What is it about?
Crop model calibration is a burdensome step of using a crop model that entails choosing values for model parameters. Regression trees give a visual representation to the relationship of candidate values and model errors. They are also assessed as a calibration method, but this approach was not as successful as the outcome of given meaning to the relationships between parameter values.
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Why is it important?
Much of the uncertainty in a model may be associated with parameter values. This study gives them a tool to aid them in the parameter value choice.
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This page is a summary of: A role for regression trees in the calibration of a new process-based crop model, European Journal of Agronomy, October 2025, Elsevier,
DOI: 10.1016/j.eja.2025.127805.
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