A Bayesian Ensemble Projection of Climate Change and Technological Impacts on Future Crop Yields

TL;DR

This paper presents a Bayesian hierarchical model for crop yield prediction under climate change, improving uncertainty quantification and informing global food security strategies.

Contribution

It extends existing crop-yield models by incorporating country-specific variances and decomposes predictive uncertainty, enhancing accuracy and transparency.

Findings

Improved calibration and probabilistic accuracy in wheat yield projections.

Systematic decomposition of uncertainty sources.

Enhanced model flexibility with country-specific error variances.

Abstract

This paper introduces a Bayesian hierarchical modeling framework within a fully probabilistic setting for crop yield estimation, model selection, and uncertainty forecasting under multiple future greenhouse gas emission scenarios. By informing on regional agricultural impacts, this approach addresses broader risks to global food security. Extending an established multivariate econometric crop-yield model to incorporate country-specific error variances, the framework systematically relaxes restrictive homogeneity assumptions and enables transparent decomposition of predictive uncertainty into contributions from climate models, emission scenarios, and crop model parameters. In both in-sample and out-of-sample analyses focused on global wheat production, the results demonstrate significant improvements in calibration and probabilistic accuracy of yield projections. These advances provide policymakers and stakeholders with detailed, risk-sensitive information to support the development of more resilient and adaptive agricultural and climate strategies in response to escalating climate-related risks.