The John Oldacre Centre for Dairy Welfare & Sustainability Research

line of dairy cows grazing in shed

Bristol Vet School has been awarded £1 million by the John Oldacre Foundation to create a new nexus for ruminant research, underpinned by a farm research platform that aspires to be the world’s most intensively monitored dairy farm at our commercial facility. Combining Bristol’s leading expertise in cohort studies, genetic epidemiology, data science, and artificial intelligence with our Sustainability, Welfare, and One Health approaches, this platform provides a foundation for tackling global livestock challenges.

Core Research Domains

  • Global Food Security: Understanding animal resilience to intensified production while ensuring sustainable resource use.

  • Welfare, Behaviour & Cognition: Tracking social and environmental interactions to advance high-welfare practices and basic behavioral science.

  • Disease & Resistance Dynamics: Modeling transmission pathways between livestock, the farm environment, and humans.

  • Precision Livestock Farming (PLF): Serving as a digital testbed for developing and validating sensors and data integration pipelines.

Infrastructure, Diagnostics & Commercial Assets

Our monitoring infrastructure, clinical resources, and commercial supply chain facilities are accessible for collaborative R&D through our regional assets and our partnership with the UK Agri-Tech Centre.

  • AI Computer Vision Network: A comprehensive 61-camera network providing 24/7 video surveillance. Automated multi-object tracking and individual cattle re-identification are driven by deep learning models developed alongside the Alan Turing Institute and the BBSRC.

  • Emissions & Physiology Monitoring: Integrated C-Lock GreenFeed systems tracking real-time individual cow emissions, inputs, and metabolic physiology.

  • IoT & Biometrics Platform: Infrastructure supporting commercial and experimental wearables, biosensors, and localized thermal or biomechanical tracking arrays.

  • Clinical & Diagnostic Assets: Direct on-site access to specialized diagnostic pathology services and the Langford Vets Farm Animal Practice.

  • Supply Chain Research Platform: Integration with our on-site commercial/research abattoir and butchery, enabling localized supply chain and agricultural economics research.

 Black and white cattle in a barn, with close-up and group feeding scene.

Active Research Portfolio & Partners

Flagship Programs

  • AI for Early Disease Detection (BBSRC / DEFRA): Using computer vision models for automated screening and early detection of endemic livestock diseases. Watch a video about this project and Isembard AI here.

  • The DECIDE Project (EPSRC): Co-creating equitable circular food systems through an integrated digital hub.

  • The TRACER Project (HORIZON REA): Leveraging real-time data streams and predictive modeling for transparent carbon and non-CO2 emission accounting.

  • Global Machine Vision Track (British Council, World Bank, Research England): Adapting and scaling computer vision frameworks for sustainable dairy farming in tropical environments.

Other Support

Our pilot studies, specialized diagnostic trials, and welfare assessments are supported by a network of research trusts and industry partners:

  • Yeo Valley

  • The Langford Trust

  • Pasture for Life

  • Animal Welfare Foundation

  • Animal Welfare Research Network (AWRN)

  • Innovate UK

 Goats standing together and sheep spread across a grassy pasture

Bristol research has focussed on the productivity and welfare issues of lameness and abortion in sheep. The goat industry is funding a major study into goat health problems which affect productivity. Our farm animal veterinary practice (Langford Vets), has client farms (mostly small holders/farms raising sheep and goats). Many of these local producers use Bristol's on-site commercial/research abattoir and butchery, providing potential for supply chain research.

Publications

Abbas, K., Afzal, Z., Raza, A., Mansouri, T., Dowsey, A.W., Inchaisri, C. and Alameer, A., 2025. Vision transformer-based multi-camera multi-object tracking framework for dairy cow monitoring. Smart Agricultural Technology, 11, p.101525. https://doi.org/10.1016/j.atech.2025.101525

Andrew, W., Gao, J., Mullan, S., Campbell, N.W., Dowsey, A.W. and Burghardt, T., 2021. Visual identification of individual Holstein-Friesian cattle via deep metric learning. Computers and Electronics in Agriculture, 185, p.106133. https://doi.org/10.1016/j.compag.2021.106133

Andrew, W., Greatwood, C. and Burghardt, T., 2020. Aerial Animal Biometrics: Individual Friesian Cattle Recovery and Visual Identification via an Autonomous UAV with Onboard Deep Inference. 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 8968-8955. https://doi.org/10.1109/IROS40897.2019.8968555

