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Saturated paste (SP) and 1:1 soillwater extractions (1:1) are commonly used to assess soil salinity for field remediation. Correlation of electrical conductivity (EC) and other analytes between the SP and 1:1 extraction methods have been documented, except the relationships were based on limited soil types and require further examination to be adequately evaluated. This study examined these relationships using 170 soils from petroleum and agriculture production sites. Saturated pastes and 1:1 extracts were prepared and analyzed for EC, major cations (Na+, K+, Mg2+, Ca2+), and major anions (Cl-, SO42-). Relationships of all analytes were established between the two methods using linear regression. Saturated paste extract EC (ECSP) was highly correlated with that of 1:1 extract EC (EC1:1) (r(2) = 0.85, P < 0.001). Significant relationships also existed (r(2) > 0.73, P < 0.001) between different ions in SP and 1:1 extracts. An independent validation set of 22 soils showed that the slopes of the regressions between predicted EC, Na+, and Cl- of SP equivalents from 1:1 extract measurements and direct SP extract measurements were very close to 1.0 suggesting that the regressions developed can accurately assess soil salinity in salt affected soils using 1:1 extract analysis instead of using the more expensive and time-consuming SP extraction.
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Reproduced from Soil Science Society of America Journal. Published by Soil Science Society of America. All copyrights reserved.
Soil Salinity Using Saturated Paste and 1:1 Soil to Water Extracts
H. Zhang, J. L. Schroder,* J. J. Pittman, J. J. Wang, and M. E. Payton
The remediation of produced water contaminated sites
has been a priority of the oil and gas industry in recent
Saturated paste (SP) and 1:1 soil/water extractions (1:1) are com-
years due to increased government regulations and pub-
monly used to assess soil salinity for field remediation. Correlation
of electrical conductivity (EC) and other analytes between the SP and
lic pressure. As a result, in Oklahoma the state’s oil and
1:1 extraction methods have been documented, except the relation-
natural gas producers and royalty owners formed the
ships were based on limited soil types and require further examination
Oklahoma Energy Resources Board, an organization that
to be adequately evaluated. This study examined these relationships
deals specifically with restoring abandoned or orphaned
using 170 soils from petroleum and agriculture production sites. Satu-
petroleum extraction sites. Since 1995, the organization
rated pastes and 1:1 extracts were prepared and analyzed for EC,
has remediated or improved over 4000 sites within the
major cations (Na
), and major anions (Cl
state that had been degraded by petroleum production.
Relationships of all analytes were established between the two meth-
Because soil remediation recommendations are based
ods using linear regression. Saturated paste extract EC (EC
on soil salt contents, soil salinity testing methods must
highly correlated with that of 1:1 extract EC (EC
0.85, P
be capable of delivering accurate and precise results in
0.001). Significant relationships also existed (r
0.73, P 0.001)
between different ions in SP and 1:1 extracts. An independent valida- a timely manner. Currently, two widely used extraction
tion set of 22 soils showed that the slopes of the regressions between
methods for soil salinity analysis are a 1:1 extraction and
predicted EC, Na
, and Cl
of SP equivalents from 1:1 extract mea-
SP extraction (USDA, 1954; Rhoades, 1996). Saturated
surements and direct SP extract measurements were very close to 1.0
paste extractions attempt to simulate the environment
suggesting that the regressions developed can accurately assess soil
of naturally occurring moisture-saturated soil. Of the ex-
salinity in salt affected soils using 1:1 extract analysis instead of using
traction methods available, results from SP extractions
the more expensive and time-consuming SP extraction.
are thought to be the best predictor of plant and soil re-
sponse to salinity (USDA, 1954; Longenecker and Lyl-
erly, 1964; Vaughn et al., 1995).
etroleum and natural gas production are impor-
Unlike the SP method, the 1:1 extraction method does
tant economic sectors of the USA contributing over
not attempt to s imula te natural soil conditions. Due to the
$110 billion to the gross domestic product (GDP) in 2001
consistency in the amount of water used and objective
(United States Department of Energy, 2003). Petroleum
nature of the method, the 1:1 extraction method can re-
production often yields contaminants such as brine or
duce the difficulties in sample preparation and reproduc-
heavy metals within the accompanying produced waters
ibility often encountered with SP extractions (USDA,
(American Petroleum Institute; 1997; Oklahoma Mid-
1954; Longenecker and Lylerly, 1963; Sonneveld and
Continent Oil and Gas Association, 1998). Before envi-
Van Den En de, 1971; Fowler and Hamm, 1980). Ion con-
ronmental regulations were established in the 1970’s,
centrations and EC
extracts are typically lower than
produced salt waters were commonly released onto the
those of S P extracts due to i ncrea sed dilution. Despite the
ground near well sites or into nearby streams. Current
differences in results between the two methods, many
regulations prohibit the release of untreated waters into
soil salinity samples are analyzed using a 1:1 extract be-
the environment, so produced waters are often injected
cause of reduced monetary and time investments.
back into wells to assist in maintaining the pressure of
Many salinity samples analyzed by the O klaho ma State
the oil reserve or deposited into large evaporation ponds
University Soil, Water and Forage Laboratory (SWA FL)
where the salts and contaminants can be contained and
originate from produced water contaminated environ-
concentrated. When released into the environment, pro-
ments associated with oil and gas exploration and pro-
duced waters often cause “salt-scars” or areas of high sa-
duction. Current regulations in Oklahoma mandate the
linity leading to the degradation of soil structure and al-
remediation of salt contaminated areas based on total
teration of the osmotic gradient between plant roots and
soluble salts (TSS) from a soil analysis (Oklahoma Mid-
the soil (Olsen and Peech, 1960; Franklin, 1969; Bar-
Continent Oil and Gas Association, 1998). Presently in
zegar et al., 1997; Holliday and Deuel, 1997). As a result,
Oklahoma, TSS is often calculate d usin g a SP or adjusted
sites affected by produced waters exhibit loss of vegeta-
1:1 analysis of soil EC (USDA, 1954). Remediation rec-
tion and increased soil erosion (Barzegar et al., 1997).
Abbreviations: Ca
, calcium in 1:1 soil/water extracts; Ca
, calcium
H. Zhang, J.L. Schroder, and J.J. Pittman, Dep. of Plant and Soil
in saturated paste extracts; Cl
, chloride in 1:1 soil/water extracts;
Sciences; M.E. Payton, Dep. of Statistics, Oklahoma State Univ.,
, chloride in saturated paste extracts; EC, electrical conductivity;
Stillwater, OK 74078; J.J. Wang, Dep. of Agronomy, Louisiana State
, 1:1 soil/water extract electrical conductivity; EC
, saturated
Univ., Baton Rouge, LA 70803. Received 9 Aug. 2004. *Correspond-
paste electrical conductivity; K
, potassium in 1:1 soil/water extracts;
ing author (
, potassium in saturated paste extracts; Mg
, magnesium in 1:1
soil/water extracts; Mg
, magnesium in saturated paste extracts;Published in Soil Sci. Soc. Am. J. 69:1146–1151 (2005).
