Comparison of spatial estimators for predicting water infiltration in calcareous, saline and sodic soils (Case study: Marvdasht plain)

Document Type : Complete scientific research article

Authors

Department of Agriculture Management, Marvedasht Branch, Islamic Azad University, Marvedasht, Iran

Abstract

Background and Objectives: Water infiltration to soil play an important role in irrigation management, storage moisture in soil especially in dry and semi-dry area and increasing agronomy yield. Understanding water infiltration to soil is of important in designing and applying water conservation methods, flood and runoff control and soil erosion management. Additionally, accurately measuring water infiltration to soil in different times is more important for predicting water storage in root zone, irrigation designing and planning and agronomy management. In other hand, water infiltration to soil is a base for precision agriculture, therefore, producing accurate maps for water infiltration to soil play an important role in land management and applying precision agriculture. Modeling soil water infiltration at the field scale with ruler of calcareous, saline and sodic conditions is important for a better understanding of infiltration processes in these soils and future of infiltration modeling. The present study aimed for estimating water infiltration to soil at different times using soil spatial prediction functions and spatial estimators.
Materials and methods: in present study, 72 soil samples were collected using a random sampling method in Marvdasht plain, Fars Province. In selected points, soil bulk density, sand silt, clay, pH, electrical conductivity, calcium carbonate, solution sodium, solution calcium and magnesium and organic carbon contents. For measuring water infiltration to soil, the double ring method were used. Multiple linear regression (MLR), artificial neural network (ANN) and spatial estimators were used for deriving soil spatial prediction functions models between water infiltration to soil in different times including 5, 10, 20, 45, 90, 150, 210 and 270 min. In this study, the readily available soil properties and auxiliary variables such as remote sensing and topography data were used in soil spatial prediction functions.
Results: The results of evaluating regression and artificial neural networks models based on the mean error (ME), coefficient of determination (R2) and root mean square error (RMSE) criteria in testing phase were showed that the developed artificial neural network models in present study performed better than multiple linear regression models in water infiltration to soil prediction at different times. Moreover, the results showed that the combined estimators (artificial neural network - Kriging) performed better than ordinary kriging model for estimating water infiltration to soil.
Conclusion: In totally, the results of this study showed that the applying soil spatial prediction functions (using auxiliary variables such as remote sensing data and topography data with the readily available soil properties) had a great potential to predict spatial estimation of water infiltration to soil at most considered times.

