Spatial Mapping of Soil Properties Using Geostatistical Methods in the Ghazvin Plains of Iran

Amir Bostani,Md Mahmudur Rahman,Maryam Salahedin,Davood Namdar Khojasteh

doi:10.5539/mas.v11n10p23

Abstract

Geostatistical interpolation is widely used to map spatial variability in physical and chemical properties of soil, such as organic matter content, particle density; and pH. Geostatistical interpolation is a branch of applied science which predicts spatial concentrations at unknown locations at a study area by incorporating limited measured data, which is a major advantage over classical statistics. Although many studies applied geostatistical interpolation in agricultural land, there are still gaps in knowledge in selecting suitable models to map soil properties on a large geographical location. The objectives of this paper were to examine and to map the spatial distribution of the soil physico-chemical properties, including electric conductivity (EC), pH, sodium absorption ratio (SAR), organic matter (OM), percentage of sand, silt and clay, bulk density (ρb), saturate percentage (SP), and mean weight diameter (MWD), at 800 hectares of agro-industrial land at Sharifabad, Qazvin, Iran. The soil samples were collected in total 275 points in a regular grid (100 × 100m) over the study area. The exploratory statistical analysis was applied on the collected data for understanding the distribution of the dataset. The silt content, clay content and OM data showed normal frequency distribution, and the pH data show near to normal frequency distribution. The remaining soil properties data, including SAR, EC, SP, MWD, sand content and bulk density showed log-normal distribution, which was identified by the normality test of Kolmogorov-Smirnov with an error probability of 1%. The spatial characteristics of the dataset were assessed by semivariogram models in GS+ and GIS 10.3 software. Among the four different semivariogram models, namely linear, exponential, Gaussian and spherical, the best performing model was chosen following the highest R2 and lowest error values. The predictive geostatistical interpolation maps for each variable were drawn using ordinary kriging model.

Highlights

The soil properties such as pH, cation exchange capacity, calcium carbonate and organic matter show spatial variation across a study area; soil properties are studied across the world as it has application in the planning and management of industrial and agricultural land (Wang, Gertner, Parysow, & Anderson, 2000).In general, the soil properties are similar to the adjacent sampling sites compared to the distant sampling sites (Wang et al, 2000).Many studies applied classical statistical method to quantify spatial characteristics in soil properties (Salehi, Safaei, Esfandiarpour-Borujeni, & Mohammadi, 2013; Yemefack, Rossiter, & Njomgang, 2005)
Soil physico-chemical characteristics often exhibit spatial dependency, which cannot be captured by classical statistical methods (Burrough, 1993; Lin, Wheeler, Bell, & Wilding, 2005).To overcome this issue, many researchers applied geostatistical interpolation methods in estimating the spatial variability in soil properties (Cambardella et al, 1994; Webster & Oliver, 2007)
The study concluded that the kriging was the preferred method in estimating spatial distribution of soil properties compared to linear regression (Mohammadi, 2000)

Summary

Introduction

The soil properties such as pH, cation exchange capacity, calcium carbonate and organic matter show spatial variation across a study area; soil properties are studied across the world as it has application in the planning and management of industrial and agricultural land (Wang, Gertner, Parysow, & Anderson, 2000).In general, the soil properties are similar to the adjacent sampling sites compared to the distant sampling sites (Wang et al, 2000).Many studies applied classical statistical method to quantify spatial characteristics in soil properties (Salehi, Safaei, Esfandiarpour-Borujeni, & Mohammadi, 2013; Yemefack, Rossiter, & Njomgang, 2005). Geostatistical interpolation models are very effective tool in estimating spatial variations of soil properties at large geographic areas (Sokouti & Mahdian, 2011). Mohammadi et al (2000) applied both kriging and linear regression models in the Iran to quantify spatial distribution of several soil properties, including electrical conductivity, saturation percent, sodium absorption ratio and its percentage. The study concluded that the kriging was the preferred method in estimating spatial distribution of soil properties compared to linear regression (Mohammadi, 2000). The kriging method was applied in many previous studies to map depth and thickness of soil materials (Bourennane, King, & Couturier, 2000; Marinoni, 2003; Penížek & Borůvka, 2006) as well as characterize soil textures (Jang, Chen, & Kuo, 2013)

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Results