A structured approach to designing soil survey schemes with prediction of sampling error from variograms

Domburg, P.; Gruijter, J.J. de; Brus, D.J.

doi:10.1016/0016-7061(94)90033-7

Cited by 39 publications

(36 citation statements)

References 3 publications

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“…The average over all simulations is an approximation of the expected sampling variance of the estimated mean. Domburg et al (1994) showed that simulation of the fields is not needed and that the expected sampling variance can be obtained by computing variance components from the variogram.…”

Section: Comparison Of Space-time Designsmentioning

confidence: 99%

Design-based Generalized Least Squares estimation of status and trend of soil properties from monitoring data

Brus

Gruijter

2011

Geoderma

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Section: Comparison Of Space-time Designsmentioning

confidence: 99%

Design-based Generalized Least Squares estimation of status and trend of soil properties from monitoring data

Brus

Gruijter

2011

Geoderma

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“…Design SY is a grid whose origin has been chosen at random. Assuming that the variogram for a polygon is known, the modelbased estimation variance of the sample mean under SI is (Domburg et al, 1994):…”

Section: Model Formmentioning

confidence: 99%

Soil carbon stock in the tropical rangelands of Australia: Effects of soil type and grazing pressure, and determination of sampling requirement

Pringle¹,

Allen²,

Dalal

et al. 2011

Geoderma

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On-going, high-profile public debate about climate change has focussed attention on how to monitor the soil organic carbon stock (C s ) of rangelands (savannas). Unfortunately, optimal sampling of the rangelands for baseline C s -the critical first step towards efficient monitoring -has received relatively little attention to date. Moreover, in the rangelands of tropical Australia relatively little is known about how C s is influenced by the practice of cattle grazing. To address these issues we used linear mixed models to: (i) unravel how grazing pressure (over a 12-year period) and soil type have affected C s and the stable carbon isotope ratio of soil organic carbon (δ 13 C) (a measure of the relative contributions of C 3 and C 4 vegetation to C s ); (ii) examine the spatial covariation of C s and δ 13 C; and, (iii) explore the amount of soil sampling required to adequately determine baseline C s . Modelling was done in the context of the material coordinate system for the soil profile, therefore the depths reported, while conventional, are only nominal. Linear mixed models revealed that soil type and grazing pressure interacted to influence C s to a depth of 0.3 m in the profile. At a depth of 0.5 m there was no effect of grazing on C s , but the soil type effect on C s was significant. Soil type influenced δ 13C to a soil depth of 0.5 m but there was no effect of grazing at any depth examined. The linear mixed model also revealed the strong negative correlation of C s with δ 13 C, particularly to a depth of 0.1 m in the soil profile. This suggested that increased C s at the study site was associated with increased input of C from C 3 trees and shrubs relative to the C 4 perennial grasses; as the latter form the bulk of the cattle diet, we contend that C sequestration may be negatively correlated with forage production. Our baseline C s sampling recommendation for cattle-grazing properties of the tropical rangelands of Australia is to: (i) divide the property into units of apparently uniform soil type and grazing management; (ii) use stratified simple random sampling to spread at least 25 soil sampling locations about each unit, with at least two samples collected per stratum. This will be adequate to accurately estimate baseline mean C s to within 20% of the true mean, to a nominal depth of 0.3 m in the profile.Crown

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“…Following Domburg et al (1994), we wanted to optimize the sample size a priori by analyzing the relation between the expected sampling error of the mean PSC as a function of sample sizes allocated to the strata. This method is based on semivariograms of the target variable (PSC ; Table 6), and requires an elaborated stratification so that each map polygon in the sample domain is assigned to one stratum.…”

Section: 3mentioning

confidence: 99%

“…• The sample size was optimized using the method described by Domburg et al (1994) and the data on phosphate saturation from the sample in map-units with water-table class III (cf. section 3.3).…”

Section: Design and Operational Phasesmentioning

confidence: 99%

A soil sampling program for the Netherlands

2007

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Soil data users in The Netherlands were inventoried for current and future data needs. Prioritized data needs were used to design the Netherlands Soil Sampling programme (NSSP) as a framework containing 3 groups of related projects: map upgrading, map updating and upgrading of pedotransfer functions. In each one group, the sampling design, performance criteria and optimal sample size were defined. This paper focuses on the upgrading of the existing soil map of the Netherlands at scale 1:50,000, and extensively treats the user inventory and the sampling strategy. The sampling design, performance criteria of the sampling and associated optimal sample size were obtained by statistical analysis of soil data available before the sampling. The Phosphate Sorption Capacity (PSC) was chosen as target variable to optimize sampling, because it dominated total cost per sample. A prior analysis of a performance criterion related to the sampling error of PSC resulted in a cost saving of 13% relative to total cost determined earlier by expert judgment. A posterior analysis showed that the set quality criterion was reached or better in 6 out of 7 cases. The NSSP resulted in a data base with soil data from 2,524 sample points selected by stratified random sampling, and a collection of 5,764 aliquots taken at these points. The NSSP has been showing its usage potential for various kinds of environmental studies and could be a sound future basis for a national scale monitoring program.

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A structured approach to designing soil survey schemes with prediction of sampling error from variograms

Cited by 39 publications

References 3 publications

Design-based Generalized Least Squares estimation of status and trend of soil properties from monitoring data

Design-based Generalized Least Squares estimation of status and trend of soil properties from monitoring data

Soil carbon stock in the tropical rangelands of Australia: Effects of soil type and grazing pressure, and determination of sampling requirement

A soil sampling program for the Netherlands

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