Nutrient Management & Soil & Plant Analysis Na
, sodium in 1:1 soil/water extracts; Na
, sodium in saturated paste
extracts; OSU, Oklahoma State University study (this study); SO
© Soil Science Society of America sulfate in 1:1 soil/water extracts; SO
; sulfate in saturated paste ex-
tracts; SP, saturated paste extraction; TSS, total soluble salts.677 S. Segoe Rd., Madison, WI 53711 USA
Published online June 2, 2005
Reproduced from Soil Science Society of America Journal. Published by Soil Science Society of America. All copyrights reserved.
Table 1. Correlation equations established by different studies to convert 1:1 soil/water (1:1) measurements to saturated paste (SP) equiv-
Parameter USDA, 1954 Hogg & Henry, 1984 Franzen, 2003 OSU (this study)
EC, ds m
SP 3.00 (1:1) SP 1.56 (1:1) 0.06 SP 3.0 (1:1) 0.77 SP 1.85 (1:1)
SP 2.78 (1:1) SP 0.95 (1:1) 5.31 SP 2.04 (1:1)
SP 1.67 (1:1) SP 1.35 (1:1)
SP 2.78 (1:1) SP 2.48 (1:1)
SP 2.78 (1:1) SP 0.95 (1:1) 30.5 SP 1.91 (1:1)
SP 1.67 (1:1) SP 0.7 (1:1) 9.39 SP 2.10 (1:1)
SP 1.67 (1:1) SP 0.7 (1:1) 9.39 SP 2.08 (1:1)
ommendations for salt-affected areas are often made generated by previous studies necessitates further exam-
ination and comparison of the two extraction methodswithout differentiating between which analytical method
was used or considering whether an adjustment of re- to generate a more refined adjustment of 1:1 analyses
over a wide range of soils. By exploring and identifyingsults was incorporated.
Because of the relative ease of the 1:1 method, theo- factors contributing to differences in 1:1 and SP extract
analyses in various soils, adjustments to the 1:1 charac-retical relationships have been developed to convert 1:1
extraction results to a SP extraction equivalent (USDA, terization of soil salinity could be improved. The objec-
tives of this study were to (i) determine the relationship1954; Freidman, 1998). Despite the reports of highly
correlated relationships between the two methods, ad- between the EC of SP and 1:1 extracts and (ii) determine
the relationship between major cations and major anionsjustment of 1:1 results to SP approximations are often
imprecise and inaccurate (Wagenet and Jurinak, 1978; of SP and 1:1 extracts.
Franzen, 2003). Therefore, further study of the relation-
ship between the results generated by 1:1 and SP extrac-
tions is needed to improve soil remediation strategies
based on adjusted 1:1 analysis of soil salinity.
Currently, the EC and major ion concentrations ac-
Approximately 170 samples from various locations in Okla-
quired using the 1:1 method are adjusted with conversion
homa and Texas were characterized using both 1:1 and SP ex-
factors from Table 2 of USDA Handbook 60 (USDA,
traction methods. These samples were from both brine contami-
nated and agricultural production areas with a broad range of
1954) (Table 1). These conversion factors were based
soil conditions and analyte concentrations (Table 2).
on soil moisture holding capacities and the theoretical
and actual chemical solubility of ions in aqueous systems
Validation of Empirical Relationships
(USDA, 1954) but not the impact of soil texture, salt
Between 1:1 and Saturated Paste
concentrations, and organic matter content on ion con-
centrations and EC. The exclusion of these soil proper-
Most remediation techniques for salt contaminated areas
ties in the conversion factors, coupled with the lack of
are based on TSS from a soil analysis (American Petroleum
extensive examination of relationships between the two
Institute, 1997; Oklahoma Mid-Continent Oil and Gas Associ-
ation, 1998). Total soluble salts are often calculated using a
methods and minimal experimental verification, could
SP or adjusted 1:1 analysis of soil EC (USDA, 1954). Twenty-
contribute to imprecise adjustment of 1:1 analyses when
two Oklahoma soil samples independent of those used to gen-
applied to a variety of soils (Franzen, 2003). To improve
erate the regressions for this study were used to validate the
on the original factors, researchers have developed new
relationships between SP and 1:1. To allow for direct com-
conversion techniques using experimental data to gener-
parisons of the predictive capabilities of the regression equa-
ate empirical relationships.
tions generated by this study (hereafter referred to as OSU)
Franzen (2003) divided EC conversion factors into
and USDA regressions, regressions that forced zero and omit-
three textural divisions and arrived at conversion factors
ted the y intercept were utilized for the validation study. The
for coarse, medium, and fine soils (Table 1). Hogg and
OSU and USDA equations were used to predict SP equiva-
Henry (1984) reported factors for EC and individual
lents of EC and ions from 1:1 measurements; the results were
then compared with actual SP measurements of EC and ions.
ionic species (Table 1). Variation in conversion factors
Table 2. Summary statistics for major ions and electrical conductivity (EC) of 170 soils used to establish relationships between 1:1 soil/
water (1:1) and saturated paste (SP) extracts.
Statistic EC Cl
ds m
mg kg
SP extract
Mean 13.4 4 930 771 43.0 2 650 539 135
Median 3.81 760 151 18.0 426 119 41.0
Minimum 0.165 5.00 15.0 0.00 5.00 5.00 1.00
Maximum 108 80 500 10 700 1270 43 200 8490 1450
1:1 extract
Mean 6.69 2 340 509 23.0 1 390 255 56.3
Median 1.81 322 78.0 13.0 272 55.0 18.5
Minimum 0.06 4.00 7.00 0.00 2.00 2.00 0.00
Maximum 49.0 33 900 7 360 273 17 900 3340 866
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1148 SOIL SCI. SOC. AM. J., VOL. 69, JULY–AUGUST 2005
Table 3. Saturated paste (SP) and 1:1 soil/water (1:1) EC dis-
Saturated Paste Extraction
tributions for 170 study soils used to establish relationships
Saturated pastes were prepared by adding deionized water
between 1:1 and SP extracts.
to approximately 500 g of soil sample as received until it reached
SP extracts 1:1 extracts
a condition of complete saturation, as described by guidelines
Range of EC Number of soils Soils Number of soils Soils
in USDA Handbook 60 (USDA, 1954). Saturated pastes were
allowed to equilibrate for 18 h. An extract from the SP was
acquired using a low-pressure filter press (Fann Equipment,
0–5 ds m
91 54.5 110 65.9
Low Pressure Filter Press, Houston, TX). The extracts were
5–10 14 8.38 24 14.4
10–20 25 15.0 14 8.38
analyzed for Na
, and Ca
using an inductively
20–50 24 14.4 19 11.4
coupled argon plasma emission spectrometer (Spectro CirOs,
50–100 13 7.78 0 0.00
ICAP, Fitchburg, MA), for Cl
using the Lachat Quickchem
8000 flow injection analyzer (Zellweger Analytics, Milwaukee,
WI); and for EC using a flow-through cell (Orion, 162A Con-
contrast with those of Hogg and Henry (1984) who
ductivity Probe, Beverly, MA). Sulfur was expressed as SO
found that concentrations of Cl
and Na
were approxi-
as commonly done by analytical labs, although total dissolved
mately equal in SP extracts and 1:1 soil/water extracts.
