<b>Multivariate technique for determination of soil pedoenvironmental indicators in Southern Amazonas

Abstract

The use of statistical techniques to evaluate soil attribute behaviour is an important tool for choosing the most adequate form of soil management. Thus, the aim of this study was to jointly assess the physical and chemical attributes and the magnetic susceptibility characteristics of three environments and to use multivariate statistics to define which attributes have the greatest potential as environmental change indicators. The study was conducted in three areas: one with archaeological black earth (and planted maize); one with pasture; and one of the forest. Sixty-four (64) deformed and non-deformed soil samples each were collected within these areas, with a 70 x 70 m grid spaced every 10 meters at depths of 0.0-0.2 and 0.4-0.6 m, to determine the chemical attributes (fertility), as well as the physical attributes (texture) and magnetic susceptibility. The data were analyzed through multivariate statistics. The archaeological dark earth areas have different behaviours between pastures and forests: chemical attributes have greater interaction in archaeological dark earth due to the high fertility of the anthropic horizons. The attributes that may indicate environmental changes are calcium, potential acidity and clay for archaeological dark earth, forest and pasture areas, respectively.

Attributes of pedoindicator soils in areas cultivated with typical crops in the Western Amazon, Brazil

“…showed an accumulation of 72%, T (29%), H + Al (29%) and Resin-P (18%); and in the 0.1-0.2 m layer showed T (30%), H+Al (29%) and Resin-P (13%) as shown in Table 2. Oliveira et al (2017) studying chemical attributes in crop systems in the Southern Amazonas, found accumulated variability between 83% for the 0.0-0.10 m layer and 74% for 0.10-0.20 m layer, corroborating the variability effect reduction on depth. The eigenvalues "scree plot" (Figure 2) confirm that the first and second major components were required to explain the total variance, since they have high values (7.01 and 1.08 at 0.0-0.05 m depth; 6.87 and 1.06 at 0.05-0.1 m depth; 5.21 and 1.00 at 0.1-0.2 m depth), justifying the use of principal component analysis 1 (PC1) and principal component analysis 2 (PC2) (KAISER, 1958).…”

“…The combined use of the multivariate techniques based on soil attribute´s patterns are effective for decision making about land usage and management, and potential indicators of major changes due to human interference (OLIVEIRA et al, 2017). The association of multivariate methods with the ternary diagrams is a powerful tool for statistical quality control, and as indicative of how the chemical variables are affected in each crop system.…”

Suscetibilidade Magnética De Argissolos Sob Floresta E Cultivados No Sul Do Amazonas

“…Em seu estudo, Freitas et al (2017), avaliando a qualidade física e química e física do solo sob diferentes sistemas de manejo, também encontraram maiores valores de Al 3+ em área de vegetação nativa e menores valores em área cultivada, atribuindo isto à realização de correções químicas do solo nos ambientes cultivados, repondo os nutrientes exportados pela produção e/ou, perdidos pela erosão e lixiviação.…”

Multivariate Statistics of Fertility Parameter Fluxes in Cement-Dust-Polluted Soils in Mfamosing, Nigeria: Impact on Agriculture

“…Multivariate statistical tools, namely, correlation analysis (CA), principal component analysis (PCA), agglomerative hierarchical cluster analysis (AHCA) and multivariate analysis of co-variance (MANCOVA) were used. These approaches have earlier been reported as successful and therefore reliable in studies on fertility parameters of humic soil cultivated with coffee (Silva and Lima, 2012), trace metal contamination of sediments (Benson et al, 2016), soil fertility parameters around nuclear power plant (Shinde et al, 2016), determination of soil pedoenvironmental indicators (Oliveira et al, 2017) and soil fertility relationships for predicting environmental persistence of pollutants (Katseanes et al, 2017).…”