Assessment of Electrical Conductivity of Saturated Soil Paste from 1:5 Soil‐Water Extracts for Reclaimed Tideland Soils in South‐Western Coastal Area of Korea

Introduction

Reclaimed tideland (RTL) accounts for 8.3% of total arable land in South Korea (MOAF, 2017). Due to high salinity of the RTL soils, rice (Oryza sativa L.) is highly recommended as a pioneer crop over other salinity‐tolerant crops after reclamation of salt‐affected soils although rice is not tolerant to salinity as excessive salts leach down below rooting zone in lowland rice culture system by irrigation of less saline water (Abrol et al., 1988). However, due to surplus production of rice over consumption, there is social pressure on decreasing rice production by converting land‐use of RTL from rice to upland crops cultivation (Lee et al., 2003a). High soil salinity is a major constraint for the growth of upland crops in RTL, however, due to disruption of plant physiology caused by osmotic stress and specific ion toxicity (Castillo et al., 2007) as well as poor soil physical conditions such as destruction of soil aggregates by sodium (Na⁺) (Rengasamy and Olsson, 1991). Therefore, careful measurement and monitoring of soil salinity and ion concentrations is strongly required for assessment the suitability for upland crop cultivation in RTL soils.

Soil salinity is conventionally assessed with electrical conductivity (EC) of saturated soil paste extracts (EC_e) as EC_e is the best indicator of the salinity experienced by plant roots, and thus EC_e is regarded as a standard method for determination of soil salinity (US Salinity Laboratory Staff, 1954; Rhoades et al., 1989; Herrero and Pérez‐Coveta, 2005). However, as saturated paste extraction is time-consuming and requires more skills for determining the correct standard point of saturation (Al‐Busaidi et al., 2006), measurement of EC_e is inconvenient and costly method to determine soil salinity when the number of sample is huge or frequent monitoring is required (Aboukila and Norton, 2017). For these reasons, soil‐water extract at varying ratio such as 1:1 (EC_1:1), 1:2 (EC_1:2), and 1:5 (EC_1:5) of soil:water mass ratio has been commonly used; the 1:5 ratio is preferred in Australia, China, Central Asia and South Korea (Rayment and Higginson, 1992; Shirokova et al., 2000; Lee et al., 2003b), whereas the 1:1 ratio in the United States and Canada (Hogg and Henry, 1984; He et al., 2013; Zhang et al., 2005). However, it is necessary to convert the EC measure with soil-water extract to EC_e for field implication as the suitability of a crop for soil salinity is established based on EC_e (US Salinity Laboratory Staff, 1954).

Salinity measured with soil‐water extract is lower than EC_e due to dilution with water, and thus dilution factor needs to be adopted to convert EC of soil‐water extract to EC_e (Zhang et al., 2005; He et al., 2013; Aboukila and Abdelaty, 2017). Many studies reported statistically significant correlation between EC_e and EC of soil‐water extract; e.g., for EC_1:5, dilution factors of 5.48‒7.98 have been reported (Sonmez et al., 2008; Khorsandi and Yazdi, 2011; Aboukila and Abdelaty, 2017; Aboukila and Norton, 2017). In South Korea, a wide range (5.49‒9.94) of dilution factor for EC_1:5 has been reported for nine RTL soils including Namyang, Daeho, Seokmun, Seosan, Nampo, Busa, Gyehwa, Sopo, and Bojeon RTLs (Lee et al., 2003b). However, most of the RTLs were constructed in 1970‒1990 and no relevant study has been conducted for new RTLs constructed since 2000s, highlighting the necessity of development of a dilution factor to convert EC_1:5 to EC_e for the new RTLs that might save time, cost, and labor for salinity monitoring. This study was conducted to find a dilution factor to estimate EC_e from EC_1:5 using the relationship between EC_1:5 and EC_e for the new RTL soils.

Materials and Methods

Soil Sampling

Soil samples (n=40) were collected from 0‐20 cm soil depth of four RTLs, Goheung (GH, n=9), Gunnae (GN, n=7), Samsan (SS, n=7), and Youngsangang (YS, n=17) in April 2018 (Table 1). The number of samples and sampling sites for each RTL was determined by considering the area of RTL and spatial distribution of the sampling locations. The soil samples were air-dried and passed through 2‐mm sieve.

