Evaluating the AgMIP calibration protocol for crop models; case study and new diagnostic tests

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. • The AgMIP calibration protocol is based on standard statistical methods. • Model-specific choices concern the order of variables and bias reduction. • New diagnostic tools are proposed to test those choices. • The tools provide in-depth evaluation, reveal problems and suggest solutions. • The diagnostics are applied in a case study.

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Publication Details

Journal
European Journal of Agronomy
Published
2025-05-05
DOI
https://doi.org/10.1016/j.eja.2025.127659
Citations
5
Primary Topic
Crop Yield and Soil Fertility
Type
article
Field-Weighted Citation Impact
7.61

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article

Evaluating the AgMIP calibration protocol for crop models; case study and new diagnostic tests

Taru Palosuo, Budong Qian, Éric Justes, Luisa Leolini et al.
5 citations
European Journal of Agronomy
Crop Yield and Soil Fertility
7.61
article

Evaluating the AgMIP calibration protocol for crop models; case study and new diagnostic tests

Taru Palosuo, Budong Qian, Éric Justes, Luisa Leolini, Marie Launay, Muhammad Zeeshan Mehmood, Gerrit Hoogenboom, Kurt Christian Kersebaum, Henrike Mielenz, Diana-Maria Seserman, Marco Moriondo, Benjamin Dumont, Vakhtang Shelia, Daniel Wallach, Samuel Buis, Phillip D. Alderman, Lutz Weihermüller, Sabine J. Seidel, Peter J. Thorburn, Qi Jing, Kwang Soo Kim, Shinwoo Hyun, Mohammad Hassan Fallah, Schulz Susanne
article en
5 citations

Abstract

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. • The AgMIP calibration protocol is based on standard statistical methods. • Model-specific choices concern the order of variables and bias reduction. • New diagnostic tools are proposed to test those choices. • The tools provide in-depth evaluation, reveal problems and suggest solutions. • The diagnostics are applied in a case study.

European Journal of AgronomyVol. 168
Oklahoma State University (US), Commonwealth Scientific and Industrial Research Organisation (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), Seoul National University (KR), University of Liège (BE), Université d'Avignon et des Pays de Vaucluse (FR), Gembloux Agro-Bio Tech (BE), 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), University of Göttingen (DE), Ferdowsi University of Mashhad (IR), BOKU University (AT)
European Commission, Deutsche Forschungsgemeinschaft, Bundesministerium für Bildung und Forschung, Rural Development Administration, Bundesministerium für Ernährung und Landwirtschaft, Bundesamt für Landwirtschaft, Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement, BonaRes
Zero hunger
Openalex Percentile: Top 3%
Crop Yield and Soil Fertility
7.61
DOIOpen Access PDFOpenAlex
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