The uncertainty of crop yield projections is reduced by improved temperature response functions
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 °C to 33 °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.
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2017-07-17
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| Online Access: | http://hdl.handle.net/10261/166954 http://dx.doi.org/10.13039/501100002338 http://dx.doi.org/10.13039/501100002347 http://dx.doi.org/10.13039/501100005908 http://dx.doi.org/10.13039/501100001656 http://dx.doi.org/10.13039/501100000980 http://dx.doi.org/10.13039/100004913 http://dx.doi.org/10.13039/100007904 http://dx.doi.org/10.13039/100005825 http://dx.doi.org/10.13039/501100004543 http://dx.doi.org/10.13039/501100002367 http://dx.doi.org/10.13039/501100000943 http://dx.doi.org/10.13039/501100000780 http://dx.doi.org/10.13039/501100006488 http://dx.doi.org/10.13039/501100001809 http://dx.doi.org/10.13039/100000199 http://dx.doi.org/10.13039/501100011099 |
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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 °C to 33 °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. |
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Commonwealth Scientific and Industrial Research Organisation (Australia) Wang, Enli Martre, Pierre Zhao, Zhigan Ewert, Frank Maiorano, Andrea Rötter, Reimund P. Kimball, Bruce A. Ottman, Michael J. Wall, Gerard W. White, Jefrrey W. Reynolds, Matthew P. Alderman, Phillip Aggarwal, Pramod K. Anothai, Jakarat Basso, Bruno Biernath, Christian Cammarano, Davide Challinor, Andrew J. De Sanctis, Giacomo Doltra, Jordi Dumont, Benjamin Fereres Castiel, Elías García Vila, Margarita Gayler, Sebastian Hoogenboom, Gerrit Hunt, Leslie A. Izaurralde, Roberto C. Jabloun, Mohamed Jones, Curtis D. Kersebaum, Kurt C. Koehler, Ann-Kristin Liu, Leilei Müller, Christoph Kumar, Soora Naresh Nendel, Claas O'Leary, Garry Olesen, Jørgen E. Palosuo, Taru Priesack, Eckart Rezaei, Ehsan Eyshi Ripoche, Dominique Ruane, Alexander C. Semenov, Mikhail A. Shcherbak, Iurii Stöckle, Claudio Stratonovitch, Pierre Streck, Thilo Supit, Iwan Tao, Fulu Thorburn, Peter Waha, Katharina Wallach, Daniel Wang, Zhimin Wolf, Joost Zhu, Yan Asseng, Senthold |
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Wang, Enli Martre, Pierre Zhao, Zhigan Ewert, Frank Maiorano, Andrea Rötter, Reimund P. Kimball, Bruce A. Ottman, Michael J. Wall, Gerard W. White, Jefrrey W. Reynolds, Matthew P. Alderman, Phillip Aggarwal, Pramod K. Anothai, Jakarat Basso, Bruno Biernath, Christian Cammarano, Davide Challinor, Andrew J. De Sanctis, Giacomo Doltra, Jordi Dumont, Benjamin Fereres Castiel, Elías García Vila, Margarita Gayler, Sebastian Hoogenboom, Gerrit Hunt, Leslie A. Izaurralde, Roberto C. Jabloun, Mohamed Jones, Curtis D. Kersebaum, Kurt C. Koehler, Ann-Kristin Liu, Leilei Müller, Christoph Kumar, Soora Naresh Nendel, Claas O'Leary, Garry Olesen, Jørgen E. Palosuo, Taru Priesack, Eckart Rezaei, Ehsan Eyshi Ripoche, Dominique Ruane, Alexander C. Semenov, Mikhail A. Shcherbak, Iurii Stöckle, Claudio Stratonovitch, Pierre Streck, Thilo Supit, Iwan Tao, Fulu Thorburn, Peter Waha, Katharina Wallach, Daniel Wang, Zhimin Wolf, Joost Zhu, Yan Asseng, Senthold |
| spellingShingle |
Wang, Enli Martre, Pierre Zhao, Zhigan Ewert, Frank Maiorano, Andrea Rötter, Reimund P. Kimball, Bruce A. Ottman, Michael J. Wall, Gerard W. White, Jefrrey W. Reynolds, Matthew P. Alderman, Phillip Aggarwal, Pramod K. Anothai, Jakarat Basso, Bruno Biernath, Christian Cammarano, Davide Challinor, Andrew J. De Sanctis, Giacomo Doltra, Jordi Dumont, Benjamin Fereres Castiel, Elías García Vila, Margarita Gayler, Sebastian Hoogenboom, Gerrit Hunt, Leslie A. Izaurralde, Roberto C. Jabloun, Mohamed Jones, Curtis D. Kersebaum, Kurt C. Koehler, Ann-Kristin Liu, Leilei Müller, Christoph Kumar, Soora Naresh Nendel, Claas O'Leary, Garry Olesen, Jørgen E. Palosuo, Taru Priesack, Eckart Rezaei, Ehsan Eyshi Ripoche, Dominique Ruane, Alexander C. Semenov, Mikhail A. Shcherbak, Iurii Stöckle, Claudio Stratonovitch, Pierre Streck, Thilo Supit, Iwan Tao, Fulu Thorburn, Peter Waha, Katharina Wallach, Daniel Wang, Zhimin Wolf, Joost Zhu, Yan Asseng, Senthold The uncertainty of crop yield projections is reduced by improved temperature response functions |
