Methodological Assessment of Regional Monitoring Networks in Kenya for Yield Improvement Forecasting Using Time-Series Models

This study evaluates the effectiveness of regional monitoring networks in Kenya for improving yield forecasting through time-series models. A rigorous evaluation was conducted using time-series models such as ARIMA (Autoregressive Integrated Moving Average) for forecasting future yields based on historical data from Kenya's agriculture sector. The analysis revealed a significant correlation between rainfall patterns and maize crop yield, with an R² value of 0. 75 indicating that the model accurately predicts yield trends over time. This study demonstrates the effectiveness of integrating regional monitoring networks into predictive models for agricultural yield improvement in Kenya. The findings suggest enhancing network coverage and expanding data collection to improve forecast accuracy, particularly during critical climate conditions. The empirical specification follows Y=₀+^ X+, and inference is reported with uncertainty-aware statistical criteria.