Mapping the patterns and drivers of antibiotic use and environmental resistance in the Argentine beef industry.

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This project will build upon preliminary work conducted by Argentine and UK partners which has already identified the presence of AMR in beef feedlots and generated the first large-scale dataset of beef farm antibiotic usage data (334 farms) anywhere in the world. It will also build upon a small existing grant of £7,700 from UK ODA seed fund to help UoL academics develop a 'UK-Argentine Beef Antibiotic Research Network' which will be used to collect and analysis samples to optimize qPCR gene selection for environmental AMR quantification in beef farms, planned for Jan-June 2019. This preliminary work provides a very robust starting point for this project.The project will use veterinary and epidemiology expertise and experience in quantifying antibiotic usage and agri-health economics expertise in mapping food value chains to build a theoretical antibiotic surveillance framework. This framework will be assessed against the available sources of information in other LMIC's to test its transferability especially in less well developed livestock systems including Kenya and Ethiopia, through existing projects which the UK the collaborators are engaged in (HORN project). The system would integrate all the available information on antibiotic prescribing and antibiotic usage along with the farm management practices across the whole range of beef farming systems in Argentina. We will then categorize antibiotic use at farm level recruiting between 200 - 1000 farms from an existing representative network of 4500 farms (organized via SENASA) into different management system types (eg. breeding, growing, feedlot finishing) and correlate the management practices used in these farms with their patterns of antibiotic usage by using the surveillance framework. The environmental AMR population load and diversity will be assessed in a stratified random sub-sample of (n = 50) of the farms according to management system type and AMU level & usage practices. Multi-level modelling, cluster and principal component analysis will be used to quantify the agreement between the classification of farms based upon the AMU surveillance framework and the microbiological results to validate the efficacy of this means of risk assessment.Deeper molecular analysis will be conducted on samples from farms with evidence of the most significant AMR diversity and load relating to High Priority Critically important antibiotics. This will include whole genome metagenomics and culture based antibiotic phenotype sensitivity to understand the population structure of the resistance profile of the most significant AMR environments. This phase of the project will allow us to use risk factor analysis to identify critical control points in the mode of antibiotic use in beef farms. This will be achieved by linking the molecular epidemiology data to detailed quantitative and qualitative interviews with farmers to understand the drivers of antibiotic use.The final phase of the project will co-develop practical AMR reduction interventions and policy advice of regulatory authorities. All the interventions recommended will be based up the risk factor analysis and their economic impact tested by cost - effectiveness analysis to identify in a clear a reproducible manner the most appropriate control measures for each farm management system. The qualitative interviews conducted with each farmer in the study will inform decision making on barriers to adoption of individual measures and feed into the co-development of policy advice to the beef industry. The collaboration provides the exciting opportunity to compare and contrast approaches in the UK and Argentina to maximizing farmer adoption of new practices and advice. The project would employ two post-doctoral researchers (PDRAs) directly through the UoL. Whilst these two PDRA's will be employed by UoL they will be supervised jointly by UK and Argentine partner senior academics.