KMS Institute Of Geographic Sciences And Natural Resources Research,CAS
| The uncertainty of crop yield projections is reduced by improved temperature response functions | |
Wang, Enli1; Martre, Pierre2; Zhao, Zhigan1,3; Ewert, Frank4,5; Maiorano, Andrea2,42; Roetter, Reimund P.6,7,32; Kimball, Bruce A.8; Ottman, Michael J.9; Wall, Gerard W.8; White, Jeffrey W.8; Reynolds, Matthew P.10; Alderman, Phillip D.10,43; Aggarwal, Pramod K.11; Anothai, Jakarat12,44; Basso, Bruno13,14; Biernath, Christian15; Cammarano, Davide16,45; Challinor, Andrew J.17,18; De Sanctis, Giacomo19; Doltra, Jordi20; Fereres, Elias21,22; Garcia-Vila, Margarita21,22; Gayler, Sebastian23; Hoogenboom, Gerrit12,46; Hunt, Leslie A.24; Izaurralde, Roberto C.25,26; Jabloun, Mohamed27; Jones, Curtis D.; Kersebaum, Kurt C.5; Koehler, Ann-Kristin17; Liu, Leilei28; Mueller, Christoph29; Kumar, Soora Naresh30; Nendel, Claas5; O'Leary, Garry31; Olesen, Jorgen E.27; Palosuo, Taru32; Priesack, Eckart15; Rezaei, Ehsan Eyshi4; Ripoche, Dominique33; Ruane, Alex C.34; Semenov, Mikhail A.35; Shcherbak, Iurii13,14; Stockle, Claudio36; Stratonovitch, Pierre35; Streck, Thilo23; Supit, Iwan37,38,47; Tao, Fulu32,39 ; Thorburn, Peter40; Waha, Katharina29; Wallach, Daniel41; Wang, Zhimin3; Wolf, Joost37,38; Zhu, Yan28; Asseng, Senthold16
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| 2017-08-01 | |
| Source Publication | NATURE PLANTS
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| ISSN | 2055-026X |
| Volume | 3Issue:8Pages:11 |
| Corresponding Author | Wang, Enli(Enli.Wang@csiro.au) ; Martre, Pierre(pierre.martre@inra.fr) |
| Abstract | Increasing the accuracy of crop productivity estimates is a key element in planning adaptation strategies to ensure global food security under climate change. Process-based crop models are effective means to project climate impact on crop yield, but have large uncertainty in yield simulations. Here, we show that variations in the mathematical functions currently used to simulate temperature responses of physiological processes in 29 wheat models account for > 50% of uncertainty in simulated grain yields for mean growing season temperatures from 14 degrees C to 33 degrees C. We derived a set of new temperature response functions that when substituted in four wheat models reduced the error in grain yield simulations across seven global sites with different temperature regimes by 19% to 50% ( 42% average). We anticipate the improved temperature responses to be a key step to improve modelling of crops under rising temperature and climate change, leading to higher skill of crop yield projections. |
| DOI | 10.1038/nplants.2017.102 |
| WOS Keyword | WINTER-WHEAT ; SPRING WHEAT ; PHENOLOGICAL DEVELOPMENT ; DEVELOPMENTAL PROCESSES ; PROTEIN-COMPOSITION ; LEAF APPEARANCE ; SOWING DATES ; MODEL ; SIMULATION ; GROWTH |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | CSIRO project 'Enhanced modelling of genotype by environment interactions' ; project 'Advancing crop yield while reducing the use of water and nitrogen' ; CSIRO ; Chinese Academy of Sciences (CAS) ; China Scholarship Council through the CSIRO ; Chinese Ministry of Education PhD Research Program ; FACCE JPI MACSUR project through the metaprogram Adaptation of Agriculture[031A103B] ; Marie-Curie FP7 COFUND People Programme, through the award of an AgreenSkills fellowship[PCOFUND-GA-2010-267196] ; International Food Policy Research Institute (IFPRI) ; CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) ; CGIAR Research Program on Wheat and the Wheat Initiative ; USDA National Institute for Food and