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Assess the effectiveness of a forecasting model for reducing agricultural risks in Kenyan smallholder farms.

March 15, 2026Open Access

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

Key Points

Assess the effectiveness of a forecasting model for reducing agricultural risks in Kenyan smallholder farms.
Time-series analysis of historical data from smallholder farmers.
Application of the Box-Jenkins ARIMA model for forecasting yields and income trends.
Evaluation of model performance using out-of-sample error measurement.
Forecasting model achieved an average risk reduction of 20% over the next five years.
Confidence intervals for forecasts set at ±5%.
The ARIMA model demonstrated utility in managing agricultural risks.

Abstract

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 =argmin_ᵢ (yᵢ, f_ (xᵢ) ) +₂², with performance evaluated using out-of-sample error.

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