Soil Analysis

The soil samples were analyzed for particle size distribution using the pipette method (Gee and Bauder, 1986). For the measurement of EC_1:5 and soluble cations, soil suspension was obtained by adding 50 mL of deionized water to 10 g soils in 100‐mL polyethylene bottles. The suspension was shaken at 150 rev min^‐1 for 30 min and the suspension was filtered through No. 2 Whatman filter paper into 100‐mL polyethylene bottles. The solution was analyzed for EC with a conductivity meter (Orion 3 STAR, Thermo Fisher Scientific Korea, Seoul, Korea) and cations (Na⁺, K⁺, Ca²⁺, and Mg²⁺) concentrations using an Atomic Absorption Spectrometer (Analyst 800, Perkin Elmer, Waltahm, USA). Sodium adsorption ratio (SAR) was calculated using the cation concentrations.

Saturated pastes were prepared by adding deionized water to approximately 500 g of soil samples in a plastic container until it reached a condition of complete saturation as described in the USDA Handbook 60 (USDA, 1954). The extracts of saturated pastes were obtained under vacuum using a vacuum extractor (SampleTek 24VE, MAVCO INDUSTREIS, Inc., Kentucky, USA), and EC was measured using the conductivity meter.

Calculation and Statistical Analysis

Prior to statistical analysis, the data set was tested for normality of distribution with Kolmogorov‐Smirnov test and homogeneity of variance with Levene’s test. The data set was normally distributed and homogenous. The linearity of EC_1:5 with cation concentration was assessed by the relationship between EC_1:5 and cation concentration (Na⁺, K⁺, Ca²⁺, and Mg²⁺). In addition, EC_1:5 was compared with the EC (EC_c) calculated using the concentration of cations following the equation (APHA‐AWWA‐WPCF, 1992):

where, k is electrical conductivity of infinitely diluted solution consisting of the cations and A is monovalent ionic coefficient. The k was calculated as follows:

where, Z_i is the absolute value of the charge, λ_i is the equivalence conductance (S cm^‐1, 1 S cm^‐1= 1000 dS m^‐1), and C_i is the concentration (mM) of i^th cation. The λ of Na⁺, K⁺, Ca²⁺, and Mg²⁺ are 50.1, 73.5, 59.5, and 53.1 S cm^‐1, respectively. The A was calculated using the Davies equation for the solution with ionic strength (IS ) < 0.5 M as follows:

where, IS was calculated as (Σ(C_i)(Z_i)²)/2000.

To find the dilution factor for conversion of EC_1:5 to EC_e, simple linear regression model was fit for the data of EC_1:5 (x variable) and EC_e (y variable). The y intercept of the regression equation was forced to 0 as EC_e should be 0 when EC_1:5 is 0. Dilution factor was obtained from the slope of the regression equation. To validate the dilution factor, EC_e was predicted using an independent data set (n=96) of GH RTL obtained from the Korea Rural Community Corporation (2016). The significance of the difference (EC_diff) between EC_e predicted and measured was assessed with paired t-test. The relationship between EC_diff and soil variables such as clay content and SAR was explored to investigate the effect of soil properties on EC_diff.

Results and Discussion

Soil texture, EC_1:5, EC_e, and Cation Composition of 1:5 Extract

Particle of the soils (n=40) was widely distributed; 0.6‒65.5% for sand, 13.6‒75.6% for silt, and 6.0‒43.9% for clay (Fig. 1a). Across the samples (n=40), EC_1:5 ranged from 0.41 to 3.37 dS m^‐1 (mean: 1.14±0.08 dS m^‐1) and EC_e ranged from 3.09 to 37.0 dS m^‐1 (mean: 9.62±0.88 dS m^‐1) (Table 2). The EC_e in the present study was within the range (1‒30 dS m^‐1) reported for other RTL soils in South Korea (Lee et al., 2003b). Among cations in the 1:5 extract, Na⁺ was dominant cation (4.94±0.23 mM) followed by Mg²⁺ (1.37±0.13 mM), Ca²⁺ (0.61±0.11 mM), and K⁺ (0.37±0.13 mM). Sodium adsorption ratio ranged from 1.46 to 9.96 (Fig. 1b). The cation concentration was correlated with EC_1:5 in a positive manner (Fig. 2), reflecting the role of cations in increasing EC (APHA‐AWWA‐WPCF, 1992).

