final print - pxrf soil salinity ss

TECHNICAL ARTICLE

Soil Salinity Measurement Via Portable X-rayFluorescence Spectrometry

Samantha Swanhart,1 David C. Weindorf,2 Somsubhra Chakraborty,3 Noura Bakr,4 Yuanda Zhu,1

Courtney Nelson,1 Kayla Shook,1 and Autumn Acree1

Abstract: Saline soils are defined as those containing appreciable saltsmore soluble than gypsum (e.g., various combinations of Na+, Mg2+,Ca2+, K+, Cl−, SO4

2-, HCO3−, and CO3

2-). Saline soils can occur across di-verse climates and geological settings. As such, salinity is not germaneto specific soil textures or parent materials. Traditional methods of measur-ing soil salinity (e.g., electrical conductance), although accurate, providelimited data and require laboratory analysis. Given the success of previousstudies using portable X-ray fluorescence (PXRF) as a tool for measuringsoil characteristics, this study evaluated its applicability for soil salinity de-termination. Portable X-ray fluorescence offers accurate quantifiable datathat can be produced rapidly, in situ, and with minimal sample preparation.For this study, 122 surface soil samples (0–15 cm) were collected fromsalt-impacted soils of coastal Louisiana. Soil samples were subjectedto standard soil characterization, including particle size analysis, loss-on-ignition organic matter, electrical conductivity (EC), and elementalquantification via PXRF. Simple and multiple linear regression modelswere developed to correlate elemental concentrations and auxiliary inputparameters (simple: Cl; multiple: Cl, S, K, Ca, sand, clay, and organicmatter) to EC results. In doing so, logarithmic transformation was usedto normalize the variables to obtain a normal distribution for the errorterm (residual, ei). Although both models resulted in similar acceptabler2 between soil EC and elemental data produced by PXRF (0.83 and0.90, respectively), multiple linear regression is recommended. In sum-mary, PXRF has the ability to predict soil EC with reasonable accuracyfrom elemental data.

Key Words: Electrical conductivity, portable X-ray fluorescence, salinity

(Soil Sci 2014;179: 417–423)

T raditionally, saline soil has been defined as soil containingsalts more soluble than gypsum (e.g., various combinations

of Na+, Mg2+, Ca2+, K+, Cl−, SO42-, HCO3

−, and CO32-) that can ad-

versely affect soil fertility (US Soil Salinity Laboratory Staff,1954). Worldwide, more than 20% of irrigated land has been neg-atively impacted by soil salinization. Salinity effectively lowersthe osmotic potential of water, making it more difficult for plantsto absorb water into their roots.

Soil salinity can develop in many different climates and/or geological settings. Thus, it is not limited to any specific

1School of Plant, Environmental, and Soil Sciences, Louisiana State UniversityAgricultural Center, Baton Rouge, Louisiana, USA. 2Department of Plant andSoil Science, Texas Tech University, Lubbock, Texas, USA. 3RamakrishnaMis-sion Vivekananda University, Kolkata, India. 4Soils andWater Use Department,National Research Centre, Cairo, Egypt.Address for correspondence: Dr. David C.Weindorf, Department of Plant Soil Sci-ence, Texas TechUniversity, Lubbock, TX, USA. E-mail: [email protected] Disclosures/Conflicts of Interest: None reported.This work was financially supported by the BL Allen Endowment of Pedologyat Texas Tech University.Received October 15, 2014.Accepted for publication December 5, 2014.Copyright © 2014 Wolters Kluwer Health, Inc. All rights reserved.ISSN: 0038-075XDOI: 10.1097/SS.0000000000000088

Soil Science • Volume 179, Number 9, September 2014

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characteristic (e.g., textures or parent materials) (Zeng and Shannon,2000; Caballero et al., 2001; Biggs and Jiang, 2009). For ex-ample, saline soils develop in coastal regions, arid to semiaridregions where evaporation exceeds precipitation, and areas of an-thropogenic impact (e.g., oil production wells pumping brine tosurface for containment in artificial ponds; irrigation with brack-ish aquifer water) (Fig. 1A) (Merrill et al., 1980; Benito et al.,1995; Hao and Chang, 2003; Saadi et al., 2007; Wang et al.,2007). In coastal Louisiana, salt accumulation in tidal marsh soilsis often inherited from sea spray or storm surge of seawater rifewith dissolved salts (electrical conductivity (EC), ∼27 dS m−1);many are composed of the anion Cl−, including NaCl, MgCl2,and CaCl2. In areas of pervasive salinity, native vegetative specieshave been displaced by salt-tolerant halophytes (Fig. 1B).

Technological innovation has produced new tools that allowfor enhanced testing and evaluation of soil quality (Soil SurveyStaff, 1993). Although newer technologies have not replaced oldertraditional methods of soil analysis, they do offer the ability tomake rapid measurements on-site in ways that were previouslynot possible. For example, where colorimetric field tests with ru-dimentary accuracy were traditionally used for field elementalanalyses (e.g., Bray, 1929), today, portable x-ray fluorescence(PXRF) spectrometry and other techniques can provide highlyaccurate results in the field with minimal to no sample pre-preparation.

