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Determination of Cd, Zn, K, pH, TNV, Organic Material and Electrical Conductivity (EC) Distribution in Agricultural Soils using Geostatistics and GIS (Case Study: South- Western of Natanz- Iran)
Abstract:Soil chemical and physical properties have important roles in compartment of the environment and agricultural sustainability and human health. The objectives of this research is determination of spatial distribution patterns of Cd, Zn, K, pH, TNV, organic material and electrical conductivity (EC) in agricultural soils of Natanz region in Esfehan province. In this study geostatistic and non-geostatistic methods were used for prediction of spatial distribution of these parameters. 64 composite soils samples were taken at 0-20 cm depth. The study area is located in south of NATANZ agricultural lands with area of 21660 hectares. Spatial distribution of Cd, Zn, K, pH, TNV, organic material and electrical conductivity (EC) was determined using geostatistic and geographic information system. Results showed that Cd, pH, TNV and K data has normal distribution and Zn, OC and EC data had not normal distribution. Kriging, Inverse Distance Weighting (IDW), Local Polynomial Interpolation (LPI) and Redial Basis functions (RBF) methods were used to interpolation. Trend analysis showed that organic carbon in north-south and east to west did not have trend while K and TNV had second degree trend. We used some error measurements include, mean absolute error(MAE), mean squared error (MSE) and mean biased error(MBE). Ordinary kriging(exponential model), LPI(Local polynomial interpolation), RBF(radial basis functions) and IDW methods have been chosen as the best methods to interpolating of the soil parameters. Prediction maps by disjunctive kriging was shown that in whole study area was intensive shortage of organic matter and more than 63.4 percent of study area had shortage of K amount.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1328007Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2270
 Cambardella CA, Karlen DL. Spatial analysis of soil fertility parameters. Precision Agriculture, 1999. 1: 5-14.
 Dercon G, Deckers J, Govers G, Poesen J, Sanchez H, Vanegas R, Ramirez M, Loaiza G. Spatial variability in soil properties on slowforming terraces in the Andes region of Ecuador. Soil & Tillage Research, 2003, 72: 31-41.
 Isaaks, E., & Srivastava, R. (1989). Applied geostatistics (p. 561). New York: Oxford University Press.
 Kolat, C., Doyuran, V., Ayday, C., Su┬¿ zen, M.L.. Preparation of a geotechnical microzonation model using Geographical Information Systems based on Multicriteria Decision Analysis. Engineering Geology, 2006, 87:241-255.
 Lark RM. Optimized spatial sampling of soil for estimation of the variogram by maximum likelihood. Geoderma,(2002, 105: 49-80.
 Li XD, Lee SL, Wong SC, Shi WZ, Thornton I. The study of metal contamination in urban soils of Hong Kong using a GIS-based approach. Environmental Pollution, 2004, 129:113- 24.
 Nelson, D., & Sommers, L. Total carbon, organic carbon and organic matter. In A. L. Page, et al. (Eds.), Methods of soil analysis, part 2, no. 9 (2nd ed., pp. 539-577). 1982. Madison: ASA Publication.
 Paz-Gonzalez A, Vieira SR, Taboada Castro MaT. The effect of cultivation on the spatial variability of selected properties of an umbric horizon. Geoderma, 2000, 97: 273-292.
 Wang, X., Qin, Y. Spatial distribution of metals in urban topsoils of Xuzhou (China): controlling factors and environmental implications. Environmental Geology. 2006, 49:905-914.