Spatial variability of hydrodynamic parameters in the native savanna of the colombian eastern plains

To understand the spatial variability of hydrodynamic parameters allows to identify the behavior of the water in the soil and to make decisions for the performance of irrigation tasks. The aim of the present study was to describe some hydro-physical attributes, the relationship between them, and the...

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Autores:
Camacho Tamayo, Jesús H.
Orjuela Matta, Helber M.
Rubiano Sanabria, Yolanda
Tipo de recurso:
Article of journal
Fecha de publicación:
2011
Institución:
Universidad Nacional de Colombia
Repositorio:
Universidad Nacional de Colombia
Idioma:
spa
OAI Identifier:
oai:repositorio.unal.edu.co:unal/29760
Acceso en línea:
https://repositorio.unal.edu.co/handle/unal/29760
http://bdigital.unal.edu.co/19808/
http://bdigital.unal.edu.co/19808/2/
Palabra clave:
soil management
cluster analysis
principal components
semivariogram
kriging.
Rights
openAccess
License
Atribución-NoComercial 4.0 Internacional
Description
Summary:To understand the spatial variability of hydrodynamic parameters allows to identify the behavior of the water in the soil and to make decisions for the performance of irrigation tasks. The aim of the present study was to describe some hydro-physical attributes, the relationship between them, and their spatial variability. The research was carried out in Puerto Lopez (Meta, Colombia), in a Typic Haplustox. The sampling was done with a mesh of 64 points, with perpendicular distances of 52 m by 45 m between points. The attributes studied were bulk density, volumetric moisture, sorptivity, saturated hydraulic conductivity, and the sand, silt, and clay contents. The data were analyzed by descriptive statistics, multivaried analysis, and geostatistics. The saturated hydraulic conductivity was the only attribute that did not show spatial dependence. The bulk density, volumetric moisture, and sand and silt contents are the attributes that best characterize the soil, having in common low variability, a high degree of spatial dependence, and greater representation in the principal components analysis. The results offer information for performing localized irrigation tasks, according to the water deficit, in order to optimize the application layer of the water and the irrigation periods.