Traditional methods of measuring soil salinity include anelectrode probe (e.g., Solubridge) that passed electrical currentsthrough the soil or extracted soil solution to measure EC in the so-lution. Higher dissolved salt concentrations were found to gener-ate stronger electrical conductance; thus, the term electricalconductivity became synonymous with soil salinity quantification(Rhoades et al., 1987; Corwin and Lesch, 2001). Although widelyused for more than five decades, electrical conductance methodsare fraught with limitations. To facilitate complete salt dissolutionwithin the soil, samples are destructively ground and mixed withdistilled water to form a saturated paste or some form of water/soil mixture (e.g., 1:2 or 1:5 vol/vol), then allowed to equilibratefor 24 h (US Salinity Laboratory Staff, 1954). Thus, performingthese analyses takes considerable time. Also, uniform preparationof the saturated paste is critical. The amount of water required tosaturate the soil varies considerably with soil texture (e.g., sandsrequire less water than clays to reach saturation). Adding toomuch water can cause a dilution effect and render atypically lowEC values (Hogg and Henry, 1984). Thus, the consistent prepara-tion of the soil paste requires considerable skill. Rhoades et al.(1989) explored the effect of soil-water slurry dilutions (e.g.,1:1, 1:2, or 1:5 vol/vol) using the aforementioned probe and foundthat larger volumes of water resulted in lower EC values. Finally,electrical conductance readings do not differentiate specific ele-ments (ions) associated with salinity; they merely report a conduc-tance measurement whereby all dissolved salts contribute toenhanced conductivity.

Recently, PXRF spectrometry has been shown to be effectiveat quantifying elemental concentrations related to soil characteristics

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FIG. 1. A, Salt-impacted soil at an old petroleum production site. B, Salt-affected organic marshland soils with halophytic vegetation inGrand Isle, Louisiana.

Swanhart et al. Soil Science • Volume 179, Number 9, September 2014

including gypsum content (Weindorf et al., 2009, 2013), soil tex-ture (Zhu et al., 2011), soil pH (Sharma et al., 2014a), soil cationexchange capacity (Sharma et al., 2014b), and pedon horizonation(Weindorf et al., 2012). A contemporary overview of PXRF andits applications for environmental, agronomic, and soil science ap-plications is provided by Weindorf et al. (2014). X-ray fluores-cence is a technique using X-rays generated from a Ta/Au, Rh,or other X-ray tube, which strike the soil.WhenX-rays strike mat-ter, they cause inner shell electrons to be ejected (Jones, 1982).Subsequently, outer shell electrons cascade down to fill the innerelectron shell void. In doing so, they must relinquish energy that isemitted as fluorescence. The wavelength (energy) of emitted radi-ation is specific to each element while the intensity is proportionalto elemental abundance. Although the technique has been sanc-tioned by the US Environmental Protection Agency (2007) foruse in soils and sediments, it does have some limitations. Piorek(1998) outlines techniques for optimizing PXRF performancethrough sample homogenization, using multiple scans per sample,and increasing X-ray beam exposure time to ensure optimal mea-surement of fluoresced X-ray photons. For example, shorter mea-surements of less than 60 sec are appropriate for initial screeningof specific elements, whereas longer measurements of up to300 sec are suitable for precise and accurate measurements. Afew sources of error must also be considered with PXRF: (i) mois-ture, (ii) sample heterogeneity, and (iii) interelemental inter-ferences. Zhu et al. (2011) noted that excessive (>20%) soilmoisture degraded the accuracy of PXRF data. Specifically, whenonly dry sample scans were considered, the correlation betweenPXRF readings and laboratory measurements improved substan-tially. Another disadvantage of in situmeasurements is the degreeof uncertainty caused by sample heterogeneity (Argyraki et al.,1997; Zhu et al., 2011). Jones (1982) noted that sample homoge-neity is promoted when soils are dried and ground to pass a 2-mmsieve; practices followed as part of this study. Importantly, manysalt-impacted soils occur in naturally dry environments such asdeserts or semiarid areas where soil moisture would be nominal.Finally, with respect to salinity assessment, current PXRF equip-ment is not able to quantify Na directly given its small stable elec-tron cloud. Nonetheless, many Na-based salts often associate withCl, which can accurately be quantified by PXRF. Given the suc-cess of previous studies using PXRF as a tool for measuring soilcharacteristics, the evaluation of soil salinity with PXRF spec-trometry seems timely. Portable X-ray fluorescence produces

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accurate quantifiable data on-site and can be uniquely used in ap-plications where nondestructive sampling is required (Weindorfet al., 2012). The present study is an extension of work originallyundertaken by Swanhart (2013), a graduate research thesis onPXRF applications in salt-impacted soils.

