-content/uploads/2022/05/Annex-II-Application-Form-for-STR-Accreditation.pdf here] on the ICAR website for the Form for ICAR laboratory accreditation for STR Microsatellite-based Parentage Testing in Cattle.

=== Appendix 3. Application form for SNP-based genotyping required for parentage analysis in cattle ===
Please refer [https://www.icar.org/wp-content/uploads/2022/05/Annex-V-Application-Form-for-SNP-Accreditation.pdf here] on the ICAR website for the Form for ICAR laboratory accreditation for SNP-based genotyping required for Parentage Analysis in Cattle.

=== Appendix 4. ICAR DNA Laboratory Accreditation Service fees ===
Please refer [https://www.icar.org/index.php/certifications/certification-and-accreditation-of-dna-genetic-laboratories/guidelines-for-str-and-snp-based-parentage-testing-in-cattle/ here] on the ICAR website for DNA testing accreditation services fees.

=== Appendix 5. ISAG recommended microsatellites for parentage verification in cattle ===
Please refer [https://www.icar.org/wp-content/uploads/2018/05/03-Annex-III-ISAG-microsatellites.pdf here] on the ICAR website for the list of ISAG recommended microsatellites for parentage verification in cattle.

=== Appendix 6. Rules for microsatellite-based parentage testing in cattle ===
Please refer [https://www.icar.org/wp-content/uploads/2018/05/01-Annex-I-guidelines-microsats-STRs.pdf here] on the ICAR website for the rules for microsatellite-based parentage verification in cattle.

=== Appendix 7. List of approved SNP for parentage verification in cattle ===
Please refer [https://www.icar.org/Guidelines/04-DNA-Technology-App7-SNP-list-for-parentage-verification.pdf here] on the ICAR website for the ICAR approved list of 200 SNP for parentage verification in cattle.

=== Appendix 8. Application form for organizations seeking ICAR parentage analysis accreditation for DNA data interpretation centres ===
Please refer [https://www.icar.org/wp-content/uploads/2016/10/6-Annex-V-Application-Form-forICAR-Accreditation-of-DNA-Centres.pdf here] on the ICAR website for the application form for organizations seeking ICAR accreditation status as a DNA data interpretation centre.

=== Appendix 9. Applicant's Guide for ICAR Parentage Analysis Accreditation for DNA Data Interpretation Centres ===
Please refer [https://www.icar.org/index.php/certifications/certification-and-accreditation-of-dna-genetic-laboratories/two-new-dna-based-services/dna-data-interpretation-centres/ here] on the ICAR website for the Applicant's Guide for ICAR Parentage Analysis Accreditation for DNA Data Interpretation Centres.

=== Appendix 10. ICAR Guidelines for Parentage Verification and Parentage Discovery Based on SNP Genotypes ===
Please refer [https://www.icar.org/Documents/GenoEx/ICAR%20Guidelines%20for%20Parentage%20Verification%20and%20Parentage%20Discovery%20based%20on%20SNP.pdf here] on the ICAR website for the ICAR Guidelines for Parentage Verification and Parentage Discovery Based on SNP Genotypes.

=== Appendix 11. List of SNP to be used for either parentage verification or parentage discovery ===
Please refer [https://www.icar.org/Guidelines/04-DNA-Technology-App-11-SNP-list-for-parentage-verification-or-discovery.pdf here] on the ICAR website for the list of SNP to be used for either parentage verification or parentage discovery.
</div>
</div>

Section 04 – DNA Technology

2025-01-31T08:53:07Z

Aashish: /* Payment of relevant fee */

Section 04 – DNA Technology

2025-01-31T08:51:31Z

Aashish: /* Payment of relevant fee */

== Molecular Genetics ==
<div class="mw-collapsible mw-collapsed">
<div>
=== Introduction ===
Advances in molecular biology, especially genomics, provide a new set of information to be incorporated into the animal industry. On one hand, the use of molecular information may contribute to the enhancement of consumers' trust in the ability to monitor and control the animal production chain. On the other hand, molecular information will greatly contribute to the achievement of genetic improvement for animal traits through the use of genomic breeding values, marker assisted selection, gene introgression, heterosis prediction, pedigree validation/prediction, and genetic defect carrier status. In most cases, advantages of using molecular information via genomic evaluations, comes from improved accuracy of animal breeding values, shortened generation intervals, and increased intensity of selection. Even with these advancements there is still a need for research and development in the search for associations between genetic markers and traits of interest, especially as new traits are included in national evaluation indexes. In addition to that, even with the current incorporation of genomic information into national selection schemes, an understanding of gene action, gene interactions, and differential gene expression to avoid negative collateral effects is needed. Cooperation between animal industries and research is required for a successful and beneficial search for genetic information in commercial livestock populations.

=== Genetic Markers ===
Genetic markers are the fundamental molecular tools for genomics, even as the type of marker used has changed. The first genetic marker associations in livestock were reported using blood typing in the 1960s, the technology then moved to microsatellites (MS) in the 1990s and more recently to the use of Single Nucleotide Polymorphism (SNP). SNP and MS are polymorphic DNA sequences (alleles) at a specific locus of a particular chromosome. While blood typing has been an ICAR approved method of parentage verification currently there are few, if any, commercial labs still offering this testing. For this reason, ICAR no longer recommends blood typing as the basis for carrying out parentage analysis in livestock species where MS or SNP technology is widely available.

==== Microsatellites ====
These are segments of DNA containing tandem repeats of simple motifs usually dimers or trimers. These segments are located throughout the genome and normally in non-coding regions. Over time, these regions are subject to the addition or subtraction of tandem repeats, which means that each microsatellite can have multiple unique alleles. Microsatellites are commonly used in many livestock species for parentage validation.

==== Single Nucleotide Polymorphism (SNP) ====
SNP are the most common type of genetic variation: each SNP represents a variation in a single nucleotide. There are millions of SNP located throughout the genome of every livestock species. For genomics the most informative SNP traditionally are either located in (a) coding regions where different alleles change the structure or function of the encoded protein, or (b) at non-coding regions that are involved in the regulatory function of the gene. For genomic breeding values, SNP that are located in other regions of the genome are also informative as they could be in linkage disequilibrium with alleles that do cause a phenotype change.

One of the big advantages of SNP is their deployment on SNP arrays with a strong parallel processing capacity whereby thousands or hundreds of thousands of SNP can be screened together in a cost-effective and efficient manner across a large number of animals. Currently, the largest livestock genotyping labs can process hundreds of thousands of animals yearly on such arrays. The availability of these large SNP panels is therefore bolstering the search for mutations underlying genetic variation for simple and complex traits. It is also revolutionizing the speed at which trait associated genes or gene regions are being discovered as well as the adoption rate of genomic selection strategies. SNP genotypes have become the international standard for the basis of parentage analysis and ICAR recommends this approach over the use of microsatellites wherever possible due to the improved accuracy and the ease of comparing results between genotyping laboratories.

=== Current and Potential Uses of DNA Technologies ===

==== Parentage verification and parental assignment authentication ====
Prior to the emergence of SNP genotyping, parentage verification was the main commercial use of genetic markers. Traditionally, parentage testing was based on the exclusion of relationship (i.e.: sire or dam) when an animal has a genotype inconsistent to a putative relationship. New trends in animal production systems are tending to encourage animal production in larger numbers per farm in response to environmental and production related constraints. In these large settings, multiple animals could be bred or give birth on the same day, which can result in more pedigree recording errors. As the cost of the analysis decreases and the number of genetic markers available increases, breed societies are now able to build up pedigree records using genetic markers to predict the pedigree of calves born in a herd at a given time. This normally requires a prior knowledge of candidate sires and dams for a calf when lower number (<200) of markers are used, but with enough SNP the correct parents can be predicted without prior knowledge being available as long as the parent is also genotyped. The probability of assignment to a correct pair of animals will depend on the number of markers used, number of alleles per loci, the minor allele frequency in the population, the number of parents, and the number of possible matings. The International Society of Animal Genetics (www.isag.us) has species-specific panels recommended of microsatellite and SNP markers for this purpose, which can be accessed via a link such as provided in ''[https://www.icar.org/Guidelines/04-DNA-Technology-App-1-Cattle-SNP-ISAG-core-additional-panel-2013.xlsx Appendix 1]. Link to SNP markers recommended by ISAG for parentage verification''. For cattle, ICAR has developed a set of parentage SNP, ICAR554, which incorporates the ISAG recommended panel and other highly informative SNP. This panel allows for highly accurate parentage validation and discovery while not allowing for accurate imputation to a higher density. Therefore, the ICAR554 panel can be shared among countries and competitors for parentage analysis without fear of others being able to use them to predict genomic breeding values. ICAR and the Interbull Centre collaborate in offering an international genotype exchange service, referred to as GenoEx, which is described further in Chapter 5 specifically for the exchange of SNP genotypes for the purposes of parentage analysis.

==== Traceability and authentication of animal products offered to consumers ====
Due to multiple crises, including BSE outbreaks to ground beef containing horsemeat, and with increased consumer interest in where their food comes from the traceability of meat products is of greater concern to the industry. Traceability is based on the availability of a verification and control system that monitors all relevant details throughout the entire livestock production chain. Since an individual’s genetic sequence is unique and does not change, its DNA remains constant from ‘conception to consumption’. Therefore, use of genetic markers allows one to match the DNA of an individual at birth to the final product.

Genetic markers for the authentication of animal products for labels of quality related to geographic location and labels of quality related to specific breeds or their crosses are/or will be very useful. However, this requires the establishment of molecular standards or allele frequencies for each breed within a species. A lot of information is coming from studies of genetic diversity among breeds. Genomic regions subject to intense selection in each population are of particular interest. With a large enough set of SNP and genotyped purebred reference animals it is also possible to predict the most likely breed composition of individuals.

==== Molecular genetic information for marker-assisted selection schemes ====
Quantitative traits are generally assumed to be controlled by a large number of genes. However, individual genes sometimes account for a significant amount of variation of the trait. Such is the case for the Myostatin gene and double muscling in beef cattle, the DGAT1 gene and milk components in dairy cattle, or the Booroola fecundity gene and ovulation rate in sheep. Since the genotype of an animal does not change during its lifetime, use of DNA information through the identification of markers linked to QTL with effects on production traits or the identification of a gene itself together with the causative variant is of great interest. Nevertheless, with complex traits there is a growing need of having a sufficiently large marker set to incorporate molecular information for selection decisions. Including genomic information as a selection criterion is of special interest for traits that are difficult and costly to measure and/or are measured late in life. By 2022, >177,000 cattle, >34,000 swine, >16,000 chicken and >4,000 sheep QTL have been identified that are associated with economically important traits such as health, carcass, milk, fertility, and body conformation. The AnimalQTLdb database housed at the [https://www.animalgenome.org/ National Animal Genome Research Program] contains up to date information on cattle, chicken, horse, pig, trout, and sheep QTL data assembled from published data.

Recording schemes have been collecting information for decades on the most common production traits measured in domestic livestock. There is an ever-increasing volume of information becoming available, but for some traits like meat quality, disease resistance and feed efficiency, those records are very expensive to measure, difficult to obtain, or are performed late in the animal’s life. Because of these challenges information for such traits is commonly collected on a reduced number of animals in any given population.

For these challenging, but economically important traits, genetic markers and genomic selection offer significant opportunities for trait selection where it was not economically feasible before. In general, genetic markers and genomics will play an important role for important traits regardless of the livestock species. Genomics can also allow us to increase selection intensities since we can predict genomic breeding values on a large number of animals and thus have more candidates for selection.

==== Disease resistance and genetic defects ====
Another group of traits with a high potential for the use of molecular data and genomics are those linked to resistance, resilience, and susceptibility to diseases. There are a number of multi-factorial or complex diseases that are the result of the interaction between an animal’s genome and environmental components. Disease resistance traits are among the most difficult to include in genetic improvement programs because they require good field measurement of the disease status of the animals and a systematic control of management or environmental conditions that allow for the identification of the environmental influence on the health status of the animal. Infectious diseases depend very much upon environmental factors such as the degree of exposure to the pathogen agent. Thus, if exposure is low, animals will show little variation. Part of the phenotypic differences for resistance may be differences in the degree of challenge. Therefore, if genes or genetic markers linked to resistance are correctly identified, resistant animals will be able to be selected on the base of their genomic information. For many diseases, identification of genes associated with resistance will require experimental conditions to be used. Genetic analysis to identify heterozygous carriers of genetic diseases caused by single, recessive genes are currently in use. Examples in dairy cattle include complex vertebral malformation (CVM), brachyspina (BY), cholesterol deficiency (CD) and several genes, gene regions or haplotypes causing embryo loss or stillbirth in different dairy breeds. In 2022, [https://www.omia.org/home/ OMIA (Online Mendelian Inheritance in Animals),] listed >1000 traits or genetic defects in livestock with a known causative mutation (cattle: 186, pig: 58, chicken: 56, sheep: 49, horse: 48, goat:17). Including these causative allele or associated haplotypes in a breeding program will allow producers to minimize their risk from genetic defects while maximizing genetic progress from beneficial traits.

=== Technical Aspects ===

==== DNA collection ====
Systematic collection of DNA is recommended in several livestock populations. DNA may be obtained from any nuclear cell in the body. Protocols for DNA extraction are now available for blood (white cells), semen, saliva (epithelial cells), hair follicles, muscle, skin, organs (such as liver, spleen etc.). Red blood cells may also be used for poultry as they retain the nuclear body while most other species do not. Small amounts of tissue material are required for routine DNA analysis. However, if there are multiple future uses of an individual’s DNA (whole genome sequencing, traceability, causative allele validations, …), then DNA storage costs, extraction costs, quality, and quantity obtained by different protocols will have to be carefully examined and optimized. Common collection methods include hair follicles, tissue samples (often ear punch) in an enclosed container, blood spots on filter paper, and nasal swabs.

==== Data organization ====
A centralised database may be organised in respect to the main uses of the genetic information:

* Parent verification, assignment, and/or discovery
* Traceability of meat products
* Breed identification or breed diversity
* Qualitative and quantitative traits 

Database tables may contain:

* Animal identification to link to all other information on the animal and its relatives.
* Number of genetic markers: n
* Standard name of each marker i (for i= 1, n)
* Accession number for marker such as the dbSNP ID
* Alleles for marker i
* Genomic location of marker i
* Effect of non-reference allele on the protein
* Phenotypic effect of the allele
* Association with other traits

==== Parentage accuracy ====
While use of microsatellite and SNP markers are both ICAR accredited methods of parentage verification they do not have the same power of parentage accuracy. Briefly the order of accuracy for ISAG and ICAR approved parentage marker panels are:

Microsatellites << small SNP panels (100 or less) < large SNP panels (500 or more)

This order is based on both genotyping accuracy and total genomic information. Comparing the genomic marker error rate in cattle microsatellites have a 1-5% error rate (Baruch and Weller, 2008<ref>Baruch, E., and J. I. Weller. 2008. 'Estimation of the number of SNP genetic markers required for parentage verification', ''Animal Genetics'', 39: 474-79.</ref>) while the SNP error rate is <0.1% (Cooper, Wiggans, and VanRaden 2013<ref>Cooper, T. A., G. R. Wiggans, and P. M. VanRaden. 2013. 'Short communication: relationship of call rate and accuracy of single nucleotide polymorphism genotypes in dairy cattle', ''Journal of dairy science'', 96: 3336-9.</ref>). As 2-3 SNP provide the same parentage exclusion accuracy as 1 microsatellite marker (Vignal et al. 2002<ref>Vignal, A., D. Milan, M. SanCristobal, and A. Eggen. 2002. 'A review on SNP and other types of molecular markers and their use in animal genetics', ''Genet Sel Evol'', 34: 275-305.

1.4.4 Genomic quality control checks</ref>), the 100 and 200 ISAG parentage SNP panels are more accurate than the 12 ISAG parentage microsatellite markers. In the same manner parentage panels of over 500 SNP (McClure et al. 2018<ref>McClure, M. C., J. McCarthy, P. Flynn, J. C. McClure, E. Dair, D. K. O'Connell, and J. F. Kearney. 2018. 'SNP Data Quality Control in a National Beef and Dairy Cattle System and Highly Accurate SNP Based Parentage Verification and Identification', ''Front Genet'', 9: 84.</ref>), such as the ICAR554, are recommended for parentage prediction which requires an even higher level of accuracy.

==== Genomic quality control checks ====
One of the most important parts of a large genomic database is to ensure that a genotype associated with an individual animal truly belongs to that animal. Most large livestock genomic databases deal with SNP data only and the quality of SNP genotyping data is of paramount importance (Wu at al., Evaluation of genotyping concordance for commercial bovine SNP arrays using quality-assurance samples, Animal Genetics, 50: 367-371, 2019). This section, therefore, focuses on quality control for that genomic data type. Both sample and SNP quality control measures are needed, and it is encouraged to develop a system for them early.

For those working with genotype data there are two main concerns. First, is ensuring that the genotype data itself is of high quality and can be trusted. Second, is ensuring that the genotype truly belongs to the individual listed. The recommended quality control checks below will work for any livestock species. The basic checks can be performed with minimal information about the individual, while some of the advanced checks require data that not everyone will have, such as historic animal location.

===== ''Basic genotype quality control checks for SNP-based genotype data'' =====
Genotype:
<ol style="list-style-type:number">
<li> Exclude SNP that have a genotype call rate below 90% when analyzed in your population. Using 500 or more animals to determine the SNP call rate is recommended. Chromosome Y SNP should have their call rate determined only in males for this filter.</li>
<li> Invalidate the individual’s genotype if its overall call rate is below <90%. For SNP-based genotypes, such as those from Illumina or Affymetrix chips, the accuracy of called genotypes is questionable when the individual’s overall call rate is <90%.</li>
<li> Check to see that the animal has all three genotype classes (i.e.: AA, AB and BB) in its full genotype file. If any genotype class is missing or has a frequency below 20% then invalidate the full genotype.</li>
<li> Check to ensure that there are no unexpected alleles in the genotype file. For example, genotypes in AB format should not have T, G, 0, 1, 2 or 9. If present, then invalidate the full genotype file.</li>

</ol>

Parentage:
<ol style="list-style-type:number">
<li> Parent (Sire or Dam) validation. If using 200 or less SNP, a listed sire will validate if <1% of the offspring-parent genotypes are in conflict. A conflicting genotype is where the offspring and listed parent have opposite homozygous genotypes.</li>
<li> Mating validation after parent validation. For all parentage validation SNP where the animal is heterozygous, if for >1% of those SNP the sire and dam are homozygous for the same allele then the listed mating is invalidated. This could represent a case where the offspring and one of the parents were mislabeled with the other’s identification (so the offspring’s genotype belongs to the sire or dam and vice versa). Under such cases, it is recommended to resample the DNA and regenotype, potentially with a panel that includes more SNP.</li>

</ol>

===== ''Advanced quality control checks for SNP-based genotype data'' =====
Animal:
<ol style="list-style-type:number">

<li> Parentage discovery. Using SNP data to predict who an animal’s likely parent is can be very useful, but steps must be taken to ensure a very high probability that the prediction is accurate. The following are recommended:</li>

# Using 500 or more SNP that have a minor allele frequency (MAF) above 20% and call rates above 90%. It is advised to calculate the MAF across your full population. Predicted parents should have <1% conflict rate with the animal.
# Sex check. Make sure that you have a process established to ensure that only males are predicted as the sire and only females as the dam.
# Date of Birth check. If you do not include a check that the predicted parent is older than the animal than the predicted individual could actually be an offspring of the animal.
# Age gap. Cattle normally reach sexual maturity at 11-12 months of age, but this can be as young as 8-9 months, and even younger if in-vitro fertilization is a technology used within the population. Under normal circumstances, a minimum of 17 months between the birth dates of the animal and its predicted parent is recommended to ensure that the predicted parent could have been sexually mature at the time of the breeding.
# Grey zone SNP conflicts. The majority of animals will have <0.5% or >1.5% conflicting genotypes with the individual when parentage discovery is conducted. Those with <0.5% pass the prediction and those with >1.5% fail. Most failed animals will have >8% conflict rates. For those animals who have between 0.5 and 1.5% conflicting SNP when a set parentage panel is used (i.e.: the ICAR554 SNP list), it is advised that the conflict rate from all available SNP be used between the two individuals and if the percent conflicting is <1% they validate as the parent, but if >1% they fail.

<li> Genetic Relationship Matrix (GRM). If an animal’s true parent is not genotyped, then it cannot be directly predicted or validated. The genetic relationship between closely related animals can be used to suggest a potential, non-genotyped, parent. It is recommended that 7,000 or more SNP be used to calculate the GRM. GRM results DO NOT validate a relationship, but only suggest. Caution should be used as GRM values can be inflated for inbred individuals. Full-sibs and parent-child should have GRM values around 50%, while half-sibs would be around 25%. The range for each group can vary 5-10% from the expected value.</li>

<li> Sex prediction. How to perform a sex prediction depends on the type and number of SNP an animal has from the X and Y chromosomes. While not every commercial chip includes chromosome Y SNP, they typically contain chromosome X markers.</li>

# Pseudo autosomal region (PAR) SNP. As both the X and Y chromosomes contain the PAR, SNP from this region should be excluded from sex prediction. If the PAR position boundaries are not published for your species they can be roughly determined by analyzing the chromosome X SNP in known males and females and identifying the region where the MAF in males for a continuous set of SNP is >1%. Non-PAR regions of chromosome X will have SNP with average MAF of <1% in males and >>1% in females.
# Chromosome X predicted. Use non-PAR SNP to determine the animal’s chromosome X heterozygosity rate (number of heterozygous chromosome X SNP / total number of chromosome X SNP). If the average heterozygosity rate is <5%, the predicted sex is male, and if >15% its female. If the rate is between 5 and 15% then the predicted sex is unknown.
# Chromosome Y predicted. Using chromosome Y SNP to predict sex is logically simpler but many commercial chips do not contain them. Say you have 7 chromosome Y SNP with high call rates in males, it is recommended using the following logic. Male is predicted when 6-7 of the Y SNP are present; female is predicted when <1 SNP is present and ambiguous sex is predicted when 2-5 Y SNP are present.
# Ambiguous sex prediction. If one set of sex chromosome SNP returns an ambiguous sex prediction and the other doesn’t it is recommended using the latter as the predicted sex. If both SNP sets are ambiguous, the animal could have Turner syndrome (X0), or Klinefelter’s syndrome (XXY), in this case it is recommended returning an ambiguous predicted sex.
# If the predicted sex from the chromosome X and Y analysis disagree, it is recommended returning an ambiguous predicted sex. This could also indicate a possible Klinefelter syndrome (XXY) animal.
# Sex selected AI semen straws. Sex prediction should not be carried out on DNA obtained from sex selected AI semen straws.

<li> Offspring Quality control. The genotyped and listed offspring of an animal can be used to identify potential cases where the animal’s genotype actually belongs to another animal. These should be used as flags to indicate a potential investigation, but it is recommended to temporarily invalidate the animal’s genotype until cleared. Advised thresholds for those flags are:</li>
# AI sire: If >80% of genotyped offspring fail if >10 offspring are genotyped.
# Stock/herd bull: If >80% of genotyped offspring fail if >5 offspring are genotyped.
# Dam: If 100% of genotyped offspring fail if 2 offspring are genotyped, else if >5 offspring are genotyped then use >80%.

<li> Duplicate genotype. The only case where two or more animals should share the exact same genotype is if they are identical twins or clones. Checking to see if >1 animal has the same genotypes is a useful quality control check. It is recommended using your parentage SNP set for initial screening and for any pair that have >99% identical genotypes and then using all available SNP to see if >99% of the genotypes match.</li>

</ol>

For standardization purposes with respect to the nomenclature of genes or loci, a web site is available at: https://www.genenames.org/about/guidelines#genenames and markers at: [https://hgvs-nomenclature.org/versions/21.0/ <nowiki>http://www.HGVS.org/varnomen</nowiki>.]

</div>
</div>

== ICAR Services Related to DNA Technology ==
ICAR offers three services that are related to the use of DNA all of which are linked to parentage analysis in one form or another, as shown in Figure 1. ICAR DNA services., and describing them in more detail in the other sections.
[[File:ICAR DNA service.png|thumb|Figure 1. ICAR DNA services.|center|415x415px]]

== ICAR Accreditation of Laboratories Providing DNA Genotyping Services ==
<div class="mw-collapsible mw-collapsed">
<div>
=== Introduction ===
Considering the need for high quality standards in all uses of molecular data, ICAR has for several years offered an accreditation service based on defined minimum requirements for laboratories providing DNA genotyping services. The basic requirements of this accreditation include proof of the minimum internal management quality assurance standards and a Rank 1 result from participation in the most recent biennial international ring test developed and offered by the International Society for Animal Genetics (ISAG).

In addition, such laboratories generally have been analyzing the resulting genotypes to carry out microsatellite- and/or SNP-based parentage analysis services including either parentage verification or animal identification confirmation. This ICAR accreditation service has previously been used for recognizing the genotyping laboratory as an accredited organization to provide parentage analysis functions without specifically testing the technical accuracy of doing so. Effective 2021, the SNP-based parentage analysis accreditation service for DNA Data Interpretation Centres has replaced the previous laboratory accreditation for SNP-based parentage verification. In the future, a similar technical process for the accreditation of microsatellite-based parentage analysis may be introduced by ICAR but until such time, the existing process for the accreditation of genotyping laboratories will remain in effect.

The following guidelines for accreditation are provided for microsatellite- and SNP-based genotyping in cattle. Minimum requirements for additional species and other DNA tests may be defined in the future.

=== Scope ===
These guidelines are for the accreditation, by ICAR, of genotyping laboratories that analyze biological samples from cattle using microsatellite- and or SNP-based genotyping, which may be subsequently used for various levels of parentage analysis, genotype imputation, estimation of genomic breeding values and other activities related to genomic selection strategies. This accreditation process also includes parentage verification based on microsatellites since ICAR has not established this service as part of the portfolio of possible accreditations for DNA data interpretation centres. For genotyping laboratories that would like to receive ICAR accreditation for SNP-based parentage verification, they must now apply separately to ICAR for its parallel service of parentage analysis accreditation for DNA data interpretation centres, as described in section 4.

=== ICAR Guidelines for Accreditation of Genotyping Laboratories ===
The accreditation process comprises the following steps:

* Application for accreditation
* Payment of relevant fee
* Review of application
* Granting of accreditation

==== Application for accreditation ====
Laboratories requesting accreditation only for microsatellite- based genotyping and parentage verification must apply by downloading and completing the appropriate form as provided in [https://www.icar.org/wp-content/uploads/2022/05/Annex-II-Application-Form-for-STR-Accreditation.pdf ''Appendix 2. Application form for microsatellite-based parentage testing in cattle''.] Laboratories seeking ICAR accreditation involving SNP-based genotyping must apply by downloading and completing the appropriate form as provided in [https://www.icar.org/wp-content/uploads/2022/05/Annex-V-Application-Form-for-SNP-Accreditation.pdf ''Appendix 3. Application form for SNP-based genotyping required for parentage analysis in cattle.''] Laboratories that have previously received ICAR accreditation for either service may re-apply prior to the expiry of any such accreditation using a shortened renewal form available on the ICAR web site. All application forms must be emailed to the ICAR secretariat at dna@icar.org and be filled out accurately and completely including the necessary documentation as required.

==== Payment of relevant fee ====
Along with the completed application form, the applicant must also provide full payment of the relevant fee as established by ICAR and given in [https://www.icar.org/index.php/certifications/certification-and-accreditation-of-dna-genetic-laboratories/guidelines-for-str-and-snp-based-parentage-testing-in-cattle/ ''Appendix 4. ICAR DNA Laboratory Accreditation Service fees''.], refer to [[#Microsatellites |hey]].

==== Review of application ====
The application will be evaluated by a committee of experts appointed by ICAR that will either:

* Approve the application
* Request additional information, or
* Reject the application

In the case of rejection, the laboratory may make a new submission as part of the ICAR annual call for applications for any subsequent year after the failed application.

==== Granting of accreditation ====
Accreditation will be given for a period of two calendar years with an expiry date of December 31st of the second year after receiving ICAR accreditation as a laboratory providing DNA genotyping services.

==== Renewal of accreditation ====
In advance of the expiry date of any existing ICAR accreditation, normally during the same year of the December 31st expiry date, a laboratory can apply for renewal of their accreditation by submitting an application as described in section 3.3.1 above and successfully completing the other steps outlined in this section 3.

==== Laboratory accreditation ====
Effective the 2022 ICAR call for accreditation of genotyping laboratories, ISO17025 accreditation, or an equivalent accreditation for ensuring quality internal management systems, is a mandatory requirement for SNP-based accreditation. In addition, effective the 2022 call for accreditation of genotyping laboratories for microsatellite (STR)-based accreditation, ISO9001 certification will no longer be acceptable and only ISO17025, or an equivalent accreditation, will be an acceptable level of accreditation to ensure quality internal management systems. During the year of application for ICAR accreditation as a laboratory providing DNA genotyping services, the applicant must provide proof of ISO17025 accreditation with an expiry date of October 31st of the following calendar year, or later.

==== Participation and performance in ring test ====
On a biennial basis, initiated during even years (i.e.: 2022, 20246, etc…) and discussed at its biennial conference in odd years (2023, 2025, etc.), ISAG conducts an international ring (comparison) test of laboratories for both microsatellite- and SNP-based genotyping. The participation in ISAG and performance within these ring tests must be disclosed, and certificates provided to ICAR, when available. Applicants must also sign a release allowing ISAG to directly disclose their ring test results to ICAR. Participation in the most recent ISAG ring test is a minimum requirement to qualify for ICAR accreditation. For the ISAG microsatellite ring test, lab genotyping performance for the official set of 12 ISAG microsatellites must be disclosed. The committee of experts will decide performance thresholds for each ring test with due consideration for the structure of the ring test and the average performance of laboratories in the ring test that year. Only those laboratories achieving Rank 1 status in the most recent biennial ISAG ring test shall automatically qualify to receive ICAR accreditation as a genotyping laboratory. Laboratories achieving a Rank 2 status in the most recent ISAG ring test may qualify to receive ICAR accreditation, at the discretion of the committee of experts, but must provide evidence of Rank 1 status for previous ISAG ring tests as well as documentation outlining the cause of the Rank 2 result and any associated actions to mitigate similar outcomes in future ISAG ring tests. Laboratories achieving a status lower than Rank 2 in the most recent ISAG ring test do not qualify for ICAR accreditation as a laboratory providing DNA genotyping services.

==== Microsatellite markers ====
The names of all microsatellites typed on all animals (marker set I) and of the additional ones assayed in the case of unresolved parentage (marker set II) must be declared, as well as the number of animals typed in at least the last two years. The minimum requirement for international exchange is the complete set of 12 official ISAG microsatellite markers. To ensure sufficient experience within the lab, analysis of 500 animals per year is set as minimum requirement for microsatellite parentage verification certification.

''[https://www.icar.org/wp-content/uploads/2018/05/03-Annex-III-ISAG-microsatellites.pdf Appendix 5. ISAG recommended microsatellites for parentage verification in cattle]'' contains the list of microsatellite markers recommended by ISAG and the method for calculating 1 parent and 2 parent exclusion probabilities. The rules for microsatellite-based parentage verification in cattle are described in ''[https://www.icar.org/wp-content/uploads/2018/05/01-Annex-I-guidelines-microsats-STRs.pdf Appendix 6. Rules for microsatellite-based parentage testing in cattle]''. Exclusion probability (PE; 2 parents and 1 parent) of each marker and of the complete marker sets must be calculated and provided in the application. The type of population and number of animals (minimum 150) used for computations are to be described. ICAR recommends using Holstein as a reference group when possible. The ICAR committee of experts will evaluate that an appropriate PE is reached for accreditation, on the basis of the population analyzed.

==== SNP markers ====
ICAR accreditation of SNP-based parentage verification is based on the full set of 200 SNP previously recommended by ISAG. The name of all SNP genotyped on all animals (marker set I, including the 100 “Core” SNP) and of the additional markers assayed in the case of unresolved parentage (marker set II, including the 100 “Additional” SNP) must be declared, as well as the number of animals SNP genotyped in at least the last two years. ICAR recommends using the full set of 200 SNP for parentage verification of all animals genotyped (''see [https://www.icar.org/Guidelines/04-DNA-Technology-App7-SNP-list-for-parentage-verification.pdf Appendix 7. List of approved SNP for parentage verification in]'' [https://www.icar.org/Guidelines/04-DNA-Technology-App7-SNP-list-for-parentage-verification.pdf cattle]). ICAR may, however, based on scientific evidence, identify specific problematic SNP that must be excluded for parentage analysis, as described in the ICAR documentation related to the accreditation of DNA data interpretation centres outlined in section 4.

==== Marker nomenclature ====
Nomenclature of markers must be described. ISAG nomenclature is required for the official ISAG 12 marker set as well as for the ISAG SNP marker set.
</div>
</div>

== Accreditation of Organisations Performing SNP-Based Parentage Analysis ==
<div class="mw-collapsible mw-collapsed">
<div>
=== Introduction ===
With the advent of SNP genotyping, the function of DNA genotyping as a laboratory activity can be separated from the functions of performing parentage verification and parentage discovery. Consequently, ICAR has established a separate accreditation for applying the results of SNP-based genotyping, which may be undertaken by laboratories, breed association societies, genetic evaluation centres and any other organization involved in parentage verification and/or the data processing of SNP genotypes.

Parentage verification and discovery are concerned with using the results that are delivered by the laboratories from DNA genotyping and require SNP genotypes for the animal itself, its recorded parents and other possible parents in the case of parentage discovery. Organizations undertaking this function may be service providers between laboratories that ICAR has accredited for microsatellite- and or SNP-based DNA genotyping and end users that may include breed societies, breeding companies, breeders and commercial farmers.

Service providers could use different laboratories for different breeds and/or species. Considering the importance of animal identification and parentage verification in animal recording, ICAR has decided to define the minimum requirements for using the results of DNA genotyping, and other information, for the purpose of:

# Parentage verification
# Parentage discovery, and
# Animal identification confirmation

The purpose of these guidelines is to provide a basis for the accreditation of processes used by organizations that use SNP genotypes in cattle. Minimum requirements for additional species and other DNA analyses may be defined in the future.

=== Scope ===
These guidelines are for the accreditation, by ICAR, of organizations that use the results of SNP-based tests for parentage analysis in cattle, which includes parentage verification, parentage discovery, and/or animal identification confirmation.

=== Accreditation of Organizations Performing Parentage Analysis ===
The ICAR accreditation process comprises the following steps:

* Application for accreditation
* Payment of relevant fee
* Review of application
* Technical processing of test data files
* Granting of accreditation

==== Application ====
Organizations carrying out SNP-based parentage analysis and requesting ICAR accreditation as a DNA Data Interpretation Centre must apply by downloading and completing the appropriate form included below as [https://www.icar.org/wp-content/uploads/2016/10/6-Annex-V-Application-Form-forICAR-Accreditation-of-DNA-Centres.pdf ''Appendix 8. Application form for organizations seeking ICAR parentage analysis accreditation for DNA data interpretation centres''.] This form must be filled out accurately and completely, providing necessary documentation as required, and submitted to ICAR with payment of the appropriate fee.

==== Review of application ====
The application will first be reviewed internally by ICAR for its completeness and additional details may be requested as needed. ICAR administration will also confirm receipt of the applicable fee.

==== Technical processing of test files ====
The applicant organization will receive a set of data files from ICAR through the Interbull Centre, for processing using its existing procedures for carrying out the level of parentage analysis for which the applicant is seeking ICAR accreditation as a DNA Data Interpretation Centre. A detailed description of this step is described in [https://www.icar.org/index.php/certifications/certification-and-accreditation-of-dna-genetic-laboratories/two-new-dna-based-services/dna-data-interpretation-centres/ ''Appendix 9. Applicant's Guide for ICAR Parentage Analysis Accreditation for DNA Data Interpretation Centres.''] In order for the applicant to be successful in obtaining the requested ICAR accreditation, it's procedures for conducting parentage analysis must exactly follow [https://www.icar.org/Documents/GenoEx/ICAR%20Guidelines%20for%20Parentage%20Verification%20and%20Parentage%20Discovery%20based%20on%20SNP.pdf ''Appendix 10. ICAR Guidelines for Parentage Verification and Parentage Discovery Based on SNP Genotypes''.] The list of SNP to be used for either parentage verification (N=200) or parentage discovery (N=554) are available in [https://www.icar.org/Guidelines/04-DNA-Technology-App-11-SNP-list-for-parentage-verification-or-discovery.pdf ''Appendix 11. List of SNP to be used for either parentage verification or parentage discovery''.] Once the applicant has completed its internal parentage analysis procedures based on the accreditation test files it received, it must send a data file of results back to the Interbull Centre. A maximum time period for 90 calendar days will be allowed for the applicant to submit acceptable files of results back to the Interbull Centre.

==== Granting of accreditation ====
Once the Interbull Centre receives the file of parentage analysis results from the applicant, it will complete the technical review and determine if the applicant has successfully completed the accreditation or not. The Interbull Centre shall inform ICAR of the results and ICAR shall issue a formal notification to the applicant. In the event the applicant was not successful in receiving ICAR accreditation, the applicant may initiate a new request for accreditation by completing and submitting the appropriate forms and providing payment of the applicable fee, as outlined above.

==== Renewal of accreditation ====
In advance of the expiry date of any existing ICAR accreditation, which coincides with the two-year anniversary date of the current accreditation, an applicant can apply for renewal of their accreditation by submitting an application as described in section 4.3.1 above and successfully completing the other required steps outlined in this section 4.
</div>
</div>

== Genotype Exchange Service – GenoEx-PSE ==
Effective 2018, ICAR has made available a genotype exchange service for parentage analysis, GenoEx-PSE, offered through the Interbull Centre. The main goal of this service is to facilitate the international exchange of SNP genotypes such that approved service users can carry out parentage analysis services at a national level in an efficient manner. The GenoEx-PSE database system and user interface has been developed to allow for the exchange of SNP genotypes for either parentage verification or parentage discovery based on the list of SNP provided in ''Appendix 11. List of SNP to be used for either parentage verification or parentage discovery''.

In order for an organization to qualify as a service user for GenoEx-PSE, it must first receive ICAR accreditation as a DNA data interpretation centre. The level of such ICAR accreditation (i.e.: for SNP-based parentage verification alone or for both SNP-based parentage verification and discovery) shall determine the highest level of SNP that may be exchanged via the GenoEx-PSE service. For details associated with this ICAR service, refer to the GenoEx-PSE web site at [https://genoex.org/ www.GenoEx.org.]

== Appendix list ==
<div class="mw-collapsible mw-collapsed">
<div>
=== Appendix 1. Link to SNP markers recommended by ISAG for parentage verification ===
https://www.icar.org/Guidelines/04-DNA-Technology-App-1-Cattle-SNP-ISAG-core-additional-panel-2013.xlsx

=== Appendix 2. Application form for microsatellite-based parentage testing in cattle ===
Please refer [https://www.icar.org/wp-content/uploads/2022/05/Annex-II-Application-Form-for-STR-Accreditation.pdf here] on the ICAR website for the Form for ICAR laboratory accreditation for STR Microsatellite-based Parentage Testing in Cattle.

=== Appendix 3. Application form for SNP-based genotyping required for parentage analysis in cattle ===
Please refer [https://www.icar.org/wp-content/uploads/2022/05/Annex-V-Application-Form-for-SNP-Accreditation.pdf here] on the ICAR website for the Form for ICAR laboratory accreditation for SNP-based genotyping required for Parentage Analysis in Cattle.

=== Appendix 4. ICAR DNA Laboratory Accreditation Service fees ===
Please refer [https://www.icar.org/index.php/certifications/certification-and-accreditation-of-dna-genetic-laboratories/guidelines-for-str-and-snp-based-parentage-testing-in-cattle/ here] on the ICAR website for DNA testing accreditation services fees.

=== Appendix 5. ISAG recommended microsatellites for parentage verification in cattle ===
Please refer [https://www.icar.org/wp-content/uploads/2018/05/03-Annex-III-ISAG-microsatellites.pdf here] on the ICAR website for the list of ISAG recommended microsatellites for parentage verification in cattle.

=== Appendix 6. Rules for microsatellite-based parentage testing in cattle ===
Please refer [https://www.icar.org/wp-content/uploads/2018/05/01-Annex-I-guidelines-microsats-STRs.pdf here] on the ICAR website for the rules for microsatellite-based parentage verification in cattle.

=== Appendix 7. List of approved SNP for parentage verification in cattle ===
Please refer [https://www.icar.org/Guidelines/04-DNA-Technology-App7-SNP-list-for-parentage-verification.pdf here] on the ICAR website for the ICAR approved list of 200 SNP for parentage verification in cattle.

=== Appendix 8. Application form for organizations seeking ICAR parentage analysis accreditation for DNA data interpretation centres ===
Please refer [https://www.icar.org/wp-content/uploads/2016/10/6-Annex-V-Application-Form-forICAR-Accreditation-of-DNA-Centres.pdf here] on the ICAR website for the application form for organizations seeking ICAR accreditation status as a DNA data interpretation centre.

=== Appendix 9. Applicant's Guide for ICAR Parentage Analysis Accreditation for DNA Data Interpretation Centres ===
Please refer [https://www.icar.org/index.php/certifications/certification-and-accreditation-of-dna-genetic-laboratories/two-new-dna-based-services/dna-data-interpretation-centres/ here] on the ICAR website for the Applicant's Guide for ICAR Parentage Analysis Accreditation for DNA Data Interpretation Centres.

=== Appendix 10. ICAR Guidelines for Parentage Verification and Parentage Discovery Based on SNP Genotypes ===
Please refer [https://www.icar.org/Documents/GenoEx/ICAR%20Guidelines%20for%20Parentage%20Verification%20and%20Parentage%20Discovery%20based%20on%20SNP.pdf here] on the ICAR website for the ICAR Guidelines for Parentage Verification and Parentage Discovery Based on SNP Genotypes.

=== Appendix 11. List of SNP to be used for either parentage verification or parentage discovery ===
Please refer [https://www.icar.org/Guidelines/04-DNA-Technology-App-11-SNP-list-for-parentage-verification-or-discovery.pdf here] on the ICAR website for the list of SNP to be used for either parentage verification or parentage discovery.
</div>
</div>

Section 04 – DNA Technology

2025-01-31T08:51:07Z

Aashish: /* Payment of relevant fee */

== Molecular Genetics ==
<div class="mw-collapsible mw-collapsed">
<div>
=== Introduction ===
Advances in molecular biology, especially genomics, provide a new set of information to be incorporated into the animal industry. On one hand, the use of molecular information may contribute to the enhancement of consumers' trust in the ability to monitor and control the animal production chain. On the other hand, molecular information will greatly contribute to the achievement of genetic improvement for animal traits through the use of genomic breeding values, marker assisted selection, gene introgression, heterosis prediction, pedigree validation/prediction, and genetic defect carrier status. In most cases, advantages of using molecular information via genomic evaluations, comes from improved accuracy of animal breeding values, shortened generation intervals, and increased intensity of selection. Even with these advancements there is still a need for research and development in the search for associations between genetic markers and traits of interest, especially as new traits are included in national evaluation indexes. In addition to that, even with the current incorporation of genomic information into national selection schemes, an understanding of gene action, gene interactions, and differential gene expression to avoid negative collateral effects is needed. Cooperation between animal industries and research is required for a successful and beneficial search for genetic information in commercial livestock populations.

=== Genetic Markers ===
Genetic markers are the fundamental molecular tools for genomics, even as the type of marker used has changed. The first genetic marker associations in livestock were reported using blood typing in the 1960s, the technology then moved to microsatellites (MS) in the 1990s and more recently to the use of Single Nucleotide Polymorphism (SNP). SNP and MS are polymorphic DNA sequences (alleles) at a specific locus of a particular chromosome. While blood typing has been an ICAR approved method of parentage verification currently there are few, if any, commercial labs still offering this testing. For this reason, ICAR no longer recommends blood typing as the basis for carrying out parentage analysis in livestock species where MS or SNP technology is widely available.

==== Microsatellites ====
These are segments of DNA containing tandem repeats of simple motifs usually dimers or trimers. These segments are located throughout the genome and normally in non-coding regions. Over time, these regions are subject to the addition or subtraction of tandem repeats, which means that each microsatellite can have multiple unique alleles. Microsatellites are commonly used in many livestock species for parentage validation.

==== Single Nucleotide Polymorphism (SNP) ====
SNP are the most common type of genetic variation: each SNP represents a variation in a single nucleotide. There are millions of SNP located throughout the genome of every livestock species. For genomics the most informative SNP traditionally are either located in (a) coding regions where different alleles change the structure or function of the encoded protein, or (b) at non-coding regions that are involved in the regulatory function of the gene. For genomic breeding values, SNP that are located in other regions of the genome are also informative as they could be in linkage disequilibrium with alleles that do cause a phenotype change.

One of the big advantages of SNP is their deployment on SNP arrays with a strong parallel processing capacity whereby thousands or hundreds of thousands of SNP can be screened together in a cost-effective and efficient manner across a large number of animals. Currently, the largest livestock genotyping labs can process hundreds of thousands of animals yearly on such arrays. The availability of these large SNP panels is therefore bolstering the search for mutations underlying genetic variation for simple and complex traits. It is also revolutionizing the speed at which trait associated genes or gene regions are being discovered as well as the adoption rate of genomic selection strategies. SNP genotypes have become the international standard for the basis of parentage analysis and ICAR recommends this approach over the use of microsatellites wherever possible due to the improved accuracy and the ease of comparing results between genotyping laboratories.

=== Current and Potential Uses of DNA Technologies ===

==== Parentage verification and parental assignment authentication ====
Prior to the emergence of SNP genotyping, parentage verification was the main commercial use of genetic markers. Traditionally, parentage testing was based on the exclusion of relationship (i.e.: sire or dam) when an animal has a genotype inconsistent to a putative relationship. New trends in animal production systems are tending to encourage animal production in larger numbers per farm in response to environmental and production related constraints. In these large settings, multiple animals could be bred or give birth on the same day, which can result in more pedigree recording errors. As the cost of the analysis decreases and the number of genetic markers available increases, breed societies are now able to build up pedigree records using genetic markers to predict the pedigree of calves born in a herd at a given time. This normally requires a prior knowledge of candidate sires and dams for a calf when lower number (<200) of markers are used, but with enough SNP the correct parents can be predicted without prior knowledge being available as long as the parent is also genotyped. The probability of assignment to a correct pair of animals will depend on the number of markers used, number of alleles per loci, the minor allele frequency in the population, the number of parents, and the number of possible matings. The International Society of Animal Genetics (www.isag.us) has species-specific panels recommended of microsatellite and SNP markers for this purpose, which can be accessed via a link such as provided in ''[https://www.icar.org/Guidelines/04-DNA-Technology-App-1-Cattle-SNP-ISAG-core-additional-panel-2013.xlsx Appendix 1]. Link to SNP markers recommended by ISAG for parentage verification''. For cattle, ICAR has developed a set of parentage SNP, ICAR554, which incorporates the ISAG recommended panel and other highly informative SNP. This panel allows for highly accurate parentage validation and discovery while not allowing for accurate imputation to a higher density. Therefore, the ICAR554 panel can be shared among countries and competitors for parentage analysis without fear of others being able to use them to predict genomic breeding values. ICAR and the Interbull Centre collaborate in offering an international genotype exchange service, referred to as GenoEx, which is described further in Chapter 5 specifically for the exchange of SNP genotypes for the purposes of parentage analysis.

==== Traceability and authentication of animal products offered to consumers ====
Due to multiple crises, including BSE outbreaks to ground beef containing horsemeat, and with increased consumer interest in where their food comes from the traceability of meat products is of greater concern to the industry. Traceability is based on the availability of a verification and control system that monitors all relevant details throughout the entire livestock production chain. Since an individual’s genetic sequence is unique and does not change, its DNA remains constant from ‘conception to consumption’. Therefore, use of genetic markers allows one to match the DNA of an individual at birth to the final product.

Genetic markers for the authentication of animal products for labels of quality related to geographic location and labels of quality related to specific breeds or their crosses are/or will be very useful. However, this requires the establishment of molecular standards or allele frequencies for each breed within a species. A lot of information is coming from studies of genetic diversity among breeds. Genomic regions subject to intense selection in each population are of particular interest. With a large enough set of SNP and genotyped purebred reference animals it is also possible to predict the most likely breed composition of individuals.

==== Molecular genetic information for marker-assisted selection schemes ====
Quantitative traits are generally assumed to be controlled by a large number of genes. However, individual genes sometimes account for a significant amount of variation of the trait. Such is the case for the Myostatin gene and double muscling in beef cattle, the DGAT1 gene and milk components in dairy cattle, or the Booroola fecundity gene and ovulation rate in sheep. Since the genotype of an animal does not change during its lifetime, use of DNA information through the identification of markers linked to QTL with effects on production traits or the identification of a gene itself together with the causative variant is of great interest. Nevertheless, with complex traits there is a growing need of having a sufficiently large marker set to incorporate molecular information for selection decisions. Including genomic information as a selection criterion is of special interest for traits that are difficult and costly to measure and/or are measured late in life. By 2022, >177,000 cattle, >34,000 swine, >16,000 chicken and >4,000 sheep QTL have been identified that are associated with economically important traits such as health, carcass, milk, fertility, and body conformation. The AnimalQTLdb database housed at the [https://www.animalgenome.org/ National Animal Genome Research Program] contains up to date information on cattle, chicken, horse, pig, trout, and sheep QTL data assembled from published data.

Recording schemes have been collecting information for decades on the most common production traits measured in domestic livestock. There is an ever-increasing volume of information becoming available, but for some traits like meat quality, disease resistance and feed efficiency, those records are very expensive to measure, difficult to obtain, or are performed late in the animal’s life. Because of these challenges information for such traits is commonly collected on a reduced number of animals in any given population.

For these challenging, but economically important traits, genetic markers and genomic selection offer significant opportunities for trait selection where it was not economically feasible before. In general, genetic markers and genomics will play an important role for important traits regardless of the livestock species. Genomics can also allow us to increase selection intensities since we can predict genomic breeding values on a large number of animals and thus have more candidates for selection.

==== Disease resistance and genetic defects ====
Another group of traits with a high potential for the use of molecular data and genomics are those linked to resistance, resilience, and susceptibility to diseases. There are a number of multi-factorial or complex diseases that are the result of the interaction between an animal’s genome and environmental components. Disease resistance traits are among the most difficult to include in genetic improvement programs because they require good field measurement of the disease status of the animals and a systematic control of management or environmental conditions that allow for the identification of the environmental influence on the health status of the animal. Infectious diseases depend very much upon environmental factors such as the degree of exposure to the pathogen agent. Thus, if exposure is low, animals will show little variation. Part of the phenotypic differences for resistance may be differences in the degree of challenge. Therefore, if genes or genetic markers linked to resistance are correctly identified, resistant animals will be able to be selected on the base of their genomic information. For many diseases, identification of genes associated with resistance will require experimental conditions to be used. Genetic analysis to identify heterozygous carriers of genetic diseases caused by single, recessive genes are currently in use. Examples in dairy cattle include complex vertebral malformation (CVM), brachyspina (BY), cholesterol deficiency (CD) and several genes, gene regions or haplotypes causing embryo loss or stillbirth in different dairy breeds. In 2022, [https://www.omia.org/home/ OMIA (Online Mendelian Inheritance in Animals),] listed >1000 traits or genetic defects in livestock with a known causative mutation (cattle: 186, pig: 58, chicken: 56, sheep: 49, horse: 48, goat:17). Including these causative allele or associated haplotypes in a breeding program will allow producers to minimize their risk from genetic defects while maximizing genetic progress from beneficial traits.

=== Technical Aspects ===

==== DNA collection ====
Systematic collection of DNA is recommended in several livestock populations. DNA may be obtained from any nuclear cell in the body. Protocols for DNA extraction are now available for blood (white cells), semen, saliva (epithelial cells), hair follicles, muscle, skin, organs (such as liver, spleen etc.). Red blood cells may also be used for poultry as they retain the nuclear body while most other species do not. Small amounts of tissue material are required for routine DNA analysis. However, if there are multiple future uses of an individual’s DNA (whole genome sequencing, traceability, causative allele validations, …), then DNA storage costs, extraction costs, quality, and quantity obtained by different protocols will have to be carefully examined and optimized. Common collection methods include hair follicles, tissue samples (often ear punch) in an enclosed container, blood spots on filter paper, and nasal swabs.

==== Data organization ====
A centralised database may be organised in respect to the main uses of the genetic information:

* Parent verification, assignment, and/or discovery
* Traceability of meat products
* Breed identification or breed diversity
* Qualitative and quantitative traits 

Database tables may contain:

* Animal identification to link to all other information on the animal and its relatives.
* Number of genetic markers: n
* Standard name of each marker i (for i= 1, n)
* Accession number for marker such as the dbSNP ID
* Alleles for marker i
* Genomic location of marker i
* Effect of non-reference allele on the protein
* Phenotypic effect of the allele
* Association with other traits

==== Parentage accuracy ====
While use of microsatellite and SNP markers are both ICAR accredited methods of parentage verification they do not have the same power of parentage accuracy. Briefly the order of accuracy for ISAG and ICAR approved parentage marker panels are:

Microsatellites << small SNP panels (100 or less) < large SNP panels (500 or more)

This order is based on both genotyping accuracy and total genomic information. Comparing the genomic marker error rate in cattle microsatellites have a 1-5% error rate (Baruch and Weller, 2008<ref>Baruch, E., and J. I. Weller. 2008. 'Estimation of the number of SNP genetic markers required for parentage verification', ''Animal Genetics'', 39: 474-79.</ref>) while the SNP error rate is <0.1% (Cooper, Wiggans, and VanRaden 2013<ref>Cooper, T. A., G. R. Wiggans, and P. M. VanRaden. 2013. 'Short communication: relationship of call rate and accuracy of single nucleotide polymorphism genotypes in dairy cattle', ''Journal of dairy science'', 96: 3336-9.</ref>). As 2-3 SNP provide the same parentage exclusion accuracy as 1 microsatellite marker (Vignal et al. 2002<ref>Vignal, A., D. Milan, M. SanCristobal, and A. Eggen. 2002. 'A review on SNP and other types of molecular markers and their use in animal genetics', ''Genet Sel Evol'', 34: 275-305.

1.4.4 Genomic quality control checks</ref>), the 100 and 200 ISAG parentage SNP panels are more accurate than the 12 ISAG parentage microsatellite markers. In the same manner parentage panels of over 500 SNP (McClure et al. 2018<ref>McClure, M. C., J. McCarthy, P. Flynn, J. C. McClure, E. Dair, D. K. O'Connell, and J. F. Kearney. 2018. 'SNP Data Quality Control in a National Beef and Dairy Cattle System and Highly Accurate SNP Based Parentage Verification and Identification', ''Front Genet'', 9: 84.</ref>), such as the ICAR554, are recommended for parentage prediction which requires an even higher level of accuracy.

==== Genomic quality control checks ====
One of the most important parts of a large genomic database is to ensure that a genotype associated with an individual animal truly belongs to that animal. Most large livestock genomic databases deal with SNP data only and the quality of SNP genotyping data is of paramount importance (Wu at al., Evaluation of genotyping concordance for commercial bovine SNP arrays using quality-assurance samples, Animal Genetics, 50: 367-371, 2019). This section, therefore, focuses on quality control for that genomic data type. Both sample and SNP quality control measures are needed, and it is encouraged to develop a system for them early.

For those working with genotype data there are two main concerns. First, is ensuring that the genotype data itself is of high quality and can be trusted. Second, is ensuring that the genotype truly belongs to the individual listed. The recommended quality control checks below will work for any livestock species. The basic checks can be performed with minimal information about the individual, while some of the advanced checks require data that not everyone will have, such as historic animal location.

===== ''Basic genotype quality control checks for SNP-based genotype data'' =====
Genotype:
<ol style="list-style-type:number">
<li> Exclude SNP that have a genotype call rate below 90% when analyzed in your population. Using 500 or more animals to determine the SNP call rate is recommended. Chromosome Y SNP should have their call rate determined only in males for this filter.</li>
<li> Invalidate the individual’s genotype if its overall call rate is below <90%. For SNP-based genotypes, such as those from Illumina or Affymetrix chips, the accuracy of called genotypes is questionable when the individual’s overall call rate is <90%.</li>
<li> Check to see that the animal has all three genotype classes (i.e.: AA, AB and BB) in its full genotype file. If any genotype class is missing or has a frequency below 20% then invalidate the full genotype.</li>
<li> Check to ensure that there are no unexpected alleles in the genotype file. For example, genotypes in AB format should not have T, G, 0, 1, 2 or 9. If present, then invalidate the full genotype file.</li>

</ol>

Parentage:
<ol style="list-style-type:number">
<li> Parent (Sire or Dam) validation. If using 200 or less SNP, a listed sire will validate if <1% of the offspring-parent genotypes are in conflict. A conflicting genotype is where the offspring and listed parent have opposite homozygous genotypes.</li>
<li> Mating validation after parent validation. For all parentage validation SNP where the animal is heterozygous, if for >1% of those SNP the sire and dam are homozygous for the same allele then the listed mating is invalidated. This could represent a case where the offspring and one of the parents were mislabeled with the other’s identification (so the offspring’s genotype belongs to the sire or dam and vice versa). Under such cases, it is recommended to resample the DNA and regenotype, potentially with a panel that includes more SNP.</li>

</ol>

===== ''Advanced quality control checks for SNP-based genotype data'' =====
Animal:
<ol style="list-style-type:number">

<li> Parentage discovery. Using SNP data to predict who an animal’s likely parent is can be very useful, but steps must be taken to ensure a very high probability that the prediction is accurate. The following are recommended:</li>

# Using 500 or more SNP that have a minor allele frequency (MAF) above 20% and call rates above 90%. It is advised to calculate the MAF across your full population. Predicted parents should have <1% conflict rate with the animal.
# Sex check. Make sure that you have a process established to ensure that only males are predicted as the sire and only females as the dam.
# Date of Birth check. If you do not include a check that the predicted parent is older than the animal than the predicted individual could actually be an offspring of the animal.
# Age gap. Cattle normally reach sexual maturity at 11-12 months of age, but this can be as young as 8-9 months, and even younger if in-vitro fertilization is a technology used within the population. Under normal circumstances, a minimum of 17 months between the birth dates of the animal and its predicted parent is recommended to ensure that the predicted parent could have been sexually mature at the time of the breeding.
# Grey zone SNP conflicts. The majority of animals will have <0.5% or >1.5% conflicting genotypes with the individual when parentage discovery is conducted. Those with <0.5% pass the prediction and those with >1.5% fail. Most failed animals will have >8% conflict rates. For those animals who have between 0.5 and 1.5% conflicting SNP when a set parentage panel is used (i.e.: the ICAR554 SNP list), it is advised that the conflict rate from all available SNP be used between the two individuals and if the percent conflicting is <1% they validate as the parent, but if >1% they fail.

<li> Genetic Relationship Matrix (GRM). If an animal’s true parent is not genotyped, then it cannot be directly predicted or validated. The genetic relationship between closely related animals can be used to suggest a potential, non-genotyped, parent. It is recommended that 7,000 or more SNP be used to calculate the GRM. GRM results DO NOT validate a relationship, but only suggest. Caution should be used as GRM values can be inflated for inbred individuals. Full-sibs and parent-child should have GRM values around 50%, while half-sibs would be around 25%. The range for each group can vary 5-10% from the expected value.</li>

<li> Sex prediction. How to perform a sex prediction depends on the type and number of SNP an animal has from the X and Y chromosomes. While not every commercial chip includes chromosome Y SNP, they typically contain chromosome X markers.</li>

# Pseudo autosomal region (PAR) SNP. As both the X and Y chromosomes contain the PAR, SNP from this region should be excluded from sex prediction. If the PAR position boundaries are not published for your species they can be roughly determined by analyzing the chromosome X SNP in known males and females and identifying the region where the MAF in males for a continuous set of SNP is >1%. Non-PAR regions of chromosome X will have SNP with average MAF of <1% in males and >>1% in females.
# Chromosome X predicted. Use non-PAR SNP to determine the animal’s chromosome X heterozygosity rate (number of heterozygous chromosome X SNP / total number of chromosome X SNP). If the average heterozygosity rate is <5%, the predicted sex is male, and if >15% its female. If the rate is between 5 and 15% then the predicted sex is unknown.
# Chromosome Y predicted. Using chromosome Y SNP to predict sex is logically simpler but many commercial chips do not contain them. Say you have 7 chromosome Y SNP with high call rates in males, it is recommended using the following logic. Male is predicted when 6-7 of the Y SNP are present; female is predicted when <1 SNP is present and ambiguous sex is predicted when 2-5 Y SNP are present.
# Ambiguous sex prediction. If one set of sex chromosome SNP returns an ambiguous sex prediction and the other doesn’t it is recommended using the latter as the predicted sex. If both SNP sets are ambiguous, the animal could have Turner syndrome (X0), or Klinefelter’s syndrome (XXY), in this case it is recommended returning an ambiguous predicted sex.
# If the predicted sex from the chromosome X and Y analysis disagree, it is recommended returning an ambiguous predicted sex. This could also indicate a possible Klinefelter syndrome (XXY) animal.
# Sex selected AI semen straws. Sex prediction should not be carried out on DNA obtained from sex selected AI semen straws.

<li> Offspring Quality control. The genotyped and listed offspring of an animal can be used to identify potential cases where the animal’s genotype actually belongs to another animal. These should be used as flags to indicate a potential investigation, but it is recommended to temporarily invalidate the animal’s genotype until cleared. Advised thresholds for those flags are:</li>
# AI sire: If >80% of genotyped offspring fail if >10 offspring are genotyped.
# Stock/herd bull: If >80% of genotyped offspring fail if >5 offspring are genotyped.
# Dam: If 100% of genotyped offspring fail if 2 offspring are genotyped, else if >5 offspring are genotyped then use >80%.

<li> Duplicate genotype. The only case where two or more animals should share the exact same genotype is if they are identical twins or clones. Checking to see if >1 animal has the same genotypes is a useful quality control check. It is recommended using your parentage SNP set for initial screening and for any pair that have >99% identical genotypes and then using all available SNP to see if >99% of the genotypes match.</li>

</ol>

For standardization purposes with respect to the nomenclature of genes or loci, a web site is available at: https://www.genenames.org/about/guidelines#genenames and markers at: [https://hgvs-nomenclature.org/versions/21.0/ <nowiki>http://www.HGVS.org/varnomen</nowiki>.]

</div>
</div>

== ICAR Services Related to DNA Technology ==
ICAR offers three services that are related to the use of DNA all of which are linked to parentage analysis in one form or another, as shown in Figure 1. ICAR DNA services., and describing them in more detail in the other sections.
[[File:ICAR DNA service.png|thumb|Figure 1. ICAR DNA services.|center|415x415px]]

== ICAR Accreditation of Laboratories Providing DNA Genotyping Services ==
<div class="mw-collapsible mw-collapsed">
<div>
=== Introduction ===
Considering the need for high quality standards in all uses of molecular data, ICAR has for several years offered an accreditation service based on defined minimum requirements for laboratories providing DNA genotyping services. The basic requirements of this accreditation include proof of the minimum internal management quality assurance standards and a Rank 1 result from participation in the most recent biennial international ring test developed and offered by the International Society for Animal Genetics (ISAG).

In addition, such laboratories generally have been analyzing the resulting genotypes to carry out microsatellite- and/or SNP-based parentage analysis services including either parentage verification or animal identification confirmation. This ICAR accreditation service has previously been used for recognizing the genotyping laboratory as an accredited organization to provide parentage analysis functions without specifically testing the technical accuracy of doing so. Effective 2021, the SNP-based parentage analysis accreditation service for DNA Data Interpretation Centres has replaced the previous laboratory accreditation for SNP-based parentage verification. In the future, a similar technical process for the accreditation of microsatellite-based parentage analysis may be introduced by ICAR but until such time, the existing process for the accreditation of genotyping laboratories will remain in effect.

The following guidelines for accreditation are provided for microsatellite- and SNP-based genotyping in cattle. Minimum requirements for additional species and other DNA tests may be defined in the future.

=== Scope ===
These guidelines are for the accreditation, by ICAR, of genotyping laboratories that analyze biological samples from cattle using microsatellite- and or SNP-based genotyping, which may be subsequently used for various levels of parentage analysis, genotype imputation, estimation of genomic breeding values and other activities related to genomic selection strategies. This accreditation process also includes parentage verification based on microsatellites since ICAR has not established this service as part of the portfolio of possible accreditations for DNA data interpretation centres. For genotyping laboratories that would like to receive ICAR accreditation for SNP-based parentage verification, they must now apply separately to ICAR for its parallel service of parentage analysis accreditation for DNA data interpretation centres, as described in section 4.

=== ICAR Guidelines for Accreditation of Genotyping Laboratories ===
The accreditation process comprises the following steps:

* Application for accreditation
* Payment of relevant fee
* Review of application
* Granting of accreditation

==== Application for accreditation ====
Laboratories requesting accreditation only for microsatellite- based genotyping and parentage verification must apply by downloading and completing the appropriate form as provided in [https://www.icar.org/wp-content/uploads/2022/05/Annex-II-Application-Form-for-STR-Accreditation.pdf ''Appendix 2. Application form for microsatellite-based parentage testing in cattle''.] Laboratories seeking ICAR accreditation involving SNP-based genotyping must apply by downloading and completing the appropriate form as provided in [https://www.icar.org/wp-content/uploads/2022/05/Annex-V-Application-Form-for-SNP-Accreditation.pdf ''Appendix 3. Application form for SNP-based genotyping required for parentage analysis in cattle.''] Laboratories that have previously received ICAR accreditation for either service may re-apply prior to the expiry of any such accreditation using a shortened renewal form available on the ICAR web site. All application forms must be emailed to the ICAR secretariat at dna@icar.org and be filled out accurately and completely including the necessary documentation as required.

==== Payment of relevant fee ====
Along with the completed application form, the applicant must also provide full payment of the relevant fee as established by ICAR and given in [https://www.icar.org/index.php/certifications/certification-and-accreditation-of-dna-genetic-laboratories/guidelines-for-str-and-snp-based-parentage-testing-in-cattle/ ''Appendix 4. ICAR DNA Laboratory Accreditation Service fees''.], refer to [[#hey|Microsatellites]].

==== Review of application ====
The application will be evaluated by a committee of experts appointed by ICAR that will either:

* Approve the application
* Request additional information, or
* Reject the application

In the case of rejection, the laboratory may make a new submission as part of the ICAR annual call for applications for any subsequent year after the failed application.

==== Granting of accreditation ====
Accreditation will be given for a period of two calendar years with an expiry date of December 31st of the second year after receiving ICAR accreditation as a laboratory providing DNA genotyping services.

==== Renewal of accreditation ====
In advance of the expiry date of any existing ICAR accreditation, normally during the same year of the December 31st expiry date, a laboratory can apply for renewal of their accreditation by submitting an application as described in section 3.3.1 above and successfully completing the other steps outlined in this section 3.

==== Laboratory accreditation ====
Effective the 2022 ICAR call for accreditation of genotyping laboratories, ISO17025 accreditation, or an equivalent accreditation for ensuring quality internal management systems, is a mandatory requirement for SNP-based accreditation. In addition, effective the 2022 call for accreditation of genotyping laboratories for microsatellite (STR)-based accreditation, ISO9001 certification will no longer be acceptable and only ISO17025, or an equivalent accreditation, will be an acceptable level of accreditation to ensure quality internal management systems. During the year of application for ICAR accreditation as a laboratory providing DNA genotyping services, the applicant must provide proof of ISO17025 accreditation with an expiry date of October 31st of the following calendar year, or later.

==== Participation and performance in ring test ====
On a biennial basis, initiated during even years (i.e.: 2022, 20246, etc…) and discussed at its biennial conference in odd years (2023, 2025, etc.), ISAG conducts an international ring (comparison) test of laboratories for both microsatellite- and SNP-based genotyping. The participation in ISAG and performance within these ring tests must be disclosed, and certificates provided to ICAR, when available. Applicants must also sign a release allowing ISAG to directly disclose their ring test results to ICAR. Participation in the most recent ISAG ring test is a minimum requirement to qualify for ICAR accreditation. For the ISAG microsatellite ring test, lab genotyping performance for the official set of 12 ISAG microsatellites must be disclosed. The committee of experts will decide performance thresholds for each ring test with due consideration for the structure of the ring test and the average performance of laboratories in the ring test that year. Only those laboratories achieving Rank 1 status in the most recent biennial ISAG ring test shall automatically qualify to receive ICAR accreditation as a genotyping laboratory. Laboratories achieving a Rank 2 status in the most recent ISAG ring test may qualify to receive ICAR accreditation, at the discretion of the committee of experts, but must provide evidence of Rank 1 status for previous ISAG ring tests as well as documentation outlining the cause of the Rank 2 result and any associated actions to mitigate similar outcomes in future ISAG ring tests. Laboratories achieving a status lower than Rank 2 in the most recent ISAG ring test do not qualify for ICAR accreditation as a laboratory providing DNA genotyping services.

==== Microsatellite markers ====
The names of all microsatellites typed on all animals (marker set I) and of the additional ones assayed in the case of unresolved parentage (marker set II) must be declared, as well as the number of animals typed in at least the last two years. The minimum requirement for international exchange is the complete set of 12 official ISAG microsatellite markers. To ensure sufficient experience within the lab, analysis of 500 animals per year is set as minimum requirement for microsatellite parentage verification certification.

''[https://www.icar.org/wp-content/uploads/2018/05/03-Annex-III-ISAG-microsatellites.pdf Appendix 5. ISAG recommended microsatellites for parentage verification in cattle]'' contains the list of microsatellite markers recommended by ISAG and the method for calculating 1 parent and 2 parent exclusion probabilities. The rules for microsatellite-based parentage verification in cattle are described in ''[https://www.icar.org/wp-content/uploads/2018/05/01-Annex-I-guidelines-microsats-STRs.pdf Appendix 6. Rules for microsatellite-based parentage testing in cattle]''. Exclusion probability (PE; 2 parents and 1 parent) of each marker and of the complete marker sets must be calculated and provided in the application. The type of population and number of animals (minimum 150) used for computations are to be described. ICAR recommends using Holstein as a reference group when possible. The ICAR committee of experts will evaluate that an appropriate PE is reached for accreditation, on the basis of the population analyzed.

==== SNP markers ====
ICAR accreditation of SNP-based parentage verification is based on the full set of 200 SNP previously recommended by ISAG. The name of all SNP genotyped on all animals (marker set I, including the 100 “Core” SNP) and of the additional markers assayed in the case of unresolved parentage (marker set II, including the 100 “Additional” SNP) must be declared, as well as the number of animals SNP genotyped in at least the last two years. ICAR recommends using the full set of 200 SNP for parentage verification of all animals genotyped (''see [https://www.icar.org/Guidelines/04-DNA-Technology-App7-SNP-list-for-parentage-verification.pdf Appendix 7. List of approved SNP for parentage verification in]'' [https://www.icar.org/Guidelines/04-DNA-Technology-App7-SNP-list-for-parentage-verification.pdf cattle]). ICAR may, however, based on scientific evidence, identify specific problematic SNP that must be excluded for parentage analysis, as described in the ICAR documentation related to the accreditation of DNA data interpretation centres outlined in section 4.

==== Marker nomenclature ====
Nomenclature of markers must be described. ISAG nomenclature is required for the official ISAG 12 marker set as well as for the ISAG SNP marker set.
</div>
</div>

== Accreditation of Organisations Performing SNP-Based Parentage Analysis ==
<div class="mw-collapsible mw-collapsed">
<div>
=== Introduction ===
With the advent of SNP genotyping, the function of DNA genotyping as a laboratory activity can be separated from the functions of performing parentage verification and parentage discovery. Consequently, ICAR has established a separate accreditation for applying the results of SNP-based genotyping, which may be undertaken by laboratories, breed association societies, genetic evaluation centres and any other organization involved in parentage verification and/or the data processing of SNP genotypes.

Parentage verification and discovery are concerned with using the results that are delivered by the laboratories from DNA genotyping and require SNP genotypes for the animal itself, its recorded parents and other possible parents in the case of parentage discovery. Organizations undertaking this function may be service providers between laboratories that ICAR has accredited for microsatellite- and or SNP-based DNA genotyping and end users that may include breed societies, breeding companies, breeders and commercial farmers.

Service providers could use different laboratories for different breeds and/or species. Considering the importance of animal identification and parentage verification in animal recording, ICAR has decided to define the minimum requirements for using the results of DNA genotyping, and other information, for the purpose of:

# Parentage verification
# Parentage discovery, and
# Animal identification confirmation

The purpose of these guidelines is to provide a basis for the accreditation of processes used by organizations that use SNP genotypes in cattle. Minimum requirements for additional species and other DNA analyses may be defined in the future.

=== Scope ===
These guidelines are for the accreditation, by ICAR, of organizations that use the results of SNP-based tests for parentage analysis in cattle, which includes parentage verification, parentage discovery, and/or animal identification confirmation.

=== Accreditation of Organizations Performing Parentage Analysis ===
The ICAR accreditation process comprises the following steps:

* Application for accreditation
* Payment of relevant fee
* Review of application
* Technical processing of test data files
* Granting of accreditation

==== Application ====
Organizations carrying out SNP-based parentage analysis and requesting ICAR accreditation as a DNA Data Interpretation Centre must apply by downloading and completing the appropriate form included below as [https://www.icar.org/wp-content/uploads/2016/10/6-Annex-V-Application-Form-forICAR-Accreditation-of-DNA-Centres.pdf ''Appendix 8. Application form for organizations seeking ICAR parentage analysis accreditation for DNA data interpretation centres''.] This form must be filled out accurately and completely, providing necessary documentation as required, and submitted to ICAR with payment of the appropriate fee.

==== Review of application ====
The application will first be reviewed internally by ICAR for its completeness and additional details may be requested as needed. ICAR administration will also confirm receipt of the applicable fee.

==== Technical processing of test files ====
The applicant organization will receive a set of data files from ICAR through the Interbull Centre, for processing using its existing procedures for carrying out the level of parentage analysis for which the applicant is seeking ICAR accreditation as a DNA Data Interpretation Centre. A detailed description of this step is described in [https://www.icar.org/index.php/certifications/certification-and-accreditation-of-dna-genetic-laboratories/two-new-dna-based-services/dna-data-interpretation-centres/ ''Appendix 9. Applicant's Guide for ICAR Parentage Analysis Accreditation for DNA Data Interpretation Centres.''] In order for the applicant to be successful in obtaining the requested ICAR accreditation, it's procedures for conducting parentage analysis must exactly follow [https://www.icar.org/Documents/GenoEx/ICAR%20Guidelines%20for%20Parentage%20Verification%20and%20Parentage%20Discovery%20based%20on%20SNP.pdf ''Appendix 10. ICAR Guidelines for Parentage Verification and Parentage Discovery Based on SNP Genotypes''.] The list of SNP to be used for either parentage verification (N=200) or parentage discovery (N=554) are available in [https://www.icar.org/Guidelines/04-DNA-Technology-App-11-SNP-list-for-parentage-verification-or-discovery.pdf ''Appendix 11. List of SNP to be used for either parentage verification or parentage discovery''.] Once the applicant has completed its internal parentage analysis procedures based on the accreditation test files it received, it must send a data file of results back to the Interbull Centre. A maximum time period for 90 calendar days will be allowed for the applicant to submit acceptable files of results back to the Interbull Centre.

==== Granting of accreditation ====
Once the Interbull Centre receives the file of parentage analysis results from the applicant, it will complete the technical review and determine if the applicant has successfully completed the accreditation or not. The Interbull Centre shall inform ICAR of the results and ICAR shall issue a formal notification to the applicant. In the event the applicant was not successful in receiving ICAR accreditation, the applicant may initiate a new request for accreditation by completing and submitting the appropriate forms and providing payment of the applicable fee, as outlined above.

==== Renewal of accreditation ====
In advance of the expiry date of any existing ICAR accreditation, which coincides with the two-year anniversary date of the current accreditation, an applicant can apply for renewal of their accreditation by submitting an application as described in section 4.3.1 above and successfully completing the other required steps outlined in this section 4.
</div>
</div>

== Genotype Exchange Service – GenoEx-PSE ==
Effective 2018, ICAR has made available a genotype exchange service for parentage analysis, GenoEx-PSE, offered through the Interbull Centre. The main goal of this service is to facilitate the international exchange of SNP genotypes such that approved service users can carry out parentage analysis services at a national level in an efficient manner. The GenoEx-PSE database system and user interface has been developed to allow for the exchange of SNP genotypes for either parentage verification or parentage discovery based on the list of SNP provided in ''Appendix 11. List of SNP to be used for either parentage verification or parentage discovery''.

In order for an organization to qualify as a service user for GenoEx-PSE, it must first receive ICAR accreditation as a DNA data interpretation centre. The level of such ICAR accreditation (i.e.: for SNP-based parentage verification alone or for both SNP-based parentage verification and discovery) shall determine the highest level of SNP that may be exchanged via the GenoEx-PSE service. For details associated with this ICAR service, refer to the GenoEx-PSE web site at [https://genoex.org/ www.GenoEx.org.]

== Appendix list ==
<div class="mw-collapsible mw-collapsed">
<div>
=== Appendix 1. Link to SNP markers recommended by ISAG for parentage verification ===
https://www.icar.org/Guidelines/04-DNA-Technology-App-1-Cattle-SNP-ISAG-core-additional-panel-2013.xlsx

=== Appendix 2. Application form for microsatellite-based parentage testing in cattle ===
Please refer [https://www.icar.org/wp-content/uploads/2022/05/Annex-II-Application-Form-for-STR-Accreditation.pdf here] on the ICAR website for the Form for ICAR laboratory accreditation for STR Microsatellite-based Parentage Testing in Cattle.

=== Appendix 3. Application form for SNP-based genotyping required for parentage analysis in cattle ===
Please refer [https://www.icar.org/wp-content/uploads/2022/05/Annex-V-Application-Form-for-SNP-Accreditation.pdf here] on the ICAR website for the Form for ICAR laboratory accreditation for SNP-based genotyping required for Parentage Analysis in Cattle.

=== Appendix 4. ICAR DNA Laboratory Accreditation Service fees ===
Please refer [https://www.icar.org/index.php/certifications/certification-and-accreditation-of-dna-genetic-laboratories/guidelines-for-str-and-snp-based-parentage-testing-in-cattle/ here] on the ICAR website for DNA testing accreditation services fees.

=== Appendix 5. ISAG recommended microsatellites for parentage verification in cattle ===
Please refer [https://www.icar.org/wp-content/uploads/2018/05/03-Annex-III-ISAG-microsatellites.pdf here] on the ICAR website for the list of ISAG recommended microsatellites for parentage verification in cattle.

=== Appendix 6. Rules for microsatellite-based parentage testing in cattle ===
Please refer [https://www.icar.org/wp-content/uploads/2018/05/01-Annex-I-guidelines-microsats-STRs.pdf here] on the ICAR website for the rules for microsatellite-based parentage verification in cattle.

=== Appendix 7. List of approved SNP for parentage verification in cattle ===
Please refer [https://www.icar.org/Guidelines/04-DNA-Technology-App7-SNP-list-for-parentage-verification.pdf here] on the ICAR website for the ICAR approved list of 200 SNP for parentage verification in cattle.

=== Appendix 8. Application form for organizations seeking ICAR parentage analysis accreditation for DNA data interpretation centres ===
Please refer [https://www.icar.org/wp-content/uploads/2016/10/6-Annex-V-Application-Form-forICAR-Accreditation-of-DNA-Centres.pdf here] on the ICAR website for the application form for organizations seeking ICAR accreditation status as a DNA data interpretation centre.

=== Appendix 9. Applicant's Guide for ICAR Parentage Analysis Accreditation for DNA Data Interpretation Centres ===
Please refer [https://www.icar.org/index.php/certifications/certification-and-accreditation-of-dna-genetic-laboratories/two-new-dna-based-services/dna-data-interpretation-centres/ here] on the ICAR website for the Applicant's Guide for ICAR Parentage Analysis Accreditation for DNA Data Interpretation Centres.

=== Appendix 10. ICAR Guidelines for Parentage Verification and Parentage Discovery Based on SNP Genotypes ===
Please refer [https://www.icar.org/Documents/GenoEx/ICAR%20Guidelines%20for%20Parentage%20Verification%20and%20Parentage%20Discovery%20based%20on%20SNP.pdf here] on the ICAR website for the ICAR Guidelines for Parentage Verification and Parentage Discovery Based on SNP Genotypes.

=== Appendix 11. List of SNP to be used for either parentage verification or parentage discovery ===
Please refer [https://www.icar.org/Guidelines/04-DNA-Technology-App-11-SNP-list-for-parentage-verification-or-discovery.pdf here] on the ICAR website for the list of SNP to be used for either parentage verification or parentage discovery.
</div>
</div>

Section 04 – DNA Technology

2025-01-31T08:50:31Z

Aashish: /* Payment of relevant fee */

== Molecular Genetics ==
<div class="mw-collapsible mw-collapsed">
<div>
=== Introduction ===
Advances in molecular biology, especially genomics, provide a new set of information to be incorporated into the animal industry. On one hand, the use of molecular information may contribute to the enhancement of consumers' trust in the ability to monitor and control the animal production chain. On the other hand, molecular information will greatly contribute to the achievement of genetic improvement for animal traits through the use of genomic breeding values, marker assisted selection, gene introgression, heterosis prediction, pedigree validation/prediction, and genetic defect carrier status. In most cases, advantages of using molecular information via genomic evaluations, comes from improved accuracy of animal breeding values, shortened generation intervals, and increased intensity of selection. Even with these advancements there is still a need for research and development in the search for associations between genetic markers and traits of interest, especially as new traits are included in national evaluation indexes. In addition to that, even with the current incorporation of genomic information into national selection schemes, an understanding of gene action, gene interactions, and differential gene expression to avoid negative collateral effects is needed. Cooperation between animal industries and research is required for a successful and beneficial search for genetic information in commercial livestock populations.

=== Genetic Markers ===
Genetic markers are the fundamental molecular tools for genomics, even as the type of marker used has changed. The first genetic marker associations in livestock were reported using blood typing in the 1960s, the technology then moved to microsatellites (MS) in the 1990s and more recently to the use of Single Nucleotide Polymorphism (SNP). SNP and MS are polymorphic DNA sequences (alleles) at a specific locus of a particular chromosome. While blood typing has been an ICAR approved method of parentage verification currently there are few, if any, commercial labs still offering this testing. For this reason, ICAR no longer recommends blood typing as the basis for carrying out parentage analysis in livestock species where MS or SNP technology is widely available.

==== Microsatellites ====
These are segments of DNA containing tandem repeats of simple motifs usually dimers or trimers. These segments are located throughout the genome and normally in non-coding regions. Over time, these regions are subject to the addition or subtraction of tandem repeats, which means that each microsatellite can have multiple unique alleles. Microsatellites are commonly used in many livestock species for parentage validation.

==== Single Nucleotide Polymorphism (SNP) ====
SNP are the most common type of genetic variation: each SNP represents a variation in a single nucleotide. There are millions of SNP located throughout the genome of every livestock species. For genomics the most informative SNP traditionally are either located in (a) coding regions where different alleles change the structure or function of the encoded protein, or (b) at non-coding regions that are involved in the regulatory function of the gene. For genomic breeding values, SNP that are located in other regions of the genome are also informative as they could be in linkage disequilibrium with alleles that do cause a phenotype change.

One of the big advantages of SNP is their deployment on SNP arrays with a strong parallel processing capacity whereby thousands or hundreds of thousands of SNP can be screened together in a cost-effective and efficient manner across a large number of animals. Currently, the largest livestock genotyping labs can process hundreds of thousands of animals yearly on such arrays. The availability of these large SNP panels is therefore bolstering the search for mutations underlying genetic variation for simple and complex traits. It is also revolutionizing the speed at which trait associated genes or gene regions are being discovered as well as the adoption rate of genomic selection strategies. SNP genotypes have become the international standard for the basis of parentage analysis and ICAR recommends this approach over the use of microsatellites wherever possible due to the improved accuracy and the ease of comparing results between genotyping laboratories.

=== Current and Potential Uses of DNA Technologies ===

==== Parentage verification and parental assignment authentication ====
Prior to the emergence of SNP genotyping, parentage verification was the main commercial use of genetic markers. Traditionally, parentage testing was based on the exclusion of relationship (i.e.: sire or dam) when an animal has a genotype inconsistent to a putative relationship. New trends in animal production systems are tending to encourage animal production in larger numbers per farm in response to environmental and production related constraints. In these large settings, multiple animals could be bred or give birth on the same day, which can result in more pedigree recording errors. As the cost of the analysis decreases and the number of genetic markers available increases, breed societies are now able to build up pedigree records using genetic markers to predict the pedigree of calves born in a herd at a given time. This normally requires a prior knowledge of candidate sires and dams for a calf when lower number (<200) of markers are used, but with enough SNP the correct parents can be predicted without prior knowledge being available as long as the parent is also genotyped. The probability of assignment to a correct pair of animals will depend on the number of markers used, number of alleles per loci, the minor allele frequency in the population, the number of parents, and the number of possible matings. The International Society of Animal Genetics (www.isag.us) has species-specific panels recommended of microsatellite and SNP markers for this purpose, which can be accessed via a link such as provided in ''[https://www.icar.org/Guidelines/04-DNA-Technology-App-1-Cattle-SNP-ISAG-core-additional-panel-2013.xlsx Appendix 1]. Link to SNP markers recommended by ISAG for parentage verification''. For cattle, ICAR has developed a set of parentage SNP, ICAR554, which incorporates the ISAG recommended panel and other highly informative SNP. This panel allows for highly accurate parentage validation and discovery while not allowing for accurate imputation to a higher density. Therefore, the ICAR554 panel can be shared among countries and competitors for parentage analysis without fear of others being able to use them to predict genomic breeding values. ICAR and the Interbull Centre collaborate in offering an international genotype exchange service, referred to as GenoEx, which is described further in Chapter 5 specifically for the exchange of SNP genotypes for the purposes of parentage analysis.

==== Traceability and authentication of animal products offered to consumers ====
Due to multiple crises, including BSE outbreaks to ground beef containing horsemeat, and with increased consumer interest in where their food comes from the traceability of meat products is of greater concern to the industry. Traceability is based on the availability of a verification and control system that monitors all relevant details throughout the entire livestock production chain. Since an individual’s genetic sequence is unique and does not change, its DNA remains constant from ‘conception to consumption’. Therefore, use of genetic markers allows one to match the DNA of an individual at birth to the final product.

Genetic markers for the authentication of animal products for labels of quality related to geographic location and labels of quality related to specific breeds or their crosses are/or will be very useful. However, this requires the establishment of molecular standards or allele frequencies for each breed within a species. A lot of information is coming from studies of genetic diversity among breeds. Genomic regions subject to intense selection in each population are of particular interest. With a large enough set of SNP and genotyped purebred reference animals it is also possible to predict the most likely breed composition of individuals.

==== Molecular genetic information for marker-assisted selection schemes ====
Quantitative traits are generally assumed to be controlled by a large number of genes. However, individual genes sometimes account for a significant amount of variation of the trait. Such is the case for the Myostatin gene and double muscling in beef cattle, the DGAT1 gene and milk components in dairy cattle, or the Booroola fecundity gene and ovulation rate in sheep. Since the genotype of an animal does not change during its lifetime, use of DNA information through the identification of markers linked to QTL with effects on production traits or the identification of a gene itself together with the causative variant is of great interest. Nevertheless, with complex traits there is a growing need of having a sufficiently large marker set to incorporate molecular information for selection decisions. Including genomic information as a selection criterion is of special interest for traits that are difficult and costly to measure and/or are measured late in life. By 2022, >177,000 cattle, >34,000 swine, >16,000 chicken and >4,000 sheep QTL have been identified that are associated with economically important traits such as health, carcass, milk, fertility, and body conformation. The AnimalQTLdb database housed at the [https://www.animalgenome.org/ National Animal Genome Research Program] contains up to date information on cattle, chicken, horse, pig, trout, and sheep QTL data assembled from published data.

Recording schemes have been collecting information for decades on the most common production traits measured in domestic livestock. There is an ever-increasing volume of information becoming available, but for some traits like meat quality, disease resistance and feed efficiency, those records are very expensive to measure, difficult to obtain, or are performed late in the animal’s life. Because of these challenges information for such traits is commonly collected on a reduced number of animals in any given population.

For these challenging, but economically important traits, genetic markers and genomic selection offer significant opportunities for trait selection where it was not economically feasible before. In general, genetic markers and genomics will play an important role for important traits regardless of the livestock species. Genomics can also allow us to increase selection intensities since we can predict genomic breeding values on a large number of animals and thus have more candidates for selection.

==== Disease resistance and genetic defects ====
Another group of traits with a high potential for the use of molecular data and genomics are those linked to resistance, resilience, and susceptibility to diseases. There are a number of multi-factorial or complex diseases that are the result of the interaction between an animal’s genome and environmental components. Disease resistance traits are among the most difficult to include in genetic improvement programs because they require good field measurement of the disease status of the animals and a systematic control of management or environmental conditions that allow for the identification of the environmental influence on the health status of the animal. Infectious diseases depend very much upon environmental factors such as the degree of exposure to the pathogen agent. Thus, if exposure is low, animals will show little variation. Part of the phenotypic differences for resistance may be differences in the degree of challenge. Therefore, if genes or genetic markers linked to resistance are correctly identified, resistant animals will be able to be selected on the base of their genomic information. For many diseases, identification of genes associated with resistance will require experimental conditions to be used. Genetic analysis to identify heterozygous carriers of genetic diseases caused by single, recessive genes are currently in use. Examples in dairy cattle include complex vertebral malformation (CVM), brachyspina (BY), cholesterol deficiency (CD) and several genes, gene regions or haplotypes causing embryo loss or stillbirth in different dairy breeds. In 2022, [https://www.omia.org/home/ OMIA (Online Mendelian Inheritance in Animals),] listed >1000 traits or genetic defects in livestock with a known causative mutation (cattle: 186, pig: 58, chicken: 56, sheep: 49, horse: 48, goat:17). Including these causative allele or associated haplotypes in a breeding program will allow producers to minimize their risk from genetic defects while maximizing genetic progress from beneficial traits.

=== Technical Aspects ===

==== DNA collection ====
Systematic collection of DNA is recommended in several livestock populations. DNA may be obtained from any nuclear cell in the body. Protocols for DNA extraction are now available for blood (white cells), semen, saliva (epithelial cells), hair follicles, muscle, skin, organs (such as liver, spleen etc.). Red blood cells may also be used for poultry as they retain the nuclear body while most other species do not. Small amounts of tissue material are required for routine DNA analysis. However, if there are multiple future uses of an individual’s DNA (whole genome sequencing, traceability, causative allele validations, …), then DNA storage costs, extraction costs, quality, and quantity obtained by different protocols will have to be carefully examined and optimized. Common collection methods include hair follicles, tissue samples (often ear punch) in an enclosed container, blood spots on filter paper, and nasal swabs.

==== Data organization ====
A centralised database may be organised in respect to the main uses of the genetic information:

* Parent verification, assignment, and/or discovery
* Traceability of meat products
* Breed identification or breed diversity
* Qualitative and quantitative traits 

Database tables may contain:

* Animal identification to link to all other information on the animal and its relatives.
* Number of genetic markers: n
* Standard name of each marker i (for i= 1, n)
* Accession number for marker such as the dbSNP ID
* Alleles for marker i
* Genomic location of marker i
* Effect of non-reference allele on the protein
* Phenotypic effect of the allele
* Association with other traits

==== Parentage accuracy ====
While use of microsatellite and SNP markers are both ICAR accredited methods of parentage verification they do not have the same power of parentage accuracy. Briefly the order of accuracy for ISAG and ICAR approved parentage marker panels are:

Microsatellites << small SNP panels (100 or less) < large SNP panels (500 or more)

This order is based on both genotyping accuracy and total genomic information. Comparing the genomic marker error rate in cattle microsatellites have a 1-5% error rate (Baruch and Weller, 2008<ref>Baruch, E., and J. I. Weller. 2008. 'Estimation of the number of SNP genetic markers required for parentage verification', ''Animal Genetics'', 39: 474-79.</ref>) while the SNP error rate is <0.1% (Cooper, Wiggans, and VanRaden 2013<ref>Cooper, T. A., G. R. Wiggans, and P. M. VanRaden. 2013. 'Short communication: relationship of call rate and accuracy of single nucleotide polymorphism genotypes in dairy cattle', ''Journal of dairy science'', 96: 3336-9.</ref>). As 2-3 SNP provide the same parentage exclusion accuracy as 1 microsatellite marker (Vignal et al. 2002<ref>Vignal, A., D. Milan, M. SanCristobal, and A. Eggen. 2002. 'A review on SNP and other types of molecular markers and their use in animal genetics', ''Genet Sel Evol'', 34: 275-305.

1.4.4 Genomic quality control checks</ref>), the 100 and 200 ISAG parentage SNP panels are more accurate than the 12 ISAG parentage microsatellite markers. In the same manner parentage panels of over 500 SNP (McClure et al. 2018<ref>McClure, M. C., J. McCarthy, P. Flynn, J. C. McClure, E. Dair, D. K. O'Connell, and J. F. Kearney. 2018. 'SNP Data Quality Control in a National Beef and Dairy Cattle System and Highly Accurate SNP Based Parentage Verification and Identification', ''Front Genet'', 9: 84.</ref>), such as the ICAR554, are recommended for parentage prediction which requires an even higher level of accuracy.

==== Genomic quality control checks ====
One of the most important parts of a large genomic database is to ensure that a genotype associated with an individual animal truly belongs to that animal. Most large livestock genomic databases deal with SNP data only and the quality of SNP genotyping data is of paramount importance (Wu at al., Evaluation of genotyping concordance for commercial bovine SNP arrays using quality-assurance samples, Animal Genetics, 50: 367-371, 2019). This section, therefore, focuses on quality control for that genomic data type. Both sample and SNP quality control measures are needed, and it is encouraged to develop a system for them early.

For those working with genotype data there are two main concerns. First, is ensuring that the genotype data itself is of high quality and can be trusted. Second, is ensuring that the genotype truly belongs to the individual listed. The recommended quality control checks below will work for any livestock species. The basic checks can be performed with minimal information about the individual, while some of the advanced checks require data that not everyone will have, such as historic animal location.

