Multimodel ensembles of wheat growth many models are better than one

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

© 2014 John Wiley & Sons Ltd.

Bibliographische Detailangaben
Veröffentlicht in:Global change biology. - 1999. - 21(2015), 2 vom: 01. Feb., Seite 911-25
1. Verfasser: Martre, Pierre (VerfasserIn)
Weitere Verfasser: 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, Hooker, Josh, Hunt, Leslie A, Ingwersen, Joachim, Izaurralde, Roberto C, Kersebaum, Kurt Christian, Müller, Christoph, Kumar, Soora Naresh, Nendel, Claas, O'leary, Garry, Olesen, Jørgen E, Osborne, Tom M, Palosuo, Taru, Priesack, Eckart, Ripoche, Dominique, Semenov, Mikhail A, Shcherbak, Iurii, Steduto, Pasquale, Stöckle, Claudio O, Stratonovitch, Pierre, Streck, Thilo, Supit, Iwan, Tao, Fulu, Travasso, Maria, Waha, Katharina, White, Jeffrey W, Wolf, Joost
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2015
Zugriff auf das übergeordnete Werk:Global change biology
Schlagworte:Journal Article Research Support, Non-U.S. Gov't ecophysiological model ensemble modeling model intercomparison process-based model uncertainty wheat (Triticum aestivum L.)
Beschreibung
Zusammenfassung:© 2014 John Wiley & Sons Ltd.
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
Beschreibung:Date Completed 22.10.2015
Date Revised 10.03.2022
published: Print-Electronic
Citation Status MEDLINE
ISSN:1365-2486
DOI:10.1111/gcb.12768