In recognition of the potential benefits PXRF affords soil sa-linity assessment, the objectives of this research were to (i) collecta wide variety of salt-impacted soil samples (low to high salinity),(ii) quantify soil salinity through traditional laboratory methodsand PXRF, and (iii) determine the relationship between elementalconcentrations and associated soil EC. If PXRF proves to be a re-liable method for quantification and differentiation of salts insoils, elemental data from PXRF soil scans could be used to pre-dict soil salinity (and other soil properties) in situ, requiring lesslaboratory analysis and time.

MATERIALS AND METHODS

Soil SamplingA total of 121 surface soil samples (0–15 cm) were collected

in Jefferson, Plaquemines, and Cameron parishes, Louisiana,per Schoeneberger et al. (2002), to represent both organic andmineral soils in 2012 and 2013. Sampling was conducted suchthat approximately 57 samples collected were predominantly sand(>80%), whereas approximately 25 samples had clay contents ofmore than 20%. Other soils were largely organic and were pre-dominantly from areas of slow drainage and mixed with fine soiltextures. Soils were collected using a small handheld shovel,which was cleaned between samples. Soil series collected in-cluded the Scatlake (Very-fine, smectitic, nonacid, hyperther-mic Sodic Hydraquent), Felicity (Mixed, hyperthermic AquicUdipsamment), Hackberry (Sandy, mixed, hyperthermic AericEndoaquept), Peveto (Mixed, thermic Typic Udipsamment), Cre-ole (Fine, smectitic, nonacid, hyperthermic Typic Hydraquent),Convent (Coarse-silty, mixed, superactive, nonacid, thermicFluvaquentic Endoaquept), and Commerce (Fine-silty, mixed,superactive, nonacid, thermic Fluvaquentic Endoaquept) (SoilSurvey Staff, 1983, 1995, 2000, 2010). Samples were sealed inplastic bags and returned to Louisiana State University for labora-tory analysis.

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Soil Science • Volume 179, Number 9, September 2014 Soil Salinity Measurement via PXRF Spectrometry

Standard Laboratory Analysis

Samples were air-dried and ground to pass a 2-mm sieve be-fore additional analysis. Standard soil characterization includedparticle size analysis, loss-on-ignition (LOI) organic matter, EC,and elemental quantification via PXRF. Soils featuring apprecia-ble organic contents were tested first with H2O2. With a positivereaction, they were thoroughly oxidized with H2O2 before particlesize analysis. Particle size analysis was conducted via the pipettemethod per Gee and Bauder (1986) with an error of ±1% clay.Sands were determined via wet sieving with a 53-μm sieve. Loss-on-ignition organic matter was determined per Ben-Dor and Banin(1989). Samples were combusted for 8 to 16 h at 400°C such thatmaximum weight loss (ashing) of all organic matter occurred withminimal dehydroxylation of clay minerals (Ben-Dor and Banin,1989). Soil ECwas determined for each sample via saturated paste.Deionized water was added to approximately 20 to 30 g of soil un-til it reached complete saturation (US Salinity Laboratory Staff,1954). Samples were allowed to equilibrate for 24 h. A model4063CC digital salinity bridge (Traceable Calibration ControlCompany, Friendswood, TX) was used to measure soil paste con-ductance (ECp). The electrical conductance probe was inserted tothe sample and allowed to equilibrate for 60 to 90 sec before a con-ductivity reading was made and reported in dS m−1. Brady andWeil (2008) further discuss how such saturated soil paste readings(ECp) relate to saturated paste extracts (ECe).

PXRF Spectrometry

A Delta Premium PXRF spectrometer (Olympus Innov-X,Woburn, MA) was used to facilitate total elemental characteriza-tion. Samples were subjected to PXRF scanning both in situ andin the laboratory; the former for initial screening to ensure salinesoil conditions and the latter for the development of regressionmodels for this research. The PXRF featured a Ta/Au x-ray tubeoperated at 10 to 40 kVand a 2-cm aperture for sample scanning.Before scanning, the instrument was calibrated with a “316”metalalloy clip tightly secured to the aperture. The PXRF was operatedin a proprietary configuration known as soil mode, with the lightelements analysis program (LEAP) engaged. Optimal Cl quantifi-cation (the element of interest for a large portion of the currentstudy) was enhanced by longer scanning time and averages ofmultiple scans. The Delta PXRF uses three beam sequential scan-ning for elemental analysis. For this study, each beam was set toscan for 30 sec. Thus, one complete scan took 90 sec. The instru-ment was then repositioned, and the sample was scanned a secondtime such that an average between scans was obtained. Quality as-surance of PXRF scan data was accomplished via scanning twoNIST-certified reference soils (2710a and 2711a). Unfortunately,S and Cl were two elements that were not reported on in the offi-cial NIST certificates. Nonetheless, the following elements werecompared and give an indication of PXRF instrument perfor-mance. The NIST values are followed by PXRF-determinedvalues in italics, with all values in mg kg−1: 2710a (As, 1,540,1,468; Ca, 9,640, 7,850; Cu, 3,420, 3,258; Fe, 43,200, 45,450;Pb, 5,520, 5,371; Mn, 2,140, 2,182; K, 21,700, 24,750; Ti,3,110, 3,514; Zn, 4,180, 4,114; Sb, 53, 57; Sr, 255, 262) and2711a (As, 107, 73; Ca, 24,200, 23,550; Cu, 140, 112; Fe,28,200, 21,950; Pb, 1,400, 1,302; Mn, 675, 572; K, 25,300,23,650; Ti, 3,170, 2,904; Zn, 414, 342; Sb, 24, 37; Sr, 242, 222).The authors of this article in noway endorse any one PXRF instru-ment over another; selection and use of equipment for this re-search project were simply reflective of the resources availableto the authors at the time the study was conducted.