S was measured by ICAP (Gavlak et al., 2003).
Relationship Between Electrical Conductivity of
1:1 Soil to Water Extraction
Saturated Paste and 1:1 Extract
One hundred milliliters of deionized water was added to
100 g of ground (2-mm sieve), oven-dried sample to create a
Electrical conductivity of SPs was highly correlated
suspension with equal parts of soil and water. The suspensions
with EC
for all the study soils (r
0.85, P 0.001)
were allowed to equilibrate for 4 h and extracts were obtained
(Fig. 1). The results of our study are similar to those
using the low-pressure filter press previously mentioned. Ana-
reported by other researchers who found that highly
lytes in the 1:1 extracts were analyzed using the same methods
significant relationships existed between the EC
as with the SP extracts.
(Hogg and Henry, 1984; Shirokova et al., 2000).
The slope of our relationship of 1.79 is very similar to
Statistical Analysis
the slope of 1.56 (Table 1) reported by Hogg and Henry
Analysis of variance (ANOVA) was performed using PROC
(1984) for a combination of coarse, medium, and fine-
GLM (SAS Institute, 2001). When significance at a 0.05 level
textured soils. However, our results differ drastically
was indicated, means were separated by a Fisher’s Least Signif-
from those reported by Franzen (2003) who found a
icant Difference Procedure. To assess the possible linear rela-
slope of 3.01 (Table 1) for the same relationship. Our
tionship of SP to 1:1, simple linear regression models were fit
results also differ drastically from the theoretically de-
with the response of 1:1 as the x variable and the response of
rived relationship published by the USDA (1954), which
SP as the y variable. PROC REG of SAS was used for these
used a slope of 3.0 (Table 1) for the relation ship. The t he-
analyses, and the analysis performed for each ion and EC.
oretically derived relationships published by the USDA
(USDA, 1954) forced the regression through zero and
omitted the y intercept. Therefo re for a more direct com -
Electrical Conductivity and Ion Concentrations
parison, a second regression was performed that forced
of Saturated Paste and 1:1 Soil/Water Extracts
the regression through zero and omitted the y intercept.
Forcing the regression line through zero slightly increased
Electrical conductivities for the soil samples studied
the slope from 1.79 to 1.85 for the study soils (Table 1).
ranged from 0.165 to 108 ds m
for the SP extracts with
the EC for the 1:1 extracts ranging from 0.06 to 49.0 ds
(Table 2). Therefore, a wide range in salinity levels
was obtained for comparing the SP with the 1:1 extrac-
tion methods. Mean EC of SP (EC
) of 13.4 ds m
was significantly greater (P 0.001) than that of 6.69 ds
in 1:1 (Table 2). Our results are similar to those of
other researchers who reported that the EC
was greater than the EC of 1:1 (EC
) soil/water extracts
(USDA, 1954; Hogg and Henry, 1984; Franzen, 2003).
The significant difference between the EC
and EC
extracts is most likely due to a dilution effect that has
been suggested by other researchers (Reitemeier, 1946;
Schofeild, 1947; USDA, 1954; Sonneveld and Van Den
Ende, 1971). Approximately 63% of the soils had an
10.0 ds m
while approximately 80% of the
soils had an EC
10.0 ds m
(Table 3).
Mean ion concentrations (Cl
Fig. 1. Relationship between electrical conductivity (EC) of saturated
and Mg
) for the SP extracts were approximately two-
paste and 1:1 soil/water extracts for 170 study soils. ***P 0.001.
fold greater (P 0.001) than those in the 1:1 soil/water
The dashed lines represent the 95% confidence interval for the re-
extracts (Table 2). Our results for ion concentrations
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Table 4. Coefficients of determination (r
) and regression equa-
tions describing the relationship between 1:1 and saturated
paste extracts for 170 study soils.
With y intercept Without y intercept
Regression Regression
Parameter equation† r
equation r
EC SP 1.79x 1.46 0.85‡ SP 1.85x 0.85
SP 2.03x 174 0.86 SP 2.04x 0.86
SP 1.32x 101 0.82 SP 1.35x 0.81
SP 2.80x 21.3 0.73 SP 2.48x 0.70
SP 1.92x 27.8 0.89 SP 1.91x 0.89
SP 2.10x 3.37 0.87 SP 2.10x 0.87
SP 2.00x 22.8 0.82 SP 2.08x 0.82
x electrical conductivity of 1:1 extract.
All regression equations were significant at P 0.001.
The slope reported by USDA (1954) was approximately
Fig. 2. Relationship between electrical conductivity (EC) of saturated
68% greater than the one found in our study.
paste predicted with measured 1:1 soil/water extract and USDA
regression equation or OSU regression equation and actual mea-
sured EC of saturated paste for 22 random soils. **P 0.01.
Relationships Between Ions Extracted Using
Saturated Paste and 1:1 Soil/Water Ratio
predicted by the USDA regression equation was signifi-
Highly significant relationships existed (P 0.001)
cantly greater (P 0.05) than the mean actual measured
between ions extracted by SP and 1:1 extracts with re-
of 9.12 ds m
. Mean measured concentrations of
gression coefficients (r
) ranging from 0.73 to 0.89 (Ta-
1490 mg Na
in SP of the validation soils were not
ble 4). Similarly, Hogg and Henry (1984) found strong
significantly different than Na
of 1400 mg kg
relationships existed between Cl
, and Na
in SP and
dicted by the OSU regression equation but was signifi-
1:1 extracts. While Hogg and Henry (1984) did not re-
cantly less than Na
of 2040 mg kg
predicted by the
port the relationship between K
and SO
in SP and
USDA regression equation. Concentrations of Cl
1:1 extracts, they noted a significant relationship existed
ally measured (2720 mg kg
) and predicted by the OSU
between the sum of Ca and Mg extracted by SP and by
regression equation (2400 mg kg
) were statistically
1:1 soil/water but did not report the individual relation-
equivalent while Cl
concentrations predicted by the
ships for Ca
or Mg
in their study.
