Reference : Multimodel ensembles improve predictions of crop–environment–management interactions
Scientific journals : Article
Life sciences : Agriculture & agronomy
Engineering, computing & technology : Computer science
http://hdl.handle.net/2268/234785
Multimodel ensembles improve predictions of crop–environment–management interactions
English
Wallach, D. [UMR AGIR, INRA, Castanet-Tolosan, 31326, France]
Martre, P. [UMR LEPSE, INRA, Montpellier SupAgro, Montpellier, France]
Liu, B. [National Engineering and Technology Center for Information Agriculture, Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture, Jiangsu Key Laboratory for Information Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, Jiangsu, China, Agricultural and Biological Engineering Department, University of Florida, Gainesville, FL, United States]
Asseng, S. [Agricultural and Biological Engineering Department, University of Florida, Gainesville, FL, United States]
Ewert, F. [Institute of Crop Science and Resource Conservation INRES, University of, Bonn, Germany, Germany, Leibniz Centre for Agricultural Landscape Research, Müncheberg, Germany]
Thorburn, P. J. [CSIRO Agriculture and Food Brisbane, St Lucia, QLD, Australia]
van Ittersum, M. [Plant Production Systems Group, Wageningen University, Wageningen, Netherlands]
Aggarwal, P. K. [CGIAR Research Program on Climate Change, Agriculture and Food Security, BISA-CIMMYT, New Delhi, India]
Ahmed, M. [Biological Systems Engineering, Washington State University, Pullman, WA, United States, Department of Agronomy, Pir Mehr Ali Shah Arid Agriculture University, Rawalpindi, Pakistan]
Basso, B. [Department of Earth and Environmental Sciences, Michigan State University, East Lansing, MI, United States, W.K. Kellogg Biological Station, Michigan State University, East Lansing, MI, United States]
Biernath, C. [Institute of Biochemical Plant Pathology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany]
Cammarano, D. [James Hutton Institute Invergowrie, Dundee, United Kingdom]
Challinor, A. J. [Institute for Climate and Atmospheric Science, School of Earth and Environment, University of Leeds, Leeds, United Kingdom, CGIAR-ESSP Program on Climate Change, Agriculture and Food Security, International Centre for Tropical Agriculture (CIAT), Cali, Colombia]
De Sanctis, G. [European Food Safety Authority, GMO Unit, Parma, Italy]
Dumont, Benjamin mailto [Université de Liège - ULiège > Agronomie, Bio-ingénierie et Chimie (AgroBioChem) > Ingénierie des productions végétales et valorisation >]
Eyshi Rezaei, E. [Institute of Crop Science and Resource Conservation INRES, University of, Bonn, Germany, Germany, Center for Development Research (ZEF), Bonn, Germany]
Fereres, E. [IAS-CSIC and University of Cordoba, Cordoba, Spain]
Fitzgerald, G. J. [Agriculture Victoria Research, Department of Economic Development, Jobs, Transport and Resources, Ballarat, VIC, Australia, Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Creswick, VIC, Australia]
Gao, Y. [Agricultural and Biological Engineering Department, University of Florida, Gainesville, FL, United States]
Garcia-Vila, M. [IAS-CSIC and University of Cordoba, Cordoba, Spain]
Gayler, S. [Institute of Soil Science and Land Evaluation, University of Hohenheim, Stuttgart, Germany]
Girousse, C. [UMR GDEC, INRA, Université Clermont Auvergne, Clermont-Ferrand, France]
Hoogenboom, G. [Agricultural and Biological Engineering Department, University of Florida, Gainesville, FL, United States, Institute for Sustainable Food Systems, University of Florida, Gainesville, FL, United States]
Horan, H. [CSIRO Agriculture and Food Brisbane, St Lucia, QLD, Australia]
Izaurralde, R. C. [Department of Geographical Sciences, University of Maryland, College Park, MD, United States, Texas A&M AgriLife Research and Extension Center, Texas A&M University, Temple, TX, United States]
Jones, C. D. [Texas A&M AgriLife Research and Extension Center, Texas A&M University, Temple, TX, United States]
Kassie, B. T. [Agricultural and Biological Engineering Department, University of Florida, Gainesville, FL, United States]
Kersebaum, K. C. [Institute of Landscape Systems Analysis, Leibniz Centre for Agricultural Landscape Research, Müncheberg, Germany]
Klein, C. [Institute of Biochemical Plant Pathology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany]
