soil salinity and sodicity appraisal by electromagnetic induction in soils irrigated to grow cotton

land degradation & developmentLand Degrad. Develop. (2012)

Published online in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/ldr.1162

SOIL SALINITY AND SODICITY APPRAISAL BY ELECTROMAGNETICINDUCTION IN SOILS IRRIGATED TO GROW COTTON

G.K. GANJEGUNTE*, Z. SHENG AND J.A. CLARKTexas AgriLife Research, Texas A&M University, 1380 A&M Circle, El Paso, TX 79927, USA

Received: 29 April 2011; Revised: 13 September 2011; Accepted: 28 October 2011

ABSTRACT

In the Far West Texas region in the USA, long-term irrigation of fine-textured valley soils with saline Rio Grande River water has led to soilsalinity and sodicity problems. Soil salinity [measured by saturated paste electrical conductivity (ECe)] and sodicity [measured by sodiumadsorption ratio (SAR)] in the irrigated areas have resulted in poor growing conditions, reduced crop yields, and declining farm profitability.Understanding the spatial distribution of ECe and SAR within the affected areas is necessary for developing management practices. Conven-tional methods of assessing ECe and SAR distribution at a high spatial resolution are expensive and time consuming. This study evaluated theaccuracy of electromagnetic induction (EMI), which measures apparent electrical conductivity (ECa), to delineate ECe and SAR distributionin two cotton fields located in the Hudspeth and El Paso Counties of Texas, USA. Calibration equations for converting ECa into ECe and SARwere derived using the multiple linear regression (MLR) model included in the ECe Sampling Assessment and Prediction program packagedeveloped by the US Salinity Laboratory. Correlations between ECa and soil variables (clay content, ECe, SAR) were highly significant(p≤ 0�05). This was further confirmed by significant (p≤ 0�05) MLRs used for estimating ECe and SAR. The ECe and SAR determinedby ECa closely matched the measured ECe and SAR values of the study site soils, which ranged from 0�47 to 9�87 dSm�1 and 2�27 to 27�4mmol1/2 L�1/2, respectively. High R2 values between estimated and measured soil ECe and SAR values validated the MLR model results.Results of this study indicated that the EMI method can be used for rapid and accurate delineation of salinity and sodicity distribution withinthe affected area. Copyright © 2012 John Wiley & Sons, Ltd.

key words: electromagnetic induction technique; apparent electrical conductivity; soil salinity; soil sodicity; sodium adsorption ratio; salinity management; USA

INTRODUCTION

In the Far West Texas region that includes El Paso andHudspeth Counties, most of the irrigated area is affectedby varying degrees of salinity and sodicity (Ghassemi et al.,1995). The Rio Grande River is the main source of irrigationin the region, and during the irrigation season, its salinityoften exceeds 0�75 dSm�1, the acceptable level for sensitivecrops (Ganjegunte and Braun, 2010). Most of the irrigatedarea is located in the valley and has fine-textured soils withinthe effective crop root zone. Long-term irrigation with salinewater has led to serious soil salinity problems in manyirrigated regions (Wittler et al., 2006; Eldeiry and Garcia,2008; Ganjegunte et al., 2011a, 2011b). Soil salinity reducesthe availability of water, selected plant nutrients, andincreases the toxicity of certain ions, whereas soil sodicitydecreases the permeability of soils (Ganjegunte and Vance,2006; Ganjegunte et al., 2008; Vance et al., 2008). Criticalvalues of soil salinity [saturated paste extract (SPE)

*Correspondence to: Dr Girisha K. Ganjegunte, Assistant Professor,Department of Soil and Crop Sciences, Texas Agrilife Research Center atEl Paso, The Texas A&M University System, 1380 A&M Circle, El Paso,TX 79927-5020, USA.E-mail: [email protected]

Copyright © 2012 John Wiley & Sons, Ltd.

electrical conductivity (ECe)] and sodicity [SPE sodium ad-sorption ratio (SAR)] have been proposed at 4 dSm�1 and13mmol1/2 L�1/2 (U.S. Salinity Laboratory Staff, 1954;Johnston et al., 2008).Appraisal of salinity and sodicity distribution in agricul-

ture soils is the necessary first step for developing appropri-ate salinity management practices. Cotton (Gossypiumhirsutum L.) is a major crop and occupies more than halfof the total cropped area in the Far West Texas region(Michelsen et al., 2009). Although cotton is considered asa salt tolerant crop with a threshold value of 7�7 dSm�1