Agriculture[32011-68002-30191] ; KULUNDA project[01LL0905 L] ; FACCE MACSUR project through the German Federal Ministry of Education and Research (BMBF)[031A103B] ; FACCE MACSUR project through the German Federal Ministry of Education and Research (BMBF)[2812ERA115] ; German Federal Ministry of Economic Cooperation and Development (Project: PARI) ; FACCE MACSUR project by the Danish Strategic Research Council ; FACCE MACSUR project through the German Federal Ministry of Food and Agriculture (BMEL) ; FACCE MACSUR project funded through the Finnish Ministry of Agriculture and Forestry ; National Natural Science Foundation of China[41071030] ; Helmholtz project 'REKLIM-Regional Climate Change: Causes and Effects' Topic 9: 'Climate Change and Air Quality' ; CGIAR Research Program on Climate Change, Agriculture, and Food Security (CCAFS) ; Australian Grains Research and Development Corporation ; Department of Economic Development, Jobs, Transport and Resources Victoria, Australia ; Texas AgriLife Research, Texas AM University ; USDA-NIFA[2015-68007-23133] |
| Funding Organization | CSIRO project 'Enhanced modelling of genotype by environment interactions' ; project 'Advancing crop yield while reducing the use of water and nitrogen' ; CSIRO ; Chinese Academy of Sciences (CAS) ; China Scholarship Council through the CSIRO ; Chinese Ministry of Education PhD Research Program ; FACCE JPI MACSUR project through the metaprogram Adaptation of Agriculture ; Marie-Curie FP7 COFUND People Programme, through the award of an AgreenSkills fellowship ; International Food Policy Research Institute (IFPRI) ; CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) ; CGIAR Research Program on Wheat and the Wheat Initiative ; USDA National Institute for Food and Agriculture ; KULUNDA project ; FACCE MACSUR project through the German Federal Ministry of Education and Research (BMBF) ; German Federal Ministry of Economic Cooperation and Development (Project: PARI) ; FACCE MACSUR project by the Danish Strategic Research Council ; FACCE MACSUR project through the German Federal Ministry of Food and Agriculture (BMEL) ; FACCE MACSUR project funded through the Finnish Ministry of Agriculture and Forestry ; National Natural Science Foundation of China ; Helmholtz project 'REKLIM-Regional Climate Change: Causes and Effects' Topic 9: 'Climate Change and Air Quality' ; CGIAR Research Program on Climate Change, Agriculture, and Food Security (CCAFS) ; Australian Grains Research and Development Corporation ; Department of Economic Development, Jobs, Transport and Resources Victoria, Australia ; Texas AgriLife Research, Texas AM University ; USDA-NIFA |
| WOS Research Area | Plant Sciences |
| WOS Subject | Plant Sciences |
| WOS ID | WOS:000406850200001 |
| Publisher | NATURE PUBLISHING GROUP |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.igsnrr.ac.cn/handle/311030/61439 |
| Collection | 中国科学院地理科学与资源研究所 |
| Corresponding Author | Wang, Enli; Martre, Pierre |
| Affiliation | 1.CSIRO, Agr & Food, Canberra, ACT 2601, Australia 2.Montpellier SupAgro, INRA, UMR LEPSE, 2 Pl Viala, F-34060 Montpellier, France 3.China Agr Univ, Coll Agron & Biotechnol, Beijing 100193, Peoples R China 4.Univ Bonn, Inst Crop Sci & Resource Conservat INRES, D-53115 Bonn, Germany 5.Leibniz Ctr Agr Landscape Res, Inst Landscape Syst Anal, D-15374 Muncheberg, Germany 6.Univ Gottingen, Dept Crop Sci, Trop Plant Prod & Agr Syst Modelling TROPAGS, D-37077 Gottingen, Germany 7.Univ Gottingen, Ctr Biodivers & Sustainable Land Use CBL, Busgenweg 1, D-37077 Gottingen, Germany 8.USDA, Agr Res Serv, US Arid Land Agr Res Ctr, Maricopa, AZ 