The uncertainty of crop yield projections is reduced by improved temperature response functions

Enli Wang; Pierre Martres; Zhigan Zhao; Frank Ewert; Andrea Maiorano; Reimund P. Rötter; Bruce A. Kimball; Michael J. Ottman; Gerard W. Wall; Jeffrey W. White; Matthew P. Reynolds; Phillip D. Alderman; Pramod K. Aggarwal; Jakarat Anothai; Bruno Basso; Christian Biernath; Davide Cammarano; Andrew J. Challinor; Giacomo de Sanctis; Jordi Doltra; Benjamin Dumont; Elias Fereres; Margarita Garcia-Vila; Sebastian Gayler; Gerrit Hoogenboom; Leslie A. Hunt; Roberto C. Izaurralde; Mohamed Jabloun; Curtis D. Jones; Kurt-Christian Kersebaum; Ann-Kristin Koehler; Lei Liu; Christoph Müller; Soora Kumar Naresh; Claas Nendel; O'Leary Garry; Jørgen E. Olesen; Taru Palosuo; Eckart Priesack; Ehsan Eyshi Rezaei; Dominique Ripoche; Alex C Ruane; Mikhail A. Semenov; Iurii Shcherbak; Claudio O. Stöckle; Pierre Stratonovitch; Thilo Streck; Iwan Supit; Fulu Tao; Peter Thorburn; Katharina Waha; Daniel Wallach; Zhimin Wang; Joost Wolf; Yan Zhu; Senthold Asseng

The uncertainty of crop yield projections is reduced by improved temperature response functions

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2017

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Anglais

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handle: 10670/1.sg0dko
hal: hal-02629102
doi: 10.1038/s41477-017-0032-6
PRODINRA: 420194
PUBMED: 28955035
WOS: 000406850200001

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Archive Ouverte d'INRAE

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info:eu-repo/semantics/altIdentifier/doi/10.1038/s41477-017-0032-6

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info:eu-repo/semantics/altIdentifier/pmid/28955035

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info:eu-repo/grantAgreement/EC/FP7/267196/EU/International Mobility Programme to Strengthen Skills and Excellence in Research for Agriculture/AGREENSKILLS

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Institut national de recherche pour l’agriculture, l’alimentation et l’environnement

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INRAE

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Enli Wang et al., « The uncertainty of crop yield projections is reduced by improved temperature response functions », Archive Ouverte d'INRAE, ID : 10.1038/s41477-017-0032-6

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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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The uncertainty of crop yield projections is reduced by improved temperature response functions

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