Selecting crop variables and parameters for the calibration of a new cultivar in a crop model: A case study of winter wheat for STICS

Crop models need to be regularly updated with parameterizations for new cultivars, but this requires calibration, which is a major challenge. Using the winter wheat cultivar Rubisko as a case study, we applied for the first time on experimental data a new calibration protocol to estimate the parameters of the STICS crop model for this new cultivar with multi-trial experimental data. We tested the calibration protocol in different conditions, with or without LAI and/or biomass experimental data, and we found that the resulting LAI and biomass dynamics strongly diverged. This study contributes to provide guidance to modelers for the calibration of a new cultivar in a crop model by focusing on the selection of variables and parameters to estimate as well as criteria for evaluating calibration strategies. With an application to winter wheat for the STICS crop model, this study has shown that the choice of calibration steps has a major impact on simulated outputs, but with a strong dependence on the structure of the experimental dataset. Firstly, this paper provides a methodology for the selection of calibration variables and associated parameters based on three criteria: 1) the relevance of the values of the estimated parameters, 2) the bias part of the mean square error, and 3) the analysis of the residuals. Secondly, by applying this methodology, we have shown that calibration based on LAI measurements is the most robust in the case of sparse observed data at the end of the cycle. Based on these results, we recommend caution when including parameters related to radiation-use efficiency; in particular, they should not be calibrated together with parameters related to leaf growth on biomass data alone. This study has enabled an appropriate calibration strategy to be defined, which will allow more modern French wheat cultivars to be parameterized in the STICS crop model. • Including LAI and/or biomass data strongly impacts cultivar calibration. • Radiation-use-efficiency and LAI parameters should not be calibrated together on biomass data. • The analysis of estimated parameter values, errors and residuals enables calibration evaluation. • Data structure is a major limiting factor for calibration performance and evaluation. • Excluding biomass data from the calibration procedure is the best option for our case study.