The (Atterberg) consistency limits of soil are important for soil mechanical properties the are used primarily in classifying cohesive soil materials for engineering purposes and are strongly correlated to other fundamental soil properties. They are also used for estimation of there useful indices for soil engineering interpretations, such as shear strength and bearing capacity, compressibility, swelling potential and specific surface. Whereas the direct measurement of there are time- consuming and require enough experiment, it is required that predict them indirectly by using some soil easily available properties with reasonable accuracy. The objective of this study was to predict soil liquid limit (LL), plastic limit (PL) and plasticity index (PI) indirectly from some easily available soil physical and chemical properties. In this research 37 loamy soil samples were randomly collected from karaj area 30 samples were used for model development and 7 samples were used for validation. Particle size distribution, bulk density, and cation exchange capacity were determined by the hydrometry, cold, and with ammonium acetate methods, respectively. Liquid limit were obtained using casagrande. The optimum combination of independent variables for estimation of LL, PL and PI were selected by using stepwise regression method. The regression equations were obtained using multiple linear regression method. The results indicated that there was a significant correlation between measured and predicted values. The correlation were significant at 0.1% for presented functions of liquid limit (R2adj=0.77), plastic limit (R2adj=0.72) and plasticity index (R2adj= 0.62). Statistical analysis for the evaluation of PTFs indicated that Geometric mean error ratio (GMER) values were close to 1 (0.99-1.06) and Geometric standard deviation of the error ratio (GSDER) values were small (1.03 - 1.32). The results indicated that presented functions were valid.

Soil quality has been defined as "the capacity of a soil to function within ecosystem and land use boundaries, to sustain biological productivity, maintain environmental quality, and promote plant as well as animal health". Its assessment is needed to improve sustainable soil management. Since soil quality can't be measured directly, it must be inferred from quality related indicators, which are measurable soil properties. The objectives of this research are: I) to identify a factor of quantitative assessment of soil quality, namely soil physical quality index (S) and II) to predict the S index by using, Pedo-transfer functions along with some conveniently measurable soil properties. Some physical and chemical properties of 84 soil samples (27 samples saline and 57 ones calcareous), such as particle size distribution, organic matter, bulk density, carbonates (CaC03), Electrical Conductivity (EC), and Sodium Adsorption Ratio (SAR) were assessed. Also soils' moisture retention curves were plotted at 0, 2.5, 5.5, 10, 20, 30, 50, 100, 200, 300, 500, 1000 kpa pressure heads. The parameters of Van Genuchten equation (1980), through a use of ROSSET A software, as well as the slope of moisture retention curve, at the inflection point, were determined. The slope of the curve was considered as soil physical quality index (S). Regression was established between this index and the conveniently measurable soil properties through a use of Pedo-transfer functions and an employment of SPSS software. The results showed that there is a significant correlation existing S index and the percentage of clay, silt and carbonates in saline, calcareous as well as in all data set (p=0.01). Also a significant correlation existed between S index and bulk density in saline and calcareous soils shown at 5 and 1% level, respectively. The results finally indicated a strong regression relationship between calculated S obtained from moisture retention curve and S predicted from conveniently measurable properties. Correlation coefficients between the two were 0.855, 0.920, and 0.919 in saline, calcareous, and all data set, respectively.

Soil saturated hydraulic conductivity is the most important physical properties that has particular importance in identifying, investigating and modeling the water, salts and pollutants transport in the porous medium. Despite numerous research, measuring saturated hydraulic conductivity with direct methods are still costly, time consuming and professional. Therefore estimating saturated hydraulic conductivity with rapid and low cost methods (pedo-transfer functions) with acceptable accuracy is essential. The purpose of this research was to estimate saturated hydraulic conductivity using easily accessible parameters such as particle size distribution, bulk density, total porosity, effective porosity, water content retained at -0.3 and -15 bar matric potentials, %CCE, %OM, pH and EC with Artificial Neural Networks. Saturated hydraulic conductivity was measured from 73 selected points at three depths (10-35, 15-35 and 20-35) with Guelph permeameter and soil samples were taken from same points. Easily accessible parameters were measured in laboratory and preliminary results were obtained. Selected parameters according to sensitivity analysis were sand and clay contents, water content at -0.3 bar matric potential, total porosity and geometric mean diameter of soil particles. Using sensitive parameters, a rapid and low cost method was selected from different designed models. Input parameters were logaritmic geometric mean diameter, total porosity, sand and clay contents with this model.

