Date of Award
5-2024
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Economics
Committee Chair/Advisor
Dr. Matthew Lewis
Committee Member
Dr. Babur de Los Santos
Committee Member
Dr. Anastasia Thayer
Abstract
Weather index-based crop insurance has promised to be a lifeline for low-income farmers in developing countries. However, high-basis risk, high administration costs, and low willingness to pay have caused the demise of many pilot programs. Building onto the principles of index insurance we propose Climate Insurance, a new form of insurance based on predictive crop models that overcomes most of the primary objections of index insurance while achieving its goals of low administration costs, greatly reduced adverse selection, and the elimination of moral hazard. The crop model presented herein utilizes an artificial neural network to predict county- level yield per acre given only weather data for a growing season and the previous high yield. This model was developed using 93 years of historical NOAA weather and USDA corn yield data for 53 counties in Iowa, though we believe it to be fully transferrable to other locations and crops. It has an RMSE of only 12.47 for 2022 weather data and an RRMSE of 6.4%.
Recommended Citation
Denning, Scott A., "Climate Insurance - Using Weather Data and AI to Manage Agricultural Risk in Developing Countries" (2024). All Theses. 4226.
https://tigerprints.clemson.edu/all_theses/4226