ICAR Wiki - User contributions [en]

Guidelines

2024-02-12T12:08:07Z

Rstephansen: /* Sections */ created a red link to Feed and gas

<center>
<big><big><big>UNDER CONSTRUCTION</big></big></big><br>
This site is being converted to a wiki for the entire ICAR Guidelines content. Please do not rely on the content until this message has been removed. That will indicate that the content has been approved.
</center>

=Forward=
Welcome to [https://www.icar.org/ ICAR's] prototype Guidelines Wiki. The content on this wiki is derived from the [https://www.icar.org/Guidelines/ Guidelines]. It is intended to grow into a production wiki over time and as an editorial organization is established. The ICAR Guidelines attempt to provide the world-wide farm livestock recording sector with
detailed standards and guidelines representing the state-of-the-art for the full range of
activities involved in the identification, performance recording and evaluation of farm
livestock.

=Sections=
<blockquote>
<div style="column-count:2">
:[[Preamble]]
:[[General Rules]]
:[[Cattle Milk Recording]]
:[[Beef Cattle Recording]]
:[https://www.icar.org/Guidelines/04-DNA-Technology.pdf DNA Technology]
:[[Conformation Recording]]
:[https://www.icar.org/Guidelines/06-AI-and-ET.pdf AI and ET Data and Fertility Analysis]
:[[Bovine Functional Traits]]
:[https://www.icar.org/Guidelines/08-Certificate-of-Quality.pdf Certificate of Quality]
:[https://www.icar.org/Guidelines/09-Dairy-Cattle-Genetic-Evaluation.pdf Dairy Cattle Genetic Evaluation]
:[[Identification Device Certification]]
:[[Testing, Approval and Checking of Measuring, Recording and Sampling Devices]]
:[[Milk Analysis]]
:[https://www.icar.org/Guidelines/13-On-farm-milk-analysis.pdf On-farm Milk Analysis]
:[https://www.icar.org/Guidelines/14-Alpaca-and-Goat-Identifcation-and-Fibre.pdf Alpaca and Goat Identification and Fibre]
:[[Data Exchange]]
:[https://www.icar.org/Guidelines/16-Dairy-Sheep-and-Goats.pdf Dairy Sheep and Goats]
:[https://www.icar.org/Guidelines/17-Buffalo-Milk-Recording.pdf Buffalo Milk Recording]
:[https://www.icar.org/Guidelines/18-Breed-Associations.pdf Breed Associations]
:[[Recording Feed Intake for Genetic Evaluation]]
:[[Methane Emission for Genetic Evaluation]]
:[[Feed and Gas Working Group]]
:[https://www.icar.org/Guidelines/21-Meat-reproduction-and-maternal-traits-in-sheep-and-goats.pdf Meat, reproduction and maternal trait in sheep and goats]
:[[Glossary]]
</div>
</blockquote>

Guidelines

2024-02-12T12:01:20Z

Rstephansen: Inserted new link to feed and gas WG

<center>
<big><big><big>UNDER CONSTRUCTION</big></big></big><br>
This site is being converted to a wiki for the entire ICAR Guidelines content. Please do not rely on the content until this message has been removed. That will indicate that the content has been approved.
</center>

=Forward=
Welcome to [https://www.icar.org/ ICAR's] prototype Guidelines Wiki. The content on this wiki is derived from the [https://www.icar.org/Guidelines/ Guidelines]. It is intended to grow into a production wiki over time and as an editorial organization is established. The ICAR Guidelines attempt to provide the world-wide farm livestock recording sector with
detailed standards and guidelines representing the state-of-the-art for the full range of
activities involved in the identification, performance recording and evaluation of farm
livestock.

=Sections=
<blockquote>
<div style="column-count:2">
:[[Preamble]]
:[[General Rules]]
:[[Cattle Milk Recording]]
:[[Beef Cattle Recording]]
:[https://www.icar.org/Guidelines/04-DNA-Technology.pdf DNA Technology]
:[[Conformation Recording]]
:[https://www.icar.org/Guidelines/06-AI-and-ET.pdf AI and ET Data and Fertility Analysis]
:[[Bovine Functional Traits]]
:[https://www.icar.org/Guidelines/08-Certificate-of-Quality.pdf Certificate of Quality]
:[https://www.icar.org/Guidelines/09-Dairy-Cattle-Genetic-Evaluation.pdf Dairy Cattle Genetic Evaluation]
:[[Identification Device Certification]]
:[[Testing, Approval and Checking of Measuring, Recording and Sampling Devices]]
:[[Milk Analysis]]
:[https://www.icar.org/Guidelines/13-On-farm-milk-analysis.pdf On-farm Milk Analysis]
:[https://www.icar.org/Guidelines/14-Alpaca-and-Goat-Identifcation-and-Fibre.pdf Alpaca and Goat Identification and Fibre]
:[[Data Exchange]]
:[https://www.icar.org/Guidelines/16-Dairy-Sheep-and-Goats.pdf Dairy Sheep and Goats]
:[https://www.icar.org/Guidelines/17-Buffalo-Milk-Recording.pdf Buffalo Milk Recording]
:[https://www.icar.org/Guidelines/18-Breed-Associations.pdf Breed Associations]
:[[Recording Feed Intake for Genetic Evaluation]]
:[[Methane Emission for Genetic Evaluation]]
:Feed and Gas Working Group
:[https://www.icar.org/Guidelines/21-Meat-reproduction-and-maternal-traits-in-sheep-and-goats.pdf Meat, reproduction and maternal trait in sheep and goats]
:[[Glossary]]
</div>
</blockquote>

User:Rstephansen/sandbox

2024-02-12T11:20:29Z

Rstephansen:

== Introduction Feed and Gas ==
Text about the group

Link to Feed intake Guidelines

Link to Methane Guidelines

== Objectives of the Feed and Gas Working Group ==
The objectives of the Feed and Gas Working Groups are to:

# Provide a forum for members of ICAR and associated researchers to collaborate, exchange information and learn from each other on all aspects of: recording and using dry matter intake data, and recording and using methane outputs data.
# Maintain, update, promote and extend universal guidelines for recording dry matter intake and methane output in cattle, sheep and goats world-wide
# Conduct and report the results of periodic international surveys on recording dry matter intake and methane output in cattle, sheep and goats worldwide.
# Develop, provide and promote a system and standards for data storage and genetic evaluation services which will form the basis of services that ICAR will provide to members of ICAR and their research associates on a user pays basis.
# Facilitate and cooordinate international collaboration in research and development on recording and using dry matter intake data, and recording and using methane outputs data.

== Members of ICAR Feed and Gas Working Group ==
Below are the members of the Feed and Gas Working Group listen in alphabetic order, including a short description.
{| class="wikitable"
|+
!Researcher
!Picture
|-
|
|
|-
|
|
|-
|Name: Rasmus Bak Stephansen
Affiliation: Aarhus University, Center for Quantitative Genetics and Genomics
E-mail: rasmus.stephansen@qgg.au.dk
Homepage: https://pure.au.dk/portal/da/persons/rasmus.stephansen%40qgg.au.dk

Research topic:

My research is dedicated to enhancing phenotyping and modeling of feed efficiency in livestock, with a specific emphasis on lactating animals. The challenges posed by the early lactation dilemma are a central focus, driving our efforts to develop robust modeling approaches. Furthermore, my investigation extends to unraveling the genetic architecture linking feed efficiency to methane emissions, as well as its impact on animal functionality and welfare. Through these multifaceted inquiries, I aim to contribute to a more sustainable and welfare-oriented livestock industry.
|[[File:Rasmus Skovgaard Stephansen.webp|thumb]]
|}
*
* Name, affiliation, picture, homepage, research topic
*
* Christine Baes
* Karoline Bakke
* Lorenzo Benzoni
* Raffaella Finocchiaro
* Maria Frizzarin
* Nina Krattenmacher
* Jan Lassen
* Jennie Pryce
* Caeli Richardson (2023 Brian Wickham Young Persons Exchange Program Bursary)
* Rasmus Bak Stephansen

User:Rstephansen/sandbox

2024-02-09T10:54:27Z

Rstephansen:

== Introduction Feed and Gas ==
Text about the group

Link to Feed intake Guidelines

Link to Methane Guidelines

== Objectives of the Feed and Gas Working Group ==
The objectives of the Feed and Gas Working Groups are to:

# Provide a forum for members of ICAR and associated researchers to collaborate, exchange information and learn from each other on all aspects of: recording and using dry matter intake data, and recording and using methane outputs data.
# Maintain, update, promote and extend universal guidelines for recording dry matter intake and methane output in cattle, sheep and goats world-wide
# Conduct and report the results of periodic international surveys on recording dry matter intake and methane output in cattle, sheep and goats worldwide.
# Develop, provide and promote a system and standards for data storage and genetic evaluation services which will form the basis of services that ICAR will provide to members of ICAR and their research associates on a user pays basis.
# Facilitate and cooordinate international collaboration in research and development on recording and using dry matter intake data, and recording and using methane outputs data.

