Geostatistics in soil science: state-of-the-art and perspectives

Goovaerts, Pierre

doi:10.1016/s0016-7061(98)00078-0

Cited by 1,007 publications

(605 citation statements)

References 77 publications

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“…It is then fitted with theoretical models such as spherical, exponential and linear models. These models provide information about the structure of the spatial variation and the input parameters for spatial prediction by kriging interpolation (Goovaerts, 1999). Selection of the best-fit variogram model was made on the basis of cross-validation.…”

Section: Geostatistical Analysismentioning

confidence: 99%

Multivariate and geostatistical analyses of the spatial distribution and origin of heavy metals in the agricultural soils in Shunyi, Beijing, China

Wang

Qin

et al. 2012

Science of The Total Environment

406

121

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An extensive survey was conducted in this study to determine the spatial distribution and possible sources of heavy metals in the agricultural soils in Shunyi, a representative agricultural suburb in Beijing, China. A total of 412 surface soil samples were collected at a density of one sample per km 2 , and concentrations of As, Cd, Cu, Hg, Pb and Zn were analyzed. The mean values of the heavy metals were 7.85± 2.13, 0.136 ±0.061, 22.4 ±6.31, 0.073 ±0.049, 20.4 ±5.2, and 69.8 ±16.5 mg kg − 1 for As, Cd, Cu, Hg, Pb, and Zn, respectively, slightly higher than their background values of Beijing topsoil with the exception of Pb, but lower than the guideline values of Chinese Environmental Quality Standard for Soils. Multivariate and geostatistical analyses suggested that soil contamination of Cd, Cu and Zn was mainly derived from agricultural practices. Whereas, As and Pb were due mainly to soil parent materials, and Hg was caused by the atmospheric deposits from Beijing City. The identification of heavy metal sources in agricultural soils is a basis for undertaking appropriate action to protect soil quality.

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Section: Geostatistical Analysismentioning

confidence: 99%

Multivariate and geostatistical analyses of the spatial distribution and origin of heavy metals in the agricultural soils in Shunyi, Beijing, China

Wang

Qin

et al. 2012

Science of The Total Environment

406

121

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“…The indicator kriging (gooVaerts, 1999;carValho and Vieira, 2001) was used considering the mean value of each of the micronutrients studied like threshold value for processing binary (0 to 1) the content of Cu, Zn, Fe and Mn in soil, thus promoting the combination between pairs of micronutrients (Cu+Zn, Cu+Fe, Cu+Mn, Zn+Fe, Zn+Mn, Fe+Mn and Zn+Cu+Fe+Mn). Indicator kriging estimates the probability of exceeding specific threshold values at a given location.…”

Section: Methodsmentioning

confidence: 99%

“…matheron (1963) developed the theory of regionalized variables using krige (1951) which concluded that the data variances depend on the separation distance between samples. Currently the application of geostatistics in agriculture has been used for the study of spatial variability of the most distinct attributes of soil and plant (mcbratney, 1984;couto and klamt, 1999;gooVaerts, 1999;White and Zasoski, 1999;Vieira, 2000;carValho and Vieira, 2001;carValho et al, 2002;ulloa guitián et al, 2002;liu et al, 2004;grego and Vieira, 2005;motomiya et al, 2006;Zanão Júnior et al, 2007;siqueira et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

Mapping of soil micronutrients in an european atlantic agricultural landscape using ordinary kriging and indicator approach

