Characterizing spatial variability of soil properties in salt affected coastal India using geostatistics and kriging

Abstract

Soil salinization is a major problem affecting 955 Mha globally and 7 Mha in India. Soil properties vary spatially and knowing the extent of spatial variability of soil physicochemical characteristics is highly essential for management of these soils and crop cultivation. This study was conducted in salt-affected coastal parts of eastern India, with the following objectives: (i) to explore the spatial variability of soil properties (soil electrical conductivity (ECe), soil pH, soil organic carbon (SOC), available soil nitrogen, available soil phosphorus, and available soil potassium) and fitting the semivariogram models; (ii) to estimate the values of soil properties at unsampled locations using geostatistical tools; and (iii) to prepare the spatial maps of soil properties using parameters of best fit semivariogram model and interpolation by ordinary kriging technique. A total of 132 soil samples were collected. Gaussian, exponential, circular, spherical, K-Bessel, and spherical semivariogram models were found to be the best fit for assessing the spatial variability in ECe, soil pH, SOC, available soil nitrogen, available soil phosphorus, and available soil potassium, respectively. The best fit model parameters were used to create the spatial maps for these soil properties by ordinary kriging. It was concluded that geostatistical and kriging tools can be used to estimate the value of soil properties at unsampled locations and ultimately to develop spatial maps for site-specific nutrient management.

Spatial analysis of soil properties using GIS based geostatistics models

“…In India majority of soil maps were prepared by conventional methods and a very little work has been done so far to use the modern spatial prediction techniques in this regard (Saha et al 2012;Pal et al 2014;Behera and Shukla 2015;Tripathi et al 2015;Bhunia et al 2016). The accurate estimation of spatial distribution of soil properties [soil pH, organic carbon (OC), electrical conductivity, phosphorous, potassium, etc.]…”

Geoinformatics for Quantifying Salt Affected Soils in Gohana, Haryana Using Soil Techniques

“…Havlin et al, (2005) reported that organic matter increases P by anion replacement of H 2 PO 4 − ion on adsorption sites and increasing the quantity of organic P mineralized to inorganic P. Phosphorus showed moderate variation in both normal and salt affected soils as indicated by CV values. Tripathi et al, (2015) have reported medium range of variation in available P with CV value of 25.95%.…”

“…The coefficient of variation varied sharply with depth indicating lateral and vertical variation. Tripathi et al, (2015) have reported medium range of variation in available K with CV value of 36.67%.…”

“…Nitrogen showed medium variation in normal soils and low to medium in salt affected soils as indicated by CV values. Tripathi et al, (2015) have reported medium range of variation in available N with CV value of 37.24%. There is a significant correlation of 0.56 (p≤0.05) between OC and N suggesting that organic matter has influence on availability of N. As organic carbon and nitrogen exhibited significantly positive correlation (Table 2) the reduction in nitrogen with depth might be due to corresponding decrease in organic carbon with soil depth.…”

“…Higher pH values were recorded at various sites across the block which may be attributed to the existence of soluble and exchangeable sodium along with bicarbonate ions which get precipitated as calcium and magnesium carbonates during evaporation (Deshmukh, 2012). Tripathi et al, (2015) also reported low CV for pH while characterizing the spatial variability of soil properties in salt affected area. The high pH in the soils suggests that groundwater was the main source of contamination in the study area.…”