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Article Dans Une Revue Environmental Modelling and Software Année : 2021

The chaos in calibrating crop models: lessons learned from a multi-model calibration exercise

1 AGIR - AGroécologie, Innovations, teRritoires
2 LUKE - Natural Resources Institute Finland
3 CSIRO - CSIRO Agriculture and Food
4 CSIRO - Commonwealth Scientific and Industrial Research Organisation [Canberra]
5 ARVALIS - Institut du végétal [Paris]
6 ARVALIS - Institut du Végétal [Boigneville]
7 UF - University of Florida [Gainesville]
8 Michigan State University [East Lansing]
9 EMMAH - Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes
10 UON - University of Nottingham, UK
11 DAGRI - Department of Agriculture, Food, Environment and Forestry
12 Gembloux Agro-Bio Tech [Gembloux]
13 INRES - Institute of Crop Science and Resource Conservation [Bonn]
14 University of Hohenheim
15 USQ - University of Southern Queensland
16 Aalto University School of Science and Technology [Aalto, Finland]
17 WUR - Wageningen University and Research [Wageningen]
18 CAU - China Agricultural University
19 KTH - KTH Royal Institute of Technology [Stockholm]
20 Agriculture and Agri-Food Canada, Saskatoon Research Centre
21 Cirad-PERSYST - Département Performances des systèmes de production et de transformation tropicaux
22 ZALF - Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research
23 CzechGlobe - Global Change Research Centre
24 IBG - Institute of Bio- and Geosciences [Jülich]
25 AGROCLIM - Agroclim
26 SLU - Swedish University of Agricultural Sciences = Sveriges lantbruksuniversitet
27 Hillridge Technology Pty Ltd
28 JKI - Julius Kühn-Institut - Federal Research Centre for Cultivated Plants
29 IBE | CNR - Institute for BioEconomy [Sesto Fiorentino]
30 Aarhus University [Aarhus]
31 CAU - Christian-Albrechts-Universität zu Kiel = Christian-Albrechts University of Kiel = Université Christian-Albrechts de Kiel
32 Helmholtz Zentrum München = German Research Center for Environmental Health
33 TU Dresden - Technische Universität Dresden = Dresden University of Technology
34 UCAR - Université de Carthage (Tunisie)
35 FZJ - Forschungszentrum Jülich GmbH | Centre de recherche de Jülich | Jülich Research Centre
36 Lincoln Agritech Ltd
37 NAU - Nanjing Agricultural University
Daniel Wallach
Gerrit Hoogenboom
Marie Launay
  • Fonction : Auteur
Yan Zhu

Résumé

Calibration, the estimation of model parameters based on fitting the model to experimental data, is among the first steps in many applications of process-based models and has an important impact on simulated values. We propose a novel method of developing guidelines for calibration of process-based models, based on development of recommendations for calibration of the phenology component of crop models. The approach was based on a multi-model study, where all teams were provided with the same data and asked to return simulations for the same conditions. All teams were asked to document in detail their calibration approach, including choices with respect to criteria for best parameters, choice of parameters to estimate and software. Based on an analysis of the advantages and disadvantages of the various choices, we propose calibration recommendations that cover a comprehensive list of decisions and that are based on actual practices.

Dates et versions

hal-03352250 , version 1 (23-09-2021)

Identifiants

Citer

Daniel Wallach, Taru Palosuo, Peter Thorburn, Zvi Hochman, Emmanuelle Gourdain, et al.. The chaos in calibrating crop models: lessons learned from a multi-model calibration exercise. Environmental Modelling and Software, 2021, 145, ⟨10.1016/j.envsoft.2021.105206⟩. ⟨hal-03352250⟩
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