How accurately do maize crop models simulate the interactions of atmospheric CO2 concentration levels with limited water supply on water use and yield?

Jean Louis Durand, Kenel Delusca, Ken Boote, Jon Lizaso, Remy Manderscheid, Hans Johachim Weigel, Alex C. Ruane, Cynthia Rosenzweig, Jim Jones, Laj Ahuja, Saseendran Anapalli, Bruno Basso, Christian Baron, Patrick Bertuzzi, Christian Biernath, Delphine Deryng, Frank Ewert, Thomas Gaiser, Sebastian Gayler, Florian HeinleinKurt Christian Kersebaum, Soo Hyung Kim, Christoph Müller, Claas Nendel, Albert Olioso, Eckart Priesack, Julian Ramirez Villegas, Dominique Ripoche, Reimund P. Rötter, Sabine I. Seidel, Amit Srivastava, Fulu Tao, Dennis Timlin, Tracy Twine, Enli Wang, Heidi Webber, Zhigan Zhao

Research output: Contribution to journalArticlepeer-review

72 Scopus citations


This study assesses the ability of 21 crop models to capture the impact of elevated CO2 concentration ([CO2]) on maize yield and water use as measured in a 2-year Free Air Carbon dioxide Enrichment experiment conducted at the Thünen Institute in Braunschweig, Germany (Manderscheid et al., 2014). Data for ambient [CO2] and irrigated treatments were provided to the 21 models for calibrating plant traits, including weather, soil and management data as well as yield, grain number, above ground biomass, leaf area index, nitrogen concentration in biomass and grain, water use and soil water content. Models differed in their representation of carbon assimilation and evapotranspiration processes. The models reproduced the absence of yield response to elevated [CO2] under well-watered conditions, as well as the impact of water deficit at ambient [CO2], with 50% of models within a range of +/−1 Mg ha−1 around the mean. The bias of the median of the 21 models was less than 1 Mg ha−1. However under water deficit in one of the two years, the models captured only 30% of the exceptionally high [CO2] enhancement on yield observed. Furthermore the ensemble of models was unable to simulate the very low soil water content at anthesis and the increase of soil water and grain number brought about by the elevated [CO2] under dry conditions. Overall, we found models with explicit stomatal control on transpiration tended to perform better. Our results highlight the need for model improvement with respect to simulating transpirational water use and its impact on water status during the kernel-set phase.

Original languageEnglish (US)
Pages (from-to)67-75
Number of pages9
JournalEuropean Journal of Agronomy
StatePublished - Oct 2018

Bibliographical note

Publisher Copyright:
© 2017 Elsevier B.V.


  • Atmospheric carbon dioxide concentration
  • Grain number
  • Multi-model ensemble
  • Stomatal conductance
  • Water use
  • Zea mays


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