Spatial dependence of sugarcane yield according to the altitude and soil physical attributes in a transect

• Autor(a) principal: Souza, Diego Henrique Silva de
• Data de Publicação: 2023
• Outros Autores: Silva, Ênio Farias de França e, Paz-González, Antonio, Siqueira, Glécio Machado, Dantas, Daniel da Costa
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Revista Caatinga
• Texto Completo: https://periodicos.ufersa.edu.br/caatinga/article/view/11617
• topic: Distribuição espacial. Física do solo. Geoestatística. Variabilidade espacial.; Spatial distribution. Soil physics. Geostatistics. Spatial variability.

Soil management carried out in areas with sugarcane production causes changes in the soil physical attributes, and geostatistics makes it possible to study the behavior of the spatial distribution of these variations. This study aimed to evaluate the behavior of the spatial distribution of sugarcane yield averages according to the altitude and soil physical attributes along a transect in Goiana, Pernambuco, Brazil. Sugarcane yield averages, altitudes, and soil physical attributes, such as soil bulk density, total porosity, micro and macro porosity, sand (total, coarse, and fine), silt, and clay, in the 0.00-0.20 and 0.20-0.40 m soil layers were determined at 20 m intervals along the transect characterized by a toposequence of Ultisols and Spodosols. Exploratory data analysis was performed with the aid of descriptive statistics. The analysis of spatial dependence was performed using geostatistics. Pairs of semivariances were obtained using the GS+ software. The development and adjustment of the semivariograms were performed using the GEOEAS geostatistical tool. The highest contents of the finest soil particles were found in the lowest sections of the transect, in the 0.20-0.40 m soil layers. The highest amount of macropores was observed in the subsurface soil layer. The areas with the lowest amount of micropores showed the lowest average sugarcane yield, around 32.00 t.ha-1. The geostatistical model that best fitted the data set was the spherical model.