Résumé
Recent advances in high-performance computing (HPC) and cloud infrastructures are transforming agricultural research by enabling large-scale simulations of cropping systems to explore climate change adaptation strategies. Gridded crop modeling platforms provide valuable tools for this purpose (Franke et al., 2020), but they are often restricted to a single Process-based crop model (PBM), tied to specific applications or computing environments (CE), and limited in computational efficiency. This raises key questions: how can we foster the integration and comparison of multiple models within a unified framework, improve the scalability and efficiency of spatially explicit multi-model simulations across heterogeneous CE, and reduce barriers to deploy these large-scale simulations across diverse CE?