Reducing uncertainty in prediction of wheat performance under climate change

Pierre Martre


Projections of climate change impacts on crop performances are inherently uncertain. However, multimodel uncertainty analysis of crop responses is rare because systematic and objective comparisons among process-based crop simulation models are difficult. Here we report on the Agricultural Model Intercomparison and Improvement Project ensemble of 30 wheat models tested using both crop and climate observed data in diverse environments, including infra-red heating field experiments, for their accuracy in simulating multiple crop growth, N economy and yield variables. The relative error averaged over models in reproducing observations was 24-38% for the different end-of-season variables. Clusters of wheat models organized by their correlations with temperature, precipitation, and solar radiation revealed common characteristics of climatic responses; however, models are rarely in the same cluster when comparing across sites. We also found that the amount of information used for calibration has only a minor effect on model ensemble climatic responses, but can be large for any single model. When simulating impacts assuming a mid-century A2 emissions scenario for climate projections from 16 downscaled general circulation models and 26 wheat models, a greater proportion of the uncertainty in climate change impact projections was due to variations among wheat models rather than to variations among climate models. Uncertainties in simulated impacts increased with atmospheric [CO2] and associated warming. Extrapolating the model ensemble temperature response (at current atmospheric [CO2]) indicated that warming is already reducing yields at a majority of wheat-growing locations. Finally, only a very weak relationship was found between the models’ sensitivities to interannual temperature variability and their response to long-term warming, suggesting that additional processes differentiate climate change impacts from observed climate variability analogs. In conclusion, uncertainties in prediction of climate change impacts on crop performance can be reduced by improving temperature and CO2 relationships in models and are better quantified through use of impact ensembles.

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Co-authors: Asseng Senthold3, Ewert Frank4, Rötter Reimund5, Lobell David6, Cammarano Davide1, Kimball Bruce7, Ottman Mike8, Wall Gerard7, White Jeffrey7, Reynolds Matthew9, Alderman Phillip9, Prasad Vara10, Aggarwal Pramod11, Anothai Jakarat12, Basso Bruno13, Biernath Christian14, Challinor Andy15,16, De Sanctis Giacomo17,18, Doltra Jordi19, Fereres E20, Garcia-Vila Margarita20, Gayler Sebastian21, Hoogenboom Gerrit 1 2, Hunt Anthony22, Izaurralde Cézar23, 24, Jabloun M25, Jones Curtis23, Kersebaum Christian26, Koehler Ann-Kristin15, Müller Christoph27, Naresh Kumar Soora28, Nendel Claas26, O’Leary Garry29, Olesen Jorgen E.25, Palosuo Taru5, Priesack Eckart14, Eyshi Rezaei Ehsan2, Ruane Alex30, Semenov Mikhail31, Shcherbak Iruii13, Stöckle Claudio32, Stratonovitch Pierre31, Streck Thilo33, Supit Iwan34, Tao Falu5,35, Thorburn Peter36, Waha Katharina27, Wang Enli37, Wallach Daniel38, Wolf Joost34, Zhao Z39,37, Zhu Yan40

INRA, UMR1095 Genetic, Diversity and Ecophysiology of Cererals (GDEC), F-63 100 Clermont-Ferrand, France; 2Now at INRA, UMR759 Laboratoire d'Ecophysiologie des Plantes sous Stress Environnementaux, Place Viala, F-34 060 Montpellier, France; 3Agricultural & Biological Engineering Department, University of Florida, Gainesville, FL 32611, USA; 4 Institute of Crop Science and Resource Conservation INRES, University of Bonn, 53115, Germany; 5Plant Production Research, MTT Agrifood Research Finland, FI-50100 Mikkeli, Finland; 6Department of Environmental Earth System Science and Center on Food Security and the Environment, Stanford University, Stanford, CA 94305; 7USDA, Agricultural Research Service, U.S. Arid-Land Agricultural Research Center, Maricopa, AZ 85138; 8The School of Plant Sciences, University of Arizona, Tucson, AZ 85721; 9CIMMYT Int. Adpo, D.F. Mexico 06600, Mexico; 10Department of Agronomy, Kansas State University, Manhattan, KS 66506, USA; 11CGIAR Research Program on Climate Change, Agriculture and Food Security, International Water Management Institute, New Delhi-110012, India; 12Biological Systems Engineering, Washington State University, Prosser, WA 99350-8694, USA; 13Department of Geological Sciences and W.K. Kellogg Biological Station, Michigan State University East Lansing, Michigan 48823, USA; 14Institute of Soil Ecology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, D-85764, Germany; 15Institute for Climate and Atmospheric Science, School of Earth and Environment, University of Leeds, Leeds LS29JT, UK; 16CGIAR-ESSP Program on Climate Change, Agriculture and Food Security, International Centre for Tropical Agriculture (CIAT), A.A. 6713, Cali, Colombia; 17INRA, US1116 AgroClim, F- 84 914 Avignon, France; 18 Now at European Commission Joint Research Center, via Enrico Fermi, 2749 Ispra, 21027 Italy; 19Cantabrian Agricultural Research and Training Centre (CIFA), 39600 Muriedas, Spain; 20IAS-CSIC and University of Cordoba, Apartado 4084, Cordoba, Spain; 21WESS-Water & Earth System Science Competence Cluster, University of Tübingen, 727074 Tübingen, Germany; 22Department of Plant Agriculture, University of Guelph, Guelph, Ontario, Canada, N1G 2W1; 23Dept. of Geographical Sciences, Univ. of Maryland, College Park, MD 20742; 24Texas A&M AgriLife Research and Extension Center, Texas A&M Univ., Temple, TX 76502; 25Department of Agroecology, Aarhus University, 8830 Tjele, Denmark; 26Institute of Landscape Systems Analysis, Leibniz Centre for Agricultural Landscape Research, 15374 Müncheberg, Germany; 27Potsdam Institute for Climate Impact Research, 14473 Potsdam, Germany; 28Centre for Environment Science and Climate Resilient Agriculture, Indian Agricultural Research Institute, IARI PUSA, New Delhi 110 012, India; 29Landscape & Water Sciences, Department of Environment and Primary Industries, Horsham 3400, Australia; 30NASA Goddard Institute for Space Studies, New York, NY 10025; 31Computational and Systems Biology Department, Rothamsted Research, Harpenden, Herts, AL5 2JQ, UK; 32Biological Systems Engineering, Washington State University, Pullman, WA 99164-6120; 33Institute of Soil Science and Land Evaluation, University of Hohenheim, 70599 Stuttgart; 34Plant Production Systems & Earth System Science, Wageningen University, 6700AA Wageningen, The Netherlands; 35Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Science, Beijing 100101, China; 36CSIRO Agriculture Flagship, Dutton Park QLD 4102, Australia; 37CSIRO Agriculture Flagship, Black Mountain ACT 2601, Australia; 38INRA, UMR 1248 Agrosystèmes et développement territorial (AGIR), 31326 Castanet-Tolosan Cedex, France; 39Department of Agronomy and Biotechnology, China Agricultural University, Yuanmingyuan West Road 2, Beijing 100193, China; 40College of Agriculture, Nanjing Agricultural University, Nanjing, Jiangsu, 210095, China

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