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=Data Collection for Seedstock Producers=
#REDIRECT [[:Category:Data Collection]]
At the core of genetic improvement is the collection of data.  While [https://en.wikipedia.org/wiki/Data_quality data quality] is critical, quantity of data collected can sometimes overcome the limitations on data quality that inherently occur in farm and ranch operations.  Along with weights and scores for economically relevant traits and their indicator traits, accurate identification of animals, parents, [[contemporary groups]], and other important details (e.g., age) are essential.  
At the core of genetic improvement is the collection of data.  While [https://en.wikipedia.org/wiki/Data_quality data quality] is critical, the quantity of data collected can sometimes overcome the limitations on data quality that inherently occur in farm and ranch operations.  Along with weights and scores for [[Economically Relevant Traits | economically relevant traits]] and their [[Indicator_Traits | indicators]], accurate [[Identification Systems | identification of animals]], parents, [[Contemporary Groups | contemporary groups]], and other important details (e.g., age) are essential. (Go [[Traits | here for a list of traits and their definitions)]].
=Collection of data to enter genetic evaluation=
At the core of genetic improvement is the collection of high-quality data. Data quality can be impacted by [https://www.precisely.com/blog/data-quality/data-quality-dimensions-measure several clearly identified factors].  While completeness, timeliness, accuracy, and conformity are all essential, consistency is often the least understood and most overlooked consideration for quality data.  Using consistent procedures for collecting, recording, manipulating and [[Data_Processing | processing data]] at both the farm and association levels is the most important aspect to maintaining quality data. 


At the core of genetic improvement is the collection of high quality data. Data quality can be impacted by [https://smartbridge.com/data-done-right-6-dimensions-of-data-quality-part-1/ several clearly identified factors].  While completeness, timeliness, accuracy, and conformity are all essential, consistency is often the least understood and most overlooked consideration for quality data.  Collecting, recording, manipulating and processing data using consistent procedures at both the farm and association levels is the most important aspect to maintaining quality data.   
In order to keep all data collected associated with an individual animal, an effective [[Identification Systems | beef cattle identification system]] is essential.  Standards have been developed for identification methods that ensure unique and accurate identification of animals during the transmission and processing of data, including [[Genomic Data | genomic information.]] Because the number of animals processed in [[:Category:Genetic Evaluation | genetic evaluation]] is routinely in the millions, it is not practical to routinely use registration number information for on-farm data collection. Ear tagging and on-farm electronic identification are often implemented in place of using a full registration identifier.   


In order to keep all data collected associated with an individual animal an effective [[beef cattle identification system]] is essential.  [[beef cattle identification system | Standards have been developed]] for identification methods that ensure unique and accurate identification of animals during the transmission and processing of data.  Because the number of animals processed in [[National Cattle Evaluations programs (NCE)]] is routinely in the millions, it is not practical to routinely use registration number information for on-farm data collection. [[Standards for ear tagging]] and on-farm electronic identification have also been implemented.  In addition, recording of animal identification is closely associated with the collection of [[Genomic Information | genomic information.]]
Historically, many beef breed genetic evaluations were based on progeny weaned and/or registered and did not require that data be recorded from females that failed to reproduce or whose progeny were not registered.  By contrast, inventory-based [[Whole Herd Reporting]] (WHR) requires the collection of annual production and performance records on all cattle within a herd.  Where possible, [[Whole_Herd_Reporting | whole herd reporting]] is recommended to capture the greatest amount of complete cowherd information.  [[Whole Herd Reporting#Performance recording requirements | Data recording on individual cows]] is essential for the prediction of female fertility. Cow fertility is often the most important determinant of profitability in the beef herd. Additionally, accurate and complete cow data are essential for the prediction of traits with a maternal influence (e.g. [[Weaning_Weight | weaning weight]]).  The [[Whole Herd Reporting#Performance recording requirements | female production data]] to be recorded on each cow must be standardized because it is often the most complex data that a producer deals with.


Historically, many beef breed genetic evaluations were based on progeny weaned and/or registered and did not require that data be recorded from females that failed to reproduce or whose progeny were not registered.  By contrast, inventory based [[Whole Herd Reporting]] (WHR) requires collection of annual production and performance records on all cattle within a herd.
Regardless of whether using an [[Whole Herd Reporting | inventory-based reporting system]] or not, accurate phenotypic data collection is vital to genetic evaluation.  Collection of complete and accurate data on [[Whole_Herd_Reporting#Performance_record_requirements | calves, bulls, heifers, mature cows]], or fed cattle (including [[Required_Carcass_Data_Collection_for_Use_in_Genetic_Evaluations| carcass data]]) is critical to making genetic improvement.  Producers may also be interested in working with their breed associations to provide data for [[Traits | novel traits]], where EPDs may be under development. When reporting these data, it is also vital to include appropriate [[Contemporary Groups | contemporary grouping]] information to ensure that the data are appropriately incorporated into the evaluation.  Using consistent methods for taking animals' weights, measures, and scores is key to accurate data.  Additionally, using a commercial or breed association supplied performance recording software helps to improve the consistency of data collection and reporting.  Producers are encouraged to contact their breed associations to obtain recommendations on what software may be compatible with their systems.


