期刊
NATURE PLANTS
卷 3, 期 8, 页码 -出版社
NATURE PORTFOLIO
DOI: 10.1038/nplants.2017.102
关键词
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资金
- 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, 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]
- Natural Environment Research Council [NE/N018125/1] Funding Source: researchfish
- NERC [NE/N018125/1] Funding Source: UKRI
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.
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