Resumo

MATIAS, Sammy Sidney Rocha et al. Liming recommendation based on the spatial variability of soil chemical attributes in the Brazilian Cerrado. Rev. de Ciências Agrárias [online]. 2019, vol.42, n.4, pp.31-40. ISSN 0871-018X.  https://doi.org/10.19084/rca.17735.

Precision farming allows the adoption of appropriate management practices in areas with chemical restrictions that may limit crop yields. However, due to the high cost with laboratory analysis, the use of this technique in some situations becomes economically unfeasible. The objective of this work was to evaluate the efficiency of sample meshes in the characterization of the spatial variability of the chemical attributes of the soil and to simulate the recommendation of the need of a corrective to be applied in an area for soybean cultivation. The work was conducted in the municipality of Corrente-PI, and soil samples were collected for chemical analysis at three sample densities of 10x10, 20x10 and 30x10 m at two depths, 0.00-0.20 and 0.20-0.40 m. The pH, calcium terrors, and magnesium were calculated and the base saturation and cation exchange capacity was calculated. The soil correction recommendation was based on base saturation. The results were analyzed based on the descriptive statistics and the geostatistical technique. The descriptive analysis identified the normality of the data. Spatial variability was confirmed, allowing mapping and recommendation of correctives in specific management areas. The geostatistical technique allows the identification of the specific management zones of the assessed attributes, allowing greater efficiency in the application of correctives. The sampling mesh 30x10 m identified the spatial variability of soil attributes with the lowest number of points. The corrective recommendation using the base saturation method is lower in variable rate than in fixed rate. The correction of the soil based on the sample mesh 30x10 m is more economical than the others, even applying 78.5 kg ha^-1 more than the 10x10 m mesh, by using less samples.

Palavras-chave : Soil correction; precision agriculture; sample grid.

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