===== ''Basic genotype quality control checks for SNP-based genotype data'' =====
Genotype:
<ol style="list-style-type:number">
<li> Exclude SNP that have a genotype call rate below 90% when analyzed in your population. Using 500 or more animals to determine the SNP call rate is recommended. Chromosome Y SNP should have their call rate determined only in males for this filter.</li>
<li> Invalidate the individual’s genotype if its overall call rate is below <90%. For SNP-based genotypes, such as those from Illumina or Affymetrix chips, the accuracy of called genotypes is questionable when the individual’s overall call rate is <90%.</li>
<li> Check to see that the animal has all three genotype classes (i.e.: AA, AB and BB) in its full genotype file. If any genotype class is missing or has a frequency below 20% then invalidate the full genotype.</li>
<li> Check to ensure that there are no unexpected alleles in the genotype file. For example, genotypes in AB format should not have T, G, 0, 1, 2 or 9. If present, then invalidate the full genotype file.</li>

</ol>

Parentage:
<ol style="list-style-type:number">
<li> Parent (Sire or Dam) validation. If using 200 or less SNP, a listed sire will validate if <1% of the offspring-parent genotypes are in conflict. A conflicting genotype is where the offspring and listed parent have opposite homozygous genotypes.</li>
<li> Mating validation after parent validation. For all parentage validation SNP where the animal is heterozygous, if for >1% of those SNP the sire and dam are homozygous for the same allele then the listed mating is invalidated. This could represent a case where the offspring and one of the parents were mislabeled with the other’s identification (so the offspring’s genotype belongs to the sire or dam and vice versa). Under such cases, it is recommended to resample the DNA and regenotype, potentially with a panel that includes more SNP.</li>

</ol>

===== ''Advanced quality control checks for SNP-based genotype data'' =====
Animal:
<ol style="list-style-type:number">

<li> Parentage discovery. Using SNP data to predict who an animal’s likely parent is can be very useful, but steps must be taken to ensure a very high probability that the prediction is accurate. The following are recommended:</li>

# Using 500 or more SNP that have a minor allele frequency (MAF) above 20% and call rates above 90%. It is advised to calculate the MAF across your full population. Predicted parents should have <1% conflict rate with the animal.
# Sex check. Make sure that you have a process established to ensure that only males are predicted as the sire and only females as the dam.
# Date of Birth check. If you do not include a check that the predicted parent is older than the animal than the predicted individual could actually be an offspring of the animal.
# Age gap. Cattle normally reach sexual maturity at 11-12 months of age, but this can be as young as 8-9 months, and even younger if in-vitro fertilization is a technology used within the population. Under normal circumstances, a minimum of 17 months between the birth dates of the animal and its predicted parent is recommended to ensure that the predicted parent could have been sexually mature at the time of the breeding.
# Grey zone SNP conflicts. The majority of animals will have <0.5% or >1.5% conflicting genotypes with the individual when parentage discovery is conducted. Those with <0.5% pass the prediction and those with >1.5% fail. Most failed animals will have >8% conflict rates. For those animals who have between 0.5 and 1.5% conflicting SNP when a set parentage panel is used (i.e.: the ICAR554 SNP list), it is advised that the conflict rate from all available SNP be used between the two individuals and if the percent conflicting is <1% they validate as the parent, but if >1% they fail.

<li> Genetic Relationship Matrix (GRM). If an animal’s true parent is not genotyped, then it cannot be directly predicted or validated. The genetic relationship between closely related animals can be used to suggest a potential, non-genotyped, parent. It is recommended that 7,000 or more SNP be used to calculate the GRM. GRM results DO NOT validate a relationship, but only suggest. Caution should be used as GRM values can be inflated for inbred individuals. Full-sibs and parent-child should have GRM values around 50%, while half-sibs would be around 25%. The range for each group can vary 5-10% from the expected value.</li>

<li> Sex prediction. How to perform a sex prediction depends on the type and number of SNP an animal has from the X and Y chromosomes. While not every commercial chip includes chromosome Y SNP, they typically contain chromosome X markers.</li>

# Pseudo autosomal region (PAR) SNP. As both the X and Y chromosomes contain the PAR, SNP from this region should be excluded from sex prediction. If the PAR position boundaries are not published for your species they can be roughly determined by analyzing the chromosome X SNP in known males and females and identifying the region where the MAF in males for a continuous set of SNP is >1%. Non-PAR regions of chromosome X will have SNP with average MAF of <1% in males and >>1% in females.
# Chromosome X predicted. Use non-PAR SNP to determine the animal’s chromosome X heterozygosity rate (number of heterozygous chromosome X SNP / total number of chromosome X SNP). If the average heterozygosity rate is <5%, the predicted sex is male, and if >15% its female. If the rate is between 5 and 15% then the predicted sex is unknown.
# Chromosome Y predicted. Using chromosome Y SNP to predict sex is logically simpler but many commercial chips do not contain them. Say you have 7 chromosome Y SNP with high call rates in males, it is recommended using the following logic. Male is predicted when 6-7 of the Y SNP are present; female is predicted when <1 SNP is present and ambiguous sex is predicted when 2-5 Y SNP are present.
# Ambiguous sex prediction. If one set of sex chromosome SNP returns an ambiguous sex prediction and the other doesn’t it is recommended using the latter as the predicted sex. If both SNP sets are ambiguous, the animal could have Turner syndrome (X0), or Klinefelter’s syndrome (XXY), in this case it is recommended returning an ambiguous predicted sex.
# If the predicted sex from the chromosome X and Y analysis disagree, it is recommended returning an ambiguous predicted sex. This could also indicate a possible Klinefelter syndrome (XXY) animal.
# Sex selected AI semen straws. Sex prediction should not be carried out on DNA obtained from sex selected AI semen straws.

<li> Offspring Quality control. The genotyped and listed offspring of an animal can be used to identify potential cases where the animal’s genotype actually belongs to another animal. These should be used as flags to indicate a potential investigation, but it is recommended to temporarily invalidate the animal’s genotype until cleared. Advised thresholds for those flags are:</li>
# AI sire: If >80% of genotyped offspring fail if >10 offspring are genotyped.
# Stock/herd bull: If >80% of genotyped offspring fail if >5 offspring are genotyped.
# Dam: If 100% of genotyped offspring fail if 2 offspring are genotyped, else if >5 offspring are genotyped then use >80%.

<li> Duplicate genotype. The only case where two or more animals should share the exact same genotype is if they are identical twins or clones. Checking to see if >1 animal has the same genotypes is a useful quality control check. It is recommended using your parentage SNP set for initial screening and for any pair that have >99% identical genotypes and then using all available SNP to see if >99% of the genotypes match.</li>

</ol>

For standardization purposes with respect to the nomenclature of genes or loci, a web site is available at: https://www.genenames.org/about/guidelines#genenames and markers at: [https://hgvs-nomenclature.org/versions/21.0/ <nowiki>http://www.HGVS.org/varnomen</nowiki>.]

</div>
</div>

== ICAR Services Related to DNA Technology ==
ICAR offers three services that are related to the use of DNA all of which are linked to parentage analysis in one form or another, as shown in Figure 1. ICAR DNA services., and describing them in more detail in the other sections.
[[File:ICAR DNA service.png|thumb|Figure 1. ICAR DNA services.|center|415x415px]]

== ICAR Accreditation of Laboratories Providing DNA Genotyping Services ==
<div class="mw-collapsible mw-collapsed">
<div>
=== Introduction ===
Considering the need for high quality standards in all uses of molecular data, ICAR has for several years offered an accreditation service based on defined minimum requirements for laboratories providing DNA genotyping services. The basic requirements of this accreditation include proof of the minimum internal management quality assurance standards and a Rank 1 result from participation in the most recent biennial international ring test developed and offered by the International Society for Animal Genetics (ISAG).

In addition, such laboratories generally have been analyzing the resulting genotypes to carry out microsatellite- and/or SNP-based parentage analysis services including either parentage verification or animal identification confirmation. This ICAR accreditation service has previously been used for recognizing the genotyping laboratory as an accredited organization to provide parentage analysis functions without specifically testing the technical accuracy of doing so. Effective 2021, the SNP-based parentage analysis accreditation service for DNA Data Interpretation Centres has replaced the previous laboratory accreditation for SNP-based parentage verification. In the future, a similar technical process for the accreditation of microsatellite-based parentage analysis may be introduced by ICAR but until such time, the existing process for the accreditation of genotyping laboratories will remain in effect.

The following guidelines for accreditation are provided for microsatellite- and SNP-based genotyping in cattle. Minimum requirements for additional species and other DNA tests may be defined in the future.

=== Scope ===
These guidelines are for the accreditation, by ICAR, of genotyping laboratories that analyze biological samples from cattle using microsatellite- and or SNP-based genotyping, which may be subsequently used for various levels of parentage analysis, genotype imputation, estimation of genomic breeding values and other activities related to genomic selection strategies. This accreditation process also includes parentage verification based on microsatellites since ICAR has not established this service as part of the portfolio of possible accreditations for DNA data interpretation centres. For genotyping laboratories that would like to receive ICAR accreditation for SNP-based parentage verification, they must now apply separately to ICAR for its parallel service of parentage analysis accreditation for DNA data interpretation centres, as described in section 4.

=== ICAR Guidelines for Accreditation of Genotyping Laboratories ===
The accreditation process comprises the following steps:

* Application for accreditation
* Payment of relevant fee
* Review of application
* Granting of accreditation

==== Application for accreditation ====
Laboratories requesting accreditation only for microsatellite- based genotyping and parentage verification must apply by downloading and completing the appropriate form as provided in [https://www.icar.org/wp-content/uploads/2022/05/Annex-II-Application-Form-for-STR-Accreditation.pdf ''Appendix 2. Application form for microsatellite-based parentage testing in cattle''.] Laboratories seeking ICAR accreditation involving SNP-based genotyping must apply by downloading and completing the appropriate form as provided in [https://www.icar.org/wp-content/uploads/2022/05/Annex-V-Application-Form-for-SNP-Accreditation.pdf ''Appendix 3. Application form for SNP-based genotyping required for parentage analysis in cattle.''] Laboratories that have previously received ICAR accreditation for either service may re-apply prior to the expiry of any such accreditation using a shortened renewal form available on the ICAR web site. All application forms must be emailed to the ICAR secretariat at dna@icar.org and be filled out accurately and completely including the necessary documentation as required.

==== Payment of relevant fee ====
Along with the completed application form, the applicant must also provide full payment of the relevant fee as established by ICAR and given in [https://www.icar.org/index.php/certifications/certification-and-accreditation-of-dna-genetic-laboratories/guidelines-for-str-and-snp-based-parentage-testing-in-cattle/ ''Appendix 4. ICAR DNA Laboratory Accreditation Service fees''.], refer to [[#Application_for_accreditation|Application for accreditation]].

==== Review of application ====
The application will be evaluated by a committee of experts appointed by ICAR that will either:

* Approve the application
* Request additional information, or
* Reject the application

In the case of rejection, the laboratory may make a new submission as part of the ICAR annual call for applications for any subsequent year after the failed application.

==== Granting of accreditation ====
Accreditation will be given for a period of two calendar years with an expiry date of December 31st of the second year after receiving ICAR accreditation as a laboratory providing DNA genotyping services.

==== Renewal of accreditation ====
In advance of the expiry date of any existing ICAR accreditation, normally during the same year of the December 31st expiry date, a laboratory can apply for renewal of their accreditation by submitting an application as described in section 3.3.1 above and successfully completing the other steps outlined in this section 3.

==== Laboratory accreditation ====
Effective the 2022 ICAR call for accreditation of genotyping laboratories, ISO17025 accreditation, or an equivalent accreditation for ensuring quality internal management systems, is a mandatory requirement for SNP-based accreditation. In addition, effective the 2022 call for accreditation of genotyping laboratories for microsatellite (STR)-based accreditation, ISO9001 certification will no longer be acceptable and only ISO17025, or an equivalent accreditation, will be an acceptable level of accreditation to ensure quality internal management systems. During the year of application for ICAR accreditation as a laboratory providing DNA genotyping services, the applicant must provide proof of ISO17025 accreditation with an expiry date of October 31st of the following calendar year, or later.

==== Participation and performance in ring test ====
On a biennial basis, initiated during even years (i.e.: 2022, 20246, etc…) and discussed at its biennial conference in odd years (2023, 2025, etc.), ISAG conducts an international ring (comparison) test of laboratories for both microsatellite- and SNP-based genotyping. The participation in ISAG and performance within these ring tests must be disclosed, and certificates provided to ICAR, when available. Applicants must also sign a release allowing ISAG to directly disclose their ring test results to ICAR. Participation in the most recent ISAG ring test is a minimum requirement to qualify for ICAR accreditation. For the ISAG microsatellite ring test, lab genotyping performance for the official set of 12 ISAG microsatellites must be disclosed. The committee of experts will decide performance thresholds for each ring test with due consideration for the structure of the ring test and the average performance of laboratories in the ring test that year. Only those laboratories achieving Rank 1 status in the most recent biennial ISAG ring test shall automatically qualify to receive ICAR accreditation as a genotyping laboratory. Laboratories achieving a Rank 2 status in the most recent ISAG ring test may qualify to receive ICAR accreditation, at the discretion of the committee of experts, but must provide evidence of Rank 1 status for previous ISAG ring tests as well as documentation outlining the cause of the Rank 2 result and any associated actions to mitigate similar outcomes in future ISAG ring tests. Laboratories achieving a status lower than Rank 2 in the most recent ISAG ring test do not qualify for ICAR accreditation as a laboratory providing DNA genotyping services.

==== Microsatellite markers ====
The names of all microsatellites typed on all animals (marker set I) and of the additional ones assayed in the case of unresolved parentage (marker set II) must be declared, as well as the number of animals typed in at least the last two years. The minimum requirement for international exchange is the complete set of 12 official ISAG microsatellite markers. To ensure sufficient experience within the lab, analysis of 500 animals per year is set as minimum requirement for microsatellite parentage verification certification.

''[https://www.icar.org/wp-content/uploads/2018/05/03-Annex-III-ISAG-microsatellites.pdf Appendix 5. ISAG recommended microsatellites for parentage verification in cattle]'' contains the list of microsatellite markers recommended by ISAG and the method for calculating 1 parent and 2 parent exclusion probabilities. The rules for microsatellite-based parentage verification in cattle are described in ''[https://www.icar.org/wp-content/uploads/2018/05/01-Annex-I-guidelines-microsats-STRs.pdf Appendix 6. Rules for microsatellite-based parentage testing in cattle]''. Exclusion probability (PE; 2 parents and 1 parent) of each marker and of the complete marker sets must be calculated and provided in the application. The type of population and number of animals (minimum 150) used for computations are to be described. ICAR recommends using Holstein as a reference group when possible. The ICAR committee of experts will evaluate that an appropriate PE is reached for accreditation, on the basis of the population analyzed.

==== SNP markers ====
ICAR accreditation of SNP-based parentage verification is based on the full set of 200 SNP previously recommended by ISAG. The name of all SNP genotyped on all animals (marker set I, including the 100 “Core” SNP) and of the additional markers assayed in the case of unresolved parentage (marker set II, including the 100 “Additional” SNP) must be declared, as well as the number of animals SNP genotyped in at least the last two years. ICAR recommends using the full set of 200 SNP for parentage verification of all animals genotyped (''see [https://www.icar.org/Guidelines/04-DNA-Technology-App7-SNP-list-for-parentage-verification.pdf Appendix 7. List of approved SNP for parentage verification in]'' [https://www.icar.org/Guidelines/04-DNA-Technology-App7-SNP-list-for-parentage-verification.pdf cattle]). ICAR may, however, based on scientific evidence, identify specific problematic SNP that must be excluded for parentage analysis, as described in the ICAR documentation related to the accreditation of DNA data interpretation centres outlined in section 4.

==== Marker nomenclature ====
Nomenclature of markers must be described. ISAG nomenclature is required for the official ISAG 12 marker set as well as for the ISAG SNP marker set.
</div>
</div>

== Accreditation of Organisations Performing SNP-Based Parentage Analysis ==
<div class="mw-collapsible mw-collapsed">
<div>
=== Introduction ===
With the advent of SNP genotyping, the function of DNA genotyping as a laboratory activity can be separated from the functions of performing parentage verification and parentage discovery. Consequently, ICAR has established a separate accreditation for applying the results of SNP-based genotyping, which may be undertaken by laboratories, breed association societies, genetic evaluation centres and any other organization involved in parentage verification and/or the data processing of SNP genotypes.

Parentage verification and discovery are concerned with using the results that are delivered by the laboratories from DNA genotyping and require SNP genotypes for the animal itself, its recorded parents and other possible parents in the case of parentage discovery. Organizations undertaking this function may be service providers between laboratories that ICAR has accredited for microsatellite- and or SNP-based DNA genotyping and end users that may include breed societies, breeding companies, breeders and commercial farmers.

Service providers could use different laboratories for different breeds and/or species. Considering the importance of animal identification and parentage verification in animal recording, ICAR has decided to define the minimum requirements for using the results of DNA genotyping, and other information, for the purpose of:

# Parentage verification
# Parentage discovery, and
# Animal identification confirmation

The purpose of these guidelines is to provide a basis for the accreditation of processes used by organizations that use SNP genotypes in cattle. Minimum requirements for additional species and other DNA analyses may be defined in the future.

=== Scope ===
These guidelines are for the accreditation, by ICAR, of organizations that use the results of SNP-based tests for parentage analysis in cattle, which includes parentage verification, parentage discovery, and/or animal identification confirmation.

=== Accreditation of Organizations Performing Parentage Analysis ===
The ICAR accreditation process comprises the following steps:

* Application for accreditation
* Payment of relevant fee
* Review of application
* Technical processing of test data files
* Granting of accreditation

==== Application ====
Organizations carrying out SNP-based parentage analysis and requesting ICAR accreditation as a DNA Data Interpretation Centre must apply by downloading and completing the appropriate form included below as [https://www.icar.org/wp-content/uploads/2016/10/6-Annex-V-Application-Form-forICAR-Accreditation-of-DNA-Centres.pdf ''Appendix 8. Application form for organizations seeking ICAR parentage analysis accreditation for DNA data interpretation centres''.] This form must be filled out accurately and completely, providing necessary documentation as required, and submitted to ICAR with payment of the appropriate fee.

==== Review of application ====
The application will first be reviewed internally by ICAR for its completeness and additional details may be requested as needed. ICAR administration will also confirm receipt of the applicable fee.

==== Technical processing of test files ====
The applicant organization will receive a set of data files from ICAR through the Interbull Centre, for processing using its existing procedures for carrying out the level of parentage analysis for which the applicant is seeking ICAR accreditation as a DNA Data Interpretation Centre. A detailed description of this step is described in [https://www.icar.org/index.php/certifications/certification-and-accreditation-of-dna-genetic-laboratories/two-new-dna-based-services/dna-data-interpretation-centres/ ''Appendix 9. Applicant's Guide for ICAR Parentage Analysis Accreditation for DNA Data Interpretation Centres.''] In order for the applicant to be successful in obtaining the requested ICAR accreditation, it's procedures for conducting parentage analysis must exactly follow [https://www.icar.org/Documents/GenoEx/ICAR%20Guidelines%20for%20Parentage%20Verification%20and%20Parentage%20Discovery%20based%20on%20SNP.pdf ''Appendix 10. ICAR Guidelines for Parentage Verification and Parentage Discovery Based on SNP Genotypes''.] The list of SNP to be used for either parentage verification (N=200) or parentage discovery (N=554) are available in [https://www.icar.org/Guidelines/04-DNA-Technology-App-11-SNP-list-for-parentage-verification-or-discovery.pdf ''Appendix 11. List of SNP to be used for either parentage verification or parentage discovery''.] Once the applicant has completed its internal parentage analysis procedures based on the accreditation test files it received, it must send a data file of results back to the Interbull Centre. A maximum time period for 90 calendar days will be allowed for the applicant to submit acceptable files of results back to the Interbull Centre.

==== Granting of accreditation ====
Once the Interbull Centre receives the file of parentage analysis results from the applicant, it will complete the technical review and determine if the applicant has successfully completed the accreditation or not. The Interbull Centre shall inform ICAR of the results and ICAR shall issue a formal notification to the applicant. In the event the applicant was not successful in receiving ICAR accreditation, the applicant may initiate a new request for accreditation by completing and submitting the appropriate forms and providing payment of the applicable fee, as outlined above.

==== Renewal of accreditation ====
In advance of the expiry date of any existing ICAR accreditation, which coincides with the two-year anniversary date of the current accreditation, an applicant can apply for renewal of their accreditation by submitting an application as described in section 4.3.1 above and successfully completing the other required steps outlined in this section 4.
</div>
</div>

== Genotype Exchange Service – GenoEx-PSE ==
Effective 2018, ICAR has made available a genotype exchange service for parentage analysis, GenoEx-PSE, offered through the Interbull Centre. The main goal of this service is to facilitate the international exchange of SNP genotypes such that approved service users can carry out parentage analysis services at a national level in an efficient manner. The GenoEx-PSE database system and user interface has been developed to allow for the exchange of SNP genotypes for either parentage verification or parentage discovery based on the list of SNP provided in ''Appendix 11. List of SNP to be used for either parentage verification or parentage discovery''.

In order for an organization to qualify as a service user for GenoEx-PSE, it must first receive ICAR accreditation as a DNA data interpretation centre. The level of such ICAR accreditation (i.e.: for SNP-based parentage verification alone or for both SNP-based parentage verification and discovery) shall determine the highest level of SNP that may be exchanged via the GenoEx-PSE service. For details associated with this ICAR service, refer to the GenoEx-PSE web site at [https://genoex.org/ www.GenoEx.org.]

== Appendix list ==
<div class="mw-collapsible mw-collapsed">
<div>
=== Appendix 1. Link to SNP markers recommended by ISAG for parentage verification ===
https://www.icar.org/Guidelines/04-DNA-Technology-App-1-Cattle-SNP-ISAG-core-additional-panel-2013.xlsx

=== Appendix 2. Application form for microsatellite-based parentage testing in cattle ===
Please refer [https://www.icar.org/wp-content/uploads/2022/05/Annex-II-Application-Form-for-STR-Accreditation.pdf here] on the ICAR website for the Form for ICAR laboratory accreditation for STR Microsatellite-based Parentage Testing in Cattle.

=== Appendix 3. Application form for SNP-based genotyping required for parentage analysis in cattle ===
Please refer [https://www.icar.org/wp-content/uploads/2022/05/Annex-V-Application-Form-for-SNP-Accreditation.pdf here] on the ICAR website for the Form for ICAR laboratory accreditation for SNP-based genotyping required for Parentage Analysis in Cattle.

=== Appendix 4. ICAR DNA Laboratory Accreditation Service fees ===
Please refer [https://www.icar.org/index.php/certifications/certification-and-accreditation-of-dna-genetic-laboratories/guidelines-for-str-and-snp-based-parentage-testing-in-cattle/ here] on the ICAR website for DNA testing accreditation services fees.

=== Appendix 5. ISAG recommended microsatellites for parentage verification in cattle ===
Please refer [https://www.icar.org/wp-content/uploads/2018/05/03-Annex-III-ISAG-microsatellites.pdf here] on the ICAR website for the list of ISAG recommended microsatellites for parentage verification in cattle.

=== Appendix 6. Rules for microsatellite-based parentage testing in cattle ===
Please refer [https://www.icar.org/wp-content/uploads/2018/05/01-Annex-I-guidelines-microsats-STRs.pdf here] on the ICAR website for the rules for microsatellite-based parentage verification in cattle.

=== Appendix 7. List of approved SNP for parentage verification in cattle ===
Please refer [https://www.icar.org/Guidelines/04-DNA-Technology-App7-SNP-list-for-parentage-verification.pdf here] on the ICAR website for the ICAR approved list of 200 SNP for parentage verification in cattle.

=== Appendix 8. Application form for organizations seeking ICAR parentage analysis accreditation for DNA data interpretation centres ===
Please refer [https://www.icar.org/wp-content/uploads/2016/10/6-Annex-V-Application-Form-forICAR-Accreditation-of-DNA-Centres.pdf here] on the ICAR website for the application form for organizations seeking ICAR accreditation status as a DNA data interpretation centre.

=== Appendix 9. Applicant's Guide for ICAR Parentage Analysis Accreditation for DNA Data Interpretation Centres ===
Please refer [https://www.icar.org/index.php/certifications/certification-and-accreditation-of-dna-genetic-laboratories/two-new-dna-based-services/dna-data-interpretation-centres/ here] on the ICAR website for the Applicant's Guide for ICAR Parentage Analysis Accreditation for DNA Data Interpretation Centres.

=== Appendix 10. ICAR Guidelines for Parentage Verification and Parentage Discovery Based on SNP Genotypes ===
Please refer [https://www.icar.org/Documents/GenoEx/ICAR%20Guidelines%20for%20Parentage%20Verification%20and%20Parentage%20Discovery%20based%20on%20SNP.pdf here] on the ICAR website for the ICAR Guidelines for Parentage Verification and Parentage Discovery Based on SNP Genotypes.

=== Appendix 11. List of SNP to be used for either parentage verification or parentage discovery ===
Please refer [https://www.icar.org/Guidelines/04-DNA-Technology-App-11-SNP-list-for-parentage-verification-or-discovery.pdf here] on the ICAR website for the list of SNP to be used for either parentage verification or parentage discovery.
</div>
</div>

Section 20 – Methane Emission for Genetic Evaluation

2024-11-10T04:19:33Z

Aashish:

= Introduction =
Increases in milk production through management and genetics have substantially improved feed efficiency and decreased costs per unit of product over recent decades. However, dairy systems are also associated with environmental costs (Baskaran et al., 2009<ref>Baskaran, R., Cullen, R., and Colombo, S. 2009. Estimating values of environmental impacts of dairy farming in New Zealand, New Zealand J. Agric. Res. 52: 377-389, DOI: 10.1080/00288230909510520.</ref>), with methane (CH4) emissions associated with rumen microbial fermentation being both an important contributor to global greenhouse gas (GHG) emissions, as well as an avoidable loss of energy that could otherwise be directed into milk production. The livestock sector is responsible for 14.5% of the global GHG (Gerber et al., 2013<ref>Gerber, P.J., Steinfeld, H., Henderson, B., Mottet, A., Opio, C., Dijkman, J., Falcucci, A. , and Tempio, G. 2013. Tackling climate change through livestock – A global assessment of emissions and mitigation opportunities. Food and Agriculture Organization of the United Nations (FAO), Rome.</ref>); dairy cattle account for 18.9% of these emissions, mainly in the form of enteric CH4 emissions (van Middelaar et al., 2014<ref>Van Middelaar, C.E., Dijkstra. J., Berentsen. P.B.M.. and De Boer. I.J.M. 2014. Cost-effectiveness of feeding strategies to reduce greenhouse gas emissions from dairy farming. J. Dairy Sci. 97:2427–2439.</ref>). Methane is a greenhouse gas with a global warming potential 28 times that of CO2 (Myhre et al., 2013<ref>Myhre, G., Shindell, D., Bréon, F., Collins, W., Fuglestvedt, J., Huang, J., Koch, D., Lamarque, J., Lee, D., Mendoza, B., and Nakajima ,T. 2013. Anthropogenic and Natural Radiative Forcing. T.F. Stocker, D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley, ed. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.</ref>). Methane from ruminant livestock is generated during microbial fermentation in the rumen and hindgut (enteric CH4), and from decomposition of manure. Enteric CH4 contributes 80% of CH4 emissions by ruminants, and manure decomposition contributes 20%. Enteric CH4 accounts for 17% of global CH4 emissions and 3.3% of total global greenhouse gas emissions from human activities (Knapp et al., 2014<ref>Knapp, J.R., Laur, G.L., Vadas, P.A., Weis,s W.P., and Tricarico, J.M. 2014. Invited review: Enteric methane in dairy cattle production: Quantifying the opportunities and impact of reducing emissions. J. Dairy Sci. 97:3231-3261.</ref>). There is, therefore, a significant research interest to find ways to reduce enteric CH4 emissions by ruminants. Ruminant animals have a digestive system to digest plant materials efficiently. Like most mammals, ruminants lack the cellulase enzyme required to break the beta-glucose linkages in cellulose, but they play host to diverse populations of rumen microbes that can digest cellulose and other plant constituents. When rumen bacteria, protozoa and fungi ferment carbohydrates and proteins of plant materials, they produce volatile fatty acids, principally acetate, propionate and butyrate. High fibre diets favour acetate synthesis. Synthesis of acetate and butyrate are accompanied by release of metabolic hydrogen, which, if allowed to accumulate in rumen fluid, has negative effects on microbial growth, and feed digestibility (Janssen, 2010<ref>Janssen, P.H. 2010. Influence of hydrogen on rumen methane formation and fermentation balances through microbial growth kinetics and fermentation thermodynamics. Anim. Feed Sci. Technol. 160:1–22. doi:10.1016/j.anifeedsci.2010.07.002.</ref>). Rumen Archaea are microorganisms that combine metabolic hydrogen with CO2 to produce CH4 and water. Archaea play a vital role, therefore, in protecting the rumen from excess metabolic hydrogen, and the CH4 they produce is an inevitable product of rumen fermentation. A number of CH4 phenotypes have been defined (Hellwing et al., 2012<ref name=":12">Hellwing, A.L.F., Lund, P., Weisbjerg, M.R., Brask, M., and Hvelplund. T. 2012. Technical note: test of a low-cost and animal-friendly system for measuring methane emissions from dairy cows. J. Dairy Sci. 95:6077–85. doi:10.3168/jds.2012-5505.</ref>); the most widely used is CH4 production (MeP) in liters or grams per day. The CH4 production trait is highly correlated with feed intake (Basarab et al., 2013<ref>Basarab, J., Beauchemin, K., Baron, V., Ominski, K., Guan, L., Miller, S., and Crowley, J. 2013. Reducing GHG emissions through genetic improvement for feed efficiency: Effects on economically important traits and enteric methane production. Animal, 7(S2):303-315. doi:10.1017/S1751731113000888</ref>; De Haas et al., 2017<ref>de Haas, Y., Pszczola, M., Soyeurt, H., Wall, E., and Lassen, J. 2017. Invited review: Phenotypes to genetically reduce greenhouse gas emissions in dairying. J. Dairy Sci. 100:855-870.</ref>) and, thereby, with the ultimate breeding goal trait: milk production in dairy cattle. The economic value of daily dry matter intake and associated methane emissions in dairy cattle showed that increasing the feed performance estimated breeding value by one unit (i.e. 1 kg of more efficiently converted DMI during the cow’s first lactation) translates to a total lifetime saving of 3.23 kg in DMI and 0.055 kg in methane (Richardson et al., 2019<ref>Richardson, C., Baes, C., Amer, P., Quinton, C., Martin, P., Osborne, V., Pryce, J.E., and Miglior, F. 2020. Determining the economic value of daily dry matter intake and associated methane emissions in dairy cattle. Animal 14:171-179. doi:10.1017/S175173111900154X</ref>). Feed Performance was defined as a 1 kg increase in more efficiently used feed in a first parity lactating cow. These results show not only the relation between DMI and CH4 production, but also the economic relationship between these traits. Persistency of lactation was found to be positively associated with increased feed efficiency and decreased methane production and intensity. Feed efficiency was associated with lower methane intensity. Feed efficiency and methane emissions can be improved by selecting for dairy cattle that are smaller and have increased persistency of lactation. Efficiency and methane emissions can be further improved by improved management of body condition score and by extending lactations beyond the conventional 305-day length (Seymour, 2019<ref>Seymour, D.J. 2019. Feed Efficiency Dynamics in Relation to Lactation and Methane Emissions in Dairy Cattle. PhD thesis, The University of Guelph, Canada.</ref>). According to Ellis et al. (2007)<ref>Ellis, J.L., Kebreab, E., Odongo, N.E., McBride, B.W., Okine, E.K., and France, J. 2007. Prediction of methane production from dairy and beef cattle. J. Dairy Sci. 90:3456–3466.</ref>, DMI predicted MeP with an R2 of 0.64, and ME intake (MJ/d) predicted MeP with an R2 of 0.53 for dairy cattle. AlternativePhenotype definitions include CH4 intensity (MeI), which is defined as liters or grams of CH4 per kg of milk, and CH4 yield (MeY), which is defined as liters or grams of CH4 per kg of dry matter intake (DMI) (Moate et al., 2016<ref>Moate, P.J., Deighton, M.H., Williams, S.R.O., Pryce, J.E., Hayes, B.J., Jacobs, J.L., Eckard, R.J., Hannah, M.C. and Wales, W.J., 2016. Reducing the carbon footprint of Australian milk production by mitigation of enteric methane emissions. Anim. Prod. Sci. 56:1017-1034.</ref>). Residual CH4 production (RMP) is calculated as observed minus predicted CH4 production (Herd et al., 2014<ref>Herd, R.M., Arthur, P.F., Bird, S.H., Donoghue, K.A., and Hegarty, R.S. 2014. Genetic variation for methane traits in beef cattle. In: Proc. 10th World Conference on Genetic Applied to Livestock Production (WCGALP), 17-22 August, 2014. Vancouver, Canada.</ref>, Berry et al., 2015<ref>Berry, D.P., Lassen, J., and de Haas, Y. 2015. Residual feed intake and breeding approaches for enteric methane mitigation In: Livestock production and climate change. P.K. Malik, R. Bhatta, J. Takahashi, R.A. Kohn, and C.S. Prasad, ed. CABI, Oxfordshire, UK. . Pages 273-291</ref>), with predicted values based on factors such as milk production, body weight and feed intake. At the moment, it is not obvious which of these phenotypes to use; but, it is important to monitor associations between the chosen CH4 phenotype and the other important traits in the breeding goal (e.g. production, fertility, longevity) to avoid unfavorable consequences. Berry and Crowley (2012)<ref>Berry, D.P., and Crowley, J.J. 2012. Residual intake and body weight gain: A new measure of efficiency in growing cattle, J. Anim. Sci. 90:109–115, <nowiki>https://doi.org/10.2527/jas.2011-4245</nowiki></ref> describe advantages and limitations of ration traits. For example, because feed efficiency traits are a linear combination of other traits it is not recommended to include them in an overall total merit index, which is a clear limitation. For all applications it is necessary to measure the CH4 emission of each animal individually. These guidelines are intended to make the right choices for this. Whilst diet changes and feed additives can be effective mitigation strategies for CH4 emissions (Beauchemin et al., 2009<ref>Beauchemin, K.A., McAllister, T.A., and McGinn, S.M. 2009. Dietary mitigation of enteric methane from cattle. CAB Rev.: Perspect. Agric., Vet. Sci., Nutr. Nat. Res. 4:1–18.</ref>; Martin et al., 2010<ref>Martin C., Morgavi, D.P., and Doreau, M. 2010. Methane mitigation in ruminants: from microbe to the farm scale. Animal 4:351–365.</ref>; Hristov et al., 2013<ref>Hristov, A.N., Oh, J., Firkins, J.L., Dijkstra, J., Kebreab, E., Waghorn, G., Makkar, H.P.S., Adesogan, A.T., Yan,g W., Lee, C., Gerber, P.J., Henderson, B., and Tricarico, J.M. 2013. Special topics - Mitigation of methane and nitrous oxide emissions from animal operations: I. A review of enteric methane mitigation options. J. Anim. Sci. 91:5045–5069.</ref>), their effects depend on the continued use of a particular diet or additive and there have been issues with the rumen microbiomes adapting to additives. Rumen bacterial communities are highly dynamic after a diet switch and did not stabilize within 5 wk of cows grazing pasture (Bainbridge et al., 2016<ref>Bainbridge, M.L., Saldinger, L.K., Barlow, J.W., Alvez, J.P., Roman, J. Kraft, J. 2016. 1609 Rumen bacterial communities continue to shift five weeks after switching diets from conserved forage to pasture. J. Anim. Sci. 94, suppl_5:783, <nowiki>https://doi.org/10.2527/jam2016-1609</nowiki> (abstr.)</ref>). In contrast, breeding for reduced CH4 emissions should result in a permanent and cumulative reduction of emissions (Wall et al., 2010<ref>Wall, E., Simm, G., and Moran, D. 2010. Developing breeding schemes to assist mitigation of greenhouse gas emissions. Animal 4:366-376.</ref>). Several studies have shown that CH4 emissions by ruminants have a genetic component, with heritability in the range 0.20 – 0.30 (de Haas et al., 2011<ref>de Haas, Y., Windig, J.J., Calus, M.P.L., Dijkstra, J., de Haan, M., Bannink, A., and Veerkamp, R F. 2011. Genetic parameters for predicted methane production and potential for reducing enteric emissions through genomic selection. J. Dairy Sci. 94:6122–6134.</ref>; Donoghue et al., 2013<ref>Donoghue K.A., Herd, R.M., Bird, S.H., Arthur, P.F., and Hegarty, R F. 2013. Preliminary genetic parameters for methane production in Australian beef cattle. In: Proceedings of the Association for the Advancement of Animal Breeding and Genetics, 20-23 October 2013, Napier, New Zealand, pp. 290–293.</ref>; Pinares-Patiño et al., 2013<ref name=":13">Pinares-Patiño C.S., Hickey, S.M., Young, E.A., Dodds, K.G., MacLean, S., Molano, G., Sandoval, E., Kjestrup, H., Harland, R., Pickering, N.K., and McEwan, J.C. 2013. Heritability estimates of methane emissions from sheep. Animal 7: 316–321.</ref>, Kandel et al., 2014A<ref name=":37">Kandel, P.B., Vanderick, S., Vanrobays, M.L., Vanlierde, A., Dehareng, F., Froidmont, E., Soyeurt, H., and Gengler, N. 2014A. Consequences of selection for environmental impact traits in dairy cows. Page 19. (<nowiki>http://orbi.ulg.ac.be/bitstream/2268/164402/164401/NSABS162014_poster_Purna_abstract.pdf</nowiki>) I:n Proc. 19th National symposium on applied biological sciences, Gembloux, Belgium.</ref>, B<ref>Kandel, P.B., Vanderick, S., Vanrobays, M.L., Vanlierde, A., Dehareng, F., Froidmont, E., Soyeur,t H., and Gengler, N. 2014B. Consequences of selection for environmental impact traits in dairy cows. In: 10th World Congress on Genetics Applied to Livestock Production (WCGALP), 17-22 August, 2014. Vancouver, Canada.</ref>; Lassen and Lovendahl, 2016<ref>Lassen, J., and Løvendahl, P. 2016. Heritability estimates for enteric methane emissions from Holstein cattle measured using noninvasive methods. J. Dairy Sci. 99:1959-1967.</ref>; López-Paredes et al. 2020<ref>Lopez-Paredes, J., Goiri, I., Atxaerandio, R., García-Rodríguez, A., Ugarte, E., Jiménez-Montero, J.A., Alenda, R and González-Recio, O. 2020. Mitigation of greenhouse gases in dairy cattle via genetic selection (i): Genetic parameters of direct methane using non-invasive methods and its proxies. J. Dairy Sci. 103.</ref>). Breeding for reduced CH4 emissions, alone or together with other mitigation strategies, could therefore be effective in reducing the environmental impact of cattle farming and, possibly, also in increasing feed efficiency. Such a breeding scheme would require, as a fundamental starting point, accurate measures of individual CH4 emissions on a large scale. Several techniques have been developed for the measurement of CH4 emissions from ruminants, with varying degrees of accuracy (see reviews by Cassandro et al., 2013<ref>Cassandro, M., Mele, M., Stefanon, B.. 2013. Genetic aspects of enteric methane emission in livestock ruminants. Italian J. Anim. Sci. 12:e73: 450-458.</ref> and Hammond et al., 2016A<ref>Hammond, K.J., Crompton, L.A., Bannink, A., Dijkstra, J., Yáñez-Ruiz, D.R., O’Kiely, P., Kebreab, E., Eugenè, M.A., Yu, Z., Shingfield, K.J., Schwarm, A., Hristov, A.N., and Reynolds, C.K. 2016A. Review of current in vivo measurement techniques for quantifying enteric methane emission from ruminants. Anim. Feed Sci. Technol. 219:13–30. doi:10.1016/j.anifeedsci.2016.05.018.</ref>), but routine individual measurements on a large scale (a requisite for genetic selection) have proven to be difficult to obtain and expensive to measure (Pickering et al., 2015<ref>Pickering, N.K., Oddy, V.H., Basarab, J.A., Cammack, K., Hayes, B J., Hegarty, R.S., McEwan, J.C., Miller, S., Pinares, C., and de Haas, Y. 2015. Invited review: Genetic possibilities to reduce enteric methane emissions from ruminants. Animal 9:1431-1440.</ref>; Negussie et al., 2016<ref>Negussie E., Lehtinen, J., Mäntysaari, P., Liinamo, A-E., Mäntysaari, E., and Lidauer, M.. 2016. Non-invasive individual methane measurements in dairy cows using photoacoustic infrared spectroscopy technique. 6th Greenhouse Gases Animal Agriculture Conference (GGAA2016) 14-18 February 2016. Melbourne, Australia. Abstract. p62.</ref>). Therefore, identifying proxies (i.e. indicators or indirect traits) that are correlated to CH4 emissions, but which are easy and relatively lowcost to record on a large scale, would be a welcome alternative. Proxies might be less accurate but could be measured repeatedly to reduce random noise and in much larger populations. These guidelines are highly indebted to Garnsworthy et al. (2019)<ref name=":11">Garnsworthy, P.C. Difford, G.F. Bell, M.J. Bayat, A.R. Huhtanen, P. Kuhla, B. Lassen, J. Peiren, N. Pszczola, M; Sorg, D. Visker, M.H., and Yan, T. 2019 Comparison of Methods to Measure Methane for Use in Genetic Evaluation of Dairy Cattle. Animals 9:837, 12p.</ref>. In this paper the methods to measure CH4 are compared with special emphasis to the genetic evaluation of dairy cattle.

= Disclaimer =
The fact that specific device manufacturers are mentioned in these guidelines is in no way an endorsement of the devices or their accuracy by ICAR.

= Scope =
A variety of technologies are being developed and employed to measure CH4 emissions of individual dairy cattle under various environmental conditions, as is evidenced by frequent reviews (Storm et al., 2012<ref>Storm, I.M., Hellwing, A.L.F., Nielsen, N.I., and Madsen, J. 2012. Methods for measuring and estimating methane emission from ruminants. Animals 2:160-183.</ref>; Cassandro et al., 2013<ref name=":28">Cassandro, M., Mele, M., Stefanon, B.. 2013. Genetic aspects of enteric methane emission in livestock ruminants. Italian J. Anim. Sci. 12:e73: 450-458.</ref>; Hammond et al., 2016A<ref>Hammond, K.J., Crompton, L.A., Bannink, A., Dijkstra, J., Yáñez-Ruiz, D.R., O’Kiely, P., Kebreab, E., Eugenè, M.A., Yu, Z., Shingfield, K.J., Schwarm, A., Hristov, A.N., and Reynolds, C.K. 2016A. Review of current in vivo measurement techniques for quantifying enteric methane emission from ruminants. Anim. Feed Sci. Technol. 219:13–30. doi:10.1016/j.anifeedsci.2016.05.018.</ref>; de Haas et al., 2017<ref name=":10">de Haas, Y., Pszczola, M., Soyeurt, H., Wall, E., and Lassen, J. 2017. Invited review: Phenotypes to genetically reduce greenhouse gas emissions in dairying. J. Dairy Sci. 100:855-870.</ref>). The first objective of the current guidelines is to review and compare the suitability of methods for large-scale measurements of CH4 output of individual animals, which may be combined with other databases for genetic evaluations. Comparisons include assessing the accuracy, precision and correlation between methods. Combining datasets from different countries and research centres could be a successful strategy for making genetic progress in this difficult to measure trait if the methods are correlated (de Haas et al., 2017<ref name=":10" />). Accuracy and precision of methods are important. Data from different sources need to be appropriately weighted or adjusted when combined, so any methods can be combined if they are suitably correlated with the ‘true’ value. The second objective of the current guidelines, therefore, is to examine correlations among results obtained by different methods, ultimately leading to an estimate of confidence limits for selecting individual animals that are high or low emitters (see also Garnsworthy et al., 2019<ref>Garnsworthy, P.C. Difford, G.F. Bell, M.J. Bayat, A.R. Huhtanen, P. Kuhla, B. Lassen, J. Peiren, N. Pszczola, M; Sorg, D. Visker, M.H., and Yan, T. 2019 Comparison of Methods to Measure Methane for Use in Genetic Evaluation of Dairy Cattle. Animals 9:837, 12p.</ref>).

= Methane determining factors =
<div class="mw-collapsible mw-collapsed">
<div>
== Diet and rumen microbiota ==
Table 1 contains a list of dietary or microbiota factors that determine CH4 production.
<center>
{| class="wikitable"
|-
! colspan="3" | ''Table 1. Methane determining factors related to diet and rumen microbiota.''
|- style="background-color:#efefef;"
|+
!Factors
!Reference
|-
|The main determinants of daily methane production are dry matter intake and diet composition: the more feed consumed, and/or the greater the fibre content of the diet, the more methane is produced per day. However, per unit of DMI, and per unit of fat+protein yield the grass diet produced less enteric CH4 per cow than the TMR diet. Nutritional approaches for methane mitigation include reducing the forage to concentrate ratio of diets, increasing dietary oil content, and dietary inclusion of rumen modifiers and methane inhibitors.
|Beauchemin et al., 2009<ref>Beauchemin, K.A., McAllister, T.A., and McGinn, S.M. 2009. Dietary mitigation of enteric methane from cattle. CAB Rev.: Perspect. Agric., Vet. Sci., Nutr. Nat. Res. 4:1–18.</ref>;
Cottle et al., 2011<ref>Cottle, D.J., Nolan, J.V., and Wiedemann, S.G. 2011. Ruminant enteric methane mitigation: A review. Anim. Prod. Sci. 51:491–514. doi:10.1071/AN10163.</ref>; Knapp et al., 2014<ref>Knapp, J.R., Laur, G.L., Vadas, P.A., Weis,s W.P., and Tricarico, J.M. 2014. Invited review: Enteric methane in dairy cattle production: Quantifying the opportunities and impact of reducing emissions. J. Dairy Sci. 97:3231-3261.</ref>; O’Neill et al., 2011<ref>O’Neill, B.F., Deighton, M.H., O’Loughlin, B.M., Mulligan, F.J., Boland, T.M., O’Donovan, M., and Lewis, E. 2011. Effects of a perennial ryegrass diet or total mixed ration diet offered to spring-calving Holstein-Friesian dairy cows on methane emissions, dry matter intake, and milk production. J. Dairy Sci. 94:1941 – 1951</ref>; Sauvant et al., 2011<ref>Sauvant, D., Giger-Reverdin, S., Serment, A., and Broudiscou, L. 2011. Influences des régimeset de leur fermentation dans le rumen sur la production de méthane par les ruminants. INRA Prod. Anim. 24:433–446.</ref>
|-
|Methane output per kg of product is affected mainly by cow milk yield or growth rate, and by herd-level factors, such as fertility, disease incidence and replacement rate.
|Garnsworthy, 2004<ref>Garnsworthy, P.C. 2004. The environmental impact of fertility in dairy cows: a modelling approach to predict methane and ammonia emissions. Anim. Feed Sci. Technol. 112:211-223.</ref>
|-
|Methane output varies considerably between individual animals. For animals fed the same feed, the between-animal coefficient of variation (CV) in methane was 8.1%.
|Blaxter and Clapperton, 1965<ref>Blaxter, K.L., and Clapperton, J.L. 1965. Prediction of the amount of methane produced by ruminants. Br. J. Nutr.19:511–522.</ref>
|-
|The amount of digestible nutrients consumed especially of the carbohydrate fraction (starch, sugar, N-free residuals) is reliable to estimate CH4 release with high precision. Furthermore, diets rich in fat reduced CH4 formation in the rumen.
|Jentsch et al., 2007<ref>Jentsch, W., Schweigel, M., Weissbach, F., Scholze, H., Pitroff, W., and Derno, M. 2007. Methane production in cattle calculated by the nutrient composition of the diet. Arch. Anim. Nutr. 61:10-19.</ref>
|-
|DMI was also the most important determining factor, but there were different regression lines for maize silage and dried grass as the main roughage component respectively:
<math>CH4\ (g)=93+16.8\times DMI(kg)</math>
<math>CH4\ (g)=81+14.0\times DMI(kg)</math>

Methane release was particularly dependent on the intake of crude fiber (CF) and ether extract (EE):

<math>CH4\ (g)=63+80xCF\ (kg)+11xNFE\ (kg)+19xCP(kg)-195xEE\ (kg)</math>
|Kirchgessner et al., 1991<ref>Kirchgessner, M., Windisch, W., Müller, H. L., and Kreuzer, M. 1991. Release of methane and of carbon dioxide by dairy cattle. Agribiol. Res. 44:91-102.</ref>
|-
|Methane linearly increased with NDF intake <math>CH4\ (L)=59.4\times NDF[kg]+ 64.6</math> for cows together with their calves independent of the breed.
|Estermann et al., 2002<ref>Estermann, B.L., Sutter, F., Schlegel, P.O., Erdin, D., Wettstein, H.R., and Kreuzer, M. 2002. Effect of calf age and dam breed on intake, energy expenditure, and excretion of nitrogen, phosphorus, and methane of beef cows with calves. J. Anim. Sci. 80:1124-1134.</ref>
|-
|Enteric CH4 could be predicted with the equation:
<math>CH4\ (g/d)=84+47\times cellulose(kg/d)+32\times starch(kg/d)+62\times sugars\ (kg/d)</math>
|Hindrichsen et al., 2005<ref>Hindrichsen, I.K., Wettstein, H.R., Machmüller, A., Jörg, B., and Kreuzer, M. 2005. Effect of the carbohydrate composition of feed concentratates on methane emission from dairy cows and their slurry. Environ. Monit. Assess., 107:329-350.</ref>
|-
|The higher the percentage concentrate the lower Ym.
|Zeitz et al., 2012<ref>Zeitz, J.O., Soliva, C.R., and Kreuzer, M. 2012. Swiss diet types for cattle: how accurately are they reflected by the Intergovernmental Panel on Climate Change default values? J. Int. Environ. Sci. 9(sup1):199-216.</ref>
|-
|Additives can sometimes have a methane reducing effect: higher dosages mitigate methane more. Saponins mitigate methanogenesis by reducing the number of protozoa, whereas condensed tannins act both by reducing the number of protozoa and by a direct toxic effect on methanogens.
|Beauchemin et al., 2008;<ref>Beauchemin, K.A., Kreuze,r M., O’Mara, F., and McAllister, T.A. 2008. Nutritional management for enteric methane abatement: A review. Aust. J. Exp. Agric. 48:21–27.</ref>
Jayanegara et al.<ref>Jayanegara, A., Leiber, F., and Kreuzer, M. 2012. Meta‐analysis of the relationship between dietary tannin level and methane formation in ruminants from in vivo and in vitro experiments. J. Anim. Physiol. Anim. Nutr. 96:365-375.</ref>, 2012; Zmora et al., 2012<ref>Zmora, P., Cieslak, A., Pers-Kamczyc, E., Nowak, A., Szczechowiak, J. and Szumacher-Strabel, M. 2012. Effect of Mentha piperita L. on in vitro rumen methanogenesis and fermentation, Acta Agr. Scan. Section A — Anim. Sci. 62:46-52, DOI: 10.1080/09064702.2012.703228.</ref>; Cieslak et al., 2013<ref>Cieslak, A., Szumacher-Strabel, M., Stochmal, A., nad Oleszek, W. 2013. Plant components with specific activities against rumen methanogens. Animal, 7(s2):253-265.</ref>; Guyader et al., 2014<ref>Guyader, J., Eugène, M., Nozière, P., Morgavi, D.P., Doreau, M., and Martin, C. 2014. Influence of rumen protozoa on methane emission in ruminants: a meta-analysis approach. Animal 8:1816-1825.</ref>
|-
|Plant essential oils have been shown as promising feed additives to mitigate CH4 and ammonia emission, but results were inconsistent.
|Cobellis et al., 2016;<ref>Cobellis, G., Trabalza-Marinucci, M. and Yu, Z. 2016. Critical evaluation of essential oils as rumen modifiers in ruminant nutrition: A review. Sci. Total Environ. 545: 556-568.</ref>
Moate et al., 2011<ref>Moate, P.J., Deighton, M.H., Williams, S.R.O., Pryce, J.E., Hayes, B.J., Jacobs, J.L., Eckard, R.J., Hannah, M.C. and Wales, W.J., 2016. Reducing the carbon footprint of Australian milk production by mitigation of enteric methane emissions. Anim. Prod. Sci. 56:1017-1034.</ref>
|-
|Nitrate and sulphate addition decreased the enteric methane emissions negatively affecting diet digestibility and milk production. The effects of the salts are additive.
|van Zijderveld et al., 2010<ref>Van Zijderveld, S.M., Gerrits, W.J.J., Apajalahti, J.A., Newbold, J.R., Dijkstra, J., Leng, R A., and Perdok, H.B. 2010. Nitrate and sulfate: effective alternative hydrogen sinks for mitigation of ruminal methane production in sheep. J. Dairy Sci. 93:5856-5866.</ref>;
van Zijderveld et al., 2011<ref>Van Zijderveld, S.M., Gerrits, W.J.J., Dijkstra, J., Newbold, J.R., Hulshof, R.B.A., and Perdok, H.B. 2011. Persistency of methane mitigation by dietary nitrate supplementation in dairy cows. J. Dairy Sci. 94:4028-4038.</ref>
|-
|The methanogenesis in the rumen of calves is associated with the development of the ruminal protozoa population. The absence of protozoa in the rumen reduced both the CH4 production and the digestibility of carbohydrates.
|Schönhusen et al., 2003<ref>Schönhusen, U., Zitnan, R., Kuhla, S., Jentsch, W., Derno, M., and Voigt, J. 2003. Effects of protozoa on methane production in rumen and hindgut of calves around time of weaning. Arch. Anim. Nutr. 57:279-295.</ref>
|-
|Implementing good grazing management reduced gross energy intake loss as CH4 by 14%.
|Wims et al., 2010<ref>Wims, C.M., Deighton, M.H., Lewis, E., O’Loughlin, B., Delaby, L., Boland, T.M., and O’Donovan, M. 2010. Effect of pregrazing herbage mass on methane production, dry matter intake, and milk production of grazing dairy cows during the mid-season period. J. Dairy Sci. 93:4976 – 4985</ref>
|}
</center>

== Host genetics, physiology and environment ==
A low-moderate proportion of variation in CH4 emissions among ruminants is under genetic control. Heritability coefficients of MeY and RMPR were h²=0.22 and 0.19 respectively in a population of 1,043 Angus growing steers and heifers measured during 2 days in RC (Donoghue et al., 2016<ref name=":14">Donoghue, K.A., Bird-Gardiner, T., Arthur, P.F., Herd, R.M., and Hegarty, R.F. 2016. Genetic and phenotypic variance and covariance components for methane emission and postweaning traits in Angus cattle. J. Anim. Sci. 94:1438–1445. doi:10.2527/jas2015-0065.</ref>). The heritability coefficient of MeY was h²=0.13 in a population of1,225 dual-purpose growing sheep measured during 2 days in RC (Pinares-Patino et al., 2013<ref>Pinares-Patiño C.S., Hickey, S.M., Young, E.A., Dodds, K.G., MacLean, S., Molano, G., Sandoval, E., Kjestrup, H., Harland, R., Pickering, N.K., and McEwan, J.C. 2013. Heritability estimates of methane emissions from sheep. Animal 7: 316–321.</ref>). Table 2 contains information of heritability of traits related to CH4 production.

{| class="wikitable"
|-
! colspan="3" | ''Table 2. Heritability information of methane-related traits and measurements.''
|- style="background-color:#efefef;"
|+
!Factors
!Reference
|-
|List with several h2
|Pickering et al., 2015<ref>Pickering, N.K., Oddy, V.H., Basarab, J.A., Cammack, K., Hayes, B J., Hegarty, R.S., McEwan, J.C., Miller, S., Pinares, C., and de Haas, Y. 2015. Invited review: Genetic possibilities to reduce enteric methane emissions from ruminants. Animal 9:1431-1440.</ref>
|-
|List with several h2
|MPWG White paper Dec 18<ref>MPWG White paper Dec 18. <nowiki>http://www.asggn.org/publications,listing,95,mpwg-white-paper.html</nowiki>. Pickering, N.K., de Haas, Y., Basarab, J., Cammack, K., Hayes, B., Hegarty, R.S., Lassen, J., McEwan, J.C., Miller, S., Pinares-Patiño, C.S., Shackell, G., Vercoe, P. and Oddy, V.H. 2013.</ref>
|-
|Methane emissions from individual cows during milking varied between individuals with the same milk yield and fed the same diet. Between-cow variation in MERm is greater than within-cow variation and ranking of cows for CH4 emissions is consistent across time. Variation related to body weight, milk yield, parity, and week of lactation/days in milk. The monitored variation might offer opportunities for genetic selection.
|Garnsworthy et al., 2011A<ref name=":15">Garnsworthy, P.C., Craigon, J., Hernandez-Medrano, J.H. and Saunders, H. 2012A. On-farm methane measurements during milking correlate with total methane production by individual dairy cows. J. Dairy Sci. 95:3166-3180.</ref>;
Garnsworthy et al., 2011B<ref name=":16">Garnsworthy, P.C., Craigon, J., Hernandez-Medrano, J.H., and Saunders, N. 2012B. Variation among individual dairy cows in methane measurements made on farm during milking. J. Dairy Sci. 95:3181–3189.</ref>
|-
|Mechanistic modelling approach: potential for dietary intervention as a means of substantially reducing CH4 emissions without adverse effects on dietary energy supply.
|Mills et al., 2001<ref>Mills, J.A.N., Dijkstra, J., Bannink, A., Cammell, S.B., Kebreab, E., and France, J. 2001. A mechanistic model of whole-tract digestion and methanogenesis in the lactating dairy cow: model development, evaluation, and application. J. Anim. Sci. 79:1584-1597.</ref>
|-
|The CH4-to-CO2 ratio measured using the non-invasive portable air sampler and analyzer unit based on Fourier transform infrared (FTIR) detection method is an asset of the individual cow and may be useful in both management and genetic evaluations.
|Lassen et al., 2012<ref name=":17">Lassen, J., Lovendahl, P., and Madsen, J. 2012. Accuracy of noninvasive breath methane measurements using Fourier transform infrared methods on individual cows. J. Dairy Sci. 95:890-898.</ref>
|-
|The estimated heritability for CH4 g/day and CH4 g/kg of FPCM were lower than common production traits but would still be useful in breeding programs.
|Kandel et al., 2013<ref>Kandel, P.B., Vanrobays, M.L., Vanlierde, A., Dehareng, F., Froidmont, E., Dardenne, P., Lewis, E., Buckley, F., Deighton, M.H., McParland, S. and Gengler, N., 2013. Genetic parameters for methane emissions predicted from milk mid-infrared spectra in dairy cows. J. Dairy Sci. 95(E-1):p.388.</ref>
|-
|Genetic correlation between CH4 intensity and milk yield (MY) was - 0.67 and with milk protein yield (PY) was -0.46 in Holstein cows.
|Kandel et al., 2014A<ref name=":37" />, B<ref>Kandel, P.B., Vanderick, S., Vanrobays, M.L., Vanlierde, A., Dehareng, F., Froidmont, E., Soyeur,t H., and Gengler, N. 2014B. Consequences of selection for environmental impact traits in dairy cows. In: 10th World Congress on Genetics Applied to Livestock Production (WCGALP), 17-22 August, 2014. Vancouver, Canada.</ref>
|-
|Milk production and CH4 emissions of dairy cows seemed to be influenced by the temperature humidity index.
|Vanrobays et al., 2013A<ref>Vanrobays, M.-L., Gengler, N., Kandel, P.B., Soyeurt, H., and Hammami, H. 2013A. Genetic effects of heat stress on milk yield and MIR predicted methane emissions of Holstein cows. 64th Annual meeting of the European Federation of Animal Science, p498</ref>
|-
|Estimate the heritability of the estimated methane emissions from 485 Polish Holstein-Friesian dairy cows at 2 commercial farms using FTIR spectroscopy during milking in an automated milking system by implementing the random regression method. The heritability level fluctuated over the course of lactation, starting at 0.23 (SE 0.12) and then increasing to its maximum value of 0.3 (SE 0.08) at 212 DIM and ending at the level of 0.27 ± 0.12. Average heritability was 0.27 ± 0.09.
|Pszczola et al., 2017<ref name=":18">Pszczola, M., Rzewuska, K., Mucha, S., and Strabel, T. 2017. Heritability of methane emissions from dairy cows over a lactation measured on commercial farms. J. Anim. Sci. 95:4813-4819. doi: 10.2527/jas2017.1842.</ref>
|-
|CH4 measured with a portable air-sampler FTIR detection method on 3,121 Holstein dairy cows from 20 herds using automatic milking systems. The heritability of CH4_MILK was 0.21 ± 0.06. It was concluded that a high genetic potential for milk production will also mean a high genetic potential for CH4 production. The results suggested that CH4 emission is partly under genetic control, that it is possible to decrease CH4 emission from dairy cattle through selection, and that selection for higher milk yield will lead to higher genetic merit for CH4 emission/cow per day.
|Lassen and Løvendahl, 2016<ref>Lassen, J., and Løvendahl, P. 2016. Heritability estimates for enteric methane emissions from Holstein cattle measured using noninvasive methods. J. Dairy Sci. 99:1959-1967.</ref>
|-
|CH4 production was measured of 184 Holstein-Friesian cows in. the milking robot with a in total 2,456 observations for CH4 production. Heritability for CH4 production ranged from 0.12 ± 0.16 to 0.45 ± 0.11, and genetic correlations with MY ranged from 0.49 ± 0.12 to 0.54 ± 0.26. The positive genetic correlation between CH4 production and milk yield indicates that care needs to be taken when genetically selecting for lower CH4 production, to avoid a decrease in MY at the animal level. However, this study shows that CH4 production is moderately heritable and therefore progress through genetic selection is possible.
|Breider et al., 2019<ref>Breider, I.S., Wall, E., Garnsworthy, P.C. 2019. Short communication: Heritability of methane production and genetic correlations with milk yield and body weight in Holstein-Friesian dairy cows, J. Dairy Sci. 102: 7277-7281.</ref>
|-
|CH4 concentration was measured with NDIR, and CH4 production was estimated from CH4 concentration and body weight. Heritability for CH4 concentration was 0.11 ± 0.03 and for CH4 production 0.12 ± 0.04. Positive genetic correlation was observed with MY (0.17-0.21), PY (0.22-0.31) and FY (0.27-0.29). Other type traits showed positive correlation with methane production (chest width=0.26, angularity =0.19, stature = 0.43 and capacity = 0.31) possibly associated to higher milk feed intake from these animals. Rumination time was negatively correlated to CH4 production (-0.24) and CH4 concentration (-0.43). However, larger CH4 production and CH4 concentration was associated with shorter days open.
|López-Paredes et al. (2020)<ref>Lopez-Paredes, J., Goiri, I., Atxaerandio, R., García-Rodríguez, A., Ugarte, E., Jiménez-Montero, J.A., Alenda, R and González-Recio, O. 2020. Mitigation of greenhouse gases in dairy cattle via genetic selection (i): Genetic parameters of direct methane using non-invasive methods and its proxies. J. Dairy Sci. 103.</ref>
|-
|Genetic parameters of CH4 emissions predicted from milk fatty acid profile (FA) and those of their predictors in 1,091 Brown Swiss cows reared on 85 farms showed that enteric CH4 emissions of dairy cows can be estimated on the basis of milk fatty acid profile. Additive genetic variation of CH4 traits was shown which could be exploited in breeding programmes.
|Bittante and Cecchinato, 2020<ref>Bittante, G., and Cecchinato, A. 2020. Heritability estimates of enteric methane emissions predicted from fatty acid profiles, and their relationships with milk composition, cheese-yield and body size and condition, It. J. An. Sci. 19:114-126, DOI: 10.1080/1828051X.2019.1698979</ref>
|-
|A total of 670 test day records were recorded on lactating Holstein Friesian cows reared in 10 commercial dairy herds. Predicted methane production (PMP) was estimated to be 15.33±1.52 MJ/d in dairy cows with 23.53±6.81 kg/d of milk yeild (MY) and 3.57±0.68% of fat content (FC). Heritability of MY was 0.09 with a posterior probability for values of h2 greater than 0.10 of 44%. Estimates of heritability for FC and protein content (PC) were 0.17 and 0.34, respectively, with a posterior probability for values of h2 greater than 0.10 of 77% and 99%. For somatic cell score (SCS), heritability was 0.13 with a posterior probability for values of h2 greater than 0.10 of 67%. Heritability for the trait PMP was moderate to low (0.12); however, posterior probability for values of h2 greater than 0.10 was 60%. Medians of the posterior distributions of genetic correlations between PMP and milk production traits were: 0.92, 0.67, 0.14, and 0.14 between PMP and MY, PMP and FC, PMP and PC, and PMP and SCS, respectively. Reduction of PMP seems to be viable through selection strategies without affecting udder health and PC.
|Cassandro et al., 2010<ref name=":29">Cassandro, M., Cecchinato, A., Battagin, M., Penasa, M., 2010. Genetic parameters of predicted methane production in Holstein Friesian cowsIn: Proc. 9th World Congr. on Genetics Applied to Livestock Production, Leipzig, Germany. . Page 181</ref>
|-
|GWAS to study the genetic architecture of CH4 production and detected genomic regions affecting CH4 production. Detected regions explained only a small proportion of the heritable variance. Potential QTL regions affecting CH4 production were located within QTLs related to feed efficiency, milk-related traits, body size and health status. Five candidate genes were found: CYP51A1 on BTA 4, PPP1R16B on BTA 13, and NTHL1, TSC2, and PKD1 on BTA 25. These candidate genes were involved in a number of metabolic processes that are possibly related to CH4 production. One of the most promising candidate genes (PKD1) was related to the development of the digestive tract. The results indicate that CH4 production is a highly polygenic trait.
|Pszczola et al., 2018<ref name=":19">Pszczola, M., Strabel, T., Mucha, S., and Sell-Kubiak, E. 2018. Genome-wide association identifies methane production level relation to genetic control of digestive tract development in dairy cows. Scientific Rep. 8 (1), 15164 <nowiki>https://doi.org/10.1038/s41598-018-33327-9</nowiki></ref>
|-
|A 1000-cow study across European countries revealed that the ruminant microbiomes can be controlled by the host animal. A 39- member subset of the core microbiome formed hubs in co-occurrence networks linking microbiome structure to host genetics and phenotype (CH4 emissions, rumen and blood metabolites, and milk production efficiency).
|Wallace et al.,
2019<ref>Wallace, R.J., Sasson, G., Garnsworthy, P.C., Tapio, I., Gregson, E., Bani, P., Huhtanen, P., Bayat, A.R., Strozzi, F., Biscarini, F., Snelling, T.J., Saunders, N., Potterton, S.L., Craigon, J., Minuti, A., Trevisi, E., Callegari, M.L., Cappelli, F.P., Cabezas-Garcia, E.H., Vilkki, J., Pinares-Patino, C., Fliegerov, K.O., Mrazek, J., Sechovcova, H., Kope, J., Bonin, A., Boyer, F., Taberlet, P., Kokou, F., Halperin, E., Williams, J.L., Shingfield, K.J., and Mizrahi, I. 2019. A heritable subset of the core rumen microbiome dictates dairy cow productivity and emissions. Sci. Adv. 5:(7):eaav8391. doi 10.1126/sciadv.aav8391.</ref>
|}

</div>
</div>

= Methane measurements methods =
<div class="mw-collapsible mw-collapsed">
<div>
Several factors influence the choice of measurement method such as cost, level of accuracy, precision, scope of application, and scale, which vary across disciplines (Cassandro et al., 2013<ref>Cassandro, M. 2013. Comparing local and cosmopolitan cattle breeds on added values for milk and cheese production and their predicted methane emissions. Animal Genetic Resources/Ressources génétiques animales/Recursos genéticos animales, available on CJO2013. doi:10.1017/S2078 63361200077X</ref>; Hammond et al., 2016A<ref>Hammond, K.J., Crompton, L.A., Bannink, A., Dijkstra, J., Yáñez-Ruiz, D.R., O’Kiely, P., Kebreab, E., Eugenè, M.A., Yu, Z., Shingfield, K.J., Schwarm, A., Hristov, A.N., and Reynolds, C.K. 2016A. Review of current in vivo measurement techniques for quantifying enteric methane emission from ruminants. Anim. Feed Sci. Technol. 219:13–30. doi:10.1016/j.anifeedsci.2016.05.018.</ref>; Garnsworthy et al., 2019<ref>Garnsworthy, P.C. Difford, G.F. Bell, M.J. Bayat, A.R. Huhtanen, P. Kuhla, B. Lassen, J. Peiren, N. Pszczola, M; Sorg, D. Visker, M.H., and Yan, T. 2019 Comparison of Methods to Measure Methane for Use in Genetic Evaluation of Dairy Cattle. Animals 9:837, 12p.</ref>). For instance, genetic selection programs require CH4 measurements on thousands of related individuals under the environmental conditions in which the animals are expected to perform (Falconer and Mackay, 1996<ref>Falconer, D., and Macka,y T. 1996. Introduction to quantitative genetics (4th edn). ISBN-13: 978-0582243026; ISBN-10: 0582243025</ref>). This can be challenging because dairy cattle perform in a wide range of conditions (e.g. grazing vs indoor housing). There are a number of different measurement methods currently being employed, each with advantages and disadvantages in terms of the factors listed above. The currently accepted and widely used measurement methods are listed and described below. The main features of methods for measuring CH4 output by individual animals are summarised in Table 3. Values for each feature are based on experience of experts in METHAGENE WG2 who have used the methods. All values are relative, and somewhat subjective, because absolute values will depend on installation and implementation of each method at different research centres. It should be noted that the measuring methods can be divided in two major sections: methods that measure the concentration and flux of CH4 (e.g. the respiration chamber), and methods that measure the flux of CH4 through the device (e.g. GreenFeed). This affects the useability of the methods for answering research questions – please see also the recommendations at the end of these guidelines.

== Respiration chambers ==
Respiration chambers are calibrated to be accurate and precise, and are the gold standard for benchmarking new methods. Only respiration chambers measure total emissions from the animal via the oral, nasal and anal routes; all other methods ignore emissions via the anus and only measure CH4 emitted in breath. Breath measurements are justified because 99% of CH4 is emitted from the mouth and nostrils, and only 1% via the anus (Murray ''et al.'', 1976<ref name=":20">Murray, R.M., Bryant, A.M., and Leng, R.A.. 1976. Rates of production of methane in the rumen and large intestine of sheep. Br. J. Nutr. 36:1-14.</ref>).

A single animal (or occasionally more) is confined in a chamber for between 2 and 7 days. Concentration of CH4 (and other gases if required) is measured at the air inlet and outlet vents of the chamber. The difference between outlet and inlet concentrations is multiplied by airflow to indicate CH4 emissions fluxes. In most installations, a single gas analyser is used to measure both inlet and outlet concentrations, often for two or more chambers. This involves switching the analyser between sampling points at set intervals, so concentrations are actually measured for only a fraction of the day. If the sampling points acquisition frequency is high it enables to draw the diurnal pattern of methane emission, comparable to the GreenFeed system.

Respiration chambers vary in construction materials, size of chamber, gas analysis equipment and airflow rate, all of which can influence results. Validation of 22 chambers at six UK research sites revealed an uncertainty of 25.7% between facilities, which was reduced to 2.1% when correction factors were applied to trace each facility to the international standard CH4 (Gardiner ''et al.'', 2015<ref name=":21">Gardiner, T.D., Coleman, M.D., Innocenti, F., Tompkins, J., Connor, A., Garnsworthy, P.C., Moorby, J.M., Reynolds, C.K., Waterhouse, A., and Wills, D. 2015. Determination of the absolute accuracy of UK chamber facilities used in measuring methane emissions from livestock. Measurement 66: 272-279.</ref>). The main sources of uncertainty were stability and measurement of airflow, which are crucial for measuring CH4 emission rate. The authors concluded, however, that chambers were accurate for comparing animals measured at the same site. This is an added challenge to benchmarking alternative methods with respiration chambers if respiration chambers themselves have not been benchmarked with respiration chambers at other facilities. It should be noted that substantial errors can occur if appropriate calibration procedures are not followed (Gardiner ''et al.'', 2015<ref name=":21" />).

For large-scale evaluation of CH4 emissions by individual animals, respiration chambers are challenging with only a single study in growing Angus steers and heifers exceeding 1000 animals and finding CH4 production to be moderately heritable h2 = 0.27 ± 0.07 (Donoghue ''et al.'', 2016<ref name=":14" />). Installation and running costs are high, as only one animal is normally measured at once. If we assume that the monitoring time is three days per animal, and chambers are run continuously, then maximum throughput would be approximately 100 animals per chamber per year. In practice, throughput is likely to be 30 to 50 animals per year. Cows are social animals and confinement in a chamber may ultimately influence their feeding behaviour resulting in less feed consumed and in a different meal pattern compared with farm conditions. Altered feeding pattern or level is not a problem for metabolic studies evaluating feeds but can be a problem when evaluating individual animals. Furthermore, the representativeness of respiration chambers to grazing systems has been called into question (Pinares-Patiño ''et al.'', 2013<ref name=":13" />). However, promising developments have led to more animal friendly respiration chambers constructed from cheaper, transparent materials. These lower the cost and reduce the stress of confinement with minimal disruptions to accuracy, precision and no drop in feed intake of the cows (Hellwing ''et al.'', 2012<ref name=":12" />).

Where an alternative method may be cheaper, less invasive, easier to implement, or have a wider scope of application, it is of value to assess the relative accuracy, precision and correlation with the gold standard to assess the relative worth of the alternative method (Barnhart ''et al.'', 2007<ref>Barnhart, H.X., Kosinski, A.S., and Haber, M.J. 2007. Assessing Individual Agreement. J. Biopharm. Stat. 17:697–719. doi:10.1080/10543400701329489.</ref>). All methods measure CH4 with some level of error, so the ‘true value’ of an individual is not known. However, when the level of measurement error increases, so too does the imprecision. When comparing two methods where one or both methods has high imprecision a phenomenon known as ‘attenuation of errors’ occurs (Spearman, 1904<ref>Spearman, C. 1904. The Proof and Measurement of Association between Two Things. Am. J. Psychol. 15:72–101.</ref>). The increased measurement error biases the correlation between the two methods downwards and reduces the efficacy of detecting significant differences in accuracy (Adolph & Hardin, 2007<ref>Adolph, S.C., and Hardin, J.S. 2007. Estimating phenotypic correlations: Correcting for bias due to intraindividual variability. Funct. Ecol. 21:178–184. doi:10.1111/j.1365-2435.2006.01209.x.</ref>). Or in terms of linear regression terms, when the observed CV of an alternative method is higher than that of the gold standard method, the slope of regression between the methods is decreased and the intercept is biased upwards.

== Portable Accumulation Chambers ==
In Australia and New Zealand an alternative method was developed for the short-term measurement of Methane Production Rate (MPR) of sheep using Portable Accumulation Chambers (PAC) during 1 hour without leading discomfort to the animals. Similarly to RC, CH4 emissions recorded in PAC include gases from flatulence in addition to eructed and expired CH4, but only during 1 hour. For a detailed comparison of the PAC and respiration chamber methods see Jonker ''et al.'' (2018).

== SF6 ==
The SF6 technique samples breath over 24 hours, whereas other techniques use spot samples of breath over periods of minutes throughout the day, so diurnal variation has to be considered. The majority of CH4 (87-99%) is released by eructation (Blaxter & Joyce, 1963; Murray ''et al.'', 1976<ref name=":20" />), which provides a clear signal for sample processing. Please note that the tracheostomy used in Murray ''et al.'' (1976)<ref name=":20" /> may have resulted in a higher percentage, but in both publications, it is clear that the majority of the CH4 is released via eructation.

The SF6 tracer gas technique was developed in an attempt to measure CH4 emissions by animals without confinement in respiration chambers (Johnson ''et al.'', 1994<ref>Johnson, K., Huyler, M., Westberg, H., Lamb, B., and Zimmerman, P. 1994. Measurement of methane emissions from ruminant livestock using a sulfur hexafluoride tracer technique. Environ. Sci. Technol. 28:359-362.</ref>). Air is sampled near the animal’s nostrils through a tube attached to a halter and connected to an evacuated canister worn around the animal’s neck or on its back. A capillary tube or orifice plate is used to restrict airflow through the tube so that the canister is between 50 and 70% full in approximately 24 hours. A permeation tube containing SF6 is placed into the rumen of each animal. The pre-determined release rate of SF6 is multiplied by the ratio of CH4 to SF6 concentrations in the canister to calculate CH4 emission rate.

Many research centres have used the SF6 technique with variations in design of sampling and collection equipment, permeation tubes, and gas analysis (Berndt ''et al.'', 2014<ref name=":22" />). Reliable results depend on following standard protocols, with greatest variation coming from accuracy of determining SF6 release rate from permeation tubes and control of sampling rate. With capillary tubes, sampling rate decreases as pressure in the canister increases, whereas an orifice plate gives a steadier sampling rate over 24 hours (Deighton ''et al.'', 2014<ref>Deighton, M.H., Williams, S.R.O., Hannah, M.C., Eckard, R.J., Boland, T.M., Wales, W.J., and Moate, P.J. 2014. A modified sulphur hexafluoride tracer technique enables accurate determination of enteric methane emissions from ruminants. Anim. Feed Sci. Technol. 197:47-63.</ref>). A source of error that has not been evaluated is that animals might interact and share CH4 emissions when the sampling tube of one animal is near the head of another animal. There is good agreement between CH4 emissions measured by the SF6 technique and respiration chambers, although results from the SF6 technique are more variable (Grainger ''et al.'', 2007<ref>Grainger, C., Clarke, T., McGinn, S.M., Auldist, M.J., Beauchemin, K.A., Hannah, M.C., Waghorn, G.C., Clark, H., and Eckard, R J. 2007. Methane emissions from dairy cows measured using the sulfur hexafluoride (SF6) tracer and chamber techniques. J. Dairy Sci. 90:2755-2766.</ref>; Muñoz ''et al.'', 2012<ref>Muñoz, C., Yan, T., Wills, D.A., Murray, S., and Gordon, A.W. 2012. Comparison of the sulfur hexafluoride tracer and respiration chamber techniques for estimating methane emissions and correction for rectum methane output from dairy cows. J. Dairy Sci. 95:3139-3148.</ref>).

== Breath sampling during milking and feeding ==
Several research groups have developed methods to measure CH4 concentration in breath of cows during milking and/or feeding. These are often referred to as ‘sniffer methods’ because they use devices originally designed to detect dangerous gas leaks. Air is sampled near the animal’s nostrils through a tube fixed in a feed bin and connected directly to a gas analyser. The feed bin might be in an automatic milking station (Garnsworthy ''et al.'', 2012A<ref name=":15" />, B<ref name=":16" />; Lassen ''et al.'', 2012<ref name=":17" />; Pszczola ''et al.'', 2017<ref name=":18" />, 2018<ref name=":19" />, 2019<ref>Pszczola, M., Calus, M.P.L., Strabel, T. 2019. Genetic correlations between methane and milk production, conformation, and functional traits. J. Dairy Sci. 102:5342-5346.</ref>) or in a concentrate feeding station (Negussie ''et al.'', 2017<ref>Negussie, E., Lehtinen, J., Mäntysaari, P., Bayat, A.R., Liinamo, A.E., Mäntysaari, E.A., and Lidauer, M.H. 2017. Non-invasive individual methane measurement in dairy cows. Animal 11:890-899.</ref>). Different research centres use different gas analysers (Nondispersive Infrared (NDIR), Fourier-transform infrared (FTIR) or photoacoustic infrared (PAIR)) and different sampling intervals (1, 5, 20 or 90-120 seconds). Methane concentration during a sampling visit of typically between 3 and 10 minutes may be specified as the overall mean, or the mean of eructation peaks. Some centres use CO2 as a tracer gas and calculate daily CH4 output according to ratio of CH4 to CO2 and daily CO2 output predicted from performance of the cow (Madsen ''et al.'', 2010<ref>Madsen, J., Bjerg, B.S., Hvelplund, T., Weisbjerg, M.R., and Lund, P. 2010. Methane and carbon dioxide ratio in excreted air for quantification of the methane production from ruminants. Livest. Sci. 129:223-227.</ref>). Repeatability and rank correlations were higher for eructation peaks than for mean concentrations, and were higher for eructation peaks than for CH4 to CO2 ratio (Bell ''et al.'', 2014<ref>Bell, M.J., Saunders, N., Wilcox, R.H., Homer, E.M., Goodman, J.R., Craigon, J., Garnsworthy, P.C. 2014 Methane emissions among individual dairy cows during milking quantified by eructation peaks or ratio with carbon dioxide. J. Dairy Sci. 97:6536–6546.</ref>). However, all methods show good repeatability.

== GreenFeed ==
GreenFeed (C-Lock Inc., Rapid City, South Dakota, USA) is a sniffer system where breath samples are provided when animals visit a bait station (Huhtanen ''et al.'', 2015<ref name=":23" />). GreenFeed Emission Monitoring (GEM) systems are designed for measuring animal emissions in their production environment. As with other sniffer systems, GreenFeed samples breath from individual animals several times (in general 4 to 6 times) per day for short periods (3 to 7 minutes in which an under pressure is created to suck the whole breath of the animal to measure the flux). They record CH4 and carbon dioxide (CO2) fluxes during short-term periods of 3-10 minutes when cattle visit an automated feeder fitted with a semi-enclosed head hood in which air is continuously drawn through an air-collection pipe (C-Lock, 2016; Huhtanen ''et al.'', 2015<ref name=":24" />; Hammond ''et al.'', 2016A<ref name=":25" />; Velazco ''et al.'', 2016<ref name=":131" />). Air samples are continually (every second) analyzed for CH4 and CO2 concentrations using non-dispersive infrared sensors. Gas fluxes are eventually calculated as the product of the air flow in the collection pipe and the concentration of gases corrected for the background concentrations and adjusted to standardized temperature, humidity and pressure. The position of the head in the feeder is detected by an infrared sensor. Gas fluxes are not calculated if the head is not correctly positioned in the feeder as not all the air in the feeder may be collected.

GreenFeed is a portable standalone system used in barn and pasture applications and incorporates an extractor fan to ensure active airflow and head position sensing for representative breath sampling (Hammond ''et al.'', 2016B<ref name=":26" />). Measurements are pre-processed by the manufacturer, and data are available in real-time through a web-based data management system (Hammond ''et al.'', 2015<ref name=":5" />). Because GreenFeed captures a high proportion of emitted air and measures airflow, which can be calibrated using a tracer gas, CH4 emission is estimated as a flux at each visit. Providing visits occur throughout the 24 hours, CH4 emission can be estimated directly as g/day (Hammond ''et al.'', 2015<ref name=":5" />; Huhtanen ''et al.'', 2015<ref name=":24" />). More importantly, repeatability of CH4 measurement must be high so the duration of the measurement period must be taken into account (Huhtanen ''et al.'', 2013; Arbre ''et al.'', 2016<ref name=":7" />); (R=0.7 after 17 days duration of measurement period, or R=0.93 after 45 days, Arbre ''et al.'', 2016<ref name=":7" />).

== Laser methane detector ==
The laser CH4 detector (LMD) is a highly responsive, hand-held device that is pointed at an animal’s nostrils and measures CH4 column density along the length of the laser beam (ppm.m). In the first implementation of LMD on a farm, measurements for each cow were taken over periods of 15 to 25 seconds between eructation events and could detect CH4 emitted each time the animal breathed out (Chagunda ''et al.'', 2009 <ref>Chagunda, M.G.G., Ross, D., and Robert,s D J. 2009. On the use of a laser methane detector in dairy cows. Comput. Electron. Agric. 68:157-160.</ref>; Sorg ''et al.'', 2016<ref>Sorg, D., Mühlbach, S., Rosner, F., Kuhla, B., Derno, M., Meese, S., Schwarm, A., Kreuzer, M., and Swalve, H. 2016. The agreement between two next-generation laser methane detectors and respiration chamber facilities in recording methane concentrations in the spent air produced by dairy cows. Comp. Electr. Agric. 143:262-272. </ref>, 2017<ref name=":27" />). In a later study with sheep and beef cattle, monitoring periods of 2 to 4 minutes allowed authors to separate breathing cycles from eructation events (Ricci ''et al.'', 2014<ref>Ricci, P., Chagunda, M.G.G., Rooke, J., Houdijk, J.G.M., Duthie, C-A., Hyslop, J., Roehe, R., and Waterhouse, A. 2014. Evaluation of the laser methane detector to estimate methane emissions from ewes and steers. J. Anim. Sci. 92:5239-5250.</ref>). Typically, animals are restrained either manually or in head yokes at a feed fence for the required length of time. The operator has to stand at the same distance (1 to 3 m) from each animal every time and must be careful to keep the laser pointed at the animal’s nostrils throughout the measurement period.
----[1] Consensus views based on experiences of METHAGENE WG2 members (www.methagene.eu).

[2] Per measuring unit or group of animals.

[3] Compared to no methane recording: low = measuring in situ; medium = some handling, training or change in routine; high = confinement.

[4] Medium if using FTIR analyser.<center>
{| class="wikitable"
|-
! colspan="7" style="text-align:left;" | ''Table 3. Summary of the main features of methods for measuring CH4 output by individual animals.''
|- style="background-color:#efefef;"
|+
! style="text-align:left;" | Method
! style="text-align:center;" | Purchase cost
! style="text-align:center;" | Running costs
! style="text-align:center;" | Labour
! style="text-align:center;" | Repeatability
! style="text-align:center;" | Behaviour alteration
! style="text-align:center;" | Throughput
|-
| style="text-align:left;" | Respiration chamber
| style="text-align:center;" | High
| style="text-align:center;" | High
| style="text-align:center;" | High
| style="text-align:center;" | High
| style="text-align:center;" | High
| style="text-align:center;" | Low
|-
| style="text-align:left;" | SF6 technique
| style="text-align:center;" | Medium
| style="text-align:center;" | High
| style="text-align:center;" | High
| style="text-align:center;" | Medium
| style="text-align:center;" | Medium
| style="text-align:center;" | Medium
|-
| style="text-align:left;" | Breath sampling during milking and feeding
| style="text-align:center;" | Low
| style="text-align:center;" | Low
| style="text-align:center;" | Low
| style="text-align:center;" | Medium
| style="text-align:center;" | None
| style="text-align:center;" | High
|-
| style="text-align:left;" | GreenFeed
| style="text-align:center;" | Medium
| style="text-align:center;" | Medium
| style="text-align:center;" | Medium
| style="text-align:center;" | Medium
| style="text-align:center;" | Medium
| style="text-align:center;" | Medium
|-
| style="text-align:left;" | Laser methane detector
| style="text-align:center;" | Low
| style="text-align:center;" | Low
| style="text-align:center;" | High
| style="text-align:center;" | Low
| style="text-align:center;" | Low-Medium
| style="text-align:center;" | Medium
|}
</center>

</div>
</div>

= Discussion of methods =
<div class="mw-collapsible mw-collapsed">
<div>
== SF6 vs Respiration Chamber ==
For large-scale evaluation of CH4 emissions by individual animals, the SF6 technique is more useful than respiration chambers. Animal behaviour and intake might be affected by wearing the apparatus, and by daily handling to exchange canisters, but the technique is considerably less intrusive than respiration chambers because cows remain in the herd. Labour and monetary costs for changing canisters each day and for lab analysis are high. Throughput is limited by the number of sets of apparatus available, handling facilities, labour, and the capacity of the lab for gas analysis. Animals need to be measured for 5 to 7 days, and it is recommended that group size should be less than 15 animals (Berndt et al., 2014<ref name=":22">Berndt, A., Boland, T.M., Deighton, M.H., Gere, J.I., Grainger, C., Hegarty, R.S., Iwaasa, A.D., Koolaard, J.P., Lassey, K.R., Luo D., Martin, R.J., Martin, C., Moate, P.J., Molano, G., Pinares-Patiño, C., Ribaux, B.E., Swainson, N.M., Waghorn, G.C., and Williams, S.R.O. 2014. Guidelines for use of sulphur hexafluoride (SF6) tracer technique to measure enteric methane emissions from ruminants. Pages 166. M. G. Lambert, ed. New Zealand Agricultural Greenhouse Gas Research Centre, New Zealand. </ref>), so maximum throughput would be about 750 animals per year. The method may be better suited for in housed conditions because of the labour and the potential movement restriction of the animals due to wearing the apparatus.

== Breath sampling during milking and feeding vs Respiration Chamber ==
For large-scale evaluation of CH4 emissions by individual animals, breath-sampling methods have significant advantages compared with other methods. Breath-sampling methods are non-invasive because, once installed, animals are unaware of the equipment and are in their normal environment. Animals follow their normal routine, which includes milking and feeding, so no training of animals, handling, or change of diet is required. Equipment is relatively cheap, although more expensive gas analysers are available, and running costs are negligible.

The compromise for non-invasiveness of breath-sampling is that concentrations of gases in the sampled air are influenced by cow head position relative to the sampling tube (Huhtanen et al., 2015<ref name=":23">Huhtanen, P., Cabezas-Garcia, E.H., Utsumi, S., and Zimmerman, S. 2015. Comparison of methods to determine methane emissions from dairy cows in farm conditions. J. Dairy Sci. 98:3394–3409. doi:10.3168/jds.2014-9118.</ref>). The use of head position sensors and data filtering algorithms can remove the effects when the cow’s head is completely out of the feed bin (Difford et al., 2016<ref>Difford, G.F., Lassen, J., and Løvendahl, P. 2016. Interchangeability between methane measurements in dairy cows assessed by comparing precision and agreement of two non-invasive infrared methods. Comput. Electron. Agric. 124:220–226. doi:10.1016/j.compag.2016.04.010.</ref>), but not within the feed bin. Consequently, sniffer measurements are more variable than flux methods, with factors like variable air flow in the barn increasing measurement error (imprecision), and head position, a highly repeatable character, inflating between-cow variability.

Using CO2 as a tracer gas partly addresses the issue but, because CO2 arises from metabolism as well as rumen fermentation, variability of CO2 emissions has to be considered. A further consideration is diurnal variation in breath concentrations of CH4 and CO2 because animals are spot-sampled at different times of day and night. Diurnal variation can be accounted for either by fitting a model derived from the whole group of animals, or by including time of measurement in the statistical model (Lassen et al., 2012<ref>Lassen, J., Lovendahl, P., and Madsen, J. 2012. Accuracy of noninvasive breath methane measurements using Fourier transform infrared methods on individual cows. J. Dairy Sci. 95:890-898.</ref>).

The number of observations per analyser is limited only by number of cows assigned to one automatic milking station or concentrate feeding station and length of time equipment is installed. Typically, each analyser will record 40 to 70 animals 2 to 7 times per day for 7 to 10 days, although the number of sampling stations per analyser can be increased by using an automatic switching system (Pszczola et al., 2017<ref>Pszczola, M., Rzewuska, K., Mucha, S., and Strabel, T. 2017. Heritability of methane emissions from dairy cows over a lactation measured on commercial farms. J. Anim. Sci. 95:4813-4819. doi: 10.2527/jas2017.1842.</ref>). Throughput per analyser is likely to be 2,000 to 3,000 animals per year.

== NDIR vs LMD ==
Both methods are low invasive. LMD needs larger labor force, wheras NDIR can be used during milking and feeding. According to Rey at al. (2019)<ref>Rey, J., Atxaerandio, R., Ruiz, R, Ugarte, E., Gonzalez-Recio, O., Garcia-Rodriguez, A., and Goiri, I. 2019. Comparison Between Non-Invasive Methane Measurement Techniques in Cattle. Animals 9(8): 563. <nowiki>https://doi.org/10.3390/ani9080563</nowiki></ref>, the repeatability of the CH4 concentration was greater for NDIR (0.42) than for LMD (0.23). Correlation between methods was moderately high and positive for CH4 concentration (0.73 and 0.74,respectively) and number of peaks (0.72 and 0.72, respectively), and the repeated measures correlation and the individual-level correlation were high (0.98 and 0.94, respectively). A high coefficient of individual agreement for the CH4 concentration (0.83) and the number of peaks (0.77) were observed between methods. The study suggests that methane concentration measurements obtained from NDIR and LMD cannot be used interchangeably. But the use of both methods could be considered for genetic selection purposes or for mitigation strategies only if sources of disagreement, which result in different between subject and within-subject variabilities, are identified and corrected for.

== Greenfeed ==
A limitation of the GreenFeed system is that animals require training to use the system, although animals which have been trained to use the system will readily use it again (Velazco et al., 2014<ref>Velazco, J.I., Cottle, D.J., and Hegarty, R.S. 2014. Methane emissions and feeding behaviour of feedlot cattle supplemented with nitrate or urea. Anim. Prod. Sci. 54:1737–1740. doi:10.1071/AN14345.</ref>). However, some animals will not use the system or will use it infrequently, and frequency of visits is affected by diet (Hammond et al., 2016B<ref name=":26">Hammond, K.J., Jones, A.K., Humphries, D.J., Crompton, L.A., and Reynolds, C.K. 2016B. Effects of diet forage source and neutral detergent fiber content on milk production of dairy cattle and methane emissions determined using GreenFeed and respiration chamber techniques. J. Dairy Sci. 99:7904–7917. doi:10.3168/jds.2015-10759.</ref>). This can be a challenge when screening commercial herds for CH4 emission under genetic evaluation. On the other hand, animals seem to get used to the equipment rapidly, and the sound produced by the system is remembered by the animals easily (personal information Dr. Finocchiaro). Alternatively, as practised in Canada, the unit is moved to individual animals in a tie-stall setting multiple times a day (personal information Prof C.F. Baes). Thus, action of individual animals is not needed. The manufacturer recommends 15 to 25 animals per GreenFeed unit, and recordings are made typically for 7 days. If all animals visit the unit adequately, throughput per unit is likely to be 750 to 1,250 animals per year. Sebek et al. (2019A, B)<ref>Sebek, L.B. 2019A. Project 11: Enterisch methaan: emissievariatie in de Nederlandse melkveestapel. 1 p. Wageningen : Wageningen University & Research.</ref> and Bannink et al. (2018)<ref>Bannink, A., Spek, J.W., Dijkstra, J., and Sebek, L.B. 2018. A Tier 3 Method for Enteric Methane in Dairy Cows Applied for Fecal N Digestibility in the Ammonia Inventory. In: Front. Sust. Food Syst. 2:66.</ref> showed the usefulness of the GreenFeed method in an on farm setting.

