ASSESSMENT OF SOIL VARIABILITY USING ELECTRICAL RESISTIVITY TECHNIQUE FOR NORMAL ALLUVIAL SOILS, EGYPT

Spatial information on soils could be resulted from direct measurements that are destructive, expensive, effort and time consuming. Methods of geophysics can be an effective, fast, economic and non-destructive tool for soil mapping for large areas. Electrical resistivity could be effective in studying soil variability as it relied on many soil characteristics. This paper was designed to study soil spatial variability using electrical resistivity technique. The Experimental Western Farm (EWF) in the faculty of agriculture, Cairo University at Giza was chosen for the present study. A GPS outlined grid points 40X40 m were initiated to cover an area of about 160 by 400 m. At each point (40 nodes) resistivity was measured using 4-electrodes Wenner array in a line perpendicular to the path direction. Soil resistivity data from a 2-depth profiling mode was considered to produce two apparent resistivity maps.using ArcGis software. Soil resistivity taxa were sampled and analyzed for soil moisture, EC and bulk density. The resistivity data were geostatistically investigated. Krigged soil resistivity maps were produced for depths (i.e. 30 and 60 cm). Kriging and Semivariogram interpretation was conducted to find out the spatial dependency of topand subsoil (Nugget / sill %). The spatial dependency of the top and subsoil resistivity were moderate (48.4% and 68.6% respectively). Highly significant negative correlations were recorded in the topsoil between apparent or true resistivity and soil moisture, EC or bulk density for the different units of the produced soil resistivity map. The best fitting relationship models ranged between linear, power, logarithmic and exponential models. In subsoil weak or insignificant relationships were recorded. The obtained models were used to produce conjugated moisture, EC and bulk density maps. The conjugated soil moisture and salinity maps were geostatistically investigated. The spatial dependency of the top and subsoil moisture contents were moderate (47.5% and 60.4% respectively), while it was for soil salinity 68.5% and 62.5%, respectively. The multiple linear regression analysis that includes moisture, EC and bulk density, showed highly significant model (R2= 0.885). The obtained factorial analysis showed that soil moisture had the highest contribution percent on soil resistivity reaching 54.99% followed by electrical conductivity by 25.92% and the least factor affecting ER was bulk density as its contribution was 18.06%. However, it could be concluded that soil moisture and EC are the most significant factors that controlling soil electrical resistivity of the investigated surface layer (0-30 cm). For the subsurface layer (30-60), the obtained linear multiple regression model was insignificant (R2=0.540). This technique can compete the other methods of soil surveys, and facilitate the development of semi-automatic soil mapping from electrical resistivity data.