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The chaos in calibrating crop models

Authors :
Daniel Wallach
Taru Palosuo
Peter Thorburn
Zvi Hochman
Emmanuelle Gourdain
Fety Andrianasolo
Senthold Asseng
Bruno Basso
Samuel Buis
Neil Crout
Camilla Dibari
Benjamin Dumont
Roberto Ferrise
Thomas Gaiser
Cecile Garcia
Sebastian Gayler
Afshin Ghahramani
Santosh Hiremath
Steven Hoek
Heidi Horan
Gerrit Hoogenboom
Mingxia Huang
Mohamed Jabloun
Per-Erik Jansson
Qi Jing
Eric Justes
Kurt Christian Kersebaum
Anne Klosterhalfen
Marie Launay
Elisabet Lewan
Qunying Luo
Bernardo Maestrini
Henrike Mielenz
Marco Moriondo
Hasti Nariman Zadeh
Gloria Padovan
Jørgen Eivind Olesen
Arne Poyda
Eckart Priesack
Johannes Wilhelmus Maria Pullens
Budong Qian
Niels Schütze
Vakhtang Shelia
Amir Souissi
Xenia Specka
Amit Kumar Srivastava
Tommaso Stella
Thilo Streck
Giacomo Trombi
Evelyn Wallor
Jing Wang
Tobias K.D. Weber
Lutz Weihermüller
Allard de Wit
Thomas Wöhling
Liujun Xiao
Chuang Zhao
Yan Zhu
Sabine J. Seidel
INRAE
Luke Natural Resources Institute Finland
CSIRO
Arvalis Institut du Végétal
Technical University of Munich
Michigan State University
University of Nottingham
University of Florence
University of Liege
University of Bonn
University of Hohenheim
University of Southern Queensland
Department of Computer Science
Wageningen University and Research Centre
University of Florida
China Agricultural University
KTH Royal Institute of Technology
Ottawa Research and Development Centre
Centre de coopération internationale en recherche agronomique pour le développement
Leibniz Centre for Agricultural Landscape Research
Jülich Research Centre
Swedish University of Agricultural Sciences
Hillridge Technology Pty Ltd
Institute for Crop and Soil Science
CNR-ENEA-EURATOM Association
Aarhus University
Kiel University
Helmholtz Zentrum München - German Research Center for Environmental Health
Technische Universität Dresden
University of Carthage
Nanjing Agricultural University
Aalto-yliopisto
Aalto University
Publication Year :
2021
Publisher :
ELSEVIER SCI LTD, 2021.

Abstract

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 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.

Details

Language :
English
Database :
OpenAIRE
Accession number :
edsair.doi.dedup.....9594ca05e25212276e3c8d03820301c1