Statistical AnalysisRegression models were developed to correlate PXRF ele-

mental concentrations with EC results using statistical analysissoftware 9.4 (SAS Institute, 2011) and XLStat version 2014(Addinsoft, Paris, France). Both simple andmultiple linear regres-sions (SLR and MLR) were used in this study. Because the origi-nal EC values were non-normally distributed (P > 0.05) andhighly influenced by outliers, Box-Cox transformation (Box andCox, 1964) was applied to both original EC and PXRF data usingλ = 0 (Log transformation) to bring the data close to a Gaussiandistribution after stabilizing the variance. Both SLR and MLRmodels were developed based on Log-transformed (λ = 0) re-sponse and predictor values. Variables included in regression anal-ysis included results from particle size analysis, organic matter,elemental concentration via PXRF, and EC. All statistical analyseswere conducted at a significance level of α = 0.05. Different sta-tistical analyses were applied to quantify significant differencesand the correlation between laboratory-measured values and pre-dicted values from the regression models for Cl and salinity.The model generalization capacity was judged in terms of coeffi-cient of determination (r2) and RMSE values. Among other errorstatistics, the mean absolute percentage error (MAPE) (Mayer andButler, 1993) was calculated per Eq.(1):

MAPE¼ 1

n∑

Actual−Forecastj jActualj j

� ��100 ð1Þ

where n denotes the number of observations,Actual represents ob-served value, andForecast indicates predicted value. Furthermore,to evaluate the best performing algorithm, the Akaike informationcriterion (AIC) was used to determine the method that most satis-factorily compromised between model accuracy and model par-simony (Akaike, 1973). It is a model selection criterion thatpenalizes models for which adding new explanatory variablesdoes not supply sufficient information to the model, the informa-tion beingmeasured through the MSE. The aim is to minimize theAIC criterion. The AIC was calculated by Eq.(2) (Viscarra Rosseland Beherens, 2010):

AIC ¼ n ln RMSE þ 2p ð2Þwhere n is the number samples and p is the number of featuresused in the prediction. The model with the smallest AIC is gener-ally considered best.

We also plotted predicted EC against SLR standardized re-siduals (Cook and Wiesberg, 1982), also known as internallystudentized residuals, which are the errors divided by their es-timated standard errors. They are used to adjust for the fact thatdifferent residuals have different variances. Moreover, MLR stan-dardized coefficients were used to compare the relative weights ofthe variables (Schroeder et al., 1986). Before fitting the MLRequation, both response and predictors are standardized bysubtracting the mean and dividing by the S.D. The standardizedregression coefficients, subsequently, indicate the change in re-sponse for a change of 1 S.D. in a predictor. The higher the abso-lute value of a coefficient, the more important the weight of thecorresponding variable.

RESULTS

Simple Linear RegressionElemental concentrations of Cl, S, K, and Ca were deter-

mined via PXRF and used to predict EC values. Salt-impactedsoils were split into five classes based on their respective EC

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TABLE 1. Average Cl Concentrations and EC (dS m−1) for AllSamples (n = 122) from Louisiana

SalinityClass

EC Range,dS m−1

Average EC,dS m−1

Average Cl,mg kg−1

0 0–2 0.39 361 2–4 3.05 8042 4–8 6.24 1,2653 8–16 11.08 2,3624 >16 37.52 6,676Total 0 ≥ 16 13.92 2,564


values: Class 0, nonsaline (0–2 dS m−1); Class 1, very slightly sa-line (2–4 dS m−1); Class 2, slightly saline (4–8 dS m−1); Class 3,moderately saline (8–16 dS m−1); and Class 4, strongly saline(>16 dS m−1). Table 1 describes the average EC and Cl concen-tration from experimental analysis. As expected, average Cl con-centration increased steadily from nonsaline to strongly salinesamples.

