Forecasting global crop yields based on El Nino Southern Oscillation early signals
文献类型：期刊论文
| 作者 | Cao, Juan; Zhang, Zhao; Tao, Fulu; Chen, Yi; Luo, Xiangzhong; Xie, Jun |
| 刊名 | AGRICULTURAL SYSTEMS
 |
| 出版日期 | 2023-02-01 |
| 卷号 | 205页码:103564 |
| ISSN号 | 0308-521X |
| 关键词 | ENSO Climate anomaly Yield variability Crop loss Food security |
| DOI | 10.1016/j.agsy.2022.103564 |
| 产权排序 | 2 |
| 文献子类 | Article |
| 英文摘要 | CONTEXT: The El-Ni (n) over tildeo Southern Oscillation (ENSO), one of the most well-known climate modes, can lead to large-scale climate variability and subsequent crop loss, posing a severe risk to global food security. OBJECTIVE: The study's main goal was to examine the synchronous impacts of ENSO and the probability of simultaneous ENSO-related crop loss on the global yields of major crops and investigate the predictability of finer-scale variation in crop yields based on ENSO-related large-scale climate precursors. METHODS: Here, using updated crop census data for similar to 12,000 political units, the study first investigated the synchronous impact of ENSO on yield variability of major crops (i.e., maize, rice, wheat, and soybean) using Synthetic Analysis and bootstrap method, and then estimated the probability of simultaneous crop loss in the top five crop-producing countries by copula approach. Finally, multiple regression was developed to identify the best forecast model, the corresponding ENSO indices, and the lead time for each political unit based on pre-occurred ENSO indices. RESULTS AND CONCLUSIONS: The results show that 12.8% (2.1%), 13.4% (6.4%), 11.8% (10.2%), and 8.4% (18.3%) of wheat, rice, maize, and soybean harvest areas were significantly negatively (positively) associated with El Ni (n) over tildeo, respectively; and 7% (11.7%), 20.2% (3.4%), 5.8% (5.6%), and 14% (6.4%) with La Ni (n) over tildea. El Ni (n) over tildeo reduced global-mean crop yield by 1.32%, 1.33%, and 0.37% for wheat, rice, and maize, respectively, but increased it for soybean by 1.9%. La Ni (n) over tildea reduced the global mean yield for rice (2.1%), maize (1.5%), and soybean (1.3%) but increased it for wheat (1.0%). Rice (6.6%) had the highest probability of simultaneous loss during the El Nino phase, whereas La Ni (n) over tildea is soybean (5.9%). Based on the early ENSO signals, crop yield could be reliably forecasted for similar to 32.05%, similar to 42.2%, similar to 21%, and similar to 26.37% of global harvest areas, with R-2 being 0.24, 0.26, 0.24, and 0.23 and a lead time of 1-12 months, for wheat, rice, maize, and soybean, respectively. The results suggest that although the reliable yield prediction based on ENSO indexes alone can be developed in a limited proportion of harvest areas, it is skillful in the ENSO-sensitive regions. SIGNIFICANCE: The findings improved the understanding of ENSO-induced crop yield variability and developed novel approaches to forecast global crop yields based on early ENSO signals. |
| WOS关键词 | CLIMATE VARIABILITY ; RICE PRODUCTION ; SUMMER MONSOON ; WHEAT YIELD ; ENSO ; CHINA ; ANOMALIES ; PACIFIC ; IMPACTS ; NORTH |
| WOS研究方向 | Agriculture |
| 语种 | 英语 |
| 出版者 | ELSEVIER SCI LTD |
| WOS记录号 | WOS:001026064600001 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/194616]  |
| 专题 | 陆地表层格局与模拟院重点实验室_外文论文 |
| 作者单位 | 1.Natural Resources Institute Finland (Luke) 2.Beijing Normal University 3.Chinese Academy of Sciences 4.Institute of Geographic Sciences & Natural Resources Research, CAS 5.University of Chinese Academy of Sciences, CAS 6.National University of Singapore 7.University of Hull |
| 推荐引用方式 GB/T 7714 | Cao, Juan,Zhang, Zhao,Tao, Fulu,et al. Forecasting global crop yields based on El Nino Southern Oscillation early signals[J]. AGRICULTURAL SYSTEMS,2023,205:103564. |
| APA | Cao, Juan,Zhang, Zhao,Tao, Fulu,Chen, Yi,Luo, Xiangzhong,&Xie, Jun.(2023).Forecasting global crop yields based on El Nino Southern Oscillation early signals.AGRICULTURAL SYSTEMS,205,103564. |
| MLA | Cao, Juan,et al."Forecasting global crop yields based on El Nino Southern Oscillation early signals".AGRICULTURAL SYSTEMS 205(2023):103564. |
入库方式： OAI收割
来源：地理科学与资源研究所
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