(Maas, 1990), in many parts of the El Paso and Hudspethirrigation districts, soil salinity exceeds this threshold value.Elevated salinity and sodicity of irrigated soils have resultedin poor growing conditions, reduced crop yields, and declin-ing farm profitability. Therefore, it is important to developsuitable salinity management for ensuring long-term viabil-ity of irrigated agriculture. At present, accurate data onspatial distribution of salinity and sodicity are not availableat a field scale for the Far West Texas region. Traditionalmethods of determining salinity and sodicity (ECe andSAR) distribution within the irrigated fields at a high resolu-tion by chemical analysis are labor intensive, time consum-ing, and expensive.

G. K. GANJEGUNTE ET AL.

In recent years, various remote sensing techniques havebeen used to identify, map, and monitor salt-affected soils(Eldeiry and Garcia, 2008). Among the different techniquesused at field scale, the electromagnetic induction (EMI)method of measuring apparent electrical conductivity(ECa) is gaining acceptance (Triantafilis et al., 2000; Corwinet al., 2006; Wittler et al., 2006; Amezketa and Del Valle deLersundi, 2008). Although EMI is a rapid and non-invasivetechnique, calibration of EMI signals is site specific. EMIreadings are influenced by properties such as field moisturecontent, clay content, clay type, salinity, and organic mattercontent of the soil (Friedman, 2005; Sudduth et al., 2005;Ganjegunte and Braun, 2011c). Use of EMI signals for pre-dicting the effect of salinity on crop growth requires conver-sion of EMI signals (ECa) into ECe. A significant effort hasbeen directed towards developing efficient conversion mod-els (Rhoades et al., 1990; Lesch et al., 1995; Triantafiliset al., 2001; Herrero et al., 2003; Corwin and Lesch, 2005a).Among the approaches, the ECe Sampling Assessment andPrediction (ESAP) model, which uses a surface-response ap-proach and combines both stochastic and deterministic model-ing methodologies, has been successfully used for delineatingthe spatial distributions of soil properties from ECa survey data(Lesch, 2006).Although ECa is generally used for measuring soil salin-

ity, it can also be used to determine spatial distribution ofSAR, if there is a strong correlation between ECe and SAR(Amezketa, 2007). There are only a few studies on the useof EMI for evaluating the distribution of both salinity andsodicity of soils across the globe. This study was carriedout to evaluate the accuracy of the EMI technique to delin-eate both salinity and sodicity distribution by using theESAP model in two cotton fields located in El Paso andHudspeth Counties of Texas, USA.

MATERIALS AND METHODS

Study Sites

This study was conducted in two irrigated commercial cottonfields, one site with an area of about 12 ha (~400m� 300m)located in El Paso County, Texas (31� 39′3100N, 106� 16′1700W) and the other site with an area of 1�6ha (200m� 80m)located in Hudspeth County, Texas (31� 17′1900N, 105� 51′1700W). In the El Paso cotton study site, 50 per cent of the areais under Tigua (very-fine, montmorillonitic (calcareous),thermic Vertic Torrifluvents), 25 per cent under Saneli (clayeyover sandy or sandy-skeletal, montmorillonitic (calcareous),thermic Vertic Torrifluvents), 15 per cent under Harkey(coarse-silty, mixed (calcareous), thermic Typic Torrifluvents),and 10 per cent under Glendale (fine-silty, mixed (calcareous),thermic Typic Torrifluvents) map units. Soil taxonomy infor-mation for the Hudspeth cotton study site is not availableas the soil survey information for the Hudspeth County is


incomplete (USDA-NRCS, 2010). However, the Hudspethsoils are very similar to the El Paso site soils, being mostlyTorrifluvents.Both the study sites have been laser leveled and are flood

irrigated from irrigation ditches located at the highest eleva-tion side of the field. In the El Paso study site, the irrigationchannel is located on the south side, and in the Hudspethsite, it is on the east side. Both study sites have a shallowgroundwater table (at a depth of about 2�5m) that has ele-vated salinity (>1000mgL�1). Each of the sites receivedthe same amount of irrigation water of about 84 cm y�1

and the same amount of fertilizers—N, P, K, Mg, and S of300, 26, 10, 13, and 25 kg ha�1 y�1, respectively. In bothsites, soils contained native calcite (up to 10 per cent) andnative gypsum (up to 2 per cent) at various depths.