USDA regression equation were greater than measured
The slopes of the relationships derived in our study
concentrations. Actual measured concentrations of Ca
for ions are different from those reported by the USDA
were significantly greater than concentrations predicted
(1954) with the exception of K
. The slope of 2.48 found
by either the OSU or the USDA regression equation
in our study for K
is close to the slope reported by
(Table 4). Concentrations of SO
predicted by the OSU
USDA (1954) (Table 4). Overall, the slopes of relation-
regression equation were significantly less than actual
ships for ions extracted by SP and 1:1 soil/water in our
measured concentrations of SO
while concentrations
study are approximately 25 to 30% less than those found
of SO
predicted by the USDA regression equation
by USDA (1954) for Cl
, and Na
while the slopes
were statistically equivalent to those actually measured
of the Ca
and Mg
relationships for our study are
(Table 5).
approximately 20 to 30% greater than the relationships
Values of EC
and SP ion concentrations predic ted
reported by USDA (1954).
by both the OSU regression equation and the USDA
regression equ ation s usin g 1:1 extrac tions were also com-
Validation of Empirical Relationships
pared with actual measurem ents via regression analysis.
Between 1:1 and Saturated Paste
A significant r elati onshi p (r
0.80, P 0. 01) wa s foun d
Mean EC
predicted by the OSU regression equation
between actual measured EC
and EC
equivalent pre-
of 9.20 ds m
was not significantly different (P 0.05)
dicted by the OSU regression equation (Fig. 2). Ad ditio n-
than mean actual measured EC
of 9.12 ds m
in the
ally, a si gnifi cant relationship (r
0.80, P 0.01) existed
validation soils (Table 5). However, mean EC
of 14.9
between actual measured EC
and EC
predicted by
the USDA regression equati on (Fig. 2). Slopes for the
Table 5. Summary of mean comparisons for actual measurements
OSU and USDA relationships were 0.97 and 1.57, respec-
of EC and ion concentrations in saturated paste (SP) and those
tively. Ideally, if the predicte d EC
were exactly the same
predicted by the Oklahoma State University study (OSU) and
USDA regression equations.
as the measured EC, the slope would equal 1.0, the y
intercept would equal zero, and r
would equal 1.0. The
Actual Predicted Predicted
Parameter measurement by USDA by OSU
slope for the relationship between predicted and mea-
sured SP EC was much closer to 1.0 for the OSU regres-
EC, ds m
9.12 14.9† 9.20‡
1490 2040† 1400‡
sion than that of the USDA regression indicating the
2720 3270† 2400‡
OSU regression equation was more accurate than the
447 251† 315†
USDA conversion factor in predicting EC
from 1:1 mea-
592 574‡ 464†
Significantly different from SP measurement at 0.05.
Not significantly different from SP measurement at 0.05.
Sodium predicted by both the OSU and USDA regr es-
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1150 SOIL SCI. SOC. AM. J., VOL. 69, JULY–AUGUST 2005
Fig. 3. Relationship between (A) Na
predicted by OSU and USDA
regression equations and measured Na
in saturated paste extracts
for 22 random soils and (B) Cl
predicted by Oklahoma State
University study and USDA regression equations and measured
Fig. 4. Relationship between (A) Ca
predicted by Oklahoma State
in saturated paste extracts for 22 random soils. **P 0.01.
University study (OSU) and USDA regression equations and mea-
sured Ca
in saturated paste extracts for 22 random soils and (B)
predicted by OSU and USDA regression equations and mea-
sion equations was highly related (r
0.92, P 0.01)
sured SO
in saturated paste extracts for 22 random soils.
with actual measured Na
extracts with slopes of 1.05
**P 0.01.
and 1.53 for the OSU and USDA relationsh ips, respec-
tively (Fig. 3A). The slope for the relationship betwee n
predicted and measur ed Na
was much closer to 1.0 for
It is possible to achieve a higher degree of precisi on
the OSU re gress ion than the USDA r egres sion indica ting
and accuracy in predicting EC and Na
, which
the OSU regression equatio n was more accurate than the
are the major ions in salt-affect ed soils, from 1:1 extracts
to their SP equivalents using the conversions generated
USDA regressio n equation in predicting Na
by this study. Because of the wide range of EC and ion
Significant relationship s (r
0.95, P 0.01) were
concentration s evaluated by this research, the derived
found between actual measured Cl
and Cl
equations have the potential to be used in a variety of soil
by the OSU and USDA regression equations (Fig. 3B).
conditions although the appropr iaten ess of these equa-
Slopes for the OSU and USDA relationshi ps were 0.92
tions for use in other regions also needs to be evaluated.
and 1.26, respectively. The closeness to 1.0 for the slope
Overall, the benefits of converting 1:1 to SP extraction
of the OSU relationship shows that it was a better pre-
equivalents are potentially large, as laboratories can mini-
dictor of Cl
than the USDA regression equation.
mize the cost and time associated with soil salinity analysis
Highly significant relationships (r
0.91, P 0.01)
by using the less costly 1:1 method while maintaining
existed between actual measured Ca
in SP extracts
a high level of accuracy and precision. Although using
and Ca
predicted by the OSU and USDA regression
adjusted 1:1 analyse s can be accurate in approximation
equations (Fig. 4A). However, extrem ely low slopes wer e
of SP measurements, adjusted 1:1 measurements are not
observed for the OSU relationshi p (slope 0.66) and
as precise as SP at characterizing soil salinity, and should
the USDA relationshi p (slope 0.53) indicating that
not be viewed as a sound substitut ion for SP measure -
neither of the regression equations was an adequate pre-
ments. Further investigation of adjusting 1:1 soil extracts
dictor of Ca
from 1:1 to its SP equivalent.
using soils from a variety of regions across the country
Measured concentrations of SO
were highly corre-
could allow for a more accurate characteriza tion of soil
lated (r
0.92, P 0.01) with SO
predicted by the
salinity using 1:1 analysis by region.
OSU and USDA regressi on equations (Fig. 4B). Slopes
for the OSU and U SDA relationships were 0.78 a nd 0.96,
respectively, indicating the USDA regression equation
American Petroleum Institute. 1997. Remediation of salt-affected soils
was better at predicting SO
than the OSU regression
at oil and gas production facilities. API Publication No. 2663. API,
Washington, DC.
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... These slope values are similar to those presented by Kargas et al. (2018) (corresponding to values of 1.83 and 6.53), as expected since the samples came from the same collection areas. Additionally, the slope of the relationship EC e -EC 1:1 has a similar value to that of Zhang et al. (2005) Although the EC e -EC 1:1 and EC e -EC 1:5 relationships are strongly linear considering all soil samples (Figure 2), differences in the slopes of linear relationships are estimated between the sampling areas. However, the corresponding relationships for each sampling area are also strongly linear, with R 2 values in the range of 0.911 < R 2 < 0.992 (data not shown). ...
... The above-mentioned results confirm previous research studies about the strong linear relationships of EC e -EC 1:1 and EC e -EC 1:5 for soils from different areas of the world (Aboukila & Norton, 2017;Chi & Wang, 2010;Datta et al., 2019;Hassannia et al., 2020;Kargas et al., 2018;Sonmez et al., 2008;Zhang et al., 2005). However, the slope of the linear relationships appears to be significantly affected by the soil texture, as well as the calcium carbonate and gypsum content for the same method of obtaining the 1:1 and 1:5 extracts and the same range of EC e values. ...
