41 Genomic Approaches to Improving Forage-based Beef Cow Efficiency

• Published in: Journal of animal science 2022-03, Vol.100 (Supplement_1), p.23-24
• Main Author: Rowan, Troy N
• Format: Article
• Language: English
• Subjects: Kunkle Symposium
• ISSN: 0021-8812; 1525-3163
• DOI: 10.1093/jas/skac028.045

Abstract In recent decades, the U.S. beef herd has seen massive gains in efficiency, driven by innovations in genetics, nutrition, reproduction, and resource management. Despite these advances, the industry remains under pressure to further reduce its environmental footprint. Perhaps the greatest opportunity to drive increases in sustainability is through targeted genetic improvement of forage-based cow-calf systems. While the industry has long focused on selecting for pre- and post-weaning performance, improvements in mature cow efficiency are more challenging to attain. One major impediment to directly selecting for cow efficiency is a lack of easily measured indicator phenotypes, leading some segments of the industry to focus exclusively on moderating mature size. The optimal solution to this problem will likely require a more complex understanding of the underlying biology of cow efficiency. As we consider how genomics might increase the environmental sustainability of forage-based beef production, we will need to focus on developing and measuring novel phenotypes and accounting for the complex interplay between an animal’s genetics, environment, and management. Using stochastic genetic simulations, we demonstrate the potential environmental and economic impacts of genetic selection for more efficient and well-adapted cows. We also describe possible solutions for measuring a cow’s efficiency on grass (e.g., forage intake to weaned calf pounds) that could be used to develop novel EPDs and economic selection indexes. Finally, we discuss the concept of precision genomic predictions that account for how genotypes interact with environmental stressors and management differences. We propose two potential solutions to address this concern: 1) Selecting robust cattle that perform well across all environments and management levels or 2) Developing precision predictions for precisely matching animals to their ideal production scheme.