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Soil Salinity Measurement Via Portable X-ray Fluorescence Spectrometry.

Authors :
Swanhart, Samantha
Weindorf, David C.
Chakraborty, Somsubhra
Bakr, Noura
Zhu, Yuanda
Nelson, Courtney
Shook, Kayla
Acree, Autumn
Source :
Soil Science; Sep2014, Vol. 179 Issue 9, p417-423, 7p
Publication Year :
2014

Abstract

Saline soils are defined as those containing appreciable salts more soluble than gypsum (e.g., various combinations of Na<superscript>+</superscript>, Mg<superscript>2+</superscript>, Ca<superscript>2+</superscript>, K<superscript>+</superscript>, Ci<superscript>-</superscript>, SO<superscript>2-</superscript><subscript>4</subscript>, HCO<superscript>-</superscript><subscript>3</subscript>, and CO<superscript>2-</superscript><subscript>3</subscript>). Saline soils can occur across diverse climates and geological settings. As such, salinity is not germane to specific soil textures o r parent materials. Traditional m ethods o f measuring soil salinity (e.g., electrical conductance), although accurate, provide limited data and require laboratory analysis. Given the success o f previous studies using portable X-ray fluorescence (PXRF) as a tool for m easuring soil characteristics, this study evaluated its applicability for soil salinity determination. Portable X-ray fluorescence offers accurate quantifiable data that can be produced rapidly, in situ, and with m inimal sample preparation. For this study, 122 surface soil samples (0-15 cm) were collected from salt-impacted soils o f coastal Louisiana. Soil samples were subjected to standard soil characterization, including particle size analysis, losson- ignition organic matter, electrical conductivity (EC), and elemental quantification via PXRF. Simple and multiple linear regression models were developed to correlate elemental concentrations and auxiliary input parameters (simple: Cl; multiple: Cl, S, K, Ca, sand, clay, and organic matter) to EC results. In doing so, logarithmic transformation was used to normalize the variables to obtain a normal distribution for the error term (residual, ei). Although both models resulted in similar acceptable r<superscript>2</superscript> between soil EC and elemental data produced by PXRF (0.83 and 0.90, respectively), multiple linear regression is recommended. In summary, PXRF has the ability to predict soil EC with reasonable accuracy from elemental data. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
0038075X
Volume :
179
Issue :
9
Database :
Supplemental Index
Journal :
Soil Science
Publication Type :
Periodical
Accession number :
101879206
Full Text :
https://doi.org/10.1097/SS.0000000000000088