Aggravation of global maize yield loss risk under various hot and dry scenarios using multiple types of prediction approaches
文献类型：期刊论文
| 作者 | Yin, Xiaomeng1,2; Leng, Guoyong1,5; Huang, Shengzhi3; Peng, Jian2,4 |
| 刊名 | INTERNATIONAL JOURNAL OF CLIMATOLOGY
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| 出版日期 | 2024-01-19 |
| 关键词 | dry hot machine learning maize yield loss risk process-based models regression |
| DOI | 10.1002/joc.8371 |
| 产权排序 | 1 |
| 英文摘要 | High temperature and drought are widely known to cause a reduction of crop yield, but the simultaneously occurring risks in major producing countries and the associated uncertainty across various climate change scenarios remain unclear at the global scale. Here, we evaluate global maize yield loss risk (i.e., the probability of yield reduction by over 10% relative to historical trend yield during 1981-2010) across 30 hot and dry scenarios using regression, machine learning and process-based models. Besides examining yield loss risk in a single country, we predict the potential risks simultaneously occurring in the top two and top ten producing countries. The three approaches agree on the aggravation of yield loss risk under dry and hot scenarios, but show large discrepancy in the magnitude and sensitivities. Specifically, 2 degrees C warming alone could lead to a global yield loss risk of 73%, 100% and 62% based on regression, long-short term memory (LSTM) and process-based models, respectively, and warming-induced risks can be further aggravated by droughts especially in process models. Global yield loss by over 10% would even become the new norm (i.e., yield loss probability is 100%) when temperature increases by over 2 degrees C in some models. Importantly, the probabilities of yield loss simultaneously occurring in the top two countries (i.e., United States and China) and top ten countries are unexpectedly high and could even become 100% under extreme hot and dry scenarios. Our results highlight the large risks that future climate change may bring to multiple exporting and importing countries simultaneously, thus threating global food market and security. We also emphasize the important value of using different types of prediction approaches for yield projection under hot and dry scenarios, which enables more realistic estimation of uncertainty range than a single type of model. The probabilities of yield loss simultaneously occurring in the top two countries (i.e., United States and China) and top ten countries are unexpectedly high and could even become 100% under extreme hot and dry scenarios.image |
| WOS关键词 | CLIMATE-CHANGE IMPACTS ; UNITED-STATES ; HEAT-STRESS ; TEMPERATURE ; WHEAT ; DROUGHT ; CHINA ; SENSITIVITY ; MANAGEMENT ; DISASTERS |
| WOS研究方向 | Meteorology & Atmospheric Sciences |
| WOS记录号 | WOS:001145285700001 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/201663]  |
| 专题 | 陆地水循环及地表过程院重点实验室_外文论文 |
| 作者单位 | 1.Chinese Acad Sci, Key Lab Water Cycle & Related Land Surface Proc, Inst Geog Sci & Nat Resources Res, Beijing, Peoples R China 2.Univ Chinese Acad Sci, Beijing, Peoples R China 3.UFZ Helmholtz Ctr Environm Res, Dept Remote Sensing, Leipzig, Germany 4.Xian Univ Technol, State Key Lab Ecohydraul Northwest Arid Reg China, Xian, Peoples R China 5.Univ Leipzig, Remote Sensing Ctr Earth Syst Res, Leipzig, Germany 6.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yin, Xiaomeng,Leng, Guoyong,Huang, Shengzhi,et al. Aggravation of global maize yield loss risk under various hot and dry scenarios using multiple types of prediction approaches[J]. INTERNATIONAL JOURNAL OF CLIMATOLOGY,2024. |
| APA | Yin, Xiaomeng,Leng, Guoyong,Huang, Shengzhi,&Peng, Jian.(2024).Aggravation of global maize yield loss risk under various hot and dry scenarios using multiple types of prediction approaches.INTERNATIONAL JOURNAL OF CLIMATOLOGY. |
| MLA | Yin, Xiaomeng,et al."Aggravation of global maize yield loss risk under various hot and dry scenarios using multiple types of prediction approaches".INTERNATIONAL JOURNAL OF CLIMATOLOGY (2024). |
入库方式： OAI收割
来源：地理科学与资源研究所
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