Mitigating basis risk in weather index-based crop insurance: harnessing models and big data to enable climate-resilient agriculture in India

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Livelihoods of millions of smallholder farmers across the developing world are under threat from extreme weather events, such as droughts, floods, and heatwaves, with risks projected to increase significantly in future years due to climate change. Crop insurance protects farmers against financial risks posed by extreme weather events, and has been widely advocated as a tool to help farmer households to escape poverty traps and invest in climate-smart high-productivity agriculture. Yet, to date, the success and uptake of crop insurance schemes across the developing world has been extremely limited. Several reasons can be identified for problems in scaling crop insurance in developing countries. Traditional indemnity-based insurance schemes require time-consuming verification of actual losses experienced by individual farmers resulting in high transaction costs, claims disputes and delays that deter farmers from purchasing insurance. To counteract these issues, governments and insurers seek to develop more cost-effective and reliable tools to determine when, and at what level, insurance should payout to farmers if an extreme weather event occurs. Parametric insurance, for example weather index-based insurance, triggers payouts based on pre-established relationships between meteorological indices and crop yields, removing the need for expensive crop loss assessments. However, a major challenge for current weather index-based insurance is that payouts often are poorly correlated with farmers' actual yield losses, a problem known as 'basis risk', creating a major barrier to use of index insurance for climate risk mitigation.In this context, how can scientists contribute to the design of smarter index insurance products that meet the needs of farmers, insurers, and governments? The overall aim of this project is to improve the current poor performance of index-based crop insurance by using state-of-the-art environmental modelling and big datasets to reduce basis risk and better protect farmers against weather risks. Our proposed research will develop novel weather index-based insurance contracts that reliably and accurately predict weather-related crop yield losses by combining crop growth modelling, satellite and smartphone imagery of crop growth status, and high-resolution gridded estimates of spatial weather variability. Importantly, our work will produce novel tools and approaches that address two stated needs of the index insurance sector: (i) to reduce temporal basis risk by designing weather index triggers that reflect accurately how yield sensitivity to extreme events varies during the growing season, and (ii) to minimise spatial basis risk by exploiting datasets that capture spatial heterogeneity in weather conditions, crop development, field conditions and management practices. Working in collaboration with HDFC ERGO General Insurance, a major provider of weather index-based insurance for smallholder farmers across India, we will apply these approaches to design and test new weather index-based insurance products to protect farmers in the major agricultural states of Punjab and Haryana - the breadbasket of India - against combined production risks from extreme temperature and heavy rainfall events. Leveraging unique field data collected through the recent IFPRI-HDFC Picture-Based Crop Insurance (PBI) Project, we will conduct agro-economic impact evaluations to quantify reductions in basis risk, increases in farmer welfare and demand for insurance from our new contracts relative to both current index insurance products and government area-yield insurance schemes. Our work will contribute directly to improvements in the quality of index insurance for farmers in India, and, more broadly, will provide the scientific foundations for weather index-based insurance to more effectively support climate-smart smallholder agriculture across the developing world.