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DOI | 10.1088/1748-9326/aae159 |
Machine learning methods for crop yield prediction and climate change impact assessment in agriculture | |
Crane-Droesch, Andrew | |
2018-11-01 | |
发表期刊 | ENVIRONMENTAL RESEARCH LETTERS |
ISSN | 1748-9326 |
出版年 | 2018 |
卷号 | 13期号:11 |
文章类型 | Article |
语种 | 英语 |
国家 | USA |
英文摘要 | Crop yields are critically dependent on weather. A growing empirical literature models this relationship in order to project climate change impacts on the sector. We describe an approach to yield modeling that uses a semiparametric variant of a deep neural network, which can simultaneously account for complex nonlinear relationships in high-dimensional datasets, as well as known parametric structure and unobserved cross-sectional heterogeneity. Using data on corn yield from the US Midwest, we show that this approach outperforms both classical statistical methods and fully-nonparametric neural networks in predicting yields of years withheld during model training. Using scenarios from a suite of climate models, we show large negative impacts of climate change on corn yield, but less severe than impacts projected using classical statistical methods. In particular, our approach is less pessimistic in the warmest regions and the warmest scenarios. |
英文关键词 | agriculture machine learning climate change impacts |
领域 | 气候变化 |
收录类别 | SCI-E |
WOS记录号 | WOS:000448445100001 |
WOS关键词 | EARTH SYSTEM MODEL ; STATISTICAL-MODELS |
WOS类目 | Environmental Sciences ; Meteorology & Atmospheric Sciences |
WOS研究方向 | Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/14910 |
专题 | 气候变化 |
作者单位 | USDA, Econ Res Serv, Washington, DC 20024 USA |
推荐引用方式 GB/T 7714 | Crane-Droesch, Andrew. Machine learning methods for crop yield prediction and climate change impact assessment in agriculture[J]. ENVIRONMENTAL RESEARCH LETTERS,2018,13(11). |
APA | Crane-Droesch, Andrew.(2018).Machine learning methods for crop yield prediction and climate change impact assessment in agriculture.ENVIRONMENTAL RESEARCH LETTERS,13(11). |
MLA | Crane-Droesch, Andrew."Machine learning methods for crop yield prediction and climate change impact assessment in agriculture".ENVIRONMENTAL RESEARCH LETTERS 13.11(2018). |
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