Time-Series Forecasting Model Evaluation for Cost-Effectiveness Analysis in Ugandan Smallholder Farm Systems

Ugandan smallholder farm systems face challenges in optimising resource allocation to enhance productivity and profitability. The study employed an autoregressive integrated moving average (ARIMA) model to predict costs and benefits over time in smallholder farm systems. Robust standard errors were used to assess the statistical significance of the model's parameters. A significant positive relationship was observed between predicted cost savings and increased output, indicating that better forecasting can lead to more effective resource management. The ARIMA model demonstrated effectiveness in measuring cost-effectiveness for Ugandan smallholder farms, with a confidence interval of the forecasted costs providing robust uncertainty bounds. Further research should explore integrating climate data into the model to enhance its predictive accuracy and applicability across different environmental conditions. The empirical specification follows Y=₀+^ X+, and inference is reported with uncertainty-aware statistical criteria.