Rocky Mountain forest soils: Evaluating spatial variability using conventional statistics and geostatistics

Rahman, S.; Munn, L. C.; Zhang, R.; Vance, George F.

doi:10.4141/cjss96-062

Cited by 27 publications

(19 citation statements)

References 17 publications

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“…Traditional statistics assume that measured observations are independent of their distribution in space. That is, that soil variation is randomly distributed and the sampling unit mean is the expected value at any location within the unit, with error expressed as the withinunit variance (Hamlett et al, 1986;Cambardella et al, 1994;Rahman et al, 1996). The spatial dependence of soil properties means that, from a mathematical standpoint, the value of a property is a function of its position.…”

Section: Understanding the Backgroundmentioning

confidence: 99%

“…The variation of soil properties, or the natural background "noise," can be estimated using Geostatistics. This provides a set of descriptive tools for incorporating the spatial coordinates of soil properties in data processing (Goovaerts, 1998(Goovaerts, , 1999 and provides insight into the nature of variability in soil properties which conventional statistics do not (Rahman et al, 1996). Geostatistics allows the description and modeling of spatial patterns, the prediction of properties at unsampled locations, and the assessment of the uncertainty attached to these predictions (Burgess et al, 1981;Isaaks and Srivastava, 1989;Desbarats, 1996;Goovaerts 1998).…”

Section: Understanding the Backgroundmentioning

confidence: 99%

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Sampling and analyzing metals in soils for archaeological prospection: A critique

Haslam

Tibbett

2004

Geoarchaeology

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This paper presents a critique of current methods of sampling and analyzing soils for metals in archaeological prospection. Commonly used methodologies in soil science are shown to be suitable for archaeological investigations, with a concomitant improvement in their resolution. Understanding the soil-fraction location, concentration range, and spatial distribution of autochthonous (native) soil metals is shown to be a vital precursor to archaeological-site investigations, as this is the background upon which anthropogenic deposition takes place. Nested sampling is suggested as the most cost-effective method of investigating the spatial variability in the autochthonous metal concentrations. The use of the appropriate soil horizon (or sampling depth) and point sampling are critical in the preparation of a sampling regime. Simultaneous extraction is proposed as the most efficient method of identifying the location and eventual fate of autochthonous and anthropogenic metals, respectively.

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Section: Understanding the Backgroundmentioning

confidence: 99%

Section: Understanding the Backgroundmentioning

confidence: 99%

Sampling and analyzing metals in soils for archaeological prospection: A critique

Haslam

Tibbett

2004

Geoarchaeology

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“…This nested model implies that the short-range variations of leaching are due to the presence of local pedotopographic characteristics such as soil properties and land-cover, according to Cambardella et al (1994), Rahman et al (1996), andFitzjohn et al (2002). This finding suggest that the increase of variability in pedotopographic properties produces a mosaic pattern of source and sink area.…”

Section: Resultsmentioning

confidence: 74%

Environinformatics in ecological risk assessment of agroecosystems pollutant leaching

Diodato¹,

Ceccarelli

2005

Stoch Environ Res Ris Assess

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In this paper, a novel approach for mapping leaching risk at large sub-regional scale under limited information is presented, with acronym environinformatics in ecological risk assessment. The problem consists into quantifying the exchange frequency of the plant available soil water (Narula et al. in J Geogr Inform Decision Anal 7(1) [32][33][34][35][36][37][38][39][40][41][42][43][44][45][46] 2002), this frequency can be adopted as a measure indicating the nutrient and contaminant leaching risk for a site. Our approach is based on integrating soil water balance with spatial analysis tools. However, any decision involved in scientific risk evaluation requires the accurate quantification of the degree of uncertainty arising from sampling, modelling and interpolation errors. The non-parametric geostatistical procedure of Indicator Kriging enables to circumvent this problem by estimating the probability that the true value exceed a set of threshold values. The transformation of leaching data to a binary response variable, known as ''indicator'', can lead to a soft description of leaching. Such soft description can mitigate the uncertainty in exchange frequency estimates of the plant available soil water. The approach was applied to a test site in Beneventan agroecosystem (South Italy) by using a long-term hydrological water balance acquired in a 40-years period. In this way, about 400 km 2 (25%) of the total 2,000 km 2 of the Benevento province were classified as areas sensitive to nutrient and contaminant leaching.

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“…In the A horizon, the delta pH was fitted to an exponential model, presenting a R 2 equal to 0.73 and a strong SDI. Rahman et al (1996) found low spatial dependence for pH in forest soils, adjusting semivariograms to a linear model with undetermined range. In soils covered by Pinus nigra stands, Basaran et al (2006) found a pure nugget effect for pH and low SDI for SOM.…”

Section: Resultsmentioning

confidence: 99%

Spatial variability of physical and chemical attributes of some forest soils in southeastern of Brazil

Álvares

Gonçalves²,

Vieira

et al. 2011

Sci. agric. (Piracicaba, Braz.)

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Capão Bonito forest soils, São Paulo state, Brazil, have been used for forestry purposes for almost one century. Detailed knowledge about the distribution of soil attributes over the landscape is of fundamental importance for proper management of natural resources. The purpose of this study was to identify the variability and spatial dependence of chemical and physical attributes of Capão Bonito forest soils. A large soil database of regional land was raised and organized. Most of the selected variables were close to the lognormal frequency range. Soil texture presented a higher range in the A horizon, and the nugget effect and sill were greater in the B horizon. These differences are attributed to the parent material of the region (Itararé Geologic Formation), which presents uneven distribution of sediments. Chemical attributes related to soil fertility presented a higher spatial dependence range in the B horizon, probably as a result of more intensive management and erosion history of the superficial soil layer. Maps for some attributes were interpolated. These had specific areas of occurrence and a wide distribution along the perimeter of the Capão Bonito District Forest, allowing a future site-specific soil management.

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Rocky Mountain forest soils: Evaluating spatial variability using conventional statistics and geostatistics

Cited by 27 publications

References 17 publications

Sampling and analyzing metals in soils for archaeological prospection: A critique

Sampling and analyzing metals in soils for archaeological prospection: A critique

Environinformatics in ecological risk assessment of agroecosystems pollutant leaching

Spatial variability of physical and chemical attributes of some forest soils in southeastern of Brazil

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