Mike Coffey

Professor Mike Coffey is a leading academic in livestock genetics and dairy cattle breeding at Scotland's Rural College (SRUC). He holds dual professorships in Animal and Veterinary Sciences and the Livestock Breeding Food Security Challenge Centre. His research focuses on optimizing dairy cattle breeding strategies to balance productivity, health, and environmental sustainability, particularly addressing feed efficiency, methane emissions, and genetic resistance to diseases like bovine tuberculosis and digital dermatitis. He has contributed to international genomic evaluation programs and collaborates on projects like the Efficient Dairy Genome Project.

Education: PhD in Livestock Genetics from the University of Edinburgh (2003), Bachelor’s in Agriculture from the University of Nottingham (1981).

Research Interests: Dairy cattle breeding, genomic selection, genetic indices, and systems approaches to trade-offs in livestock traits. He explores how genetic and environmental factors influence traits like energy mobilization, fertility, and disease susceptibility. Recent work includes detecting genetic variability in TB infectivity and modeling claw horn lesions in Holstein cattle.

Projects and Grants: Active in projects like Cool Cows (EnviroCow+) for methane reduction, genetic solutions for lameness in dairy cattle, and data-driven livestock improvement funded by the Scottish Government. He has secured grants totaling over £multi-million, addressing feed efficiency, disease resistance, and smallholder dairy challenges in sub-Saharan Africa.

• Key Awards: Queen’s Anniversary Prize (2017, 2019) for contributions to animal science and sustainability.
• Teaching: Leads SRUC’s Agriculture degree courses, lectures on bovine reproduction, and developed the Vetnomics CPD course for veterinary practitioners.
• External Roles: Member of the British Cattle Breeders Club and European Association of Animal Production.

Labs/Teams: Collaborates with the Roslin Institute and Agrimetrics Agritech Centre. His work integrates clinical data, genomics, and AI for livestock improvement, as seen in projects like host-pathogen interaction studies and automated phenotype extraction using deep learning.