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Martre, Pierre; Wallach, Daniel; Asseng, Senthold; Ewert, Frank; Jones, James W.; Rötter, Reimund P.; Boote, Kenneth J.; Ruane, Alex C.; Thorburn, Peter J.; Cammarano, Davide; Hatfield, Jerry L.; Rosenzweig, Cynthia; Aggarwal, Pramod K.; Angulo, Carlos; Basso, Bruno; Bertuzzi, Patrick; Biernath, Christian; Brisson, Nadine; Challinor, Andrew J.; Doltra, Jordi; Gayler, Sebastian; Goldberg, Richie; Grant, Robert F.; Heng, Lee; [...]

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

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  • Medientyp: E-Artikel
  • Titel: Multimodel ensembles of wheat growth: many models are better than one
  • Beteiligte: Martre, Pierre; Wallach, Daniel; Asseng, Senthold; Ewert, Frank; Jones, James W.; Rötter, Reimund P.; Boote, Kenneth J.; Ruane, Alex C.; Thorburn, Peter J.; Cammarano, Davide; Hatfield, Jerry L.; Rosenzweig, Cynthia; Aggarwal, Pramod K.; Angulo, Carlos; Basso, Bruno; Bertuzzi, Patrick; Biernath, Christian; Brisson, Nadine; Challinor, Andrew J.; Doltra, Jordi; Gayler, Sebastian; Goldberg, Richie; Grant, Robert F.; Heng, Lee; [...]
  • Erschienen: Wiley, 2015
  • Erschienen in: Global Change Biology
  • Sprache: Englisch
  • DOI: 10.1111/gcb.12768
  • ISSN: 1354-1013; 1365-2486
  • Schlagwörter: General Environmental Science ; Ecology ; Environmental Chemistry ; Global and Planetary Change
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  • Beschreibung: <jats:title>Abstract</jats:title><jats:p>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 (<jats:styled-content style="fixed-case">GY</jats:styled-content>) and grain protein concentration (<jats:styled-content style="fixed-case">GPC</jats:styled-content>). There was little relation between error of a model for <jats:styled-content style="fixed-case">GY</jats:styled-content> or <jats:styled-content style="fixed-case">GPC</jats:styled-content> and error for in‐season variables. Thus, most models did not arrive at accurate simulations of <jats:styled-content style="fixed-case">GY</jats:styled-content> and <jats:styled-content style="fixed-case">GPC</jats:styled-content> 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 <jats:styled-content style="fixed-case">GY</jats:styled-content> and third in <jats:styled-content style="fixed-case">GPC</jats:styled-content>. 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.</jats:p>