气候变化领域集成服务门户(CCPortal): Comparative assessment of environmental variables and machine learning algorithms for maize yield prediction in the US Midwest

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DOI	10.1088/1748-9326/ab7df9
	Comparative assessment of environmental variables and machine learning algorithms for maize yield prediction in the US Midwest
	Kang Y.; Ozdogan M.; Zhu X.; Ye Z.; Hain C.; Anderson M.
发表日期	2020
ISSN	17489318
卷号	15 期号:6
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. © 2020 The Author(s). Published by IOP Publishing Ltd.
英文关键词	climate impact; crop yields; data-driven; deep learning; machine learning
语种	英语
scopus关键词	Agricultural robots; Convolutional neural networks; Crops; Decision trees; Deep learning; Deep neural networks; Economics; Learning systems; Long short-term memory; Weather forecasting; Comparative assessment; Crop yield forecasting; Environmental conditions; Environmental indicators; Environmental variables; Observation frequencies; Support vector regressor; United states department of agricultures; Learning algorithms; algorithm; artificial neural network; assessment method; comparative study; crop yield; environmental factor; environmental indicator; environmental stress; estimation method; machine learning; maize; phenology; Midwest; United States; Zea mays
来源期刊	Environmental Research Letters
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/154019
作者单位	Department of Geography, University of Wisconsin-Madison, 550 N. Park St., Madison, WI 53706, United States; Nelson Institute Center for Sustainability and the Global Environment, University of Wisconsin-Madison, 1710 University Avenue, Madison, WI 53726, United States; Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, 1630 Linden Drive, Madison, WI 53706, United States; Department of Computer Sciences, University of Wisconsin-Madison, 1210 W. Dayton Street, Madison, WI 53706, United States; NASA Marshall Space Flight Center, Earth Science Branch, Huntsville, AL 35805, United States; Hydrology and Remote Sensing Laboratory, USDA ARS, Beltsville, MD 20705, United States
推荐引用方式 GB/T 7714	Kang Y.,Ozdogan M.,Zhu X.,et al. Comparative assessment of environmental variables and machine learning algorithms for maize yield prediction in the US Midwest[J],2020,15(6).
APA	Kang Y.,Ozdogan M.,Zhu X.,Ye Z.,Hain C.,&Anderson M..(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 Y.,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).