Abstract
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.
| Original language | English |
|---|---|
| Article number | 105206 |
| Journal | Environmental Modelling and Software |
| Volume | 145 |
| DOIs | |
| Publication status | Published - Nov 2021 |
| MoE publication type | A1 Journal article-refereed |
Keywords
- Calibration recommendations
- Parameter estimation
- Phenology
- Process-based models
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In: Environmental Modelling and Software, Vol. 145, 105206, 11.2021.
Research output: Contribution to journal › Article › Scientific › peer-review
TY - JOUR
T1 - The chaos in calibrating crop models
T2 - Lessons learned from a multi-model calibration exercise
AU - Wallach, Daniel
AU - Palosuo, Taru
AU - Thorburn, Peter
AU - Hochman, Zvi
AU - Gourdain, Emmanuelle
AU - Andrianasolo, Fety
AU - Asseng, Senthold
AU - Basso, Bruno
AU - Buis, Samuel
AU - Crout, Neil
AU - Dibari, Camilla
AU - Dumont, Benjamin
AU - Ferrise, Roberto
AU - Gaiser, Thomas
AU - Garcia, Cecile
AU - Gayler, Sebastian
AU - Ghahramani, Afshin
AU - Hiremath, Santosh
AU - Hoek, Steven
AU - Horan, Heidi
AU - Hoogenboom, Gerrit
AU - Huang, Mingxia
AU - Jabloun, Mohamed
AU - Jansson, Per Erik
AU - Jing, Qi
AU - Justes, Eric
AU - Kersebaum, Kurt Christian
AU - Klosterhalfen, Anne
AU - Launay, Marie
AU - Lewan, Elisabet
AU - Luo, Qunying
AU - Maestrini, Bernardo
AU - Mielenz, Henrike
AU - Moriondo, Marco
AU - Nariman Zadeh, Hasti
AU - Padovan, Gloria
AU - Olesen, Jørgen Eivind
AU - Poyda, Arne
AU - Priesack, Eckart
AU - Pullens, Johannes Wilhelmus Maria
AU - Qian, Budong
AU - Schütze, Niels
AU - Shelia, Vakhtang
AU - Souissi, Amir
AU - Specka, Xenia
AU - Srivastava, Amit Kumar
AU - Stella, Tommaso
AU - Streck, Thilo
AU - Trombi, Giacomo
AU - Wallor, Evelyn
AU - Wang, Jing
AU - Weber, Tobias K.D.
AU - Weihermüller, Lutz
AU - de Wit, Allard
AU - Wöhling, Thomas
AU - Xiao, Liujun
AU - Zhao, Chuang
AU - Zhu, Yan
AU - Seidel, Sabine J.
N1 - Funding Information: This work was in part supported by the Collaborative Research Center 1253 CAMPOS (Project 7: Stochastic Modeling 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 ) and Natural Resources Institute Finland (Luke) through a strategic project BoostIA, the BonaRes project ''Soil3'' ( BOMA 03037514 ) of the Federal Ministry of Education and Research ( BMBF ), Germany, the Deutsche Forschungsgemeinschaft ( DFG , German Research Foundation ) under Germany's Excellence Strategy - EXC 2070–390732324 EXC (PhenoRob), the project BiomassWeb of the GlobeE programme (Grant number: FKZ031A258B ) funded by the Federal Ministry of Education and Research ( BMBF , Germany), the INRA ACCAF meta-programme, the German Federal Ministry of Education and Research ( BMBF ) in the framework of the funding measure “Soil as a Sustainable Resource for the Bioeconomy – BonaRes”, project “BonaRes (Module B): 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 ), the Priority Academic Program Development of Jiangsu Higher Education Institutions ( PAPD ), the 111 Project ( B16026 ), and China Scholarship Council , the Agriculture and Agri-Food Canada's Project 1387 under he Canadian Agricultural Partnership, the DFG Research Unit FOR 1695 ‘Agricultural Landscapes under Global Climate Change – Processes and Feedbacks on a Regional Scale, the U.S. 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 ), The Broadacre Agriculture Initiative, a research partnership between University of Southern Queensland and the Queensland Department of Agriculture and Fisheries , the Academy of Finland through project AI-CropPro ( 315896 ), the JPI FACCE MACSUR2 project, funded by the Italian Ministry for Agricultural, Food, and Forestry Policies ( D.M. 24064/7303/15 of 6/Nov/2015), the Ministry of Education, Youth and Sports of Czech Republic through SustES - Adaption strategies for sustainable ecosystem services and food security under adverse environmental conditions (project no. CZ.02.1.01/0.0/0.0/16_019/000797 ). The order in which the donors are listed is arbitrary. Funding Information: This work was in part supported by the Collaborative Research Center 1253 CAMPOS (Project 7: Stochastic Modeling 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) and Natural Resources Institute Finland (Luke) through a strategic project BoostIA, the BonaRes project ''Soil3'' (BOMA 03037514) of the Federal Ministry of Education and Research (BMBF), Germany, the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy - EXC 2070?390732324 EXC (PhenoRob), the project BiomassWeb of the GlobeE programme (Grant number: FKZ031A258B) funded by the Federal Ministry of Education and Research (BMBF, Germany), the INRA ACCAF meta-programme, the German Federal Ministry of Education and Research (BMBF) in the framework of the funding measure ?Soil as a Sustainable Resource for the Bioeconomy ? BonaRes?, project ?BonaRes (Module B): 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), the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD), the 111 Project (B16026), and China Scholarship Council, the Agriculture and Agri-Food Canada's Project 1387 under he Canadian Agricultural Partnership, the DFG Research Unit FOR 1695 ?Agricultural Landscapes under Global Climate Change ? Processes and Feedbacks on a Regional Scale, the U.S. 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), The Broadacre Agriculture Initiative, a research partnership between University of Southern Queensland and the Queensland Department of Agriculture and Fisheries, the Academy of Finland through project AI-CropPro (315896), the JPI FACCE MACSUR2 project, funded by the Italian Ministry for Agricultural, Food, and Forestry Policies (D.M. 24064/7303/15 of 6/Nov/2015), the Ministry of Education, Youth and Sports of Czech Republic through SustES - Adaption strategies for sustainable ecosystem services and food security under adverse environmental conditions (project no. CZ.02.1.01/0.0/0.0/16_019/000797). The order in which the donors are listed is arbitrary. Publisher Copyright: © 2021 Elsevier Ltd
PY - 2021/11
Y1 - 2021/11
N2 - 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.
AB - 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.
KW - Calibration recommendations
KW - Parameter estimation
KW - Phenology
KW - Process-based models
UR - http://www.scopus.com/inward/record.url?scp=85117249704&partnerID=8YFLogxK
U2 - 10.1016/j.envsoft.2021.105206
DO - 10.1016/j.envsoft.2021.105206
M3 - Article
AN - SCOPUS:85117249704
SN - 1364-8152
VL - 145
JO - Environmental Modelling and Software
JF - Environmental Modelling and Software
M1 - 105206
ER -