An SLR model was constructed considering Ln (EC) and Ln(Cl) as response and predictor variables, respectively. Samples be-low the Cl detection limit for PXRF (60–100 mg kg−1) were ex-cluded from regression analysis because their associated ECswould not be considered saline soil (Soil Survey Staff, 1993;Hoppin et al., 1995; Papachristodoulou et al., 2006). This resultedin a final data set (n = 90) that was randomly distributed into cal-ibration (n = 68, ∼75%) and validation (n = 22, ∼25%) data setsand subsequently used in both SLR and MLR models. Notably,among these 90 soil samples, soil salinity (EC) varied from 0to 79.70 dS m−1. Substantial variability was also observed forsoil S (∼114–13,328 mg kg−1), K (∼1,240–13,410 mg kg−1),Ca (∼113–100,876mg kg−1), sand (1.90%–98.60%), clay content(2.00%–61.50%), and organic matter (0.20%–24.50%) (Table 2).Figure 2A shows the SLR model representing PXRF (Cl)-pre-dicted EC versus measured EC. The calibration model exhibiteda reasonable coefficient of determination (r2 = 0.83) (Table 3).Moreover, Fig. 2B represents the standardized residuals. Appar-ently, the prediction deteriorates significantly with decreasingEC, which could be caused by the scarcity of soil samples withlow EC values. Independent validation with the test set (n = 22)produced an r2 value of 0.78, further confirming the potentialityof PXRF (Cl) in predicting soil EC (Table 3). The MAPE, whichis a measure of how high or low the differences are between thepredictions and actual data, exhibited that, on average, the predic-tions from the SLR model were approximately 58% higher orlower than actual values. Notably, log transformation substantiallyimproved the SLR model predictability in terms of coefficient of

TABLE 2. Summary Statistics of Samples (n = 90) Used in Predictive

Statistics

EC S K

dS m−1 mg kg−1

Minimum 0.11 114.30 1,240.50Maximum 79.70 13,328.30 13,410.60Mean 18.63 1,827.38 8,011.44Range 79.59 13,214.00 12,170.10First Quartile 5.26 583.45 7,121.525Median 11.20 1,203.60 8,055.85Third Quartile 28.20 1,914.25 9,170.10Variance 341.77 6,269,354.49 5,058,735.77

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determination (r2 = 0.83) when compared with untransformedvariables (r2 = 0.66). Table 4 exhibits both SLR and MLR modelequations.

Multiple Linear RegressionIn consideration of the possibility of more diverse types of

salt contributing to soil salinity, MLR was used to compare ECreadings with concentrations of Cl, K, S, and Ca, with sand, clay,and organic matter as auxiliary input variables. As such, an MLRmodel was created, including K, S, Ca, and Cl from PXRFas con-stituent elements of common salt compounds.

Considering the calibration data set (n = 68), the MLR-predicted ECversusmeasuredECproduced an r2 of 0.90 (Fig. 2C),whereas the validation data set (n = 22) produced an r2 of 0.70(Table 3). The MLR calibration model produced a lower MAPEvalue (21.77) than SLR, indicating that MLR calibration wassubstantially better than SLR. Moreover, MLR produced lowerRMSE (0.475 Ln dS m−1), MAPE (21.77%), and AIC (-89.37)values than the SLR model (MAPE, 57.73%; AIC, -69.83).

DISCUSSIONOne limitation of using single-element analysis (e.g., Cl) via

PXRF is the potential for matrix interference from other elementswith higher concentrations. However, such limitations can bemanaged with extended scanning time, sample homogenization,correction via NIST standards, and consideration of multiplescans (Anderson and Olin, 1990). We minimized the aforemen-tioned limitation by scanning each sample in duplicate (physicallyrepositioning the instrument between each scan such that differentareas of soil were scanned to obtain an average), homogenizingthe soil before scanning through drying/grinding to pass a 2-mmsieve and through substantial scanning time (90 sec) for eachindividual scan.

Interestingly, the MLR-standardized coefficients exhibitedmajor influences of organic matter, clay, Ca, and K apart fromCl content (Fig. 2D). Although systems that offer electrostatic at-traction to free cations in soil solution may effectively bind themto the exchange complex of clays or integrate them into the molec-ular structure of complex organics, anions such as Cl would stillbe freely available as like charges repel each other. However, be-cause of binding, clays and organics may contribute only limitedcations to dissociated active soil salinity, which would be reflectedin a lower overall soil EC, whereas PXRF elemental readings arenot affected by binding versus dissociation. This is likely the ratio-nale behind the influence of soil organic matter and clay on soilsalinity results.

