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

Wallach, Daniel and Palosuo, Taru and Thorburn, Peter and Hochman, Zvi and Gourdain, Emmanuelle and Andrianasolo, Fety and Asseng, Senthold and Basso, Bruno and Buis, Samuel and Crout, Neil and Dibari, Camilla and Dumont, Benjamin and Ferrise, Roberto and Gaiser, Thomas and Garcia, Cecile and Gayler, Sebastian and Ghahramani, Afshin ORCID: https://orcid.org/0000-0002-9648-4606 and Hiremath, Santosh and Hoek, Steven and Horan, Heidi and Hoogenboom, Gerrit and Huang, Mingxia and Jabloun, Mohamed and Jansson, Per-Erik and Jing, Qi and Justes, Eric and Kersebaum, Kurt Christian and Klosterhalfen, Anne and Launay, Marie and Lewan, Elisabet and Luo, Qunying and Maestrini, Bernardo and Mielenz, Henrike and Moriondo, Marco and Zadeh, Hasti Nariman and Padovan, Gloria and Olesen, Jorgen Eivind and Poyda, Arne and Priesack, Eckart and Pullens, Johannes Wilhelmus Maria and Qian, Budong and Schutze, Niels and Shelia, Vakhtang and Souissi, Amir and Specka, Xenia and Srivastava, Amit Kumar and Stella, Tommaso and Streck, Thilo and Trombi, Giacomo and Wallor, Evelyn and Wang, Jing and Weber, Tobias K. D. and Weihermuller, Lutz and de Wit, Allard and Wohling, Thomas and Xiao, Liujun and Zhao, Chuang and Zhu, Yan and Seidel, Sabine J. (2021) The chaos in calibrating crop models: lessons learned from a multi-model calibration exercise. Environmental Modelling and Software, 145. ISSN 1364-8152


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.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Files associated with this item cannot be displayed due to copyright restrictions.
Faculty/School / Institute/Centre: Current - Institute for Life Sciences and the Environment - Centre for Sustainable Agricultural Systems (1 Aug 2018 -)
Faculty/School / Institute/Centre: Current - Institute for Life Sciences and the Environment - Centre for Sustainable Agricultural Systems (1 Aug 2018 -)
Date Deposited: 25 Oct 2021 01:36
Last Modified: 10 Nov 2021 02:05
Uncontrolled Keywords: calibration recommendations; process-based models; parameter estimation; phenology
Fields of Research (2008): 07 Agricultural and Veterinary Sciences > 0701 Agriculture, Land and Farm Management > 070105 Agricultural Systems Analysis and Modelling
Fields of Research (2020): 30 AGRICULTURAL, VETERINARY AND FOOD SCIENCES > 3002 Agriculture, land and farm management > 300207 Agricultural systems analysis and modelling
Socio-Economic Objectives (2008): D Environment > 96 Environment > 9609 Land and Water Management > 960904 Farmland, Arable Cropland and Permanent Cropland Land Management
Socio-Economic Objectives (2020): 18 ENVIRONMENTAL MANAGEMENT > 1806 Terrestrial systems and management > 180603 Evaluation, allocation, and impacts of land use
Identification Number or DOI: https://doi.org/10.1016/j.envsoft.2021.105206
URI: http://eprints.usq.edu.au/id/eprint/43985

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