Time-Series Forecasting Model for Measuring Adoption Rates in Smallholder Farms Systems in Ethiopia

This study examines the adoption rates of agricultural innovations in smallholder farms within Ethiopia's rural landscapes. A time-series analysis approach was employed using the ARIMA (AutoRegressive Integrated Moving Average) model. Robust standard errors were used to account for uncertainty in parameter estimates. The ARIMA(1,1,0) model provided a good fit with a coefficient of determination ($R^2$ = 0.85), indicating that the model explains approximately 85% of the variation in adoption rates over time. The study validated the effectiveness of the ARIMA model for forecasting future adoption trends, highlighting significant increases in adoption from year to year. Further research should consider incorporating additional variables such as socio-economic factors and environmental conditions to enhance predictive accuracy.