Crop model; Model ensemble; Prediction error; Protocol; Variability; Environmental Engineering; Agronomy and Crop Science
Abstract :
[en] 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%.
Disciplines :
Agriculture & agronomy Computer science
Author, co-author :
Wallach, Daniel ; Institute of Crop Science and Resource Conservation, University of Bonn, Bonn, Germany
Palosuo, Taru; Natural Resources Institute Finland (Luke), Helsinki, Finland
Thorburn, Peter; CSIRO Agriculture and Food, Brisbane, Australia
Mielenz, Henrike; Institute for Crop and Soil Science, Julius Kühn Institute (JKI) – Federal Research Centre for Cultivated Plants, Braunschweig, Germany
Buis, Samuel; INRAE, UMR 1114 EMMAH, Avignon, France
Hochman, Zvi; CSIRO Agriculture and Food, Brisbane, Australia
Gourdain, Emmanuelle; ARVALIS - Institut du végétal Paris, Paris, France
Andrianasolo, Fety; ARVALIS - Institut du végétal Paris, Paris, France
Dumont, Benjamin ; Université de Liège - ULiège > TERRA Research Centre > Plant Sciences
Ferrise, Roberto; Department of Agriculture, Food, Environment and Forestry (DAGRI), University of Florence, Florence, Italy
Gaiser, Thomas; Institute of Crop Science and Resource Conservation, University of Bonn, Bonn, Germany
Garcia, Cecile; ARVALIS - Institut du végétal Paris, Paris, France
Gayler, Sebastian; Institute of Soil Science and Land Evaluation, Biogeophysics, University of Hohenheim, Stuttgart, Germany
Harrison, Matthew; Tasmanian Institute of Agriculture, University of Tasmania, Launceston, Australia
Hiremath, Santosh; Aalto University School of Science, Espoo, Finland
Horan, Heidi; CSIRO Agriculture and Food, Brisbane, Australia
Hoogenboom, Gerrit; Agricultural and Biological Engineering Department, University of Florida, Gainesville, United States ; Global Food Systems Institute, University of Florida, Gainesville, United States
Jansson, Per-Erik; Royal Institute of Technology (KTH), Stockholm, Sweden
Jing, Qi; Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, Canada
Justes, Eric; PERSYST Department, CIRAD, Montpellier, France
Kersebaum, Kurt-Christian; Leibniz Centre for Agricultural Landscape Research (ZALF), Müncheberg, Germany ; Global Change Research Institute CAS, Brno, Czech Republic ; Tropical Plant Production and Agricultural Systems Modelling (TROPAGS), University of Göttingen, Göttingen, Germany
Launay, Marie; INRAE, US 1116 AgroClim, Avignon, France
Lewan, Elisabet; Department of Soil and Environment, Swedish University of Agricultural Sciences (SLU), Uppsala, Sweden
Liu, Ke; Tasmanian Institute of Agriculture, University of Tasmania, Launceston, Australia
Mequanint, Fasil; Institute of Soil Science and Land Evaluation, Biogeophysics, University of Hohenheim, Stuttgart, Germany
Moriondo, Marco; CNR-IBE, Firenze, Italy
Nendel, Claas; Leibniz Centre for Agricultural Landscape Research (ZALF), Müncheberg, Germany ; Global Change Research Institute CAS, Brno, Czech Republic ; Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany
Padovan, Gloria; Department of Agriculture, Food, Environment and Forestry (DAGRI), University of Florence, Florence, Italy
Qian, Budong; Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, Canada
Schütze, Niels; Institute of Hydrology and Meteorology, Chair of Hydrology, Technische Universität Dresden, Dresden, Germany
Seserman, Diana-Maria; Leibniz Centre for Agricultural Landscape Research (ZALF), Müncheberg, Germany
Shelia, Vakhtang; Agricultural and Biological Engineering Department, University of Florida, Gainesville, United States ; Global Food Systems Institute, University of Florida, Gainesville, United States
Souissi, Amir; Swift Current Research and Development Centre, Agriculture and Agri-Food Canada, Swift Current, Canada
Specka, Xenia; Leibniz Centre for Agricultural Landscape Research (ZALF), Müncheberg, Germany
Srivastava, Amit Kumar; Institute of Crop Science and Resource Conservation, University of Bonn, Bonn, Germany
Trombi, Giacomo; Department of Agriculture, Food, Environment and Forestry (DAGRI), University of Florence, Florence, Italy
Weber, Tobias K. D.; Institute of Soil Science and Land Evaluation, Biogeophysics, University of Hohenheim, Stuttgart, Germany ; Faculty of Organic Agriculture, Soil Science Section, University of Kassel, Witzenhausen, Germany
Weihermüller, Lutz; Institute of Bio- and Geosciences - IBG-3, Agrosphere, Forschungszentrum Jülich GmbH, Jülich, Germany
Wöhling, Thomas; Institute of Hydrology and Meteorology, Chair of Hydrology, Technische Universität Dresden, Dresden, Germany ; Lincoln Agritech Ltd., Hamilton, New Zealand
Seidel, Sabine J. ; Institute of Crop Science and Resource Conservation, University of Bonn, Bonn, Germany
DFG - Deutsche Forschungsgemeinschaft Academy of Finland Rheinische Friedrich-Wilhelms-Universität Bonn
Funding text :
Open Access funding enabled and organized by Projekt DEAL. This study was implemented as a co-operative project under the umbrella of the Agricultural Model Intercomparison and Improvement Project (AgMIP). This work was supported by the Academy of Finland through projects AI-CropPro (316172 and 315896) and DivCSA (316215) and Natural Resources Institute Finland (Luke) through a strategic project EFFI, 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, Phase 3): BonaRes Centre for Soil Research, subproject B” (grant 031B1064B), the BonaRes project “I4S” (031B0513I) 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 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 Agriculture and Agri-Food Canada’s Project J-002303 “Sustainable crop production in Canada under climate change” under the Interdepartmental Research Initiative in Agriculture, 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), and the INRAE CLIMAE meta-program and AgroEcoSystem department. The order in which the donors are listed is arbitrary.
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