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

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Enli Wang
Enli Wang
Enli Wang
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Pierre Martre at French National Institute for Agriculture, Food, and Environment (INRAE)
Pierre Martre
Zhigan Zhao
Zhigan Zhao
Zhigan Zhao
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Frank Ewert at University of Bonn
Frank Ewert
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Abstract

Nature Plants 3, 17102 (2017); published online 17 July 2017; corrected online 27 September 2017.
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AuThor CorreCTion
DOI: 10.1038/s41477-017-0032-6
© 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved. © 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.
Author Correction: The uncertainty of crop yield projections is reduced by improved
temperature response functions
Enli Wang1*, Pierre Martre2, Zhigan Zhao1,3, Frank Ewert4,5, Andrea Maiorano2, Reimund P. Rötter6,7, Bruce A. Kimball8,
Michael J. Ottman9, Gerard W. Wall8, Jerey W. White8, Matthew P. Reynolds10, Phillip D. Alderman10, Pramod K. Aggarwal11,
Jakarat Anothai12, Bruno Basso13, Christian Biernath14, Davide Cammarano15, Andrew J. Challinor16,17, Giacomo De Sanctis18,
Jordi Doltra19, Benjamin Dumont13, Elias Fereres20,21, Margarita Garcia-Vila20,21, Sebastian Gayler22, Gerrit Hoogenboom12,
Leslie A. Hunt23, Roberto C. Izaurralde24,25, Mohamed Jabloun26, Curtis D. Jones24, Kurt C. Kersebaum5, Ann-Kristin Koehler16,
Leilei Liu27, Christoph Müller28, Soora Naresh Kumar29, Claas Nendel5, Garry O’Leary30, Jørgen E. Olesen26, Taru Palosuo31,
Eckart Priesack14, Ehsan Eyshi Rezaei4, Dominique Ripoche32, Alex C. Ruane33, Mikhail A. Semenov34, Iurii Shcherbak13,
Claudio Stöckle35, Pierre Stratonovitch34, Thilo Streck22, Iwan Supit36, Fulu Tao31,37, Peter Thorburn38, Katharina Waha28,
Daniel Wallach39, Zhimin Wang3, Joost Wolf36, Yan Zhu27 and Senthold Asseng15
Nature Plants 3, 17102 (2017); published online 17 July 2017; corrected online 27 September 2017.
In the original version of this Article, the name of one co-author was omitted. This has now been corrected by the addition of Benjamin
Dumont to the author list.
Nature PlaNts | VOL 3 | OCTOBER 2017 | 833 | www.nature.com/natureplants 833
Content courtesy of Springer Nature, terms of use apply. Rights reserved
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... The main weather drivers of plant phenological development are temperature, photoperiod and solar radiation, which have been extensively used in crop modelling for predicting the days to anthesis and maturity (e.g. Challinor et al. 2004;Craufurd and Wheeler 2009;Ottman et al. 2013;Wang et al. 2017;Baumont et al. 2019). Here, photoperiod and vernalization were disregarded, since the wheat cultivars of this study are not sensitive to them Martre et al. 2017). ...
... Nevertheless, the use of a simple sum of degree days for phenology often leads to lower performance than more complex process-based algorithms (Wallach et al. 2021a). Wang et al. (2017) make skilful predictions with a curvilinear temperature response function based on a minimum, optimum and maximum cardinal temperature for wheat. In the future, the introduction of a feature of thermal time accumulation with a more complex, wheat-based function could increase the ML skill and may decrease the bias in the simulations for the progression of the crop phenological stages. ...
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The uncertainty of crop yield projections is reduced by improved temperature response functions
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