The regression equation between EC_1:5 measured and calculated using the equations 1‒3 suggests that the contribution of the measured cations to EC_1:5 was 34.9% (Fig. 3). The remainder should be attributed to counterbalancing anions such as NO₃^‐, PO₄^3‐, and SO₄^2‐ as well as undetermined cations including H⁺, Al species (Al³⁺ and Al(OH)_n^m), and trace cations.

Relationship between EC_1:5 and EC_e

There was a linear relationship between EC_1:5 and EC_e and the dilution factor for conversion of EC_1:5 to EC_e was estimated to be 8.70 from the slope of the regression equation (Fig. 4). A wide range of dilution factor for EC_1:5 has been reported; 5.37 and 5.48 (Khorsandi and Yazdi, 2011), 5.79 (Aboukila and Norton, 2017), 7.89 (Aboukila and Abdelaty, 2017), 7.98 (Sonmez et al., 2008), and 5.49‒9.94 (Lee et al., 2003b).

The differences in the dilution factor should be ascribed to the difference in the ionic compositions (Tolgyessy, 1993), silt content (Lee et al., 2003b), type of clay (Sonmez et al., 2008), type of salts present (Richard and Gouny, 1965), gypsum content (Khorsandi and Yazdi, 2007, 2011), equilibration times and methods (He et al., 2013), and the EC_e range of soil samples used to develop the conversion equations (Lee et al., 2003b; Aboukila and Norton, 2017). Soil texture might also affect the dilution factor (Monteleone et al., 2016) though a narrow range (0.05‒0.86) of difference in the dilution factor among different texture group has been reported (Hogg and Henry, 1984; Sonmez et al., 2008). Such a wide range of dilution factors suggests that it is not straightforward to apply a dilution factor of one study to soils from different area as such variables that might affect the dilution factor should differ with sites.

Validation of the Dilution Factor

There was a significant difference between EC_e measured and predicted by applying the dilution factor (8.70) to EC_1:5 of the independent data set from GH RTL (Table 3). However, the EC_e predicted and measured was linearly correlated, and the regression equation (EC_e predicted =1.16×EC_e measured) indicated that the dilution factor overestimate EC_e by 16% on average (Fig. 5). Ideally, if the predicted values of EC_e were exactly the same as the measured EC values, the slope would be 1.0, r² would equal 1.0, and the y intercept would equal 0. However, when monitoring of the relative changes in EC_e rather than determination of the exact EC_e is interested, the dilution factor could be successfully adopted.

The EC_diff between EC_e predicted and measured was positively correlated with clay content and negatively with SAR (Fig. 6). The positive correlation between EC_diff with clay indicates that EC_e predicted is likely to be overestimated with increasing clay content. Clay content may affect the relationship between EC_e and EC_1:5 as saturation percentage of soils, which is defined as the ratio of the amount of water added to saturate dry soil samples, differs with soil texture; i.e., saturation percentage tends to increase with clay content (Aali et al., 2009). Slavich and Petterson (1993) reported that the dilution factor for the conversion of EC_1:5 to EC_e decreases with increasing saturation percentage, indicating that soil with a high clay content and thus a high saturation percentage might have a lower dilution factor compared to soil with a low clay content and thus a low saturation percentage. Therefore, in the present study, the increased magnitude of the overestimation of EC_e predicted with clay content indicates that a real dilution factor for the soils decreases with increasing clay content. This result suggests that the application of dilution factor to the soils with high clay content should be cautioned.

The negative correlation between EC_diff and SAR suggests that ionic composition also affects dilution factor due to difference in solubility of the cation-anion pairs, particularly the presence of gypsum (CaSO₄) (Khorsandi and Yazdi, 2007). Though the pattern of dilution factor as affected by gypsum content is not uncovered yet, it is widely reported that the presence of gypsum abates the relationship between EC_1:5 and EC_e, leading to loss of linearity of the regression (Visconti et al., 2010; He et al., 2013). Considering the low solubility of salts consisted of divalent cations (e.g., Ca²⁺ and Mg²⁺) than those of monovalent (e.g., Na⁺), we postulate that the dilution factor is more applicable the soils with high SAR (i.e., containing more Na⁺ compared to Ca²⁺ and Mg²⁺) due to high solubility of the Na‐based salts in the soils. Considering the potential effect of clay content and SAR on the EC_diff, the strong relationship between EC_1:5 and EC_e could be attributed to the narrow range (1.46 to 9.96) of SAR of the soils used for development of the dilution factor rather than clay content as clay content varied widely (6.0‒43.9%) (Fig. 1), suggesting that SAR may be more influential than clay content in the variations in the dilution factor among soils.

