Multimodel ensembles of wheat growth: Many models are better than one

Pierre Martre, Daniel Wallach, Senthold Asseng, Frank Ewert, James W. Jones, Reimund P. Rötter, Kenneth J. Boote, Alex C. Ruane, Peter J. Thorburn, Davide Cammarano, Jerry L. Hatfield, Cynthia Rosenzweig, Pramod K. Aggarwal, Carlos Angulo, Bruno Basso, Patrick Bertuzzi, Christian Biernath, Nadine Brisson, Andrew J. Challinor, Jordi DoltraSebastian Gayler, Richie Goldberg, Robert F. Grant, Lee Heng, Josh Hooker, Leslie A. Hunt, Joachim Ingwersen, Roberto C. Izaurralde, Kurt Christian Kersebaum, Christoph Müller, Soora Naresh Kumar, Claas Nendel, Garry O'leary, Jørgen E. Olesen, Tom M. Osborne, Taru Palosuo, Eckart Priesack, Dominique Ripoche, Mikhail A. Semenov, Iurii Shcherbak, Pasquale Steduto, Claudio O. Stöckle, Pierre Stratonovitch, Thilo Streck, Iwan Supit, Fulu Tao, Maria Travasso, Katharina Waha, Jeffrey W. White, Joost Wolf

Research output: Contribution to journalArticlepeer-review

385 Scopus citations

Abstract

Crop models of crop growth are increasingly used to quantify the impact of global changes due to climate or crop management. Therefore, accuracy of simulation results is a major concern. Studies with ensembles of crop models can give valuable information about model accuracy and uncertainty, but such studies are difficult to organize and have only recently begun. We report on the largest ensemble study to date, of 27 wheat models tested in four contrasting locations for their accuracy in simulating multiple crop growth and yield variables. The relative error averaged over models was 24-38% for the different end-of-season variables including grain yield (GY) and grain protein concentration (GPC). There was little relation between error of a model for GY or GPC and error for in-season variables. Thus, most models did not arrive at accurate simulations of GY and GPC by accurately simulating preceding growth dynamics. Ensemble simulations, taking either the mean (e-mean) or median (e-median) of simulated values, gave better estimates than any individual model when all variables were considered. Compared to individual models, e-median ranked first in simulating measured GY and third in GPC. The error of e-mean and e-median declined with an increasing number of ensemble members, with little decrease beyond 10 models. We conclude that multimodel ensembles can be used to create new estimators with improved accuracy and consistency in simulating growth dynamics. We argue that these results are applicable to other crop species, and hypothesize that they apply more generally to ecological system models.

Original languageEnglish
Pages (from-to)911-925
Number of pages15
JournalGlobal Change Biology
Volume21
Issue number2
DOIs
StatePublished - 1 Feb 2015
Externally publishedYes

Keywords

  • Ecophysiological model
  • Ensemble modeling
  • Model intercomparison
  • Process-based model
  • Uncertainty
  • Wheat (Triticum aestivum L.)

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