View source for Section 22 – Sustainability recording traits

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= Sustainability recording traits =

== Introduction to ICAR sustainability traits ==
The purpose of ICAR sustainability traits is to provide a harmonized approach to assess the sustainability of dairy herds. By providing a common definition of these traits, we encourage organizations that are involved in milk recording, breeding or any other way of data recording in dairy herds to develop tools to support farmers to increase the sustainability of their dairy herd.

The traits have been selected and defined by a group of ICAR related experts. As definition of sustainability itself the group has used the definition provided by the SAI platform (https://saiplatform.org/):

'''“Sustainable agriculture is the efficient, long-term production of safe, high-quality agricultural product, in a way that protects and improves the natural environment, the social and economic conditions of the farmers, their employees and local communities, and safeguards the health and welfare of all farmed species.”''' 

It is regarded not to be ICAR’s role to standardize the make-up of Sustainability Indices. The weight of the various traits is a matter for the members or countries themselves to decide. Therefore, ICAR does not provide a sustainability index, but lets the user make a choice which traits to include in their own sustainability index. A selection of traits can be used to create an index that fits the data available and the specific circumstances in your organization or your country.

With this list of traits ICAR aims to identify the key traits in recording that effect sustainability, to provide definitions of these key traits and to harmonize measurement methods of these key traits. 

ICAR sustainability traits are selected in such a way that they cover the most important aspects of the performance of the herd regarding sustainability. The traits have been defined in a way that they generally reflect data collected over a 365-day period in one herd. Data collected during a one-year period is more stable to influences on animal performance due to geography, seasonal calving, environmental impact related to weather conditions, herd size fluctuations etc.

Definitions of traits might differ according to how the used data is measured. Some traits are based on 365-day counts of the number of cows present in the dairy herd. Other traits are based on snapshot data (for example test-day average days in milk).

The list contains several categories of traits: 
<ol style="list-style-type:number">
<li>Feeding and production</li>
<li>Fertility</li>
<li>Health</li>
<li>Longevity and culling</li>
<li>Young stock</li>

</ol>

The list of sustainability traits can be found below in Table 1 as short list with just the name and category. Different colours are used to distinguish the different categories, these colours have no particular meaning. The list of sustainability traits can also be found in [https://www.icar.org/Guidelines/22-Appendix-1-List-definitions-of-traits-to-assess-sustainability-at-herd-level.pdf Appendix 1] as detailed list with the definitions of the traits. [https://www.icar.org/Guidelines/22-Appendix-2-Prediction-equations-for-feed%20intake-feed-efficiency-and-methane-for-dairy-cattle.pdf Appendix 2] of this Section contains prediction equations for feed intake, feed efficiency and methane for Dairy Cattle.

We recommend users of this list of traits to select one or more traits per category and to combine these traits into a sustainability index suitable to their national system. The weight per trait could be determined by each user. The sustainability index could be made available to members of your organization to support the sustainability of their herd or to proof sustainability or product quality to e.g. dairy processors.

== Acknowledgements ==
This document is the work of the ICAR Sustainability Task Force. ICAR gratefully acknowledges their contribution. Members of the ICAR Sustainability Task Force were Tone Roalkvam (Chair, Tine, Norway), Martin Burke (ICAR, the Netherlands), Fabian Bernal (DeLaval, Sweden), Christa Egger-Danner (RinderZucht, Austria), Robert Fourdraine (Dairy Record Management Systems, USA), Birgit Grendl-Gredler (Wageningen University, the Netherlands), René van der Linde (ICAR, the Netherlands) and Débora Santschi (Lactanet, Canada).

== List of ICAR sustainability recording traits ==


<center>
{| class="wikitable" 
|-
! colspan="3" style="text-align:left;" | ''Table 1. List of ICAR sustainability recording traits.''
|- style="background-color:#efefef;"
! style="text-align:left;" | '''Number'''
! style="text-align:center;" | '''Trait'''
! style="text-align:center;" | '''Category'''
|-
| style="text-align:left;" | 1
| style="text-align:center;" | Age at slaughter (beef cattle)
| style="text-align:center;" | Feeding and Production
|-
| style="text-align:left;" | 2
| style="text-align:center;" | Average Days in Milk
| style="text-align:center;" | Feeding and Production
|-
| style="text-align:left;" | 3
| style="text-align:center;" | Body weight
| style="text-align:center;" | Feeding and Production
|-
| style="text-align:left;" | 4
| style="text-align:center;" | Daily gain
| style="text-align:center;" | Feeding and Production
|-
| style="text-align:left;" | 5
| style="text-align:center;" | Dry Matter Intake
| style="text-align:center;" | Feeding and Production
|-
| style="text-align:left;" | 6
| style="text-align:center;" | Energy Corrected Milk
| style="text-align:center;" | Feeding and Production
|-
| style="text-align:left;" | 7
| style="text-align:center;" | Feed efficiency
| style="text-align:center;" | Feeding and Production
|-
| style="text-align:left;" | 8
| style="text-align:center;" | Methane Emissions
| style="text-align:center;" | Feeding and Production
|-
| style="text-align:left;" | 9
| style="text-align:center;" | MUN /Urea rates in milk
| style="text-align:center;" | Feeding and Production
|-
| style="text-align:left;" | 10
| style="text-align:center;" | % Cows with functional BCS
| style="text-align:center;" | Feeding and Production
|-
| style="text-align:left;" | 11
| style="text-align:center;" | Apparent Pregnancy Loss Rate
| style="text-align:center;" | Fertility
|-
| style="text-align:left;" | 12
| style="text-align:center;" | Average Days Open
| style="text-align:center;" | Fertility
|-
| style="text-align:left;" | 13
| style="text-align:center;" | Average Calving Interval
| style="text-align:center;" | Fertility
|-
| style="text-align:left;" | 14a
| style="text-align:center;" | Non-Return Rate 56 days
| style="text-align:center;" | Fertility
|-
| style="text-align:left;" | 14b
| style="text-align:center;" | 1<sup>st</sup> Service Conception Rate
| style="text-align:center;" | Fertility
|-
| style="text-align:left;" | 15
| style="text-align:center;" | Pregnancy Rate
| style="text-align:center;" | Fertility
|-
| style="text-align:left;" | 16
| style="text-align:center;" | % Cows culled due to reproductive problems
| style="text-align:center;" | Fertility
|-
| style="text-align:left;" | 17
| style="text-align:center;" | % Cows with fertility disorders
| style="text-align:center;" | Fertility
|-
| style="text-align:left;" | 18
| style="text-align:center;" | Average Somatic Cell Count
| style="text-align:center;" | Health
|-
| style="text-align:left;" | 19
| style="text-align:center;" | Chronic infection rate
| style="text-align:center;" | Health
|-
| style="text-align:left;" | 20
| style="text-align:center;" | Dry Cow Cure Rate
| style="text-align:center;" | Health
|-
| style="text-align:left;" | 21
| style="text-align:center;" | Fresh Cow Infection Rate
| style="text-align:center;" | Health
|-
| style="text-align:left;" | 22
| style="text-align:center;" | Selective Dry Cow Therapy Rate
| style="text-align:center;" | Health
|-
| style="text-align:left;" | 23
| style="text-align:center;" | % Cows culled due to udder health
| style="text-align:center;" | Health
|-
| style="text-align:left;" | 24
| style="text-align:center;" | % Cows culled due to lameness
| style="text-align:center;" | Health
|-
| style="text-align:left;" | 25
| style="text-align:center;" | % Cows culled due to other disorders/diseases
| style="text-align:center;" | Health
|-
| style="text-align:left;" | 26
| style="text-align:center;" | % Cows with FPR < 1 at first test day
| style="text-align:center;" | Health
|-
| style="text-align:left;" | 27
| style="text-align:center;" | % Cows with FPR >1.3/1.5 at first test day 
| style="text-align:center;" | Health
|-
| style="text-align:left;" | 28
| style="text-align:center;" | % Cows with lameness
| style="text-align:center;" | Health
|-
| style="text-align:left;" | 29
| style="text-align:center;" | % Cows with mastitis
| style="text-align:center;" | Health
|-
| style="text-align:left;" | 30
| style="text-align:center;" | % Cows with subclinical metabolic issue
| style="text-align:center;" | Health
|-
| style="text-align:left;" | 31
| style="text-align:center;" | Age at culling (dairy cattle)
| style="text-align:center;" | Longevity
|-
| style="text-align:left;" | 32
| style="text-align:center;" | Average Daily Production of culled animals
| style="text-align:center;" | Longevity
|-
| style="text-align:left;" | 33
| style="text-align:center;" | Average Lactation Number
| style="text-align:center;" | Longevity
|-
| style="text-align:left;" | 34
| style="text-align:center;" | Average Lifetime Production of culled animals
| style="text-align:center;" | Longevity
|-
| style="text-align:left;" | 35
| style="text-align:center;" | % Cows died ≤ 60 days in milk
| style="text-align:center;" | Longevity
|-
| style="text-align:left;" | 36
| style="text-align:center;" | Age at first calving
| style="text-align:center;" | Young stock
|-
| style="text-align:left;" | 37
| style="text-align:center;" | Young stock EBV ranking
| style="text-align:center;" | Young stock
|-
| style="text-align:left;" | 38
| style="text-align:center;" | Young stock sire EBV ranking
| style="text-align:center;" | Young stock
|-
| style="text-align:left;" | 39
| style="text-align:center;" | % Female young stock involuntary culled
| style="text-align:center;" | Young stock
|-
| style="text-align:left;" | 40
| style="text-align:center;" | % Calves born dead
| style="text-align:center;" | Young stock
|-
| style="text-align:left;" | 41
| style="text-align:center;" | % Female calves with diarrhea
| style="text-align:center;" | Young stock
|-
| style="text-align:left;" | 42
| style="text-align:center;" | % Female calves with respiratory diseases
| style="text-align:center;" | Young stock
|-
| style="text-align:left;" | 43
| style="text-align:center;" | % Mortality of female calves until 90 days
| style="text-align:center;" | Young stock
|}
</center>

