Amostral Optimization of Mechanical Resistance to the Penetration of a Yellow Oxisol Under Pasture

Abstract

The degradation of pastures can be characterized by several factors, mainly due to the management adopted, so in view of the country's territorial extension and the peculiarity of each region and soil type, it is essential to develop research to improve the monitoring of the system. The objective of this study was to evaluate the effect of different sample densities to establish a mesh that gives precision in maps of spatial variability of soil mechanical resistance to root penetration to pasture areas in the coastal tableland region of Northeast Brazil. In a pasture area, three sampling meshes were demarcated for georeferenced evaluation of soil mechanical resistance to root penetration: mesh 1 established in the dimensions of 50 × 50 m, mesh 2 of 100 × 100 m and mesh 3 of 150 × 150 m, totaling an area of 9 ha. The soil resistance to penetration was measured using an automated apparatus, coupled to a tractor. The variation found in the values of penetration resistance in subsurface can be related to the management adopted in the area, as well as the trampling of the animals. Data on soil penetration resistance in pasture showed that the most compacted zone was always below 30 cm depth by using different sample densities. The results allow us to conclude that the higher the density of the sampling mesh, the greater the accuracy of the data and that, independently of the sample mesh, it was possible to identify the layer of higher soil mechanical resistance to root penetration.

Reduction of Sample Size in the Analysis of Spatial Variability of Nonstationary Soil Chemical Attributes

“…These results were similar to those obtained in simulations and lower than the sample size optimized by simulated annealing proposed by Guedes et al (2014) or the fixed sample size (50% of the initial grid) in the optimization of a sample configuration proposed by Guedes et al (2011) using a hybrid genetic algorithm and considering the efficiency of spatial prediction. In addition, these results corroborate the findings of Dias et al (2018), who evaluated the effect of sample densities and observed that a reduction in an interval of 60 to 80% of the sample grid allowed the identification of spatial variability.…”