4.0 Article

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

期刊

REVISTA CIENCIA AGRONOMICA
卷 43, 期 3, 页码 429-438

出版社

UNIV FEDERAL CEARA, DEPT GEOL
DOI: 10.1590/S1806-66902012000300003

关键词

Soil Science; Geomorphology; Multivariate analysis

向作者/读者索取更多资源

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.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.0
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据