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Geostatistical methods to identify and map spatial variations of soil salinity
dc.contributor.author | Juan, Pablo | |
dc.contributor.author | Mateu, Jorge | |
dc.contributor.author | Jordán Vidal, Manuel Miguel | |
dc.contributor.author | Mataix-Solera, Jorge | |
dc.contributor.author | Meléndez-Pastor, I. | |
dc.contributor.author | Navarro Pedreño, José | |
dc.date.accessioned | 2012-10-16T13:54:07Z | |
dc.date.available | 2012-10-16T13:54:07Z | |
dc.date.issued | 2011-01 | |
dc.identifier.citation | Journal of Geochemical Exploration Volume 108, Issue 1, January 2011 | |
dc.identifier.issn | 0375-6742 | |
dc.identifier.uri | http://hdl.handle.net/10234/48754 | |
dc.description.abstract | The problem of estimating and predicting spatial distribution of a spatial stochastic process, observed at irregular locations in space, is considered in this paper. Environmental variables usually show spatial dependencies among observations, with lead one to use geostatisticalmethods to model the spatial distributions of those observations. This is particularly important in the study of soil properties and their spatial variability. In this study geostatistical techniques were used to describe the spatial dependence and to quantify the scale and intensity of spatialvariations of soil properties, which provide the essential spatial information for local estimation. In this contribution, we propose a spatial Gaussian linear mixed model that involves (a) a non-parametric term for accounting deterministic trend due to exogenous variables and (b) a parametric component for defining the purely spatial random variation due possibly to latent spatial processes. We focus here on the analysis of the relationship between soil electrical conductivity and Na content to identifyspatialvariations of soilsalinity. This analysis can be useful for agricultural and environmental land management. | ca_CA |
dc.format.extent | 10 p. | ca_CA |
dc.language.iso | eng | ca_CA |
dc.publisher | Elsevier | ca_CA |
dc.rights | Copyright © 2010 Elsevier B.V. All rights reserved. | ca_CA |
dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | * |
dc.subject | Bayesian methodology | ca_CA |
dc.subject | Electrical conductivity | ca_CA |
dc.subject | Spatial Gaussian linear mixed model | ca_CA |
dc.subject | Hierarchical modelling | ca_CA |
dc.subject | Sodium | ca_CA |
dc.subject | Soilsalinity | ca_CA |
dc.title | Geostatistical methods to identify and map spatial variations of soil salinity | ca_CA |
dc.type | info:eu-repo/semantics/article | ca_CA |
dc.identifier.doi | http://dx.doi.org/10.1016/j.gexplo.2010.10.003 | |
dc.rights.accessRights | info:eu-repo/semantics/restrictedAccess | ca_CA |
dc.relation.publisherVersion | http://www.sciencedirect.com/science/article/pii/S0375674210001482 | ca_CA |
dc.type.version | info:eu-repo/semantics/publishedVersion | ca_CA |
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