Integrated Excel-Based Stochastic Model for Predicting Maize Crop Resilience under Climate Variability in Kenya

Abstract

Climate change is threatening agricultural systems in Sub-Saharan Africa due to unpredictable rainfall levels and increases in temperature, resulting in adverse effects on crop productivity and food security. This paper introduces a stochastic model for predicting crop resilience based on climate change in a shifting climate regime, specifically focusing on maize cultivation in Kenya. The analysis is based on a 30-year data set from 1995 to 2024 on annual rainfall levels, temperature changes, and maize yield. The stochastic modeling process includes the use of probabilistic tools like Monte Carlo simulations. Statistical measures, including yield variation, crop failure probability, and Crop Resilience Index (CRI), will be used to measure system stability under varying climates. Results show high inter-annual variability in precipitation, but a consistent rise in temperatures has led to highly variable levels of maize production. The results of simulation demonstrate that there is a moderately resilient nature of crops, with about 18% to 22% chances of a low yield in cases of unfavourable weather. This study concludes that rainfall variability plays the biggest role in affecting crop yields, whereas rising temperatures contribute to increased stress on the crops. Results shows that stochastic models based on Microsoft Excel can be used effectively as a low-cost decision support system for evaluating climate risks in resource-scarce regions.