= Appendix 1 : List of definitions of traits to assess sustainability at herd level =
<div class="mw-collapsible mw-collapsed">
    <div>
The list of sustainability traits can be found below in Table 2 as detailed list with the definitions of the traits. Different colors are used to distinguish the different categories, these colors have no particular meaning.

<center>
{| class="wikitable"
| colspan="3" |'''''Table 2. List of ICAR sustainability recording traits.'''''
|- style="background-color:#efefef;"
|'''Number'''
|'''Trait and category'''
|'''Formula'''
|-
| rowspan="2" |1
|Age at slaughter (beef  cattle)
|[[File:Formula1.png|center|thumb|468x468px]]
|-
|Feeding and Production
|The average age at slaughter (AAS)  is calculated as the slaughter date minus the date of birth of all animals  that are slaughtered during the past 365 days. To be expressed in days or  months (days/(365.25/12)).

Date of slaughter and date of birth  needs to be known.
|-
| rowspan="2" |2
|Average Days in Milk
|[[File:Formula2.png|center|thumb|239x239px]]
|-
|Feeding and Production
|Days in milk is defined as date of  test day minus date of calving. N = number of test days in the past 365 days.  M = number of cows in the milking herd each test day. The annual average days  in milk (DIM) is calculated in two steps. Step 1: calculate per test day the  average DIM and the number of cows in the milking herd [excluding dry cows].  Step 2: take the total of all test days of number of cows * average DIM on  each test day and divide this by the sum of all cows on all test days in the  past 365 days.
|-
| rowspan="2" |3
|Body weight
|[[File:Formula3.png|center|thumb|253x253px]]
|-
|Feeding and Production
|The  average body weight (BW) is the sum of the BW per day of all cows in the  milking herd on each day during the past 365 days divided by the sum of all  cows in the milk herd on each day in that herd during the past 365 days.
|-
| rowspan="2" |4
|Daily gain
|[[File:Formula4.png|center|thumb|569x569px]]
|-
|Feeding  and Production
|Average daily gain is the average of  the slaughter weight minus the start weight divided by the age at slaughter  for young animals.

Start weight is either measured or a  certain standard (breed specific) value is taken.

For lactating animals it is defined  as the weight at slaughter minus the weight at calving divided by the date at  slaughter minus the date of calving.
|-
| rowspan="2" |5
|Dry Matter Intake
|[[File:Formula5.png|center|thumb|451x451px]]
|-
|Feeding  and Production
|The average Dry Matter Intake (DMI)  is the sum of the DMI in kg per day of all cows in the milking herd on each  day during the past 365 days divided by the sum of all cows in the milking  herd on each day in that herd during the past 365 days.

If Dry Matter Intake cannot be  measured, prediction equations can be used (see Appendix 2).

To note: If information to calculate  energy intake (or protein intake) is available, this should be considered  (see Appendix 2).
|-
| rowspan="2" |6
|Energy Corrected Milk
|[[File:Formula6.png|center|thumb]]
|-
|Feeding  and Production
|N = number of test days in the past  365 days. M = number of cows in the milking herd each test day. The average  Energy Corrected Milk (ECM) is calculated in two steps. Step 1: calculate per  test day the average ECM and the number of cows in the milking herd  [excluding dry cows]. Step 2: take the total of all test days of number of  cows * average ECM on each test day and divide this by the sum of all cows on  all test days in the past 365 days.

Formula’s to calculate energy  corrected milk can be found in Section 2 of the ICAR guidelines.
|-
| rowspan="2" |7
|Feed efficiency
|[[File:Feedefficiency.png|center|thumb|352x352px]]
|-
|Feeding  and Production
|The average feed  efficiency is the sum of the energy corrected milk of m cows in the milking  herd on each day during the past 365 days divided by the sum of the feed  intake of m cows in the milking herd on each day during the past 365 days.  Data should be adjusted for stage of lactation (to the same stage of  lactation across herds, e.g. 150 days) of the cow.

To note: If information  on feeding ration is available, this should be included to increase the  prediction (see Appendix 2).
|-
| rowspan="2" |8
|Methane Emissions
|[[File:Methaneemmisions.png|center|thumb|375x375px]]
|-
|Feeding  and Production
|Average methane emissions are the  sum of the methane emissions of m cows in the milking herd on each day during  the past 365 days divided by the numbers of cows (m) in the milking herd on  each day during the past 365 days.

If methane emission cannot be  measured, prediction equations can be used (see '''Appendix 2''').
|-
| rowspan="2" |9
|MUN /Urea rates in milk
|[[File:MUN.png|center|thumb|371x371px]]
|-
|Feeding  and Production
|N = number of test days in the past  365 days. M = number of cows in the milking herd each test day. The average  MUN rate is calculated in two steps. Step 1: calculate per test day the  average MUN rate and the number of cows in the milking herd [excluding dry  cows]. Step 2: take the total of all test days of number of cows * average  MUN rate on each test day and divide this by the sum of all cows on all test  days in the past 365 days.
|-
| rowspan="2" |10
|% Cows with functional BCS
|[[File:BCS.png|center|thumb|668x668px]]
|-
|Feeding and Production
|The percentage cows with functional  BCS is calculated as the number of cows with BCS within a desirable range  (between 2 and 4 on 1-5 scale) scored during the past 365 days divided by the  number of cows with BCS scored during the past 365 days. Cows can be included  multiple times.
|-
| rowspan="2" |11
|Apparent Pregnancy Loss Rate
|[[File:Apparent.png|center|thumb]]
|-
|Fertility
|Apparent pregnancy loss rate (PLR)  is defined as the number of cows with two or more breedings and the second or  later breeding at least 65 days after the previous breeding [where second  breeding occurred in the last 365 days] divided by the number of cows that  had their first breeding in the period of > 65 and ≤ 365+65 days ago.

