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Browsing by Person "Souissi, Amir"

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    Proposal and extensive test of a calibration protocol for crop phenology models
    (2023) 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.; 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; Plant Sciences & TERRA Teaching and Research Centre, Gembloux Agro-Bio Tech, University of Liege, Gembloux, Belgium; 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, Tasmania, Australia; Hiremath, Santosh; Aalto University School of Science, Espoo, Finland; Horan, Heidi; CSIRO Agriculture and Food, Brisbane, Australia; Hoogenboom, Gerrit; Global Food Systems Institute, University of Florida, Gainesville, USA; 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; 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, Tasmania, Australia; Mequanint, Fasil; Institute of Soil Science and Land Evaluation, Biogeophysics, University of Hohenheim, Stuttgart, Germany; Moriondo, Marco; CNR-IBE, Firenze, Italy; Nendel, Claas; 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; Global Food Systems Institute, University of Florida, Gainesville, USA; 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.; 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; Lincoln Agritech Ltd., Hamilton, New Zealand; Seidel, Sabine J.; Institute of Crop Science and Resource Conservation, University of Bonn, Bonn, Germany
    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%.