Why is there so much variability in crop multi-model studies?

• It is important to quantify sources of uncertainty in crop multi-model studies • Parameter uncertainty includes default values and calibration approach • Parameter uncertainty can be estimated using multi-model studies • In 3 case studies, parameter uncertainty contributed 69 % % of total uncertainty • Improved parameter estimates are essential for crop model improvement 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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Publication Details

Journal
Agricultural and Forest Meteorology
Published
2025-07-05
DOI
https://doi.org/10.1016/j.agrformet.2025.110697
Citations
3
Primary Topic
Climate change impacts on agriculture
Type
article
Field-Weighted Citation Impact
9.08

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article

Why is there so much variability in crop multi-model studies?

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3 citations
Agricultural and Forest Meteorology
Climate change impacts on agriculture
9.08
article

Why is there so much variability in crop multi-model studies?

Zvi Hochman, Elisabet Lewan, Taru Palosuo, Budong Qian, Éric Justes, Jørgen Eivind Olesen, Claas Nendel, Senthold Asseng, Ke Liu, Sebastian Gayler, Marie Launay, Xenia Specka, Roberto Ferrise, Gerrit Hoogenboom, Kurt Christian Kersebaum, Fasil Mequanint Rettie, Henrike Mielenz, Diana-Maria Seserman, Benjamin Dumont, Vakhtang Shelia, Daniel Wallach, Johannes Wilhelmus Maria Pullens, Matthew Tom Harrison, Afshin Ghahramani, Gloria Padovan, Samuel Buis, Lutz Weihermüller, Sabine J. Seidel, Peter J. Thorburn, Qi Jing, Amir Souissi, Jing Wang, Mingxia Huang, Tobias Karl David Weber, Qunying Luo
article en
3 citations

Abstract

• It is important to quantify sources of uncertainty in crop multi-model studies • Parameter uncertainty includes default values and calibration approach • Parameter uncertainty can be estimated using multi-model studies • In 3 case studies, parameter uncertainty contributed 69 % % of total uncertainty • Improved parameter estimates are essential for crop model improvement 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.

Agricultural and Forest MeteorologyVol. 372
University of Hohenheim (DE), Commonwealth Scientific and Industrial Research Organisation (AU), University of Tasmania (AU), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (FR), Julius Kühn-Institut (DE), Agriculture and Agri-Food Canada (CA), University of Bonn (DE), University of Liège (BE), Université d'Avignon et des Pays de Vaucluse (FR), Université de Montpellier (FR), Gembloux Agro-Bio Tech (BE), Queensland Government (AU), Swedish University of Agricultural Sciences (SE), Natural Resources Institute Finland (FI), University of Florida (US), Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (FR), Czech Academy of Sciences, Global Change Research Institute (CZ), Leibniz Centre for Agricultural Landscape Research (DE), Agriculture and Food (AU), Ottawa Research and Development Centre, University of Florence (IT), China Agricultural University (CN), Technical University of Munich (DE), University of Göttingen (DE)
National Science Foundation, U.S. Department of Agriculture, Commonwealth Scientific and Industrial Research Organisation, Grains Research and Development Corporation, Deutsche Forschungsgemeinschaft, University of Southern Queensland, Bundesministerium für Bildung und Forschung, China Scholarship Council, Institut National de la Recherche Agronomique, Priority Academic Program Development of Jiangsu Higher Education Institutions, Higher Education Discipline Innovation Project, National Institute of Food and Agriculture, Agriculture and Agri-Food Canada, National Key Research and Development Program of China, National Science Fund for Distinguished Young Scholars, BonaRes
Zero hunger
Openalex Percentile: Top 3%
Climate change impacts on agriculture
9.08
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