Spatial analysis of soil parameters in Domagor-Pahuj watershed using Geostatistical methods of GIS

R H RIZVI; R S YADAV; RAMESH SINGH; S B PANDEY; S P WANI; S K DHYANI

doi:10.56093/ijas.v85i4.47949

Authors

R H RIZVI Senior Scientist, National Research Centre for Agroforestry, Jhansi, Uttar Pradesh 284 003
R S YADAV Principal Scientist, Project Directorate for Farming Systems Research, Modipuram
RAMESH SINGH Senior Scientist, National Research Centre for Agroforestry, Jhansi, Uttar Pradesh 284 003
S B PANDEY Programme Manager, Development Alternative, Taragram, Jhansi
S P WANI Principal Scientist, ICRISAT, Patancheru, Hyderabad
S K DHYANI Director, National Research Centre for Agroforestry, Jhansi, Uttar Pradesh 284 003

https://doi.org/10.56093/ijas.v85i4.47949

Keywords:

Geostatistical, GIS, Kriging, Soil, Spatial, Watershed

Abstract

The water and land management is an important aspect in watershed programme increasing the productivity visa- vis sustainability of resources. Watersheds are natural hydraulic entity where water flows in a definite path to a common point. For land use planning of any watershed, soil characterization for its area is very essential and important. This would help in knowing the soil fertility status in watershed area. GIS is a tool that can be effectively used for spatial analysis of soil fertility for a desired geographical area. Domagor-Pahuj watershed is situated in Jhansi district of Bundelkhand region and located between 25o28’ to 25o31’ N latitude and 078o25’ to 078o28’ E longitude. This watershed has a total geographical area of 1 646 ha out of which 1 373 ha area is treatable and consists of three villages namely Domagor, Dikauli and Naya Khera. A total number of 103 representative soil samples were taken from Domagor-Pahuj watershed during May-June 2010. These samples were analyzed for soil parameters like pH, OC, EC, P, K, Zn, B and S. These sample points were geographically referenced and soil maps were generated using Geostatistical interpolation methods in Arc GIS 10. Ordinary and Universal Krigging methods were applied and compared on the basis of RMSE. Out of the two methods applied, ordinary Kriging was found better for prediction of soil EC, P, B and S, whereas universal Krigging was found better for prediction of soil pH, OC, K and Zn. Thus, spatial maps were generated by Ordinary and Universal Krigging methodsfor these soil parameters and hence may be used for soil fertility status and land use planning of Domagor-Pahuj watershed.

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References

Erashin S. 2003. Comparing Kriging and Cokriging to estimate infiltration rate. Soil Science 67: 1 848–55. DOI: https://doi.org/10.2136/sssaj2003.1848

Goovaerts P. 1999. Geostatistics in soil science: state-of the-art and perspectives. Geoderma 89: 1–45. DOI: https://doi.org/10.1016/S0016-7061(98)00078-0

Gotway C A, Ferguson R B, Hergert, G W and Peterson, T A. 1996. Comparison of Kriging and Inverse distance methods for mapping soil parameters. American Journal of Soil Science 60: 1 237–47. DOI: https://doi.org/10.2136/sssaj1996.03615995006000040040x

Johnston K, Ver Hoef J M, Krivoruchko K and Lucas N. 2001. Using Arc GIS Geostatistical Analyst. Environmental Systems Research Institute, Redlands, USA.

Kravchwnko A N and Bullock D G. 1999. A comparative study of interpolation methods for mapping soil properties. Journal of Agronomy 91: 393–400. DOI: https://doi.org/10.2134/agronj1999.00021962009100030007x

Lark R M. 2000. A comparison of some robust estimators of the variogram for use in soil survey. European Journal of Soil Science 51: 137–57. DOI: https://doi.org/10.1046/j.1365-2389.2000.00280.x

Meul M and Van Meirvenne M. 2003. Kriging soil texture under different type of non-stationarity. Geoderma 112: 217–33. DOI: https://doi.org/10.1016/S0016-7061(02)00308-7

Robinson T P and Metternicht G. 2006. Testing the performance of spatial interpolation techniques for mapping soil properties. Computer and Electronics in Agriculture 50: 97–108. DOI: https://doi.org/10.1016/j.compag.2005.07.003

Shi Z, Li Y, Makeshchine F and Wang R C. 2005. Assessment of temporal and spatial variability of soil salinity in a wastal saline field. Environmental Geology 48: 171–8. DOI: https://doi.org/10.1007/s00254-005-1285-3

Sokoti S, Mahdian M, Mahmoodi Sh. and Ghahramani A. 2006. Comparing the applicability of some geo-statistical methods to predict, soil salinity a case study of Urmia plain. Pajuhesh and Sazandgi 74: 90–8 (In Persian).

Wei J B, Xio D N, Zhang X Y, Li X Z, Li X Y. 2006. Spatial variability of soil organic carbon in relation to environmental factor of a typical small watershed in the black soil region, Northeast China. Environmental Monitoring and Assessment 121: 597–613. DOI: https://doi.org/10.1007/s10661-005-9158-5

Zare-Mahrjardi M, Taghizadeh-Mehrjardi R, Akbarzadah A. 2010. Evaluation of geospatial techniques for mapping soil pH, salinity and plant cover affected by environmental factors in southern Iran. Not Sci Biol 2: 92–103. DOI: https://doi.org/10.15835/nsb244997