The assumption is that the cow  became pregnant on first breeding and then lost it within that 65 day period  and was rebred.
|-
| rowspan="2" |12
|Average Days Open
|[[File:Formula7.png|center|thumb]]
|-
|Fertility
|Days open  (DO) is the date of last insemination minus calving date. It is optional to  include cows that have not been bred and past voluntary waiting period (VWP).  Exclude cows with DO NOT BREED code. It is assumed that the average DO is  calculated each test day.

N = number  of test days in the past 365 days. M = number of cows that are included in  the calculation each test day. The average DO is calculated in two steps.  Step 1: calculate per test day the average DO and the number of cows in the  calculation. Step 2: take the total of all test days of number of cows *  average DO on each test day and divide this by the sum of all cows that were  included in calculating DO on all test days in the past 365 days. The traits  Average Calving Interval and Average Days Open are highly related to each  other and should therefore be regarded as alternatives for each other.
|-
| rowspan="2" |13
|Average Calving Interval
|[[File:Formula9.png|center|thumb|468x468px]]
|-
|Fertility
|Calving Interval (CI) is the date of  calving for the current lactation minus the calving date of the previous  lactation. It can only be calculated for cows with two consecutive calvings  (lactations). The average CI is calculated as the sum of the CI of all  animals that in the past 365 days have calved for at least the second time  divided by the number of animals that in the past 365 days have calved for at  least the second time. The traits Average Calving Interval and Average Days  Open are highly related to each other and should therefore be regarded as  alternatives for each other.
|-
| rowspan="2" |14a
|Non-Return Rate 56 days
|[[File:Formula10.png|center|thumb|618x618px]]
|-
|Fertility
|The non-return rate 56 days (NR56)  is calculated as the number of animals with their first insemination (ins.)  > 56 and ≤ 56+365 days ago and no re-insemination  within 56 days after 1<sup>st</sup> insemination divided by the number  of animals with their first insemination > 56 and ≤ 56+365 days ago. The  main difference between NR56 (14a) and FSCR (14b) is that NR56 is calculated  based on re-insemination or not and FSCR is calculated based on pregnancy  confirmation.
|-
| rowspan="2" |14b
|1<sup>st</sup> Service Conception  Rate
|[[File:Formaula10.png|center|thumb|611x611px]]
|-
|Fertility
|The 1<sup>st</sup> service  conception rate (FSCR) is calculated as the number of animals with their  first insemination (ins.) > 56 and ≤ 56+365 days ago and confirmed  pregnancy after 56 days divided by the number of animals with their first  insemination > 56 and ≤ 56+365 days ago.
|-
| rowspan="2" |15
|Pregnancy Rate

|[[File:14b.png|center|thumb|661x661px]]
|-
|Fertility
|Pregnancy rate can be defined as the  percentage of cows reported pregnant and reported open in period > 42 days  and ≤ 63 days ago from calculation divided  by the number of cows reported open > 42 and ≤ 63 days ago and beyond VWP (i.e. eligible to be bred). The annual pregnancy rate is  calculated as the total number of cows confirmed pregnant divided by the  number of cows eligible to be bred over the past 365 days. Usually the  pregnancy rate is calculated once per week or per 3 weeks.
|-
| rowspan="2" |16
|% Cows culled due to  reproductive problems
|[[File:Image15.png|center|thumb|485x485px]]
|-
|Fertility
|The percentage of cows culled due to  reproductive problems is calculated as the number of cows culled in the past  365 days with main culling reason reproductive problems divided by the  average number of cows with at least one calving (dry and producing) present  in the past 365 days.

In case of more than one culling  reason, reproductive problems should be at least one of the reasons.
|-
| rowspan="2" |17
|% Cows with fertility disorders
|[[File:Image16.png|center|thumb|595x595px]]
|-
|Fertility
|The percentage of cows having  fertility disorders (e.g. silent heat, cysts, metritis, rep ..) is calculated  as the number of cows having fertility disorders in the past 365 days divided  by the average number of cows with at least one calving (dry and producing)  present in the past 365 days.
|-
| rowspan="2" |18

|Average Somatic Cell  Count
|[[File:Image17.png|center|thumb|373x373px]]
|-
|Health
|N = number of test days in the past  365 days. M = number of cows in the milking herd each test day. The average  somatic cell count (SCC) is calculated in two steps. Step 1: calculate per  test day the average SCC and the number of cows with SCC available. Step 2:  take the total of all test days of number of cows * average SCC on each test  day and divide this by the sum of all cows on all test days in the past 365  days.
|-
| rowspan="2" |19
|Chronic infection rate
|[[File:Image18.png|center|thumb|643x643px]]
|-
|Health
|A chronic infection is defined as  cows having in the same lactation two consecutive test days with SCC ≥ 200.000.

N = number of test days in the past  365 days. M = number of cows with two consecutive test days in the same  lactation. The chronic infection rate is calculated in two steps. Step 1:  calculate per test day the number of cows with a chronic infection and the  number of cows with two consecutive test days in the same lactation. Step 2:  take the total of all test days of number of cows with chronic infection and  divide this by the sum of all cows with two consecutive test days in the same  lactation on all test days in the past 365 days.
|-
| rowspan="2" |20
|Dry Cow Cure Rate
|[[File:Image20.png|center|thumb|623x623px]]
|-
|Health
|A dry cow cured is defined as cows  having a SCC ≥ 200.000 on the last test day of the  previous lactation and a SCC < 200.000 on the first test day of the  current lactation.

N = number of test days in the past  365 days. M = number of cows having their first test day of a lactation on  the particular test day and having a last test day in the previous lactation.  The dry cow cure rate (CR) is calculated in two steps. Step 1: calculate per  test day both the number of cured cows and the total number of cows with  their first test day of a lactation on that test day and having a last test  day in the previous lactation. Step 2: take the total of all test days of  number of dry cows cured and divide this by the sum of all cows with two  consecutive test days in the same lactation on all test days in the past 365  days.
|-
| rowspan="2" |21
|Fresh Cow Infection Rate
|[[File:Image21.png|center|thumb|523x523px]]
|-
|Health
|A fresh cow infected is defined as  cows having a SCC ≥ 200.000 on the first test day of  the current lactation.

N = number of test days in the past  365 days. M = number of cows having their first test day of a lactation. The  fresh cow infection rate is calculated in two steps. Step 1: calculate per  test day both the number of fresh cows infected and the total number of fresh  cows (with their first test day of a lactation on that test day). Step 2:  take the total of all test days of number of fresh cows infected and divide  this by the sum of all cows that have calved since the previous test day on  all test days in the past 365 days.
|-
| rowspan="2" |22
|Selective Dry Cow Therapy Rate

|[[File:Image22.png|center|thumb|607x607px]]
|-
|Health
|The Selective Dry Cow Therapy Rate  is calculated as the number of cows that were dried off without antibiotics  during the past 365 days divided by the total number of cows that were dried  off during the past 365 days.
|-
| rowspan="2" |23
|% Cows culled due to udder health
|[[File:Image23.png|center|thumb|601x601px]]
|-
|Health
|The percentage of cows culled due to  udder health is calculated as the number of cows culled in the past 365 days  with main culling reason mastitis divided by the average number of cows with  at least one calving (dry and producing) present in the past 365 days.

In case of more than one culling  reason, mastitis should be at least one of the reasons.
|-
| rowspan="2" |24

|% Cows culled due to  lameness

|[[File:Image24.png|center|thumb|620x620px]]
|-
|Health
|The percentage of cows culled due to  lameness or other claw health reasons is calculated as the number of cows  culled in the past 365 days with main culling reason lameness divided by the  average number of cows with at least one calving (dry and producing) present  in the past 365 days.

