Speaker: Professor Guilherme J. M. Rosa, Department of Animal & Dairy Sciences, University of Wisconsin, Madison, USA
Genotype-by-environment interaction (G × E) has long been recognized as a critical factor in beef cattle improvement, particularly given the wide range of environmental conditions in which cattle are raised globally and the added challenges posed by climate change. Traits such as heat tolerance, disease resistance, and overall adaptability are becoming increasingly essential for sustaining productivity in these diverse and fluctuating production systems. Traditionally, G × E has been studied in livestock breeding through multi-trait models—treating performance in each discrete environment as a separate trait—or via reaction norms, where breeding values are modeled as linear functions of an environmental gradient. More recently, the concept of enviromics has emerged as a powerful and integrative framework for addressing G × E. Enviromics involves the high-resolution characterization of the environments in which animals are raised, enabling the use of advanced data analytics and predictive modeling to uncover complex interactions among genetics, environmental variables, and management practices. This approach provides a more nuanced understanding of how specific environmental and managerial factors influence animal performance and supports the development of breeding strategies that produce animals better adapted to their environments. In this seminar, we will discuss ongoing research in our lab that applies enviromics and machine learning techniques to investigate G × E × M (genetics × environment × management) interactions in beef cattle populations in both Brazil and the United States. We will present examples of how these tools can be used to predict performance across heterogeneous environments and to guide precision breeding strategies aimed at enhancing robustness, efficiency, and sustainability in beef production systems.
