Why is there so much variability in crop multi-model studies?
2025
Wallach, Daniel | Palosuo, Taru | Mielenz, Henrike | Buis, Samuel | Thorburn, Peter | Asseng, Senthold | Dumont, Benjamin | Ferrise, Roberto | Gayler, Sebastian | Ghahramani, Afshin | Harrison, Matthew Tom | Hochman, Zvi | Hoogenboom, Gerrit | Huang, Mingxia | Jing, Qi | Justes, Eric | Kersebaum, Kurt Christian | Launay, Marie | Lewan, Elisabet | Liu, Ke | Luo, Qunying | Mequanint, Fasil | Nendel, Claas | Padovan, Gloria | Olesen, Jørgen Eivind | Pullens, Johannes Wilhelmus Maria | Qian, Budong | Seserman, Diana-Maria | Shelia, Vakhtang | Souissi, Amir | Specka, Xenia | Wang, Jing | Weber, Tobias K.D. | Weihermüller, Lutz | Seidel, Sabine, J | Universität Bonn = University of Bonn | Natural Resources Institute Finland (LUKE) | Technische Universität Braunschweig = Technical University of Braunschweig [Braunschweig] | 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 Entomology ; Commonwealth Scientific and Industrial Research Organisation [Australia] (CSIRO) | University of Applied Sciences [Munich] | Université de Liège = University of Liège = Universiteit van Luik = Universität Lüttich (ULiège) | Università degli Studi di Firenze = University of Florence = Université de Florence (UniFI) | Universität Hohenheim = University of Hohenheim | Queensland Government - Department of Agriculture and Fisheries (DAF) | University of Tasmania [Hobart] (UTAS) | CSIRO Atmospheric Research ; Commonwealth Scientific and Industrial Research Organisation [Australia] (CSIRO) | University of Florida [Gainesville] (UF) | China Agricultural University (CAU) | Ottawa Research and Development Center | Département Performances des systèmes de production et de transformation tropicaux (Cirad-PERSYST) ; 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) | Swedish University of Agricultural Sciences = Sveriges lantbruksuniversitet (SLU) | Hillridge Technology Pty Ltd | Aarhus University [Aarhus] | University of Ottawa [Ottawa] | University of Saskatchewan [Saskatoon, Canada] (U of S) | University of Kassel | Jülich Center for Structural Biology [Jülich] (JuStruct) | Universität für Bodenkultur Wien = University of Natural Resources and Life Sciences [Vienne, Autriche] (BOKU) | (Project 7: Stochastic Modelling Framework), funded by the German Research Foundation (DFG, Grant Agreement SFB 1253/1 2017), the Academy of Finland through projects AICropPro (316172) and DivCSA (316215), the BonaRes projects ''Soil3′' (BOMA 03037514) and “I4S” (031B053I) of the Federal Ministry of Education and Research (BMBF)Germany’s Excellence Strategy - EXC 2070 – 390732324 EXC (PhenoRob), the project BiomassWeb of the GlobeE programme (Grant number: FKZ031A258B) BonaRes Centre for Soil Research, subproject B” (grant 031B0511B), the National Key Research and Development Program of China (2017YFD0300205), the National Science Foundation for Distinguished Young Scholars (31725020)Department of Agriculture National Institute of Food and Agriculture (award no. 2015-68007-23133) and USDA/NIFA HATCH grant N. MCL02368, the National Key Research and Development Program of China (2016YFD0300105)(D.M. 24064/7303/15 of 6/Nov/2015), the SYSTEMIC funded by JPI HDHL, JPI-OCEANS and FACCE-JPI under ERA-NET (n.696295), the SustEs project funded by the Ministry of Education, Youth and Sports of the Czech Republic (CZ.02.1.01/0.0/0.0/16_019/000797)Project UOT1906-002RTX). GRDC’s National Variety Trial Network" project (CSA00027)
International audience
Afficher plus [+] Moins [-]anglais. It has become common to compare crop model results in multi-model simulation experiments. In general, one observes a large variability in such studies, which reduces the confidence one can have in such models. It is important to understand the causes of this variability as a first step toward reducing it. For a given data set, the variability in a multi-model study can arise from uncertainty in model structure or in parameter values for a given structure. Previous studies have made assumptions about the origin of parameter uncertainty, and then quantified its contribution, generally finding that parameter uncertainty is less important than structure uncertainty. However, those studies do not take account of the full parameter variability in multi-model studies. Here we propose estimating parameter uncertainty based on open-call multi-model ensembles where the same structure is used by more than one modeling group. The variability in such a case is due to the full variability of parameters among modeling groups. Then structure and parameter contributions can be estimated using random effects analysis of variance. Based on three multi-model studies for simulating wheat phenology, it is found that the contribution of parameter uncertainty to total uncertainty is, on average, more than twice as large as the uncertainty from structure. A second estimate, based on a comparison of two different calibration approaches for multiple models leads to a very similar result. We conclude that improvement of crop models requires as much attention to parameters as to model structure.
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