Crop model improvement reduces the uncertainty of the response to temperature of multi-model ensembles

Andrea Maiorano, Pierre Martre, Senthold Asseng, Frank Ewert, Christoph Müller, Reimund P. Rötter, Alex C. Ruane, Mikhail A. Semenov, Daniel Wallach, Enli Wang, Phillip D. Alderman, Belay T. Kassie, Christian Biernath, Bruno Basso, Davide Cammarano, Andrew J. Challinor, Jordi Doltra, Benjamin Dumont, Ehsan Eyshi Rezaei, Sebastian GaylerKurt Christian Kersebaum, Bruce A. Kimball, Ann Kristin Koehler, Bing Liu, Garry J. O'Leary, Jørgen E. Olesen, Michael J. Ottman, Eckart Priesack, Matthew Reynolds, Pierre Stratonovitch, Thilo Streck, Peter J. Thorburn, Katharina Waha, Gerard W. Wall, Jeffrey W. White, Zhigan Zhao, Yan Zhu

Research output: Contribution to journalArticlepeer-review

112 Scopus citations


To improve climate change impact estimates and to quantify their uncertainty, multi-model ensembles (MMEs) have been suggested. Model improvements can improve the accuracy of simulations and reduce the uncertainty of climate change impact assessments. Furthermore, they can reduce the number of models needed in a MME. Herein, 15 wheat growth models of a larger MME were improved through re-parameterization and/or incorporating or modifying heat stress effects on phenology, leaf growth and senescence, biomass growth, and grain number and size using detailed field experimental data from the USDA Hot Serial Cereal experiment (calibration data set). Simulation results from before and after model improvement were then evaluated with independent field experiments from a CIMMYT world-wide field trial network (evaluation data set). Model improvements decreased the variation (10th to 90th model ensemble percentile range) of grain yields simulated by the MME on average by 39% in the calibration data set and by 26% in the independent evaluation data set for crops grown in mean seasonal temperatures >24 °C. MME mean squared error in simulating grain yield decreased by 37%. A reduction in MME uncertainty range by 27% increased MME prediction skills by 47%. Results suggest that the mean level of variation observed in field experiments and used as a benchmark can be reached with half the number of models in the MME. Improving crop models is therefore important to increase the certainty of model-based impact assessments and allow more practical, i.e. smaller MMEs to be used effectively.

Original languageEnglish (US)
Pages (from-to)5-20
Number of pages16
JournalField Crops Research
StatePublished - Feb 15 2017


  • High temperature
  • Impact uncertainty
  • Model improvement
  • Multi-model ensemble
  • Wheat crop model

ASJC Scopus subject areas

  • Agronomy and Crop Science
  • Soil Science


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