... 3.3 | Correlations of ion concentrations in saturated paste extract and in 1:1 and 1:5 soil/water extracts In Table 1, the mean, maximum and minimum values of cation concentrations in the saturated paste, 1:1 and 1:5 soil/water extracts are presented. Zhang et al. (2005) reported that the mean concentration of saturated paste extract ions (Cl À , SO 4 2À , Na + , Ca 2+ and Mg 2+ ) was approximately twofold greater than those in the 1:1 soil/water extracts. However, in our study, this occurred only in the case of Mg 2+ (Table 1). ...
This study investigates the relationship between the sodium adsorption ratio acquired by the saturated paste extract (SARe) method and those acquired by 1:1 and 1:5 soil/water extracts (SAR1:1 and SAR1:5) by using 122 surface soil samples from 5 agricultural areas of Greece. The soil samples are classified as medium to fine textured with negligible gypsum content and low organic carbon content. The United States Department of Agriculture (USDA) method was used to obtain all extracts. The results showed that the relationships between SARe and SAR1:1, as well as SARe and SAR1:5, are strongly linear, with a higher coefficient of determination (R2) observed in the case of the SARe–SAR1:1 relationship. The linear regression equations were SARe = 1.21·SAR1:1 (R2 = 0.954) and SARe = 2.22·SAR1:5 (R2 = 0.828). The linearity of the SARe–SAR1:5 relationship was stronger when the calcium carbonate content of soils was greater than 4%. Linear relationships were also observed between the electrical conductivity (EC) relationships ECe–EC1:1 and ECe–EC1:5 and between the concentrations of ions Na+, Ca2+ and Mg2+ derived from saturated paste extract and each of the 1:1 and 1:5 extracts. The 1:1 method was more effective than the 1:5 method in the prediction of EC, SAR and ion concentrations of the preferred index of the saturated paste extract method. Cette étude examine la relation entre le rapport d'adsorption du sodium acquis par la méthode de l'extrait de pâte saturée (SARe) et ceux acquis par des extraits sol/eau 1:1 et 1:5 (SAR1:1 et SAR1:5) en utilisant 122 échantillons de sol de surface provenant de 5 zones agricoles de Grèce. Les échantillons de sol sont classés comme ayant une texture moyenne à fine, une teneur en gypse négligeable et une faible teneur en carbone organique. La méthode du département de l'agriculture des Etats Unis (USDA) a été utilisée pour obtenir tous les extraits. Les résultats ont montré que les relations entre SARe et SAR1:1, ainsi que SARe et SAR1:5, sont fortement linéaires, avec un coefficient de détermination (R2) plus élevé observé dans le cas de la relation SARe–SAR1:1. Les équations de régression linéaire étaient SARe = 1.21·SAR1:1 (R2 = 0.954) et SARe = 2.22·SAR1:5 (R2 = 0.828). La linéarité de la relation SARe–SAR1:5 était plus forte lorsque la teneur en carbonate de calcium des sols était supérieure à 4%. Des relations linéaires ont également été observées entre les relations de conductivité électrique (CE) ECe–EC1:1 et ECe–EC1:5 et entre les concentrations d'ions Na+, Ca2+ et Mg2+ dérivées de l'extrait de pâte saturée et de chacun des extraits 1:1 et 1:5. La méthode 1:1 s'est avérée plus efficace que la méthode 1:5 pour la prédiction de la CE, du DAS/SAR et des concentrations d'ions de l'indice préféré de la méthode de l'extrait de pâte saturée.
... The monitoring of soil salinization is a necessity for the authorities to produce knowledge on the state of the soil at all times. Traditional methods such as the measurement of the electrical conductivity of saturated soil pulp [18][19][20] and require sampling missions, preparation, treatment, and analysis in the laboratory, which is costly in resources and in time. Hence, remote sensing and processing satellite images show as promising tools for monitoring soil conditions over large areas and over long periods. ...
... Hence, remote sensing and processing satellite images show as promising tools for monitoring soil conditions over large areas and over long periods. Over the past decades, the field of remote sensing and the GIS (geographic information system) has evolved considerably and provides an opportunity to map the spatio-temporal evolution of soil salinity as well as the extraction of instantaneous information on large perimeters [18,. ...
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Water stress is one of the factors controlling agricultural land salinization and is also a major problem worldwide. According to FAO and the most recent estimates, it already affects more than 400 million hectares. The Tafilalet plain in Southeastern Morocco suffers from soil salinization. In this regard, the GIS tools and remote sensing were used in the processing of 19 satellite images acquired from Landsat 4-5, (Landsat 7), (Landsat 8), and (Sentinel 2) sensors. The most used indices in the literature were (16 indices) tested and correlated with the results obtained from 25 samples taken from the first soil horizon at a constant depth of 0.20 m from the 2018 campaign. The linear model, at first, allows the selection of five better indices of the soil salinity discrimination (SI-Khan, VSSI, BI, S3, and SI-Dehni). These last indices were the subject of the application of a logarithmic model and polynomial models of degree two and four to increase the prediction of saline soil. After studies and analysis, we concluded that the second-degree polynomial model of the salinity index (SI-KHAN) is the most efficient one for detecting and mapping soil salinity in the Tafilalet oasis, with a coefficient of determination (R 2) and the Nash-Sutcliffe efficiency (NSE) equal to 0.93 and 0.86, respectively. Percent bias (PBIAS) calculated for this model equal was 1.868% < 10%, and the low value of the root mean square error (RMSE) confirms its very good performance. The drought cyclicity led to the intensification of the soil salinization process and accelerated soil degradation. The standardized precipitation anomaly index (SPAI) is strongly correlated to soil salinity. The hy-droclimate condition is the factor that further controls this phenomenon. An increase in salinized surfaces is observed during the periods of 1984-1996 and 2000-2005, which cover a surface of 11.50 and 24.20 km 2 , respectively, while a decrease of about 50% is observed during the periods of 1996-2000 and 2005-2018. Citation: Rafik, A.; Ibouh, H.; Fels, A.E.A.E.; Eddahby, L.; Mezzane, D.; Bousfoul, M.; Amazirh, A.; Ouhamdouch, S.; Bahir, M.; Gourfi, A.; et al. Soil Salinity Detection and Mapping in an Environment under Water Stress between 1984 and 2018 (Case of the Largest Oasis in Africa-Morocco).
... Determining ECe for salinity monitoring is related to natural conditions, but using it for the massive number of soil samples during the growing season is time-consuming and laborious. Therefore, researchers proposed to determine EC for various ratios of soil-to-water extracts (e.g., soil/water = 1:1, 1:2, 1:2.5, 1:5) in the laboratory [16][17][18][19][20][21] (Table 1). Table 1. ...
