Spatial variability of Amazonian soil attributes under different managements
Sustainability, fertility, agricultural soils, environmental quality, ecosystems.
The growth of world population and purchasing power has led to the expansion of agricultural areas to produce food that meet demand, making it necessary to increase productivity and maintain sustainability in these areas. In this work was evaluated the effect of different Land Use and Land Cover (LULC) types, namely native forest, pasture, rice and soybean crops on the spatial variability of soil fertility and texture. Descriptive statistical analysis take base on pH, H+Al, Al, Ca, Mg, P, K, Cu, Fe, Mn, Zn, V, m, organic matter, clay, silt and sand values from soil samples about the different LULC. To verify the normality of the data, the Shapiro-Wilk test at 5% significance was performed, outlier analysis using boxplot graphics, principal component analysis and cluster analysis, using R software. In addition, the data were submitted to geostatistical analysis to verify the degree of spatial dependence of the variables through semivariograms, for interpolated kriging maps generated in the GS+ software. It was verified that the forest area has less fertility and greater acidity, whereas crop areas presented the opposite result. Except for silt, all variables were well represented in the factor map, in relation to PCA values the variability can be explained mainly by pH, V, Ca, K and Zn values inversely proportional to m, P and Sand. Through geostatistical analysis, was observed greater range of spatial dependence in the area with rice planting, in addition to performing the reliable prediction of most attributes in the pasture, rice and soybean areas, the native forest generated the greatest amount of pure nugget effect.