== Laser Methane Detector ==
The LMD can be used in the animal’s normal environment, although for consistency restraint is required during measurement. Because the LMD measures CH4 in the plume originating from the animal’s nostrils, results can be affected by factors such as: distance from the animal; pointing angle; animal’s head orientation and head movement; air movement and temperature in the barn; adjacent animals; and operator variation (Sorg et al., 2017<ref name=":27">Sorg, D., Difford, G.F., Mühlbach, S., Kuhla, B., Swalve, H.H., Lassen, J., Strabel, T., and Pszczola, M. 2017. Comparison of a laser methane detector with the GreenFeed and two breath analysers for on-farm measurements of methane emissions from dairy cows. Comp. Elec. Agric. 153:285-294.</ref>). Operator variation is likely to be one of the biggest factors because the operator controls distance and pointing angle, and is responsible for ensuring the laser remains on target. The structure of the barn and the resulting ventilation conditions and wind speed at the location of the measurement are also considerable sources of variation in recorded CH4. Assuming operator fatigue does not limit measurements, each LMD could record up to 10 animals per hour. If each animal is recorded 3 times (on 3 consecutive days, for example, as in Mühlbach et al. (2018)<ref>Mühlbach, S., Sorg, D., Rosner, F., Kecman, J., and Swalve, H.H. 2018. Genetic analyses for CH₄ concentrations in the breath of dairy cows measured on-farm with the Laser Methane Detector. In: Proceedings of the World Congress on Genetics Applied to Livestock Production, Abstract No. 186, 11-16th February, Auckland, New Zealand.</ref>), throughput is likely to be up to 1000 animals per year.</div>
</div>

= Comparison of methods to measure methane =
<div class="mw-collapsible mw-collapsed"><div>
== Correlations among methods ==
Correlations among methods
Table 4 shows correlations between the respiratory chamber method as the gold standard to measure CH4 emission from cows and other methods from Garnsworthy et al. (2019)<ref name=":11" />.
<center>
{| class="wikitable"
|-
! colspan="3" style="text-align:left;" | ''Table 4. Correlations between CH4 measuring methods. Data were taken from Garnsworthy et al. (2019)''<ref name=":11" />''.''
|- style="background-color:#efefef;"
|+
! style="text-align:left;" | Method
! style="text-align:center;" | Correlation
! style="text-align:center;" | S.E.
|-
| style="text-align:left;" | Respiratory chamber - SF6
| style="text-align:center;" | 0.87
| style="text-align:center;" | - 0.08
|-
| style="text-align:left;" | Respiratory chamber - Greenfeed
| style="text-align:center;" | 0.81
| style="text-align:center;" | - 0.1
|-
| style="text-align:left;" | Respiratory chamber - NDIR
| style="text-align:center;" | - 0.07
| style="text-align:center;" | 0.88
|-
| style="text-align:left;" | Respiratory chamber - NDIR peak
| style="text-align:center;" | 0.72
| style="text-align:center;" | - 0.11
|-
| style="text-align:left;" | Respiratory chamber - PAIR
| style="text-align:center;" | - 0.08
| style="text-align:center;" | 0.7
|-
| style="text-align:left;" | SF6 - Greenfeed
| style="text-align:center;" | 0.4
| style="text-align:center;" | - 0.8
|-
| style="text-align:left;" | LMD - Greenfeed
| style="text-align:center;" | 0.77
| style="text-align:center;" | - 0.23
|-
| style="text-align:left;" | NDIR - Greenfeed
| style="text-align:center;" | 0.64
| style="text-align:center;" | - 0.18
|-
| style="text-align:left;" | NDIR - LMD
| style="text-align:center;" | 0.6
| style="text-align:center;" | - 0.11
|-
| style="text-align:left;" | FTIR - LMD
| style="text-align:center;" | 0.57
| style="text-align:center;" | - 0.25
|-
| style="text-align:left;" | NDIR - NDIR peaks
| style="text-align:center;" | 0.58
| style="text-align:center;" | - 0.15
|-
| style="text-align:left;" | FTIR - NDIR
| style="text-align:center;" | 0.97
| style="text-align:center;" | - 0.02
|}
</center>

In method comparison studies, simultaneous repeated measures per cow with two or more methods are required in order to assess systematic differences between methods (means) and random differences (precision) and correlation between methods free of residual error. Furthermore, adequately short time differences between repeated measures per subject are needed to ensure the underlying biology of the cow has not changed. Not all methods can be recorded simultaneously and CH4 emission of cows’ changes both within day and over the lactation period. In such instances either cross-over designs or matched pair repeated measures designs are needed. Members of METHAGENE WG2 provided data from studies in which two or more methods had been used to measure CH4 output (g/day) by individual dairy cows. Methods were applied to each cow either concurrently or consecutively within a short timeframe.

Seven main methods were represented: respiration chambers; SF6; GreenFeed; LMD; and three breath-sampling systems based on different gas analysers. Gas analysers incorporated different technologies to measure CH4, which were NDIR (e.g. Guardian Plus, Edinburgh Instruments, Edinburgh, UK), FTIR (e.g. Gasmet 4030, Gasmet Technologies Oy, Helsinki, Finland), or PAIR (e.g. F10, Gasera Ltd, Turku, Finland). In the contributing studies, NDIR and FTIR were used in automatic milking stations, and PAIR was used in concentrate feeding stations. One NDIR study and all FTIR and PAIR studies used CO2 as a tracer gas, with daily CO2 output calculated either from milk yield, live weight and days pregnant or from metabolisable energy intake. Two NDIR studies were based on CH4 concentration in eructation peaks rather than mean CH4 concentration, so were treated as separate methods. By separating NDIR studies, a total of 8 distinct methods were available giving a matrix of 28 potential combinations for comparisons. Data were available for 13 method combinations (Garnsworthy et al., 2019<ref name=":11" />).

Method comparisons were conducted using bivariate models (repeatability animal models) to obtain correlations between ‘true values’, also known as repeated measures correlations or individual level correlations (Bakdash and Marusich, 2017<ref>Bakdash, J.Z., and Marusich, L.R. 2017. Repeated measures correlation. Front. Psychol. 8:1–13. doi:10.3389/fpsyg.2017.00456.</ref>). Variance components including between cow variation and within cow variation (precision) and means (accuracy) were used in the calculation of between cow coefficient of variation (CV, %) and total CV and repeatability. Where single measurements were available for each method Pearson’s correlation was reported and where repeated measures per subject were available repeated measures correlation was reported.

Respiration chambers were the most precise method, as can be seen by the smaller between cow CV% and total CV compared to alternative methods, and respiration chambers are by definition the most accurate. All methods tested showed high correlations with respiration chambers but none of the correlations exceeded 0.90. This is in part due to the increased imprecision of alternative methods, as even the most accurate and precise method will compare poorly to a less precise method. These correlations are also likely to be underestimated because none of the methods could be recorded simultaneously with respiration chambers and had to be recorded in cross over designs. Consequently, the true value for each cow may have changed due to changes in the underlying biology of the cow over time between measurements. Comparisons among alternative methods generally had lower correlations than comparisons with respiration chambers, despite having relatively higher numbers of animals and in most cases simultaneous or near simultaneous repeated measures per cow per method due to the increased variability and imprecision of alternative methods as is seen by the increased CVs or due to the possibility that different aspects of CH4 emission are captured using different methods.

For the methods with repeated measures per cow the two mass flux methods, SF6 and GreenFeed, had the highest repeated measures correlations (0.87 ± 0.08 and 0.81 ± 0.10) which outperformed the concentration based NDIR method using CO2 tracer gas. Of the two concentration methods evaluated against respiration chambers using single measurements, NDIR Peaks had a higher correlation (0.89 ± 0.07) than the PAIR CO2 tracer gas (0.80 ± 0.10). The study of Hristov et al. (2016)<ref>Hristov, A.N., O,h J., Giallongo, F., Frederick, T., Harper, M.T., Weeks, H., Branco, F., Price, W.J., Moate, P.J., Deighto,n M.H., Williams, S.R.O., Kindermann, M., and Duval, S. 2016. Short communication: Comparison of the GreenFeed system with the sulfur hexafluoride tracer technique for measuring enteric methane emissions from dairy cows. J. Dairy Sci. 5461–5465. doi:10.3168/jds.2016-10897.</ref> comparing SF6 and GreenFeed reported a low Pearson correlation of 0.40, despite having a large number of animals with repeated measures per method, the authors appear not to have estimated a repeated measures correlation, which could be larger. Estimating a repeated measures correlation between these two mass flux methods is a priority as it would clarify the inexplicable disagreement between two methods which both correlate highly with the gold standard method. With the exception of the aforementioned study, the imprecision was low in the mass flux measure comparisons as compared to the concentration-based methods.

Two of the sniffer methods evaluated, FTIR CO2t1 and NDIR CO2t1, correlated close to unity (0.97), most likely due to the shared prediction equation for CO2 tracer gas. Nevertheless, all correlations derived from actual data were positive. This suggests that combination of datasets obtained with different methods is a realistic proposition for genetic studies. Calculation of adjustment or weighting factors for bias, accuracy and precision would improve the value of combined datasets.

== Pro’s and con’s of devices ==

=== Daily methane emission measures ===
Due to the large diurnal variation in enteric CH4 emission in relation with feeding pattern (Grainger et al., 2007<ref>Grainger, C., Clarke, T., McGinn, S.M., Auldist, M.J., Beauchemin, K.A., Hannah, M.C., Waghorn, G.C., Clark, H., and Eckard, R J. 2007. Methane emissions from dairy cows measured using the sulfur hexafluoride (SF6) tracer and chamber techniques. J. Dairy Sci. 90:2755-2766.</ref>; Jonker et al. 2014<ref>Jonker, A., Molano, G., Antwi, C., Waghorn, G.. 2014. Feeding lucerne silage to beef cattle at three allowances and four feeding frequencies affects circadian patterns of methane emissions, but not emissions per unit of intake. Anim. Prod. Sci.54:1350-1353.</ref>), the highest accuracy of daily CH4 production rate (DMPR) will be obtained with methods that encompass the whole day emissions. Two methods are available: Respiration Chambers (RC) and SF6 methods.

Alternative methods are based on short-term measures of CH4 production rate: Portable Accumulation Chambers (PAC) for sheep and GreenFeed Emission Monitoring (GEM) systems for cattle and sheep (Hegarty, 2013<ref>Hegarty, R.S. 2013. Applicability of short term emission measurements for on-farm quantification of enteric methane. Animal 7, s2:401-408.</ref>).

=== DMPR with Respiration Chamber (RC) ===
It should be noted that CH4 emissions recorded in RC also include gases from flatulence in addition to eructed and expired CH4. Compared with mouth exhaled CH4, CH4 from flatulence is generally considered as limited.

Feed intake in the RC may not be representative of the normal animal feed intake (Bickell et al., 2014<ref>Bickell, S.L., Revell, D.K., Toovey, A.F., and Vercoe, P. E. 2014. Feed intake of sheep when allowed ad libitum access to feed in methane respiration chambers. J. Anim. Sci. 92:2259-2264.</ref>; Llonch et al., 2016<ref>Llonch, P., Somarriba, M.,. Duthie, C-A, Haskell, M.J., Rooke, J.A., Troy, S., Roehe, R., and . Turner, S.P. 2016 Association of temperament and acute stress responsiveness with productivity, feed efficiency, and methane emissions in beef cattle: an observational study. Front. Vet. Sci. 3: 43.</ref>; Troy et al., 2016<ref name=":0a">Troy, S.M., Duthie, C.A., Ross, D.W., Hyslop, J.J., Roehe, R., Waterhouse, A., and Rooke, J.A. 2016. A comparison of methane emissions from beef cattle measured using methane hoods with those measured using respiration chambers. Anim. Feed Sci. Technol. 211:227-240.</ref>). As a consequence, the DMPR measured could be biased. Animals are usually not fed ad libitum when recorded in RC. It is therefore recommended to compare animal or diet effects on Methane Yield (MY) calculated as the ratio of the observed DMPR/DMI during the RC recording in order to take into account possible differences among animals in DMI bias. Animal effects can also be compared on the Residual Methane Production Rate (RMPR) the difference between the observed DMPR and the expected DMPR obtained by regression of observed DMPR on DMI recorded during RC test. Residual traits, however, require a large number of recorded animals for valid adjustment.

Repeatability coefficients between measures taken on consecutive days are very high, rep=0.85 [0.75 to 0.94] for MeY and RMPR of cattle and sheep (Grainger et al., 2007<ref>Grainger, C., Clarke, T., McGinn, S.M., Auldist, M.J., Beauchemin, K.A., Hannah, M.C., Waghorn, G.C., Clark, H., and Eckard, R J. 2007. Methane emissions from dairy cows measured using the sulfur hexafluoride (SF6) tracer and chamber techniques. J. Dairy Sci. 90:2755-2766.</ref>; Donoghue et al., 2016<ref name=":14" />; Pinares-Patino et al., 2013<ref name=":2">Pinares-Patiño C.S., Hickey, S.M., Young, E.A., Dodds, K.G., MacLean, S., Molano, G., Sandoval, E., Kjestrup, H., Harland, R., Pickering, N.K., and McEwan, J.C. 2013. Heritability estimates of methane emissions from sheep. Animal 7: 316–321.</ref>). It has been concluded that 1-day measurement duration could be recommended as it will have a limited impact, less than 5%, on the efficiency of selection of MeY as compared to a selection on a 2-day measurement duration.

When repeated measures of CH4 emission of sheep are taken few days to two weeks apart the repeatability coefficients of MeY and RMPR drops to rep=0.36 [0.26 to 0.41] on average (Pinares-Patino et al., 2013<ref name=":2" />; Robinson et al., 2014a<ref name=":3b">Robinson, D.L., Goopy, J.P., Donaldson, A.J., Woodgate, R.T., Oddy, V.H., and Hegarty, R.S. 2014. Sire and liveweight affect feed intake and methane emissions of sheep confined in respiration chambers. Anima, 8:1935-1944.</ref>). Interestingly, repeatability maintains at a moderate level, rep=0.27 [0.23 to 0.53], when animals were measured several months or even years apart. Similar results were found in Angus cattle, rep=0.20, between MeY and RMPR measures taken more than 60 days apart (Donoghue et al., 2016<ref name=":14" />).

==== Conclusions and reccomendations ====
All these results show that animal effects exist on daily CH4 emissions and animal differences are partially under genetic determinism. This trait, as any other physiology trait, is subject to number of environmental effects and to evolution with time. Ranking animals on their CH4 emission requires standardization of the testing environment. Although highly precise, a single measure recorded in RC is not sufficient for characterizing an animals emission aptitude. In order to characterize a long term phenotype it is therefore recommended to record several 1-day measures, each a few weeks apart, instead of one single 2-day measure, keeping the testing environment as constant as possible.

=== DMPR with GEM ===
At each visit CH4 and CO2 fluxes are measured and animal emission rates are obtained by averaging the short-term flux measures recorded during the testing period. In a review of published results (Dorich et al., 2015<ref name=":4">Dorich, C.D., Varner, R.K., Pereira, A.B.D., Martineau, R., Soder, K.J., and Brito, A.F. 2015. Use of a portable, automated, open-circuit gas quantification system and the sulfur hexafluoride tracer technique for measuring enteric methane emissions in Holstein cows fed ad libitum or restricted. J. Dairy Sci. 98:2676-2681.</ref>; Hammond et al., 2015<ref name=":5">Hammond, K.J., Humphries, D.J., Crompton, L.A., Green, C., and Reynolds, C.K. 2015. Methane emissions from cattle: Estimates from short-term measurements using a GreenFeed system compared with measurements obtained using respiration chambers or sulphur hexafluoride tracer. Anim. Feed Sci. Technol. 203:41-52. doi:10.1016/j.anifeedsci.2015.02.008.</ref>; Velazco et al., 2016<ref name=":131">Velazco, J. I., Hegarty, R., Cottle, D., and Li, L. 2016. Quantifying daily methane production of beef cattle from multiple short-term measures using the GreenFeed system. <nowiki>https://rune.une.edu.au/web/handle/1959.11/23580</nowiki>.</ref>) Hammond et al. (2016A)<ref>Hammond, K.J., Crompton, L.A., Bannink, A., Dijkstra, J., Yáñez-Ruiz, D.R., O’Kiely, P., Kebreab, E., Eugenè, M.A., Yu, Z., Shingfield, K.J., Schwarm, A., Hristov, A.N., and Reynolds, C.K. 2016A. Review of current in vivo measurement techniques for quantifying enteric methane emission from ruminants. Anim. Feed Sci. Technol. 219:13–30. doi:10.1016/j.anifeedsci.2016.05.018</ref> concluded that the GEM system provides similar DMPR values as the RC or SF6 methods. Similar accuracy was found by Arbre et al. (2016)<ref name=":7">Arbre, M., Rochette, Y., Guyader, J., Lascoux, C., Gómez, L.M., Eugène, M., Morgavi, D.P., Renand, G., Doreau, M. and Martin, C. 2016. Repeatability of enteric methane determinations from cattle using either the SF6 tracer technique or the GreenFeed system. Anim. Prod. Sci. 56:238-243.</ref> for CH4 yield measured with GEM as compared with RC and SF6 measures.

The spot measures are highly variable since they include, in addition to the animal and environment effects, an important within-animal and within-day variance. The latter is considered as an error term. Consequently, the precision of the animal estimates increase with the number of spot measures averaged per animal. From the results reported by Renand and Maupetit (2016)<ref name=":8">Renand, G., and Maupetit, D. 2016. Assessing individual differences in enteric methane emission among beef heifers using the GreenFeed Emission Monitoring system: effect of the length of testing period on precision. Anim. Prod. Sci. 56:218-223.</ref> with 124 beef heifers controlled indoors, it can be shown that the coefficient of variation of that error term (CVe) decreases exponentially with the number of spot measures: 13.7%, 10.8%, 7.9% and 4.9% with 5, 10, 25 and 100 measures respectively. Results reported by Arbre et al. (2016)<ref name=":7" /> with 7 lactating dairy cows controlled indoors, also show that CVe decreases from 12.8% to 11.4%, 9.5% and 6.8% when the number of measures increases from 5 to 10, 25 and 100. With dairy cows at pasture, Waghorn et al. (2016)<ref name=":9">Waghorn, G.C., Jonker, A., and Macdonald, K A. (2016). Measuring methane from grazing dairy cows using GreenFeed. Anim. Prod. Sci. 56:252-257.</ref> showed that the coefficient of variation among 36 dairy cows at pasture was half (6.6 and 7.5%) when CH4 production rate was averaged over 16 days with approximately 18 to 26 measures per cow, as compared with 4 day averages with 4 to 6 measures per cow (13.0 and 17.2%). These authors concluded that at least 16 days are required to give confident estimates.

With 45 to 50 spot measures recorded during 2 weeks Arbre et al. (2016)<ref name=":7" /> and Renand and Maupetit (2016)<ref name=":8" /> obtained repeatabilityof 0.78 and 0.73 for DMPR estimates of 7 dairy cows and 124 beef heifers, respectively. A similar repeatability coefficient (0.74) was obtained by Huhtanen et al. (2015)<ref name=":24">Huhtanen, P., Cabezas-Garcia, E.H., Utsumi, S., and Zimmerman, S. 2015. Comparison of methods to determine methane emissions from dairy cows in farm conditions. J. Dairy Sci. 98:3394–3409. doi:10.3168/jds.2014-9118.</ref> with 25 dairy cows recorded during 3 weeks, with 20 to 30 samples per cow. Interestingly, these latter authors fitted gas concentration, airflow and head position measurement equipments into two automatic milking systems that were used to measure CH4 emission of 59 dairy cows during two periods of 10 days. After filtering data for acceptable head-position, the repeatability of DMPR was 0.75.

Considering the need to average enough spot measures and the advantage of measuring DMPR over long periods to take into account the emission variability with time, the GEM system should be run over several weeks. Averaging 40 to 50 spot measures per animal should provide a precise measure of the animal DMPR. The minimum duration of CH4 recording will depend on the number of spot measures actually recorded per day.

The GEM system relies on animals that voluntarily visit the GEM unit when attracted with pellets dispensed by a feeder at a controlled rate. The visitation frequency appears to be highly variable among different studies reported up to now. While some experiments report a very high frequency of cattle visiting the GEM units (up to 96%), the proportion of not visiting animals may be very high in other studies (up to 60%) (Dorich et al., 2015<ref name=":4" />; Hammond et al, 2015A<ref name=":5" />, Arbre et al., 2016<ref name=":7" />; Renand and Maupetit, 2016<ref name=":8" />; Velazco et al., 2016<ref name=":131" />; Waghorn et al., 2016<ref name=":9" />). The reason why some animals may not visit the unit is not obvious. That problem of no or low visiting frequency may jeopardize the precise ranking of animals on their DMRP. Training them is an important requisite for the success of DMPR recording with the GEM system (see recommendations on the C-Lock website). Palatability of the pellets used to attract the cattle should be high compared with the diet they receive in the trough or the grass they are grazing.

In addition to the effect on precision, the low visiting frequency may have an impact on accuracy if associated in some animals with specific time of visiting. Enteric CH4 emissions have a diurnal variation with a minimum at the end of night, before the first feeding, and a steady increase after each feeding. A weak diurnal pattern in CH4 emission was detected by Velazco et al. (2016)<ref name=":131" /> using GEM systems. Renand et al. (2013)<ref>Renand, G., Ricard, E., Maupetit, D., Thouly, J.C. 2013. Variability among individual young beef bulls and heifers in methane emissions. In: Book of abstracts of EAAP 64th annual meeting, Nantes, France. p. 195. (Wageningen Academic Publishers: Wageningen)</ref> observed significant differences between visit hours (CV=10%). If some animals visit the GEM at specific hours of the day, the rough average of spot measures will be biased. In order to get rid of this time effect on the DMPR measure, Dorich et al. (2015)<ref name=":4" /> and Hristov et al. (2016)<ref>Hristov, A.N., O,h J., Giallongo, F., Frederick, T., Harper, M.T., Weeks, H., Branco, F., Price, W.J., Moate, P.J., Deighto,n M.H., Williams, S.R.O., Kindermann, M., and Duval, S. 2016. Short communication: Comparison of the GreenFeed system with the sulfur hexafluoride tracer technique for measuring enteric methane emissions from dairy cows. J. Dairy Sci. 5461–5465. doi:10.3168/jds.2016-10897.</ref> came up with a protocol where the GEM units were moved sequentially from one cow to the next one over several days, so that all the cows were equally measured during different hours of the day. That protocol is possible only with tie stall cattle and is obviously not applicable for measuring large number of animals. However, with animals controlled in their production environment, the bias generated by potential specific visiting patterns can actually be removed if the measuring hour is taken into account in the linear model when estimating the animal effect.

As voluntary visiting of the GEM system may be a limiting factor under some conditions, measures of DMPR can be designed when animals are drinking or eating, i.e. several times per day. Velazco et al. (2016)<ref name=":131" /> showed that a GEM water unit prototype designed and built by C-Lock Inc., displayed different eructation patterns as compared with a plain GEM unit. They concluded that further development appears necessary before any application. Troy et al. (2016)<ref name=":0a" /> tested a CH4 hood (MH) system placed above an automated feeding bin. That system includes an air extraction fan for each hood with continuously recorded airflow. Methane concentration was measured using 4 infrared analyzers, one for 8 hoods. In this system one CH4 concentration value was recorded every 6 min. With 9 to 12 feeding events per day on average and feeding visits averaging 8 min, there were between 12 to 16 CH4 concentration values recorded and CH4 production rates calculated per day. The measurements were recorded during 46 days and ranking of animals in relation with the test duration was studied. However no repeatability coefficient was given for comparison with other methods. That system was compared with respiratory chambers results in two experiments with 82 and 80 steers fed different diet-treatment combinations. Over the whole experimental design, a good concordance was found between MH and RC results as a consequence that both methods detected similar effects for the diet-treatment effects. However no correlation was given between both methods within diet-treatment samples that are the essential information needed to evaluate the ability of this new method to predict individual DMPR.

==== Conclusions and reccomendations ====
With only a single gas analyzer for 8 feed bins, the time when useful CH4 concentration is recorded is certainly too short for including several eructation peaks. Fitting one gas analyzer per feed bin will combine advantages of the measurement time during visits of the GEM system with the visiting frequency allowed by the MH system.

=== MPR with PAC ===
The delay between the measurement and the last feeding has to be carefully monitored and taken into account when calculating animal emission values. As individual DMI is difficult to record, direct measurement of CH4 yield (MY=MPR/DMI) turns out to be impossible. Although not representative of a whole day production rate, that method can be used to characterize individual CH4 emission rates if standardized protocols are applied. It was first validated with 40 ewes measured 1 hour in PAC after three 22-hour measures in RC: a correlation of 0.71 was found between the two measures of CH4 production rate over 1 or 22 hours (Goopy et al., 2011<ref>Goopy, J.P., Woodgate, R., Donaldson, A., Robinson, D.L., and Hegarty, R.S. 2011. Validation of a short term methane measurement using portable static chambers to estimate methane production in sheep. Anim. Feed Sci. Technol. 166-167;219-226.</ref>). The 1-hour CH4 production measure in PAC has a moderate repeatability of rep=0.50 [0.37 to 0.60] when taken few days to seven weeks apart (Robinson et al., 2015<ref>Robinson, D.L., Goopy, J.P., Hegarty, R.S., and Oddy, V.H. 2015. Comparison of repeated measurements of methane production in sheep over 5 years and a range of measurement protocols. J. Anim. Sci. 93:4637-4650.</ref>; Goopy et al., 2016<ref>Goopy, J.P., Robinson, D.L., Woodgate, R.T., Donaldson, A.J., Oddy, V.H., Vercoe, P. E., and Hegarty, R.S. 2016. Estimates of repeatability and heritability of methane production in sheep using portable accumulation chambers. Anim. Prod. Sci. 56:116-122.</ref>). Heritability coefficient of this 1-hour CH4 production measure is estimated to h²=0.12 in a population of 2,279 sheep (Robinson et al., 2014<ref name=":3b" />) with a repeatability coefficient rep=0.25.

==== Conclusions and recomendations ====
The authors recommend using the mean of 3 PAC measurements in order to get accurate phenotype estimates.</div>
</div>

= Proxies =
<div class="mw-collapsible mw-collapsed">
<div>
== Introduction ==
Large-scale measurements of enteric CH4 emissions from dairy cows are needed for effective monitoring of strategies to reduce the carbon footprint of milk production, as well as for incorporation of CH4 emissions into breeding programs. However, measurements on a sufficiently large scale are difficult and expensive. Proxies for CH4 emissions can provide an alternative, but each approach has limitations. Negussie et al. (2019)<ref name=":30">Negussie, E., González Recio, O., de Haas, Y., Gengler N., Soyeurt, H., Peiren, N., Pszczola, M., Garnsworthy, P., Battagin, M., Bayat, A., Lassen, J., Yan, T., Boland, T., Kuhla, B., Strabel, T., Schwarm, A., Vanlierde, A., and Biscarini, F. 2019. Machine learning ensemble algorithms in predictive analytics of dairy cattle methane emission using imputed versus non-imputed datasets. 7th GGAA – Greenhouse Gas and Animal Agriculture Conference held from August 4th to 8th, Iguassu Falls/Brazil. Oral communication, Book of Abstracts Page 40. <nowiki>http://www.ggaa2019.org/sites/default/files/proceedings-ggaa2019.pdf</nowiki></ref> recently showed the potential of proxies proxies that are easy to record in the farm. These proxies can be gathered in most farms and are a realistic threshold accuracy that can be obtained without more fancy proxies. Several techniques have been developed for the measurement of CH4 emissions from ruminants, with varying degrees of accuracy (see reviews by Cassandro et al., 2013<ref>Cassandro, M., Mele, M., Stefanon, B.. 2013. Genetic aspects of enteric methane emission in livestock ruminants. Italian J. Anim. Sci. 12:e73: 450-458.</ref> and Hammond et al., 2016A<ref name=":25">Hammond, K.J., Crompton, L.A., Bannink, A., Dijkstra, J., Yáñez-Ruiz, D.R., O’Kiely, P., Kebreab, E., Eugenè, M.A., Yu, Z., Shingfield, K.J., Schwarm, A., Hristov, A.N., and Reynolds, C.K. 2016A. Review of current in vivo measurement techniques for quantifying enteric methane emission from ruminants. Anim. Feed Sci. Technol. 219:13–30. doi:10.1016/j.anifeedsci.2016.05.018.</ref>), but routine individual measurements on a large scale (a requisite for genetic selection) have proven to be difficult and expensive (Pickering et al., 2015<ref>Pickering, N.K., Oddy, V.H., Basarab, J.A., Cammack, K., Hayes, B J., Hegarty, R.S., McEwan, J.C., Miller, S., Pinares, C., and de Haas, Y. 2015. Invited review: Genetic possibilities to reduce enteric methane emissions from ruminants. Animal 9:1431-1440.</ref>; Negussie et al., 2016<ref>Negussie E., Lehtinen, J., Mäntysaari, P., Liinamo, A-E., Mäntysaari, E., and Lidauer, M.. 2016. Non-invasive individual methane measurements in dairy cows using photoacoustic infrared spectroscopy technique. 6th Greenhouse Gases Animal Agriculture Conference (GGAA2016) 14-18 February 2016. Melbourne, Australia. Abstract. p62.</ref>). Therefore, identifying proxies (i.e. indicators or indirect traits) that are correlated to CH4 emission, but which are easy and relatively low-cost to record on a large scale, is a much needed alternative. Proxies might be less accurate, but could be measured repeatedly to reduce random noise. The (potential) proxies range from simple and low-cost measurements such as body weight, to high-throughput milk MIR, to more demanding measures like rumen morphology, rumen metabolites or microbiome profiling.

Combining proxies that are easy to measure and cheap to record could provide predictions of CH4 emissions that are sufficiently accurate for selection and management of cows with low CH4 emissions.

== Available Proxies ==
A large array of CH4 proxies differing widely in accuracy and applicability under different conditions have been reported. The ideal proxy would be highly phenotypically and genetically correlated with CH4 emissions and could easily, and potentially repeatedly, be measured on a large scale. A systematic summary and assessment of existing knowledge is needed for the identification of robust and accurate CH4 proxies for future use. Table 5 summarizes proxies for CH4 production, and Table 6 summarizes results from combining proxies to improve predictability of proxies for CH4 prediction.
{| class="wikitable"
|-
! colspan="3" | ''Table 5. Available methane proxies include: (1) feed intake and feeding behaviour, (2) rumen function, metabolites and microbiome, (3) milk production and composition, (4) hind-gut and faeces, and (5) measurements at the level of the whole animal.''
|- style="background-color:#efefef;"
|+
!Proxy
!Description / conclusion
!Reference
|-
| colspan="3" |'''(1) Feed intake and feeding behavior'''
|-
|Dry Matter Intake (DMI)
|DMI predict MeP with R2= 0.06-0.64, and ME intake predict MeP with R2= 0.53-0,55
| Ellis et al. (2007);<ref>Ellis, J.L., Kebreab, E., Odongo, N.E., McBride, B.W., Okine, E.K., and France, J. 2007. Prediction of methane production from dairy and beef cattle. J. Dairy Sci. 90:3456–3466.</ref>
Mills et al. (2003)<ref>Mills, J.A.N., Kebreab, E., Yates, C.M., Crompton, L.A., Cammell, S.B., Dhanoa, M.S., Agnew, R.E., and France, J. 2003. Alternative approaches to predicting methane emissions from dairy cows. J. Anim. Sci. 81:3141–3150.</ref>;

Negussie et al. (2019)<ref>Negussie, E., González Recio, O., de Haas, Y., Gengler N., Soyeurt, H., Peiren, N., Pszczola, M., Garnsworthy, P., Battagin, M., Bayat, A., Lassen, J., Yan, T., Boland, T., Kuhla, B., Strabel, T., Schwarm, A., Vanlierde, A., and Biscarini, F. 2019. Machine learning ensemble algorithms in predictive analytics of dairy cattle methane emission using imputed versus non-imputed datasets. 7th GGAA – Greenhouse Gas and Animal Agriculture Conference held from August 4th to 8th, Iguassu Falls/Brazil. Oral communication, Book of Abstracts Page 40. <nowiki>http://www.ggaa2019.org/sites/default/files/proceedings-ggaa2019.pdf</nowiki></ref>
|-
|Gross Energy intake (GE)
|Predict MeP with RMSPE= 3.01.
| Moraes et al. (2014)<ref name=":0b">Moraes, L.E., Strathe, A.B., Fadel, J.G., Casper, D.P., and Kebreab, E. 2014. Prediction of enteric methane emissions from cattle. Glob. Change Biol. 20:2140–2148.</ref>
|-
|Feeding behavior
| Magnitude and direction of relation to MeP varies across studies
|Nkrumah et al. (2006);<ref>Nkrumah, J.D., Okine, E.K., Mathison, G.W., Schmid, K., Li, C., Basarab, J.A., Price, M.A., Wang, Z., and Moore, S.S. 2006. Relationships of feedlot feed efficiency, performance, and feeding behavior with metabolic rate, methane production, and energy partitioning in beef cattle. J. Anim. Sci. 84:145-153.</ref>
Jonker et al., 2014<ref>Jonker, A., Molano, G., Antwi, C., Waghorn, G.. 2014. Feeding lucerne silage to beef cattle at three allowances and four feeding frequencies affects circadian patterns of methane emissions, but not emissions per unit of intake. Anim. Prod. Sci.54:1350-1353.</ref>
|-
| Rumination time
|High rumination relates to more milk, consume more concentrate and produce more CH4, lower RMP and MeI
| Watt et al. (2015)<ref>Watt, L.J., Clark, C.E.F., Krebs, G.L., Petzel, C.E., Nielsen, S., and Utsumi, S.A. 2015. Differential rumination, intake, and enteric methane production of dairy cows in a pasture-based automatic milking system. J. Dairy Sci. 98:7248–7263.</ref>;
López- Paredes et al. (2020)<ref>Lopez-Paredes, J., Goiri, I., Atxaerandio, R., García-Rodríguez, A., Ugarte, E., Jiménez-Montero, J.A., Alenda, R and González-Recio, O. 2020. Mitigation of greenhouse gases in dairy cattle via genetic selection (i): Genetic parameters of direct methane using non-invasive methods and its proxies. J. Dairy Sci. 103.</ref>
|-
|Rumen microbiome
|The metagenome can predict DMI, and classify high vs low intakes
|Delgado et al. (2019)<ref>Delgado, B., Bach A., Guasch I., González C, Elcoso G., Pryce J.E., Gonzalez-Recio O. (2019).Whole rumen metagenome sequencing allows classifying and predicting feed efficiency and intake levels in cattle. Scientific Reports 9: 11. doi:10.1038/s41598-018-36673-w</ref>
|-
| colspan="3" |'''(2) Rumen function, metabolites and microbiome'''
|-
|Dietary antimethanogenic compounds
|Inhibitors of the enzyme methyl coenzyme-M reductase: bromochoromethane; chloroform; 3- nitrooxypropanol (not always)
|Denman et al., 2007;<ref>Denman, S.E., Tomkins, N.W., and McSweeney, C.S. 2007. Quantitation and diversity analysis of ruminal methanogenic populations in response to the antimethanogenic compound bromochloromethane. FEMS Microbiol. Ecol. 62:313-322.</ref>
Knight et al., 2011;<ref>Knight, T., Ronimus, R.S., Dey, D., Tootill, C., Naylor, G., Evans, P., Molano, G., Smith, A., Tavendale, M., Pinares-Patiño, C.S., and Clark, H. 2011. Chloroform decreases rumen methanogenesis and methanogen populations without altering rumen function in cattle. Anim. Feed Sci. Technol. 166-167:101-112.</ref>

Haisan et al., 2014<ref>Haisan, J., Sun, Y., Guan, L.L., Beauchemin, K.A., Iwaasa, A., Duval, S., Barreda, D.R., and Oba, M. 2014. The effects of feeding 3-nitrooxypropanol on methane emissions and productivity of Holstein cows in mid lactation. J. Dairy Sci. 97:3110-3119.</ref>;

Romero-Perez et al., 2014<ref>Romero-Perez, A., Okine, E.K., McGinn, S.M., Guan, L.L., Oba, M., Duval, S.M., Kinderman,n M., and Beauchemin, K.A. 2014. The potential of 3-nitrooxypropanol to lower enteric methane emissions from beef cattle. J. Anim. Sci. 92:4682-4693.</ref>
|-
|Dietary antimicrobial compounds
|Induce reductions in both MeP and methanogens numbers: nitrates, anacardic acid (cashew nut shell liquid), monensin, isobutyrate
|Iwamoto et al., 2002;<ref>Iwamoto, M., Asanuma, N., and Hin,o T. 2002. Ability of Selenomonas ruminantium, Veillonella parvula, and Wolinella succinogenes to reduce nitrate and nitrite with special reference to the suppression of ruminal methanogenesis. Anaerobe. 8:209-215.</ref>
Kubo et al., 1993<ref>Kubo, I., Muroi, H., Himejima, M., Yamagiwa, Y., Mera, H., Tokushima, K., Ohta, S., and Kamikawa, T. 1993. Structure-antibacterial activity relationships of anacardic acids. J. Agric. Food Chem. 41:1016-1019.</ref>;

van Zijderveld et al., 2010<ref>Van Zijderveld, S.M., Gerrits, W.J.J., Apajalahti, J.A., Newbold, J.R., Dijkstra, J., Leng, R A., and Perdok, H.B. 2010. Nitrate and sulfate: effective alternative hydrogen sinks for mitigation of ruminal methane production in sheep. J. Dairy Sci. 93:5856-5866.</ref>;

Veneman et al., 2015<ref>Veneman, J.B., Muetzel, S., Hart, K.J., Faulkner, C.L., Moorby, J.M., Perdok, H.B., and Newbold, C.J. 2015. Does Dietary Mitigation of Enteric Methane Production Affect Rumen Function and Animal Productivity in Dairy Cows? PLoS ONE 10(10): e0140282. doi: 10.1371/journal.pone.0140282</ref>;

Shinkai et al., 2012<ref>Shinkai, T., Enishi, O., Mitsumori, M., Higuchi, K., Kobayashi, Y., Takenaka, A., Nagashima, K., and Mochizuki, M. 2012. Mitigation of methane production from cattle by feeding cashew nut shell liquid. J. Dairy Sci. 95:5308-5316.</ref>;

Wang et al., 2015<ref>Wang, C., Liu, Q., Zhang, Y.L., Pei, C.X., Zhang, S.L., Wang, Y.X., Yang, W.Z., Bai, Y.S., Shi, Z.G., and Liu, X.N. 2015. Effects of isobutyrate supplementation on ruminal microflora, rumen enzyme activities and methane emissions in Simmental steers. J. Anim. Physiol. Anim. Nutr. (Berl). 99:123-131.</ref>
|-
|Rumen microbiome profile
|High Fibrobacteres, Quinella ovalis and Veillonellaceae and low Ruminococcaceae, Lachnospiraceae and Clostridiales associate with low- CH4 phenotypes and high propionate
Protozoa concentration
|Kittelmann et al., 2014<ref>Kittelmann, S., Pinares-Patiño, C.S., Seedorf, H., Kirk, M.R., Ganesh, S., McEwan, J.C., and Janssen, P.H. 2014. Two different bacterial community types are linked with the low-methane emission trait in sheep. PLoS One 9:e103171.</ref>;
Wallace et al., 2014<ref>Wallace, R. ., Rooke, J A., Duthie, C.-A., Hyslop, J.J., Ross, D.W., McKain, N., de Souza, S.M., Snelling, T.J., Waterhouse, A., and Roehe, R. 2014. Archaeal abundance in post-mortem ruminal digesta may help predict methane emissions from beef cattle. Sci. Rep. 4:5892.</ref>;

Sun et al., 2015<ref>Sun, X., Henderson, G., Cox, F., Molan,o G., Harrison, S.J., Luo, D., Janssen, P.H., and Pacheco, D. 2015. Lambs Fed Fresh Winter Forage Rape (Brassica napus L.) Emit Less Methane than Those Fed Perennial Ryegrass (Lolium perenne L.), and Possible Mechanisms behind the Difference. PLoS One 10(3):e0119697: DOI: 10.1371/journal.pone.0119697</ref>;

Guyader et al., 2014<ref>Guyader, J., Eugène, M., Nozière, P., Morgavi, D.P., Doreau, M., and Martin, C. 2014. Influence of rumen protozoa on methane emission in ruminants: a meta-analysis approach. Animal 8:1816-1825.</ref>
|-
|Rumen microbiome profile
|Predict MeP with R2 up to 0.55
|Ross et al. 2013a<ref>Ross, E. M., P.J . Moate, L.C. Marett, B.G. Cocks and B.J. Hayes. 2013a. Investigating the effect of two methane-mitigating diets on the rumen microbiome using massively parallel sequencing. J. Dairy Sci. 96:6030–6046.</ref>;
Ross et al. (2013b)<ref>Ross, E. M., Moate, P. J., Marett, L.C., Cocks, B.G., and Hayes, B.J. 2013b. Metagenomic Predictions: From Microbiome to Complex Health and Environmental Phenotypes in Humans and Cattle. PLoS One DOI: 10.1371/journal.pone.0073056.</ref>
|-
|Microbial genes
|20 (out of 3970 identified) related to CH4 emissions
|Roehe et al. (2016)<ref>Roehe R., Dewhurst, R.J., Duthie, C-A., Rooke, J.A., McKain, N., Ross, D.W., Hyslop, J.J., Waterhouse, A., Freeman, T.C., Watson, M., and Wallace, R.J. 2016. Bovine Host Genetic Variation Influences Rumen Microbial Methane Production with Best Selection Criterion for Low Methane Emitting and Efficiently Feed Converting Hosts Based on Metagenomic Gene Abundance. PLoS Genet 12(2): e1005846. doi:10.1371/journal.pgen.1005846.</ref>
|-
|Rumen volume (Xray Computed Tomography) and retention time
|Low-MeY sheep had smaller rumens. Faster passage= less time to ferment substrate - explained 28% of variation in MeP
|Pinares Patiño et al., 2003<ref>Pinares-Patiño C.S., Hickey, S.M., Young, E.A., Dodds, K.G., MacLean, S., Molano, G., Sandoval, E., Kjestrup, H., Harland, R., Pickering, N.K., and McEwan, J.C. 2013. Heritability estimates of methane emissions from sheep. Animal 7: 316–321.</ref>;
Goopy et al., 2014<ref>Goopy, J.P., Donaldson, A., Hegarty, R., Vercoe, P.E., Haynes, F., Barnett, M., and Oddy, V.H. 2014. Low-methane yield sheep have smaller rumens and shorter rumen retention time. Br. J. Nutr. 111:578-585.</ref>;

Okine et al. (1989)<ref>Okine, E. ., Mathiso,n G.W., and Hardin, R.T. 1989. Effects of changes in frequency of reticular contractions on fluid and particulate passage rates in cattle. J. Anim. Sci. 67:3388–3396.</ref>
|-
|Blood triiodothyronine concentration
|Reduced MeY
|Barnett et al. (2012)<ref>Barnett, M.C., Goopy, J.P., McFarlane, J.R., Godwin, I.R., Nolan, J.V., and Hegarty, R.S. (2012). Triiodothyronine influences digesta kinetics and methane yield in sheep. Anim. Prod. Sci. 52:572-577.</ref>
|-
|Acetate to propionate ratio in ruminal fluid
|Positively associated with CH4 emissions, but not confirmed in all studies, sometimes opposite relation
|Mohammed et al., 2011;<ref name=":1b">Mohammed, R., McGinn, S.M., and Beauchemin, K.A. 2011. Prediction of enteric methane output from milk fatty acid concentrations and rumen fermentation parameters in dairy cows fed sunflower, flax, or canola seeds. J. Dairy Sci. 94:6057–6068.</ref>
Fievez et al., 2012<ref>Fievez V., Colma,n E., Castro-Montoya, J.M., Stefanov, I., and Vlaeminck, B. 2012. Milk odd- and branched-chain fatty acids as biomarkers of rumen function – An update. Anim. Feed. Sci. Technol. 172:51–65.</ref>;

Chung et al., 2011<ref>Chung, Y.-H., Walker, N.D., McGinn, S.M., and Beauchemin, K.A. 2011. Differing effects of 2 active dried yeast (Saccharomyces cerevisiae) strains on ruminal acidosis and methane production in nonlactating dairy cows. J. Dairy Sci. 94:2431–2439.</ref>;

Van Zijderveld et al., 2010<ref>Van Zijderveld, S.M., Gerrits, W.J.J., Apajalahti, J.A., Newbold, J.R., Dijkstra, J., Leng, R A., and Perdok, H.B. 2010. Nitrate and sulfate: effective alternative hydrogen sinks for mitigation of ruminal methane production in sheep. J. Dairy Sci. 93:5856-5866.</ref>
|-
| colspan="3" |'''(3) Milk production and composition'''
|-
|Modelling approach
|Doubling milk production only adds 5 kg to the MeP and so greatly reduces MeY
|Kirchgessner et al. (1995)<ref>Kirchgessner M., Windisch, W., and Muller, H.L. 1995. Nutritional factors for the quantification of methane production. In: Ruminant Physiology: Digestion, Metabolism, Growth and Reproduction. Proceedings 8th International Symposium on Ruminant Physiology (eds W. Von Engelhardt, S. Leonhard-Marek, G. Breves and D. Giesecke). Reproduction Proceedings 8th International Symposium on Ruminant Physiology. Ferdinand Enke Verlag, Stuttgart, Germany. pp. 333-348.</ref>;
Hristov et al. (2014)<ref>Hristov, A.N., Johnson, K.A., and Kebreab, E, 2014. Livestock methane emissions in the United States. Proc. Natl. Aacad. Sci. 111:E1320; doi:10.1073/pnas.1401046111</ref>
|-
|Milk fat content
|key explanatory variable for predicting CH4: A moderate negative genetic correlation with infrared predicted
MeI: correlations MeP = 0,08 and MeI = - 0.13
|Moraes et al. (2014)<ref name=":0b" />;
Kandel et al., 2014A<ref name=":37" />, B<ref name=":3">Kandel, P.B., Vanderick, S., Vanrobays, M.L., Vanlierde, A., Dehareng, F., Froidmont, E., Soyeur,t H., and Gengler, N. 2014B. Consequences of selection for environmental impact traits in dairy cows. In: 10th World Congress on Genetics Applied to Livestock Production (WCGALP), 17-22 August, 2014. Vancouver, Canada.</ref>;

Vanlierde et al. (2015)<ref name=":4a">Vanlierde, A., Vanrobays, M.L., Dehareng, F., Froidmont, E., Soyeurt, H., McParland, S., Lewis, E., Deighton, M.H., Grandl, F., Kreuzer, M., Grendler, B., Dardenne, P., and Gengler, N. 2015. Hot topic: Innovative lactation-stage-dependent prediction of methane emissions from milk mid-infrared spectra. J. Dairy Sci. 98:5740–5747.</ref>
|-
|Milk fat content
|A positive relationship between VFA proportions and methanogenesis is expected as a consequence of the common biochemical pathways; Dietary unsaturated fatty acids are negatively associated with CH4 emissions
|Vlaeminck et al., 2006<ref>Vlaeminck, B., Fievez, V., Cabrita, A.R.J., Fonseca, A.J.M., and Dewhurst, R.J. 2006. Factors affecting odd- and branched-chain fatty acids in milk: A review. Anim. Feed Sci. Technol. 131:389–417.</ref>;
Van Lingen et al., 2014<ref name=":5a">Van Lingen H.J., Crompton, L.A., Hendriks, W.H., Reynolds, C.K., Dijkstra, J. 2014. Meta-analysis of relationships between enteric methane yield and milk fatty acid profile in dairy cattle. J. Dairy Sci. 97:7115-7132.</ref>
|-
|Milk protein yield
|Correlation with Mel = - 0.47 or -0.09, MeP = 0.53
|Kandel et al. (2014A)<ref name=":37" />;
Vanlierde et al. (2015)<ref name=":4a" />
|-
|Lactose
|Variable correlations: MeP = 0,33; MeI = - 0.21; R = 0.19 for CH4 emission
|Miettinen and Huhtanen (1996)<ref>Miettinen, H., and Huhtanen, P. 1996. Effects of the ration of ruminal propionate to butyrate on milk yield and blood metabolites in dairy cows. J. Dairy Sci. 79:851–861.</ref>;
Dehareng et al. (2012)<ref name=":32">Dehareng, F., Delfosse, C., Froidmont, E., Soyeurt, H., Martin, C., Gengler, N., Vanlierde, A., and Dardenne, P. 2012. Potential use of milk mid-infrared spectra to predict individual methane emission of dairy cows. Animal 6:1694-701.</ref>
|-
|Somatic cell score
|Genetic correlation with infrared predicted MeI: R = 0.07
|Kandel et al. (2014A<ref name=":37" />, B<ref name=":3" />)
|-
|Prediction equations Milk FA and CH4 emissions, including from MIR data
|R² ranged between 47 and 95%; relationships between the individual milk FA and MeP differed considerably and the correlations between CH4 and milk FA vary throughout the lactation
|Chilliard et al. (2009)<ref>Chilliard Y., Martin ,C., Rouel, J., and Doreau, M. 2009. Milk fatty acids in dairy cows fed whole crude linseed, extruded linseed, or linseed oil, and their relationship with methane output. J. Dairy Sci. 92:5199-5211.</ref>;
Delfosse et al. (2010)<ref>Delfosse, O., Froidmont, E., Fernandez Pierna, J. A., Martin, C., and Dehareng, F. 2010. Estimation of methane emissions by dairy cows on the basis of milk composition. In: Greenhouse Gases and Animal Agriculture Conference. 2010; GGAA2010: 4. Greenhouse Gases and Animal Agriculture Conference, Banff, CAN, 2010-10-03-2010-10-08, 60-61.</ref>;

Castro-Montoya et al. (2011)<ref>Castro Montoya, J., Bhagwat, A.M., Peiren, N., De Campeneere, S., De Baets, B., and Fievez, V. 2011. Relationships between odd- and branched-chain fatty acid profiles in milk and calculated enteric methane proportion for lactating dairy cattle. Anim. Feed Sci. Technol.166:596–602.</ref>;

Dijkstra et al. (2011)<ref>Dijkstra, J., van Zijderveld, S.M., Apajalahti, J.A., Bannink, A., Gerrits, W.J.J., Newbold, J.R., Perdok, H.B.,, and Berends, H. 2011. Relationships between methane production and milk fatty acid profiles in dairy cattle. Anim. Feed Sci. Technol. 166–167:590–595.</ref>;

Kandel et al. (2013)<ref>Kandel, P.B., Vanrobays, M.L., Vanlierde, A., Dehareng, F., Froidmont, E., Dardenne, P., Lewis, E., Buckley, F., Deighton, M.H., McParland, S. and Gengler, N., 2013. Genetic parameters for methane emissions predicted from milk mid-infrared spectra in dairy cows. J. Dairy Sci. 95(E-1):p.388.</ref>;

Mohammed et al. (2011)<ref name=":1b" />;

Van Lingen et al. (2014)<ref name=":5a" />;

Williams et al. (2014)<ref>Williams, S.R.O., Williams, B., Moate, P.J., Deighton, M.H., Hannah, M.C., and Wales, W.J. 2014. Methane emissions of dairy cows cannot be predicted by the concentrations of C8:0 and total C18 fatty acids in milk. Anim. Prod. Sci. 54:1757–1761. <nowiki>http://dx.doi.org/10.1071/AN14292</nowiki>.</ref>;

Dijkstra et al. (2016)<ref>Dijkstra, J., van Zijderveld, S.M., Apajalahti, J.A., Bannink, A., Gerrits, W.J.J., Newbold, J.R., Perdok, H.B.,, and Berends, H. 2011. Relationships between methane production and milk fatty acid profiles in dairy cattle. Anim. Feed Sci. Technol. 166–167:590–595.</ref>;

Rico et al. (2016)<ref name=":33">Rico D.E., Chouinard, P.Y., Hassanat, F., Benchaar, C., and Gervais, R. 2016. Prediction of enteric methane emissions from Holstein dairy cows fed various forage sources. Animal 10:203-211. doi:10.1017/S1751731115001949</ref>;

Van Gastelen and Dijkstra (2016)<ref name=":36">Van Gastelen, S., and Dijkstra, J.. 2016. Prediction of methane emission from lactating dairy cows using milk fatty acids and mid-infrared spectroscopy. J. Sci. Food Agric. 96:3963-3968. DOI: 10.1002/jsfa.7718.</ref>;

Vanrobays et al. (2016);<ref>Vanrobays, M.-L., Bastin, C., Vandenplas, J., Hammami, H., Soyeurt, H., Vanlierde, A., Dehareng, F., Froidmont, E., and Gengler, N. 2016. Changes throughout lactation in phenotypic and genetic correlations between methane emissions and milk fatty acid contents predicted from milk mid-infrared spectra. J. Dairy Sci. 99:1–14. <nowiki>http://dx.doi.org/10.3168/jds.2015-10646</nowiki></ref>

Bougoin et al., (2019)<ref name=":34">Bougouin, A., Appuhamy, J.A.D.R.N., Ferlay, A., Kebreab, E., Martin, C., Moate, P.J., Benchaar, C., Lund, P., and Eugène, M. 2019. Individual milk fatty acids are potential predictors of enteric methane emissions from dairy cows fed a wide range of diets: Approach by meta-analysis. J. Dairy Sci. 102:10616–10631. DOI: <nowiki>http://doi.org/10.3168/jds.2018-15940</nowiki></ref>
|-
| colspan="3" |'''(4) Hind-gut and feces'''
|-
|Whole tract digestibility (potential as supporting factors in the prediction of enteric CH4 emissions)
|Main effects relate to rumen (see above), but energy digestibility as a supporting factor to GE intake improved the accuracy of CH4 prediction, despite the fact that there was no direct linear relationship between energy digestibility and MeY and in % of GE intake
|Yan et al., 2009<ref name=":6">Yan, T., Porter, M.G., and Mayne, S.C. 2009. Prediction of methane emission from beef cattle using data measured in indirect open-circuit respiration calorimeters. Animal 3:1455-1462.</ref>
|-
|Ratio of acetic and butyric acid divided by propionic acid
|Methane yield positive relation
|Moss et al., 2000<ref>Moss A.R., Jouany, J.P., and Newbold, J. 2000. Methane production by ruminants: Its contribution to global warming. Annal. Zootech. 49:231-253.</ref>
|-
| colspan="3" |'''(5) Whole animal measurements'''
|-
|Body weight and conformation
|Prediction models; primary predictor for enteric MeP
|Moraes et al. (2014)<ref name=":0b" />;
Holter and Young, 1992; <ref>Holter J.B., and Young, A.J. 1992. Methane production in dry and lactating Holstein cows. J. Dairy Sci. 75:2165–2175.</ref>

Yan et al., 2009<ref name=":6" />
|-
|Body weight
|Relationship with MeI: r = 0.44; relationship between body weight and rumen capacity
|Antunes-Fernandes et al. (2016)<ref>Antunes-Fernandes, E.C., van Gastelen, S., Dijkstra, J., Hettinga K.A., and Vervoort, J. 2016. Milk metabolome relates enteric methane emission to milk synthesis, and energy metabolism pathways. J. Dairy. Sci. 99:6251-6262.</ref>;
Demment and Van Soest, 1985<ref>Demment, M.W., and Van Soest, P.J. 1985. A nutritional explanation for body-size patterns of ruminant and nonruminant herbivores. Am. Nat. 125:641–672.</ref>
|-
|Body weight
|Key explanatory variable for enteric MeP
|No reference available
|-
|Conformation traits: affects enteric MeP
|Indicators for rumen volume (via feed intake and rumen passage rates); BCS
|Agnew and Yan, 2000<ref>Agnew, R.E. and Yan, T. 2000. The impact of recent research on energy feeding systems for dairy cattle. Livest. Prod. Sci. 66:197-215.</ref>
|-
|Lactation stage
|Complementary proxy
|Vanlierde et al. (2015)<ref name=":4a" />
|}</div>It is evident that no single proxy offers a good solution in terms of all of these attributes, though the low cost and high throughput make milk MIR a good candidate for further work on refining methods, improving calibrations and exploring combinations with other proxies.

== Combining proxies for methane ==
Although milk MIR shows promise as a single proxy for CH4 emissions, there may be advantages in using two or more proxies in combination. There are two potential reasons why a combination of proxies might be appropriate: (i) proxies may describe independent sources of variation in CH4 emissions, and (ii) one proxy allows correction for shortcomings in the way the other proxy describes CH4 emissions (e.g. taking into account lactation stage if CH4 emissions prediction coefficients change during the lactation). See also Negussie ''et al.'' (2019)<ref name=":30" />.
<center>
{| class="wikitable"
| colspan="3" |'''''Table 7. Combinations of proxies for methane.'''''
|- style="background-color:#efefef;"
|'''Proxy combinations'''
|'''Results'''
|'''References'''
|-
|Rumen microbiome + VFA
|Combination of rumen VFA proportions and pH + modelling may be more informative
|Brask ''et al.'' 2015<ref>Brask, M., Weisbjerg, M.R., Hellwing, A.L.F., Bannink, A., and Lund, P. 2015. Methane production and diurnal variation measured in dairy cows and predicted from fermentation pattern and nutrient or carbon flow Animal. 9:1795-1806. DOI: <nowiki>http://dx.doi.org/10.1017/S1751731115001184</nowiki></ref>
|-
|Methanogen abundance in rumen fluid + proxy, a chemical marker for methanogens (archaeol)
|
|McCartney ''et al.'' (2013)<ref>McCartney, C.A., Bull, I.D., Waters, S.M., and Dewhurst, R.J. 2013. Technical note: Comparison of biomarker and molecular biological methods for estimating methanogen abundance. J. Anim. Sci. 91:5724–5728.</ref>
|-
|Fecal ether lipids (ratio of diether to tetraether lipids) + rumen pH
|Combining measurements of rumen VFA, pH and the microbiome should be more informative for predicting CH4 emissions
|McCartney ''et al.'' (2014)<ref>McCartney, C.A., Dewhurst, R.J. and Bull, I.D. 2014. Changes in the ratio of tetraether to diether lipids in cattle feces in response to altered dietary ratio of grass silage and concentrates. J. Anim. Sci. 92:4095-4098.</ref>; Ann ''et al.'', 1996
|-
|Feed intake (determined by body weight, production level, growth rate and feed quality)
|Main driver for CH4 emissions; should be all included in models for CH4
|Moraes ''et al.'' 2014,<ref name=":0b" />
|-
|DMI and diet composition
|Combine database to predict CH4
|Niu ''et al.'', 2018<ref>Niu, M., Kebreab, E., Hristov, A. N., Oh, J., Arndt, C., Bannink, A., Bayat, A. R., Brito, A. F., Boland, T., Casper, D., Crompton, L. A., Dijkstra, J., Eugène, M. A., Garnsworthy, P. C., Haque, M. N., Hellwing, A.L.F., Huhtanen, P., Kreuzer, M., Kuhla, B., Lund, P., Madsen, J., Martin, C., Mcclelland, S. C., Mcgee, M., Moate, P. J., Muetzel, S., Muñoz, C., O'Kiely, P., Peiren, N., Reynolds, C. K., Schwarm, A., Shingfield, K. J., Storlien, T. M., Weisbjerg, M. R., Yáñez-Ruiz, D. R., and Yu, Z. 2018. Prediction of enteric methane production, yield, and intensity in dairy cattle using an intercontinental database. Glob. Change Biol. 2018;1–22, DOI: 10.1111/gcb.14094.</ref>; Van Lingen ''et al.'', 2019<ref>Van Lingen, H.J., Niu, M., Kebreab, E., Valadares Filho, S.C., Rooke, J.A., Duthie, C.A., Schwarm, A., Kreuzer, M., Hynd, P.I., Caetano, M., and Eugène, M. 2019. Prediction of enteric methane production, yield and intensity of beef cattle using an intercontinental database. Agric. Ecosyst. Environ. 283:106575.</ref>
|-
|Range of prediction equations for CH4 production
|Feed intake = primary predictor of total CH4 production (accounted for 52 to 64%); Combining more factors did indeed improve the prediction equation by 15 to 35%
|Ramin & Huhtanen (2013)<ref>Ramin, M. and Huhtanen, P.. 2013. Development of equations for predicting methane emissions from ruminants. J. Dairy Sci. 96:2476-2493.</ref>; Knapp ''et al.'', 2015<ref>Knapp J.R., Laur, G.L., Vadas, P.A.. Weiss, , and Tricarico, J.M. 2015. Invited review: Enteric methane in dairy cattle production: Quantifying the opportunities and impact of reducing emissions. J. Dairy Sci. 97:3231–3261.</ref>; Sauvant & Nozière (2016)<ref>Sauvant, D., and Nozière, P. 2016. Quantification of the main digestive processes in ruminants: the equations involved in the renewed energy and protein feed evaluation systems. Animal 10:755–770. <nowiki>https://doi:10.1017/S1751731115002670</nowiki>. </ref>
|-
|Rumen measurements (VFA, pH, protozoa counts) + feed intake (total DMI, forage DMI and FA intake) + production parameters (milk yield and composition) + milk FA
|Suggest that milk FA predict CH4 emission better (R2= 0.74) compared to rumen variables, feed intake and production parameters (R2 < 0.58). Total combination: R2= 0.90
|Mohammed ''et al.'' (2011)<ref name=":1b" />
|-
|Modelling
|specific prediction equations may need to be developed, or diet composition may need to be included in the prediction equations
|Mohammed ''et al.'' (2011)<ref name=":1b" />
|-
|Feed intake + diet composition + milk production + milk FA
|CH4 prediction equations: best fit = combining milk FA, feed intake, diet composition, and milk production (R2 = 0.84)
|Rico ''et al.'' (2016)<ref name=":33" />; Bougoin ''et al.'' (2019)<ref name=":34" />
|-
|MIR + lactation stage
|MIR spectroscopy (coefficient of determination = 0.68 and 0.79), predictions at different stages of lactation were not biologically meaningful + lactation stage refined the model: showing a biologically meaningful behavior throughout lactation: an increase in CH4 production after calving up to approximately 100 DIM, followed by gradual decline towards the end of lactation
|Dehareng ''et al.'' (2012)<ref name=":32" />; Vanlierde ''et al.'' (2015)<ref name=":4a" />
|-
|Milk yield, fat percentage + type traits
|Combine database to predict CH4 using official milk recording system and type evaluation.
|Cassandro ''et al.'', (2010)<ref name=":29" />; Cassandro, (2013)<ref name=":28" />
|}
</center>

== Building an index for methane ==
For some of the proxies, the heritability and correlations with CH4 output are known: e.g. Vanrobays ''et al.'' (2016)<ref>Vanrobays, M.-L., Bastin, C., Vandenplas, J., Hammami, H., Soyeurt, H., Vanlierde, A., Dehareng, F., Froidmont, E., and Gengler, N. 2016. Changes throughout lactation in phenotypic and genetic correlations between methane emissions and milk fatty acid contents predicted from milk mid-infrared spectra. J. Dairy Sci. 99:1–14. <nowiki>http://dx.doi.org/10.3168/jds.2015-10646</nowiki></ref> estimated heritability of 0.25 for CH4 production (g/d) and in the range 0.17 - 0.42 for different classes of milk FA; phenotypic and genetic correlations between MeP and milk FA varied between -0.03 and 0.16, and between -0.02 and 0.32 (C18:0), respectively. The genetic correlation between MeI and milk yield was estimated by Dehareng ''et al.'' (2012)<ref name=":32" /> at -0.45; that between milk yield and protein percentage at -0.54 (Miglior ''et al.'' 2007<ref>Miglior, F., Sewalem, A., Jamrozik, J., Bohmanova, J., Lefebvre, D.M., Moore, R.K. 2007. Genetic analysis of milk urea nitrogen and lactose and their relationships with other production traits in Canadian Holstein cattle. J. Dairy Sci. 90:2468-2479.</ref>). This would give a genetic correlation between MeI and protein percentage in the range [-0.5, 0.9], with likelier values for positive correlations. The most probable value in the given range could then be estimated (from the prior distribution of the missing correlation and the joint likelihood of the two known correlations given the values in the range). Such data could in the future be used to develop an index for breeding on CH4 emission.
</div>

= Proxies discussion =
<div class="mw-collapsible mw-collapsed">
<div>
The greatest limitation of proxies today is the lack of robustness in their general applicability. Future efforts should therefore be directed towards developing combinations of proxies that are robust and applicable across diverse production systems and environments. Here we present the present status of the knowledge of proxies and their predictive value for CH4 emission. Proxies related to body weight or milk yield and composition are relatively simple, low-cost, high throughput, and are easy to implement in practice. In particular, DMI and milk MIR, along with covariates such as lactation stage, are a promising option for prediction of CH4 emission in dairy cows. No single proxy was found to accurately predict CH4, whilst combinations of two or more proxies are likely to be a better solution. Combining proxies can increase the accuracy of predictions by up to 15 - 35%, mainly because different proxies describe independent sources of variation in CH4 and one proxy can correct for shortcomings in the other(s). One plausible strategy could be to increase animal productive efficiency whilst reducing CH4 emissions per animal. This could be achieved by reducing MeY and/or decreasing DMI provided that there is no concomitant reduction in productivity or increase in feed consumption (Pickering et al., 2015<ref>Pickering, N.K., Oddy, V.H., Basarab, J.A., Cammack, K., Hayes, B J., Hegarty, R.S., McEwan, J.C., Miller, S., Pinares, C., and de Haas, Y. 2015. Invited review: Genetic possibilities to reduce enteric methane emissions from ruminants. Animal 9:1431-1440.</ref>).

== Combining diet-based measurements with other proxies for methane emissions ==
Feed intake appears a reasonably adequate predictor of MeP: generally, heavier animals have higher maintenance requirements, so eat more and produce more CH4. However, a substantial level of variation is left unaccounted for. This suggests that more detailed information on dietary composition is needed. This is also important when one wants to account for MeP on diets of similar DMI but of different nutrient profiles.

The prediction accuracy of MeP strongly depends on the accuracy of quantifying the VFA produced in the rumen (Alemu et al., 2011<ref>Alemu, A.W., Dijkstra, J., Bannink, A., France, J., and Kebreab, E. 2011. Rumen stoichiometric models and their contribution and challenges in predicting enteric methane production. Anim. Feed Sci. Technol. 166-167:761-778.</ref>). The type of VFA formed during rumen fermentation depends on the type of substrate fermented (Bannink et al., 2011<ref>Bannink, A., van Schijndel, M.W., and Dijkstra, J. 2011. A model of enteric fermentation in dairy cows to estimate methane emission for the Dutch National Inventory Report using the IPCC Tier 3 approach. Anim. Feed Sci. Technol. 166-167:603-618.</ref>), such as the dietary content of neutral detergent fiber and starch. The type of substrate fermented thus appears a useful factor for predicting MeP (Ellis et al., 2007<ref>Ellis, J.L., Kebreab, E., Odongo, N.E., McBride, B.W., Okine, E.K., and France, J. 2007. Prediction of methane production from dairy and beef cattle. J. Dairy Sci. 90:3456–3466.</ref>), indicating that including a description of variation in dietary quality caused by nutritional factors results in improved prediction accuracy of CH4 emission (Ellis et al., 2010<ref>Ellis, J.L., Bannink, A., France, J., Kebreab, E., and Dijkstra, J. 2010. Evaluation of enteric methane prediction equations for dairy cows used in whole farm models. Glob. Change Biol. 16:3246–3256.</ref>; Moraes et al., 2014<ref name=":0b" />).

== Rumen ==
When feed intake is kept constant, a higher rumen capacity results in a lower passage rate (Demment and Van Soest, 1985<ref>Demment, M.W., and Van Soest, P.J. 1985. A nutritional explanation for body-size patterns of ruminant and nonruminant herbivores. Am. Nat. 125:641–672.</ref>), resulting in a higher MeP (Moraes et al., 2014<ref name=":0b" />). Proxies based on rumen samples are generally poor to moderately accurate predictors of CH4, and are costly and difficult to measure routinely on-farm. VFA are a proxy for rumen CH4 emissions. Using rumen fermentation data obtained from in vitro gas production, Moss et al. (2000)<ref>Moss A.R., Jouany, J.P., and Newbold, J. 2000. Methane production by ruminants: Its contribution to global warming. Annal. Zootech. 49:231-253.</ref> reported a negative linear relationship between CH4 production and the ratio of (acetic + butyric acid)/propionic acid. However, by combining different information sources, either related to feed intake or to the impact of feed intake on the VFA composition, a better proxy with an improved accuracy can be achieved. This way, the prediction equation for CH4 production can be optimized (higher accuracy).

The relationship between rumen methanogen abundance and methanogenesis is less clear when changes in enteric CH4 emissions are modulated by diet or are a consequence of selecting phenotypes related to feed efficiency or MeY. Whereas in some reports there was a significant positive relationship (Aguinaga Casanas et al., 2015<ref name=":1c">Aguinaga Casanas, M.A., , N., Krattenmacher, N., Thalle,r G., Metges, C.C., and Kuhla, B. 2015. Methyl-coenzyme M reductase A as an indicator to estimate methane production from dairy cows. J. Dairy Sci. 98:4074-4083.</ref>; Arndt et al., 2015<ref>Arndt, C., Powell, J.M., Aguerre, M.J., Crump, P.M., and Wattiaux, M.A. 2015. Feed conversion efficiency in dairy cows: Repeatability, variation in digestion and metabolism of energy and nitrogen, and ruminal methanogens. J. Dairy Sci. 98:3938-3950.</ref>; Sun et al., 2015<ref>Sun, X., Henderson, G., Cox, F., Molan,o G., Harrison, S.J., Luo, D., Janssen, P.H., and Pacheco, D. 2015. Lambs Fed Fresh Winter Forage Rape (Brassica napus L.) Emit Less Methane than Those Fed Perennial Ryegrass (Lolium perenne L.), and Possible Mechanisms behind the Difference. PLoS One 10(3):e0119697: DOI: 10.1371/journal.pone.0119697</ref>; Wallace et al., 2015<ref name=":2b">Wallace, R., Rooke, J., McKain, N., Duthie, C.-A., Hyslop, J., Ross, D., Waterhouse, A., Watson, M., and Roehe, R. 2015. The rumen microbial metagenome associated with high methane production in cattle. BMC Genomics. 16:839.</ref>), in many others the concentration of methanogens was unrelated to methanogenesis (Morgavi et al., 2012<ref>Morgavi, D.P., Martin, C., Jouany, J.P., and Ranilla, M.J. 2012. Rumen protozoa and methanogenesis: not a simple cause-effect relationship. Br. J. Nutr. 107:388-397. 10.1017/S0007114511002935.</ref>; Kittelmann et al., 2014<ref>Kittelmann, S., Pinares-Patiño, C.S., Seedorf, H., Kirk, M.R., Ganesh, S., McEwan, J.C., and Janssen, P.H. 2014. Two different bacterial community types are linked with the low-methane emission trait in sheep. PLoS One 9:e103171.</ref>; Shi et al., 2014<ref name=":3a">Shi W., Moon, C.D., Leahy, S.C., Kang, D., Froula, J., Kittelmann, S., Fan, C., Deutsch, S., Gagic, D., Seedorf, H., Kelly, W.J., Atua, R., Sang, C., Soni, P., Li, D., Pinares-Patiño, C.S., McEwan, J.C., Janssen, P.H., Chen, F., Visel, A., Wang, Z., Attwood, G.T., and Rubin, E.M. 2014. Methane yield phenotypes linked to differential gene expression in the sheep rumen microbiome. Genome Res. Doi:10.1101/gr.168245.113.</ref>; Bouchard et al., 2015<ref name=":4b">Bouchard, K., Wittenberg, K.M., Legesse, G., Krause, D.O., Khafipour, E., Buckley, K.E., and Ominski, K.H. 2015. Comparison of feed intake, body weight gain, enteric methane emission and relative abundance of rumen microbes in steers fed sainfoin and lucerne silages under western Canadian conditions. Grass a Forage Sci. 70:116-129.</ref>). Bouchard et al. (2015)<ref name=":4b" /> even reported a reduction in methanogens withoutsignificant decrease in MeP for steers fed sainfoin silage. Sheep selected for high or low MeY showed no differences in methanogen abundance, though there was a strong correlation with expression of archaeal genes involved in methanogenesis (Shi et al., 2014<ref name=":3" />).

Hindgut and Feces: whole tract digestibility variables cannot serve as primary predictors for enteric MeP in cattle or sheep, but might be used as supporting factors to improve the accuracy of prediction of CH4 output.

== Protozoa and other rumen microbes ==
Protozoa are net producers of H2 and their absence from the rumen is associated with an average reduction in enteric MeP of approximately 11% (Hegarty, 1999<ref>Hegarty, R.S. 1999. Reducing rumen methane emissions through elimination of rumen protozoa. Aust. J. Agric. Res. 50:1321-1327.</ref>; Morgavi et al., 2010<ref>Morgavi, D.P., Forano, E., Martin, C., and Newbold, C.J. 2010. Microbial ecosystem and methanogenesis in ruminants. Animal 4:1024-1036.</ref>; Newbold et al., 2015<ref>Newbold, C.J., de la Fuente, G., Belanche, A., Ramos-Morales, E., and McEwan, N. 2015. The role of ciliate protozoa in the rumen. Front. Microbiol. 6:1313.doi: 10.3389/fmicb.2015.01313</ref>). Using a database of 28 experiments and 91 dietary treatments, Guyader et al. (2014)<ref>Guyader, J., Eugène, M., Nozière, P., Morgavi, D.P., Doreau, M., and Martin, C. 2014. Influence of rumen protozoa on methane emission in ruminants: a meta-analysis approach. Animal 8:1816-1825.</ref> showed a significant decrease of 8.14 g CH4/kg DMI for each log unit reduction in rumen protozoal abundance. About 21% of experiments within this dataset reported CH4 changes unrelated to protozoal abundance, highlighting the multifactorial nature of methanogenesis. Roehe et al. (2016)<ref>Roehe R., Dewhurst, R.J., Duthie, C-A., Rooke, J.A., McKain, N., Ross, D.W., Hyslop, J.J., Waterhouse, A., Freeman, T.C., Watson, M., and Wallace, R.J. 2016. Bovine Host Genetic Variation Influences Rumen Microbial Methane Production with Best Selection Criterion for Low Methane Emitting and Efficiently Feed Converting Hosts Based on Metagenomic Gene Abundance. PLoS Genet 12(2): e1005846. doi:10.1371/journal.pgen.1005846.</ref> observed that the ranking of sire groups for CH4 emissions measured with respiration chambers was the same as that for ranking on archaea/bacteria ratio, providing further evidence that host control of archaeal abundance contributes to genetic variation in CH4 emissions - at least in some circumstances. Across a wide geographical range, the methanogenic archaea were shown to be highly conserved across the world (Henderson et al., 2015<ref>Henderson, G., Cox, F., Ganesh, S., Jonker, A., Young, W., Global Rumen Census Collaborators, and Janssen, P.H. 2015. Rumen microbial community composition varies with diet and host, but a core microbiome is found across a wide geographical range. Sci. Rep. 5:14567. doi:10.1038/srep14567</ref>). This universality and limited diversity could make it possible to mitigate CH4 emissions by developing strategies that target the few dominant methanogens. However, one clear limitation of metagenomic predictions compared to genomic predictions was that the microbiome of the host is variable - that is, it may change in response to diet or other environmental factors over time, whereas the hosts DNA remains constant.

== Rumen microbial genes ==
These included genes involved in the first and last steps of methanogenesis: formylmethanofuran dehydrogenase subunit B (fmdB) and methyl-coenzyme M reductase alpha subunit (mcrA), which were 170 times more abundant in high CH4 emitting cattle. Whilst gene-centric metagenomics is not low-cost or high-throughput, these results point to potential future proxy approaches using low-cost gene chips.

The difference in gene expression activity as opposed to abundance was also reported by others (Popova et al., 2011<ref>Popova, M., Martin, C., Eugène, M., Mialon, M.M., Doreau, M., and Morgavi, D.P. 2011. Effect of fibre- and starch-rich finishing diets on methanogenic Archaea diversity and activity in the rumen of feedlot bulls. Anim. Feed Sci. Technol. 166-167:113-121.</ref>). However, there are also studies in which there was no relationship with gene expression (Aguinaga Casanas et al., 2015<ref name=":1">Garnsworthy, P.C. Difford, G.F. Bell, M.J. Bayat, A.R. Huhtanen, P. Kuhla, B. Lassen, J. Peiren, N. Pszczola, M; Sorg, D. Visker, M.H., and Yan, T. 2019 Comparison of Methods to Measure Methane for Use in Genetic Evaluation of Dairy Cattle. Animals 9:837, 12p.</ref>). There are some methodological and experimental differences that might explain some of the apparent contradictions, such as the type of gene target and primers used for nucleic acid amplification. Effects are seen most clearly when the difference in MeP between groups of animals is large (e.g. Wallace et al. (2015)<ref name=":2b" /> used treatments that generated a 1.9-fold difference CH4 emissions).

== Proxies based on measurements in milk ==
Milk yield alone does not provide a good prediction of MeP by dairy cows. Yan et al. (2010)<ref>Yan. T., Mayne, C.S., Gordon, F.G., Porter, M.G., Agnew, R.E., Patterson, D.C., Ferris, C.P., and Kilpatrick, D.J. 2010. Mitigation of enteric methane emissions through improving efficiency of energy utilization and productivity in lactating dairy cows. J. Dairy Sci. 93:2630–2638. doi: 10.3168/jds.2009-2929.</ref> indicated that CH4 as a proportion of GE intake or milk energy output was negatively related to milk production. It is less clear if MeY can be predicted from milk yield when making comparisons across studies.

Milk MIR spectroscopy is relatively inexpensive, rapid and already routinely used technology in milk recording systems to predict fat, protein, lactose and urea contents in dairy milk to assist farm management decisions and breeding. It can be used as a promising strategy to exploit the link between enteric CH4 emission from ruminants and microbial digestion in the rumen by assessing the signature of digestion in milk composition. Milk MIR data can be obtained through regular milk recording schemes, as well as, on a herd level, through analysis used for milk payment systems. Diverse milk phenotypes can be obtained by MIR spectrometry – including detailed milk composition (e.g. FA as reported by Soyeurt et al., 2011<ref>Soyeurt H., Dehareng, F., Gengler, N., McParland, S., Wall, E., Berry, D.P., Coffey, M., and Dardenne, P. 2011. Mid-infrared prediction of bovine milk fatty acids across multiple breeds, production systems and countries. J. Dairy Sci. 94:1657–1667.</ref>), technological properties of milk, and cow physiological status (De Marchi et al., 2014<ref>De Marchi, M., Toffanin, V., Cassandro, M., and Penasa, M. 2014. Invited review: Mid-infrared spectroscopy as phenotyping tool for milk traits. J. Dairy Sci. 97:1171–1186. <nowiki>http://dx.doi.org/10.3168/jds.2013-6799</nowiki>.</ref>; Gengler et al., 2016<ref>Gengler, N., Soyeurt, H., Dehareng, F., Bastin, C., Colinet, F., Hammami, H., Vanrobays, M.-L., Lainé, A., Vanderick, S., Grelet, C., Vanlierde, A., Froidmont, E., and Dardenne, P. 2016. Capitalizing on fine milk composition for breeding and management of dairy cows. J. Dairy Sci. 99:4071-4079.</ref>). Several of these novel traits (i.e. FA composition) have been identified as potential indicators of CH4 emission. Therefore, using MIR to predict MeP (Dehareng et al. 2012<ref name=":5b">Dehareng, F., Delfosse, C., Froidmont, E., Soyeurt, H., Martin, C., Gengler, N., Vanlierde, A., and Dardenne, P. 2012. Potential use of milk mid-infrared spectra to predict individual methane emission of dairy cows. Animal 6:1694-701.</ref>; Vanlierde et al. 2013<ref>Vanlierde, A., Dehareng, F., Froidmont, E., Dardenne, P., Kandel, P.B., Gengler, N., Deighton, M.H., buckley, F., Lewis, E., McParland, S., Berry, D.P., and Soyeurt, H. 2013. Prediction of the individual enteric methane emission of dairy cows from milk-mid-infrared spectra. Advances in Animal Biosciences. 5th Greenhouse Gases Animal Agriculture Conference (GGAA2013) 23-26 June 2014. Dublin, Ireland. p 433.</ref>, 2015<ref name=":4a" />; Van Gastelen and Dijkstra, 2016<ref name=":7a">Van Gastelen, S., and Dijkstra, J.. 2016. Prediction of methane emission from lactating dairy cows using milk fatty acids and mid-infrared spectroscopy. J. Sci. Food Agric. 96:3963-3968. DOI: 10.1002/jsfa.7718.</ref>) is also a logical extension of its use to quantify the major milk components (i.e. fat, protein, casein, lactose, and urea) and minor components (e.g. FA). Dehareng et al. (2012)<ref name=":32" /> assessed the feasibility to predict individual MeP from dairy cows using milk MIR spectra. Their initial results suggest that this approach could be useful to predict MeP at the farm or regional scale, as well as to identify low-CH4 emitting cows. According to Van Gastelen and Dijkstra (2016)<ref name=":36" />, MIR spectroscopy has the disadvantage that it has a moderate predictive power for CH4 emission, both direct and indirect (i.e. via milk FA), and that it lacks the ability to predict important milk FA for CH4 prediction. They concluded that it may not be sufficient to predict MeP based on MIR alone. It is, however, possible to improve the accuracy of prediction through the combination of MIR with some animal characteristics such as lactation stage (Vanlierde et al., 2015<ref name=":4a" />). The advantage of this latter development is that this type of prediction can be done on a very large scale inside a routine milk recording system (Vanlierde et al., 2015<ref name=":4a" />).

== Proxy: future developments and perspectives ==
There is currently limited consensus on which phenotype to use to lower the carbon footprint of milk production through genetic selection. This could be MeP, MeI or MeY. The direct goal would be CH4 production; the relationship with milk production and/or feed intake could be accounted for by including these in the final selection index or scheme. However, one might argue that it would be more effective/accurate to directly use milk production- or feed intakecorrected CH4 (e.g. CH4 intensity or yield) as breeding goal.

The analysis of proxies in terms of their attributes shows that proxies that are based on samples from the rumen or related to rumen sources are poor to moderately accurate predictors of CH4. In addition, these proxies are too costly and difficult for routine on-farm implementation. On the other hand, proxies related to BW, milk yield and composition (e.g. milk FA) are moderately to highly accurate predictors of CH4 and relatively simple, low-cost and easier to implement in practice (Cassandro et al.,2010<ref>Cassandro, M., Cecchinato, A., Battagin, M., Penasa, M., 2010. Genetic parameters of predicted methane production in Holstein Friesian cowsIn: Proc. 9th World Congr. on Genetics Applied to Livestock Production, Leipzig, Germany. . Page 181</ref>; Cassandro, 2013<ref>Cassandro, M. 2013. Comparing local and cosmopolitan cattle breeds on added values for milk and cheese production and their predicted methane emissions. Animal Genetic Resources/Ressources génétiques animales/Recursos genéticos animales, available on CJO2013. doi:10.1017/S2078 63361200077X. </ref>). Particularly, milk MIR and the prediction of CH4 based on milk MIR along with other covariates such as lactation stage is a promising alternative: that is accurate, cheaper and easy to be implemented in routine milk analysis at no extra cost.

Therefore, in the future advances in infrared, photoacoustic and related technologies will push the boundaries, particularly in focusing on developments of fast and portable technologies. Such developments will lead to better and promising proxies for CH4 that will enable a sizable throughput of CH4 phenotypes in dairy cows. Antunes-Fernandes et al. (2016)<ref>Antunes-Fernandes, E.C., van Gastelen, S., Dijkstra, J., Hettinga K.A., and Vervoort, J. 2016. Milk metabolome relates enteric methane emission to milk synthesis, and energy metabolism pathways. J. Dairy. Sci. 99:6251-6262.</ref> already presented the use of metabolomics on milk to better understand the biological pathways involved in CH4 production in dairy cattle. The techniques used in that study are not suitable for large scale measurements, but rapid developments in omics may offer tests and assay methodologies on blood, urine or milk samples that will provide an additional tool for developing new / additional proxies for CH4 emissions in dairy cattle.</div>
</div>

= Merging and sharing data in genetic evaluations =
Early 2016 an attempt to make cross country evaluations of CH4 emissions from Holstein dairy cattle was initiated. The work was based on data from NL, DK, AUS, UK and IR. In total, 12,820 weekly CH4 emission records from 2,857 cows were available. Although different equipment was used across countries to measure CH4 emissions, the research aimed to define similar CH4 output phenotypes in each country. The analysed CH4 traitres, that are available in each country, are (1) CH4 production in g/d, and (2) CH4 intensity in g/d per kg fat protein corrected milk (FPCM). In addition to these CH4 traits, CH4 concentration (in ppm) was available in Denmark, the Netherlands and UK, and the ratio between CH4 and CO2 concentration was available in Denmark and the Netherlands.

Bivariate analyses were carried out to estimate genetic correlations between countries, using an animal linear mixed model for all traits. Both univariate and bivariate analyses were repeated with the GRM as well. With all weekly records, standardizing the trait in the full dataset increased the heritability for CH4 production from 0.03 to 0.06. The heritability for CH4 intensity was slightly higher. The highest heritability with the full dataset is estimated for the standardized CH4 concentration (0.19). Correlations estimated among CH4 traits estimated with either the pedigree or the GRM were in same direction and of similar magnitude. The genetic correlations show that when CH4 production increased, the CH4 concentration and the ratio between CH4 and CO2 increased as well.

The approach is novel, and no other attempt has been performed to make genetic analysis of CH4 traits across countries. The analysis can be repeated in future studies where more data hopefully will be available, and more effort can be made into improving both the fixed and random part of the model.

= Recommendations =
The most important question: what method to use if you need to measure CH4? The answer may be: it depends on what you like to do. In the Table 6 we summarize some experimental conditions and designs, and make recommendations.
<center>
{| class="wikitable"
|-
! colspan="3" style="text-align:left;" | ''Table 6. Recommendations for measuring methane in diverse experimental conditions and designs.''
|- style="background-color:#efefef;"
|+
! style="text-align:center;" | Experimental condition and design
! style="text-align:center;" | Methane measurement method recommendation
|-
| style="text-align:center;" | Need to measure absolute methane values – animal numbers and location not important
| style="text-align:center;" | Respiration chamber;
SF6; GreenFeed
|-
| style="text-align:center;" | Need to rank animals from low to high methane emission
| style="text-align:center;" | Sniffer method
|-
| style="text-align:center;" | Need to measure methane on farm
| style="text-align:center;" | Sniffer method;
GreenFeed; PAC
|-
| style="text-align:center;" | Low budget measurements needed
| style="text-align:center;" | Proxy;
Proxies measurement
|-
| style="text-align:center;" | High animal numbers required
| style="text-align:center;" | Sniffer method;
Proxies measurement; LMD
|}
</center>

= Conclusions =
Measuring CH4 emission on large numbers of cows is a challenge. The high costs and low throughput of RC restrict their use to research studies measuring CH4 emissions on small numbers of individual animals. Respiration chambers remain the gold standard method, but benchmarking alternative methods against RC is challenging because simultaneous replicate measures per cow are not feasible. Methods like SF6 and GreenFeed require lower capital investment and running costs than RC, and have higher throughput and potential for use in extensive and grazing situations, but costs are still prohibitive for recording large numbers of animals. Methods based on concentration are less precise and accurate than flux methods, but they are viable for large scale measurement, which is a prerequisite of genetic evaluations. Further development is needed to increase accuracy and precision of concentration methods. Several reviews of methods for measuring CH4 have made qualitative judgements based on individual comparison studies without expanding scope to genetic evaluations and considering repeated measure correlations between methods as proxies for genetic correlations. Results confirm that there is sufficient correlation between methods for all to be combined for international genetic studies and provide a much needed framework for comparing genetic correlations between methods should these be made available. Proxies have the potential to be used as predictors of CH4 production and emission. Although proxies are less accurate than direct CH4 measurements they can be easier, cheaper, and at high throughput, and may be therefore the best method in practical situations, especially proxies related to milk measurements. Therefore, these proxies at the population level, can provide useful information at genetic improvement that can be used to reduce emissions following 3 ways: (1) intensification of animal production; (2) improving of system efficiency and (3) the direct reduction of GHG emissions by breeding for reduced predicting animals that are high or low GHG emitters.

= Acknowledgements =
This document is the result of the ICAR Feed and Gas Working Group and its industry and research liaison group. The members of the ICAR Feed and Gas Working Group at the time of publication are, in alphabetical order:

* Christine Baes, Department of Animal Biosciences, University of Guelph, Canada and Institute of Genetics, Vetsuisse Faculty, University of Bern, Switzerland
* Yvette de Haas, Animal Breeding and Genomics, Wageningen Livestock Research
* Raffaella Finocchiaro, ANAFI, Italy
* Phil Garnsworthy, School of Biosciences, University of Nottingham, United Kingdom
* Birgit Gredler-Grandl, Animal Breeding and Genomics, Wageningen Livestock Research
* Nina Krattenmacher, Institute of Animal Breeding and Husbandry, Christian-Albrechts-University, Germany
* Jan Lassen, Viking Genetics, Denmark
* Jennie Pryce, Centre for AgriBioscience, AgriBio, Agriculture Victoria Research and School of Applied Systems Biology, La Trobe University, Australia
* Roel Veerkamp, Animal Breeding and Genomics, Wageningen Livestock Research (chairperson)

The work of Marinus te Pas (Animal Breeding and Genomics, Wageningen Livestock Research) in compiling draft versions is highly acknowledged. The content of the guidelines are partly based on results of the network of internationally recognized experts within the METHAGENE COST Action FA1320.

