How do various maize crop models vary in their responses to climate change factors?

S. Bassu, N. Brisson, J. Durand, K.J. Boote, J. Lizaso, J.W. Jones, C. Rosenzweig, A.C. Ruane, M. Adam, C. Baron, B. Basso, C. Biernath, H.L. Boogaard, S. Conijn, M. Corbeels, D. Deryng, G. De Sanctis, S. Gayler, P. Grassini, J.L. Hatfield & 2 others S.B. Hoek, C. Izaurralde

Research output: Contribution to journalArticleAcademicpeer-review

273 Citations (Scopus)

Abstract

Potential consequences of climate change on crop production can be studied using mechanistic crop simulation models. While a broad variety of maize simulation models exist, it is not known whether different models diverge on grain yield responses to changes in climatic factors, or whether they agree in their general trends related to phenology, growth, and yield. With the goal of analyzing the sensitivity of simulated yields to changes in temperature and atmospheric carbon dioxide concentrations [CO2], we present the largest maize crop model intercomparison to date, including 23 different models. These models were evaluated for four locations representing a wide range of maize production conditions in the world: Lusignan (France), Ames (USA), Rio Verde (Brazil) and Morogoro (Tanzania). While individual models differed considerably in absolute yield simulation at the four sites, an ensemble of a minimum number of models was able to simulate absolute yields accurately at the four sites even with low data for calibration, thus suggesting that using an ensemble of models has merit. Temperature increase had strong negative influence on modeled yield response of roughly -0.5 Mg ha-1 per °C. Doubling [CO2] from 360 to 720 µmol mol-1 increased grain yield by 7.5% on average across models and the sites. That would therefore make temperature the main factor altering maize yields at the end of this century. Furthermore, there was a large uncertainty in the yield response to [CO2] among models. Model responses to temperature and [CO2] did not differ whether models were simulated with low calibration information or, simulated with high level of calibration information.
Original languageEnglish
Pages (from-to)2301-2320
JournalGlobal Change Biology
Volume20
Issue number7
DOIs
Publication statusPublished - 2014

Fingerprint

Climate change
Crops
maize
crop
climate change
yield response
Calibration
calibration
temperature
simulation
Temperature
crop production
Carbon Dioxide
phenology
carbon dioxide

Keywords

  • water-use efficiency
  • air co2 enrichment
  • simulation-model
  • elevated co2
  • systems simulation
  • nitrogen dynamics
  • carbon-dioxide
  • yield
  • wheat
  • agriculture

Cite this

Bassu, S., Brisson, N., Durand, J., Boote, K. J., Lizaso, J., Jones, J. W., ... Izaurralde, C. (2014). How do various maize crop models vary in their responses to climate change factors? Global Change Biology, 20(7), 2301-2320. https://doi.org/10.1111/gcb.12520
Bassu, S. ; Brisson, N. ; Durand, J. ; Boote, K.J. ; Lizaso, J. ; Jones, J.W. ; Rosenzweig, C. ; Ruane, A.C. ; Adam, M. ; Baron, C. ; Basso, B. ; Biernath, C. ; Boogaard, H.L. ; Conijn, S. ; Corbeels, M. ; Deryng, D. ; De Sanctis, G. ; Gayler, S. ; Grassini, P. ; Hatfield, J.L. ; Hoek, S.B. ; Izaurralde, C. / How do various maize crop models vary in their responses to climate change factors?. In: Global Change Biology. 2014 ; Vol. 20, No. 7. pp. 2301-2320.
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abstract = "Potential consequences of climate change on crop production can be studied using mechanistic crop simulation models. While a broad variety of maize simulation models exist, it is not known whether different models diverge on grain yield responses to changes in climatic factors, or whether they agree in their general trends related to phenology, growth, and yield. With the goal of analyzing the sensitivity of simulated yields to changes in temperature and atmospheric carbon dioxide concentrations [CO2], we present the largest maize crop model intercomparison to date, including 23 different models. These models were evaluated for four locations representing a wide range of maize production conditions in the world: Lusignan (France), Ames (USA), Rio Verde (Brazil) and Morogoro (Tanzania). While individual models differed considerably in absolute yield simulation at the four sites, an ensemble of a minimum number of models was able to simulate absolute yields accurately at the four sites even with low data for calibration, thus suggesting that using an ensemble of models has merit. Temperature increase had strong negative influence on modeled yield response of roughly -0.5 Mg ha-1 per °C. Doubling [CO2] from 360 to 720 µmol mol-1 increased grain yield by 7.5{\%} on average across models and the sites. That would therefore make temperature the main factor altering maize yields at the end of this century. Furthermore, there was a large uncertainty in the yield response to [CO2] among models. Model responses to temperature and [CO2] did not differ whether models were simulated with low calibration information or, simulated with high level of calibration information.",
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Bassu, S, Brisson, N, Durand, J, Boote, KJ, Lizaso, J, Jones, JW, Rosenzweig, C, Ruane, AC, Adam, M, Baron, C, Basso, B, Biernath, C, Boogaard, HL, Conijn, S, Corbeels, M, Deryng, D, De Sanctis, G, Gayler, S, Grassini, P, Hatfield, JL, Hoek, SB & Izaurralde, C 2014, 'How do various maize crop models vary in their responses to climate change factors?', Global Change Biology, vol. 20, no. 7, pp. 2301-2320. https://doi.org/10.1111/gcb.12520

