Volume XL-8
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-8, 1353-1363, 2014
https://doi.org/10.5194/isprsarchives-XL-8-1353-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-8, 1353-1363, 2014
https://doi.org/10.5194/isprsarchives-XL-8-1353-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.

  23 Dec 2014

23 Dec 2014

Spatial variability of Soil Nutrients Using Geospatial Techniques: A case study in soils of Sanwer Tehsil of Indore district of Madhya Pradesh

G. S. Tagore1, G. D. Bairagi2, R. Sharma2, and P. K. Verma2 G. S. Tagore et al.
  • 1Department of Soil Science and Agril. Chemistry, JNKVV, Jabalpur (M.P.), India
  • 2M. P. Council of Science and Technology, Vigyan Bhawan, Bhopal (M.P.), India

Keywords: Spatial Variability, Soil Properties, semi -variogram, Sanwer, Kriging, GPS

Abstract. A study was conducted to explore the spatial variability of major soil nutrients in a soybean grown region of Malwa plateau. From the study area, one hundred sixty two surface soil samples were collected by a random sampling strategy using GPS. Then soil physico-chemical properties i.e., pH, EC, organic carbon, soil available nutrients (N, P, K, S and Zn) were measured in laboratory. After data normalization, classical and geo-statistical analyses were used to describe soil properties and spatial correlation of soil characteristics. Spatial variability of soil physico-chemical properties was quantified through semi-variogram analysis and the respective surface maps were prepared through ordinary Kriging. Exponential model fits well with experimental semi-variogram of pH, EC, OC, available N, P, K, S and Zn. pH, EC, OC, N, P, and K has displayed moderate spatial dependence whereas S and Zn showed weak spatial dependence. Cross validation of kriged map shows that spatial prediction of soil nutrients using semi-variogram parameters is better than assuming mean of observed value for any un-sampled location. Therefore it is a suitable alternative method for accurate estimation of chemical properties of soil in un-sampled positions as compared to direct measurement which has time and costs concerned.