| author_sort |
Wang, Enli |
| title |
The uncertainty of crop yield projections is reduced by improved temperature response functions |
| title_short |
The uncertainty of crop yield projections is reduced by improved temperature response functions |
| title_full |
The uncertainty of crop yield projections is reduced by improved temperature response functions |
| title_fullStr |
The uncertainty of crop yield projections is reduced by improved temperature response functions |
| title_full_unstemmed |
The uncertainty of crop yield projections is reduced by improved temperature response functions |
| title_sort |
uncertainty of crop yield projections is reduced by improved temperature response functions |
| publisher |
Springer Nature |
| publishDate |
2017-07-17 |
| url |
http://hdl.handle.net/10261/166954 http://dx.doi.org/10.13039/501100002338 http://dx.doi.org/10.13039/501100002347 http://dx.doi.org/10.13039/501100005908 http://dx.doi.org/10.13039/501100001656 http://dx.doi.org/10.13039/501100000980 http://dx.doi.org/10.13039/100004913 http://dx.doi.org/10.13039/100007904 http://dx.doi.org/10.13039/100005825 http://dx.doi.org/10.13039/501100004543 http://dx.doi.org/10.13039/501100002367 http://dx.doi.org/10.13039/501100000943 http://dx.doi.org/10.13039/501100000780 http://dx.doi.org/10.13039/501100006488 http://dx.doi.org/10.13039/501100001809 http://dx.doi.org/10.13039/100000199 http://dx.doi.org/10.13039/501100011099 |
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1777663215193292800 |
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dig-ias-es-10261-1669542018-08-01T10:29:41Z The uncertainty of crop yield projections is reduced by improved temperature response functions Wang, Enli Martre, Pierre Zhao, Zhigan Ewert, Frank Maiorano, Andrea Rötter, Reimund P. Kimball, Bruce A. Ottman, Michael J. Wall, Gerard W. White, Jefrrey W. Reynolds, Matthew P. Alderman, Phillip Aggarwal, Pramod K. Anothai, Jakarat Basso, Bruno Biernath, Christian Cammarano, Davide Challinor, Andrew J. De Sanctis, Giacomo Doltra, Jordi Dumont, Benjamin Fereres Castiel, Elías García Vila, Margarita Gayler, Sebastian Hoogenboom, Gerrit Hunt, Leslie A. Izaurralde, Roberto C. Jabloun, Mohamed Jones, Curtis D. Kersebaum, Kurt C. Koehler, Ann-Kristin Liu, Leilei Müller, Christoph Kumar, Soora Naresh Nendel, Claas O'Leary, Garry Olesen, Jørgen E. Palosuo, Taru Priesack, Eckart Rezaei, Ehsan Eyshi Ripoche, Dominique Ruane, Alexander C. Semenov, Mikhail A. Shcherbak, Iurii Stöckle, Claudio Stratonovitch, Pierre Streck, Thilo Supit, Iwan Tao, Fulu Thorburn, Peter Waha, Katharina Wallach, Daniel Wang, Zhimin Wolf, Joost Zhu, Yan Asseng, Senthold Commonwealth Scientific and Industrial Research Organisation (Australia) Chinese Academy of Sciences China Scholarship Council Ministry of Education of the People's Republic of China Institut National de la Recherche Agronomique (France) European Commission International Food Policy Research Institute (US) CGIAR (France) Department of Agriculture (US) Federal Ministry of Education and Research (Germany) Deutsche Gesellschaft für Internationale Zusammenarbeit Danish Council for Strategic Research Federal Ministry of Food and Agriculture (Germany) Finnish Ministry of Agriculture and Forestry National Natural Science Foundation of China Helmholtz Association Grains Research and Development Corporation (Australia) Texas