Determination of optimal maize unit area of insurance to support agriculture insurance

Agriculture is vital for food security, economic stability, and rural livelihoods but is highly vulnerable to climate risks, making index insurance essential for financial protection. However, the effectiveness of index insurance depends on the ability to estimate yield variability across different regions, which influences basis risk. In addition, index insurance designs that adopt administrative boundaries as unit areas of insurance (UAI) amplify basis risk. We build on existing research by proposing a more homogeneous agro-ecologically defined UAIs, hypothesizing that these refined units will improve yield predictability and reduce basis risk in index insurance. To test this approach, we identified key variables—including satellite-derived vegetation indices and climate data—to assess their ability to capture maize yield variability in Kenya’s maize-growing regions. We then applied
K-means clustering to delineate homogeneous UAIs, ensuring greater spatial accuracy and yield uniformity within each zone. Five distinct clusters were found to provide better performance in zone delineation, with average silhouette coefficient and variance reduction of 0.59 and 0.65, respectively. Our findings reveal that agroecologically defined UAIs outperform traditional administrative boundaries in yield predictability, improving the coefficient of determination from 67% to 72% and reducing the root mean square error from 0.24 to 0.14 mt/ha. Furthermore, Tukey’s test indicates that the optimized zones have significant differences in yield and other agro-ecological variables. These results demonstrate that refined UAIs can effectively reduce basis risk, offering valuable insights for insurance providers and policymakers striving to enhance the accuracy, fairness, and reliability of agricultural index insurance schemes.