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

Abstract

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. large areas, mainly through the inadequate conversion of natural environments into agricultural areas (Fonseca et al., 2007; Rojas et al., 2016). The main impact of LUC is on in the soils, which are directly responsible for the sustainability and productivity of natural and agricultural ecosystems (Castilho et al., 2016; Novak et al., 2017; Sanabria et al., 2016). Studying the physical, chemical and biological attributes of soil in different applications and comparing these attributes to those in areas

CO2 emission and its relation to soil temperature, moisture, and O2 absorption in the reforested areas of Cerrado biome, Central Brazil

“…3). Our findings were similar to those of Carvalho et al (2018) conducted in different agricultural systems in the southern Amazon in Brazil. Soil fertility, pH, soil temperature, and soil water content are important regulators of microbial decomposition activity (Six et al 2006) and can maximise soil biological activity.…”

Spatial and multivariate analysis of soybean productivity and soil physical-chemical attributes

“…This justifies the need for research that integrates soil and plant attributes in order to determine specific management zones. Gong et al (2015), Rojas et al (2016) and Carvalho et al (2018) successfully used the multivariate approach to characterize soil biological, physical, and chemical attributes.…”

“…Several studies have used successfully multivariate techniques to evaluate, characterize, and integrate multiple soil and plant attributes (Córdoba et al, 2013;Yao et al, 2014;Gong et al, 2015;Haghverdi et al, 2015;Rojas et al, 2016;Freddi et al, 2017;Carvalho et al, 2018;Silva et al, 2018). The integration of geostatistics tools and multivariate analysis allows the design of management zones with greater accuracy.…”

Soil Fertility: Challenges to Soybean (𝘎𝘭𝘺𝘤𝘪𝘯𝘦 𝘮𝘢𝘹 L.) Productivity in Consolidated No-Tillage System

“…Thus, the interaction between these attributes can contribute to the change in nutrient availability, or even to how plants perceive fertility in the first centimeters of soil and throughout the area explored by the roots. This is due to the so-called rhizospheric effect, since the existing interrelationships between chemical, physical and biological attributes control the processes and aspects related to the variation of time and space, with effects on soil quality and agricultural productivity (CARVALHO et al, 2018).…”

“…Thus, for a better understanding of these interactions (chemical, physical and biological) as well as the use of a broad dataset, it is necessary to use multivariate data analysis tools (BELINATO et al, 2020). The use of multivariate analysis techniques is more efficient to explore the correlations between the variables and determine those that most contribute to soil characterization and changes (CARVALHO et al, 2018).…”