== Members of ICAR Feed and Gas Working Group ==
Below are the members of the Feed and Gas Working Group listen in alphabetic order, including a short description.
{| class="wikitable"
|+
!Researcher
!Picture
|-
|
|
|-
|
|
|-
|Name: Rasmus Bak Stephansen
Affiliation: Aarhus University, Center for Quantitative Genetics and Genomics
E-mail: rasmus.stephansen@qgg.au.dk
Homepage: https://pure.au.dk/portal/da/persons/rasmus.stephansen%40qgg.au.dk

Research topic:
|[[File:Rasmus Skovgaard Stephansen.webp|thumb]]
|}
*
* Name, affiliation, picture, homepage, research topic
*
* Christine Baes
* Karoline Bakke
* Lorenzo Benzoni
* Raffaella Finocchiaro
* Maria Frizzarin
* Nina Krattenmacher
* Jan Lassen
* Jennie Pryce
* Caeli Richardson (2023 Brian Wickham Young Persons Exchange Program Bursary)
* Rasmus Bak Stephansen

File:Image AU.png

2024-02-09T10:53:17Z

Rstephansen:

Image AU

User:Rstephansen/sandbox

2024-02-09T08:39:33Z

Rstephansen:

== Introduction Feed and Gas ==
Text

link

link

== Objectives of the Feed and Gas WG ==
The objectives of the Feed and Gas WG are to:

# Provide a forum for members of ICAR and associated researchers to collaborate, exchange information and learn from each other on all aspects of: recording and using dry matter intake data, and recording and using methane outputs data.
# Maintain, update, promote and extend universal guidelines for recording dry matter intake and methane output in cattle, sheep and goats world-wide
# Conduct and report the results of periodic international surveys on recording dry matter intake and methane output in cattle, sheep and goats worldwide.
# Develop, provide and promote a system and standards for data storage and genetic evaluation services which will form the basis of services that ICAR will provide to members of ICAR and their research associates on a user pays basis.
# Facilitate and cooordinate international collaboration in research and development on recording and using dry matter intake data, and recording and using methane outputs data.

== Members ==

* Name, affiliation, picture, homepage, research topic
*
* Christine Baes
* Karoline Bakke
* Lorenzo Benzoni
* Raffaella Finocchiaro
* Maria Frizzarin
* Nina Krattenmacher
* Jan Lassen
* Jennie Pryce
* Caeli Richardson (2023 Brian Wickham Young Persons Exchange Program Bursary)
* Rasmus Bak Stephansen

User:Rstephansen/sandbox

2024-02-09T08:34:46Z

Rstephansen:

== Introduction Feed and Gas ==
Text

link

link

== Objectives of the Feed and Gas WG ==
The objectives of the Feed and Gas WG are to:

# Provide a forum for members of ICAR and associated researchers to collaborate, exchange information and learn from each other on all aspects of: recording and using dry matter intake data, and recording and using methane outputs data.
# Maintain, update, promote and extend universal guidelines for recording dry matter intake and methane output in cattle, sheep and goats world-wide
# Conduct and report the results of periodic international surveys on recording dry matter intake and methane output in cattle, sheep and goats worldwide.
# Develop, provide and promote a system and standards for data storage and genetic evaluation services which will form the basis of services that ICAR will provide to members of ICAR and their research associates on a user pays basis.
# Facilitate and cooordinate international collaboration in research and development on recording and using dry matter intake data, and recording and using methane outputs data.

== Members ==

* Christine Baes
* Karoline Bakke
* Lorenzo Benzoni
* Raffaella Finocchiaro
* Maria Frizzarin
* Nina Krattenmacher
* Jan Lassen
* Jennie Pryce
* Caeli Richardson (2023 Brian Wickham Young Persons Exchange Program Bursary)
* Rasmus Bak Stephansen

Section 19 – Recording Feed Intake for Genetic Evaluation

2024-02-07T18:48:49Z

Rstephansen:

== Introduction ==
The widespread use of genomic information in dairy cattle breeding programs has presented the opportunity to select for feed intake and feed efficiency. This is because animals from research herds can be used as a reference population to calibrate a genomic prediction equation, which is then used to predict the breeding values for selection candidates based on their own genotype. Phenotypes for feed intake, however, are still an important limiting factor for obtaining high accuracy breeding values and performing genetic analysis for feed intake. Based on the experiences gained through several initiatives, we provide guidelines on the recording and handling of feed intake records for genomic and genetic evaluations.

== 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 ==
Figure 1 illustrates the main elements of this guideline. The numbers in this figure refer to the chapter numbers of this guideline. The scope is to give guidelines of recording feed intake for genetic and genomic evaluations. The elements of the guidelines (Figure 1) are presented in the subsections (see links below). [[File:FeedIntakeFigure1.jpg |center|''Figure 1. Scope of Guidelines''|alt=Figure 1.|frame]]

=Sub-sections=
<blockquote>
<div style="column-count:2">
: [[Feed Intake: Definitions, Terminology, and Scope | Definitions, Terminology, and Scope]]
: [[Feed Intake: Utilizing existing feed intake data | Utilizing existing feed intake data]]
: [[Feed Intake: Setting up optimal data recording | Setting up optimal data recording]]
: [[Feed Intake: Recording intake indoors and at pasture | Recording intake indoors and at pasture]]
: [[Feed Intake: Calibration | Calibration]]
: [[Feed Intake: Chemical analysis | Chemical analysis]]
: [[Feed Intake: Number animals per bin | Number animals per bin]]
: [[Feed Intake: Additional recording | Additional recording]]
: [[Feed Intake: Bulls, cows and young stock | Bulls, cows and young stock]]
: [[Feed Intake: Lactation period | Lactation period]]
: [[Feed Intake: Feeding system | Feeding system]]
: [[Feed Intake: Genotyping and imputation | Genotyping and imputation]]
: [[Feed Intake: Merging and sharing data in genetic evaluations | Merging and sharing data in genetic evaluations]]
: [[Feed Intake: Proxies for feed intake | Proxies for feed intake]]
: [[Feed Intake: Feed efficiency in the breeding goal | Feed efficiency in the breeding goal]]
: [[Feed Intake: Acknowledgements | Acknowledgements]]
: [[Feed Intake: Appendix A: Standard Operating Procedure Nottingham | Appendix A: Standard Operating Procedure Nottingham]]
: [[Feed Intake: Appendix B Alkane Feed intake technique Piacenza, Italy | Appendix B Alkane Feed intake technique Piacenza, Italy ]]
</div>
</blockquote>

== Summary of Changes ==
{| class="wikitable"
|+
!Date of Change
!Nature of Change
|-
|March 2020
|Draft from Feed & Gas WG put into standard template for ICAR Guidelines.
|-
|April 2020
|Edits and acknowledgements added by Feed & Gas WG.
|-
|April 2020
|File paginated according the agreed template.
|-
|May 2020
|Approved by ICAR Board on 26th May subject to addition of disclaimer. Disclaimer added as new chapter 2 - the fact specific device manufacturers are mentioned in these guidelines is in no way an endorsement of the devices or their accuracy by ICAR.
|-
|March 2024
|Moove of Feed intake for genetic evaluation guidelines to ICAR WIKI format.
|}

Section 19 – Recording Feed Intake for Genetic Evaluation

2024-02-04T12:36:44Z

Rstephansen:

Section 19 – Recording Feed Intake for Genetic Evaluation

2024-02-04T12:32:13Z

Rstephansen:

== Introduction ==
The widespread use of genomic information in dairy cattle breeding programs has presented the opportunity to select for feed intake and feed efficiency. This is because animals from research herds can be used as a reference population to calibrate a genomic prediction equation, which is then used to predict the breeding values for selection candidates based on their own genotype. Phenotypes for feed intake, however, are still an important limiting factor for obtaining high accuracy breeding values and performing genetic analysis for feed intake. Based on the experiences gained through several initiatives, we provide guidelines on the recording and handling of feed intake records for genomic and genetic evaluations.

== 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.

