Breakthroughs in precision livestock technology are bringing the beef cattle industry to a pivotal transition: moving from standardized genetic predictions to environment-specific evaluations. In a recent episode of the Iowa Beef Collective podcast, host Patrick Wall sat down with Troy Rowan, an associate professor and beef cattle genomics specialist at the University of Tennessee, to discuss how automated data collection in commercial herds is revolutionizing genomic selection.
Automated Phenotyping in Commercial Herds
Rowan explains he is excited to see the growth in collecting relevant phenotypes in an automated way, rather than with pen and paper. There has been a rapid increase with these technologies allowing for that kind of data collection.
Most work being completed at the University of Tennessee currently ties back to cow efficiency, whether that be fertility, grazing, structural soundness and more.
“There are plenty of tools to capture this data to scale, but more specifically in commercial settings,” Rowan says.
Shifting data-collection efforts toward commercial herds, rather than focusing solely on elite seedstock operations, allows researchers and producers to evaluate how genetics perform across diverse, real-world environments.
The Myth of the ‘Average Environment’
Wall notes current genetic tools often carry a built-in blind spot: “One of the biggest weaknesses of genetic tools right now is we assume as how they are printed, they all live in the same environment, but that is not the case.”
Standard genetic evaluations can struggle to account for regional adaptability.
“Our EPDs [expected progeny differences] assume an average environment but an average environment doesn’t exist,” Rowan says.
There are genetic differences in cattle that perform better in different areas. Natural selection and adaptation show this, and as producers grow herds and keep back replacement females, they pool together genetics that work well in that specific area.
Rowan explains the next step of this natural selection is being more precise about it. For example, if a producer is bringing an artificial insemination (AI) sire from one side of the country to the other, they are taking a trial-and-error risk.
A project led by the University of Wisconsin, in conjunction with the American Angus Association, is helping develop tools that could help with these decisions. Data including environment, indicators of animal tolerance to stressors and management strategies is being collected to further understand animal adaptations.
Rowan explains you can have producers in the same ZIP code but “a fenceline between one neighbor and another can represent a completely different environment just due to the management differences.”
Biometric ear tags, GPS collars, and automated wearables are becoming critical for capturing these fine-scale differences, tracking grazing behavior, bull breeding activity, and fertility markers without requiring additional ranch labor.
“The cows that have such a massive genetic footprint are often in embryo transfer programs or bred via artificial insemination,” Rowan says. However, the conditions under which seedstock donors perform do not always mirror commercial reality.
Wearable Sensors and the Next Frontier in Herd Health
With wearable technologies, these ideal phenotypes are factors that can be tracked without having to touch cows or spend labor on. Then, this information can flow right into genetic evaluation.
Rowan observes commercial producers are often not engaged in data reporting to a breed association or a genetic evaluation, nor are they using wearable technology for data collection, but rather for ease of management. But the passive data that could be collected in this way is exciting.
“We can now kill two birds with one stone: We can help them [producers] on a labor front and then collect information that we’ve never collected before,” Wall adds.
Beyond reproduction, Rowan explains his outlook on the wearable technology is also to gather data through the life cycle of a terminal animal.
“One of the biggest areas that we still don’t have great genetic selection tools for relates to animal health,” he adds.
These wearables could be used for tracking health in calves from birth to feedlot, potentially allowing for more information on health phenotypes. But this is a difficult area of data to pursue, given the number of diseases and health factors in beef cattle.
Rowan stresses this as an area to continue to pursue since it has such a huge impact on all factors that make the beef industry successful.
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