ῐ
J. Jpn. Soc. Soil Phys.
ΐῑῐ῔῕ῒ
No. +*., p. ,1ῌ.* ῍,**0῎Soil Salinity Assessment by Some Destructive and
Non Destructive Methods in Calcareous Soils
AHMED Al-Busaidi*, Tahei YAMAMOTO*, Charles BAKHEIT** and Peter COOKSON**** Arid Land Research Center, Tottori University, +-3* Hamasaka, Tottori 02*ῌ***+, Japan** Department of Mathematics and Statistics, College of Science*** Department of Soil and Water Sciences, College of Agriculture, Sultan Qaboos University,
PO Box -. Al-Khod +,-, Sultanate of Oman
Abstract
Salinity is a major problem limiting plant growth throughout the world. Various methods havebeen used to measure soil salinity. The destructive (standard) method involves soil sampling
followed by laboratory measurement of electrical conductivity (EC) in saturated paste extracts, + :+, and + : / soil and water suspensions. Whereas, a non destructive (Sigma probe) method give a
direct measurement of soil pore water salinity. Various methods for measuring salinity arecommonly correlated in similar soils. The objective of this study was to determine the relationships between salinity readings obtained from standard methods and Sigma probe in calcareous
soils of di#erent texture and salinity. Results from the laboratory methods (saturated paste
extracts, + : +, and + : / soil/water ratios) were generally highly correlated (R,῏*.32). Moreover,present of many salts in the soil were a#ecting linearity of the relationship between saturated
paste extracts and + : / ratio in which the best correlation was found when the EC of saturated
paste extract was less than ,* dS m῎+. Sigma probe was successfully correlated with standard
methods. Its readings were relatively a#ected by soil salts and water content, in which the best
correlation was found between Sigma probe reading and EC measured at saturated paste extracts
values, at low water content with EC less than +* dS m῎+. Many salts (ex. Na῍, Ca,῍, Mg,῍ ῌetc.)
were interfering the correlation equations between methods and as soil water increased more salts
were dissolved and gave weak correlation factor (R,). Using Sigma probe removes the need forsoil sampling prior to salinity measurements and should allow in situ soil salinity measurements
to be taken that could be related to standard methods. Moreover, readings of Sigma probe at low
water content, which is around ﬁeld capacity considered as the best measuring conditions.
However, more researches with gradient increase in soil salinity and water content need to be
done. Finally, soil properties and measuring conditions should be considered before applying any
published method or equation for soil and water salinity evaluation. However, if the equations
found in this study are suitable for certain soils, salinity measurements could be done in shorttime by Sigma probe and related to the standard methods.
Key words : Electrical conductivity, Sigma probe, saturated paste extract, soil/solution ratios+.

Introduction

Plant response is more related to the salt
concentration of the soil solution than to thetotal salt content of the soil, on a weight basis(U.S. Salinity Laboratory Sta#, +3/.). Soil salinity can be assessed from the electrical conductivity (EC) measurements of soil water suspensions representative of the soil solution.
Methods are based on the principle that the

῏

28



amount of electrical current transmitted by asalt solution under standardized conditions
will increase as the salt concentration of thesolution is increased (Rowell, +33.).
Salinity of the soil solution can also be estimated from the EC obtained from water extract of the soil. In general, the higher thewater content, the easier it will be to obtain theextract, but the less representative the extracted solution will be the solution to whichplant roots are exposed in the soil. Conductivity of the saturation extract is recommended asa general method for appraising soil salinity inrelation to plant growth. The advantage ofthis method is that the saturation percentage isdirectly related to the ﬁeld water content
range. In addition, it is an easy and accurate
method. Salinity estimates based on conductivity of + : + and + : / extracts are convenient
for rapid determinations, particularly if theamount of soil sample is limited, or when repeated samplings is to be made in the same soil
(Page et al., +32,). Methods of saturation extract and soil/water ratios are destructive,money and time consuming especially if alarge area should be sampled. Thus, there is agreat demand for a new method.
Many sensors (ex. EC probe, four electrode,
Sigma probe and TDR) were invented for easy
and in situ measurements of soil salinity. The
latest invention was time domain reﬂectometer(TDR) meter. It is a famous method for watercontent and salinity determinations based on
detecting reﬂected signals as a function oftime. This method required long time forcalibrations and the initial costs are relatively
high compared to other methods (Hamed et al.,,**-). The other method that was used in thisstudy is Sigma probe. It is a non-destructive
method for a direct measurement of soil salinity. It is an easy method for measuring soil
salinity based on measuring accurately thepermittivity and conductivity of the bulk soil.
Its measures the soil pore water EC independently from soil water content and degree ofcontact between probe electrodes and the soil+*.

,**0(Hamed et al., ,**- and Hilhorst, ,***). For
mapping large areas, evaluating lands for agricultural purposes and monitoring changes insoil salinity over time, Sigma probe is fasterand involves less labor than standard methods.
It is a new device in the ﬁeld of science and its
advantages and limitations need to be evaluated and clariﬁed to the other users. Doing
this study could also help in evaluating theaccuracy of Sigma probe when it was used in
di#erent soil water content in calcareous soils
compared with other standard methods.
Standard methods are well-known and good
indicators for soil salinity but their limitations
directed researchers to look for other methods
and search for correlations between them. The
objective of this study was to determine relationships between methods for measuring soil
salinity, speciﬁcally results from Sigma probe
verse standard methods.
,.

