The future of phenomics in dairy cattle breeding

J.B. Cole^1*, S.A.E. Eaglen², C. Maltecca³, H.A. Mulder⁴, and .J.E. Pryce^5,6

¹Animal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Center, Agricultural Research Service, United States Department of Agriculture, Beltsville, Maryland, 20715, USA
²National Association of Animal Breeders, Madison, Wisconsin, 53717, USA
³Department of Animal Science, North Carolina State University, Raleigh, North Carolina, 27695, USA
⁴Department of Animal Science, Wageningen University and Research, 6700AH Wageningen, The Netherlands
⁵Agriculture Victoria Research, AgriBio, Centre for AgriBioscience, Bundoora, Victoria 3083, Australia
⁶School of Applied Systems Biology, La Trobe University, Bundoora, Victoria 3083, Australia

^*Corresponding author

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Implications

• Increasingly complex dairy cattle production systems require that all aspects of animal performance are measured across individuals’ lifetimes.
• Selection emphasis is shifting away from traits related to animal productivity towards those related to efficient resource utilization.
• The goal of phenomics is to provide information for making decisions related to on-farm management, as well as genetic improvement.

Introduction

Genetic selection has been a very successful tool for the long-term improvement of livestock populations, and the rapid adoption of genomic selection over the last decade has doubled the rate at which some populations are improving (García-Ruiz et al., 2016). While details differ somewhat between livestock species, the general objective of breeding programs is the same: the identification of genetically superior males and females that are used as the parents of the next generation. However, the expression of genetic potential also requires that animals are placed in environments that support such performance. For example, Figure 1 shows the increase since 1970 in milk protein yield in US Holstein cattle, partitioned into gains due to increased genetic potential and those associated with improved environment (housing, feeding, etc.). Improved animal efficiency has also resulted in reduced environmental impacts throughout the production chain, which is of importance to consumers around the world (Capper and Cady, 2019).

Key Words: analytics, big data, dairy cattle, machine learning, phenomics, sensors