Although a small difference was observed in the results, par-ticularly in terms of validation r2 between both SLR (0.77) and

Models

Ca Sand Clay LOI

%

113.00 1.90 2.00 0.20100,876.30 98.60 61.50 24.5016,104.86 60.54 14.13 3.26100,763.30 96.70 59.50 24.306,447.15 33.25 2.60 1.2013,338.20 66.60 9.55 2.1517,084.40 85.30 19.30 3.40

288,867,636.47 833.34 222.30 20.35



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FIG. 2. Plots showing (A) SLR predicted Ln EC versus measured Ln EC (outer lines represent 95% confidence interval), (B) SLR standardizedresiduals, (C) MLR predicted Ln EC versus measured Ln EC (outer lines represent 95% confidence interval), and (D) MLR standardizedregression coefficients (gray bars). The magnitude of the regression coefficient at each variable is proportional to the height of the bar.The higher the absolute value of a coefficient, the more important the weight of the corresponding variable. LOI represents loss-on-ignitionorganic matter. The EC values were measured in the laboratory using standard procedures, whereas Cl, Ca, K, and S values were obtainedvia PXRF spectrometry for salt-impacted soils in Louisiana.


MLR (0.70), the latter is recommended with deference to modelaccuracy (Table 3). This is reflected in both the MAPE and AIC,which suggest that MLR is the best EC predictive model. As evi-dent in Fig. 2D, inclusion of influential auxiliary predictors likeorganic matter and clay (when available) plays a crucial role insubstantially lowering the MAPE.

However, in-depth elucidation of the differences betweenSLR and MLR dynamics would require the study of a largernumber of samples with a better control of the factors that can in-fluence differences. Yet, it is possible to conclude that, at least inthe analysis soil EC, MLR provided satisfactory generalization

TABLE 3. Calibration (n = 68) and Validation Statistics (n = 22) of SLRCa, Sand, Clay, and Organic Matter for Soil Samples from Louisiana

Model Calibration r2 Validation r2

SLR 0.83 0.77MLR 0.90 0.70

AIC, Akaike information criterion; LOI, loss-on-ignition organic matter; M



capability. Notably, for research of specific salts, the use of mul-tivariate models may be preferable. Also, other elements may pro-vide increased predictive power. For example, a differentconfiguration of the Delta Premium PXRF features an Rh X-raytube that is capable of measuring Mg. Clearly, this would be animportant predictor variable for salts such as MgCl2, Mg(NO3)2,and MgSO4.

Summarily, PXRF shows considerable promise in providingrapid EC prediction in soils with reasonable accuracy. Acquisitionof PXRF data is rapid, easy, and cost-effective, especially forunusual circumstances where nondestructive sampling is required.

Model Using PXRF ln Cl, andMLRModel Using PXRF ln Cl, S, K,

RMSE, Ln dS m−1 MAPE, % AIC

0.590 57.73 -69.830.475 21.77 -89.37

APE, mean absolute percentage error.

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TABLE 4. Calibration Equations for SLR Model Using PXRF ln Cl, and MLRModel Using PXRF ln Cl, S, K, Ca, Sand, Clay, and OrganicMatter for Soil Samples from Louisiana

PredictorsRegressionModel Regression Equation r2

Cl Simple (SLR) Ln EC= -3.28 + 0.75*ln Cl 0.83Cl, S, K, Ca,sand, clay, LOI

Multiple (MLR) Ln EC = -4.34 + 0.79*ln Cl + 5.35E - 03*ln S +0.14*ln K + 1.75E - 02*ln Ca - 9.87E - 03*sand - 2.042E - 04*clay - 5.33E - 02*LOI

0.90


Additional research should be continued to include larger geo-graphical ranges along with other soil properties, but the futureof PXRF-based soil EC characterization seems promising. Appli-cations of PXRF for prediction of soil EC are particularly advan-tageous for salinity determination in situ and in instances whereproximally sensed data are already being collected for otherparameters of interest. Our study indicates that soil salinity canbe reasonably predicted using simple elemental data and predic-tive models—results that can also be extended to soil spatial andtemporal variability analysis. Other approaches seek to combinePXRF data with other remotely or proximally sensed data to im-prove model predictability. Aldabaa et al. (2015) demonstratedthat utilization of PXRF data in tandemwith VisNIR and remotelysensed spectral data substantially improved the prediction of soilsalinity in playas of West Texas.

CONCLUSIONSPrevious studies successfully used PXRF to measure physi-

cal, chemical, and morphological properties in soils. Applied tosoil salinity assessment, PXRF is capable of providing data onup to 20 elements more quickly (seconds to minutes) than tradi-tional soil analysis. This research sought to develop a method ofusing proximally sensed PXRFelemental data to directly predict soilsalinity. In doing so, the PXRF yielded information on the elementalabundance of the various ions commonly contributing to soil salin-ity (e.g., Ca, K, S, Cl). Furthermore, this technique has the potentialto be conducted on-site with minimal to no sample pre-preparationand no destruction of the sample in conducting the analysis.