Conclusions

This study shows that the dilution factor to convert EC_1:5 to EC_e in the study RTL soils is 8.70. Though the EC_e predicted by using the dilution factor overestimated EC_e by 16% when compared to the measured EC_e, the linear relationship between EC_e predicted and measured suggested that the dilution factor can be used in monitoring of the changes in soil EC_e of the study RTLs. Using the dilution factor is expected to save time and cost when huge number of soil samples are monitored. However, the relationship between EC_diff and soil variables indicated cautions are required for the soils with high clay content and low SAR. Further study is necessary to investigate the effect of clay content and SAR on the relationship between EC_1:5 and EC_e and thus on the dilution factor.

Note

The authors declare no conflict of interest.

ACKNOWLEDGEMENT

This work was carried out with the support of ”Cooperative Research Program for Agriculture Science and Technology Development (Project No. PJ013873042019)”, Rural Development Administration, Republic of Korea.

Tables & Figures

Table 1. Brief information of reclaimed tideland (RTL) investigated in this study

Fig. 1. Selected characteristics of the soils: (a) particle distribution and (b) sodium adsorption ratio (SAR).

Table 2. Summary of electrical conductivity (EC_1:5) of 1:5 soil‐water extract and electrical conductivity (EC_e) of saturated soil paste (n=40)

Fig. 2. Relationship between cation concentration and EC of 1:5 soil‐water extract. For Na⁺ and K⁺, the data was best fitted with an exponential model. For K⁺, to obtain a general trend, four outliers of which K⁺ concentration was greater than 0.4 mM were not used for the regression analysis.

Fig. 3. Relationship between EC_1:5 measured and calculated using the equations 1‒3. The broken line is 1:1 line. The slope of the regression equation indicates that the contribution of measured cations to EC_1:5 measured was 34.9%.

Fig. 4. Relationship between EC_1:5 and EC_e. The slope of the regression equation indicates that the dilution factor to convert EC_1:5 to EC_e is 8.70.

Table 3. Statistics summary for the paired t‐test (n=24) between EC_e measured (EC_{e_measured}) and predicted (EC_{e‐pridicted}) by applying the dilution factor (8.70) obtained in the present study to another data set (n=96) of Goheung RTL

Fig. 5. Relationship between EC_e measured and predicted using the dilution factor (8.70) for independent data set (n=96) of Goheung RTL.

Fig. 6. Relationship of the difference in EC_e predicted and measured with soil variables: (a) clay content and (b) sodium adsorption ratio (SAR). The EC_e was predicted by applying the dilution factor (8.70) obtained in the present study (shown in Fig. 4) to independent data set (n=96) of Goheung RTL (shown in Fig. 5).

