Hybrid modelling for crop carbon cycle

Given the ongoing climate change and the increasing frequency of extreme weather events, accurately assessing their impacts on crop productivity is crucial for developing adaptation strategies to mitigate negative impacts and ensure sustainable food security in the future. Process-based crop models are the preferred tools to simulate and predict crop yields under climate change. However, due to the simplified representations of complex biophysical processes, these models generally introduce uncertainty when used to account for crop yield losses. Integrating process-based crop models with data-driven machine learning methods shows great promise. In our study, we are developing a hybrid crop model, particularly the carbon cycle components (photosynthesis, carbon allocation, soil carbon decomposition, etc.), based on the state-of-the-art process-based vegetation model LPJmL (Lund-Potsdam-Jena managed Land). The empirical processes and parameters in the carbon cycle of LPJmL are replaced or augmented with neural networks. The resulting hybrid crop model can leverage information from observational data to simulate previously unresolved processes while maintaining the process-based understanding. We showcase how the hybrid crop model generalizes from the LPJmL to capture the carbon cycle under unseen climate conditions.