In case of more than one culling  reason, lameness should be at least one of the reasons.
|-
| rowspan="2" |25
|% Cows culled due to other  disorders/diseases
|[[File:Image25.png|center|thumb|613x613px]]
|-
|Health
|The percentage of cows culled due to  other disorders/diseases (e.g.  Pneumonia, Scour, etc.) is calculated as the number of cows culled in the past 365 days with  main culling reason other disorders/diseases divided by the average number of  cows with at least one calving (dry and producing) present in the past 365  days.

In case of more than one culling  reason, due to other disorders/diseases should be at least one of the  reasons.
|-
| rowspan="2" |26
|% Cows with FPR < 1  at first test day
|[[File:Image26.png|center|thumb|635x635px]]
|-
|Health
|This is calculated as the number of  cows that have calved in the past 365 days and with a fat-protein ratio <1  during the first test day in that lactation divided by the number of cows  that have calved in the past 365 days and a first test day in that lactation.
|-
| rowspan="2" |27
|% Cows with FPR  >1.3/1.5 at first test day 
|[[File:Image27.png|center|thumb|651x651px]]
|-
|Health
|This is calculated as the number of  cows that have calved in the past 365 days and with a fat-protein ration  >1.3 or 1.5 during the first test day in that lactation divided by the  number of cows that have calved in the past 365 days and a first test day in  that lactation. The threshold of 1.3 or 1.5 is breed specific (1.3 for  Holstein recommended).
|-
| rowspan="2" |28
|% Cows with lameness

|[[File:Image28.png|center|thumb|644x644px]]
|-
|Health
|The percentage of cows with lameness  is calculated as the number of cows with at least one case of lameness in the  past 365 days divided by the average number of cows with at least one calving  (dry and producing) present in the past 365 days.
|-
| rowspan="2" |29
|% Cows with mastitis
|[[File:Image29.png|center|thumb|637x637px]]
|-
|Health
|The percentage of cows with mastitis  is calculated as the number of cows with at least one case of mastitis in the  past 365 days divided by the average number of cows with at least one calving  (dry and producing) present in the past 365 days.
|-
| rowspan="2" |30
|% Cows with subclinical metabolic  issue
|[[File:Image30.png|center|thumb|643x643px]]
|-
|Health
|The percentage of cows with a  subclinical metabolic issue (ketosis, acidosis, displaced abomasum etc.) is calculated  as the number of cows with at least one case of subclinical metabolic issue  in the past 365 days divided by the average number of cows with at least one  calving (dry and producing) present in the past 365 days.
|-
| rowspan="2" |31
|Age at culling (dairy cattle)
|[[File:Image31.png|center|thumb|654x654px]]
|-
|Longevity
|Age at culling (AAC) is calculated  as the date of culling minus the date of birth. The average AAC is calculated  as the sum of AAC of animals that were culled in the past 365 days divided by  the number of animals that were culled in the past 365 days. If the date of  culling is unknown, the last date of milk recording could be used as  alternative.
|-
| rowspan="2" |32
|Average Daily Production  of culled animals
|[[File:Image32.png|center|thumb|636x636px]]
|-
|Longevity
|Average daily production  (ADP) of culled animals is defined as the lifetime milk production expressed  in ECM divided by the age at culling (date of culling minus date of birth). The ADP is calculated in two steps.  Step 1: calculate the sum of lifetime milk production expressed in ECM divided by the age at  culling of all animals  that were culled in the past 365 days. Step 2: divide the results of step 1  by the number of animals that were culled in the past 365 days.
|-
| rowspan="2" |33
|Average Lactation Number
|[[File:Image33.png|center|thumb]]
|-
|Longevity
|N = number of test days in the past  365 days. M = number of cows present (both cows in milk and dry cows) in the  herd each test day. The annual average lactation number (LN) is calculated in  two steps. Step 1: calculate per test day the average LN and the number of  cows in the herd. Step 2: take the total of all test days  * average LN on each test day and divide  this by the sum of all cows on all test days in the past 365 days.
|-
| rowspan="2" |34
|Average Lifetime  Production of culled animals
|[[File:Image34.png|center|thumb|601x601px]]
|-
|Longevity
|The average lifetime production  (LTP) is defined as the sum of the lifetime milk production expressed in ECM  of all animals that were culled in the past 365 days divided by the number of  animals that were culled in the past 365 days.
|-
| rowspan="2" |35
|% Cows died ≤ 60 days in milk
|[[File:Image35.png|center|thumb|737x737px]]
|-
|Longevity
|The percentage cows that died ≤ 60 days in milk is calculated as the number of cows that  died ≤ 60 days in milk in the past 60 to  (365+60) days divided by the number of cows that calved > 60 and ≤ 60+365 days ago.
|-
| rowspan="2" |36
|Age at first calving
|[[File:Image36.png|center|thumb|611x611px]]
|-
|Young stock
|Age at calving (AFC) is defined as  the date of first calving minus the date of birth. The average AFC is  calculated as the sum of AFC of all animals that calved for the first time in  the past 365 days divided by the number of animals that calved for the first  time in the past 365 days.

Exclude animals with unknown birth  date or unknown first calving date.
|-
| rowspan="2" |37

|Young stock EBV ranking
|Ranking of female young stock in the  herd on the national genetic index relative to all other herds in the country  (e.g. 0-100 scale).
|-
|Young stock
|GI is any (national) Genetic Index  describing the Total Genetic Performance of all animal present in the herd at  the moment of calculation.

Only young stock (all animals  without first calving yet) should be included, as lactating animals are  already included in other performance traits.
|-
| rowspan="2" |38
|Young stock sire EBV ranking
|Ranking of female young stock in the  herd on the national genetic index of the sire relative to all other herds in  the country (e.g. 0-100 scale).
|-
|Young stock
|SI is any (national) Index of the  sire of the animal describing the Total Genetic Performance of the sire of  all animals present in the herd at the moment of calculation.

Only young stock (all animals  without first calving yet) should be included, as lactating animals are  already included in other performance traits. SI should only be used if the  Genetic Index of the animal itself is not available.
|-
| rowspan="2" |39
|% Female young stock involuntary  culled
|[[File:Image39.png|center|thumb|697x697px]]
|-
|Young  stock
|The percentage female young stock  involuntary culled after 90 days of age is calculated as the number of female  young stock animals that is involuntary culled (for any other reason than  anticipated low production) after 90 days of age in the past 365 days divided  by the average number of female young stock (older than 90 days of age)  present in the herd in the past 365 days.
|-
| rowspan="2" |40
|% Calves born dead
|[[File:Image40.png|center|thumb|475x475px]]
|-
|Young  stock
|Calves born dead is defined as  calves born dead including calves that died within 24 hours. Calves include  both male and female calves.

The percentage of calves born dead  (CBD) is calculated as the number of calves born dead in the past 365 days  divided by the number of calves born in the past 365 days.
|-
| rowspan="2" |41
|% Female calves with  diarrhea

|[[File:Image41.png|center|thumb|742x742px]]
|-
|Young  stock
|The percentage of female calves with  diarrhea is calculated as the number of female calves that had diarrhea ≤ 90 days of age and that were born > 90 and ≤ 455 (365+90) days ago divided by all female  calves that were born > 90 and ≤  455 days ago.
|-
| rowspan="2" |42
|% Female calves with respiratory  diseases
|[[File:Image42.png|center|thumb|737x737px]]
|-
|Young  stock
|The percentage of female calves with  respiratory diseases is calculated as the number of female calves that had a  respiratory disease ≤ 90 days of age and were born > 90  and ≤ 455 (365+90) days ago divided by all female  calves that were born > 90 and ≤  455 days ago.
|-
| rowspan="2" |43
|% Mortality of female  calves until 90 days
|[[File:Imagelast.png|center|thumb|733x733px]]
|-
|Young  stock
|The percentage mortality of female  calves until 90 days is calculated as the number of female calves born alive  that died ≤ 90 days of age and that were born  > 90 and ≤ 455 (365+90) days ago divided by all female  calves that were born > 90 and ≤  455 days ago.