== Definitions and Terminology ==
Table 1 contains a list of important definitions for terms and abbreviations used in these guidelines.
{| class="wikitable"
|+
! colspan="2" |''Table 1. Definitions of Terms used in these guidelines.''
|-
!Term
!Definition
|-
|DM
|Dry matter
|-
|DMI
|Dry matter intake
|-
|EID
|Electronic identification device
|-
|PMR
|Partly mixed ration
|-
|REML
|Restricted maximum likelihood
|-
|RFI
|Residual feed intake
|-
|RFID
|Radio-frequency identification
|-
|RIC
|Roughage intake control system
|}

== Scope ==
Figure 1 illustrates the main elements of this guideline. The numbers in this figure refer to the chapter numbers of this guideline. The scope is to give guidelines of recording feed intake for genetic and genomic evaluations.[[File:FeedIntakeFigure1.jpg |center|''Figure 1. Scope of Guidelines''|alt=Figure 1.|frame]]

=Sub-sections=
<blockquote>
<div style="column-count:2">
: [[Feed Intake: Definitions, Terminology, and Scope | Definitions, Terminology, and Scope]]
: [[Feed Intake: Utilizing existing feed intake data | Utilizing existing feed intake data]]
: [[Feed Intake: Setting up optimal data recording | Setting up optimal data recording]]
: [[Feed Intake: Recording intake indoors and at pasture | Recording intake indoors and at pasture]]
: [[Feed Intake: Calibration | Calibration]]
: [[Feed Intake: Chemical analysis | Chemical analysis]]
: [[Feed Intake: Number animals per bin | Number animals per bin]]
: [[Feed Intake: Additional recording | Additional recording]]
: [[Feed Intake: Bulls, cows and young stock | Bulls, cows and young stock]]
: [[Feed Intake: Lactation period | Lactation period]]
: [[Feed Intake: Feeding system | Feeding system]]
: [[Feed Intake: Genotyping and imputation | Genotyping and imputation]]
: [[Feed Intake: Merging and sharing data in genetic evaluations | Merging and sharing data in genetic evaluations]]
: [[Feed Intake: Proxies for feed intake | Proxies for feed intake]]
: [[Feed Intake: Feed efficiency in the breeding goal | Feed efficiency in the breeding goal]]
: [[Feed Intake: Acknowledgements | Acknowledgements]]
: [[Feed Intake: Appendix A: Standard Operating Procedure Nottingham | Appendix A: Standard Operating Procedure Nottingham]]
: [[Feed Intake: Appendix B Alkane Feed intake technique Piacenza, Italy | Appendix B Alkane Feed intake technique Piacenza, Italy ]]
</div>
</blockquote>

== Summary of Changes ==
{| class="wikitable"
|+
!Date of Change
!Nature of Change
|-
|March 2020
|Draft from Feed & Gas WG put into standard template for ICAR Guidelines.
|-
|April 2020
|Edits and acknowledgements added by Feed & Gas WG.
|-
|April 2020
|File paginated according the agreed template.
|-
|May 2020
|Approved by ICAR Board on 26th May subject to addition of disclaimer. Disclaimer added as new chapter 2 - the fact specific device manufacturers are mentioned in these guidelines is in no way an endorsement of the devices or their accuracy by ICAR.
|}

Section 19 – Recording Feed Intake for Genetic Evaluation

2024-01-23T20:00:48Z

Rstephansen: /* Animal groups (Bulls, young stock and cows) */

== Summary of Changes ==
{| class="wikitable"
|+
!Date of Change
!Nature of Change
|-
|March 2020
|Draft from Feed & Gas WG put into standard template for ICAR Guidelines.
|-
|April 2020
|Edits and acknowledgements added by Feed & Gas WG.
|-
|April 2020
|File paginated according the agreed template.
|-
|May 2020
|Approved by ICAR Board on 26th May subject to addition of disclaimer. Disclaimer added as new chapter 2 - the fact specific device manufacturers are mentioned in these guidelines is in no way an endorsement of the devices or their accuracy by ICAR.
|}

== Introduction ==
The widespread use of genomic information in dairy cattle breeding programs has presented the opportunity to select for feed intake and feed efficiency. This is because animals from research herds can be used as a reference population to calibrate a genomic prediction equation, which is then used to predict the breeding values for selection candidates based on their own genotype. Phenotypes for feed intake, however, are still an important limiting factor for obtaining high accuracy breeding values and performing genetic analysis for feed intake. Based on the experiences gained through several initiatives, we provide guidelines on the recording and handling of feed intake records for genomic and genetic evaluations.

== 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.

== Definitions and Terminology ==
Table 1 contains a list of important definitions for terms and abbreviations used in these guidelines.
{| class="wikitable"
|+
! colspan="2" |''Table 1. Definitions of Terms used in these guidelines.''
|-
!Term
!Definition
|-
|DM
|Dry matter
|-
|DMI
|Dry matter intake
|-
|EID
|Electronic identification device
|-
|PMR
|Partly mixed ration
|-
|REML
|Restricted maximum likelihood
|-
|RFI
|Residual feed intake
|-
|RFID
|Radio-frequency identification
|-
|RIC
|Roughage intake control system
|}

== Scope ==
Figure 1 illustrates the main elements of this guideline. The numbers in this figure refer to the chapter numbers of this guideline. The scope is to give guidelines of recording feed intake for genetic and genomic evaluations.[[File:FeedIntakeFigure1.jpg |center|''Figure 1. Scope of Guidelines''|alt=Figure 1.|frame]]

== Utilizing existing Feed Intake data ==
Worldwide, there are relatively few designed experiments specifically suited for genetic analysis of feed intake (and related traits). Examples are, the long term experiment at the Scottish Agricultural College (now SRUC) Dairy Research Centre based at Langhill herd, Edinburgh<ref>Pryce, J.E., Nielsen, B.L., Veerkamp, R.F., and Simm, G. 1999. Genotype and feeding system effects and interactions for health and fertility traits in dairy cattle. Livest. Prod. Sci. 57:193-201.</ref> <ref>Veerkamp, R.F. 1996. Liveweight and feed intake in dairy cattle breeding. INTERBULL BULLETIN. 1996(12).</ref> the experiment at the Dutch farm ‘t Gen (Lelystad, the Netherlands) <ref>Veerkamp, R. F., Oldenbroek, J.K., Van Der Gaast, H.J., and VanDer Werf, J.H.J. 2000. Genetic Correlation Between Days Until Start of Luteal Activity and Milk Yield, Energy Balance, and Live Weights. J. Dairy Sci. 83: 577–583.</ref>, the data collection at the dairy research farm Karkendamm of the Christian-Albrechts-University Kiel in northern Germany , or more recently data collected on young heifers in Australia<ref>Williams, Y., Pryce, J., Grainger, C., Wales, W., Linden, N., Porker, M., and Hayes, B. 2011. Variation in residual feed intake in holstein-friesian dairy heifers in southern australia. J. Dairy Sci. 94:4715-4725.</ref> and New Zealand<ref>Waghorn, G., Macdonald, K., Williams, Y., Davis, S., and Spelman, R. 2012. Measuring residual feed intake in dairy heifers fed an alfalfa (medicago sativa) cube diet. J. Dairy Sci. 95:1462-1471.</ref>. The common denominator across these studies is that approximately 1000 animals were recorded, that were fed a total mixed ration (TMR) diet ad libitum, and the dairy cows were recorded from the start of lactation up to a fixed point in lactation (10, 26 or 38 weeks).

The designed studies are too small for a reference population using genomic prediction, and therefore in many countries’ additional sources of feed intake records have been added. For example, from nutritional experiments <ref name="Templeman">Tempelman, R.J., Spurlock, D.M., Coffe,y M., Veerkamp, R.F., Armentano, L.E., Weigel, K.A., De Haas, Y., Staples, C.R., Connor, E.E., Lu, Y., and Vandehaar, M.J. 2015. Heterogeneity in genetic and nongenetic variation and energy sink relationships for residual feed intake across research stations and countries. J. Dairy Sci. 98:2013-2026.</ref><ref>Veerkamp, R.F., Calus, M.P.L., De Jong, G., Van Der Linde, R., and De Haas, Y.. 2014. Breeding value for dry matter intake for dutch bulls based on dgv for dmi and bv for predictors. in Proc. Proceedings of the 10th World congress of genetics applied to livestock production, Vacouver, Canada.</ref>, consortia have been formed that combined data across countries<ref>Banos, G., Coffey, M.P., Veerkam,p R.F., Berry, D.P., and Wall, E. 2012. Merging and characterising phenotypic data on conventional and rare traits from dairy cattle experimental resources in three countries. Animal 6:1040-1048.</ref><ref>Berry, D.P., Coffey, M.P., Pryce, J.E., De Haas, Y., Lovendahl, P., Krattenmacher N., Crowley, J.J., Wang, Z., Spurlock, D., Weigel K., Macdonald, K., and Veerkamp, R.F. 2014. International genetic evaluations for feed intake in dairy cattle through the collation of data from multiple sources. J. Dairy Sci. 97:3894-3905.</ref><ref>De Haas, Y., Calus, M.P.L., Veerkam,p R.F., Wall, E., Coffey, M.P., Daetwyler, H.D., Hayes, B.J., and Pryce, J.E. 2012. Improved accuracy of genomic prediction for dry matter intake of dairy cattle from combined european and australian data sets. J. Dairy Sci. 95:6103-6112.</ref><ref>Pryce, J.E., Arias, J., Bowman, P.J., Davis, S.R., Macdonald, K.A., Waghorn, G.C., Wales, W.J., Williams, Y.J., Spelman, R.J., and Hayes, B.J. 2012. Accuracy of genomic predictions of residual feed intake and 250-day body weight in growing heifers using 625,000 single nucleotide polymorphism markers. J. Dairy Sci. 95:2108-2119.</ref>, or utilising genomic information from beef breeds for dairy cattle<ref>Khansefid, M., Pryce, J.E., Bolormaa, S., Miller, S.P., Wang, Z., Li, C., and Goddard, M.E. 2014. Estimation of genomic breeding values for residual feed intake in a multibreed cattle population. J. Anim. Sci. 92:3270-3283.</ref>. Combining all these types of data is an attractive and cost-effective way of increasing the reference population, but at the same time the data becomes more heterogeneous in many aspects. For example, the recording period during lactation might be different, repeated records within and across lactation might be available or not, and feeding systems might be different, especially across nutritional experiments. This heterogeneous data collection directed attention to a statistical “use what we have”-approach rather than attention to design of the most optimal recording of feed intake. This is further described in chapter 15 Genotyping and imputation.
== Setting up Optimal data Recording ==
When starting to collect feed intake records, an important question is how to optimize the “number of gate-days per year” by “number of cows x recording period per cow”? The number of cows can be established by the theoretical prediction from Daetwyler et al. The actual accuracy obtained with cross validation when records were used within country follows this pattern closely (Figure 2). In this example, empirical accuracies were slightly higher than theoretical, likely because validation and training animals were closely related. Overall it is clear that thousands of cows need to be recorded to obtain accurate prediction equations.

