Evaluating the AgMIP calibration protocol for soil-crop models: Case study and new diagnostic tests
2025
Wallach, Daniel | Kim, Kwang Soo | Hyun, Shinwoo | Buis, Samuel | Thorburn, Peter J. | Mielenz, Henrike | Seidel, Sabine Julia | Alderman, Phillip D. | Dumont, Benjamin | Fallah, Mohammad Hassan | Hoogenboom, Gerrit | Justes, Eric | Kersebaum, Kurt Christian | Launay, Marie | Leolini, Luisa | Mehmood, Muhammad Zeeshan | Moriondo, Marco | Jing, Qi | Qian, Budong | Schulz, Susanne | Palosuo, Taru
Crop simulation models are important tools in agronomy. Typically, they need to be calibrated before being used for new environments or cultivars. However, there is a large variability in calibration approaches, which contributes to uncertainty in simulated values, so it is important to develop improved calibration procedures that are widely applicable. The AgMIP calibration group recently proposed a comprehensive, generic calibration protocol that is directly based on standard statistical parameter estimation in regression models. Weighted least squares (WLS) is used to handle multiple response variables and forward regression using the corrected Akaike Information Criterion (AICc) is used to select the parameters to be calibrated. The protocol includes two adaptations, which are specific to each model and data set. First, initial approximations to the WLS parameters are obtained by fitting variables one group at a time. Secondly, “major” parameters are identified that are intended to reduce bias, analogously to the constant in linear regression. In this study, new diagnostic tools to be included in the protocol are proposed and tested in a case study. The diagnostics test whether the protocol does indeed lead to good initial approximations to the WLS parameters, and whether the protocol does indeed substantially reduce bias. These diagnostics provide in-depth understanding of the calibration process, reveal problems and help suggest solutions. The diagnostics should increase confidence in the results of the protocol. Having a reliable, generic calibration approach, like the augmented AgMIP protocol, is essential to using crop models more effectively.
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