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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doaj-2a3247f26e0045d18e4f25468107b9382020-11-25T02:33:33ZengPublic Library of Science (PLoS)PLoS ONE1932-62032018-01-011310e020515210.1371/journal.pone.0205152Downscaling of national crop area statistics using drivers of cropland productivity measured at fine resolutions.Jingyu SongMichael S DelgadoPaul V PreckelNelson B VilloriaDespite 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.http://europepmc.org/articles/PMC6181340?pdf=render |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jingyu Song Michael S Delgado Paul V Preckel Nelson B Villoria |
spellingShingle |
Jingyu Song Michael S Delgado Paul V Preckel Nelson B Villoria Downscaling of national crop area statistics using drivers of cropland productivity measured at fine resolutions. PLoS ONE |
author_facet |
Jingyu Song Michael S Delgado Paul V Preckel Nelson B Villoria |
author_sort |
Jingyu Song |
title |
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_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_sort |
downscaling of national crop area statistics using drivers of cropland productivity measured at fine resolutions. |
publisher |
Public Library of Science (PLoS) |
series |
PLoS ONE |
issn |
1932-6203 |
publishDate |
2018-01-01 |
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. |
url |
http://europepmc.org/articles/PMC6181340?pdf=render |
work_keys_str_mv |
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1724813277587832832 |