== Recording intake indoors and at pasture ==
The minimal requirement for recording individual feed intake is the amount of fresh feed offered and refused per cow per day, with the associated dry matter percentage. Direct measurement of stalled cows is straightforward, but contamination of refused feed by drinking water must be prevented, and stalled cows may not behave in the same way as cows in freestalls. Also, measurement of concentrates is relatively easy, since concentrate are often dispersed in limited and fixed amounts by the farmer or an automated system. Measuring the ad libitum intake of roughage or total mixed rations (TMR) is more complicated. Automated systems have been developed by Calan Broadbent (American Calan Inc. Northwood, NH, USA), Gallagher Animal Management Systems (Hamilton, New Zealand), GrowSafe 4000 System (GrowSafe Systems, Ltd., Airdrie, AB, Canada), the RIC-system (Insentec B.V., Marknesse, The Netherlands), the CRFI (BioControl, Technology for biology), SmartFeed (C-lock Inc., Rapid City, SD, USA). Each system has unique features along with general features like opening a gate and weighing feed in a bin.

With the Calan Broadbent Feeding Gate, animals access food via ‘electronic Calan gates’, with each gate allowing access to a feed box mounted on a weigh scale and linked to an automatic cow identification system (Griffith Elder; Bury St Edmunds, UK). Opening of the Calan gates is controlled via a transponder mounted on a neck collar.

With the Gallagher Animal Management Systems, feed intake units are hard-wired to data loggers, which poll each electronic identification reader and weight read out indicator every second to determine if an animal was present, its identification, and the weight of the feed bin at that particular time point (Williams et al., 2011).

In the Growsafe 4000 System, each bunk is equipped with an antenna to detect animal presence at the feed bunk, load cells to measure the eaten feed, a stanchion equipped with neck bars to allow only one animal to enter the feed bunk at a time, and data acquisition software (GrowSafe DAQ; v. 9.25). This records all the feeding behavior and intake data. The GrowSafe system was designed to monitor feeding behavior by continuously recording the presence of an animal at the feed bunk once an EID crosses the neck bars of the feed bunk stanchion. Concurrently, the electronic system measures individual feed intake by continuously weighing feed during each bunk visit. These data (EID number, bunk number, time stamp of each transponder recording, and scale weight) are continuously recorded via wireless transfer to the data-acquisition computer.

The RIC system consists of intelligent feeding in combination with so-called RIC feed-weigh troughs, where feed is accurately weighed continously. This version enables restricted and unrestricted (ad-lib) feeding. If an animal approaches one of the feeding troughs, it is identified. Then the animal number and starting time of the visit are recorded. After the visit, the end time and amount of feed intake is recorded. The amount of roughage intake is electronically measured by the RIC weighing trough and stored in the RIC database (Hokofarm Group, n.d.).

CRFI gives individual cows access to specific mangers. The mangers are placed on weighing cells that measure the weight of the manger before and after cow access. This weight difference is transferred to the central computer for analysis. Cows can be identified by ear tags or neck transponders. The cows will be let in, after identification by a gate that goes down. After a specific time set for feeding, the access gate rises and pushes the cow away from the manger. There are versions available without the access gate, a cow weighscale can be added, and a lid for the manger is available. The manger is easy to clean because it can tilt (BioControl, n.d.). Please note that these features are available for RIC feeders as well.

SmartFeed is a portable, self-contained system designed to measure total daily feed intake from individual large animals. The SmartFeed system includes an RFID reader, weigh scales, and feed bin which continuously logs data to determine the feed intake per visit per animal. The system calculates the feed intake in real-time within each SmartFeed system. They are different versions, one is for pasture

Bloch et al. argued that present measurement methods do not allow to determine individual animal feed efficiency, and that the existing nutrition models could only be used for group-wise feed intake prediction. They developed model systems for individual animal feed efficiency using measurements of behavior and production sensors combined with a mathematical model with accuracies up to R2=0.93. The method is described in a patent (PCT/L2014/051071).

In each case, consideration must be given to minimize the amount of feed wasted or stolen. Sorting of feeds should also be minimized, especially in systems that use multiple cows per feeding station. All animals must have adequate space and time to eat an intake that is truly ad libitum and does not cause alterations in feeding behaviour.

Measurement of dry matter intake (DMI) at pasture can be difficult. As animals graze in a herd or group it can be particularly challenging to obtain individual estimates of DMI. It is somewhat easier to obtain group dry matter intakes, although these can be crude and prone to error. Genetic analyses rely on comparison of animals within the same group, so therefore group estimates are not particularly useful. The n-alkane technique is one method by which individual cow DM intake can be estimated. The technique requires animals to be dosed twice daily with a synthetic even-chain length n-alkane, at a known amount perday, adjacent in chain length to a naturally occurring odd-chain length n-alkane present in the herbage consumed. The best estimates of intake have been typically achieved with the n-alkane pair C33 and C32, as a result of similar faecal n-alkane recovery rates. Wright et al. recently validated the accuracy of the n-alkane technique compared to measurement of the quantity of grass offered compared to that refused on a daily basis and found the n-alkane technique was a good estimate of dry matter intake. There are a number of factors however which are critical for this technique. They are:

* homogenous perennial ryegrass swards,
* good herbage sampling technique (i.e. representative of what the cow actually ate paying particular attention to post-grazing height),
* no contamination of faecal samples (i.e. no urine, grass, soil, faeces from another cow)
* representative bulking of samples collected over a six-day measurement period
* extreme attention to detail in the lab when extracting the alkanes

== Additional Recording ==
In addition to recording feed intake, it seems obvious that cow identification and pedigree must be recorded for genetic analysis. However, nutritional experiments often use their own anonymous local identification number for cows, which makes it hard to trace the cow back to a national pedigree register. Ideally a DNA, or a hair or blood sample, should be collected from each cow to allow subsequent genotyping.

Other traits that should be considered for weekly recording are milk yield and composition, liveweight, and body condition score. These energy sink traits can be especially useful for feed intake records that are of short duration and might be related to temporary production records that are not representative of the whole lactation. The utilisation of feed intake records determines which traits to record. For example, for national breeding values for AI bulls, there is little extra information in recording milk yield on this limited number of cows. On growing bulls and heifers in Italy also weekly height and chest width is measured. However, to establish the genetic correlation between yield and feed intake, it might be very useful to collect these extra records, or when a genomic prediction is to be developed for residual feed intake (RFI). For RFI, recording the energy sink traits is essential. Health and fertility traits should also be considered for examining relationships with both intake and production. Ration composition might be useful for understanding Genotype x Diet interactions or calculating energy or protein efficiency. As mentioned, the choice depends on the purpose of recording. However, sometimes extra records are recorded as insurance: records might be important in the future and, compared with recording feed intake, the costs are relatively small.

== Bulls, young stock and cows ==
Recording on milking cows might be difficult, and practically it might be easier to record feed intake on young stock or bulls. Genetic correlations of these non-lactating animals with lactating animals were above 0.74 indicating that more than 50% of the genetic variation among lactating animals can be observed in the non-lactating animals. In Australia and New Zealand, RFI was calculated for growing heifers. The extremes (top and bottom 10%) were retained for a lactating cow experiment, where it was shown that divergence for RFI was maintained (<0.01).

When feed intake records are available on bulls and cows, these can be combined in one reference population. Hence, there is potential in recording the number of records combining cow and bull reference populations. There is still little information on the optimal design for recording feed intake, and genetic parameters are likely not yet accurate enough, to do precise index calculations.

== Lactation period ==
Ideally, feed intake should be measured across the lifetime of an animal. This is not realistic and often only short periods of recording are available. In lactating animals, it might be of particular importance to consider the period that feed intake is measured. Cows might compensate a more negative energy balance in early lactation by a higher intake during late lactation. Hence, biological feed intake might be a different trait during different parts of the lactation, which is supported by the relatively low genetic correlation between DMI during early and late lactation. One way to overcome this bias due to tissue mobilisation might be to use RFI, which is adjusted for energy sinks. Hence, in terms of improving feed efficiency across the whole lactation, it seems necessary to have feed intake records available during all stages of lactation, rather than focus collection on the first part of the lactation alone, as most designed experiment have done.

