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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
Descripción
Sumario: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.