Mapping global yields of four major crops at 5-minute resolution from 1982 to 2015 using multi-source data and machine learning
文献类型：期刊论文
| 作者 | Cao, Juan3,4; Zhang, Zhao4; Luo, Xiangzhong2; Luo, Yuchuan4; Xu, Jialu4; Xie, Jun4; Han, Jichong4; Tao, Fulu1,3 |
| 刊名 | SCIENTIFIC DATA
 |
| 出版日期 | 2025-02-28 |
| 卷号 | 12期号:1页码:357 |
| DOI | 10.1038/s41597-025-04650-4 |
| 产权排序 | 2 |
| 文献子类 | Article |
| 英文摘要 | Accurate, historical, and continuous global crop yield data are essential for assessing risks to the global food system. However, existing datasets often have limited spatial and temporal resolution. Here, we introduce GlobalCropYield5min, a novel gridded dataset providing crop yield data for major crops - including maize, rice, wheat, and soybean - from 1982 to 2015, with a spatial resolution of 5 arc-minutes. We developed three machine learning (ML) models for each country and crop, using crop statistics from approximately 12,000 administrative units, along with satellite data, climate variables, soil properties, agricultural practices, and climate modes. The optimal predictors and ML model were selected to estimate annual crop yield for each 5 x 5 arc-minute grid cell. Results show good model performance, with R2 ranging from 0.70 to 0.95, and RMSE (NRMSE) from 0.16 t/ha (5%) to 1.1 t/ha (20%). GlobalCropYield5min outperforms other global yield datasets in spatial resolution, temporal coverage, and accuracy. This dataset is crucial for investigating climate-crop yield interactions and managing agricultural disaster risks. |
| URL标识 | 查看原文 |
| WOS关键词 | CLIMATE-CHANGE ; WINTER-WHEAT ; VARIABILITY ; MODEL ; RESPONSES ; TRENDS ; IMPACT ; GROWTH ; MAIZE ; CHINA |
| WOS研究方向 | Science & Technology - Other Topics |
| 语种 | 英语 |
| WOS记录号 | WOS:001434950500007 |
| 出版者 | NATURE PORTFOLIO |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/213310]  |
| 专题 | 陆地表层格局与模拟院重点实验室_外文论文 |
| 通讯作者 | Zhang, Zhao; Tao, Fulu |
| 作者单位 | 1.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing, Peoples R China 2.Natl Univ Singapore, Dept Geog, Singapore City, Singapore; 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing 100101, Peoples R China; 4.Beijing Normal Univ, Sch Natl Safety & Emergency Management, Beijing 100875, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Cao, Juan,Zhang, Zhao,Luo, Xiangzhong,et al. Mapping global yields of four major crops at 5-minute resolution from 1982 to 2015 using multi-source data and machine learning[J]. SCIENTIFIC DATA,2025,12(1):357. |
| APA | Cao, Juan.,Zhang, Zhao.,Luo, Xiangzhong.,Luo, Yuchuan.,Xu, Jialu.,...&Tao, Fulu.(2025).Mapping global yields of four major crops at 5-minute resolution from 1982 to 2015 using multi-source data and machine learning.SCIENTIFIC DATA,12(1),357. |
| MLA | Cao, Juan,et al."Mapping global yields of four major crops at 5-minute resolution from 1982 to 2015 using multi-source data and machine learning".SCIENTIFIC DATA 12.1(2025):357. |
入库方式： OAI收割
来源：地理科学与资源研究所
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