The chaos in calibrating crop models: Lessons learned from a multi-model calibration exercise. (November 2021) - Wallach, Daniel; Palosuo, Taru; Thorburn, Peter; Hochman, Zvi; Gourdain, Emmanuelle; Andrianasolo, Fety; Asseng, Senthold; Basso, Bruno; Buis, Samuel; Crout, Neil; Dibari, Camilla; Dumont, Benjamin; et al.
Abstract: Calibration, the estimation of model parameters based on fitting the model to experimental data, is among the first steps in many applications of process-based models and has an important impact on simulated values. We propose a novel method of developing guidelines for calibration of process-based models, based on development of recommendations for calibration of the phenology component of crop models. The approach was based on a multi-model study, where all teams were provided with the same data and asked to return simulations for the same conditions. All teams were asked to document in detail their calibration approach, including choices with respect to criteria for best parameters, choice of parameters to estimate and software. Based on an analysis of the advantages and disadvantages of the various choices, we propose calibration recommendations that cover a comprehensive list of decisions and that are based on actual practices. Highlights: We propose a new approach to calibration recommendations for process-based models. Approach is based on analyzing calibration in multi-model simulation exercises. Resulting recommendations are holistic and anchored in actual practice. We derive calibration recommendations for crop models used to simulate phenology. Recommendations concern: objective function, parameters to estimate, software used.