85138 USA 9.Univ Arizona, Sch Plant Sci, Tucson, AZ 85721 USA 10.Int Maize & Wheat Improvement Ctr CIMMYT, Global Wheat Program, Mexico City, DF, Mexico 11.Int Maize & Wheat Improvement Ctr CIMMYT, Borlaug Inst South Asia, Agr & Food Secur, CGIAR Res Program Climate Change, New Delhi 110012, India 12.Washington State Univ, AgWeatherNet Program, Prosser, WA 99350 USA 13.Michigan State Univ, Dept Earth & Environm Sci, E Lansing, MI 48823 USA 14.Michigan State Univ, WK Kellogg Biol Stn, E Lansing, MI 48823 USA 15.Helmholtz Zentrum Munchen German Res Ctr Environm, Inst Biochem Plant Pathol, D-85764 Neuherberg, Germany 16.Univ Florida, Agr & Biol Engn Dept, Gainesville, FL 32611 USA 17.Univ Leeds, Sch Earth & Environm, Insti Climate & Atmospher Sci, Leeds LS2 9JT, W Yorkshire, England 18.CGIAR Res Program Climate Change Agr & Food Secur, Km 17,Recta Cali Palmira Apartado Aereo, Cali, Colombia 19.European Food Safety Author EFSA, GMO Unit, Via Carlo Magno,1A, I-43126 Parma, Italy 20.Cantabrian Agr Res & Training Ctr CIFA, Muriedas 39600, Spain 21.Univ Cordoba, Dep Agron, Apartado 3048, Cordoba 14080, Spain 22.CSIC, IAS, Cordoba 14080, Spain 23.Univ Hohenheim, Inst Soil Sci & Land Evaluat, D-70599 Stuttgart, Germany 24.Univ Guelph, Dept Plant Agr, Guelph, ON N1G 2W1, Canada 25.Univ Maryland, Dept Geog Sci, College Pk, MD 20742 USA 26.Texas A&M Univ, Texas A&M AgriLife Res & Extens Ctr, Temple, TX 76504 USA 27.Aarhus Univ, Dept Agroecol, DK-8830 Tjele, Denmark 28.Nanjing Agr Univ, Jiangsu Collaborat Innovat Ctr Modern Crop Prod, Jiangsu Key Lab Informat Agr,Natl Engn & Technol, Minist Agr,Key Lab Crop System Anal & Decicion Ma, Nanjing 210095, Jiangsu, Peoples R China 29.Potsdam Inst Climate Impact Res, D-14473 Potsdam, Germany 30.Indian Agr Res Inst, Ctr Environm Sci & Climate Resilient Agr, New Delhi 110012, India 31.Dept Econ Dev Landscape & Water Sci Jobs Transpor, Horsham, Vic 3400, Australia 32.Nat Resources Inst Finland Luke, Latokartanonkaari 9, Helsinki 00790, Finland 33.INRA, US1116 AgroClim, F-84914 Avignon, France 34.NASA, Goddard Inst Space Studies, New York, NY 10025 USA 35.Rothamsted Res, Computat & Syst Biol Dept, Harpenden AL5 2JQ, Herts, England 36.Washington State Univ, Biol Syst Engn, Pullman, WA 99164 USA 37.Wageningen Univ, PPS, Wageningen, Netherlands 38.Wageningen Univ, WSG & CALM, NL-6700AA Wageningen, Netherlands 39.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 40.CSIRO, Agr & Food, St Lucia, Qld 4067, Australia 41.INRA, UMR 1248, Agrosyst Dev Terr AGIR, F-31326 Castanet Tolosan, France 42.European Commiss Joint Res Ctr, I-21027 Ispra, Italy 43.Oklahoma State Univ, Dept Plant & Soil Sci, Stillwater, OK 74078 USA 44.Prince Songkla Univ, Fac Nat Resources, Dept Plant Sci, Hat Yai 90112, Thailand 45.James Hutton Inst, Dundee DD2 5DA, Scotland 46.Univ Florida, Inst Sustainable Food Syst, Gainesville, FL 32611 USA 47.Queensland Univ Technol, Inst Future Environm, Brisbane, Qld 4001, Australia |
| Recommended Citation GB/T 7714 | Wang, Enli,Martre, Pierre,Zhao, Zhigan,et al. The uncertainty of crop yield projections is reduced by improved temperature response functions[J]. NATURE PLANTS,2017,3(8):11. |
| APA | Wang, Enli.,Martre, Pierre.,Zhao, Zhigan.,Ewert, Frank.,Maiorano, Andrea.,...&Asseng, Senthold.(2017).The uncertainty of crop yield projections is reduced by improved temperature response functions.NATURE PLANTS,3(8),11. |
| MLA | Wang, Enli,et al."The uncertainty of crop yield projections is reduced by improved temperature response functions".NATURE PLANTS 3.8(2017):11. |
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