<references />

Section 20 – Methane Emission for Genetic Evaluation

2024-11-10T04:18:13Z

Aashish: /* Introduction */

= vaIntroduction =
Increases in milk production through management and genetics have substantially improved feed efficiency and decreased costs per unit of product over recent decades. However, dairy systems are also associated with environmental costs (Baskaran et al., 2009<ref>Baskaran, R., Cullen, R., and Colombo, S. 2009. Estimating values of environmental impacts of dairy farming in New Zealand, New Zealand J. Agric. Res. 52: 377-389, DOI: 10.1080/00288230909510520.</ref>), with methane (CH4) emissions associated with rumen microbial fermentation being both an important contributor to global greenhouse gas (GHG) emissions, as well as an avoidable loss of energy that could otherwise be directed into milk production. The livestock sector is responsible for 14.5% of the global GHG (Gerber et al., 2013<ref>Gerber, P.J., Steinfeld, H., Henderson, B., Mottet, A., Opio, C., Dijkman, J., Falcucci, A. , and Tempio, G. 2013. Tackling climate change through livestock – A global assessment of emissions and mitigation opportunities. Food and Agriculture Organization of the United Nations (FAO), Rome.</ref>); dairy cattle account for 18.9% of these emissions, mainly in the form of enteric CH4 emissions (van Middelaar et al., 2014<ref>Van Middelaar, C.E., Dijkstra. J., Berentsen. P.B.M.. and De Boer. I.J.M. 2014. Cost-effectiveness of feeding strategies to reduce greenhouse gas emissions from dairy farming. J. Dairy Sci. 97:2427–2439.</ref>). Methane is a greenhouse gas with a global warming potential 28 times that of CO2 (Myhre et al., 2013<ref>Myhre, G., Shindell, D., Bréon, F., Collins, W., Fuglestvedt, J., Huang, J., Koch, D., Lamarque, J., Lee, D., Mendoza, B., and Nakajima ,T. 2013. Anthropogenic and Natural Radiative Forcing. T.F. Stocker, D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley, ed. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.</ref>). Methane from ruminant livestock is generated during microbial fermentation in the rumen and hindgut (enteric CH4), and from decomposition of manure. Enteric CH4 contributes 80% of CH4 emissions by ruminants, and manure decomposition contributes 20%. Enteric CH4 accounts for 17% of global CH4 emissions and 3.3% of total global greenhouse gas emissions from human activities (Knapp et al., 2014<ref>Knapp, J.R., Laur, G.L., Vadas, P.A., Weis,s W.P., and Tricarico, J.M. 2014. Invited review: Enteric methane in dairy cattle production: Quantifying the opportunities and impact of reducing emissions. J. Dairy Sci. 97:3231-3261.</ref>). There is, therefore, a significant research interest to find ways to reduce enteric CH4 emissions by ruminants. Ruminant animals have a digestive system to digest plant materials efficiently. Like most mammals, ruminants lack the cellulase enzyme required to break the beta-glucose linkages in cellulose, but they play host to diverse populations of rumen microbes that can digest cellulose and other plant constituents. When rumen bacteria, protozoa and fungi ferment carbohydrates and proteins of plant materials, they produce volatile fatty acids, principally acetate, propionate and butyrate. High fibre diets favour acetate synthesis. Synthesis of acetate and butyrate are accompanied by release of metabolic hydrogen, which, if allowed to accumulate in rumen fluid, has negative effects on microbial growth, and feed digestibility (Janssen, 2010<ref>Janssen, P.H. 2010. Influence of hydrogen on rumen methane formation and fermentation balances through microbial growth kinetics and fermentation thermodynamics. Anim. Feed Sci. Technol. 160:1–22. doi:10.1016/j.anifeedsci.2010.07.002.</ref>). Rumen Archaea are microorganisms that combine metabolic hydrogen with CO2 to produce CH4 and water. Archaea play a vital role, therefore, in protecting the rumen from excess metabolic hydrogen, and the CH4 they produce is an inevitable product of rumen fermentation. A number of CH4 phenotypes have been defined (Hellwing et al., 2012<ref name=":12">Hellwing, A.L.F., Lund, P., Weisbjerg, M.R., Brask, M., and Hvelplund. T. 2012. Technical note: test of a low-cost and animal-friendly system for measuring methane emissions from dairy cows. J. Dairy Sci. 95:6077–85. doi:10.3168/jds.2012-5505.</ref>); the most widely used is CH4 production (MeP) in liters or grams per day. The CH4 production trait is highly correlated with feed intake (Basarab et al., 2013<ref>Basarab, J., Beauchemin, K., Baron, V., Ominski, K., Guan, L., Miller, S., and Crowley, J. 2013. Reducing GHG emissions through genetic improvement for feed efficiency: Effects on economically important traits and enteric methane production. Animal, 7(S2):303-315. doi:10.1017/S1751731113000888</ref>; De Haas et al., 2017<ref>de Haas, Y., Pszczola, M., Soyeurt, H., Wall, E., and Lassen, J. 2017. Invited review: Phenotypes to genetically reduce greenhouse gas emissions in dairying. J. Dairy Sci. 100:855-870.</ref>) and, thereby, with the ultimate breeding goal trait: milk production in dairy cattle. The economic value of daily dry matter intake and associated methane emissions in dairy cattle showed that increasing the feed performance estimated breeding value by one unit (i.e. 1 kg of more efficiently converted DMI during the cow’s first lactation) translates to a total lifetime saving of 3.23 kg in DMI and 0.055 kg in methane (Richardson et al., 2019<ref>Richardson, C., Baes, C., Amer, P., Quinton, C., Martin, P., Osborne, V., Pryce, J.E., and Miglior, F. 2020. Determining the economic value of daily dry matter intake and associated methane emissions in dairy cattle. Animal 14:171-179. doi:10.1017/S175173111900154X</ref>). Feed Performance was defined as a 1 kg increase in more efficiently used feed in a first parity lactating cow. These results show not only the relation between DMI and CH4 production, but also the economic relationship between these traits. Persistency of lactation was found to be positively associated with increased feed efficiency and decreased methane production and intensity. Feed efficiency was associated with lower methane intensity. Feed efficiency and methane emissions can be improved by selecting for dairy cattle that are smaller and have increased persistency of lactation. Efficiency and methane emissions can be further improved by improved management of body condition score and by extending lactations beyond the conventional 305-day length (Seymour, 2019<ref>Seymour, D.J. 2019. Feed Efficiency Dynamics in Relation to Lactation and Methane Emissions in Dairy Cattle. PhD thesis, The University of Guelph, Canada.</ref>). According to Ellis et al. (2007)<ref>Ellis, J.L., Kebreab, E., Odongo, N.E., McBride, B.W., Okine, E.K., and France, J. 2007. Prediction of methane production from dairy and beef cattle. J. Dairy Sci. 90:3456–3466.</ref>, DMI predicted MeP with an R2 of 0.64, and ME intake (MJ/d) predicted MeP with an R2 of 0.53 for dairy cattle. AlternativePhenotype definitions include CH4 intensity (MeI), which is defined as liters or grams of CH4 per kg of milk, and CH4 yield (MeY), which is defined as liters or grams of CH4 per kg of dry matter intake (DMI) (Moate et al., 2016<ref>Moate, P.J., Deighton, M.H., Williams, S.R.O., Pryce, J.E., Hayes, B.J., Jacobs, J.L., Eckard, R.J., Hannah, M.C. and Wales, W.J., 2016. Reducing the carbon footprint of Australian milk production by mitigation of enteric methane emissions. Anim. Prod. Sci. 56:1017-1034.</ref>). Residual CH4 production (RMP) is calculated as observed minus predicted CH4 production (Herd et al., 2014<ref>Herd, R.M., Arthur, P.F., Bird, S.H., Donoghue, K.A., and Hegarty, R.S. 2014. Genetic variation for methane traits in beef cattle. In: Proc. 10th World Conference on Genetic Applied to Livestock Production (WCGALP), 17-22 August, 2014. Vancouver, Canada.</ref>, Berry et al., 2015<ref>Berry, D.P., Lassen, J., and de Haas, Y. 2015. Residual feed intake and breeding approaches for enteric methane mitigation In: Livestock production and climate change. P.K. Malik, R. Bhatta, J. Takahashi, R.A. Kohn, and C.S. Prasad, ed. CABI, Oxfordshire, UK. . Pages 273-291</ref>), with predicted values based on factors such as milk production, body weight and feed intake. At the moment, it is not obvious which of these phenotypes to use; but, it is important to monitor associations between the chosen CH4 phenotype and the other important traits in the breeding goal (e.g. production, fertility, longevity) to avoid unfavorable consequences. Berry and Crowley (2012)<ref>Berry, D.P., and Crowley, J.J. 2012. Residual intake and body weight gain: A new measure of efficiency in growing cattle, J. Anim. Sci. 90:109–115, <nowiki>https://doi.org/10.2527/jas.2011-4245</nowiki></ref> describe advantages and limitations of ration traits. For example, because feed efficiency traits are a linear combination of other traits it is not recommended to include them in an overall total merit index, which is a clear limitation. For all applications it is necessary to measure the CH4 emission of each animal individually. These guidelines are intended to make the right choices for this. Whilst diet changes and feed additives can be effective mitigation strategies for CH4 emissions (Beauchemin et al., 2009<ref>Beauchemin, K.A., McAllister, T.A., and McGinn, S.M. 2009. Dietary mitigation of enteric methane from cattle. CAB Rev.: Perspect. Agric., Vet. Sci., Nutr. Nat. Res. 4:1–18.</ref>; Martin et al., 2010<ref>Martin C., Morgavi, D.P., and Doreau, M. 2010. Methane mitigation in ruminants: from microbe to the farm scale. Animal 4:351–365.</ref>; Hristov et al., 2013<ref>Hristov, A.N., Oh, J., Firkins, J.L., Dijkstra, J., Kebreab, E., Waghorn, G., Makkar, H.P.S., Adesogan, A.T., Yan,g W., Lee, C., Gerber, P.J., Henderson, B., and Tricarico, J.M. 2013. Special topics - Mitigation of methane and nitrous oxide emissions from animal operations: I. A review of enteric methane mitigation options. J. Anim. Sci. 91:5045–5069.</ref>), their effects depend on the continued use of a particular diet or additive and there have been issues with the rumen microbiomes adapting to additives. Rumen bacterial communities are highly dynamic after a diet switch and did not stabilize within 5 wk of cows grazing pasture (Bainbridge et al., 2016<ref>Bainbridge, M.L., Saldinger, L.K., Barlow, J.W., Alvez, J.P., Roman, J. Kraft, J. 2016. 1609 Rumen bacterial communities continue to shift five weeks after switching diets from conserved forage to pasture. J. Anim. Sci. 94, suppl_5:783, <nowiki>https://doi.org/10.2527/jam2016-1609</nowiki> (abstr.)</ref>). In contrast, breeding for reduced CH4 emissions should result in a permanent and cumulative reduction of emissions (Wall et al., 2010<ref>Wall, E., Simm, G., and Moran, D. 2010. Developing breeding schemes to assist mitigation of greenhouse gas emissions. Animal 4:366-376.</ref>). Several studies have shown that CH4 emissions by ruminants have a genetic component, with heritability in the range 0.20 – 0.30 (de Haas et al., 2011<ref>de Haas, Y., Windig, J.J., Calus, M.P.L., Dijkstra, J., de Haan, M., Bannink, A., and Veerkamp, R F. 2011. Genetic parameters for predicted methane production and potential for reducing enteric emissions through genomic selection. J. Dairy Sci. 94:6122–6134.</ref>; Donoghue et al., 2013<ref>Donoghue K.A., Herd, R.M., Bird, S.H., Arthur, P.F., and Hegarty, R F. 2013. Preliminary genetic parameters for methane production in Australian beef cattle. In: Proceedings of the Association for the Advancement of Animal Breeding and Genetics, 20-23 October 2013, Napier, New Zealand, pp. 290–293.</ref>; Pinares-Patiño et al., 2013<ref name=":13">Pinares-Patiño C.S., Hickey, S.M., Young, E.A., Dodds, K.G., MacLean, S., Molano, G., Sandoval, E., Kjestrup, H., Harland, R., Pickering, N.K., and McEwan, J.C. 2013. Heritability estimates of methane emissions from sheep. Animal 7: 316–321.</ref>, Kandel et al., 2014A<ref name=":37">Kandel, P.B., Vanderick, S., Vanrobays, M.L., Vanlierde, A., Dehareng, F., Froidmont, E., Soyeurt, H., and Gengler, N. 2014A. Consequences of selection for environmental impact traits in dairy cows. Page 19. (<nowiki>http://orbi.ulg.ac.be/bitstream/2268/164402/164401/NSABS162014_poster_Purna_abstract.pdf</nowiki>) I:n Proc. 19th National symposium on applied biological sciences, Gembloux, Belgium.</ref>, B<ref>Kandel, P.B., Vanderick, S., Vanrobays, M.L., Vanlierde, A., Dehareng, F., Froidmont, E., Soyeur,t H., and Gengler, N. 2014B. Consequences of selection for environmental impact traits in dairy cows. In: 10th World Congress on Genetics Applied to Livestock Production (WCGALP), 17-22 August, 2014. Vancouver, Canada.</ref>; Lassen and Lovendahl, 2016<ref>Lassen, J., and Løvendahl, P. 2016. Heritability estimates for enteric methane emissions from Holstein cattle measured using noninvasive methods. J. Dairy Sci. 99:1959-1967.</ref>; López-Paredes et al. 2020<ref>Lopez-Paredes, J., Goiri, I., Atxaerandio, R., García-Rodríguez, A., Ugarte, E., Jiménez-Montero, J.A., Alenda, R and González-Recio, O. 2020. Mitigation of greenhouse gases in dairy cattle via genetic selection (i): Genetic parameters of direct methane using non-invasive methods and its proxies. J. Dairy Sci. 103.</ref>). Breeding for reduced CH4 emissions, alone or together with other mitigation strategies, could therefore be effective in reducing the environmental impact of cattle farming and, possibly, also in increasing feed efficiency. Such a breeding scheme would require, as a fundamental starting point, accurate measures of individual CH4 emissions on a large scale. Several techniques have been developed for the measurement of CH4 emissions from ruminants, with varying degrees of accuracy (see reviews by Cassandro et al., 2013<ref>Cassandro, M., Mele, M., Stefanon, B.. 2013. Genetic aspects of enteric methane emission in livestock ruminants. Italian J. Anim. Sci. 12:e73: 450-458.</ref> and Hammond et al., 2016A<ref>Hammond, K.J., Crompton, L.A., Bannink, A., Dijkstra, J., Yáñez-Ruiz, D.R., O’Kiely, P., Kebreab, E., Eugenè, M.A., Yu, Z., Shingfield, K.J., Schwarm, A., Hristov, A.N., and Reynolds, C.K. 2016A. Review of current in vivo measurement techniques for quantifying enteric methane emission from ruminants. Anim. Feed Sci. Technol. 219:13–30. doi:10.1016/j.anifeedsci.2016.05.018.</ref>), but routine individual measurements on a large scale (a requisite for genetic selection) have proven to be difficult to obtain and expensive to measure (Pickering et al., 2015<ref>Pickering, N.K., Oddy, V.H., Basarab, J.A., Cammack, K., Hayes, B J., Hegarty, R.S., McEwan, J.C., Miller, S., Pinares, C., and de Haas, Y. 2015. Invited review: Genetic possibilities to reduce enteric methane emissions from ruminants. Animal 9:1431-1440.</ref>; Negussie et al., 2016<ref>Negussie E., Lehtinen, J., Mäntysaari, P., Liinamo, A-E., Mäntysaari, E., and Lidauer, M.. 2016. Non-invasive individual methane measurements in dairy cows using photoacoustic infrared spectroscopy technique. 6th Greenhouse Gases Animal Agriculture Conference (GGAA2016) 14-18 February 2016. Melbourne, Australia. Abstract. p62.</ref>). Therefore, identifying proxies (i.e. indicators or indirect traits) that are correlated to CH4 emissions, but which are easy and relatively lowcost to record on a large scale, would be a welcome alternative. Proxies might be less accurate but could be measured repeatedly to reduce random noise and in much larger populations. These guidelines are highly indebted to Garnsworthy et al. (2019)<ref name=":11">Garnsworthy, P.C. Difford, G.F. Bell, M.J. Bayat, A.R. Huhtanen, P. Kuhla, B. Lassen, J. Peiren, N. Pszczola, M; Sorg, D. Visker, M.H., and Yan, T. 2019 Comparison of Methods to Measure Methane for Use in Genetic Evaluation of Dairy Cattle. Animals 9:837, 12p.</ref>. In this paper the methods to measure CH4 are compared with special emphasis to the genetic evaluation of dairy cattle.

= Disclaimer =
The fact that specific device manufacturers are mentioned in these guidelines is in no way an endorsement of the devices or their accuracy by ICAR.

= Scope =
A variety of technologies are being developed and employed to measure CH4 emissions of individual dairy cattle under various environmental conditions, as is evidenced by frequent reviews (Storm et al., 2012<ref>Storm, I.M., Hellwing, A.L.F., Nielsen, N.I., and Madsen, J. 2012. Methods for measuring and estimating methane emission from ruminants. Animals 2:160-183.</ref>; Cassandro et al., 2013<ref name=":28">Cassandro, M., Mele, M., Stefanon, B.. 2013. Genetic aspects of enteric methane emission in livestock ruminants. Italian J. Anim. Sci. 12:e73: 450-458.</ref>; Hammond et al., 2016A<ref>Hammond, K.J., Crompton, L.A., Bannink, A., Dijkstra, J., Yáñez-Ruiz, D.R., O’Kiely, P., Kebreab, E., Eugenè, M.A., Yu, Z., Shingfield, K.J., Schwarm, A., Hristov, A.N., and Reynolds, C.K. 2016A. Review of current in vivo measurement techniques for quantifying enteric methane emission from ruminants. Anim. Feed Sci. Technol. 219:13–30. doi:10.1016/j.anifeedsci.2016.05.018.</ref>; de Haas et al., 2017<ref name=":10">de Haas, Y., Pszczola, M., Soyeurt, H., Wall, E., and Lassen, J. 2017. Invited review: Phenotypes to genetically reduce greenhouse gas emissions in dairying. J. Dairy Sci. 100:855-870.</ref>). The first objective of the current guidelines is to review and compare the suitability of methods for large-scale measurements of CH4 output of individual animals, which may be combined with other databases for genetic evaluations. Comparisons include assessing the accuracy, precision and correlation between methods. Combining datasets from different countries and research centres could be a successful strategy for making genetic progress in this difficult to measure trait if the methods are correlated (de Haas et al., 2017<ref name=":10" />). Accuracy and precision of methods are important. Data from different sources need to be appropriately weighted or adjusted when combined, so any methods can be combined if they are suitably correlated with the ‘true’ value. The second objective of the current guidelines, therefore, is to examine correlations among results obtained by different methods, ultimately leading to an estimate of confidence limits for selecting individual animals that are high or low emitters (see also Garnsworthy et al., 2019<ref>Garnsworthy, P.C. Difford, G.F. Bell, M.J. Bayat, A.R. Huhtanen, P. Kuhla, B. Lassen, J. Peiren, N. Pszczola, M; Sorg, D. Visker, M.H., and Yan, T. 2019 Comparison of Methods to Measure Methane for Use in Genetic Evaluation of Dairy Cattle. Animals 9:837, 12p.</ref>).

= Methane determining factors =
<div class="mw-collapsible mw-collapsed">
<div>
== Diet and rumen microbiota ==
Table 1 contains a list of dietary or microbiota factors that determine CH4 production.
<center>
{| class="wikitable"
|-
! colspan="3" | ''Table 1. Methane determining factors related to diet and rumen microbiota.''
|- style="background-color:#efefef;"
|+
!Factors
!Reference
|-
|The main determinants of daily methane production are dry matter intake and diet composition: the more feed consumed, and/or the greater the fibre content of the diet, the more methane is produced per day. However, per unit of DMI, and per unit of fat+protein yield the grass diet produced less enteric CH4 per cow than the TMR diet. Nutritional approaches for methane mitigation include reducing the forage to concentrate ratio of diets, increasing dietary oil content, and dietary inclusion of rumen modifiers and methane inhibitors.
|Beauchemin et al., 2009<ref>Beauchemin, K.A., McAllister, T.A., and McGinn, S.M. 2009. Dietary mitigation of enteric methane from cattle. CAB Rev.: Perspect. Agric., Vet. Sci., Nutr. Nat. Res. 4:1–18.</ref>;
Cottle et al., 2011<ref>Cottle, D.J., Nolan, J.V., and Wiedemann, S.G. 2011. Ruminant enteric methane mitigation: A review. Anim. Prod. Sci. 51:491–514. doi:10.1071/AN10163.</ref>; Knapp et al., 2014<ref>Knapp, J.R., Laur, G.L., Vadas, P.A., Weis,s W.P., and Tricarico, J.M. 2014. Invited review: Enteric methane in dairy cattle production: Quantifying the opportunities and impact of reducing emissions. J. Dairy Sci. 97:3231-3261.</ref>; O’Neill et al., 2011<ref>O’Neill, B.F., Deighton, M.H., O’Loughlin, B.M., Mulligan, F.J., Boland, T.M., O’Donovan, M., and Lewis, E. 2011. Effects of a perennial ryegrass diet or total mixed ration diet offered to spring-calving Holstein-Friesian dairy cows on methane emissions, dry matter intake, and milk production. J. Dairy Sci. 94:1941 – 1951</ref>; Sauvant et al., 2011<ref>Sauvant, D., Giger-Reverdin, S., Serment, A., and Broudiscou, L. 2011. Influences des régimeset de leur fermentation dans le rumen sur la production de méthane par les ruminants. INRA Prod. Anim. 24:433–446.</ref>
|-
|Methane output per kg of product is affected mainly by cow milk yield or growth rate, and by herd-level factors, such as fertility, disease incidence and replacement rate.
|Garnsworthy, 2004<ref>Garnsworthy, P.C. 2004. The environmental impact of fertility in dairy cows: a modelling approach to predict methane and ammonia emissions. Anim. Feed Sci. Technol. 112:211-223.</ref>
|-
|Methane output varies considerably between individual animals. For animals fed the same feed, the between-animal coefficient of variation (CV) in methane was 8.1%.
|Blaxter and Clapperton, 1965<ref>Blaxter, K.L., and Clapperton, J.L. 1965. Prediction of the amount of methane produced by ruminants. Br. J. Nutr.19:511–522.</ref>
|-
|The amount of digestible nutrients consumed especially of the carbohydrate fraction (starch, sugar, N-free residuals) is reliable to estimate CH4 release with high precision. Furthermore, diets rich in fat reduced CH4 formation in the rumen.
|Jentsch et al., 2007<ref>Jentsch, W., Schweigel, M., Weissbach, F., Scholze, H., Pitroff, W., and Derno, M. 2007. Methane production in cattle calculated by the nutrient composition of the diet. Arch. Anim. Nutr. 61:10-19.</ref>
|-
|DMI was also the most important determining factor, but there were different regression lines for maize silage and dried grass as the main roughage component respectively:
<math>CH4\ (g)=93+16.8\times DMI(kg)</math>
<math>CH4\ (g)=81+14.0\times DMI(kg)</math>

Methane release was particularly dependent on the intake of crude fiber (CF) and ether extract (EE):

<math>CH4\ (g)=63+80xCF\ (kg)+11xNFE\ (kg)+19xCP(kg)-195xEE\ (kg)</math>
|Kirchgessner et al., 1991<ref>Kirchgessner, M., Windisch, W., Müller, H. L., and Kreuzer, M. 1991. Release of methane and of carbon dioxide by dairy cattle. Agribiol. Res. 44:91-102.</ref>
|-
|Methane linearly increased with NDF intake <math>CH4\ (L)=59.4\times NDF[kg]+ 64.6</math> for cows together with their calves independent of the breed.
|Estermann et al., 2002<ref>Estermann, B.L., Sutter, F., Schlegel, P.O., Erdin, D., Wettstein, H.R., and Kreuzer, M. 2002. Effect of calf age and dam breed on intake, energy expenditure, and excretion of nitrogen, phosphorus, and methane of beef cows with calves. J. Anim. Sci. 80:1124-1134.</ref>
|-
|Enteric CH4 could be predicted with the equation:
<math>CH4\ (g/d)=84+47\times cellulose(kg/d)+32\times starch(kg/d)+62\times sugars\ (kg/d)</math>
|Hindrichsen et al., 2005<ref>Hindrichsen, I.K., Wettstein, H.R., Machmüller, A., Jörg, B., and Kreuzer, M. 2005. Effect of the carbohydrate composition of feed concentratates on methane emission from dairy cows and their slurry. Environ. Monit. Assess., 107:329-350.</ref>
|-
|The higher the percentage concentrate the lower Ym.
|Zeitz et al., 2012<ref>Zeitz, J.O., Soliva, C.R., and Kreuzer, M. 2012. Swiss diet types for cattle: how accurately are they reflected by the Intergovernmental Panel on Climate Change default values? J. Int. Environ. Sci. 9(sup1):199-216.</ref>
|-
|Additives can sometimes have a methane reducing effect: higher dosages mitigate methane more. Saponins mitigate methanogenesis by reducing the number of protozoa, whereas condensed tannins act both by reducing the number of protozoa and by a direct toxic effect on methanogens.
|Beauchemin et al., 2008;<ref>Beauchemin, K.A., Kreuze,r M., O’Mara, F., and McAllister, T.A. 2008. Nutritional management for enteric methane abatement: A review. Aust. J. Exp. Agric. 48:21–27.</ref>
Jayanegara et al.<ref>Jayanegara, A., Leiber, F., and Kreuzer, M. 2012. Meta‐analysis of the relationship between dietary tannin level and methane formation in ruminants from in vivo and in vitro experiments. J. Anim. Physiol. Anim. Nutr. 96:365-375.</ref>, 2012; Zmora et al., 2012<ref>Zmora, P., Cieslak, A., Pers-Kamczyc, E., Nowak, A., Szczechowiak, J. and Szumacher-Strabel, M. 2012. Effect of Mentha piperita L. on in vitro rumen methanogenesis and fermentation, Acta Agr. Scan. Section A — Anim. Sci. 62:46-52, DOI: 10.1080/09064702.2012.703228.</ref>; Cieslak et al., 2013<ref>Cieslak, A., Szumacher-Strabel, M., Stochmal, A., nad Oleszek, W. 2013. Plant components with specific activities against rumen methanogens. Animal, 7(s2):253-265.</ref>; Guyader et al., 2014<ref>Guyader, J., Eugène, M., Nozière, P., Morgavi, D.P., Doreau, M., and Martin, C. 2014. Influence of rumen protozoa on methane emission in ruminants: a meta-analysis approach. Animal 8:1816-1825.</ref>
|-
|Plant essential oils have been shown as promising feed additives to mitigate CH4 and ammonia emission, but results were inconsistent.
|Cobellis et al., 2016;<ref>Cobellis, G., Trabalza-Marinucci, M. and Yu, Z. 2016. Critical evaluation of essential oils as rumen modifiers in ruminant nutrition: A review. Sci. Total Environ. 545: 556-568.</ref>
Moate et al., 2011<ref>Moate, P.J., Deighton, M.H., Williams, S.R.O., Pryce, J.E., Hayes, B.J., Jacobs, J.L., Eckard, R.J., Hannah, M.C. and Wales, W.J., 2016. Reducing the carbon footprint of Australian milk production by mitigation of enteric methane emissions. Anim. Prod. Sci. 56:1017-1034.</ref>
|-
|Nitrate and sulphate addition decreased the enteric methane emissions negatively affecting diet digestibility and milk production. The effects of the salts are additive.
|van Zijderveld et al., 2010<ref>Van Zijderveld, S.M., Gerrits, W.J.J., Apajalahti, J.A., Newbold, J.R., Dijkstra, J., Leng, R A., and Perdok, H.B. 2010. Nitrate and sulfate: effective alternative hydrogen sinks for mitigation of ruminal methane production in sheep. J. Dairy Sci. 93:5856-5866.</ref>;
van Zijderveld et al., 2011<ref>Van Zijderveld, S.M., Gerrits, W.J.J., Dijkstra, J., Newbold, J.R., Hulshof, R.B.A., and Perdok, H.B. 2011. Persistency of methane mitigation by dietary nitrate supplementation in dairy cows. J. Dairy Sci. 94:4028-4038.</ref>
|-
|The methanogenesis in the rumen of calves is associated with the development of the ruminal protozoa population. The absence of protozoa in the rumen reduced both the CH4 production and the digestibility of carbohydrates.
|Schönhusen et al., 2003<ref>Schönhusen, U., Zitnan, R., Kuhla, S., Jentsch, W., Derno, M., and Voigt, J. 2003. Effects of protozoa on methane production in rumen and hindgut of calves around time of weaning. Arch. Anim. Nutr. 57:279-295.</ref>
|-
|Implementing good grazing management reduced gross energy intake loss as CH4 by 14%.
|Wims et al., 2010<ref>Wims, C.M., Deighton, M.H., Lewis, E., O’Loughlin, B., Delaby, L., Boland, T.M., and O’Donovan, M. 2010. Effect of pregrazing herbage mass on methane production, dry matter intake, and milk production of grazing dairy cows during the mid-season period. J. Dairy Sci. 93:4976 – 4985</ref>
|}
</center>

== Host genetics, physiology and environment ==
A low-moderate proportion of variation in CH4 emissions among ruminants is under genetic control. Heritability coefficients of MeY and RMPR were h²=0.22 and 0.19 respectively in a population of 1,043 Angus growing steers and heifers measured during 2 days in RC (Donoghue et al., 2016<ref name=":14">Donoghue, K.A., Bird-Gardiner, T., Arthur, P.F., Herd, R.M., and Hegarty, R.F. 2016. Genetic and phenotypic variance and covariance components for methane emission and postweaning traits in Angus cattle. J. Anim. Sci. 94:1438–1445. doi:10.2527/jas2015-0065.</ref>). The heritability coefficient of MeY was h²=0.13 in a population of1,225 dual-purpose growing sheep measured during 2 days in RC (Pinares-Patino et al., 2013<ref>Pinares-Patiño C.S., Hickey, S.M., Young, E.A., Dodds, K.G., MacLean, S., Molano, G., Sandoval, E., Kjestrup, H., Harland, R., Pickering, N.K., and McEwan, J.C. 2013. Heritability estimates of methane emissions from sheep. Animal 7: 316–321.</ref>). Table 2 contains information of heritability of traits related to CH4 production.

{| class="wikitable"
|-
! colspan="3" | ''Table 2. Heritability information of methane-related traits and measurements.''
|- style="background-color:#efefef;"
|+
!Factors
!Reference
|-
|List with several h2
|Pickering et al., 2015<ref>Pickering, N.K., Oddy, V.H., Basarab, J.A., Cammack, K., Hayes, B J., Hegarty, R.S., McEwan, J.C., Miller, S., Pinares, C., and de Haas, Y. 2015. Invited review: Genetic possibilities to reduce enteric methane emissions from ruminants. Animal 9:1431-1440.</ref>
|-
|List with several h2
|MPWG White paper Dec 18<ref>MPWG White paper Dec 18. <nowiki>http://www.asggn.org/publications,listing,95,mpwg-white-paper.html</nowiki>. Pickering, N.K., de Haas, Y., Basarab, J., Cammack, K., Hayes, B., Hegarty, R.S., Lassen, J., McEwan, J.C., Miller, S., Pinares-Patiño, C.S., Shackell, G., Vercoe, P. and Oddy, V.H. 2013.</ref>
|-
|Methane emissions from individual cows during milking varied between individuals with the same milk yield and fed the same diet. Between-cow variation in MERm is greater than within-cow variation and ranking of cows for CH4 emissions is consistent across time. Variation related to body weight, milk yield, parity, and week of lactation/days in milk. The monitored variation might offer opportunities for genetic selection.
|Garnsworthy et al., 2011A<ref name=":15">Garnsworthy, P.C., Craigon, J., Hernandez-Medrano, J.H. and Saunders, H. 2012A. On-farm methane measurements during milking correlate with total methane production by individual dairy cows. J. Dairy Sci. 95:3166-3180.</ref>;
Garnsworthy et al., 2011B<ref name=":16">Garnsworthy, P.C., Craigon, J., Hernandez-Medrano, J.H., and Saunders, N. 2012B. Variation among individual dairy cows in methane measurements made on farm during milking. J. Dairy Sci. 95:3181–3189.</ref>
|-
|Mechanistic modelling approach: potential for dietary intervention as a means of substantially reducing CH4 emissions without adverse effects on dietary energy supply.
|Mills et al., 2001<ref>Mills, J.A.N., Dijkstra, J., Bannink, A., Cammell, S.B., Kebreab, E., and France, J. 2001. A mechanistic model of whole-tract digestion and methanogenesis in the lactating dairy cow: model development, evaluation, and application. J. Anim. Sci. 79:1584-1597.</ref>
|-
|The CH4-to-CO2 ratio measured using the non-invasive portable air sampler and analyzer unit based on Fourier transform infrared (FTIR) detection method is an asset of the individual cow and may be useful in both management and genetic evaluations.
|Lassen et al., 2012<ref name=":17">Lassen, J., Lovendahl, P., and Madsen, J. 2012. Accuracy of noninvasive breath methane measurements using Fourier transform infrared methods on individual cows. J. Dairy Sci. 95:890-898.</ref>
|-
|The estimated heritability for CH4 g/day and CH4 g/kg of FPCM were lower than common production traits but would still be useful in breeding programs.
|Kandel et al., 2013<ref>Kandel, P.B., Vanrobays, M.L., Vanlierde, A., Dehareng, F., Froidmont, E., Dardenne, P., Lewis, E., Buckley, F., Deighton, M.H., McParland, S. and Gengler, N., 2013. Genetic parameters for methane emissions predicted from milk mid-infrared spectra in dairy cows. J. Dairy Sci. 95(E-1):p.388.</ref>
|-
|Genetic correlation between CH4 intensity and milk yield (MY) was - 0.67 and with milk protein yield (PY) was -0.46 in Holstein cows.
|Kandel et al., 2014A<ref name=":37" />, B<ref>Kandel, P.B., Vanderick, S., Vanrobays, M.L., Vanlierde, A., Dehareng, F., Froidmont, E., Soyeur,t H., and Gengler, N. 2014B. Consequences of selection for environmental impact traits in dairy cows. In: 10th World Congress on Genetics Applied to Livestock Production (WCGALP), 17-22 August, 2014. Vancouver, Canada.</ref>
|-
|Milk production and CH4 emissions of dairy cows seemed to be influenced by the temperature humidity index.
|Vanrobays et al., 2013A<ref>Vanrobays, M.-L., Gengler, N., Kandel, P.B., Soyeurt, H., and Hammami, H. 2013A. Genetic effects of heat stress on milk yield and MIR predicted methane emissions of Holstein cows. 64th Annual meeting of the European Federation of Animal Science, p498</ref>
|-
|Estimate the heritability of the estimated methane emissions from 485 Polish Holstein-Friesian dairy cows at 2 commercial farms using FTIR spectroscopy during milking in an automated milking system by implementing the random regression method. The heritability level fluctuated over the course of lactation, starting at 0.23 (SE 0.12) and then increasing to its maximum value of 0.3 (SE 0.08) at 212 DIM and ending at the level of 0.27 ± 0.12. Average heritability was 0.27 ± 0.09.
|Pszczola et al., 2017<ref name=":18">Pszczola, M., Rzewuska, K., Mucha, S., and Strabel, T. 2017. Heritability of methane emissions from dairy cows over a lactation measured on commercial farms. J. Anim. Sci. 95:4813-4819. doi: 10.2527/jas2017.1842.</ref>
|-
|CH4 measured with a portable air-sampler FTIR detection method on 3,121 Holstein dairy cows from 20 herds using automatic milking systems. The heritability of CH4_MILK was 0.21 ± 0.06. It was concluded that a high genetic potential for milk production will also mean a high genetic potential for CH4 production. The results suggested that CH4 emission is partly under genetic control, that it is possible to decrease CH4 emission from dairy cattle through selection, and that selection for higher milk yield will lead to higher genetic merit for CH4 emission/cow per day.
|Lassen and Løvendahl, 2016<ref>Lassen, J., and Løvendahl, P. 2016. Heritability estimates for enteric methane emissions from Holstein cattle measured using noninvasive methods. J. Dairy Sci. 99:1959-1967.</ref>
|-
|CH4 production was measured of 184 Holstein-Friesian cows in. the milking robot with a in total 2,456 observations for CH4 production. Heritability for CH4 production ranged from 0.12 ± 0.16 to 0.45 ± 0.11, and genetic correlations with MY ranged from 0.49 ± 0.12 to 0.54 ± 0.26. The positive genetic correlation between CH4 production and milk yield indicates that care needs to be taken when genetically selecting for lower CH4 production, to avoid a decrease in MY at the animal level. However, this study shows that CH4 production is moderately heritable and therefore progress through genetic selection is possible.
|Breider et al., 2019<ref>Breider, I.S., Wall, E., Garnsworthy, P.C. 2019. Short communication: Heritability of methane production and genetic correlations with milk yield and body weight in Holstein-Friesian dairy cows, J. Dairy Sci. 102: 7277-7281.</ref>
|-
|CH4 concentration was measured with NDIR, and CH4 production was estimated from CH4 concentration and body weight. Heritability for CH4 concentration was 0.11 ± 0.03 and for CH4 production 0.12 ± 0.04. Positive genetic correlation was observed with MY (0.17-0.21), PY (0.22-0.31) and FY (0.27-0.29). Other type traits showed positive correlation with methane production (chest width=0.26, angularity =0.19, stature = 0.43 and capacity = 0.31) possibly associated to higher milk feed intake from these animals. Rumination time was negatively correlated to CH4 production (-0.24) and CH4 concentration (-0.43). However, larger CH4 production and CH4 concentration was associated with shorter days open.
|López-Paredes et al. (2020)<ref>Lopez-Paredes, J., Goiri, I., Atxaerandio, R., García-Rodríguez, A., Ugarte, E., Jiménez-Montero, J.A., Alenda, R and González-Recio, O. 2020. Mitigation of greenhouse gases in dairy cattle via genetic selection (i): Genetic parameters of direct methane using non-invasive methods and its proxies. J. Dairy Sci. 103.</ref>
|-
|Genetic parameters of CH4 emissions predicted from milk fatty acid profile (FA) and those of their predictors in 1,091 Brown Swiss cows reared on 85 farms showed that enteric CH4 emissions of dairy cows can be estimated on the basis of milk fatty acid profile. Additive genetic variation of CH4 traits was shown which could be exploited in breeding programmes.
|Bittante and Cecchinato, 2020<ref>Bittante, G., and Cecchinato, A. 2020. Heritability estimates of enteric methane emissions predicted from fatty acid profiles, and their relationships with milk composition, cheese-yield and body size and condition, It. J. An. Sci. 19:114-126, DOI: 10.1080/1828051X.2019.1698979</ref>
|-
|A total of 670 test day records were recorded on lactating Holstein Friesian cows reared in 10 commercial dairy herds. Predicted methane production (PMP) was estimated to be 15.33±1.52 MJ/d in dairy cows with 23.53±6.81 kg/d of milk yeild (MY) and 3.57±0.68% of fat content (FC). Heritability of MY was 0.09 with a posterior probability for values of h2 greater than 0.10 of 44%. Estimates of heritability for FC and protein content (PC) were 0.17 and 0.34, respectively, with a posterior probability for values of h2 greater than 0.10 of 77% and 99%. For somatic cell score (SCS), heritability was 0.13 with a posterior probability for values of h2 greater than 0.10 of 67%. Heritability for the trait PMP was moderate to low (0.12); however, posterior probability for values of h2 greater than 0.10 was 60%. Medians of the posterior distributions of genetic correlations between PMP and milk production traits were: 0.92, 0.67, 0.14, and 0.14 between PMP and MY, PMP and FC, PMP and PC, and PMP and SCS, respectively. Reduction of PMP seems to be viable through selection strategies without affecting udder health and PC.
|Cassandro et al., 2010<ref name=":29">Cassandro, M., Cecchinato, A., Battagin, M., Penasa, M., 2010. Genetic parameters of predicted methane production in Holstein Friesian cowsIn: Proc. 9th World Congr. on Genetics Applied to Livestock Production, Leipzig, Germany. . Page 181</ref>
|-
|GWAS to study the genetic architecture of CH4 production and detected genomic regions affecting CH4 production. Detected regions explained only a small proportion of the heritable variance. Potential QTL regions affecting CH4 production were located within QTLs related to feed efficiency, milk-related traits, body size and health status. Five candidate genes were found: CYP51A1 on BTA 4, PPP1R16B on BTA 13, and NTHL1, TSC2, and PKD1 on BTA 25. These candidate genes were involved in a number of metabolic processes that are possibly related to CH4 production. One of the most promising candidate genes (PKD1) was related to the development of the digestive tract. The results indicate that CH4 production is a highly polygenic trait.
|Pszczola et al., 2018<ref name=":19">Pszczola, M., Strabel, T., Mucha, S., and Sell-Kubiak, E. 2018. Genome-wide association identifies methane production level relation to genetic control of digestive tract development in dairy cows. Scientific Rep. 8 (1), 15164 <nowiki>https://doi.org/10.1038/s41598-018-33327-9</nowiki></ref>
|-
|A 1000-cow study across European countries revealed that the ruminant microbiomes can be controlled by the host animal. A 39- member subset of the core microbiome formed hubs in co-occurrence networks linking microbiome structure to host genetics and phenotype (CH4 emissions, rumen and blood metabolites, and milk production efficiency).
|Wallace et al.,
2019<ref>Wallace, R.J., Sasson, G., Garnsworthy, P.C., Tapio, I., Gregson, E., Bani, P., Huhtanen, P., Bayat, A.R., Strozzi, F., Biscarini, F., Snelling, T.J., Saunders, N., Potterton, S.L., Craigon, J., Minuti, A., Trevisi, E., Callegari, M.L., Cappelli, F.P., Cabezas-Garcia, E.H., Vilkki, J., Pinares-Patino, C., Fliegerov, K.O., Mrazek, J., Sechovcova, H., Kope, J., Bonin, A., Boyer, F., Taberlet, P., Kokou, F., Halperin, E., Williams, J.L., Shingfield, K.J., and Mizrahi, I. 2019. A heritable subset of the core rumen microbiome dictates dairy cow productivity and emissions. Sci. Adv. 5:(7):eaav8391. doi 10.1126/sciadv.aav8391.</ref>
|}

</div>
</div>

= Methane measurements methods =
<div class="mw-collapsible mw-collapsed">
<div>
Several factors influence the choice of measurement method such as cost, level of accuracy, precision, scope of application, and scale, which vary across disciplines (Cassandro et al., 2013<ref>Cassandro, M. 2013. Comparing local and cosmopolitan cattle breeds on added values for milk and cheese production and their predicted methane emissions. Animal Genetic Resources/Ressources génétiques animales/Recursos genéticos animales, available on CJO2013. doi:10.1017/S2078 63361200077X</ref>; Hammond et al., 2016A<ref>Hammond, K.J., Crompton, L.A., Bannink, A., Dijkstra, J., Yáñez-Ruiz, D.R., O’Kiely, P., Kebreab, E., Eugenè, M.A., Yu, Z., Shingfield, K.J., Schwarm, A., Hristov, A.N., and Reynolds, C.K. 2016A. Review of current in vivo measurement techniques for quantifying enteric methane emission from ruminants. Anim. Feed Sci. Technol. 219:13–30. doi:10.1016/j.anifeedsci.2016.05.018.</ref>; Garnsworthy et al., 2019<ref>Garnsworthy, P.C. Difford, G.F. Bell, M.J. Bayat, A.R. Huhtanen, P. Kuhla, B. Lassen, J. Peiren, N. Pszczola, M; Sorg, D. Visker, M.H., and Yan, T. 2019 Comparison of Methods to Measure Methane for Use in Genetic Evaluation of Dairy Cattle. Animals 9:837, 12p.</ref>). For instance, genetic selection programs require CH4 measurements on thousands of related individuals under the environmental conditions in which the animals are expected to perform (Falconer and Mackay, 1996<ref>Falconer, D., and Macka,y T. 1996. Introduction to quantitative genetics (4th edn). ISBN-13: 978-0582243026; ISBN-10: 0582243025</ref>). This can be challenging because dairy cattle perform in a wide range of conditions (e.g. grazing vs indoor housing). There are a number of different measurement methods currently being employed, each with advantages and disadvantages in terms of the factors listed above. The currently accepted and widely used measurement methods are listed and described below. The main features of methods for measuring CH4 output by individual animals are summarised in Table 3. Values for each feature are based on experience of experts in METHAGENE WG2 who have used the methods. All values are relative, and somewhat subjective, because absolute values will depend on installation and implementation of each method at different research centres. It should be noted that the measuring methods can be divided in two major sections: methods that measure the concentration and flux of CH4 (e.g. the respiration chamber), and methods that measure the flux of CH4 through the device (e.g. GreenFeed). This affects the useability of the methods for answering research questions – please see also the recommendations at the end of these guidelines.

== Respiration chambers ==
Respiration chambers are calibrated to be accurate and precise, and are the gold standard for benchmarking new methods. Only respiration chambers measure total emissions from the animal via the oral, nasal and anal routes; all other methods ignore emissions via the anus and only measure CH4 emitted in breath. Breath measurements are justified because 99% of CH4 is emitted from the mouth and nostrils, and only 1% via the anus (Murray ''et al.'', 1976<ref name=":20">Murray, R.M., Bryant, A.M., and Leng, R.A.. 1976. Rates of production of methane in the rumen and large intestine of sheep. Br. J. Nutr. 36:1-14.</ref>).

A single animal (or occasionally more) is confined in a chamber for between 2 and 7 days. Concentration of CH4 (and other gases if required) is measured at the air inlet and outlet vents of the chamber. The difference between outlet and inlet concentrations is multiplied by airflow to indicate CH4 emissions fluxes. In most installations, a single gas analyser is used to measure both inlet and outlet concentrations, often for two or more chambers. This involves switching the analyser between sampling points at set intervals, so concentrations are actually measured for only a fraction of the day. If the sampling points acquisition frequency is high it enables to draw the diurnal pattern of methane emission, comparable to the GreenFeed system.

Respiration chambers vary in construction materials, size of chamber, gas analysis equipment and airflow rate, all of which can influence results. Validation of 22 chambers at six UK research sites revealed an uncertainty of 25.7% between facilities, which was reduced to 2.1% when correction factors were applied to trace each facility to the international standard CH4 (Gardiner ''et al.'', 2015<ref name=":21">Gardiner, T.D., Coleman, M.D., Innocenti, F., Tompkins, J., Connor, A., Garnsworthy, P.C., Moorby, J.M., Reynolds, C.K., Waterhouse, A., and Wills, D. 2015. Determination of the absolute accuracy of UK chamber facilities used in measuring methane emissions from livestock. Measurement 66: 272-279.</ref>). The main sources of uncertainty were stability and measurement of airflow, which are crucial for measuring CH4 emission rate. The authors concluded, however, that chambers were accurate for comparing animals measured at the same site. This is an added challenge to benchmarking alternative methods with respiration chambers if respiration chambers themselves have not been benchmarked with respiration chambers at other facilities. It should be noted that substantial errors can occur if appropriate calibration procedures are not followed (Gardiner ''et al.'', 2015<ref name=":21" />).

For large-scale evaluation of CH4 emissions by individual animals, respiration chambers are challenging with only a single study in growing Angus steers and heifers exceeding 1000 animals and finding CH4 production to be moderately heritable h2 = 0.27 ± 0.07 (Donoghue ''et al.'', 2016<ref name=":14" />). Installation and running costs are high, as only one animal is normally measured at once. If we assume that the monitoring time is three days per animal, and chambers are run continuously, then maximum throughput would be approximately 100 animals per chamber per year. In practice, throughput is likely to be 30 to 50 animals per year. Cows are social animals and confinement in a chamber may ultimately influence their feeding behaviour resulting in less feed consumed and in a different meal pattern compared with farm conditions. Altered feeding pattern or level is not a problem for metabolic studies evaluating feeds but can be a problem when evaluating individual animals. Furthermore, the representativeness of respiration chambers to grazing systems has been called into question (Pinares-Patiño ''et al.'', 2013<ref name=":13" />). However, promising developments have led to more animal friendly respiration chambers constructed from cheaper, transparent materials. These lower the cost and reduce the stress of confinement with minimal disruptions to accuracy, precision and no drop in feed intake of the cows (Hellwing ''et al.'', 2012<ref name=":12" />).

Where an alternative method may be cheaper, less invasive, easier to implement, or have a wider scope of application, it is of value to assess the relative accuracy, precision and correlation with the gold standard to assess the relative worth of the alternative method (Barnhart ''et al.'', 2007<ref>Barnhart, H.X., Kosinski, A.S., and Haber, M.J. 2007. Assessing Individual Agreement. J. Biopharm. Stat. 17:697–719. doi:10.1080/10543400701329489.</ref>). All methods measure CH4 with some level of error, so the ‘true value’ of an individual is not known. However, when the level of measurement error increases, so too does the imprecision. When comparing two methods where one or both methods has high imprecision a phenomenon known as ‘attenuation of errors’ occurs (Spearman, 1904<ref>Spearman, C. 1904. The Proof and Measurement of Association between Two Things. Am. J. Psychol. 15:72–101.</ref>). The increased measurement error biases the correlation between the two methods downwards and reduces the efficacy of detecting significant differences in accuracy (Adolph & Hardin, 2007<ref>Adolph, S.C., and Hardin, J.S. 2007. Estimating phenotypic correlations: Correcting for bias due to intraindividual variability. Funct. Ecol. 21:178–184. doi:10.1111/j.1365-2435.2006.01209.x.</ref>). Or in terms of linear regression terms, when the observed CV of an alternative method is higher than that of the gold standard method, the slope of regression between the methods is decreased and the intercept is biased upwards.

== Portable Accumulation Chambers ==
In Australia and New Zealand an alternative method was developed for the short-term measurement of Methane Production Rate (MPR) of sheep using Portable Accumulation Chambers (PAC) during 1 hour without leading discomfort to the animals. Similarly to RC, CH4 emissions recorded in PAC include gases from flatulence in addition to eructed and expired CH4, but only during 1 hour. For a detailed comparison of the PAC and respiration chamber methods see Jonker ''et al.'' (2018).

== SF6 ==
The SF6 technique samples breath over 24 hours, whereas other techniques use spot samples of breath over periods of minutes throughout the day, so diurnal variation has to be considered. The majority of CH4 (87-99%) is released by eructation (Blaxter & Joyce, 1963; Murray ''et al.'', 1976<ref name=":20" />), which provides a clear signal for sample processing. Please note that the tracheostomy used in Murray ''et al.'' (1976)<ref name=":20" /> may have resulted in a higher percentage, but in both publications, it is clear that the majority of the CH4 is released via eructation.

The SF6 tracer gas technique was developed in an attempt to measure CH4 emissions by animals without confinement in respiration chambers (Johnson ''et al.'', 1994<ref>Johnson, K., Huyler, M., Westberg, H., Lamb, B., and Zimmerman, P. 1994. Measurement of methane emissions from ruminant livestock using a sulfur hexafluoride tracer technique. Environ. Sci. Technol. 28:359-362.</ref>). Air is sampled near the animal’s nostrils through a tube attached to a halter and connected to an evacuated canister worn around the animal’s neck or on its back. A capillary tube or orifice plate is used to restrict airflow through the tube so that the canister is between 50 and 70% full in approximately 24 hours. A permeation tube containing SF6 is placed into the rumen of each animal. The pre-determined release rate of SF6 is multiplied by the ratio of CH4 to SF6 concentrations in the canister to calculate CH4 emission rate.

Many research centres have used the SF6 technique with variations in design of sampling and collection equipment, permeation tubes, and gas analysis (Berndt ''et al.'', 2014<ref name=":22" />). Reliable results depend on following standard protocols, with greatest variation coming from accuracy of determining SF6 release rate from permeation tubes and control of sampling rate. With capillary tubes, sampling rate decreases as pressure in the canister increases, whereas an orifice plate gives a steadier sampling rate over 24 hours (Deighton ''et al.'', 2014<ref>Deighton, M.H., Williams, S.R.O., Hannah, M.C., Eckard, R.J., Boland, T.M., Wales, W.J., and Moate, P.J. 2014. A modified sulphur hexafluoride tracer technique enables accurate determination of enteric methane emissions from ruminants. Anim. Feed Sci. Technol. 197:47-63.</ref>). A source of error that has not been evaluated is that animals might interact and share CH4 emissions when the sampling tube of one animal is near the head of another animal. There is good agreement between CH4 emissions measured by the SF6 technique and respiration chambers, although results from the SF6 technique are more variable (Grainger ''et al.'', 2007<ref>Grainger, C., Clarke, T., McGinn, S.M., Auldist, M.J., Beauchemin, K.A., Hannah, M.C., Waghorn, G.C., Clark, H., and Eckard, R J. 2007. Methane emissions from dairy cows measured using the sulfur hexafluoride (SF6) tracer and chamber techniques. J. Dairy Sci. 90:2755-2766.</ref>; Muñoz ''et al.'', 2012<ref>Muñoz, C., Yan, T., Wills, D.A., Murray, S., and Gordon, A.W. 2012. Comparison of the sulfur hexafluoride tracer and respiration chamber techniques for estimating methane emissions and correction for rectum methane output from dairy cows. J. Dairy Sci. 95:3139-3148.</ref>).

== Breath sampling during milking and feeding ==
Several research groups have developed methods to measure CH4 concentration in breath of cows during milking and/or feeding. These are often referred to as ‘sniffer methods’ because they use devices originally designed to detect dangerous gas leaks. Air is sampled near the animal’s nostrils through a tube fixed in a feed bin and connected directly to a gas analyser. The feed bin might be in an automatic milking station (Garnsworthy ''et al.'', 2012A<ref name=":15" />, B<ref name=":16" />; Lassen ''et al.'', 2012<ref name=":17" />; Pszczola ''et al.'', 2017<ref name=":18" />, 2018<ref name=":19" />, 2019<ref>Pszczola, M., Calus, M.P.L., Strabel, T. 2019. Genetic correlations between methane and milk production, conformation, and functional traits. J. Dairy Sci. 102:5342-5346.</ref>) or in a concentrate feeding station (Negussie ''et al.'', 2017<ref>Negussie, E., Lehtinen, J., Mäntysaari, P., Bayat, A.R., Liinamo, A.E., Mäntysaari, E.A., and Lidauer, M.H. 2017. Non-invasive individual methane measurement in dairy cows. Animal 11:890-899.</ref>). Different research centres use different gas analysers (Nondispersive Infrared (NDIR), Fourier-transform infrared (FTIR) or photoacoustic infrared (PAIR)) and different sampling intervals (1, 5, 20 or 90-120 seconds). Methane concentration during a sampling visit of typically between 3 and 10 minutes may be specified as the overall mean, or the mean of eructation peaks. Some centres use CO2 as a tracer gas and calculate daily CH4 output according to ratio of CH4 to CO2 and daily CO2 output predicted from performance of the cow (Madsen ''et al.'', 2010<ref>Madsen, J., Bjerg, B.S., Hvelplund, T., Weisbjerg, M.R., and Lund, P. 2010. Methane and carbon dioxide ratio in excreted air for quantification of the methane production from ruminants. Livest. Sci. 129:223-227.</ref>). Repeatability and rank correlations were higher for eructation peaks than for mean concentrations, and were higher for eructation peaks than for CH4 to CO2 ratio (Bell ''et al.'', 2014<ref>Bell, M.J., Saunders, N., Wilcox, R.H., Homer, E.M., Goodman, J.R., Craigon, J., Garnsworthy, P.C. 2014 Methane emissions among individual dairy cows during milking quantified by eructation peaks or ratio with carbon dioxide. J. Dairy Sci. 97:6536–6546.</ref>). However, all methods show good repeatability.

== GreenFeed ==
GreenFeed (C-Lock Inc., Rapid City, South Dakota, USA) is a sniffer system where breath samples are provided when animals visit a bait station (Huhtanen ''et al.'', 2015<ref name=":23" />). GreenFeed Emission Monitoring (GEM) systems are designed for measuring animal emissions in their production environment. As with other sniffer systems, GreenFeed samples breath from individual animals several times (in general 4 to 6 times) per day for short periods (3 to 7 minutes in which an under pressure is created to suck the whole breath of the animal to measure the flux). They record CH4 and carbon dioxide (CO2) fluxes during short-term periods of 3-10 minutes when cattle visit an automated feeder fitted with a semi-enclosed head hood in which air is continuously drawn through an air-collection pipe (C-Lock, 2016; Huhtanen ''et al.'', 2015<ref name=":24" />; Hammond ''et al.'', 2016A<ref name=":25" />; Velazco ''et al.'', 2016<ref name=":131" />). Air samples are continually (every second) analyzed for CH4 and CO2 concentrations using non-dispersive infrared sensors. Gas fluxes are eventually calculated as the product of the air flow in the collection pipe and the concentration of gases corrected for the background concentrations and adjusted to standardized temperature, humidity and pressure. The position of the head in the feeder is detected by an infrared sensor. Gas fluxes are not calculated if the head is not correctly positioned in the feeder as not all the air in the feeder may be collected.

GreenFeed is a portable standalone system used in barn and pasture applications and incorporates an extractor fan to ensure active airflow and head position sensing for representative breath sampling (Hammond ''et al.'', 2016B<ref name=":26" />). Measurements are pre-processed by the manufacturer, and data are available in real-time through a web-based data management system (Hammond ''et al.'', 2015<ref name=":5" />). Because GreenFeed captures a high proportion of emitted air and measures airflow, which can be calibrated using a tracer gas, CH4 emission is estimated as a flux at each visit. Providing visits occur throughout the 24 hours, CH4 emission can be estimated directly as g/day (Hammond ''et al.'', 2015<ref name=":5" />; Huhtanen ''et al.'', 2015<ref name=":24" />). More importantly, repeatability of CH4 measurement must be high so the duration of the measurement period must be taken into account (Huhtanen ''et al.'', 2013; Arbre ''et al.'', 2016<ref name=":7" />); (R=0.7 after 17 days duration of measurement period, or R=0.93 after 45 days, Arbre ''et al.'', 2016<ref name=":7" />).

== Laser methane detector ==
The laser CH4 detector (LMD) is a highly responsive, hand-held device that is pointed at an animal’s nostrils and measures CH4 column density along the length of the laser beam (ppm.m). In the first implementation of LMD on a farm, measurements for each cow were taken over periods of 15 to 25 seconds between eructation events and could detect CH4 emitted each time the animal breathed out (Chagunda ''et al.'', 2009 <ref>Chagunda, M.G.G., Ross, D., and Robert,s D J. 2009. On the use of a laser methane detector in dairy cows. Comput. Electron. Agric. 68:157-160.</ref>; Sorg ''et al.'', 2016<ref>Sorg, D., Mühlbach, S., Rosner, F., Kuhla, B., Derno, M., Meese, S., Schwarm, A., Kreuzer, M., and Swalve, H. 2016. The agreement between two next-generation laser methane detectors and respiration chamber facilities in recording methane concentrations in the spent air produced by dairy cows. Comp. Electr. Agric. 143:262-272. </ref>, 2017<ref name=":27" />). In a later study with sheep and beef cattle, monitoring periods of 2 to 4 minutes allowed authors to separate breathing cycles from eructation events (Ricci ''et al.'', 2014<ref>Ricci, P., Chagunda, M.G.G., Rooke, J., Houdijk, J.G.M., Duthie, C-A., Hyslop, J., Roehe, R., and Waterhouse, A. 2014. Evaluation of the laser methane detector to estimate methane emissions from ewes and steers. J. Anim. Sci. 92:5239-5250.</ref>). Typically, animals are restrained either manually or in head yokes at a feed fence for the required length of time. The operator has to stand at the same distance (1 to 3 m) from each animal every time and must be careful to keep the laser pointed at the animal’s nostrils throughout the measurement period.
----[1] Consensus views based on experiences of METHAGENE WG2 members (www.methagene.eu).

[2] Per measuring unit or group of animals.

[3] Compared to no methane recording: low = measuring in situ; medium = some handling, training or change in routine; high = confinement.

[4] Medium if using FTIR analyser.<center>
{| class="wikitable"
|-
! colspan="7" style="text-align:left;" | ''Table 3. Summary of the main features of methods for measuring CH4 output by individual animals.''
|- style="background-color:#efefef;"
|+
! style="text-align:left;" | Method
! style="text-align:center;" | Purchase cost
! style="text-align:center;" | Running costs
! style="text-align:center;" | Labour
! style="text-align:center;" | Repeatability
! style="text-align:center;" | Behaviour alteration
! style="text-align:center;" | Throughput
|-
| style="text-align:left;" | Respiration chamber
| style="text-align:center;" | High
| style="text-align:center;" | High
| style="text-align:center;" | High
| style="text-align:center;" | High
| style="text-align:center;" | High
| style="text-align:center;" | Low
|-
| style="text-align:left;" | SF6 technique
| style="text-align:center;" | Medium
| style="text-align:center;" | High
| style="text-align:center;" | High
| style="text-align:center;" | Medium
| style="text-align:center;" | Medium
| style="text-align:center;" | Medium
|-
| style="text-align:left;" | Breath sampling during milking and feeding
| style="text-align:center;" | Low
| style="text-align:center;" | Low
| style="text-align:center;" | Low
| style="text-align:center;" | Medium
| style="text-align:center;" | None
| style="text-align:center;" | High
|-
| style="text-align:left;" | GreenFeed
| style="text-align:center;" | Medium
| style="text-align:center;" | Medium
| style="text-align:center;" | Medium
| style="text-align:center;" | Medium
| style="text-align:center;" | Medium
| style="text-align:center;" | Medium
|-
| style="text-align:left;" | Laser methane detector
| style="text-align:center;" | Low
| style="text-align:center;" | Low
| style="text-align:center;" | High
| style="text-align:center;" | Low
| style="text-align:center;" | Low-Medium
| style="text-align:center;" | Medium
|}
</center>

</div>
</div>

= Discussion of methods =
<div class="mw-collapsible mw-collapsed">
<div>
== SF6 vs Respiration Chamber ==
For large-scale evaluation of CH4 emissions by individual animals, the SF6 technique is more useful than respiration chambers. Animal behaviour and intake might be affected by wearing the apparatus, and by daily handling to exchange canisters, but the technique is considerably less intrusive than respiration chambers because cows remain in the herd. Labour and monetary costs for changing canisters each day and for lab analysis are high. Throughput is limited by the number of sets of apparatus available, handling facilities, labour, and the capacity of the lab for gas analysis. Animals need to be measured for 5 to 7 days, and it is recommended that group size should be less than 15 animals (Berndt et al., 2014<ref name=":22">Berndt, A., Boland, T.M., Deighton, M.H., Gere, J.I., Grainger, C., Hegarty, R.S., Iwaasa, A.D., Koolaard, J.P., Lassey, K.R., Luo D., Martin, R.J., Martin, C., Moate, P.J., Molano, G., Pinares-Patiño, C., Ribaux, B.E., Swainson, N.M., Waghorn, G.C., and Williams, S.R.O. 2014. Guidelines for use of sulphur hexafluoride (SF6) tracer technique to measure enteric methane emissions from ruminants. Pages 166. M. G. Lambert, ed. New Zealand Agricultural Greenhouse Gas Research Centre, New Zealand. </ref>), so maximum throughput would be about 750 animals per year. The method may be better suited for in housed conditions because of the labour and the potential movement restriction of the animals due to wearing the apparatus.

== Breath sampling during milking and feeding vs Respiration Chamber ==
For large-scale evaluation of CH4 emissions by individual animals, breath-sampling methods have significant advantages compared with other methods. Breath-sampling methods are non-invasive because, once installed, animals are unaware of the equipment and are in their normal environment. Animals follow their normal routine, which includes milking and feeding, so no training of animals, handling, or change of diet is required. Equipment is relatively cheap, although more expensive gas analysers are available, and running costs are negligible.

The compromise for non-invasiveness of breath-sampling is that concentrations of gases in the sampled air are influenced by cow head position relative to the sampling tube (Huhtanen et al., 2015<ref name=":23">Huhtanen, P., Cabezas-Garcia, E.H., Utsumi, S., and Zimmerman, S. 2015. Comparison of methods to determine methane emissions from dairy cows in farm conditions. J. Dairy Sci. 98:3394–3409. doi:10.3168/jds.2014-9118.</ref>). The use of head position sensors and data filtering algorithms can remove the effects when the cow’s head is completely out of the feed bin (Difford et al., 2016<ref>Difford, G.F., Lassen, J., and Løvendahl, P. 2016. Interchangeability between methane measurements in dairy cows assessed by comparing precision and agreement of two non-invasive infrared methods. Comput. Electron. Agric. 124:220–226. doi:10.1016/j.compag.2016.04.010.</ref>), but not within the feed bin. Consequently, sniffer measurements are more variable than flux methods, with factors like variable air flow in the barn increasing measurement error (imprecision), and head position, a highly repeatable character, inflating between-cow variability.

Using CO2 as a tracer gas partly addresses the issue but, because CO2 arises from metabolism as well as rumen fermentation, variability of CO2 emissions has to be considered. A further consideration is diurnal variation in breath concentrations of CH4 and CO2 because animals are spot-sampled at different times of day and night. Diurnal variation can be accounted for either by fitting a model derived from the whole group of animals, or by including time of measurement in the statistical model (Lassen et al., 2012<ref>Lassen, J., Lovendahl, P., and Madsen, J. 2012. Accuracy of noninvasive breath methane measurements using Fourier transform infrared methods on individual cows. J. Dairy Sci. 95:890-898.</ref>).

The number of observations per analyser is limited only by number of cows assigned to one automatic milking station or concentrate feeding station and length of time equipment is installed. Typically, each analyser will record 40 to 70 animals 2 to 7 times per day for 7 to 10 days, although the number of sampling stations per analyser can be increased by using an automatic switching system (Pszczola et al., 2017<ref>Pszczola, M., Rzewuska, K., Mucha, S., and Strabel, T. 2017. Heritability of methane emissions from dairy cows over a lactation measured on commercial farms. J. Anim. Sci. 95:4813-4819. doi: 10.2527/jas2017.1842.</ref>). Throughput per analyser is likely to be 2,000 to 3,000 animals per year.

== NDIR vs LMD ==
Both methods are low invasive. LMD needs larger labor force, wheras NDIR can be used during milking and feeding. According to Rey at al. (2019)<ref>Rey, J., Atxaerandio, R., Ruiz, R, Ugarte, E., Gonzalez-Recio, O., Garcia-Rodriguez, A., and Goiri, I. 2019. Comparison Between Non-Invasive Methane Measurement Techniques in Cattle. Animals 9(8): 563. <nowiki>https://doi.org/10.3390/ani9080563</nowiki></ref>, the repeatability of the CH4 concentration was greater for NDIR (0.42) than for LMD (0.23). Correlation between methods was moderately high and positive for CH4 concentration (0.73 and 0.74,respectively) and number of peaks (0.72 and 0.72, respectively), and the repeated measures correlation and the individual-level correlation were high (0.98 and 0.94, respectively). A high coefficient of individual agreement for the CH4 concentration (0.83) and the number of peaks (0.77) were observed between methods. The study suggests that methane concentration measurements obtained from NDIR and LMD cannot be used interchangeably. But the use of both methods could be considered for genetic selection purposes or for mitigation strategies only if sources of disagreement, which result in different between subject and within-subject variabilities, are identified and corrected for.

== Greenfeed ==
A limitation of the GreenFeed system is that animals require training to use the system, although animals which have been trained to use the system will readily use it again (Velazco et al., 2014<ref>Velazco, J.I., Cottle, D.J., and Hegarty, R.S. 2014. Methane emissions and feeding behaviour of feedlot cattle supplemented with nitrate or urea. Anim. Prod. Sci. 54:1737–1740. doi:10.1071/AN14345.</ref>). However, some animals will not use the system or will use it infrequently, and frequency of visits is affected by diet (Hammond et al., 2016B<ref name=":26">Hammond, K.J., Jones, A.K., Humphries, D.J., Crompton, L.A., and Reynolds, C.K. 2016B. Effects of diet forage source and neutral detergent fiber content on milk production of dairy cattle and methane emissions determined using GreenFeed and respiration chamber techniques. J. Dairy Sci. 99:7904–7917. doi:10.3168/jds.2015-10759.</ref>). This can be a challenge when screening commercial herds for CH4 emission under genetic evaluation. On the other hand, animals seem to get used to the equipment rapidly, and the sound produced by the system is remembered by the animals easily (personal information Dr. Finocchiaro). Alternatively, as practised in Canada, the unit is moved to individual animals in a tie-stall setting multiple times a day (personal information Prof C.F. Baes). Thus, action of individual animals is not needed. The manufacturer recommends 15 to 25 animals per GreenFeed unit, and recordings are made typically for 7 days. If all animals visit the unit adequately, throughput per unit is likely to be 750 to 1,250 animals per year. Sebek et al. (2019A, B)<ref>Sebek, L.B. 2019A. Project 11: Enterisch methaan: emissievariatie in de Nederlandse melkveestapel. 1 p. Wageningen : Wageningen University & Research.</ref> and Bannink et al. (2018)<ref>Bannink, A., Spek, J.W., Dijkstra, J., and Sebek, L.B. 2018. A Tier 3 Method for Enteric Methane in Dairy Cows Applied for Fecal N Digestibility in the Ammonia Inventory. In: Front. Sust. Food Syst. 2:66.</ref> showed the usefulness of the GreenFeed method in an on farm setting.

== Laser Methane Detector ==
The LMD can be used in the animal’s normal environment, although for consistency restraint is required during measurement. Because the LMD measures CH4 in the plume originating from the animal’s nostrils, results can be affected by factors such as: distance from the animal; pointing angle; animal’s head orientation and head movement; air movement and temperature in the barn; adjacent animals; and operator variation (Sorg et al., 2017<ref name=":27">Sorg, D., Difford, G.F., Mühlbach, S., Kuhla, B., Swalve, H.H., Lassen, J., Strabel, T., and Pszczola, M. 2017. Comparison of a laser methane detector with the GreenFeed and two breath analysers for on-farm measurements of methane emissions from dairy cows. Comp. Elec. Agric. 153:285-294.</ref>). Operator variation is likely to be one of the biggest factors because the operator controls distance and pointing angle, and is responsible for ensuring the laser remains on target. The structure of the barn and the resulting ventilation conditions and wind speed at the location of the measurement are also considerable sources of variation in recorded CH4. Assuming operator fatigue does not limit measurements, each LMD could record up to 10 animals per hour. If each animal is recorded 3 times (on 3 consecutive days, for example, as in Mühlbach et al. (2018)<ref>Mühlbach, S., Sorg, D., Rosner, F., Kecman, J., and Swalve, H.H. 2018. Genetic analyses for CH₄ concentrations in the breath of dairy cows measured on-farm with the Laser Methane Detector. In: Proceedings of the World Congress on Genetics Applied to Livestock Production, Abstract No. 186, 11-16th February, Auckland, New Zealand.</ref>), throughput is likely to be up to 1000 animals per year.</div>
</div>

= Comparison of methods to measure methane =
<div class="mw-collapsible mw-collapsed"><div>
== Correlations among methods ==
Correlations among methods
Table 4 shows correlations between the respiratory chamber method as the gold standard to measure CH4 emission from cows and other methods from Garnsworthy et al. (2019)<ref name=":11" />.
<center>
{| class="wikitable"
|-
! colspan="3" style="text-align:left;" | ''Table 4. Correlations between CH4 measuring methods. Data were taken from Garnsworthy et al. (2019)''<ref name=":11" />''.''
|- style="background-color:#efefef;"
|+
! style="text-align:left;" | Method
! style="text-align:center;" | Correlation
! style="text-align:center;" | S.E.
|-
| style="text-align:left;" | Respiratory chamber - SF6
| style="text-align:center;" | 0.87
| style="text-align:center;" | - 0.08
|-
| style="text-align:left;" | Respiratory chamber - Greenfeed
| style="text-align:center;" | 0.81
| style="text-align:center;" | - 0.1
|-
| style="text-align:left;" | Respiratory chamber - NDIR
| style="text-align:center;" | - 0.07
| style="text-align:center;" | 0.88
|-
| style="text-align:left;" | Respiratory chamber - NDIR peak
| style="text-align:center;" | 0.72
| style="text-align:center;" | - 0.11
|-
| style="text-align:left;" | Respiratory chamber - PAIR
| style="text-align:center;" | - 0.08
| style="text-align:center;" | 0.7
|-
| style="text-align:left;" | SF6 - Greenfeed
| style="text-align:center;" | 0.4
| style="text-align:center;" | - 0.8
|-
| style="text-align:left;" | LMD - Greenfeed
| style="text-align:center;" | 0.77
| style="text-align:center;" | - 0.23
|-
| style="text-align:left;" | NDIR - Greenfeed
| style="text-align:center;" | 0.64
| style="text-align:center;" | - 0.18
|-
| style="text-align:left;" | NDIR - LMD
| style="text-align:center;" | 0.6
| style="text-align:center;" | - 0.11
|-
| style="text-align:left;" | FTIR - LMD
| style="text-align:center;" | 0.57
| style="text-align:center;" | - 0.25
|-
| style="text-align:left;" | NDIR - NDIR peaks
| style="text-align:center;" | 0.58
| style="text-align:center;" | - 0.15
|-
| style="text-align:left;" | FTIR - NDIR
| style="text-align:center;" | 0.97
| style="text-align:center;" | - 0.02
|}
</center>

In method comparison studies, simultaneous repeated measures per cow with two or more methods are required in order to assess systematic differences between methods (means) and random differences (precision) and correlation between methods free of residual error. Furthermore, adequately short time differences between repeated measures per subject are needed to ensure the underlying biology of the cow has not changed. Not all methods can be recorded simultaneously and CH4 emission of cows’ changes both within day and over the lactation period. In such instances either cross-over designs or matched pair repeated measures designs are needed. Members of METHAGENE WG2 provided data from studies in which two or more methods had been used to measure CH4 output (g/day) by individual dairy cows. Methods were applied to each cow either concurrently or consecutively within a short timeframe.

Seven main methods were represented: respiration chambers; SF6; GreenFeed; LMD; and three breath-sampling systems based on different gas analysers. Gas analysers incorporated different technologies to measure CH4, which were NDIR (e.g. Guardian Plus, Edinburgh Instruments, Edinburgh, UK), FTIR (e.g. Gasmet 4030, Gasmet Technologies Oy, Helsinki, Finland), or PAIR (e.g. F10, Gasera Ltd, Turku, Finland). In the contributing studies, NDIR and FTIR were used in automatic milking stations, and PAIR was used in concentrate feeding stations. One NDIR study and all FTIR and PAIR studies used CO2 as a tracer gas, with daily CO2 output calculated either from milk yield, live weight and days pregnant or from metabolisable energy intake. Two NDIR studies were based on CH4 concentration in eructation peaks rather than mean CH4 concentration, so were treated as separate methods. By separating NDIR studies, a total of 8 distinct methods were available giving a matrix of 28 potential combinations for comparisons. Data were available for 13 method combinations (Garnsworthy et al., 2019<ref name=":11" />).

Method comparisons were conducted using bivariate models (repeatability animal models) to obtain correlations between ‘true values’, also known as repeated measures correlations or individual level correlations (Bakdash and Marusich, 2017<ref>Bakdash, J.Z., and Marusich, L.R. 2017. Repeated measures correlation. Front. Psychol. 8:1–13. doi:10.3389/fpsyg.2017.00456.</ref>). Variance components including between cow variation and within cow variation (precision) and means (accuracy) were used in the calculation of between cow coefficient of variation (CV, %) and total CV and repeatability. Where single measurements were available for each method Pearson’s correlation was reported and where repeated measures per subject were available repeated measures correlation was reported.

Respiration chambers were the most precise method, as can be seen by the smaller between cow CV% and total CV compared to alternative methods, and respiration chambers are by definition the most accurate. All methods tested showed high correlations with respiration chambers but none of the correlations exceeded 0.90. This is in part due to the increased imprecision of alternative methods, as even the most accurate and precise method will compare poorly to a less precise method. These correlations are also likely to be underestimated because none of the methods could be recorded simultaneously with respiration chambers and had to be recorded in cross over designs. Consequently, the true value for each cow may have changed due to changes in the underlying biology of the cow over time between measurements. Comparisons among alternative methods generally had lower correlations than comparisons with respiration chambers, despite having relatively higher numbers of animals and in most cases simultaneous or near simultaneous repeated measures per cow per method due to the increased variability and imprecision of alternative methods as is seen by the increased CVs or due to the possibility that different aspects of CH4 emission are captured using different methods.

For the methods with repeated measures per cow the two mass flux methods, SF6 and GreenFeed, had the highest repeated measures correlations (0.87 ± 0.08 and 0.81 ± 0.10) which outperformed the concentration based NDIR method using CO2 tracer gas. Of the two concentration methods evaluated against respiration chambers using single measurements, NDIR Peaks had a higher correlation (0.89 ± 0.07) than the PAIR CO2 tracer gas (0.80 ± 0.10). The study of Hristov et al. (2016)<ref>Hristov, A.N., O,h J., Giallongo, F., Frederick, T., Harper, M.T., Weeks, H., Branco, F., Price, W.J., Moate, P.J., Deighto,n M.H., Williams, S.R.O., Kindermann, M., and Duval, S. 2016. Short communication: Comparison of the GreenFeed system with the sulfur hexafluoride tracer technique for measuring enteric methane emissions from dairy cows. J. Dairy Sci. 5461–5465. doi:10.3168/jds.2016-10897.</ref> comparing SF6 and GreenFeed reported a low Pearson correlation of 0.40, despite having a large number of animals with repeated measures per method, the authors appear not to have estimated a repeated measures correlation, which could be larger. Estimating a repeated measures correlation between these two mass flux methods is a priority as it would clarify the inexplicable disagreement between two methods which both correlate highly with the gold standard method. With the exception of the aforementioned study, the imprecision was low in the mass flux measure comparisons as compared to the concentration-based methods.

Two of the sniffer methods evaluated, FTIR CO2t1 and NDIR CO2t1, correlated close to unity (0.97), most likely due to the shared prediction equation for CO2 tracer gas. Nevertheless, all correlations derived from actual data were positive. This suggests that combination of datasets obtained with different methods is a realistic proposition for genetic studies. Calculation of adjustment or weighting factors for bias, accuracy and precision would improve the value of combined datasets.

== Pro’s and con’s of devices ==

=== Daily methane emission measures ===
Due to the large diurnal variation in enteric CH4 emission in relation with feeding pattern (Grainger et al., 2007<ref>Grainger, C., Clarke, T., McGinn, S.M., Auldist, M.J., Beauchemin, K.A., Hannah, M.C., Waghorn, G.C., Clark, H., and Eckard, R J. 2007. Methane emissions from dairy cows measured using the sulfur hexafluoride (SF6) tracer and chamber techniques. J. Dairy Sci. 90:2755-2766.</ref>; Jonker et al. 2014<ref>Jonker, A., Molano, G., Antwi, C., Waghorn, G.. 2014. Feeding lucerne silage to beef cattle at three allowances and four feeding frequencies affects circadian patterns of methane emissions, but not emissions per unit of intake. Anim. Prod. Sci.54:1350-1353.</ref>), the highest accuracy of daily CH4 production rate (DMPR) will be obtained with methods that encompass the whole day emissions. Two methods are available: Respiration Chambers (RC) and SF6 methods.

Alternative methods are based on short-term measures of CH4 production rate: Portable Accumulation Chambers (PAC) for sheep and GreenFeed Emission Monitoring (GEM) systems for cattle and sheep (Hegarty, 2013<ref>Hegarty, R.S. 2013. Applicability of short term emission measurements for on-farm quantification of enteric methane. Animal 7, s2:401-408.</ref>).

=== DMPR with Respiration Chamber (RC) ===
It should be noted that CH4 emissions recorded in RC also include gases from flatulence in addition to eructed and expired CH4. Compared with mouth exhaled CH4, CH4 from flatulence is generally considered as limited.

Feed intake in the RC may not be representative of the normal animal feed intake (Bickell et al., 2014<ref>Bickell, S.L., Revell, D.K., Toovey, A.F., and Vercoe, P. E. 2014. Feed intake of sheep when allowed ad libitum access to feed in methane respiration chambers. J. Anim. Sci. 92:2259-2264.</ref>; Llonch et al., 2016<ref>Llonch, P., Somarriba, M.,. Duthie, C-A, Haskell, M.J., Rooke, J.A., Troy, S., Roehe, R., and . Turner, S.P. 2016 Association of temperament and acute stress responsiveness with productivity, feed efficiency, and methane emissions in beef cattle: an observational study. Front. Vet. Sci. 3: 43.</ref>; Troy et al., 2016<ref name=":0a">Troy, S.M., Duthie, C.A., Ross, D.W., Hyslop, J.J., Roehe, R., Waterhouse, A., and Rooke, J.A. 2016. A comparison of methane emissions from beef cattle measured using methane hoods with those measured using respiration chambers. Anim. Feed Sci. Technol. 211:227-240.</ref>). As a consequence, the DMPR measured could be biased. Animals are usually not fed ad libitum when recorded in RC. It is therefore recommended to compare animal or diet effects on Methane Yield (MY) calculated as the ratio of the observed DMPR/DMI during the RC recording in order to take into account possible differences among animals in DMI bias. Animal effects can also be compared on the Residual Methane Production Rate (RMPR) the difference between the observed DMPR and the expected DMPR obtained by regression of observed DMPR on DMI recorded during RC test. Residual traits, however, require a large number of recorded animals for valid adjustment.

Repeatability coefficients between measures taken on consecutive days are very high, rep=0.85 [0.75 to 0.94] for MeY and RMPR of cattle and sheep (Grainger et al., 2007<ref>Grainger, C., Clarke, T., McGinn, S.M., Auldist, M.J., Beauchemin, K.A., Hannah, M.C., Waghorn, G.C., Clark, H., and Eckard, R J. 2007. Methane emissions from dairy cows measured using the sulfur hexafluoride (SF6) tracer and chamber techniques. J. Dairy Sci. 90:2755-2766.</ref>; Donoghue et al., 2016<ref name=":14" />; Pinares-Patino et al., 2013<ref name=":2">Pinares-Patiño C.S., Hickey, S.M., Young, E.A., Dodds, K.G., MacLean, S., Molano, G., Sandoval, E., Kjestrup, H., Harland, R., Pickering, N.K., and McEwan, J.C. 2013. Heritability estimates of methane emissions from sheep. Animal 7: 316–321.</ref>). It has been concluded that 1-day measurement duration could be recommended as it will have a limited impact, less than 5%, on the efficiency of selection of MeY as compared to a selection on a 2-day measurement duration.

When repeated measures of CH4 emission of sheep are taken few days to two weeks apart the repeatability coefficients of MeY and RMPR drops to rep=0.36 [0.26 to 0.41] on average (Pinares-Patino et al., 2013<ref name=":2" />; Robinson et al., 2014a<ref name=":3b">Robinson, D.L., Goopy, J.P., Donaldson, A.J., Woodgate, R.T., Oddy, V.H., and Hegarty, R.S. 2014. Sire and liveweight affect feed intake and methane emissions of sheep confined in respiration chambers. Anima, 8:1935-1944.</ref>). Interestingly, repeatability maintains at a moderate level, rep=0.27 [0.23 to 0.53], when animals were measured several months or even years apart. Similar results were found in Angus cattle, rep=0.20, between MeY and RMPR measures taken more than 60 days apart (Donoghue et al., 2016<ref name=":14" />).

==== Conclusions and reccomendations ====
All these results show that animal effects exist on daily CH4 emissions and animal differences are partially under genetic determinism. This trait, as any other physiology trait, is subject to number of environmental effects and to evolution with time. Ranking animals on their CH4 emission requires standardization of the testing environment. Although highly precise, a single measure recorded in RC is not sufficient for characterizing an animals emission aptitude. In order to characterize a long term phenotype it is therefore recommended to record several 1-day measures, each a few weeks apart, instead of one single 2-day measure, keeping the testing environment as constant as possible.

=== DMPR with GEM ===
At each visit CH4 and CO2 fluxes are measured and animal emission rates are obtained by averaging the short-term flux measures recorded during the testing period. In a review of published results (Dorich et al., 2015<ref name=":4">Dorich, C.D., Varner, R.K., Pereira, A.B.D., Martineau, R., Soder, K.J., and Brito, A.F. 2015. Use of a portable, automated, open-circuit gas quantification system and the sulfur hexafluoride tracer technique for measuring enteric methane emissions in Holstein cows fed ad libitum or restricted. J. Dairy Sci. 98:2676-2681.</ref>; Hammond et al., 2015<ref name=":5">Hammond, K.J., Humphries, D.J., Crompton, L.A., Green, C., and Reynolds, C.K. 2015. Methane emissions from cattle: Estimates from short-term measurements using a GreenFeed system compared with measurements obtained using respiration chambers or sulphur hexafluoride tracer. Anim. Feed Sci. Technol. 203:41-52. doi:10.1016/j.anifeedsci.2015.02.008.</ref>; Velazco et al., 2016<ref name=":131">Velazco, J. I., Hegarty, R., Cottle, D., and Li, L. 2016. Quantifying daily methane production of beef cattle from multiple short-term measures using the GreenFeed system. <nowiki>https://rune.une.edu.au/web/handle/1959.11/23580</nowiki>.</ref>) Hammond et al. (2016A)<ref>Hammond, K.J., Crompton, L.A., Bannink, A., Dijkstra, J., Yáñez-Ruiz, D.R., O’Kiely, P., Kebreab, E., Eugenè, M.A., Yu, Z., Shingfield, K.J., Schwarm, A., Hristov, A.N., and Reynolds, C.K. 2016A. Review of current in vivo measurement techniques for quantifying enteric methane emission from ruminants. Anim. Feed Sci. Technol. 219:13–30. doi:10.1016/j.anifeedsci.2016.05.018</ref> concluded that the GEM system provides similar DMPR values as the RC or SF6 methods. Similar accuracy was found by Arbre et al. (2016)<ref name=":7">Arbre, M., Rochette, Y., Guyader, J., Lascoux, C., Gómez, L.M., Eugène, M., Morgavi, D.P., Renand, G., Doreau, M. and Martin, C. 2016. Repeatability of enteric methane determinations from cattle using either the SF6 tracer technique or the GreenFeed system. Anim. Prod. Sci. 56:238-243.</ref> for CH4 yield measured with GEM as compared with RC and SF6 measures.

The spot measures are highly variable since they include, in addition to the animal and environment effects, an important within-animal and within-day variance. The latter is considered as an error term. Consequently, the precision of the animal estimates increase with the number of spot measures averaged per animal. From the results reported by Renand and Maupetit (2016)<ref name=":8">Renand, G., and Maupetit, D. 2016. Assessing individual differences in enteric methane emission among beef heifers using the GreenFeed Emission Monitoring system: effect of the length of testing period on precision. Anim. Prod. Sci. 56:218-223.</ref> with 124 beef heifers controlled indoors, it can be shown that the coefficient of variation of that error term (CVe) decreases exponentially with the number of spot measures: 13.7%, 10.8%, 7.9% and 4.9% with 5, 10, 25 and 100 measures respectively. Results reported by Arbre et al. (2016)<ref name=":7" /> with 7 lactating dairy cows controlled indoors, also show that CVe decreases from 12.8% to 11.4%, 9.5% and 6.8% when the number of measures increases from 5 to 10, 25 and 100. With dairy cows at pasture, Waghorn et al. (2016)<ref name=":9">Waghorn, G.C., Jonker, A., and Macdonald, K A. (2016). Measuring methane from grazing dairy cows using GreenFeed. Anim. Prod. Sci. 56:252-257.</ref> showed that the coefficient of variation among 36 dairy cows at pasture was half (6.6 and 7.5%) when CH4 production rate was averaged over 16 days with approximately 18 to 26 measures per cow, as compared with 4 day averages with 4 to 6 measures per cow (13.0 and 17.2%). These authors concluded that at least 16 days are required to give confident estimates.

With 45 to 50 spot measures recorded during 2 weeks Arbre et al. (2016)<ref name=":7" /> and Renand and Maupetit (2016)<ref name=":8" /> obtained repeatabilityof 0.78 and 0.73 for DMPR estimates of 7 dairy cows and 124 beef heifers, respectively. A similar repeatability coefficient (0.74) was obtained by Huhtanen et al. (2015)<ref name=":24">Huhtanen, P., Cabezas-Garcia, E.H., Utsumi, S., and Zimmerman, S. 2015. Comparison of methods to determine methane emissions from dairy cows in farm conditions. J. Dairy Sci. 98:3394–3409. doi:10.3168/jds.2014-9118.</ref> with 25 dairy cows recorded during 3 weeks, with 20 to 30 samples per cow. Interestingly, these latter authors fitted gas concentration, airflow and head position measurement equipments into two automatic milking systems that were used to measure CH4 emission of 59 dairy cows during two periods of 10 days. After filtering data for acceptable head-position, the repeatability of DMPR was 0.75.

Considering the need to average enough spot measures and the advantage of measuring DMPR over long periods to take into account the emission variability with time, the GEM system should be run over several weeks. Averaging 40 to 50 spot measures per animal should provide a precise measure of the animal DMPR. The minimum duration of CH4 recording will depend on the number of spot measures actually recorded per day.

The GEM system relies on animals that voluntarily visit the GEM unit when attracted with pellets dispensed by a feeder at a controlled rate. The visitation frequency appears to be highly variable among different studies reported up to now. While some experiments report a very high frequency of cattle visiting the GEM units (up to 96%), the proportion of not visiting animals may be very high in other studies (up to 60%) (Dorich et al., 2015<ref name=":4" />; Hammond et al, 2015A<ref name=":5" />, Arbre et al., 2016<ref name=":7" />; Renand and Maupetit, 2016<ref name=":8" />; Velazco et al., 2016<ref name=":131" />; Waghorn et al., 2016<ref name=":9" />). The reason why some animals may not visit the unit is not obvious. That problem of no or low visiting frequency may jeopardize the precise ranking of animals on their DMRP. Training them is an important requisite for the success of DMPR recording with the GEM system (see recommendations on the C-Lock website). Palatability of the pellets used to attract the cattle should be high compared with the diet they receive in the trough or the grass they are grazing.

In addition to the effect on precision, the low visiting frequency may have an impact on accuracy if associated in some animals with specific time of visiting. Enteric CH4 emissions have a diurnal variation with a minimum at the end of night, before the first feeding, and a steady increase after each feeding. A weak diurnal pattern in CH4 emission was detected by Velazco et al. (2016)<ref name=":131" /> using GEM systems. Renand et al. (2013)<ref>Renand, G., Ricard, E., Maupetit, D., Thouly, J.C. 2013. Variability among individual young beef bulls and heifers in methane emissions. In: Book of abstracts of EAAP 64th annual meeting, Nantes, France. p. 195. (Wageningen Academic Publishers: Wageningen)</ref> observed significant differences between visit hours (CV=10%). If some animals visit the GEM at specific hours of the day, the rough average of spot measures will be biased. In order to get rid of this time effect on the DMPR measure, Dorich et al. (2015)<ref name=":4" /> and Hristov et al. (2016)<ref>Hristov, A.N., O,h J., Giallongo, F., Frederick, T., Harper, M.T., Weeks, H., Branco, F., Price, W.J., Moate, P.J., Deighto,n M.H., Williams, S.R.O., Kindermann, M., and Duval, S. 2016. Short communication: Comparison of the GreenFeed system with the sulfur hexafluoride tracer technique for measuring enteric methane emissions from dairy cows. J. Dairy Sci. 5461–5465. doi:10.3168/jds.2016-10897.</ref> came up with a protocol where the GEM units were moved sequentially from one cow to the next one over several days, so that all the cows were equally measured during different hours of the day. That protocol is possible only with tie stall cattle and is obviously not applicable for measuring large number of animals. However, with animals controlled in their production environment, the bias generated by potential specific visiting patterns can actually be removed if the measuring hour is taken into account in the linear model when estimating the animal effect.

As voluntary visiting of the GEM system may be a limiting factor under some conditions, measures of DMPR can be designed when animals are drinking or eating, i.e. several times per day. Velazco et al. (2016)<ref name=":131" /> showed that a GEM water unit prototype designed and built by C-Lock Inc., displayed different eructation patterns as compared with a plain GEM unit. They concluded that further development appears necessary before any application. Troy et al. (2016)<ref name=":0a" /> tested a CH4 hood (MH) system placed above an automated feeding bin. That system includes an air extraction fan for each hood with continuously recorded airflow. Methane concentration was measured using 4 infrared analyzers, one for 8 hoods. In this system one CH4 concentration value was recorded every 6 min. With 9 to 12 feeding events per day on average and feeding visits averaging 8 min, there were between 12 to 16 CH4 concentration values recorded and CH4 production rates calculated per day. The measurements were recorded during 46 days and ranking of animals in relation with the test duration was studied. However no repeatability coefficient was given for comparison with other methods. That system was compared with respiratory chambers results in two experiments with 82 and 80 steers fed different diet-treatment combinations. Over the whole experimental design, a good concordance was found between MH and RC results as a consequence that both methods detected similar effects for the diet-treatment effects. However no correlation was given between both methods within diet-treatment samples that are the essential information needed to evaluate the ability of this new method to predict individual DMPR.

==== Conclusions and reccomendations ====
With only a single gas analyzer for 8 feed bins, the time when useful CH4 concentration is recorded is certainly too short for including several eructation peaks. Fitting one gas analyzer per feed bin will combine advantages of the measurement time during visits of the GEM system with the visiting frequency allowed by the MH system.

=== MPR with PAC ===
The delay between the measurement and the last feeding has to be carefully monitored and taken into account when calculating animal emission values. As individual DMI is difficult to record, direct measurement of CH4 yield (MY=MPR/DMI) turns out to be impossible. Although not representative of a whole day production rate, that method can be used to characterize individual CH4 emission rates if standardized protocols are applied. It was first validated with 40 ewes measured 1 hour in PAC after three 22-hour measures in RC: a correlation of 0.71 was found between the two measures of CH4 production rate over 1 or 22 hours (Goopy et al., 2011<ref>Goopy, J.P., Woodgate, R., Donaldson, A., Robinson, D.L., and Hegarty, R.S. 2011. Validation of a short term methane measurement using portable static chambers to estimate methane production in sheep. Anim. Feed Sci. Technol. 166-167;219-226.</ref>). The 1-hour CH4 production measure in PAC has a moderate repeatability of rep=0.50 [0.37 to 0.60] when taken few days to seven weeks apart (Robinson et al., 2015<ref>Robinson, D.L., Goopy, J.P., Hegarty, R.S., and Oddy, V.H. 2015. Comparison of repeated measurements of methane production in sheep over 5 years and a range of measurement protocols. J. Anim. Sci. 93:4637-4650.</ref>; Goopy et al., 2016<ref>Goopy, J.P., Robinson, D.L., Woodgate, R.T., Donaldson, A.J., Oddy, V.H., Vercoe, P. E., and Hegarty, R.S. 2016. Estimates of repeatability and heritability of methane production in sheep using portable accumulation chambers. Anim. Prod. Sci. 56:116-122.</ref>). Heritability coefficient of this 1-hour CH4 production measure is estimated to h²=0.12 in a population of 2,279 sheep (Robinson et al., 2014<ref name=":3b" />) with a repeatability coefficient rep=0.25.

==== Conclusions and recomendations ====
The authors recommend using the mean of 3 PAC measurements in order to get accurate phenotype estimates.</div>
</div>

= Proxies =
<div class="mw-collapsible mw-collapsed">
<div>
== Introduction ==
Large-scale measurements of enteric CH4 emissions from dairy cows are needed for effective monitoring of strategies to reduce the carbon footprint of milk production, as well as for incorporation of CH4 emissions into breeding programs. However, measurements on a sufficiently large scale are difficult and expensive. Proxies for CH4 emissions can provide an alternative, but each approach has limitations. Negussie et al. (2019)<ref name=":30">Negussie, E., González Recio, O., de Haas, Y., Gengler N., Soyeurt, H., Peiren, N., Pszczola, M., Garnsworthy, P., Battagin, M., Bayat, A., Lassen, J., Yan, T., Boland, T., Kuhla, B., Strabel, T., Schwarm, A., Vanlierde, A., and Biscarini, F. 2019. Machine learning ensemble algorithms in predictive analytics of dairy cattle methane emission using imputed versus non-imputed datasets. 7th GGAA – Greenhouse Gas and Animal Agriculture Conference held from August 4th to 8th, Iguassu Falls/Brazil. Oral communication, Book of Abstracts Page 40. <nowiki>http://www.ggaa2019.org/sites/default/files/proceedings-ggaa2019.pdf</nowiki></ref> recently showed the potential of proxies proxies that are easy to record in the farm. These proxies can be gathered in most farms and are a realistic threshold accuracy that can be obtained without more fancy proxies. Several techniques have been developed for the measurement of CH4 emissions from ruminants, with varying degrees of accuracy (see reviews by Cassandro et al., 2013<ref>Cassandro, M., Mele, M., Stefanon, B.. 2013. Genetic aspects of enteric methane emission in livestock ruminants. Italian J. Anim. Sci. 12:e73: 450-458.</ref> and Hammond et al., 2016A<ref name=":25">Hammond, K.J., Crompton, L.A., Bannink, A., Dijkstra, J., Yáñez-Ruiz, D.R., O’Kiely, P., Kebreab, E., Eugenè, M.A., Yu, Z., Shingfield, K.J., Schwarm, A., Hristov, A.N., and Reynolds, C.K. 2016A. Review of current in vivo measurement techniques for quantifying enteric methane emission from ruminants. Anim. Feed Sci. Technol. 219:13–30. doi:10.1016/j.anifeedsci.2016.05.018.</ref>), but routine individual measurements on a large scale (a requisite for genetic selection) have proven to be difficult and expensive (Pickering et al., 2015<ref>Pickering, N.K., Oddy, V.H., Basarab, J.A., Cammack, K., Hayes, B J., Hegarty, R.S., McEwan, J.C., Miller, S., Pinares, C., and de Haas, Y. 2015. Invited review: Genetic possibilities to reduce enteric methane emissions from ruminants. Animal 9:1431-1440.</ref>; Negussie et al., 2016<ref>Negussie E., Lehtinen, J., Mäntysaari, P., Liinamo, A-E., Mäntysaari, E., and Lidauer, M.. 2016. Non-invasive individual methane measurements in dairy cows using photoacoustic infrared spectroscopy technique. 6th Greenhouse Gases Animal Agriculture Conference (GGAA2016) 14-18 February 2016. Melbourne, Australia. Abstract. p62.</ref>). Therefore, identifying proxies (i.e. indicators or indirect traits) that are correlated to CH4 emission, but which are easy and relatively low-cost to record on a large scale, is a much needed alternative. Proxies might be less accurate, but could be measured repeatedly to reduce random noise. The (potential) proxies range from simple and low-cost measurements such as body weight, to high-throughput milk MIR, to more demanding measures like rumen morphology, rumen metabolites or microbiome profiling.

Combining proxies that are easy to measure and cheap to record could provide predictions of CH4 emissions that are sufficiently accurate for selection and management of cows with low CH4 emissions.

