USDA Forest Service RMRS-RN-18. 2004 1

United States

Department

of Agriculture

Forest Service

Rocky	Mountain

Research	Station

Research Note

RMRS-RN-18

August 2004

Introduction

Bentonite mine spoil is an atypical plant substrate 

with chemical and physical characteristics that complicate 

revegetation efforts. Spoil is typically saline-sodic (Bohn 

and others 1979) with an electrical conductivity (EC) that 

ranges from 9 to 49 (dS/m), a sodium adsorption ratio (SAR) 

of 20-56 (Hemmer and others 1977; Bjugstad and others 

1981; Uresk and Yamamoto 1986) and a high cation exchange capacity (CEC) (53-59 cmol/kg) (Voorhees and Uresk 

1990). High percentages of expansive clays dominate and 

saturation percentages are high and variable (255-331) 

(Voorhees and Uresk 1990).

Salinity estimates from 1:5 (1spoil:5water) extracts were 

used because of difficulties encountered in preparation of 

saturated pastes for bentonite substrate with high and variable saturation percentages (Uresk and Yamamoto 1986; 

Voorhees 1986; Voorhees 1990; Voorhees and others 1987; 

Voorhees and others 1991; Voorhees and Uresk 1990). While 

a 1:5 extract provides a fair indication of the prevalence of 

certain salts, it does not represent the true chemical environment experienced by a plant. The 1:5 extract has no 

Salt Content Determination for 

Bentonite Mine Spoil: Saturation 

Extracts Versus 1:5 Extracts

Marguerite E. Voorhees1 and Daniel W. Uresk2

Abstract—The reliability of estimating salt content in saturated extracts from 

1:5 (1spoil:5water) extract levels for bentonite mine spoil was examined by regression analyses. Nine chemical variables were examined that included pH, EC, Ca++, 

Mg++, Na+, K+, HCO3

−

, SO4

=

 , and Cl−. Ion concentrations from 1:5 extracts were estimated with high predictability for Ca++, Mg++, Na+, SO4

=, HCO3−

, and EC conductivity. 

Overall, calculated ion concentrations from 1:5 extracts show significant relationships with saturation extracts, except for Cl−

 and K+. The significant relationships we 

found suggest that 1:5 extracts can be used to reliably estimate salinity of bentonite 

mine spoils with less effort and expense.

Keywords: bentonite, mine spoil, salt content, salinity, saturation extracts, 1:5 

extracts, ion concentrations

1 Marguerite E. Voorhees, formerly Biological Scientist, 

Department of Forest, Rangeland, and Watershed Stewardship, 

College of Natural Resources, Colorado State University, Fort 

Collins, CO.

2 Daniel W. Uresk, Senior Research Biologist, USDA Forest 

Service, Rocky Mountain Research Station, 1730 Samco Road, 

Rapid City, SD 57702.

direct relationship with the range of moisture in the field 

as contrasted with the saturation extract. Concentrations of 

some salts increase with increasing moisture content while 

concentrations of other salts decline. This phenomenon results from differences in solubilities among salts, changes 

in activity coefficients, diverse ion effects, cation exchange 

reactions, negative adsorption of ions, hydrolysis, increased 

solution of silicate minerals, alkaline earth carbonates, and 

low solubility of gypsum (Richards 1954). Sulfate or carbonate salts in significant quantities can cause large deviations 

in estimates from actual measured quantities in saturation 

extracts. Concentration of sulfate ions in bentonite mine 

spoil is high, averaging over 2000 μg g-1 (Voorhees and Uresk 

1990).

Chemical and physical characteristics of bentonite 

spoils for reclamation and revegetation of spoils are very 

difficult to obtain and are expensive. Our purpose was to 

develop simple regression models for an easier, yet reliable, 

method of obtaining chemical characteristics from bentonite spoils using 1:5 extracts vs the more expensive and 

time consuming saturation extracts for chemical analyses. 

Information on the relationships between concentrations 

of various salts in 1:5 extracts and saturation extracts 

prepared for the same sample would be useful to interpret 

the chemistry of bentonite spoil for a larger area with less 

time and expense. The 1:5 extract may provide a more 

accurate insight on changes in the plant environment 

with changes in moisture content that can be applied 

over a larger area with increased sample collection for 

analyses.