Materials and Methods,. + Sigma probe
Sigma probe (type EC+, Delta T Devices,
Cambridge, U.K) is a portable device (weightῌ
/** g) measures pore water conductivity independently from both soil moisture content andthe degree of contact between the probe andsoil (Fig. +). The measurement is made possible
by use of an integrated circuit to measure accurately the permittivity and conductivity of thebulk soil. This circuit is running inside Sigmaprobe and producing a continuous electricalﬁeld (voltage) around the sensor with frequency of -* MHz (input signals). The ions (salts)
around the electrical ﬁeld will carry the energy(polarization) and the energy lost will be considered as the ions concentrations in the porewater (i.e. reduction in the output signals will
be converted to salinity unit, dS m+). The
conductivity of the pore water (sp) is calculatedusing the following equation :spep*sbῌebe*

ῌWhere ep Permittivity of pore water ; sb
Conductivity of bulk soil ; ebPermittivity ofῒῑ : Soil Salinity Assessment by Some Destructive and Non Destructive Methods in Calcareous Soils 29

bulk soil ; and e*῍Permittivity of dry soil materials.
Sigma probe program eliminates the need forsoil speciﬁc calibration and makes the readings
insensitive to the degree of contact between
probe and soil. The probe tip is speciallydesigned both for ease of insertion into the soil
and to enable local measurements of permittivity and conductivity. It is the combinationof these features, which enables the pore water
conductivity to be calculated reliably from thedielectric readings. The software provided
with Sigma probe processes the dielectric datafrom the probe, calculates and displays thepore water conductivity and temperature.
Readings usually stored electronically in a
Psion Workabout sheet (Fig. +). This sheet contains readings of EC, temperature, date, time,sample number, ﬁle name and note space.
Measurements usually performed by turningon the device, write the date, time and ﬁlename. Inserting the probe in the soil and press
enter for EC measurement. The device takes +*seconds to read and store one reading (UserFig. +

manual for Sigma probe, ,***, Hamed et al.,,**- and Hilhorst, ,***).
This sensor was updated to W.E.T. sensor
that working under same principles but it canmeasure soil water content in addition to thesoil salinity and temperature (WET sensor,,**.).
,. , Soil sampling and preparations
Thirty soil samples from surface (*ῌ+* cm)and subsurface (+*ῌ,* cm) were obtained from+/ cultivated lands of Oman (,,῎-*῏N /-῎-*῏E).
Soils were selected from agricultural areas (ex.
date palm, alfalfa, barley, tomato, ῌetc) that
had a wide range of salinity and textures. All
samples were air dried at ,/ῐ and passed
through a ,-mm sieve (Tan, +33/). All soil
samples were analyzed following chart in Fig.
,. Soil initial conditions (pH, cation exchange
capacity, calcium carbonate, gypsum and clay
percentage) were measured by following soil
analysis methods (Tan, +33/ and Page et al.,+32,). Moreover, the end results were presented in Table +.

Sigma probe (type EC+, Delta T Devices, Cambridge, U.K).



30

,. - Soil salinity determination
For each soil sample, salinity was determined from electrical conductivity (EC) measurements in soil/water suspensions at threesoil : water ratios, and measurements of soil
pore water using a Sigma probe. Electricalconductivity (EC) of soil/water suspensions
was measured by using distilled water in soil
saturated paste extract, + : +, and + : / ratios.
After mixing, soil saturated pastes were left tostand for over night (,*) before ﬁltration
under vacuum. Soil/water suspensions wereshaken automatically for + hour and thenﬁltered through *.++ mm Whatman ﬁlter paper(Page et al., +32,). EC extracts (ECsat. paste) andsuspensions (EC+ : +, + : /) were measured by using a calibrated conductivity meter (Jenway,
Conductivity Meter .*,*, UK) connected to an
EC electrode. Soil pore water salinity wasmeasured using Sigma probe. It was inserted
into either saturated soils, soils at ,/ῌ or +/ῌ
water content (volume/mass basis) preparedby mixing soil and water (bulk density+., gcm-) to reach required percentage. In thepaper, these water contents are referred tohigh, medium and low, respectively. EC read-Fig. ,+*.  ,**0

ings by Sigma probe were stored electronicallyand the average of three readings per sample
was taken.
Soluble ions (Na, Ca, and Mg,) and soil
adsorption ratio (SAR) were measured in saturated paste extract by using PYE UNICAM SP
3 Atomic Absorption Spectrophotometer (Tan,+33/ and Page et al., +32,).
Results of soil salinity from di#erent methods of assessment were compared by regression analysis, using statistical software (Statistical Package for Social Science, version 0,
Coakes and Lyndall, +331).
-.

Results and Discussion-. + Standard methods
Table + details the properties of the soil studied. It includes estimates of soil parametersthat interfere with soil salinity measurements.
The collection of soils exhibited a range of physical and chemical properties, and the range ofsalinities covered that are found in agriculturally cultivated soils. Moreover, water holding capacity of the studied soils were estimatedby the study done by Israelsen and Hansen(+30,) in which he estimated ﬁeld capacity (FC)General ﬂowchart of soil analysis.

