Variabilidade espacial de propriedades físicas do solo em uma parcela experimental

Grego, C. R.; Vieira, Sidney Rosa

doi:10.1590/s0100-06832005000200002

Cited by 135 publications

(127 citation statements)

References 16 publications

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“…the range of spatial dependence for FdM was 9.7 m, so that the extreme values for the Bd and Ma ranged from 9.0 m (Bd2) to 38.0 m (Ma2c). these results were similar to those reported by Carvalho et al (2002Carvalho et al ( , 2003 and Grego & Vieira (2005), varying from 3.9 to 23.7 m (tp) and 1.0 to 13.1 m (Sd).…”

Section: Methodssupporting

confidence: 93%

“…diSCUSSion the variability of a property can be classified according to the magnitude of the variation coefficient (Freddi et al, 2006). in this study, the variability of FdM, Ma2c and the Ma3c was average, of Ma1 it was high and for the other variables low (table 1), in agreement with Carvalho et al (2002Carvalho et al ( , 2003, Johann et al (2004), Souza et al (2004a, b) and Grego & Vieira (2005). Soil variability is the product of interaction between the factors and processes of its formation, soil management and tilling.…”

Section: Methodssupporting

confidence: 89%

“…For soil bulk density (table 1), the frequency distribution was normal for Bd1, Bd2 and Bd3, as also observed by Johann et al (2004), Souza et al (2004a), and Grego & Vieira (2005). the average values were 1.64 kg dm -3 (Bd1), 1.68 kg dm -3 (Bd2) and 1.67 kg dm -3 (Bd3), following a positive linear trend in relation to the increase in soil depth, corroborating Souza et al (2001) and Carvalho et al (2002), who reported an increased density in deeper layers, due to the reduced content of organic matter, and in disagreement with Grego &Vieira (2005) andMelo Filho et al (2006), who found a decreasing gradient.…”

Section: Methodssupporting

confidence: 75%

“…the average values were 1.64 kg dm -3 (Bd1), 1.68 kg dm -3 (Bd2) and 1.67 kg dm -3 (Bd3), following a positive linear trend in relation to the increase in soil depth, corroborating Souza et al (2001) and Carvalho et al (2002), who reported an increased density in deeper layers, due to the reduced content of organic matter, and in disagreement with Grego &Vieira (2005) andMelo Filho et al (2006), who found a decreasing gradient. the first pair (FdM x Ma1) indicated an inverse function of cause and effect, i.e., the lower the macroporosity, in the 0.00-0.10 m layer, the greater is FdM.…”

Section: Methodssupporting

confidence: 65%

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Spatial and linear correlations between soil and corn

Chioderoli¹,

Mello

Noronha³

et al. 2012

Rev. Bras. Ciênc. Solo

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SUMMarYthe technologies setting at agricultural production system have the main characteristics the vertical productivity, reduced costs, soil physical, chemical and biological improvement to promote production sustainable growth. thus, the study aimed to determine the variability and the linear and special correlations between the plant and soil attributes in order to select and indicate good representation of soil physical quality for forage productivity. in the growing season of 2006, on the Fazenda Bonança in pereira Barreto (Sp), the productivity of autumn corn forage (FdM) in an irrigated no-tillage system and the soil physical properties were analyzed. the purpose was to study the variability and the linear and spatial correlations between the plant and soil properties, to select an indicator of soil physical quality related to corn forage yield. a geostatistical grid was installed to collect soil and plant data, with 125 sampling points in an area of 2,500 m 2 . the results show that the studied properties did not vary randomly and that data variability was low to very high, with well-defined spatial patterns, ranging from 7.8 to 38.0 m. on the other hand, the linear correlation between the plant and the soil properties was low and highly significant. the pairs forage dry matter versus microporosity and stem diameter versus bulk density were best correlated in the 0-0.10 m layer, while the other pairs -forage dry matter versus macro -and total porosity -were inversely correlated in the same layer. However, from the spatial point of view, there was a high inverse correlation between forage dry matter with microporosity, so that microporosity in the 0-0.10 m layer can be considered a good indicator of soil physical quality, with a view to corn forage yield. index terms: soil physical properties, soil management, geostatistics, no-tillage, forage crops.