The question on when to record feed intake can also be approached from the quantitative genetics perspective using selection index methodology. Genetic parameters can be used to estimate the accuracy of DMI breeding values across the whole lactation, when only part of the lactation is recorded. These authors concluded that recording DMI for 15 weeks gave an accuracy of 0.58, which was on average 0.25 more accurate than recording DMI for 5 weeks, and 0.11 more accurate than recording DMI for 10 weeks. Also, starting to record DMI in mid or late lactation gave more accurate estimates for predicting lactation DMI than starting recording in early lactation. Still, more reliable estimates for genetic correlations between feed intake measurements across lactation are required to define more precisely when to record DMI.

If feed intake is measured as part of a comprehensive analysis of feed efficiency examining all energy sinks and calculating RFI, then the time and duration to record feed intake can be shortened and conducted earlier in lactation. Connor et al. found that correlations for weekly RFI across weeks, even as early as 4 wk, were high (r=0.8) and that the heritability of RFI was similar (h2=0.45) whether only the first 50 DIM or the first 100 DIM were considered; heritability for earlier assessments of RFI were lower.

== Feeding system ==
The diet fed to the cows should be balanced to meet requirements for energy, protein, minerals, and vitamins, and feeding a well-mixed TMR using silages or other wet feeds is likely to minimize sorting. If the feed is dry, such as a dry cubed feed, it can be important to measure the %DM in the refused feed separately.

Common practice is that the diet is fed ad libitum, but there is little known about the effects of not doing so. The diet should be comparable to that fed to commercial herds, representative of the average for a country. This is not always easy for a diverse country like the USA, where diets may differ markedly by region. RFI might then be useful, as this can be calculated within contemporary group which makes the composition of the diet less important. A high correlation of RFI was obtained when a cow was fed a high starch or low starch diet. However, it is clear that feeding a diet formulated according to level of production leads to bias; this would unfairly result in feeding a more digestible diet to the high producers and also might increase the correlation between DMI and milk yield. In some countries, in nutrition trials cows get a partly mixed ration (PMR) instead of TMR and are individually supplemented with concentrates according to their needs (and milk production). Perhaps the most important criterion is that diet must be the same for all cows that are in the same contemporary group, and contemporary groups should be sufficiently large (> 5 animals) and genetically connected through common (grand-)sires.

== Genotyping and Imputation ==
One of the issues with combining data from research herds for genomic analysis is that different SNP chips may be used for each experiment. Fortunately, most of the commercially available SNP chips have many SNP in common, so a set of common SNPs can generally be identified. Another option is to impute genotypes from a low density to a higher one (e.g. from 50K to 777K (HD)), which may be advantageous for some (Bayesian) approaches for genomic prediction. This strategy relies on a reference dataset of bulls or cows that are genotyped at high density and that have some genetic ties (i.e. haplotypes in common) with the animals in the dataset that is to be imputed. Pryce et al. showed that it was possible to impute the genotypes of research herd animals from 50K to HD using two independent reference datasets with high concordance. For animals with historical feed intake records, but no DNA information, imputation of the genotypes might be considered when offspring are genotyped.

== Merging and sharing data in genetic evaluations ==
To increase the accuracy, it is important that feed intake records are collected on animals as closely related to the selection candidates as possible. A large genetic distance between historical data and current selection candidates is therefore a disadvantage. Also, the genetic connectedness between contemporary groups, through common sires, is important to separate the permanent environmental and genetic effects in the data. With many small, disconnected nutritional experiments, this could be a problem.

One aspect when considering suitable statistical approaches is dealing with the different recording periods within and across parity. Nutritional experiments are often done on second or later parity animals, and data collection might focus on the transition period (early lactation) or mid to late lactation. Experiments might be short (a month) or several months. Hence, a solution is required to standardize the data. One solution may be to standardise the records to one DMI record for each cow, and to use that one record in subsequent genetic analysis. The one trait could be standardised based on a random regression model prediction for a cow, based on the (repeated) records collected during different parties and the covariance structure found in the population, or the one trait could be based on the phenotypic records available for a cow in a standardised time, e.g. first 28-d period between 50 and 205 days. An alternative to standardising to one trait is to utilise all available feed intake records in the genetic analyses accounting for no genetic permanent cow effects by using a fixed regressions test-day model.

A second important aspect when considering statistical approaches is to account for differences in the mean and variance of DMI. The most common method to account for mean differences in genetic analysis is to perform the analysis within contemporary group: comparing daughters of different sires within a group of herd mates that receive the same treatment. Well established REML techniques are common practice for this. Traditionally, contemporary groups are based on treatment and season of calving, however, feeding treatments within studies change over time, and rations might also change over time (i.e., all cows on a given day get silage out of the same silage pit, independent of calving date). Therefore it is often wise to adjust for time-dependent contemporary groups. Also, differences between animals might be larger due to experimental treatment, herd, diet or lactation stage of recording. For this reason, often heterogeneous residual variances across treatments, or herds are fitted. Tempelman et al. demonstrated that care should be taken to allow for different relationships between DMI and for example, yield or live weight across environments.

A third important aspect when considering statistical approaches is to assume that trait definitions vary across countries, and therefore fitting a multitrait model allowing for non-unity genetic correlations between countries. Although this might appear obvious, this is only possible when each country has enough data to estimate the genetic correlation with reasonable precision. Ideally this requires common sires between the environments, but using genomic relationships assists in establishing genetic links between the countries.

Altogether, a reasonable amount of statistics is required to merge DMI data and perform subsequent genetic analysis; however, the common experience is generally positive. Genetic correlations between countries are relatively large and genomic predictions across countries have higher reliabilities than using a smaller within country dataset.

== Proxies for Feed Intake ==
Regardless of international collaboration, recording of feed intake will always be a limiting factor for accurate breeding values. Therefore, there is an interest in using predictors to improve the accuracy of the breeding values. DMI might be predicted by the yield traits with reasonable accuracy. However, this predicted DMI can never be used to identify genetic variation in feed efficiency as all variation between animals is due to difference in the milk yield traits. So DMI data are always required to select for improved feed utilisation.
Next to yield, an obvious group of predictor traits are the conformation traits; chest width, stature, body depth, and angularity which help to predict live weight, and therefore provide a good predictor for estimating feed required for maintenance. Other potential predictors are:

* Body condition score (BCS) to indicate levels of body fat and protein
* Activity data can be used to account for variation in maintenance,
* Thermal infra-red cameras can be used to collect heat measures (related to DMI)
* MIR analysis of milk samples performed regularly by milk recording agencies have been shown to be correlated to energy balance, which is mathematically related to RFI (McParland et al., 2011)
* Changes in eating pattern is informative to predict disease. Eating pattern might also be affected by social hierarchy.

== Feed efficiency in the breeding goal ==
At first sight, the inclusion of feed intake or efficiency in the breeding goal may seem a relatively simple matter. The goal is more milk with less feed. Several factors, however, complicate the inclusion of feed intake or efficiency in a balanced breeding goal.

Life-time feed efficiency, as well as including milk performance and feed intake, must also consider longevity, reproductive performance, days dry, and body weight when slaughtered (income from beef). This suggests that efficiency must be quantified at the production system level and so it might be more complex than feed efficiency at a single cow level. However, in the short term it was assumed for the discussion that a small change at the cow level will contribute to efficiency of the whole system.

More milk (i.e., output) per kg feed intake (i.e., input) suggests that feed efficiency should be presented as a ratio of input and output, i.e., gross feed efficiency or feed conversion ratio. Such traits appeal to producers since they appear easily interpretable, but ratio traits have several disadvantages in animal breeding. Gross feed efficiency favours animals with high output, because maintenance costs are diluted. Even worse, increasing gross efficiency does not necessarily favour more efficient feed conversion towards milk. For these reasons the outcome of the discussion of gDMI was that the ratio traits, feed conversion or gross feed efficiency, could be presented as a stand-alone trait because of its appeal. However, its direct inclusion in the overall breeding goal could be complicated and may be best represented as a linearised expression of the ratio. Then there are two options, which in an ideal world should result in a similar outcome.

The first option is to calculate residual feed intake (RFI) for all animals that have feed intake records. RFI is the measured feed intake minus the expected feed intake for milk production, growth (including body tissue mobilisation) and maintenance (as well as other energy sinks if data are available) based on feed requirement equations. RFI is popular in growing cattle and is probably the closest approximation of net feed efficiency at a population level for genetic/genomic evaluations. However, RFI is made to be independent of milk production and maintenance costs. Hence, these feed costs for yield, growth and maintenance should ideally also be considered in the breeding goal as traits in themselves with their respective economic values. This complexity may result in a negative economic value on body size which may affect producer acceptance. Nonetheless a negative economic value for body size is already adopted and accepted by producers in New Zealand, Ireland, and in the US ($NM - by means of reduced Stature).

The second option for linearized inclusion of feed efficiency in the breeding goal is not to predict the breeding value for RFI, but to predict the breeding value for DMI itself; this would need to be undertaken in any case if RFI is to be defined at the genetic level. The breeding goal can then be defined as the milk returns, minus the cost of DMI. Since DMI automatically includes the feed consumed for growth, maintenance and production, there is no need to separately account for the cost of differently sized animals or differences in for example the fat:protein ratio in the milk. Subsequent inclusion of body size in the breeding goal can be left to the desired outcome of the index.

