Like most crop models, the CSM models require detailed data on management, soil physical and chemical conditions, and daily weather. Furthermore, for most applications, the model should be evaluated through comparison with measured crop responses. Obtaining all of the required data is often one of the biggest challenges for model applications.
We note that data quality is often problematic as well, so users should cross-compare or otherwise assess data whenever possible.
Through the links below we list multiple data resources. In many cases, the data are not pre-formatted for use in DSSAT, so the user must reformat the data and in some cases reformat or apply conversion factors. Various DSSAT tools can assist in this process, but for large volumes of similar data (e.g., numerous locations for weather data), it is often more efficient to process the data using scripting languages (e.g., Python) or statistical packages with utilities for reading and writing in different data formats.
The Agricultural Modeling Intercomparison and Improvement Project (AgMIP) has collaborated extensively with the DSSAT community in developing tools for data access and management.