Bridging literature and models: a workflow for creating agricultural datasets for model application using AI

Agricultural simulation models can provide insights into complex crop-soil interactions, potentially informing decisions around sustainable cropping system management. However, their use is limited by insufficient datasets for model testing.Published research describing experiments and presenting results are a valuable source of information for model development and calibration.