The chaos in calibrating crop models

Abstract

Calibration, the estimation of model parameters based on fitting the model to experimental data, is among the first steps in many applications of system models and has an important impact on simulated values. Here we propose and illustrate a novel method of developing guidelines for calibration of system models. Our example is calibration of the phenology component of crop models. The approach is based on a multi-model study, where all teams are provided with the same data and asked to return simulations for the same conditions. All teams are 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. HighlightsO_LIWe propose a new approach to deriving calibration recommendations for system models C_LIO_LIApproach is based on analyzing calibration in multi-model simulation exercises C_LIO_LIResulting recommendations are holistic and anchored in actual practice C_LIO_LIWe apply the approach to calibration of crop models used to simulate phenology C_LIO_LIRecommendations concern: objective function, parameters to estimate, software used C_LI