Index-based insurance and hydroclimatic risk management in agriculture: A systematic review of index selection and yield-index modelling methods

In this study we systematically reviewed the available literature on weather index insurance design between 2001 and 2020. In recent years, there has been a marked increase in ex-ante studies of index-based insurance as a financial risk management tool for agricultural risks. New and improved indices have emerged, and new methods for quantifying the yield-index relationship have been explored to minimise basis risk in contract design. Our review indicated that rainfall-followed by temperature-based indices were most prevalent, while indices based on droughts and floods, vegetation, soil moisture, humidity, and sunshine hours were underrepresented despite their demonstrated potentials. Ordinary least square-based correlation and linear regression methods dominated yield-index modelling, while methods addressing extremes such as quantile regression and copulas have increased prominence in developed countries recently. We highlighted several new research trends to guide future research studies, in particular, the use of remote sensing data and hydrological and crop modelling to address data scarcity, geographical basis risk, and climate change.

Automated summary

Research on weather index insurance design has been increasing in recent years, with new indices and methods emerging. Rainfall and temperature-based indices are most prevalent, while other promising indices are underrepresented. Ordinary least square-based correlation and linear regression methods dominate yield-index modeling.