Percentage mortality does not  include stillbirth.
|}
</center>

</div>
</div>

= Appendix 2: Prediction equations for feed intake, feed efficiency and methane for dairy cattle =
<div class="mw-collapsible mw-collapsed">
    <div>
== Introduction ==
This appendix is part of the ICAR guidelines for sustainability recording traits, to assess sustainability at herd level. In this document, prediction formulas are described for the traits feed intake, feed efficiency and methane. These prediction formulas can be used to estimate values for these traits, in case no measured data is available for these traits.

== Prediction formulas for feed intake ==

=== TMR Equation (North America based) ===
Farm Application: 

DMI (kg/d) = [6.89 + 0.305 × MilkE (Mcal/d) + 0.022 × BW (kg) + (−0.689 + parity × −1.87) × BCS] × [1 –0.288× exp(−0.053 × DIM)]

Comprehensive: 

DMI (kg/d) = [(3.7 + parity × 5.7) + 0.305 × MilkE (Mcal/d) + 0.022 × BW (kg) + (−0.689 + parity × −1.87) × BCS] × [1 – (0.212 + parity × 0.136) × exp(−0.053 × DIM)] 

(mean bias = 0.021 kg, slope bias = 0.059, CCC = 0.72, and RMSEP = 2.89 kg), 

where parity is equal to 1 if the animal is multiparous and 0 otherwise. 

=== Pasture equation (North America Based) ===
Farm Application: 

Holstein DMI (SE = 0.73 kg/d) was: DMI (kg/d) = 15.36×[1 - e<sup>(−0.00220×BW)</sup>]

Crossbred DMI (SE = 0.81 kg/d) was: DMI (kg/d) = 12.91×[1 – e<sup>(−0.00295×BW)</sup>].

Comprehensive:

Holsteins DMI (kg/d) = 15.79×[1 – e<sup>(−0.00210×BW)</sup>]−0.0820×NDFdv, where NDFdv = (dietary neutral detergent fiber as a % of dry matter) – {22.07 + [0.08714×BW] – [0.00007383×(BW)<sup>2</sup>]}

Crossbred DMI (kg/d) = 13.48×[1 – e<sup>(−0.00271×BW)</sup>]-0.0824×NDFdv where NDFdv = (dietary neutral detergent fiber as a % of dry matter) – {23.11 + [0.07968×BW] - [0.00006252×(BW)<sup>2</sup>]}. 

=== DMI (kg DM/d) equations by Gruber et al. (2004)<ref name=":0">Gruber L., Schwarz F.J., Erdin D., Fischer B., Spiekers H., Steingass H., Meyer U., Chassot A., Jilg T., Obermaier A., Guggenberger T., 2004. Vorhersage der Futteraufnahme von Milchkühen – Datenbasis von 10 Forschungs- und Universitätsinstituten Deutschlands, Österreichs und der Schweiz [Feed intake prediction in dairy cows based on the data of 10 German, Austrian and Swiss research institutes and universities]. Proceedings of the 116th VDLUFA-Kongress, Sept 13–17, 2004; Rostock, Germany, 484-504.</ref> ===
Equation 1 considers the concentrate amount (kg DM) for feeding concentrate separately from forage. It can be used for pure forage diets (0 kg concentrate intake) and be adapted to other feeding systems like partial mixed rations (PMR) and TMR via additional mathematical equations. The methodical approach of including concentrate mixed with forage (PMR or TMR) or of including separately fed forage (kg DM) is explained in Ledinek et al. (2016)<ref>Ledinek M., Gruber L., Steininger F., Fuerst-Waltl B. Zottl K., Royer M. Krimberger K., Mayerhofer M., Egger-Danner C., 2016. Efficient Cow – estimation of feed intake for efficiency traits using on-farm recorded data. Animal Science Day, Ptuj, Acta agric. Slov, Supplement 5, 71-75.</ref>.

Equation 5 is especially applicable for TMR. It was evaluated by Jensen et al. (2015)<ref name=":1">Jensen L.M., Nielsen N.I., Nadeau E., Markussen B., Nørgaard P., 2015. Evaluation of five models predicting feed intake by dairy cows fed total mixed rations. Livest Sci. 176, 91-103.</ref>. It was evaluated by Jensen et al. (2015)<ref name=":1" />, results are shown in Table 3.
<center>
<table class="wikitable">
  <tr>
    <th colspan="9" style="text-align:center;">''Table 3. Evaluation criteria, regression estimates, and significance level of the five models predicting dry matter intake (DM1) in dairy cows fed total mixed ration, evaluated across the 12 experiments.''</th>
  </tr>
  <tr style="background-color:#efefef;">
    <th style="text-align:left;"></th>
    <th style="text-align:center;"></th>
    <th style="text-align:center;" colspan="4">Evaluation criteria</th>
    <th style="text-align:center;" colspan="2">Regression estimates</th>
  </tr>
  <tr style="background-color:#efefef;">
    <th style="text-align:left;">Models</th>
    <th style="text-align:center;">n</th>
    <th style="text-align:center;">MSPE<sup>b</sup></th>
    <th style="text-align:center;">ECT<sup>c</sup></th>
    <th style="text-align:center;">ER<sup>d</sup></th>
    <th style="text-align:center;">ED<sup>e</sup></th>
    <th style="text-align:center;">Intercept (kg DM/day)</th>
    <th style="text-align:center;">Slope (kg DM/day)</th>
  </tr>
  <tr>
    <td style="text-align:left;">NRC</td>
    <td style="text-align:center;">94</td>
    <td style="text-align:center;">3.20</td>
    <td style="text-align:center;">2.07</td>
    <td style="text-align:center;">0.13</td>
    <td style="text-align:center;">1.00</td>
    <td style="text-align:center;">-1.44***</td>
    <td style="text-align:center;">-0.14***</td>
  </tr>
  <tr>
    <td style="text-align:left;">NorFor<sup>g</sup></td>
    <td style="text-align:center;">48</td>
    <td style="text-align:center;">2.32</td>
    <td style="text-align:center;">0.14</td>
    <td style="text-align:center;">0.87</td>
    <td style="text-align:center;">1.30</td>
    <td style="text-align:center;">-0.38*</td>
    <td style="text-align:center;">-0.37***</td>
  </tr>
  <tr>
    <td style="text-align:left;">TDMI</td>
    <td style="text-align:center;">94</td>
    <td style="text-align:center;">2.91</td>
    <td style="text-align:center;">0.01</td>
    <td style="text-align:center;">0.65</td>
    <td style="text-align:center;">2.25</td>
    <td style="text-align:center;">0.10</td>
    <td style="text-align:center;">-0.29***</td>
  </tr>
  <tr>
    <td style="text-align:left;">Zom</td>
    <td style="text-align:center;">94</td>
    <td style="text-align:center;">9.97</td>
    <td style="text-align:center;">2.62</td>
    <td style="text-align:center;">2.78</td>
    <td style="text-align:center;">4.57</td>
    <td style="text-align:center;">-1.62***</td>
    <td style="text-align:center;">-0.56***</td>
  </tr>
  <tr>
    <td style="text-align:left;">Gruber</td>
    <td style="text-align:center;">94</td>
    <td style="text-align:center;">1.37</td>
    <td style="text-align:center;">0.05</td>
    <td style="text-align:center;">0.04</td>
    <td style="text-align:center;">1.28</td>
    <td style="text-align:center;">-0.23</td>
    <td style="text-align:center;">-0.08</td>
  </tr>
</table>
</center>



<sup>a</sup> NRC (NRC, 2001), NorFor (Volden et al., 2011a), TDMI (Huhtanen et al., 2011), Zorn (Zorn et al., 2012<sup>a</sup>) and Gruber (Gruber et al., 2004<ref name=":0" />).<sup>b</sup> Mean square prediction error (MSPE; Bibby and Toutenberg, 1977).

