Clinical outcomes in smallholder farm systems have been studied extensively, but methods for longitudinal analysis are often underutilized. A longitudinal study employing a Bayesian hierarchical model was conducted on smallholder farm systems in South Africa. The model accounts for spatial, temporal, and individual variability within the data. The analysis revealed significant variation in clinical outcomes across different farms over two years, with a notable increase in disease prevalence among poultry from 20% to 45%. The Bayesian hierarchical model provided robust insights into the dynamics of smallholder farm health and identified key areas for intervention. Policy makers should consider targeting interventions where clinical outcomes are most critical, based on our findings. The empirical specification follows $Y=\beta_0+\beta^\top X+\varepsilon$, and inference is reported with uncertainty-aware statistical criteria.