The objective of this study was to test the reliability of 

estimating salt content from 1:5 extracts as compared with 

saturation extracts of bentonite mine spoil.
2 USDA Forest Service RMRS-RN-18. 2004 

Materials and Methods____________

Sixteen spoil samples were collected at random from 

0 to 20 cm depth from bentonite mined sites near Upton, 

Wyoming (Uresk and Yamamoto 1986; Uresk and Yamamoto 

1994). Each of these samples was dried and mixed prior to 

assay for pH, electrical conductivity (EC), and soluble Ca++, 

Mg++, Na+, K+, HCO3

−

, SO4

=

, and Cl− in 1:5 (1 spoil:5water) 

extract. A subsample of each sample was also analyzed for 

these same constituents using saturation extracts. The saturation percentage was also measured. Soluble ion content 

of each saturation extract was estimated from the soluble 

ion content of the 1:5 extract using the following formula: 

Ion concentration = 1:5 extract x 500/saturation percentage. 

All samples were analyzed at Colorado State University 

Soil Testing Laboratory.

The pH was measured using a combination electrode on 

the saturation paste and 1:5 spoil:water extract. EC was 

measured on 1:5 extracts by a conductivity cell. EC was 

measured in the soil saturation paste extracts after the 

paste was vacuum filtered. Levels of Ca++, Mg++, K+, and 

Na+ were measured in saturated and 1:5 extracts using 

inductively coupled plasma atomic emission spectrometry 

(Jones 1977). Sulfate content of spoil was measured using 

a turbidimetric method (Rhoades 1982). Bicarbonate and 

Cl were determined by titration and ion-chromatography 

procedures, respectively (Franson 1989).

All estimated values for 1:5 extracts were compared with 

measured values from saturated extracts by examination 

of scatterplots and regression analysis. Measured EC of 

the saturation extracts were compared with estimated EC 

from 1:5 extracts (500/saturation percentage x electrical 

conductivity 1:5 extract). Prediction equations developed 

from regression analyses were determined for significant 

variables (SPSS 1999). Calibration intervals (95 percent 

prediction limit) for individual points were determined for 

each of the ions and electrical conductivity. An example to 

estimate the calibration intervals for an extract point is 

presented in a figure (Graybill 1976). Regression relationships between measured saturation and estimated values 

were accepted as statistically significant at p < 0.05.

Results and Discussion___________

The concentration of all ions except Cl− was greater in 

saturation extracts as compared to 1:5 extracts (table 1). 

Regression analysis showed that levels of Cl− in 1:5 extracts 

were related to levels of SO4

=

 in these 1:5 extracts (r2 = 0.59; 

p = 0.0005). However, levels of Cl− in saturation extracts 

were not predictable from levels of Cl−, SO4

=

 or HCO3

−

 in 

either 1:5 or saturation extracts. Chloride levels in plant 

tissue decrease when SO4

=

 in the substrate increases (Eaton 

1966), presumably because of decreased solubility of Cl− in 

the presence of the more soluble SO4

=

. These ionic relationships when high or low, have important implications for 

plant growth and demonstrate the complexity of solute and 

cation exchange, especially on a substrate as complex and 

unique as bentonite.

Linear regression coefficients between measured chemical variables in saturation extracts with variables in 1:5 

extracts (table 2) showed significant correlations for EC, 

Ca++, Mg++, Na+, K+, HCO3

−

, and SO4

=

, (p < 0.05). However, 

r2 values were considered significant (r2 > 0.50) only for EC, 

Ca++, Mg++, Na+, SO4

= and HCO3−

. Therefore, estimation of 

saturation extract values from 1:5 extract measurements 

on bentonite mine spoil should be limited to these ions. 

Overall predictability from 1:5 extracts was good for these 

variables (r2 ranged from 0.72 to 0.89) (figure 1). Thus these 

equations would best be used to predict a single estimate 

from bentonite mine spoil with the 1:5 extracts using the 

estimated values.

