Clinical outcomes in agricultural settings often require robust statistical models to ensure accurate evaluations of interventions and practices. The article employs Bayesian Hierarchical Models (BHMs) to analyse data collected from multiple sites across different regions of South Africa. The BHMs are designed to account for variability between sites while estimating treatment effects accurately. The study underscores the effectiveness and adaptability of BHMs for evaluating clinical outcomes in agricultural contexts. The results highlight the need for continued methodological refinement and wider adoption of these models in similar studies. Researchers are encouraged to use BHMs when conducting field research in agriculture, particularly focusing on enhancing model robustness through more comprehensive data collection and site-specific adjustments. The empirical specification follows $Y=\beta_0+\beta^\top X+\varepsilon$, and inference is reported with uncertainty-aware statistical criteria.