It can be a lengthy discussion choosing between these two approaches and both have their own advantages and disadvantages and many different definitions have been applied (Table 5). Both approaches however are a linear combination of the same traits, and therefore are expected to yield the same result (Kennedy, 1993). When selecting animals based on RFI it is certain that animals with a negative RFI eat less than expected based on their outputs (assuming the definition of RFI is correct). Feed efficient animals are more difficult to identify if DMI itself is used. Also, computation of RFI is more flexible (e.g., relationship between DMI and production or maintenance may be non-linear), but must be properly modelled (i.e., cognisance must be taken of the contribution of body tissue mobilisation to energy kinetics). However, RFI may be more difficult to understand as it is currently not clear what exactly RFI is and whether it is simply an accumulation of variance associated with an inaccurate statistical model. RFI might depend on lactation stage, and so within parity the correlations among, and the contributions of, the components to RFI may change. Therefore, if you use one set of parameters, RFI may not be calculated correctly. Also, RFI breeding values will be based on a small reference population, whereas alongside DMI, body size and yield can be used as predictor traits, as well as other potential predictors like milk Mid Infra-Red (MIR) (McParland et al., 2011). RFI, however, is essentially a sub-index and there is already a precedence of decomposing total merit indexes into sub-indexes. Using DMI makes the index more amenable to individual herd customisation of the index by altering the economic value on feed costs for that farm. Wulfhorst et al. (2010) concluded that RFI is a difficult concept and therefore including feed intake directly in breeding objectives may avoid confusion among the end-users.

One compromise that has currently be chosen in most countries is the feed saved definition (Australia and the Netherlands). This allows for the extra expected intake due to an increase in milk yield, but not for extra intake for maintenance or residual feed intake.

== Acknowledgements ==
Authors of the document are, in alphabetical order:

Maria Frizzarin, Teagasc, Ireland

Birgit Gredler-Grandl, Wageningen Livestock Research, Netherlands

Jan Lassen, VikingGenetics & Aarhus University, Denmark

Rasmus Bak Stephansen, Aarhus University, Denmark

Karoline Bakke Wethal, Geno, Norway

=Sub-sections=
<blockquote>
<div style="column-count:2">
: [[Feed Intake: Definitions, Terminology, and Scope | Definitions, Terminology, and Scope]]
: [[Feed Intake: Utilizing existing feed intake data | Utilizing existing feed intake data]]
: [[Feed Intake: Setting up optimal data recording | Setting up optimal data recording]]
: [[Feed Intake: Recording intake indoors and at pasture | Recording intake indoors and at pasture]]
: [[Feed Intake: Calibration | Calibration]]
: [[Feed Intake: Chemical analysis | Chemical analysis]]
: [[Feed Intake: Number animals per bin | Number animals per bin]]
: [[Feed Intake: Additional recording | Additional recording]]
: [[Feed Intake: Bulls, cows and young stock | Bulls, cows and young stock]]
: [[Feed Intake: Lactation period | Lactation period]]
: [[Feed Intake: Feeding system | Feeding system]]
: [[Feed Intake: Genotyping and imputation | Genotyping and imputation]]
: [[Feed Intake: Merging and sharing data in genetic evaluations | Merging and sharing data in genetic evaluations]]
: [[Feed Intake: Proxies for feed intake | Proxies for feed intake]]
: [[Feed Intake: Feed efficiency in the breeding goal | Feed efficiency in the breeding goal]]
: [[Feed Intake: Acknowledgements | Acknowledgements]]
: [[Feed Intake: Appendix A: Standard Operating Procedure Nottingham | Appendix A: Standard Operating Procedure Nottingham]]
: [[Feed Intake: Appendix B Alkane Feed intake technique Piacenza, Italy | Appendix B Alkane Feed intake technique Piacenza, Italy ]]
</div>
</blockquote>

Section 19 – Recording Feed Intake for Genetic Evaluation

2024-01-23T19:56:40Z

Rstephansen: /* Animal groups (Bulls, young stock and cows) */

Section 19 – Recording Feed Intake for Genetic Evaluation

2024-01-23T19:56:13Z

Rstephansen: /* Additional Recording */

Section 19 – Recording Feed Intake for Genetic Evaluation

2024-01-23T19:55:50Z

Rstephansen: /* Additional Recording */

Section 19 – Recording Feed Intake for Genetic Evaluation

2024-01-23T19:55:06Z

Rstephansen: Update mooving to Wiki

Section 19 – Recording Feed Intake for Genetic Evaluation

2024-01-23T13:06:35Z

Rstephansen: test reference

== Introduction ==
The widespread use of genomic information in dairy cattle breeding programs has presented the opportunity to select for feed intake and feed efficiency. This is because animals from research herds can be used as a reference population to calibrate a genomic prediction equation, which is then used to predict the breeding values for selection candidates based on their own genotype. Phenotypes for feed intake, however, are still an important limiting factor for obtaining high accuracy breeding values and performing genetic analysis for feed intake. Based on the experiences gained through several initiatives, we provide guidelines on the recording and handling of feed intake records for genomic and genetic evaluations.

== 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 ==
The scope is to provide guidelines for recording of feed intake for genetic and genomic evaluations of feed intake and/or feed efficiency. Figure 1 illustrates the main elements of the guidelines and a detailed description can be found below.

== Setting up feed intake recording schemes ==
Before starting collection of feed intake records, important questions to address are how to optimize the data collection in terms of, should we collect full lactations or periods of the lactation? number of cows? The trade-off is typically the capacity of the recording system, in order to maximize the value of the collected data for genetic evaluation. An expected effect for the number of cows can be established by the theoretical prediction from Daetwyler et al. (2008)<ref>Daetwyler, H.D., Villanueva, B., and Woolliams, J.A. 2008. Accuracy of predicting the genetic risk of disease using a genome-wide approach. PLoS One. 2008 3(10).</ref>. The actual accuracy obtained with cross validation when records were used within country follows this pattern closely (Figure 2). In this example, empirical accuracies were slightly higher than theoretical, likely because validation and training animals were closely related. Overall it is clear that thousands of cows need to be recorded to obtain accurate prediction equations, as seen for example for milk production at ~0.80 accuracy (Oliveira et al.,2019).

== Recording Feed Intake ==
Text

=== Animal groups (Bulls, young stock and cows) ===
Text

=== Feeding system ===
Text

== Utilizing existing Feed Intake data ==
Text

== Important Records besides Feed Intake ==
Text

=== Going from intake to efficiency ===
Text

=== Phenotypes ===
Text

=== Genotyping ===
Text

== Proxies for Feed Intake ==
Text

== Quality control of Feed Intake data ==
Text

=== Visit level quality control of data ===
Text

=== Daily/weekly quality control of data ===
Text

== Feed efficiency in the breeding goal ==
Text

== Acknowledgements ==
Authors of the document are, in alphabetical order:

Maria Frizzarin, Teagasc, Ireland

Birgit Gredler-Grandl, Wageningen Livestock Research, Netherlands

Jan Lassen, VikingGenetics & Aarhus University, Denmark

Rasmus Bak Stephansen, Aarhus University, Denmark

Karoline Bakke Wethal, Geno, Norway

=Sub-sections=
<blockquote>
<div style="column-count:2">
: [[Feed Intake: Definitions, Terminology, and Scope | Definitions, Terminology, and Scope]]
: [[Feed Intake: Utilizing existing feed intake data | Utilizing existing feed intake data]]
: [[Feed Intake: Setting up optimal data recording | Setting up optimal data recording]]
: [[Feed Intake: Recording intake indoors and at pasture | Recording intake indoors and at pasture]]
: [[Feed Intake: Calibration | Calibration]]
: [[Feed Intake: Chemical analysis | Chemical analysis]]
: [[Feed Intake: Number animals per bin | Number animals per bin]]
: [[Feed Intake: Additional recording | Additional recording]]
: [[Feed Intake: Bulls, cows and young stock | Bulls, cows and young stock]]
: [[Feed Intake: Lactation period | Lactation period]]
: [[Feed Intake: Feeding system | Feeding system]]
: [[Feed Intake: Genotyping and imputation | Genotyping and imputation]]
: [[Feed Intake: Merging and sharing data in genetic evaluations | Merging and sharing data in genetic evaluations]]
: [[Feed Intake: Proxies for feed intake | Proxies for feed intake]]
: [[Feed Intake: Feed efficiency in the breeding goal | Feed efficiency in the breeding goal]]
: [[Feed Intake: Acknowledgements | Acknowledgements]]
: [[Feed Intake: Appendix A: Standard Operating Procedure Nottingham | Appendix A: Standard Operating Procedure Nottingham]]
: [[Feed Intake: Appendix B Alkane Feed intake technique Piacenza, Italy | Appendix B Alkane Feed intake technique Piacenza, Italy ]]
</div>
</blockquote>

Section 19 – Recording Feed Intake for Genetic Evaluation

2024-01-23T13:00:39Z

Rstephansen: Insert section titles

== Introduction ==
The widespread use of genomic information in dairy cattle breeding programs has presented the opportunity to select for feed intake and feed efficiency. This is because animals from research herds can be used as a reference population to calibrate a genomic prediction equation, which is then used to predict the breeding values for selection candidates based on their own genotype. Phenotypes for feed intake, however, are still an important limiting factor for obtaining high accuracy breeding values and performing genetic analysis for feed intake. Based on the experiences gained through several initiatives, we provide guidelines on the recording and handling of feed intake records for genomic and genetic evaluations.

