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Current Journal of Applied Science and Technology, ISSN: 2457-1024; 2231-0843 (old),Vol.: 26, Issue.: 2


Assessment of Spatial Variability of Soil Properties in Gopalapur Microwatershed, Gundlupet Taluk, Chamarajanagar District, Karnataka, India


Rajendra Hegde1, K. V. Niranjan1, Gopali Bardhan1, B. P. Bhaskar1* and S. K. Singh2

1ICAR-NBSS & LUP, Regional Centre, Hebbal, Bangalore-560024, India.

2ICAR-NBSS & LUP, Amravati Road, Nagpur-440033, India.

Article Information
(1) Jakub Kostecki, Professor, Faculty of Civil Engineering, Architecture and Environmental Engineering, University of Zielona Góra, Poland.
(2) Jose Navarro Pedreno, Professor, Department of Agrochemistry and Environment, University Miguel Hernandez, Spain.
(1) Ade Onanuga, Canada.
(2) M. Diack, Université Gaston Berger, Senegal.
(3) M. Pandimadevi, SRM Institute of Science and Technology, India.
(4) Godwin Michael Ubi, University of Calabar, Nigeria.
Complete Peer review History: http://www.sciencedomain.org/review-history/23481


Evaluation of geostatistical tools was used for assessing top soil variability in Gopalapur micro-watershed, Gundlupet taluk, Chamarajanagar district, Karnataka, India to explore a scientific basis for predicting soil properties from unknown locations and to derive site-specific nutrient management strategies. The study area is a part of Central Karnataka Plateau, have hot, moist semi-arid with medium to deep Red loamy soils, low AWC and LGP 120-150 days. The Grid survey at 250 × 250 m interval was carried out and collected 97 georeferenced surface soil samples (0-15 cm) from five land use systems such as agriculture, scrubland, forest, grassland and fallow land. Three interpolation methods such as ordinary kriging, inverse distance weighting (IDW) and spline were used to generate spatial distribution of eleven soil variables viz. pH, EC, organic carbon, available K2O, P2O5, sulphur/boron and DTPA extractable Cu, Fe, Mn and Zn. Experimental variograms were fitted with the exponential, spherical, Gaussian and linear models using weighted least squares. The model with the smallest residual sum of squares (RSS) was further interrogated to find the number of neighbours that returned the best cross-validation result. The choice of the exponent value for IDW and splines as well as the number of the closest neighbours was decided from the root mean squared error (RMSE) statistic, obtained from a cross-validation procedure. On this experimental field, ordinary kriging performed best for topsoil and exponential method of kriging gave the best results of interpolation with the smallest residual sum of squares (RSS). We conclude that ordinary kriging is a superior method with the least RMSE and lowest RSS value for interpolation of topsoil spatial distribution.

Keywords :

Cross-validation; geostatistical interpolation; kriging; spatial prediction; soil properties; watershed.

Full Article - PDF    Page 1-16

DOI : 10.9734/CJAST/2018/38787

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