Dr Katharina Watson

Biography 

Dr Katharina Watson is an Associate Professor in Animal Science. She holds a PhD in Veterinary Epidemiology from the University of Warwick. Before joining the RAU in 2020 she spent four years as a postdoctoral researcher working in the areas of livestock disease, antimicrobial resistance and livestock precision technologies. She is an active researcher and research grant holder of a range of research projects in the areas of animal science, veterinary epidemiology, AgriTech and environmental sciences.

University responsibilities:

Associate Professor in Animal Science.

Research Culture Coordinator.

Qualifications

  • 2013 - 2017: Doctor of Philosophy (PhD) in Life Sciences University of Warwick, Project Title: Persistence of Dichelobacter nodosus, the causal agent of ovine footrot.   
  • 2009 - 2012: BSc (first class Hons) in Animal Science University of Essex/Writtle College, Dissertation title: The effect of providing a plant extract or a high intensity sweetener on welfare and performance of weanling pigs   

Further Education

  • 2021: Postgraduate Certificate in Academic Practice, University of Gloucestershire
  • 2017: Postgraduate Certificate in transferable skills in science, University of Warwick
  • 2016: Postgraduate Award in Teaching and Learning in HE, University of Warwick

Membership of professional bodies

2021: Fellow of the Higher Education Academy

Research

Research keywords

Animal Health and welfare, infectious diseases of wild, domestic and farm animals, epidemiology, antimicrobial resistance, microbiology, molecular biology, proteomics/mass spectrometry, precision livestock technologies.

ORCID

https://orcid.org/0000-0001-6037-0618

Current research projects

  • Breeding Better Beef and Sheep: Co-designed breeding strategies to achieve net zero (2024-2025, UKRI Agri-food for Net Zero Network)
  • A bright future: The use of Far UV as a disinfectant in the livestock industry—Feasibility studies in the dairy and poultry sectors (2023-2025, Innovate UK Farming Innovation programme)
  • 2023 – 2025: Academic PI, Innovate UK Farming Innovation programme: A bright future: The use of Far UV as a disinfectant in the livestock industry—Feasibility studies in the dairy and poultry sectors. In collaboration with Applied Group and UVcare 222. £321,000 award to RAU 160,000

Completed research projects

  • 2022 – 2023: C0-PI BBSRC endemic livestock disease initiative: Unravelling Enterococcus cecorum infection in UK broilers. In collaboration with APHA, Queens University Belfast and Slate Hall Veterinary Group: £250,000 awarded to RAU £50,000

PhD students

  • Iris McCormick: Virtual livestock fencing and animal welfare 
  • Jennifer Howse: Supporting farmer wellbeing

Teaching

  • 1441 Applied Animal Science
  • 1431 Wildlife handling, ethics and conservation
  • 1430 Wildlife Ecology and Survey
  • 3333: Wildlife behaviour and evolution

Publications

  • Eltholth, M.,  Hurley, P., Morgans, L., Pearl, J.E., Sakrabani, R.,  Shorthall, O., Watson, K., and Cole, J. (2024). Understanding the interests of academics from diverse disciplines to identify the prospective focus for a UK-based transdisciplinary network involving farm-to-fork stakeholders on antimicrobial resistance in agrifood systems: An online survey. One Health,19. https://doi.org/10.1016/j.onehlt.2024.100884.
  • Watson, K., Arais, L., Green, S.,  O'Kane, P., Kirchner, M., Demmers, T., Commins, C., Smith, R.,  Cordoni, G., Kyriazakis, I., Schock, A., Anjum, M.F (2024).Towards the identification of transmission pathways and early detection of Enterococcus cecorum infection in broiler chickens, Poultry Science, 103 (11).https://doi.org/10.1016/j.psj.2024.104224.
  • Mclaughlin, D., Bradley, A., Dottorini, T., Giebel, K., Leach, K., Hyde, R., Green, M. (2022) Identifying associations between management practices and antimicrobial resistances of sentinel bacteria recovered from bulk tank milk on dairy farms. Preventive Veterinary Medicine, 105666, https://doi.org/10.1016/j.prevetmed.2022.105666
  • Esener, N., Marciel-Guerra, A., Giebel, K., Lea, D., Green., M.J., Bradley, A.J., Dottorini, T. (2021) Mass spectrometry and machine learning for the accurate diagnosis of benzylpenicillin and multi-drug resistance of Staphylococcus aureus in bovine mastitis. PLOS Computational Biology 17 (6) https://doi.org/10.1371/journal.pcbi.1009108s
  • Maciel-Guerra, A., Esener, N., Giebel, K. et al. (2021) Prediction of Streptococcus uberis clinical mastitis treatment success in dairy herds by means of mass  spectrometry and machine-learning, Nature Scientific Reports 11, 7736. https://doi.org/10.1038/s41598-021-87300-0
  • Giebel K., Green L.E., Purdy K.J. (2021). A Pilot Study to Investigate the Feasibility of a Multiple Locus Variable Number Tandem Repeat Analysis to Understand the Epidemiology of Dichelobacter nodosus in Ovine Footrot. Frontiers in Veterinary Science, 7,581342. https://doi.org/10.1016/j.vetmic.2017.11.017
  • Miller, G., Mitchell, M., Barker, Z., Giebel, K., Codling, E., Amory, J., Michie, C., Davison, Tachtatzis, C., Andonovic, I. and Duthie, C.-A, (2020). Using animal-mounted sensor technology and machine learning to predict time-to-calving in beef and dairy cows. Animal, 1-9. https://doi.org/10.1017/S1751731119003380
  • Clifton, R.1, Giebel, K.1, Liu, N.L.B.H., Purdy, K.J., Green. L.E. (2019) A paradigm shift on the persistence of Dichelobacter nodosus and Fusobacterium necrophorum in sheep and their environment. Nature Scientific Reports. 9, 14429.https://doi.org/10.1038/s41598-019-50822-9
  • Giebel, K, Green, L.E., Purdy, K.J.P. (2017) Persistence of Dichelobacter nodosus, the causal agent of ovine footrot. Proceedings of the British Society of Animal Science, 8 (1), p94.

External engagement and impact

Network memberships and roles

  • Animal Welfare Research network (AWRN): Member of the outreach committee
  • AMR in Agrifood Systems Transdisciplinary Network: Partner in stakeholder engagement in the animal compartment of one Health
  • BroilerNet
  • Calf Action Network
  • Agrifood for net zero (AFN) network