Koehler, A.-K. [Institute for Climate and Atmospheric Science, School of Earth and Environment, University of Leeds, Leeds, United Kingdom]
Maiorano, A. [UMR LEPSE, INRA, Montpellier SupAgro, Montpellier, France, European Food Safety Authority—EFSA, Parma, Italy]
Minoli, S. [Potsdam Institute for Climate Impact Research, Potsdam, Germany]
Müller, C. [Potsdam Institute for Climate Impact Research, Potsdam, Germany]
Naresh Kumar, S. [Centre for Environment Science and Climate Resilient Agriculture, Indian Agricultural Research Institute, IARI PUSA, New Delhi, India]
Nendel, C. [Institute of Landscape Systems Analysis, Leibniz Centre for Agricultural Landscape Research, Müncheberg, Germany]
O'Leary, G. J. [Grains Innovation Park, Department of Economic Development, Jobs, Transport and Resources, Agriculture Victoria Research, Horsham, VIC, Australia]
Palosuo, T. [Natural Resources Institute Finland (Luke), Helsinki, Finland]
Priesack, E. [Institute of Biochemical Plant Pathology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany]
Ripoche, D. [US AgroClim, INRA, Avignon, France]
Rötter, R. P. [Tropical Plant Production and Agricultural Systems Modelling (TROPAGS), University of Göttingen, Göttingen, Germany, Centre of Biodiversity and Sustainable Land Use (CBL), University of Göttingen, Göttingen, Germany]
Semenov, M. A. [Computational and Systems Biology Department, Rothamsted Research, Harpenden, Herts, United Kingdom]
Stöckle, C. [Biological Systems Engineering, Washington State University, Pullman, WA, United States]
Stratonovitch, P. [Computational and Systems Biology Department, Rothamsted Research, Harpenden, Herts, United Kingdom]
Streck, T. [Institute of Soil Science and Land Evaluation, University of Hohenheim, Stuttgart, Germany]
Supit, I. [Water & Food and Water Systems & Global Change Group, Wageningen University, Wageningen, Netherlands]
Tao, F. [Natural Resources Institute Finland (Luke), Helsinki, Finland, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Science, Beijing, China]
Wolf, J. [Plant Production Systems, Wageningen University, Wageningen, Netherlands]
Zhang, Z. [State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing, China]
2018
Global Change Biology
Blackwell Publishing Ltd
24
11
5072-5083
Yes (verified by ORBi)
International
13541013
13652486
[en] Triticum aestivum ; Agriculture ; Climate Change ; Environment ; Models, Theoretical ; Triticum
[en] A recent innovation in assessment of climate change impact on agricultural production has been to use crop multimodel ensembles (MMEs). These studies usually find large variability between individual models but that the ensemble mean (e-mean) and median (e-median) often seem to predict quite well. However, few studies have specifically been concerned with the predictive quality of those ensemble predictors. We ask what is the predictive quality of e-mean and e-median, and how does that depend on the ensemble characteristics. Our empirical results are based on five MME studies applied to wheat, using different data sets but the same 25 crop models. We show that the ensemble predictors have quite high skill and are better than most and sometimes all individual models for most groups of environments and most response variables. Mean squared error of e-mean decreases monotonically with the size of the ensemble if models are added at random, but has a minimum at usually 2–6 models if best-fit models are added first. Our theoretical results describe the ensemble using four parameters: average bias, model effect variance, environment effect variance, and interaction variance. We show analytically that mean squared error of prediction (MSEP) of e-mean will always be smaller than MSEP averaged over models and will be less than MSEP of the best model if squared bias is less than the interaction variance. If models are added to the ensemble at random, MSEP of e-mean will decrease as the inverse of ensemble size, with a minimum equal to squared bias plus interaction variance. This minimum value is not necessarily small, and so it is important to evaluate the predictive quality of e-mean for each target population of environments. These results provide new information on the advantages of ensemble predictors, but also show their limitations. © 2018 John Wiley & Sons Ltd
http://hdl.handle.net/2268/234785
10.1111/gcb.14411

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