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Downscaling of national crop area statistics using drivers of cropland productivity measured at fine resolutions

Despite substantial research and policy interest in pixel level cropland allocation data, few sources are available that span a large geographic area. The data used for much of this research are derived from complex modeling techniques that may include model simulation and other data processing. We...

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Detalles Bibliográficos
Autores principales: Song, Jingyu, Delgado, Michael S., Preckel, Paul V., Villoria, Nelson B.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6181340/
https://www.ncbi.nlm.nih.gov/pubmed/30307995
http://dx.doi.org/10.1371/journal.pone.0205152
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author Song, Jingyu
Delgado, Michael S.
Preckel, Paul V.
Villoria, Nelson B.
author_facet Song, Jingyu
Delgado, Michael S.
Preckel, Paul V.
Villoria, Nelson B.
author_sort Song, Jingyu
collection PubMed
description Despite substantial research and policy interest in pixel level cropland allocation data, few sources are available that span a large geographic area. The data used for much of this research are derived from complex modeling techniques that may include model simulation and other data processing. We develop a transparent econometric framework that uses pixel level biophysical measurements and aggregate cropland statistics to develop pixel level cropland allocation predictions. Such pixel level land use data can be used to investigate the impact of human activities on the environment. Validation exercises show that our approach is effective at downscaling cropland allocation to multiple levels of resolution.
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spelling pubmed-61813402018-10-26 Downscaling of national crop area statistics using drivers of cropland productivity measured at fine resolutions Song, Jingyu Delgado, Michael S. Preckel, Paul V. Villoria, Nelson B. PLoS One Research Article Despite substantial research and policy interest in pixel level cropland allocation data, few sources are available that span a large geographic area. The data used for much of this research are derived from complex modeling techniques that may include model simulation and other data processing. We develop a transparent econometric framework that uses pixel level biophysical measurements and aggregate cropland statistics to develop pixel level cropland allocation predictions. Such pixel level land use data can be used to investigate the impact of human activities on the environment. Validation exercises show that our approach is effective at downscaling cropland allocation to multiple levels of resolution. Public Library of Science 2018-10-11 /pmc/articles/PMC6181340/ /pubmed/30307995 http://dx.doi.org/10.1371/journal.pone.0205152 Text en © 2018 Song et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Song, Jingyu
Delgado, Michael S.
Preckel, Paul V.
Villoria, Nelson B.
Downscaling of national crop area statistics using drivers of cropland productivity measured at fine resolutions
title Downscaling of national crop area statistics using drivers of cropland productivity measured at fine resolutions
title_full Downscaling of national crop area statistics using drivers of cropland productivity measured at fine resolutions
title_fullStr Downscaling of national crop area statistics using drivers of cropland productivity measured at fine resolutions
title_full_unstemmed Downscaling of national crop area statistics using drivers of cropland productivity measured at fine resolutions
title_short Downscaling of national crop area statistics using drivers of cropland productivity measured at fine resolutions
title_sort downscaling of national crop area statistics using drivers of cropland productivity measured at fine resolutions
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6181340/
https://www.ncbi.nlm.nih.gov/pubmed/30307995
http://dx.doi.org/10.1371/journal.pone.0205152
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