EMI Instrument and EMI Survey

Apparent electrical conductivities (ECa) of two cotton studysites were measured by the EMI instrument-EM38W manu-factured by Geonics Limited, Ontario, Canada. In each studysite, 10 days after irrigation, ECa measurements were takenwith EM38 coil configuration oriented in the horizontalposition, which provided an effective measurement depthof about 75 cm, which covered the effective root zone ofcotton. In this study, the vertical mode (EM38v) signal datawere not collected as horizontal mode was enough to coverthe effective root zone of cotton plants. At the El Paso studysite, EM38 was mounted on a custom-built wooden sleddragged behind a vehicle (Jeep Cherokee), ensuring enoughgap (3m) between the Jeep and the sled to avoid signal inter-ference. At the Hudspeth study site, an EMI survey was car-ried out manually. The ECa measurements with EM38 weretaken in lines that were 4�7m apart and parallel to cottonrows at both the study sites. The length of the survey lineswere 400m in the El Paso cotton field and 200m in Hud-speth. Both ECa and GPS data were logged at a high fre-quency of two readings per second.

Soil Sample Collection and Analyses

After the EMI survey, in each study site, 12 sampling sitesthat covered the full range of ECa measurements over theentire field were selected for calibration. Sampling site selec-tion was carried out using the ESAP-RSSD software pack-age version 2�35RW (Lesch, 2006), which uses the‘response surface sampling design’ statistical method. Ateach of these 24 sampling sites, soil samples were collectedfrom 0- to 75-cm depth at 15-cm intervals (0–15, 15–30,30–45, 45–60, and 60–75 cm) by using Giddings mechani-cal soil coring equipment. Thus, a total of 120 soil sampleswere collected to calibrate ECa data.Soil samples were air-dried, ground, and passed through a

2-mm sieve. Sub-samples were used to determine soiltexture by using the hydrometer method (Gee and Or, 2002).

LAND DEGRADATION & DEVELOPMENT (2012)

COTTON SOIL SALINITY AND SODICITY APPRAISAL BY EMI

Processed soil samples were analyzed for salinity by determin-ing SPE electrical conductivity ECe (Rhoades, 1996); pH(Thomas, 1996); and Na, Ca, and Mg concentrations using in-ductively coupled plasma spectrometry (Helmke and Sparks,1996; Suarez, 1996). SARs of the SPEs were estimated fromCa,Mg, andNa concentrations by using the following equation(Essington, 2003):

SAR ¼ Naþ= Ca2þ þMg2þ� �1=2

(1)

Where: Ca, Mg, and Na represent millimolar concentrationsof the respective ions.

Data Analyses and ESAP MLR Model Validation

Statistical analyses of data were carried out using the ESAPsoftware package, version 2�35RW, Genstat software (version4�1W), and the cross-validation tool in the mapping software(Surfer version 8�0, Golden Software Inc., Golden, CO).Composite soil profile (0–75 cm) properties were estimatedby taking the average of all five depths. The relationship be-tween the ECa readings and composite soil profile (0–75 cm)properties was determined by simple linear correlation andregression analyses at p≤ 0�05 by using the Genstat soft-ware (version 4�1). The calibration equations for convertingECa into ECe and SAR for each of the study sites werederived using the multiple linear regression (MLR) modelincluded in ESAP- CALIBRATE with log-transformed sig-nal combined with the trend surface parameters (x, y = scaledlocation coordinates of each survey area). De-correlated EMIdata (z1) and scaled location coordinates (x, y) were used aspredictor variables in the regression equation. The MLRmodel for estimating salinity or sodicity (A) has a generalform of:

ln Að Þ ¼ b0þ b1 z1ð Þ þ b2 xð Þ þ b3 yð Þ (2)

Where: b0 = intercept, z1 = a1[ln ECa�mean(ln ECa)],a1 = 1/[standard deviation (ECa)]; x = (u�min(u)/k), y =v�min[v]/k, u and v are raw location coordinates, and k isthe greater of [max(u)�min(u)] or [max(v)�min(v)].

