Methodological Evaluation of Smallholder Farm Systems in Rwanda Using Time-Series Forecasting Models

Smallholder farming in Rwanda is a critical sector for agricultural productivity and rural livelihoods. The study employs ARIMA (AutoRegressive Integrated Moving Average) model to analyse time-series data from Rwanda's Ministry of Agriculture. The ARIMA(2,1,0)[0] model showed an adoption rate trend with a confidence interval around the mean forecast value of 95%. ARIMA models effectively predict smallholder farm technology adoption over time in Rwanda, offering insights for policy makers and farmers. Continue monitoring trends using ARIMA analysis for future intervention planning. Smallholder farming, Rwanda, Time-series forecasting, ARIMA model, Adoption rates The empirical specification follows $Y=\beta_0+\beta^\top X+\varepsilon$, and inference is reported with uncertainty-aware statistical criteria.