Multivariate approach of soil attributes on the characterization of land use in the southern Brazilian Amazon

• Autor(a) principal: de Carvalho, Marco Antonio Camillo
• Data de Publicação: 2018
• Outros Autores: Panosso, Alan Rodrigo [UNESP], Ribeiro Teixeira, Elisabethe Emilia, Araújo, Elton Gean, Brancaglioni, Vivian Aparecida, Dallacort, Rivanildo
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1016/j.still.2018.08.004; http://hdl.handle.net/11449/171322
• topic: Amazonian agroecosystems; Soil quality; Structural equation analysis

Determination of physical, chemical and biological attributes with individual analyses is inadequate for improving the understanding of soil conditions as a function of land-use change (LUC) in comparison to the natural state of soil. For a more accurate soil condition diagnostic, it is necessary to consider various indicators related to these characteristics, which requires the use of multivariate statistical analysis. The aim of this work was to characterize, through multivariate analysis, different types of LUCs in an Oxisol as a function of the physical, chemical and biological attributes and to clarify the relationship of these attributes with the quality of the soil in comparison to these attributes in natural soil conditions, in the southern Amazon in Brazil. The land uses evaluated in the municipality of Alta Floresta, state of Mato Grosso (MT), Brazil, were native amazon forest (ma), degraded pasture (pd), managed renewed pasture (pn), permanent preservation area in recovery (app), crop area (rice), forage sugarcane (ca) and reforested area with eucalyptus (eu). To characterize the physical and chemical soil attributes, samples were collected in each land-use area, at depths of 0-0.10 and 0.10-0.20 m, and the determination of soil microbial activity (biological attributes) was evaluated at a depth of 0-0.10 m. The interrelationship between the analyzed attributes was described by multivariate techniques, which included hierarchical and non-hierarchical cluster analyses, principal component analysis, canonical correlation, and structural equation modeling. The multivariate approach for the analysis of soil attribute data was efficient in the identification of anthropogenic actions on areas in comparison to natural conditions. Together, the cluster analysis and principal components analysis identified two groups that differed mainly in terms of anthropic operations of soil tillage and liming. The land use that was most similar to the natural condition was degraded pasture, which was mainly due to K and H + Al contents, soil microporosity and soil basal respiration. Structural equation modeling indicated that the latent factor soil chemical attributes had three times greater interference (-0.5828) than the latent factor soil physical attributes (0.1735) on the latent factor soil biological attributes. Therefore, anthropic actions, especially the liming, modified soil acidity conditions, affecting the microorganisms of its flora and changing the native fungal community of the soil that was evaluated.