ῒῑ : Soil Salinity Assessment by Some Destructive and Non Destructive Methods in Calcareous Soils 31

Table +

Physical and chemical properties of the soil
Soil properties

Soil
No.
A+
A,
B+
B,
C+
C,
D+
D,
E+
E,
F+
F,
G+
G,
H+
H,
I+
I,
G+
G,
K+
K,
L+
L,
M+
M,
N+
N,
O+
O,

Sampling
EC῍si῎ atdepth
EC῍+ : +῎
EC῍+ : /῎ EC῍sat. paste῎ Low WC
ῐ+
ῐ+
ῐ+
(cm)
(dS m ) (dS m ) (dS m ) (dS mῐ+)
*ῌ+*
+*ῌ,*
*ῌ+*
+*ῌ,*
*ῌ+*
+*ῌ,*
*ῌ+*
+*ῌ,*
*ῌ+*
+*ῌ,*
*ῌ+*
+*ῌ,*
*ῌ+*
+*ῌ,*
*ῌ+*
+*ῌ,*
*ῌ+*
+*ῌ,*
*ῌ+*
+*ῌ,*
*ῌ+*
+*ῌ,*
*ῌ+*
+*ῌ,*
*ῌ+*
+*ῌ,*
*ῌ+*
+*ῌ,*
*ῌ+*
+*ῌ,*

*4,3
*41+
*4-.
*4.2
+4*3
*431
+4-*
*41/
+4*,
+4,*
+14.
+4,+
,4+/
,4-*
.4/+
,4*/
14,1
-4*1
24.0
-4*0
/4,1
-4*2
+34/
1403
24/+
+.4+
+/40
,.42
,/43
-.42

*4+/
*4,/
*4+0
*4,*
*4-0
*4-*4-1
*4,/
*4-/
*4-.
-433
*4-3
*4/3
*40+
+4*1
*4/*
+4-*410+422*41+4+3*41.
.4.+410
+430
-4+*
-4-2
04+.
14.0
34.,

*401
+4/,
*411
*43*
+412
+4/+
.4,/
+422
+42,
,41.
-+4.
+40-41
.4*+
+,40
04*3
++4,
/4,1
+1404//
+-40
1410
/-4.
,-42
,*41
-+40
-34+
/340
0341
2042

+4+,
+41+
*432
+4,2
,4+,
+43
-4+1
+430
+43+
,4/+
ῌ
,4++
-4+*
,430
+*4.4*+
04-,
-40+
+-4,
.4/34-+
/4/1
ῌ
+-4*
+34.
ῌ
ῌ
ῌ
ῌ
ῌ

and permanent welting point (PWP) of eachsoil depending on soil texture of the soil. Generally the studied soils were calcareous, alkaline and mostly light in texture. Present of all
this variations in soils properties can give good
reﬂections for di#erent possibilities, interactions and ﬁnally more reliable equations andconclusions for di#erent soils, in which theconcluded equation can be generalized and applied to many di#erent soils.
Table , presents linear regression equations

Na
Ca
Mg
SAR (mmol῍῏῎ Lῐ+) (mmol῍῏῎ Lῐ+) (mmol῍῏῎ Lῐ+)
,400
.410
.4,+
,400
/4-/4-24.,
1433
1433
34/+/4*1433
24*/+.4+140
24/.
+/42
+*42
,-41
+142
+.40
+-42
-,4/
14-+
,-41
,+4/
+342
.+40
-*4+
/,4+

/4..
+*43
/4..
/4..
+*43
+*43
,14,
+04+04,+41+-*4.
+04,+41
-,40
104+
-,40
214*
-24*
+-*4.
/.4.
214*
/.4.
-.142
104+
+.+4+1-43
,+14.
-.142
-.142
/0/4,

04,/
04,/
+4,/
04,/
04,/
04,/
+,4/
04,/
04,/
04,/
0242
04,/
04,/
04,/
+,4/
+,4/
-+4+,4/
+242
+,4/
,/4*
+,4/
0,4/
/*4*
,/4*
/041/4*/04+**4*
0242

,4*2
.4+1
,4*2
,4*2
,4*2
,4*2
24-,4*2,4*2.4+1
2-4,4*2
24-.4+1
,/4*
+041
,341
+,4/
.+41
+,4/
./42
+242
+0041
+0041
./42
1/4*
+0041
2-4+0041+0041

relating di#erent standard methods of soil salinity determination, with R, values (i.e. theproportion of variance accounted for by theequation). For example, from the relationshipbetween EC῍+ : /῎ and EC῍sat. paste῎, it is necessary
to multiply by 3.1 to convert EC῍+ : /῎ toEC῍sat. paste῎ data. This ﬁnding was also clariﬁed by Fig. -, in which it can be seen that approximately a linear relationship was connecting those two di#erent methods. It is shownthat higher correlation was occurred when



32

+*.  ,**0

Table +(Continue)

Soil properties
a

b

Soil
No.

pH
(+ : ,./)

CaCO(g g+)

CaSO.
(cmolc kg+)CEC(cmolc kg+)

Clay
(ῌ)

Texture

FC
(ῌ)

A+
A,
B+
B,
C+
C,
D+
D,
E+
E,
F+
F,
G+
G,
H+
H,
I+
I,
G+
G,
K+
K,
L+
L,
M+
M,
N+
N,
O+
O,

34*,
241+
34,/
2410
242/
2402
2410
34*0
243.
24324+*
2420
2401
242+
2403
2413
24-24.*
24-+2430
24-24.*
24,24*1
24-24,,
24+3
24.,
24*24-*

*4-.
*4.3
*40*
*4-*4.,
*4.,
*4.1
*4/*
*4.*4.*4-/
*4.+
*4.,
*4-3
*4-2
*4/2
*4-3
*4.,
*4.,
*4.1
*4..
*4.3
*4.,
*4,2
*4.0
*4-0
*4.,
*4.+
*4.*
*4-/

+40*
+40*
+40*
+40*
+40*
+40*
+40*
+40*
+40*
+40*
+40*
+40*
+40*
+40*
+40*
+40*
/4**
+40*
+40*
+40*
+40*
+40*
+40*
+40*
+40*
+40*
+40*
+40*
040*
+*4*

