Integrating Enviromics, Genomics, and Machine Learning for Precision Breeding of Resilient Beef Cattle

Abstract Animal breeding is one of the main pillars of livestock production. Statistics, computer science, and genomics have transformed productivity in the industry. Another round of breakthroughs is expected to come from harnessing the power of big data and machine learning analytics to address the complex interaction between animal genetics and the environment. Developing methods that enable precise selection decisions for animals in diverse production environments is expected to result in significant productivity gains and a reduction in welfare issues, while also mitigating the environmental impact of suboptimal allocation of resources to ill-adapted animals. Due to climate uncertainty, an understanding of these genetic and environmental interactions will be critical for decision-making. Our multi-institutional group initiated a project to generate a comprehensive enviromics data lake to develop and apply novel methods for breeding more adapted and resilient beef cattle for varying environments. Using GIS technology and integrating various sources of environmental information from publicly available databases and satellite imaging, detailed descriptions of soil, climate, forage, and weather conditions will be created for thousands of U.S. farms, representing millions of cattle, with phenotypic and genotypic data. Farms will be comprehensively described in terms of their facilities and management practices through surveys. Machine learning and artificial intelligence techniques will then be used to predict future animal performance for precision livestock management. The proposed models will also be biologically validated by measuring direct indicators of animal resilience. Results will be communicated to beef cattle producers and industry groups to foster the implementation of environment-aware approaches into routine genetic evaluations. In this talk, we will discuss this ongoing project in detail and present some preliminary results.