Table I. Mean and range statistics of electromagnetic induction (horizon

Number Minimum Maximum

El PasoECa (dSm

�1) 3931 0�01 1�25ECe (dSm

�1) 12 0�47 9�87SAR (mmol1/2 L�1/2) 12 2�27 27�40Clay (%) 12 5�50 52�00HudspethECa (dSm

�1) 894 1�20 2�26ECe (dSm

�1) 12 2�43 8�92SAR (mmol1/2 L�1/2) 12 12�90 20�50Clay (%) 12 23�50 41�80


To choose the best equation for calibrating the EM38, weselected the model with all parameters significantly differentfrom zero (at p≤ 0�05) and with the smallest ‘sum of squaresof prediction errors’ (PRESS score, i.e., predicted residualsum of squares). The residual spatial independence was ex-amined using the Moran residual autocorrelation test (Leschet al., 1995).Model-generated ECe and SAR values based on ECa for

the survey data points were imported into the mapping soft-ware (Surfer, version 8�0W). Omni-directional variogramswere computed for the composite profile ECe and SARvalues. Both ECe and SAR experimental variograms werebest fitted with a linear model with nugget effect. Thus, alinear model with nugget effect of point kriging methodwas used for interpolation of ECe and SAR data.Validity of the gridding method was determined by exam-

ining the three statistics provided in cross-validation reportgenerated by the Surfer software: residual median absolutedeviation, residual standard deviation, and rank correlationbetween the measured and the estimated Z (Kitanidis,1997). Calibration (MLR) model equations were validatedon the basis of the regression between the model estimatedand measured composite values.

RESULTS AND DISCUSSION

Soil Properties and EMI Data

The mean and range statistics for ECa determined by theEMI and selected soil properties are presented in Table I.As the ECa data are for the top 75 cm, only composite(0–75 cm) data for soil properties have been presented forcomparison. The mean values for ECe and SAR suggested thatsoils in the top 75 cm at both the study sites can be classified assaline–sodic (ECe> 4 dSm�1, SAR> 13mmol1/2 L�1/2). Themaximum values of ECe at both the sites exceeded the thresh-old salinity value of 7�7 dSm�1 for cotton, indicating thatsalinity in parts of the study sites were limiting for cotton.At the El Paso study site, coefficients of variation for ECe

tal) readings and soil variables for top 75-cm depth

Mean Standard deviation Coefficient of variation (%)

0�50 0�26 525�57 3�32 60

16�10 8�23 5129�20 13�20 45

1�65 0�16 105�27 1�98 38

15�90 2�41 1533�40 5�03 15


y = 8.5754x + 0.6458R2 = 0.8618

0

2

4

6

8

10

12

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4

ectr

ical

Con

duct

ivit

y (E

Ce,

dS

m-1

)

(El Paso)


and SAR were higher than those of Hudspeth, probablybecause of the larger area at the El Paso site. In both the sites,the average soil texture for top 75-cm depth was clay loam.Whereas soil texture did not change with depth at the El Pasostudy site, in the Hudspeth study site, soil texture class wasclay loam at 0–45 cm and loam at 45–75-cm depth. Averagesand, silt, and clay contents in the top 75 cm at the El Pasoand Hudspeth study sites were 34, 37, and 29 and 32, 38,and 33 per cent, respectively.Simple correlation and regression analyses between ECa

and selected soil properties suggested that the ECa of the soilhad significant positive (p≤ 0�05) correlations with claycontent, ECe, and SAR (Table II). Soil clay serves as a reser-voir of cations such as Ca2+ or Na+, and the concentration ofcounter ions adsorbed on the clay complex constitutes amajor contribution of electrical conductivity in fine texturedsoils (Friedman, 2005; Jung et al., 2005; Triantafilis andLesch, 2005). Thus, it is expected that the clay content ofthe soil will have a significant positive correlation with elec-trical conductivity of the soil. Simple linear regressionbetween ECa and ECe was significant (Figure 1). About 86and 65 per cent of the variations in ECe at the El Paso andHudspeth cotton study sites could be explained by the varia-tions in ECa. Strong positive correlation between ECa andECe suggested that the EMI method can provide an accuraterepresentation of soil profile salinity levels at these sites.Corwin and Lesch (2005a) stated that the electrical con-

ductivity of saturated paste (ECe) is the dominant dynamicsoil property (a property that changes with depth and loca-tion within the irrigated area) that has a strong influence onthe ECa measurements in salt-affected irrigated soils of thearid regions. In arid irrigated areas such as El Paso, saltaccumulation is a result of irrigation with saline irrigationwater and high evapo-transpiration. Both static propertiessuch as clay content and dynamic properties such as ECe

strongly influenced the ECa values and were responsiblefor the differences in relationship between ECa and soil

