Building a communicative pathway to reduce AMR; a study of cattle farmers’ perceptions of on-farm E.coli infections in the UK

PI: Ray Chan, University of Exeter.

Co-Is: Ross Booton (University of Bristol), Jonathan Tyrrell (University of Bristol / Cardiff University), Sion Bayliss (University of Bath), Lisa Morgans (Innovation for Agriculture).

Antimicrobial resistance (AMR) is a multifaceted and dynamic process driven by various One Health factors. Cattle farming is a significant reservoir of Escherichia. coli, a diarrheagenic pathogen with zoonotic potential, due to high antimicrobial use, high population densities and low host genetic diversity. Inappropriate usage of antibiotics in agricultural environments promotes increased AMR and therefore increases risk to both human and animal health.

Many studies illustrate transmission of antimicrobial resistant E. coli within the farm environment, including within the food chain, in cowsheds (Venegas-Vargas et al., 2016) and at the herd level. However, it remains unclear to what extent cattle farmers are aware of this information or are able to implement it into daily decision-making strategies for the use of antimicrobials on the farm.

We will develop a programme of work focusing on the co-production of knowledge between researchers and farmers to promote prudent antimicrobial usage and desirable disease management practices on farms. We use E. coli prevalence and antimicrobial resistance data aggregated from contemporaneous literature to produce a detailed transmission model of E. coli and antibiotic resistance on the UK cattle farm environment. The outputs of this model will be used to guide discussion with farmers in a workshop environment about behavioural changes promoting the reduction of AMR transmission on farms and to produce bespoke protocols for improved on farm biosecurity. Focus groups (pre- and post-workshop) will be used to both guide the topics for discussion and evaluate the influence of the programme on farmers’ understanding of AMR in the farm environment.