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Chilean Journal of Agricultural Research
Instituto de Investigaciones Agropecuarias, INIA
ISSN: 0718-5820
EISSN: 0718-5839
Vol. 73, No. 4, 2013, pp. 414-423
Bioline Code: cj13060
Full paper language: English
Document type: Research Article
Document available free of charge

Chilean Journal of Agricultural Research, Vol. 73, No. 4, 2013, pp. 414-423

 en Influence of incorporating geometric anisotropy on the construction of thematic maps of simulated data and chemical attributes of soil
Carvalho-Guedes, Luciana Pagliosa; Uribe-Opazo, Miguel Angel & Ribeiro Junior, Paulo Justiniano

Abstract

The study on spatial variability of soil properties performed through geostatistical techniques allow us to identify the spatial distribution of phenomena by means of a spatial model that considers degree of dependence among observed data, depending on distance and also the direction that separate them, if there is geometric anisotropy, in other words, a directional trend in spatial continuity. However, the main difficulty in decision making regarding the use of anisotropic spatial model focuses on its relevance to the parameters that express the geometric anisotropy in a spatial model exercise in relation to the estimation space. This study aims at identifying the degree of influence of geometric anisotropy on the accuracy of spatial estimation using simulated data sets with different sample sizes and soil chemical properties such as: Fe, potential acidity (H + Al), organic matter and Mn. Comparing the isotropic and anisotropic models, especially for smaller sample sizes (100 and 169) showed an increased sum of squares of differences between predictions anisotropy factor (Fa) equals 2. Furthermore, from Fa equals 2.5, over 50% of the simulations showed values of overall accuracy (OA) of less than 0.80 and values for the concordance index Kappa (K) and Tau (T) from 0.67 to 0.80, indicating differences between thematic maps. Similar conclusions were obtained for chemical properties of the soil, from Fa equals 2, showing that there are relevant differences regarding the inclusion or not of geometric anisotropy.

Keywords
Anisotropy factor; geostatistics; spatial variability

 
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