Spatial variability of chemical pedoindicators in Haplic Inceptisol under different management systems in southern Amazonas state, Brazil - de Oliveira, Ivanildo Amorim; Campos, Milton César Costa; de Aquino, Renato Eleotério [UNESP]; de Freitas, Ludmila; de Souza, Zigomar Menezes; da Cunha, José Maurício; Soares, Marcelo Dayron Rodrigues; da Silva, Douglas Marcelo Pinheiro; Fonseca, Julimar da Silva; Silva, Laércio Santos [UNESP]; de Souza, Fernando Gomes (Southern Cross Publishing, 2018)
Inceptisols are poorly developed soils with original material characteristics and high or low base saturation depending on the environment in which they developed. This study aimed to evaluate the spatial variability of chemical pedoindicators in an Inceptisol under different management systems. The study was carried out in three agricultural areas cultivated with cassava, sugarcane and an agroforestry system, with cultivation time of 15, 8 and 17 years, respectively. All the areas investigated were located in Southern Amazonas state, Brazil. In these areas, a square mesh was established measuring 70.00 × 70.00 m, with cross points spaced 10.00 m apart and a total of 64 sampling points in each grid. Soil samples were then collected at a depth between 0.00 and 0.10 m to determine organic matter, pH, calcium, magnesium, potassium, available phosphorus, potential acidity, sum of bases, cation exchange capacity and base saturation, in addition to magnetic susceptibility in all the samples, using 10 cm3 of soil. Data were analyzed using multivariate, descriptive and geostatistics. It was found that magnetic susceptibility may be a pedoindicador for areas under cassava cultivation, potential acidity for the agroforestry environment, and pH for areas under sugarcane cultivation. The agroforestry area had higher data heterogeneity, since a nugget effect was encountered for all the properties analyzed. Based on the scaled semivariograms, the lower range values indicate greater spatial variability in the sugarcane and agroforestry systems, due to the specificities of the management system.
Keywords: Geostatistics; Principal component analysis; Scaled semivariogram