Multimodel ensembles of wheat growth: many models are better than one

Global Change Biology - Tập 21 Số 2 - Trang 911-925 - 2015
Pierre Martre1,2, Daniel Wallach3, Senthold Asseng4, Frank Ewert5, James W. Jones4, Reimund P. Rötter6, Kenneth J. Boote4, Alex C. Ruane7, Peter J. Thorburn8, Davide Cammarano4, Jerry L. Hatfield9, Cynthia Rosenzweig7, Pramod Aggarwal10, Carlos Angulo5, Bruno Basso11,12, Ary Bruand13, Nadine Brisson14,15, Andrew J. Challinor16,17, Jordi Doltra18, Sebastian Gayler19, Robert K. Goldberg7, R. F. Grant20, Lee Heng21, Josh Hooker22, R. C. Izaurralde23, Joachim Ingwersen24, Kurt Christian Kersebaum25, Christoph Müller26, Claas Nendel27, Garry J. O’Leary28, Jørgen E. Olesen29, Thomas M. Osborne30, Taru Palosuo6, Eckart Priesack31, Mikhail A. Semenov32, Iurii Shcherbak11,12, Pasquale Steduto33, Claudio O. Stöckle34, Pierre Stratonovitch32, Thilo Streck24, Fulu Tao35, Maria Travasso36, Katharina Waha26, Jeffrey W. White37, Heidi Webber38
1Blaise Pascal University UMR1095 GDEC F‐63 170 Aubière France
2INRA UMR1095 Genetics, Diversity and Ecophysiology of Cereals (GDEC) 5 chemin de Beaulieu F‐63 100 Clermont‐Ferrand France
3INRA UMR1248 Agrosystèmes et Développement Territorial F‐31 326 Castanet‐Tolosan France
4UF|ABE - Department of Agricultural and Biological Engineering [Gainesville] (University of Florida, Institute of Food and Agricultural Sciences, 1741 Museum Road, Gainesville, Florida 32611-0570 - United States)
5Institute of Crop Science and Resource Conservation Universität Bonn D‐53 115 Bonn Germany
6Plant Production Research MTT Agrifood Research Finland FI‐50 100 Mikkeli Finland
7National Aeronautics and Space Administration Goddard Institute for Space Studies New York NY 10025 USA
8Commonwealth Scientific and Industrial Research Organization Ecosystem Sciences Dutton Park QLD 4102 Australia
9National Laboratory for Agriculture and Environment Ames IA 50011 USA
10Consultative Group on International Agricultural Research Research Program on Climate Change, Agriculture and Food Security International Water Management Institute New Delhi 110012 India
11Department of Geological Sciences and Kellogg Biological Station Michigan State University East Lansing MI 48823 USA
12KBS - W. K. Kellogg Biological Station (3700 East Gull Lake Drive, Hickory Corners, MI 49060 - United States)
13INRA US1116 AgroClim F‐84 914 Avignon France
14AgroParisTech UMR0211 Agronomie F‐78 750 Thiverval‐Grignon France
15INRA UMR0211 Agronomie F‐78 750 Thiverval‐Grignon France
16CGIAR‐ESSP Program on Climate Change, Agriculture and Food Security International Centre for Tropical Agriculture A.A. 6713 Cali Colombia
17SEE - School of Earth and Environment [Leeds] (Maths/Earth and Environment Building, The University of Leeds, Leeds. LS2 9JT - United Kingdom)
18Cantabrian Agricultural Research and Training Centre 39600 Muriedas Spain
19Water & Earth System Science Competence Cluster c/o University of Tübingen D‐72 074 Tübingen Germany
20Department of Renewable Resources, University of Alberta, Edmonton, AB T6G 2E3, Canada
21IAEA - International Atomic Energy Agency [Vienna] (Vienna International Centre, POB 100, A-1400 Vienna - Austria)
22School of Agriculture, Policy and Development University of Reading RG6 6AR Reading UK
23Department of Plant Agriculture, University of Guelph, Guelph, ON N1G 2W1, Canada
24Institute of Soil Science and Land Evaluation Universität Hohenheim D‐70 599 Stuttgart Germany
25Institute of Landscape Systems Analysis Leibniz Centre for Agricultural Landscape Research D‐15 374 Müncheberg Germany
26PIK - Potsdam Institute for Climate Impact Research (Telegrafenberg A 31, 14473 Potsdam - Germany)
27Centre for Environment Science and Climate Resilient Agriculture Indian Agricultural Research Institute New Delhi 110 012 India
28Department of Primary Industries Landscape & Water Sciences Horsham Vic. 3400 Australia
29Department of Agroecology, Aarhus University, 8830 Tjele, Denmark
30National Centre for Atmospheric Science Department of Meteorology University of Reading RG6 6BB Reading UK
31Institute of Soil Ecology Helmholtz Zentrum München German Research Center for Environmental Health Neuherberg D‐85 764 Germany
32Computational and Systems Biology Department, Rothamsted Research, Harpenden, Herts, AL5 2JQ, UK
33Food and Agriculture Organization of the United Nations Rome 00153 Italy
34Biological Systems Engineering Washington State University Pullman WA 99164‐6120 USA
35Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Science, Beijing 100101, China
36Institute for Climate and Water INTA‐CIRN 1712 Castelar Argentina
37Arid‐Land Agricultural Research Center USDA Maricopa AZ 85138 USA
38WUR - Wageningen University and Research [Wageningen] (Droevendaalsesteeg 4, 6708 PB Wageningen - Netherlands)

