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	Multimodel ensembles of wheat growth: many models are better than one
	Martre P.; Wallach, D.; Asseng, S.; Ewert, F.; Jones, J. W.; Rotter, R. P.; Boote, K. J.; Ruane, A. C.; Thorburn, P. J.; Cammarano, D.
	2015
关键词	ecophysiological model ensemble modeling model intercomparison process-based model uncertainty wheat (Triticum aestivum L.) climate-change crop production impacts yield simulations calibration australia billion europe grain
英文摘要	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.
出处	Global Change Biology
卷	21
期	2
页	911-925
收录类别	SCI
语种	英语
ISSN号	1354-1013
内容类型	SCI/SSCI论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/38779]
专题	地理科学与资源研究所_历年回溯文献
推荐引用方式 GB/T 7714	Martre P.,Wallach, D.,Asseng, S.,et al. Multimodel ensembles of wheat growth: many models are better than one. 2015.

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