.4-/
+*43
+430
241*
+*43
+04,4+1
,4+1
+*43
.4-/
+*43
+04+04,+41
,4-3
,4-3
+04,+41+*43+0404/,
241*
.4-/
.4-/
241*
+*43
04/,
04/,
04/,
241*

04*34++
0411+04,+41
,.43
.4*,
+*4,
+142
+*4*
-*4*
-/4.
+34.
+343
/4+.4,,*4+
,*4,
3410
+,42
04*/411
.4*+
.4*.
++42
+.4*
342/
++41
+*4*
+/43

LS
LS
S
SL
L
L
LS
LS
SL
SL
CL
CL
L
L
S
S
L
L
SL
SL
LS
LS
LS
LS
SL
SL
SL
SL
SL
SL

+*ῌ+2
+*ῌ+2
0ῌ+,
+*ῌ+2
+2ῌ,0
+2ῌ,0
+*ῌ+2
+*ῌ+2
+*ῌ+2
+*ῌ+2
,-ῌ-+
,-ῌ-+
+2ῌ,0
+2ῌ,0
0ῌ+,
0ῌ+,
+2ῌ,0
+2ῌ,0
+*ῌ+2
+*ῌ+2
+*ῌ+2
+*ῌ+2
+*ῌ+2
+*ῌ+2
+*ῌ+2
+*ῌ+2
+*ῌ+2
+*ῌ+2
+*ῌ+2
+*ῌ+2

c

PWP
(ῌ)

.ῌ 2
.ῌ 2
,ῌ 0
.ῌ 2
2ῌ+,
2ῌ+,
.ῌ 2
.ῌ 2
.ῌ 2
.ῌ 2
++ῌ+/
++ῌ+/
2ῌ+,
2ῌ+,
,ῌ 0
,ῌ 0
2ῌ+,
2ῌ+,
.ῌ 2
.ῌ 2
.ῌ 2
.ῌ 2
.ῌ 2
.ῌ 2
.ῌ 2
.ῌ 2
.ῌ 2
.ῌ 2
.ῌ 2
.ῌ 2

a
Where L, S, LS, SL and CL are loam, sand, loamy sand, sandy loam and clay loam textures, respectively, b FCField Capacity with ,ῌ, c PWPPermanent Wilting Point with +ῌ.

ECsat. paste values were less than ,* dS m+ andafter that this relationship was a#ected byhigh values of soil salinity. Moreover, when
the EC values were less than +* dS m+, therelationship was much stronger and the regression line was approximately connecting all
points of EC values (Fig. -). Generally equations for this conversion have been developedby many researchers using other soils (Landon,+32., Hernandez Bastida et al., ,**., Rhoades etal., +323 and Elprince, +32/), and it was concluded that no one equation is suitable for all
soil types. For other conversions other thansaturated paste extract and + : / ratio were nothighly studied by di#erent researches because
the most needed method for soil evaluation issaturated paste extract but the most easy wayof getting the extract is by + : / ratio. In thisstudy all methods were highly correlated(Table ,) with R, values of *.32 and conversion

ΐῒ : Soil Salinity Assessment by Some Destructive and Non Destructive Methods in Calcareous Soils 33

Table ,

Fig. -

Relationships between salinity standard methods

Method comparison

Relationship

R,

EC(+ : /), EC(sat. paste)
EC(+ : +), EC(sat. paste)
EC(+ : /), EC(+ : +)EC῍sat. paste῎ῑ3.1** EC῍+ : /῎EC῍sat. paste῎ῑ,./** EC῍+ : +῎
EC῍+ : +῎ῑ-.2** EC῍+ : /῎

*.32
*.32
*.32

Relationship between electrical conductivity (EC) measured by saturated past extract
and + : / ratio.

factors within acceptable level as comparedwith published equations.
Landon (+32.) reported that the EC in saturated paste extract is higher by factors of 0..
and ,., for EC῍+ : /῎ and EC῍+ : +῎ respectively. He
established the following equations :EC῍satῌ paste῎ῑ0..*EC῍+ : /῎ῌEC῍satῌ paste῎ῑ,.,*EC῍+ : +῎῍

In other side, Elprince (+32/) correlated EC῍+ : -῎values with saturated paste and converted
values to (+ : /) basis, and found approximately
same relationship as Landon (+32.) but withlower values of R,. The reported equation was
as follows :EC῍satῌ paste῎ῑ-.2+*EC῍+ : -῎῍R,ῑ*.11῎῎When EC῍+ : /῎ values exceed 0 dS mῐ+, Elpince(+32/) predicted that soil solutions would beover-saturated with respect to gypsum, and heconcluded that this was the main reason forfactor converting EC in the saturated paste
extract to that in extracts of higher dilution to
exceed the value 0...
Various causes for the di#erences in conver-sion factors between soils have been suggested.
One of the causes may be the presence of carbonates and gypsum in soil, especially when inhigh amounts. Di#erences in soil salinity canalso cause deviations in the conversion factor.
Hernandez Bastida et al. (,**.) reported thatthe measurement of EC in saturation extract
was a good saline content indicator only when
the salt content was low but less suitable in thepresence of high Clῐ or Na῏ concentrations.
The behavior of the ions in the + : + soil solution extract was the same. Increasing of EC
above + S mῐ+ result in a great dispersion of thepoints and a corresponding loss in linearity
between the di#erent parameters. The best
correlation model between the EC value measured saturation and + : + extracts was a thirdorder polynomial, although good results wereobtained with linear and quadratic models(Hernandez Bastida et al., ,**.).
Any dilution above ﬁeld conditions introduces errors in the interpretation of soil salinity data. The higher the dilution, the greater
will be the deviation between ionic ratios in thesample and the soil solution under ﬁeld conditions. These errors are associated with mineraldissolution, ion hydrolysis, and changes in exchangeable cation ratios. The ionic strength ofsoil solution in soils containing gypsum deviate more than other soils because the calcium
and sulfate concentrations remain nearly constant with sample dilution, while the concentrations of other ions decrease with dilution(Tanji, +33*). As a consequence the uniformityof relationships between EC in extracts andthe conditions of the soil solution for di#erent
soils is lost with increasing sample dilution.
Rhoades et al. (+323) reported that the water