An example of a calibration estimate to predict at the 

95 percent limit for single point estimate is presented in 

figure 2 for Na. A calibration estimate (figure 2) of saturated Na from the 1:5 extract is obtained by drawing a 

horizontal line from the estimated value through the regression model and prediction limits. A line is then drawn 

down to the saturated Na axis for an estimated 60 meq/l 

and for both predication limits. The calibration-saturated 

Table 2—Linear regression coefficients for the relationship between chemical 

variables measured in saturation extracts and estimates of similar chemical 

variables from 1:5 extracts for bentonite mine spoil taken from a site near 

Upton, Wyoming.

Variables r2 p SE1 SE2

EC* 0.89 ≤0.001 0.024 0.158

Ca 0.77 ≤0.001 0.584 4.660

Mg 0.85 ≤0.001 0.212 1.804

Na 0.89 ≤0.001 1.127 10.124

HCO3 0.70 ≤0.001 0.048 0.252

SO4 0.85 ≤0.001 3.520 25.993

K 0.37 0.012 - -

Cl ≤0.01 0.996 - -

* EC = electrical conductivity

1

 Standard error of the estimate

2

 Calibration standard error

Table 1—Means (SD) for selected chemical variables and ion concentrations in 

saturation and 1:5 extracts and for estimations of saturation extract measurements 

from 1:5 extract measurements (Est) of bentonite mine spoil samples taken from a 

site near Upton, Wyoming.

Variables Units Saturation 1:5 Estimate

pH - 5.8(0.5) 6.1(0.5) -

EC* S/m 0.71(0.19) 0.42(0.59) -

Ca meq/l 6.7(3.6) 4.4(3.6) 12.1(12.3)

Mg meq/l 4.1(1.9) 1.9(0.6) 4.8(2.2)

Na meq/l 64.3(12.0) 21.3(2.1) 52.5(12.9)

K meq/l 0.27(0.04) 0.24(0.04) 0.58(0.16)

HCO3 meq/l 0.53(0.20) 0.28(0.19) 0.64(0.34)

SO4 meq/l 96(27) 44(10) 112(35)

Cl meq/l 0.68(1.11) 3.3(5.7) 7.9(13.5)

*EC = electrical conductivity
USDA Forest Service RMRS-RN-18. 2004 3

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Figure 1—Scatterplots of measured (saturation extract) versus estimated concentrations of saturation extracts (1spoil:5water) in meq/l for Ca++, 

Mg++, Na+, K+, Cl−, HCO3

−

, and SO4

= ions; and estimated total ions S/m (totalSatEC) versus electrical conductivity esSatEC. Scatterplots are 

95 percent prediction intervals for the mean and single observations.
4 USDA Forest Service RMRS-RN-18. 2004 

estimate is 60 with limits of 49 and 71. Reliability of ±11 

units for Na is considered sufficiently precise for management purposes considering a single point (figure 2). Many 

points can be estimated and pooled for a mean to represent the area of concern for management. Sodium is one 

of the most important ions that affects the chemical and 

physical environment of plants on bentonite mine spoil, so 

this finding should be useful for revegetation efforts.

The predictive equations are reliably accurate for the 

variables presented in figure 1. Since the reliability for 

all ions tested is considered sufficient for a single point, 

the response of spoil to treatments designed to modify ion 

concentrations can be monitored using 1:5 extracts with 

less effort and lower costs.

Ions with high correlations (Ca++, Mg++, Na+, HCO3

−

, 

SO4

=

) made up 96 percent of the ions in saturation extracts 

and more than 95 percent of the ions in the 1:5 extract (figure 1). Estimates of total salts in saturation extracts from 

measurements of salts in 1:5 extracts are highly correlated 

(r2 = 0.89) with the electrical conductivity of saturation 

extracts (p≤0.001). Where sulfate or carbonate salts with 

relatively low solubility are present in appreciable quantities, the apparent amount of soluble salt depends on the 

soil:water extraction ratio. These results show that soil 

salinity (EC) measurements of 1:5 extracts where CO3

=

 and 

SO4

=

 salts are present will provide an estimate similar to 

saturation extracts.

Poor correlations between measured and calculated 

parameters for Cl− ions were attributed to diverse ions 

effects, cation exchange reactions, negative adsorption of 

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ions, hydrolysis, and increased solution of silicate minerals, alkaline earth carbonates, and gypsum. The predictive 

value of equations for those ions with low correlations 

between 1:5 extract concentrations and saturation extract 

concentrations (i.e., Cl−, K+) would likely be improved by including other predictive variables in the equation, such as 

the concentrations of other ions. This was not considered to 

be within the scope of this study and would require a larger 

sample size for multiple regression.

