Assessment of antimicrobial resistance in dairy cattle within Green Valley Farm Industry

Thesis Abstract

The escalating global concern over antimicrobial resistance (AMR) necessitates targeted investigations into its prevalence and contributing factors within specific agricultural settings, such as dairy farming, which plays a pivotal role in food security and economic stability. This study aims to assess the prevalence, patterns, and determinants of antimicrobial resistance present in bacterial isolates obtained from dairy cattle within Green Valley Farm Industry, a leading dairy producer. The research specifically seeks to quantify antimicrobial resistance levels, identify resistant bacterial strains, and explore associations between antimicrobial use practices, farm management strategies, and resistance patterns. The study employs a cross-sectional survey design integrating microbiological, molecular, and statistical methods to provide a comprehensive understanding of the AMR scenario. The population comprises all lactating dairy cattle within Green Valley Farm Industry, totaling approximately 1,200 animals. A stratified random sampling approach selected 150 cattle from different herd groups to ensure representativeness across various management units. Data collection involved obtaining milk and fecal samples from selected animals, alongside administering structured questionnaires to farm managers and veterinarians to capture antimicrobial usage patterns, biosecurity measures, and healthcare records. Microbiological analysis included isolation and identification of common pathogenic bacteria such as Escherichia coli and Staphylococcus aureus using standard culture techniques. Antimicrobial susceptibility testing employed the Kirby-Bauer disc diffusion method following Clinical and Laboratory Standards Institute (CLSI) guidelines, with interpretation according to zone diameter breakpoints. Molecular characterization of resistance determinants focused on PCR detection of resistance genes such as blaCTX-M, mecA, and tet(M). Quantitative data were analyzed using descriptive statistics, chi-square tests, and logistic regression models to identify significant predictors of resistance. Hierarchical clustering and principal component analysis facilitated the visualization of resistance patterns and their associations with farm practices. Expected findings include a high prevalence of multidrug-resistant bacterial strains, particularly resistance to beta-lactams, tetracyclines, and aminoglycosides. The study anticipates identifying key risk factors such as unregulated antimicrobial use, inadequate biosecurity, and poor infection control practices that significantly correlate with higher resistance levels. These results are expected to reveal localized resistance trends, including the presence of mobile genetic elements facilitating horizontal gene transfer among bacteria. The research contributes novel insights into the epidemiology of AMR within dairy farm settings, highlighting the impact of farm management and antimicrobial stewardship programs on resistance development. The study's primary conclusion emphasizes the urgent need for implementing rigorous antimicrobial stewardship protocols, establishing farm-specific hygiene standards, and promoting prudent antimicrobial use to curtail the spread of resistance. Recommendations include developing targeted intervention strategies, training farm personnel on antimicrobial best practices, and establishing routine resistance monitoring frameworks. This research advances theoretical understanding by integrating the One Health framework with empirical microbiological data, illustrating the interconnectedness of animal health, antimicrobial use, and resistance dissemination. It provides a foundation for policymakers, veterinary practitioners, and farm managers to formulate evidence-based interventions aimed at mitigating AMR in dairy industry contexts. Overall, the findings underscore the importance of comprehensive antimicrobial resistance surveillance at the farm level and contribute to global efforts to combat AMR through localized, data-driven strategies.