Spatial variability of the chemical characteristics of a trace-element-contaminated soil before and after remediation

Burgos, Pilar; Madejón, Engracia; Pérez‐de‐Mora, Alfredo; Cabrera, Francisco

doi:10.1016/j.geoderma.2005.01.016

Cited by 140 publications

(72 citation statements)

References 29 publications

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“…Table 2 represents characteristic parameters of best fitted semivariogram models of tea quality parameters in the regions of study area. Semivariograms showed a lower nugget effect on TF, TR faction and organoleptic properties of black tea, indicating that the sampling density was adequate to reveal the spatial structures (Burgos et al 2006). The results showed that the TF, TF1, TF3, TF4, TR fractions (TR1 and TR2), TDE and TSS semivariograms were well described with the Gaussian model, while the TF2 semivariogram was well described with TF TR TR1 TR2 TSS TF1 TF2 TF3 TF4 TDE TR Fig.…”

Section: Resultsmentioning

confidence: 89%

“…(Goovaerts 1997;Santra et al 2012). Geostatistics is based on the theory of a regionalized variable (Burgos et al 2006), which is distributed in space (with spatial coordinates) and shows spatial autocorrelation such that samples close together in space are more alike than those that are farther apart. The geostatistical approach consists of two parts: one is the calculation of an experimental variogram from the data and the model fitting and the second is the estimation or prediction at unsampled locations.…”

Section: Introductionmentioning

confidence: 99%

“…Among the several estimation methods, kriging is the most popular (Santra et al 2008). Kriging is not just used to estimate unsampled areas; it is also used to build probabilistic models of uncertainty about the unknown but estimated predicted values (Burgos et al 2006). The kriging estimates can be mapped to reveal the overall trend of data.…”

Section: Introductionmentioning

confidence: 99%

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Spatial variability of theaflavins and thearubigins fractions and their impact on black tea quality

et al. 2015

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The spatial distribution of theaflavin and thearubigin fractions and their impact on black tea quality were investigated using multivariate and geostatistics techniques. Black tea samples were collected from tea gardens of six geographical regions of Assam and West Bengal, India. Total theaflavin (TF) and its four fractions of upper Assam, south bank and North Bank teas were higher than the other regions. Simple theaflavin showed highest significant correlation with tasters' quality. Low molecular weight thearubigins of south bank and North Bank were significantly higher than other regions. Total thearubigin (TR) and its fractions revealed significant positive correlation with tasters' organoleptic valuations. Tea tasters' parameters were significantly and positively correlated with each other. The semivariogram for quality parameters were best represented by gaussian models. The nugget/sill ratio indicated a strong/ moderate spatial dependence of the studied parameters. Spatial variation of tea quality parameters may be used for quality assessment in the tea growing areas of India.

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Section: Resultsmentioning

confidence: 89%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Spatial variability of theaflavins and thearubigins fractions and their impact on black tea quality

et al. 2015

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“…The kriging estimates can be mapped, to reveal the overall trend of the data (Burgos et al, 2006). Maps provide useful visual displays of the spatial variability in the field and can be used to represent and summarize soil properties (Goodchild et al, 1993).…”

Section: Geostatistics and Data Transformationmentioning

confidence: 99%

Spatial variation of soil test phosphorus in a long‐term grazed experimental grassland field

Tunney

Zhang

2010

Z. Pflanzenernähr. Bodenk.

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The spatial variation of soil test P (STP) in grassland soils is becoming important because of the use of STP as a basis for policies such as the recently EU-introduced Nitrate Directive. This research investigates the spatial variation of soil P in grazed grassland plots with a long-term (38 y) experiment. A total of 326 soil samples (including 14 samples from an adjacent grasswood buffer zone) were collected based on a 10 × 10 m 2 grid system. The samples were measured for STP and other nutrients. The results were analyzed using conventional statistics, geostatistics, and a geographic information system (GIS). Soil test P concentrations followed a lognormal distribution, with a median of 5.30 mg L -1 and a geometric mean of 5.35 mg L -1 . Statistically significant (p < 0.01) positive correlation between STP and pH was found. Spatial clusters and spatial outliers were detected using the local Moran's I index (a local indicator of spatial association) and were mapped using GIS. An obvious low-value spatial-cluster area was observed on the plots that received zero-P fertilizer application from 1968 to 1998 and a large high-value spatial-cluster area was found on the relatively high-P fertilizer application plots (15 kg ha -1 y -1 ). The local Moran's I index was also effective in detecting spatial outliers, especially at locations close to spatial-cluster areas. To obtain a reliable and stable spatial structure, semivariogram of soil-P data was produced after elimination of spatial outliers. A spherical model with a nugget effect was chosen to fit the experimental semivariogram. The spatial-distribution map of soil P was produced using the kriging interpolation method. The interpolated distribution map was dominated by medium STP values, ranging from 3 mg to 8 mg L -1 . An evidently low-P-value area was present in the upper side of the study area, as zero or short-term P fertilizer was applied on the plots. Meanwhile, high-P-value area was located mainly on the plots receiving 15 kg P ha -1 y -1 (for 38 y) as these plots accumulated excess P after a long-term P-fertilizer spreading. The high-or low-value patterns were in line with the spatial clusters. Geostatistics, combined with GIS and the local spatial autocorrelation index, provides a useful tool for analyzing the spatial variation in soil nutrients.

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“…However, crop establishment also modifies soil variability (Burgos et al, 2006), especially by farming work and implementation of agricultural inputs, especially on the surface (Camacho-Tamayo et al, 2008). Mello et al (2006) found heterogeneous attributes in different soil types and indicated that spatial dependence is conditioned by relief, which causes to determine different management areas from each evaluated attribute of the soil, evidencing the need of carrying out researches in different soil classes.…”

Section: Introductionmentioning

confidence: 99%

Spatial variability of an Alfisol cultivated with sugarcane

et al. 2011

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Soil variability occurs due to natural and anthropogenic factors acting at various spatial and temporal scales. The purpose of this study was to evaluate the spatial variability as well as the relation of some chemical attributes using different statistical techniques. The study was conducted in the municipality of Maracanaú (CE, Brazil) in the commercial area of sugar cane in a typic Hapludalf. Soils were sampled in a 100 x 100 m grid every 10 m and at a depth between 0 and 20 cm for determination of exchangeable acidity, pH (KCl), Ca, Na, K, Mg, P, N, sum of bases, cationic exchange capacity, base saturation and organic matter. Data were subjected to analysis of descriptive statistics, multivariate analysis and geostatistics. Attributes showed low and medium variability, except for pH that did not show spatial dependence. Different statistical tools helped to identify the soil spatial variability and to get a better understanding of relationships among different attributes. Techniques also allowed to better plan and control agricultural production, especially in carrying out site specific management of agricultural inputs.

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Spatial variability of the chemical characteristics of a trace-element-contaminated soil before and after remediation

Cited by 140 publications

References 29 publications

Spatial variability of theaflavins and thearubigins fractions and their impact on black tea quality

Spatial variability of theaflavins and thearubigins fractions and their impact on black tea quality

Spatial variation of soil test phosphorus in a long‐term grazed experimental grassland field

Spatial variability of an Alfisol cultivated with sugarcane

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