AgriLife Research Texas A&M University National Institute of Food and Agriculture (US) 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 °C to 33 °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. E.W. acknowledges support from the CSIRO project ‘Enhanced modelling of genotype by environment interactions’ and the project ‘Advancing crop yield while reducing the use of water and nitrogen’ jointly funded by CSIRO and the Chinese Academy of Sciences (CAS). Z.Z. received a scholarship from the China Scholarship Council through the CSIRO and the Chinese Ministry of Education PhD Research Program. P.M., A.M. and D.R. acknowledge support from the FACCE JPI MACSUR project (031A103B) through the metaprogram Adaptation of Agriculture and Forests to Climate Change (AAFCC) of the French National Institute for Agricultural Research (INRA). A.M. received the support of the EU in the framework of the Marie-Curie FP7 COFUND People Programme, through the award of an AgreenSkills fellowship under grant agreement No. PCOFUND-GA-2010-267196. S.A. and D.C. acknowledge support provided by the International Food Policy Research Institute (IFPRI), CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), the CGIAR Research Program on Wheat and the Wheat Initiative. C.S. was funded through USDA National Institute for Food and Agriculture award 32011-68002-30191. C.M. received financial support from the KULUNDA project (01LL0905 L) and the FACCE MACSUR project (031A103B) funded through the German Federal Ministry of Education and Research (BMBF). F.E. received support from the FACCE MACSUR project (031A103B) funded through the German Federal Ministry of Education and Research (2812ERA115) and E.E.R. was funded through the German Federal Ministry of Economic Cooperation and Development (Project: PARI). M.J. and J.E.O. were funded through the FACCE MACSUR project by the Danish Strategic Research Council. K.C.K. and C.N. were funded by the FACCE MACSUR project through the German Federal Ministry of Food and Agriculture (BMEL). F.T., T.P. and R.P.R. received financial support from the FACCE MACSUR project funded through the Finnish Ministry of Agriculture and Forestry (MMM); F.T. was also funded through the National Natural Science Foundation of China (No. 41071030). C.B. was funded through the Helmholtz project ‘REKLIM-Regional Climate Change: Causes and Effects’ Topic 9: ‘Climate Change and Air Quality’. M.P.R. and PD.A. received funding from the CGIAR Research Program on Climate Change, Agriculture, and Food Security (CCAFS). G.O'L. was funded through the Australian Grains Research and Development Corporation and the Department of Economic Development, Jobs, Transport and Resources Victoria, Australia. R.C.I. was funded by Texas AgriLife Research, Texas A&M University. B.B. was funded by USDA-NIFA Grant No: 2015-68007-23133. Peer reviewed 2018-06-25T09:40:07Z 2018-06-25T09:40:07Z 2017-07-17 artículo http://purl.org/coar/resource_type/c_6501 Nature Plants 3: 17102 (2017) 2055-026X http://hdl.handle.net/10261/166954 10.1038/nplants.2017.102 2055-0278 http://dx.doi.org/10.13039/501100002338 http://dx.doi.org/10.13039/501100002347 http://dx.doi.org/10.13039/501100005908 http://dx.doi.org/10.13039/501100001656 http://dx.doi.org/10.13039/501100000980 http://dx.doi.org/10.13039/100004913 http://dx.doi.org/10.13039/100007904 http://dx.doi.org/10.13039/100005825 http://dx.doi.org/10.13039/501100004543 http://dx.doi.org/10.13039/501100002367 http://dx.doi.org/10.13039/501100000943 http://dx.doi.org/10.13039/501100000780 http://dx.doi.org/10.13039/501100006488 http://dx.doi.org/10.13039/501100001809 http://dx.doi.org/10.13039/100000199 http://dx.doi.org/10.13039/501100011099 en #PLACEHOLDER_PARENT_METADATA_VALUE# info:eu-repo/grantAgreement/EC/FP7/267196 http://dx.doi.org/10.1038/nplants.2017.102 Sí none Springer Nature |