How do various maize crop models vary in their responses to climate change factors? / Bassu, S.; Brisson, N.; Durand, J.; Boote, K.J.; Lizaso, J.; Jones, J.W.; Rosenzweig, C.; Ruane, A.C.; Adam, M.; Baron, C.; Basso, B.; Biernath, C.; Boogaard, H.L.; Conijn, S.; Corbeels, M.; Deryng, D.; De Sanctis, G.; Gayler, S.; Grassini, P.; Hatfield, J.L.; Hoek, S.B.; Izaurralde, C.

In: Global Change Biology, Vol. 20, No. 7, 2014, p. 2301-2320.

Research output: Contribution to journalArticleAcademicpeer-review

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T1 - How do various maize crop models vary in their responses to climate change factors?

AU - Bassu, S.

AU - Brisson, N.

AU - Durand, J.

AU - Boote, K.J.

AU - Lizaso, J.

AU - Jones, J.W.

AU - Rosenzweig, C.

AU - Ruane, A.C.

AU - Adam, M.

AU - Baron, C.

AU - Basso, B.

AU - Biernath, C.

AU - Boogaard, H.L.

AU - Conijn, S.

AU - Corbeels, M.

AU - Deryng, D.

AU - De Sanctis, G.

AU - Gayler, S.

AU - Grassini, P.

AU - Hatfield, J.L.

AU - Hoek, S.B.

AU - Izaurralde, C.

PY - 2014

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N2 - Potential consequences of climate change on crop production can be studied using mechanistic crop simulation models. While a broad variety of maize simulation models exist, it is not known whether different models diverge on grain yield responses to changes in climatic factors, or whether they agree in their general trends related to phenology, growth, and yield. With the goal of analyzing the sensitivity of simulated yields to changes in temperature and atmospheric carbon dioxide concentrations [CO2], we present the largest maize crop model intercomparison to date, including 23 different models. These models were evaluated for four locations representing a wide range of maize production conditions in the world: Lusignan (France), Ames (USA), Rio Verde (Brazil) and Morogoro (Tanzania). While individual models differed considerably in absolute yield simulation at the four sites, an ensemble of a minimum number of models was able to simulate absolute yields accurately at the four sites even with low data for calibration, thus suggesting that using an ensemble of models has merit. Temperature increase had strong negative influence on modeled yield response of roughly -0.5 Mg ha-1 per °C. Doubling [CO2] from 360 to 720 µmol mol-1 increased grain yield by 7.5% on average across models and the sites. That would therefore make temperature the main factor altering maize yields at the end of this century. Furthermore, there was a large uncertainty in the yield response to [CO2] among models. Model responses to temperature and [CO2] did not differ whether models were simulated with low calibration information or, simulated with high level of calibration information.

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KW - water-use efficiency

KW - air co2 enrichment

KW - simulation-model

KW - elevated co2

KW - systems simulation

KW - nitrogen dynamics

KW - carbon-dioxide

KW - yield

KW - wheat

KW - agriculture

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DO - 10.1111/gcb.12520

M3 - Article

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JO - Global Change Biology

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