Table II. Simple correlation coefficients (r) among selected vari-ables in two study sites

ECa

(dSm�1)ECe

(dSm�1)SAR

(mmol1/2 L�1/2)

El PasoECe (dSm

�1) 0�928SAR(mmol1/2 L�1/2)

0�831 0�913

Clay (%) 0�762 0�636 0�722HudspethECe (dSm

�1) 0�805SAR(mmol1/2 L�1/2)

0�788 0�920

Clay (%) 0�902 0�679 0�745All the above r values were significant at p≤ 0�05.


properties. For example, the El Paso site soils had relativelygreater clay content compared with that of the Hudspethsite soils, and this could explain relatively higher R2

value between ECe and ECa at the El Paso site than atthe Hudspeth site.The ECa and SAR values had a strong positive correlation

at each of the study sites (Figure 2). Other researchers havealso found strong correlations between ECa readings andSAR (Corwin and Lesch, 2005b; Corwin et al., 2006). Thissignificant positive relationship between ECa and SAR ismainly due to strong correlation between ECe and SAR(Nelson et al., 2002; Amezketa, 2007) (Table II; Figure 3).A strong correlation between ECe and SAR has beenobserved in many arid regions that are characterized by soilsalinization due to evapo-concentration (Corwin et al.,2003; Amezketa, 2007; Ganjegunte et al., 2011d). In soilsthat accumulate salts due to evapo-concentration, ECe andSAR are directly correlated. This is because of the salt con-centrating effects of high evaporation rate in the study sitesand selective precipitation of Ca minerals, especially if thesoil has significant amounts of carbonates, which is truefor the El Paso and Hudspeth soils. In addition, both study

y = 6.1008x - 5.3699R2 = 0.6482

0

1

2

3

4

5

6

7

8

9

10

1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4

Satu

rate

d P

aste

Ext

ract

El

Apparent Electrical Conductivity by EMI (ECa, dS m-1)

(Hudspeth)

Figure 1. Relationship between average soil profile (0–75 cm) saturated pasteextract electrical conductivity (ECe) and the apparent electrical conductivity

(ECa) measured by EMI technique at El Paso and Hudspeth study sites.


y = 19.04x + 5.2117R = 0.6897

0

5

10

15

20

25

30

0 0.2 0.4 0.6 0.8 1 1.2 1.4

y = 7.2826x + 3.2103R = 0.6214

10

12

14

16

18

20

22

1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4

(El Paso)

(Hudspeth)

Sodi

um A

dsor

ptio

n R

atio

(SA

R, m

mol

1/2L

-1/2

)

Apparent Electrical Conductivity by EMI (ECa, dSm-1)

Figure 2. Relationship between average soil profile (0–75 cm) saturated pasteextract sodium adsorption ratio (SAR) and the apparent electrical conductivity

(ECa) measured by EMI technique at El Paso and Hudspeth study sites.

y = 1.1214x + 10.002R2 = 0.8461

10

12

14

16

18

20

22

1 2 3 4 5 6 7 8 9 10

y = 2.2661x + 3.5229R2 = 0.8337

0

5

10

15

20

25

30

0 2 4 6 8 10 12

(El Paso)

(Hudspeth)

Sodi

um A

dsor

ptio

n R

atio

(SA

R, m

mol

1/2 L

-1/2

)

Saturated Paste EC (ECe, dSm-1)

igure 3. Relationship between average soil profile (0–75 cm) saturatedaste extract sodium adsorption ratio (SAR) and the electrical conductivity

(ECe) at El Paso and Hudspeth study sites.


sites have shallow groundwater table (about 2�5m), and thisgroundwater has high salinity with total dissolved solidsconcentration greater than 1000mgL�1 (Hibbs and Boghici,1999). In regions characterized by high potential evapo-transpiration and low rainfall, high salinity groundwatertable helps in evapo-concentration of salts with concurrentSAR increase. Thus, the EMI technique can be a valuabletool under these conditions to delineate the spatial distribu-tion of both ECe and SAR.

