globalchange  > 影响、适应和脆弱性
DOI: 10.1111/gcb.12768
Title:
Multimodel ensembles of wheat growth: Many models are better than one
Author: Martre P.; Wallach D.; Asseng S.; Ewert F.; Jones J.W.; Rötter R.P.; Boote K.J.; Ruane A.C.; Thorburn P.J.; Cammarano D.; Hatfield J.L.; Rosenzweig C.; Aggarwal P.K.; Angulo C.; Basso B.; Bertuzzi P.; Biernath C.; Brisson N.; Challinor A.J.; Doltra J.; Gayler S.; Goldberg R.; Grant R.F.; Heng L.; Hooker J.; Hunt L.A.; Ingwersen J.; Izaurralde R.C.; Kersebaum K.C.; Müller C.; Kumar S.N.; Nendel C.; O'leary G.; Olesen J.E.; Osborne T.M.; Palosuo T.; Priesack E.; Ripoche D.; Semenov M.A.; Shcherbak I.; Steduto P.; Stöckle C.O.; Stratonovitch P.; Streck T.; Supit I.; Tao F.; Travasso M.; Waha K.; White J.W.; Wolf J.
Source Publication: Global Change Biology
ISSN: 13541013
Publishing Year: 2015
Volume: 21, Issue:2
pages begin: 911
pages end: 925
Language: 英语
Keyword: Ecophysiological model ; Ensemble modeling ; Model intercomparison ; Process-based model ; Uncertainty ; Wheat (Triticum aestivum L.)
Scopus Keyword: climate change ; crop production ; ecophysiology ; ensemble forecasting ; global change ; growth rate ; growth response ; numerical model ; uncertainty analysis ; wheat ; yield response ; Triticum aestivum ; biological model ; climate ; climate change ; environment ; growth, development and aging ; season ; wheat ; Climate ; Climate Change ; Environment ; Models, Biological ; Seasons ; Triticum
English Abstract: Crop models of crop growth are increasingly used to quantify the impact of global changes due to climate or crop management. Therefore, accuracy of simulation results is a major concern. Studies with ensembles of crop models can give valuable information about model accuracy and uncertainty, but such studies are difficult to organize and have only recently begun. We report on the largest ensemble study to date, of 27 wheat models tested in four contrasting locations for their accuracy in simulating multiple crop growth and yield variables. The relative error averaged over models was 24-38% for the different end-of-season variables including grain yield (GY) and grain protein concentration (GPC). There was little relation between error of a model for GY or GPC and error for in-season variables. Thus, most models did not arrive at accurate simulations of GY and GPC by accurately simulating preceding growth dynamics. Ensemble simulations, taking either the mean (e-mean) or median (e-median) of simulated values, gave better estimates than any individual model when all variables were considered. Compared to individual models, e-median ranked first in simulating measured GY and third in GPC. The error of e-mean and e-median declined with an increasing number of ensemble members, with little decrease beyond 10 models. We conclude that multimodel ensembles can be used to create new estimators with improved accuracy and consistency in simulating growth dynamics. We argue that these results are applicable to other crop species, and hypothesize that they apply more generally to ecological system models. © 2014 John Wiley & Sons Ltd.
Citation statistics:
被引频次[WOS]:166   [查看WOS记录]     [查看WOS中相关记录]
Document Type: 期刊论文
Identifier: http://119.78.100.158/handle/2HF3EXSE/61753
Appears in Collections:影响、适应和脆弱性

Files in This Item:

There are no files associated with this item.


Affiliation: INRA, UMR1095 Genetics, Diversity and Ecophysiology of Cereals (GDEC), 5 chemin de Beaulieu, Clermont-Ferrand, France; Blaise Pascal University, UMR1095 GDEC, Aubière, France; INRA, UMR1248 Agrosystèmes et Développement Territorial, Castanet-Tolosan, France; Agricultural and Biological Engineering Department, University of Florida, Gainesville, FL, United States; Institute of Crop Science and Resource Conservation, Universität Bonn, Bonn, Germany; Plant Production Research, MTT Agrifood Research Finland, Mikkeli, Finland; National Aeronautics and Space Administration, Goddard Institute for Space Studies, New York, NY, United States; Commonwealth Scientific and Industrial Research Organization, Ecosystem Sciences, Dutton Park, QLD, Australia; National Laboratory for Agriculture and Environment, Ames, IA, United States; Consultative Group on International Agricultural Research, Research Program on Climate Change, Agriculture and Food Security, International Water Management Institute, New Delhi, India; Department of Geological Sciences and Kellogg Biological Station, Michigan State University, East Lansing, MI, United States; INRA, US1116 AgroClim, Avignon, France; Institute of Soil Ecology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany; INRA, UMR0211 Agronomie, Thiverval-Grignon, France; AgroParisTech, UMR0211 Agronomie, Thiverval-Grignon, France; 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, Cali, Colombia; Cantabrian Agricultural Research and Training Centre, Muriedas, Spain; Water and Earth System Science Competence Cluster, C/o University of Tübingen, Tübingen, Germany; Department of Renewable Resources, University of Alberta, Edmonton, AB, Canada; International Atomic Energy Agency, Vienna, Austria; School of Agriculture, Policy and Development, University of Reading, Reading, United Kingdom; Department of Plant Agriculture, University of Guelph, Guelph, ON, Canada; Institute of Soil Science and Land Evaluation, Universität Hohenheim, Stuttgart, Germany; Department of Geographical Sciences, University of Maryland, College Park, MD, United States; Institute of Landscape Systems Analysis, Leibniz Centre for Agricultural Landscape Research, Müncheberg, Germany; Potsdam Institute for Climate Impact Research, Potsdam, Germany; Centre for Environment Science and Climate Resilient Agriculture, Indian Agricultural Research Institute, New Delhi, India; Department of Primary Industries, Landscape and Water Sciences, Horsham, VIC, Australia; Department of Agroecology, Aarhus University, Tjele, Denmark; National Centre for Atmospheric Science, Department of Meteorology, University of Reading, Reading, United Kingdom; Computational and Systems Biology Department, Rothamsted Research, Harpenden, Herts, United Kingdom; Food and Agriculture Organization of the United Nations, Rome, Italy; Biological Systems Engineering, Washington State University, Pullman, WA, United States; Earth System Science-Climate Change, Wageningen University, Wageningen, Netherlands; Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Science, Beijing, China; Institute for Climate and Water, INTA-CIRN, Castelar, Argentina; Arid-Land Agricultural Research Center, USDA, Maricopa, AZ, United States; Plant Production Systems, Wageningen University, Wageningen, Netherlands

Recommended Citation:
Martre P.,Wallach D.,Asseng S.,et al. Multimodel ensembles of wheat growth: Many models are better than one[J]. Global Change Biology,2015-01-01,21(2)
Service
Recommend this item
Sava as my favorate item
Show this item's statistics
Export Endnote File
Google Scholar
Similar articles in Google Scholar
[Martre P.]'s Articles
[Wallach D.]'s Articles
[Asseng S.]'s Articles
百度学术
Similar articles in Baidu Scholar
[Martre P.]'s Articles
[Wallach D.]'s Articles
[Asseng S.]'s Articles
CSDL cross search
Similar articles in CSDL Cross Search
[Martre P.]‘s Articles
[Wallach D.]‘s Articles
[Asseng S.]‘s Articles
Related Copyright Policies
Null
收藏/分享
所有评论 (0)
暂无评论
 

Items in IR are protected by copyright, with all rights reserved, unless otherwise indicated.