Gaussian process modeling of nonstationary crop yield distributions with applications to crop insurance

Abstract

Purpose

The purpose of this paper is to bring out the development of a flexible model for nonstationary crop yield distributions and its applications to decision-making in crop insurance.

Design/methodology/approach

The authors design a nonparametric Bayesian approach based on Gaussian process regressions to model crop yields over time. Further flexibility is obtained via Bayesian model averaging that results in mixed Gaussian processes.

Findings

Simulation results on crop insurance premium rates show that the proposed method compares favorably with conventional estimators, especially when the underlying distributions are nonstationary.

Originality/value

Unlike conventional two-stage estimation, the proposed method models nonstationary crop yields in a single stage. The authors further adopt a decision theoretic framework in its empirical application and demonstrate that insurance companies can use the proposed method to effectively identify profitable policies under symmetric or asymmetric loss functions.

Citation

Wu, W., Wu, X., Zhang, Y.Y. and Leatham, D. (2021), "Gaussian process modeling of nonstationary crop yield distributions with applications to crop insurance", Agricultural Finance Review, Vol. 81 No. 5, pp. 767-783. https://doi.org/10.1108/AFR-09-2020-0144