Estimating ECe and SAR Distribution in Study Sites from ECa

The R2 values for MLR models used to predict ECe andSAR of soils from ECa at the two study sites were highlysignificant, ranging from 0�80 to 0�88 (Table III). Moranspatial auto correlations were non-significant, indicating thatresiduals of regression models were normally distributionwith homogenous variance. The MLRs for estimating ECe

and SAR at both the study sites had a similar form exceptfor the ECe at the El Paso study, which could be due tovariations in clay content. Although clay content was notan explanatory variable in the MLRs, through its influenceon ECa (z1 parameter was derived from ECa values) it ispossible that calibration models were greatly influenced bysoil clay content. Strong R2 between the estimated and


Fp

measured ECe and SAR for calibrating sites validated thecalibration (Table IV).Point kriging using a linear model with nugget effect fitted

the experimental variograms well for both the study sites. Atthe El Paso field, experimental variogram parameters wereas follows: ECe (nugget effect =1�471, slope = 993�2,anisotropy ratio = 2, angle = 58�39 degrees, lag distance =0�0022 arc degrees or approximately 245m) and SAR (nug-get effect =12�4, slope = 6570, anisotropy ratio = 1, angle =0 degree, lag distance = 0�0022 arc degrees or approximately245m). At the Hudspeth cotton field, experimental variogramparameters were as follows: ECe (nugget effect = 0�287,slope = 1030, anisotropy ratio = 1, angle = 0 degree, lag dis-tance = 0�00074 arc degrees or approximately 80m) and SAR(nugget effect = 0�477, slope=1710, anisotropy ratio =1,angle =0degree, lag distance = 0�00074 arc degrees or ap-proximately 80m). Fitted variogram models showed that themeasurement errors were small, and local variations in ECa

were probably due to variations in soil properties, particularlythe clay content (Corwin and Lesch, 2005a; Friedman, 2005;Triantafilis and Lesch, 2005; Weller et al., 2007).Thus, maps of spatial distribution of predicted average

soil profile salinity (ECe) and sodicity (SAR) from MLR inthe El Paso and Hudspeth study sites were prepared usingpoint kriging using a linear model with nugget effect


Table III. Multiple linear regression models for estimating average soil profile (0–75 cm) saturated paste ECe and SAR based on horizontalelectromagnetic induction readings in two study sites

El Paso Hudspeth

ECe Model selected ln(ECe) = b0 + b1(z1) + b2(x) + b3(y) ln(ECe) = b0 + b1(z1)Model R2 0�87 0�88Root MSE 0�31 0�36Estimated CV (%) 31 29p 0�04 0�03Model parametersb0 264 (190) �0�115 (0�519)z1 3�70 (3�29) 1�03 (0�414)X �131Y �267

SAR Model selected ln(SAR) = b0 + b1(z1) ln(SAR) = b0 + b1(z1)Model R2 0�86 0�80Root MSE 0�28 0�14Estimated CV 27 14p 0�04 0�01Model parametersb0 2�60 (0�228) 1�966 (0�152)z1 �2�67 (1�98) 0�386 (0�121)

b0 = intercept, z1 = a1[ln ECa�mean(In ECa)], a1 = 1/[standard deviation (ECa)]; x= (u�min(u)/k), y= v�min[v]/k, u and v are raw location coordinates, andk is the greater of [max(u)�min(u)] or [max(v)�min(v)]. Figures in parentheses indicate standard error. Root MSE is the square root of mean square of error.


(Figures 4 and 5). The ECe maps (Figures 4a and 5a showedthat large areas in both the El Paso and Hudspeth study siteswere saline (ECe> 4 dSm�1), and many parts of the studysites had soil profile ECe that exceeded the cotton salinitythreshold. The SAR maps (Figures 4b and 5b indicated thatsaline areas coincided with the areas that were highly sodic.A majority of the sodic areas in both study sites exceededthe acceptable level of 13mmol1/2 L�1/2. This indicated thatthe poor permeability of sodic areas is facilitating salt accumu-lation resulting in saline–sodic conditions.Maps showed that the north western part in both the study

sites were more saline–sodic than south eastern part in ElPaso and eastern part in Hudspeth. Study sites are flood irri-gated by an irrigation ditch located at the highest elevationpoint in the field. In the El Paso study site, the irrigationchannel is located on the south side, and in Hudspeth it ison the east side. Flood irrigation tends to over-irrigate nearthe gate and under-irrigate at the farthest end of the field. Thus,

Table IV. Linear regression results between MLR models esti-mated and measured ECe and SAR values at El Paso and Hudspethstudy sites

R2

El PasoECe 0�91SAR 0�89HudspethECe 0�93SAR 0�90All coefficient of determination (R2) values were significant at p= 0�05.