Conference Paper
Introduction, scope and main objectives. Soil salinity severely affects ecosystem quality and crop production. Large amount of data on soil salinity has been collected in the Commonwealth of Independent States (CIS, formerly USSR) and many other countries during more than 70 years, but its current use is complicated because in these countries salinity was expressed by (i) total soluble salts (total soluble salts, TSS, %) and (ii) eight salinity types (chemistry) determined by the ratios of the anions and cations (Cl-, SO4 ²+, HCO3 ²-, and Na+, Ca²+, Mg²+) in diluted 1:5 soil/water extract without assessing electrical conductivity (EC) (Basilevich and Pankova, 1968; Hazelton and Murphy, 2016). Measuring the EC (1:5) is more convenient and can be easily linked to saturated paste extract, ECe (Sonmez et al., 2008; He et al., 2013; Kargas et al., 2020). Yet, EC is not only affected by salt concentration but also by salinity chemistry (Corwin and Scudiero, 2019, Ismayilov et al., 2021). The latter also influences soil physical characteristics, soil-water-plant relations and abiotic stresses (Levy et al., 2005; Rengasamy, 2010). The objective of this study was to examine the relationship between EC and TSS of soils in a diluted extract (1:5) for the eight classic salinity types used in CIS. Methodology. Extracts (1:5) of 1100 samples of a clayey soil (0–30 cm) collected from cultivated semi-arid and arid regions of the Kur-Araz basin, Azerbaijan, were analysed for EC, TSS, soluble cations (Na+, Ca²+, Mg²+), and anions (HCO3 ²- , Cl-, SO4 ²+). Eight types of salinity chemistry were formed in light of the geomorphological conditions, irrigation, and drainage history in the basin. Results. Results revealed that (i) the variation in the proportional relations (R2=0.91–0.98) between TSS (0.05%–2.5%) and EC (0.12–5.6 dS/m) could be related to salinity type, and (ii) the proportionality coefficient of the relations (TSS = a EC; a= 0.313–0.447) decreased in the following salinity chemistry order: SO4 > Cl(SO4)–HCO3 > Cl(HCO3)–SO4 > SO4 (HCO3)–Cl > Cl. Formerly reported mean value of the coefficient (a = 0.336) was significantly lower than our mean value (a = 0.408), but still within the range of coefficients obtained in our study (a = 0.313– 0.447). Discussion. The traditional reported coefficient (TSS = 0.336 EC) is based on soil salinity dominated by NaCl. This coefficient was (i) comparable for chloride dominant salinity type (0.313, 0.323, and 0.336 for Cl, SO4–Cl, and HCO3–Cl, respectively); (ii) similar but somewhat lower for the sulfate dominant type of salinity (0.369 and 0.371 for Cl–SO4 and HCO3–SO4, respectively); and (iii) lower for sulfate itself and the carbonate and bicarbonate dominant type of salinity (0.447, 0.402, and 0.396 for SO4, Cl–HCO3, and SO4–HCO3, respectively). Thus, new TSS= a EC relation were (and should be) determined by ion characteristics or salinity type (Ismayilov et al., 2021). Conclusions. The findings suggest that once soil salinity type is established, EC (1:5) values can be used for evaluation of salinity degree in irrigated land in the context of sustainable soil and crop management. Results can assist in application of advanced precision agriculture and management strategies associated with mapping, leaching fraction, salinity stress, and selection of cultivars tolerance to salinity level and deterioration of soil physical quality. Acknowledgements. The support of Arid Land Research Center, Tottori University and Institute of Soil Science and Agrochemistrty, ANAS, is acknowledged. References. Corwin, D.L. & Scudiero, E. 2019. Chapter One–Review of soil salinity assessment for agriculture across multiple scales using proximal and/or remote sensors. In D.L. Sparks, ed. Advances in Agronomy, pp. 1–130. Academic Press. Bazilevich, N.I. & Pankova, E. I. 1968. An experience of soil classification according to the salinity status. Soviet Soil Science, 11: 3–16 (in Russian with English abstract). Hazelton, P. & Murphy, B. 2016. Interpreting Soil Test Results. What Do All the Numbers Mean? CSIRO Publishing. 201 pp. He, Y., DeSutter, T., Hopkins, D., Jia, X. & Wysocki, D.A. 2013. Predicting ECe of the saturated paste extract from value of EC1:5. Canadian Journal of Soil Science, 93(5): 585–594. Ismayilov, A.I., Mamedov, A.I., Fujimaki, H., Tsunekawa, A. & Levy, G.J. 2021. Soil Salinity Type Effects on the Relationship between the Electrical Conductivity and Salt Content for 1:5 Soil-to-Water Extract. Sustainability, 13(6): 3395. Kargas, G., Londra, P. & Sgoubopoulou, A. 2020. Comparison of Soil EC Values from Methods Based on 1:1 and 1:5 Soil to Water Ratios and ECe from Saturated Paste Extract Based Method. Water, 12(4): 1010. Levy, G., Goldstein, D. & Mamedov, A. 2005. Saturated Hydraulic Conductivity of Semiarid Soils: Combined Effects of Salinity, Sodicity, and Rate of Wetting. Soil Science Society of America Journal, 69. Rengasamy, P. 2010. Soil processes affecting crop production in salt-affected soils. Functional Plant Biology, 37. Sonmez, S., Buyuktas, D., Okturen, F. & Citak, S. 2008. Assessment of different soil to water ratios (1:1, 1:2.5, 1:5) in soil salinity studies. Geoderma, 1–2(144): 361–369.
... As it is expected, a wide range in the levels of salinity has been found when comparing EC1:1 with ECe values, because the ECe values calculated from EC1:1 by multiplying a conversion factor of 3 (USDA, 1954). Previously, in literature, it was reported that the values of ECe, which were measured in the saturated paste, were higher than those of EC1:1, mainly due to dilution effect (USDA, 1954;Zhang et al., 2005). The soil cultivated with wheat has 22.93% of non-saline soil and 8.33% of slightly saline ones with a total of 31.22% that have less than 4 dS m -1 based on the calculated ECe. ...
... sieve and all the coarse fragments were eliminated. This test employed the saturated soil paste extract method to quantify EC, as recommended by worldwide soil science (Richards 1954;Zhang et al. 2005;Burt 2014) and is considered for an accurate soil salinity measurement (Al-Ali et al. 2021). The soil sample was prepared by adding distilled water and carefully mixing to form a paste or saturation that smoothly glides out of the spatula and was left undisturbed for the entire night. ...