34

content of a saturate paste is about twice theﬁeld capacity for most soils. However, exceptions included sandy soils, organic soils, andsoils containing gypsum. After taking into
account these exceptions, the advantage of thesaturated paste technique was that the procedure enabled the development of crop tolerance tables that could be interpreted across soil
texture classes (U.S. Salinity Laboratory Sta#,+3/.). In general, there were no great variationin the behavior of the di#erent ions between
the + : + and soil saturation extract, except incase of Ca,, which showed the greatest propensity to form ionic pairs, which might inﬂuence EC because of the lower ionic activity ofthe elements in the extract (Hernandez Bastida
et al., ,**.).
All reviewers in this ﬁnding are relaying
di#erences in EC equations are due to di#erent
association with mineral dissolution, ion hydrolysis, and changes in exchangeable cation
ratios. Some of them are relaying on NaCl,
Ca, or gypsum in playing a main rule in these
di#erences. Soil salinity usually has a strong
relationship with many salts in the soil. From
Fig. ., it is clear that studied soils had a good
relationship with Na, Ca, and Mg, (R,*.33), so it is expected that any variations intheir concentration will a#ect soil salinity andpredicted equation. Soil Na, which usuallyconnected with Cl was participating by 1/ῌ
in evaluating soil salinity measured by saturat-

Fig. .

+*.

,**0

ed paste extract methods and followed by Ca,and Mg, using standardized data by standard
regression coe$cient. This high percent ofNa can play one of the main rules in controlling soil salinity and di#erent conversion
factors. In addition, the studied soils are calcareous soils and present of calcium ions andother salts (Table +) could be the major reasonsfor interfering di#erences in equations between di#erent methods. From other side,
more water added to each soil as a dilution
factor (ex. from + : + to + : / ratio) could give
more chance for other salts to be dissolved anda#ect some physical (surface area) or chemical(pH) reactions. Whereas having a common
criterion between soils (calcareous) could help
in directing EC reactions and giving good relationships between methods (Table ,).
-. , Evaluation of Sigma probe
In the present study, the actual values ofSigma probe readings (ECsi) at di#erent soil
water content and standard methods were correlated (Table -). It can be seen that the highest correlation (R,) among all equations waswith Sigma probe readings at low water content and saturated paste extract. However all
standers methods were giving the best correlation factors (*.2/, *.3*, *.3+) with low water content of Sigma probe readings. It seems thatmedium and high water contents were nothighly preferable because under these conditions more water will di#use salts away fromInﬂuence of soil soluble ions on soil salinity (ECsat. paste) values.

ῑῐ : Soil Salinity Assessment by Some Destructive and Non Destructive Methods in Calcareous Soils 35

Table -

Relationships between salinity standard methods and Sigmaprobe readings

Method comparison

RelationshipR,

S.I. (low WC), EC (sat. paste)
S.I. (low WC), EC (+ : /)S.I. (low WC), EC (+ : +)S.I. (medium WC), EC (sat. paste)
S.I. (medium WC), EC (+ : /)S.I. (medium WC), EC (+ : +)S.I. (high WC), EC (sat. paste)
S.I. (high WC), EC (+ : /)S.I. (high WC), EC (+ : +)ECῌsat. paste῍῏+.-** ECῌsi῍ECῌ+ : /῍῏*.++* ECῌsi῍
EC+ : +῏*./,* ECῌsi῍ECῌsat. paste)῏+..1* ECῌsi῍ECῌ+ : /῍῏*.+,* ECῌsi῍ECῌ+ : +῍῏*./1* ECῌsi῍ECῌsat. paste῍῏*.,.* ECῌsi῍ECῌ+ : /῍῏*.*,* ECῌsi῍ECῌ+ : +῍῏*.+** ECῌsi῍

*43+
*43*
*42/
*421
*420
*42+
*4.+
*4./
*4.*

WC is soil water content, where high, medium and low refer to saturated
soils, ,/ῌ and +/ῌ (v/m basis), respectively ; S.I. refers to Sigma probe.

the probe and give inaccurate readings.
Whereas low water content is like a ﬁeld capacity conditions which give more actual values
and representing salts concentration in the soil
solution extract. Sigma probe readings at low
water content are around ﬁeld capacity rang
for most soils in Table + in which it can berecommended to use Sigma probe when thewater content in the ﬁeld is around ﬁeld capacity.
Fig. / shows the best ﬁtting line between
measured values of Sigma probe and saturated
paste extracts. Generally it isn’t a linear relationship. It was started with linear relationship when soil salinity was low but as soil
salinity getting higher many salts were interfering this relationship and the correlation line
smoothly went down. However, the most reliable readings of Sigma probe were approximately up to +* dS m῎+ of saturated paste extractand it was di$cult for the probe to detectsalinity when the salinity of saturated paste
extract exceed ,* dS m῎+. In addition, the maximum value of the EC that can be measured by
Sigma probe is up to ,* dS m῎+ (Table +) andabove that, it is di$cult for the probe to detectsoil salinity. This limitation was due to manufacturing design and calibration line, which ismainly controlled by the manufacturers. In
addition, present of many salts could a#ect

