Explainable AI shows climate impacts on wheat yields: insights from 30 years of field data.

Abstract

Wheat (Triticum aestivum L.) is amongst the worlds most important staple crops and primary food source for an estimated 35% of the global population. Climate impacts have caused global yield stagnation and quantifying the climatic variables influencing wheat yield is critical to anticipate yield losses and design climate-resilient agricultural strategies. Here, we use a unique 30-year dataset on winter wheat variety trials in six sites across Switzerland, explainable artificial intelligence (XAI) and interpretable machine learning (IML) methods (i.e., decision trees and gradient boosting models combined with post hoc tests) to elucidate climate drivers on wheat yields. We showed based on 405 varieties and over 10,000 observations, that climatic variables such as cumulative solar radiation, precipitation from sowing to harvest and genotype makeup are significant yield drivers. Partial dependence plots and variable interaction analyses revealed, for example, a yield plateau above cumulative solar radiation levels of [~]3000 MJ m-{superscript 2}, suggesting complex genotype-by-environment interactions. These findings suggest that XAI adds important biological interpretability to predictive performance, and reveals the mechanisms how climate affects wheat yields. Our methodological framework and results can inform breeding activities, agronomic management, and adaptation strategies under climate change across environmental conditions in Switzerland and with global ramifications.