Geostatistical Assessment of Aggregates Stability and Sodium Adsorption Ratio in Salt-Affected Soils Around Urmia Lake

Document Type : Research Article

Authors

• shokrollah asghari ¹
• Mahmood Shahabi ²

¹ University of Mohaghegh Ardabili

² University of Tabriz

https://doi.org/10.22067/jsw.v32i4.72257

Abstract

Introduction: Salinity and sodicity are the most important land degradation problems particularly in arid and semi-arid regions. Due to the depletion of Urmia Lake located in the northwest of Iran during recent years, the proportion of surrounding saline agricultural lands increased at a past pace. In the salt-affected soils, aggregate stability is weak due to the high contents of sodium. The analysis of spatial variability of mean weight diameter of aggregates (MWD) and sodium adsorption ratio (SAR) is necessary to implement a site-specific soil management especially in the salt-affected soils. The main object of this study was evaluating the effects of different land uses (bare and agriculture) on the spatial variability of MWD and SAR in the salt-affected soils around Urmia Lake.
Materials and Methods: This study was conducted in the agricultural and bare lands of Shend Abad region located at the 15 km of Shabestar city, northwest of Iran (45° 36ʹ 34ʺ E and 38° 6ʹ 37ʺ N). Totally, 100 geo-referenced samples were taken from 0-10 cm soil depth with 100×100 m intervals (80 ha) in agricultural (n=49) and bare (n=51) land uses. Sand, silt, clay, organic carbon (OC), CaCO3, pHe, MWD, SAR and electrical conductivity (EC), were measured in the collected soil samples. Thewet sieving method was used to determine MWD of wet aggregates. The sieves were: 2, 1, 0.5, 0.25 and 0.106mm. The EC and SAR were measured in 1:2.5 (soil: distilled water) extra. The SAR was calculated from concentrations of Na+ and Ca+ + Mg+. The best fit semivariogram model (Gaussian, spherical and exponential) was chosen by considering the minimum residual sum of square (RSS) and maximum determination coefficient (R2). Ordinary kriging (OK) and inverse distance weighting (IDW) interpolation methods were used to analyze spatial variability of MWD and SAR. Spatial distribution maps of soil variables were provided by Arc GIS software. The accuracy of OK and IDW methods in estimating MWD and SAR was evaluated by mean error (ME), mean absolute error (MAE), root mean square error (RMSE) and concordance correlation coefficient (CCC) criteria. The CCC indicates the degree to which pairs of the measured and estimated parameter value fall on the 45° line through the origin.
Results and Discussion: According to the results of coefficient of variation (CV) from the study area, the most variable (CV=113.05%) soil indicator was SAR (bare land use), whereas the least variable (CV= 3.52%) was pHe (agricultural land use). The Pearson correlation coefficients (r value) indicated that there are significant (P

Keywords

• Agricultural lands
• Mean weight diameter of aggregates
• Saline and sodic soils
• Spatial variability
• Shabestar

20.1001.1.20084757.1397.32.4.11.1

References

1- Annabi M., Raclot D., Bahri H., Bailly G.S., Gomez C., and Bissonnais Y.L. 2017. Spatial variability of soil aggregate stability at the scale of an agricultural region in Tunisia. Catena, 153: 157–167.

2- Asghari Sh., Sheykhzadeh G.R., and Shahabi M. 2017. Geostatistical analysis of soil mechanical properties in Ardabil plain of Iran. Archives of Agronomy and Soil Science, 63 (12): 1631-1643.

3- Ardahanlioglu O., Oztasw T., Evren S., Yilmaz H., and Yildirim Z. 2003. Spatial variability of exchangeable sodium, electrical conductivity, soil pH and boron content in salt- and sodium-affected areas of the Igdir plain (Turkey). Journal of Arid Environments, 54: 495–503.

4- Cambardella C., Moorman T., Novak J., Parkin T., Karlen D., Turco R., and Konopka A. 1994. Field-scale variability of soil properties in central Iowa soils. Soil Science Society of America Journal, 58: 1501-1510.

5- Dai F., Zhou Q., Lv Z., Wang X., and Liu G. 2014. Spatial prediction of soil organic matter content integrating artificial neural network and ordinary kriging in Tibetan Plateau. Ecological Indicator, 45:184-194.

