AUTHOR=Sun Yan 

TITLE=Enhanced Weather-Based Index Insurance Design for Hedging Crop Yield Risk

JOURNAL=Frontiers in Plant Science

VOLUME=Volume 13 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2022.895183

DOI=10.3389/fpls.2022.895183

ISSN=1664-462X

ABSTRACT=In this paper, we propose an optimization-based weather-yield model to reduce the basis risk of weather-based index insurance. This weather-yield model helps us to capture the monthly variation of the growing season as it involves monthly explanatory weather indices. In addition, it can capture additional extreme weather effects by including extreme cooling or heating weather indices. This paper presents an innovative machine learning framework incorporating optimization approaches to ensure the parsimony of weather index models and the accuracy of crop yield predictions, which can be integrated into the conventional policy design and pricing process. The advantages of this modeling approach and the effectiveness of weather index-based insurance based on this model in reducing policy basis risk are demonstrated by applying county-level yield data for mid-season rice in Anhui Province, China.