Climate Change Data Portal
DOI | 10.1007/s10584-019-02432-7 |
Predicting high-magnitude, low-frequency crop losses using machine learning: an application to cereal crops in Ethiopia | |
Mann, Michael L.1; Warner, James M.2; Malik, Arun S.3 | |
发表日期 | 2019 |
ISSN | 0165-0009 |
EISSN | 1573-1480 |
卷号 | 154期号:1-2页码:211-227 |
英文摘要 | Timely and accurate agricultural impact assessments for droughts are critical for designing appropriate interventions and policy. These assessments are often ad hoc, late, or spatially imprecise, with reporting at the zonal or regional level. This is problematic as we find substantial variability in losses at the village-level, which is missing when reporting at the zonal level. In this paper, we propose a new data fusion methodcombining remotely sensed data with agricultural survey datathat might address these limitations. We apply the method to Ethiopia, which is regularly hit by droughts and is a substantial recipient of ad hoc imported food aid. We then utilize remotely sensed data obtained near mid-season to predict substantial crop losses of greater than or equal to 25% due to drought at the village level for five primary cereal crops. We train machine learning models to predict the likelihood of losses and explore the most influential variables. On independent samples, the models identify substantial drought loss cases with up to 81% accuracy by mid- to late-September. We believe the proposed models could be used to help monitor and predict yields for disaster response teams and policy makers, particularly with further development of the models and integration of soon-to-be available high-resolution, remotely sensed data such as the Harmonized Landsat Sentinel (HLS) data set. |
WOS研究方向 | Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences |
来源期刊 | CLIMATIC CHANGE |
文献类型 | 期刊论文 |
条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/90228 |
作者单位 | 1.George Washington Univ, Dept Geog, Washington, DC 20052 USA; 2.Int Food Policy Res Inst, Addis Ababa, Ethiopia; 3.George Washington Univ, Dept Econ, Washington, DC USA |
推荐引用方式 GB/T 7714 | Mann, Michael L.,Warner, James M.,Malik, Arun S.. Predicting high-magnitude, low-frequency crop losses using machine learning: an application to cereal crops in Ethiopia[J],2019,154(1-2):211-227. |
APA | Mann, Michael L.,Warner, James M.,&Malik, Arun S..(2019).Predicting high-magnitude, low-frequency crop losses using machine learning: an application to cereal crops in Ethiopia.CLIMATIC CHANGE,154(1-2),211-227. |
MLA | Mann, Michael L.,et al."Predicting high-magnitude, low-frequency crop losses using machine learning: an application to cereal crops in Ethiopia".CLIMATIC CHANGE 154.1-2(2019):211-227. |
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