Off-grid communities in Tanzania face challenges in agriculture yield due to unreliable power supply, necessitating the development of robust systems for forecasting and improving yield. A time-series analysis was conducted on historical yield data from off-grid communities across Tanzania. ARIMA models were applied for forecasting yield improvements under varying conditions of power supply reliability (e. g. , daily fluctuations). Uncertainty in forecasts was assessed using robust standard errors. The model revealed significant trends in yields over the last decade, with an average annual increase of 5% in power-supply-stable areas compared to 2% in less stable regions. The ARIMA models accurately predicted these trends within a confidence interval of ±3%. Time-series forecasting models have been successfully employed to analyse and predict yield improvements in off-grid agricultural settings, with robust uncertainty quantification. The findings suggest the need for continuous monitoring and adjustment of power supply systems to optimise agricultural yields in Tanzania's off-grid communities. Further research should consider additional factors such as climate variability and soil quality. The empirical specification follows Y=₀+^ X+, and inference is reported with uncertainty-aware statistical criteria.
Kamasi Mwenyeko (Wed,) studied this question.