Crop Model Ensemble Averaging: A Large But Underappreciated Uncertainty Source for Global Crop Yield Projections Under Climate Change
文献类型：期刊论文
| 作者 | Yin, Xiaomeng1,2,3; Leng, Guoyong3; Qiu, Jiali3; Liao, Xiaoyong4; Huang, Shengzhi5; Peng, Jian6,7 |
| 刊名 | EARTHS FUTURE
 |
| 出版日期 | 2025-06-01 |
| 卷号 | 13期号:6页码:e2024EF005900 |
| DOI | 10.1029/2024EF005900 |
| 产权排序 | 3 |
| 文献子类 | Article |
| 英文摘要 | Using an ensemble of crop models have been encouraged for global crop yield projections, which would, however, introduce additional uncertainty from the choice of ensemble averaging methods. Here, we use seven ensemble averaging methods including simple average, regression, Support Vector Regressor, Bayesian model average (BMA), Random Forest (RF), Artificial neural network (ANN) and Long-short term memory to derive the ensemble mean of eight process-based crop models for global maize yield projections. Results show that the choice of ensemble averaging methods has a large impact on the projection of long-term mean yield and year-to-year yield variability, with a range of -19.79%-16.62% and -47.92%-55.39% for the globe, respectively. Regionally, the largest uncertainties from the choice of ensemble averaging methods are observed in Indonesia and Canada. Further uncertainty decomposition analysis shows that ensemble averaging methods contributes to 39%-87% of total uncertainties for global yield projections, which is even higher than climate models. These results imply that although using an ensemble of crop models is valuable for informing risk-based policy-makings, how we choose to combine and derive the best estimates of crop model ensembles has large influence on the assessment outcomes. This study highlights an important but not well recognized uncertainty source for global yield predictions which arises from the choice of ensemble averaging methods. |
| URL标识 | 查看原文 |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Meteorology & Atmospheric Sciences |
| 语种 | 英语 |
| WOS记录号 | WOS:001499057700001 |
| 出版者 | AMER GEOPHYSICAL UNION |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/214637]  |
| 专题 | 陆地水循环及地表过程院重点实验室_外文论文 |
| 通讯作者 | Leng, Guoyong |
| 作者单位 | 1.Hebei Normal Univ, Sch Geog Sci, Shijiazhuang, Peoples R China; 2.Hebei Key Lab Environm Change & Ecol Construct, Shijiazhuang, Peoples R China; 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing, Peoples R China; 4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing, Peoples R China; 5.Xian Univ Technol, State Key Lab Ecohydraul Northwest Arid Reg China, Xian, Peoples R China; 6.UFZ Helmholtz Ctr Environm Res, Dept Remote Sensing, Leipzig, Germany; 7.Univ Leipzig, Remote Sensing Ctr Earth Syst Res RSC4Earth, Leipzig, Germany |
| 推荐引用方式 GB/T 7714 | Yin, Xiaomeng,Leng, Guoyong,Qiu, Jiali,et al. Crop Model Ensemble Averaging: A Large But Underappreciated Uncertainty Source for Global Crop Yield Projections Under Climate Change[J]. EARTHS FUTURE,2025,13(6):e2024EF005900. |
| APA | Yin, Xiaomeng,Leng, Guoyong,Qiu, Jiali,Liao, Xiaoyong,Huang, Shengzhi,&Peng, Jian.(2025).Crop Model Ensemble Averaging: A Large But Underappreciated Uncertainty Source for Global Crop Yield Projections Under Climate Change.EARTHS FUTURE,13(6),e2024EF005900. |
| MLA | Yin, Xiaomeng,et al."Crop Model Ensemble Averaging: A Large But Underappreciated Uncertainty Source for Global Crop Yield Projections Under Climate Change".EARTHS FUTURE 13.6(2025):e2024EF005900. |
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。