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Multimodel ensembles of wheat growth: many models are better than one
Author(s) -
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,
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
Publication year - 2015
Publication title -
global change biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 4.146
H-Index - 255
eISSN - 1365-2486
pISSN - 1354-1013
DOI - 10.1111/gcb.12768
Subject(s) - estimator , statistics , consistency (knowledge bases) , crop , yield (engineering) , crop yield , variable (mathematics) , simulation modeling , mathematics , econometrics , computer science , ecology , artificial intelligence , mathematical analysis , materials science , mathematical economics , metallurgy , biology
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.