Classifying multi-model wheat yield impact response surfaces showing sensitivity to temperature and precipitation change
2017
Fronzek, Stefan | Pirttioja, Nina | Carter, Timothy R. | Bindi, Marco | Hoffmann, Holger | Palosuo, Taru | Ruiz-Ramos, Margarita | Tao, Fulu | Trnka, Miroslav | Acutis, Marco | Asseng, Senthold | Baranowski, Piotr | Basso, Bruno | Bodin, Per | Buis, Samuel | Cammarano, Davide | Deligios, Paola | Destain, Marie-France | Dumont, Benjamin | Ewert, Frank | Ferrise, Roberto | François, Louis | Gaiser, Thomas | Hlavinka, Petr | Jacquemin, Ingrid | Kersebaum, Kurt Christian | Kollas, Chris | Krzyszczak, Jaromir | Lorite, Ignacio J. | Minet, Julien | Minguez, M. Ines | Montesino, Manuel | Moriondo, Marco | Müller, Christoph | Nendel, Claas | Perego, Alessia | Rodríguez, Alfredo | Ruane, Alex C. | Ruget, Francoise | Sanna, Mattia | Semenov, Mikhail A. | Slawinski, Cezary | Stratonovitch, Pierre | Supit, Iwan | Waha, Katharina | Wang, Enli | Wu, Lianhai | Zhao, Zhigan | Rötter, Reimund P. | Finnish Environment Institute (SYKE) | Università degli Studi di Firenze = University of Florence = Université de Florence (UniFI) | Rheinische Friedrich-Wilhelms-Universität Bonn | Institut für Nutzpflanzenwissenschaften und Ressourcenschutz (INRES) ; Rheinische Friedrich-Wilhelms-Universität Bonn | Natural Resources Institute Finland (LUKE) | Centro de Estudios e Investigación para la Gestión de Riesgos Agrarios y Medioambientales (CEIGRAM) ; Universidad Politécnica de Madrid (UPM) | Institute of Agrosystems and Bioclimatology ; Mendel University in Brno (MENDELU) | Global Change Research Centre (CzechGlobe) | Institut de Recherche pour le Développement (IRD [Nouvelle-Calédonie]) | University of Florida [Gainesville] (UF) | Institute of Agrophysics, ; Polska Akademia Nauk = Polish Academy of Sciences = Académie polonaise des sciences (PAN) | Michigan State University [East Lansing] ; Michigan State University System | Skane University Hospital [Lund] | Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH) ; Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE) | The James Hutton Institute | Università degli Studi di Sassari = University of Sassari [Sassari] (UNISS) | Université de Liège = University of Liège = Universiteit van Luik = Universität Lüttich (ULiège) | Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF) | Potsdam Institute for Climate Impact Research (PIK) | Instituto Andaluz de Investigación y Formación Agraria y Pesquera (IFAPA) | University of Copenhagen = Københavns Universitet (UCPH) | Istituto di Biometeorologia [Firenze] (IBIMET) ; National Research Council of Italy | Consiglio Nazionale delle Ricerche (CNR) | NASA Goddard Institute for Space Studies (GISS) ; NASA Goddard Space Flight Center (GSFC) | Rothamsted Research ; Biotechnology and Biological Sciences Research Council (BBSRC) | Wageningen University and Research [Wageningen] (WUR) | Commonwealth Scientific and Industrial Research Organisation [Australia] (CSIRO) | China Agricultural University (CAU) | Tropical Plant Production and Agricultural Systems Modelling (TROPAGS) ; Georg-August-University of Göttingen = Georg-August-Universität Göttingen | Centre for Biodiversity and Sustainable Land Use (CBL) ; Georg-August-University of Göttingen = Georg-August-Universität Göttingen | cademy of Finland 277276 277403 292836; Finnish Ministry of Agriculture and Forestry (FACCE-MACSUR) ; Polish National Centre for Research and Development BIOSTRATEG1/271322/3/NCBR/2015;BIOSTRATEG2 298782 ; German Federal Ministry of Food and Agriculture through the Federal Office for Agriculture and Food 2851ERA01J; FACCE MACSUR 2812ERA 147;German Federal Ministry of Education and Research via the 'Limpopo Living Landscapes' project within the SPACES programme 01LL1304A;MACMIT project 01LN1317A;Italian Ministry of Agricultural Food and Forest Policies (AGROSCENARI Project); Italian Ministry of Education, University and Research (FIRB) RBFR12B2K4_004 | European Project: 603416,EC:FP7:ENV,FP7-ENV-2013-two-stage,IMPRESSIONS(2013)
Crop growth simulation models can differ greatly in their treatment of key processes and hence in their response to environmental conditions. Here, we used an ensemble of 26 process-based wheat models applied at sites across a European transect to compare their sensitivity to changes in temperature (−2 to +9°C) and precipitation (−50 to +50%). Model results were analysed by plotting them as impact response surfaces (IRSs), classifying the IRS patterns of individual model simulations, describing these classes and analysing factors that may explain the major differences in model responses. The model ensemble was used to simulate yields of winter and spring wheat at four sites in Finland, Germany and Spain. Results were plotted as IRSs that show changes in yields relative to the baseline with respect to temperature and precipitation. IRSs of 30-year means and selected extreme years were classified using two approaches describing their pattern.The expert diagnostic approach (EDA) combines two aspects of IRS patterns: location of the maximum yield (nine classes) and strength of the yield response with respect to climate (four classes), resulting in a total of 36 combined classes defined using criteria pre-specified by experts. The statistical diagnostic approach (SDA) groups IRSs by comparing their pattern and magnitude, without attempting to interpret these features. It applies a hierarchical clustering method, grouping response patterns using a distance metric that combines the spatial correlation and Euclidian distance between IRS pairs. The two approaches were used to investigate whether different patterns of yield response could be related to different properties of the crop models, specifically their genealogy, calibration and process description. Although no single model property across a large model ensemble was found to explain the integrated yield response to temperature and precipitation perturbations, the application of the EDA and SDA approaches revealed their capability to distinguish: (i) stronger yield responses to precipitation for winter wheat than spring wheat; (ii) differing strengths of response to climate changes for years with anomalous weather conditions compared to period-average conditions; (iii) the influence of site conditions on yield patterns; (iv) similarities in IRS patterns among models with related genealogy; (v) similarities in IRS patterns for models with simpler process descriptions of root growth and water uptake compared to those with more complex descriptions; and (vi) a closer correspondence of IRS patterns in models using partitioning schemes to represent yield formation than in those using a harvest index. Such results can inform future crop modelling studies that seek to exploit the diversity of multi-model ensembles, by distinguishing ensemble members that span a wide range of responses as well as those that display implausible behaviour or strong mutual similarities.
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