Geostatistical methods to identify and map spatial variations of soil salinity |
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| Authors: | P Juan J Mateu MM Jordan J Mataix-Solera I Meléndez-Pastor J Navarro-Pedreño |
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| Institution: | 1. Department of Mathematics, Campus Riu Sec. University Jaume I. E-12071. Castellón, Spain;2. Departamento de Agroquímica y Medio Ambiente, Universidad Miguel Hernández de Elche. E-03202. Elche (Alicante), Spain |
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| 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 geostatistical methods 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 spatial variations 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 identify spatial variations of soil salinity. This analysis can be useful for agricultural and environmental land management. |
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| Keywords: | Bayesian methodology Electrical conductivity Spatial Gaussian linear mixed model Hierarchical modelling Sodium Soil salinity |
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