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Section: Methodssupporting

confidence: 93%

Section: Methodssupporting

confidence: 89%

Section: Methodssupporting

confidence: 75%

Section: Methodssupporting

confidence: 65%

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Spatial and linear correlations between soil and corn

Chioderoli¹,

Mello

Noronha³

et al. 2012

Rev. Bras. Ciênc. Solo

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“…The semivariograms adjusted by spherical, exponential e Gaussian functions presented spatial dependence for the majority of soil physical and chemical parameters, with prevalence of spherical adjustment model, in accordance with GREGO & VIEIRA (2005) who demonstrated that the spherical model was adjusted to all soil physical attributes, considered the major model in soil science studies (Figure 2). A spatial dependence for soil variables was not achieved only for penetration resistance (PR) measured in crop line, and for organic matter (MO), a phenomenon called "Pure Nugget Effect" (Table 2).…”

Section: Resultssupporting

confidence: 79%

Spatial influence of physical and chemical parameters on management zone definition in apple orchards

Gebler

Grego

Vieira³

et al. 2015

Eng. Agríc.

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Precision agriculture adoption in Brazilian apple orchards is still incipient. This study aimed at evaluating the spatial variability of certain soil properties as soil density, soil penetration resistance, electrical conductivity, yield, and fruit quality in an apple orchard through digital mapping, as well as assessing the correlation between these factors by means of geostatistics, establishing management zones. Forty representative points were set within 2.5 hectares of apple orchard, wherein soil samples were collected and analyzed, besides measurements of fruit quality (Brix degree, size or diameter, pulp firmness and color) to generate an overall index quality. We concluded that the fruit quality indexes, when isolated, did not show strong spatial dependence, unlike the index of fruit quality (FQI), derived from a combination of these parameters, allowing orchard planning according to management zones based on quality.KEYWORDS: apple trees, precision fruit-growing, electrical conductivity in orchards. INFLUÊNCIA ESPACIAL DE PARÂMETROS FÍSICO-QUÍMICOS DE SOLO NA DEFINIÇÃO DE ZONAS DE MANEJO EM POMAR DE MAÇÃS RESUMO:A aplicação da agricultura de precisão em pomares de maçã no Brasil ainda é incipiente. O objetivo do presente trabalho foi avaliar a variabilidade espacial, através de mapeamento digital, da densidade do solo, resistência do solo à penetração, condutividade elétrica, produtividade e qualidade dos frutos em um pomar de maçãs e verificar a correlação entre estes fatores através de uso de geoestatística para o estabelecimento de zonas de manejo em pomar de maçã. Foram estabelecidos 40 pontos representativos e m 2,5 hectares de pomar, de onde foram coletadas e analisadas amostras de solo e mensurados dados relativo à qualidade dos frutos ( o Brix, calibre ou diâmetro, firmeza e cor), utilizados para geração de um índice geral de qualidade de frutos, também mapeado. Concluiu-se que os índices de qualidade de frutas isolados não apresentaram forte dependência espacial, ao contrário do índice de qualidade dos frutos (IQF), derivado da junção desses parâmetros, permitindo o planejamento do pomar segundo zonas de manejo baseadas na qualidade.PALAVRAS-CHAVE: macieiras, fruticultura de precisão, condutividade elétrica em pomares.

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Distribution of rare earth elements in soils of contrasting geological and pedological settings to support human health assessment and environmental policies

Landim

Silva

Nascimento

et al. 2021

Environ Geochem Health

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Establishing quality reference values (QRVs) for rare earth elements (REEs) in soils is essential for the screening of these emergent contaminants. Currently, Brazil has the second-largest reserve of REEs, but data regarding background concentrations and distributions in soils remain scarce. The aim of this study was to establish the QRVs and assess the spatial distribution of REEs in soils, including REE fractionations and anomalies in (Piauí) state (251,529.186 km 2 ), northeastern Brazil. This study reports the most detailed data on REE geochemistry in Brazilian soils. A total of 243 composite soil samples was collected at 0-20 cm depth. The mean background concentrations in soils followed the abundance of the earth's upper crust:REEs (mg kg -1 ) showed the following order based on the individual mesoregions of Piauí state: Southeast (262.75) ). The highest QRVs were observed in the Southeast mesoregion. The establishment of QRVs based on the mesoregion scale improves data representativeness and the monitoring of natural REE values by identifying hot spots. Geostatistical modeling indicated significant local variability, especially in the Southeast mesoregion. The levels of these elements in this spatial zone are naturally higher than the other values across Piauí state and the mesoregion itself and indicate a high potential to exceed the QRVs. Our approach provides much needed data to help strengthen policies for both human health and environmental protection.

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Variabilidade espacial de propriedades físicas do solo em uma parcela experimental

Abstract: RESUMO

Cited by 135 publications

References 16 publications

Spatial and linear correlations between soil and corn

Spatial and linear correlations between soil and corn

Spatial influence of physical and chemical parameters on management zone definition in apple orchards

Distribution of rare earth elements in soils of contrasting geological and pedological settings to support human health assessment and environmental policies

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