Evaluating the AgMIP calibration protocol for crop models; case study and new diagnostic tests
2025
Wallach, Daniel | Kim, Kwang, Soo | Hyun, Shinwoo | Buis, Samuel | Thorburn, Peter | 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 | Susanne, Schulz | Seserman, Diana-Maria | Shelia, Vakhtang | Weihermüller, Lutz | Palosuo, Taru | Universität Bonn = University of Bonn | Seoul National University [Seoul] (SNU) | Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH) ; Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE) | CSIRO Agriculture and Food (CSIRO AF) ; Commonwealth Scientific and Industrial Research Organisation [Australia] (CSIRO) | Julius Kühn-Institut (JKI) | Institute of Crop Science and Resource Conservation [Bonn] (INRES) ; Rheinische Friedrich-Wilhelms-Universität Bonn | Oklahoma State University [Stillwater] (OSU) | Université de Liège = University of Liège = Universiteit van Luik = Universität Lüttich (ULiège) | Ferdowsi University of Mashhad (FUM) | University of Florida [Gainesville] (UF) | Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad) | Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF) | Agroclim (AGROCLIM) ; Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE) | Università degli Studi di Firenze = University of Florence = Université de Florence (UniFI) | Institute for BioEconomy [Sesto Fiorentino] (IBE | CNR) ; National Research Council of Italy | Consiglio Nazionale delle Ricerche (CNR) | Ottawa Research and Development Center | Forschungszentrum Jülich = Research Center Juelich (FZ Juelich) | Natural Resources Institute Finland (LUKE) | German Research Foundation) under Germany’s Excellence Strategy - EXC 2070 – 390732324, by funds of the Federal Ministry of Food and Agriculture (BMEL) based on a decision of the Parliament of the Federal Republic of Germany via the Federal Office for Agriculture and Food (BLE), grant number 2822ABS010, IntercropVALUES, grant agreement No 101081973), the Federal Ministry of Education and Research (BMBF) BonaRes Project I4S (grant Number 031B1069B), (Project No. RS-2024-00361442.(CZ.02.01.01/00/22_008/0004635)
International audience
Show more [+] Less [-]English. 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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