== Available Proxies ==
A large array of CH4 proxies differing widely in accuracy and applicability under different conditions have been reported. The ideal proxy would be highly phenotypically and genetically correlated with CH4 emissions and could easily, and potentially repeatedly, be measured on a large scale. A systematic summary and assessment of existing knowledge is needed for the identification of robust and accurate CH4 proxies for future use. Table 5 summarizes proxies for CH4 production, and Table 6 summarizes results from combining proxies to improve predictability of proxies for CH4 prediction.
{| class="wikitable"
|-
! colspan="3" | ''Table 5. Available methane proxies include: (1) feed intake and feeding behaviour, (2) rumen function, metabolites and microbiome, (3) milk production and composition, (4) hind-gut and faeces, and (5) measurements at the level of the whole animal.''
|- style="background-color:#efefef;"
|+
!Proxy
!Description / conclusion
!Reference
|-
| colspan="3" |'''(1) Feed intake and feeding behavior'''
|-
|Dry Matter Intake (DMI)
|DMI predict MeP with R2= 0.06-0.64, and ME intake predict MeP with R2= 0.53-0,55
| Ellis et al. (2007);<ref>Ellis, J.L., Kebreab, E., Odongo, N.E., McBride, B.W., Okine, E.K., and France, J. 2007. Prediction of methane production from dairy and beef cattle. J. Dairy Sci. 90:3456–3466.</ref>
Mills et al. (2003)<ref>Mills, J.A.N., Kebreab, E., Yates, C.M., Crompton, L.A., Cammell, S.B., Dhanoa, M.S., Agnew, R.E., and France, J. 2003. Alternative approaches to predicting methane emissions from dairy cows. J. Anim. Sci. 81:3141–3150.</ref>;

Negussie et al. (2019)<ref>Negussie, E., González Recio, O., de Haas, Y., Gengler N., Soyeurt, H., Peiren, N., Pszczola, M., Garnsworthy, P., Battagin, M., Bayat, A., Lassen, J., Yan, T., Boland, T., Kuhla, B., Strabel, T., Schwarm, A., Vanlierde, A., and Biscarini, F. 2019. Machine learning ensemble algorithms in predictive analytics of dairy cattle methane emission using imputed versus non-imputed datasets. 7th GGAA – Greenhouse Gas and Animal Agriculture Conference held from August 4th to 8th, Iguassu Falls/Brazil. Oral communication, Book of Abstracts Page 40. <nowiki>http://www.ggaa2019.org/sites/default/files/proceedings-ggaa2019.pdf</nowiki></ref>
|-
|Gross Energy intake (GE)
|Predict MeP with RMSPE= 3.01.
| Moraes et al. (2014)<ref name=":0b">Moraes, L.E., Strathe, A.B., Fadel, J.G., Casper, D.P., and Kebreab, E. 2014. Prediction of enteric methane emissions from cattle. Glob. Change Biol. 20:2140–2148.</ref>
|-
|Feeding behavior
| Magnitude and direction of relation to MeP varies across studies
|Nkrumah et al. (2006);<ref>Nkrumah, J.D., Okine, E.K., Mathison, G.W., Schmid, K., Li, C., Basarab, J.A., Price, M.A., Wang, Z., and Moore, S.S. 2006. Relationships of feedlot feed efficiency, performance, and feeding behavior with metabolic rate, methane production, and energy partitioning in beef cattle. J. Anim. Sci. 84:145-153.</ref>
Jonker et al., 2014<ref>Jonker, A., Molano, G., Antwi, C., Waghorn, G.. 2014. Feeding lucerne silage to beef cattle at three allowances and four feeding frequencies affects circadian patterns of methane emissions, but not emissions per unit of intake. Anim. Prod. Sci.54:1350-1353.</ref>
|-
| Rumination time
|High rumination relates to more milk, consume more concentrate and produce more CH4, lower RMP and MeI
| Watt et al. (2015)<ref>Watt, L.J., Clark, C.E.F., Krebs, G.L., Petzel, C.E., Nielsen, S., and Utsumi, S.A. 2015. Differential rumination, intake, and enteric methane production of dairy cows in a pasture-based automatic milking system. J. Dairy Sci. 98:7248–7263.</ref>;
López- Paredes et al. (2020)<ref>Lopez-Paredes, J., Goiri, I., Atxaerandio, R., García-Rodríguez, A., Ugarte, E., Jiménez-Montero, J.A., Alenda, R and González-Recio, O. 2020. Mitigation of greenhouse gases in dairy cattle via genetic selection (i): Genetic parameters of direct methane using non-invasive methods and its proxies. J. Dairy Sci. 103.</ref>
|-
|Rumen microbiome
|The metagenome can predict DMI, and classify high vs low intakes
|Delgado et al. (2019)<ref>Delgado, B., Bach A., Guasch I., González C, Elcoso G., Pryce J.E., Gonzalez-Recio O. (2019).Whole rumen metagenome sequencing allows classifying and predicting feed efficiency and intake levels in cattle. Scientific Reports 9: 11. doi:10.1038/s41598-018-36673-w</ref>
|-
| colspan="3" |'''(2) Rumen function, metabolites and microbiome'''
|-
|Dietary antimethanogenic compounds
|Inhibitors of the enzyme methyl coenzyme-M reductase: bromochoromethane; chloroform; 3- nitrooxypropanol (not always)
|Denman et al., 2007;<ref>Denman, S.E., Tomkins, N.W., and McSweeney, C.S. 2007. Quantitation and diversity analysis of ruminal methanogenic populations in response to the antimethanogenic compound bromochloromethane. FEMS Microbiol. Ecol. 62:313-322.</ref>
Knight et al., 2011;<ref>Knight, T., Ronimus, R.S., Dey, D., Tootill, C., Naylor, G., Evans, P., Molano, G., Smith, A., Tavendale, M., Pinares-Patiño, C.S., and Clark, H. 2011. Chloroform decreases rumen methanogenesis and methanogen populations without altering rumen function in cattle. Anim. Feed Sci. Technol. 166-167:101-112.</ref>

Haisan et al., 2014<ref>Haisan, J., Sun, Y., Guan, L.L., Beauchemin, K.A., Iwaasa, A., Duval, S., Barreda, D.R., and Oba, M. 2014. The effects of feeding 3-nitrooxypropanol on methane emissions and productivity of Holstein cows in mid lactation. J. Dairy Sci. 97:3110-3119.</ref>;

Romero-Perez et al., 2014<ref>Romero-Perez, A., Okine, E.K., McGinn, S.M., Guan, L.L., Oba, M., Duval, S.M., Kinderman,n M., and Beauchemin, K.A. 2014. The potential of 3-nitrooxypropanol to lower enteric methane emissions from beef cattle. J. Anim. Sci. 92:4682-4693.</ref>
|-
|Dietary antimicrobial compounds
|Induce reductions in both MeP and methanogens numbers: nitrates, anacardic acid (cashew nut shell liquid), monensin, isobutyrate
|Iwamoto et al., 2002;<ref>Iwamoto, M., Asanuma, N., and Hin,o T. 2002. Ability of Selenomonas ruminantium, Veillonella parvula, and Wolinella succinogenes to reduce nitrate and nitrite with special reference to the suppression of ruminal methanogenesis. Anaerobe. 8:209-215.</ref>
Kubo et al., 1993<ref>Kubo, I., Muroi, H., Himejima, M., Yamagiwa, Y., Mera, H., Tokushima, K., Ohta, S., and Kamikawa, T. 1993. Structure-antibacterial activity relationships of anacardic acids. J. Agric. Food Chem. 41:1016-1019.</ref>;

van Zijderveld et al., 2010<ref>Van Zijderveld, S.M., Gerrits, W.J.J., Apajalahti, J.A., Newbold, J.R., Dijkstra, J., Leng, R A., and Perdok, H.B. 2010. Nitrate and sulfate: effective alternative hydrogen sinks for mitigation of ruminal methane production in sheep. J. Dairy Sci. 93:5856-5866.</ref>;

Veneman et al., 2015<ref>Veneman, J.B., Muetzel, S., Hart, K.J., Faulkner, C.L., Moorby, J.M., Perdok, H.B., and Newbold, C.J. 2015. Does Dietary Mitigation of Enteric Methane Production Affect Rumen Function and Animal Productivity in Dairy Cows? PLoS ONE 10(10): e0140282. doi: 10.1371/journal.pone.0140282</ref>;

Shinkai et al., 2012<ref>Shinkai, T., Enishi, O., Mitsumori, M., Higuchi, K., Kobayashi, Y., Takenaka, A., Nagashima, K., and Mochizuki, M. 2012. Mitigation of methane production from cattle by feeding cashew nut shell liquid. J. Dairy Sci. 95:5308-5316.</ref>;

Wang et al., 2015<ref>Wang, C., Liu, Q., Zhang, Y.L., Pei, C.X., Zhang, S.L., Wang, Y.X., Yang, W.Z., Bai, Y.S., Shi, Z.G., and Liu, X.N. 2015. Effects of isobutyrate supplementation on ruminal microflora, rumen enzyme activities and methane emissions in Simmental steers. J. Anim. Physiol. Anim. Nutr. (Berl). 99:123-131.</ref>
|-
|Rumen microbiome profile
|High Fibrobacteres, Quinella ovalis and Veillonellaceae and low Ruminococcaceae, Lachnospiraceae and Clostridiales associate with low- CH4 phenotypes and high propionate
Protozoa concentration
|Kittelmann et al., 2014<ref>Kittelmann, S., Pinares-Patiño, C.S., Seedorf, H., Kirk, M.R., Ganesh, S., McEwan, J.C., and Janssen, P.H. 2014. Two different bacterial community types are linked with the low-methane emission trait in sheep. PLoS One 9:e103171.</ref>;
Wallace et al., 2014<ref>Wallace, R. ., Rooke, J A., Duthie, C.-A., Hyslop, J.J., Ross, D.W., McKain, N., de Souza, S.M., Snelling, T.J., Waterhouse, A., and Roehe, R. 2014. Archaeal abundance in post-mortem ruminal digesta may help predict methane emissions from beef cattle. Sci. Rep. 4:5892.</ref>;

Sun et al., 2015<ref>Sun, X., Henderson, G., Cox, F., Molan,o G., Harrison, S.J., Luo, D., Janssen, P.H., and Pacheco, D. 2015. Lambs Fed Fresh Winter Forage Rape (Brassica napus L.) Emit Less Methane than Those Fed Perennial Ryegrass (Lolium perenne L.), and Possible Mechanisms behind the Difference. PLoS One 10(3):e0119697: DOI: 10.1371/journal.pone.0119697</ref>;

Guyader et al., 2014<ref>Guyader, J., Eugène, M., Nozière, P., Morgavi, D.P., Doreau, M., and Martin, C. 2014. Influence of rumen protozoa on methane emission in ruminants: a meta-analysis approach. Animal 8:1816-1825.</ref>
|-
|Rumen microbiome profile
|Predict MeP with R2 up to 0.55
|Ross et al. 2013a<ref>Ross, E. M., P.J . Moate, L.C. Marett, B.G. Cocks and B.J. Hayes. 2013a. Investigating the effect of two methane-mitigating diets on the rumen microbiome using massively parallel sequencing. J. Dairy Sci. 96:6030–6046.</ref>;
Ross et al. (2013b)<ref>Ross, E. M., Moate, P. J., Marett, L.C., Cocks, B.G., and Hayes, B.J. 2013b. Metagenomic Predictions: From Microbiome to Complex Health and Environmental Phenotypes in Humans and Cattle. PLoS One DOI: 10.1371/journal.pone.0073056.</ref>
|-
|Microbial genes
|20 (out of 3970 identified) related to CH4 emissions
|Roehe et al. (2016)<ref>Roehe R., Dewhurst, R.J., Duthie, C-A., Rooke, J.A., McKain, N., Ross, D.W., Hyslop, J.J., Waterhouse, A., Freeman, T.C., Watson, M., and Wallace, R.J. 2016. Bovine Host Genetic Variation Influences Rumen Microbial Methane Production with Best Selection Criterion for Low Methane Emitting and Efficiently Feed Converting Hosts Based on Metagenomic Gene Abundance. PLoS Genet 12(2): e1005846. doi:10.1371/journal.pgen.1005846.</ref>
|-
|Rumen volume (Xray Computed Tomography) and retention time
|Low-MeY sheep had smaller rumens. Faster passage= less time to ferment substrate - explained 28% of variation in MeP
|Pinares Patiño et al., 2003<ref>Pinares-Patiño C.S., Hickey, S.M., Young, E.A., Dodds, K.G., MacLean, S., Molano, G., Sandoval, E., Kjestrup, H., Harland, R., Pickering, N.K., and McEwan, J.C. 2013. Heritability estimates of methane emissions from sheep. Animal 7: 316–321.</ref>;
Goopy et al., 2014<ref>Goopy, J.P., Donaldson, A., Hegarty, R., Vercoe, P.E., Haynes, F., Barnett, M., and Oddy, V.H. 2014. Low-methane yield sheep have smaller rumens and shorter rumen retention time. Br. J. Nutr. 111:578-585.</ref>;

Okine et al. (1989)<ref>Okine, E. ., Mathiso,n G.W., and Hardin, R.T. 1989. Effects of changes in frequency of reticular contractions on fluid and particulate passage rates in cattle. J. Anim. Sci. 67:3388–3396.</ref>
|-
|Blood triiodothyronine concentration
|Reduced MeY
|Barnett et al. (2012)<ref>Barnett, M.C., Goopy, J.P., McFarlane, J.R., Godwin, I.R., Nolan, J.V., and Hegarty, R.S. (2012). Triiodothyronine influences digesta kinetics and methane yield in sheep. Anim. Prod. Sci. 52:572-577.</ref>
|-
|Acetate to propionate ratio in ruminal fluid
|Positively associated with CH4 emissions, but not confirmed in all studies, sometimes opposite relation
|Mohammed et al., 2011;<ref name=":1b">Mohammed, R., McGinn, S.M., and Beauchemin, K.A. 2011. Prediction of enteric methane output from milk fatty acid concentrations and rumen fermentation parameters in dairy cows fed sunflower, flax, or canola seeds. J. Dairy Sci. 94:6057–6068.</ref>
Fievez et al., 2012<ref>Fievez V., Colma,n E., Castro-Montoya, J.M., Stefanov, I., and Vlaeminck, B. 2012. Milk odd- and branched-chain fatty acids as biomarkers of rumen function – An update. Anim. Feed. Sci. Technol. 172:51–65.</ref>;

Chung et al., 2011<ref>Chung, Y.-H., Walker, N.D., McGinn, S.M., and Beauchemin, K.A. 2011. Differing effects of 2 active dried yeast (Saccharomyces cerevisiae) strains on ruminal acidosis and methane production in nonlactating dairy cows. J. Dairy Sci. 94:2431–2439.</ref>;

Van Zijderveld et al., 2010<ref>Van Zijderveld, S.M., Gerrits, W.J.J., Apajalahti, J.A., Newbold, J.R., Dijkstra, J., Leng, R A., and Perdok, H.B. 2010. Nitrate and sulfate: effective alternative hydrogen sinks for mitigation of ruminal methane production in sheep. J. Dairy Sci. 93:5856-5866.</ref>
|-
| colspan="3" |'''(3) Milk production and composition'''
|-
|Modelling approach
|Doubling milk production only adds 5 kg to the MeP and so greatly reduces MeY
|Kirchgessner et al. (1995)<ref>Kirchgessner M., Windisch, W., and Muller, H.L. 1995. Nutritional factors for the quantification of methane production. In: Ruminant Physiology: Digestion, Metabolism, Growth and Reproduction. Proceedings 8th International Symposium on Ruminant Physiology (eds W. Von Engelhardt, S. Leonhard-Marek, G. Breves and D. Giesecke). Reproduction Proceedings 8th International Symposium on Ruminant Physiology. Ferdinand Enke Verlag, Stuttgart, Germany. pp. 333-348.</ref>;
Hristov et al. (2014)<ref>Hristov, A.N., Johnson, K.A., and Kebreab, E, 2014. Livestock methane emissions in the United States. Proc. Natl. Aacad. Sci. 111:E1320; doi:10.1073/pnas.1401046111</ref>
|-
|Milk fat content
|key explanatory variable for predicting CH4: A moderate negative genetic correlation with infrared predicted
MeI: correlations MeP = 0,08 and MeI = - 0.13
|Moraes et al. (2014)<ref name=":0b" />;
Kandel et al., 2014A<ref name=":37" />, B<ref name=":3">Kandel, P.B., Vanderick, S., Vanrobays, M.L., Vanlierde, A., Dehareng, F., Froidmont, E., Soyeur,t H., and Gengler, N. 2014B. Consequences of selection for environmental impact traits in dairy cows. In: 10th World Congress on Genetics Applied to Livestock Production (WCGALP), 17-22 August, 2014. Vancouver, Canada.</ref>;

Vanlierde et al. (2015)<ref name=":4a">Vanlierde, A., Vanrobays, M.L., Dehareng, F., Froidmont, E., Soyeurt, H., McParland, S., Lewis, E., Deighton, M.H., Grandl, F., Kreuzer, M., Grendler, B., Dardenne, P., and Gengler, N. 2015. Hot topic: Innovative lactation-stage-dependent prediction of methane emissions from milk mid-infrared spectra. J. Dairy Sci. 98:5740–5747.</ref>
|-
|Milk fat content
|A positive relationship between VFA proportions and methanogenesis is expected as a consequence of the common biochemical pathways; Dietary unsaturated fatty acids are negatively associated with CH4 emissions
|Vlaeminck et al., 2006<ref>Vlaeminck, B., Fievez, V., Cabrita, A.R.J., Fonseca, A.J.M., and Dewhurst, R.J. 2006. Factors affecting odd- and branched-chain fatty acids in milk: A review. Anim. Feed Sci. Technol. 131:389–417.</ref>;
Van Lingen et al., 2014<ref name=":5a">Van Lingen H.J., Crompton, L.A., Hendriks, W.H., Reynolds, C.K., Dijkstra, J. 2014. Meta-analysis of relationships between enteric methane yield and milk fatty acid profile in dairy cattle. J. Dairy Sci. 97:7115-7132.</ref>
|-
|Milk protein yield
|Correlation with Mel = - 0.47 or -0.09, MeP = 0.53
|Kandel et al. (2014A)<ref name=":37" />;
Vanlierde et al. (2015)<ref name=":4a" />
|-
|Lactose
|Variable correlations: MeP = 0,33; MeI = - 0.21; R = 0.19 for CH4 emission
|Miettinen and Huhtanen (1996)<ref>Miettinen, H., and Huhtanen, P. 1996. Effects of the ration of ruminal propionate to butyrate on milk yield and blood metabolites in dairy cows. J. Dairy Sci. 79:851–861.</ref>;
Dehareng et al. (2012)<ref name=":32">Dehareng, F., Delfosse, C., Froidmont, E., Soyeurt, H., Martin, C., Gengler, N., Vanlierde, A., and Dardenne, P. 2012. Potential use of milk mid-infrared spectra to predict individual methane emission of dairy cows. Animal 6:1694-701.</ref>
|-
|Somatic cell score
|Genetic correlation with infrared predicted MeI: R = 0.07
|Kandel et al. (2014A<ref name=":37" />, B<ref name=":3" />)
|-
|Prediction equations Milk FA and CH4 emissions, including from MIR data
|R² ranged between 47 and 95%; relationships between the individual milk FA and MeP differed considerably and the correlations between CH4 and milk FA vary throughout the lactation
|Chilliard et al. (2009)<ref>Chilliard Y., Martin ,C., Rouel, J., and Doreau, M. 2009. Milk fatty acids in dairy cows fed whole crude linseed, extruded linseed, or linseed oil, and their relationship with methane output. J. Dairy Sci. 92:5199-5211.</ref>;
Delfosse et al. (2010)<ref>Delfosse, O., Froidmont, E., Fernandez Pierna, J. A., Martin, C., and Dehareng, F. 2010. Estimation of methane emissions by dairy cows on the basis of milk composition. In: Greenhouse Gases and Animal Agriculture Conference. 2010; GGAA2010: 4. Greenhouse Gases and Animal Agriculture Conference, Banff, CAN, 2010-10-03-2010-10-08, 60-61.</ref>;

Castro-Montoya et al. (2011)<ref>Castro Montoya, J., Bhagwat, A.M., Peiren, N., De Campeneere, S., De Baets, B., and Fievez, V. 2011. Relationships between odd- and branched-chain fatty acid profiles in milk and calculated enteric methane proportion for lactating dairy cattle. Anim. Feed Sci. Technol.166:596–602.</ref>;

Dijkstra et al. (2011)<ref>Dijkstra, J., van Zijderveld, S.M., Apajalahti, J.A., Bannink, A., Gerrits, W.J.J., Newbold, J.R., Perdok, H.B.,, and Berends, H. 2011. Relationships between methane production and milk fatty acid profiles in dairy cattle. Anim. Feed Sci. Technol. 166–167:590–595.</ref>;

Kandel et al. (2013)<ref>Kandel, P.B., Vanrobays, M.L., Vanlierde, A., Dehareng, F., Froidmont, E., Dardenne, P., Lewis, E., Buckley, F., Deighton, M.H., McParland, S. and Gengler, N., 2013. Genetic parameters for methane emissions predicted from milk mid-infrared spectra in dairy cows. J. Dairy Sci. 95(E-1):p.388.</ref>;

Mohammed et al. (2011)<ref name=":1b" />;

Van Lingen et al. (2014)<ref name=":5a" />;

Williams et al. (2014)<ref>Williams, S.R.O., Williams, B., Moate, P.J., Deighton, M.H., Hannah, M.C., and Wales, W.J. 2014. Methane emissions of dairy cows cannot be predicted by the concentrations of C8:0 and total C18 fatty acids in milk. Anim. Prod. Sci. 54:1757–1761. <nowiki>http://dx.doi.org/10.1071/AN14292</nowiki>.</ref>;

Dijkstra et al. (2016)<ref>Dijkstra, J., van Zijderveld, S.M., Apajalahti, J.A., Bannink, A., Gerrits, W.J.J., Newbold, J.R., Perdok, H.B.,, and Berends, H. 2011. Relationships between methane production and milk fatty acid profiles in dairy cattle. Anim. Feed Sci. Technol. 166–167:590–595.</ref>;

Rico et al. (2016)<ref name=":33">Rico D.E., Chouinard, P.Y., Hassanat, F., Benchaar, C., and Gervais, R. 2016. Prediction of enteric methane emissions from Holstein dairy cows fed various forage sources. Animal 10:203-211. doi:10.1017/S1751731115001949</ref>;

Van Gastelen and Dijkstra (2016)<ref name=":36">Van Gastelen, S., and Dijkstra, J.. 2016. Prediction of methane emission from lactating dairy cows using milk fatty acids and mid-infrared spectroscopy. J. Sci. Food Agric. 96:3963-3968. DOI: 10.1002/jsfa.7718.</ref>;

Vanrobays et al. (2016);<ref>Vanrobays, M.-L., Bastin, C., Vandenplas, J., Hammami, H., Soyeurt, H., Vanlierde, A., Dehareng, F., Froidmont, E., and Gengler, N. 2016. Changes throughout lactation in phenotypic and genetic correlations between methane emissions and milk fatty acid contents predicted from milk mid-infrared spectra. J. Dairy Sci. 99:1–14. <nowiki>http://dx.doi.org/10.3168/jds.2015-10646</nowiki></ref>

Bougoin et al., (2019)<ref name=":34">Bougouin, A., Appuhamy, J.A.D.R.N., Ferlay, A., Kebreab, E., Martin, C., Moate, P.J., Benchaar, C., Lund, P., and Eugène, M. 2019. Individual milk fatty acids are potential predictors of enteric methane emissions from dairy cows fed a wide range of diets: Approach by meta-analysis. J. Dairy Sci. 102:10616–10631. DOI: <nowiki>http://doi.org/10.3168/jds.2018-15940</nowiki></ref>
|-
| colspan="3" |'''(4) Hind-gut and feces'''
|-
|Whole tract digestibility (potential as supporting factors in the prediction of enteric CH4 emissions)
|Main effects relate to rumen (see above), but energy digestibility as a supporting factor to GE intake improved the accuracy of CH4 prediction, despite the fact that there was no direct linear relationship between energy digestibility and MeY and in % of GE intake
|Yan et al., 2009<ref name=":6">Yan, T., Porter, M.G., and Mayne, S.C. 2009. Prediction of methane emission from beef cattle using data measured in indirect open-circuit respiration calorimeters. Animal 3:1455-1462.</ref>
|-
|Ratio of acetic and butyric acid divided by propionic acid
|Methane yield positive relation
|Moss et al., 2000<ref>Moss A.R., Jouany, J.P., and Newbold, J. 2000. Methane production by ruminants: Its contribution to global warming. Annal. Zootech. 49:231-253.</ref>
|-
| colspan="3" |'''(5) Whole animal measurements'''
|-
|Body weight and conformation
|Prediction models; primary predictor for enteric MeP
|Moraes et al. (2014)<ref name=":0b" />;
Holter and Young, 1992; <ref>Holter J.B., and Young, A.J. 1992. Methane production in dry and lactating Holstein cows. J. Dairy Sci. 75:2165–2175.</ref>

Yan et al., 2009<ref name=":6" />
|-
|Body weight
|Relationship with MeI: r = 0.44; relationship between body weight and rumen capacity
|Antunes-Fernandes et al. (2016)<ref>Antunes-Fernandes, E.C., van Gastelen, S., Dijkstra, J., Hettinga K.A., and Vervoort, J. 2016. Milk metabolome relates enteric methane emission to milk synthesis, and energy metabolism pathways. J. Dairy. Sci. 99:6251-6262.</ref>;
Demment and Van Soest, 1985<ref>Demment, M.W., and Van Soest, P.J. 1985. A nutritional explanation for body-size patterns of ruminant and nonruminant herbivores. Am. Nat. 125:641–672.</ref>
|-
|Body weight
|Key explanatory variable for enteric MeP
|No reference available
|-
|Conformation traits: affects enteric MeP
|Indicators for rumen volume (via feed intake and rumen passage rates); BCS
|Agnew and Yan, 2000<ref>Agnew, R.E. and Yan, T. 2000. The impact of recent research on energy feeding systems for dairy cattle. Livest. Prod. Sci. 66:197-215.</ref>
|-
|Lactation stage
|Complementary proxy
|Vanlierde et al. (2015)<ref name=":4a" />
|}</div>It is evident that no single proxy offers a good solution in terms of all of these attributes, though the low cost and high throughput make milk MIR a good candidate for further work on refining methods, improving calibrations and exploring combinations with other proxies.

== Combining proxies for methane ==
Although milk MIR shows promise as a single proxy for CH4 emissions, there may be advantages in using two or more proxies in combination. There are two potential reasons why a combination of proxies might be appropriate: (i) proxies may describe independent sources of variation in CH4 emissions, and (ii) one proxy allows correction for shortcomings in the way the other proxy describes CH4 emissions (e.g. taking into account lactation stage if CH4 emissions prediction coefficients change during the lactation). See also Negussie ''et al.'' (2019)<ref name=":30" />.
<center>
{| class="wikitable"
| colspan="3" |'''''Table 7. Combinations of proxies for methane.'''''
|- style="background-color:#efefef;"
|'''Proxy combinations'''
|'''Results'''
|'''References'''
|-
|Rumen microbiome + VFA
|Combination of rumen VFA proportions and pH + modelling may be more informative
|Brask ''et al.'' 2015<ref>Brask, M., Weisbjerg, M.R., Hellwing, A.L.F., Bannink, A., and Lund, P. 2015. Methane production and diurnal variation measured in dairy cows and predicted from fermentation pattern and nutrient or carbon flow Animal. 9:1795-1806. DOI: <nowiki>http://dx.doi.org/10.1017/S1751731115001184</nowiki></ref>
|-
|Methanogen abundance in rumen fluid + proxy, a chemical marker for methanogens (archaeol)
|
|McCartney ''et al.'' (2013)<ref>McCartney, C.A., Bull, I.D., Waters, S.M., and Dewhurst, R.J. 2013. Technical note: Comparison of biomarker and molecular biological methods for estimating methanogen abundance. J. Anim. Sci. 91:5724–5728.</ref>
|-
|Fecal ether lipids (ratio of diether to tetraether lipids) + rumen pH
|Combining measurements of rumen VFA, pH and the microbiome should be more informative for predicting CH4 emissions
|McCartney ''et al.'' (2014)<ref>McCartney, C.A., Dewhurst, R.J. and Bull, I.D. 2014. Changes in the ratio of tetraether to diether lipids in cattle feces in response to altered dietary ratio of grass silage and concentrates. J. Anim. Sci. 92:4095-4098.</ref>; Ann ''et al.'', 1996
|-
|Feed intake (determined by body weight, production level, growth rate and feed quality)
|Main driver for CH4 emissions; should be all included in models for CH4
|Moraes ''et al.'' 2014,<ref name=":0b" />
|-
|DMI and diet composition
|Combine database to predict CH4
|Niu ''et al.'', 2018<ref>Niu, M., Kebreab, E., Hristov, A. N., Oh, J., Arndt, C., Bannink, A., Bayat, A. R., Brito, A. F., Boland, T., Casper, D., Crompton, L. A., Dijkstra, J., Eugène, M. A., Garnsworthy, P. C., Haque, M. N., Hellwing, A.L.F., Huhtanen, P., Kreuzer, M., Kuhla, B., Lund, P., Madsen, J., Martin, C., Mcclelland, S. C., Mcgee, M., Moate, P. J., Muetzel, S., Muñoz, C., O'Kiely, P., Peiren, N., Reynolds, C. K., Schwarm, A., Shingfield, K. J., Storlien, T. M., Weisbjerg, M. R., Yáñez-Ruiz, D. R., and Yu, Z. 2018. Prediction of enteric methane production, yield, and intensity in dairy cattle using an intercontinental database. Glob. Change Biol. 2018;1–22, DOI: 10.1111/gcb.14094.</ref>; Van Lingen ''et al.'', 2019<ref>Van Lingen, H.J., Niu, M., Kebreab, E., Valadares Filho, S.C., Rooke, J.A., Duthie, C.A., Schwarm, A., Kreuzer, M., Hynd, P.I., Caetano, M., and Eugène, M. 2019. Prediction of enteric methane production, yield and intensity of beef cattle using an intercontinental database. Agric. Ecosyst. Environ. 283:106575.</ref>
|-
|Range of prediction equations for CH4 production
|Feed intake = primary predictor of total CH4 production (accounted for 52 to 64%); Combining more factors did indeed improve the prediction equation by 15 to 35%
|Ramin & Huhtanen (2013)<ref>Ramin, M. and Huhtanen, P.. 2013. Development of equations for predicting methane emissions from ruminants. J. Dairy Sci. 96:2476-2493.</ref>; Knapp ''et al.'', 2015<ref>Knapp J.R., Laur, G.L., Vadas, P.A.. Weiss, , and Tricarico, J.M. 2015. Invited review: Enteric methane in dairy cattle production: Quantifying the opportunities and impact of reducing emissions. J. Dairy Sci. 97:3231–3261.</ref>; Sauvant & Nozière (2016)<ref>Sauvant, D., and Nozière, P. 2016. Quantification of the main digestive processes in ruminants: the equations involved in the renewed energy and protein feed evaluation systems. Animal 10:755–770. <nowiki>https://doi:10.1017/S1751731115002670</nowiki>. </ref>
|-
|Rumen measurements (VFA, pH, protozoa counts) + feed intake (total DMI, forage DMI and FA intake) + production parameters (milk yield and composition) + milk FA
|Suggest that milk FA predict CH4 emission better (R2= 0.74) compared to rumen variables, feed intake and production parameters (R2 < 0.58). Total combination: R2= 0.90
|Mohammed ''et al.'' (2011)<ref name=":1b" />
|-
|Modelling
|specific prediction equations may need to be developed, or diet composition may need to be included in the prediction equations
|Mohammed ''et al.'' (2011)<ref name=":1b" />
|-
|Feed intake + diet composition + milk production + milk FA
|CH4 prediction equations: best fit = combining milk FA, feed intake, diet composition, and milk production (R2 = 0.84)
|Rico ''et al.'' (2016)<ref name=":33" />; Bougoin ''et al.'' (2019)<ref name=":34" />
|-
|MIR + lactation stage
|MIR spectroscopy (coefficient of determination = 0.68 and 0.79), predictions at different stages of lactation were not biologically meaningful + lactation stage refined the model: showing a biologically meaningful behavior throughout lactation: an increase in CH4 production after calving up to approximately 100 DIM, followed by gradual decline towards the end of lactation
|Dehareng ''et al.'' (2012)<ref name=":32" />; Vanlierde ''et al.'' (2015)<ref name=":4a" />
|-
|Milk yield, fat percentage + type traits
|Combine database to predict CH4 using official milk recording system and type evaluation.
|Cassandro ''et al.'', (2010)<ref name=":29" />; Cassandro, (2013)<ref name=":28" />
|}
</center>

== Building an index for methane ==
For some of the proxies, the heritability and correlations with CH4 output are known: e.g. Vanrobays ''et al.'' (2016)<ref>Vanrobays, M.-L., Bastin, C., Vandenplas, J., Hammami, H., Soyeurt, H., Vanlierde, A., Dehareng, F., Froidmont, E., and Gengler, N. 2016. Changes throughout lactation in phenotypic and genetic correlations between methane emissions and milk fatty acid contents predicted from milk mid-infrared spectra. J. Dairy Sci. 99:1–14. <nowiki>http://dx.doi.org/10.3168/jds.2015-10646</nowiki></ref> estimated heritability of 0.25 for CH4 production (g/d) and in the range 0.17 - 0.42 for different classes of milk FA; phenotypic and genetic correlations between MeP and milk FA varied between -0.03 and 0.16, and between -0.02 and 0.32 (C18:0), respectively. The genetic correlation between MeI and milk yield was estimated by Dehareng ''et al.'' (2012)<ref name=":32" /> at -0.45; that between milk yield and protein percentage at -0.54 (Miglior ''et al.'' 2007<ref>Miglior, F., Sewalem, A., Jamrozik, J., Bohmanova, J., Lefebvre, D.M., Moore, R.K. 2007. Genetic analysis of milk urea nitrogen and lactose and their relationships with other production traits in Canadian Holstein cattle. J. Dairy Sci. 90:2468-2479.</ref>). This would give a genetic correlation between MeI and protein percentage in the range [-0.5, 0.9], with likelier values for positive correlations. The most probable value in the given range could then be estimated (from the prior distribution of the missing correlation and the joint likelihood of the two known correlations given the values in the range). Such data could in the future be used to develop an index for breeding on CH4 emission.
</div>

= Proxies discussion =
<div class="mw-collapsible mw-collapsed">
<div>
The greatest limitation of proxies today is the lack of robustness in their general applicability. Future efforts should therefore be directed towards developing combinations of proxies that are robust and applicable across diverse production systems and environments. Here we present the present status of the knowledge of proxies and their predictive value for CH4 emission. Proxies related to body weight or milk yield and composition are relatively simple, low-cost, high throughput, and are easy to implement in practice. In particular, DMI and milk MIR, along with covariates such as lactation stage, are a promising option for prediction of CH4 emission in dairy cows. No single proxy was found to accurately predict CH4, whilst combinations of two or more proxies are likely to be a better solution. Combining proxies can increase the accuracy of predictions by up to 15 - 35%, mainly because different proxies describe independent sources of variation in CH4 and one proxy can correct for shortcomings in the other(s). One plausible strategy could be to increase animal productive efficiency whilst reducing CH4 emissions per animal. This could be achieved by reducing MeY and/or decreasing DMI provided that there is no concomitant reduction in productivity or increase in feed consumption (Pickering et al., 2015<ref>Pickering, N.K., Oddy, V.H., Basarab, J.A., Cammack, K., Hayes, B J., Hegarty, R.S., McEwan, J.C., Miller, S., Pinares, C., and de Haas, Y. 2015. Invited review: Genetic possibilities to reduce enteric methane emissions from ruminants. Animal 9:1431-1440.</ref>).

== Combining diet-based measurements with other proxies for methane emissions ==
Feed intake appears a reasonably adequate predictor of MeP: generally, heavier animals have higher maintenance requirements, so eat more and produce more CH4. However, a substantial level of variation is left unaccounted for. This suggests that more detailed information on dietary composition is needed. This is also important when one wants to account for MeP on diets of similar DMI but of different nutrient profiles.

The prediction accuracy of MeP strongly depends on the accuracy of quantifying the VFA produced in the rumen (Alemu et al., 2011<ref>Alemu, A.W., Dijkstra, J., Bannink, A., France, J., and Kebreab, E. 2011. Rumen stoichiometric models and their contribution and challenges in predicting enteric methane production. Anim. Feed Sci. Technol. 166-167:761-778.</ref>). The type of VFA formed during rumen fermentation depends on the type of substrate fermented (Bannink et al., 2011<ref>Bannink, A., van Schijndel, M.W., and Dijkstra, J. 2011. A model of enteric fermentation in dairy cows to estimate methane emission for the Dutch National Inventory Report using the IPCC Tier 3 approach. Anim. Feed Sci. Technol. 166-167:603-618.</ref>), such as the dietary content of neutral detergent fiber and starch. The type of substrate fermented thus appears a useful factor for predicting MeP (Ellis et al., 2007<ref>Ellis, J.L., Kebreab, E., Odongo, N.E., McBride, B.W., Okine, E.K., and France, J. 2007. Prediction of methane production from dairy and beef cattle. J. Dairy Sci. 90:3456–3466.</ref>), indicating that including a description of variation in dietary quality caused by nutritional factors results in improved prediction accuracy of CH4 emission (Ellis et al., 2010<ref>Ellis, J.L., Bannink, A., France, J., Kebreab, E., and Dijkstra, J. 2010. Evaluation of enteric methane prediction equations for dairy cows used in whole farm models. Glob. Change Biol. 16:3246–3256.</ref>; Moraes et al., 2014<ref name=":0b" />).

== Rumen ==
When feed intake is kept constant, a higher rumen capacity results in a lower passage rate (Demment and Van Soest, 1985<ref>Demment, M.W., and Van Soest, P.J. 1985. A nutritional explanation for body-size patterns of ruminant and nonruminant herbivores. Am. Nat. 125:641–672.</ref>), resulting in a higher MeP (Moraes et al., 2014<ref name=":0b" />). Proxies based on rumen samples are generally poor to moderately accurate predictors of CH4, and are costly and difficult to measure routinely on-farm. VFA are a proxy for rumen CH4 emissions. Using rumen fermentation data obtained from in vitro gas production, Moss et al. (2000)<ref>Moss A.R., Jouany, J.P., and Newbold, J. 2000. Methane production by ruminants: Its contribution to global warming. Annal. Zootech. 49:231-253.</ref> reported a negative linear relationship between CH4 production and the ratio of (acetic + butyric acid)/propionic acid. However, by combining different information sources, either related to feed intake or to the impact of feed intake on the VFA composition, a better proxy with an improved accuracy can be achieved. This way, the prediction equation for CH4 production can be optimized (higher accuracy).

The relationship between rumen methanogen abundance and methanogenesis is less clear when changes in enteric CH4 emissions are modulated by diet or are a consequence of selecting phenotypes related to feed efficiency or MeY. Whereas in some reports there was a significant positive relationship (Aguinaga Casanas et al., 2015<ref name=":1c">Aguinaga Casanas, M.A., , N., Krattenmacher, N., Thalle,r G., Metges, C.C., and Kuhla, B. 2015. Methyl-coenzyme M reductase A as an indicator to estimate methane production from dairy cows. J. Dairy Sci. 98:4074-4083.</ref>; Arndt et al., 2015<ref>Arndt, C., Powell, J.M., Aguerre, M.J., Crump, P.M., and Wattiaux, M.A. 2015. Feed conversion efficiency in dairy cows: Repeatability, variation in digestion and metabolism of energy and nitrogen, and ruminal methanogens. J. Dairy Sci. 98:3938-3950.</ref>; Sun et al., 2015<ref>Sun, X., Henderson, G., Cox, F., Molan,o G., Harrison, S.J., Luo, D., Janssen, P.H., and Pacheco, D. 2015. Lambs Fed Fresh Winter Forage Rape (Brassica napus L.) Emit Less Methane than Those Fed Perennial Ryegrass (Lolium perenne L.), and Possible Mechanisms behind the Difference. PLoS One 10(3):e0119697: DOI: 10.1371/journal.pone.0119697</ref>; Wallace et al., 2015<ref name=":2b">Wallace, R., Rooke, J., McKain, N., Duthie, C.-A., Hyslop, J., Ross, D., Waterhouse, A., Watson, M., and Roehe, R. 2015. The rumen microbial metagenome associated with high methane production in cattle. BMC Genomics. 16:839.</ref>), in many others the concentration of methanogens was unrelated to methanogenesis (Morgavi et al., 2012<ref>Morgavi, D.P., Martin, C., Jouany, J.P., and Ranilla, M.J. 2012. Rumen protozoa and methanogenesis: not a simple cause-effect relationship. Br. J. Nutr. 107:388-397. 10.1017/S0007114511002935.</ref>; Kittelmann et al., 2014<ref>Kittelmann, S., Pinares-Patiño, C.S., Seedorf, H., Kirk, M.R., Ganesh, S., McEwan, J.C., and Janssen, P.H. 2014. Two different bacterial community types are linked with the low-methane emission trait in sheep. PLoS One 9:e103171.</ref>; Shi et al., 2014<ref name=":3a">Shi W., Moon, C.D., Leahy, S.C., Kang, D., Froula, J., Kittelmann, S., Fan, C., Deutsch, S., Gagic, D., Seedorf, H., Kelly, W.J., Atua, R., Sang, C., Soni, P., Li, D., Pinares-Patiño, C.S., McEwan, J.C., Janssen, P.H., Chen, F., Visel, A., Wang, Z., Attwood, G.T., and Rubin, E.M. 2014. Methane yield phenotypes linked to differential gene expression in the sheep rumen microbiome. Genome Res. Doi:10.1101/gr.168245.113.</ref>; Bouchard et al., 2015<ref name=":4b">Bouchard, K., Wittenberg, K.M., Legesse, G., Krause, D.O., Khafipour, E., Buckley, K.E., and Ominski, K.H. 2015. Comparison of feed intake, body weight gain, enteric methane emission and relative abundance of rumen microbes in steers fed sainfoin and lucerne silages under western Canadian conditions. Grass a Forage Sci. 70:116-129.</ref>). Bouchard et al. (2015)<ref name=":4b" /> even reported a reduction in methanogens withoutsignificant decrease in MeP for steers fed sainfoin silage. Sheep selected for high or low MeY showed no differences in methanogen abundance, though there was a strong correlation with expression of archaeal genes involved in methanogenesis (Shi et al., 2014<ref name=":3" />).

Hindgut and Feces: whole tract digestibility variables cannot serve as primary predictors for enteric MeP in cattle or sheep, but might be used as supporting factors to improve the accuracy of prediction of CH4 output.

== Protozoa and other rumen microbes ==
Protozoa are net producers of H2 and their absence from the rumen is associated with an average reduction in enteric MeP of approximately 11% (Hegarty, 1999<ref>Hegarty, R.S. 1999. Reducing rumen methane emissions through elimination of rumen protozoa. Aust. J. Agric. Res. 50:1321-1327.</ref>; Morgavi et al., 2010<ref>Morgavi, D.P., Forano, E., Martin, C., and Newbold, C.J. 2010. Microbial ecosystem and methanogenesis in ruminants. Animal 4:1024-1036.</ref>; Newbold et al., 2015<ref>Newbold, C.J., de la Fuente, G., Belanche, A., Ramos-Morales, E., and McEwan, N. 2015. The role of ciliate protozoa in the rumen. Front. Microbiol. 6:1313.doi: 10.3389/fmicb.2015.01313</ref>). Using a database of 28 experiments and 91 dietary treatments, Guyader et al. (2014)<ref>Guyader, J., Eugène, M., Nozière, P., Morgavi, D.P., Doreau, M., and Martin, C. 2014. Influence of rumen protozoa on methane emission in ruminants: a meta-analysis approach. Animal 8:1816-1825.</ref> showed a significant decrease of 8.14 g CH4/kg DMI for each log unit reduction in rumen protozoal abundance. About 21% of experiments within this dataset reported CH4 changes unrelated to protozoal abundance, highlighting the multifactorial nature of methanogenesis. Roehe et al. (2016)<ref>Roehe R., Dewhurst, R.J., Duthie, C-A., Rooke, J.A., McKain, N., Ross, D.W., Hyslop, J.J., Waterhouse, A., Freeman, T.C., Watson, M., and Wallace, R.J. 2016. Bovine Host Genetic Variation Influences Rumen Microbial Methane Production with Best Selection Criterion for Low Methane Emitting and Efficiently Feed Converting Hosts Based on Metagenomic Gene Abundance. PLoS Genet 12(2): e1005846. doi:10.1371/journal.pgen.1005846.</ref> observed that the ranking of sire groups for CH4 emissions measured with respiration chambers was the same as that for ranking on archaea/bacteria ratio, providing further evidence that host control of archaeal abundance contributes to genetic variation in CH4 emissions - at least in some circumstances. Across a wide geographical range, the methanogenic archaea were shown to be highly conserved across the world (Henderson et al., 2015<ref>Henderson, G., Cox, F., Ganesh, S., Jonker, A., Young, W., Global Rumen Census Collaborators, and Janssen, P.H. 2015. Rumen microbial community composition varies with diet and host, but a core microbiome is found across a wide geographical range. Sci. Rep. 5:14567. doi:10.1038/srep14567</ref>). This universality and limited diversity could make it possible to mitigate CH4 emissions by developing strategies that target the few dominant methanogens. However, one clear limitation of metagenomic predictions compared to genomic predictions was that the microbiome of the host is variable - that is, it may change in response to diet or other environmental factors over time, whereas the hosts DNA remains constant.

== Rumen microbial genes ==
These included genes involved in the first and last steps of methanogenesis: formylmethanofuran dehydrogenase subunit B (fmdB) and methyl-coenzyme M reductase alpha subunit (mcrA), which were 170 times more abundant in high CH4 emitting cattle. Whilst gene-centric metagenomics is not low-cost or high-throughput, these results point to potential future proxy approaches using low-cost gene chips.

The difference in gene expression activity as opposed to abundance was also reported by others (Popova et al., 2011<ref>Popova, M., Martin, C., Eugène, M., Mialon, M.M., Doreau, M., and Morgavi, D.P. 2011. Effect of fibre- and starch-rich finishing diets on methanogenic Archaea diversity and activity in the rumen of feedlot bulls. Anim. Feed Sci. Technol. 166-167:113-121.</ref>). However, there are also studies in which there was no relationship with gene expression (Aguinaga Casanas et al., 2015<ref name=":1">Garnsworthy, P.C. Difford, G.F. Bell, M.J. Bayat, A.R. Huhtanen, P. Kuhla, B. Lassen, J. Peiren, N. Pszczola, M; Sorg, D. Visker, M.H., and Yan, T. 2019 Comparison of Methods to Measure Methane for Use in Genetic Evaluation of Dairy Cattle. Animals 9:837, 12p.</ref>). There are some methodological and experimental differences that might explain some of the apparent contradictions, such as the type of gene target and primers used for nucleic acid amplification. Effects are seen most clearly when the difference in MeP between groups of animals is large (e.g. Wallace et al. (2015)<ref name=":2b" /> used treatments that generated a 1.9-fold difference CH4 emissions).

== Proxies based on measurements in milk ==
Milk yield alone does not provide a good prediction of MeP by dairy cows. Yan et al. (2010)<ref>Yan. T., Mayne, C.S., Gordon, F.G., Porter, M.G., Agnew, R.E., Patterson, D.C., Ferris, C.P., and Kilpatrick, D.J. 2010. Mitigation of enteric methane emissions through improving efficiency of energy utilization and productivity in lactating dairy cows. J. Dairy Sci. 93:2630–2638. doi: 10.3168/jds.2009-2929.</ref> indicated that CH4 as a proportion of GE intake or milk energy output was negatively related to milk production. It is less clear if MeY can be predicted from milk yield when making comparisons across studies.

Milk MIR spectroscopy is relatively inexpensive, rapid and already routinely used technology in milk recording systems to predict fat, protein, lactose and urea contents in dairy milk to assist farm management decisions and breeding. It can be used as a promising strategy to exploit the link between enteric CH4 emission from ruminants and microbial digestion in the rumen by assessing the signature of digestion in milk composition. Milk MIR data can be obtained through regular milk recording schemes, as well as, on a herd level, through analysis used for milk payment systems. Diverse milk phenotypes can be obtained by MIR spectrometry – including detailed milk composition (e.g. FA as reported by Soyeurt et al., 2011<ref>Soyeurt H., Dehareng, F., Gengler, N., McParland, S., Wall, E., Berry, D.P., Coffey, M., and Dardenne, P. 2011. Mid-infrared prediction of bovine milk fatty acids across multiple breeds, production systems and countries. J. Dairy Sci. 94:1657–1667.</ref>), technological properties of milk, and cow physiological status (De Marchi et al., 2014<ref>De Marchi, M., Toffanin, V., Cassandro, M., and Penasa, M. 2014. Invited review: Mid-infrared spectroscopy as phenotyping tool for milk traits. J. Dairy Sci. 97:1171–1186. <nowiki>http://dx.doi.org/10.3168/jds.2013-6799</nowiki>.</ref>; Gengler et al., 2016<ref>Gengler, N., Soyeurt, H., Dehareng, F., Bastin, C., Colinet, F., Hammami, H., Vanrobays, M.-L., Lainé, A., Vanderick, S., Grelet, C., Vanlierde, A., Froidmont, E., and Dardenne, P. 2016. Capitalizing on fine milk composition for breeding and management of dairy cows. J. Dairy Sci. 99:4071-4079.</ref>). Several of these novel traits (i.e. FA composition) have been identified as potential indicators of CH4 emission. Therefore, using MIR to predict MeP (Dehareng et al. 2012<ref name=":5b">Dehareng, F., Delfosse, C., Froidmont, E., Soyeurt, H., Martin, C., Gengler, N., Vanlierde, A., and Dardenne, P. 2012. Potential use of milk mid-infrared spectra to predict individual methane emission of dairy cows. Animal 6:1694-701.</ref>; Vanlierde et al. 2013<ref>Vanlierde, A., Dehareng, F., Froidmont, E., Dardenne, P., Kandel, P.B., Gengler, N., Deighton, M.H., buckley, F., Lewis, E., McParland, S., Berry, D.P., and Soyeurt, H. 2013. Prediction of the individual enteric methane emission of dairy cows from milk-mid-infrared spectra. Advances in Animal Biosciences. 5th Greenhouse Gases Animal Agriculture Conference (GGAA2013) 23-26 June 2014. Dublin, Ireland. p 433.</ref>, 2015<ref name=":4a" />; Van Gastelen and Dijkstra, 2016<ref name=":7a">Van Gastelen, S., and Dijkstra, J.. 2016. Prediction of methane emission from lactating dairy cows using milk fatty acids and mid-infrared spectroscopy. J. Sci. Food Agric. 96:3963-3968. DOI: 10.1002/jsfa.7718.</ref>) is also a logical extension of its use to quantify the major milk components (i.e. fat, protein, casein, lactose, and urea) and minor components (e.g. FA). Dehareng et al. (2012)<ref name=":32" /> assessed the feasibility to predict individual MeP from dairy cows using milk MIR spectra. Their initial results suggest that this approach could be useful to predict MeP at the farm or regional scale, as well as to identify low-CH4 emitting cows. According to Van Gastelen and Dijkstra (2016)<ref name=":36" />, MIR spectroscopy has the disadvantage that it has a moderate predictive power for CH4 emission, both direct and indirect (i.e. via milk FA), and that it lacks the ability to predict important milk FA for CH4 prediction. They concluded that it may not be sufficient to predict MeP based on MIR alone. It is, however, possible to improve the accuracy of prediction through the combination of MIR with some animal characteristics such as lactation stage (Vanlierde et al., 2015<ref name=":4a" />). The advantage of this latter development is that this type of prediction can be done on a very large scale inside a routine milk recording system (Vanlierde et al., 2015<ref name=":4a" />).

== Proxy: future developments and perspectives ==
There is currently limited consensus on which phenotype to use to lower the carbon footprint of milk production through genetic selection. This could be MeP, MeI or MeY. The direct goal would be CH4 production; the relationship with milk production and/or feed intake could be accounted for by including these in the final selection index or scheme. However, one might argue that it would be more effective/accurate to directly use milk production- or feed intakecorrected CH4 (e.g. CH4 intensity or yield) as breeding goal.

The analysis of proxies in terms of their attributes shows that proxies that are based on samples from the rumen or related to rumen sources are poor to moderately accurate predictors of CH4. In addition, these proxies are too costly and difficult for routine on-farm implementation. On the other hand, proxies related to BW, milk yield and composition (e.g. milk FA) are moderately to highly accurate predictors of CH4 and relatively simple, low-cost and easier to implement in practice (Cassandro et al.,2010<ref>Cassandro, M., Cecchinato, A., Battagin, M., Penasa, M., 2010. Genetic parameters of predicted methane production in Holstein Friesian cowsIn: Proc. 9th World Congr. on Genetics Applied to Livestock Production, Leipzig, Germany. . Page 181</ref>; Cassandro, 2013<ref>Cassandro, M. 2013. Comparing local and cosmopolitan cattle breeds on added values for milk and cheese production and their predicted methane emissions. Animal Genetic Resources/Ressources génétiques animales/Recursos genéticos animales, available on CJO2013. doi:10.1017/S2078 63361200077X. </ref>). Particularly, milk MIR and the prediction of CH4 based on milk MIR along with other covariates such as lactation stage is a promising alternative: that is accurate, cheaper and easy to be implemented in routine milk analysis at no extra cost.

Therefore, in the future advances in infrared, photoacoustic and related technologies will push the boundaries, particularly in focusing on developments of fast and portable technologies. Such developments will lead to better and promising proxies for CH4 that will enable a sizable throughput of CH4 phenotypes in dairy cows. Antunes-Fernandes et al. (2016)<ref>Antunes-Fernandes, E.C., van Gastelen, S., Dijkstra, J., Hettinga K.A., and Vervoort, J. 2016. Milk metabolome relates enteric methane emission to milk synthesis, and energy metabolism pathways. J. Dairy. Sci. 99:6251-6262.</ref> already presented the use of metabolomics on milk to better understand the biological pathways involved in CH4 production in dairy cattle. The techniques used in that study are not suitable for large scale measurements, but rapid developments in omics may offer tests and assay methodologies on blood, urine or milk samples that will provide an additional tool for developing new / additional proxies for CH4 emissions in dairy cattle.</div>
</div>

= Merging and sharing data in genetic evaluations =
Early 2016 an attempt to make cross country evaluations of CH4 emissions from Holstein dairy cattle was initiated. The work was based on data from NL, DK, AUS, UK and IR. In total, 12,820 weekly CH4 emission records from 2,857 cows were available. Although different equipment was used across countries to measure CH4 emissions, the research aimed to define similar CH4 output phenotypes in each country. The analysed CH4 traitres, that are available in each country, are (1) CH4 production in g/d, and (2) CH4 intensity in g/d per kg fat protein corrected milk (FPCM). In addition to these CH4 traits, CH4 concentration (in ppm) was available in Denmark, the Netherlands and UK, and the ratio between CH4 and CO2 concentration was available in Denmark and the Netherlands.

Bivariate analyses were carried out to estimate genetic correlations between countries, using an animal linear mixed model for all traits. Both univariate and bivariate analyses were repeated with the GRM as well. With all weekly records, standardizing the trait in the full dataset increased the heritability for CH4 production from 0.03 to 0.06. The heritability for CH4 intensity was slightly higher. The highest heritability with the full dataset is estimated for the standardized CH4 concentration (0.19). Correlations estimated among CH4 traits estimated with either the pedigree or the GRM were in same direction and of similar magnitude. The genetic correlations show that when CH4 production increased, the CH4 concentration and the ratio between CH4 and CO2 increased as well.

The approach is novel, and no other attempt has been performed to make genetic analysis of CH4 traits across countries. The analysis can be repeated in future studies where more data hopefully will be available, and more effort can be made into improving both the fixed and random part of the model.

= Recommendations =
The most important question: what method to use if you need to measure CH4? The answer may be: it depends on what you like to do. In the Table 6 we summarize some experimental conditions and designs, and make recommendations.
<center>
{| class="wikitable"
|-
! colspan="3" style="text-align:left;" | ''Table 6. Recommendations for measuring methane in diverse experimental conditions and designs.''
|- style="background-color:#efefef;"
|+
! style="text-align:center;" | Experimental condition and design
! style="text-align:center;" | Methane measurement method recommendation
|-
| style="text-align:center;" | Need to measure absolute methane values – animal numbers and location not important
| style="text-align:center;" | Respiration chamber;
SF6; GreenFeed
|-
| style="text-align:center;" | Need to rank animals from low to high methane emission
| style="text-align:center;" | Sniffer method
|-
| style="text-align:center;" | Need to measure methane on farm
| style="text-align:center;" | Sniffer method;
GreenFeed; PAC
|-
| style="text-align:center;" | Low budget measurements needed
| style="text-align:center;" | Proxy;
Proxies measurement
|-
| style="text-align:center;" | High animal numbers required
| style="text-align:center;" | Sniffer method;
Proxies measurement; LMD
|}
</center>

= Conclusions =
Measuring CH4 emission on large numbers of cows is a challenge. The high costs and low throughput of RC restrict their use to research studies measuring CH4 emissions on small numbers of individual animals. Respiration chambers remain the gold standard method, but benchmarking alternative methods against RC is challenging because simultaneous replicate measures per cow are not feasible. Methods like SF6 and GreenFeed require lower capital investment and running costs than RC, and have higher throughput and potential for use in extensive and grazing situations, but costs are still prohibitive for recording large numbers of animals. Methods based on concentration are less precise and accurate than flux methods, but they are viable for large scale measurement, which is a prerequisite of genetic evaluations. Further development is needed to increase accuracy and precision of concentration methods. Several reviews of methods for measuring CH4 have made qualitative judgements based on individual comparison studies without expanding scope to genetic evaluations and considering repeated measure correlations between methods as proxies for genetic correlations. Results confirm that there is sufficient correlation between methods for all to be combined for international genetic studies and provide a much needed framework for comparing genetic correlations between methods should these be made available. Proxies have the potential to be used as predictors of CH4 production and emission. Although proxies are less accurate than direct CH4 measurements they can be easier, cheaper, and at high throughput, and may be therefore the best method in practical situations, especially proxies related to milk measurements. Therefore, these proxies at the population level, can provide useful information at genetic improvement that can be used to reduce emissions following 3 ways: (1) intensification of animal production; (2) improving of system efficiency and (3) the direct reduction of GHG emissions by breeding for reduced predicting animals that are high or low GHG emitters.

= Acknowledgements =
This document is the result of the ICAR Feed and Gas Working Group and its industry and research liaison group. The members of the ICAR Feed and Gas Working Group at the time of publication are, in alphabetical order:

* Christine Baes, Department of Animal Biosciences, University of Guelph, Canada and Institute of Genetics, Vetsuisse Faculty, University of Bern, Switzerland
* Yvette de Haas, Animal Breeding and Genomics, Wageningen Livestock Research
* Raffaella Finocchiaro, ANAFI, Italy
* Phil Garnsworthy, School of Biosciences, University of Nottingham, United Kingdom
* Birgit Gredler-Grandl, Animal Breeding and Genomics, Wageningen Livestock Research
* Nina Krattenmacher, Institute of Animal Breeding and Husbandry, Christian-Albrechts-University, Germany
* Jan Lassen, Viking Genetics, Denmark
* Jennie Pryce, Centre for AgriBioscience, AgriBio, Agriculture Victoria Research and School of Applied Systems Biology, La Trobe University, Australia
* Roel Veerkamp, Animal Breeding and Genomics, Wageningen Livestock Research (chairperson)

The work of Marinus te Pas (Animal Breeding and Genomics, Wageningen Livestock Research) in compiling draft versions is highly acknowledged. The content of the guidelines are partly based on results of the network of internationally recognized experts within the METHAGENE COST Action FA1320.

<references />

Section 20 – Methane Emission for Genetic Evaluation

2024-11-10T04:15:22Z

Aashish: /* Proxies based on measurements in milk */

= vaIntroduction =
Increases in milk production through management and genetics have substantially improved feed efficiency and decreased costs per unit of product over recent decades. However, dairy systems are also associated with environmental costs (Baskaran et al., 2009<ref>Baskaran, R., Cullen, R., and Colombo, S. 2009. Estimating values of environmental impacts of dairy farming in New Zealand, New Zealand J. Agric. Res. 52: 377-389, DOI: 10.1080/00288230909510520.</ref>), with methane (CH4) emissions associated with rumen microbial fermentation being both an important contributor to global greenhouse gas (GHG) emissions, as well as an avoidable loss of energy that could otherwise be directed into milk production. The livestock sector is responsible for 14.5% of the global GHG (Gerber et al., 2013<ref>Gerber, P.J., Steinfeld, H., Henderson, B., Mottet, A., Opio, C., Dijkman, J., Falcucci, A. , and Tempio, G. 2013. Tackling climate change through livestock – A global assessment of emissions and mitigation opportunities. Food and Agriculture Organization of the United Nations (FAO), Rome.</ref>); dairy cattle account for 18.9% of these emissions, mainly in the form of enteric CH4 emissions (van Middelaar et al., 2014<ref>Van Middelaar, C.E., Dijkstra. J., Berentsen. P.B.M.. and De Boer. I.J.M. 2014. Cost-effectiveness of feeding strategies to reduce greenhouse gas emissions from dairy farming. J. Dairy Sci. 97:2427–2439.</ref>). Methane is a greenhouse gas with a global warming potential 28 times that of CO2 (Myhre et al., 2013<ref>Myhre, G., Shindell, D., Bréon, F., Collins, W., Fuglestvedt, J., Huang, J., Koch, D., Lamarque, J., Lee, D., Mendoza, B., and Nakajima ,T. 2013. Anthropogenic and Natural Radiative Forcing. T.F. Stocker, D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley, ed. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.</ref>). Methane from ruminant livestock is generated during microbial fermentation in the rumen and hindgut (enteric CH4), and from decomposition of manure. Enteric CH4 contributes 80% of CH4 emissions by ruminants, and manure decomposition contributes 20%. Enteric CH4 accounts for 17% of global CH4 emissions and 3.3% of total global greenhouse gas emissions from human activities (Knapp et al., 2014<ref>Knapp, J.R., Laur, G.L., Vadas, P.A., Weis,s W.P., and Tricarico, J.M. 2014. Invited review: Enteric methane in dairy cattle production: Quantifying the opportunities and impact of reducing emissions. J. Dairy Sci. 97:3231-3261.</ref>). There is, therefore, a significant research interest to find ways to reduce enteric CH4 emissions by ruminants. Ruminant animals have a digestive system to digest plant materials efficiently. Like most mammals, ruminants lack the cellulase enzyme required to break the beta-glucose linkages in cellulose, but they play host to diverse populations of rumen microbes that can digest cellulose and other plant constituents. When rumen bacteria, protozoa and fungi ferment carbohydrates and proteins of plant materials, they produce volatile fatty acids, principally acetate, propionate and butyrate. High fibre diets favour acetate synthesis. Synthesis of acetate and butyrate are accompanied by release of metabolic hydrogen, which, if allowed to accumulate in rumen fluid, has negative effects on microbial growth, and feed digestibility (Janssen, 2010<ref>Janssen, P.H. 2010. Influence of hydrogen on rumen methane formation and fermentation balances through microbial growth kinetics and fermentation thermodynamics. Anim. Feed Sci. Technol. 160:1–22. doi:10.1016/j.anifeedsci.2010.07.002.</ref>). Rumen Archaea are microorganisms that combine metabolic hydrogen with CO2 to produce CH4 and water. Archaea play a vital role, therefore, in protecting the rumen from excess metabolic hydrogen, and the CH4 they produce is an inevitable product of rumen fermentation. A number of CH4 phenotypes have been defined (Hellwing et al., 2012<ref name=":12">Hellwing, A.L.F., Lund, P., Weisbjerg, M.R., Brask, M., and Hvelplund. T. 2012. Technical note: test of a low-cost and animal-friendly system for measuring methane emissions from dairy cows. J. Dairy Sci. 95:6077–85. doi:10.3168/jds.2012-5505.</ref>); the most widely used is CH4 production (MeP) in liters or grams per day. The CH4 production trait is highly correlated with feed intake (Basarab et al., 2013<ref>Basarab, J., Beauchemin, K., Baron, V., Ominski, K., Guan, L., Miller, S., and Crowley, J. 2013. Reducing GHG emissions through genetic improvement for feed efficiency: Effects on economically important traits and enteric methane production. Animal, 7(S2):303-315. doi:10.1017/S1751731113000888</ref>; De Haas et al., 2017<ref>de Haas, Y., Pszczola, M., Soyeurt, H., Wall, E., and Lassen, J. 2017. Invited review: Phenotypes to genetically reduce greenhouse gas emissions in dairying. J. Dairy Sci. 100:855-870.</ref>) and, thereby, with the ultimate breeding goal trait: milk production in dairy cattle. The economic value of daily dry matter intake and associated methane emissions in dairy cattle showed that increasing the feed performance estimated breeding value by one unit (i.e. 1 kg of more efficiently converted DMI during the cow’s first lactation) translates to a total lifetime saving of 3.23 kg in DMI and 0.055 kg in methane (Richardson et al., 2019<ref>Richardson, C., Baes, C., Amer, P., Quinton, C., Martin, P., Osborne, V., Pryce, J.E., and Miglior, F. 2020. Determining the economic value of daily dry matter intake and associated methane emissions in dairy cattle. Animal 14:171-179. doi:10.1017/S175173111900154X</ref>). Feed Performance was defined as a 1 kg increase in more efficiently used feed in a first parity lactating cow. These results show not only the relation between DMI and CH4 production, but also the economic relationship between these traits. Persistency of lactation was found to be positively associated with increased feed efficiency and decreased methane production and intensity. Feed efficiency was associated with lower methane intensity. Feed efficiency and methane emissions can be improved by selecting for dairy cattle that are smaller and have increased persistency of lactation. Efficiency and methane emissions can be further improved by improved management of body condition score and by extending lactations beyond the conventional 305-day length (Seymour, 2019<ref>Seymour, D.J. 2019. Feed Efficiency Dynamics in Relation to Lactation and Methane Emissions in Dairy Cattle. PhD thesis, The University of Guelph, Canada.</ref>). According to Ellis et al. (2007)<ref>Ellis, J.L., Kebreab, E., Odongo, N.E., McBride, B.W., Okine, E.K., and France, J. 2007. Prediction of methane production from dairy and beef cattle. J. Dairy Sci. 90:3456–3466.</ref>, DMI predicted MeP with an R2 of 0.64, and ME intake (MJ/d) predicted MeP with an R2 of 0.53 for dairy cattle. AlternativePhenotype definitions include CH4 intensity (MeI), which is defined as liters or grams of CH4 per kg of milk, and CH4 yield (MeY), which is defined as liters or grams of CH4 per kg of dry matter intake (DMI) (Moate et al., 2016<ref>Moate, P.J., Deighton, M.H., Williams, S.R.O., Pryce, J.E., Hayes, B.J., Jacobs, J.L., Eckard, R.J., Hannah, M.C. and Wales, W.J., 2016. Reducing the carbon footprint of Australian milk production by mitigation of enteric methane emissions. Anim. Prod. Sci. 56:1017-1034.</ref>). Residual CH4 production (RMP) is calculated as observed minus predicted CH4 production (Herd et al., 2014<ref>Herd, R.M., Arthur, P.F., Bird, S.H., Donoghue, K.A., and Hegarty, R.S. 2014. Genetic variation for methane traits in beef cattle. In: Proc. 10th World Conference on Genetic Applied to Livestock Production (WCGALP), 17-22 August, 2014. Vancouver, Canada.</ref>, Berry et al., 2015<ref>Berry, D.P., Lassen, J., and de Haas, Y. 2015. Residual feed intake and breeding approaches for enteric methane mitigation In: Livestock production and climate change. P.K. Malik, R. Bhatta, J. Takahashi, R.A. Kohn, and C.S. Prasad, ed. CABI, Oxfordshire, UK. . Pages 273-291</ref>), with predicted values based on factors such as milk production, body weight and feed intake. At the moment, it is not obvious which of these phenotypes to use; but, it is important to monitor associations between the chosen CH4 phenotype and the other important traits in the breeding goal (e.g. production, fertility, longevity) to avoid unfavorable consequences. Berry and Crowley (2012)<ref>Berry, D.P., and Crowley, J.J. 2012. Residual intake and body weight gain: A new measure of efficiency in growing cattle, J. Anim. Sci. 90:109–115, <nowiki>https://doi.org/10.2527/jas.2011-4245</nowiki></ref> describe advantages and limitations of ration traits. For example, because feed efficiency traits are a linear combination of other traits it is not recommended to include them in an overall total merit index, which is a clear limitation. For all applications it is necessary to measure the CH4 emission of each animal individually. These guidelines are intended to make the right choices for this. Whilst diet changes and feed additives can be effective mitigation strategies for CH4 emissions (Beauchemin et al., 2009<ref>Beauchemin, K.A., McAllister, T.A., and McGinn, S.M. 2009. Dietary mitigation of enteric methane from cattle. CAB Rev.: Perspect. Agric., Vet. Sci., Nutr. Nat. Res. 4:1–18.</ref>; Martin et al., 2010<ref>Martin C., Morgavi, D.P., and Doreau, M. 2010. Methane mitigation in ruminants: from microbe to the farm scale. Animal 4:351–365.</ref>; Hristov et al., 2013<ref>Hristov, A.N., Oh, J., Firkins, J.L., Dijkstra, J., Kebreab, E., Waghorn, G., Makkar, H.P.S., Adesogan, A.T., Yan,g W., Lee, C., Gerber, P.J., Henderson, B., and Tricarico, J.M. 2013. Special topics - Mitigation of methane and nitrous oxide emissions from animal operations: I. A review of enteric methane mitigation options. J. Anim. Sci. 91:5045–5069.</ref>), their effects depend on the continued use of a particular diet or additive and there have been issues with the rumen microbiomes adapting to additives. Rumen bacterial communities are highly dynamic after a diet switch and did not stabilize within 5 wk of cows grazing pasture (Bainbridge et al., 2016<ref>Bainbridge, M.L., Saldinger, L.K., Barlow, J.W., Alvez, J.P., Roman, J. Kraft, J. 2016. 1609 Rumen bacterial communities continue to shift five weeks after switching diets from conserved forage to pasture. J. Anim. Sci. 94, suppl_5:783, <nowiki>https://doi.org/10.2527/jam2016-1609</nowiki> (abstr.)</ref>). In contrast, breeding for reduced CH4 emissions should result in a permanent and cumulative reduction of emissions (Wall et al., 2010<ref>Wall, E., Simm, G., and Moran, D. 2010. Developing breeding schemes to assist mitigation of greenhouse gas emissions. Animal 4:366-376.</ref>). Several studies have shown that CH4 emissions by ruminants have a genetic component, with heritability in the range 0.20 – 0.30 (de Haas et al., 2011<ref>de Haas, Y., Windig, J.J., Calus, M.P.L., Dijkstra, J., de Haan, M., Bannink, A., and Veerkamp, R F. 2011. Genetic parameters for predicted methane production and potential for reducing enteric emissions through genomic selection. J. Dairy Sci. 94:6122–6134.</ref>; Donoghue et al., 2013<ref>Donoghue K.A., Herd, R.M., Bird, S.H., Arthur, P.F., and Hegarty, R F. 2013. Preliminary genetic parameters for methane production in Australian beef cattle. In: Proceedings of the Association for the Advancement of Animal Breeding and Genetics, 20-23 October 2013, Napier, New Zealand, pp. 290–293.</ref>; Pinares-Patiño et al., 2013<ref name=":13">Pinares-Patiño C.S., Hickey, S.M., Young, E.A., Dodds, K.G., MacLean, S., Molano, G., Sandoval, E., Kjestrup, H., Harland, R., Pickering, N.K., and McEwan, J.C. 2013. Heritability estimates of methane emissions from sheep. Animal 7: 316–321.</ref>, Kandel et al., 2014A<ref name=":37">Kandel, P.B., Vanderick, S., Vanrobays, M.L., Vanlierde, A., Dehareng, F., Froidmont, E., Soyeurt, H., and Gengler, N. 2014A. Consequences of selection for environmental impact traits in dairy cows. Page 19. (<nowiki>http://orbi.ulg.ac.be/bitstream/2268/164402/164401/NSABS162014_poster_Purna_abstract.pdf</nowiki>) I:n Proc. 19th National symposium on applied biological sciences, Gembloux, Belgium.</ref>, B<ref>Kandel, P.B., Vanderick, S., Vanrobays, M.L., Vanlierde, A., Dehareng, F., Froidmont, E., Soyeur,t H., and Gengler, N. 2014B. Consequences of selection for environmental impact traits in dairy cows. In: 10th World Congress on Genetics Applied to Livestock Production (WCGALP), 17-22 August, 2014. Vancouver, Canada.</ref>; Lassen and Lovendahl, 2016<ref>Lassen, J., and Løvendahl, P. 2016. Heritability estimates for enteric methane emissions from Holstein cattle measured using noninvasive methods. J. Dairy Sci. 99:1959-1967.</ref>; López-Paredes et al. 2020<ref>Lopez-Paredes, J., Goiri, I., Atxaerandio, R., García-Rodríguez, A., Ugarte, E., Jiménez-Montero, J.A., Alenda, R and González-Recio, O. 2020. Mitigation of greenhouse gases in dairy cattle via genetic selection (i): Genetic parameters of direct methane using non-invasive methods and its proxies. J. Dairy Sci. 103.</ref>). Breeding for reduced CH4 emissions, alone or together with other mitigation strategies, could therefore be effective in reducing the environmental impact of cattle farming and, possibly, also in increasing feed efficiency. Such a breeding scheme would require, as a fundamental starting point, accurate measures of individual CH4 emissions on a large scale. Several techniques have been developed for the measurement of CH4 emissions from ruminants, with varying degrees of accuracy (see reviews by Cassandro et al., 2013<ref>Cassandro, M., Mele, M., Stefanon, B.. 2013. Genetic aspects of enteric methane emission in livestock ruminants. Italian J. Anim. Sci. 12:e73: 450-458.</ref> and Hammond et al., 2016A<ref>Hammond, K.J., Crompton, L.A., Bannink, A., Dijkstra, J., Yáñez-Ruiz, D.R., O’Kiely, P., Kebreab, E., Eugenè, M.A., Yu, Z., Shingfield, K.J., Schwarm, A., Hristov, A.N., and Reynolds, C.K. 2016A. Review of current in vivo measurement techniques for quantifying enteric methane emission from ruminants. Anim. Feed Sci. Technol. 219:13–30. doi:10.1016/j.anifeedsci.2016.05.018.</ref>), but routine individual measurements on a large scale (a requisite for genetic selection) have proven to be difficult to obtain and expensive to measure (Pickering et al., 2015<ref>Pickering, N.K., Oddy, V.H., Basarab, J.A., Cammack, K., Hayes, B J., Hegarty, R.S., McEwan, J.C., Miller, S., Pinares, C., and de Haas, Y. 2015. Invited review: Genetic possibilities to reduce enteric methane emissions from ruminants. Animal 9:1431-1440.</ref>; Negussie et al., 2016<ref>Negussie E., Lehtinen, J., Mäntysaari, P., Liinamo, A-E., Mäntysaari, E., and Lidauer, M.. 2016. Non-invasive individual methane measurements in dairy cows using photoacoustic infrared spectroscopy technique. 6th Greenhouse Gases Animal Agriculture Conference (GGAA2016) 14-18 February 2016. Melbourne, Australia. Abstract. p62.</ref>). Therefore, identifying proxies (i.e. indicators or indirect traits) that are correlated to CH4 emissions, but which are easy and relatively lowcost to record on a large scale, would be a welcome alternative. Proxies might be less accurate but could be measured repeatedly to reduce random noise and in much larger populations. These guidelines are highly indebted to Garnsworthy et al. (2019)<ref name=":11">Garnsworthy, P.C. Difford, G.F. Bell, M.J. Bayat, A.R. Huhtanen, P. Kuhla, B. Lassen, J. Peiren, N. Pszczola, M; Sorg, D. Visker, M.H., and Yan, T. 2019 Comparison of Methods to Measure Methane for Use in Genetic Evaluation of Dairy Cattle. Animals 9:837, 12p.</ref>. In this paper the methods to measure CH4 are compared with special emphasis to the genetic evaluation of dairy cattle.

= Disclaimer =
The fact that specific device manufacturers are mentioned in these guidelines is in no way an endorsement of the devices or their accuracy by ICAR.

= Scope =
A variety of technologies are being developed and employed to measure CH4 emissions of individual dairy cattle under various environmental conditions, as is evidenced by frequent reviews (Storm et al., 2012<ref>Storm, I.M., Hellwing, A.L.F., Nielsen, N.I., and Madsen, J. 2012. Methods for measuring and estimating methane emission from ruminants. Animals 2:160-183.</ref>; Cassandro et al., 2013<ref name=":28">Cassandro, M., Mele, M., Stefanon, B.. 2013. Genetic aspects of enteric methane emission in livestock ruminants. Italian J. Anim. Sci. 12:e73: 450-458.</ref>; Hammond et al., 2016A<ref>Hammond, K.J., Crompton, L.A., Bannink, A., Dijkstra, J., Yáñez-Ruiz, D.R., O’Kiely, P., Kebreab, E., Eugenè, M.A., Yu, Z., Shingfield, K.J., Schwarm, A., Hristov, A.N., and Reynolds, C.K. 2016A. Review of current in vivo measurement techniques for quantifying enteric methane emission from ruminants. Anim. Feed Sci. Technol. 219:13–30. doi:10.1016/j.anifeedsci.2016.05.018.</ref>; de Haas et al., 2017<ref name=":10">de Haas, Y., Pszczola, M., Soyeurt, H., Wall, E., and Lassen, J. 2017. Invited review: Phenotypes to genetically reduce greenhouse gas emissions in dairying. J. Dairy Sci. 100:855-870.</ref>). The first objective of the current guidelines is to review and compare the suitability of methods for large-scale measurements of CH4 output of individual animals, which may be combined with other databases for genetic evaluations. Comparisons include assessing the accuracy, precision and correlation between methods. Combining datasets from different countries and research centres could be a successful strategy for making genetic progress in this difficult to measure trait if the methods are correlated (de Haas et al., 2017<ref name=":10" />). Accuracy and precision of methods are important. Data from different sources need to be appropriately weighted or adjusted when combined, so any methods can be combined if they are suitably correlated with the ‘true’ value. The second objective of the current guidelines, therefore, is to examine correlations among results obtained by different methods, ultimately leading to an estimate of confidence limits for selecting individual animals that are high or low emitters (see also Garnsworthy et al., 2019<ref>Garnsworthy, P.C. Difford, G.F. Bell, M.J. Bayat, A.R. Huhtanen, P. Kuhla, B. Lassen, J. Peiren, N. Pszczola, M; Sorg, D. Visker, M.H., and Yan, T. 2019 Comparison of Methods to Measure Methane for Use in Genetic Evaluation of Dairy Cattle. Animals 9:837, 12p.</ref>).

= Methane determining factors =
<div class="mw-collapsible mw-collapsed">
<div>
== Diet and rumen microbiota ==
Table 1 contains a list of dietary or microbiota factors that determine CH4 production.
<center>
{| class="wikitable"
|-
! colspan="3" | ''Table 1. Methane determining factors related to diet and rumen microbiota.''
|- style="background-color:#efefef;"
|+
!Factors
!Reference
|-
|The main determinants of daily methane production are dry matter intake and diet composition: the more feed consumed, and/or the greater the fibre content of the diet, the more methane is produced per day. However, per unit of DMI, and per unit of fat+protein yield the grass diet produced less enteric CH4 per cow than the TMR diet. Nutritional approaches for methane mitigation include reducing the forage to concentrate ratio of diets, increasing dietary oil content, and dietary inclusion of rumen modifiers and methane inhibitors.
|Beauchemin et al., 2009<ref>Beauchemin, K.A., McAllister, T.A., and McGinn, S.M. 2009. Dietary mitigation of enteric methane from cattle. CAB Rev.: Perspect. Agric., Vet. Sci., Nutr. Nat. Res. 4:1–18.</ref>;
Cottle et al., 2011<ref>Cottle, D.J., Nolan, J.V., and Wiedemann, S.G. 2011. Ruminant enteric methane mitigation: A review. Anim. Prod. Sci. 51:491–514. doi:10.1071/AN10163.</ref>; Knapp et al., 2014<ref>Knapp, J.R., Laur, G.L., Vadas, P.A., Weis,s W.P., and Tricarico, J.M. 2014. Invited review: Enteric methane in dairy cattle production: Quantifying the opportunities and impact of reducing emissions. J. Dairy Sci. 97:3231-3261.</ref>; O’Neill et al., 2011<ref>O’Neill, B.F., Deighton, M.H., O’Loughlin, B.M., Mulligan, F.J., Boland, T.M., O’Donovan, M., and Lewis, E. 2011. Effects of a perennial ryegrass diet or total mixed ration diet offered to spring-calving Holstein-Friesian dairy cows on methane emissions, dry matter intake, and milk production. J. Dairy Sci. 94:1941 – 1951</ref>; Sauvant et al., 2011<ref>Sauvant, D., Giger-Reverdin, S., Serment, A., and Broudiscou, L. 2011. Influences des régimeset de leur fermentation dans le rumen sur la production de méthane par les ruminants. INRA Prod. Anim. 24:433–446.</ref>
|-
|Methane output per kg of product is affected mainly by cow milk yield or growth rate, and by herd-level factors, such as fertility, disease incidence and replacement rate.
|Garnsworthy, 2004<ref>Garnsworthy, P.C. 2004. The environmental impact of fertility in dairy cows: a modelling approach to predict methane and ammonia emissions. Anim. Feed Sci. Technol. 112:211-223.</ref>
|-
|Methane output varies considerably between individual animals. For animals fed the same feed, the between-animal coefficient of variation (CV) in methane was 8.1%.
|Blaxter and Clapperton, 1965<ref>Blaxter, K.L., and Clapperton, J.L. 1965. Prediction of the amount of methane produced by ruminants. Br. J. Nutr.19:511–522.</ref>
|-
|The amount of digestible nutrients consumed especially of the carbohydrate fraction (starch, sugar, N-free residuals) is reliable to estimate CH4 release with high precision. Furthermore, diets rich in fat reduced CH4 formation in the rumen.
|Jentsch et al., 2007<ref>Jentsch, W., Schweigel, M., Weissbach, F., Scholze, H., Pitroff, W., and Derno, M. 2007. Methane production in cattle calculated by the nutrient composition of the diet. Arch. Anim. Nutr. 61:10-19.</ref>
|-
|DMI was also the most important determining factor, but there were different regression lines for maize silage and dried grass as the main roughage component respectively:
<math>CH4\ (g)=93+16.8\times DMI(kg)</math>
<math>CH4\ (g)=81+14.0\times DMI(kg)</math>

Methane release was particularly dependent on the intake of crude fiber (CF) and ether extract (EE):

<math>CH4\ (g)=63+80xCF\ (kg)+11xNFE\ (kg)+19xCP(kg)-195xEE\ (kg)</math>
|Kirchgessner et al., 1991<ref>Kirchgessner, M., Windisch, W., Müller, H. L., and Kreuzer, M. 1991. Release of methane and of carbon dioxide by dairy cattle. Agribiol. Res. 44:91-102.</ref>
|-
|Methane linearly increased with NDF intake <math>CH4\ (L)=59.4\times NDF[kg]+ 64.6</math> for cows together with their calves independent of the breed.
|Estermann et al., 2002<ref>Estermann, B.L., Sutter, F., Schlegel, P.O., Erdin, D., Wettstein, H.R., and Kreuzer, M. 2002. Effect of calf age and dam breed on intake, energy expenditure, and excretion of nitrogen, phosphorus, and methane of beef cows with calves. J. Anim. Sci. 80:1124-1134.</ref>
|-
|Enteric CH4 could be predicted with the equation:
<math>CH4\ (g/d)=84+47\times cellulose(kg/d)+32\times starch(kg/d)+62\times sugars\ (kg/d)</math>
|Hindrichsen et al., 2005<ref>Hindrichsen, I.K., Wettstein, H.R., Machmüller, A., Jörg, B., and Kreuzer, M. 2005. Effect of the carbohydrate composition of feed concentratates on methane emission from dairy cows and their slurry. Environ. Monit. Assess., 107:329-350.</ref>
|-
|The higher the percentage concentrate the lower Ym.
|Zeitz et al., 2012<ref>Zeitz, J.O., Soliva, C.R., and Kreuzer, M. 2012. Swiss diet types for cattle: how accurately are they reflected by the Intergovernmental Panel on Climate Change default values? J. Int. Environ. Sci. 9(sup1):199-216.</ref>
|-
|Additives can sometimes have a methane reducing effect: higher dosages mitigate methane more. Saponins mitigate methanogenesis by reducing the number of protozoa, whereas condensed tannins act both by reducing the number of protozoa and by a direct toxic effect on methanogens.
|Beauchemin et al., 2008;<ref>Beauchemin, K.A., Kreuze,r M., O’Mara, F., and McAllister, T.A. 2008. Nutritional management for enteric methane abatement: A review. Aust. J. Exp. Agric. 48:21–27.</ref>
Jayanegara et al.<ref>Jayanegara, A., Leiber, F., and Kreuzer, M. 2012. Meta‐analysis of the relationship between dietary tannin level and methane formation in ruminants from in vivo and in vitro experiments. J. Anim. Physiol. Anim. Nutr. 96:365-375.</ref>, 2012; Zmora et al., 2012<ref>Zmora, P., Cieslak, A., Pers-Kamczyc, E., Nowak, A., Szczechowiak, J. and Szumacher-Strabel, M. 2012. Effect of Mentha piperita L. on in vitro rumen methanogenesis and fermentation, Acta Agr. Scan. Section A — Anim. Sci. 62:46-52, DOI: 10.1080/09064702.2012.703228.</ref>; Cieslak et al., 2013<ref>Cieslak, A., Szumacher-Strabel, M., Stochmal, A., nad Oleszek, W. 2013. Plant components with specific activities against rumen methanogens. Animal, 7(s2):253-265.</ref>; Guyader et al., 2014<ref>Guyader, J., Eugène, M., Nozière, P., Morgavi, D.P., Doreau, M., and Martin, C. 2014. Influence of rumen protozoa on methane emission in ruminants: a meta-analysis approach. Animal 8:1816-1825.</ref>
|-
|Plant essential oils have been shown as promising feed additives to mitigate CH4 and ammonia emission, but results were inconsistent.
|Cobellis et al., 2016;<ref>Cobellis, G., Trabalza-Marinucci, M. and Yu, Z. 2016. Critical evaluation of essential oils as rumen modifiers in ruminant nutrition: A review. Sci. Total Environ. 545: 556-568.</ref>
Moate et al., 2011<ref>Moate, P.J., Deighton, M.H., Williams, S.R.O., Pryce, J.E., Hayes, B.J., Jacobs, J.L., Eckard, R.J., Hannah, M.C. and Wales, W.J., 2016. Reducing the carbon footprint of Australian milk production by mitigation of enteric methane emissions. Anim. Prod. Sci. 56:1017-1034.</ref>
|-
|Nitrate and sulphate addition decreased the enteric methane emissions negatively affecting diet digestibility and milk production. The effects of the salts are additive.
|van Zijderveld et al., 2010<ref>Van Zijderveld, S.M., Gerrits, W.J.J., Apajalahti, J.A., Newbold, J.R., Dijkstra, J., Leng, R A., and Perdok, H.B. 2010. Nitrate and sulfate: effective alternative hydrogen sinks for mitigation of ruminal methane production in sheep. J. Dairy Sci. 93:5856-5866.</ref>;
van Zijderveld et al., 2011<ref>Van Zijderveld, S.M., Gerrits, W.J.J., Dijkstra, J., Newbold, J.R., Hulshof, R.B.A., and Perdok, H.B. 2011. Persistency of methane mitigation by dietary nitrate supplementation in dairy cows. J. Dairy Sci. 94:4028-4038.</ref>
|-
|The methanogenesis in the rumen of calves is associated with the development of the ruminal protozoa population. The absence of protozoa in the rumen reduced both the CH4 production and the digestibility of carbohydrates.
|Schönhusen et al., 2003<ref>Schönhusen, U., Zitnan, R., Kuhla, S., Jentsch, W., Derno, M., and Voigt, J. 2003. Effects of protozoa on methane production in rumen and hindgut of calves around time of weaning. Arch. Anim. Nutr. 57:279-295.</ref>
|-
|Implementing good grazing management reduced gross energy intake loss as CH4 by 14%.
|Wims et al., 2010<ref>Wims, C.M., Deighton, M.H., Lewis, E., O’Loughlin, B., Delaby, L., Boland, T.M., and O’Donovan, M. 2010. Effect of pregrazing herbage mass on methane production, dry matter intake, and milk production of grazing dairy cows during the mid-season period. J. Dairy Sci. 93:4976 – 4985</ref>
|}
</center>

== Host genetics, physiology and environment ==
A low-moderate proportion of variation in CH4 emissions among ruminants is under genetic control. Heritability coefficients of MeY and RMPR were h²=0.22 and 0.19 respectively in a population of 1,043 Angus growing steers and heifers measured during 2 days in RC (Donoghue et al., 2016<ref name=":14">Donoghue, K.A., Bird-Gardiner, T., Arthur, P.F., Herd, R.M., and Hegarty, R.F. 2016. Genetic and phenotypic variance and covariance components for methane emission and postweaning traits in Angus cattle. J. Anim. Sci. 94:1438–1445. doi:10.2527/jas2015-0065.</ref>). The heritability coefficient of MeY was h²=0.13 in a population of1,225 dual-purpose growing sheep measured during 2 days in RC (Pinares-Patino et al., 2013<ref>Pinares-Patiño C.S., Hickey, S.M., Young, E.A., Dodds, K.G., MacLean, S., Molano, G., Sandoval, E., Kjestrup, H., Harland, R., Pickering, N.K., and McEwan, J.C. 2013. Heritability estimates of methane emissions from sheep. Animal 7: 316–321.</ref>). Table 2 contains information of heritability of traits related to CH4 production.

