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

Enli Wang; Pierre Martre; 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; 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; Leilei Liu; Christoph Müller; Soora Naresh Kumar; Claas Nendel; Garry O'Leary; 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 J. Thorburn; Katharina Waha; Daniel Wallach; Zhimin Wang; Joost Wolf; Yan Zhu; Senthold Asseng; Benjamin Dumont

doi:10.1038/nplants.2017.102

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

Enli Wang^*, Pierre Martre, 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 DoltraElias 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, Leilei Liu, Christoph Müller, Soora Naresh Kumar, Claas Nendel, Garry O'Leary, 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 J. Thorburn, Katharina Waha, Daniel Wallach, Zhimin Wang, Joost Wolf, Yan Zhu, Senthold Asseng, Benjamin Dumont

^*Corresponding author for this work

Research output: Contribution to journal › Article › Academic › peer-review

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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 °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.

Original language	English
Article number	17102
Journal	Nature Plants
Volume	3
DOIs	https://doi.org/10.1038/nplants.2017.102
Publication status	Published - 2017

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Wang, E., Martre, P., Zhao, Z., Ewert, F., Maiorano, A., Rötter, R. P., Kimball, B. A., Ottman, M. J., Wall, G. W., White, J. W., Reynolds, M. P., Alderman, P. D., Aggarwal, P. K., Anothai, J., Basso, B., Biernath, C., Cammarano, D., Challinor, A. J., De Sanctis, G., ... Dumont, B. (2017). The uncertainty of crop yield projections is reduced by improved temperature response functions. Nature Plants, 3, Article 17102. https://doi.org/10.1038/nplants.2017.102

@article{c648f2cd1dcb45f393fdcccf4edd0f4b,

title = "The uncertainty of crop yield projections is reduced by improved temperature response functions",

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 °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.",

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

year = "2017",

doi = "10.1038/nplants.2017.102",

language = "English",

volume = "3",

journal = "Nature Plants",

issn = "2055-026X",

publisher = "Nature",

}

Wang, E, Martre, P, Zhao, Z, Ewert, F, Maiorano, A, Rötter, RP, Kimball, BA, Ottman, MJ, Wall, GW, White, JW, Reynolds, MP, Alderman, PD, Aggarwal, PK, Anothai, J, Basso, B, Biernath, C, Cammarano, D, Challinor, AJ, De Sanctis, G, Doltra, J, Fereres, E, Garcia-Vila, M, Gayler, S, Hoogenboom, G, Hunt, LA, Izaurralde, RC, Jabloun, M, Jones, CD, Kersebaum, KC, Koehler, AK, Liu, L, Müller, C, Naresh Kumar, S, Nendel, C, O'Leary, G, Olesen, JE, Palosuo, T, Priesack, E, Eyshi Rezaei, E, Ripoche, D, Ruane, AC, Semenov, MA, Shcherbak, I, Stöckle, CO, Stratonovitch, P, Streck, T, Supit, I, Tao, F, Thorburn, PJ, Waha, K, Wallach, D, Wang, Z, Wolf, J, Zhu, Y, Asseng, S & Dumont, B 2017, 'The uncertainty of crop yield projections is reduced by improved temperature response functions', Nature Plants, vol. 3, 17102. https://doi.org/10.1038/nplants.2017.102

TY - JOUR

T1 - The uncertainty of crop yield projections is reduced by improved temperature response functions

AU - Wang, Enli

AU - Martre, Pierre

AU - Zhao, Zhigan

AU - Ewert, Frank

AU - Maiorano, Andrea

AU - Rötter, Reimund P.

AU - Kimball, Bruce A.

AU - Ottman, Michael J.

AU - Wall, Gerard W.

AU - White, Jeffrey W.

AU - Reynolds, Matthew P.

AU - Alderman, Phillip D.

AU - Aggarwal, Pramod K.

AU - Anothai, Jakarat

AU - Basso, Bruno

AU - Biernath, Christian

AU - Cammarano, Davide

AU - Challinor, Andrew J.

AU - De Sanctis, Giacomo

AU - Doltra, Jordi

AU - Fereres, Elias

AU - Garcia-Vila, Margarita

AU - Gayler, Sebastian

AU - Hoogenboom, Gerrit

AU - Hunt, Leslie A.

AU - Izaurralde, Roberto C.

AU - Jabloun, Mohamed

AU - Jones, Curtis D.

AU - Kersebaum, Kurt Christian

AU - Koehler, Ann Kristin

AU - Liu, Leilei

AU - Müller, Christoph

AU - Naresh Kumar, Soora

AU - Nendel, Claas

AU - O'Leary, Garry

AU - Olesen, Jørgen E.

AU - Palosuo, Taru

AU - Priesack, Eckart

AU - Eyshi Rezaei, Ehsan

AU - Ripoche, Dominique

AU - Ruane, Alex C.

AU - Semenov, Mikhail A.

AU - Shcherbak, Iurii

AU - Stöckle, Claudio O.

AU - Stratonovitch, Pierre

AU - Streck, Thilo

AU - Supit, Iwan

AU - Tao, Fulu

AU - Thorburn, Peter J.

AU - Waha, Katharina

AU - Wallach, Daniel

AU - Wang, Zhimin

AU - Wolf, Joost

AU - Zhu, Yan

AU - Asseng, Senthold

AU - Dumont, Benjamin

PY - 2017

Y1 - 2017

N2 - 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.

AB - 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.

U2 - 10.1038/nplants.2017.102

DO - 10.1038/nplants.2017.102

M3 - Article

AN - SCOPUS:85025114987

SN - 2055-026X

VL - 3

JO - Nature Plants

JF - Nature Plants

M1 - 17102

ER -

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

Abstract

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