Fig. /

Relationship between electrical conductivity (EC) measured by saturated past extract
and Sigma probe at low water content.

calibration model and the reading will be out ofdesigned range (*ῌ,* dS m῎+).
For other concern of this study, the readingsof Sigma probe were compared to the standard
saturated paste extract (ECῌsat. paste῍) method,with the aim of generating a set of calibration
formulas to convert a reading from the probe
to that of the recommended standard method.
The latter method is time consuming andhence expensive to perform. Most calibration
studies use pre-selected values of the independent variable (Sche#é, +31-). However, in thisstudy, both independent and dependent variables are subject to error. The aim was to compare the methods and investigate their relationship under the conditions in which theprobe will normally be used. But because of
the accuracy with which the values of36ECsat. paste were measured, it is reasonable toassume that they have negligible variability.
A logarithmic transformation of both pairs ofdata was found to achieve reasonably linear
predictor-response relationships. The standard
linear regression techniques were used to ﬁt
the transformed data, the results of which werethen converted to the original units of measurement. Table . gives the calibration models
ﬁnally adopted, together with the corresponding 3/ῑ conﬁdence interval equations. All
three equations with high (sat.), medium (,/ῑ)
and low (+/ῑ) water content were found highly
signiﬁcant, and the coe$cient of determinations (R,) is *.31, *.30 and *.33, respectively. The
residual analysis of all three models indicated agood ﬁt. Only the regression line of the readings for the low soil water content had a zerointercept. In addition, the coe$cient of variations of all three values of the slope of theregression lines were less than *.*/, indicatingthat this particular parameter was accurately
determined for each model. The conﬁdence
interval equations should therefore provide
good approximations of the exact intervals.
Equations in Table . can be applied to the ﬁeld

Table .

 +*.  ,**0

conditions by justifying soil water content indicated by ﬁeld capacity condition or water
holding capacity of the soil. In this study, thebest readings of Sigma probe were found to be
at low water content (+/ῑ) in which it is nearor around the range of the ﬁeld capacity of
most studied soils (Table +).
Hamed et al. (,**-) reported that Sigmaprobe gives fairly good results in the sandy soil
but as the clay content increases, the correlation factor between Sigma probe readings andsoil water salinity decreases, especially at low
soil water salinity. In average, Sigma probe
measured soil water salinity was within ,*ῑ ofthe correct value for all soil types when soil
water salinity + dS m+. This study was also
conﬁrming this ﬁnding and that can bee seenin equations between Sigma probe readings
and soil texture (Table /). It is clear that coarse
texture, which contains clay particlesῐ+*ῑ,
give better correlation (R,*./*) than ﬁne soils(R,*.,,). Usually clay soils contain more elements than sandy soils in which they area#ecting or deviating EC values. From Table/, it can be seen also that ECsi with low watercontent gave a good relationship (R,*.31)

Calibration equations and the corresponding 3/ῌ conﬁdence region equations of the probereadings for the three soil water contents

Soil water content

EquationHigh (sat.)Ln «x*.-.+.-2 lny

Medium (,/ῑ)

Ln «x*.-,+../ lny

Low (+/ῑ)xy+.+2«

3/ῑ Conﬁdence interval equations(R,)ῌln y+.,0 ῎
῍
῏
+-.0

*.31

ῌln y+.+3+ ῎
῍
῏
+-.0

*.30

(y+.+,, y+.,.)

*.33

,

+/,

ln «
x*.-3

,

+/,

ln «
x*..-

Where «
x and y are EC (saturated paste extract) and EC (Sigma probe) values, respectively.

Table /

Inﬂuence of soil texture and ions on soil salinity measured by Sigma probe(low water content)

Soil factor

RelationshipR,

Fine soils (Clay+*ῑ)
Coarse soils (Clayῐ+*ῑ)Ions

ECsi*.+- Clay (ῑ)0.*.
ECsi+.-2 Clay (ῑ)+..,ECsi+.+,*.+, Na*.,* Ca,*.*2 Mg,

*4,,
*4/*
*431

ῒῑ : Soil Salinity Assessment by Some Destructive and Non Destructive Methods in Calcareous Soils 37

with soil ions (Na῎, Ca,῎ and Mg,῎) which meanthat Sigma probe is like any meter that can bea#ected by salt concentration in the soil.
To assess the appropriateness of each model,
some values of ECῌsi῍ were used to obtain thecorresponding ECῌsat. paste῍ values by the formulas in Table .. Table 0 shows the estimatedvalues of ECῌsat. paste῍ together with their 3/ῌ
conﬁdence intervals. While lower readings ofECῌsi῍ appeared to give good estimates ofECῌsat. paste῍, high values of ECῌsi῍ gave unacceptably high estimates when high and medium
soil water contents formulas were used. Judging by the conﬁdence widths of the estimates,
the power function obtained from the low soil
water content readings gave the best results,
while medium soil water based formula,having the widest conﬁdence widths had theworst results. For instance, for ECῌsi῍ῐ+*.*, we
get the estimate of ECῌsat. paste῍ as +1.* (++.-, ,/.0),,*.. (+,.3, -,.-) and +/.+ (+-.+, +1./) using theequations for high, medium and low soil watercontents, respectively. It is clear that the conﬁdence interval width is least for the last estimate, indication that it is also the best estimateof the three. Auerswald et al. (,**+) found thatelectrical conductivity of the soil depend
highly on water content, when it was low, EC

