Submitted to: Journal of Animal Science
Publication Type: Abstract Only
Publication Acceptance Date: 6/7/2022
Publication Date: 9/1/2022
Citation: Miles, A.M. 2022. Sustainable dairy breeding: working within the US National Evaluation System [abstract]. Journal of Animal Science. 100(Suppl. S3):14(abstr. 25). https://doi.org/10.1093/jas/skac247.025.
Technical Abstract: We define sustainable agriculture as a balance of practices protecting the natural environment, promoting economic vitality, and building healthy communities in the present without compromising the future. The dairy industry has been working for decades with these goals in mind, but under the brand of productive efficiency. This has resulted in tremendous gains in farm profitability and cow performance, though dairy systems are continually impacted by resource availability, advancing technology, and changing consumer values that all affect the environment in which cows must perform. As we redefine selection goals for sustainability we should prioritize overall fitness, encompassing a dairy cattle population that is genetically diverse, resilient, and adaptable. Greater genetic gains correspond to a faster rate of inbreeding, which has long been recognized as an antagonist to health and performance. Male genetic variation is very limited due to the marketability of high genetic merit sires and so conserving female genetic diversity must be a top priority. National genetic and genomic evaluations are delivered with the cooperation of key industry partners through a complex system of data sharing and management; incorporating new phenotypes (e.g., emissions) will require operating within the parameters of this system. Examples of changing industry trends include the rise in embryo transfer, increasing interest in evaluations for heat tolerance, higher frequency of robotic milkers in the United States, and the popularity of beef-on-dairy crossbreds as managers seek to sustain farm profits. Genetic improvement tools need to be just as dynamic as the dairy industry, but they all depend upon reliable and accurate data flow. Autonomous, sensor-based phenotyping represents a huge opportunity for genomic improvement, but translating this rapidly generated data into something that can be used for genomic selection will be a new challenge.