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Article
2023
Proposal and extensive test of a calibration protocol for crop phenology models
Proposal and extensive test of a calibration protocol for crop phenology models
Wallach, Daniel Palosuo, Taru Thorburn, Peter Mielenz, Henrike Buis, Samuel Hochman, Zvi Gourdain, Emmanuelle Andrianasolo, Fety Dumont, Benjamin Ferrise, Roberto Gaiser, Thomas Garcia, Cecile Gayler, Sebastian Harrison, Matthew Hiremath, Santosh Horan, Heidi Hoogenboom, Gerrit Jansson, Per-Erik Jing, Qi Justes, Eric Kersebaum, Kurt-Christian Launay, Marie Lewan, Elisabet Liu, Ke Mequanint, Fasil Moriondo, Marco Nendel, Claas Padovan, Gloria Qian, Budong Schütze, Niels Seserman, Diana-Maria Shelia, Vakhtang Souissi, Amir Specka, Xenia Srivastava, Amit Kumar Trombi, Giacomo Weber, Tobias K. D. Weihermüller, Lutz Wöhling, Thomas Seidel, Sabine J.
Abstract (English)
A major effect of environment on crops is through crop phenology, and therefore, the capacity to predict phenology for new environments is important. Mechanistic crop models are a major tool for such predictions, but calibration of crop phenology models is difficult and there is no consensus on the best approach. We propose an original, detailed approach for calibration of such models, which we refer to as a calibration protocol. The protocol covers all the steps in the calibration workflow, namely choice of default parameter values, choice of objective function, choice of parameters to estimate from the data, calculation of optimal parameter values, and diagnostics. The major innovation is in the choice of which parameters to estimate from the data, which combines expert knowledge and data-based model selection. First, almost additive parameters are identified and estimated. This should make bias (average difference between observed and simulated values) nearly zero. These are “obligatory” parameters, that will definitely be estimated. Then candidate parameters are identified, which are parameters likely to explain the remaining discrepancies between simulated and observed values. A candidate is only added to the list of parameters to estimate if it leads to a reduction in BIC (Bayesian Information Criterion), which is a model selection criterion. A second original aspect of the protocol is the specification of documentation for each stage of the protocol. The protocol was applied by 19 modeling teams to three data sets for wheat phenology. All teams first calibrated their model using their “usual” calibration approach, so it was possible to compare usual and protocol calibration. Evaluation of prediction error was based on data from sites and years not represented in the training data. Compared to usual calibration, calibration following the new protocol reduced the variability between modeling teams by 22% and reduced prediction error by 11%.
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Agronomy for Sustainable Development, 43 (2023), 4.
https://doi.org/10.1007/s13593-023-00900-0.
ISSN: 1773-0155
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English
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630 Agriculture
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Sustainable Development Goals
BibTeX
@article{Wallach2023-07-13,
doi = {10.1007/s13593-023-00900-0},
author = {Wallach, Daniel and Palosuo, Taru and Thorburn, Peter et al.},
title = {Proposal and extensive test of a calibration protocol for crop phenology models},
journal = {Agronomy for Sustainable Development},
year = {2023-07-13},
volume = {43},
number = {4},
}