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        <identifier>oai:figshare.com:article/31332454</identifier>
        <datestamp>2026-09-17T07:37:29Z</datestamp>
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          <dc:title>Spatial analysis of interpolation techniques for soil attributes</dc:title>
          <dc:creator>Henja Glas (23179537)</dc:creator>
          <dc:subject>Spatial statistics</dc:subject>
          <dc:subject>Spatial interpolation</dc:subject>
          <dc:subject>Interpolation</dc:subject>
          <dc:subject>Kriging</dc:subject>
          <dc:subject>Soil analysis</dc:subject>
          <dc:subject>Cation exchange capacity (CEC)</dc:subject>
          <dc:description>&lt;p dir="ltr"&gt;The research examines the impact of the Kriging method, variogram model, and grid configuration on the spatial interpolation outcomes for soil data in South Africa. Two independent spatial datasets containing measurements of cation exchange capacity (CEC) were analysed. Five Kriging techniques, Simple Kriging, Ordinary Kriging, Universal Kriging, Ordinary Co-Kriging, and Regression Kriging, were used in a thorough interpolation framework to capture a variety of spatial modelling strategies, ranging from strictly deterministic mean structures to methods that include auxiliary variables and spatial crosscorrelation. Three popular theoretical variogram models, spherical, exponential, and Gaussian, were fitted for each Kriging technique to characterise the spatial autocorrelation structure of the main variable. To assess the effects of spatial sample density and layout on model outputs and their spatial patterns, predictions were produced across four different grid configurations, each with a different resolution and arrangement.&lt;/p&gt;</dc:description>
          <dc:date>2026-09-17T07:37:29Z</dc:date>
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          <dc:identifier>10.25403/UPresearchdata.31332454.v1</dc:identifier>
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