{| class="wikitable"
|-
! colspan="3" | ''Table 2. Heritability information of methane-related traits and measurements.''
|- style="background-color:#efefef;"
|+
!Factors
!Reference
|-
|List with several h2
|Pickering et al., 2015<ref>Pickering, N.K., Oddy, V.H., Basarab, J.A., Cammack, K., Hayes, B J., Hegarty, R.S., McEwan, J.C., Miller, S., Pinares, C., and de Haas, Y. 2015. Invited review: Genetic possibilities to reduce enteric methane emissions from ruminants. Animal 9:1431-1440.</ref>
|-
|List with several h2
|MPWG White paper Dec 18<ref>MPWG White paper Dec 18. <nowiki>http://www.asggn.org/publications,listing,95,mpwg-white-paper.html</nowiki>. Pickering, N.K., de Haas, Y., Basarab, J., Cammack, K., Hayes, B., Hegarty, R.S., Lassen, J., McEwan, J.C., Miller, S., Pinares-Patiño, C.S., Shackell, G., Vercoe, P. and Oddy, V.H. 2013.</ref>
|-
|Methane emissions from individual cows during milking varied between individuals with the same milk yield and fed the same diet. Between-cow variation in MERm is greater than within-cow variation and ranking of cows for CH4 emissions is consistent across time. Variation related to body weight, milk yield, parity, and week of lactation/days in milk. The monitored variation might offer opportunities for genetic selection.
|Garnsworthy et al., 2011A<ref name=":15">Garnsworthy, P.C., Craigon, J., Hernandez-Medrano, J.H. and Saunders, H. 2012A. On-farm methane measurements during milking correlate with total methane production by individual dairy cows. J. Dairy Sci. 95:3166-3180.</ref>;
Garnsworthy et al., 2011B<ref name=":16">Garnsworthy, P.C., Craigon, J., Hernandez-Medrano, J.H., and Saunders, N. 2012B. Variation among individual dairy cows in methane measurements made on farm during milking. J. Dairy Sci. 95:3181–3189.</ref>
|-
|Mechanistic modelling approach: potential for dietary intervention as a means of substantially reducing CH4 emissions without adverse effects on dietary energy supply.
|Mills et al., 2001<ref>Mills, J.A.N., Dijkstra, J., Bannink, A., Cammell, S.B., Kebreab, E., and France, J. 2001. A mechanistic model of whole-tract digestion and methanogenesis in the lactating dairy cow: model development, evaluation, and application. J. Anim. Sci. 79:1584-1597.</ref>
|-
|The CH4-to-CO2 ratio measured using the non-invasive portable air sampler and analyzer unit based on Fourier transform infrared (FTIR) detection method is an asset of the individual cow and may be useful in both management and genetic evaluations.
|Lassen et al., 2012<ref name=":17">Lassen, J., Lovendahl, P., and Madsen, J. 2012. Accuracy of noninvasive breath methane measurements using Fourier transform infrared methods on individual cows. J. Dairy Sci. 95:890-898.</ref>
|-
|The estimated heritability for CH4 g/day and CH4 g/kg of FPCM were lower than common production traits but would still be useful in breeding programs.
|Kandel et al., 2013<ref>Kandel, P.B., Vanrobays, M.L., Vanlierde, A., Dehareng, F., Froidmont, E., Dardenne, P., Lewis, E., Buckley, F., Deighton, M.H., McParland, S. and Gengler, N., 2013. Genetic parameters for methane emissions predicted from milk mid-infrared spectra in dairy cows. J. Dairy Sci. 95(E-1):p.388.</ref>
|-
|Genetic correlation between CH4 intensity and milk yield (MY) was - 0.67 and with milk protein yield (PY) was -0.46 in Holstein cows.
|Kandel et al., 2014A<ref name=":37" />, B<ref>Kandel, P.B., Vanderick, S., Vanrobays, M.L., Vanlierde, A., Dehareng, F., Froidmont, E., Soyeur,t H., and Gengler, N. 2014B. Consequences of selection for environmental impact traits in dairy cows. In: 10th World Congress on Genetics Applied to Livestock Production (WCGALP), 17-22 August, 2014. Vancouver, Canada.</ref>
|-
|Milk production and CH4 emissions of dairy cows seemed to be influenced by the temperature humidity index.
|Vanrobays et al., 2013A<ref>Vanrobays, M.-L., Gengler, N., Kandel, P.B., Soyeurt, H., and Hammami, H. 2013A. Genetic effects of heat stress on milk yield and MIR predicted methane emissions of Holstein cows. 64th Annual meeting of the European Federation of Animal Science, p498</ref>
|-
|Estimate the heritability of the estimated methane emissions from 485 Polish Holstein-Friesian dairy cows at 2 commercial farms using FTIR spectroscopy during milking in an automated milking system by implementing the random regression method. The heritability level fluctuated over the course of lactation, starting at 0.23 (SE 0.12) and then increasing to its maximum value of 0.3 (SE 0.08) at 212 DIM and ending at the level of 0.27 ± 0.12. Average heritability was 0.27 ± 0.09.
|Pszczola et al., 2017<ref name=":18">Pszczola, M., Rzewuska, K., Mucha, S., and Strabel, T. 2017. Heritability of methane emissions from dairy cows over a lactation measured on commercial farms. J. Anim. Sci. 95:4813-4819. doi: 10.2527/jas2017.1842.</ref>
|-
|CH4 measured with a portable air-sampler FTIR detection method on 3,121 Holstein dairy cows from 20 herds using automatic milking systems. The heritability of CH4_MILK was 0.21 ± 0.06. It was concluded that a high genetic potential for milk production will also mean a high genetic potential for CH4 production. The results suggested that CH4 emission is partly under genetic control, that it is possible to decrease CH4 emission from dairy cattle through selection, and that selection for higher milk yield will lead to higher genetic merit for CH4 emission/cow per day.
|Lassen and Løvendahl, 2016<ref>Lassen, J., and Løvendahl, P. 2016. Heritability estimates for enteric methane emissions from Holstein cattle measured using noninvasive methods. J. Dairy Sci. 99:1959-1967.</ref>
|-
|CH4 production was measured of 184 Holstein-Friesian cows in. the milking robot with a in total 2,456 observations for CH4 production. Heritability for CH4 production ranged from 0.12 ± 0.16 to 0.45 ± 0.11, and genetic correlations with MY ranged from 0.49 ± 0.12 to 0.54 ± 0.26. The positive genetic correlation between CH4 production and milk yield indicates that care needs to be taken when genetically selecting for lower CH4 production, to avoid a decrease in MY at the animal level. However, this study shows that CH4 production is moderately heritable and therefore progress through genetic selection is possible.
|Breider et al., 2019<ref>Breider, I.S., Wall, E., Garnsworthy, P.C. 2019. Short communication: Heritability of methane production and genetic correlations with milk yield and body weight in Holstein-Friesian dairy cows, J. Dairy Sci. 102: 7277-7281.</ref>
|-
|CH4 concentration was measured with NDIR, and CH4 production was estimated from CH4 concentration and body weight. Heritability for CH4 concentration was 0.11 ± 0.03 and for CH4 production 0.12 ± 0.04. Positive genetic correlation was observed with MY (0.17-0.21), PY (0.22-0.31) and FY (0.27-0.29). Other type traits showed positive correlation with methane production (chest width=0.26, angularity =0.19, stature = 0.43 and capacity = 0.31) possibly associated to higher milk feed intake from these animals. Rumination time was negatively correlated to CH4 production (-0.24) and CH4 concentration (-0.43). However, larger CH4 production and CH4 concentration was associated with shorter days open.
|López-Paredes et al. (2020)<ref>Lopez-Paredes, J., Goiri, I., Atxaerandio, R., García-Rodríguez, A., Ugarte, E., Jiménez-Montero, J.A., Alenda, R and González-Recio, O. 2020. Mitigation of greenhouse gases in dairy cattle via genetic selection (i): Genetic parameters of direct methane using non-invasive methods and its proxies. J. Dairy Sci. 103.</ref>
|-
|Genetic parameters of CH4 emissions predicted from milk fatty acid profile (FA) and those of their predictors in 1,091 Brown Swiss cows reared on 85 farms showed that enteric CH4 emissions of dairy cows can be estimated on the basis of milk fatty acid profile. Additive genetic variation of CH4 traits was shown which could be exploited in breeding programmes.
|Bittante and Cecchinato, 2020<ref>Bittante, G., and Cecchinato, A. 2020. Heritability estimates of enteric methane emissions predicted from fatty acid profiles, and their relationships with milk composition, cheese-yield and body size and condition, It. J. An. Sci. 19:114-126, DOI: 10.1080/1828051X.2019.1698979</ref>
|-
|A total of 670 test day records were recorded on lactating Holstein Friesian cows reared in 10 commercial dairy herds. Predicted methane production (PMP) was estimated to be 15.33±1.52 MJ/d in dairy cows with 23.53±6.81 kg/d of milk yeild (MY) and 3.57±0.68% of fat content (FC). Heritability of MY was 0.09 with a posterior probability for values of h2 greater than 0.10 of 44%. Estimates of heritability for FC and protein content (PC) were 0.17 and 0.34, respectively, with a posterior probability for values of h2 greater than 0.10 of 77% and 99%. For somatic cell score (SCS), heritability was 0.13 with a posterior probability for values of h2 greater than 0.10 of 67%. Heritability for the trait PMP was moderate to low (0.12); however, posterior probability for values of h2 greater than 0.10 was 60%. Medians of the posterior distributions of genetic correlations between PMP and milk production traits were: 0.92, 0.67, 0.14, and 0.14 between PMP and MY, PMP and FC, PMP and PC, and PMP and SCS, respectively. Reduction of PMP seems to be viable through selection strategies without affecting udder health and PC.
|Cassandro et al., 2010<ref name=":29">Cassandro, M., Cecchinato, A., Battagin, M., Penasa, M., 2010. Genetic parameters of predicted methane production in Holstein Friesian cowsIn: Proc. 9th World Congr. on Genetics Applied to Livestock Production, Leipzig, Germany. . Page 181</ref>
|-
|GWAS to study the genetic architecture of CH4 production and detected genomic regions affecting CH4 production. Detected regions explained only a small proportion of the heritable variance. Potential QTL regions affecting CH4 production were located within QTLs related to feed efficiency, milk-related traits, body size and health status. Five candidate genes were found: CYP51A1 on BTA 4, PPP1R16B on BTA 13, and NTHL1, TSC2, and PKD1 on BTA 25. These candidate genes were involved in a number of metabolic processes that are possibly related to CH4 production. One of the most promising candidate genes (PKD1) was related to the development of the digestive tract. The results indicate that CH4 production is a highly polygenic trait.
|Pszczola et al., 2018<ref name=":19">Pszczola, M., Strabel, T., Mucha, S., and Sell-Kubiak, E. 2018. Genome-wide association identifies methane production level relation to genetic control of digestive tract development in dairy cows. Scientific Rep. 8 (1), 15164 <nowiki>https://doi.org/10.1038/s41598-018-33327-9</nowiki></ref>
|-
|A 1000-cow study across European countries revealed that the ruminant microbiomes can be controlled by the host animal. A 39- member subset of the core microbiome formed hubs in co-occurrence networks linking microbiome structure to host genetics and phenotype (CH4 emissions, rumen and blood metabolites, and milk production efficiency).
|Wallace et al.,
2019<ref>Wallace, R.J., Sasson, G., Garnsworthy, P.C., Tapio, I., Gregson, E., Bani, P., Huhtanen, P., Bayat, A.R., Strozzi, F., Biscarini, F., Snelling, T.J., Saunders, N., Potterton, S.L., Craigon, J., Minuti, A., Trevisi, E., Callegari, M.L., Cappelli, F.P., Cabezas-Garcia, E.H., Vilkki, J., Pinares-Patino, C., Fliegerov, K.O., Mrazek, J., Sechovcova, H., Kope, J., Bonin, A., Boyer, F., Taberlet, P., Kokou, F., Halperin, E., Williams, J.L., Shingfield, K.J., and Mizrahi, I. 2019. A heritable subset of the core rumen microbiome dictates dairy cow productivity and emissions. Sci. Adv. 5:(7):eaav8391. doi 10.1126/sciadv.aav8391.</ref>
|}

</div>
</div>

= Methane measurements methods =
<div class="mw-collapsible mw-collapsed">
<div>
Several factors influence the choice of measurement method such as cost, level of accuracy, precision, scope of application, and scale, which vary across disciplines (Cassandro et al., 2013<ref>Cassandro, M. 2013. Comparing local and cosmopolitan cattle breeds on added values for milk and cheese production and their predicted methane emissions. Animal Genetic Resources/Ressources génétiques animales/Recursos genéticos animales, available on CJO2013. doi:10.1017/S2078 63361200077X</ref>; Hammond et al., 2016A<ref>Hammond, K.J., Crompton, L.A., Bannink, A., Dijkstra, J., Yáñez-Ruiz, D.R., O’Kiely, P., Kebreab, E., Eugenè, M.A., Yu, Z., Shingfield, K.J., Schwarm, A., Hristov, A.N., and Reynolds, C.K. 2016A. Review of current in vivo measurement techniques for quantifying enteric methane emission from ruminants. Anim. Feed Sci. Technol. 219:13–30. doi:10.1016/j.anifeedsci.2016.05.018.</ref>; Garnsworthy et al., 2019<ref>Garnsworthy, P.C. Difford, G.F. Bell, M.J. Bayat, A.R. Huhtanen, P. Kuhla, B. Lassen, J. Peiren, N. Pszczola, M; Sorg, D. Visker, M.H., and Yan, T. 2019 Comparison of Methods to Measure Methane for Use in Genetic Evaluation of Dairy Cattle. Animals 9:837, 12p.</ref>). For instance, genetic selection programs require CH4 measurements on thousands of related individuals under the environmental conditions in which the animals are expected to perform (Falconer and Mackay, 1996<ref>Falconer, D., and Macka,y T. 1996. Introduction to quantitative genetics (4th edn). ISBN-13: 978-0582243026; ISBN-10: 0582243025</ref>). This can be challenging because dairy cattle perform in a wide range of conditions (e.g. grazing vs indoor housing). There are a number of different measurement methods currently being employed, each with advantages and disadvantages in terms of the factors listed above. The currently accepted and widely used measurement methods are listed and described below. The main features of methods for measuring CH4 output by individual animals are summarised in Table 3. Values for each feature are based on experience of experts in METHAGENE WG2 who have used the methods. All values are relative, and somewhat subjective, because absolute values will depend on installation and implementation of each method at different research centres. It should be noted that the measuring methods can be divided in two major sections: methods that measure the concentration and flux of CH4 (e.g. the respiration chamber), and methods that measure the flux of CH4 through the device (e.g. GreenFeed). This affects the useability of the methods for answering research questions – please see also the recommendations at the end of these guidelines.

== Respiration chambers ==
Respiration chambers are calibrated to be accurate and precise, and are the gold standard for benchmarking new methods. Only respiration chambers measure total emissions from the animal via the oral, nasal and anal routes; all other methods ignore emissions via the anus and only measure CH4 emitted in breath. Breath measurements are justified because 99% of CH4 is emitted from the mouth and nostrils, and only 1% via the anus (Murray ''et al.'', 1976<ref name=":20">Murray, R.M., Bryant, A.M., and Leng, R.A.. 1976. Rates of production of methane in the rumen and large intestine of sheep. Br. J. Nutr. 36:1-14.</ref>).

A single animal (or occasionally more) is confined in a chamber for between 2 and 7 days. Concentration of CH4 (and other gases if required) is measured at the air inlet and outlet vents of the chamber. The difference between outlet and inlet concentrations is multiplied by airflow to indicate CH4 emissions fluxes. In most installations, a single gas analyser is used to measure both inlet and outlet concentrations, often for two or more chambers. This involves switching the analyser between sampling points at set intervals, so concentrations are actually measured for only a fraction of the day. If the sampling points acquisition frequency is high it enables to draw the diurnal pattern of methane emission, comparable to the GreenFeed system.

Respiration chambers vary in construction materials, size of chamber, gas analysis equipment and airflow rate, all of which can influence results. Validation of 22 chambers at six UK research sites revealed an uncertainty of 25.7% between facilities, which was reduced to 2.1% when correction factors were applied to trace each facility to the international standard CH4 (Gardiner ''et al.'', 2015<ref name=":21">Gardiner, T.D., Coleman, M.D., Innocenti, F., Tompkins, J., Connor, A., Garnsworthy, P.C., Moorby, J.M., Reynolds, C.K., Waterhouse, A., and Wills, D. 2015. Determination of the absolute accuracy of UK chamber facilities used in measuring methane emissions from livestock. Measurement 66: 272-279.</ref>). The main sources of uncertainty were stability and measurement of airflow, which are crucial for measuring CH4 emission rate. The authors concluded, however, that chambers were accurate for comparing animals measured at the same site. This is an added challenge to benchmarking alternative methods with respiration chambers if respiration chambers themselves have not been benchmarked with respiration chambers at other facilities. It should be noted that substantial errors can occur if appropriate calibration procedures are not followed (Gardiner ''et al.'', 2015<ref name=":21" />).

For large-scale evaluation of CH4 emissions by individual animals, respiration chambers are challenging with only a single study in growing Angus steers and heifers exceeding 1000 animals and finding CH4 production to be moderately heritable h2 = 0.27 ± 0.07 (Donoghue ''et al.'', 2016<ref name=":14" />). Installation and running costs are high, as only one animal is normally measured at once. If we assume that the monitoring time is three days per animal, and chambers are run continuously, then maximum throughput would be approximately 100 animals per chamber per year. In practice, throughput is likely to be 30 to 50 animals per year. Cows are social animals and confinement in a chamber may ultimately influence their feeding behaviour resulting in less feed consumed and in a different meal pattern compared with farm conditions. Altered feeding pattern or level is not a problem for metabolic studies evaluating feeds but can be a problem when evaluating individual animals. Furthermore, the representativeness of respiration chambers to grazing systems has been called into question (Pinares-Patiño ''et al.'', 2013<ref name=":13" />). However, promising developments have led to more animal friendly respiration chambers constructed from cheaper, transparent materials. These lower the cost and reduce the stress of confinement with minimal disruptions to accuracy, precision and no drop in feed intake of the cows (Hellwing ''et al.'', 2012<ref name=":12" />).

Where an alternative method may be cheaper, less invasive, easier to implement, or have a wider scope of application, it is of value to assess the relative accuracy, precision and correlation with the gold standard to assess the relative worth of the alternative method (Barnhart ''et al.'', 2007<ref>Barnhart, H.X., Kosinski, A.S., and Haber, M.J. 2007. Assessing Individual Agreement. J. Biopharm. Stat. 17:697–719. doi:10.1080/10543400701329489.</ref>). All methods measure CH4 with some level of error, so the ‘true value’ of an individual is not known. However, when the level of measurement error increases, so too does the imprecision. When comparing two methods where one or both methods has high imprecision a phenomenon known as ‘attenuation of errors’ occurs (Spearman, 1904<ref>Spearman, C. 1904. The Proof and Measurement of Association between Two Things. Am. J. Psychol. 15:72–101.</ref>). The increased measurement error biases the correlation between the two methods downwards and reduces the efficacy of detecting significant differences in accuracy (Adolph & Hardin, 2007<ref>Adolph, S.C., and Hardin, J.S. 2007. Estimating phenotypic correlations: Correcting for bias due to intraindividual variability. Funct. Ecol. 21:178–184. doi:10.1111/j.1365-2435.2006.01209.x.</ref>). Or in terms of linear regression terms, when the observed CV of an alternative method is higher than that of the gold standard method, the slope of regression between the methods is decreased and the intercept is biased upwards.

== Portable Accumulation Chambers ==
In Australia and New Zealand an alternative method was developed for the short-term measurement of Methane Production Rate (MPR) of sheep using Portable Accumulation Chambers (PAC) during 1 hour without leading discomfort to the animals. Similarly to RC, CH4 emissions recorded in PAC include gases from flatulence in addition to eructed and expired CH4, but only during 1 hour. For a detailed comparison of the PAC and respiration chamber methods see Jonker ''et al.'' (2018).

== SF6 ==
The SF6 technique samples breath over 24 hours, whereas other techniques use spot samples of breath over periods of minutes throughout the day, so diurnal variation has to be considered. The majority of CH4 (87-99%) is released by eructation (Blaxter & Joyce, 1963; Murray ''et al.'', 1976<ref name=":20" />), which provides a clear signal for sample processing. Please note that the tracheostomy used in Murray ''et al.'' (1976)<ref name=":20" /> may have resulted in a higher percentage, but in both publications, it is clear that the majority of the CH4 is released via eructation.

The SF6 tracer gas technique was developed in an attempt to measure CH4 emissions by animals without confinement in respiration chambers (Johnson ''et al.'', 1994<ref>Johnson, K., Huyler, M., Westberg, H., Lamb, B., and Zimmerman, P. 1994. Measurement of methane emissions from ruminant livestock using a sulfur hexafluoride tracer technique. Environ. Sci. Technol. 28:359-362.</ref>). Air is sampled near the animal’s nostrils through a tube attached to a halter and connected to an evacuated canister worn around the animal’s neck or on its back. A capillary tube or orifice plate is used to restrict airflow through the tube so that the canister is between 50 and 70% full in approximately 24 hours. A permeation tube containing SF6 is placed into the rumen of each animal. The pre-determined release rate of SF6 is multiplied by the ratio of CH4 to SF6 concentrations in the canister to calculate CH4 emission rate.

Many research centres have used the SF6 technique with variations in design of sampling and collection equipment, permeation tubes, and gas analysis (Berndt ''et al.'', 2014<ref name=":22" />). Reliable results depend on following standard protocols, with greatest variation coming from accuracy of determining SF6 release rate from permeation tubes and control of sampling rate. With capillary tubes, sampling rate decreases as pressure in the canister increases, whereas an orifice plate gives a steadier sampling rate over 24 hours (Deighton ''et al.'', 2014<ref>Deighton, M.H., Williams, S.R.O., Hannah, M.C., Eckard, R.J., Boland, T.M., Wales, W.J., and Moate, P.J. 2014. A modified sulphur hexafluoride tracer technique enables accurate determination of enteric methane emissions from ruminants. Anim. Feed Sci. Technol. 197:47-63.</ref>). A source of error that has not been evaluated is that animals might interact and share CH4 emissions when the sampling tube of one animal is near the head of another animal. There is good agreement between CH4 emissions measured by the SF6 technique and respiration chambers, although results from the SF6 technique are more variable (Grainger ''et al.'', 2007<ref>Grainger, C., Clarke, T., McGinn, S.M., Auldist, M.J., Beauchemin, K.A., Hannah, M.C., Waghorn, G.C., Clark, H., and Eckard, R J. 2007. Methane emissions from dairy cows measured using the sulfur hexafluoride (SF6) tracer and chamber techniques. J. Dairy Sci. 90:2755-2766.</ref>; Muñoz ''et al.'', 2012<ref>Muñoz, C., Yan, T., Wills, D.A., Murray, S., and Gordon, A.W. 2012. Comparison of the sulfur hexafluoride tracer and respiration chamber techniques for estimating methane emissions and correction for rectum methane output from dairy cows. J. Dairy Sci. 95:3139-3148.</ref>).

== Breath sampling during milking and feeding ==
Several research groups have developed methods to measure CH4 concentration in breath of cows during milking and/or feeding. These are often referred to as ‘sniffer methods’ because they use devices originally designed to detect dangerous gas leaks. Air is sampled near the animal’s nostrils through a tube fixed in a feed bin and connected directly to a gas analyser. The feed bin might be in an automatic milking station (Garnsworthy ''et al.'', 2012A<ref name=":15" />, B<ref name=":16" />; Lassen ''et al.'', 2012<ref name=":17" />; Pszczola ''et al.'', 2017<ref name=":18" />, 2018<ref name=":19" />, 2019<ref>Pszczola, M., Calus, M.P.L., Strabel, T. 2019. Genetic correlations between methane and milk production, conformation, and functional traits. J. Dairy Sci. 102:5342-5346.</ref>) or in a concentrate feeding station (Negussie ''et al.'', 2017<ref>Negussie, E., Lehtinen, J., Mäntysaari, P., Bayat, A.R., Liinamo, A.E., Mäntysaari, E.A., and Lidauer, M.H. 2017. Non-invasive individual methane measurement in dairy cows. Animal 11:890-899.</ref>). Different research centres use different gas analysers (Nondispersive Infrared (NDIR), Fourier-transform infrared (FTIR) or photoacoustic infrared (PAIR)) and different sampling intervals (1, 5, 20 or 90-120 seconds). Methane concentration during a sampling visit of typically between 3 and 10 minutes may be specified as the overall mean, or the mean of eructation peaks. Some centres use CO2 as a tracer gas and calculate daily CH4 output according to ratio of CH4 to CO2 and daily CO2 output predicted from performance of the cow (Madsen ''et al.'', 2010<ref>Madsen, J., Bjerg, B.S., Hvelplund, T., Weisbjerg, M.R., and Lund, P. 2010. Methane and carbon dioxide ratio in excreted air for quantification of the methane production from ruminants. Livest. Sci. 129:223-227.</ref>). Repeatability and rank correlations were higher for eructation peaks than for mean concentrations, and were higher for eructation peaks than for CH4 to CO2 ratio (Bell ''et al.'', 2014<ref>Bell, M.J., Saunders, N., Wilcox, R.H., Homer, E.M., Goodman, J.R., Craigon, J., Garnsworthy, P.C. 2014 Methane emissions among individual dairy cows during milking quantified by eructation peaks or ratio with carbon dioxide. J. Dairy Sci. 97:6536–6546.</ref>). However, all methods show good repeatability.

== GreenFeed ==
GreenFeed (C-Lock Inc., Rapid City, South Dakota, USA) is a sniffer system where breath samples are provided when animals visit a bait station (Huhtanen ''et al.'', 2015<ref name=":23" />). GreenFeed Emission Monitoring (GEM) systems are designed for measuring animal emissions in their production environment. As with other sniffer systems, GreenFeed samples breath from individual animals several times (in general 4 to 6 times) per day for short periods (3 to 7 minutes in which an under pressure is created to suck the whole breath of the animal to measure the flux). They record CH4 and carbon dioxide (CO2) fluxes during short-term periods of 3-10 minutes when cattle visit an automated feeder fitted with a semi-enclosed head hood in which air is continuously drawn through an air-collection pipe (C-Lock, 2016; Huhtanen ''et al.'', 2015<ref name=":24" />; Hammond ''et al.'', 2016A<ref name=":25" />; Velazco ''et al.'', 2016<ref name=":131" />). Air samples are continually (every second) analyzed for CH4 and CO2 concentrations using non-dispersive infrared sensors. Gas fluxes are eventually calculated as the product of the air flow in the collection pipe and the concentration of gases corrected for the background concentrations and adjusted to standardized temperature, humidity and pressure. The position of the head in the feeder is detected by an infrared sensor. Gas fluxes are not calculated if the head is not correctly positioned in the feeder as not all the air in the feeder may be collected.

GreenFeed is a portable standalone system used in barn and pasture applications and incorporates an extractor fan to ensure active airflow and head position sensing for representative breath sampling (Hammond ''et al.'', 2016B<ref name=":26" />). Measurements are pre-processed by the manufacturer, and data are available in real-time through a web-based data management system (Hammond ''et al.'', 2015<ref name=":5" />). Because GreenFeed captures a high proportion of emitted air and measures airflow, which can be calibrated using a tracer gas, CH4 emission is estimated as a flux at each visit. Providing visits occur throughout the 24 hours, CH4 emission can be estimated directly as g/day (Hammond ''et al.'', 2015<ref name=":5" />; Huhtanen ''et al.'', 2015<ref name=":24" />). More importantly, repeatability of CH4 measurement must be high so the duration of the measurement period must be taken into account (Huhtanen ''et al.'', 2013; Arbre ''et al.'', 2016<ref name=":7" />); (R=0.7 after 17 days duration of measurement period, or R=0.93 after 45 days, Arbre ''et al.'', 2016<ref name=":7" />).

== Laser methane detector ==
The laser CH4 detector (LMD) is a highly responsive, hand-held device that is pointed at an animal’s nostrils and measures CH4 column density along the length of the laser beam (ppm.m). In the first implementation of LMD on a farm, measurements for each cow were taken over periods of 15 to 25 seconds between eructation events and could detect CH4 emitted each time the animal breathed out (Chagunda ''et al.'', 2009 <ref>Chagunda, M.G.G., Ross, D., and Robert,s D J. 2009. On the use of a laser methane detector in dairy cows. Comput. Electron. Agric. 68:157-160.</ref>; Sorg ''et al.'', 2016<ref>Sorg, D., Mühlbach, S., Rosner, F., Kuhla, B., Derno, M., Meese, S., Schwarm, A., Kreuzer, M., and Swalve, H. 2016. The agreement between two next-generation laser methane detectors and respiration chamber facilities in recording methane concentrations in the spent air produced by dairy cows. Comp. Electr. Agric. 143:262-272. </ref>, 2017<ref name=":27" />). In a later study with sheep and beef cattle, monitoring periods of 2 to 4 minutes allowed authors to separate breathing cycles from eructation events (Ricci ''et al.'', 2014<ref>Ricci, P., Chagunda, M.G.G., Rooke, J., Houdijk, J.G.M., Duthie, C-A., Hyslop, J., Roehe, R., and Waterhouse, A. 2014. Evaluation of the laser methane detector to estimate methane emissions from ewes and steers. J. Anim. Sci. 92:5239-5250.</ref>). Typically, animals are restrained either manually or in head yokes at a feed fence for the required length of time. The operator has to stand at the same distance (1 to 3 m) from each animal every time and must be careful to keep the laser pointed at the animal’s nostrils throughout the measurement period.
----[1] Consensus views based on experiences of METHAGENE WG2 members (www.methagene.eu).

[2] Per measuring unit or group of animals.

[3] Compared to no methane recording: low = measuring in situ; medium = some handling, training or change in routine; high = confinement.

[4] Medium if using FTIR analyser.<center>
{| class="wikitable"
|-
! colspan="7" style="text-align:left;" | ''Table 3. Summary of the main features of methods for measuring CH4 output by individual animals.''
|- style="background-color:#efefef;"
|+
! style="text-align:left;" | Method
! style="text-align:center;" | Purchase cost
! style="text-align:center;" | Running costs
! style="text-align:center;" | Labour
! style="text-align:center;" | Repeatability
! style="text-align:center;" | Behaviour alteration
! style="text-align:center;" | Throughput
|-
| style="text-align:left;" | Respiration chamber
| style="text-align:center;" | High
| style="text-align:center;" | High
| style="text-align:center;" | High
| style="text-align:center;" | High
| style="text-align:center;" | High
| style="text-align:center;" | Low
|-
| style="text-align:left;" | SF6 technique
| style="text-align:center;" | Medium
| style="text-align:center;" | High
| style="text-align:center;" | High
| style="text-align:center;" | Medium
| style="text-align:center;" | Medium
| style="text-align:center;" | Medium
|-
| style="text-align:left;" | Breath sampling during milking and feeding
| style="text-align:center;" | Low
| style="text-align:center;" | Low
| style="text-align:center;" | Low
| style="text-align:center;" | Medium
| style="text-align:center;" | None
| style="text-align:center;" | High
|-
| style="text-align:left;" | GreenFeed
| style="text-align:center;" | Medium
| style="text-align:center;" | Medium
| style="text-align:center;" | Medium
| style="text-align:center;" | Medium
| style="text-align:center;" | Medium
| style="text-align:center;" | Medium
|-
| style="text-align:left;" | Laser methane detector
| style="text-align:center;" | Low
| style="text-align:center;" | Low
| style="text-align:center;" | High
| style="text-align:center;" | Low
| style="text-align:center;" | Low-Medium
| style="text-align:center;" | Medium
|}
</center>

</div>
</div>

= Discussion of methods =
<div class="mw-collapsible mw-collapsed">
<div>
== SF6 vs Respiration Chamber ==
For large-scale evaluation of CH4 emissions by individual animals, the SF6 technique is more useful than respiration chambers. Animal behaviour and intake might be affected by wearing the apparatus, and by daily handling to exchange canisters, but the technique is considerably less intrusive than respiration chambers because cows remain in the herd. Labour and monetary costs for changing canisters each day and for lab analysis are high. Throughput is limited by the number of sets of apparatus available, handling facilities, labour, and the capacity of the lab for gas analysis. Animals need to be measured for 5 to 7 days, and it is recommended that group size should be less than 15 animals (Berndt et al., 2014<ref name=":22">Berndt, A., Boland, T.M., Deighton, M.H., Gere, J.I., Grainger, C., Hegarty, R.S., Iwaasa, A.D., Koolaard, J.P., Lassey, K.R., Luo D., Martin, R.J., Martin, C., Moate, P.J., Molano, G., Pinares-Patiño, C., Ribaux, B.E., Swainson, N.M., Waghorn, G.C., and Williams, S.R.O. 2014. Guidelines for use of sulphur hexafluoride (SF6) tracer technique to measure enteric methane emissions from ruminants. Pages 166. M. G. Lambert, ed. New Zealand Agricultural Greenhouse Gas Research Centre, New Zealand. </ref>), so maximum throughput would be about 750 animals per year. The method may be better suited for in housed conditions because of the labour and the potential movement restriction of the animals due to wearing the apparatus.

== Breath sampling during milking and feeding vs Respiration Chamber ==
For large-scale evaluation of CH4 emissions by individual animals, breath-sampling methods have significant advantages compared with other methods. Breath-sampling methods are non-invasive because, once installed, animals are unaware of the equipment and are in their normal environment. Animals follow their normal routine, which includes milking and feeding, so no training of animals, handling, or change of diet is required. Equipment is relatively cheap, although more expensive gas analysers are available, and running costs are negligible.

The compromise for non-invasiveness of breath-sampling is that concentrations of gases in the sampled air are influenced by cow head position relative to the sampling tube (Huhtanen et al., 2015<ref name=":23">Huhtanen, P., Cabezas-Garcia, E.H., Utsumi, S., and Zimmerman, S. 2015. Comparison of methods to determine methane emissions from dairy cows in farm conditions. J. Dairy Sci. 98:3394–3409. doi:10.3168/jds.2014-9118.</ref>). The use of head position sensors and data filtering algorithms can remove the effects when the cow’s head is completely out of the feed bin (Difford et al., 2016<ref>Difford, G.F., Lassen, J., and Løvendahl, P. 2016. Interchangeability between methane measurements in dairy cows assessed by comparing precision and agreement of two non-invasive infrared methods. Comput. Electron. Agric. 124:220–226. doi:10.1016/j.compag.2016.04.010.</ref>), but not within the feed bin. Consequently, sniffer measurements are more variable than flux methods, with factors like variable air flow in the barn increasing measurement error (imprecision), and head position, a highly repeatable character, inflating between-cow variability.

Using CO2 as a tracer gas partly addresses the issue but, because CO2 arises from metabolism as well as rumen fermentation, variability of CO2 emissions has to be considered. A further consideration is diurnal variation in breath concentrations of CH4 and CO2 because animals are spot-sampled at different times of day and night. Diurnal variation can be accounted for either by fitting a model derived from the whole group of animals, or by including time of measurement in the statistical model (Lassen et al., 2012<ref>Lassen, J., Lovendahl, P., and Madsen, J. 2012. Accuracy of noninvasive breath methane measurements using Fourier transform infrared methods on individual cows. J. Dairy Sci. 95:890-898.</ref>).

The number of observations per analyser is limited only by number of cows assigned to one automatic milking station or concentrate feeding station and length of time equipment is installed. Typically, each analyser will record 40 to 70 animals 2 to 7 times per day for 7 to 10 days, although the number of sampling stations per analyser can be increased by using an automatic switching system (Pszczola et al., 2017<ref>Pszczola, M., Rzewuska, K., Mucha, S., and Strabel, T. 2017. Heritability of methane emissions from dairy cows over a lactation measured on commercial farms. J. Anim. Sci. 95:4813-4819. doi: 10.2527/jas2017.1842.</ref>). Throughput per analyser is likely to be 2,000 to 3,000 animals per year.

== NDIR vs LMD ==
Both methods are low invasive. LMD needs larger labor force, wheras NDIR can be used during milking and feeding. According to Rey at al. (2019)<ref>Rey, J., Atxaerandio, R., Ruiz, R, Ugarte, E., Gonzalez-Recio, O., Garcia-Rodriguez, A., and Goiri, I. 2019. Comparison Between Non-Invasive Methane Measurement Techniques in Cattle. Animals 9(8): 563. <nowiki>https://doi.org/10.3390/ani9080563</nowiki></ref>, the repeatability of the CH4 concentration was greater for NDIR (0.42) than for LMD (0.23). Correlation between methods was moderately high and positive for CH4 concentration (0.73 and 0.74,respectively) and number of peaks (0.72 and 0.72, respectively), and the repeated measures correlation and the individual-level correlation were high (0.98 and 0.94, respectively). A high coefficient of individual agreement for the CH4 concentration (0.83) and the number of peaks (0.77) were observed between methods. The study suggests that methane concentration measurements obtained from NDIR and LMD cannot be used interchangeably. But the use of both methods could be considered for genetic selection purposes or for mitigation strategies only if sources of disagreement, which result in different between subject and within-subject variabilities, are identified and corrected for.

== Greenfeed ==
A limitation of the GreenFeed system is that animals require training to use the system, although animals which have been trained to use the system will readily use it again (Velazco et al., 2014<ref>Velazco, J.I., Cottle, D.J., and Hegarty, R.S. 2014. Methane emissions and feeding behaviour of feedlot cattle supplemented with nitrate or urea. Anim. Prod. Sci. 54:1737–1740. doi:10.1071/AN14345.</ref>). However, some animals will not use the system or will use it infrequently, and frequency of visits is affected by diet (Hammond et al., 2016B<ref name=":26">Hammond, K.J., Jones, A.K., Humphries, D.J., Crompton, L.A., and Reynolds, C.K. 2016B. Effects of diet forage source and neutral detergent fiber content on milk production of dairy cattle and methane emissions determined using GreenFeed and respiration chamber techniques. J. Dairy Sci. 99:7904–7917. doi:10.3168/jds.2015-10759.</ref>). This can be a challenge when screening commercial herds for CH4 emission under genetic evaluation. On the other hand, animals seem to get used to the equipment rapidly, and the sound produced by the system is remembered by the animals easily (personal information Dr. Finocchiaro). Alternatively, as practised in Canada, the unit is moved to individual animals in a tie-stall setting multiple times a day (personal information Prof C.F. Baes). Thus, action of individual animals is not needed. The manufacturer recommends 15 to 25 animals per GreenFeed unit, and recordings are made typically for 7 days. If all animals visit the unit adequately, throughput per unit is likely to be 750 to 1,250 animals per year. Sebek et al. (2019A, B)<ref>Sebek, L.B. 2019A. Project 11: Enterisch methaan: emissievariatie in de Nederlandse melkveestapel. 1 p. Wageningen : Wageningen University & Research.</ref> and Bannink et al. (2018)<ref>Bannink, A., Spek, J.W., Dijkstra, J., and Sebek, L.B. 2018. A Tier 3 Method for Enteric Methane in Dairy Cows Applied for Fecal N Digestibility in the Ammonia Inventory. In: Front. Sust. Food Syst. 2:66.</ref> showed the usefulness of the GreenFeed method in an on farm setting.

== Laser Methane Detector ==
The LMD can be used in the animal’s normal environment, although for consistency restraint is required during measurement. Because the LMD measures CH4 in the plume originating from the animal’s nostrils, results can be affected by factors such as: distance from the animal; pointing angle; animal’s head orientation and head movement; air movement and temperature in the barn; adjacent animals; and operator variation (Sorg et al., 2017<ref name=":27">Sorg, D., Difford, G.F., Mühlbach, S., Kuhla, B., Swalve, H.H., Lassen, J., Strabel, T., and Pszczola, M. 2017. Comparison of a laser methane detector with the GreenFeed and two breath analysers for on-farm measurements of methane emissions from dairy cows. Comp. Elec. Agric. 153:285-294.</ref>). Operator variation is likely to be one of the biggest factors because the operator controls distance and pointing angle, and is responsible for ensuring the laser remains on target. The structure of the barn and the resulting ventilation conditions and wind speed at the location of the measurement are also considerable sources of variation in recorded CH4. Assuming operator fatigue does not limit measurements, each LMD could record up to 10 animals per hour. If each animal is recorded 3 times (on 3 consecutive days, for example, as in Mühlbach et al. (2018)<ref>Mühlbach, S., Sorg, D., Rosner, F., Kecman, J., and Swalve, H.H. 2018. Genetic analyses for CH₄ concentrations in the breath of dairy cows measured on-farm with the Laser Methane Detector. In: Proceedings of the World Congress on Genetics Applied to Livestock Production, Abstract No. 186, 11-16th February, Auckland, New Zealand.</ref>), throughput is likely to be up to 1000 animals per year.</div>
</div>

= Comparison of methods to measure methane =
<div class="mw-collapsible mw-collapsed"><div>
== Correlations among methods ==
Correlations among methods
Table 4 shows correlations between the respiratory chamber method as the gold standard to measure CH4 emission from cows and other methods from Garnsworthy et al. (2019)<ref name=":11" />.
<center>
{| class="wikitable"
|-
! colspan="3" style="text-align:left;" | ''Table 4. Correlations between CH4 measuring methods. Data were taken from Garnsworthy et al. (2019)''<ref name=":11" />''.''
|- style="background-color:#efefef;"
|+
! style="text-align:left;" | Method
! style="text-align:center;" | Correlation
! style="text-align:center;" | S.E.
|-
| style="text-align:left;" | Respiratory chamber - SF6
| style="text-align:center;" | 0.87
| style="text-align:center;" | - 0.08
|-
| style="text-align:left;" | Respiratory chamber - Greenfeed
| style="text-align:center;" | 0.81
| style="text-align:center;" | - 0.1
|-
| style="text-align:left;" | Respiratory chamber - NDIR
| style="text-align:center;" | - 0.07
| style="text-align:center;" | 0.88
|-
| style="text-align:left;" | Respiratory chamber - NDIR peak
| style="text-align:center;" | 0.72
| style="text-align:center;" | - 0.11
|-
| style="text-align:left;" | Respiratory chamber - PAIR
| style="text-align:center;" | - 0.08
| style="text-align:center;" | 0.7
|-
| style="text-align:left;" | SF6 - Greenfeed
| style="text-align:center;" | 0.4
| style="text-align:center;" | - 0.8
|-
| style="text-align:left;" | LMD - Greenfeed
| style="text-align:center;" | 0.77
| style="text-align:center;" | - 0.23
|-
| style="text-align:left;" | NDIR - Greenfeed
| style="text-align:center;" | 0.64
| style="text-align:center;" | - 0.18
|-
| style="text-align:left;" | NDIR - LMD
| style="text-align:center;" | 0.6
| style="text-align:center;" | - 0.11
|-
| style="text-align:left;" | FTIR - LMD
| style="text-align:center;" | 0.57
| style="text-align:center;" | - 0.25
|-
| style="text-align:left;" | NDIR - NDIR peaks
| style="text-align:center;" | 0.58
| style="text-align:center;" | - 0.15
|-
| style="text-align:left;" | FTIR - NDIR
| style="text-align:center;" | 0.97
| style="text-align:center;" | - 0.02
|}
</center>

In method comparison studies, simultaneous repeated measures per cow with two or more methods are required in order to assess systematic differences between methods (means) and random differences (precision) and correlation between methods free of residual error. Furthermore, adequately short time differences between repeated measures per subject are needed to ensure the underlying biology of the cow has not changed. Not all methods can be recorded simultaneously and CH4 emission of cows’ changes both within day and over the lactation period. In such instances either cross-over designs or matched pair repeated measures designs are needed. Members of METHAGENE WG2 provided data from studies in which two or more methods had been used to measure CH4 output (g/day) by individual dairy cows. Methods were applied to each cow either concurrently or consecutively within a short timeframe.

Seven main methods were represented: respiration chambers; SF6; GreenFeed; LMD; and three breath-sampling systems based on different gas analysers. Gas analysers incorporated different technologies to measure CH4, which were NDIR (e.g. Guardian Plus, Edinburgh Instruments, Edinburgh, UK), FTIR (e.g. Gasmet 4030, Gasmet Technologies Oy, Helsinki, Finland), or PAIR (e.g. F10, Gasera Ltd, Turku, Finland). In the contributing studies, NDIR and FTIR were used in automatic milking stations, and PAIR was used in concentrate feeding stations. One NDIR study and all FTIR and PAIR studies used CO2 as a tracer gas, with daily CO2 output calculated either from milk yield, live weight and days pregnant or from metabolisable energy intake. Two NDIR studies were based on CH4 concentration in eructation peaks rather than mean CH4 concentration, so were treated as separate methods. By separating NDIR studies, a total of 8 distinct methods were available giving a matrix of 28 potential combinations for comparisons. Data were available for 13 method combinations (Garnsworthy et al., 2019<ref name=":11" />).

Method comparisons were conducted using bivariate models (repeatability animal models) to obtain correlations between ‘true values’, also known as repeated measures correlations or individual level correlations (Bakdash and Marusich, 2017<ref>Bakdash, J.Z., and Marusich, L.R. 2017. Repeated measures correlation. Front. Psychol. 8:1–13. doi:10.3389/fpsyg.2017.00456.</ref>). Variance components including between cow variation and within cow variation (precision) and means (accuracy) were used in the calculation of between cow coefficient of variation (CV, %) and total CV and repeatability. Where single measurements were available for each method Pearson’s correlation was reported and where repeated measures per subject were available repeated measures correlation was reported.

Respiration chambers were the most precise method, as can be seen by the smaller between cow CV% and total CV compared to alternative methods, and respiration chambers are by definition the most accurate. All methods tested showed high correlations with respiration chambers but none of the correlations exceeded 0.90. This is in part due to the increased imprecision of alternative methods, as even the most accurate and precise method will compare poorly to a less precise method. These correlations are also likely to be underestimated because none of the methods could be recorded simultaneously with respiration chambers and had to be recorded in cross over designs. Consequently, the true value for each cow may have changed due to changes in the underlying biology of the cow over time between measurements. Comparisons among alternative methods generally had lower correlations than comparisons with respiration chambers, despite having relatively higher numbers of animals and in most cases simultaneous or near simultaneous repeated measures per cow per method due to the increased variability and imprecision of alternative methods as is seen by the increased CVs or due to the possibility that different aspects of CH4 emission are captured using different methods.

For the methods with repeated measures per cow the two mass flux methods, SF6 and GreenFeed, had the highest repeated measures correlations (0.87 ± 0.08 and 0.81 ± 0.10) which outperformed the concentration based NDIR method using CO2 tracer gas. Of the two concentration methods evaluated against respiration chambers using single measurements, NDIR Peaks had a higher correlation (0.89 ± 0.07) than the PAIR CO2 tracer gas (0.80 ± 0.10). The study of Hristov et al. (2016)<ref>Hristov, A.N., O,h J., Giallongo, F., Frederick, T., Harper, M.T., Weeks, H., Branco, F., Price, W.J., Moate, P.J., Deighto,n M.H., Williams, S.R.O., Kindermann, M., and Duval, S. 2016. Short communication: Comparison of the GreenFeed system with the sulfur hexafluoride tracer technique for measuring enteric methane emissions from dairy cows. J. Dairy Sci. 5461–5465. doi:10.3168/jds.2016-10897.</ref> comparing SF6 and GreenFeed reported a low Pearson correlation of 0.40, despite having a large number of animals with repeated measures per method, the authors appear not to have estimated a repeated measures correlation, which could be larger. Estimating a repeated measures correlation between these two mass flux methods is a priority as it would clarify the inexplicable disagreement between two methods which both correlate highly with the gold standard method. With the exception of the aforementioned study, the imprecision was low in the mass flux measure comparisons as compared to the concentration-based methods.

Two of the sniffer methods evaluated, FTIR CO2t1 and NDIR CO2t1, correlated close to unity (0.97), most likely due to the shared prediction equation for CO2 tracer gas. Nevertheless, all correlations derived from actual data were positive. This suggests that combination of datasets obtained with different methods is a realistic proposition for genetic studies. Calculation of adjustment or weighting factors for bias, accuracy and precision would improve the value of combined datasets.

== Pro’s and con’s of devices ==

=== Daily methane emission measures ===
Due to the large diurnal variation in enteric CH4 emission in relation with feeding pattern (Grainger et al., 2007<ref>Grainger, C., Clarke, T., McGinn, S.M., Auldist, M.J., Beauchemin, K.A., Hannah, M.C., Waghorn, G.C., Clark, H., and Eckard, R J. 2007. Methane emissions from dairy cows measured using the sulfur hexafluoride (SF6) tracer and chamber techniques. J. Dairy Sci. 90:2755-2766.</ref>; Jonker et al. 2014<ref>Jonker, A., Molano, G., Antwi, C., Waghorn, G.. 2014. Feeding lucerne silage to beef cattle at three allowances and four feeding frequencies affects circadian patterns of methane emissions, but not emissions per unit of intake. Anim. Prod. Sci.54:1350-1353.</ref>), the highest accuracy of daily CH4 production rate (DMPR) will be obtained with methods that encompass the whole day emissions. Two methods are available: Respiration Chambers (RC) and SF6 methods.

Alternative methods are based on short-term measures of CH4 production rate: Portable Accumulation Chambers (PAC) for sheep and GreenFeed Emission Monitoring (GEM) systems for cattle and sheep (Hegarty, 2013<ref>Hegarty, R.S. 2013. Applicability of short term emission measurements for on-farm quantification of enteric methane. Animal 7, s2:401-408.</ref>).

=== DMPR with Respiration Chamber (RC) ===
It should be noted that CH4 emissions recorded in RC also include gases from flatulence in addition to eructed and expired CH4. Compared with mouth exhaled CH4, CH4 from flatulence is generally considered as limited.

Feed intake in the RC may not be representative of the normal animal feed intake (Bickell et al., 2014<ref>Bickell, S.L., Revell, D.K., Toovey, A.F., and Vercoe, P. E. 2014. Feed intake of sheep when allowed ad libitum access to feed in methane respiration chambers. J. Anim. Sci. 92:2259-2264.</ref>; Llonch et al., 2016<ref>Llonch, P., Somarriba, M.,. Duthie, C-A, Haskell, M.J., Rooke, J.A., Troy, S., Roehe, R., and . Turner, S.P. 2016 Association of temperament and acute stress responsiveness with productivity, feed efficiency, and methane emissions in beef cattle: an observational study. Front. Vet. Sci. 3: 43.</ref>; Troy et al., 2016<ref name=":0a">Troy, S.M., Duthie, C.A., Ross, D.W., Hyslop, J.J., Roehe, R., Waterhouse, A., and Rooke, J.A. 2016. A comparison of methane emissions from beef cattle measured using methane hoods with those measured using respiration chambers. Anim. Feed Sci. Technol. 211:227-240.</ref>). As a consequence, the DMPR measured could be biased. Animals are usually not fed ad libitum when recorded in RC. It is therefore recommended to compare animal or diet effects on Methane Yield (MY) calculated as the ratio of the observed DMPR/DMI during the RC recording in order to take into account possible differences among animals in DMI bias. Animal effects can also be compared on the Residual Methane Production Rate (RMPR) the difference between the observed DMPR and the expected DMPR obtained by regression of observed DMPR on DMI recorded during RC test. Residual traits, however, require a large number of recorded animals for valid adjustment.

Repeatability coefficients between measures taken on consecutive days are very high, rep=0.85 [0.75 to 0.94] for MeY and RMPR of cattle and sheep (Grainger et al., 2007<ref>Grainger, C., Clarke, T., McGinn, S.M., Auldist, M.J., Beauchemin, K.A., Hannah, M.C., Waghorn, G.C., Clark, H., and Eckard, R J. 2007. Methane emissions from dairy cows measured using the sulfur hexafluoride (SF6) tracer and chamber techniques. J. Dairy Sci. 90:2755-2766.</ref>; Donoghue et al., 2016<ref name=":14" />; Pinares-Patino et al., 2013<ref name=":2">Pinares-Patiño C.S., Hickey, S.M., Young, E.A., Dodds, K.G., MacLean, S., Molano, G., Sandoval, E., Kjestrup, H., Harland, R., Pickering, N.K., and McEwan, J.C. 2013. Heritability estimates of methane emissions from sheep. Animal 7: 316–321.</ref>). It has been concluded that 1-day measurement duration could be recommended as it will have a limited impact, less than 5%, on the efficiency of selection of MeY as compared to a selection on a 2-day measurement duration.

When repeated measures of CH4 emission of sheep are taken few days to two weeks apart the repeatability coefficients of MeY and RMPR drops to rep=0.36 [0.26 to 0.41] on average (Pinares-Patino et al., 2013<ref name=":2" />; Robinson et al., 2014a<ref name=":3b">Robinson, D.L., Goopy, J.P., Donaldson, A.J., Woodgate, R.T., Oddy, V.H., and Hegarty, R.S. 2014. Sire and liveweight affect feed intake and methane emissions of sheep confined in respiration chambers. Anima, 8:1935-1944.</ref>). Interestingly, repeatability maintains at a moderate level, rep=0.27 [0.23 to 0.53], when animals were measured several months or even years apart. Similar results were found in Angus cattle, rep=0.20, between MeY and RMPR measures taken more than 60 days apart (Donoghue et al., 2016<ref name=":14" />).

==== Conclusions and reccomendations ====
All these results show that animal effects exist on daily CH4 emissions and animal differences are partially under genetic determinism. This trait, as any other physiology trait, is subject to number of environmental effects and to evolution with time. Ranking animals on their CH4 emission requires standardization of the testing environment. Although highly precise, a single measure recorded in RC is not sufficient for characterizing an animals emission aptitude. In order to characterize a long term phenotype it is therefore recommended to record several 1-day measures, each a few weeks apart, instead of one single 2-day measure, keeping the testing environment as constant as possible.

=== DMPR with GEM ===
At each visit CH4 and CO2 fluxes are measured and animal emission rates are obtained by averaging the short-term flux measures recorded during the testing period. In a review of published results (Dorich et al., 2015<ref name=":4">Dorich, C.D., Varner, R.K., Pereira, A.B.D., Martineau, R., Soder, K.J., and Brito, A.F. 2015. Use of a portable, automated, open-circuit gas quantification system and the sulfur hexafluoride tracer technique for measuring enteric methane emissions in Holstein cows fed ad libitum or restricted. J. Dairy Sci. 98:2676-2681.</ref>; Hammond et al., 2015<ref name=":5">Hammond, K.J., Humphries, D.J., Crompton, L.A., Green, C., and Reynolds, C.K. 2015. Methane emissions from cattle: Estimates from short-term measurements using a GreenFeed system compared with measurements obtained using respiration chambers or sulphur hexafluoride tracer. Anim. Feed Sci. Technol. 203:41-52. doi:10.1016/j.anifeedsci.2015.02.008.</ref>; Velazco et al., 2016<ref name=":131">Velazco, J. I., Hegarty, R., Cottle, D., and Li, L. 2016. Quantifying daily methane production of beef cattle from multiple short-term measures using the GreenFeed system. <nowiki>https://rune.une.edu.au/web/handle/1959.11/23580</nowiki>.</ref>) Hammond et al. (2016A)<ref>Hammond, K.J., Crompton, L.A., Bannink, A., Dijkstra, J., Yáñez-Ruiz, D.R., O’Kiely, P., Kebreab, E., Eugenè, M.A., Yu, Z., Shingfield, K.J., Schwarm, A., Hristov, A.N., and Reynolds, C.K. 2016A. Review of current in vivo measurement techniques for quantifying enteric methane emission from ruminants. Anim. Feed Sci. Technol. 219:13–30. doi:10.1016/j.anifeedsci.2016.05.018</ref> concluded that the GEM system provides similar DMPR values as the RC or SF6 methods. Similar accuracy was found by Arbre et al. (2016)<ref name=":7">Arbre, M., Rochette, Y., Guyader, J., Lascoux, C., Gómez, L.M., Eugène, M., Morgavi, D.P., Renand, G., Doreau, M. and Martin, C. 2016. Repeatability of enteric methane determinations from cattle using either the SF6 tracer technique or the GreenFeed system. Anim. Prod. Sci. 56:238-243.</ref> for CH4 yield measured with GEM as compared with RC and SF6 measures.

The spot measures are highly variable since they include, in addition to the animal and environment effects, an important within-animal and within-day variance. The latter is considered as an error term. Consequently, the precision of the animal estimates increase with the number of spot measures averaged per animal. From the results reported by Renand and Maupetit (2016)<ref name=":8">Renand, G., and Maupetit, D. 2016. Assessing individual differences in enteric methane emission among beef heifers using the GreenFeed Emission Monitoring system: effect of the length of testing period on precision. Anim. Prod. Sci. 56:218-223.</ref> with 124 beef heifers controlled indoors, it can be shown that the coefficient of variation of that error term (CVe) decreases exponentially with the number of spot measures: 13.7%, 10.8%, 7.9% and 4.9% with 5, 10, 25 and 100 measures respectively. Results reported by Arbre et al. (2016)<ref name=":7" /> with 7 lactating dairy cows controlled indoors, also show that CVe decreases from 12.8% to 11.4%, 9.5% and 6.8% when the number of measures increases from 5 to 10, 25 and 100. With dairy cows at pasture, Waghorn et al. (2016)<ref name=":9">Waghorn, G.C., Jonker, A., and Macdonald, K A. (2016). Measuring methane from grazing dairy cows using GreenFeed. Anim. Prod. Sci. 56:252-257.</ref> showed that the coefficient of variation among 36 dairy cows at pasture was half (6.6 and 7.5%) when CH4 production rate was averaged over 16 days with approximately 18 to 26 measures per cow, as compared with 4 day averages with 4 to 6 measures per cow (13.0 and 17.2%). These authors concluded that at least 16 days are required to give confident estimates.

With 45 to 50 spot measures recorded during 2 weeks Arbre et al. (2016)<ref name=":7" /> and Renand and Maupetit (2016)<ref name=":8" /> obtained repeatabilityof 0.78 and 0.73 for DMPR estimates of 7 dairy cows and 124 beef heifers, respectively. A similar repeatability coefficient (0.74) was obtained by Huhtanen et al. (2015)<ref name=":24">Huhtanen, P., Cabezas-Garcia, E.H., Utsumi, S., and Zimmerman, S. 2015. Comparison of methods to determine methane emissions from dairy cows in farm conditions. J. Dairy Sci. 98:3394–3409. doi:10.3168/jds.2014-9118.</ref> with 25 dairy cows recorded during 3 weeks, with 20 to 30 samples per cow. Interestingly, these latter authors fitted gas concentration, airflow and head position measurement equipments into two automatic milking systems that were used to measure CH4 emission of 59 dairy cows during two periods of 10 days. After filtering data for acceptable head-position, the repeatability of DMPR was 0.75.

Considering the need to average enough spot measures and the advantage of measuring DMPR over long periods to take into account the emission variability with time, the GEM system should be run over several weeks. Averaging 40 to 50 spot measures per animal should provide a precise measure of the animal DMPR. The minimum duration of CH4 recording will depend on the number of spot measures actually recorded per day.

The GEM system relies on animals that voluntarily visit the GEM unit when attracted with pellets dispensed by a feeder at a controlled rate. The visitation frequency appears to be highly variable among different studies reported up to now. While some experiments report a very high frequency of cattle visiting the GEM units (up to 96%), the proportion of not visiting animals may be very high in other studies (up to 60%) (Dorich et al., 2015<ref name=":4" />; Hammond et al, 2015A<ref name=":5" />, Arbre et al., 2016<ref name=":7" />; Renand and Maupetit, 2016<ref name=":8" />; Velazco et al., 2016<ref name=":131" />; Waghorn et al., 2016<ref name=":9" />). The reason why some animals may not visit the unit is not obvious. That problem of no or low visiting frequency may jeopardize the precise ranking of animals on their DMRP. Training them is an important requisite for the success of DMPR recording with the GEM system (see recommendations on the C-Lock website). Palatability of the pellets used to attract the cattle should be high compared with the diet they receive in the trough or the grass they are grazing.

In addition to the effect on precision, the low visiting frequency may have an impact on accuracy if associated in some animals with specific time of visiting. Enteric CH4 emissions have a diurnal variation with a minimum at the end of night, before the first feeding, and a steady increase after each feeding. A weak diurnal pattern in CH4 emission was detected by Velazco et al. (2016)<ref name=":131" /> using GEM systems. Renand et al. (2013)<ref>Renand, G., Ricard, E., Maupetit, D., Thouly, J.C. 2013. Variability among individual young beef bulls and heifers in methane emissions. In: Book of abstracts of EAAP 64th annual meeting, Nantes, France. p. 195. (Wageningen Academic Publishers: Wageningen)</ref> observed significant differences between visit hours (CV=10%). If some animals visit the GEM at specific hours of the day, the rough average of spot measures will be biased. In order to get rid of this time effect on the DMPR measure, Dorich et al. (2015)<ref name=":4" /> and Hristov et al. (2016)<ref>Hristov, A.N., O,h J., Giallongo, F., Frederick, T., Harper, M.T., Weeks, H., Branco, F., Price, W.J., Moate, P.J., Deighto,n M.H., Williams, S.R.O., Kindermann, M., and Duval, S. 2016. Short communication: Comparison of the GreenFeed system with the sulfur hexafluoride tracer technique for measuring enteric methane emissions from dairy cows. J. Dairy Sci. 5461–5465. doi:10.3168/jds.2016-10897.</ref> came up with a protocol where the GEM units were moved sequentially from one cow to the next one over several days, so that all the cows were equally measured during different hours of the day. That protocol is possible only with tie stall cattle and is obviously not applicable for measuring large number of animals. However, with animals controlled in their production environment, the bias generated by potential specific visiting patterns can actually be removed if the measuring hour is taken into account in the linear model when estimating the animal effect.

As voluntary visiting of the GEM system may be a limiting factor under some conditions, measures of DMPR can be designed when animals are drinking or eating, i.e. several times per day. Velazco et al. (2016)<ref name=":131" /> showed that a GEM water unit prototype designed and built by C-Lock Inc., displayed different eructation patterns as compared with a plain GEM unit. They concluded that further development appears necessary before any application. Troy et al. (2016)<ref name=":0a" /> tested a CH4 hood (MH) system placed above an automated feeding bin. That system includes an air extraction fan for each hood with continuously recorded airflow. Methane concentration was measured using 4 infrared analyzers, one for 8 hoods. In this system one CH4 concentration value was recorded every 6 min. With 9 to 12 feeding events per day on average and feeding visits averaging 8 min, there were between 12 to 16 CH4 concentration values recorded and CH4 production rates calculated per day. The measurements were recorded during 46 days and ranking of animals in relation with the test duration was studied. However no repeatability coefficient was given for comparison with other methods. That system was compared with respiratory chambers results in two experiments with 82 and 80 steers fed different diet-treatment combinations. Over the whole experimental design, a good concordance was found between MH and RC results as a consequence that both methods detected similar effects for the diet-treatment effects. However no correlation was given between both methods within diet-treatment samples that are the essential information needed to evaluate the ability of this new method to predict individual DMPR.

==== Conclusions and reccomendations ====
With only a single gas analyzer for 8 feed bins, the time when useful CH4 concentration is recorded is certainly too short for including several eructation peaks. Fitting one gas analyzer per feed bin will combine advantages of the measurement time during visits of the GEM system with the visiting frequency allowed by the MH system.

=== MPR with PAC ===
The delay between the measurement and the last feeding has to be carefully monitored and taken into account when calculating animal emission values. As individual DMI is difficult to record, direct measurement of CH4 yield (MY=MPR/DMI) turns out to be impossible. Although not representative of a whole day production rate, that method can be used to characterize individual CH4 emission rates if standardized protocols are applied. It was first validated with 40 ewes measured 1 hour in PAC after three 22-hour measures in RC: a correlation of 0.71 was found between the two measures of CH4 production rate over 1 or 22 hours (Goopy et al., 2011<ref>Goopy, J.P., Woodgate, R., Donaldson, A., Robinson, D.L., and Hegarty, R.S. 2011. Validation of a short term methane measurement using portable static chambers to estimate methane production in sheep. Anim. Feed Sci. Technol. 166-167;219-226.</ref>). The 1-hour CH4 production measure in PAC has a moderate repeatability of rep=0.50 [0.37 to 0.60] when taken few days to seven weeks apart (Robinson et al., 2015<ref>Robinson, D.L., Goopy, J.P., Hegarty, R.S., and Oddy, V.H. 2015. Comparison of repeated measurements of methane production in sheep over 5 years and a range of measurement protocols. J. Anim. Sci. 93:4637-4650.</ref>; Goopy et al., 2016<ref>Goopy, J.P., Robinson, D.L., Woodgate, R.T., Donaldson, A.J., Oddy, V.H., Vercoe, P. E., and Hegarty, R.S. 2016. Estimates of repeatability and heritability of methane production in sheep using portable accumulation chambers. Anim. Prod. Sci. 56:116-122.</ref>). Heritability coefficient of this 1-hour CH4 production measure is estimated to h²=0.12 in a population of 2,279 sheep (Robinson et al., 2014<ref name=":3b" />) with a repeatability coefficient rep=0.25.

==== Conclusions and recomendations ====
The authors recommend using the mean of 3 PAC measurements in order to get accurate phenotype estimates.</div>
</div>

= Proxies =
<div class="mw-collapsible mw-collapsed">
<div>
== Introduction ==
Large-scale measurements of enteric CH4 emissions from dairy cows are needed for effective monitoring of strategies to reduce the carbon footprint of milk production, as well as for incorporation of CH4 emissions into breeding programs. However, measurements on a sufficiently large scale are difficult and expensive. Proxies for CH4 emissions can provide an alternative, but each approach has limitations. Negussie et al. (2019)<ref name=":30">Negussie, E., González Recio, O., de Haas, Y., Gengler N., Soyeurt, H., Peiren, N., Pszczola, M., Garnsworthy, P., Battagin, M., Bayat, A., Lassen, J., Yan, T., Boland, T., Kuhla, B., Strabel, T., Schwarm, A., Vanlierde, A., and Biscarini, F. 2019. Machine learning ensemble algorithms in predictive analytics of dairy cattle methane emission using imputed versus non-imputed datasets. 7th GGAA – Greenhouse Gas and Animal Agriculture Conference held from August 4th to 8th, Iguassu Falls/Brazil. Oral communication, Book of Abstracts Page 40. <nowiki>http://www.ggaa2019.org/sites/default/files/proceedings-ggaa2019.pdf</nowiki></ref> recently showed the potential of proxies proxies that are easy to record in the farm. These proxies can be gathered in most farms and are a realistic threshold accuracy that can be obtained without more fancy proxies. Several techniques have been developed for the measurement of CH4 emissions from ruminants, with varying degrees of accuracy (see reviews by Cassandro et al., 2013<ref>Cassandro, M., Mele, M., Stefanon, B.. 2013. Genetic aspects of enteric methane emission in livestock ruminants. Italian J. Anim. Sci. 12:e73: 450-458.</ref> and Hammond et al., 2016A<ref name=":25">Hammond, K.J., Crompton, L.A., Bannink, A., Dijkstra, J., Yáñez-Ruiz, D.R., O’Kiely, P., Kebreab, E., Eugenè, M.A., Yu, Z., Shingfield, K.J., Schwarm, A., Hristov, A.N., and Reynolds, C.K. 2016A. Review of current in vivo measurement techniques for quantifying enteric methane emission from ruminants. Anim. Feed Sci. Technol. 219:13–30. doi:10.1016/j.anifeedsci.2016.05.018.</ref>), but routine individual measurements on a large scale (a requisite for genetic selection) have proven to be difficult and expensive (Pickering et al., 2015<ref>Pickering, N.K., Oddy, V.H., Basarab, J.A., Cammack, K., Hayes, B J., Hegarty, R.S., McEwan, J.C., Miller, S., Pinares, C., and de Haas, Y. 2015. Invited review: Genetic possibilities to reduce enteric methane emissions from ruminants. Animal 9:1431-1440.</ref>; Negussie et al., 2016<ref>Negussie E., Lehtinen, J., Mäntysaari, P., Liinamo, A-E., Mäntysaari, E., and Lidauer, M.. 2016. Non-invasive individual methane measurements in dairy cows using photoacoustic infrared spectroscopy technique. 6th Greenhouse Gases Animal Agriculture Conference (GGAA2016) 14-18 February 2016. Melbourne, Australia. Abstract. p62.</ref>). Therefore, identifying proxies (i.e. indicators or indirect traits) that are correlated to CH4 emission, but which are easy and relatively low-cost to record on a large scale, is a much needed alternative. Proxies might be less accurate, but could be measured repeatedly to reduce random noise. The (potential) proxies range from simple and low-cost measurements such as body weight, to high-throughput milk MIR, to more demanding measures like rumen morphology, rumen metabolites or microbiome profiling.

Combining proxies that are easy to measure and cheap to record could provide predictions of CH4 emissions that are sufficiently accurate for selection and management of cows with low CH4 emissions.

== Available Proxies ==
A large array of CH4 proxies differing widely in accuracy and applicability under different conditions have been reported. The ideal proxy would be highly phenotypically and genetically correlated with CH4 emissions and could easily, and potentially repeatedly, be measured on a large scale. A systematic summary and assessment of existing knowledge is needed for the identification of robust and accurate CH4 proxies for future use. Table 5 summarizes proxies for CH4 production, and Table 6 summarizes results from combining proxies to improve predictability of proxies for CH4 prediction.
{| class="wikitable"
|-
! colspan="3" | ''Table 5. Available methane proxies include: (1) feed intake and feeding behaviour, (2) rumen function, metabolites and microbiome, (3) milk production and composition, (4) hind-gut and faeces, and (5) measurements at the level of the whole animal.''
|- style="background-color:#efefef;"
|+
!Proxy
!Description / conclusion
!Reference
|-
| colspan="3" |'''(1) Feed intake and feeding behavior'''
|-
|Dry Matter Intake (DMI)
|DMI predict MeP with R2= 0.06-0.64, and ME intake predict MeP with R2= 0.53-0,55
| Ellis et al. (2007);<ref>Ellis, J.L., Kebreab, E., Odongo, N.E., McBride, B.W., Okine, E.K., and France, J. 2007. Prediction of methane production from dairy and beef cattle. J. Dairy Sci. 90:3456–3466.</ref>
Mills et al. (2003)<ref>Mills, J.A.N., Kebreab, E., Yates, C.M., Crompton, L.A., Cammell, S.B., Dhanoa, M.S., Agnew, R.E., and France, J. 2003. Alternative approaches to predicting methane emissions from dairy cows. J. Anim. Sci. 81:3141–3150.</ref>;

Negussie et al. (2019)<ref>Negussie, E., González Recio, O., de Haas, Y., Gengler N., Soyeurt, H., Peiren, N., Pszczola, M., Garnsworthy, P., Battagin, M., Bayat, A., Lassen, J., Yan, T., Boland, T., Kuhla, B., Strabel, T., Schwarm, A., Vanlierde, A., and Biscarini, F. 2019. Machine learning ensemble algorithms in predictive analytics of dairy cattle methane emission using imputed versus non-imputed datasets. 7th GGAA – Greenhouse Gas and Animal Agriculture Conference held from August 4th to 8th, Iguassu Falls/Brazil. Oral communication, Book of Abstracts Page 40. <nowiki>http://www.ggaa2019.org/sites/default/files/proceedings-ggaa2019.pdf</nowiki></ref>
|-
|Gross Energy intake (GE)
|Predict MeP with RMSPE= 3.01.
| Moraes et al. (2014)<ref name=":0b">Moraes, L.E., Strathe, A.B., Fadel, J.G., Casper, D.P., and Kebreab, E. 2014. Prediction of enteric methane emissions from cattle. Glob. Change Biol. 20:2140–2148.</ref>
|-
|Feeding behavior
| Magnitude and direction of relation to MeP varies across studies
|Nkrumah et al. (2006);<ref>Nkrumah, J.D., Okine, E.K., Mathison, G.W., Schmid, K., Li, C., Basarab, J.A., Price, M.A., Wang, Z., and Moore, S.S. 2006. Relationships of feedlot feed efficiency, performance, and feeding behavior with metabolic rate, methane production, and energy partitioning in beef cattle. J. Anim. Sci. 84:145-153.</ref>
Jonker et al., 2014<ref>Jonker, A., Molano, G., Antwi, C., Waghorn, G.. 2014. Feeding lucerne silage to beef cattle at three allowances and four feeding frequencies affects circadian patterns of methane emissions, but not emissions per unit of intake. Anim. Prod. Sci.54:1350-1353.</ref>
|-
| Rumination time
|High rumination relates to more milk, consume more concentrate and produce more CH4, lower RMP and MeI
| Watt et al. (2015)<ref>Watt, L.J., Clark, C.E.F., Krebs, G.L., Petzel, C.E., Nielsen, S., and Utsumi, S.A. 2015. Differential rumination, intake, and enteric methane production of dairy cows in a pasture-based automatic milking system. J. Dairy Sci. 98:7248–7263.</ref>;
López- Paredes et al. (2020)<ref>Lopez-Paredes, J., Goiri, I., Atxaerandio, R., García-Rodríguez, A., Ugarte, E., Jiménez-Montero, J.A., Alenda, R and González-Recio, O. 2020. Mitigation of greenhouse gases in dairy cattle via genetic selection (i): Genetic parameters of direct methane using non-invasive methods and its proxies. J. Dairy Sci. 103.</ref>
|-
|Rumen microbiome
|The metagenome can predict DMI, and classify high vs low intakes
|Delgado et al. (2019)<ref>Delgado, B., Bach A., Guasch I., González C, Elcoso G., Pryce J.E., Gonzalez-Recio O. (2019).Whole rumen metagenome sequencing allows classifying and predicting feed efficiency and intake levels in cattle. Scientific Reports 9: 11. doi:10.1038/s41598-018-36673-w</ref>
|-
| colspan="3" |'''(2) Rumen function, metabolites and microbiome'''
|-
|Dietary antimethanogenic compounds
|Inhibitors of the enzyme methyl coenzyme-M reductase: bromochoromethane; chloroform; 3- nitrooxypropanol (not always)
|Denman et al., 2007;<ref>Denman, S.E., Tomkins, N.W., and McSweeney, C.S. 2007. Quantitation and diversity analysis of ruminal methanogenic populations in response to the antimethanogenic compound bromochloromethane. FEMS Microbiol. Ecol. 62:313-322.</ref>
Knight et al., 2011;<ref>Knight, T., Ronimus, R.S., Dey, D., Tootill, C., Naylor, G., Evans, P., Molano, G., Smith, A., Tavendale, M., Pinares-Patiño, C.S., and Clark, H. 2011. Chloroform decreases rumen methanogenesis and methanogen populations without altering rumen function in cattle. Anim. Feed Sci. Technol. 166-167:101-112.</ref>

Haisan et al., 2014<ref>Haisan, J., Sun, Y., Guan, L.L., Beauchemin, K.A., Iwaasa, A., Duval, S., Barreda, D.R., and Oba, M. 2014. The effects of feeding 3-nitrooxypropanol on methane emissions and productivity of Holstein cows in mid lactation. J. Dairy Sci. 97:3110-3119.</ref>;

Romero-Perez et al., 2014<ref>Romero-Perez, A., Okine, E.K., McGinn, S.M., Guan, L.L., Oba, M., Duval, S.M., Kinderman,n M., and Beauchemin, K.A. 2014. The potential of 3-nitrooxypropanol to lower enteric methane emissions from beef cattle. J. Anim. Sci. 92:4682-4693.</ref>
|-
|Dietary antimicrobial compounds
|Induce reductions in both MeP and methanogens numbers: nitrates, anacardic acid (cashew nut shell liquid), monensin, isobutyrate
|Iwamoto et al., 2002;<ref>Iwamoto, M., Asanuma, N., and Hin,o T. 2002. Ability of Selenomonas ruminantium, Veillonella parvula, and Wolinella succinogenes to reduce nitrate and nitrite with special reference to the suppression of ruminal methanogenesis. Anaerobe. 8:209-215.</ref>
Kubo et al., 1993<ref>Kubo, I., Muroi, H., Himejima, M., Yamagiwa, Y., Mera, H., Tokushima, K., Ohta, S., and Kamikawa, T. 1993. Structure-antibacterial activity relationships of anacardic acids. J. Agric. Food Chem. 41:1016-1019.</ref>;

van Zijderveld et al., 2010<ref>Van Zijderveld, S.M., Gerrits, W.J.J., Apajalahti, J.A., Newbold, J.R., Dijkstra, J., Leng, R A., and Perdok, H.B. 2010. Nitrate and sulfate: effective alternative hydrogen sinks for mitigation of ruminal methane production in sheep. J. Dairy Sci. 93:5856-5866.</ref>;

Veneman et al., 2015<ref>Veneman, J.B., Muetzel, S., Hart, K.J., Faulkner, C.L., Moorby, J.M., Perdok, H.B., and Newbold, C.J. 2015. Does Dietary Mitigation of Enteric Methane Production Affect Rumen Function and Animal Productivity in Dairy Cows? PLoS ONE 10(10): e0140282. doi: 10.1371/journal.pone.0140282</ref>;

Shinkai et al., 2012<ref>Shinkai, T., Enishi, O., Mitsumori, M., Higuchi, K., Kobayashi, Y., Takenaka, A., Nagashima, K., and Mochizuki, M. 2012. Mitigation of methane production from cattle by feeding cashew nut shell liquid. J. Dairy Sci. 95:5308-5316.</ref>;

Wang et al., 2015<ref>Wang, C., Liu, Q., Zhang, Y.L., Pei, C.X., Zhang, S.L., Wang, Y.X., Yang, W.Z., Bai, Y.S., Shi, Z.G., and Liu, X.N. 2015. Effects of isobutyrate supplementation on ruminal microflora, rumen enzyme activities and methane emissions in Simmental steers. J. Anim. Physiol. Anim. Nutr. (Berl). 99:123-131.</ref>
|-
|Rumen microbiome profile
|High Fibrobacteres, Quinella ovalis and Veillonellaceae and low Ruminococcaceae, Lachnospiraceae and Clostridiales associate with low- CH4 phenotypes and high propionate
Protozoa concentration
|Kittelmann et al., 2014<ref>Kittelmann, S., Pinares-Patiño, C.S., Seedorf, H., Kirk, M.R., Ganesh, S., McEwan, J.C., and Janssen, P.H. 2014. Two different bacterial community types are linked with the low-methane emission trait in sheep. PLoS One 9:e103171.</ref>;
Wallace et al., 2014<ref>Wallace, R. ., Rooke, J A., Duthie, C.-A., Hyslop, J.J., Ross, D.W., McKain, N., de Souza, S.M., Snelling, T.J., Waterhouse, A., and Roehe, R. 2014. Archaeal abundance in post-mortem ruminal digesta may help predict methane emissions from beef cattle. Sci. Rep. 4:5892.</ref>;

Sun et al., 2015<ref>Sun, X., Henderson, G., Cox, F., Molan,o G., Harrison, S.J., Luo, D., Janssen, P.H., and Pacheco, D. 2015. Lambs Fed Fresh Winter Forage Rape (Brassica napus L.) Emit Less Methane than Those Fed Perennial Ryegrass (Lolium perenne L.), and Possible Mechanisms behind the Difference. PLoS One 10(3):e0119697: DOI: 10.1371/journal.pone.0119697</ref>;

Guyader et al., 2014<ref>Guyader, J., Eugène, M., Nozière, P., Morgavi, D.P., Doreau, M., and Martin, C. 2014. Influence of rumen protozoa on methane emission in ruminants: a meta-analysis approach. Animal 8:1816-1825.</ref>
|-
|Rumen microbiome profile
|Predict MeP with R2 up to 0.55
|Ross et al. 2013a<ref>Ross, E. M., P.J . Moate, L.C. Marett, B.G. Cocks and B.J. Hayes. 2013a. Investigating the effect of two methane-mitigating diets on the rumen microbiome using massively parallel sequencing. J. Dairy Sci. 96:6030–6046.</ref>;
Ross et al. (2013b)<ref>Ross, E. M., Moate, P. J., Marett, L.C., Cocks, B.G., and Hayes, B.J. 2013b. Metagenomic Predictions: From Microbiome to Complex Health and Environmental Phenotypes in Humans and Cattle. PLoS One DOI: 10.1371/journal.pone.0073056.</ref>
|-
|Microbial genes
|20 (out of 3970 identified) related to CH4 emissions
|Roehe et al. (2016)<ref>Roehe R., Dewhurst, R.J., Duthie, C-A., Rooke, J.A., McKain, N., Ross, D.W., Hyslop, J.J., Waterhouse, A., Freeman, T.C., Watson, M., and Wallace, R.J. 2016. Bovine Host Genetic Variation Influences Rumen Microbial Methane Production with Best Selection Criterion for Low Methane Emitting and Efficiently Feed Converting Hosts Based on Metagenomic Gene Abundance. PLoS Genet 12(2): e1005846. doi:10.1371/journal.pgen.1005846.</ref>
|-
|Rumen volume (Xray Computed Tomography) and retention time
|Low-MeY sheep had smaller rumens. Faster passage= less time to ferment substrate - explained 28% of variation in MeP
|Pinares Patiño et al., 2003<ref>Pinares-Patiño C.S., Hickey, S.M., Young, E.A., Dodds, K.G., MacLean, S., Molano, G., Sandoval, E., Kjestrup, H., Harland, R., Pickering, N.K., and McEwan, J.C. 2013. Heritability estimates of methane emissions from sheep. Animal 7: 316–321.</ref>;
Goopy et al., 2014<ref>Goopy, J.P., Donaldson, A., Hegarty, R., Vercoe, P.E., Haynes, F., Barnett, M., and Oddy, V.H. 2014. Low-methane yield sheep have smaller rumens and shorter rumen retention time. Br. J. Nutr. 111:578-585.</ref>;

Okine et al. (1989)<ref>Okine, E. ., Mathiso,n G.W., and Hardin, R.T. 1989. Effects of changes in frequency of reticular contractions on fluid and particulate passage rates in cattle. J. Anim. Sci. 67:3388–3396.</ref>
|-
|Blood triiodothyronine concentration
|Reduced MeY
|Barnett et al. (2012)<ref>Barnett, M.C., Goopy, J.P., McFarlane, J.R., Godwin, I.R., Nolan, J.V., and Hegarty, R.S. (2012). Triiodothyronine influences digesta kinetics and methane yield in sheep. Anim. Prod. Sci. 52:572-577.</ref>
|-
|Acetate to propionate ratio in ruminal fluid
|Positively associated with CH4 emissions, but not confirmed in all studies, sometimes opposite relation
|Mohammed et al., 2011;<ref name=":1b">Mohammed, R., McGinn, S.M., and Beauchemin, K.A. 2011. Prediction of enteric methane output from milk fatty acid concentrations and rumen fermentation parameters in dairy cows fed sunflower, flax, or canola seeds. J. Dairy Sci. 94:6057–6068.</ref>
Fievez et al., 2012<ref>Fievez V., Colma,n E., Castro-Montoya, J.M., Stefanov, I., and Vlaeminck, B. 2012. Milk odd- and branched-chain fatty acids as biomarkers of rumen function – An update. Anim. Feed. Sci. Technol. 172:51–65.</ref>;

Chung et al., 2011<ref>Chung, Y.-H., Walker, N.D., McGinn, S.M., and Beauchemin, K.A. 2011. Differing effects of 2 active dried yeast (Saccharomyces cerevisiae) strains on ruminal acidosis and methane production in nonlactating dairy cows. J. Dairy Sci. 94:2431–2439.</ref>;

Van Zijderveld et al., 2010<ref>Van Zijderveld, S.M., Gerrits, W.J.J., Apajalahti, J.A., Newbold, J.R., Dijkstra, J., Leng, R A., and Perdok, H.B. 2010. Nitrate and sulfate: effective alternative hydrogen sinks for mitigation of ruminal methane production in sheep. J. Dairy Sci. 93:5856-5866.</ref>
|-
| colspan="3" |'''(3) Milk production and composition'''
|-
|Modelling approach
|Doubling milk production only adds 5 kg to the MeP and so greatly reduces MeY
|Kirchgessner et al. (1995)<ref>Kirchgessner M., Windisch, W., and Muller, H.L. 1995. Nutritional factors for the quantification of methane production. In: Ruminant Physiology: Digestion, Metabolism, Growth and Reproduction. Proceedings 8th International Symposium on Ruminant Physiology (eds W. Von Engelhardt, S. Leonhard-Marek, G. Breves and D. Giesecke). Reproduction Proceedings 8th International Symposium on Ruminant Physiology. Ferdinand Enke Verlag, Stuttgart, Germany. pp. 333-348.</ref>;
Hristov et al. (2014)<ref>Hristov, A.N., Johnson, K.A., and Kebreab, E, 2014. Livestock methane emissions in the United States. Proc. Natl. Aacad. Sci. 111:E1320; doi:10.1073/pnas.1401046111</ref>
|-
|Milk fat content
|key explanatory variable for predicting CH4: A moderate negative genetic correlation with infrared predicted
MeI: correlations MeP = 0,08 and MeI = - 0.13
|Moraes et al. (2014)<ref name=":31">Moraes, L.E., Strathe, A.B., Fadel, J.G., Casper, D.P., and Kebreab, E. 2014. Prediction of enteric methane emissions from cattle. Glob. Change Biol. 20:2140–2148.</ref>;
Kandel et al., 2014A<ref name=":37" />, B<ref name=":3">Kandel, P.B., Vanderick, S., Vanrobays, M.L., Vanlierde, A., Dehareng, F., Froidmont, E., Soyeur,t H., and Gengler, N. 2014B. Consequences of selection for environmental impact traits in dairy cows. In: 10th World Congress on Genetics Applied to Livestock Production (WCGALP), 17-22 August, 2014. Vancouver, Canada.</ref>;

Vanlierde et al. (2015)<ref name=":4a">Vanlierde, A., Vanrobays, M.L., Dehareng, F., Froidmont, E., Soyeurt, H., McParland, S., Lewis, E., Deighton, M.H., Grandl, F., Kreuzer, M., Grendler, B., Dardenne, P., and Gengler, N. 2015. Hot topic: Innovative lactation-stage-dependent prediction of methane emissions from milk mid-infrared spectra. J. Dairy Sci. 98:5740–5747.</ref>
|-
|Milk fat content
|A positive relationship between VFA proportions and methanogenesis is expected as a consequence of the common biochemical pathways; Dietary unsaturated fatty acids are negatively associated with CH4 emissions
|Vlaeminck et al., 2006<ref>Vlaeminck, B., Fievez, V., Cabrita, A.R.J., Fonseca, A.J.M., and Dewhurst, R.J. 2006. Factors affecting odd- and branched-chain fatty acids in milk: A review. Anim. Feed Sci. Technol. 131:389–417.</ref>;
Van Lingen et al., 2014<ref name=":5a">Van Lingen H.J., Crompton, L.A., Hendriks, W.H., Reynolds, C.K., Dijkstra, J. 2014. Meta-analysis of relationships between enteric methane yield and milk fatty acid profile in dairy cattle. J. Dairy Sci. 97:7115-7132.</ref>
|-
|Milk protein yield
|Correlation with Mel = - 0.47 or -0.09, MeP = 0.53
|Kandel et al. (2014A)<ref name=":37" />;
Vanlierde et al. (2015)<ref name=":4a" />
|-
|Lactose
|Variable correlations: MeP = 0,33; MeI = - 0.21; R = 0.19 for CH4 emission
|Miettinen and Huhtanen (1996)<ref>Miettinen, H., and Huhtanen, P. 1996. Effects of the ration of ruminal propionate to butyrate on milk yield and blood metabolites in dairy cows. J. Dairy Sci. 79:851–861.</ref>;
Dehareng et al. (2012)<ref name=":32">Dehareng, F., Delfosse, C., Froidmont, E., Soyeurt, H., Martin, C., Gengler, N., Vanlierde, A., and Dardenne, P. 2012. Potential use of milk mid-infrared spectra to predict individual methane emission of dairy cows. Animal 6:1694-701.</ref>
|-
|Somatic cell score
|Genetic correlation with infrared predicted MeI: R = 0.07
|Kandel et al. (2014A<ref name=":37" />, B<ref name=":3" />)
|-
|Prediction equations Milk FA and CH4 emissions, including from MIR data
|R² ranged between 47 and 95%; relationships between the individual milk FA and MeP differed considerably and the correlations between CH4 and milk FA vary throughout the lactation
|Chilliard et al. (2009)<ref>Chilliard Y., Martin ,C., Rouel, J., and Doreau, M. 2009. Milk fatty acids in dairy cows fed whole crude linseed, extruded linseed, or linseed oil, and their relationship with methane output. J. Dairy Sci. 92:5199-5211.</ref>;
Delfosse et al. (2010)<ref>Delfosse, O., Froidmont, E., Fernandez Pierna, J. A., Martin, C., and Dehareng, F. 2010. Estimation of methane emissions by dairy cows on the basis of milk composition. In: Greenhouse Gases and Animal Agriculture Conference. 2010; GGAA2010: 4. Greenhouse Gases and Animal Agriculture Conference, Banff, CAN, 2010-10-03-2010-10-08, 60-61.</ref>;

Castro-Montoya et al. (2011)<ref>Castro Montoya, J., Bhagwat, A.M., Peiren, N., De Campeneere, S., De Baets, B., and Fievez, V. 2011. Relationships between odd- and branched-chain fatty acid profiles in milk and calculated enteric methane proportion for lactating dairy cattle. Anim. Feed Sci. Technol.166:596–602.</ref>;

Dijkstra et al. (2011)<ref>Dijkstra, J., van Zijderveld, S.M., Apajalahti, J.A., Bannink, A., Gerrits, W.J.J., Newbold, J.R., Perdok, H.B.,, and Berends, H. 2011. Relationships between methane production and milk fatty acid profiles in dairy cattle. Anim. Feed Sci. Technol. 166–167:590–595.</ref>;

Kandel et al. (2013)<ref>Kandel, P.B., Vanrobays, M.L., Vanlierde, A., Dehareng, F., Froidmont, E., Dardenne, P., Lewis, E., Buckley, F., Deighton, M.H., McParland, S. and Gengler, N., 2013. Genetic parameters for methane emissions predicted from milk mid-infrared spectra in dairy cows. J. Dairy Sci. 95(E-1):p.388.</ref>;

Mohammed et al. (2011)<ref name=":1b" />;

Van Lingen et al. (2014)<ref name=":5a" />;

Williams et al. (2014)<ref>Williams, S.R.O., Williams, B., Moate, P.J., Deighton, M.H., Hannah, M.C., and Wales, W.J. 2014. Methane emissions of dairy cows cannot be predicted by the concentrations of C8:0 and total C18 fatty acids in milk. Anim. Prod. Sci. 54:1757–1761. <nowiki>http://dx.doi.org/10.1071/AN14292</nowiki>.</ref>;

Dijkstra et al. (2016)<ref>Dijkstra, J., van Zijderveld, S.M., Apajalahti, J.A., Bannink, A., Gerrits, W.J.J., Newbold, J.R., Perdok, H.B.,, and Berends, H. 2011. Relationships between methane production and milk fatty acid profiles in dairy cattle. Anim. Feed Sci. Technol. 166–167:590–595.</ref>;

Rico et al. (2016)<ref name=":33">Rico D.E., Chouinard, P.Y., Hassanat, F., Benchaar, C., and Gervais, R. 2016. Prediction of enteric methane emissions from Holstein dairy cows fed various forage sources. Animal 10:203-211. doi:10.1017/S1751731115001949</ref>;

Van Gastelen and Dijkstra (2016)<ref name=":36">Van Gastelen, S., and Dijkstra, J.. 2016. Prediction of methane emission from lactating dairy cows using milk fatty acids and mid-infrared spectroscopy. J. Sci. Food Agric. 96:3963-3968. DOI: 10.1002/jsfa.7718.</ref>;

Vanrobays et al. (2016);<ref>Vanrobays, M.-L., Bastin, C., Vandenplas, J., Hammami, H., Soyeurt, H., Vanlierde, A., Dehareng, F., Froidmont, E., and Gengler, N. 2016. Changes throughout lactation in phenotypic and genetic correlations between methane emissions and milk fatty acid contents predicted from milk mid-infrared spectra. J. Dairy Sci. 99:1–14. <nowiki>http://dx.doi.org/10.3168/jds.2015-10646</nowiki></ref>

Bougoin et al., (2019)<ref name=":34">Bougouin, A., Appuhamy, J.A.D.R.N., Ferlay, A., Kebreab, E., Martin, C., Moate, P.J., Benchaar, C., Lund, P., and Eugène, M. 2019. Individual milk fatty acids are potential predictors of enteric methane emissions from dairy cows fed a wide range of diets: Approach by meta-analysis. J. Dairy Sci. 102:10616–10631. DOI: <nowiki>http://doi.org/10.3168/jds.2018-15940</nowiki></ref>
|-
| colspan="3" |'''(4) Hind-gut and feces'''
|-
|Whole tract digestibility (potential as supporting factors in the prediction of enteric CH4 emissions)
|Main effects relate to rumen (see above), but energy digestibility as a supporting factor to GE intake improved the accuracy of CH4 prediction, despite the fact that there was no direct linear relationship between energy digestibility and MeY and in % of GE intake
|Yan et al., 2009<ref name=":6">Yan, T., Porter, M.G., and Mayne, S.C. 2009. Prediction of methane emission from beef cattle using data measured in indirect open-circuit respiration calorimeters. Animal 3:1455-1462.</ref>
|-
|Ratio of acetic and butyric acid divided by propionic acid
|Methane yield positive relation
|Moss et al., 2000<ref>Moss A.R., Jouany, J.P., and Newbold, J. 2000. Methane production by ruminants: Its contribution to global warming. Annal. Zootech. 49:231-253.</ref>
|-
| colspan="3" |'''(5) Whole animal measurements'''
|-
|Body weight and conformation
|Prediction models; primary predictor for enteric MeP
|Moraes et al. (2014)<ref name=":0">de Haas, Y., Pszczola, M., Soyeurt, H., Wall, E., and Lassen, J. 2017. Invited review: Phenotypes to genetically reduce greenhouse gas emissions in dairying. J. Dairy Sci. 100:855-870.</ref>;
Holter and Young, 1992; <ref>Holter J.B., and Young, A.J. 1992. Methane production in dry and lactating Holstein cows. J. Dairy Sci. 75:2165–2175.</ref>

Yan et al., 2009<ref name=":6" />
|-
|Body weight
|Relationship with MeI: r = 0.44; relationship between body weight and rumen capacity
|Antunes-Fernandes et al. (2016)<ref>Antunes-Fernandes, E.C., van Gastelen, S., Dijkstra, J., Hettinga K.A., and Vervoort, J. 2016. Milk metabolome relates enteric methane emission to milk synthesis, and energy metabolism pathways. J. Dairy. Sci. 99:6251-6262.</ref>;
Demment and Van Soest, 1985<ref>Demment, M.W., and Van Soest, P.J. 1985. A nutritional explanation for body-size patterns of ruminant and nonruminant herbivores. Am. Nat. 125:641–672.</ref>
|-
|Body weight
|Key explanatory variable for enteric MeP
|No reference available
|-
|Conformation traits: affects enteric MeP
|Indicators for rumen volume (via feed intake and rumen passage rates); BCS
|Agnew and Yan, 2000<ref>Agnew, R.E. and Yan, T. 2000. The impact of recent research on energy feeding systems for dairy cattle. Livest. Prod. Sci. 66:197-215.</ref>
|-
|Lactation stage
|Complementary proxy
|Vanlierde et al. (2015)<ref name=":4a" />
|}</div>It is evident that no single proxy offers a good solution in terms of all of these attributes, though the low cost and high throughput make milk MIR a good candidate for further work on refining methods, improving calibrations and exploring combinations with other proxies.