References

1. 1. Aali, K. A., Parsinejad, M., & Rahmani,B. ((2009)). Estimation of saturation percentage of soil using multiple regression, ANN, and ANFIS techniques.. Computer and Information Science 2. 127 -136.
2. 2. Aboukila, E. F., & Abdelaty,E. F. ((2017)). Assessment of saturated soil paste salinity from 1:2.5 and 1:5 soilwater extracts for coarse textured soils.. Alexandria Science Exchange Journal 38. 722 -732.
3. 3. Aboukila, E. F., & Norton,J. B. ((2017)). Estimation of saturated soil paste salinity from soil‐water extracts.. Soil Science 182. 107 -113.
4. 4. Abrol, I. P., Yadav, J. S. P., & Massoud,F. I. ((1988)). Saltaffected soils and their management. FAO Soils Bulletin 39..
5. 5. Al‐Busaidi, A., Yamamoto, T., Bakheit, C., & Cookson,P. ((2006)). Soil salinity assessment by some destructive and non destructive methods in calcareous soils.. Journal of the Japanese Society of Soil Physics 104. 27 -40.
6. 6. Castillo, E. G., Tuong, T. P., Ismail, A. M., & Inubushi,K. ((2007)). Response to salinity in rice: comparative effects of osmotic and ionic stresses.. Plant Production Science 10. 159 -170.
7. 7. Gee, G. W., & Bauder,J. W. ((1986)). Particle‐size analysis. Methods of Soil Analysis. Part 1: Physical and Mineralogical Methods (ed. Champbell, G. S.). 383 -412.
8. 8. He, Y., De Sutter, T., Prunty, L., Hopkins, D., Jia, X., & Wysocki,D. ((2013)). Predicting EC_e of the saturated paste extract from value of EC_1:5.. Canadian Journal of Soil Science 93. 585 -594.
9. 9. Hogg, T. J., & Henry,J. L. ((1984)). Comparison of 1:1 and 1:2 suspensions and extracts with the saturation extract in estimating salinity in Saskatchewan soils.. Canadian Journal of Soil Science 64. 669 -704.
10. 10. Khorsandi, F., & Yazdi,F. A. ((2007)). Gypsum and texture effects on the estimation of saturated paste electrical conductivity by two extraction methods.. Communications in Soil Science and Plant Analysis 38. 1105 -1117.
11. 11. Khorsandi, F., & Yazdi,F. A. ((2011)). Estimation of saturated paste extracts’ electrical conductivity from 1:5 soil/water suspension and gypsum.. Communications in Soil Sciecne and Plant Analysis 42. 315 -321.
12. 12. Lee, S. H., Hong, B. D., An, Y., & Ro,H. M. ((2003a)). Relation between growth condition of six upland‐crops and soil salinity in reclaimed land.. Korean Journal of Soil Science and Fertilizer 36. 66 -71.
13. 13. Lee, S. H., Hong, B. D., An, Y., & Ro,H. M. ((2003b)). Estimation of conversion factors for electrical conductivities measured by saturation‐paste and 1:5 water extraction.. Korean Journal of Soil Science and Fertilizer 36. 193 -199.
14. 14. Monteleone, M., Lacolla, G., Caranfa, G., & Cucci,G. ((2016)). Indirect measurement of electrical conductivity and exchangeable cations on soil water extracts: assessing the precision of the estimates.. Soil Science 181. 465 -471.
15. 15. Rayment, G. E., & Higginson,F. R. ((1992)). Australian soil and land survey handbook. Australian Laboratory Handbook of Soil and Water Chemical Methods..
16. 16. Rengasamy, P., & Olsson,K. A. ((1991)). Sodicity and soil structure.. Australian Journal of Soil Research 29. 935 -952.
17. 17. Rhoades, J. D., Manteghi, N. A., Shouse, P. J., & Alves,W. J. ((1989)). Estimating soil salinity from saturated soil ‐paste electrical conductivity.. Soil Science Society of America Journal 53. 428 -433.
18. 18. Richard, M., & Gouny,P. ((1965)). Soil salinity test.. Annales agronomiques 16. 625 -635.
19. 19. Shirokova, Y., Forkutsa, I., & Sharafutdinova,N. ((2000)). Use of electrical conductivity instead of soluble salts for soil salinity monitoring in Central Asia.. Irrigation and Drainage Systems 14. 199 -205.
20. 20. Slavich, P. G., & Petterson,G. H. ((1993)). Estimating the electrical conductivity of saturated paste extracts from 1:5 soil, water suspensions and texture.. Australian Journal of Soil Research 31. 73 -81.
21. 21. 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 144. 361 -369.
22. 22. Tolgyessy,J. ((1993)). Chemistry and Biology of Water, Air and Soil: Environmental Aspects..
23. 23. Visconti, F., de Paz, J. M., & Rubio,J. L. ((2010)). What information does the electrical conductivity of soil water extracts of 1 and 5 ratio (w/v) provide for soil salinity assessment of agricultural irrigated lands?. Geoderma 154. 387 -397.
24. 24. Zhang, H., Schroder, J. L., Pittman, J. J., Wang, J. J., & Payton,M. E. ((2005)). Soil salinity using saturated paste and 1:1 soil to water extracts.. Soil Science Society of America Journal 69. 1146 -1151.