Climate change impacts on crop yields: A review of empirical findings, statistical crop models, and machine learning methods
文献类型：期刊论文
| 作者 | Hu, Tongxi6,7; Zhang, Xuesong5; Khanal, Sami4; Wilson, Robyn7; Leng, Guoyong3; Toman, Elizabeth M.2; Wang, Xuhui1; Li, Yang7; Zhao, Kaiguang7 |
| 刊名 | ENVIRONMENTAL MODELLING & SOFTWARE
 |
| 出版日期 | 2024-08-01 |
| 卷号 | 179页码:16 |
| 关键词 | Climate change Statistical crop models Process-based models Food security Machine learning Digital Twin Agriculture 5.0 Global Warming |
| ISSN号 | 1364-8152 |
| DOI | 10.1016/j.envsoft.2024.106119 |
| 英文摘要 | Understanding crop responses to climate change is crucial for ensuring food security. Here, we reviewed similar to 230 statistical crop modeling studies for major crops and summarized recent progress in estimating climate change impacts on crop yields. Evidence was strong that increasing temperatures reduce crop yields. A 1 degrees C warming decreased the yields by 7.5 +/- 5.3% (maize), 6.0 +/- 3.3% (wheat), 6.8 +/- 5.9% (soybean), and 1.2 +/- 5.2% (rice) across the world, but spatial heterogeneity was noticeable, due partly to asymmetric nonlinear crop responses to temperature (e.g., warming-induced gains in cold regions). Yield responses to precipitation were not consistent across the studies or geographical areas. On average, climate explained 37% of yield variability. We also observed a methodological shift from linear regression to machine learning (e.g., explainable AI and interpretable machine learning), which on average reduced predictve errors by 44%. Furthermore, we discussed the opportunities and challenges facing statistical crop modeling, such as ensemble modeling, physics-informed machine learning, spatiotemporal heterogeneity in crop responses, climate extremes, extrapolation under novel climates, and the confounding from technology, management, CO2, and O-3. |
| WOS关键词 | SIMULATING IMPACTS ; WHEAT YIELDS ; MAIZE YIELD ; ADAPTATION ; DROUGHT ; WEATHER ; RISK ; VARIABILITY ; PREDICTION ; MANAGEMENT |
| 资助项目 | U.S. Department of Agriculture National Institute of Food and Agriculture (NIFA)[2018-68002-27932] ; Agricultural Research Service ; SCINet/AI-COE Fellowship |
| WOS研究方向 | Computer Science ; Engineering ; Environmental Sciences & Ecology ; Water Resources |
| 语种 | 英语 |
| WOS记录号 | WOS:001262840800001 |
| 出版者 | ELSEVIER SCI LTD |
| 资助机构 | U.S. Department of Agriculture National Institute of Food and Agriculture (NIFA) ; Agricultural Research Service ; SCINet/AI-COE Fellowship |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/207605]  |
| 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 | Zhang, Xuesong; Zhao, Kaiguang |
| 作者单位 | 1.Peking Univ, Sino French Inst Earth Syst Sci, Beijing, Peoples R China 2.Colorado State Univ, Dept Ecosyst Sci & Sustainabil, Ft Collins, CO 80523 USA 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 4.Ohio State Univ, Dept Food Agr & Biol Engn, Columbus, OH 43210 USA 5.USDA ARS, Hydrol & Remote Sensing Lab, Beltsville, MD 20705 USA 6.Univ Illinois, Inst Sustainabil Energy & Environm, Agroecosystems Sustainabil Ctr, Urbana, IL 61801 USA 7.Ohio State Univ, Sch Nat Resources, Environm Sci Grad Program, Columbus, OH 43210 USA |
| 推荐引用方式 GB/T 7714 | Hu, Tongxi,Zhang, Xuesong,Khanal, Sami,et al. Climate change impacts on crop yields: A review of empirical findings, statistical crop models, and machine learning methods[J]. ENVIRONMENTAL MODELLING & SOFTWARE,2024,179:16. |
| APA | Hu, Tongxi.,Zhang, Xuesong.,Khanal, Sami.,Wilson, Robyn.,Leng, Guoyong.,...&Zhao, Kaiguang.(2024).Climate change impacts on crop yields: A review of empirical findings, statistical crop models, and machine learning methods.ENVIRONMENTAL MODELLING & SOFTWARE,179,16. |
| MLA | Hu, Tongxi,et al."Climate change impacts on crop yields: A review of empirical findings, statistical crop models, and machine learning methods".ENVIRONMENTAL MODELLING & SOFTWARE 179(2024):16. |
入库方式： OAI收割
来源：地理科学与资源研究所
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