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References

 1.Shukla, M. K., Lal, R., & Unkefer, P. (2003). Experimental evaluation of infiltration models for different land use and soil management systems. Soil Science, 168 (3), 178-191.
2.Jenny, H. (1941). Factors of Soil Formation. A System of Quantitative Pedology. McGraw Hill, New York, 281p.
3.Minasny, B., Sulaeman, Y., & Mcbratney, A.B. (2011). Is soil carbon disappearing? The dynamics of soil organic carbon
in Java. Global Change Biology, 17 (5), 1917-1924.
4.Guo, P. T., Wu, W., Sheng, Q. K., Li, M. F., Liu, H. B., & Wang, Z.Y. (2013). Prediction of soil organic matter using artificial neural network and topographic indicators in hilly areas. Nutrient cycling in agroecosystems, 95, 333-344.
5.Zolfaghari, Z., Mosaddeghi, M. R., & Ayoubi, S. (2015). ANN‐based pedotransfer and soil spatial prediction functions for predicting Atterberg consistency limits and indices from easily available properties at the watershed scale in western Iran. Soil Use and Management, 31 (1), 142-154.
6.Shahriari, M., Delbari, M., Afrasiab, P., & Pahlavan-Rad, M. R. (2019). Predicting regional spatial distribution of soil texture in floodplains using remote sensing data: A case of southeastern Iran. Catena, 182, 104149.
7.Yao, X., Yu, K., Deng, Y., Liu, J., & Lai, Z. (2020). Spatial variability of soil organic carbon and total nitrogen in the hilly red soil region of Southern China. Journal of Forestry Research, 31 (6), 2385-2394.
8.Mirzaee, S., Ghorbani-Dashtaki, S., & Kerry, R. (2020). Comparison of a spatial, spatial and hybrid methods for predicting inter-rill and rill soil sensitivity to erosion at the field scale. Catena, 188, 104439.
9.Abbaspour, K. C., Schulin, R., van Genuchten, M. T., & Schläppi, E. (1998). An alternative to cokriging for situations with small sample sizes. Mathematical geology, 30, 259-274.
10.Mohammadi, J. (2006). Pedometric vol 2 (spatial statistics). Tehran: Pelk.
11.Wu, J., Norvell, W. A., Hopkins, D. G., Smith, D. B., Ulmer, M. G., & Welch, R. M. (2003). Improved prediction and mapping of soil copper by kriging with auxiliary data for cation‐exchange capacity. Soil Science Society of America Journal, 67 (3), 919-927.
12.Wu, C., Wu, J., Luo, Y., Zhang, L., & DeGloria, S. D. (2009). Spatial prediction of soil organic matter content using cokriging with remotely sensed data. Soil Science Society of America Journal, 73 (4), 1202-1208.
13.Liao, K., Xu, S., Wu, J., & Zhu, Q. (2013). Spatial estimation of surface soil texture using remote sensing data. Soil science and plant nutrition, 59 (4), 488-500.
14.Triantafilis, J., Ward, W. T., & McBratney, A. B. (2001). Land suitability assessment in the Namoi Valley of Australia, using a continuous model. Soil Research, 39 (2), 273-289.
15.Knotters, M., Brus, D. J., & Voshaar, J. O. (1995). A comparison of kriging, co-kriging and kriging combined with regression for spatial interpolation of horizon depth with censored observations. Geoderma, 67 (3-4), 227-246.
16.Bishop, T. F. A., & McBratney, A. B. (2001). A comparison of prediction methods for the creation of field-
extent soil property maps. Geoderma, 103 (1-2), 149-160.
17.Hengl, T., Heuvelink, G. B., & Stein, A. (2004). A generic framework for spatial prediction of soil variables
based on regression-kriging. Geoderma, 120 (1-2), 75-93.
18.Minasny, B., & McBratney, A. B. (2007). Spatial prediction of soil properties using EBLUP with the Matérn covariance function. Geoderma, 140 (4), 324-336.
19.Eldeiry, A. A., & Garcia, L. A. (2010). Comparison of ordinary kriging, regression kriging, and cokriging techniques to estimate soil salinity using LANDSAT images. Journal of Irrigation and Drainage Engineering, 136 (6), 355-364.
20.Dashtaki, S. G., Baniani, S. D., Khodaverdiloo, H., Mohammadi, J., & Khalilmoghaddam, B. (2012). Estimation of saturated hydraulic conductivity and inverse of macroscopic capillary length using PTFs. Journal of Science and Technology of Agriculture and Natural Resources, 16 (60 (B)), 145-157.
21.Dai, F., Zhou, Q., Lv, Z., Wang, X., & Liu, G. (2014). Spatial prediction of soil organic matter content integrating artificial neural network and ordinary kriging in Tibetan Plateau. Ecological Indicators, 45, 184-194.
22.Piccini, C., Marchetti, A., & Francaviglia, R. (2014). Estimation of soil organic matter by geostatistical methods: Use of auxiliary information in agricultural and environmental assessment. Ecological Indicators, 36, 301-314.
23.Watt, M. S., & Palmer, D. J. (2012). Use of regression kriging to develop a carbon: nitrogen ratio surface for New Zealand. Geoderma. 183-184, 49-57.
24.Blake, G. R., & Hartge, K. H. (1986). Bulk density. P 363-375. Methods of Soil Analysis: Part, 1 (10.2136).
25.Gee, G. W., & Bauder, J. W. (1986). Particle‐size analysis. Methods of soil analysis: Part 1 Physical and mineralogical methods, 5, 383-411.
26.Nelson, D. A., & Sommers, L. (1983). Total carbon, organic carbon, and organic matter. Methods of soil analysis: Part 2 chemical and microbiological properties, 9, 539-579.
27.Walkley, A., & Black, I. A. (1934). An examination of Degtjareff method for determining soil organic matter and a proposed modification of the chromic acid in soil analysis. 1. Experimental. Soil Sci. 79, 459-465.
28.Shirazi, M. A., & Boersma, L. (1984). A unifying quantitative analysis of soil texture. Soil Science Society of America Journal, 48 (1), 142-147.
29.US Department of Agriculture Natural Resources and Conservation Service (NRCS). (2005). National Engineering Handbook, Part 623, Surface Irrigation. National Technical Information Service, Washington, DC (Chapter 4).
30.Alavipanah, S.K. (2003). Application of Remote Sensing in the Earth Sciences (soil). Tehran university publication, Tehran, 478p.
31.Natural Resources Conservation Service, & Agriculture Department (Eds.). (2010). Keys to soil taxonomy. Government Printing Office.
32.Havaee, S., Mosaddeghi, M. R., & Ayoubi, S. (2015). In situ surface shear strength as affected by soil characteristics and land use in calcareous soils of central Iran. Geoderma. 237-238, 137-148.
33.Mirzaee, S., Ghorbani-Dashtaki, S., Mohammadi, J., Asadzadeh, F., & Kerry, R. (2017). Modeling WEPP erodibility parameters in calcareous soils in northwest Iran. Ecological Indicators, 74, 302-310.
34.Shainberg, I., Gal, M., Ferreira, A. G., & Goldstein, D. (1991). Effect of water quality and amendments on the hydraulic properties and erosion from several Mediterranean soils. Soil Technology, 4 (2), 135-146.
35.Suarez, D. L., Wood, J. D., & Lesch, S. M. (2006). Effect of SAR on water infiltration under a sequential rain–irrigation management system. Agricultural Water Management, 86 (1-2), 150-164.
Journal of Soil Management and Sustainable Production
Volume 13, Issue 1
March 2023
Pages 53-73

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