Table 0changed little but when water content wasaround ﬁeld capacity the changes becomelarger. There is little di#erence among thethree models for small values of ECῌsi῍. To illustrate this we estimated ECῌsat. paste῍ for ECῌsi῍ῐ,.*, and obtained +.2/ (+.,., ,.11), +.33 (+.,1, -.+*),
and ,.,0 (,.+1, ,.-1), respectively. The last model
still had the smallest conﬁdence interval widthof *.,* compared to +./- and +.2- for the models
from high and medium soil water contents,
respectively.
The soil liquid and solid fractions both contribute to the total soil EC. The contribution of
the solid fraction depends on the number of theexchangeable ions adsorbed to the surfaces ofclay and organic matter (Nadler and Frenkel,+32*). This contribution tend to depend on thewater content (Rhoades et al., +323) because areduction in water content results in increased
Coulomb interactions, due to attractions between free ions and the solid particles. Polarization, dispersion, and the arrangement ofwater molecules close to the clay surface(Cremers et al., +300) have inﬂuence on the ECof the solids. The combined e#ects of theseinteractions make the contribution of the solid
fraction to EC complex (Amente et al., ,***).
Bottraud and Rhoades (+32/) reported that

Applications of the calibration equations for selected values of Sigma probe readings(Nῌ,- soils)

Sigma probereading (dS m῏+)

Soil water content

Estimate ofEC (sat. paste)
value (dS m῏+)

3/ῌ Conﬁdence
intervalConﬁdence width,4*High (sat.)
Medium (,/ῌ)
Low (+/ῌ)High (sat.)
Medium (,/ῌ)
Low (+/ῌ)High (sat.)
Medium (,/ῌ)
Low (+/ῌ)High (sat.)
Medium (,/ῌ)
Low (+/ῌ)

+42/
+433
,4,0
04//
14.2
0402
+14*
,*4.
+/4+
,341
-041
,.4.

ῌ+4,., ,411῍ῌ+4,1, -4+*῍
ῌ,4+1, ,4-1῍
ῌ.4.*, 341/῍
ῌ.42*, ++41῍
ῌ04*., 14.*῍ῌ++4-, ,/40῍ῌ+,43, -,4-῍
ῌ+-4+, +14/῍ῌ+34., ./4.῍ῌ,,42, /243῍ῌ,*40, ,34*῍+4/+42*4,*
/4-/
043*
+4-0
+.4+34.
.4.*
,04*
-04+
24.*

/4*

+*4*

+/4*

38



volumetric water content, soil water EC, apparent EC of the solid phase and transmission
coe$cient have high relation to the soil EC. It
is possible that some other expressions or parameters would be better than those describedabove, either in general or for speciﬁc situations and uses. In all cases, the prediction
value for each calculation depends on prediction technique, calculation model and EC measurement methods (Rhoades et al., +33*). Since
this study tries to generalize a common idea or
equation so generally all those opinions andexpectation are applicable. Adding more water
to the soil will help in dissolving more salt andcomplicating soil reaction, which leads ﬁnallyto a weak correlation coe$cient (R,). Whileless water content keep the soil simple, easy tocontrol and produce a good relationship between methods (Table -). In addition to thatinteractions between measured values andchoosing the appropriate model helped a lot in
ﬁnalizing each equation (Tables . & 0). If thisstudy was done in one kind of soil, the variations between parameters will be small andlead to better understanding of soil system,measured values and predicted equations, but
the ﬁnal ﬁndings can not be applied or
generalized to di#erent soils. In this study,
di#erent soils were used so di#erent parameters were a#ected measured values and the
expected or ﬁnal equation could be applicablefor many soils.
Hamed et al. (,**-) concluded that Sigmaprobe was relatively in expensive, easy tohandle and can be used for di#erent soil types
without calibration. Variations between soils,
methods and meters can lead to di#erent equations and models. If those variables can be
speciﬁed, many parameters that interfering
plant growth can be predicted. Each soil salinity method has its own limitation that can besolved by other methods. Sigma probe andstandard methods are still in use until today
and those equations that were predicted in thisstudy can help in correlating many methods
with each other. Sigma probe is an easy device+*.

,**0

for pore water salinity measurements but it
was seen that this device was relatively a#ected by soil water content. In addition, the accuracy of measured value was also a#ected bysoil salinity in which the best range was foundto be between *ῌ+* dS m+ of EC saturated
paste extract and maximum values was up to,* dS m+. Generally, each EC meter has its
own advantages and limitations but soil is acomplex system and deciding the right meteror equation for each soil or di#erent soils need
many e#orts and researches. Finally, generalizing a certain equation in any ﬁeld need
many conﬁrmations by many scientists andresearchers and those information’s found in
this study could help in progressing scienceand be useful for many users.
..

Conclusion

The measurement of electrical conductivity
in mixtures of soil and water is a convenientmethod for assessing soil salinity. However,diluting soils with increasing amounts of water
not only reduces the electrical conductivity of
the resulting suspensions but also changes theconversion factor required when soil salinity
has been determined using di#erent soil towater ratios. For calcareous soils from Oman,
the relationships between ECsat. paste, EC+ : +and EC+ : / were statistically reliable, with R,values equal to *.32. Moreover, the presence ofsoluble calcium and other salts in the soils,
probably contributed to the high factors required for converting between ECsat. paste andEC+ : + or EC+ : /, i.e. ,./ and 3.1 respectively,
compared to the literature values of ,., and 0...
The use of Sigma probe to estimate soil porewater salinity was rapid, requiring only about+* seconds from probe insertion into the soil totaking a reading. Results from Sigma probe
were signiﬁcantly linearly related to the other
EC methods under study (soil saturated paste
extract, + : +, and + : / soil/water ratios), moreclosely associated when data were transformed
to a power function. The use of power functions to relate Sigma probe readings toῑῐ : Soil Salinity Assessment by Some Destructive and Non Destructive Methods in Calcareous Soils 39

