Spatial variability of soil chemical atributes from different sampling grid in two agricultural seasons

• Autor(a) principal: Leandro Junior, Eraldo Fernandes Leandro Junior
• Data de Publicação: 2020
• Outros Autores: Cunha, Ricardo Manoel Cordeiro, Nascimento, Jackeline Matos, Arcoverde, Sálvio Napoleão Soares, Secretti, Mateus Luiz
• Tipo de documento: Artigo
• Idioma: por
• Título da fonte: Caderno de Ciências Agrárias (Online)
• Texto Completo: https://periodicos.ufmg.br/index.php/ccaufmg/article/view/25115
• topic: Dependência espacial; Geoestatística; Krigagem; Latossolos; Semivariogramas; Spatial dependence; Geostatistics; Kriging; Oxisol; Semivariograms

This research had as objective the study of spatial variability of chemical properties of the soil soybean culture (Glycine max (L.) Merrill) in a typic haplorthox. The soil samples were collected with the aid of smartphone app C7 GPS Dados e C7 GPS Malha. In the first year, a sampling grid of 1: 3 was used in the sampling grid and in the subsequent year this sampling grid was 1: 5. In the first agricultural year, mechanical soil management was necessary with its correction. In both agricultural years, collections and analyzes were made before the implantation of the soybean crop, and in possession of these data, exploratory analysis was carried out, which aimed to perform the calculation of descriptive statistics. For the classification of the variability of the analyzed attributes, the coefficient of variation (CV) was used, and geostatistics was applied, with which mathematical models were adjusted with the criteria of the high coefficient of determination (R²) and the low sum of squares of residues for a better adjustment of the semivariogram. The reduction of the sample density from 1: 3 with a maximum range of 173 m to 1: 5 with a maximum range of 223 m, proved to be viable, attested by geostatistics, maintaining its high precision, strong spatial dependence and reducing the cost.