Advancing dairy and beef genetics through genomic technologies

Authors: BANERJEE, PRIYANKA - Auburn University; DINIZ, WELLISON - Auburn University

Submitted to: Veterinary Clinics of North America
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/1/2024
Publication Date: 11/1/2024
Citation: Banerjee, P., Diniz, W.J. 2024. Advancing dairy and beef genetics through genomic technologies. Veterinary Clinics of North America. 40(3):447-458. https://doi.org/10.1016/j.cvfa.2024.05.009.
DOI: https://doi.org/10.1016/j.cvfa.2024.05.009

Interpretive Summary: Purpose The beef industry is experiencing pressure to increase the efficiency and sustainability of forage-based cow-calf production. Dozens of traits affect a cow’s ability to be biologically, economically, and environmentally efficient. Furthermore, in an increasingly volatile climate, an animal’s genetic merit must be considered in the context of its environment. This review discusses the complex problem of cow efficiency and how the industry can leverage genetic selection tools to improve these traits. I review currently available selection tools for increasing cow-calf production efficiency and discuss developing technologies and research that will drive future innovation. Sources This review draws on primary literature from animal breeding, beef production, engineering, and agricultural sustainability to broadly discuss the challenges of genetically improving cow-calf efficiency and adaptability. Synthesis Decision support tools allow breeders to select sires that will have more efficient daughters. Historically, these tools have focused on moderating mature cow size and milk production. They have recently expanded to include traits that directly measure feed efficiency and other related phenotypes such as longevity and fertility. Genetic selection tools also exist for adaptive traits to help producers select sires that produce offspring that respond better to environmental stressors such as heat stress or high elevation. Conclusions and Applications Future work to develop genetic selection tools for forage-based beef systems will rely on integrating genomics, animal breeding, and precision livestock technologies. These developing technologies, coupled with existing tools, will enable producers to more directly make breeding decisions focused on cow efficiency and greenhouse gas emissions.

Technical Abstract: Purpose The beef industry is experiencing pressure to increase the efficiency and sustainability of forage-based cow-calf production. Dozens of traits affect a cow’s ability to be biologically, economically, and environmentally efficient. Furthermore, in an increasingly volatile climate, an animal’s genetic merit must be considered in the context of its environment. This review discusses the complex problem of cow efficiency and how the industry can leverage genetic selection tools to improve these traits. I review currently available selection tools for increasing cow-calf production efficiency and discuss developing technologies and research that will drive future innovation. Sources This review draws on primary literature from animal breeding, beef production, engineering, and agricultural sustainability to broadly discuss the challenges of genetically improving cow-calf efficiency and adaptability. Synthesis Decision support tools allow breeders to select sires that will have more efficient daughters. Historically, these tools have focused on moderating mature cow size and milk production. They have recently expanded to include traits that directly measure feed efficiency and other related phenotypes such as longevity and fertility. Genetic selection tools also exist for adaptive traits to help producers select sires that produce offspring that respond better to environmental stressors such as heat stress or high elevation. Conclusions and Applications Future work to develop genetic selection tools for forage-based beef systems will rely on integrating genomics, animal breeding, and precision livestock technologies. These developing technologies, coupled with existing tools, will enable producers to more directly make breeding decisions focused on cow efficiency and greenhouse gas emissions.