Identifying robust climate drivers of wheat yield failure in Europe from high-dimensional, multivariate spatiotemporal data

Identifying the weather conditions that lead to crop yield failure is critical for early warning systems and climate adaptation planning. However, yield at harvest time is driven by nonlinear interactions between weather and other variables across different stages of plant development. While machine learning models excel at capturing such complex relationships from high-dimensional data, they can easily overfit to the dependencies inherent to spatiotemporal agroclimatic data. We apply a data-driven framework to multivariate observational data to identify key climate drivers of wheat yield failure in Europe. The method, previously validated using process-based crop model simulations, yields parsimonious sets of drivers that are able to effectively reproduce interannual variability, based on their contribution to the predictive performance of models across held-out spatial regions and years and in combination with different sets of predictive features. The resulting drivers are physically interpretable and align with agronomic understanding. In addition, using both observational data and process-based model simulations, we explore the impact of different model evaluation strategies on the drivers that are identified and the transferability of resulting models to unseen regions. The approach allows researchers to exploit the information available in high-resolution multivariate datasets using machine learning, while making use of parsimonious, interpretable statistical models. Beyond agriculture, this framework may be useful for the study, modelling and mapping of other societally relevant climate impacts, such as forest mortality, wildfires, floods, and landslides.