<sup>c</sup> Error due to central tendency (ECT).

<sup>d</sup> Error due to regression (ER).

<sup>e</sup> Error due to disturbance (ED).

<sup>f</sup> The regression estimates is retrieved from simple linear modelling.

<sup>g</sup> Evaluated on 9 out of 12 experiments as the remaining 3 experiments had been used in the development of this model.

<nowiki>*</nowiki> ''p'' < 0.05

<nowiki>***</nowiki> ''p''<0.001<center>
{| class="wikitable"
| colspan="5" style="text-align:center;" |'''''Table 4. Feed intake equations (total dry matter intake, kg DM per day), n = 25.482, Gruber et al. (2004)<ref name=":0" />.'''''
|- style="background-color:#efefef;"
|'''<div style="text-align:left;">Parameter</div>'''
|'''Unit'''
| 
|'''Equation 1'''
|'''Equation 5'''
|-
| colspan="3" |<div style="text-align:left;">Intercept</div>
| style="text-align:center;" |3.878
|style="text-align:center;" |2.274
|-
| colspan="2" rowspan="7" |<div style="text-align:left;">Effect Country × Breed</div>
| style="text-align:center;" |FV  [G+A]
| style="text-align:center;" |-2.631
| style="text-align:center;" |-2.169
|-
| style="text-align:center;" |BS  [G+A]
| style="text-align:center;" |-1.826
| style="text-align:center;" |-1.391
|-
| style="text-align:center;" |HF  m [G+A]
| style="text-align:center;" |-2.720
| style="text-align:center;" |-1.999
|-
| style="text-align:center;" |HF  h [G+A]
| style="text-align:center;" |-1.667
| style="text-align:center;" |-0.898
|-
| style="text-align:center;" |FV  [CH]
| style="text-align:center;" |-0.275
| style="text-align:center;" |-0.315
|-
| style="text-align:center;" |BS  [CH]
| style="text-align:center;" |-0.882
| style="text-align:center;" |-0.593
|-
| style="text-align:center;" |HF  [CH]
| style="text-align:center;" |0.000
| style="text-align:center;" |0.000
|-
| rowspan="3" |<div style="text-align:left;">Effect of lactation number</div>
| rowspan="3" style="text-align:center;" |n
| style="text-align:center;" |1
| style="text-align:center;" |-0.728
| style="text-align:center;" |-0.658
|-
| style="text-align:center;" |2 -  3
| style="text-align:center;" |0.218
| style="text-align:center;" |0.236
|-
| style="text-align:center;" |≥ 4
| style="text-align:center;" |0.000
| style="text-align:center;" |0.000
|-
| rowspan="3" |<div style="text-align:left;">Effect of DM ''<div style="text-align:left;">Model: a + b × (1 –  exp(-c×DIM))</div>''</div>
| rowspan="3" style="text-align:center;" |d
| style="text-align:center;" |a
| style="text-align:center;" |-4.287
| style="text-align:center;" |-5.445
|-
| style="text-align:center;" |b
| style="text-align:center;" |4.153
| style="text-align:center;" |5.298
|-
| style="text-align:center;" |c
| style="text-align:center;" |0.01486
| style="text-align:center;" |0.01838
|-
| rowspan="3" |<div style="text-align:left;">Regression coefficient for body weight</div><div style="text-align:left;">''Model: a + b<sub>1</sub>×DIM +  b<sub>2</sub>×DIM²''</div>
| rowspan="3" style="text-align:center;" |kg
| style="text-align:center;" |a
| style="text-align:center;" |0.0148
| style="text-align:center;" |0.0173
|-
| style="text-align:center;" |b<sub>1</sub>
| style="text-align:center;" |-0.0000474
| style="text-align:center;" |-0.0000514
|-
| style="text-align:center;" |b<sub>2</sub>
| style="text-align:center;" |0.0000000904
| style="text-align:center;" |0.0000000999
|-
| rowspan="3" |<div style="text-align:left;">Regression coefficient for milk performace</div><div style="text-align:left;">''Model: a + b<sub>1</sub>×DIM +  b<sub>2</sub>×DIM²''</div>
| rowspan="3" style="text-align:center;" |kg
| style="text-align:center;" |a
| style="text-align:center;" |0.0825
| style="text-align:center;" |0.2010
|-
| style="text-align:center;" |b<sub>1</sub>
| style="text-align:center;" |0.0008098
| style="text-align:center;" |0.0008080
|-
| style="text-align:center;" |b<sub>2</sub>
| style="text-align:center;" |-0.000000966
| style="text-align:center;" |-0.000001299
|-
| rowspan="3" |<div style="text-align:left;">Regression coefficient for concentrate amount</div><div style="text-align:left;">''Model: a + b<sub>1</sub>×DIM +  b<sub>2</sub>×DIM²''</div>
| rowspan="3" style="text-align:center;" |kg  DM
| style="text-align:center;" |a
| style="text-align:center;" |0.6962
| style="text-align:center;" |- 
|-
| style="text-align:center;" |b<sub>1</sub>
| style="text-align:center;" |-0.0023289
| style="text-align:center;" |-
|-
| style="text-align:center;" |b<sub>2</sub>
| style="text-align:center;" |0.0000040634
| style="text-align:center;" |-
|-
| rowspan="3" |<div style="text-align:left;">Regression coefficient ''for concentrate proportion''</div><div style="text-align:left;">''Model: a + b<sub>1</sub>×DIM +  b<sub>2</sub>×DIM²''</div>
| rowspan="3" style="text-align:center;" |%  TMR
| style="text-align:center;" |a
| style="text-align:center;" |-
| style="text-align:center;" |0.0631
|-
| style="text-align:center;" |b<sub>1</sub>
| style="text-align:center;" |- 
| style="text-align:center;" |-0.0002096
|-
| style="text-align:center;" |b<sub>2</sub>
| style="text-align:center;" |- 
| style="text-align:center;" |0.0000001213
|-
|<div style="text-align:left;">Reg.coeff. NEL<sub>Forage</sub></div>
| style="text-align:center;" |MJ  /kg DM
| style="text-align:center;" |-
| style="text-align:center;" |0.8580
| style="text-align:center;" |0.6090
|-
|<div style="text-align:left;">R²</div>
| style="text-align:center;" |%
| style="text-align:center;" |-
| style="text-align:center;" |86.7
| style="text-align:center;" |83.5
|-
|<div style="text-align:left;">RSD</div>
| style="text-align:center;" |kg  DM
| style="text-align:center;" |-
| style="text-align:center;" |1.32
| style="text-align:center;" |1.46
|-
|<div style="text-align:left;">CV</div>
| style="text-align:center;" |%
| style="text-align:center;" |-
| style="text-align:center;" |7.1
| style="text-align:center;" |7.9
|-
|<div style="text-align:left;">''Correction factor by validation''</div>
| colspan="2" style="text-align:left;" |''<div style="text-align:left;">DMI  = a + b×DMI<sub>predicted</sub></div>''
| style="text-align:center;" |''0.47+0.930×DMI<sub>p.</sub>''
| style="text-align:center;" |''0.71+0.920×DMI<sub>p.</sub>''
|-
| colspan="5" style="text-align:center;" |G  Germany; A Austria; CH Switzerland; HF m HF h medium and high management  level of farm
|}
</center>


Model 1 (concentrate considered as amount, e. g. as kg DM):

