Guilherme Rosa, professor in the Department of Animal and Dairy Sciences, received $1 million for his project “Integrating enviromics, genomics, and machine learning for precision breeding of resilient beef cattle” through NIFA’s Inter-Disciplinary Engagement in Animal Systems (IDEAS) program area priority within the Agriculture and Food Research Initiative. It was among 12 projects sharing $9.6 million in funding.

Project summary (from CRIS website): Animal breeding is one of the main pillars of livestock production. Statistics, computer science, and genomics have transformed the industry’s productivity. 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 precision selection decisions for animals in diverse production environments is expected to result in large productivity gains and a reduction in welfare issues, all while mitigating the environmental impact from 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. We will generate a wide-ranging 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 models proposed will also be biologically validated by measuring direct indicators of animal resilience. Following this integrated research-extension project, we will work with producers and industry groups to implement environment-aware approaches into routine genetic evaluations.