Cation Exchange Capacity (CEC) is an important characteristic of soil in view point of nutrient and water holding capacity and contamination management. Measurement of CEC is difficult and time-consuming. Therefore, CEC estimation through other easily-measurable properties is desirable. The purpose of this research was to investigate CEC estimating using easily accessible parameters with Artificial Neural Network. In this study, the easily accessible parameters were sand, silt and clay contents, bulk density, particle density, organic matter (%OM), calcium carbonate equivalent (%CCE), pH, geometric mean diameter (dg) and geometric standard deviation of particle size (sg) in 69 points from a 1´2 km sampling grid. The results showed that Artificial Neural Network is a precise method to predict CEC that it can predict 82% of CEC variation. The most important influential factor on CEC was soil texture. The sensitivity analysis of the model developed by using of Artificial Neural Network represented that clay%, silt%, sand%, geometric mean diameter and geometric standard deviation of particle size, OM% and total porosity were the most sensitive parameters, respectively. The model with clay%, silt%, sand%, geometric mean diameter and geometric standard deviation of particle size as inputs data was selected as the base model to predict CEC at studied area.

and Objectives: One of the most important requirements in planning production and processing of medicinal plants in order to obtain high yield and high-quality is the initial assessment of the soil physical and chemical properties, which can reduce the production cost by avoiding the use of unnecessary soil analysis. Summer savory (Satureja hortensis L. ) is one the most widely used medicinal plants that quality index of plant is related to the quantity and the constituent of its essential oil content. Understanding the relations between the quantity and quality of medicinal plants with the several physical and chemical properties of soil is very complex and the estimation of parameters changes of medicinal plants affect by soil quality characteristics is more difficult. Today, with the introduction of multivariable regression models and artificial network models in the research, many complex relationships found in nature is understandable. Hence the need for estimation of the essential oil yield of savory using fast, cheap and acceptable accuracy methods is necessary. Materials and Methods: The present study was performed as pot experiment based on completely randomized design with 3 replications. Fifty three soil samples were collected from different parts of Nishabur and easily available soil properties including sand, silt and clay percentage, organic matter, pH, salinity, phosphorus, potassium, nitrogen and carbon contents of the soil samples were measured at laboratory and the primary results were obtained. Approximately 90 days after seed planting in mentioned soil samples, the sampling of plants was done based on the treatments. Samples were placed for 24 hours in an oven at 40 ° C, for drying. Finally, the relationship between the essential oil yield and easily available soil parameters was determined using artificial neural network by Matlab7. 9 software. To obtain the most sensitive parameters, sensitivity analysis was calculated by using sensitivity coefficient without dimension method. So that, if the parameter value is more than 0. 1, then that parameter is considered as the sensitive parameter of the model. Results: An artificial neural network is simulated from a human neural network model, which, after training, estimates the output parameters by applying the input parameters. In this research, the perceptron neural network structure was used with Marcoat Levenberg training algorithm to estimate the essential oil yield from easily available soil parameters such as soil texture, organic matter and macro elements. The high R2 values and the low RMSE values indicate that predictive data are close to the measurement data and high accuracy of the model in the estimation of summer savory essential oil yield. Based on this, soil texture parameters (sand, silt and clay percentages) and organic carbon, organic matter, salinity, potassium and soil acidity were selected as the most sensitive parameters, respectively. High values of R2 and low levels of RMSE mentioned the proximity of the forecast data with measurement data and high accuracy of the model in summer savory essential oil yield estimation. Accordingly, the parameters of organic carbon, nitrogen, phosphorus, organic matter, potassium, pH, salinity, clay, silt and sand respectively were selected as the most sensitive parameters. Conclusion: The results showed that the created neural models were not able to estimate the essential oil yield of summer savory with a maximum accuracy (R2 = 0. 50). Among the 8 fitted models, a model based on independent variables EC + texture + carbon + organic matter + potassium + pH was better than the other, but the high number of input factors of this model is considered to be a limitation. Since the present study is an initial assessment of the essential oil yield of medicinal plants, it is recommended to continue the research in this regard as well as to predict the performance of other medicinal herbs.

Cation exchange capacity (CEC) is one of the main factors in monitoring and management of soil quality in order to achieve sustainable production. Although CEC can be measured directly, these measurements are difficult, time-consuming and costly, especially for Aridisols due to their high amounts of calcium and gypsum. One alternative method to direct measurements is the use of pedotransfer functions (PTF), in which CEC of soils is estimated through available soil information. A dataset including 1141 data points was used as the calibration set for development and accuracy test of functions and another independent data set with 232 data points was used for validation of the PTFs. The results showed that, grouping the soils based on clay and organic carbon contents are generally reduced the coefficient of variation of clay, organic carbon and CEC. In general, the grouping of soils declined the correlation of clay and organic carbon with CEC. In soils with organic carbon contents of less than 0.5%, CEC was predicted with reasonable accuracy using only the clay as input. Generally, grouping the soils enhanced the accuracy of the PTFs, while in most cases decreased their reliability. However grouping the soils based on their clay content, except for soils with clay content ≥35%, was more effective in improving the accuracy and reliability of the functions. Results also showed that clay (except for soil with clay content of ≥35%) was the most influential factor in predicting the CEC of the soils, so that including the other variables did not significantly improve the accuracy and reliability of the functions.