Discrimination of geomorphic surfaces with multivariate analysis of soil attributes in sandstone - basalt lithosequence

• Autor(a) principal: Campos,Milton César Costa
• Data de Publicação: 2012
• Outros Autores: Marques Júnior,José, Souza,Zigomar Menezes de, Siqueira,Diego Silva, Pereira,Gener Tadeu
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Revista ciência agronômica (Online)
• Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1806-66902012000300003
• topic: Soil Science; Geomorphology; Multivariate analysis

The geomorphic surface concept allows interrelationship among various branches of soil sciences, such as geology, geomorphology and pedology. This association enhances the understanding of spatial soil distribution through landscape, pointing out the soil attributes behavior, which are mainly related to stratigraphy and relief forms. Therefore, this study aims to apply multivariate statistics to categorize geomorphic surfaces in sandstone - basalt lithosequence, so as to provide a basis for soil assessment in similar areas. The study area is located in Pereira Barreto County, SP, Brazil. An area of 530 hectare was selected, where three geomorphic surfaces (I, II and III) were located and mapped. In this area, 134 soil samples were collected at depths of 0.0-0.2 m and 0.8-1.0 m below ground surface. Sand, silt and clay contents were determined, pH in CaCl2 solution, OM, P, Ca, Mg, K, Al and H+Al contents were also evaluated. Based on the results, univariate, multivariate analysis of variance, cluster and principal-component analysis were performed in order to compare the three geomorphic surfaces. The univariate statistical analysis of soil attributes was not efficient enough to categorize the three geomorphic surfaces. By using the physical and chemical soil properties, the multivariate statistical techniques enabled the differentiation of the three groups of soil natural bodies which were equivalent to the same three mapped geomorphic surfaces (GS). These results are interestingin order to demonstrate the feasibility of the numerical classification use on geomorphic surfaces to assist the soil mapping.