Abstract:RESUMO -O objetivo desse trabalho foi avaliar a variabilidade espacial de atributos físicos do solo para áreas de Latossolo, Argissolo e Cambissolo ocupadas por pastagens. O conhecimento dessa variabilidade é importante, pois ela influencia a qualidade das pastagens e a recarga de água subterrânea. Foram coletadas 154 amostras georreferenciadas para cada área de estudo. Para analisar os dados foi usada a estatística descritiva e a geoestatística. A dependência espacial ocorreu para a maioria dos atributos físi… Show more
“…The TP and K 0 decreased as PR and BD increased (Figures 2, 3, 4, and 5), the results similar to findings of Guimarães et al (2016). According to Mion et al (2012), TP shows a strong correlation with PR, which tends to increase as TP is reduced.…”
Section: Resultssupporting
confidence: 80%
“…The PR for 0.0 -0.1 m showed high variability, as also reported by Mion et al (2012), who analyzed the spatial variability of the physical attributes in a yellow argisol under alternate sheep grazing; those authors attributed their results to high variability of the average, showing a distribution with high heterogeneity of data. After grazing, BD and TP had low variability for all depths sampled, which was also found by Guimarães, Junior, Marques, Santos, and Fernandes (2016), who evaluated the spatial variability of soil physical attributes in latosol, argisol, and cambisol pastures and reported that their results were due to bovines having preferred spots in a pasture, which can promote greater soil heterogeneity. The PR, GM, and K 0 showed moderate variability (Table 3).…”
Section: Resultssupporting
confidence: 66%
“…Thus, the variability of this variable can be considered aleatory, and lower spacing will be necessary for sample collection to detect spatial dependence, as suggested by Cambardella et al (1994). Likewise, Guimarães et al (2016) reported a pure nugget effect for TP at 0.10 and 0.15 m after applying 10 x 10 m spacing to estimate the spatial dependence of the physical attributes of soil under pasture. The spatial dependence degree (SDD) was classified as strong for other variables analyzed.…”
Section: Resultsmentioning
confidence: 96%
“…According to Reichert, Reinert, and Braida (2003), a density values of 1.65 kg dm -3 in sandy soils indicates a high probability of root growth restriction. In conventional grazing over 10 years, Guimarães et al (2016) reported 1.27 kg dm -3 BD. The BD increase, which occurred mainly at the shallowest depths, can be related to high-intensity bovine trampling and, consequently, pasture degradation.…”
Soils under pastures suffer physical modifications, in greater or lesser intensity, via the action of animal trampling. Thus, the aim was to evaluate the spatial dependence of soil physical attributes under bovine trampling. The trial was performed at Roçadinho Farm, Agreste of Pernambuco, Brazil, in a 40 x 40 m paddock that was managed with continuous stocking by bovines and 12 AU ha-1 stocking rate. Soil samples were collected before and after grazing using a 6 x 6 m grid, totaling 36 sampling points. At each point, the bulk density, total porosity, moisture, soil penetration resistance at 0.00-0.10, 0.10-0.20, and 0.20-0.30 m depth were estimated, as was the hydraulic conductivity on the saturated soil surface. Descriptive statistics and geostatistics supported the data analysis. A normal distribution was verified for all variables, which were scored as either low or high variability in terms of the variation coefficient. The physical attributes (density, total porosity, moisture, soil penetration resistance and hydraulic conductivity) of the soil sampled presented a strong spatial dependence before and after grazing.
“…The TP and K 0 decreased as PR and BD increased (Figures 2, 3, 4, and 5), the results similar to findings of Guimarães et al (2016). According to Mion et al (2012), TP shows a strong correlation with PR, which tends to increase as TP is reduced.…”
Section: Resultssupporting
confidence: 80%
“…The PR for 0.0 -0.1 m showed high variability, as also reported by Mion et al (2012), who analyzed the spatial variability of the physical attributes in a yellow argisol under alternate sheep grazing; those authors attributed their results to high variability of the average, showing a distribution with high heterogeneity of data. After grazing, BD and TP had low variability for all depths sampled, which was also found by Guimarães, Junior, Marques, Santos, and Fernandes (2016), who evaluated the spatial variability of soil physical attributes in latosol, argisol, and cambisol pastures and reported that their results were due to bovines having preferred spots in a pasture, which can promote greater soil heterogeneity. The PR, GM, and K 0 showed moderate variability (Table 3).…”
Section: Resultssupporting
confidence: 66%
“…Thus, the variability of this variable can be considered aleatory, and lower spacing will be necessary for sample collection to detect spatial dependence, as suggested by Cambardella et al (1994). Likewise, Guimarães et al (2016) reported a pure nugget effect for TP at 0.10 and 0.15 m after applying 10 x 10 m spacing to estimate the spatial dependence of the physical attributes of soil under pasture. The spatial dependence degree (SDD) was classified as strong for other variables analyzed.…”
Section: Resultsmentioning
confidence: 96%
“…According to Reichert, Reinert, and Braida (2003), a density values of 1.65 kg dm -3 in sandy soils indicates a high probability of root growth restriction. In conventional grazing over 10 years, Guimarães et al (2016) reported 1.27 kg dm -3 BD. The BD increase, which occurred mainly at the shallowest depths, can be related to high-intensity bovine trampling and, consequently, pasture degradation.…”
Soils under pastures suffer physical modifications, in greater or lesser intensity, via the action of animal trampling. Thus, the aim was to evaluate the spatial dependence of soil physical attributes under bovine trampling. The trial was performed at Roçadinho Farm, Agreste of Pernambuco, Brazil, in a 40 x 40 m paddock that was managed with continuous stocking by bovines and 12 AU ha-1 stocking rate. Soil samples were collected before and after grazing using a 6 x 6 m grid, totaling 36 sampling points. At each point, the bulk density, total porosity, moisture, soil penetration resistance at 0.00-0.10, 0.10-0.20, and 0.20-0.30 m depth were estimated, as was the hydraulic conductivity on the saturated soil surface. Descriptive statistics and geostatistics supported the data analysis. A normal distribution was verified for all variables, which were scored as either low or high variability in terms of the variation coefficient. The physical attributes (density, total porosity, moisture, soil penetration resistance and hydraulic conductivity) of the soil sampled presented a strong spatial dependence before and after grazing.
“…Similar fits were found by Resende et al (2014) for clay, silt, and total sand in a Typic Hapludults, by Sana et al (2014) for clay in a Typic Hapludox, and by Guimarães et al (2016) for Ma, TPV, and BD in an Oxisol.…”
Establishing the number of samples required to determine values of soil physical properties ultimately results in optimization of labor and allows better representation of such attributes. The objective of this study was to analyze the spatial variability of soil physical properties in a Conilon coffee field and propose a soil sampling method better attuned to conditions of the management system. The experiment was performed in a Conilon coffee field in Espírito Santo state, Brazil, under a 3.0 × 2.0 × 1.0 m (4,000 plants ha -1 ) double spacing design. An irregular grid, with dimensions of 107 × 95.7 m and 65 sampling points, was set up. Soil samples were collected from the 0.00-0.20 m depth from each sampling point. Data were analyzed under descriptive statistical and geostatistical methods. Using statistical parameters, the adequate number of samples for analyzing the attributes under study was established, which ranged from 1 to 11 sampling points. With the exception of particle density, all soil physical properties showed a spatial dependence structure best fitted to the spherical model. Establishment of the number of samples and spatial variability for the physical properties of soils may be useful in developing sampling strategies that minimize costs for farmers within a tolerable and predictable level of error.
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