Selection for resilient dairy cows
PI: Kent Weigel
Weigel is Professor and Chair of the Department of Animal and Dairy Sciences at UW–Madison. He holds research, extension and teaching appointments and serves a technical consultant for numerous companies and organizations in the dairy genetics industry. His research focuses on genetic selection and genome-guided management programs to improve the productivity, health, fertility, and feed efficiency of dairy cattle using tools such as whole genome selection, advanced reproductive technologies, crossbreeding, electronic data capture systems, and artificial intelligence algorithms.
Postdoc: Ligia Cavani (pictured above), a prior educator, received her PhD in animal science from Sao Paulo University in Brazil. She previously taught classes on animal breeding, ruminants, beef cattle, and statistics, and has experience gene mapping and making genomic predictions. Cavani will be mentored by Kent Weigel from the Department of Animal and Dairy Sciences.
This project seeks to improve the health and welfare of dairy cows and the sustainability of dairy farms by genetic selection for resistance to, and rapid recovery from, environmental and management disturbances. Researchers will use data from approximately 220 commercial dairy farms to develop and evaluate measures of resilience based on deviations in daily milk yield from expected performance. The research team will validate the ability of resilience traits that can be measured on commercial farms to reflect underlying changes in feeding behavior, feed consumption, and energy balance using dry matter intake and residual feed intake data from nearly 10,000 cows at research stations throughout North America, Europe, and Australia. Lastly, researchers will use pedigree, genome, and performance data from the research stations and cooperating dairy farms to develop a prototype for routine genetic evaluation of resilience in U.S. Dairy cattle, with the aim of enhancing their ability to cope with challenges and preform at a higher level under variable management and environmental conditions.