et al. 2010

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Nutrient maps based on intensive soil sampling are useful to develop site-specific management practices. Geostatistical methods have been widely used to determine the spatial correlation and the range of spatial dependence at different sampling scales. If spatial dependence is detected, the modelled semivariograms can then be used to map the interested variable by kriging, an interpolation method that produces unbiased estimates with minimal estimation variance. The objectives of this paper were to examine and to map the spatial distribution of the micronutrients Cu, Zn, Fe and Mn on an agricultural area in Galicia, Spain, under European Atlantic climatic conditions. The ordinary kriging was first used to determine the values for the non-sampled locations, then the indicator approach was used to transform the micronutrient content values into binary values having the mean values of each nutrient as the threshold content. All four elements analyzed showed spatial dependence using the indicator semivariograms. The strength of spatial dependence was assessed using the values of nugget effect and range from the semivariogram, the fitted range values decreased in the order Mn >Fe >Zn >Cu. The spatial dependence of the combination of two or more of the studied micronutrients was also examined using indicator semivariograms. In opposition to spatial analysis of individual microelements, indicator semivariograms obtained for the binary coding of the variables showed a great nugget effect value or a low proportion of sill. The maps for each nutrient obtained using indicator kriging showed some similarity in the spatial distribution, suggesting the delimitation of uniform management areas.Key words: soil management; geostatistics; spatial variability; soil chemistry. RESUMO MAPEAMENTO DE MICRONUTRIENTES DO SOLO DE UMA PAISAGEM AGRÍCOLA EUROPÉIAATLÂNTICA UTILIZANDO KRIGAGEM ORDINÁRIA E INDICATRIZ Mapas de nutrientes do solo com base em uma amostragem intensiva são úteis para desenvolver práticas específicas de manejo. Os métodos geoestatísticos cada vez mais têm sido utilizados para determinar a correlação espacial e o alcance da dependência espacial de amostragens em diferentes escalas. Uma vez detectada a variabilidade espacial, o semivariograma experimental é modelado e usado para mapear as variáveis investigadas por meio da krigagem, um método de interpolação sem tendência e com variância mínima. O objetivo deste trabalho foi avaliar e mapear a distribuição espacial dos micronutrientes Cu, Zn, Mn e Fe em uma área agrícola, na Galícia, Espanha, sob condições climáticas do Atlântico Europeu. A krigagem ordinária foi aplicada inicialmente para se determinar os valores para os locais não amostrados. Posteriormente, a krigagem indicatriz foi utilizada para transformar o conteúdo de micronutrientes em valores binários, tendo os valores médios de cada nutriente como referência. Nos quatro micronutrientes estudados havia dependência espacial, expressa por meio dos parâmetros do semivariograma. A razão de dependência es...

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“…By using geo-statistics, it is possible to assess uncertainty of non-sampled data, and provides map that describes probability of exceeding critical values, such as criteria of soil pollution or soil quality [Goovaerts, 1998]. Several soil variability assessments in order to improve agricultural productivity and sustainable farming design have been done.…”

Section: Introductionmentioning

confidence: 99%

Spatial Distribution of Trace Elements in Rice Field at Prafi District Manokwari

Bless

Baan

Darmawan

2016

IJG

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Mapping spatial variability of trace elements in rice field is necessary to obtain soil quality information to enhance rice production. This study was aimed to measure concentration and distribution of Zn, Cu, Fe, Pb, and Cd in two different sites (SP1, SP2) of Prafi rice field in Manokwari West Papua. The representative 26 soil samples were analysed for their available trace metal concentration (DTPA), soil pH, and C-organic and soil texture. The result indicated that Fe toxicity and Zn deficient problems were encountered in both sites. Rice field in SP2 was more deficient in Zn than SP1. Site with the highest trace elements (Zn, Fe, Cu, and Cd) concentration had low soil pH and high C-organic. Acidic soil has higher solubility of metals; while high C-organic could improve the formation of dissolve organic carbon-metal binding, hence it improving the trace metals concentration in soil solution. Abstrak

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Geostatistics in soil science: state-of-the-art and perspectives

Cited by 1,007 publications

References 77 publications

Multivariate and geostatistical analyses of the spatial distribution and origin of heavy metals in the agricultural soils in Shunyi, Beijing, China

Multivariate and geostatistical analyses of the spatial distribution and origin of heavy metals in the agricultural soils in Shunyi, Beijing, China

Mapping of soil micronutrients in an european atlantic agricultural landscape using ordinary kriging and indicator approach

Spatial Distribution of Trace Elements in Rice Field at Prafi District Manokwari

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