Tóm tắt

AbstractCrop 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.

Từ khóa


Tài liệu tham khảo

10.2134/advagricsystmodel2.c15

10.1016/j.agrformet.2012.11.017

10.1016/S0378-4290(97)00117-2

10.1038/nclimate1916

10.1111/gcb.12520

10.1051/agro/2009001

10.1093/jxb/erp377

10.1126/science.1209290

10.1175/2011JCLI4175.1

10.3354/cr00779

10.1038/nclimate2117

10.3354/cr00916

10.1093/bioinformatics/bti063

Easterling WE, 2007, Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the intergovernmental Panel on Climate Change, 273

FAOSTAT(2014)Food and Agricultural organization of the United Nations (FAO).FAO Statistical Databases. Available at:faostat3.fao.org(accessed 28 October 2014).

10.1093/jxb/erq300

10.1126/science.1185383

10.1051/agro/2009059

10.1111/j.1600-0587.2010.06152.x

10.1007/978-94-011-3434-7_19

10.1111/j.1600-0870.2005.00103.x

10.1006/anbo.1994.1133

10.1007/s10584-010-9800-2

10.1016/j.agrformet.2010.06.004

10.1007/978-3-642-60684-7_1

10.1016/j.eja.2008.01.005

10.1126/science.1204531

NaveenN(1986)Evaluation of soil water status plant growth and canopy environment in relation to variable water supply to wheat. Unpublished PhD IARI New Delhi.

10.1016/j.eja.2011.05.001

10.1098/rstb.2005.1752

R Core Team, 2013, R: A Language and Environment for Statistical Computing

10.1175/1520-0442(2001)014<3212:APADMA>2.0.CO;2

10.1073/pnas.1222463110

10.1016/j.agrformet.2012.09.011

10.1038/nclimate1152

10.1016/j.fcr.2012.03.016

StackhouseP(2006)Prediction of worldwide energy resources. Available at:http://power.larc.nasa.gov(accessed 28 October 2014).

10.1016/j.fcr.2011.07.015

10.1098/rsta.2007.2076

Travasso MI, 2005, MODSIM 2005 International Congress on Modelling and Simulation, 366

10.1105/tpc.106.042267

10.1073/pnas.0701728104

10.2134/agronj2010.0432

Wallach D, 2013, Working with Dynamic Crop Models. Methods Tools and Examples for Agriculture and Environment

10.1016/j.fcr.2011.07.001

10.1111/j.1365-3180.1974.tb01084.x

Thông tin
Thông tin xuất bản

Global Change Biology

Tập 21 Số 2

911-925

Thông tin tác giả