Spatial variability structure of the surface layer attributes of gleysols from the coastal plain of Rio Grande do Sul

Abstract

The spatial variability structure of soil attributes in a certain area might influence the semivariogram fitting model and, consequently, the attribute behavior mapping in this area leading to different decisions regarding crop management. This study aimed to identify, characterize and quantify the spatial variability of chemical attributes and the clay content in the superficial layer of a Gleysoils mapping unit (MU) at reconnaissance scale in the coastal plain of Rio Grande do Sul, through descriptive statistics and geostatistics and compare the results taking into consideration the existence of three Gleysoils mapping units at semi-detailed scale through the scaled semivariogram technique. A 403 ha area located in the Rio Grande do Sul Coastal Plain, in the city of Jaguarão was sub-divided into three mapping units (GL-mo, GL-mo.lv and GL-lv), a sampling grid with 403 points, 100 m far one from another was established. In a 5 m radius around each sampling point, 10 sub-samples of disturbed soil were collected from the 0-0.20 m layer, making up a soil compound sample, and the following attributes were determined for each sample: pH in water, organic carbon, phosphorus, potassium, sodium, calcium, magnesium, aluminum, potential acidity and clay content. The cation Exchange capacity (pH=7.0) and base saturation were also calculated. The identification, characterization and quantification of the spatial variability of attributes from the soil Ap horizons were carried out through descriptive statistics and geostatistics, considering the mapping unit at the reconnaissance scale and the three units at the semi-detailed scale. In the geostatistics analysis, the scaled semivariogram technique was employed aiming to compare the spatial variability structure for each soil attribute in the total area and in the three MUs at the semi-detailed scale. Regarding the descriptive statistics, the Ap horizon attributes behavior in GL-lv was similar to that in the total area of the soil layer under analysis; however, when considering the spatial coordinates, the spatial variability structure of the GL-mo.lv attributes was the one that best described the attributes variability in the total area. The scaled semivariogram technique revealed that the spatial behavior of the attributes pH and exchangeable sodium was similar, regardless of the evaluation scale adopted or the factor used for the scaled semivariogram.

Keywords

• geostatistics
• scaled semivariograms
• wetlands
• soil map units
• precision farming