Observational constraint of process crop models suggests higher risks for global maize yield under climate change
文献类型：期刊论文
| 作者 | Yin, Xiaomeng1,2; Leng, Guoyong1,2 |
| 刊名 | ENVIRONMENTAL RESEARCH LETTERS
 |
| 出版日期 | 2022-07-01 |
| 卷号 | 17期号:7页码:14 |
| 关键词 | emergent constraint machine learning yield change yield variability process crop model global |
| ISSN号 | 1748-9326 |
| DOI | 10.1088/1748-9326/ac7ac7 |
| 通讯作者 | Leng, Guoyong(lenggy@igsnrr.ac.cn) |
| 英文摘要 | Projecting future changes in crop yield usually relies on process-based crop models, but the associated uncertainties (i.e. the range between models) are often high. In this study, a Machine Learning (i.e. Random Forest, RF) based observational constraining approach is proposed for reducing the uncertainties of future maize yield projections by seven process-based crop models. Based on the observationally constrained crop models, future changes in yield average and yield variability for the period 2080-2099 are investigated for the globe and top ten producing countries. Results show that the uncertainties of crop models for projecting future changes in yield average and yield variability can be largely reduced by 62% and 52% by the RF-based constraint, respectively, while only 4% and 16% of uncertainty reduction is achieved by traditional linear regression-based constraint. Compared to the raw simulations of future change in yield average (-5.13 +/- 18.19%) and yield variability (-0.24 +/- 1.47%), the constrained crop models project a much higher yield loss (-34.58 +/- 6.93%) and an increase in yield variability (3.15 +/- 0.71%) for the globe. Regionally, the constrained models show the largest increase in yield loss magnitude in Brazil, India and Indonesia. Our results suggest more agricultural risks under climate change than previously expected after observationally constraining crop models. The results obtained in this study point to the importance for observationally constraining process crop models for robust yield projections, and highlight the added value of using Machine Learning for reducing the associated uncertainties. |
| WOS关键词 | EMERGENT CONSTRAINTS ; SOWING DATE ; WHEAT YIELD ; RICE YIELD ; UNCERTAINTY ; TEMPERATURE ; CARBON ; PROJECTIONS ; CALIBRATION ; SYSTEM |
| 资助项目 | National Natural Science Foundation of China[42077420] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA28060100] |
| WOS研究方向 | Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences |
| 语种 | 英语 |
| WOS记录号 | WOS:000820456000001 |
| 出版者 | IOP Publishing Ltd |
| 资助机构 | National Natural Science Foundation of China ; Strategic Priority Research Program of the Chinese Academy of Sciences |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/181052]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Leng, Guoyong |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing 100101, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yin, Xiaomeng,Leng, Guoyong. Observational constraint of process crop models suggests higher risks for global maize yield under climate change[J]. ENVIRONMENTAL RESEARCH LETTERS,2022,17(7):14. |
| APA | Yin, Xiaomeng,&Leng, Guoyong.(2022).Observational constraint of process crop models suggests higher risks for global maize yield under climate change.ENVIRONMENTAL RESEARCH LETTERS,17(7),14. |
| MLA | Yin, Xiaomeng,et al."Observational constraint of process crop models suggests higher risks for global maize yield under climate change".ENVIRONMENTAL RESEARCH LETTERS 17.7(2022):14. |
入库方式： OAI收割
来源：地理科学与资源研究所
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