 
[[Data Collection for Commercial Producers | Data collected by commercial cattle producers]] are, in most cases, substantially different than data collection requirements for seedstock producers.
[[Female Production Data | Data recording on individual cows]] is essential for the prediction of female fertility. Cow fertility is often the most impactful factor on profitability in the beef herd. Additionally, accurate and complete cow data are essential for prediction of traits with a maternal influence (e.g. weaning weight).
 
The [[Female Production Data]] to be recorded on each cow must be standardized because it is often the most complex data that a producer deals with.
 
Data collection of complete and accurate data on individual calf performance through slaughter or breeding is critical to making genetic improvement.  Using consistent methods for taking [[Animal Performance Data | animals' weights, measures, and scores]] is key to accurate data.  Additionally, using a commercial or breed association supplied [[Performance Recording Software | performance recording software]] helps to improve consistency of data collection and reporting.
 
==ID Systems==
===Herd IDs===
===Tattoos===
===Breed Association Registration Numbers===
===International Registration Numbers===
====Breed Codes====
=====ICAR=====
=====NAAB=====
==Whole Herd Reporting==
===Basics===
===Timeline===
===Disposal and Reason Codes===
==Contemporary Groups==
content by Jennifer Bormann
 
===Basics===
 
===Type of Birth===
====Multiple Births and Freemartins====
====ET Calves====
===Components by Trait===
 
==Data Collection on Calves==
===Survival to Weaning===
====Disposal====
====Disease====
===Weights===
====BW====
=====[[Hoof Tape | Hoof Tapes?]]=====
 
====[[Weaning Weight]]====
 
====[[Yearling Weight | Yearling Weight]]====
 
Content by Michael Gonda
 
===CE Scores===
===Hip Height/Frame===
====Discuss whether to include====
===Docility===
===Ultrasound (link to UGC website)===
==Data Collection on Yearling Bulls==
Content by Madison Butler/Megan Rolf
===[[Breeding Soundness Exam | Breeding Soundness Exam]]===
===[[Scrotal Circumference | Scrotal Circumference]]===
 
==Data Collection on Yearling Heifers==
===[[Pelvic Measurements | Pelvic Measurements]]===
 
Content by Dave Patterson
 
===[[Reproductive Tract Scores | Reproductive Tract Scores]]===
 
Content by Dave Patterson
 
===Exposure Data===
===Pregnancy Data===
===CE Scores on Calves===
==Data Collection on Mature Cows==
===Calf Record/Reason Code (for Stayability)===
===Exposure and Pregnancy Data===
===Gestation Length===
===Calving Interval===
===[[Mature Height and Weight | Mature Height and Weight]]===
content by Heather Bradford
 
===[[Body Condition Scores | Body Condition Scores]]===
 
Content by Dave Lalman
 
===[[Teat and Udder Scores | Teat and Udder Scores]]===
 
Content by David Riley
 
===[[Foot and Leg Scores | Foot and Leg Scores]]===
content by Lane Giess
 
===Intake===
 
==Adaptability-Related Traits==
===[[PAP Scores | PAP Scores]]===
Content by Mark Enns, Milt Thomas, and Scott Speidel
 
===[[Hair Shedding | Hair Shedding]]===
Content by Trent Smith
 
==Genomic Data==
Use of genomic data requires quality [http://guidelines.thetasolutionsllc.com/index.php/Genotyping sample collection].  Once samples are acquired and processed according to breed association specifications, the data can be incorporated into reporting systems for breed associations, including reporting schemes for [http://guidelines.thetasolutionsllc.com/index.php/Monogenic_Traits monogenic traits] such as horned/polled genotype or [http://guidelines.thetasolutionsllc.com/index.php/Recessive_Genetic_Defects genetic abnormality] carrier status as well as for quantitative traits, which will be utilized within either [http://guidelines.thetasolutionsllc.com/index.php/Single-step_Genomic_BLUP single-step genomic BLUP] or [http://guidelines.thetasolutionsllc.com/index.php/Single-step_Hybrid_Marker_Effects_Models single-step hybrid marker effects models] for genetic prediction.  Genotype data can also be utilized for other applications, as detailed below.
 
===[[Parentage Testing| Parentage Testing]]===
content by Megan Rolf
 
Genotype data is often used for parentage testing for embryo transfer calves or in the case of ambiguous birth date.  It can also be helpful for producers who utilize multiple-sire pastures.  The concept behind using genetic markers for parentage testing is based on the fact that each animal receives one allele from each parent, which makes up the animal's genotype (i.e. for an animal that is Aa, the A comes from one parent, while the a comes from the other).  Therefore, an animal's genotypes at many loci can be compared to genotypes of potential parents at those same loci to determine if those markers are consistent with that individual being a parent of the offspring in question. 
 
