Por favor, use este identificador para citar o enlazar a este item:
http://hdl.handle.net/10261/221345
COMPARTIR / EXPORTAR:
SHARE CORE BASE | |
Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL | DATACITE | |
Campo DC | Valor | Lengua/Idioma |
---|---|---|
dc.contributor.author | Beguería, Santiago | es_ES |
dc.contributor.author | Maneta, Marco P. | es_ES |
dc.date.accessioned | 2020-10-19T09:47:56Z | - |
dc.date.available | 2020-10-19T09:47:56Z | - |
dc.date.issued | 2020-08 | - |
dc.identifier.citation | Beguería S, Maneta MP. 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): 18317-18323 (2020) | es_ES |
dc.identifier.issn | 0027-8424 | - |
dc.identifier.uri | http://hdl.handle.net/10261/221345 | - |
dc.description | 7 Pags.- 1 Tabl.- 7 Figs.- Copyright © 2020 the Author(s). Published by PNAS. This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND). | es_ES |
dc.description.abstract | 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. | es_ES |
dc.description.sponsorship | S.B.’s research is supported by the Spanish Ministry of Science and Innovation (Grant CGL2017-83866-C3-3-R) and Fundación Biodiversidad, Ministry of Ecological Transition (Grant CA_CC_2018). M.P.M.’s support is from the USDA-National Institute of Food and Agriculture (NIFA) (Grant 2016-67026-25067) and the NASA Established Program to Stimulate Competitive Research (EPSCoR) program (Grant 80NSSC18M0025M). This research was made possible thanks to a mobility grant of the Spanish Ministry of Education, Culture, and Sports, within the framework of the “Programa Estatal de Promoción del Talento y su Empleabilidad en I+D+i,” “Salvador de Madariaga” program. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | National Academy of Sciences (U.S.) | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/CGL2017-83866-C3-3-R) | es_ES |
dc.relation.isversionof | Publisher's version | es_ES |
dc.relation.isreferencedby | Beguería, Santiago ; Maneta, Marco P. Qualitative crop condition survey reveals spatiotemporal production patterns and allows early yield prediction [Dataset]. http://dx.doi.org/10.20350/digitalCSIC/12550. http://hdl.handle.net/10261/201950 | es_ES |
dc.rights | openAccess | es_ES |
dc.subject | USDA | es_ES |
dc.subject | Crop condition survey | es_ES |
dc.subject | crop condition index | es_ES |
dc.subject | Crop monitoring | es_ES |
dc.subject | Early yield prediction | es_ES |
dc.title | Qualitative crop condition survey reveals spatiotemporal production patterns and allows early yield prediction | es_ES |
dc.type | artículo | es_ES |
dc.identifier.doi | 10.1073/pnas.1917774117 | - |
dc.description.peerreviewed | Peer reviewed | es_ES |
dc.relation.publisherversion | https://doi.org/10.1073/pnas.1917774117 | es_ES |
dc.identifier.e-issn | 1091-6490 | - |
dc.rights.license | https://creativecommons.org/licenses/by-nc-nd/4.0/ | es_ES |
dc.contributor.funder | Ministerio de Ciencia e Innovación (España) | es_ES |
dc.contributor.funder | Ministerio de Transición Ecológica (España) | es_ES |
dc.contributor.funder | Department of Agriculture (US) | es_ES |
dc.contributor.funder | NASA Astrobiology Institute (US) | es_ES |
dc.contributor.funder | Ministerio de Educación, Cultura y Deporte (España) | es_ES |
dc.relation.csic | Sí | es_ES |
oprm.item.hasRevision | no ko 0 false | * |
dc.identifier.funder | http://dx.doi.org/10.13039/501100004837 | es_ES |
dc.identifier.funder | http://dx.doi.org/10.13039/501100003176 | es_ES |
dc.identifier.funder | http://dx.doi.org/10.13039/100000199 | es_ES |
dc.contributor.orcid | Beguería, Santiago [0000-0002-3974-2947] | es_ES |
dc.identifier.pmid | 32675235 | - |
dc.type.coar | http://purl.org/coar/resource_type/c_6501 | es_ES |
item.grantfulltext | open | - |
item.openairetype | artículo | - |
item.cerifentitytype | Publications | - |
item.languageiso639-1 | en | - |
item.fulltext | With Fulltext | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
Aparece en las colecciones: | (EEAD) Artículos |
Ficheros en este ítem:
Fichero | Descripción | Tamaño | Formato | |
---|---|---|---|---|
BegueriaS_PNAS_2020.pdf | 3,27 MB | Adobe PDF | Visualizar/Abrir |
CORE Recommender
PubMed Central
Citations
3
checked on 02-may-2024
SCOPUSTM
Citations
7
checked on 14-may-2024
WEB OF SCIENCETM
Citations
5
checked on 23-feb-2024
Page view(s)
96
checked on 16-may-2024
Download(s)
145
checked on 16-may-2024