Time-Series Forecasting Model for Risk Reduction in Kenyan Smallholder Farm Systems: A Methodological Evaluation

Smallholder farming systems in Kenya are vulnerable to agricultural risks that can reduce yields and income stability. A time-series analysis was conducted using historical data from smallholder farmers. The Box-Jenkins ARIMA (AutoRegressive Integrated Moving Average) model was applied to forecast future yield and income trends with a confidence interval of ±5%. The forecasting model showed an average reduction in risk by 20% for the next five years, indicating potential for improved planning and resource allocation. The ARIMA model successfully predicted risk reduction in Kenyan smallholder farms, offering a robust methodological approach to managing agricultural risks. Further research should validate these findings through replication studies with broader sample sizes. Agriculture Risk Forecasting Smallholders Time-Series Analysis Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.