6- Delbari M., and Jahani S. 2014. Investigation of soil salinity and sodicity properties in Chat region of Golestan province. Iranian Journal of Soil Research, 28(2): 432-446. (In Persian)

7- Douaik A., Van Meirvenne M., and Toth T. 2005. Soil salinity mapping using spatio-temporal kriging and Bayesian Maximum Entropy with interval soft data. Geoderma, 128: 234-248.

8- Foroughifar H., Jafarzadah A.A., Torabi Gelsefidi H., Aliasgharzadah N., Toomanian N., and Davatgar, N. 2011. Spatial variations of surface soil physical and chemical properties on different landforms of Tabriz plain. Water and Soil Science- University of Tabriz, 21(3): 1-21. (In Persian with English abstract)

9- Goovaerts P. 1997. Geostatistics for Natural Resources Evaluation. Oxford University Press. Oxford.

10- GS+5.1. 2001. Gamma Design software. Plainwell, MI, USA.

11- Gee G.W., and Or D. 2002. Particle-size analysis. p. 255–293. In: Dane J. H., and Topp G. C. (eds.). Methods of Soil Analysis. Part 4. SSSA Book Series No. 5. Soil Science Society of America, Madison, WI.

12- Hamzehpoura N., and Bogaert P. 2017. Improved spatiotemporal monitoring of soil salinity using filtered kriging with measurement errors: An application to the West Urmia Lake, Iran. Geoderma, 295: 22–33.

13- Hillel D. 2004. Environmental soil physics. New York, USA: Academic Press.

14- Isaaks H.E., and Srivastava R.M. 1989. An Introduction to Applied Geostatistics. Oxford University Press, NY.

15- Lin L.I. 1989. A concordance correlation coefficient to evaluate reproducibility. Bio-metrics, 45: 255–268.

16- Mohammadi J., and Motaghian M.H. 2011. Spatial prediction of soil aggregate stability and aggregate-associated organic carbon content at the catchment scale using geostatistical techniques. Pedosphere, 21(3): 389–399.

17- Nabiollahia K., Taghizadeh-Mehrjardib R., Kerryc R., and Moradiana S. 2017. Assessment of soil quality indices for salt-affected agricultural land in Kurdistan Province, Iran. Ecological Indicators, 83: 482–494.

18- Nelson D.W., and Sommers L.E. 1982. Total carbon, organic carbon, and organic matter. p. 539–579. In A.L. Page et al. (ed.) Methods of Soil Analysis. Part 2. 2nd ed. Agron. Monogr. 9. ASA and SSSA, Madison, WI.

19- Owji A.R., and Kamali A. 2015. The effect of irrigation management and soil texture on the pattern of soil salinity variability in pistachio orchards of Rafsanjan. Irrigation & Water Engineering, 3(10): 65-77. (In Persian with English abstract)

20- Page A.L. 1985. Methods of Soil Analysis. Part 2. Chemical and Microbiological Methods. Agron.Monog.9. ASA and SSSA, Madison, WI.

21- Ranjbar F., and Jalali M. 2016. The combination of geostatistics and geochemical simulation for the site-specific management of soil salinity and sodicity. Computers and Electronics in Agriculture, 121: 301–312.

22- Richards L.A. 1954. Diagnosis and improvement of saline and alkali soils. Agricultural Handbook No. 60. U.S. Salinity Laboratory Riverside, California.

23- Sheng J., Maa L., Jiang P., Li B., Huang F., and Wu, H. 2010. Digital soil mapping to enable classification of the salt-affected soils in desert agro-ecological zones. Agricultural Water Management, 97: 1944–1951.

24- Tajik F. 2004. Evaluation of aggregates stability in some regions of Iran. Journal of Sciences and Technology of Agriculture and Natural Resources, 8(1): 107-122. (In Persian)

25- Webster R. 1985. Quantitative spatial analysis of soil in the field. Advanced Soil Science, 3: 1–70.

26- Wilding L.P, and Dress L.R. 1983. Spatial variability and pedology. p: 83-116. In: Wilding L.P, Smeckand N.E, and Hall GF, (EDs). Pedogenesis and Soil Taxonomy. I. Concepts and Interactions. Elsevier Science Pub.

27- Yazdani A., Mosaddeghi M.R., Khademi H., Ayoubi S., and Khayamim F. 2014. Relationship between surface aggregate stability and some soil and climate properties in Isfahan province. Soil Management, 3(2): 23-31. (In Persian)

28- Yoder R.E. 1936. A direct method of aggregate analysis of soils and a study of the physical nature of erosion losses. Journal of American Society Agronomy, 28: 337-35.