== Combining proxies for methane ==
Although milk MIR shows promise as a single proxy for CH4 emissions, there may be advantages in using two or more proxies in combination. There are two potential reasons why a combination of proxies might be appropriate: (i) proxies may describe independent sources of variation in CH4 emissions, and (ii) one proxy allows correction for shortcomings in the way the other proxy describes CH4 emissions (e.g. taking into account lactation stage if CH4 emissions prediction coefficients change during the lactation). See also Negussie ''et al.'' (2019)<ref name=":30" />.
<center>
{| class="wikitable"
| colspan="3" |'''''Table 7. Combinations of proxies for methane.'''''
|- style="background-color:#efefef;"
|'''Proxy combinations'''
|'''Results'''
|'''References'''
|-
|Rumen microbiome + VFA
|Combination of rumen VFA proportions and pH + modelling may be more informative
|Brask ''et al.'' 2015<ref>Brask, M., Weisbjerg, M.R., Hellwing, A.L.F., Bannink, A., and Lund, P. 2015. Methane production and diurnal variation measured in dairy cows and predicted from fermentation pattern and nutrient or carbon flow Animal. 9:1795-1806. DOI: <nowiki>http://dx.doi.org/10.1017/S1751731115001184</nowiki></ref>
|-
|Methanogen abundance in rumen fluid + proxy, a chemical marker for methanogens (archaeol)
|
|McCartney ''et al.'' (2013)<ref>McCartney, C.A., Bull, I.D., Waters, S.M., and Dewhurst, R.J. 2013. Technical note: Comparison of biomarker and molecular biological methods for estimating methanogen abundance. J. Anim. Sci. 91:5724–5728.</ref>
|-
|Fecal ether lipids (ratio of diether to tetraether lipids) + rumen pH
|Combining measurements of rumen VFA, pH and the microbiome should be more informative for predicting CH4 emissions
|McCartney ''et al.'' (2014)<ref>McCartney, C.A., Dewhurst, R.J. and Bull, I.D. 2014. Changes in the ratio of tetraether to diether lipids in cattle feces in response to altered dietary ratio of grass silage and concentrates. J. Anim. Sci. 92:4095-4098.</ref>; Ann ''et al.'', 1996
|-
|Feed intake (determined by body weight, production level, growth rate and feed quality)
|Main driver for CH4 emissions; should be all included in models for CH4
|Moraes ''et al.'' 2014,<ref name=":31" />
|-
|DMI and diet composition
|Combine database to predict CH4
|Niu ''et al.'', 2018<ref>Niu, M., Kebreab, E., Hristov, A. N., Oh, J., Arndt, C., Bannink, A., Bayat, A. R., Brito, A. F., Boland, T., Casper, D., Crompton, L. A., Dijkstra, J., Eugène, M. A., Garnsworthy, P. C., Haque, M. N., Hellwing, A.L.F., Huhtanen, P., Kreuzer, M., Kuhla, B., Lund, P., Madsen, J., Martin, C., Mcclelland, S. C., Mcgee, M., Moate, P. J., Muetzel, S., Muñoz, C., O'Kiely, P., Peiren, N., Reynolds, C. K., Schwarm, A., Shingfield, K. J., Storlien, T. M., Weisbjerg, M. R., Yáñez-Ruiz, D. R., and Yu, Z. 2018. Prediction of enteric methane production, yield, and intensity in dairy cattle using an intercontinental database. Glob. Change Biol. 2018;1–22, DOI: 10.1111/gcb.14094.</ref>; Van Lingen ''et al.'', 2019<ref>Van Lingen, H.J., Niu, M., Kebreab, E., Valadares Filho, S.C., Rooke, J.A., Duthie, C.A., Schwarm, A., Kreuzer, M., Hynd, P.I., Caetano, M., and Eugène, M. 2019. Prediction of enteric methane production, yield and intensity of beef cattle using an intercontinental database. Agric. Ecosyst. Environ. 283:106575.</ref>
|-
|Range of prediction equations for CH4 production
|Feed intake = primary predictor of total CH4 production (accounted for 52 to 64%); Combining more factors did indeed improve the prediction equation by 15 to 35%
|Ramin & Huhtanen (2013)<ref>Ramin, M. and Huhtanen, P.. 2013. Development of equations for predicting methane emissions from ruminants. J. Dairy Sci. 96:2476-2493.</ref>; Knapp ''et al.'', 2015<ref>Knapp J.R., Laur, G.L., Vadas, P.A.. Weiss, , and Tricarico, J.M. 2015. Invited review: Enteric methane in dairy cattle production: Quantifying the opportunities and impact of reducing emissions. J. Dairy Sci. 97:3231–3261.</ref>; Sauvant & Nozière (2016)<ref>Sauvant, D., and Nozière, P. 2016. Quantification of the main digestive processes in ruminants: the equations involved in the renewed energy and protein feed evaluation systems. Animal 10:755–770. <nowiki>https://doi:10.1017/S1751731115002670</nowiki>. </ref>
|-
|Rumen measurements (VFA, pH, protozoa counts) + feed intake (total DMI, forage DMI and FA intake) + production parameters (milk yield and composition) + milk FA
|Suggest that milk FA predict CH4 emission better (R2= 0.74) compared to rumen variables, feed intake and production parameters (R2 < 0.58). Total combination: R2= 0.90
|Mohammed ''et al.'' (2011)<ref name=":1b" />
|-
|Modelling
|specific prediction equations may need to be developed, or diet composition may need to be included in the prediction equations
|Mohammed ''et al.'' (2011)<ref name=":1b" />
|-
|Feed intake + diet composition + milk production + milk FA
|CH4 prediction equations: best fit = combining milk FA, feed intake, diet composition, and milk production (R2 = 0.84)
|Rico ''et al.'' (2016)<ref name=":33" />; Bougoin ''et al.'' (2019)<ref name=":34" />
|-
|MIR + lactation stage
|MIR spectroscopy (coefficient of determination = 0.68 and 0.79), predictions at different stages of lactation were not biologically meaningful + lactation stage refined the model: showing a biologically meaningful behavior throughout lactation: an increase in CH4 production after calving up to approximately 100 DIM, followed by gradual decline towards the end of lactation
|Dehareng ''et al.'' (2012)<ref name=":32" />; Vanlierde ''et al.'' (2015)<ref name=":4a" />
|-
|Milk yield, fat percentage + type traits
|Combine database to predict CH4 using official milk recording system and type evaluation.
|Cassandro ''et al.'', (2010)<ref name=":29" />; Cassandro, (2013)<ref name=":28" />
|}
</center>

== Building an index for methane ==
For some of the proxies, the heritability and correlations with CH4 output are known: e.g. Vanrobays ''et al.'' (2016)<ref>Vanrobays, M.-L., Bastin, C., Vandenplas, J., Hammami, H., Soyeurt, H., Vanlierde, A., Dehareng, F., Froidmont, E., and Gengler, N. 2016. Changes throughout lactation in phenotypic and genetic correlations between methane emissions and milk fatty acid contents predicted from milk mid-infrared spectra. J. Dairy Sci. 99:1–14. <nowiki>http://dx.doi.org/10.3168/jds.2015-10646</nowiki></ref> estimated heritability of 0.25 for CH4 production (g/d) and in the range 0.17 - 0.42 for different classes of milk FA; phenotypic and genetic correlations between MeP and milk FA varied between -0.03 and 0.16, and between -0.02 and 0.32 (C18:0), respectively. The genetic correlation between MeI and milk yield was estimated by Dehareng ''et al.'' (2012)<ref name=":32" /> at -0.45; that between milk yield and protein percentage at -0.54 (Miglior ''et al.'' 2007<ref>Miglior, F., Sewalem, A., Jamrozik, J., Bohmanova, J., Lefebvre, D.M., Moore, R.K. 2007. Genetic analysis of milk urea nitrogen and lactose and their relationships with other production traits in Canadian Holstein cattle. J. Dairy Sci. 90:2468-2479.</ref>). This would give a genetic correlation between MeI and protein percentage in the range [-0.5, 0.9], with likelier values for positive correlations. The most probable value in the given range could then be estimated (from the prior distribution of the missing correlation and the joint likelihood of the two known correlations given the values in the range). Such data could in the future be used to develop an index for breeding on CH4 emission.
</div>

= Proxies discussion =
<div class="mw-collapsible mw-collapsed">
<div>
The greatest limitation of proxies today is the lack of robustness in their general applicability. Future efforts should therefore be directed towards developing combinations of proxies that are robust and applicable across diverse production systems and environments. Here we present the present status of the knowledge of proxies and their predictive value for CH4 emission. Proxies related to body weight or milk yield and composition are relatively simple, low-cost, high throughput, and are easy to implement in practice. In particular, DMI and milk MIR, along with covariates such as lactation stage, are a promising option for prediction of CH4 emission in dairy cows. No single proxy was found to accurately predict CH4, whilst combinations of two or more proxies are likely to be a better solution. Combining proxies can increase the accuracy of predictions by up to 15 - 35%, mainly because different proxies describe independent sources of variation in CH4 and one proxy can correct for shortcomings in the other(s). One plausible strategy could be to increase animal productive efficiency whilst reducing CH4 emissions per animal. This could be achieved by reducing MeY and/or decreasing DMI provided that there is no concomitant reduction in productivity or increase in feed consumption (Pickering et al., 2015<ref>Pickering, N.K., Oddy, V.H., Basarab, J.A., Cammack, K., Hayes, B J., Hegarty, R.S., McEwan, J.C., Miller, S., Pinares, C., and de Haas, Y. 2015. Invited review: Genetic possibilities to reduce enteric methane emissions from ruminants. Animal 9:1431-1440.</ref>).

== Combining diet-based measurements with other proxies for methane emissions ==
Feed intake appears a reasonably adequate predictor of MeP: generally, heavier animals have higher maintenance requirements, so eat more and produce more CH4. However, a substantial level of variation is left unaccounted for. This suggests that more detailed information on dietary composition is needed. This is also important when one wants to account for MeP on diets of similar DMI but of different nutrient profiles.

The prediction accuracy of MeP strongly depends on the accuracy of quantifying the VFA produced in the rumen (Alemu et al., 2011<ref>Alemu, A.W., Dijkstra, J., Bannink, A., France, J., and Kebreab, E. 2011. Rumen stoichiometric models and their contribution and challenges in predicting enteric methane production. Anim. Feed Sci. Technol. 166-167:761-778.</ref>). The type of VFA formed during rumen fermentation depends on the type of substrate fermented (Bannink et al., 2011<ref>Bannink, A., van Schijndel, M.W., and Dijkstra, J. 2011. A model of enteric fermentation in dairy cows to estimate methane emission for the Dutch National Inventory Report using the IPCC Tier 3 approach. Anim. Feed Sci. Technol. 166-167:603-618.</ref>), such as the dietary content of neutral detergent fiber and starch. The type of substrate fermented thus appears a useful factor for predicting MeP (Ellis et al., 2007<ref>Ellis, J.L., Kebreab, E., Odongo, N.E., McBride, B.W., Okine, E.K., and France, J. 2007. Prediction of methane production from dairy and beef cattle. J. Dairy Sci. 90:3456–3466.</ref>), indicating that including a description of variation in dietary quality caused by nutritional factors results in improved prediction accuracy of CH4 emission (Ellis et al., 2010<ref>Ellis, J.L., Bannink, A., France, J., Kebreab, E., and Dijkstra, J. 2010. Evaluation of enteric methane prediction equations for dairy cows used in whole farm models. Glob. Change Biol. 16:3246–3256.</ref>; Moraes et al., 2014<ref name=":0c">Moraes, L.E., Strathe, A.B., Fadel, J.G., Casper, D.P., and Kebreab, E. 2014. Prediction of enteric methane emissions from cattle. Glob. Change Biol. 20:2140–2148.</ref>).

== Rumen ==
When feed intake is kept constant, a higher rumen capacity results in a lower passage rate (Demment and Van Soest, 1985<ref>Demment, M.W., and Van Soest, P.J. 1985. A nutritional explanation for body-size patterns of ruminant and nonruminant herbivores. Am. Nat. 125:641–672.</ref>), resulting in a higher MeP (Moraes et al., 2014<ref name=":0" />). Proxies based on rumen samples are generally poor to moderately accurate predictors of CH4, and are costly and difficult to measure routinely on-farm. VFA are a proxy for rumen CH4 emissions. Using rumen fermentation data obtained from in vitro gas production, Moss et al. (2000)<ref>Moss A.R., Jouany, J.P., and Newbold, J. 2000. Methane production by ruminants: Its contribution to global warming. Annal. Zootech. 49:231-253.</ref> reported a negative linear relationship between CH4 production and the ratio of (acetic + butyric acid)/propionic acid. However, by combining different information sources, either related to feed intake or to the impact of feed intake on the VFA composition, a better proxy with an improved accuracy can be achieved. This way, the prediction equation for CH4 production can be optimized (higher accuracy).

The relationship between rumen methanogen abundance and methanogenesis is less clear when changes in enteric CH4 emissions are modulated by diet or are a consequence of selecting phenotypes related to feed efficiency or MeY. Whereas in some reports there was a significant positive relationship (Aguinaga Casanas et al., 2015<ref name=":1c">Aguinaga Casanas, M.A., , N., Krattenmacher, N., Thalle,r G., Metges, C.C., and Kuhla, B. 2015. Methyl-coenzyme M reductase A as an indicator to estimate methane production from dairy cows. J. Dairy Sci. 98:4074-4083.</ref>; Arndt et al., 2015<ref>Arndt, C., Powell, J.M., Aguerre, M.J., Crump, P.M., and Wattiaux, M.A. 2015. Feed conversion efficiency in dairy cows: Repeatability, variation in digestion and metabolism of energy and nitrogen, and ruminal methanogens. J. Dairy Sci. 98:3938-3950.</ref>; Sun et al., 2015<ref>Sun, X., Henderson, G., Cox, F., Molan,o G., Harrison, S.J., Luo, D., Janssen, P.H., and Pacheco, D. 2015. Lambs Fed Fresh Winter Forage Rape (Brassica napus L.) Emit Less Methane than Those Fed Perennial Ryegrass (Lolium perenne L.), and Possible Mechanisms behind the Difference. PLoS One 10(3):e0119697: DOI: 10.1371/journal.pone.0119697</ref>; Wallace et al., 2015<ref name=":2b">Wallace, R., Rooke, J., McKain, N., Duthie, C.-A., Hyslop, J., Ross, D., Waterhouse, A., Watson, M., and Roehe, R. 2015. The rumen microbial metagenome associated with high methane production in cattle. BMC Genomics. 16:839.</ref>), in many others the concentration of methanogens was unrelated to methanogenesis (Morgavi et al., 2012<ref>Morgavi, D.P., Martin, C., Jouany, J.P., and Ranilla, M.J. 2012. Rumen protozoa and methanogenesis: not a simple cause-effect relationship. Br. J. Nutr. 107:388-397. 10.1017/S0007114511002935.</ref>; Kittelmann et al., 2014<ref>Kittelmann, S., Pinares-Patiño, C.S., Seedorf, H., Kirk, M.R., Ganesh, S., McEwan, J.C., and Janssen, P.H. 2014. Two different bacterial community types are linked with the low-methane emission trait in sheep. PLoS One 9:e103171.</ref>; Shi et al., 2014<ref name=":3a">Shi W., Moon, C.D., Leahy, S.C., Kang, D., Froula, J., Kittelmann, S., Fan, C., Deutsch, S., Gagic, D., Seedorf, H., Kelly, W.J., Atua, R., Sang, C., Soni, P., Li, D., Pinares-Patiño, C.S., McEwan, J.C., Janssen, P.H., Chen, F., Visel, A., Wang, Z., Attwood, G.T., and Rubin, E.M. 2014. Methane yield phenotypes linked to differential gene expression in the sheep rumen microbiome. Genome Res. Doi:10.1101/gr.168245.113.</ref>; Bouchard et al., 2015<ref name=":4b">Bouchard, K., Wittenberg, K.M., Legesse, G., Krause, D.O., Khafipour, E., Buckley, K.E., and Ominski, K.H. 2015. Comparison of feed intake, body weight gain, enteric methane emission and relative abundance of rumen microbes in steers fed sainfoin and lucerne silages under western Canadian conditions. Grass a Forage Sci. 70:116-129.</ref>). Bouchard et al. (2015)<ref name=":4b" /> even reported a reduction in methanogens withoutsignificant decrease in MeP for steers fed sainfoin silage. Sheep selected for high or low MeY showed no differences in methanogen abundance, though there was a strong correlation with expression of archaeal genes involved in methanogenesis (Shi et al., 2014<ref name=":3" />).

Hindgut and Feces: whole tract digestibility variables cannot serve as primary predictors for enteric MeP in cattle or sheep, but might be used as supporting factors to improve the accuracy of prediction of CH4 output.

== Protozoa and other rumen microbes ==
Protozoa are net producers of H2 and their absence from the rumen is associated with an average reduction in enteric MeP of approximately 11% (Hegarty, 1999<ref>Hegarty, R.S. 1999. Reducing rumen methane emissions through elimination of rumen protozoa. Aust. J. Agric. Res. 50:1321-1327.</ref>; Morgavi et al., 2010<ref>Morgavi, D.P., Forano, E., Martin, C., and Newbold, C.J. 2010. Microbial ecosystem and methanogenesis in ruminants. Animal 4:1024-1036.</ref>; Newbold et al., 2015<ref>Newbold, C.J., de la Fuente, G., Belanche, A., Ramos-Morales, E., and McEwan, N. 2015. The role of ciliate protozoa in the rumen. Front. Microbiol. 6:1313.doi: 10.3389/fmicb.2015.01313</ref>). Using a database of 28 experiments and 91 dietary treatments, Guyader et al. (2014)<ref>Guyader, J., Eugène, M., Nozière, P., Morgavi, D.P., Doreau, M., and Martin, C. 2014. Influence of rumen protozoa on methane emission in ruminants: a meta-analysis approach. Animal 8:1816-1825.</ref> showed a significant decrease of 8.14 g CH4/kg DMI for each log unit reduction in rumen protozoal abundance. About 21% of experiments within this dataset reported CH4 changes unrelated to protozoal abundance, highlighting the multifactorial nature of methanogenesis. Roehe et al. (2016)<ref>Roehe R., Dewhurst, R.J., Duthie, C-A., Rooke, J.A., McKain, N., Ross, D.W., Hyslop, J.J., Waterhouse, A., Freeman, T.C., Watson, M., and Wallace, R.J. 2016. Bovine Host Genetic Variation Influences Rumen Microbial Methane Production with Best Selection Criterion for Low Methane Emitting and Efficiently Feed Converting Hosts Based on Metagenomic Gene Abundance. PLoS Genet 12(2): e1005846. doi:10.1371/journal.pgen.1005846.</ref> observed that the ranking of sire groups for CH4 emissions measured with respiration chambers was the same as that for ranking on archaea/bacteria ratio, providing further evidence that host control of archaeal abundance contributes to genetic variation in CH4 emissions - at least in some circumstances. Across a wide geographical range, the methanogenic archaea were shown to be highly conserved across the world (Henderson et al., 2015<ref>Henderson, G., Cox, F., Ganesh, S., Jonker, A., Young, W., Global Rumen Census Collaborators, and Janssen, P.H. 2015. Rumen microbial community composition varies with diet and host, but a core microbiome is found across a wide geographical range. Sci. Rep. 5:14567. doi:10.1038/srep14567</ref>). This universality and limited diversity could make it possible to mitigate CH4 emissions by developing strategies that target the few dominant methanogens. However, one clear limitation of metagenomic predictions compared to genomic predictions was that the microbiome of the host is variable - that is, it may change in response to diet or other environmental factors over time, whereas the hosts DNA remains constant.

== Rumen microbial genes ==
These included genes involved in the first and last steps of methanogenesis: formylmethanofuran dehydrogenase subunit B (fmdB) and methyl-coenzyme M reductase alpha subunit (mcrA), which were 170 times more abundant in high CH4 emitting cattle. Whilst gene-centric metagenomics is not low-cost or high-throughput, these results point to potential future proxy approaches using low-cost gene chips.

The difference in gene expression activity as opposed to abundance was also reported by others (Popova et al., 2011<ref>Popova, M., Martin, C., Eugène, M., Mialon, M.M., Doreau, M., and Morgavi, D.P. 2011. Effect of fibre- and starch-rich finishing diets on methanogenic Archaea diversity and activity in the rumen of feedlot bulls. Anim. Feed Sci. Technol. 166-167:113-121.</ref>). However, there are also studies in which there was no relationship with gene expression (Aguinaga Casanas et al., 2015<ref name=":1">Garnsworthy, P.C. Difford, G.F. Bell, M.J. Bayat, A.R. Huhtanen, P. Kuhla, B. Lassen, J. Peiren, N. Pszczola, M; Sorg, D. Visker, M.H., and Yan, T. 2019 Comparison of Methods to Measure Methane for Use in Genetic Evaluation of Dairy Cattle. Animals 9:837, 12p.</ref>). There are some methodological and experimental differences that might explain some of the apparent contradictions, such as the type of gene target and primers used for nucleic acid amplification. Effects are seen most clearly when the difference in MeP between groups of animals is large (e.g. Wallace et al. (2015)<ref name=":2b" /> used treatments that generated a 1.9-fold difference CH4 emissions).

== Proxies based on measurements in milk ==
Milk yield alone does not provide a good prediction of MeP by dairy cows. Yan et al. (2010)<ref>Yan. T., Mayne, C.S., Gordon, F.G., Porter, M.G., Agnew, R.E., Patterson, D.C., Ferris, C.P., and Kilpatrick, D.J. 2010. Mitigation of enteric methane emissions through improving efficiency of energy utilization and productivity in lactating dairy cows. J. Dairy Sci. 93:2630–2638. doi: 10.3168/jds.2009-2929.</ref> indicated that CH4 as a proportion of GE intake or milk energy output was negatively related to milk production. It is less clear if MeY can be predicted from milk yield when making comparisons across studies.

Milk MIR spectroscopy is relatively inexpensive, rapid and already routinely used technology in milk recording systems to predict fat, protein, lactose and urea contents in dairy milk to assist farm management decisions and breeding. It can be used as a promising strategy to exploit the link between enteric CH4 emission from ruminants and microbial digestion in the rumen by assessing the signature of digestion in milk composition. Milk MIR data can be obtained through regular milk recording schemes, as well as, on a herd level, through analysis used for milk payment systems. Diverse milk phenotypes can be obtained by MIR spectrometry – including detailed milk composition (e.g. FA as reported by Soyeurt et al., 2011<ref>Soyeurt H., Dehareng, F., Gengler, N., McParland, S., Wall, E., Berry, D.P., Coffey, M., and Dardenne, P. 2011. Mid-infrared prediction of bovine milk fatty acids across multiple breeds, production systems and countries. J. Dairy Sci. 94:1657–1667.</ref>), technological properties of milk, and cow physiological status (De Marchi et al., 2014<ref>De Marchi, M., Toffanin, V., Cassandro, M., and Penasa, M. 2014. Invited review: Mid-infrared spectroscopy as phenotyping tool for milk traits. J. Dairy Sci. 97:1171–1186. <nowiki>http://dx.doi.org/10.3168/jds.2013-6799</nowiki>.</ref>; Gengler et al., 2016<ref>Gengler, N., Soyeurt, H., Dehareng, F., Bastin, C., Colinet, F., Hammami, H., Vanrobays, M.-L., Lainé, A., Vanderick, S., Grelet, C., Vanlierde, A., Froidmont, E., and Dardenne, P. 2016. Capitalizing on fine milk composition for breeding and management of dairy cows. J. Dairy Sci. 99:4071-4079.</ref>). Several of these novel traits (i.e. FA composition) have been identified as potential indicators of CH4 emission. Therefore, using MIR to predict MeP (Dehareng et al. 2012<ref name=":5b">Dehareng, F., Delfosse, C., Froidmont, E., Soyeurt, H., Martin, C., Gengler, N., Vanlierde, A., and Dardenne, P. 2012. Potential use of milk mid-infrared spectra to predict individual methane emission of dairy cows. Animal 6:1694-701.</ref>; Vanlierde et al. 2013<ref>Vanlierde, A., Dehareng, F., Froidmont, E., Dardenne, P., Kandel, P.B., Gengler, N., Deighton, M.H., buckley, F., Lewis, E., McParland, S., Berry, D.P., and Soyeurt, H. 2013. Prediction of the individual enteric methane emission of dairy cows from milk-mid-infrared spectra. Advances in Animal Biosciences. 5th Greenhouse Gases Animal Agriculture Conference (GGAA2013) 23-26 June 2014. Dublin, Ireland. p 433.</ref>, 2015<ref name=":4a" />; Van Gastelen and Dijkstra, 2016<ref name=":7a">Van Gastelen, S., and Dijkstra, J.. 2016. Prediction of methane emission from lactating dairy cows using milk fatty acids and mid-infrared spectroscopy. J. Sci. Food Agric. 96:3963-3968. DOI: 10.1002/jsfa.7718.</ref>) is also a logical extension of its use to quantify the major milk components (i.e. fat, protein, casein, lactose, and urea) and minor components (e.g. FA). Dehareng et al. (2012)<ref name=":32" /> assessed the feasibility to predict individual MeP from dairy cows using milk MIR spectra. Their initial results suggest that this approach could be useful to predict MeP at the farm or regional scale, as well as to identify low-CH4 emitting cows. According to Van Gastelen and Dijkstra (2016)<ref name=":36" />, MIR spectroscopy has the disadvantage that it has a moderate predictive power for CH4 emission, both direct and indirect (i.e. via milk FA), and that it lacks the ability to predict important milk FA for CH4 prediction. They concluded that it may not be sufficient to predict MeP based on MIR alone. It is, however, possible to improve the accuracy of prediction through the combination of MIR with some animal characteristics such as lactation stage (Vanlierde et al., 2015<ref name=":4a" />). The advantage of this latter development is that this type of prediction can be done on a very large scale inside a routine milk recording system (Vanlierde et al., 2015<ref name=":4a" />).

== Proxy: future developments and perspectives ==
There is currently limited consensus on which phenotype to use to lower the carbon footprint of milk production through genetic selection. This could be MeP, MeI or MeY. The direct goal would be CH4 production; the relationship with milk production and/or feed intake could be accounted for by including these in the final selection index or scheme. However, one might argue that it would be more effective/accurate to directly use milk production- or feed intakecorrected CH4 (e.g. CH4 intensity or yield) as breeding goal.

The analysis of proxies in terms of their attributes shows that proxies that are based on samples from the rumen or related to rumen sources are poor to moderately accurate predictors of CH4. In addition, these proxies are too costly and difficult for routine on-farm implementation. On the other hand, proxies related to BW, milk yield and composition (e.g. milk FA) are moderately to highly accurate predictors of CH4 and relatively simple, low-cost and easier to implement in practice (Cassandro et al.,2010<ref>Cassandro, M., Cecchinato, A., Battagin, M., Penasa, M., 2010. Genetic parameters of predicted methane production in Holstein Friesian cowsIn: Proc. 9th World Congr. on Genetics Applied to Livestock Production, Leipzig, Germany. . Page 181</ref>; Cassandro, 2013<ref>Cassandro, M. 2013. Comparing local and cosmopolitan cattle breeds on added values for milk and cheese production and their predicted methane emissions. Animal Genetic Resources/Ressources génétiques animales/Recursos genéticos animales, available on CJO2013. doi:10.1017/S2078 63361200077X. </ref>). Particularly, milk MIR and the prediction of CH4 based on milk MIR along with other covariates such as lactation stage is a promising alternative: that is accurate, cheaper and easy to be implemented in routine milk analysis at no extra cost.

Therefore, in the future advances in infrared, photoacoustic and related technologies will push the boundaries, particularly in focusing on developments of fast and portable technologies. Such developments will lead to better and promising proxies for CH4 that will enable a sizable throughput of CH4 phenotypes in dairy cows. Antunes-Fernandes et al. (2016)<ref>Antunes-Fernandes, E.C., van Gastelen, S., Dijkstra, J., Hettinga K.A., and Vervoort, J. 2016. Milk metabolome relates enteric methane emission to milk synthesis, and energy metabolism pathways. J. Dairy. Sci. 99:6251-6262.</ref> already presented the use of metabolomics on milk to better understand the biological pathways involved in CH4 production in dairy cattle. The techniques used in that study are not suitable for large scale measurements, but rapid developments in omics may offer tests and assay methodologies on blood, urine or milk samples that will provide an additional tool for developing new / additional proxies for CH4 emissions in dairy cattle.</div>
</div>

= Merging and sharing data in genetic evaluations =
Early 2016 an attempt to make cross country evaluations of CH4 emissions from Holstein dairy cattle was initiated. The work was based on data from NL, DK, AUS, UK and IR. In total, 12,820 weekly CH4 emission records from 2,857 cows were available. Although different equipment was used across countries to measure CH4 emissions, the research aimed to define similar CH4 output phenotypes in each country. The analysed CH4 traitres, that are available in each country, are (1) CH4 production in g/d, and (2) CH4 intensity in g/d per kg fat protein corrected milk (FPCM). In addition to these CH4 traits, CH4 concentration (in ppm) was available in Denmark, the Netherlands and UK, and the ratio between CH4 and CO2 concentration was available in Denmark and the Netherlands.

Bivariate analyses were carried out to estimate genetic correlations between countries, using an animal linear mixed model for all traits. Both univariate and bivariate analyses were repeated with the GRM as well. With all weekly records, standardizing the trait in the full dataset increased the heritability for CH4 production from 0.03 to 0.06. The heritability for CH4 intensity was slightly higher. The highest heritability with the full dataset is estimated for the standardized CH4 concentration (0.19). Correlations estimated among CH4 traits estimated with either the pedigree or the GRM were in same direction and of similar magnitude. The genetic correlations show that when CH4 production increased, the CH4 concentration and the ratio between CH4 and CO2 increased as well.

The approach is novel, and no other attempt has been performed to make genetic analysis of CH4 traits across countries. The analysis can be repeated in future studies where more data hopefully will be available, and more effort can be made into improving both the fixed and random part of the model.

= Recommendations =
The most important question: what method to use if you need to measure CH4? The answer may be: it depends on what you like to do. In the Table 6 we summarize some experimental conditions and designs, and make recommendations.
<center>
{| class="wikitable"
|-
! colspan="3" style="text-align:left;" | ''Table 6. Recommendations for measuring methane in diverse experimental conditions and designs.''
|- style="background-color:#efefef;"
|+
! style="text-align:center;" | Experimental condition and design
! style="text-align:center;" | Methane measurement method recommendation
|-
| style="text-align:center;" | Need to measure absolute methane values – animal numbers and location not important
| style="text-align:center;" | Respiration chamber;
SF6; GreenFeed
|-
| style="text-align:center;" | Need to rank animals from low to high methane emission
| style="text-align:center;" | Sniffer method
|-
| style="text-align:center;" | Need to measure methane on farm
| style="text-align:center;" | Sniffer method;
GreenFeed; PAC
|-
| style="text-align:center;" | Low budget measurements needed
| style="text-align:center;" | Proxy;
Proxies measurement
|-
| style="text-align:center;" | High animal numbers required
| style="text-align:center;" | Sniffer method;
Proxies measurement; LMD
|}
</center>

= Conclusions =
Measuring CH4 emission on large numbers of cows is a challenge. The high costs and low throughput of RC restrict their use to research studies measuring CH4 emissions on small numbers of individual animals. Respiration chambers remain the gold standard method, but benchmarking alternative methods against RC is challenging because simultaneous replicate measures per cow are not feasible. Methods like SF6 and GreenFeed require lower capital investment and running costs than RC, and have higher throughput and potential for use in extensive and grazing situations, but costs are still prohibitive for recording large numbers of animals. Methods based on concentration are less precise and accurate than flux methods, but they are viable for large scale measurement, which is a prerequisite of genetic evaluations. Further development is needed to increase accuracy and precision of concentration methods. Several reviews of methods for measuring CH4 have made qualitative judgements based on individual comparison studies without expanding scope to genetic evaluations and considering repeated measure correlations between methods as proxies for genetic correlations. Results confirm that there is sufficient correlation between methods for all to be combined for international genetic studies and provide a much needed framework for comparing genetic correlations between methods should these be made available. Proxies have the potential to be used as predictors of CH4 production and emission. Although proxies are less accurate than direct CH4 measurements they can be easier, cheaper, and at high throughput, and may be therefore the best method in practical situations, especially proxies related to milk measurements. Therefore, these proxies at the population level, can provide useful information at genetic improvement that can be used to reduce emissions following 3 ways: (1) intensification of animal production; (2) improving of system efficiency and (3) the direct reduction of GHG emissions by breeding for reduced predicting animals that are high or low GHG emitters.

= Acknowledgements =
This document is the result of the ICAR Feed and Gas Working Group and its industry and research liaison group. The members of the ICAR Feed and Gas Working Group at the time of publication are, in alphabetical order:

* Christine Baes, Department of Animal Biosciences, University of Guelph, Canada and Institute of Genetics, Vetsuisse Faculty, University of Bern, Switzerland
* Yvette de Haas, Animal Breeding and Genomics, Wageningen Livestock Research
* Raffaella Finocchiaro, ANAFI, Italy
* Phil Garnsworthy, School of Biosciences, University of Nottingham, United Kingdom
* Birgit Gredler-Grandl, Animal Breeding and Genomics, Wageningen Livestock Research
* Nina Krattenmacher, Institute of Animal Breeding and Husbandry, Christian-Albrechts-University, Germany
* Jan Lassen, Viking Genetics, Denmark
* Jennie Pryce, Centre for AgriBioscience, AgriBio, Agriculture Victoria Research and School of Applied Systems Biology, La Trobe University, Australia
* Roel Veerkamp, Animal Breeding and Genomics, Wageningen Livestock Research (chairperson)

The work of Marinus te Pas (Animal Breeding and Genomics, Wageningen Livestock Research) in compiling draft versions is highly acknowledged. The content of the guidelines are partly based on results of the network of internationally recognized experts within the METHAGENE COST Action FA1320.

<references />

Section 20 – Methane Emission for Genetic Evaluation

2024-11-10T04:14:06Z

Aashish:

= vaIntroduction =
Increases in milk production through management and genetics have substantially improved feed efficiency and decreased costs per unit of product over recent decades. However, dairy systems are also associated with environmental costs (Baskaran et al., 2009<ref>Baskaran, R., Cullen, R., and Colombo, S. 2009. Estimating values of environmental impacts of dairy farming in New Zealand, New Zealand J. Agric. Res. 52: 377-389, DOI: 10.1080/00288230909510520.</ref>), with methane (CH4) emissions associated with rumen microbial fermentation being both an important contributor to global greenhouse gas (GHG) emissions, as well as an avoidable loss of energy that could otherwise be directed into milk production. The livestock sector is responsible for 14.5% of the global GHG (Gerber et al., 2013<ref>Gerber, P.J., Steinfeld, H., Henderson, B., Mottet, A., Opio, C., Dijkman, J., Falcucci, A. , and Tempio, G. 2013. Tackling climate change through livestock – A global assessment of emissions and mitigation opportunities. Food and Agriculture Organization of the United Nations (FAO), Rome.</ref>); dairy cattle account for 18.9% of these emissions, mainly in the form of enteric CH4 emissions (van Middelaar et al., 2014<ref>Van Middelaar, C.E., Dijkstra. J., Berentsen. P.B.M.. and De Boer. I.J.M. 2014. Cost-effectiveness of feeding strategies to reduce greenhouse gas emissions from dairy farming. J. Dairy Sci. 97:2427–2439.</ref>). Methane is a greenhouse gas with a global warming potential 28 times that of CO2 (Myhre et al., 2013<ref>Myhre, G., Shindell, D., Bréon, F., Collins, W., Fuglestvedt, J., Huang, J., Koch, D., Lamarque, J., Lee, D., Mendoza, B., and Nakajima ,T. 2013. Anthropogenic and Natural Radiative Forcing. T.F. Stocker, D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley, ed. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.</ref>). Methane from ruminant livestock is generated during microbial fermentation in the rumen and hindgut (enteric CH4), and from decomposition of manure. Enteric CH4 contributes 80% of CH4 emissions by ruminants, and manure decomposition contributes 20%. Enteric CH4 accounts for 17% of global CH4 emissions and 3.3% of total global greenhouse gas emissions from human activities (Knapp et al., 2014<ref>Knapp, J.R., Laur, G.L., Vadas, P.A., Weis,s W.P., and Tricarico, J.M. 2014. Invited review: Enteric methane in dairy cattle production: Quantifying the opportunities and impact of reducing emissions. J. Dairy Sci. 97:3231-3261.</ref>). There is, therefore, a significant research interest to find ways to reduce enteric CH4 emissions by ruminants. Ruminant animals have a digestive system to digest plant materials efficiently. Like most mammals, ruminants lack the cellulase enzyme required to break the beta-glucose linkages in cellulose, but they play host to diverse populations of rumen microbes that can digest cellulose and other plant constituents. When rumen bacteria, protozoa and fungi ferment carbohydrates and proteins of plant materials, they produce volatile fatty acids, principally acetate, propionate and butyrate. High fibre diets favour acetate synthesis. Synthesis of acetate and butyrate are accompanied by release of metabolic hydrogen, which, if allowed to accumulate in rumen fluid, has negative effects on microbial growth, and feed digestibility (Janssen, 2010<ref>Janssen, P.H. 2010. Influence of hydrogen on rumen methane formation and fermentation balances through microbial growth kinetics and fermentation thermodynamics. Anim. Feed Sci. Technol. 160:1–22. doi:10.1016/j.anifeedsci.2010.07.002.</ref>). Rumen Archaea are microorganisms that combine metabolic hydrogen with CO2 to produce CH4 and water. Archaea play a vital role, therefore, in protecting the rumen from excess metabolic hydrogen, and the CH4 they produce is an inevitable product of rumen fermentation. A number of CH4 phenotypes have been defined (Hellwing et al., 2012<ref name=":12">Hellwing, A.L.F., Lund, P., Weisbjerg, M.R., Brask, M., and Hvelplund. T. 2012. Technical note: test of a low-cost and animal-friendly system for measuring methane emissions from dairy cows. J. Dairy Sci. 95:6077–85. doi:10.3168/jds.2012-5505.</ref>); the most widely used is CH4 production (MeP) in liters or grams per day. The CH4 production trait is highly correlated with feed intake (Basarab et al., 2013<ref>Basarab, J., Beauchemin, K., Baron, V., Ominski, K., Guan, L., Miller, S., and Crowley, J. 2013. Reducing GHG emissions through genetic improvement for feed efficiency: Effects on economically important traits and enteric methane production. Animal, 7(S2):303-315. doi:10.1017/S1751731113000888</ref>; De Haas et al., 2017<ref>de Haas, Y., Pszczola, M., Soyeurt, H., Wall, E., and Lassen, J. 2017. Invited review: Phenotypes to genetically reduce greenhouse gas emissions in dairying. J. Dairy Sci. 100:855-870.</ref>) and, thereby, with the ultimate breeding goal trait: milk production in dairy cattle. The economic value of daily dry matter intake and associated methane emissions in dairy cattle showed that increasing the feed performance estimated breeding value by one unit (i.e. 1 kg of more efficiently converted DMI during the cow’s first lactation) translates to a total lifetime saving of 3.23 kg in DMI and 0.055 kg in methane (Richardson et al., 2019<ref>Richardson, C., Baes, C., Amer, P., Quinton, C., Martin, P., Osborne, V., Pryce, J.E., and Miglior, F. 2020. Determining the economic value of daily dry matter intake and associated methane emissions in dairy cattle. Animal 14:171-179. doi:10.1017/S175173111900154X</ref>). Feed Performance was defined as a 1 kg increase in more efficiently used feed in a first parity lactating cow. These results show not only the relation between DMI and CH4 production, but also the economic relationship between these traits. Persistency of lactation was found to be positively associated with increased feed efficiency and decreased methane production and intensity. Feed efficiency was associated with lower methane intensity. Feed efficiency and methane emissions can be improved by selecting for dairy cattle that are smaller and have increased persistency of lactation. Efficiency and methane emissions can be further improved by improved management of body condition score and by extending lactations beyond the conventional 305-day length (Seymour, 2019<ref>Seymour, D.J. 2019. Feed Efficiency Dynamics in Relation to Lactation and Methane Emissions in Dairy Cattle. PhD thesis, The University of Guelph, Canada.</ref>). According to Ellis et al. (2007)<ref>Ellis, J.L., Kebreab, E., Odongo, N.E., McBride, B.W., Okine, E.K., and France, J. 2007. Prediction of methane production from dairy and beef cattle. J. Dairy Sci. 90:3456–3466.</ref>, DMI predicted MeP with an R2 of 0.64, and ME intake (MJ/d) predicted MeP with an R2 of 0.53 for dairy cattle. AlternativePhenotype definitions include CH4 intensity (MeI), which is defined as liters or grams of CH4 per kg of milk, and CH4 yield (MeY), which is defined as liters or grams of CH4 per kg of dry matter intake (DMI) (Moate et al., 2016<ref>Moate, P.J., Deighton, M.H., Williams, S.R.O., Pryce, J.E., Hayes, B.J., Jacobs, J.L., Eckard, R.J., Hannah, M.C. and Wales, W.J., 2016. Reducing the carbon footprint of Australian milk production by mitigation of enteric methane emissions. Anim. Prod. Sci. 56:1017-1034.</ref>). Residual CH4 production (RMP) is calculated as observed minus predicted CH4 production (Herd et al., 2014<ref>Herd, R.M., Arthur, P.F., Bird, S.H., Donoghue, K.A., and Hegarty, R.S. 2014. Genetic variation for methane traits in beef cattle. In: Proc. 10th World Conference on Genetic Applied to Livestock Production (WCGALP), 17-22 August, 2014. Vancouver, Canada.</ref>, Berry et al., 2015<ref>Berry, D.P., Lassen, J., and de Haas, Y. 2015. Residual feed intake and breeding approaches for enteric methane mitigation In: Livestock production and climate change. P.K. Malik, R. Bhatta, J. Takahashi, R.A. Kohn, and C.S. Prasad, ed. CABI, Oxfordshire, UK. . Pages 273-291</ref>), with predicted values based on factors such as milk production, body weight and feed intake. At the moment, it is not obvious which of these phenotypes to use; but, it is important to monitor associations between the chosen CH4 phenotype and the other important traits in the breeding goal (e.g. production, fertility, longevity) to avoid unfavorable consequences. Berry and Crowley (2012)<ref>Berry, D.P., and Crowley, J.J. 2012. Residual intake and body weight gain: A new measure of efficiency in growing cattle, J. Anim. Sci. 90:109–115, <nowiki>https://doi.org/10.2527/jas.2011-4245</nowiki></ref> describe advantages and limitations of ration traits. For example, because feed efficiency traits are a linear combination of other traits it is not recommended to include them in an overall total merit index, which is a clear limitation. For all applications it is necessary to measure the CH4 emission of each animal individually. These guidelines are intended to make the right choices for this. Whilst diet changes and feed additives can be effective mitigation strategies for CH4 emissions (Beauchemin et al., 2009<ref>Beauchemin, K.A., McAllister, T.A., and McGinn, S.M. 2009. Dietary mitigation of enteric methane from cattle. CAB Rev.: Perspect. Agric., Vet. Sci., Nutr. Nat. Res. 4:1–18.</ref>; Martin et al., 2010<ref>Martin C., Morgavi, D.P., and Doreau, M. 2010. Methane mitigation in ruminants: from microbe to the farm scale. Animal 4:351–365.</ref>; Hristov et al., 2013<ref>Hristov, A.N., Oh, J., Firkins, J.L., Dijkstra, J., Kebreab, E., Waghorn, G., Makkar, H.P.S., Adesogan, A.T., Yan,g W., Lee, C., Gerber, P.J., Henderson, B., and Tricarico, J.M. 2013. Special topics - Mitigation of methane and nitrous oxide emissions from animal operations: I. A review of enteric methane mitigation options. J. Anim. Sci. 91:5045–5069.</ref>), their effects depend on the continued use of a particular diet or additive and there have been issues with the rumen microbiomes adapting to additives. Rumen bacterial communities are highly dynamic after a diet switch and did not stabilize within 5 wk of cows grazing pasture (Bainbridge et al., 2016<ref>Bainbridge, M.L., Saldinger, L.K., Barlow, J.W., Alvez, J.P., Roman, J. Kraft, J. 2016. 1609 Rumen bacterial communities continue to shift five weeks after switching diets from conserved forage to pasture. J. Anim. Sci. 94, suppl_5:783, <nowiki>https://doi.org/10.2527/jam2016-1609</nowiki> (abstr.)</ref>). In contrast, breeding for reduced CH4 emissions should result in a permanent and cumulative reduction of emissions (Wall et al., 2010<ref>Wall, E., Simm, G., and Moran, D. 2010. Developing breeding schemes to assist mitigation of greenhouse gas emissions. Animal 4:366-376.</ref>). Several studies have shown that CH4 emissions by ruminants have a genetic component, with heritability in the range 0.20 – 0.30 (de Haas et al., 2011<ref>de Haas, Y., Windig, J.J., Calus, M.P.L., Dijkstra, J., de Haan, M., Bannink, A., and Veerkamp, R F. 2011. Genetic parameters for predicted methane production and potential for reducing enteric emissions through genomic selection. J. Dairy Sci. 94:6122–6134.</ref>; Donoghue et al., 2013<ref>Donoghue K.A., Herd, R.M., Bird, S.H., Arthur, P.F., and Hegarty, R F. 2013. Preliminary genetic parameters for methane production in Australian beef cattle. In: Proceedings of the Association for the Advancement of Animal Breeding and Genetics, 20-23 October 2013, Napier, New Zealand, pp. 290–293.</ref>; Pinares-Patiño et al., 2013<ref name=":13">Pinares-Patiño C.S., Hickey, S.M., Young, E.A., Dodds, K.G., MacLean, S., Molano, G., Sandoval, E., Kjestrup, H., Harland, R., Pickering, N.K., and McEwan, J.C. 2013. Heritability estimates of methane emissions from sheep. Animal 7: 316–321.</ref>, Kandel et al., 2014A<ref name=":37">Kandel, P.B., Vanderick, S., Vanrobays, M.L., Vanlierde, A., Dehareng, F., Froidmont, E., Soyeurt, H., and Gengler, N. 2014A. Consequences of selection for environmental impact traits in dairy cows. Page 19. (<nowiki>http://orbi.ulg.ac.be/bitstream/2268/164402/164401/NSABS162014_poster_Purna_abstract.pdf</nowiki>) I:n Proc. 19th National symposium on applied biological sciences, Gembloux, Belgium.</ref>, B<ref>Kandel, P.B., Vanderick, S., Vanrobays, M.L., Vanlierde, A., Dehareng, F., Froidmont, E., Soyeur,t H., and Gengler, N. 2014B. Consequences of selection for environmental impact traits in dairy cows. In: 10th World Congress on Genetics Applied to Livestock Production (WCGALP), 17-22 August, 2014. Vancouver, Canada.</ref>; Lassen and Lovendahl, 2016<ref>Lassen, J., and Løvendahl, P. 2016. Heritability estimates for enteric methane emissions from Holstein cattle measured using noninvasive methods. J. Dairy Sci. 99:1959-1967.</ref>; López-Paredes et al. 2020<ref>Lopez-Paredes, J., Goiri, I., Atxaerandio, R., García-Rodríguez, A., Ugarte, E., Jiménez-Montero, J.A., Alenda, R and González-Recio, O. 2020. Mitigation of greenhouse gases in dairy cattle via genetic selection (i): Genetic parameters of direct methane using non-invasive methods and its proxies. J. Dairy Sci. 103.</ref>). Breeding for reduced CH4 emissions, alone or together with other mitigation strategies, could therefore be effective in reducing the environmental impact of cattle farming and, possibly, also in increasing feed efficiency. Such a breeding scheme would require, as a fundamental starting point, accurate measures of individual CH4 emissions on a large scale. Several techniques have been developed for the measurement of CH4 emissions from ruminants, with varying degrees of accuracy (see reviews by Cassandro et al., 2013<ref>Cassandro, M., Mele, M., Stefanon, B.. 2013. Genetic aspects of enteric methane emission in livestock ruminants. Italian J. Anim. Sci. 12:e73: 450-458.</ref> and Hammond et al., 2016A<ref>Hammond, K.J., Crompton, L.A., Bannink, A., Dijkstra, J., Yáñez-Ruiz, D.R., O’Kiely, P., Kebreab, E., Eugenè, M.A., Yu, Z., Shingfield, K.J., Schwarm, A., Hristov, A.N., and Reynolds, C.K. 2016A. Review of current in vivo measurement techniques for quantifying enteric methane emission from ruminants. Anim. Feed Sci. Technol. 219:13–30. doi:10.1016/j.anifeedsci.2016.05.018.</ref>), but routine individual measurements on a large scale (a requisite for genetic selection) have proven to be difficult to obtain and expensive to measure (Pickering et al., 2015<ref>Pickering, N.K., Oddy, V.H., Basarab, J.A., Cammack, K., Hayes, B J., Hegarty, R.S., McEwan, J.C., Miller, S., Pinares, C., and de Haas, Y. 2015. Invited review: Genetic possibilities to reduce enteric methane emissions from ruminants. Animal 9:1431-1440.</ref>; Negussie et al., 2016<ref>Negussie E., Lehtinen, J., Mäntysaari, P., Liinamo, A-E., Mäntysaari, E., and Lidauer, M.. 2016. Non-invasive individual methane measurements in dairy cows using photoacoustic infrared spectroscopy technique. 6th Greenhouse Gases Animal Agriculture Conference (GGAA2016) 14-18 February 2016. Melbourne, Australia. Abstract. p62.</ref>). Therefore, identifying proxies (i.e. indicators or indirect traits) that are correlated to CH4 emissions, but which are easy and relatively lowcost to record on a large scale, would be a welcome alternative. Proxies might be less accurate but could be measured repeatedly to reduce random noise and in much larger populations. These guidelines are highly indebted to Garnsworthy et al. (2019)<ref name=":11">Garnsworthy, P.C. Difford, G.F. Bell, M.J. Bayat, A.R. Huhtanen, P. Kuhla, B. Lassen, J. Peiren, N. Pszczola, M; Sorg, D. Visker, M.H., and Yan, T. 2019 Comparison of Methods to Measure Methane for Use in Genetic Evaluation of Dairy Cattle. Animals 9:837, 12p.</ref>. In this paper the methods to measure CH4 are compared with special emphasis to the genetic evaluation of dairy cattle.

= Disclaimer =
The fact that specific device manufacturers are mentioned in these guidelines is in no way an endorsement of the devices or their accuracy by ICAR.

= Scope =
A variety of technologies are being developed and employed to measure CH4 emissions of individual dairy cattle under various environmental conditions, as is evidenced by frequent reviews (Storm et al., 2012<ref>Storm, I.M., Hellwing, A.L.F., Nielsen, N.I., and Madsen, J. 2012. Methods for measuring and estimating methane emission from ruminants. Animals 2:160-183.</ref>; Cassandro et al., 2013<ref name=":28">Cassandro, M., Mele, M., Stefanon, B.. 2013. Genetic aspects of enteric methane emission in livestock ruminants. Italian J. Anim. Sci. 12:e73: 450-458.</ref>; Hammond et al., 2016A<ref>Hammond, K.J., Crompton, L.A., Bannink, A., Dijkstra, J., Yáñez-Ruiz, D.R., O’Kiely, P., Kebreab, E., Eugenè, M.A., Yu, Z., Shingfield, K.J., Schwarm, A., Hristov, A.N., and Reynolds, C.K. 2016A. Review of current in vivo measurement techniques for quantifying enteric methane emission from ruminants. Anim. Feed Sci. Technol. 219:13–30. doi:10.1016/j.anifeedsci.2016.05.018.</ref>; de Haas et al., 2017<ref name=":10">de Haas, Y., Pszczola, M., Soyeurt, H., Wall, E., and Lassen, J. 2017. Invited review: Phenotypes to genetically reduce greenhouse gas emissions in dairying. J. Dairy Sci. 100:855-870.</ref>). The first objective of the current guidelines is to review and compare the suitability of methods for large-scale measurements of CH4 output of individual animals, which may be combined with other databases for genetic evaluations. Comparisons include assessing the accuracy, precision and correlation between methods. Combining datasets from different countries and research centres could be a successful strategy for making genetic progress in this difficult to measure trait if the methods are correlated (de Haas et al., 2017<ref name=":10" />). Accuracy and precision of methods are important. Data from different sources need to be appropriately weighted or adjusted when combined, so any methods can be combined if they are suitably correlated with the ‘true’ value. The second objective of the current guidelines, therefore, is to examine correlations among results obtained by different methods, ultimately leading to an estimate of confidence limits for selecting individual animals that are high or low emitters (see also Garnsworthy et al., 2019<ref>Garnsworthy, P.C. Difford, G.F. Bell, M.J. Bayat, A.R. Huhtanen, P. Kuhla, B. Lassen, J. Peiren, N. Pszczola, M; Sorg, D. Visker, M.H., and Yan, T. 2019 Comparison of Methods to Measure Methane for Use in Genetic Evaluation of Dairy Cattle. Animals 9:837, 12p.</ref>).

= Methane determining factors =
<div class="mw-collapsible mw-collapsed">
<div>
== Diet and rumen microbiota ==
Table 1 contains a list of dietary or microbiota factors that determine CH4 production.
<center>
{| class="wikitable"
|-
! colspan="3" | ''Table 1. Methane determining factors related to diet and rumen microbiota.''
|- style="background-color:#efefef;"
|+
!Factors
!Reference
|-
|The main determinants of daily methane production are dry matter intake and diet composition: the more feed consumed, and/or the greater the fibre content of the diet, the more methane is produced per day. However, per unit of DMI, and per unit of fat+protein yield the grass diet produced less enteric CH4 per cow than the TMR diet. Nutritional approaches for methane mitigation include reducing the forage to concentrate ratio of diets, increasing dietary oil content, and dietary inclusion of rumen modifiers and methane inhibitors.
|Beauchemin et al., 2009<ref>Beauchemin, K.A., McAllister, T.A., and McGinn, S.M. 2009. Dietary mitigation of enteric methane from cattle. CAB Rev.: Perspect. Agric., Vet. Sci., Nutr. Nat. Res. 4:1–18.</ref>;
Cottle et al., 2011<ref>Cottle, D.J., Nolan, J.V., and Wiedemann, S.G. 2011. Ruminant enteric methane mitigation: A review. Anim. Prod. Sci. 51:491–514. doi:10.1071/AN10163.</ref>; Knapp et al., 2014<ref>Knapp, J.R., Laur, G.L., Vadas, P.A., Weis,s W.P., and Tricarico, J.M. 2014. Invited review: Enteric methane in dairy cattle production: Quantifying the opportunities and impact of reducing emissions. J. Dairy Sci. 97:3231-3261.</ref>; O’Neill et al., 2011<ref>O’Neill, B.F., Deighton, M.H., O’Loughlin, B.M., Mulligan, F.J., Boland, T.M., O’Donovan, M., and Lewis, E. 2011. Effects of a perennial ryegrass diet or total mixed ration diet offered to spring-calving Holstein-Friesian dairy cows on methane emissions, dry matter intake, and milk production. J. Dairy Sci. 94:1941 – 1951</ref>; Sauvant et al., 2011<ref>Sauvant, D., Giger-Reverdin, S., Serment, A., and Broudiscou, L. 2011. Influences des régimeset de leur fermentation dans le rumen sur la production de méthane par les ruminants. INRA Prod. Anim. 24:433–446.</ref>
|-
|Methane output per kg of product is affected mainly by cow milk yield or growth rate, and by herd-level factors, such as fertility, disease incidence and replacement rate.
|Garnsworthy, 2004<ref>Garnsworthy, P.C. 2004. The environmental impact of fertility in dairy cows: a modelling approach to predict methane and ammonia emissions. Anim. Feed Sci. Technol. 112:211-223.</ref>
|-
|Methane output varies considerably between individual animals. For animals fed the same feed, the between-animal coefficient of variation (CV) in methane was 8.1%.
|Blaxter and Clapperton, 1965<ref>Blaxter, K.L., and Clapperton, J.L. 1965. Prediction of the amount of methane produced by ruminants. Br. J. Nutr.19:511–522.</ref>
|-
|The amount of digestible nutrients consumed especially of the carbohydrate fraction (starch, sugar, N-free residuals) is reliable to estimate CH4 release with high precision. Furthermore, diets rich in fat reduced CH4 formation in the rumen.
|Jentsch et al., 2007<ref>Jentsch, W., Schweigel, M., Weissbach, F., Scholze, H., Pitroff, W., and Derno, M. 2007. Methane production in cattle calculated by the nutrient composition of the diet. Arch. Anim. Nutr. 61:10-19.</ref>
|-
|DMI was also the most important determining factor, but there were different regression lines for maize silage and dried grass as the main roughage component respectively:
<math>CH4\ (g)=93+16.8\times DMI(kg)</math>
<math>CH4\ (g)=81+14.0\times DMI(kg)</math>

Methane release was particularly dependent on the intake of crude fiber (CF) and ether extract (EE):

<math>CH4\ (g)=63+80xCF\ (kg)+11xNFE\ (kg)+19xCP(kg)-195xEE\ (kg)</math>
|Kirchgessner et al., 1991<ref>Kirchgessner, M., Windisch, W., Müller, H. L., and Kreuzer, M. 1991. Release of methane and of carbon dioxide by dairy cattle. Agribiol. Res. 44:91-102.</ref>
|-
|Methane linearly increased with NDF intake <math>CH4\ (L)=59.4\times NDF[kg]+ 64.6</math> for cows together with their calves independent of the breed.
|Estermann et al., 2002<ref>Estermann, B.L., Sutter, F., Schlegel, P.O., Erdin, D., Wettstein, H.R., and Kreuzer, M. 2002. Effect of calf age and dam breed on intake, energy expenditure, and excretion of nitrogen, phosphorus, and methane of beef cows with calves. J. Anim. Sci. 80:1124-1134.</ref>
|-
|Enteric CH4 could be predicted with the equation:
<math>CH4\ (g/d)=84+47\times cellulose(kg/d)+32\times starch(kg/d)+62\times sugars\ (kg/d)</math>
|Hindrichsen et al., 2005<ref>Hindrichsen, I.K., Wettstein, H.R., Machmüller, A., Jörg, B., and Kreuzer, M. 2005. Effect of the carbohydrate composition of feed concentratates on methane emission from dairy cows and their slurry. Environ. Monit. Assess., 107:329-350.</ref>
|-
|The higher the percentage concentrate the lower Ym.
|Zeitz et al., 2012<ref>Zeitz, J.O., Soliva, C.R., and Kreuzer, M. 2012. Swiss diet types for cattle: how accurately are they reflected by the Intergovernmental Panel on Climate Change default values? J. Int. Environ. Sci. 9(sup1):199-216.</ref>
|-
|Additives can sometimes have a methane reducing effect: higher dosages mitigate methane more. Saponins mitigate methanogenesis by reducing the number of protozoa, whereas condensed tannins act both by reducing the number of protozoa and by a direct toxic effect on methanogens.
|Beauchemin et al., 2008;<ref>Beauchemin, K.A., Kreuze,r M., O’Mara, F., and McAllister, T.A. 2008. Nutritional management for enteric methane abatement: A review. Aust. J. Exp. Agric. 48:21–27.</ref>
Jayanegara et al.<ref>Jayanegara, A., Leiber, F., and Kreuzer, M. 2012. Meta‐analysis of the relationship between dietary tannin level and methane formation in ruminants from in vivo and in vitro experiments. J. Anim. Physiol. Anim. Nutr. 96:365-375.</ref>, 2012; Zmora et al., 2012<ref>Zmora, P., Cieslak, A., Pers-Kamczyc, E., Nowak, A., Szczechowiak, J. and Szumacher-Strabel, M. 2012. Effect of Mentha piperita L. on in vitro rumen methanogenesis and fermentation, Acta Agr. Scan. Section A — Anim. Sci. 62:46-52, DOI: 10.1080/09064702.2012.703228.</ref>; Cieslak et al., 2013<ref>Cieslak, A., Szumacher-Strabel, M., Stochmal, A., nad Oleszek, W. 2013. Plant components with specific activities against rumen methanogens. Animal, 7(s2):253-265.</ref>; Guyader et al., 2014<ref>Guyader, J., Eugène, M., Nozière, P., Morgavi, D.P., Doreau, M., and Martin, C. 2014. Influence of rumen protozoa on methane emission in ruminants: a meta-analysis approach. Animal 8:1816-1825.</ref>
|-
|Plant essential oils have been shown as promising feed additives to mitigate CH4 and ammonia emission, but results were inconsistent.
|Cobellis et al., 2016;<ref>Cobellis, G., Trabalza-Marinucci, M. and Yu, Z. 2016. Critical evaluation of essential oils as rumen modifiers in ruminant nutrition: A review. Sci. Total Environ. 545: 556-568.</ref>
Moate et al., 2011<ref>Moate, P.J., Deighton, M.H., Williams, S.R.O., Pryce, J.E., Hayes, B.J., Jacobs, J.L., Eckard, R.J., Hannah, M.C. and Wales, W.J., 2016. Reducing the carbon footprint of Australian milk production by mitigation of enteric methane emissions. Anim. Prod. Sci. 56:1017-1034.</ref>
|-
|Nitrate and sulphate addition decreased the enteric methane emissions negatively affecting diet digestibility and milk production. The effects of the salts are additive.
|van Zijderveld et al., 2010<ref>Van Zijderveld, S.M., Gerrits, W.J.J., Apajalahti, J.A., Newbold, J.R., Dijkstra, J., Leng, R A., and Perdok, H.B. 2010. Nitrate and sulfate: effective alternative hydrogen sinks for mitigation of ruminal methane production in sheep. J. Dairy Sci. 93:5856-5866.</ref>;
van Zijderveld et al., 2011<ref>Van Zijderveld, S.M., Gerrits, W.J.J., Dijkstra, J., Newbold, J.R., Hulshof, R.B.A., and Perdok, H.B. 2011. Persistency of methane mitigation by dietary nitrate supplementation in dairy cows. J. Dairy Sci. 94:4028-4038.</ref>
|-
|The methanogenesis in the rumen of calves is associated with the development of the ruminal protozoa population. The absence of protozoa in the rumen reduced both the CH4 production and the digestibility of carbohydrates.
|Schönhusen et al., 2003<ref>Schönhusen, U., Zitnan, R., Kuhla, S., Jentsch, W., Derno, M., and Voigt, J. 2003. Effects of protozoa on methane production in rumen and hindgut of calves around time of weaning. Arch. Anim. Nutr. 57:279-295.</ref>
|-
|Implementing good grazing management reduced gross energy intake loss as CH4 by 14%.
|Wims et al., 2010<ref>Wims, C.M., Deighton, M.H., Lewis, E., O’Loughlin, B., Delaby, L., Boland, T.M., and O’Donovan, M. 2010. Effect of pregrazing herbage mass on methane production, dry matter intake, and milk production of grazing dairy cows during the mid-season period. J. Dairy Sci. 93:4976 – 4985</ref>
|}
</center>

== Host genetics, physiology and environment ==
A low-moderate proportion of variation in CH4 emissions among ruminants is under genetic control. Heritability coefficients of MeY and RMPR were h²=0.22 and 0.19 respectively in a population of 1,043 Angus growing steers and heifers measured during 2 days in RC (Donoghue et al., 2016<ref name=":14">Donoghue, K.A., Bird-Gardiner, T., Arthur, P.F., Herd, R.M., and Hegarty, R.F. 2016. Genetic and phenotypic variance and covariance components for methane emission and postweaning traits in Angus cattle. J. Anim. Sci. 94:1438–1445. doi:10.2527/jas2015-0065.</ref>). The heritability coefficient of MeY was h²=0.13 in a population of1,225 dual-purpose growing sheep measured during 2 days in RC (Pinares-Patino et al., 2013<ref>Pinares-Patiño C.S., Hickey, S.M., Young, E.A., Dodds, K.G., MacLean, S., Molano, G., Sandoval, E., Kjestrup, H., Harland, R., Pickering, N.K., and McEwan, J.C. 2013. Heritability estimates of methane emissions from sheep. Animal 7: 316–321.</ref>). Table 2 contains information of heritability of traits related to CH4 production.

{| class="wikitable"
|-
! colspan="3" | ''Table 2. Heritability information of methane-related traits and measurements.''
|- style="background-color:#efefef;"
|+
!Factors
!Reference
|-
|List with several h2
|Pickering et al., 2015<ref>Pickering, N.K., Oddy, V.H., Basarab, J.A., Cammack, K., Hayes, B J., Hegarty, R.S., McEwan, J.C., Miller, S., Pinares, C., and de Haas, Y. 2015. Invited review: Genetic possibilities to reduce enteric methane emissions from ruminants. Animal 9:1431-1440.</ref>
|-
|List with several h2
|MPWG White paper Dec 18<ref>MPWG White paper Dec 18. <nowiki>http://www.asggn.org/publications,listing,95,mpwg-white-paper.html</nowiki>. Pickering, N.K., de Haas, Y., Basarab, J., Cammack, K., Hayes, B., Hegarty, R.S., Lassen, J., McEwan, J.C., Miller, S., Pinares-Patiño, C.S., Shackell, G., Vercoe, P. and Oddy, V.H. 2013.</ref>
|-
|Methane emissions from individual cows during milking varied between individuals with the same milk yield and fed the same diet. Between-cow variation in MERm is greater than within-cow variation and ranking of cows for CH4 emissions is consistent across time. Variation related to body weight, milk yield, parity, and week of lactation/days in milk. The monitored variation might offer opportunities for genetic selection.
|Garnsworthy et al., 2011A<ref name=":15">Garnsworthy, P.C., Craigon, J., Hernandez-Medrano, J.H. and Saunders, H. 2012A. On-farm methane measurements during milking correlate with total methane production by individual dairy cows. J. Dairy Sci. 95:3166-3180.</ref>;
Garnsworthy et al., 2011B<ref name=":16">Garnsworthy, P.C., Craigon, J., Hernandez-Medrano, J.H., and Saunders, N. 2012B. Variation among individual dairy cows in methane measurements made on farm during milking. J. Dairy Sci. 95:3181–3189.</ref>
|-
|Mechanistic modelling approach: potential for dietary intervention as a means of substantially reducing CH4 emissions without adverse effects on dietary energy supply.
|Mills et al., 2001<ref>Mills, J.A.N., Dijkstra, J., Bannink, A., Cammell, S.B., Kebreab, E., and France, J. 2001. A mechanistic model of whole-tract digestion and methanogenesis in the lactating dairy cow: model development, evaluation, and application. J. Anim. Sci. 79:1584-1597.</ref>
|-
|The CH4-to-CO2 ratio measured using the non-invasive portable air sampler and analyzer unit based on Fourier transform infrared (FTIR) detection method is an asset of the individual cow and may be useful in both management and genetic evaluations.
|Lassen et al., 2012<ref name=":17">Lassen, J., Lovendahl, P., and Madsen, J. 2012. Accuracy of noninvasive breath methane measurements using Fourier transform infrared methods on individual cows. J. Dairy Sci. 95:890-898.</ref>
|-
|The estimated heritability for CH4 g/day and CH4 g/kg of FPCM were lower than common production traits but would still be useful in breeding programs.
|Kandel et al., 2013<ref>Kandel, P.B., Vanrobays, M.L., Vanlierde, A., Dehareng, F., Froidmont, E., Dardenne, P., Lewis, E., Buckley, F., Deighton, M.H., McParland, S. and Gengler, N., 2013. Genetic parameters for methane emissions predicted from milk mid-infrared spectra in dairy cows. J. Dairy Sci. 95(E-1):p.388.</ref>
|-
|Genetic correlation between CH4 intensity and milk yield (MY) was - 0.67 and with milk protein yield (PY) was -0.46 in Holstein cows.
|Kandel et al., 2014A<ref name=":37" />, B<ref>Kandel, P.B., Vanderick, S., Vanrobays, M.L., Vanlierde, A., Dehareng, F., Froidmont, E., Soyeur,t H., and Gengler, N. 2014B. Consequences of selection for environmental impact traits in dairy cows. In: 10th World Congress on Genetics Applied to Livestock Production (WCGALP), 17-22 August, 2014. Vancouver, Canada.</ref>
|-
|Milk production and CH4 emissions of dairy cows seemed to be influenced by the temperature humidity index.
|Vanrobays et al., 2013A<ref>Vanrobays, M.-L., Gengler, N., Kandel, P.B., Soyeurt, H., and Hammami, H. 2013A. Genetic effects of heat stress on milk yield and MIR predicted methane emissions of Holstein cows. 64th Annual meeting of the European Federation of Animal Science, p498</ref>
|-
|Estimate the heritability of the estimated methane emissions from 485 Polish Holstein-Friesian dairy cows at 2 commercial farms using FTIR spectroscopy during milking in an automated milking system by implementing the random regression method. The heritability level fluctuated over the course of lactation, starting at 0.23 (SE 0.12) and then increasing to its maximum value of 0.3 (SE 0.08) at 212 DIM and ending at the level of 0.27 ± 0.12. Average heritability was 0.27 ± 0.09.
|Pszczola et al., 2017<ref name=":18">Pszczola, M., Rzewuska, K., Mucha, S., and Strabel, T. 2017. Heritability of methane emissions from dairy cows over a lactation measured on commercial farms. J. Anim. Sci. 95:4813-4819. doi: 10.2527/jas2017.1842.</ref>
|-
|CH4 measured with a portable air-sampler FTIR detection method on 3,121 Holstein dairy cows from 20 herds using automatic milking systems. The heritability of CH4_MILK was 0.21 ± 0.06. It was concluded that a high genetic potential for milk production will also mean a high genetic potential for CH4 production. The results suggested that CH4 emission is partly under genetic control, that it is possible to decrease CH4 emission from dairy cattle through selection, and that selection for higher milk yield will lead to higher genetic merit for CH4 emission/cow per day.
|Lassen and Løvendahl, 2016<ref>Lassen, J., and Løvendahl, P. 2016. Heritability estimates for enteric methane emissions from Holstein cattle measured using noninvasive methods. J. Dairy Sci. 99:1959-1967.</ref>
|-
|CH4 production was measured of 184 Holstein-Friesian cows in. the milking robot with a in total 2,456 observations for CH4 production. Heritability for CH4 production ranged from 0.12 ± 0.16 to 0.45 ± 0.11, and genetic correlations with MY ranged from 0.49 ± 0.12 to 0.54 ± 0.26. The positive genetic correlation between CH4 production and milk yield indicates that care needs to be taken when genetically selecting for lower CH4 production, to avoid a decrease in MY at the animal level. However, this study shows that CH4 production is moderately heritable and therefore progress through genetic selection is possible.
|Breider et al., 2019<ref>Breider, I.S., Wall, E., Garnsworthy, P.C. 2019. Short communication: Heritability of methane production and genetic correlations with milk yield and body weight in Holstein-Friesian dairy cows, J. Dairy Sci. 102: 7277-7281.</ref>
|-
|CH4 concentration was measured with NDIR, and CH4 production was estimated from CH4 concentration and body weight. Heritability for CH4 concentration was 0.11 ± 0.03 and for CH4 production 0.12 ± 0.04. Positive genetic correlation was observed with MY (0.17-0.21), PY (0.22-0.31) and FY (0.27-0.29). Other type traits showed positive correlation with methane production (chest width=0.26, angularity =0.19, stature = 0.43 and capacity = 0.31) possibly associated to higher milk feed intake from these animals. Rumination time was negatively correlated to CH4 production (-0.24) and CH4 concentration (-0.43). However, larger CH4 production and CH4 concentration was associated with shorter days open.
|López-Paredes et al. (2020)<ref>Lopez-Paredes, J., Goiri, I., Atxaerandio, R., García-Rodríguez, A., Ugarte, E., Jiménez-Montero, J.A., Alenda, R and González-Recio, O. 2020. Mitigation of greenhouse gases in dairy cattle via genetic selection (i): Genetic parameters of direct methane using non-invasive methods and its proxies. J. Dairy Sci. 103.</ref>
|-
|Genetic parameters of CH4 emissions predicted from milk fatty acid profile (FA) and those of their predictors in 1,091 Brown Swiss cows reared on 85 farms showed that enteric CH4 emissions of dairy cows can be estimated on the basis of milk fatty acid profile. Additive genetic variation of CH4 traits was shown which could be exploited in breeding programmes.
|Bittante and Cecchinato, 2020<ref>Bittante, G., and Cecchinato, A. 2020. Heritability estimates of enteric methane emissions predicted from fatty acid profiles, and their relationships with milk composition, cheese-yield and body size and condition, It. J. An. Sci. 19:114-126, DOI: 10.1080/1828051X.2019.1698979</ref>
|-
|A total of 670 test day records were recorded on lactating Holstein Friesian cows reared in 10 commercial dairy herds. Predicted methane production (PMP) was estimated to be 15.33±1.52 MJ/d in dairy cows with 23.53±6.81 kg/d of milk yeild (MY) and 3.57±0.68% of fat content (FC). Heritability of MY was 0.09 with a posterior probability for values of h2 greater than 0.10 of 44%. Estimates of heritability for FC and protein content (PC) were 0.17 and 0.34, respectively, with a posterior probability for values of h2 greater than 0.10 of 77% and 99%. For somatic cell score (SCS), heritability was 0.13 with a posterior probability for values of h2 greater than 0.10 of 67%. Heritability for the trait PMP was moderate to low (0.12); however, posterior probability for values of h2 greater than 0.10 was 60%. Medians of the posterior distributions of genetic correlations between PMP and milk production traits were: 0.92, 0.67, 0.14, and 0.14 between PMP and MY, PMP and FC, PMP and PC, and PMP and SCS, respectively. Reduction of PMP seems to be viable through selection strategies without affecting udder health and PC.
|Cassandro et al., 2010<ref name=":29">Cassandro, M., Cecchinato, A., Battagin, M., Penasa, M., 2010. Genetic parameters of predicted methane production in Holstein Friesian cowsIn: Proc. 9th World Congr. on Genetics Applied to Livestock Production, Leipzig, Germany. . Page 181</ref>
|-
|GWAS to study the genetic architecture of CH4 production and detected genomic regions affecting CH4 production. Detected regions explained only a small proportion of the heritable variance. Potential QTL regions affecting CH4 production were located within QTLs related to feed efficiency, milk-related traits, body size and health status. Five candidate genes were found: CYP51A1 on BTA 4, PPP1R16B on BTA 13, and NTHL1, TSC2, and PKD1 on BTA 25. These candidate genes were involved in a number of metabolic processes that are possibly related to CH4 production. One of the most promising candidate genes (PKD1) was related to the development of the digestive tract. The results indicate that CH4 production is a highly polygenic trait.
|Pszczola et al., 2018<ref name=":19">Pszczola, M., Strabel, T., Mucha, S., and Sell-Kubiak, E. 2018. Genome-wide association identifies methane production level relation to genetic control of digestive tract development in dairy cows. Scientific Rep. 8 (1), 15164 <nowiki>https://doi.org/10.1038/s41598-018-33327-9</nowiki></ref>
|-
|A 1000-cow study across European countries revealed that the ruminant microbiomes can be controlled by the host animal. A 39- member subset of the core microbiome formed hubs in co-occurrence networks linking microbiome structure to host genetics and phenotype (CH4 emissions, rumen and blood metabolites, and milk production efficiency).
|Wallace et al.,
2019<ref>Wallace, R.J., Sasson, G., Garnsworthy, P.C., Tapio, I., Gregson, E., Bani, P., Huhtanen, P., Bayat, A.R., Strozzi, F., Biscarini, F., Snelling, T.J., Saunders, N., Potterton, S.L., Craigon, J., Minuti, A., Trevisi, E., Callegari, M.L., Cappelli, F.P., Cabezas-Garcia, E.H., Vilkki, J., Pinares-Patino, C., Fliegerov, K.O., Mrazek, J., Sechovcova, H., Kope, J., Bonin, A., Boyer, F., Taberlet, P., Kokou, F., Halperin, E., Williams, J.L., Shingfield, K.J., and Mizrahi, I. 2019. A heritable subset of the core rumen microbiome dictates dairy cow productivity and emissions. Sci. Adv. 5:(7):eaav8391. doi 10.1126/sciadv.aav8391.</ref>
|}

</div>
</div>

= Methane measurements methods =
<div class="mw-collapsible mw-collapsed">
<div>
Several factors influence the choice of measurement method such as cost, level of accuracy, precision, scope of application, and scale, which vary across disciplines (Cassandro et al., 2013<ref>Cassandro, M. 2013. Comparing local and cosmopolitan cattle breeds on added values for milk and cheese production and their predicted methane emissions. Animal Genetic Resources/Ressources génétiques animales/Recursos genéticos animales, available on CJO2013. doi:10.1017/S2078 63361200077X</ref>; Hammond et al., 2016A<ref>Hammond, K.J., Crompton, L.A., Bannink, A., Dijkstra, J., Yáñez-Ruiz, D.R., O’Kiely, P., Kebreab, E., Eugenè, M.A., Yu, Z., Shingfield, K.J., Schwarm, A., Hristov, A.N., and Reynolds, C.K. 2016A. Review of current in vivo measurement techniques for quantifying enteric methane emission from ruminants. Anim. Feed Sci. Technol. 219:13–30. doi:10.1016/j.anifeedsci.2016.05.018.</ref>; Garnsworthy et al., 2019<ref>Garnsworthy, P.C. Difford, G.F. Bell, M.J. Bayat, A.R. Huhtanen, P. Kuhla, B. Lassen, J. Peiren, N. Pszczola, M; Sorg, D. Visker, M.H., and Yan, T. 2019 Comparison of Methods to Measure Methane for Use in Genetic Evaluation of Dairy Cattle. Animals 9:837, 12p.</ref>). For instance, genetic selection programs require CH4 measurements on thousands of related individuals under the environmental conditions in which the animals are expected to perform (Falconer and Mackay, 1996<ref>Falconer, D., and Macka,y T. 1996. Introduction to quantitative genetics (4th edn). ISBN-13: 978-0582243026; ISBN-10: 0582243025</ref>). This can be challenging because dairy cattle perform in a wide range of conditions (e.g. grazing vs indoor housing). There are a number of different measurement methods currently being employed, each with advantages and disadvantages in terms of the factors listed above. The currently accepted and widely used measurement methods are listed and described below. The main features of methods for measuring CH4 output by individual animals are summarised in Table 3. Values for each feature are based on experience of experts in METHAGENE WG2 who have used the methods. All values are relative, and somewhat subjective, because absolute values will depend on installation and implementation of each method at different research centres. It should be noted that the measuring methods can be divided in two major sections: methods that measure the concentration and flux of CH4 (e.g. the respiration chamber), and methods that measure the flux of CH4 through the device (e.g. GreenFeed). This affects the useability of the methods for answering research questions – please see also the recommendations at the end of these guidelines.

== Respiration chambers ==
Respiration chambers are calibrated to be accurate and precise, and are the gold standard for benchmarking new methods. Only respiration chambers measure total emissions from the animal via the oral, nasal and anal routes; all other methods ignore emissions via the anus and only measure CH4 emitted in breath. Breath measurements are justified because 99% of CH4 is emitted from the mouth and nostrils, and only 1% via the anus (Murray ''et al.'', 1976<ref name=":20">Murray, R.M., Bryant, A.M., and Leng, R.A.. 1976. Rates of production of methane in the rumen and large intestine of sheep. Br. J. Nutr. 36:1-14.</ref>).

A single animal (or occasionally more) is confined in a chamber for between 2 and 7 days. Concentration of CH4 (and other gases if required) is measured at the air inlet and outlet vents of the chamber. The difference between outlet and inlet concentrations is multiplied by airflow to indicate CH4 emissions fluxes. In most installations, a single gas analyser is used to measure both inlet and outlet concentrations, often for two or more chambers. This involves switching the analyser between sampling points at set intervals, so concentrations are actually measured for only a fraction of the day. If the sampling points acquisition frequency is high it enables to draw the diurnal pattern of methane emission, comparable to the GreenFeed system.

Respiration chambers vary in construction materials, size of chamber, gas analysis equipment and airflow rate, all of which can influence results. Validation of 22 chambers at six UK research sites revealed an uncertainty of 25.7% between facilities, which was reduced to 2.1% when correction factors were applied to trace each facility to the international standard CH4 (Gardiner ''et al.'', 2015<ref name=":21">Gardiner, T.D., Coleman, M.D., Innocenti, F., Tompkins, J., Connor, A., Garnsworthy, P.C., Moorby, J.M., Reynolds, C.K., Waterhouse, A., and Wills, D. 2015. Determination of the absolute accuracy of UK chamber facilities used in measuring methane emissions from livestock. Measurement 66: 272-279.</ref>). The main sources of uncertainty were stability and measurement of airflow, which are crucial for measuring CH4 emission rate. The authors concluded, however, that chambers were accurate for comparing animals measured at the same site. This is an added challenge to benchmarking alternative methods with respiration chambers if respiration chambers themselves have not been benchmarked with respiration chambers at other facilities. It should be noted that substantial errors can occur if appropriate calibration procedures are not followed (Gardiner ''et al.'', 2015<ref name=":21" />).

For large-scale evaluation of CH4 emissions by individual animals, respiration chambers are challenging with only a single study in growing Angus steers and heifers exceeding 1000 animals and finding CH4 production to be moderately heritable h2 = 0.27 ± 0.07 (Donoghue ''et al.'', 2016<ref name=":14" />). Installation and running costs are high, as only one animal is normally measured at once. If we assume that the monitoring time is three days per animal, and chambers are run continuously, then maximum throughput would be approximately 100 animals per chamber per year. In practice, throughput is likely to be 30 to 50 animals per year. Cows are social animals and confinement in a chamber may ultimately influence their feeding behaviour resulting in less feed consumed and in a different meal pattern compared with farm conditions. Altered feeding pattern or level is not a problem for metabolic studies evaluating feeds but can be a problem when evaluating individual animals. Furthermore, the representativeness of respiration chambers to grazing systems has been called into question (Pinares-Patiño ''et al.'', 2013<ref name=":13" />). However, promising developments have led to more animal friendly respiration chambers constructed from cheaper, transparent materials. These lower the cost and reduce the stress of confinement with minimal disruptions to accuracy, precision and no drop in feed intake of the cows (Hellwing ''et al.'', 2012<ref name=":12" />).

Where an alternative method may be cheaper, less invasive, easier to implement, or have a wider scope of application, it is of value to assess the relative accuracy, precision and correlation with the gold standard to assess the relative worth of the alternative method (Barnhart ''et al.'', 2007<ref>Barnhart, H.X., Kosinski, A.S., and Haber, M.J. 2007. Assessing Individual Agreement. J. Biopharm. Stat. 17:697–719. doi:10.1080/10543400701329489.</ref>). All methods measure CH4 with some level of error, so the ‘true value’ of an individual is not known. However, when the level of measurement error increases, so too does the imprecision. When comparing two methods where one or both methods has high imprecision a phenomenon known as ‘attenuation of errors’ occurs (Spearman, 1904<ref>Spearman, C. 1904. The Proof and Measurement of Association between Two Things. Am. J. Psychol. 15:72–101.</ref>). The increased measurement error biases the correlation between the two methods downwards and reduces the efficacy of detecting significant differences in accuracy (Adolph & Hardin, 2007<ref>Adolph, S.C., and Hardin, J.S. 2007. Estimating phenotypic correlations: Correcting for bias due to intraindividual variability. Funct. Ecol. 21:178–184. doi:10.1111/j.1365-2435.2006.01209.x.</ref>). Or in terms of linear regression terms, when the observed CV of an alternative method is higher than that of the gold standard method, the slope of regression between the methods is decreased and the intercept is biased upwards.

== Portable Accumulation Chambers ==
In Australia and New Zealand an alternative method was developed for the short-term measurement of Methane Production Rate (MPR) of sheep using Portable Accumulation Chambers (PAC) during 1 hour without leading discomfort to the animals. Similarly to RC, CH4 emissions recorded in PAC include gases from flatulence in addition to eructed and expired CH4, but only during 1 hour. For a detailed comparison of the PAC and respiration chamber methods see Jonker ''et al.'' (2018).

== SF6 ==
The SF6 technique samples breath over 24 hours, whereas other techniques use spot samples of breath over periods of minutes throughout the day, so diurnal variation has to be considered. The majority of CH4 (87-99%) is released by eructation (Blaxter & Joyce, 1963; Murray ''et al.'', 1976<ref name=":20" />), which provides a clear signal for sample processing. Please note that the tracheostomy used in Murray ''et al.'' (1976)<ref name=":20" /> may have resulted in a higher percentage, but in both publications, it is clear that the majority of the CH4 is released via eructation.

The SF6 tracer gas technique was developed in an attempt to measure CH4 emissions by animals without confinement in respiration chambers (Johnson ''et al.'', 1994<ref>Johnson, K., Huyler, M., Westberg, H., Lamb, B., and Zimmerman, P. 1994. Measurement of methane emissions from ruminant livestock using a sulfur hexafluoride tracer technique. Environ. Sci. Technol. 28:359-362.</ref>). Air is sampled near the animal’s nostrils through a tube attached to a halter and connected to an evacuated canister worn around the animal’s neck or on its back. A capillary tube or orifice plate is used to restrict airflow through the tube so that the canister is between 50 and 70% full in approximately 24 hours. A permeation tube containing SF6 is placed into the rumen of each animal. The pre-determined release rate of SF6 is multiplied by the ratio of CH4 to SF6 concentrations in the canister to calculate CH4 emission rate.

Many research centres have used the SF6 technique with variations in design of sampling and collection equipment, permeation tubes, and gas analysis (Berndt ''et al.'', 2014<ref name=":22" />). Reliable results depend on following standard protocols, with greatest variation coming from accuracy of determining SF6 release rate from permeation tubes and control of sampling rate. With capillary tubes, sampling rate decreases as pressure in the canister increases, whereas an orifice plate gives a steadier sampling rate over 24 hours (Deighton ''et al.'', 2014<ref>Deighton, M.H., Williams, S.R.O., Hannah, M.C., Eckard, R.J., Boland, T.M., Wales, W.J., and Moate, P.J. 2014. A modified sulphur hexafluoride tracer technique enables accurate determination of enteric methane emissions from ruminants. Anim. Feed Sci. Technol. 197:47-63.</ref>). A source of error that has not been evaluated is that animals might interact and share CH4 emissions when the sampling tube of one animal is near the head of another animal. There is good agreement between CH4 emissions measured by the SF6 technique and respiration chambers, although results from the SF6 technique are more variable (Grainger ''et al.'', 2007<ref>Grainger, C., Clarke, T., McGinn, S.M., Auldist, M.J., Beauchemin, K.A., Hannah, M.C., Waghorn, G.C., Clark, H., and Eckard, R J. 2007. Methane emissions from dairy cows measured using the sulfur hexafluoride (SF6) tracer and chamber techniques. J. Dairy Sci. 90:2755-2766.</ref>; Muñoz ''et al.'', 2012<ref>Muñoz, C., Yan, T., Wills, D.A., Murray, S., and Gordon, A.W. 2012. Comparison of the sulfur hexafluoride tracer and respiration chamber techniques for estimating methane emissions and correction for rectum methane output from dairy cows. J. Dairy Sci. 95:3139-3148.</ref>).

== Breath sampling during milking and feeding ==
Several research groups have developed methods to measure CH4 concentration in breath of cows during milking and/or feeding. These are often referred to as ‘sniffer methods’ because they use devices originally designed to detect dangerous gas leaks. Air is sampled near the animal’s nostrils through a tube fixed in a feed bin and connected directly to a gas analyser. The feed bin might be in an automatic milking station (Garnsworthy ''et al.'', 2012A<ref name=":15" />, B<ref name=":16" />; Lassen ''et al.'', 2012<ref name=":17" />; Pszczola ''et al.'', 2017<ref name=":18" />, 2018<ref name=":19" />, 2019<ref>Pszczola, M., Calus, M.P.L., Strabel, T. 2019. Genetic correlations between methane and milk production, conformation, and functional traits. J. Dairy Sci. 102:5342-5346.</ref>) or in a concentrate feeding station (Negussie ''et al.'', 2017<ref>Negussie, E., Lehtinen, J., Mäntysaari, P., Bayat, A.R., Liinamo, A.E., Mäntysaari, E.A., and Lidauer, M.H. 2017. Non-invasive individual methane measurement in dairy cows. Animal 11:890-899.</ref>). Different research centres use different gas analysers (Nondispersive Infrared (NDIR), Fourier-transform infrared (FTIR) or photoacoustic infrared (PAIR)) and different sampling intervals (1, 5, 20 or 90-120 seconds). Methane concentration during a sampling visit of typically between 3 and 10 minutes may be specified as the overall mean, or the mean of eructation peaks. Some centres use CO2 as a tracer gas and calculate daily CH4 output according to ratio of CH4 to CO2 and daily CO2 output predicted from performance of the cow (Madsen ''et al.'', 2010<ref>Madsen, J., Bjerg, B.S., Hvelplund, T., Weisbjerg, M.R., and Lund, P. 2010. Methane and carbon dioxide ratio in excreted air for quantification of the methane production from ruminants. Livest. Sci. 129:223-227.</ref>). Repeatability and rank correlations were higher for eructation peaks than for mean concentrations, and were higher for eructation peaks than for CH4 to CO2 ratio (Bell ''et al.'', 2014<ref>Bell, M.J., Saunders, N., Wilcox, R.H., Homer, E.M., Goodman, J.R., Craigon, J., Garnsworthy, P.C. 2014 Methane emissions among individual dairy cows during milking quantified by eructation peaks or ratio with carbon dioxide. J. Dairy Sci. 97:6536–6546.</ref>). However, all methods show good repeatability.

== GreenFeed ==
GreenFeed (C-Lock Inc., Rapid City, South Dakota, USA) is a sniffer system where breath samples are provided when animals visit a bait station (Huhtanen ''et al.'', 2015<ref name=":23" />). GreenFeed Emission Monitoring (GEM) systems are designed for measuring animal emissions in their production environment. As with other sniffer systems, GreenFeed samples breath from individual animals several times (in general 4 to 6 times) per day for short periods (3 to 7 minutes in which an under pressure is created to suck the whole breath of the animal to measure the flux). They record CH4 and carbon dioxide (CO2) fluxes during short-term periods of 3-10 minutes when cattle visit an automated feeder fitted with a semi-enclosed head hood in which air is continuously drawn through an air-collection pipe (C-Lock, 2016; Huhtanen ''et al.'', 2015<ref name=":24" />; Hammond ''et al.'', 2016A<ref name=":25" />; Velazco ''et al.'', 2016<ref name=":131" />). Air samples are continually (every second) analyzed for CH4 and CO2 concentrations using non-dispersive infrared sensors. Gas fluxes are eventually calculated as the product of the air flow in the collection pipe and the concentration of gases corrected for the background concentrations and adjusted to standardized temperature, humidity and pressure. The position of the head in the feeder is detected by an infrared sensor. Gas fluxes are not calculated if the head is not correctly positioned in the feeder as not all the air in the feeder may be collected.

GreenFeed is a portable standalone system used in barn and pasture applications and incorporates an extractor fan to ensure active airflow and head position sensing for representative breath sampling (Hammond ''et al.'', 2016B<ref name=":26" />). Measurements are pre-processed by the manufacturer, and data are available in real-time through a web-based data management system (Hammond ''et al.'', 2015<ref name=":5" />). Because GreenFeed captures a high proportion of emitted air and measures airflow, which can be calibrated using a tracer gas, CH4 emission is estimated as a flux at each visit. Providing visits occur throughout the 24 hours, CH4 emission can be estimated directly as g/day (Hammond ''et al.'', 2015<ref name=":5" />; Huhtanen ''et al.'', 2015<ref name=":24" />). More importantly, repeatability of CH4 measurement must be high so the duration of the measurement period must be taken into account (Huhtanen ''et al.'', 2013; Arbre ''et al.'', 2016<ref name=":7" />); (R=0.7 after 17 days duration of measurement period, or R=0.93 after 45 days, Arbre ''et al.'', 2016<ref name=":7" />).

== Laser methane detector ==
The laser CH4 detector (LMD) is a highly responsive, hand-held device that is pointed at an animal’s nostrils and measures CH4 column density along the length of the laser beam (ppm.m). In the first implementation of LMD on a farm, measurements for each cow were taken over periods of 15 to 25 seconds between eructation events and could detect CH4 emitted each time the animal breathed out (Chagunda ''et al.'', 2009 <ref>Chagunda, M.G.G., Ross, D., and Robert,s D J. 2009. On the use of a laser methane detector in dairy cows. Comput. Electron. Agric. 68:157-160.</ref>; Sorg ''et al.'', 2016<ref>Sorg, D., Mühlbach, S., Rosner, F., Kuhla, B., Derno, M., Meese, S., Schwarm, A., Kreuzer, M., and Swalve, H. 2016. The agreement between two next-generation laser methane detectors and respiration chamber facilities in recording methane concentrations in the spent air produced by dairy cows. Comp. Electr. Agric. 143:262-272. </ref>, 2017<ref name=":27" />). In a later study with sheep and beef cattle, monitoring periods of 2 to 4 minutes allowed authors to separate breathing cycles from eructation events (Ricci ''et al.'', 2014<ref>Ricci, P., Chagunda, M.G.G., Rooke, J., Houdijk, J.G.M., Duthie, C-A., Hyslop, J., Roehe, R., and Waterhouse, A. 2014. Evaluation of the laser methane detector to estimate methane emissions from ewes and steers. J. Anim. Sci. 92:5239-5250.</ref>). Typically, animals are restrained either manually or in head yokes at a feed fence for the required length of time. The operator has to stand at the same distance (1 to 3 m) from each animal every time and must be careful to keep the laser pointed at the animal’s nostrils throughout the measurement period.
----[1] Consensus views based on experiences of METHAGENE WG2 members (www.methagene.eu).

[2] Per measuring unit or group of animals.

[3] Compared to no methane recording: low = measuring in situ; medium = some handling, training or change in routine; high = confinement.

[4] Medium if using FTIR analyser.<center>
{| class="wikitable"
|-
! colspan="7" style="text-align:left;" | ''Table 3. Summary of the main features of methods for measuring CH4 output by individual animals.''
|- style="background-color:#efefef;"
|+
! style="text-align:left;" | Method
! style="text-align:center;" | Purchase cost
! style="text-align:center;" | Running costs
! style="text-align:center;" | Labour
! style="text-align:center;" | Repeatability
! style="text-align:center;" | Behaviour alteration
! style="text-align:center;" | Throughput
|-
| style="text-align:left;" | Respiration chamber
| style="text-align:center;" | High
| style="text-align:center;" | High
| style="text-align:center;" | High
| style="text-align:center;" | High
| style="text-align:center;" | High
| style="text-align:center;" | Low
|-
| style="text-align:left;" | SF6 technique
| style="text-align:center;" | Medium
| style="text-align:center;" | High
| style="text-align:center;" | High
| style="text-align:center;" | Medium
| style="text-align:center;" | Medium
| style="text-align:center;" | Medium
|-
| style="text-align:left;" | Breath sampling during milking and feeding
| style="text-align:center;" | Low
| style="text-align:center;" | Low
| style="text-align:center;" | Low
| style="text-align:center;" | Medium
| style="text-align:center;" | None
| style="text-align:center;" | High
|-
| style="text-align:left;" | GreenFeed
| style="text-align:center;" | Medium
| style="text-align:center;" | Medium
| style="text-align:center;" | Medium
| style="text-align:center;" | Medium
| style="text-align:center;" | Medium
| style="text-align:center;" | Medium
|-
| style="text-align:left;" | Laser methane detector
| style="text-align:center;" | Low
| style="text-align:center;" | Low
| style="text-align:center;" | High
| style="text-align:center;" | Low
| style="text-align:center;" | Low-Medium
| style="text-align:center;" | Medium
|}
</center>

</div>
</div>

= Discussion of methods =
<div class="mw-collapsible mw-collapsed">
<div>
== SF6 vs Respiration Chamber ==
For large-scale evaluation of CH4 emissions by individual animals, the SF6 technique is more useful than respiration chambers. Animal behaviour and intake might be affected by wearing the apparatus, and by daily handling to exchange canisters, but the technique is considerably less intrusive than respiration chambers because cows remain in the herd. Labour and monetary costs for changing canisters each day and for lab analysis are high. Throughput is limited by the number of sets of apparatus available, handling facilities, labour, and the capacity of the lab for gas analysis. Animals need to be measured for 5 to 7 days, and it is recommended that group size should be less than 15 animals (Berndt et al., 2014<ref name=":22">Berndt, A., Boland, T.M., Deighton, M.H., Gere, J.I., Grainger, C., Hegarty, R.S., Iwaasa, A.D., Koolaard, J.P., Lassey, K.R., Luo D., Martin, R.J., Martin, C., Moate, P.J., Molano, G., Pinares-Patiño, C., Ribaux, B.E., Swainson, N.M., Waghorn, G.C., and Williams, S.R.O. 2014. Guidelines for use of sulphur hexafluoride (SF6) tracer technique to measure enteric methane emissions from ruminants. Pages 166. M. G. Lambert, ed. New Zealand Agricultural Greenhouse Gas Research Centre, New Zealand. </ref>), so maximum throughput would be about 750 animals per year. The method may be better suited for in housed conditions because of the labour and the potential movement restriction of the animals due to wearing the apparatus.

== Breath sampling during milking and feeding vs Respiration Chamber ==
For large-scale evaluation of CH4 emissions by individual animals, breath-sampling methods have significant advantages compared with other methods. Breath-sampling methods are non-invasive because, once installed, animals are unaware of the equipment and are in their normal environment. Animals follow their normal routine, which includes milking and feeding, so no training of animals, handling, or change of diet is required. Equipment is relatively cheap, although more expensive gas analysers are available, and running costs are negligible.

The compromise for non-invasiveness of breath-sampling is that concentrations of gases in the sampled air are influenced by cow head position relative to the sampling tube (Huhtanen et al., 2015<ref name=":23">Huhtanen, P., Cabezas-Garcia, E.H., Utsumi, S., and Zimmerman, S. 2015. Comparison of methods to determine methane emissions from dairy cows in farm conditions. J. Dairy Sci. 98:3394–3409. doi:10.3168/jds.2014-9118.</ref>). The use of head position sensors and data filtering algorithms can remove the effects when the cow’s head is completely out of the feed bin (Difford et al., 2016<ref>Difford, G.F., Lassen, J., and Løvendahl, P. 2016. Interchangeability between methane measurements in dairy cows assessed by comparing precision and agreement of two non-invasive infrared methods. Comput. Electron. Agric. 124:220–226. doi:10.1016/j.compag.2016.04.010.</ref>), but not within the feed bin. Consequently, sniffer measurements are more variable than flux methods, with factors like variable air flow in the barn increasing measurement error (imprecision), and head position, a highly repeatable character, inflating between-cow variability.

