气候变化领域集成服务门户(CCPortal): Qualitative crop condition survey reveals spatiotemporal production patterns and allows early yield prediction

CCPortal

DOI	10.1073/pnas.1917774117
	Qualitative crop condition survey reveals spatiotemporal production patterns and allows early yield prediction
	Begueriá S.; Maneta M.P.
发表日期	2020
ISSN	0027-8424
起始页码	18317
结束页码	18323
卷号	117 期号:31
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.
英文关键词	Crop condition index; Crop condition survey; Crop monitoring; Early yield prediction; Usda
语种	英语
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
来源期刊	Proceedings of the National Academy of Sciences of the United States of America
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/160854
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
推荐引用方式 GB/T 7714	Begueriá S.,Maneta M.P.. Qualitative crop condition survey reveals spatiotemporal production patterns and allows early yield prediction[J],2020,117(31).
APA	Begueriá S.,&Maneta M.P..(2020).Qualitative crop condition survey reveals spatiotemporal production patterns and allows early yield prediction.Proceedings of the National Academy of Sciences of the United States of America,117(31).
MLA	Begueriá S.,et al."Qualitative crop condition survey reveals spatiotemporal production patterns and allows early yield prediction".Proceedings of the National Academy of Sciences of the United States of America 117.31(2020).