== 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 ==
The scope is to provide guidelines for recording of feed intake for genetic and genomic evaluations of feed intake and/or feed efficiency. Figure 1 illustrates the main elements of the guidelines and a detailed description can be found below.

== Setting up feed intake recording schemes ==
Before starting collection of feed intake records, important questions to address are how to optimize the data collection in terms of, should we collect full lactations or periods of the lactation? number of cows? The trade-off is typically the capacity of the recording system, in order to maximize the value of the collected data for genetic evaluation. An expected effect for the number of cows can be established by the theoretical prediction from Daetwyler et al. (2008). The actual accuracy obtained with cross validation when records were used within country follows this pattern closely (Figure 2). In this example, empirical accuracies were slightly higher than theoretical, likely because validation and training animals were closely related. Overall it is clear that thousands of cows need to be recorded to obtain accurate prediction equations, as seen for example for milk production at ~0.80 accuracy (Oliveira et al.,2019).

== Recording Feed Intake ==
Text

=== Animal groups (Bulls, young stock and cows) ===
Text

=== Feeding system ===
Text

== Utilizing existing Feed Intake data ==
Text

== Important Records besides Feed Intake ==
Text

=== Going from intake to efficiency ===
Text

=== Phenotypes ===
Text

=== Genotyping ===
Text

== Proxies for Feed Intake ==
Text

== Quality control of Feed Intake data ==
Text

=== Visit level quality control of data ===
Text

=== Daily/weekly quality control of data ===
Text

== Feed efficiency in the breeding goal ==
Text

== Acknowledgements ==
Authors of the document are, in alphabetical order:

Maria Frizzarin, Teagasc, Ireland

Birgit Gredler-Grandl, Wageningen Livestock Research, Netherlands

Jan Lassen, VikingGenetics & Aarhus University, Denmark

Rasmus Bak Stephansen, Aarhus University, Denmark

Karoline Bakke Wethal, Geno, Norway

=Sub-sections=
<blockquote>
<div style="column-count:2">
: [[Feed Intake: Definitions, Terminology, and Scope | Definitions, Terminology, and Scope]]
: [[Feed Intake: Utilizing existing feed intake data | Utilizing existing feed intake data]]
: [[Feed Intake: Setting up optimal data recording | Setting up optimal data recording]]
: [[Feed Intake: Recording intake indoors and at pasture | Recording intake indoors and at pasture]]
: [[Feed Intake: Calibration | Calibration]]
: [[Feed Intake: Chemical analysis | Chemical analysis]]
: [[Feed Intake: Number animals per bin | Number animals per bin]]
: [[Feed Intake: Additional recording | Additional recording]]
: [[Feed Intake: Bulls, cows and young stock | Bulls, cows and young stock]]
: [[Feed Intake: Lactation period | Lactation period]]
: [[Feed Intake: Feeding system | Feeding system]]
: [[Feed Intake: Genotyping and imputation | Genotyping and imputation]]
: [[Feed Intake: Merging and sharing data in genetic evaluations | Merging and sharing data in genetic evaluations]]
: [[Feed Intake: Proxies for feed intake | Proxies for feed intake]]
: [[Feed Intake: Feed efficiency in the breeding goal | Feed efficiency in the breeding goal]]
: [[Feed Intake: Acknowledgements | Acknowledgements]]
: [[Feed Intake: Appendix A: Standard Operating Procedure Nottingham | Appendix A: Standard Operating Procedure Nottingham]]
: [[Feed Intake: Appendix B Alkane Feed intake technique Piacenza, Italy | Appendix B Alkane Feed intake technique Piacenza, Italy ]]
</div>
</blockquote>

Section 19 – Recording Feed Intake for Genetic Evaluation

2024-01-23T12:55:13Z

Rstephansen:

=== Introduction ===
The widespread use of genomic information in dairy cattle breeding programs has presented the opportunity to select for feed intake and feed efficiency. This is because animals from research herds can be used as a reference population to calibrate a genomic prediction equation, which is then used to predict the breeding values for selection candidates based on their own genotype. Phenotypes for feed intake, however, are still an important limiting factor for obtaining high accuracy breeding values and performing genetic analysis for feed intake. Based on the experiences gained through several initiatives, we provide guidelines on the recording and handling of feed intake records for genomic and genetic evaluations.

=== 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.

=Sub-sections=
<blockquote>
<div style="column-count:2">
: [[Feed Intake: Definitions, Terminology, and Scope | Definitions, Terminology, and Scope]]
: [[Feed Intake: Utilizing existing feed intake data | Utilizing existing feed intake data]]
: [[Feed Intake: Setting up optimal data recording | Setting up optimal data recording]]
: [[Feed Intake: Recording intake indoors and at pasture | Recording intake indoors and at pasture]]
: [[Feed Intake: Calibration | Calibration]]
: [[Feed Intake: Chemical analysis | Chemical analysis]]
: [[Feed Intake: Number animals per bin | Number animals per bin]]
: [[Feed Intake: Additional recording | Additional recording]]
: [[Feed Intake: Bulls, cows and young stock | Bulls, cows and young stock]]
: [[Feed Intake: Lactation period | Lactation period]]
: [[Feed Intake: Feeding system | Feeding system]]
: [[Feed Intake: Genotyping and imputation | Genotyping and imputation]]
: [[Feed Intake: Merging and sharing data in genetic evaluations | Merging and sharing data in genetic evaluations]]
: [[Feed Intake: Proxies for feed intake | Proxies for feed intake]]
: [[Feed Intake: Feed efficiency in the breeding goal | Feed efficiency in the breeding goal]]
: [[Feed Intake: Acknowledgements | Acknowledgements]]
: [[Feed Intake: Appendix A: Standard Operating Procedure Nottingham | Appendix A: Standard Operating Procedure Nottingham]]
: [[Feed Intake: Appendix B Alkane Feed intake technique Piacenza, Italy | Appendix B Alkane Feed intake technique Piacenza, Italy ]]
</div>
</blockquote>

Section 20 – Methane Emission for Genetic Evaluation

2023-12-18T08:35:03Z

Rstephansen:

[[Feed and Gas Working Group Members]]

[[Activity of Feed and Gas Working Group|Activity of the Feed and Gas Working Group]]

[[Definition and Terminology]]

[[Recording Dairy Cattle Methane Emission for Genetic Evaluation Guideline|Recording Dairy Cattle Methane Emission for Genetic Evaluation - Guideline]]

[[Recording Feed Intake for Genetic Evaluation - Guideline|Recording Feed Intake for Genetic Evaluation]] - Guideline

[[Greenfeed SOP]]

[[Sniffer SOP]]

Section 20 – Methane Emission for Genetic Evaluation

2023-12-18T08:29:36Z

Rstephansen: Inserted red link

Section 20 – Methane Emission for Genetic Evaluation

2023-12-18T07:27:19Z

Rstephansen: Inserted title for link to feed intake guidelined

[[Feed and Gas Working Group Members]]

[[Activity of Feed and Gas Working Group|Activity of the Feed and Gas Working Group]]

[[Definition and Terminology]]

[[Recording Dairy Cattle Methane Emission for Genetic Evaluation Guideline|Recording Dairy Cattle Methane Emission for Genetic Evaluation - Guideline]]

Recording Feed Intake for Genetic Evaluation

[[Greenfeed SOP]]

[[Sniffer SOP]]

User:Rstephansen

2023-12-15T13:30:39Z

Rstephansen:

{| class="wikitable"
|+
!Rasmus Bak Stephansen
Member of the ICAR Feed and Gas working group

PhD fellow at Aarhus University, Center for Quantitative Genetics and Genomics

rasmus.stephansen@qgg.au.dk
![[File:Rasmus.jpg|thumb]]
|}

User:Rstephansen

2023-12-15T13:29:08Z

Rstephansen: Uploaded description of my self

{| class="wikitable"
|+
!Rasmus Bak Stephansen
Member of the ICAR Feed and Gas working group

PhD fellow at Aarhus University, Center for Quantitative Genetics and Genomics
![[File:Rasmus.jpg|thumb]]
|}

File:Rasmus.jpg

2023-12-15T13:28:39Z

Rstephansen:

File:Rasmus Skovgaard Stephansen.webp

2023-12-15T13:26:00Z

Rstephansen:

Picture from AU QGG of Rasmus Bak Stephansen

User:Rstephansen/sandbox

2023-12-12T13:49:06Z

Rstephansen: /* Sub-sections */

== Guideline for Recording Feed Intake for Genetic Evaluation ==

== Introduction ==
The widespread use of genomic information in dairy cattle breeding programs has presented the opportunity to select for scares phenotypes of feed intake and feed efficiency. That means animals from research and commercial herds can serve as reference population to calibrate a genomic prediction equation. Genomic breeding values of selection candidates are then used to enhance genetic progress of feed intake and/or feed efficiency. Phenotypes for feed intake, however, are still an important limiting factor for obtaining high accurate breeding values and performing genetic analysis for feed intake and feed efficiency. Based on the experiences gained through several initiatives, we provide guidelines on the recording and quality control of feed intake records for genomic and genetic evaluations.

