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vol.32 número1FUNÇÕES DE DISTRIBUIÇÃO DE PROBABILIDADE APLICADAS NA ESTIMATIVA DA NECESSIDADE HÍDRICA EM PROJETOS DE IRRIGAÇÃOPOTENCIAL PRODUTIVO DO GIRASSOL SOB DOSES DE BIOFERTILIZANTE CAPRINO E LÂMINAS DE IRRIGAÇÃO índice de autoresíndice de assuntospesquisa de artigos
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Revista Caatinga

versão impressa ISSN 0100-316Xversão On-line ISSN 1983-2125

Resumo

OLIVEIRA JUNIOR, Antônio Italcy de et al. STATISTICAL MULTIVARIATE ANALYSIS APPLIED TO ENVIRONMENTAL CHARACTERIZATION OF SOIL IN SEMIARID REGION1. Rev. Caatinga [online]. 2019, vol.32, n.1, pp.200-210.  Epub 09-Maio-2019. ISSN 0100-316X.  http://dx.doi.org/10.1590/1983-21252019v32n120rc.

Soil is a dynamic and complex system that requires a considerable number of samples for analysis and research purposes. Using multivariate statistical methods, favorable conditions can be created by analyzing the samples, i.e., structural reduction and simplification of the data. The objective of this study was to use multivariate statistical analysis, including factorial analysis (FA) and hierarchical groupings, for the environmental characterization of soils in semiarid regions, considering anthropic (land use and occupation) and topographic aspects (altitude, moisture, granulometry, PR, and organic-matter content). As a case study, the São José Hydrographic Microbasin, which is located in the Cariri region of Ceará, was considered. An FA was performed using the principal component method, with normalized varimax rotation. In hierarchical grouping analysis, the “farthest neighbor” method was used as the hierarchical criterion for grouping, with the measure of dissimilarity given by the “square Euclidean distance.” The FA indicated that two factors explain 75.76% of the total data variance. In the analysis of hierarchical groupings, the samples were agglomerated in three groups with similar characteristics: one with samples collected in an area of the preserved forest and two with samples collected in areas with more anthropized soils. This indicates that the statistical tool used showed sensitivity to distinguish the most conserved soils and soils with different levels of anthropization.

Palavras-chave : Factor analysis; Cluster analysis; Soil variables.

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