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The land is a vital source of agriculture. Agricultural land is uti-lized for the purpose of cropping. Delineation of potential agricul-tural land is pertinent when population pressure incrementallyincreases and a competitive trade-off between different land uses.The present study is conducted to evaluate the agrarian land suit-ability in the Ranchi district (Plateau region in India) using fuzzyanalytical hierarchy process (FAHP). A land suitability evaluationtechnique is designed, combining nineteen thematic layers fromfive categories: climatic parameters, topographic features, soilcharacteristics, euclidean distance, and land elements. All thematiclayers were combined into a single layer using the FAHP modelto create agricultural land suitability map in a GIS environment.Our analysis demonstrates that 11.17% of the total area(509700 ha) is highly suitable, while about 20.73% and 13.81% ofthe study area is moderately suitable and marginally suitable foragricultural activities. The study also reveals that approximately54.29% of the lands are not suitable for agriculture. Our findingsprovide additional information, and local governments can usethe land suitability map to manage and develop agriculturalresources. The study contributes to the studies on agriculturalsuitability by bringing in multiple variables centring on the FAHPmethod, which can be replicated in studies on plateau regionsacross the globe.
... Soil samples collected from the surface horizon were air-dried and gently grounded to pass through a 2-mm mesh sieve. The soil salinity was determined by mixing an aliquot of a sample with deionized water, at a soil/liquid ratio of 1:1 (w/v) (Zhang et al. 2005;Matthees et al. 2017). In brief, a 10-g sample of air-dried soil was pretreated with 10-ml deionized water (10 ml) at room temperature (25  C). ...
Background: Several coastal regions in Vietnam have been suffered from salinity intrusion as a consequence of global climate change. However, there are limited studies on saline intrusion in Vietnam. This paper aimed to investigate the salinity intrusion of water and soil samples in paddy fields along Tam Giang lagoon, Thua Thien Hue province and clarify the factors affecting the salinity level. Methods: We measured the salinity concentrations (EC, Electrical conductivity) of water and soil samples in paddy fields at different distances (400, 600, 800, 1000 and 1200 m) from Tam Giang lagoon. The multiple regression analysis was performed to figure out the factors affecting the salinity concentrations. Result: The salinity concentrations of water were assessed as 48% high saline (10-25 dS m-1), 34% moderately saline (2-10 dS m-1), 2% slightly saline (0.7-2 dS m-1) and 15% non-saline ( less than 0.7 dS m-1). As for surface soil in paddy field, 14.3% moderately saline (4-8 dS m-1), 35.4% slightly saline (2-4 dS m-1) and 50.3% non-saline (0-2 dS m-1) were measured. A significantly positive correlation was found between salinity concentrations of water and soil (n = 175, r = 0.886, p less than 0.01). The distances from salinity sources, Tam Giang lagoon and shrimp pond, were major factors affecting the salinity concentrations. The paddy fields near Tam Giang lagoon and shrimp pond have higher salinity concentrations compared to those areas close to the residential area. The surface water in the paddy field within 1000 m from the salinity source was assessed as saline that might harm the paddy soil and rice production. The results of this study provide highly useful information for local policymakers and farmers about the status of salinity intrusion in paddy land.
The electrical conductivity of saturated paste extracts (ECe) is more widely used as the laboratory method for estimating soil salinity compared to the soil-water ratio method. However, the current saturated paste process is time consuming and mainly based on subjective experience, and the quantitative relationships among the factors influencing the saturated paste process are not clear. In order to understand and optimize the process of soil salinity determination by saturated paste method, an experiment involving the process of making saturated paste and the key factors in salt determination was carried out on coastal saline silt and sandy loam soils: amount of water added, soaking time, number of centrifugations, and soil particle size. These test factors had clear impact on soil salinity results (both ECe and mass salt content) and pH. Based on the principle of obtaining more mass salt content in this study, suitable parameters for the production of saturated slurries were proposed as follows: (1) the amount of distilled water added to the soil sample should be 2.2 times the saturated water content of the soil; (2) the equilibration time of saturated paste was reduced from 18 to 12 h; (3) only 40 % of the full salt content was obtained by one centrifugation and obtaining 80 % requires three centrifugations; and (4) 0.25–0.50 and 1–2 mm of particle-size sieving should be used to prepare samples of silt and sandy loam soils, respectively.
Electrical conductivity (EC) of soil-water extracts is commonly used to assess soil salinity. However, its conversion to the EC of saturated soil paste extracts (ECe), the standard measure of soil salinity, is currently required for practical applications. Although many regression models can be used to obtain ECe from the EC of soil-water extracts, the application of a site-specific model to different sites is not straightforward due to confounding soil factors such as soil texture. This study was conducted to develop a universal regression model to estimate a conversion factor (CF) for predicting ECe from EC of soil-water extracts at a 1:5 ratio (EC1:5), by employing a site-specific soil texture (i.e., sand content). A regression model, CF = 8.910 5e0.010 6sand/1.298 4 (r² = 0.97, P < 0.001), was developed based on the results of coastal saline soil surveys (n = 173) and laboratory experiments using artificial saline soils with different textures (n = 6, sand content = 10%–65%) and salinity levels (n = 7, salinity = 1–24 dS m⁻¹). Model performance was validated using an independent dataset and demonstrated that ECe prediction using the developed model is more suitable for highly saline soils than for low saline soils. The feasibility of the regression model should be tested at other sites. Other soil factors affecting EC conversion factor also need to be explored to revise and improve the model through further studies.
Conference Paper
The diagnosis of saline soils requires the analysis of electrical conductivity in saturated soil paste extract. Its analysis is expensive, tedious, and highly time-consuming, therefore, commercial laboratories analyze the aqueous extract in a 1:1 ratio and then transform the value into saturation extract using equations. The research aimed to calibrate a statistical learning method to predict the electrical conductivity adapted to Peruvian conditions. For this, we apply different models from highly interpretable to black-box, such as multiple linear model, generalized additive models, Bayesian additive regression tree, extreme gradient boosting trees, and neural networks. In general, the models with beast predictive power were neural network and extreme gradient boosting trees, and the beast interpretable was Bayesian additive regression trees. The generalized additive models present the best balance between prediction power and interpretability with low application on extremely salty soils.
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Soil salinity is considered the most serious socio-economic and environmental problem in arid and semi-arid regions. This studym was carried out at an oasis in Zelfana, aims to (1) assess the quality of water used for irrigation, (2) estimate soil salinity/sodicity and follow their spatio-temporal and vertical variations, (3) compare two geostatistical methods of IDW and OK interpolation, (4) provide suggestions for a better management of the oasis agricultural system To achieve these objectives, samples of the irrigation and drainage water involved in this plot were collected and analyzed. Soil sample points were taken in two different periods (May, November) with two depths (0-30cm) and (30-60cm). The analysis of the irrigation water of the palm grove coming from the Albian water table, shows that it is admissible only for the palm orchards and poor for the other crops which hold a weak resistance to the saline stress. The content of Na+, Ca2+ and Mg2+ increased in the drainage water in the same proportion as the electrical conductivity, which may prove that the soil has a large reserve of these elements, responsible for salinity. The results of the mapping analysis in the vertical direction of soil-bound salt dynamics indicate a trend of accumulation at depth that can be explained by leaching processes associated with the sandy texture and flood irrigation system. However, the seasonal spatial distribution showed strong differences in salt movement, related to the direction of water flow, lack of maintenance of the drainage system and furthermore the influence of topography (presence of stagnation zones). The efficiency and best model between two geostatistical methods inverse distance weighting (IDW) and Ordinary Kriging (OK) were evaluated by calculating the mean error (ME) and root mean square error (RMSE). These results showing that the ME of both interpolation methods was satisfactory for soil salinity (EC) and sodicity (SAR), but the RMSE value was lower using IDW for both periods. This may explain the accuracy of the IDW interpolation method. This research could be applied to other studies on the factors controlling salt dynamics in agricultural soils, especially in an oasis environment. It should be noted that our study was only conducted on oasis farmland in dry areas and needs to be extended to other similar regions on a large scale.