== 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.

== Definitions and Terminology ==
Table 1. Definitions of Terms used in the guideline of recording feed intake for genetic evaluation
{| class="wikitable"
|+
!Term
!Definition
|-
|DM
|Dry Matter
|-
|DMI
|Dry Matter Intake
|-
|EID
|Electronic identification device
|-
|FS
|Feed Saved
|-
|PMR
|Partial mixed ration
|-
|REML
|Restricted maximum likelihood
|-
|RFI
|Residual feed intake
|-
|TMR
|Total mixed ration
|}

== Scope ==
The scope here is to provide guidelines for of recording feed intake for genetic and genomic evaluations of feed intake and/or feed efficiency. Figure 1 illustrates the main elements of the guidelines and links for a detailed description can be found below.

== Sub-sections ==

* Setting up feed intake recording schemes
* Recording intake indoors and at pasture
* Phenotypes to support feed intake in feed efficiency evaluations
* Utilizing existing feed intake data nationally and internationally
* Quality control of feed intake data
*

== Acknowledgements ==
Authors of the document are, in alphabetical order:

''Maria Frizzarin, Teagasc, Ireland''

''Birgit Gredler-Grandl, Wageningen Livestock Research, Netherlands''

''Jan Lassen, VikingGenetics & Aarhus University, Denmark''

''Rasmus Bak Stephansen, Aarhus University, Denmark''

''Karoline Bakke Wethal, Geno, Norway''

User:Rstephansen/sandbox

2023-12-12T11:42:01Z

Rstephansen:

== Guideline for Recording Feed Intake for Genetic Evaluation ==

== Introduction ==
The widespread use of genomic information in dairy cattle breeding programs has presented the opportunity to select for scares phenotypes of feed intake and feed efficiency. That means animals from research and commercial herds can serve as reference population to calibrate a genomic prediction equation. Genomic breeding values of selection candidates are then used to enhance genetic progress of feed intake and/or feed efficiency. Phenotypes for feed intake, however, are still an important limiting factor for obtaining high accurate breeding values and performing genetic analysis for feed intake and feed efficiency. Based on the experiences gained through several initiatives, we provide guidelines on the recording and handling of feed intake records for genomic and genetic evaluations.

== 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.

== Definitions and Terminology ==
Table 1. Definitions of Terms used in the guideline of recording feed intake for genetic evaluation
{| class="wikitable"
|+
!Term
!Definition
|-
|DM
|Dry Matter
|-
|DMI
|Dry Matter Intake
|-
|EID
|Electronic identification device
|-
|FS
|Feed Saved
|-
|PMR
|Partial mixed ration
|-
|REML
|Restricted maximum likelihood
|-
|RFI
|Residual feed intake
|-
|TMR
|Total mixed ration
|}

== Scope ==
The scope here is to provide guidelines for of recording feed intake for genetic and genomic evaluations of feed intake and/or feed efficiency. Figure 1 illustrates the main elements of the guidelines and links for a detailed description can be found below.

== Sub-sections ==
Links

== Acknowledgements ==
Authors of the document are, in alphabetical order:

''Maria Frizzarin, Teagasc, Ireland''

''Birgit Gredler-Grandl, Wageningen Livestock Research, Netherlands''

''Jan Lassen, VikingGenetics & Aarhus University, Denmark''

''Rasmus Bak Stephansen, Aarhus University, Denmark''

''Karoline Bakke Wethal, Geno, Norway''

User:Rstephansen/sandbox

2023-12-12T11:26:43Z

Rstephansen:

== Guideline for Recording Feed Intake for Genetic Evaluation ==

=== Introduction ===
The widespread use of genomic information in dairy cattle breeding programs has presented the opportunity to select for scares phenotypes of feed intake and feed efficiency. That means animals from research and commercial herds can serve as reference population to calibrate a genomic prediction equation. Genomic breeding values of selection candidates are then used to enhance genetic progress of feed intake and/or feed efficiency. Phenotypes for feed intake, however, are still an important limiting factor for obtaining high accurate breeding values and performing genetic analysis for feed intake and feed efficiency. Based on the experiences gained through several initiatives, we provide guidelines on the recording and handling of feed intake records for genomic and genetic evaluations.

=== 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.

=== Definitions and Terminology ===
Table 1. Definitions of Terms used in the guideline of recording feed intake for genetic evaluation
{| class="wikitable"
|+
!Term
!Definition
|-
|DM
|Dry Matter
|-
|DMI
|Dry Matter Intake
|-
|EID
|Electronic identification device
|-
|FS
|Feed Saved
|-
|PMR
|Partial mixed ration
|-
|REML
|Restricted maximum likelihood
|-
|RFI
|Residual feed intake
|-
|TMR
|Total mixed ration
|-
|
|
|-
|
|
|}

=== Scope ===
Figure 1 illustrates the main elements of this guideline. The numbers in this figure refer to the chapter numbers of this guideline. The scope is to give guidelines of recording feed intake for genetic and genomic evaluations of feed intake and/or feed efficiency.

=== Sub-sections ===
Links

=== Acknowledgements ===

User:Rstephansen/sandbox

2023-12-12T11:25:20Z

Rstephansen:

User:Rstephansen/sandbox

2023-12-12T11:24:18Z

Rstephansen:

User:Rstephansen/sandbox

2023-12-12T11:17:17Z

Rstephansen:

== Guideline for Recording Feed Intake for Genetic Evaluation ==

=== Grass based ===
[https://doi.org/10.3168/jds.S0022-0302(03)73976-9]
#Fist Item
# Second Item
* Bulleted list item

<math>
R_W = \frac{W_t - W_B}{A_t}\times 200 + W_B
</math>

{| class="wikitable"
|- style="font-weight:bold;"
!<br />Term
!<br />Definition
|-
|<br />DM
|<br />Dry Matter
|-
|<br />DMI
|<br />Dry Matter Intake
|-
| <br />EID
|<br />Electronic identification device
|-
|<br />FS
|<br />Feed Saved
|-
|<br />PMR
|<br />Partial mixed ration
|-
|<br />REML
|<br />Restricted maximum likelihood
|-
| <br />RFI
|<br />Residual feed intake
|-
|<br />RIC
|<br />Roughage intake control system
|-
|<br />TMR
|<br />Total mixed ration
|}

User:Rstephansen/sandbox

2023-11-28T15:02:14Z

Rstephansen:

==Feed Intake Recording==

=== Grass based ===
[https://doi.org/10.3168/jds.S0022-0302(03)73976-9]
#Fist Item
# Second Item
* Bulleted list item

<math>
R_W = \frac{W_t - W_B}{A_t}\times 200 + W_B
</math>

{| class="wikitable"
|- style="font-weight:bold;"
!<br />Term
!<br />Definition
|-
|<br />DM
|<br />Dry Matter
|-
|<br />DMI
|<br />Dry Matter Intake
|-
| <br />EID
|<br />Electronic identification device
|-
|<br />FS
|<br />Feed Saved
|-
|<br />PMR
|<br />Partial mixed ration
|-
|<br />REML
|<br />Restricted maximum likelihood
|-
| <br />RFI
|<br />Residual feed intake
|-
|<br />RIC
|<br />Roughage intake control system
|-
|<br />TMR
|<br />Total mixed ration
|}

User:Rstephansen/sandbox

2023-11-28T14:43:34Z

Rstephansen:

==Feed Intake Recording==

=== Grass based ===
#Fist Item
# Second Item
* Bulleted list item

{| class="wikitable"
|- style="font-weight:bold;"
! <br />Term
! <br />Definition
|-
| <br />DM
| <br />Dry Matter
|-
| <br />DMI
| <br />Dry Matter Intake
|-
| <br />EID
| <br />Electronic identification device
|-
| <br />FS
| <br />Feed Saved
|-
| <br />PMR
| <br />Partial mixed ration
|-
| <br />REML
| <br />Restricted maximum likelihood
|-
| <br />RFI
| <br />Residual feed intake
|-
| <br />RIC
| <br />Roughage intake control system
|-
| <br />TMR
| <br />Total mixed ration
|}

User:Rstephansen/sandbox

2023-11-28T14:32:36Z

Rstephansen: Created page with " ==Feed Intake Recording== ===Grass based=== #Fist Item # Second Item * Bulleted list item <ref>Kennedy et al., (1993)</ref> <references />"

==Feed Intake Recording==
===Grass based===
#Fist Item
# Second Item
* Bulleted list item

<ref>Kennedy et al., (1993)</ref>

<references />