Multi-model ensembles improve predictions of crop-environment-management interactions
2018
Wallach, Daniel | Martre, Pierre | Liu, Bo | Asseng, Senthold | Ewert, Frank | Thorburn, P.J. | van Ittersum, Martin K. | Aggarwal, Pramod K. | Ahmed, Melika Ben | Basso, Bruno | Biernath, Christian | Cammarano, Davide | Challinor, Andrew J. | de Sanctis, Giacomo | Dumont, Benjamin | Eyshi Rezaei, Ehsan | Fereres, Elias | Fitzgerald, Gerry J | Gao, Yimin | Garcia-Vila, Margarita | Gayler, Sebastian | Girousse, Christine | Hoogenboom, Gerrit | Horan, Heidi | Izaurralde, Roberto C. | Jones, Corbin D. | Kassie, Belay T. | Kersebaum, Kurt Christian | Klein, C. | Koehler, Ann-Kristin | Maiorano, Andrea | Minoli, Sara | Müller, Christoph | Kumar Naresh, Soora | Nendel, Claas | O'Leary, Garry J. | Palosuo, Taru | Priesack, Eckart | Ripoche, Dominique | Rötter, Reimund Paul | Semenov, Mikhail A. | Stöckle, Claudio O. | Stratonovitch, Pierre | Streck, Thilo | Supit, Iwan | Tao, Fulu | Wolf, Joost | Zhang, Ze | AGroécologie, Innovations, teRritoires (AGIR) ; Institut National de la Recherche Agronomique (INRA)-Institut National Polytechnique (Toulouse) (Toulouse INP) ; Université de Toulouse (UT)-Université de Toulouse (UT) | Écophysiologie des Plantes sous Stress environnementaux (LEPSE) ; Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro) | Nanjing Agricultural University (NAU) | Department of Agricultural and Biological Engineering [Gainesville] (UF|ABE) ; Institute of Food and Agricultural Sciences [Gainesville] (UF|IFAS) ; University of Florida [Gainesville] (UF)-University of Florida [Gainesville] (UF) | Institute of Crop Science and Resource Conservation [Bonn] (INRES) ; Rheinische Friedrich-Wilhelms-Universität Bonn | Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF) | Commonwealth Scientific and Industrial Research Organisation [Australia] (CSIRO) | Plant Production Systems Group ; Wageningen University and Research [Wageningen] (WUR) | Agriculture and Food Security (CCAFS) | Biological Systems Engineering ; Washington State University (WSU) | Department of Agronomy ; University of El-Tarf | Department of Earth and Environmental Sciences [East Lansing] ; Michigan State University [East Lansing] ; Michigan State University System-Michigan State University System | Plant Pathology | The James Hutton Institute | University of Leeds | Consultative Group on International Agricultural Research (CGIAR) | GMO Unit ; European Food Safety Authority = Autorité européenne de sécurité des aliments | Department Terra & AgroBioChem, Gembloux Agro‐Bio Tech ; Université de Liège = University of Liège = Universiteit van Luik = Universität Lüttich (ULiège) | Center for Development Research | Universidad de Córdoba = University of Córdoba [Córdoba] | Agriculture Victoria (AgriBio) | University of Melbourne | Institute of Soil Science and Land Evaluation ; Universität Hohenheim = University of Hohenheim | Génétique Diversité et Ecophysiologie des Céréales (GDEC) ; Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020]) | University of Florida [Gainesville] (UF) | Department of Geographical Sciences ; University of Maryland [College Park] (UMD) ; University System of Maryland-University System of Maryland | Texas A and M AgriLife Research ; Texas A&M University System | Institute of landscape systems analysis ; Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF) | School of Earth and Environment [Leeds] (SEE) ; University of Leeds | European Food Safety Authority (EFSA) | Potsdam Institute for Climate Impact Research (PIK) | Centre for Environment Science and Climate Resilient Agriculture (CESCRA) ; Indian Agricultural Research Institute (IARI) | Department of Economic Development, Jobs, Transport and Resources (DEDJTR) | Natural Resources Institute Finland (LUKE) | Agroclim (AGROCLIM) ; Institut National de la Recherche Agronomique (INRA) | University Medical Center Göttingen (UMG) | Computational and Systems Biology Department ; Rothamsted Research ; Biotechnology and Biological Sciences Research Council (BBSRC)-Biotechnology and Biological Sciences Research Council (BBSRC) | Water & Food and Water Systems & Global Change Group ; Wageningen University and Research [Wageningen] (WUR) | Institute of geographical sciences and natural resources research [CAS] (IGSNRR) ; Chinese Academy of Sciences [Beijing] (CAS) | Plant Production Systems ; Wageningen University and Research [Wageningen] (WUR) | State Key Laboratory of Earth Surface Processes and Resource Ecology ; Beijing Normal University (BNU) | European Project: 267196,EC:FP7:PEOPLE,FP7-PEOPLE-2010-COFUND,AGREENSKILLS(2012)
International audience
Show more [+] Less [-]English. A recent innovation in assessment of climate change impact on agricultural production has been to use crop multi model 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.
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