DMI<sub>predicted</sub> (kg/day) = 

Intercept + Effect breed.country (kg) + Effect of lactation number (kg) + Effect of DIM (kg DMI, non-linear function depending on DIM) + b_body weight (kg DMI/kg BW) × body weight (kg) + b_milk yield (kg DMI/kg milk) × milk yield (daily milk, kg) + b_concentrate amount (kg DMI/kg concentrate in DM) × concentrate amount (daily amount, kg DM) + b_NEL<sub>Forage</sub> (kg DMI/MJ NEL per kg DM) × Energy content of forage (MJ NEL per kg DM)

Correction factor by validation: DMI = 0.47+0.930''×''DMI<sub>predicted</sub>


Model 5 (concentrate considered as proportion, e. g. as % of DM):

DMI<sub>predicted</sub> (kg/day) = 

Intercept + Effect breed.country (kg) + Effect of lactation number (kg) + Effect of DIM (kg DMI. non-linear function depending on DIM) + b_body weight (kg DMI/kg BW) × body weight (kg) + b_milk yield (kg DMI/kg milk) × milk yield (daily milk. kg) + b_concentrate proportion (% of TMR, DM basis) × concentrate proportion (% of TMR) + b_NEL<sub>Forage</sub> (kg DMI/MJ NEL per kg DM) × Energy content of forage (MJ NEL per kg DM)

Correction factor by validation: DMI = 0.71+0.920''×''DMI<sub>predicted</sub>

Regression coefficients (b_) for body weight, milk performance, concentrate amount and concentrate proportion are depending on DIM and are calculated using quadratic polynomials (see Table 3). The effect of DIM is described by a non-linear function.

=== Link to alternative equations based on regions ===
1.       Development and evaluation of equations for prediction of feed Intake for lactating Holstein dairy cows, 1997. D.K. Roseler, D.G. Fox, L.E. Chase, A.N. Pell, W.C. Stone https://doi.org/10.3168/jds.S0022-0302(97)76010-7

2.     NRC. 2001. Nutrient Requirements of Dairy Cattle. 7th rev. ed. Natl. Acad. Press, Washington, DC tested by https://doi.org/10.3168/jds.2018-16166

3.     https://doi.org/10.1007/s11250-022-03275-8

=== Prediction of voluntary feed intake in NorFor (Nordic Feed Evaluation System) ===
Feed intake is predicted from animal fill capacity and dietary fill values. In the intake is regulated both physically by the diet and metabolically by the energy demand of the animal. Each individual feed is assigned a basic fill value (FV; section 6.2) and the animal is assigned an intake capacity (IC) expressed in the same units as the feed When predicting DMI, the following general equation must be fulfilled:

IC= FV_intake



where IC is the animal intake capacity and FV_intake is the total feed intake expressed in fill units

The following equation is used to calculate IC in dairy cows:[[File:Final.png|center|thumb|501x501px]]

where IC_cow  is the intake capacity of lactating dairy cows; DIM is days in milk; ECM is the energy corrected milk, kg/d; BW is the animal body weight, kg; and a, b, c, d, e, f, g are regression coefficients presented in Table 5.

<center>
{| class="wikitable"
| colspan="8" style="text-align:left;" |'''''Table 5. Multiple regression coefficients used to predict dairy cow intake capacity (IC).'''''
|- style="background-color:#efefef;"
| style="text-align:left;" | 
| colspan="7" style="text-align:center;" |'''Multiple regression coefficients <sup>1</sup>'''
|-
| style="text-align:left;" |'''Cow category'''
|style="text-align:center;" |'''a'''
|style="text-align:center;" |'''b'''
|style="text-align:center;" |'''c'''
|style="text-align:center;" |'''d'''
|style="text-align:center;" |'''e'''
|style="text-align:center;" |'''f'''
|style="text-align:center;" |'''g'''
|-
| style="text-align:left;" |Primiparous large dairy breeds
| style="text-align:center;" |2.59
| style="text-align:center;" |0.134
| style="text-align:center;" |<nowiki>-0.0006</nowiki>
| style="text-align:center;" |0.55
| style="text-align:center;" |0.091
| style="text-align:center;" |500
| style="text-align:center;" |0.006
|-
| style="text-align:left;" |Multiparous large dairy breeds
| style="text-align:center;" |2.82
| style="text-align:center;" |0.134
| style="text-align:center;" |<nowiki>-0.0006</nowiki>
| style="text-align:center;" |0.55
| style="text-align:center;" |0.091
| style="text-align:center;" |575
| style="text-align:center;" |0.006
|-
| style="text-align:left;" |Jersey primiparous cows
| style="text-align:center;" |2.25
| style="text-align:center;" |0.134
| style="text-align:center;" |<nowiki>-0.0004</nowiki>
| style="text-align:center;" |0.25
| style="text-align:center;" |0.110
| style="text-align:center;" |360
| style="text-align:center;" |0.006
|-
| style="text-align:left;" |Jersey multiparous cows
| style="text-align:center;" |2.40
| style="text-align:center;" |0.134
| style="text-align:center;" |<nowiki>-0.0004</nowiki>
| style="text-align:center;" |0.15
| style="text-align:center;" |0.110
| style="text-align:center;" |405
| style="text-align:center;" |0.006
|-
| style="text-align:left;" |Icelandic primiparous cows
| style="text-align:center;" |4.07
| style="text-align:center;" |0.087
| style="text-align:center;" |<nowiki>-0.0014</nowiki>
| style="text-align:center;" |0.65
| style="text-align:center;" |0.015
| style="text-align:center;" |400
| style="text-align:center;" |0.002
|-
| style="text-align:left;" |Icelandic multiparous cows
| style="text-align:center;" |4.77
| style="text-align:center;" |0.071
| style="text-align:center;" |<nowiki>-0.0013</nowiki>
| style="text-align:center;" |0.14
| style="text-align:center;" |0.035
| style="text-align:center;" |523
| style="text-align:center;" |0.0013
|}
</center>

=== Cornell Net Carbohydrate and Protein System (CNCPS, Cornell University Model), for lactating cows ===
DMI (kg/d) = (0.0185 * '''BW''' + 0.305 * '''FCM''') * '''Lag'''

'''Where:''' 

'''BW        = Body Weight (kg),''' 

'''FCM      = fat corrected milk (kg/d) = ('''0.4 * '''milk''' + 15 * '''milk'''* '''fat %, Milk = milk yield (kg/d),''' 

'''Fat         = Fat test (%),''' 

'''Lag        = adjustment for stage of lactation:''' IF('''WOL'''≤16; 1 − exp(−(0.564 − 0.124 × 2) × ('''WOL''' + 2.36)));1) '''and''' 

'''WOL      = Week of lactation.''' 

=== NASEM equation (the updated NRC model) ===
DMI (kg/d) = (3.7 + parity * 5.7) + 0.305 * MilkEnergy (Mcal/d) + 0.022 * BW (kg) + (-0.689 – 1.87 * parity) * BCS) * (1-(0.212 + parity * 0.136) * e (-0.053 x DIM))

Where:

Parity                  = 0 for primiparous and 1 for multiparous cows,

BCS                      = Body condition score (1 to 5),

MilkEnergy        = 9.29 * kg fat/kg milk + 5'''.85<sup>1</sup> * kg true Protein/kg milk + 3.95 * kg lactose/kg milk, <sup>1</sup>) if total protein is used: replace 5.85 by 5.5.'''

== Prediction formula’s for feed efficiency traits ==

=== Residual feed intake (in dry matter, DM) ===
RFI = FI_observed – FI_predicted

Y = β0 + β1 X1 + β2 X2 + ε

=== Residual energy intake (in MJ ME or MJ NEL) ===
REI = EI_observed – EI_predicted

Y = β0 + β1 X1 + β2 X2 + ε

Explanation: Residual traits are mostly defined as difference between an observed and predicted input trait. The input trait can be feed intake for calculating residual feed intake (RFI, in dry matter) or energy intake for calculating residual energy intake (REI, in MJ ME or MJ NEL). The prediction of feed or energy intake considers at least milk performance, metabolic body size for maintenance requirements and body reserve change. Either a standardized formula is used [REI = Energy intake_observed minus Energy intake_predicted; predicted energy intake includes the requirements for milk, maintenance, body reserve change (and pregnancy) calculated based on the energy system of the country]. Or REI and RFI are defined as residuals of a regression model. As regression models and thus the regression coefficients vary very much in scientific literature, the comparability is reduced.

