globalchange  > 气候变化与战略
DOI: 10.1073/pnas.1917774117
论文题名:
Qualitative crop condition survey reveals spatiotemporal production patterns and allows early yield prediction
作者: Begueriá S.; Maneta M.P.
刊名: Proceedings of the National Academy of Sciences of the United States of America
ISSN: 0027-8424
出版年: 2020
卷: 117, 期:31
起始页码: 18317
结束页码: 18323
语种: 英语
英文关键词: Crop condition index ; Crop condition survey ; Crop monitoring ; Early yield prediction ; Usda
Scopus关键词: Article ; controlled study ; crop production ; decision making ; environmental monitoring ; geographic distribution ; information processing ; meteorological phenomena ; molecular stability ; nonhuman ; plant development ; plant growth ; plant yield ; priority journal ; qualitative analysis ; quantitative analysis ; remote sensing ; satellite imagery ; spatiotemporal analysis ; biological model ; crop ; government ; growth, development and aging ; season ; United States ; Crops, Agricultural ; Models, Biological ; Seasons ; United States ; United States Department of Agriculture
英文摘要: Large-scale continuous crop monitoring systems (CMS) are key to detect and manage agricultural production anomalies. Current CMS exploit meteorological and crop growth models, and satellite imagery, but have underutilized legacy sources of information such as operational crop expert surveys with long and uninterrupted records. We argue that crop expert assessments, despite their subjective and categorical nature, capture the complexities of assessing the "status" of a crop better than any model or remote sensing retrieval. This is because crop rating data naturally encapsulates the broad expert knowledge of many individual surveyors spread throughout the country, constituting a sophisticated network of "people as sensors" that provide consistent and accurate information on crop progress. We analyze data from the US Department of Agriculture (USDA) Crop Progress and Condition (CPC) survey between 1987 and 2019 for four major crops across the US, and show how to transform the original qualitative data into a continuous, probabilistic variable better suited to quantitative analysis. Although the CPC reflects the subjective perception of many surveyors at different locations, the underlying models that describe the reported crop status are statistically robust and maintain similar characteristics across different crops, exhibit long-term stability, and have nation-wide validity. We discuss the origin and interpretation of existing spatial and temporal biases in the survey data. Finally, we propose a quantitative Crop Condition Index based on the CPC survey and demonstrate how this index can be used to monitor crop status and provide earlier and more precise predictions of crop yields than official USDA forecasts released midseason. © 2020 National Academy of Sciences. All rights reserved.
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/164097
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作者单位: Begueriá, S., Department of Soil and Water, EstacioÁ N Exp. de Aula Dei-Consejo Sup. de Invest. CientÁAficas (EEAD-CSIC), Zaragoza, 50014, Spain; Maneta, M.P., Geosciences Department, University of Montana, Missoula, MT 59812, United States, Department of Ecosystem and Conservation Sciences, W.A. Franke College of Forestry and Conservation, University of Montana, Missoula, MT 59812, United States

Recommended Citation:
Begueriá S.,Maneta M.P.. Qualitative crop condition survey reveals spatiotemporal production patterns and allows early yield prediction[J]. Proceedings of the National Academy of Sciences of the United States of America,2020-01-01,117(31)
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