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Please use this identifier to cite or link to this item:
http://krishi.icar.gov.in/jspui/handle/123456789/10568
Title: | Digital soil mapping of sand content in arid western India using geostatistical approaches |
Other Titles: | Not Available |
Authors: | Santra, P., Kumar, M., Panwar, N.R. |
ICAR Data Use Licennce: | http://krishi.icar.gov.in/PDF/ICAR_Data_Use_Licence.pdf |
Author's Affiliated institute: | ICAR::Central Arid Zone Research Institute |
Published/ Complete Date: | 2017-03-27 |
Project Code: | Not Available |
Keywords: | Digital soil mapping Arid Western India Semivariogram Kriging Aridisol Inceptisol Entisol |
Publisher: | Elsevier |
Citation: | 5 |
Series/Report no.: | Not Available; |
Abstract/Description: | Digitalmaps of sand content of arid western Indiawere prepared using legacy soil data published by National Bureau of Soil Survey and Land Use Planning, Nagpur following digital soil mapping (DSM) approach. In the first step, profile data was harmonized to standard depths as followed by GlobalSoilMapping programme e.g. 0– 5 cm, 5–15 cm, 15–30 cm, 30–60 cm, 60–100 cm and 100–200 cm using mass preserving spline tool in R. Four different approaches of DSM methodology were applied to prepare sand content map of arid western India and these are ordinary kriging (OK), universal kriging (UK)/kriging with eternal drift (KED), random forest regression and regression kriging (RK). Apart from legacy soil data, information on auxiliary and environmental variables e.g. soil map, terrain attributes and bioclimatic variables were used in the DSM methodology. Trend of covariates were fitted using random forest regression and the R2 of fitted trend was found 0.21–0.28. The accuracy of the prepared digital products was evaluated through k-fold cross validation approach. Lin's concordance correlation coefficient (LCCC) was found 0.47–0.55 for KED, 0.45–0.51 for RK, 0.43–0.51 for random forest regression and 0.28–0.43 for OK. Apart from LCCC, other evaluation indices e.g. R2, root mean squared error (RMSE) and bias also showed the best performance of KED to predict sand content followed by RK, random forest regression and OK. The prepared digital products will be quite useful to take decisions on appropriate and region specific soilmanagements. The prepared maps may further be uploaded in webmap services for itswider access by end users. |
Description: | Not Available |
ISSN: | Not Available |
Type(s) of content: | Research Paper |
Sponsors: | Not Available |
Language: | English |
Name of Journal: | Geoderma Regional |
NAAS Rating: | 8.67 |
Volume No.: | 9 |
Page Number: | 56-72 |
Name of the Division/Regional Station: | Division of Agricultural Engineering and Renewable Energy |
Source, DOI or any other URL: | https://doi.org/10.1016/j.geodrs.2017.03.003 |
URI: | http://krishi.icar.gov.in/jspui/handle/123456789/10568 |
Appears in Collections: | NRM-CAZRI-Publication |
Files in This Item:
File | Description | Size | Format | |
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Santra et al 2017_DSM_Geoderma Regional.pdf | 4.11 MB | Adobe PDF | View/Open |
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