Article CC BY 4.0
refereed
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Proposal and extensive test of a calibration protocol for crop phenology models

ORCID
0000-0003-3500-8179
Affiliation
University of Bonn, Institute of Crop Science and Resource Conservation, Germany
Wallach, Daniel;
Affiliation
Natural Resources Institute Finland (Luke), Finland
Palosuo, Taru;
Affiliation
CSIRO Agriculture and Food, Brisbane, Australia
Thorburn, Peter;
GND
1244239623
Affiliation
Julius Kühn-Institute (JKI), Institute for Crop and Soil Science, Germany
Mielenz, Henrike;
Affiliation
INRAE, UMR 1114 EMMAH, France
Buis, Samuel;
Affiliation
CSIRO Agriculture and Food, Brisbane, Australia
Hochman, Zvi;
Affiliation
ARVALIS - Institut du végétal Paris, France
Gourdain, Emmanuelle;
Affiliation
ARVALIS - Institut du végétal Paris, France
Andrianasolo, Fety;
Affiliation
University of Liege, Plant Sciences & TERRA Teaching and Research Centre, Gembloux Agro-Bio Tech, Belgium
Dumont, Benjamin;
Affiliation
University of Florence, Department of Agriculture, Food, Environment and Forestry (DAGRI), Italy
Ferrise, Roberto;
Affiliation
University of Bonn, Institute of Crop Science and Resource Conservation, Germany
Gaiser, Thomas;
Affiliation
ARVALIS - Institut du végétal Paris, France
Garcia, Cecile;
Affiliation
University of Hohenheim, Institute of Soil Science and Land Evaluation, Biogeophysics, Germany
Gayler, Sebastian;
Affiliation
University of Tasmania, Tasmanian Institute of Agriculture, Australia
Harrison, Matthew;
Affiliation
Aalto University School of Science, Finland
Hiremath, Santosh;
Affiliation
CSIRO Agriculture and Food, Brisbane, Australia
Horan, Heidi;
Affiliation
University of Florida, Agricultural and Biological Engineering Department, USA ; University of Florida, Global Food Systems Institute, USA
Hoogenboom, Gerrit;
Affiliation
Royal Institute of Technology (KTH), Sweden
Jansson, Per-Erik;
Affiliation
Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Canada
Jing, Qi;
Affiliation
PERSYST Department, CIRAD, France
Justes, Eric;
Affiliation
Leibniz Centre for Agricultural Landscape Research (ZALF), Germany ; Global Change Research Institute CAS, Czech Republic ; University of Göttingen, Tropical Plant Production and Agricultural Systems Modelling (TROPAGS), Germany
Kersebaum, Kurt-Christian;
Affiliation
INRAE, US 1116 AgroClim, France
Launay, Marie;
Affiliation
Swedish University of Agricultural Sciences (SLU), Department of Soil and Environment, Sweden
Lewan, Elisabet;
Affiliation
University of Tasmania, Tasmanian Institute of Agriculture, Australia
Liu, Ke;
Affiliation
University of Hohenheim, Institute of Soil Science and Land Evaluation, Biogeophysics, Germany
Mequanint, Fasil;
Affiliation
CNR-IBE, Firenze, Italy
Moriondo, Marco;
Affiliation
Leibniz Centre for Agricultural Landscape Research (ZALF), Germany ; Global Change Research Institute CAS, Czech Republic ; University of Potsdam, Institute of Biochemistry and Biology, Germany
Nendel, Claas;
Affiliation
University of Florence, Department of Agriculture, Food, Environment and Forestry (DAGRI), Italy
Padovan, Gloria;
Affiliation
Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Canada
Qian, Budong;
Affiliation
Technische Universität Dresden, Institute of Hydrology and Meteorology, Chair of Hydrology, Germany
Schütze, Niels;
Affiliation
Leibniz Centre for Agricultural Landscape Research (ZALF), Germany
Seserman, Diana-Maria;
Affiliation
University of Florida, Agricultural and Biological Engineering Department, USA ; University of Florida, Global Food Systems Institute, USA
Shelia, Vakhtang;
Affiliation
Swift Current Research and Development Centre, Agriculture and Agri-Food Canada, Swift Current, Canada
Souissi, Amir;
Affiliation
Leibniz Centre for Agricultural Landscape Research (ZALF), Germany
Specka, Xenia;
Affiliation
University of Bonn, Institute of Crop Science and Resource Conservation, Germany
Srivastava, Amit Kumar;
Affiliation
University of Florence, Department of Agriculture, Food, Environment and Forestry (DAGRI), Italy
Trombi, Giacomo;
Affiliation
University of Hohenheim, Institute of Soil Science and Land Evaluation, Biogeophysics, Germany
Weber, Tobias K. D.;
Affiliation
Institute of Bio- and Geosciences - IBG-3, Agrosphere, Forschungszentrum Jülich GmbH, Germany
Weihermüller, Lutz;
Affiliation
Technische Universität Dresden, Institute of Hydrology and Meteorology, Chair of Hydrology, Germany ; Lincoln Agritech Ltd., New Zealand
Wöhling, Thomas;
ORCID
0000-0003-3283-8361
Affiliation
University of Bonn, Institute of Crop Science and Resource Conservation, Germany
Seidel, Sabine J.

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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