Rapid Assessment and Mapping of Soil Salinity in Subsurface Drainage Project using Electromagnetic Induction and Geostatistical Analysis
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Authors
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Sagar D. Vibhute
Division of Irrigation and Drainage Engineering, ICAR-CSSRI, Karnal -132 001, Haryana, India
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S. K. Kamra
Ex-Principal Scientist, Division of Irrigation and Drainage Engineering, ICAR-CSSRI, Karnal -132 001, India
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Aslam. L. Pathan
Division of Irrigation and Drainage Engineering, ICAR-CSSRI, Karnal -132 001, Haryana, India
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D. S. Bundela
Division of Irrigation and Drainage Engineering, ICAR-CSSRI, Karnal -132 001, Haryana, India
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R. Abhishek
Assistant Professor, Agricultural Engineering, SGT University, Gurugram – 122505, India
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Satyendra Kumar
Division of Irrigation and Drainage Engineering, ICAR-CSSRI, Karnal -132 001, Haryana, India
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Jitendra Kumar
Division of Irrigation and Drainage Engineering, ICAR-CSSRI, Karnal -132 001, India
Keywords:
Soil salinity, multiple linear regression, surfer, electrical conductivityAbstract
Assessment of soil salinity is essential in subsurface drainage (SSD) projects not only for identifying suitable locations for SSD installation but to monitor its performance in reducing soil salinity. A study was undertaken in Katwara SSD site of Rohtak district of Haryana using EM 38 and surfer tool for rapid assessment and mapping of soil salinity. An EM38 survey was conducted in a block of 36 ha and values of apparent electrical conductivity (ECa) of soil were recorded in vertical (ECav) and horizontal (ECah) positions at 91 locations. Simultaneously soil samples with auger were collected at 9 locations (10% of the EM38 locations) to develop a multiple linear regression (MLR) equation for converting ECa values to ECe. The equations were developed for 0-30, 30-60, 60-90 and 0-90 cm soil depths with R2 0.68, 0.89, 0.94 and 0.70, respectively. This shows that the combination of EM38 and MLR equation are effective in rapid assessment of soil salinity in subsurface drainage project. Furthermore, to convert this point data of EM values in spatial variability maps, krigging method was applied using surfer tool. The developed maps depicted spatial variation in soil salinity in drainage block for different soil depths. The scatter plot of predicted and measured ECe values was closer to 1:1 line and this indicated better prediction accuracy. The methodology used for assessment of initial soil salinity can be further used for monitoring salt movement throughout the drainage block.
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References
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