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Please use this identifier to cite or link to this item:
http://krishi.icar.gov.in/jspui/handle/123456789/24068
Title: | Digital mapping of soil salinity at various depths using an EM38 |
Other Titles: | Not Available |
Authors: | Bhaskar Narjary Murli Dhar Meena Satyendra Kumar Sushil Kumar Kamra Dinesh Kumar Sharma John Triantafilis |
ICAR Data Use Licennce: | http://krishi.icar.gov.in/PDF/ICAR_Data_Use_Licence.pdf |
Author's Affiliated institute: | ICAR::Central Soil Salinity Research Institute |
Published/ Complete Date: | 2018-11-29 |
Project Code: | NRMACSSRISIL201300200847 |
Keywords: | Electrical Conductivity Sampling Assessment and Prediction EM-38 ordinary kriging soil salinity |
Publisher: | Wiley |
Citation: | Narjary B, Meena MD, Kumar S, Kamra SK, Sharma DK, Traintafilis J. Digital mapping of soil salinity at various depths using an EM38. Soil Use Manage. 2019;35:232–244 |
Series/Report no.: | Not Available; |
Abstract/Description: | Problem definition: Spatial information on salinity is required at the farm level to enable suitable soil, crop and water management practices. Rationale: To facilitate this, we used an electromagnetic (EM) induction instrument for rapid measurement of apparent soil electrical conductivity (ECa—mS m–1) across the 11 ha area of the Central Soil Salinity Research Institute experimental farm in Nain, Haryana, India. Methods: The ECa was measured using an EM38 in horizontal (ECah) and vertical (ECav) modes on a grid survey. Using the ECa data, we selected 21 locations using the response surface sampling design (RSSD) module of Electrical Conductivity Sampling Assessment and Prediction (ESAP) software. We collected soil samples at four depth increments, including two topsoil (0–0.15 and 0.15–0.30 m), a subsurface (0.3–0.6m) and a subsoil (0.6–0.9m) and measured the soil electrical conductivity (ECe—dS m–1). Results: We developed multiple linear regression to predict ECe using the ESAP software from ECah and ECav and two trend surface parameters (i.e., Easting and Northing) across the farm. The prediction accuracy and bias were compared at different depth increments, and results of the spatial distributions of ECe using ordinary kriging (OK) interpolation were described in terms of the crop and soil use and management implications. Conclusions: We conclude the overall approach allows for generations of a digital soil maps (DSMs) of ECe which serve as baseline data that will allow the monitoring of any rehabilitation effort of salt-affected soils according to their actual degree of salinity. |
Description: | Not Available |
ISSN: | Not Available |
Type(s) of content: | Research Paper |
Sponsors: | ICAR-CSSRI |
Language: | English |
Name of Journal: | Soil Use and Management |
NAAS Rating: | 7.69 |
Volume No.: | 35 |
Page Number: | 232-244 |
Name of the Division/Regional Station: | Irrigation and Drainage Engineering |
Source, DOI or any other URL: | https://doi. org/10.1111/sum.12468 |
URI: | http://krishi.icar.gov.in/jspui/handle/123456789/24068 |
Appears in Collections: | NRM-CSSRI-Publication |
Files in This Item:
File | Description | Size | Format | |
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SUM-2018-017.R3.pdf | 1.52 MB | Adobe PDF | View/Open |
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