EC῍sat. paste῎ values at soil water contents varying
from +/ῌ to saturation resulted in R, values
exceeding *.30. Sigma probe readings fromsoils low in water content had the highest correlation with EC῍sat. paste῎ values. The calibration
equation obtained from these readings had theleast conﬁdence widths compared to the ones
from either saturated soils or at moisture contents of ,/ῌ.
It is concluded that the most reliable readings of Sigma probe were obtained when theEC῍sat. paste῎ value was less than +* dS m῏+, corresponding to low and moderate salinity. Sigmaprobe readings from increasingly saline soils
provided progressively less reliable estimatesof EC῍sat. paste῎ values. Moreover, Sigma probereadings gave the best correlations with othermethods when soil water content was at lower
value (+/ῌ), which is near or around ﬁeld capacity condition. Further experiences with theprobe need to be examined, especially withhighly saline soils and gradual increment ofsoil water content to determine the yield response of di#erent crops to salinity at prescribed soil pore water salinity, as measured
using Sigma probe. Moreover, relationships
between studied and published methods (destructive and non destructive) were highly dependent on soil properties and measuring conditions in which those speciﬁcations should beconsidered before applying any of thosemethods or correlated equations.
References
Amente, G., Baker, J.M. and Reece, C.F. (,***) : Estimation of soil electrical conductivity frombulk soil electrical conductivity in sandy soils.
Soil Sci. Soc. Am. J., 0. : +3-+ῌ+3-3.
Auerswald, K., Simon, S. and Stanjek, H. (,**+) :Inﬂuence of soil properties on electrical conductivity under humid water regimes. Soil Sci.,+00 : -2,ῌ-3*.
Bottraud, J.C. and Rhoades, J.D. (+32/) : Referencing water content e#ects on soil electrical conductivity ῌ salinity calibrations. Soil Sci. Soc.
Am. J., .3 : +/13ῌ+/2+.
Coakes, S. J. and Lyndall, G.S. (+331) : Statistical
Analysis for Social Science (SPSS), Version 0.

John Wiley and Sons, New York.
Cremers, A., Loon, J.V. and Laudelout, H. (+300) :Geometry e#ects for speciﬁc electrical conductance in clays soils. Proc. +.th Nat. Conf.
Clays and Clay Miner., ,0 : +.3ῌ+0,.
Elprince, A.M. (+32/) : Model for the soil solution
composition of an oasis. Soil Sci. Soc. Am. J.,.3 : ++,+ῌ++,2.
Hamed, Y., Persson, M. and Berndtsson, R. (,**-) :Soil solution electrical conductivity measurements using di#erent dielectric techniques.
Soil Sci. Soc. Am. J., 01 : +*1+ῌ+*12.
Hernandez Bastida, J.A., Vela De Oro N. and Ortiz
Silla R. (,**.) : Electrolytic conductivity ofsemiarid soils (Southeastern Spain) in relation
to ion composition. Arid Land Research andManagement, +2 : ,0/ῌ,2+.
Hilhorst, M.A. (,***) : A pore water conductivity
sensor. Soil Sci. Soc. Am. J., 0. : +3,,ῌ+3,/.
Israelsen, O.W. and Hansen, V.E. (+30,) : Irrigation
principles and practices. John Wiley and Sons,
Inc., New York, p. .++.
Landon, J.R. (+32.) : Booker Tropical Manual.
Longman Inc., New York, pp. +/1ῌ+0-.
Nadler, A. and Frenkel, H. (+32*) : Determination ofsoil solution electrical conductivity from bulk
soil electrical conductivity measurementsfrom the four-electrode method. Soil Sci. Soc.
Am. J., .. : +,+0ῌ+,,+.
Page, A.L., Miller, R.H. and Keeney, D.R. (+32,) :Methods of soil analysis, chemical and microbiological properties. Ed. ,. Madison, Wisconsin, USA.
Rhoades, J.D., Manteghi, A.N., Shouse P. J. andAlves, W. J. (+323) : Estimating soil salinityfrom saturated soil ῌ paste electrical conductivity. Soil Sci. Soc. Am. J., /- : .,2ῌ.-3.
Rhoades, J.D., Shouse, P. J., Alves, W. J., Manteghi,
N.A. and Lesch, S.M. (+33*) : Determining soil
salinity from electrical conductivity using
di#erent models and estimates. Soil Sci. Soc.
Am. J., /. : .0ῌ/..
Rowell, D.L. (+33.) : Soil science, methods and applications. Salinity and sodicity. Longman,
London, p. ,22.
Sche#é, H. (+31-) : A statistical theory of calibration. The Annals of Statistics, + : +ῌ-1.
Tan, H.K. (+33/) : Soil sampling, preparation andanalysis. Marcel Dekker, New York.
Tanji, K.K. (+33*) : Agriculture salinity assessmentand management. Sampling, monitoring andmeasurements. American society of civil engineers, New York, pp. ,*-ῌ,*..
User manual for Sigma probe (,***) : Type EC+.

/3 9x » +*. ¼ T,**0U

40

Delta-T Devices Ltd. Burwell, UK.
U.S. Salinity Laboratory Sta# (+3/.) : Diagnosis
and improvement of saline and alkali soils.
USDA Hand Book. 0*. U.S. Gov. Print. O$ce,
 

Washington, DC.
WET sensor (,**.) : Delta-T Devices Ltd, Burwell,UK.

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