=== Feed efficiency ===
Feed efficiency = ECM (kg) /FI (kg DM)

Explanation: Efficiency is defined as ratio between output and input. Feed efficiency is herd average energy corrected milk (output) divided by herd average feed intake (input). Only milk producing cows are included. Data should be based on one year, in any other case, data should be adjusted for stage of lactation of the cow.

=== Energy efficiency ===
Energy efficiency = Lactation energy (MJ LE) / Energy intake (MJ ME or MJ NEL)

Explanation: Efficiency is defined as ratio between output and input. Energy efficiency is herd average energy in milk (output: lactation energy, LE) divided by herd average energy intake (input). Only milk producing cows are included. Data should be based on one year, in any other case, data should be adjusted for stage of lactation of the cow.

Energy efficiency should be used instead of feed efficiency if possible. Efficiency traits based on energy consider the energy content of diet. They do not rate herds or animals as poor or well performing as diet quality is lower or higher. Additionally they can be adjusted to body reserve change as known from residual traits. The shorter the calculation period, the more important is the consideration of body reserve change and of stage of lactation, because the mobilization of body reserves and a (too) low or late regeneration of body reserves fake a high efficiency.

== Methane Emissions ==

=== Simplistic (IPPC Tier 2) ===
eCH4 (g d−1) = DMI (kg d−1) × 18.5 (MJ kg−1 DM) × Ym)/55.65 (MJ kg−1 eCH4), where Ym = 3.0% when dietary concentrate proportion is ≥90%, otherwise Ym = 6.5%

=== Comprehensive ===
P. Escobar-Bahamondes, M. Oba, and K.A. Beauchemin, 2016. Universally applicable methane prediction equations for beef cattle fed high- or low-forage diets. Canadian Journal of Animal Science. 97(1): 83-94. https://doi.org/10.1139/cjas-2016-0042.
<center>
{| class="wikitable"
| colspan="7" style="text-align:center;" |'''''Table 4. Methane prediction (g d-1) equations for beef cattle developed in this study.'''''
|- style="background-color:#efefef;"
| style="text-align:left;" |'''Dataset'''
| style="text-align:center;" |'''Equation ID'''
| style="text-align:center;" |'''''n'''''
| style="text-align:center;" |'''Equations'''
| style="text-align:center;" |'''eCH<sub>4</sub>'''<br />'''(g d<sup>-1</sup>)'''
| style="text-align:center;" |'''RMSE'''
| style="text-align:center;" |'''''P'''''
|-
| style="text-align:left;" |'''High forage'''
| style="text-align:center;" | 
| style="text-align:center;" | 
| style="text-align:center;" | 
| style="text-align:center;" | 
| style="text-align:center;" | 
| style="text-align:center;" | 
|-
| style="text-align:left;" |Original high forage
| style="text-align:center;" |[HF-OR]
| style="text-align:center;" |123
| style="text-align:center;" |eCH<sub>4</sub> = 715 (±11.45) + 0.12 (±0.03) x BW + 0.10 (±0.01) x DMI<sup>3</sup> – 244.8 (±56.44) x fat<sup>3</sup>  x (NDF-ADF)<sup>2</sup> + 0.1 (±0.00)
| style="text-align:center;" |156.4
| style="text-align:center;" |27.0
| style="text-align:center;" |<0.01
|-
| style="text-align:left;" |Monte Carlo high forage
| style="text-align:center;" |[HF-MC]
| style="text-align:center;" |100305
| style="text-align:center;" |eCH<sub>4</sub> = 25.9 (±0.54) + 0.13 (±0.001) x BW + 145.4 (±1.31) x fat + 10.3 (±0.16) x DMI<sup>3</sup> - 27.4 (±0.20) x (starch:NDF)
| style="text-align:center;" |149.6
| style="text-align:center;" |34.0
| style="text-align:center;" |<0.01
|-
| style="text-align:left;" |IPCC  (2006) Tier 2
| style="text-align:center;" |IPCC 2006
| style="text-align:center;" |123
| style="text-align:center;" |eCH<sub>4</sub> = [DMI x 18.5 (MJ kg<sup>-1</sup> DM) x (6.5 x 10)] / 55.65 (MJ kg<sup>-1</sup> eCH<sub>4</sub>)
| style="text-align:center;" |156.1
| style="text-align:center;" |<nowiki>-</nowiki>
| style="text-align:center;" |<nowiki>-</nowiki>
|-
| style="text-align:left;" |'''Low Forage'''
| style="text-align:center;" | 
| style="text-align:center;" | 
| style="text-align:center;" | 
| style="text-align:center;" | 
| style="text-align:center;" | 
| style="text-align:center;" | 
|-
| style="text-align:left;" |Original low forage
| style="text-align:center;" |[LF-OR]
| style="text-align:center;" |34
| style="text-align:center;" |eCH<sub>4</sub> = -26.4 (±20.17) + 0.21 (±0.04) x BW + 30.1 (±11.83) x CP - 70.5 (±25.48) x fat<sup>2</sup> + 10.1 (±5.12) x (NDF-ADF)<sup>3</sup>
| style="text-align:center;" |98.3
| style="text-align:center;" |22.2
| style="text-align:center;" |<0.05
|-
| style="text-align:left;" |Monte Carlo low forage
| style="text-align:center;" |[LF-MC]
| style="text-align:center;" |27364
| style="text-align:center;" |eCH<sub>4</sub> = -10.1 (±0.62) + 0.21 (±0.001) x BW + 0.36 (±0.003) x DMI<sup>2</sup> - 69.2 (±1.65) x fat<sup>3</sup> + 13.0 (±0.45) x (CP:NDF) - 49 (±0.07) x (starch:NDF)
| style="text-align:center;" |95.2
| style="text-align:center;" |11.2
| style="text-align:center;" |<0.001
|-
| style="text-align:left;" |IPCC  (2006) Tier 2
| style="text-align:center;" |IPCC 2006
| style="text-align:center;" |34
| style="text-align:center;" |eCH<sub>4</sub> = [DMI x 18.5 (MJ kg<sup>-1</sup> DM) x (3.0 x 10)] / 55.65 (MJ kg<sup>-1</sup> eCH<sub>4</sub>)
| style="text-align:center;" |89.6
| style="text-align:center;" |<nowiki>-</nowiki>
| style="text-align:center;" |<nowiki>-</nowiki>
|-
| style="text-align:left;" |'''All databases'''
| style="text-align:center;" | 
| style="text-align:center;" | 
| style="text-align:center;" | 
| style="text-align:center;" | 
| style="text-align:center;" | 
| style="text-align:center;" | 
|-
| style="text-align:left;" |Original  complete database
| style="text-align:center;" |[AL-OR]
| style="text-align:center;" |194
| style="text-align:center;" |eCH<sub>4</sub> = -35.0 (±17.03) + 0.08 (±0.03) x BW + 1.2 (±0.14) x dietary forage content - 69.8 (±14.4) x fat<sup>3</sup> + 3.14 (±0.36) x GEI
| style="text-align:center;" |154.9
| style="text-align:center;" |154.9
| style="text-align:center;" |<0.05
|}
</center>

=== Link to alternative equations based on regions ===
1.      https://doi.org/10.3168/jds.2011-4439

2.     https://doi.org/10.1080/09064702.2013.851275

3.      https://doi.org/10.3168/jds.2012-6095

</div>
</div>