Abstract: Researchers have long had trouble in measurement of
Exchangeable Sodium Ratio (ESR) at salt-affected soils. this
parameter are often determined using laborious and time consuming
laboratory tests, but it may be more appropriate and economical to
develop a method which uses a more simple soil salinity index. The
aim of this study was to determine the relationship between
exchangeable sodium ratio (ESR) and sodium adsorption ratio (SAR)
in some salt-affected soils of Khuzestan plain. To this purpose, two
experimental areas (S1, S2) of Khuzestan province-IRAN were
selected and four treatments with three replications by series of
double rings were applied. The treatments were included 25cm,
50cm, 75cm and 100cm water application. The statistical results of
the study indicated that in order to predict soil ESR based on soil
SAR the linear regression model ESR=0.2048+0.0066 SAR
(R2=0.53) & ESR=0.0564+0.0171 SAR (R2=0.76) can be
recommended in Pilot S1 and S2 respectively.
Abstract: Salinity is a measure of the amount of salts in the
water. Total Dissolved Solids (TDS) as salinity parameter are often
determined using laborious and time consuming laboratory tests, but
it may be more appropriate and economical to develop a method
which uses a more simple soil salinity index. Because dissolved ions
increase salinity as well as conductivity, the two measures are
related. The aim of this research was determine of constant
coefficients for predicting of Total Dissolved Solids (TDS) based on
Electrical Conductivity (EC) with Statistics of Correlation
coefficient, Root mean square error, Maximum error, Mean Bias
error, Mean absolute error, Relative error and Coefficient of residual
mass. For this purpose, two experimental areas (S1, S2) of Khuzestan
province-IRAN were selected and four treatments with three
replications by series of double rings were applied. The treatments
were included 25cm, 50cm, 75cm and 100cm water application. The
results showed the values 16.3 & 12.4 were the best constant
coefficients for predicting of Total Dissolved Solids (TDS) based on
EC in Pilot S1 and S2 with correlation coefficient 0.977 & 0.997 and
191.1 & 106.1 Root mean square errors (RMSE) respectively.
Abstract: Saturated hydraulic conductivity of Soil is an
important property in processes involving water and solute flow in
soils. Saturated hydraulic conductivity of soil is difficult to measure
and can be highly variable, requiring a large number of replicate
samples. In this study, 60 sets of soil samples were collected at
Saqhez region of Kurdistan province-IRAN. The statistics such as
Correlation Coefficient (R), Root Mean Square Error (RMSE), Mean
Bias Error (MBE) and Mean Absolute Error (MAE) were used to
evaluation the multiple linear regression models varied with number
of dataset. In this study the multiple linear regression models were
evaluated when only percentage of sand, silt, and clay content (SSC)
were used as inputs, and when SSC and bulk density, Bd, (SSC+Bd)
were used as inputs. The R, RMSE, MBE and MAE values of the 50
dataset for method (SSC), were calculated 0.925, 15.29, -1.03 and
12.51 and for method (SSC+Bd), were calculated 0.927, 15.28,-1.11
and 12.92, respectively, for relationship obtained from multiple
linear regressions on data. Also the R, RMSE, MBE and MAE values
of the 10 dataset for method (SSC), were calculated 0.725, 19.62, -
9.87 and 18.91 and for method (SSC+Bd), were calculated 0.618,
24.69, -17.37 and 22.16, respectively, which shows when number of
dataset increase, precision of estimated saturated hydraulic
conductivity, increases.
Abstract: Hydraulic conductivity is one parameter important for predicting the movement of water and contaminants dissolved in the water through the soil. The hydraulic conductivity is measured on soil samples in the lab and sometimes tests carried out in the field. The hydraulic conductivity has been related to soil particle diameter by a number of investigators. In this study, 25 set of soil samples with sand texture. The results show approximately success in predicting hydraulic conductivity from particle diameters data. The following relationship obtained from multiple linear regressions on data (R2 = 0.52): Where d10, d50 and d60, are the soil particle diameter (mm) that 10%, 50% and 60% of all soil particles are finer (smaller) by weight and Ks, saturated hydraulic conductivity is expressed in m/day. The results of regression analysis showed that d10 play a more significant role with respect to Ks, saturated hydraulic conductivity (m/day), and has been named as the effective parameter in Ks calculation.