Sampling in environmental matrices: a critical review

Bhave, Prashant P.; Sadhwani, Karan

doi:10.1080/15275922.2021.1887971

Cited by 1 publication

(1 citation statement)

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“…The choice of sampling methodology depends on the objective of the study, the nature of the underlying population, prior knowledge of the sampling region and spatial features of the grid structure. If the sampling region has some sort of spatial pattern, a sampling design that provides a good spatial coverage over the region may provide more information than a sampling plan providing a poor coverage (Bhave and Sadhwani 2021). A spatial pattern in a region may be a function of environmental factors, or chemical, physical and microbiological patterns of the soil map.…”

Section: Introductionmentioning

confidence: 99%

Row–Column Sampling Design Using Auxiliary Ranking Variables

Öztürk

Kravchuk

Correll

2022

JABES

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In this paper, we present a new class of row–column sampling design when a sampling region is presented by non-overlapping quadrats of an rp-by-p row–column grid. Quadrats are rank-ordered on a relevant auxiliary variable, first in the rp rows of the grid and then in the so formed p ranked columns. This ranking enhances the precision of the sampling using ideas from standard rank set sampling methods. The sampling design falls in the traditional sampling framework where the sample selection probabilities are independent of the variables of interest. Selection probabilities are governed by a spatial design and the auxiliary information to facilitate better sampling coverage over the sampling region. The paper constructs unbiased estimators for population mean, total and provides variance estimates for them. It also shows when the new sampling design performs better than its competitors. The new sampling design is applied to a forest and two agricultural field samples to estimate the population means. Supplementary materials accompanying this paper appear online.

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Section: Introductionmentioning

confidence: 99%