Data Collection

Data Collection for Seedstock Producers

At the core of genetic improvement is the collection of data. While 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 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. 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 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. 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. 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.

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.

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 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.

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 animals' weights, measures, and scores is key to accurate data. Additionally, using a commercial or breed association supplied performance recording software helps to improve consistency of data collection and reporting.

Contemporary Groups

content by Jennifer Bormann

Data Collection on Calves

Weights

Birth Weight

Content by Michael Gonda and Bradie Schmidt, SDSU

Yearling Weight

Content by Michael Gonda and Bradie Schmidt, SDSU

Data Collection on Yearling Bulls

Content by Madison Butler/Megan Rolf

Data Collection on Yearling Heifers

Pelvic Measurements

Content by Dave Patterson

Reproductive Tract Scores

Content by Dave Patterson

Exposure Data

Content by Dave Patterson?

Pregnancy Data

Content by Dave Patterson?

CE Scores

Content by Dave Patterson?

Data Collection on Mature Cows

Stayability

Content by Warren Snelling

Mature Height and Weight

content by Heather Bradford

Body Condition Scores

Content by Dave Lalman

Teat and Udder Scores

Content by David Riley

Foot and Leg Scores

content by Lane Giess

Adaptability-Related Traits

PAP Scores

Content by Mark Enns, Milt Thomas, and Scott Speidel

Hair Shedding

Content by Trent Smith

Genomic Data

Use of genomic data requires quality 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 monogenic traits such as horned/polled genotype or genetic abnormality carrier status as well as for quantitative traits, which will be utilized within either single-step genomic BLUP or single-step hybrid marker effects models for genetic prediction. Genotype data can also be utilized for other applications, as detailed below.

Parentage Testing

content by Megan Rolf

Data Collection for Commercial Producers

Content by Jackie Atkins and Chip Kemp

Data Collection at Feedlots

Content by Larry Kuehn