Multimodel ensembles improve predictions of crop-environment-management interactions
文献类型：期刊论文
| 作者 | Wallach, Daniel1; Martre, Pierre2; Liu, Bing3,4; Asseng, Senthold4; Ewert, Frank5,6; Thorburn, Peter J.7; van Ittersum, Martin8; Aggarwal, Pramod K.9; Ahmed, Mukhtar10,11; Basso, Bruno12,13 |
| 刊名 | GLOBAL CHANGE BIOLOGY
 |
| 出版日期 | 2018-11-01 |
| 卷号 | 24期号:11页码:5072-5083 |
| 关键词 | climate change impact crop models ensemble mean ensemble median multimodel ensemble prediction |
| ISSN号 | 1354-1013 |
| DOI | 10.1111/gcb.14411 |
| 通讯作者 | Wallach, Daniel(daniel.wallach@inra.fr) |
| 英文摘要 | A recent innovation in assessment of climate change impact on agricultural production has been to use crop multimodel ensembles (MMEs). These studies usually find large variability between individual models but that the ensemble mean (e-mean) and median (e-median) often seem to predict quite well. However, few studies have specifically been concerned with the predictive quality of those ensemble predictors. We ask what is the predictive quality of e-mean and e-median, and how does that depend on the ensemble characteristics. Our empirical results are based on five MME studies applied to wheat, using different data sets but the same 25 crop models. We show that the ensemble predictors have quite high skill and are better than most and sometimes all individual models for most groups of environments and most response variables. Mean squared error of e-mean decreases monotonically with the size of the ensemble if models are added at random, but has a minimum at usually 2-6 models if best-fit models are added first. Our theoretical results describe the ensemble using four parameters: average bias, model effect variance, environment effect variance, and interaction variance. We show analytically that mean squared error of prediction (MSEP) of e-mean will always be smaller than MSEP averaged over models and will be less than MSEP of the best model if squared bias is less than the interaction variance. If models are added to the ensemble at random, MSEP of e-mean will decrease as the inverse of ensemble size, with a minimum equal to squared bias plus interaction variance. This minimum value is not necessarily small, and so it is important to evaluate the predictive quality of e-mean for each target population of environments. These results provide new information on the advantages of ensemble predictors, but also show their limitations. |
| WOS关键词 | CLIMATE-CHANGE ; MODELS ; WHEAT ; YIELD ; UNCERTAINTY ; EUROPE ; SKILL |
| WOS研究方向 | Biodiversity & Conservation ; Environmental Sciences & Ecology |
| 语种 | 英语 |
| WOS记录号 | WOS:000447760300007 |
| 出版者 | WILEY |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/52677]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Wallach, Daniel |
| 作者单位 | 1.INRA, UMR AGIR, F-31326 Castanet Tolosan, France 2.Montpellier SupAgro, INRA, UMR LEPSE, Montpellier, France 3.Nanjing Agr Univ, Jiangsu Collaborat Innovat Ctr Modern Crop Prod, Key Lab Crop Syst Anal & Decis Making,Minist Agr, Natl Engn & Technol Ctr Informat Agr,Jiangsu Key, Nanjing, Jiangsu, Peoples R China 4.Univ Florida, Agr & Biol Engn Dept, Gainesville, FL USA 5.Univ Bonn, Inst Crop Sci & Resource Conservat, INRES, Bonn, Germany 6.Leibniz Ctr Agr Landscape Res, Muncheberg, Germany 7.CSIRO Agr & Food Brisbane, St Lucia, Qld, Australia 8.Wageningen Univ, Plant Prod Syst Grp, Wageningen, Netherlands 9.BISA CIMMYT, CGIAR Res Program Climate Change Agr & Food Secur, New Delhi, India 10.Washington State Univ, Biol Syst Engn, Pullman, WA 99164 USA |
| 推荐引用方式 GB/T 7714 | Wallach, Daniel,Martre, Pierre,Liu, Bing,et al. Multimodel ensembles improve predictions of crop-environment-management interactions[J]. GLOBAL CHANGE BIOLOGY,2018,24(11):5072-5083. |
| APA | Wallach, Daniel.,Martre, Pierre.,Liu, Bing.,Asseng, Senthold.,Ewert, Frank.,...&Zhang, Zhao.(2018).Multimodel ensembles improve predictions of crop-environment-management interactions.GLOBAL CHANGE BIOLOGY,24(11),5072-5083. |
| MLA | Wallach, Daniel,et al."Multimodel ensembles improve predictions of crop-environment-management interactions".GLOBAL CHANGE BIOLOGY 24.11(2018):5072-5083. |
入库方式： OAI收割
来源：地理科学与资源研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。