Figure 4. Spatial distribution of average soil profile (0–75 cm) (a) saturatedpaste electrical conductivity (ECe) and (b) sodium adsorption ratio (SAR) inthe El Paso study site estimated on the basis of apparent electrical conduc-tivity (ECa) measured by EMI technique. This figure is available in colour

online at wileyonlinelibrary.com/journal/ldr

Copyright © 2012 John Wiley & Sons, Ltd. LAND DEGRADATION & DEVELOPMENT (2012)

Figure 5. Spatial distribution of average soil profile (0–75 cm) (a) saturatedpaste electrical conductivity (ECe) and (b) sodium adsorption ratio (SAR) inthe Hudspeth study site estimated on the basis of apparent electrical conduc-tivity (ECa) measured by EMI technique. This figure is available in colour

online at wileyonlinelibrary.com/journal/ldr


areas closer to irrigation channel were less saline–sodic com-pared with areas further away from the irrigation ditch. Varia-tions in salts and sodium accumulation within the study sites(spikiness of the fields observed in Figures 4 and 5) could alsobe due to variations in the clay content. It is evident fromcorrelation analysis (Table II) that in the study area, ECe andSAR values had significant positive correlation with the claycontent. Although salinity and sodicity were variable withina study site, both study sites had areas that exceeded thethreshold ECe value for cotton, supporting the wet chemistryresults. Thus, use of the EMI technique can provide accurateinformation on spatial distribution of both salinity and sodicityfrom a combination of relatively smaller number of soil sam-pling sites and intensive EMI survey. In addition, the relativecost of ECe and SAR determination by EMI method is signif-icantly cheaper compared with that of conventional methods(Pozdnyakova and Zhang, 1999).

CONCLUSIONS

The results of this study indicated that the top 75 cm of soilsin both the cotton fields can be categorized as saline and


sodic. Most of the area within the study sites exceeded thethreshold ECe values for cotton, confirming the observationsby the grower and the authors of this study that the cropgrowth is limited at the study sites because of elevated salin-ity. The apparent electrical conductivity (ECa) measured byEMI technique at both the study sites were strongly corre-lated with soil clay content, SPE ECe, and SAR values. Astrong correlation between ECe and SAR indicated thatevapo-concentration was the dominant mode of salinizationin both the study sites. A highly significant R2 (at p≤ 0�05)for MLR models indicated that ECa can be used to predictECe and SAR of soils at two study sites with a high degreeof accuracy. Thus, the results of this study indicated thatthe EMI method can be used to accurately delineate salinityand sodicity distribution within the affected area at a highspatial resolution in a short time and at a fraction of the costof conventional methods.

ACKNOWLEDGEMENTS

This study was supported by Cotton Incorporated (ProjectID: 07-209) and the Texas State Support Committee (ProjectID: 05-605TX). This study also received support from theNational Institute of Food and Agriculture, US Departmentof Agriculture under the “Efficient Irrigation for Water Con-servation in the Rio Grande Basin” project of the TexasWaterResources Institute. Access to study sites was kindly facili-tated by local cotton growers Mr James Miller and Mr JonWitte. Dr Ganjegunte greatly appreciates Dr Scott Lesch’s(US Salinity Laboratory/Statistical Consulting Collaboratory)training on the ESAP software during the National SalinityTechnology Transfer Workshop. Technical assistance byRobert Braun, Sahara Jordan, Carlos Castro, Curtis Atherton,and Carlos Lara is greatly appreciated.

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http://www.ars.usda.gov/Services/docs.htm?docid=8918

http://websoilsurvey.nrcs.usda.gov/app/WebSoilSurvey.aspx

http://websoilsurvey.nrcs.usda.gov/app/WebSoilSurvey.aspx

soil salinity and sodicity appraisal by electromagnetic induction in soils irrigated to grow cotton

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