Crop simulation models are essential tools in agricultural research and crop systems analysis, where they are typically used to forecast crop performance under varying environmental conditions. However, their potential as decision-support systems in crop management remains largely untapped. Crop models require diverse input data, including crop trial measurements, soil surveys, weather time series, and remote sensing data. Integrating these heterogeneous sources requires expertise across multiple disciplines and robust workflows for data integration, transformation, and quality control. Additionally, the output data from these models require extensive quality checking, annotation, and preparation for publication and long-term archiving. Current challenges include data quality and format heterogeneity, dispersed data sources, and the lack of domain-specific standards adopted by both agronomists and modelers, making the data preparation process cumbersome and non-replicable. Use Case 6 of FAIRagro aims to establish research data management guidelines for agronomists to facilitate data exchange between data producers and data users. We will develop FAIR-compliant workflows to streamline data processing from field experiments to simulations. This includes integrating heterogeneous data sources—such as manual measurements, drone images, and field sensors—deriving valid model inputs from raw data, automating simulations and parameter fitting, and documenting and archiving all (meta)data in an online catalog. All the developed tools will be implemented in a pilot trial to demonstrate the decision-support capabilities of crop models and the potential of FAIR research data management for fostering collaboration among agronomists and modelers through data exchange.