Andrew, W., Greatwood, C. and Burghardt, T., 2020. Fusing Animal Biometrics with Autonomous Robotics: Drone-based Search and Individual ID of Friesian Cattle. 2020 IEEE Winter Conference on Applications of Computer Vision (WACVW), pp. 9096-9049. https://doi.org/10.1109/WACVW50321.2020.9096949

Andrew, W., Greatwood, C. and Burghardt, T., 2018. Visual Localisation and Individual Identification of Holstein Friesian Cattle via Deep Learning. 2017 IEEE International Conference on Computer Vision Workshop (ICCVW), pp. 336-344. https://doi.org/10.1109/ICCVW.2017.336

Andrew, W., Hannuna, S.L., Campbell, N.W. and Tilo Burghardt., 2017. Automatic individual holstein friesian cattle identification via selective local coat pattern matching in RGB-D imagery. 2016 IEEE International Conference on Image Processing (ICIP), pp. 7532-7540. https://doi.org/10.1109/ICIP.2016.7532404

Colston, K.P.J., Ede, T., Mendl, M.T. and Lecorps, B., 2024. Cold therapy and pain relief after hot-iron disbudding in dairy calves. PLoS One, 19(7), p.e0306889. https://doi.org/10.1371/journal.pone.0306889

Gao, J., Burghardt, T. and Campbell, N.W., 2022. Label a Herd in Minutes: Individual Holstein-Friesian Cattle Identification. International Conference on Image Analysis and Processing Workshops (ICIAPW), pp. 33-44. https://doi.org/10.1007/978-3-031-13324-4_33

Gao, J., Burghardt, T., Andrew, W., Dowsey, A.W. and Campbell, N.W., 2021. Towards Self-Supervision for Video Identification of Individual Holstein-Friesian Cattle: The Cows2021 Dataset. arXiv preprint arXiv:2105.01938. https://arxiv.org/abs/2105.01938

Hall, S. and Dauksta, K., 2024. Los primeros pasos sobre la investigación sobre el manejo agroecológico de praderas en la Universidad de Bristol. Cadernos de Agroecologia, 19(1). https://cadernos.aba-agroecologia.org.br/cadernos/article/view/10147

Hendricks, J., Mendl, M.T. and Lecorps, B., 2026. Watching makes it worse: dairy calves are averse to watching another calf feed while hungry. Animal Behaviour, 211, pp. 123-132. https://doi.org/10.1016/j.anbehav.2025.123380

Lafon, C., Mendl, M.T. and Lecorps, B., 2024. Using the conditioned place preference paradigm to assess hunger in dairy calves: Preliminary results and methodological issues. Animal Welfare, 33, p.e24. https://doi.org/10.1017/awf.2024.24

Ledger, E.M., Ede, T., Mendl, M. and Lecorps, B., 2026. Calves disbudded with local nerve block and analgesic show conditioned place aversion two days later but not in the hours post-disbudding. Animal Welfare, 35, p.e25. https://doi.org/10.1017/awf.2026.10082

Ramirez Montes de Oca, M., Mendl, M.T. and Lecorps, B., 2024. An exploration of surface temperature asymmetries as potential markers of affective states in calves experiencing or observing disbudding. Animal Welfare, 33, p.e47. https://doi.org/10.1017/awf.2024.47

Rivero, M.J., Evans, A.C., Berndt, A., Cartmill, A., Dowsey, A., Farruggia, A., Mignolet, C., Enriquez-Hidalgo, D., Chadwick, D., McCracken, D.I., Busch, D., Eisler, M.C. and Lee, M.R.F., 2021. Taking the steps toward sustainable livestock: our multidisciplinary global farm platform journey. Animal Frontiers, 11(5), pp. 52–58. https://doi.org/10.1093/af/vfab048

Sharma, A., Randewich, L., Andrew, W., Hannuna, S., Campbell, N., Mullan, S., Dowsey, A.W., Smith, M., Hansen, M. and Burghardt, T., 2024. Universal Bovine Identification via Depth Data and Deep Metric Learning. Computers and Electronics in Agriculture, 220, p.109657. https://doi.org/10.1016/j.compag.2024.109657

St John Wallis, A., Held, S.D.E., Mendl, M.T., von Keyserlingk, M.A.G., Weary, D.M. and Lecorps, B., 2025. Pain and pessimism affect calves' play behaviours post-disbudding. Discover Animals, 2, p.10105. https://link.springer.com/article/10.1007/s44338-025-00105-7

Yu, P., Burghardt, T., Dowsey, A. and Campbell, N.W., 2025. Holstein-Friesian re-identification using multiple cameras and self-supervision on a working farm. Computers and Electronics in Agriculture, 229, p.110568. https://doi.org/10.1016/j.compag.2025.110568