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About

Jarrod Doucette is an emerging researcher in animal genetics and genomics, specializing in the application of advanced computational and statistical methods to improve dairy cattle breeding programs. His work sits at the intersection of precision livestock farming, quantitative genetics, and machine learning, with a particular focus on North American Holstein cattle. Doucette's most impactful contributions center on harnessing data generated by automatic milking systems (AMS) to derive novel genetic parameters for traits previously difficult to measure at scale. His highly cited 2023 study on resilience across lactations (46 citations) addresses the urgent challenge of breeding cattle capable of withstanding climate-related and environmental stressors — a critical concern for sustainable agriculture. Complementing this, his work on milkability traits (36 citations) and udder conformation derived from robotic milking systems (20 citations) demonstrates his commitment to translating precision farming technologies into actionable breeding tools. Perhaps most forward-looking is his exploration of deep learning and machine learning for genomic prediction of behavioral traits (18 citations), pushing the boundaries of traditional genomic selection methodologies. Across his body of work, Doucette has established himself as a creative and practically minded geneticist helping to shape the future of resilient, welfare-conscious dairy production.