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A geostatistical analysis of soil salinity in an agricultural area in the San Joaquin Valley included measurements of electrical conductivity of soil paste extract and water content of soil samples supplemented by surface measurements of apparent electrical conductivity. -from Authors
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The salinity tolerance of six annual crop species, wheat, oats, barley, rye, flax and rapeseed, were determined on saline soils that occur north of the Quill Lakes in the northeastern corner of the agricultural area of Saskatchewan. The relative merits of a salt-tolerant grass-legume mixture were also given consideration. The effects of salt stress on spring-sown cultivars became most apparent following exposure to hot, dry summer weather. In contrast, maximum salt tolerance for both winter wheat and winter rye was a function of winterkill. The winterhardiness of both winter annuals was reduced by saline conditions, but winter rye was more adversely affected than winter wheat. Large decreases in seed yield, plant dry weight and height occurred before the effects of increased soil conductivity were expressed for hectoliter weight, 1000-kernel weight, date of maturity, protein content and oil content. Among the spring and winter annual cultivars considered, Bonanza barley and Garry oats demonstrated the greatest salt tolerance. However, where severely saline conditions occurred, mixtures of salt-tolerant perennial grasses and alfalfa proved to be more productive than either barley or oats. The salinity tolerance of all cultivars was greater for years with more favorable growing conditions. It was apparent that stress factors, such as soil salinity, cold, heat, drought, etc., have a cumulative effect in reducing crop performance. This observation emphasizes the importance of minimizing all stress factors when attempting to crop saline soils. Detailed soil analyses indicated that where salts were a problem, the level of salinity was extremely variable, often changing dramatically over short distances. This extreme variability made it difficult to assess the magnitude of the salinity problem. In this regard, crop performance, especially plant height, provided a good indicator for identifying saline areas for purposes of soil testing.
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Organic matter influences aggregation in non-sodic soils, but less is known about its role in sodic soils. The purpose of this study was to examine the role of organic matter in aggregation and clay dispersion in soils with different levels of sodicity. Prepared soils with 15% clay (smectitic or illitic) and natural soil aggregates (from a smectitic clay soil [Pellustert] with 25 g kg-1 organic C and a pair of illitic loams [Rhodoxeralfs] with 15 and 29 g kg-1 organic C) were equilibrated with solutions having sodium adsorption ratios (SAR) of 0, 5, 15, and 30. Pea (Pisum sativum L.) straw was added at 50 g kg-1 to the prepared soils, which were then incubated. Changes in aggregation during incubation were similar irrespective of clay type. After 7 d incubation with no added straw at SAR 0 and 30, the amounts of spontaneously dispersible clay were 5.9 and 23.7 g kg-1 soil, and mechanically dispersible clay was 11.9 and 23.3 g kg-1 soil. Macroaggregation (>250 μm) was 125 and 41 g kg-1 soil at SAR 0 and 30. After 67 d incubation with pea straw, spontaneously dispersible clay contents were 2.1 and 5.4 g kg-1, mechanically dispersible clay contents were 9.0 and 17.7 g kg-1 soil, and macroaggregation was 533 and 416 g kg-1 soil at SAR 0 and 30. The effects of sodicity and organic matter on structural stability of the natural soil aggregates were similar to those in the prepared soils, but macroaggregation was less, and the smectitic clay soil was more sensitive to sodicity than the illitic loams. This work showed that organic matter has at least as great a role in aggregation in sodic soils as in non-sodic soils.
Electrical conductivity of saturation extracts was related to that of 1:1 and 1:2 (soil:H2O) suspensions and extracts for a wide range of Saskatchewan soils. The conductivity of 1:1 extracts was 1.75 times greater than for 1:1 suspensions and the conductivity of 1:2 extracts was 1.38 times greater than that of 1:1 suspensions. The conductivity of the saturation extract was closely related to all of 1:1 extract or suspension; 1:2 extract or suspension (r = 0.96–0.98). The regression coefficient relating the conductivity of 1:1 and 1:2 extracts and suspensions to that of the saturated paste extract decreased in going from coarse to medium to fine soil textures. The concentrations of Na⁺, Ca²⁺ + Mg²⁺ and Cl⁻ in 1:1 and 1:2 (soil:H2O) extracts were highly correlated with the amounts in the saturation extract (r = 0.93 to 0.99). Key words: Soil salinity, 1:1 and 1:2 suspension and extract, saturated paste
A common method for estimating soil salinity is by in situ measurements of the apparent electrical conductivity, EC(a), usually by a four-electrode probe. It is assumed that the EC(a) can be regarded as taking place in parallel mode by two conductors on a bulk scale: the dissolved (EC(b)) and the adsorbed (EC(s)) ions. Therefore, the contribution to the EC(a) by the soil solution electrolytes, EC(b), which serves to assess its salinity, can be deduced by subtracting the estimated EC(s) from the measured EC(a). This assumption is wrong and leads to an evaluation of EC(b) higher than its real value. This study was conducted to characterize the error in estimating soil solution electrical conductivity, EC(w). A simplified model of a randomly diluted and pore-size-distribution-decorated simple cubic lattice serves to describe the pore network of a saturated soil. It is assumed that only within each pore can the electrical conductance be represented by a sum of two conductors: the dissolved and the adsorbed ions, acting in parallel. Using Monte Carlo lattice simulations, it was shown that the error due to the assumption of parallel mode on a bulk scale increases with increasing broadness of the pore-size distribution, decreasing connectivities, and increasing cation-exchange capacity. An illustrative example of real soils, typical of irrigated soils, indicated errors of up to 25% for electrolyte concentrations.
A study was conducted to examine data collected in the microwatershed land process studies with regard to quantifying spatially variable soil properties. All 35 sampling sities were classified as occurring on the Mancos shale formation within a 777 km2 (300 mi2) area of the Price River Basin. Samples were taken at 0-2.5, 2.5-7.5, and 7.5-15.0-cm depths. Using the electrical conductivity (EC) of either the 1:1 or saturation extract as the salinity index parameter, it was found that EC values were distributed log-normally about the mean EC value of 35 observations. The coefficient of determination for the log-normal statistical plots was r2 = 1.00 for all three depths sampled at the 35 sites. The variance in the EC values increased with depth.