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A geospatial method for estimating soil moisture variability in prehistoric agricultural landscapes

Prehistoric peoples chose farming locations based on environmental conditions, such as soil moisture, which plays a crucial role in crop production. Ancestral Pueblo communities of the central Mesa Verde region became increasingly reliant on maize agriculture for their subsistence needs by AD 900. P...

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Detalles Bibliográficos
Autores principales: Gillreath-Brown, Andrew, Nagaoka, Lisa, Wolverton, Steve
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6703677/
https://www.ncbi.nlm.nih.gov/pubmed/31433812
http://dx.doi.org/10.1371/journal.pone.0220457
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author Gillreath-Brown, Andrew
Nagaoka, Lisa
Wolverton, Steve
author_facet Gillreath-Brown, Andrew
Nagaoka, Lisa
Wolverton, Steve
author_sort Gillreath-Brown, Andrew
collection PubMed
description Prehistoric peoples chose farming locations based on environmental conditions, such as soil moisture, which plays a crucial role in crop production. Ancestral Pueblo communities of the central Mesa Verde region became increasingly reliant on maize agriculture for their subsistence needs by AD 900. Prehistoric agriculturalists (e.g., Ancestral Pueblo farmers) were dependent on having sufficient soil moisture for successful plant growth. To better understand the quality of farmland in terms of soil moisture, this study develops a static geospatial soil moisture model, the Soil Moisture Proxy Model, which uses soil and topographic variables to estimate soil moisture potential across a watershed. The model is applied to the semi-arid region of the Goodman watershed in the central Mesa Verde region of southwestern Colorado. We evaluate the model by comparing the Goodman watershed output to two other watersheds and to soil moisture sensor values. The simple framework can be used in other regions of the world, where water is also an important limiting factor for farming. The general outcome of this research is an improved understanding of potential farmland and human-environmental relationships across the local landscape.
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spelling pubmed-67036772019-09-04 A geospatial method for estimating soil moisture variability in prehistoric agricultural landscapes Gillreath-Brown, Andrew Nagaoka, Lisa Wolverton, Steve PLoS One Research Article Prehistoric peoples chose farming locations based on environmental conditions, such as soil moisture, which plays a crucial role in crop production. Ancestral Pueblo communities of the central Mesa Verde region became increasingly reliant on maize agriculture for their subsistence needs by AD 900. Prehistoric agriculturalists (e.g., Ancestral Pueblo farmers) were dependent on having sufficient soil moisture for successful plant growth. To better understand the quality of farmland in terms of soil moisture, this study develops a static geospatial soil moisture model, the Soil Moisture Proxy Model, which uses soil and topographic variables to estimate soil moisture potential across a watershed. The model is applied to the semi-arid region of the Goodman watershed in the central Mesa Verde region of southwestern Colorado. We evaluate the model by comparing the Goodman watershed output to two other watersheds and to soil moisture sensor values. The simple framework can be used in other regions of the world, where water is also an important limiting factor for farming. The general outcome of this research is an improved understanding of potential farmland and human-environmental relationships across the local landscape. Public Library of Science 2019-08-21 /pmc/articles/PMC6703677/ /pubmed/31433812 http://dx.doi.org/10.1371/journal.pone.0220457 Text en © 2019 Gillreath-Brown 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
Gillreath-Brown, Andrew
Nagaoka, Lisa
Wolverton, Steve
A geospatial method for estimating soil moisture variability in prehistoric agricultural landscapes
title A geospatial method for estimating soil moisture variability in prehistoric agricultural landscapes
title_full A geospatial method for estimating soil moisture variability in prehistoric agricultural landscapes
title_fullStr A geospatial method for estimating soil moisture variability in prehistoric agricultural landscapes
title_full_unstemmed A geospatial method for estimating soil moisture variability in prehistoric agricultural landscapes
title_short A geospatial method for estimating soil moisture variability in prehistoric agricultural landscapes
title_sort geospatial method for estimating soil moisture variability in prehistoric agricultural landscapes
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6703677/
https://www.ncbi.nlm.nih.gov/pubmed/31433812
http://dx.doi.org/10.1371/journal.pone.0220457
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