Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/22620
Title: Georeferenced soil provenancing with digital signatures
Contributor(s): Tighe, Matthew  (author)orcid ; Forster, Nicola (author); Guppy, Christopher  (author)orcid ; Savage, D G (author); Grave, Peter  (author)orcid ; Young, I (author)
Publication Date: 2018
Open Access: Yes
DOI: 10.1038/s41598-018-21530-7Open Access Link
Handle Link: https://hdl.handle.net/1959.11/22620
Abstract: The provenance or origin of a soil sample is of interest in soil forensics, archaeology, and biosecurity. In all of these fields, highly specialized and often expensive analysis is usually combined with expert interpretation to estimate sample origin. In this proof of concept study we apply rapid and non-destructive spectral analysis to the question of direct soil provenancing. This approach is based on one of the underlying tenets of soil science - that soil pedogenesis is spatially unique, and thus digital spectral signatures of soil can be related directly, rather than via individual soil properties, to a georeferenced location. We examine three different multivariate regression techniques to predict GPS coordinates in two nested datasets. With a minimum of data processing, we show that in most instances Eastings and Northings can be predicted to within 20% of the range of each within the dataset using the spectral signatures produced via portable x-ray fluorescence. We also generate 50 and 95% confidence intervals of prediction and express these as a range of GPS coordinates. This approach has promise for future application in soil and environmental provenancing.
Publication Type: Journal Article
Source of Publication: Scientific Reports, v.8, p. 1-9
Publisher: Nature Publishing Group
Place of Publication: United Kingdom
ISSN: 2045-2322
Fields of Research (FoR) 2008: 070104 Agricultural Spatial Analysis and Modelling
010401 Applied Statistics
050304 Soil Chemistry (excl. Carbon Sequestration Science)
Fields of Research (FoR) 2020: 300206 Agricultural spatial analysis and modelling
490501 Applied statistics
410604 Soil chemistry and soil carbon sequestration (excl. carbon sequestration science)
Socio-Economic Objective (SEO) 2008: 961402 Farmland, Arable Cropland and Permanent Cropland Soils
960906 Forest and Woodlands Land Management
960904 Farmland, Arable Cropland and Permanent Cropland Land Management
Socio-Economic Objective (SEO) 2020: 180605 Soils
180607 Terrestrial erosion
180603 Evaluation, allocation, and impacts of land use
Peer Reviewed: Yes
HERDC Category Description: C1 Refereed Article in a Scholarly Journal
Appears in Collections:Journal Article
School of Environmental and Rural Science
School of Humanities, Arts and Social Sciences

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