Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1088/1748-9326/ab7df9 |
Comparative assessment of environmental variables and machine learning algorithms for maize yield prediction in the US Midwest | |
Kang, Yanghui1,2; Ozdogan, Mutlu2,3; Zhu, Xiaojin4; Ye, Zhiwei2; Hain, Christopher5; Anderson, Martha6 | |
2020-06-01 | |
发表期刊 | ENVIRONMENTAL RESEARCH LETTERS |
ISSN | 1748-9326 |
出版年 | 2020 |
卷号 | 15期号:6 |
文章类型 | Article |
语种 | 英语 |
国家 | USA |
英文摘要 | Crop yield estimates over large areas are conventionally made using weather observations, but a comprehensive understanding of the effects of various environmental indicators, observation frequency, and the choice of prediction algorithm remains elusive. Here we present a thorough assessment of county-level maize yield prediction in U.S. Midwest using six statistical/machine learning algorithms (Lasso, Support Vector Regressor, Random Forest, XGBoost, Long-short term memory (LSTM), and Convolutional Neural Network (CNN)) and an extensive set of environmental variables derived from satellite observations, weather data, land surface model results, soil maps, and crop progress reports. Results show that seasonal crop yield forecasting benefits from both more advanced algorithms and a large composite of information associated with crop canopy, environmental stress, phenology, and soil properties (i.e. hundreds of features). The XGBoost algorithm outperforms other algorithms both in accuracy and stability, while deep neural networks such as LSTM and CNN are not advantageous. The compositing interval (8-day, 16-day or monthly) of time series variable does not have significant effects on the prediction. Combining the best algorithm and inputs improves the prediction accuracy by 5% when compared to a baseline statistical model (Lasso) using only basic climatic and satellite observations. Reasonable county-level yield foresting is achievable from early June, almost four months prior to harvest. At the national level, early-season (June and July) prediction from the best model outperforms that of the United States Department of Agriculture (USDA) World Agricultural Supply and Demand Estimates (WASDE). This study provides insights into practical crop yield forecasting and the understanding of yield response to climatic and environmental conditions. |
英文关键词 | crop yields climate impact machine learning deep learning data-driven |
领域 | 气候变化 |
收录类别 | SCI-E |
WOS记录号 | WOS:000538483900001 |
WOS关键词 | SENSED VEGETATION INDEXES ; EVAPORATIVE STRESS INDEX ; CROP YIELD ; AGRICULTURAL DROUGHT ; CLIMATE-CHANGE ; UNITED-STATES ; WINTER-WHEAT ; MODEL ; CORN ; EVAPOTRANSPIRATION |
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/279348 |
专题 | 气候变化 |
作者单位 | 1.Univ Wisconsin, Dept Geog, 550 N Pk St, Madison, WI 53706 USA; 2.Univ Wisconsin, Nelson Inst Ctr Sustainabil & Global Environm, 1710 Univ Ave, Madison, WI 53726 USA; 3.Univ Wisconsin, Dept Forest & Wildlife Ecol, 1630 Linden Dr, Madison, WI 53706 USA; 4.Univ Wisconsin, Dept Comp Sci, 1210 W Dayton St, Madison, WI 53706 USA; 5.NASA, Marshall Space Flight Ctr, Earth Sci Branch, Huntsville, AL 35805 USA; 6.USDA ARS, Hydrol & Remote Sensing Lab, Beltsville, MD 20705 USA |
推荐引用方式 GB/T 7714 | Kang, Yanghui,Ozdogan, Mutlu,Zhu, Xiaojin,et al. Comparative assessment of environmental variables and machine learning algorithms for maize yield prediction in the US Midwest[J]. ENVIRONMENTAL RESEARCH LETTERS,2020,15(6). |
APA | Kang, Yanghui,Ozdogan, Mutlu,Zhu, Xiaojin,Ye, Zhiwei,Hain, Christopher,&Anderson, Martha.(2020).Comparative assessment of environmental variables and machine learning algorithms for maize yield prediction in the US Midwest.ENVIRONMENTAL RESEARCH LETTERS,15(6). |
MLA | Kang, Yanghui,et al."Comparative assessment of environmental variables and machine learning algorithms for maize yield prediction in the US Midwest".ENVIRONMENTAL RESEARCH LETTERS 15.6(2020). |
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