Uganda's agricultural sector is crucial for national economic stability, with livestock health monitoring being a significant aspect of this sector. The study employs advanced statistical techniques including ARIMA (AutoRegressive Integrated Moving Average) for forecasting and evaluating the reliability of agricultural monitoring systems. The application of time-series models has revealed robust system performance metrics, which are essential for improving surveillance and management strategies. Further research should focus on integrating these findings into policy frameworks to enhance agricultural productivity and sustainability. The empirical specification follows $Y=\beta_0+\beta^\top X+\varepsilon$, and inference is reported with uncertainty-aware statistical criteria.