Engenharia Agrícola - Artigos
URI permanente para esta coleçãohttps://locus.ufv.br/handle/123456789/11739
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Item Cokrigagem na estimativa dos teores de Ca e Mg em um Latossolo Vermelho distroférrico(Revista Ciência Agronômica, 2011-10) Bottega, Eduardo Leonel; Silva, Samuel de Assis; Costa, Marcelo Marques; Bottega, Simone PriscilaBuscou-se, com este estudo, estimar, através do uso da cokrigagem, valores de cálcio e magnésio, utilizando como co-variável valores de pH. Em um Latossolo Vermelho Distroférrico, amostrou-se a camada de 0-0,2 m, utilizando grade de 25 x 25 m, totalizando 60 pontos em 5,02 ha. Realizou-se, inicialmente, análise descritiva e de correlação e, posteriormente, análise geoestatística, para identificar a dependência espacial das variáveis estudadas. Utilizando a técnica da cokrigagem, os valores de pH mostraram-se eficientes na estimativa de teores de Ca e Mg, devido a elevada correlação entre esta co-variável e as variáveis a serem estimadas.Item The use of different sampling grids in determining the variability of soil physical attributes of Oxisol(Comunicata Scientiae, 2014-04) Bottega, Eduardo Leonel; Queiroz, Daniel Marçal de; Pinto, Francisco de Assis de Carvalho; Santos, Nerilson TerraThis study aimed to analyze the influence of different sampling grids in determining the spatial variability of physical attributes of Oxisol. It was used to study an area of approximately 90 hectares, where soil was sampled at depth from 0 to 0.20 meters, using a grid of 2 points per hectare (G1). Each soil sample was composed of four subsamples and obtained using a soil sampler Dutch type. The samples were sent to the laboratory to granulometric analysis. From the initial grid, the area was divided into sampling cells of 2.9 (G2) and 4.7 hectares (G3), and assigned a coordinate value representative of the center of each cell. Classical statistical and geostatistical methods were used to characterize the data and to model the spatial dependence. Spatial dependence was detected for all physical variables of the soil, regardless of the sampling grid used. The utilization of sampling grid of 1 point for each 2.9 hectares, and the sampling cell characterized by 12 subsamples, showed itself capable of detecting the spatial variability of the physical attributes of the soil, guaranteeing reliability in the estimates, even reducing the quantity of points when compared to the densest grid.