Interpolation and spatial prediction methodologies for the analysis of the physical properties of the soil in the hoya del río Suárez (Colombia)

In Colombia, agriculture can be developed on sloping land that requires zonal soil conservation practices. To do this, the area of ​​interest is analyzed for its determining properties, usually with sampling at specific points. Techniques of interpolation such as inverse distance weighting (IDW) and...

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Príomhchruthaitheoir: Vargas Diaz, Ruy Edeymar (author)
Rannpháirtithe: Galindo Pacheco, Julio Ricardo (author), Giraldo Henao, Ramón (author), Varón Ramírez, Viviana Marcela (author), Wilches Ortiz, Wilmar Alexander (author), Franco Florez, Clara Viviana (author)
Formáid: article
Teanga:spa
Foilsithe / Cruthaithe: 2023
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Rochtain ar líne:https://revistadigital.uce.edu.ec/index.php/SIEMBRA/article/view/4118
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Achoimre:In Colombia, agriculture can be developed on sloping land that requires zonal soil conservation practices. To do this, the area of ​​interest is analyzed for its determining properties, usually with sampling at specific points. Techniques of interpolation such as inverse distance weighting (IDW) and of prediction such as kriging can be used for prediction and estimation of values ​​at unsampled locations. The objective of this study was to compare the IDW and kriging methodologies for modeling the spatial distribution of the physical characteristics of the soil in the agricultural zone of the slope of the Hoya del Rio Suárez (HDRS). Data on five physical properties of the soil associated with erodibility: percentage of sand, weighted average diameter, available water retention capacity, bulk density, and real density, were used, corresponding to 932 points observed on the HDRS in a 700x700 m grid. Cross-validation was applied for each variable and the error of the evaluated techniques was compared. In addition, zoning maps of spatial variability were prepared to visually compare both procedures. The graphical representation of the IDW and the kriging estimates predictions were similar for all soil characteristics evaluated. However, cross-validation analysis yielded better results with kriging. The variance maps (kriging) showed that the estimation uncertainty was homogeneous for most of the HDRS. The kriging technique turned out to be more accurate than IDW in estimating values ​​at non-sampled points.