Salt-impacted soil samples were collected from Louisianacoastal parishes, representing awide variety in organicmatter, par-ticle size distribution, and salinity. Samples were subjected to tra-ditional methods of measuring physical and chemical properties,with subsequent elemental quantification via PXRF. Simple andmultiple linear regression models were created to relate EC toPXRF data as a method of measuring salinity in situ. Althoughboth models resulted in similar acceptable calibration r2 (0.83,and 0.90, respectively), multiple linear regression is recom-mended given its superior predictive accuracy. Summarily, thespeed, portability, and accuracy of PXRF offer formidable advan-tages over traditional analysis of soil salinity.

ACKNOWLEDGMENTSThe authors gratefully acknowledge the contributions of

Kelly Polander and the support from the BL Allen Endowment inPedology at Texas Tech University in conducting this research.

REFERENCESAkaike H. 1973. Information theory and an extension of maximum likelihood

principle. In: Petrov B. N., and F. Csáki (eds.). Second International Sympo-sium on Information Theory. Akadémia Kiadó, Budapest, Hungary,pp. 267–281.

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Aldabaa A. A. A., D. C.Weindorf, S. Chakraborty, A. Sharma, and B. Li. 2015.Combination of proximal and remote sensing methods for rapid soil salinityquantification. Geoderma 239-240:34–46.

Anderson M., and A. Olin. 1990. Determination of bromine, chlorine, sulfur,and phosphorus in peat by X-ray fluorescence spectrometry combined withsingle element and multi-element standard addition. Talanta. 37:185–190.

Argyraki A., M. H. Ramsey, and P. J. Potts. 1997. Evaluation of portable X-rayfluorescence instrumentation for in situ measurements of lead on contami-nated land. The Analyst. 122:743–749.

Ben-Dor E., and A. Banin. 1989. Determination of organic matter content inarid-zone soils using a simply “loss-on-ignition” method. Comm. SoilSci. Plant Anal. 20:1675–1695.

Benito G., P. Perez del Campo, M. Gutierrez-Elorza, and C. Sancho. 1995. Nat-ural and human-induced sinkholes in gypsum terrain and associated envi-ronmental problems in NE Spain. Environ. Ecol. 25:156–164.

Biggs T.W., and B. Jiang. 2009. Soil salinity and exchangeable cations inwaste-water irrigated area, India. J. Environ. Qual. 38:887–896.

Box G. E. P., and D. R. Cox. 1964. An analysis of transformations. J. Royal Sta-tistical Soc. B Methodol. 26:211–252.

Brady N. C., and R. R.Weil. 2008. The nature and properties of soils. (14th ed.)Upper Saddle River, NJ, Prentice Hall.

Bray R. H. 1929. A field test for available phosphorous in soilsUniversity of Illi-nois (Urbana-Champaign) Agricultural Experiment Station Bulletin No. 337.

Caballero R., A. Bustos, and R. Roman. 2001. Soil salinity under traditionaland improved irrigation schedules in Central Spain. Soil Sci. Soc. Am. J.65:1210–1218.

Cook D., and S. Wiesberg. 1982. Residuals and influence in regression. NewYork, NY, Chapman and Hall.

Corwin D. L., and S. M. Lesch. 2001. Application of soil electrical conductivityto precision agriculture. Agron. J. 95:455–471.

Gee G. W., and J. W. Bauder. 1986. Methods of Soil Analysis-Part 1: PhysicalandMineralogicalMethods. Soil Science Society ofAmerica.WI,Madison.

Hao X., and C. Chang. 2003. Does long-term heavy cattle manure applicationincrease salinity of clay loam sol in semi-arid southern Alberta? Agric.Ecosyst. Environ. 94:89–103.

Hogg T. J., and J. L. Henry. 1984. Comparison of 1:1 and 1:2 suspensions andextracts with the saturation extract in estimating salinity in Saskatchewansoils. Can. J. Soil Sci. 64:699–704.

Hoppin J. A., A. C. A. Aro, P. L. Williams, H. Hu, and P. B. Ryan. 1995. Vali-dation of K-XRF bone lead measurement in young adults. Environ. HealthProspect. 103:78–83.

Jones A. A. 1982. Methods of Soil Analysis-Part 2: Chemical andMicrobiolog-ical Properties Soil Science Society of America, Madison, WI.

Mayer D.G., andD.G. Butler. 1993. Statistical validation. Ecol.Model. 68:21–32.

Merrill S. D., E. J. Doering, and J. F. Power. 1980. Changes in sodicity and salinityin soils reconstructed on strip-mined land. NorthDakota FarmRes. 37:13–16.

PapachristodoulouC., A.Oikonomou,K. Ioannides, andK.Gravani. 2006.A studyof ancient pottery by means of X-ray fluorescence spectroscopy, multivariatestatistics and mineralogical analysis. Anal. Chim. Acta. 573-574:347–353.



www.soilsci.com


Piorek S. 1998. Determination of Metals in Soils by Field-Portable XRF Spec-trometry. Current Protocols in Field Analytical Chemistry. John Wiley &Sons, New York, NY, pp. 3B.1.1–3B.1.18.

