Still reeling from the science outlined by Filippo Miglior, PhD, in episode 6 of the pod? Us too. That’s why we invited special guest Christine Baes, PhD, to give us a download on the follow-up project. Christine is a professor and department chair of the Department of Animal Biosciences and Research Chair in Livestock Genomics at the University of Guelph. She is interested in improving dairy cows’ health, welfare, and productivity through genetics and is the lead investigator of The Resilient Dairy Genome Project, a massive, international, collaborative research project aiming to develop genomic tools to breed more resilient dairy cows. Christine explains the project’s focus on interrelated activities—fertility, health (especially for calves), feed efficiency, and methane emissions—and how they can be translated and applied on farms to help them prepare for the future. Bringing together more than 30 co-investigators and 35 international partner organizations, this huge undertaking recognizes the complexity of dairy cows and the dairy sector. It takes an interdisciplinary approach, considering the ethical, environmental, economic, legal, and societal impacts. Matt and Christine also connect the dots between resilience and longevity, acknowledging the tension between maximizing genetic gains and ensuring cows have the long, healthy, and happy lives expected by customers and dairy professionals who cherish their animals. Christine also explains her journey from farm kid to geneticist and lets listeners peek behind the curtain of the decisions that go into updating the Lifetime Performance Index. Finally, the two farmers-at-heart bond

Episode Sixteen Show Notes

Learn more about Christine Baes’s work and connect with her on LinkedIn and Twitter/X. Explore The Resilient Dairy Genome Project and its precursor, The Efficient Dairy Genome Project.

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Catch up on the papers discussed in the episode:

The Resilient Dairy Genome Project—A general overview of methods and objectives related to feed efficiency and methane emissions, Journal of Dairy Science (2024)

Estimation of genetic parameters for feed efficiency traits using random regression models in dairy cattle, Journal of Dairy Science (2024)

Estimates of genetic parameters for rumination time, feed efficiency, and methane production traits in first lactation Holstein cows, Journal of Dairy Science (2024)

Predicting methane emission in Canadian Holstein dairy cattle using milk mid-infrared reflectance spectroscopy and other commonly available predictors via artificial neural networks, Journal of Dairy Science (2024)

Recording of calf diseases for potential use in breeding programs: a case study on calf respiratory illness and diarrhea, Canadian Journal of Dairy Science (2023)