One common misconception of parentage testing is that the test determines parentage absolutely.  Rather, parentage testing excludes animals that cannot be the parents of a particular offspring, rather than proving that an animal is the parent.  In the simplest terms, we use the genetic markers to exclude animals as a possible parent, with the goal of having only one animal left as the most likely parent for that offspring after all others have been excluded.  This is why all possible parents must be genotyped and included in the comparison to get accurate results, but the inclusion of animals that can be excluded due to location, color, or other factors is discouraged.
 
The newest type of parentage panels typically utilize 96 SNP (or single nucleotide polymorphism) markers.  To account for genotyping errors, one exclusion out of all the markers in the panel is typically allowed and still determine parentage.  Two to three exclusions would indicate a need to re-test the sample to rule out contamination, poor DNA quality, or poor genotyping results.  More than three exclusions will lead to a complete exclusion of that animal as a potential parent.  Research has shown that parentage can be determined with greater specificity with a larger number of SNP markers (around 400), but at a greater cost.  A balance between cost effectiveness and having a reasonable ability to eliminate animals that could not have been the parents must be achieved.
 
Older parentage panels utilized Microsatelllite markers, and these panels typically had a smaller number of markers. Parentage cannot be determined if animals are not genotyped on the same type of panel, as the markers at the same loci cannot be compared between these panels.   
 
References:
 
International Society for Animal Genetics.  (2012).  Guidelines for cattle parentage verification based on SNP markers.  http://www.isag.us/Docs/Guideline-for-cattle-SNP-use-for-parentage-2012.pdf.
 
 
More detailed infomration on parentage testing along with relevant examples can be found [https://articles.extension.org/pages/74048/parentage-testing here].
 
=Data Collection for Commercial Producers=
Content by Jackie Atkins (and Chip Kemp?)
 
===See Seedstock Data Collection (link)===
===Herd Measurements===
===Calving Distribution===
===Bull Measurements===
===Cow Measurements===
===MPPA===
 
=Data Collection at Feedlots=
Content by Larry Kuehn
===Average Daily Gain===
===Intake and Feed Efficiency===
===Health Traits===
 
=Data Collection at Packers=
==Carcass Traits==
===Cooperation Between Packer and Producer===
===Required Data for Genetic Evaluation===
===Hot Carcass Weight===
===Ribeye Area===
===Marbling Score===
===Fat Thickness===
===Other Traits (e.g. KPH)===
===Warner-Bratzler Shear Force (link)===
===Quality Grade and Yield Grade (link)===
 
 
=Herd Management Software (link to Data Prep section)=

Latest revision as of 17:48, 12 April 2021

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At the core of genetic improvement is the collection of data. While data quality is critical, the quantity of data collected can sometimes overcome the limitations on data quality that inherently occur in farm and ranch operations. Along with weights and scores for economically relevant traits and their indicators, accurate identification of animals, parents, contemporary groups, and other important details (e.g., age) are essential. (Go here for a list of traits and their definitions).

Collection of data to enter genetic evaluation

At the core of genetic improvement is the collection of high-quality data. Data quality can be impacted by several clearly identified factors. While completeness, timeliness, accuracy, and conformity are all essential, consistency is often the least understood and most overlooked consideration for quality data. Using consistent procedures for collecting, recording, manipulating and processing data at both the farm and association levels is the most important aspect to maintaining quality data.

In order to keep all data collected associated with an individual animal, an effective beef cattle identification system is essential. Standards have been developed for identification methods that ensure unique and accurate identification of animals during the transmission and processing of data, including genomic information. Because the number of animals processed in genetic evaluation is routinely in the millions, it is not practical to routinely use registration number information for on-farm data collection. Ear tagging and on-farm electronic identification are often implemented in place of using a full registration identifier.

Historically, many beef breed genetic evaluations were based on progeny weaned and/or registered and did not require that data be recorded from females that failed to reproduce or whose progeny were not registered.  By contrast, inventory-based Whole Herd Reporting (WHR) requires the collection of annual production and performance records on all cattle within a herd. Where possible, whole herd reporting is recommended to capture the greatest amount of complete cowherd information. Data recording on individual cows is essential for the prediction of female fertility. Cow fertility is often the most important determinant of profitability in the beef herd. Additionally, accurate and complete cow data are essential for the prediction of traits with a maternal influence (e.g. weaning weight). The female production data to be recorded on each cow must be standardized because it is often the most complex data that a producer deals with.

Regardless of whether using an inventory-based reporting system or not, accurate phenotypic data collection is vital to genetic evaluation. Collection of complete and accurate data on calves, bulls, heifers, mature cows, or fed cattle (including carcass data) is critical to making genetic improvement. Producers may also be interested in working with their breed associations to provide data for novel traits, where EPDs may be under development. When reporting these data, it is also vital to include appropriate contemporary grouping information to ensure that the data are appropriately incorporated into the evaluation. Using consistent methods for taking animals' weights, measures, and scores is key to accurate data. Additionally, using a commercial or breed association supplied performance recording software helps to improve the consistency of data collection and reporting. Producers are encouraged to contact their breed associations to obtain recommendations on what software may be compatible with their systems.

Data collected by commercial cattle producers are, in most cases, substantially different than data collection requirements for seedstock producers.

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