A Relational Model for Predicting Farm-Level Crop Yield Distributions in the Absence of Farm-Level Data

Abstract

Individual farm-level expected yields serve as the foundation for crop insurance design and rating. Therefore, constructing a reasonable, accurate, and robust model for the farm-level loss distribution is essential. Unfortunately, farm-level yield data is often insufficient or unavailable in many regions to conduct sound statistical inference, especially in developing countries. This paper develops a new two-stage relational model to predict farm-level crop yield distributions for a country (base country) in the absence of farm yield losses, through “borrowing” information from a neighbouring country (reference country), where detailed farm-level yield experience is available. Detailed farm-level and county-level corn yield data in the U.S. and Canada are used to empirically validate the performance of the proposed relational model. Empirical results show that the approach developed in this paper can predict farm-level data accurately and hence may be useful in improving yield forecasts and pricing in the case where farm-level data is limited or not available. Further, this approach may also help to address the issue of aggregation bias, when county-level data is used as a substitute for farm-level data, which tend to result in underestimating the predicted risk relative to the true risk.