Using CO2 as a tracer gas partly addresses the issue but, because CO2 arises from metabolism as well as rumen fermentation, variability of CO2 emissions has to be considered. A further consideration is diurnal variation in breath concentrations of CH4 and CO2 because animals are spot-sampled at different times of day and night. Diurnal variation can be accounted for either by fitting a model derived from the whole group of animals, or by including time of measurement in the statistical model (Lassen et al., 2012<ref>Lassen, J., Lovendahl, P., and Madsen, J. 2012. Accuracy of noninvasive breath methane measurements using Fourier transform infrared methods on individual cows. J. Dairy Sci. 95:890-898.</ref>).

The number of observations per analyser is limited only by number of cows assigned to one automatic milking station or concentrate feeding station and length of time equipment is installed. Typically, each analyser will record 40 to 70 animals 2 to 7 times per day for 7 to 10 days, although the number of sampling stations per analyser can be increased by using an automatic switching system (Pszczola et al., 2017<ref>Pszczola, M., Rzewuska, K., Mucha, S., and Strabel, T. 2017. Heritability of methane emissions from dairy cows over a lactation measured on commercial farms. J. Anim. Sci. 95:4813-4819. doi: 10.2527/jas2017.1842.</ref>). Throughput per analyser is likely to be 2,000 to 3,000 animals per year.

== NDIR vs LMD ==
Both methods are low invasive. LMD needs larger labor force, wheras NDIR can be used during milking and feeding. According to Rey at al. (2019)<ref>Rey, J., Atxaerandio, R., Ruiz, R, Ugarte, E., Gonzalez-Recio, O., Garcia-Rodriguez, A., and Goiri, I. 2019. Comparison Between Non-Invasive Methane Measurement Techniques in Cattle. Animals 9(8): 563. <nowiki>https://doi.org/10.3390/ani9080563</nowiki></ref>, the repeatability of the CH4 concentration was greater for NDIR (0.42) than for LMD (0.23). Correlation between methods was moderately high and positive for CH4 concentration (0.73 and 0.74,respectively) and number of peaks (0.72 and 0.72, respectively), and the repeated measures correlation and the individual-level correlation were high (0.98 and 0.94, respectively). A high coefficient of individual agreement for the CH4 concentration (0.83) and the number of peaks (0.77) were observed between methods. The study suggests that methane concentration measurements obtained from NDIR and LMD cannot be used interchangeably. But the use of both methods could be considered for genetic selection purposes or for mitigation strategies only if sources of disagreement, which result in different between subject and within-subject variabilities, are identified and corrected for.

== Greenfeed ==
A limitation of the GreenFeed system is that animals require training to use the system, although animals which have been trained to use the system will readily use it again (Velazco et al., 2014<ref>Velazco, J.I., Cottle, D.J., and Hegarty, R.S. 2014. Methane emissions and feeding behaviour of feedlot cattle supplemented with nitrate or urea. Anim. Prod. Sci. 54:1737–1740. doi:10.1071/AN14345.</ref>). However, some animals will not use the system or will use it infrequently, and frequency of visits is affected by diet (Hammond et al., 2016B<ref name=":26">Hammond, K.J., Jones, A.K., Humphries, D.J., Crompton, L.A., and Reynolds, C.K. 2016B. Effects of diet forage source and neutral detergent fiber content on milk production of dairy cattle and methane emissions determined using GreenFeed and respiration chamber techniques. J. Dairy Sci. 99:7904–7917. doi:10.3168/jds.2015-10759.</ref>). This can be a challenge when screening commercial herds for CH4 emission under genetic evaluation. On the other hand, animals seem to get used to the equipment rapidly, and the sound produced by the system is remembered by the animals easily (personal information Dr. Finocchiaro). Alternatively, as practised in Canada, the unit is moved to individual animals in a tie-stall setting multiple times a day (personal information Prof C.F. Baes). Thus, action of individual animals is not needed. The manufacturer recommends 15 to 25 animals per GreenFeed unit, and recordings are made typically for 7 days. If all animals visit the unit adequately, throughput per unit is likely to be 750 to 1,250 animals per year. Sebek et al. (2019A, B)<ref>Sebek, L.B. 2019A. Project 11: Enterisch methaan: emissievariatie in de Nederlandse melkveestapel. 1 p. Wageningen : Wageningen University & Research.</ref> and Bannink et al. (2018)<ref>Bannink, A., Spek, J.W., Dijkstra, J., and Sebek, L.B. 2018. A Tier 3 Method for Enteric Methane in Dairy Cows Applied for Fecal N Digestibility in the Ammonia Inventory. In: Front. Sust. Food Syst. 2:66.</ref> showed the usefulness of the GreenFeed method in an on farm setting.

== Laser Methane Detector ==
The LMD can be used in the animal’s normal environment, although for consistency restraint is required during measurement. Because the LMD measures CH4 in the plume originating from the animal’s nostrils, results can be affected by factors such as: distance from the animal; pointing angle; animal’s head orientation and head movement; air movement and temperature in the barn; adjacent animals; and operator variation (Sorg et al., 2017<ref name=":27">Sorg, D., Difford, G.F., Mühlbach, S., Kuhla, B., Swalve, H.H., Lassen, J., Strabel, T., and Pszczola, M. 2017. Comparison of a laser methane detector with the GreenFeed and two breath analysers for on-farm measurements of methane emissions from dairy cows. Comp. Elec. Agric. 153:285-294.</ref>). Operator variation is likely to be one of the biggest factors because the operator controls distance and pointing angle, and is responsible for ensuring the laser remains on target. The structure of the barn and the resulting ventilation conditions and wind speed at the location of the measurement are also considerable sources of variation in recorded CH4. Assuming operator fatigue does not limit measurements, each LMD could record up to 10 animals per hour. If each animal is recorded 3 times (on 3 consecutive days, for example, as in Mühlbach et al. (2018)<ref>Mühlbach, S., Sorg, D., Rosner, F., Kecman, J., and Swalve, H.H. 2018. Genetic analyses for CH₄ concentrations in the breath of dairy cows measured on-farm with the Laser Methane Detector. In: Proceedings of the World Congress on Genetics Applied to Livestock Production, Abstract No. 186, 11-16th February, Auckland, New Zealand.</ref>), throughput is likely to be up to 1000 animals per year.</div>
</div>

= Comparison of methods to measure methane =
<div class="mw-collapsible mw-collapsed"><div>
== Correlations among methods ==
Correlations among methods
Table 4 shows correlations between the respiratory chamber method as the gold standard to measure CH4 emission from cows and other methods from Garnsworthy et al. (2019)<ref name=":11" />.
<center>
{| class="wikitable"
|-
! colspan="3" style="text-align:left;" | ''Table 4. Correlations between CH4 measuring methods. Data were taken from Garnsworthy et al. (2019)''<ref name=":11" />''.''
|- style="background-color:#efefef;"
|+
! style="text-align:left;" | Method
! style="text-align:center;" | Correlation
! style="text-align:center;" | S.E.
|-
| style="text-align:left;" | Respiratory chamber - SF6
| style="text-align:center;" | 0.87
| style="text-align:center;" | - 0.08
|-
| style="text-align:left;" | Respiratory chamber - Greenfeed
| style="text-align:center;" | 0.81
| style="text-align:center;" | - 0.1
|-
| style="text-align:left;" | Respiratory chamber - NDIR
| style="text-align:center;" | - 0.07
| style="text-align:center;" | 0.88
|-
| style="text-align:left;" | Respiratory chamber - NDIR peak
| style="text-align:center;" | 0.72
| style="text-align:center;" | - 0.11
|-
| style="text-align:left;" | Respiratory chamber - PAIR
| style="text-align:center;" | - 0.08
| style="text-align:center;" | 0.7
|-
| style="text-align:left;" | SF6 - Greenfeed
| style="text-align:center;" | 0.4
| style="text-align:center;" | - 0.8
|-
| style="text-align:left;" | LMD - Greenfeed
| style="text-align:center;" | 0.77
| style="text-align:center;" | - 0.23
|-
| style="text-align:left;" | NDIR - Greenfeed
| style="text-align:center;" | 0.64
| style="text-align:center;" | - 0.18
|-
| style="text-align:left;" | NDIR - LMD
| style="text-align:center;" | 0.6
| style="text-align:center;" | - 0.11
|-
| style="text-align:left;" | FTIR - LMD
| style="text-align:center;" | 0.57
| style="text-align:center;" | - 0.25
|-
| style="text-align:left;" | NDIR - NDIR peaks
| style="text-align:center;" | 0.58
| style="text-align:center;" | - 0.15
|-
| style="text-align:left;" | FTIR - NDIR
| style="text-align:center;" | 0.97
| style="text-align:center;" | - 0.02
|}
</center>

In method comparison studies, simultaneous repeated measures per cow with two or more methods are required in order to assess systematic differences between methods (means) and random differences (precision) and correlation between methods free of residual error. Furthermore, adequately short time differences between repeated measures per subject are needed to ensure the underlying biology of the cow has not changed. Not all methods can be recorded simultaneously and CH4 emission of cows’ changes both within day and over the lactation period. In such instances either cross-over designs or matched pair repeated measures designs are needed. Members of METHAGENE WG2 provided data from studies in which two or more methods had been used to measure CH4 output (g/day) by individual dairy cows. Methods were applied to each cow either concurrently or consecutively within a short timeframe.

Seven main methods were represented: respiration chambers; SF6; GreenFeed; LMD; and three breath-sampling systems based on different gas analysers. Gas analysers incorporated different technologies to measure CH4, which were NDIR (e.g. Guardian Plus, Edinburgh Instruments, Edinburgh, UK), FTIR (e.g. Gasmet 4030, Gasmet Technologies Oy, Helsinki, Finland), or PAIR (e.g. F10, Gasera Ltd, Turku, Finland). In the contributing studies, NDIR and FTIR were used in automatic milking stations, and PAIR was used in concentrate feeding stations. One NDIR study and all FTIR and PAIR studies used CO2 as a tracer gas, with daily CO2 output calculated either from milk yield, live weight and days pregnant or from metabolisable energy intake. Two NDIR studies were based on CH4 concentration in eructation peaks rather than mean CH4 concentration, so were treated as separate methods. By separating NDIR studies, a total of 8 distinct methods were available giving a matrix of 28 potential combinations for comparisons. Data were available for 13 method combinations (Garnsworthy et al., 2019<ref name=":11" />).

Method comparisons were conducted using bivariate models (repeatability animal models) to obtain correlations between ‘true values’, also known as repeated measures correlations or individual level correlations (Bakdash and Marusich, 2017<ref>Bakdash, J.Z., and Marusich, L.R. 2017. Repeated measures correlation. Front. Psychol. 8:1–13. doi:10.3389/fpsyg.2017.00456.</ref>). Variance components including between cow variation and within cow variation (precision) and means (accuracy) were used in the calculation of between cow coefficient of variation (CV, %) and total CV and repeatability. Where single measurements were available for each method Pearson’s correlation was reported and where repeated measures per subject were available repeated measures correlation was reported.

Respiration chambers were the most precise method, as can be seen by the smaller between cow CV% and total CV compared to alternative methods, and respiration chambers are by definition the most accurate. All methods tested showed high correlations with respiration chambers but none of the correlations exceeded 0.90. This is in part due to the increased imprecision of alternative methods, as even the most accurate and precise method will compare poorly to a less precise method. These correlations are also likely to be underestimated because none of the methods could be recorded simultaneously with respiration chambers and had to be recorded in cross over designs. Consequently, the true value for each cow may have changed due to changes in the underlying biology of the cow over time between measurements. Comparisons among alternative methods generally had lower correlations than comparisons with respiration chambers, despite having relatively higher numbers of animals and in most cases simultaneous or near simultaneous repeated measures per cow per method due to the increased variability and imprecision of alternative methods as is seen by the increased CVs or due to the possibility that different aspects of CH4 emission are captured using different methods.

For the methods with repeated measures per cow the two mass flux methods, SF6 and GreenFeed, had the highest repeated measures correlations (0.87 ± 0.08 and 0.81 ± 0.10) which outperformed the concentration based NDIR method using CO2 tracer gas. Of the two concentration methods evaluated against respiration chambers using single measurements, NDIR Peaks had a higher correlation (0.89 ± 0.07) than the PAIR CO2 tracer gas (0.80 ± 0.10). The study of Hristov et al. (2016)<ref>Hristov, A.N., O,h J., Giallongo, F., Frederick, T., Harper, M.T., Weeks, H., Branco, F., Price, W.J., Moate, P.J., Deighto,n M.H., Williams, S.R.O., Kindermann, M., and Duval, S. 2016. Short communication: Comparison of the GreenFeed system with the sulfur hexafluoride tracer technique for measuring enteric methane emissions from dairy cows. J. Dairy Sci. 5461–5465. doi:10.3168/jds.2016-10897.</ref> comparing SF6 and GreenFeed reported a low Pearson correlation of 0.40, despite having a large number of animals with repeated measures per method, the authors appear not to have estimated a repeated measures correlation, which could be larger. Estimating a repeated measures correlation between these two mass flux methods is a priority as it would clarify the inexplicable disagreement between two methods which both correlate highly with the gold standard method. With the exception of the aforementioned study, the imprecision was low in the mass flux measure comparisons as compared to the concentration-based methods.

Two of the sniffer methods evaluated, FTIR CO2t1 and NDIR CO2t1, correlated close to unity (0.97), most likely due to the shared prediction equation for CO2 tracer gas. Nevertheless, all correlations derived from actual data were positive. This suggests that combination of datasets obtained with different methods is a realistic proposition for genetic studies. Calculation of adjustment or weighting factors for bias, accuracy and precision would improve the value of combined datasets.

== Pro’s and con’s of devices ==

=== Daily methane emission measures ===
Due to the large diurnal variation in enteric CH4 emission in relation with feeding pattern (Grainger et al., 2007<ref>Grainger, C., Clarke, T., McGinn, S.M., Auldist, M.J., Beauchemin, K.A., Hannah, M.C., Waghorn, G.C., Clark, H., and Eckard, R J. 2007. Methane emissions from dairy cows measured using the sulfur hexafluoride (SF6) tracer and chamber techniques. J. Dairy Sci. 90:2755-2766.</ref>; Jonker et al. 2014<ref>Jonker, A., Molano, G., Antwi, C., Waghorn, G.. 2014. Feeding lucerne silage to beef cattle at three allowances and four feeding frequencies affects circadian patterns of methane emissions, but not emissions per unit of intake. Anim. Prod. Sci.54:1350-1353.</ref>), the highest accuracy of daily CH4 production rate (DMPR) will be obtained with methods that encompass the whole day emissions. Two methods are available: Respiration Chambers (RC) and SF6 methods.

Alternative methods are based on short-term measures of CH4 production rate: Portable Accumulation Chambers (PAC) for sheep and GreenFeed Emission Monitoring (GEM) systems for cattle and sheep (Hegarty, 2013<ref>Hegarty, R.S. 2013. Applicability of short term emission measurements for on-farm quantification of enteric methane. Animal 7, s2:401-408.</ref>).

=== DMPR with Respiration Chamber (RC) ===
It should be noted that CH4 emissions recorded in RC also include gases from flatulence in addition to eructed and expired CH4. Compared with mouth exhaled CH4, CH4 from flatulence is generally considered as limited.

Feed intake in the RC may not be representative of the normal animal feed intake (Bickell et al., 2014<ref>Bickell, S.L., Revell, D.K., Toovey, A.F., and Vercoe, P. E. 2014. Feed intake of sheep when allowed ad libitum access to feed in methane respiration chambers. J. Anim. Sci. 92:2259-2264.</ref>; Llonch et al., 2016<ref>Llonch, P., Somarriba, M.,. Duthie, C-A, Haskell, M.J., Rooke, J.A., Troy, S., Roehe, R., and . Turner, S.P. 2016 Association of temperament and acute stress responsiveness with productivity, feed efficiency, and methane emissions in beef cattle: an observational study. Front. Vet. Sci. 3: 43.</ref>; Troy et al., 2016<ref name=":0a">Troy, S.M., Duthie, C.A., Ross, D.W., Hyslop, J.J., Roehe, R., Waterhouse, A., and Rooke, J.A. 2016. A comparison of methane emissions from beef cattle measured using methane hoods with those measured using respiration chambers. Anim. Feed Sci. Technol. 211:227-240.</ref>). As a consequence, the DMPR measured could be biased. Animals are usually not fed ad libitum when recorded in RC. It is therefore recommended to compare animal or diet effects on Methane Yield (MY) calculated as the ratio of the observed DMPR/DMI during the RC recording in order to take into account possible differences among animals in DMI bias. Animal effects can also be compared on the Residual Methane Production Rate (RMPR) the difference between the observed DMPR and the expected DMPR obtained by regression of observed DMPR on DMI recorded during RC test. Residual traits, however, require a large number of recorded animals for valid adjustment.

Repeatability coefficients between measures taken on consecutive days are very high, rep=0.85 [0.75 to 0.94] for MeY and RMPR of cattle and sheep (Grainger et al., 2007<ref>Grainger, C., Clarke, T., McGinn, S.M., Auldist, M.J., Beauchemin, K.A., Hannah, M.C., Waghorn, G.C., Clark, H., and Eckard, R J. 2007. Methane emissions from dairy cows measured using the sulfur hexafluoride (SF6) tracer and chamber techniques. J. Dairy Sci. 90:2755-2766.</ref>; Donoghue et al., 2016<ref name=":14" />; Pinares-Patino et al., 2013<ref name=":2">Pinares-Patiño C.S., Hickey, S.M., Young, E.A., Dodds, K.G., MacLean, S., Molano, G., Sandoval, E., Kjestrup, H., Harland, R., Pickering, N.K., and McEwan, J.C. 2013. Heritability estimates of methane emissions from sheep. Animal 7: 316–321.</ref>). It has been concluded that 1-day measurement duration could be recommended as it will have a limited impact, less than 5%, on the efficiency of selection of MeY as compared to a selection on a 2-day measurement duration.

When repeated measures of CH4 emission of sheep are taken few days to two weeks apart the repeatability coefficients of MeY and RMPR drops to rep=0.36 [0.26 to 0.41] on average (Pinares-Patino et al., 2013<ref name=":2" />; Robinson et al., 2014a<ref name=":3b">Robinson, D.L., Goopy, J.P., Donaldson, A.J., Woodgate, R.T., Oddy, V.H., and Hegarty, R.S. 2014. Sire and liveweight affect feed intake and methane emissions of sheep confined in respiration chambers. Anima, 8:1935-1944.</ref>). Interestingly, repeatability maintains at a moderate level, rep=0.27 [0.23 to 0.53], when animals were measured several months or even years apart. Similar results were found in Angus cattle, rep=0.20, between MeY and RMPR measures taken more than 60 days apart (Donoghue et al., 2016<ref name=":14" />).

==== Conclusions and reccomendations ====
All these results show that animal effects exist on daily CH4 emissions and animal differences are partially under genetic determinism. This trait, as any other physiology trait, is subject to number of environmental effects and to evolution with time. Ranking animals on their CH4 emission requires standardization of the testing environment. Although highly precise, a single measure recorded in RC is not sufficient for characterizing an animals emission aptitude. In order to characterize a long term phenotype it is therefore recommended to record several 1-day measures, each a few weeks apart, instead of one single 2-day measure, keeping the testing environment as constant as possible.

=== DMPR with GEM ===
At each visit CH4 and CO2 fluxes are measured and animal emission rates are obtained by averaging the short-term flux measures recorded during the testing period. In a review of published results (Dorich et al., 2015<ref name=":4">Dorich, C.D., Varner, R.K., Pereira, A.B.D., Martineau, R., Soder, K.J., and Brito, A.F. 2015. Use of a portable, automated, open-circuit gas quantification system and the sulfur hexafluoride tracer technique for measuring enteric methane emissions in Holstein cows fed ad libitum or restricted. J. Dairy Sci. 98:2676-2681.</ref>; Hammond et al., 2015<ref name=":5">Hammond, K.J., Humphries, D.J., Crompton, L.A., Green, C., and Reynolds, C.K. 2015. Methane emissions from cattle: Estimates from short-term measurements using a GreenFeed system compared with measurements obtained using respiration chambers or sulphur hexafluoride tracer. Anim. Feed Sci. Technol. 203:41-52. doi:10.1016/j.anifeedsci.2015.02.008.</ref>; Velazco et al., 2016<ref name=":131">Velazco, J. I., Hegarty, R., Cottle, D., and Li, L. 2016. Quantifying daily methane production of beef cattle from multiple short-term measures using the GreenFeed system. <nowiki>https://rune.une.edu.au/web/handle/1959.11/23580</nowiki>.</ref>) Hammond et al. (2016A)<ref>Hammond, K.J., Crompton, L.A., Bannink, A., Dijkstra, J., Yáñez-Ruiz, D.R., O’Kiely, P., Kebreab, E., Eugenè, M.A., Yu, Z., Shingfield, K.J., Schwarm, A., Hristov, A.N., and Reynolds, C.K. 2016A. Review of current in vivo measurement techniques for quantifying enteric methane emission from ruminants. Anim. Feed Sci. Technol. 219:13–30. doi:10.1016/j.anifeedsci.2016.05.018</ref> concluded that the GEM system provides similar DMPR values as the RC or SF6 methods. Similar accuracy was found by Arbre et al. (2016)<ref name=":7">Arbre, M., Rochette, Y., Guyader, J., Lascoux, C., Gómez, L.M., Eugène, M., Morgavi, D.P., Renand, G., Doreau, M. and Martin, C. 2016. Repeatability of enteric methane determinations from cattle using either the SF6 tracer technique or the GreenFeed system. Anim. Prod. Sci. 56:238-243.</ref> for CH4 yield measured with GEM as compared with RC and SF6 measures.

The spot measures are highly variable since they include, in addition to the animal and environment effects, an important within-animal and within-day variance. The latter is considered as an error term. Consequently, the precision of the animal estimates increase with the number of spot measures averaged per animal. From the results reported by Renand and Maupetit (2016)<ref name=":8">Renand, G., and Maupetit, D. 2016. Assessing individual differences in enteric methane emission among beef heifers using the GreenFeed Emission Monitoring system: effect of the length of testing period on precision. Anim. Prod. Sci. 56:218-223.</ref> with 124 beef heifers controlled indoors, it can be shown that the coefficient of variation of that error term (CVe) decreases exponentially with the number of spot measures: 13.7%, 10.8%, 7.9% and 4.9% with 5, 10, 25 and 100 measures respectively. Results reported by Arbre et al. (2016)<ref name=":7" /> with 7 lactating dairy cows controlled indoors, also show that CVe decreases from 12.8% to 11.4%, 9.5% and 6.8% when the number of measures increases from 5 to 10, 25 and 100. With dairy cows at pasture, Waghorn et al. (2016)<ref name=":9">Waghorn, G.C., Jonker, A., and Macdonald, K A. (2016). Measuring methane from grazing dairy cows using GreenFeed. Anim. Prod. Sci. 56:252-257.</ref> showed that the coefficient of variation among 36 dairy cows at pasture was half (6.6 and 7.5%) when CH4 production rate was averaged over 16 days with approximately 18 to 26 measures per cow, as compared with 4 day averages with 4 to 6 measures per cow (13.0 and 17.2%). These authors concluded that at least 16 days are required to give confident estimates.

With 45 to 50 spot measures recorded during 2 weeks Arbre et al. (2016)<ref name=":7" /> and Renand and Maupetit (2016)<ref name=":8" /> obtained repeatabilityof 0.78 and 0.73 for DMPR estimates of 7 dairy cows and 124 beef heifers, respectively. A similar repeatability coefficient (0.74) was obtained by Huhtanen et al. (2015)<ref name=":24">Huhtanen, P., Cabezas-Garcia, E.H., Utsumi, S., and Zimmerman, S. 2015. Comparison of methods to determine methane emissions from dairy cows in farm conditions. J. Dairy Sci. 98:3394–3409. doi:10.3168/jds.2014-9118.</ref> with 25 dairy cows recorded during 3 weeks, with 20 to 30 samples per cow. Interestingly, these latter authors fitted gas concentration, airflow and head position measurement equipments into two automatic milking systems that were used to measure CH4 emission of 59 dairy cows during two periods of 10 days. After filtering data for acceptable head-position, the repeatability of DMPR was 0.75.

Considering the need to average enough spot measures and the advantage of measuring DMPR over long periods to take into account the emission variability with time, the GEM system should be run over several weeks. Averaging 40 to 50 spot measures per animal should provide a precise measure of the animal DMPR. The minimum duration of CH4 recording will depend on the number of spot measures actually recorded per day.

The GEM system relies on animals that voluntarily visit the GEM unit when attracted with pellets dispensed by a feeder at a controlled rate. The visitation frequency appears to be highly variable among different studies reported up to now. While some experiments report a very high frequency of cattle visiting the GEM units (up to 96%), the proportion of not visiting animals may be very high in other studies (up to 60%) (Dorich et al., 2015<ref name=":4" />; Hammond et al, 2015A<ref name=":5" />, Arbre et al., 2016<ref name=":7" />; Renand and Maupetit, 2016<ref name=":8" />; Velazco et al., 2016<ref name=":131" />; Waghorn et al., 2016<ref name=":9" />). The reason why some animals may not visit the unit is not obvious. That problem of no or low visiting frequency may jeopardize the precise ranking of animals on their DMRP. Training them is an important requisite for the success of DMPR recording with the GEM system (see recommendations on the C-Lock website). Palatability of the pellets used to attract the cattle should be high compared with the diet they receive in the trough or the grass they are grazing.

In addition to the effect on precision, the low visiting frequency may have an impact on accuracy if associated in some animals with specific time of visiting. Enteric CH4 emissions have a diurnal variation with a minimum at the end of night, before the first feeding, and a steady increase after each feeding. A weak diurnal pattern in CH4 emission was detected by Velazco et al. (2016)<ref name=":131" /> using GEM systems. Renand et al. (2013)<ref>Renand, G., Ricard, E., Maupetit, D., Thouly, J.C. 2013. Variability among individual young beef bulls and heifers in methane emissions. In: Book of abstracts of EAAP 64th annual meeting, Nantes, France. p. 195. (Wageningen Academic Publishers: Wageningen)</ref> observed significant differences between visit hours (CV=10%). If some animals visit the GEM at specific hours of the day, the rough average of spot measures will be biased. In order to get rid of this time effect on the DMPR measure, Dorich et al. (2015)<ref name=":4" /> and Hristov et al. (2016)<ref>Hristov, A.N., O,h J., Giallongo, F., Frederick, T., Harper, M.T., Weeks, H., Branco, F., Price, W.J., Moate, P.J., Deighto,n M.H., Williams, S.R.O., Kindermann, M., and Duval, S. 2016. Short communication: Comparison of the GreenFeed system with the sulfur hexafluoride tracer technique for measuring enteric methane emissions from dairy cows. J. Dairy Sci. 5461–5465. doi:10.3168/jds.2016-10897.</ref> came up with a protocol where the GEM units were moved sequentially from one cow to the next one over several days, so that all the cows were equally measured during different hours of the day. That protocol is possible only with tie stall cattle and is obviously not applicable for measuring large number of animals. However, with animals controlled in their production environment, the bias generated by potential specific visiting patterns can actually be removed if the measuring hour is taken into account in the linear model when estimating the animal effect.

As voluntary visiting of the GEM system may be a limiting factor under some conditions, measures of DMPR can be designed when animals are drinking or eating, i.e. several times per day. Velazco et al. (2016)<ref name=":131" /> showed that a GEM water unit prototype designed and built by C-Lock Inc., displayed different eructation patterns as compared with a plain GEM unit. They concluded that further development appears necessary before any application. Troy et al. (2016)<ref name=":0a" /> tested a CH4 hood (MH) system placed above an automated feeding bin. That system includes an air extraction fan for each hood with continuously recorded airflow. Methane concentration was measured using 4 infrared analyzers, one for 8 hoods. In this system one CH4 concentration value was recorded every 6 min. With 9 to 12 feeding events per day on average and feeding visits averaging 8 min, there were between 12 to 16 CH4 concentration values recorded and CH4 production rates calculated per day. The measurements were recorded during 46 days and ranking of animals in relation with the test duration was studied. However no repeatability coefficient was given for comparison with other methods. That system was compared with respiratory chambers results in two experiments with 82 and 80 steers fed different diet-treatment combinations. Over the whole experimental design, a good concordance was found between MH and RC results as a consequence that both methods detected similar effects for the diet-treatment effects. However no correlation was given between both methods within diet-treatment samples that are the essential information needed to evaluate the ability of this new method to predict individual DMPR.

==== Conclusions and reccomendations ====
With only a single gas analyzer for 8 feed bins, the time when useful CH4 concentration is recorded is certainly too short for including several eructation peaks. Fitting one gas analyzer per feed bin will combine advantages of the measurement time during visits of the GEM system with the visiting frequency allowed by the MH system.

=== MPR with PAC ===
The delay between the measurement and the last feeding has to be carefully monitored and taken into account when calculating animal emission values. As individual DMI is difficult to record, direct measurement of CH4 yield (MY=MPR/DMI) turns out to be impossible. Although not representative of a whole day production rate, that method can be used to characterize individual CH4 emission rates if standardized protocols are applied. It was first validated with 40 ewes measured 1 hour in PAC after three 22-hour measures in RC: a correlation of 0.71 was found between the two measures of CH4 production rate over 1 or 22 hours (Goopy et al., 2011<ref>Goopy, J.P., Woodgate, R., Donaldson, A., Robinson, D.L., and Hegarty, R.S. 2011. Validation of a short term methane measurement using portable static chambers to estimate methane production in sheep. Anim. Feed Sci. Technol. 166-167;219-226.</ref>). The 1-hour CH4 production measure in PAC has a moderate repeatability of rep=0.50 [0.37 to 0.60] when taken few days to seven weeks apart (Robinson et al., 2015<ref>Robinson, D.L., Goopy, J.P., Hegarty, R.S., and Oddy, V.H. 2015. Comparison of repeated measurements of methane production in sheep over 5 years and a range of measurement protocols. J. Anim. Sci. 93:4637-4650.</ref>; Goopy et al., 2016<ref>Goopy, J.P., Robinson, D.L., Woodgate, R.T., Donaldson, A.J., Oddy, V.H., Vercoe, P. E., and Hegarty, R.S. 2016. Estimates of repeatability and heritability of methane production in sheep using portable accumulation chambers. Anim. Prod. Sci. 56:116-122.</ref>). Heritability coefficient of this 1-hour CH4 production measure is estimated to h²=0.12 in a population of 2,279 sheep (Robinson et al., 2014<ref name=":3b" />) with a repeatability coefficient rep=0.25.

==== Conclusions and recomendations ====
The authors recommend using the mean of 3 PAC measurements in order to get accurate phenotype estimates.</div>
</div>

= Proxies =
<div class="mw-collapsible mw-collapsed">
<div>
== Introduction ==
Large-scale measurements of enteric CH4 emissions from dairy cows are needed for effective monitoring of strategies to reduce the carbon footprint of milk production, as well as for incorporation of CH4 emissions into breeding programs. However, measurements on a sufficiently large scale are difficult and expensive. Proxies for CH4 emissions can provide an alternative, but each approach has limitations. Negussie et al. (2019)<ref name=":30">Negussie, E., González Recio, O., de Haas, Y., Gengler N., Soyeurt, H., Peiren, N., Pszczola, M., Garnsworthy, P., Battagin, M., Bayat, A., Lassen, J., Yan, T., Boland, T., Kuhla, B., Strabel, T., Schwarm, A., Vanlierde, A., and Biscarini, F. 2019. Machine learning ensemble algorithms in predictive analytics of dairy cattle methane emission using imputed versus non-imputed datasets. 7th GGAA – Greenhouse Gas and Animal Agriculture Conference held from August 4th to 8th, Iguassu Falls/Brazil. Oral communication, Book of Abstracts Page 40. <nowiki>http://www.ggaa2019.org/sites/default/files/proceedings-ggaa2019.pdf</nowiki></ref> recently showed the potential of proxies proxies that are easy to record in the farm. These proxies can be gathered in most farms and are a realistic threshold accuracy that can be obtained without more fancy proxies. Several techniques have been developed for the measurement of CH4 emissions from ruminants, with varying degrees of accuracy (see reviews by Cassandro et al., 2013<ref>Cassandro, M., Mele, M., Stefanon, B.. 2013. Genetic aspects of enteric methane emission in livestock ruminants. Italian J. Anim. Sci. 12:e73: 450-458.</ref> and Hammond et al., 2016A<ref name=":25">Hammond, K.J., Crompton, L.A., Bannink, A., Dijkstra, J., Yáñez-Ruiz, D.R., O’Kiely, P., Kebreab, E., Eugenè, M.A., Yu, Z., Shingfield, K.J., Schwarm, A., Hristov, A.N., and Reynolds, C.K. 2016A. Review of current in vivo measurement techniques for quantifying enteric methane emission from ruminants. Anim. Feed Sci. Technol. 219:13–30. doi:10.1016/j.anifeedsci.2016.05.018.</ref>), but routine individual measurements on a large scale (a requisite for genetic selection) have proven to be difficult and expensive (Pickering et al., 2015<ref>Pickering, N.K., Oddy, V.H., Basarab, J.A., Cammack, K., Hayes, B J., Hegarty, R.S., McEwan, J.C., Miller, S., Pinares, C., and de Haas, Y. 2015. Invited review: Genetic possibilities to reduce enteric methane emissions from ruminants. Animal 9:1431-1440.</ref>; Negussie et al., 2016<ref>Negussie E., Lehtinen, J., Mäntysaari, P., Liinamo, A-E., Mäntysaari, E., and Lidauer, M.. 2016. Non-invasive individual methane measurements in dairy cows using photoacoustic infrared spectroscopy technique. 6th Greenhouse Gases Animal Agriculture Conference (GGAA2016) 14-18 February 2016. Melbourne, Australia. Abstract. p62.</ref>). Therefore, identifying proxies (i.e. indicators or indirect traits) that are correlated to CH4 emission, but which are easy and relatively low-cost to record on a large scale, is a much needed alternative. Proxies might be less accurate, but could be measured repeatedly to reduce random noise. The (potential) proxies range from simple and low-cost measurements such as body weight, to high-throughput milk MIR, to more demanding measures like rumen morphology, rumen metabolites or microbiome profiling.

Combining proxies that are easy to measure and cheap to record could provide predictions of CH4 emissions that are sufficiently accurate for selection and management of cows with low CH4 emissions.

== Available Proxies ==
A large array of CH4 proxies differing widely in accuracy and applicability under different conditions have been reported. The ideal proxy would be highly phenotypically and genetically correlated with CH4 emissions and could easily, and potentially repeatedly, be measured on a large scale. A systematic summary and assessment of existing knowledge is needed for the identification of robust and accurate CH4 proxies for future use. Table 5 summarizes proxies for CH4 production, and Table 6 summarizes results from combining proxies to improve predictability of proxies for CH4 prediction.
{| class="wikitable"
|-
! colspan="3" | ''Table 5. Available methane proxies include: (1) feed intake and feeding behaviour, (2) rumen function, metabolites and microbiome, (3) milk production and composition, (4) hind-gut and faeces, and (5) measurements at the level of the whole animal.''
|- style="background-color:#efefef;"
|+
!Proxy
!Description / conclusion
!Reference
|-
| colspan="3" |'''(1) Feed intake and feeding behavior'''
|-
|Dry Matter Intake (DMI)
|DMI predict MeP with R2= 0.06-0.64, and ME intake predict MeP with R2= 0.53-0,55
| Ellis et al. (2007);<ref>Ellis, J.L., Kebreab, E., Odongo, N.E., McBride, B.W., Okine, E.K., and France, J. 2007. Prediction of methane production from dairy and beef cattle. J. Dairy Sci. 90:3456–3466.</ref>
Mills et al. (2003)<ref>Mills, J.A.N., Kebreab, E., Yates, C.M., Crompton, L.A., Cammell, S.B., Dhanoa, M.S., Agnew, R.E., and France, J. 2003. Alternative approaches to predicting methane emissions from dairy cows. J. Anim. Sci. 81:3141–3150.</ref>;

Negussie et al. (2019)<ref>Negussie, E., González Recio, O., de Haas, Y., Gengler N., Soyeurt, H., Peiren, N., Pszczola, M., Garnsworthy, P., Battagin, M., Bayat, A., Lassen, J., Yan, T., Boland, T., Kuhla, B., Strabel, T., Schwarm, A., Vanlierde, A., and Biscarini, F. 2019. Machine learning ensemble algorithms in predictive analytics of dairy cattle methane emission using imputed versus non-imputed datasets. 7th GGAA – Greenhouse Gas and Animal Agriculture Conference held from August 4th to 8th, Iguassu Falls/Brazil. Oral communication, Book of Abstracts Page 40. <nowiki>http://www.ggaa2019.org/sites/default/files/proceedings-ggaa2019.pdf</nowiki></ref>
|-
|Gross Energy intake (GE)
|Predict MeP with RMSPE= 3.01.
| Moraes et al. (2014)<ref name=":0b">Moraes, L.E., Strathe, A.B., Fadel, J.G., Casper, D.P., and Kebreab, E. 2014. Prediction of enteric methane emissions from cattle. Glob. Change Biol. 20:2140–2148.</ref>
|-
|Feeding behavior
| Magnitude and direction of relation to MeP varies across studies
|Nkrumah et al. (2006);<ref>Nkrumah, J.D., Okine, E.K., Mathison, G.W., Schmid, K., Li, C., Basarab, J.A., Price, M.A., Wang, Z., and Moore, S.S. 2006. Relationships of feedlot feed efficiency, performance, and feeding behavior with metabolic rate, methane production, and energy partitioning in beef cattle. J. Anim. Sci. 84:145-153.</ref>
Jonker et al., 2014<ref>Jonker, A., Molano, G., Antwi, C., Waghorn, G.. 2014. Feeding lucerne silage to beef cattle at three allowances and four feeding frequencies affects circadian patterns of methane emissions, but not emissions per unit of intake. Anim. Prod. Sci.54:1350-1353.</ref>
|-
| Rumination time
|High rumination relates to more milk, consume more concentrate and produce more CH4, lower RMP and MeI
| Watt et al. (2015)<ref>Watt, L.J., Clark, C.E.F., Krebs, G.L., Petzel, C.E., Nielsen, S., and Utsumi, S.A. 2015. Differential rumination, intake, and enteric methane production of dairy cows in a pasture-based automatic milking system. J. Dairy Sci. 98:7248–7263.</ref>;
López- Paredes et al. (2020)<ref>Lopez-Paredes, J., Goiri, I., Atxaerandio, R., García-Rodríguez, A., Ugarte, E., Jiménez-Montero, J.A., Alenda, R and González-Recio, O. 2020. Mitigation of greenhouse gases in dairy cattle via genetic selection (i): Genetic parameters of direct methane using non-invasive methods and its proxies. J. Dairy Sci. 103.</ref>
|-
|Rumen microbiome
|The metagenome can predict DMI, and classify high vs low intakes
|Delgado et al. (2019)<ref>Delgado, B., Bach A., Guasch I., González C, Elcoso G., Pryce J.E., Gonzalez-Recio O. (2019).Whole rumen metagenome sequencing allows classifying and predicting feed efficiency and intake levels in cattle. Scientific Reports 9: 11. doi:10.1038/s41598-018-36673-w</ref>
|-
| colspan="3" |'''(2) Rumen function, metabolites and microbiome'''
|-
|Dietary antimethanogenic compounds
|Inhibitors of the enzyme methyl coenzyme-M reductase: bromochoromethane; chloroform; 3- nitrooxypropanol (not always)
|Denman et al., 2007;<ref>Denman, S.E., Tomkins, N.W., and McSweeney, C.S. 2007. Quantitation and diversity analysis of ruminal methanogenic populations in response to the antimethanogenic compound bromochloromethane. FEMS Microbiol. Ecol. 62:313-322.</ref>
Knight et al., 2011;<ref>Knight, T., Ronimus, R.S., Dey, D., Tootill, C., Naylor, G., Evans, P., Molano, G., Smith, A., Tavendale, M., Pinares-Patiño, C.S., and Clark, H. 2011. Chloroform decreases rumen methanogenesis and methanogen populations without altering rumen function in cattle. Anim. Feed Sci. Technol. 166-167:101-112.</ref>

Haisan et al., 2014<ref>Haisan, J., Sun, Y., Guan, L.L., Beauchemin, K.A., Iwaasa, A., Duval, S., Barreda, D.R., and Oba, M. 2014. The effects of feeding 3-nitrooxypropanol on methane emissions and productivity of Holstein cows in mid lactation. J. Dairy Sci. 97:3110-3119.</ref>;

Romero-Perez et al., 2014<ref>Romero-Perez, A., Okine, E.K., McGinn, S.M., Guan, L.L., Oba, M., Duval, S.M., Kinderman,n M., and Beauchemin, K.A. 2014. The potential of 3-nitrooxypropanol to lower enteric methane emissions from beef cattle. J. Anim. Sci. 92:4682-4693.</ref>
|-
|Dietary antimicrobial compounds
|Induce reductions in both MeP and methanogens numbers: nitrates, anacardic acid (cashew nut shell liquid), monensin, isobutyrate
|Iwamoto et al., 2002;<ref>Iwamoto, M., Asanuma, N., and Hin,o T. 2002. Ability of Selenomonas ruminantium, Veillonella parvula, and Wolinella succinogenes to reduce nitrate and nitrite with special reference to the suppression of ruminal methanogenesis. Anaerobe. 8:209-215.</ref>
Kubo et al., 1993<ref>Kubo, I., Muroi, H., Himejima, M., Yamagiwa, Y., Mera, H., Tokushima, K., Ohta, S., and Kamikawa, T. 1993. Structure-antibacterial activity relationships of anacardic acids. J. Agric. Food Chem. 41:1016-1019.</ref>;

van Zijderveld et al., 2010<ref>Van Zijderveld, S.M., Gerrits, W.J.J., Apajalahti, J.A., Newbold, J.R., Dijkstra, J., Leng, R A., and Perdok, H.B. 2010. Nitrate and sulfate: effective alternative hydrogen sinks for mitigation of ruminal methane production in sheep. J. Dairy Sci. 93:5856-5866.</ref>;

Veneman et al., 2015<ref>Veneman, J.B., Muetzel, S., Hart, K.J., Faulkner, C.L., Moorby, J.M., Perdok, H.B., and Newbold, C.J. 2015. Does Dietary Mitigation of Enteric Methane Production Affect Rumen Function and Animal Productivity in Dairy Cows? PLoS ONE 10(10): e0140282. doi: 10.1371/journal.pone.0140282</ref>;

Shinkai et al., 2012<ref>Shinkai, T., Enishi, O., Mitsumori, M., Higuchi, K., Kobayashi, Y., Takenaka, A., Nagashima, K., and Mochizuki, M. 2012. Mitigation of methane production from cattle by feeding cashew nut shell liquid. J. Dairy Sci. 95:5308-5316.</ref>;

Wang et al., 2015<ref>Wang, C., Liu, Q., Zhang, Y.L., Pei, C.X., Zhang, S.L., Wang, Y.X., Yang, W.Z., Bai, Y.S., Shi, Z.G., and Liu, X.N. 2015. Effects of isobutyrate supplementation on ruminal microflora, rumen enzyme activities and methane emissions in Simmental steers. J. Anim. Physiol. Anim. Nutr. (Berl). 99:123-131.</ref>
|-
|Rumen microbiome profile
|High Fibrobacteres, Quinella ovalis and Veillonellaceae and low Ruminococcaceae, Lachnospiraceae and Clostridiales associate with low- CH4 phenotypes and high propionate
Protozoa concentration
|Kittelmann et al., 2014<ref>Kittelmann, S., Pinares-Patiño, C.S., Seedorf, H., Kirk, M.R., Ganesh, S., McEwan, J.C., and Janssen, P.H. 2014. Two different bacterial community types are linked with the low-methane emission trait in sheep. PLoS One 9:e103171.</ref>;
Wallace et al., 2014<ref>Wallace, R. ., Rooke, J A., Duthie, C.-A., Hyslop, J.J., Ross, D.W., McKain, N., de Souza, S.M., Snelling, T.J., Waterhouse, A., and Roehe, R. 2014. Archaeal abundance in post-mortem ruminal digesta may help predict methane emissions from beef cattle. Sci. Rep. 4:5892.</ref>;

Sun et al., 2015<ref>Sun, X., Henderson, G., Cox, F., Molan,o G., Harrison, S.J., Luo, D., Janssen, P.H., and Pacheco, D. 2015. Lambs Fed Fresh Winter Forage Rape (Brassica napus L.) Emit Less Methane than Those Fed Perennial Ryegrass (Lolium perenne L.), and Possible Mechanisms behind the Difference. PLoS One 10(3):e0119697: DOI: 10.1371/journal.pone.0119697</ref>;

Guyader et al., 2014<ref>Guyader, J., Eugène, M., Nozière, P., Morgavi, D.P., Doreau, M., and Martin, C. 2014. Influence of rumen protozoa on methane emission in ruminants: a meta-analysis approach. Animal 8:1816-1825.</ref>
|-
|Rumen microbiome profile
|Predict MeP with R2 up to 0.55
|Ross et al. 2013a<ref>Ross, E. M., P.J . Moate, L.C. Marett, B.G. Cocks and B.J. Hayes. 2013a. Investigating the effect of two methane-mitigating diets on the rumen microbiome using massively parallel sequencing. J. Dairy Sci. 96:6030–6046.</ref>;
Ross et al. (2013b)<ref>Ross, E. M., Moate, P. J., Marett, L.C., Cocks, B.G., and Hayes, B.J. 2013b. Metagenomic Predictions: From Microbiome to Complex Health and Environmental Phenotypes in Humans and Cattle. PLoS One DOI: 10.1371/journal.pone.0073056.</ref>
|-
|Microbial genes
|20 (out of 3970 identified) related to CH4 emissions
|Roehe et al. (2016)<ref>Roehe R., Dewhurst, R.J., Duthie, C-A., Rooke, J.A., McKain, N., Ross, D.W., Hyslop, J.J., Waterhouse, A., Freeman, T.C., Watson, M., and Wallace, R.J. 2016. Bovine Host Genetic Variation Influences Rumen Microbial Methane Production with Best Selection Criterion for Low Methane Emitting and Efficiently Feed Converting Hosts Based on Metagenomic Gene Abundance. PLoS Genet 12(2): e1005846. doi:10.1371/journal.pgen.1005846.</ref>
|-
|Rumen volume (Xray Computed Tomography) and retention time
|Low-MeY sheep had smaller rumens. Faster passage= less time to ferment substrate - explained 28% of variation in MeP
|Pinares Patiño et al., 2003<ref>Pinares-Patiño C.S., Hickey, S.M., Young, E.A., Dodds, K.G., MacLean, S., Molano, G., Sandoval, E., Kjestrup, H., Harland, R., Pickering, N.K., and McEwan, J.C. 2013. Heritability estimates of methane emissions from sheep. Animal 7: 316–321.</ref>;
Goopy et al., 2014<ref>Goopy, J.P., Donaldson, A., Hegarty, R., Vercoe, P.E., Haynes, F., Barnett, M., and Oddy, V.H. 2014. Low-methane yield sheep have smaller rumens and shorter rumen retention time. Br. J. Nutr. 111:578-585.</ref>;

Okine et al. (1989)<ref>Okine, E. ., Mathiso,n G.W., and Hardin, R.T. 1989. Effects of changes in frequency of reticular contractions on fluid and particulate passage rates in cattle. J. Anim. Sci. 67:3388–3396.</ref>
|-
|Blood triiodothyronine concentration
|Reduced MeY
|Barnett et al. (2012)<ref>Barnett, M.C., Goopy, J.P., McFarlane, J.R., Godwin, I.R., Nolan, J.V., and Hegarty, R.S. (2012). Triiodothyronine influences digesta kinetics and methane yield in sheep. Anim. Prod. Sci. 52:572-577.</ref>
|-
|Acetate to propionate ratio in ruminal fluid
|Positively associated with CH4 emissions, but not confirmed in all studies, sometimes opposite relation
|Mohammed et al., 2011;<ref name=":1b">Mohammed, R., McGinn, S.M., and Beauchemin, K.A. 2011. Prediction of enteric methane output from milk fatty acid concentrations and rumen fermentation parameters in dairy cows fed sunflower, flax, or canola seeds. J. Dairy Sci. 94:6057–6068.</ref>
Fievez et al., 2012<ref>Fievez V., Colma,n E., Castro-Montoya, J.M., Stefanov, I., and Vlaeminck, B. 2012. Milk odd- and branched-chain fatty acids as biomarkers of rumen function – An update. Anim. Feed. Sci. Technol. 172:51–65.</ref>;

Chung et al., 2011<ref>Chung, Y.-H., Walker, N.D., McGinn, S.M., and Beauchemin, K.A. 2011. Differing effects of 2 active dried yeast (Saccharomyces cerevisiae) strains on ruminal acidosis and methane production in nonlactating dairy cows. J. Dairy Sci. 94:2431–2439.</ref>;

Van Zijderveld et al., 2010<ref>Van Zijderveld, S.M., Gerrits, W.J.J., Apajalahti, J.A., Newbold, J.R., Dijkstra, J., Leng, R A., and Perdok, H.B. 2010. Nitrate and sulfate: effective alternative hydrogen sinks for mitigation of ruminal methane production in sheep. J. Dairy Sci. 93:5856-5866.</ref>
|-
| colspan="3" |'''(3) Milk production and composition'''
|-
|Modelling approach
|Doubling milk production only adds 5 kg to the MeP and so greatly reduces MeY
|Kirchgessner et al. (1995)<ref>Kirchgessner M., Windisch, W., and Muller, H.L. 1995. Nutritional factors for the quantification of methane production. In: Ruminant Physiology: Digestion, Metabolism, Growth and Reproduction. Proceedings 8th International Symposium on Ruminant Physiology (eds W. Von Engelhardt, S. Leonhard-Marek, G. Breves and D. Giesecke). Reproduction Proceedings 8th International Symposium on Ruminant Physiology. Ferdinand Enke Verlag, Stuttgart, Germany. pp. 333-348.</ref>;
Hristov et al. (2014)<ref>Hristov, A.N., Johnson, K.A., and Kebreab, E, 2014. Livestock methane emissions in the United States. Proc. Natl. Aacad. Sci. 111:E1320; doi:10.1073/pnas.1401046111</ref>
|-
|Milk fat content
|key explanatory variable for predicting CH4: A moderate negative genetic correlation with infrared predicted
MeI: correlations MeP = 0,08 and MeI = - 0.13
|Moraes et al. (2014)<ref name=":31">Moraes, L.E., Strathe, A.B., Fadel, J.G., Casper, D.P., and Kebreab, E. 2014. Prediction of enteric methane emissions from cattle. Glob. Change Biol. 20:2140–2148.</ref>;
Kandel et al., 2014A<ref name=":37" />, B<ref name=":3">Kandel, P.B., Vanderick, S., Vanrobays, M.L., Vanlierde, A., Dehareng, F., Froidmont, E., Soyeur,t H., and Gengler, N. 2014B. Consequences of selection for environmental impact traits in dairy cows. In: 10th World Congress on Genetics Applied to Livestock Production (WCGALP), 17-22 August, 2014. Vancouver, Canada.</ref>;

Vanlierde et al. (2015)<ref name=":4a">Vanlierde, A., Vanrobays, M.L., Dehareng, F., Froidmont, E., Soyeurt, H., McParland, S., Lewis, E., Deighton, M.H., Grandl, F., Kreuzer, M., Grendler, B., Dardenne, P., and Gengler, N. 2015. Hot topic: Innovative lactation-stage-dependent prediction of methane emissions from milk mid-infrared spectra. J. Dairy Sci. 98:5740–5747.</ref>
|-
|Milk fat content
|A positive relationship between VFA proportions and methanogenesis is expected as a consequence of the common biochemical pathways; Dietary unsaturated fatty acids are negatively associated with CH4 emissions
|Vlaeminck et al., 2006<ref>Vlaeminck, B., Fievez, V., Cabrita, A.R.J., Fonseca, A.J.M., and Dewhurst, R.J. 2006. Factors affecting odd- and branched-chain fatty acids in milk: A review. Anim. Feed Sci. Technol. 131:389–417.</ref>;
Van Lingen et al., 2014<ref name=":5a">Van Lingen H.J., Crompton, L.A., Hendriks, W.H., Reynolds, C.K., Dijkstra, J. 2014. Meta-analysis of relationships between enteric methane yield and milk fatty acid profile in dairy cattle. J. Dairy Sci. 97:7115-7132.</ref>
|-
|Milk protein yield
|Correlation with Mel = - 0.47 or -0.09, MeP = 0.53
|Kandel et al. (2014A)<ref name=":37" />;
Vanlierde et al. (2015)<ref name=":4a" />
|-
|Lactose
|Variable correlations: MeP = 0,33; MeI = - 0.21; R = 0.19 for CH4 emission
|Miettinen and Huhtanen (1996)<ref>Miettinen, H., and Huhtanen, P. 1996. Effects of the ration of ruminal propionate to butyrate on milk yield and blood metabolites in dairy cows. J. Dairy Sci. 79:851–861.</ref>;
Dehareng et al. (2012)<ref name=":32">Dehareng, F., Delfosse, C., Froidmont, E., Soyeurt, H., Martin, C., Gengler, N., Vanlierde, A., and Dardenne, P. 2012. Potential use of milk mid-infrared spectra to predict individual methane emission of dairy cows. Animal 6:1694-701.</ref>
|-
|Somatic cell score
|Genetic correlation with infrared predicted MeI: R = 0.07
|Kandel et al. (2014A<ref name=":37" />, B<ref name=":3" />)
|-
|Prediction equations Milk FA and CH4 emissions, including from MIR data
|R² ranged between 47 and 95%; relationships between the individual milk FA and MeP differed considerably and the correlations between CH4 and milk FA vary throughout the lactation
|Chilliard et al. (2009)<ref>Chilliard Y., Martin ,C., Rouel, J., and Doreau, M. 2009. Milk fatty acids in dairy cows fed whole crude linseed, extruded linseed, or linseed oil, and their relationship with methane output. J. Dairy Sci. 92:5199-5211.</ref>;
Delfosse et al. (2010)<ref>Delfosse, O., Froidmont, E., Fernandez Pierna, J. A., Martin, C., and Dehareng, F. 2010. Estimation of methane emissions by dairy cows on the basis of milk composition. In: Greenhouse Gases and Animal Agriculture Conference. 2010; GGAA2010: 4. Greenhouse Gases and Animal Agriculture Conference, Banff, CAN, 2010-10-03-2010-10-08, 60-61.</ref>;

Castro-Montoya et al. (2011)<ref>Castro Montoya, J., Bhagwat, A.M., Peiren, N., De Campeneere, S., De Baets, B., and Fievez, V. 2011. Relationships between odd- and branched-chain fatty acid profiles in milk and calculated enteric methane proportion for lactating dairy cattle. Anim. Feed Sci. Technol.166:596–602.</ref>;

Dijkstra et al. (2011)<ref>Dijkstra, J., van Zijderveld, S.M., Apajalahti, J.A., Bannink, A., Gerrits, W.J.J., Newbold, J.R., Perdok, H.B.,, and Berends, H. 2011. Relationships between methane production and milk fatty acid profiles in dairy cattle. Anim. Feed Sci. Technol. 166–167:590–595.</ref>;

Kandel et al. (2013)<ref>Kandel, P.B., Vanrobays, M.L., Vanlierde, A., Dehareng, F., Froidmont, E., Dardenne, P., Lewis, E., Buckley, F., Deighton, M.H., McParland, S. and Gengler, N., 2013. Genetic parameters for methane emissions predicted from milk mid-infrared spectra in dairy cows. J. Dairy Sci. 95(E-1):p.388.</ref>;

Mohammed et al. (2011)<ref name=":1b" />;

Van Lingen et al. (2014)<ref name=":5a" />;

Williams et al. (2014)<ref>Williams, S.R.O., Williams, B., Moate, P.J., Deighton, M.H., Hannah, M.C., and Wales, W.J. 2014. Methane emissions of dairy cows cannot be predicted by the concentrations of C8:0 and total C18 fatty acids in milk. Anim. Prod. Sci. 54:1757–1761. <nowiki>http://dx.doi.org/10.1071/AN14292</nowiki>.</ref>;

Dijkstra et al. (2016)<ref>Dijkstra, J., van Zijderveld, S.M., Apajalahti, J.A., Bannink, A., Gerrits, W.J.J., Newbold, J.R., Perdok, H.B.,, and Berends, H. 2011. Relationships between methane production and milk fatty acid profiles in dairy cattle. Anim. Feed Sci. Technol. 166–167:590–595.</ref>;

Rico et al. (2016)<ref name=":33">Rico D.E., Chouinard, P.Y., Hassanat, F., Benchaar, C., and Gervais, R. 2016. Prediction of enteric methane emissions from Holstein dairy cows fed various forage sources. Animal 10:203-211. doi:10.1017/S1751731115001949</ref>;

Van Gastelen and Dijkstra (2016)<ref name=":36">Van Gastelen, S., and Dijkstra, J.. 2016. Prediction of methane emission from lactating dairy cows using milk fatty acids and mid-infrared spectroscopy. J. Sci. Food Agric. 96:3963-3968. DOI: 10.1002/jsfa.7718.</ref>;

Vanrobays et al. (2016);<ref>Vanrobays, M.-L., Bastin, C., Vandenplas, J., Hammami, H., Soyeurt, H., Vanlierde, A., Dehareng, F., Froidmont, E., and Gengler, N. 2016. Changes throughout lactation in phenotypic and genetic correlations between methane emissions and milk fatty acid contents predicted from milk mid-infrared spectra. J. Dairy Sci. 99:1–14. <nowiki>http://dx.doi.org/10.3168/jds.2015-10646</nowiki></ref>

Bougoin et al., (2019)<ref name=":34">Bougouin, A., Appuhamy, J.A.D.R.N., Ferlay, A., Kebreab, E., Martin, C., Moate, P.J., Benchaar, C., Lund, P., and Eugène, M. 2019. Individual milk fatty acids are potential predictors of enteric methane emissions from dairy cows fed a wide range of diets: Approach by meta-analysis. J. Dairy Sci. 102:10616–10631. DOI: <nowiki>http://doi.org/10.3168/jds.2018-15940</nowiki></ref>
|-
| colspan="3" |'''(4) Hind-gut and feces'''
|-
|Whole tract digestibility (potential as supporting factors in the prediction of enteric CH4 emissions)
|Main effects relate to rumen (see above), but energy digestibility as a supporting factor to GE intake improved the accuracy of CH4 prediction, despite the fact that there was no direct linear relationship between energy digestibility and MeY and in % of GE intake
|Yan et al., 2009<ref name=":6">Yan, T., Porter, M.G., and Mayne, S.C. 2009. Prediction of methane emission from beef cattle using data measured in indirect open-circuit respiration calorimeters. Animal 3:1455-1462.</ref>
|-
|Ratio of acetic and butyric acid divided by propionic acid
|Methane yield positive relation
|Moss et al., 2000<ref>Moss A.R., Jouany, J.P., and Newbold, J. 2000. Methane production by ruminants: Its contribution to global warming. Annal. Zootech. 49:231-253.</ref>
|-
| colspan="3" |'''(5) Whole animal measurements'''
|-
|Body weight and conformation
|Prediction models; primary predictor for enteric MeP
|Moraes et al. (2014)<ref name=":0">de Haas, Y., Pszczola, M., Soyeurt, H., Wall, E., and Lassen, J. 2017. Invited review: Phenotypes to genetically reduce greenhouse gas emissions in dairying. J. Dairy Sci. 100:855-870.</ref>;
Holter and Young, 1992; <ref>Holter J.B., and Young, A.J. 1992. Methane production in dry and lactating Holstein cows. J. Dairy Sci. 75:2165–2175.</ref>

Yan et al., 2009<ref name=":6" />
|-
|Body weight
|Relationship with MeI: r = 0.44; relationship between body weight and rumen capacity
|Antunes-Fernandes et al. (2016)<ref>Antunes-Fernandes, E.C., van Gastelen, S., Dijkstra, J., Hettinga K.A., and Vervoort, J. 2016. Milk metabolome relates enteric methane emission to milk synthesis, and energy metabolism pathways. J. Dairy. Sci. 99:6251-6262.</ref>;
Demment and Van Soest, 1985<ref>Demment, M.W., and Van Soest, P.J. 1985. A nutritional explanation for body-size patterns of ruminant and nonruminant herbivores. Am. Nat. 125:641–672.</ref>
|-
|Body weight
|Key explanatory variable for enteric MeP
|No reference available
|-
|Conformation traits: affects enteric MeP
|Indicators for rumen volume (via feed intake and rumen passage rates); BCS
|Agnew and Yan, 2000<ref>Agnew, R.E. and Yan, T. 2000. The impact of recent research on energy feeding systems for dairy cattle. Livest. Prod. Sci. 66:197-215.</ref>
|-
|Lactation stage
|Complementary proxy
|Vanlierde et al. (2015)<ref name=":4a" />
|}</div>It is evident that no single proxy offers a good solution in terms of all of these attributes, though the low cost and high throughput make milk MIR a good candidate for further work on refining methods, improving calibrations and exploring combinations with other proxies.

== Combining proxies for methane ==
Although milk MIR shows promise as a single proxy for CH4 emissions, there may be advantages in using two or more proxies in combination. There are two potential reasons why a combination of proxies might be appropriate: (i) proxies may describe independent sources of variation in CH4 emissions, and (ii) one proxy allows correction for shortcomings in the way the other proxy describes CH4 emissions (e.g. taking into account lactation stage if CH4 emissions prediction coefficients change during the lactation). See also Negussie ''et al.'' (2019)<ref name=":30" />.
<center>
{| class="wikitable"
| colspan="3" |'''''Table 7. Combinations of proxies for methane.'''''
|- style="background-color:#efefef;"
|'''Proxy combinations'''
|'''Results'''
|'''References'''
|-
|Rumen microbiome + VFA
|Combination of rumen VFA proportions and pH + modelling may be more informative
|Brask ''et al.'' 2015<ref>Brask, M., Weisbjerg, M.R., Hellwing, A.L.F., Bannink, A., and Lund, P. 2015. Methane production and diurnal variation measured in dairy cows and predicted from fermentation pattern and nutrient or carbon flow Animal. 9:1795-1806. DOI: <nowiki>http://dx.doi.org/10.1017/S1751731115001184</nowiki></ref>
|-
|Methanogen abundance in rumen fluid + proxy, a chemical marker for methanogens (archaeol)
|
|McCartney ''et al.'' (2013)<ref>McCartney, C.A., Bull, I.D., Waters, S.M., and Dewhurst, R.J. 2013. Technical note: Comparison of biomarker and molecular biological methods for estimating methanogen abundance. J. Anim. Sci. 91:5724–5728.</ref>
|-
|Fecal ether lipids (ratio of diether to tetraether lipids) + rumen pH
|Combining measurements of rumen VFA, pH and the microbiome should be more informative for predicting CH4 emissions
|McCartney ''et al.'' (2014)<ref>McCartney, C.A., Dewhurst, R.J. and Bull, I.D. 2014. Changes in the ratio of tetraether to diether lipids in cattle feces in response to altered dietary ratio of grass silage and concentrates. J. Anim. Sci. 92:4095-4098.</ref>; Ann ''et al.'', 1996
|-
|Feed intake (determined by body weight, production level, growth rate and feed quality)
|Main driver for CH4 emissions; should be all included in models for CH4
|Moraes ''et al.'' 2014,<ref name=":31" />
|-
|DMI and diet composition
|Combine database to predict CH4
|Niu ''et al.'', 2018<ref>Niu, M., Kebreab, E., Hristov, A. N., Oh, J., Arndt, C., Bannink, A., Bayat, A. R., Brito, A. F., Boland, T., Casper, D., Crompton, L. A., Dijkstra, J., Eugène, M. A., Garnsworthy, P. C., Haque, M. N., Hellwing, A.L.F., Huhtanen, P., Kreuzer, M., Kuhla, B., Lund, P., Madsen, J., Martin, C., Mcclelland, S. C., Mcgee, M., Moate, P. J., Muetzel, S., Muñoz, C., O'Kiely, P., Peiren, N., Reynolds, C. K., Schwarm, A., Shingfield, K. J., Storlien, T. M., Weisbjerg, M. R., Yáñez-Ruiz, D. R., and Yu, Z. 2018. Prediction of enteric methane production, yield, and intensity in dairy cattle using an intercontinental database. Glob. Change Biol. 2018;1–22, DOI: 10.1111/gcb.14094.</ref>; Van Lingen ''et al.'', 2019<ref>Van Lingen, H.J., Niu, M., Kebreab, E., Valadares Filho, S.C., Rooke, J.A., Duthie, C.A., Schwarm, A., Kreuzer, M., Hynd, P.I., Caetano, M., and Eugène, M. 2019. Prediction of enteric methane production, yield and intensity of beef cattle using an intercontinental database. Agric. Ecosyst. Environ. 283:106575.</ref>
|-
|Range of prediction equations for CH4 production
|Feed intake = primary predictor of total CH4 production (accounted for 52 to 64%); Combining more factors did indeed improve the prediction equation by 15 to 35%
|Ramin & Huhtanen (2013)<ref>Ramin, M. and Huhtanen, P.. 2013. Development of equations for predicting methane emissions from ruminants. J. Dairy Sci. 96:2476-2493.</ref>; Knapp ''et al.'', 2015<ref>Knapp J.R., Laur, G.L., Vadas, P.A.. Weiss, , and Tricarico, J.M. 2015. Invited review: Enteric methane in dairy cattle production: Quantifying the opportunities and impact of reducing emissions. J. Dairy Sci. 97:3231–3261.</ref>; Sauvant & Nozière (2016)<ref>Sauvant, D., and Nozière, P. 2016. Quantification of the main digestive processes in ruminants: the equations involved in the renewed energy and protein feed evaluation systems. Animal 10:755–770. <nowiki>https://doi:10.1017/S1751731115002670</nowiki>. </ref>
|-
|Rumen measurements (VFA, pH, protozoa counts) + feed intake (total DMI, forage DMI and FA intake) + production parameters (milk yield and composition) + milk FA
|Suggest that milk FA predict CH4 emission better (R2= 0.74) compared to rumen variables, feed intake and production parameters (R2 < 0.58). Total combination: R2= 0.90
|Mohammed ''et al.'' (2011)<ref name=":1b" />
|-
|Modelling
|specific prediction equations may need to be developed, or diet composition may need to be included in the prediction equations
|Mohammed ''et al.'' (2011)<ref name=":1b" />
|-
|Feed intake + diet composition + milk production + milk FA
|CH4 prediction equations: best fit = combining milk FA, feed intake, diet composition, and milk production (R2 = 0.84)
|Rico ''et al.'' (2016)<ref name=":33" />; Bougoin ''et al.'' (2019)<ref name=":34" />
|-
|MIR + lactation stage
|MIR spectroscopy (coefficient of determination = 0.68 and 0.79), predictions at different stages of lactation were not biologically meaningful + lactation stage refined the model: showing a biologically meaningful behavior throughout lactation: an increase in CH4 production after calving up to approximately 100 DIM, followed by gradual decline towards the end of lactation
|Dehareng ''et al.'' (2012)<ref name=":32" />; Vanlierde ''et al.'' (2015)<ref name=":4a" />
|-
|Milk yield, fat percentage + type traits
|Combine database to predict CH4 using official milk recording system and type evaluation.
|Cassandro ''et al.'', (2010)<ref name=":29" />; Cassandro, (2013)<ref name=":28" />
|}
</center>

== Building an index for methane ==
For some of the proxies, the heritability and correlations with CH4 output are known: e.g. Vanrobays ''et al.'' (2016)<ref>Vanrobays, M.-L., Bastin, C., Vandenplas, J., Hammami, H., Soyeurt, H., Vanlierde, A., Dehareng, F., Froidmont, E., and Gengler, N. 2016. Changes throughout lactation in phenotypic and genetic correlations between methane emissions and milk fatty acid contents predicted from milk mid-infrared spectra. J. Dairy Sci. 99:1–14. <nowiki>http://dx.doi.org/10.3168/jds.2015-10646</nowiki></ref> estimated heritability of 0.25 for CH4 production (g/d) and in the range 0.17 - 0.42 for different classes of milk FA; phenotypic and genetic correlations between MeP and milk FA varied between -0.03 and 0.16, and between -0.02 and 0.32 (C18:0), respectively. The genetic correlation between MeI and milk yield was estimated by Dehareng ''et al.'' (2012)<ref name=":32" /> at -0.45; that between milk yield and protein percentage at -0.54 (Miglior ''et al.'' 2007<ref>Miglior, F., Sewalem, A., Jamrozik, J., Bohmanova, J., Lefebvre, D.M., Moore, R.K. 2007. Genetic analysis of milk urea nitrogen and lactose and their relationships with other production traits in Canadian Holstein cattle. J. Dairy Sci. 90:2468-2479.</ref>). This would give a genetic correlation between MeI and protein percentage in the range [-0.5, 0.9], with likelier values for positive correlations. The most probable value in the given range could then be estimated (from the prior distribution of the missing correlation and the joint likelihood of the two known correlations given the values in the range). Such data could in the future be used to develop an index for breeding on CH4 emission.
</div>

= Proxies discussion =
<div class="mw-collapsible mw-collapsed">
<div>
The greatest limitation of proxies today is the lack of robustness in their general applicability. Future efforts should therefore be directed towards developing combinations of proxies that are robust and applicable across diverse production systems and environments. Here we present the present status of the knowledge of proxies and their predictive value for CH4 emission. Proxies related to body weight or milk yield and composition are relatively simple, low-cost, high throughput, and are easy to implement in practice. In particular, DMI and milk MIR, along with covariates such as lactation stage, are a promising option for prediction of CH4 emission in dairy cows. No single proxy was found to accurately predict CH4, whilst combinations of two or more proxies are likely to be a better solution. Combining proxies can increase the accuracy of predictions by up to 15 - 35%, mainly because different proxies describe independent sources of variation in CH4 and one proxy can correct for shortcomings in the other(s). One plausible strategy could be to increase animal productive efficiency whilst reducing CH4 emissions per animal. This could be achieved by reducing MeY and/or decreasing DMI provided that there is no concomitant reduction in productivity or increase in feed consumption (Pickering et al., 2015<ref>Pickering, N.K., Oddy, V.H., Basarab, J.A., Cammack, K., Hayes, B J., Hegarty, R.S., McEwan, J.C., Miller, S., Pinares, C., and de Haas, Y. 2015. Invited review: Genetic possibilities to reduce enteric methane emissions from ruminants. Animal 9:1431-1440.</ref>).

== Combining diet-based measurements with other proxies for methane emissions ==
Feed intake appears a reasonably adequate predictor of MeP: generally, heavier animals have higher maintenance requirements, so eat more and produce more CH4. However, a substantial level of variation is left unaccounted for. This suggests that more detailed information on dietary composition is needed. This is also important when one wants to account for MeP on diets of similar DMI but of different nutrient profiles.

The prediction accuracy of MeP strongly depends on the accuracy of quantifying the VFA produced in the rumen (Alemu et al., 2011<ref>Alemu, A.W., Dijkstra, J., Bannink, A., France, J., and Kebreab, E. 2011. Rumen stoichiometric models and their contribution and challenges in predicting enteric methane production. Anim. Feed Sci. Technol. 166-167:761-778.</ref>). The type of VFA formed during rumen fermentation depends on the type of substrate fermented (Bannink et al., 2011<ref>Bannink, A., van Schijndel, M.W., and Dijkstra, J. 2011. A model of enteric fermentation in dairy cows to estimate methane emission for the Dutch National Inventory Report using the IPCC Tier 3 approach. Anim. Feed Sci. Technol. 166-167:603-618.</ref>), such as the dietary content of neutral detergent fiber and starch. The type of substrate fermented thus appears a useful factor for predicting MeP (Ellis et al., 2007<ref>Ellis, J.L., Kebreab, E., Odongo, N.E., McBride, B.W., Okine, E.K., and France, J. 2007. Prediction of methane production from dairy and beef cattle. J. Dairy Sci. 90:3456–3466.</ref>), indicating that including a description of variation in dietary quality caused by nutritional factors results in improved prediction accuracy of CH4 emission (Ellis et al., 2010<ref>Ellis, J.L., Bannink, A., France, J., Kebreab, E., and Dijkstra, J. 2010. Evaluation of enteric methane prediction equations for dairy cows used in whole farm models. Glob. Change Biol. 16:3246–3256.</ref>; Moraes et al., 2014<ref name=":0c">Moraes, L.E., Strathe, A.B., Fadel, J.G., Casper, D.P., and Kebreab, E. 2014. Prediction of enteric methane emissions from cattle. Glob. Change Biol. 20:2140–2148.</ref>).

== Rumen ==
When feed intake is kept constant, a higher rumen capacity results in a lower passage rate (Demment and Van Soest, 1985<ref>Demment, M.W., and Van Soest, P.J. 1985. A nutritional explanation for body-size patterns of ruminant and nonruminant herbivores. Am. Nat. 125:641–672.</ref>), resulting in a higher MeP (Moraes et al., 2014<ref name=":0" />). Proxies based on rumen samples are generally poor to moderately accurate predictors of CH4, and are costly and difficult to measure routinely on-farm. VFA are a proxy for rumen CH4 emissions. Using rumen fermentation data obtained from in vitro gas production, Moss et al. (2000)<ref>Moss A.R., Jouany, J.P., and Newbold, J. 2000. Methane production by ruminants: Its contribution to global warming. Annal. Zootech. 49:231-253.</ref> reported a negative linear relationship between CH4 production and the ratio of (acetic + butyric acid)/propionic acid. However, by combining different information sources, either related to feed intake or to the impact of feed intake on the VFA composition, a better proxy with an improved accuracy can be achieved. This way, the prediction equation for CH4 production can be optimized (higher accuracy).

The relationship between rumen methanogen abundance and methanogenesis is less clear when changes in enteric CH4 emissions are modulated by diet or are a consequence of selecting phenotypes related to feed efficiency or MeY. Whereas in some reports there was a significant positive relationship (Aguinaga Casanas et al., 2015<ref name=":1c">Aguinaga Casanas, M.A., , N., Krattenmacher, N., Thalle,r G., Metges, C.C., and Kuhla, B. 2015. Methyl-coenzyme M reductase A as an indicator to estimate methane production from dairy cows. J. Dairy Sci. 98:4074-4083.</ref>; Arndt et al., 2015<ref>Arndt, C., Powell, J.M., Aguerre, M.J., Crump, P.M., and Wattiaux, M.A. 2015. Feed conversion efficiency in dairy cows: Repeatability, variation in digestion and metabolism of energy and nitrogen, and ruminal methanogens. J. Dairy Sci. 98:3938-3950.</ref>; Sun et al., 2015<ref>Sun, X., Henderson, G., Cox, F., Molan,o G., Harrison, S.J., Luo, D., Janssen, P.H., and Pacheco, D. 2015. Lambs Fed Fresh Winter Forage Rape (Brassica napus L.) Emit Less Methane than Those Fed Perennial Ryegrass (Lolium perenne L.), and Possible Mechanisms behind the Difference. PLoS One 10(3):e0119697: DOI: 10.1371/journal.pone.0119697</ref>; Wallace et al., 2015<ref name=":2b">Wallace, R., Rooke, J., McKain, N., Duthie, C.-A., Hyslop, J., Ross, D., Waterhouse, A., Watson, M., and Roehe, R. 2015. The rumen microbial metagenome associated with high methane production in cattle. BMC Genomics. 16:839.</ref>), in many others the concentration of methanogens was unrelated to methanogenesis (Morgavi et al., 2012<ref>Morgavi, D.P., Martin, C., Jouany, J.P., and Ranilla, M.J. 2012. Rumen protozoa and methanogenesis: not a simple cause-effect relationship. Br. J. Nutr. 107:388-397. 10.1017/S0007114511002935.</ref>; Kittelmann et al., 2014<ref>Kittelmann, S., Pinares-Patiño, C.S., Seedorf, H., Kirk, M.R., Ganesh, S., McEwan, J.C., and Janssen, P.H. 2014. Two different bacterial community types are linked with the low-methane emission trait in sheep. PLoS One 9:e103171.</ref>; Shi et al., 2014<ref name=":3a">Shi W., Moon, C.D., Leahy, S.C., Kang, D., Froula, J., Kittelmann, S., Fan, C., Deutsch, S., Gagic, D., Seedorf, H., Kelly, W.J., Atua, R., Sang, C., Soni, P., Li, D., Pinares-Patiño, C.S., McEwan, J.C., Janssen, P.H., Chen, F., Visel, A., Wang, Z., Attwood, G.T., and Rubin, E.M. 2014. Methane yield phenotypes linked to differential gene expression in the sheep rumen microbiome. Genome Res. Doi:10.1101/gr.168245.113.</ref>; Bouchard et al., 2015<ref name=":4b">Bouchard, K., Wittenberg, K.M., Legesse, G., Krause, D.O., Khafipour, E., Buckley, K.E., and Ominski, K.H. 2015. Comparison of feed intake, body weight gain, enteric methane emission and relative abundance of rumen microbes in steers fed sainfoin and lucerne silages under western Canadian conditions. Grass a Forage Sci. 70:116-129.</ref>). Bouchard et al. (2015)<ref name=":4b" /> even reported a reduction in methanogens withoutsignificant decrease in MeP for steers fed sainfoin silage. Sheep selected for high or low MeY showed no differences in methanogen abundance, though there was a strong correlation with expression of archaeal genes involved in methanogenesis (Shi et al., 2014<ref name=":3" />).

Hindgut and Feces: whole tract digestibility variables cannot serve as primary predictors for enteric MeP in cattle or sheep, but might be used as supporting factors to improve the accuracy of prediction of CH4 output.

== Protozoa and other rumen microbes ==
Protozoa are net producers of H2 and their absence from the rumen is associated with an average reduction in enteric MeP of approximately 11% (Hegarty, 1999<ref>Hegarty, R.S. 1999. Reducing rumen methane emissions through elimination of rumen protozoa. Aust. J. Agric. Res. 50:1321-1327.</ref>; Morgavi et al., 2010<ref>Morgavi, D.P., Forano, E., Martin, C., and Newbold, C.J. 2010. Microbial ecosystem and methanogenesis in ruminants. Animal 4:1024-1036.</ref>; Newbold et al., 2015<ref>Newbold, C.J., de la Fuente, G., Belanche, A., Ramos-Morales, E., and McEwan, N. 2015. The role of ciliate protozoa in the rumen. Front. Microbiol. 6:1313.doi: 10.3389/fmicb.2015.01313</ref>). Using a database of 28 experiments and 91 dietary treatments, Guyader et al. (2014)<ref>Guyader, J., Eugène, M., Nozière, P., Morgavi, D.P., Doreau, M., and Martin, C. 2014. Influence of rumen protozoa on methane emission in ruminants: a meta-analysis approach. Animal 8:1816-1825.</ref> showed a significant decrease of 8.14 g CH4/kg DMI for each log unit reduction in rumen protozoal abundance. About 21% of experiments within this dataset reported CH4 changes unrelated to protozoal abundance, highlighting the multifactorial nature of methanogenesis. Roehe et al. (2016)<ref>Roehe R., Dewhurst, R.J., Duthie, C-A., Rooke, J.A., McKain, N., Ross, D.W., Hyslop, J.J., Waterhouse, A., Freeman, T.C., Watson, M., and Wallace, R.J. 2016. Bovine Host Genetic Variation Influences Rumen Microbial Methane Production with Best Selection Criterion for Low Methane Emitting and Efficiently Feed Converting Hosts Based on Metagenomic Gene Abundance. PLoS Genet 12(2): e1005846. doi:10.1371/journal.pgen.1005846.</ref> observed that the ranking of sire groups for CH4 emissions measured with respiration chambers was the same as that for ranking on archaea/bacteria ratio, providing further evidence that host control of archaeal abundance contributes to genetic variation in CH4 emissions - at least in some circumstances. Across a wide geographical range, the methanogenic archaea were shown to be highly conserved across the world (Henderson et al., 2015<ref>Henderson, G., Cox, F., Ganesh, S., Jonker, A., Young, W., Global Rumen Census Collaborators, and Janssen, P.H. 2015. Rumen microbial community composition varies with diet and host, but a core microbiome is found across a wide geographical range. Sci. Rep. 5:14567. doi:10.1038/srep14567</ref>). This universality and limited diversity could make it possible to mitigate CH4 emissions by developing strategies that target the few dominant methanogens. However, one clear limitation of metagenomic predictions compared to genomic predictions was that the microbiome of the host is variable - that is, it may change in response to diet or other environmental factors over time, whereas the hosts DNA remains constant.

== Rumen microbial genes ==
These included genes involved in the first and last steps of methanogenesis: formylmethanofuran dehydrogenase subunit B (fmdB) and methyl-coenzyme M reductase alpha subunit (mcrA), which were 170 times more abundant in high CH4 emitting cattle. Whilst gene-centric metagenomics is not low-cost or high-throughput, these results point to potential future proxy approaches using low-cost gene chips.

The difference in gene expression activity as opposed to abundance was also reported by others (Popova et al., 2011<ref>Popova, M., Martin, C., Eugène, M., Mialon, M.M., Doreau, M., and Morgavi, D.P. 2011. Effect of fibre- and starch-rich finishing diets on methanogenic Archaea diversity and activity in the rumen of feedlot bulls. Anim. Feed Sci. Technol. 166-167:113-121.</ref>). However, there are also studies in which there was no relationship with gene expression (Aguinaga Casanas et al., 2015<ref name=":1">Garnsworthy, P.C. Difford, G.F. Bell, M.J. Bayat, A.R. Huhtanen, P. Kuhla, B. Lassen, J. Peiren, N. Pszczola, M; Sorg, D. Visker, M.H., and Yan, T. 2019 Comparison of Methods to Measure Methane for Use in Genetic Evaluation of Dairy Cattle. Animals 9:837, 12p.</ref>). There are some methodological and experimental differences that might explain some of the apparent contradictions, such as the type of gene target and primers used for nucleic acid amplification. Effects are seen most clearly when the difference in MeP between groups of animals is large (e.g. Wallace et al. (2015)<ref name=":2b" /> used treatments that generated a 1.9-fold difference CH4 emissions).

== Proxies based on measurements in milk ==
Milk yield alone does not provide a good prediction of MeP by dairy cows. Yan et al. (2010)<ref>Yan. T., Mayne, C.S., Gordon, F.G., Porter, M.G., Agnew, R.E., Patterson, D.C., Ferris, C.P., and Kilpatrick, D.J. 2010. Mitigation of enteric methane emissions through improving efficiency of energy utilization and productivity in lactating dairy cows. J. Dairy Sci. 93:2630–2638. doi: 10.3168/jds.2009-2929.</ref> indicated that CH4 as a proportion of GE intake or milk energy output was negatively related to milk production. It is less clear if MeY can be predicted from milk yield when making comparisons across studies.

Milk MIR spectroscopy is relatively inexpensive, rapid and already routinely used technology in milk recording systems to predict fat, protein, lactose and urea contents in dairy milk to assist farm management decisions and breeding. It can be used as a promising strategy to exploit the link between enteric CH4 emission from ruminants and microbial digestion in the rumen by assessing the signature of digestion in milk composition. Milk MIR data can be obtained through regular milk recording schemes, as well as, on a herd level, through analysis used for milk payment systems. Diverse milk phenotypes can be obtained by MIR spectrometry – including detailed milk composition (e.g. FA as reported by Soyeurt et al., 2011<ref>Soyeurt H., Dehareng, F., Gengler, N., McParland, S., Wall, E., Berry, D.P., Coffey, M., and Dardenne, P. 2011. Mid-infrared prediction of bovine milk fatty acids across multiple breeds, production systems and countries. J. Dairy Sci. 94:1657–1667.</ref>), technological properties of milk, and cow physiological status (De Marchi et al., 2014<ref>De Marchi, M., Toffanin, V., Cassandro, M., and Penasa, M. 2014. Invited review: Mid-infrared spectroscopy as phenotyping tool for milk traits. J. Dairy Sci. 97:1171–1186. <nowiki>http://dx.doi.org/10.3168/jds.2013-6799</nowiki>.</ref>; Gengler et al., 2016<ref>Gengler, N., Soyeurt, H., Dehareng, F., Bastin, C., Colinet, F., Hammami, H., Vanrobays, M.-L., Lainé, A., Vanderick, S., Grelet, C., Vanlierde, A., Froidmont, E., and Dardenne, P. 2016. Capitalizing on fine milk composition for breeding and management of dairy cows. J. Dairy Sci. 99:4071-4079.</ref>). Several of these novel traits (i.e. FA composition) have been identified as potential indicators of CH4 emission. Therefore, using MIR to predict MeP (Dehareng et al. 2012<ref name=":5b">Dehareng, F., Delfosse, C., Froidmont, E., Soyeurt, H., Martin, C., Gengler, N., Vanlierde, A., and Dardenne, P. 2012. Potential use of milk mid-infrared spectra to predict individual methane emission of dairy cows. Animal 6:1694-701.</ref>; Vanlierde et al. 2013<ref>Vanlierde, A., Dehareng, F., Froidmont, E., Dardenne, P., Kandel, P.B., Gengler, N., Deighton, M.H., buckley, F., Lewis, E., McParland, S., Berry, D.P., and Soyeurt, H. 2013. Prediction of the individual enteric methane emission of dairy cows from milk-mid-infrared spectra. Advances in Animal Biosciences. 5th Greenhouse Gases Animal Agriculture Conference (GGAA2013) 23-26 June 2014. Dublin, Ireland. p 433.</ref>, 2015<ref name=":4a" />; Van Gastelen and Dijkstra, 2016<ref name=":7a">Van Gastelen, S., and Dijkstra, J.. 2016. Prediction of methane emission from lactating dairy cows using milk fatty acids and mid-infrared spectroscopy. J. Sci. Food Agric. 96:3963-3968. DOI: 10.1002/jsfa.7718.</ref>) is also a logical extension of its use to quantify the major milk components (i.e. fat, protein, casein, lactose, and urea) and minor components (e.g. FA). Dehareng et al. (2012)<ref name=":32" /> assessed the feasibility to predict individual MeP from dairy cows using milk MIR spectra. Their initial results suggest that this approach could be useful to predict MeP at the farm or regional scale, as well as to identify low-CH4 emitting cows. According to Van Gastelen and Dijkstra (2016)<ref name=":36" />, MIR spectroscopy has the disadvantage that it has a moderate predictive power for CH4 emission, both direct and indirect (i.e. via milk FA), and that it lacks the ability to predict important milk FA for CH4 prediction. They concluded that it may not be sufficient to predict MeP based on MIR alone. It is, however, possible to improve the accuracy of prediction through the combination of MIR with some animal characteristics such as lactation stage (Vanlierde et al., 2015<ref name=":6" />). The advantage of this latter development is that this type of prediction can be done on a very large scale inside a routine milk recording system (Vanlierde et al., 2015<ref name=":6" />).

== Proxy: future developments and perspectives ==
There is currently limited consensus on which phenotype to use to lower the carbon footprint of milk production through genetic selection. This could be MeP, MeI or MeY. The direct goal would be CH4 production; the relationship with milk production and/or feed intake could be accounted for by including these in the final selection index or scheme. However, one might argue that it would be more effective/accurate to directly use milk production- or feed intakecorrected CH4 (e.g. CH4 intensity or yield) as breeding goal.

The analysis of proxies in terms of their attributes shows that proxies that are based on samples from the rumen or related to rumen sources are poor to moderately accurate predictors of CH4. In addition, these proxies are too costly and difficult for routine on-farm implementation. On the other hand, proxies related to BW, milk yield and composition (e.g. milk FA) are moderately to highly accurate predictors of CH4 and relatively simple, low-cost and easier to implement in practice (Cassandro et al.,2010<ref>Cassandro, M., Cecchinato, A., Battagin, M., Penasa, M., 2010. Genetic parameters of predicted methane production in Holstein Friesian cowsIn: Proc. 9th World Congr. on Genetics Applied to Livestock Production, Leipzig, Germany. . Page 181</ref>; Cassandro, 2013<ref>Cassandro, M. 2013. Comparing local and cosmopolitan cattle breeds on added values for milk and cheese production and their predicted methane emissions. Animal Genetic Resources/Ressources génétiques animales/Recursos genéticos animales, available on CJO2013. doi:10.1017/S2078 63361200077X. </ref>). Particularly, milk MIR and the prediction of CH4 based on milk MIR along with other covariates such as lactation stage is a promising alternative: that is accurate, cheaper and easy to be implemented in routine milk analysis at no extra cost.

Therefore, in the future advances in infrared, photoacoustic and related technologies will push the boundaries, particularly in focusing on developments of fast and portable technologies. Such developments will lead to better and promising proxies for CH4 that will enable a sizable throughput of CH4 phenotypes in dairy cows. Antunes-Fernandes et al. (2016)<ref>Antunes-Fernandes, E.C., van Gastelen, S., Dijkstra, J., Hettinga K.A., and Vervoort, J. 2016. Milk metabolome relates enteric methane emission to milk synthesis, and energy metabolism pathways. J. Dairy. Sci. 99:6251-6262.</ref> already presented the use of metabolomics on milk to better understand the biological pathways involved in CH4 production in dairy cattle. The techniques used in that study are not suitable for large scale measurements, but rapid developments in omics may offer tests and assay methodologies on blood, urine or milk samples that will provide an additional tool for developing new / additional proxies for CH4 emissions in dairy cattle.</div>
</div>

= Merging and sharing data in genetic evaluations =
Early 2016 an attempt to make cross country evaluations of CH4 emissions from Holstein dairy cattle was initiated. The work was based on data from NL, DK, AUS, UK and IR. In total, 12,820 weekly CH4 emission records from 2,857 cows were available. Although different equipment was used across countries to measure CH4 emissions, the research aimed to define similar CH4 output phenotypes in each country. The analysed CH4 traitres, that are available in each country, are (1) CH4 production in g/d, and (2) CH4 intensity in g/d per kg fat protein corrected milk (FPCM). In addition to these CH4 traits, CH4 concentration (in ppm) was available in Denmark, the Netherlands and UK, and the ratio between CH4 and CO2 concentration was available in Denmark and the Netherlands.

Bivariate analyses were carried out to estimate genetic correlations between countries, using an animal linear mixed model for all traits. Both univariate and bivariate analyses were repeated with the GRM as well. With all weekly records, standardizing the trait in the full dataset increased the heritability for CH4 production from 0.03 to 0.06. The heritability for CH4 intensity was slightly higher. The highest heritability with the full dataset is estimated for the standardized CH4 concentration (0.19). Correlations estimated among CH4 traits estimated with either the pedigree or the GRM were in same direction and of similar magnitude. The genetic correlations show that when CH4 production increased, the CH4 concentration and the ratio between CH4 and CO2 increased as well.

The approach is novel, and no other attempt has been performed to make genetic analysis of CH4 traits across countries. The analysis can be repeated in future studies where more data hopefully will be available, and more effort can be made into improving both the fixed and random part of the model.

= Recommendations =
The most important question: what method to use if you need to measure CH4? The answer may be: it depends on what you like to do. In the Table 6 we summarize some experimental conditions and designs, and make recommendations.
<center>
{| class="wikitable"
|-
! colspan="3" style="text-align:left;" | ''Table 6. Recommendations for measuring methane in diverse experimental conditions and designs.''
|- style="background-color:#efefef;"
|+
! style="text-align:center;" | Experimental condition and design
! style="text-align:center;" | Methane measurement method recommendation
|-
| style="text-align:center;" | Need to measure absolute methane values – animal numbers and location not important
| style="text-align:center;" | Respiration chamber;
SF6; GreenFeed
|-
| style="text-align:center;" | Need to rank animals from low to high methane emission
| style="text-align:center;" | Sniffer method
|-
| style="text-align:center;" | Need to measure methane on farm
| style="text-align:center;" | Sniffer method;
GreenFeed; PAC
|-
| style="text-align:center;" | Low budget measurements needed
| style="text-align:center;" | Proxy;
Proxies measurement
|-
| style="text-align:center;" | High animal numbers required
| style="text-align:center;" | Sniffer method;
Proxies measurement; LMD
|}
</center>

= Conclusions =
Measuring CH4 emission on large numbers of cows is a challenge. The high costs and low throughput of RC restrict their use to research studies measuring CH4 emissions on small numbers of individual animals. Respiration chambers remain the gold standard method, but benchmarking alternative methods against RC is challenging because simultaneous replicate measures per cow are not feasible. Methods like SF6 and GreenFeed require lower capital investment and running costs than RC, and have higher throughput and potential for use in extensive and grazing situations, but costs are still prohibitive for recording large numbers of animals. Methods based on concentration are less precise and accurate than flux methods, but they are viable for large scale measurement, which is a prerequisite of genetic evaluations. Further development is needed to increase accuracy and precision of concentration methods. Several reviews of methods for measuring CH4 have made qualitative judgements based on individual comparison studies without expanding scope to genetic evaluations and considering repeated measure correlations between methods as proxies for genetic correlations. Results confirm that there is sufficient correlation between methods for all to be combined for international genetic studies and provide a much needed framework for comparing genetic correlations between methods should these be made available. Proxies have the potential to be used as predictors of CH4 production and emission. Although proxies are less accurate than direct CH4 measurements they can be easier, cheaper, and at high throughput, and may be therefore the best method in practical situations, especially proxies related to milk measurements. Therefore, these proxies at the population level, can provide useful information at genetic improvement that can be used to reduce emissions following 3 ways: (1) intensification of animal production; (2) improving of system efficiency and (3) the direct reduction of GHG emissions by breeding for reduced predicting animals that are high or low GHG emitters.

= Acknowledgements =
This document is the result of the ICAR Feed and Gas Working Group and its industry and research liaison group. The members of the ICAR Feed and Gas Working Group at the time of publication are, in alphabetical order:

* Christine Baes, Department of Animal Biosciences, University of Guelph, Canada and Institute of Genetics, Vetsuisse Faculty, University of Bern, Switzerland
* Yvette de Haas, Animal Breeding and Genomics, Wageningen Livestock Research
* Raffaella Finocchiaro, ANAFI, Italy
* Phil Garnsworthy, School of Biosciences, University of Nottingham, United Kingdom
* Birgit Gredler-Grandl, Animal Breeding and Genomics, Wageningen Livestock Research
* Nina Krattenmacher, Institute of Animal Breeding and Husbandry, Christian-Albrechts-University, Germany
* Jan Lassen, Viking Genetics, Denmark
* Jennie Pryce, Centre for AgriBioscience, AgriBio, Agriculture Victoria Research and School of Applied Systems Biology, La Trobe University, Australia
* Roel Veerkamp, Animal Breeding and Genomics, Wageningen Livestock Research (chairperson)

The work of Marinus te Pas (Animal Breeding and Genomics, Wageningen Livestock Research) in compiling draft versions is highly acknowledged. The content of the guidelines are partly based on results of the network of internationally recognized experts within the METHAGENE COST Action FA1320.

<references />