Surrogate impact modelling for crop yield assessment with nested RNNs

Climate variability and change significantly influence crop production, presenting challenges that extend from understanding the basic crop growth principles to evaluating the effects of extreme weather events on crop development. Addressing this requires effective agro-management strategies guided by tailored climate services. However, a critical gap exists between scientific insights and their practical application. This study introduces and evaluates an AI-driven methodology designed to simulate crop growth and predict grain maize yields across Europe. Specifically, nested Recurrent Neural Networks (RNNs) are tested as a computationally efficient surrogate model for the process-based ECroPS model developed by the European Commission’s Joint Research Centre. Traditional mechanistic crop models, like ECroPS, require numerous meteorological inputs and significant computational resources, limiting scalability for applications such as large-scale climate simulations or ensemble modeling that explore variables like climate projections and CO₂ effects. In contrast, the surrogate AI model relies on just three weather inputs—daily minimum and maximum temperatures and daily precipitation—trained using ECMWF-ERA5 reanalysis data. This streamlined approach demonstrates the potential to bridge the gap between resource-intensive crop modeling and scalable, data-driven solutions for climate impact assessments.