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)
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.
[1] Cambardella CA, Karlen DL. Spatial analysis of soil fertility parameters.
Precision Agriculture, 1999. 1: 5-14.
[2] 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.
[3] Isaaks, E., & Srivastava, R. (1989). Applied geostatistics (p. 561). New
York: Oxford University Press.
[4] 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.
[5] Lark RM. Optimized spatial sampling of soil for estimation of the
variogram by maximum likelihood. Geoderma,(2002, 105: 49-80.
[6] 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.
[7] 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.
[8] 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.
[9] 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.
[1] Cambardella CA, Karlen DL. Spatial analysis of soil fertility parameters.
Precision Agriculture, 1999. 1: 5-14.
[2] 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.
[3] Isaaks, E., & Srivastava, R. (1989). Applied geostatistics (p. 561). New
York: Oxford University Press.
[4] 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.
[5] Lark RM. Optimized spatial sampling of soil for estimation of the
variogram by maximum likelihood. Geoderma,(2002, 105: 49-80.
[6] 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.
[7] 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.
[8] 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.
[9] 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.
@article{"International Journal of Biological, Life and Agricultural Sciences:49407", author = "Abbas Hani and Seyed Ali Hoseini Abari", title = "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.", keywords = "Electrical conductivity, Geostatistics, Geographical
Information System, TNV", volume = "5", number = "12", pages = "852-4", }