M. B. Farhangi, M. R. Mosaddeghi, A. A. Safari Sinegani, A. A. Mahboubi,
Volume 16, Issue 59 (4-2012)
Abstract
In agriculture, cow manures are used to enhance soil fertility and productivity. Escherichia coli is the most common fecal coliform in cow manure and considered as an index for microbial contamination of groundwater resources. The objective of this study was to investigate the transport of Escherichia coli (released from cow manure) through the field soil. Lysimeters (with internal diameter of 20.5 and height of 50 cm) were inserted into an in situ clay loam soil. Unsaturated soil water flow was controlled at an inlet matric potential of –5 cm using a tension infiltrometer. When the steady-state flow was established, air-dried fresh cow manure was applied on the lysimeters at a rate of 10 Mg ha-1 (dry basis) and the soil-manure leaching started. Soil solution was sampled at 1, 2, 4, 6, 12 and 24 h after leaching initiation using plastic samplers installed at depths of 20 and 40 cm. Concentrations of Escherichia coli in the soil solution (C) and the influent (C0) were measured using the plate count method. Impacts of soil depth, sampling time, and their interaction on C and C/C0 were significant (P<0.01). In all leaching times, relative adsorption index (SR) was lower when both soil layers were considered and the filtration increased with soil depth. When the concentration was corrected for the second layer (i.e. 20–40 cm), the SR values in this layer were considerable and greater than those in the first layer at 4 and 6 h. The influence of surface layer was substantial in bacterial filtration however, the preferential flows especially in the initial leaching times resulted in bacterial movement towards the second layer. Temperature drop reduced bacteria release from the manure, increased viscosity of the flowing water, and consequently diminished significantly the bacteria concentration in the soil solution at 24 h. Overall, it was found that similar to surface layer, subsurface layer might have great role in bacterial filtration due to its higher clay and carbonate contents
S. Ashrafi-Saeidlou, Mh. Rasouli-Sadghiani, M. Barin,
Volume 21, Issue 3 (11-2017)
Abstract
The Firing effect on soil depends on its intensity and duration. In order to investigate influence of different firing backgrounds on some soil physical and chemical properties, 80 soil samples were taken from two depths (0-5 cm and 5-20 cm) with different time of firing background (2 and 12 months). Some soil physical and chemical characteristics were measured at soil samples. The results showed that there was a significant difference in the amount of pH, EC, bulk density and ammonium in soils with different history of burning. The amount of studied indices increased after firing in burned soils compared to the control ones. However 12 months later they reach to their pre-fire levels. Total nitrogen amount in soils with 2 and 12 months firing history were 1.18 and 1.11 times higher than the control soils, respectively. The amount of organic carbon in surface depth (0-5 cm) of burned soils with 2 and 12 month firing backgrounds 37.25 and 24.7 percent increased in comparison to control soils, respectively. Also, fire led to a significant reduction in the amount of clay (29.25 percent) in burned areas compared to the control ones. Soil particle size distribution in control sites were in clay up to loam and in burned areas were in clay loam up to sandy loam classes. Therefore forest firing causes obvious changes in soil properties, remediation of which takes more than one year.
S. Zahedi, K. Shahedi, M. Habibnejhad Roshan, K. Solaimani, K. Dadkhah,
Volume 21, Issue 4 (2-2018)
Abstract
Soil depth is a major soil characteristic commonly used in distributed hydrological modeling in order to present watershed subsurface attributes. It strongly affects water infiltration and accordingly runoff generation, subsurface moisture storage, vertical and lateral moisture movement, saturation thickness and plant root depth in the soil. The objective of this study is to develop a statistical model that predicts the spatial pattern of soil depth over the watershed from topographic and land cover variables derived from DEM and satellite image, respectively. A 10 m resolution DEM was prepared using 1:25000 topographic maps. Landsat8 imagery, OLI sensor (May 06, 2015) was used to derive different land cover attributes. Soil depth, topographic curvature, land use and vegetation characteristics were surveyed at 426 profiles within the four sub-watersheds. Box Cox transformations were used to normalize the measured soil depth and each explanatory variable. Random Forest prediction model was used to predict soil depth using the explanatory variables. The model was run using 336 data points in the calibration dataset with all 31 explanatory variables (18 variables from DEM and 13 variables from remote sensing image), and soil depth as the response of the model. Prediction errors were computed for validation data set. Testing dataset was done with the model soil depth values at testing locations (93 points). The Nash-Sutcliffe Efficiency coefficient (NSE) for testing data set was 0.689. The results showed that land use, Specific Catchment Area (SCA), NDVI, Aspect, Slope and PCA1 are the most important explanatory variables in predicting soil depth.