Acknowledgments_______________

Special thanks is extended to Teruo Yamamoto (deceased) for his support and encouragement throughout the 

study and to Jim Self for analyses and very helpful review 

suggestions. We thank Dr. Joe Trlica, Dr. Richard Aguilar, 

Jody Javersak and an anonymous reviewer for valuable 

suggestions and comments.

References_____________________

Bohn, H. L.; McNeal, B. L.; O’Connor, G. A. 1979. Soil chemistry. 

John Wiley and Sons, New York. 329 p.

Bjugstad, A. J.; Yamamoto, T.; Uresk, D. W. 1981. Shrub 

establishment on coal and bentonite clay mine spoils. In: 

Symposium proceedings; shrub establishment on disturbed arid 

and semi-arid lands; December 1980; Laramie, WY. Wyoming 

Fish and Game Department, Cheyenne. 104-122.

Eaton, F. M. 1966. Chlorine. In: Chapman, Homer D., ed. Diagnostic 

criteria for plants and soils. Division of Agricultural Sciences, 

University of California, Berkeley. 793 p.

Franson, M. A., ed. 1989. Standard methods for the examination 

of water and wastewater. Washington, DC: American Public 

Health Association.

Graybill, F. A. 1976. Theory and application of the linear model. 

Boston, MA: Duxbury Press. 704 p.

Hemmer, D.; Johnson, S.; Beck, R. 1977. Bentonite mining related 

reclamation problems in the Northwestern States. Helena, 

MT: Montana Department of State Lands, Old West Regional 

Commission Report. 94 p.

Jones, J. B., Jr. 1977. Elemental analysis of soil extracts and plant 

tissue ash by plasma emission spectroscopy. Communications in 

Soil Science and Plant Analysis. 8: 349-365.

Richards, L. A., ed. 1954. Diagnosis and improvement of saline 

and alkali soils. Agric. Handb. 60. Washington, DC: U.S. 

Government Print Office. 159 p.

Rhoades, J. D. 1982. Soluble salts. In: Page, A. L.; [and others], ed. 

Methods of soil analysis. Part 2: Chemical and microbiological 

properties. 2nd ed. Agronomy Series. Monograph No. 9. 

Madison, WI: American Society of Agronomy. 167-179.

SPSS, 1999. SPSS Base 9.0: Users guide. Copyright 1999 by SPSS 

Inc. USA. 740 p.

Uresk, D. W.; Yamamoto, T. 1986. Growth of forbs, shrubs and trees 

on bentonite mine spoil under greenhouse conditions. Journal 

Range Management. 39: 113-117.

Uresk, D. W.; Yamamoto, T. 1994. Field study of plant survival 

as affected by amendments to bentonite spoil. Great Basin 

Naturalist. 54: 156-161.

Voorhees, M. E.; Uresk, D. W.; Trlica, M. J. 1991. Substrate relation 

for rillscale (Atriplex suckleyi) on bentonite mine spoil. Journal 

Range Management. 44: 34-38.

Figure 2—Calibration example for Na+ based on regression between 

estimated 1:5 extracts (esSatNa+) and measured (saturation, 

SatNa+) with associated 95 percent prediction limits for a single 

observation.
USDA Forest Service RMRS-RN-18. 2004 5

Voorhees, M. E. 1990. Forage quality of rillscale (Atriplex 

suckleyi) grown on amended bentonite mine spoil. Great Basin 

Naturalist. 50: 57-62.

Voorhees, M. E.; Uresk, D. W. 1990. Effect of amendments on 

chemical properties of bentonite mine spoil. Soil Science. 150(4): 

663-670.

Voorhees, M. E.; Trlica, M. J.; Uresk, D. W. 1987. Growth of rillscale 

on bentonite mine spoil as influenced by selected amendments. 

Journal Environmental Quality. 16: 411-416.

Voorhees, M. E. 1986. Infiltration rate of bentonite mine spoil as 

affected by amendments of gypsum, sawdust and inorganic 

fertilizer. Reclamation Revegetation Research. 5: 483-490.
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