Rhoades J. D., N. A. Manteghi, P. J. Shouse, and W. J. Alves. 1987. Soil electri-cal conductivity and soil salinity: new formulation and calibrations. SoilSci. Soc. Am. J.. 53(2):433–439.

Rhoades J. D., B. L.Waggoner, P. J. Shouse, andW. J. Alves. 1989. Determiningsoil salinity from soil and soil-paste electrical conductivities: Sensitivityanalysis of models. Soil Sci. Soc. Am. J. 53:1368–1374.

Saadi I., Y. Laor, M. Raviv, and S. Medina. 2007. Land spreading of olive millwastewater: Effects on soil microbiology activity and potential phytotoxic-ity. Chemosphere. 66:75–83.

Institute SAS. 2011. The SAS System for Windows, Release 9.3, SAS Institute,Cary, NC.

Schoeneberger P. J., D. A. Wysocki, E. C. Benham, and W. D. Broderson (eds.).2002. Field Book for Describing and Sampling Soils, Version 2.0. USDA-NRCS. National Soil Survey Center, Lincoln, NE.

Schroeder L. D., D. L. Sjoquist, and P. E. Stephan. 1986. Understanding regres-sion analysis. New York, NY, Sage Publications.

Sharma A., D. C. Weindorf, T. Man, A. Aldabaa, and S. Chakraborty. 2014a.Characterizing soils via portable X-ray fluorescence spectrometer: 3. Soilreaction (pH). Geoderma 232-234:141–147.

Sharma A., D. C.Weindorf, D. D.Wang, and S. Chakraborty. 2014b. Character-izing soils via portable X-ray fluorescence spectrometer: 4. Cation ex-change capacity (CEC). Geoderma 239–240:130–134.

Soil Survey Staff. 1993. Soil Survey Manual. United States Department ofAgriculture Handbook No. 18.USDA-NRCS. USGovernment Printing Of-fice, Washington, DC.

Soil Survey Staff. 1995. Soil Survey of Cameron Parish, Louisiana. USDA-NRCS, US Government Printing Office, Washington, DC.

Staff S. S. 1983. Soil Survey of Jefferson Parish, Louisiana. In: USDA-NRCS.US Government Printing Office, Washington, DC.

Staff S. S. 2000. Soil Survey of Plaquemines Parish, Louisiana. In: USDA-NRCS. US Government Printing Office, Washington, DC.



Staff S. S. 2010. Keys to Soil Taxonomy, 11th ed. In: USDA-NRCS. US Gov-ernment Printing Office, Washington, DC.

Swanhart S. 2013.Measuring soluble salts in soils via portable X-ray fluorescencespectrometry. MS Thesis, Louisiana State University, Baton Rouge, LA.

US Salinity Laboratory Staff. 1954. Diagnosis and Improvement of Saline andAlkali Soils. United States Department of Agriculture Handbook No. 60.US Government Printing Office, Washington, DC.

US Environmental Protection Agency. 2007. Method 6200: Field portableX-ray fluorescence spectrometry for the determination of elemental concen-trations in soil and sediment. Available at www.epa.gov (verified January11, 2012), USEPA, Washington, DC.

Viscarra Rossel R. A., and T. Beherens. 2010. Using data mining to model andinterpret soil diffuse reflectance spectra. Geoderma. 158:46–54.

Wang H., Y. P. Hsieh, M. A. Harwell, andW. Huang. 2007. Modeling soil salin-ity distribution along topographic gradients in tidal salt marshes in Atlanticand Gulf coastal regions. Ecol. Model. 201:429–439.

Weindorf, D. C., Y. Zhu, R. Ferrell, N. Rolong, T. Barnett, B. Allen, J. Herrero,and W. Hudnall. 2009. Evaluation of portable x-ray fluorescence for gyp-sum quantification in soils. Soil Sci. 174:556–562.

Weindorf, D. C., J. Herrero, N. Bakr, and S. Swanhart. 2013. Direct soil gypsumquantification via portable x-ray fluorescence spectrometry. Soil Sci. Soc.Am. J.

Weindorf D. C., Y. Zhu, B. Haggard, J. Lofton, S. Chakraborty, N. Bakr, W.Zhang, W. Weindorf, and M. Legoria. 2012. Enhanced pedon horizonationusing portable X-ray fluorescence spectrometry. Soil Sci. Soc. Am. J.76:522–531.

Weindorf D. C, N. Bakr, and Y. Zhu. 2014. Advances in portable X-ray fluores-cence (PXRF) for environmental, pedological, and agronomic applications.Adv. Agron. 128:1–45.

Zeng L., and M. C. Shannon. 2000. Salinity effects on seedling growth andyield components of rice. Crop Sci. 40:996–1003.

Zhu, Y., D. C. Weindorf, and W. Zhang. 2011. Characterizing soils using aportable X-ray fluorescence spectrometer: 1. Soil texture. Geoderma.167–168:167–177.

www.soilsci.com 423


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