Integrating Biophysical and Socio-Economic Factors to improve weather index insurance in Eastern Tigray, Ethiopia

Climate variability and recurrent droughts pose significant challenges to agricultural productivity and rural livelihoods in Ethiopia, particularly in drought-prone regions such as Eastern Tigray. Weather index insurance (WII) has emerged as a promising climate risk management tool; however, its effectiveness is often limited by high basis risk, which arises from the mismatch between index-based payouts and actual farm-level losses. This study aimed to improve the performance of weather index insurance by integrating biophysical and socioeconomic factors to enhance the accuracy and reliability of drought representation in Eastern Tigray, Ethiopia. The study utilized a combination of satellite-derived rainfall data (CHIRPS), vegetation indices (MODIS NDVI), and socio-economic data obtained through focus group discussions (FGDs) with farmers. Meteorological drought was assessed using the Standardized Precipitation Index (SPI), while agricultural drought was characterized using NDVI deviation (DevNDVI). Historical drought years were identified and ranked based on farmer recall and compared with results from SPI and NDVI analyses. The findings revealed some discrepancies between scientific drought indicators and farmer-reported experiences, highlighting the limitations of single-indicator approaches and the presence of significant basis risk. To address these challenges, the study developed an improved Weather Insurance Index (WII) that integrates SPI and NDVI deviation into a composite drought index. The model transforms these indicators into standardized drought severity scores and combines them using a weighted framework to better capture both climatic drivers and vegetation response. Additionally, a dekad-based (10-day) analysis was introduced to capture intra-seasonal variability, such as late onset and early cessation of rainfall, which are critical for crop growth and yield outcomes. The inclusion of farmer-reported drought data in the calibration process further enhanced the model’s relevance and alignment with local conditions. The results demonstrate that the integrated WII model improves the representation of drought conditions and reduces basis risk compared to traditional rainfall-based indices. By capturing both meteorological and agricultural dimensions of drought, as well as incorporating local knowledge, the model provides a more robust and reliable basis for insurance design. The study concludes that multi-indicator and multi-temporal approaches are essential for improving the effectiveness of weather index insurance in data-scarce and climate-vulnerable regions. The findings have important implications for policymakers, insurance providers, and development practitioners seeking to enhance climate risk management strategies. The proposed framework offers a scalable and adaptable solution that can be applied in similar agro-ecological contexts to improve resilience among smallholder farmers and support sustainable agricultural development.