Spatial variability and geostatistical analysis of selected soil

Abstract

A study was conducted to explore the spatial variability of major soil nutrients of Agricultural fields in South-western region of Bangladesh. From the study area, 40 surface soil samples were collected by a random sampling strategy using GPS. Then soil physico-chemical properties i.e., pH, electrical conductivity (EC), organic matter (OM), total nitrogen (TN) N, soil available nutrients (P, K and S) were measured in laboratory. After data normalization, classical and geo-statistical analyses were used to describe soil properties and spatial correlation of soil characteristics. Spatial variability of soil physico-chemical properties was quantified through semi-variogram analysis and the respective surface maps were prepared through ordinary Kriging. Spherical model fits well with experimental semi-variogram of pH, EC, OM, TN, available P, K and S. Soil pH, available phosphorus (Av P), potassium (Av K) and sulfur (Av S) have the moderate spatial dependence, with nugget/sill ratios of 41.13% to 72.21%. The others have the strong dependence with nugget/sill ratios of less than 25%. The spatial variability of estimating soil properties varies within range of 0.0142 for Av P to 0.0383 for Av S and this model can calculate the un-sampled within neighboring distance of about 12.65 m for Av S to 150.82 m for TN, respectively. Cross validation of kriged map shows that spatial prediction of soil nutrients using semi-variogram parameters is better than assuming mean of observed value for any un-sampled location. Therefore, it is a suitable alternative method for accurate estimation of chemical properties of soil in un-sampled positions as compared to direct measurement which has time and costs concerned.
 Bangladesh J. Sci. Ind. Res.54(1), 55-66, 2019

Spatial Variability and Geostatistical Analysis of Soil Properties in the Diversified Cropping Regions of Bangladesh Using Geographic Information System Techniques

“…45 (Figure 11). In this study area, the lower contents of sulfur may be the consequence of predominantly washed-out sulfur in the form of sulfates, especially under leaching circumstances [69,78].…”

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Agricultural land suitability assessment with GIS-based multi-criteria decision analysis and geostatistical approach in semi-arid regions

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Integrated Use of Hyperspectral Remote Sensing and Geostatistics in Spatial Prediction of Soil Organic Carbon Content

“…Geostatistical techniques incorporating the theory of regionalized variables and spatial autocorrelation are more effective tools for characterizing and understanding spatial variability of measured variables compared with the traditional statistical interpolation techniques (Khan et al, 2019). Semivariogram and kriging-based geostatistical techniques are widely used to map and characterize the spatial variability of soil chemical and physical properties in different agro-ecosystems (Awal et al, 2019;Bhunia et al, 2018;Khan et al, 2019;Reza et al, 2016;Yan et al, 2021). Kriging is a geostatistical interpolation technique known by the acronym BLUE or best linear unbiased estimator.…”