To obtain Soil-Moisture Characteristic Curve experimentally is time-consuming and usually subject to considerable errors. So, many investigators have tried to predict Soil-Moisture Characteristic Curve by different models. One of these models predicts Soil Moisture Characteristic Curve based on Soil particle size distribution and bulk density. In this model, Soil particle size distribution Curve is divided into a number of segments, each with a specific particle radius and cumulative particle mass greater than that of the radius. Using these data, Soil-Moisture Characteristic Curve was estimated. In this model, a scale factor, a , is used which may be considered as a constant, or obtained by logistic or linear procedures. The average values of a for clay, silty clay, sandy loam, two loam Soils, and two silty clay loam Soils were 1.159, 1.229, 1.494, 1.391, 1.393, 1.253 and 1.254, respectively. For most conditions, Soil particle size distribution Curve is not available, but only the percentages of clay, silt, and sand could be obtained using Soil textural data, which is not enough to draw a precise Soil particle size distribution Curve. In this situation, a precise Soil particle size distribution Curve must be initially developed on the basis of which the Soil Moisture Characteristic Curve can be predicted. In this study, using Soil textural data of seven different Soils, Soil Moisture Characteristic Curve of each was estimated. In these estimations, logistic and linear methods were used to obtain the a value. Then, the results were compared with those of measured Soil Moisture Characteristic Curve. For estimation of Soil particle size distribution Curve, two extreme values for Soil particle radius, 125 and 999 mm, were used.The results indicated that using particle radius of 999 mm is more appropriate. On the other hand, it was found that for clay, silty clay, and sitly clay loam texture, it is more appropriate to employ a linear equation to determine a for estimating Soil-Moisture Characteristic Curve while the logistic equation can be more appropriately used for loam and sand loam textures.

Background and Objectives: Soft computational techniques have been widely used in scientific research and engineering in recent decades. Since the measurement of hydraulic properties by direct laboratory methods is hard, time consuming and expensive, Thus, there is need to use alternative methods based on conveniently available Soil properties to estimate it with less effort, time and cost. One of the new methods for estimating Soil hydraulic properties, such as Soil Moisture Characteristic Curve, is non-parametric methods. This study was performed to determine the efficiency of the decision tree method in separation of effective properties in estimating Soil Moisture Characteristic Curve parameters. Materials and Methods: To perform this study, number of 72 points were selected in the village of Marghmalek and Sharekord city. Samples were collected from depth of 0-20 cm and then were transferred to the laboratory for required measurements. Some properties such as pH, EC, saturated Moisture, calcium carbonate, organic matter, clay and sand, bulk density, mean weight diameter of dry aggregate, mean weight diameter of wet aggregate, geometric mean and standard deviation of particle diameter were measured in the laboratory. Also, the Moisture Characteristic Curves were determined at 0, 1, 3, 5, 10, 30, 50, 100, 150, 1000, 1500 kPa suctions and were fitted to the van Genuchten model. The input variables were introduced into the MATLAB software in two scenarios (first scenario: pH, EC, %clay and sand, organic matter, calcium carbonate, mean weight diameter of wet and dry aggregate, bulk density, saturated Moisture and the second scenario: pH, EC, geometric mean and standard deviation of particle diameter, organic matter, calcium carbonate, mean weight diameter of wet and dry aggregate, bulk density, saturated Moisture) and modeled by decision tree and error estimators of cross validation and resub stitution. Evaluation statistics of each model including R2, RMSE and %RMSE were calculated. Results: The results obtained from decision tree modeling showed that the most important factors affecting Moisture content in PWP suction, were saturated Moisture and clay. The PWP target variable has the highest correlation in the first scenario (0. 88) and in the second scenario (0. 91) and the least error rate among the other variables and after that, FC has the highest correlation (0. 86) in the second scenario. Target variables n had the highest error rate and α the lowest correlation in both scenarios. Generally, the second scenario performed better than the first scenario by replacing the geometric mean and standard deviation of particle diameter with the percentage of clay and sand. The sensitivity analysis showed that PWP was the most sensitive among the input parameters to pH, BD, calcium carbonate and organic matter and FC was the most sensitive to geometric standard deviation of particle and MWDwet. Conclusion: In general, modeling has been successful in both scenarios. But by substituting geometric mean and standard deviation of particle diameter instead of clay and sand percentage, a better performance was obtained in estimating Moisture Characteristic Curve variables in the second scenario.

Measurement of Soil Moisture Characteristic Curve in laboratory and field is costly, time consuming and difficult. Since, this Curve is necessary for studying water movement in unsaturated Soil, therefore using the estimated methods for this Curve is common. The model of Grykson et al. (1987) is a single one parameter model based on the logarithmic shape. This model has two coefficients with negative correlation and the other unknown parameter. In this study, Soil bulk density and geometric standard deviation of the particle-size diameter were used for determining the unknown parameter of model. For this purpose, 160 Soils from UNSODA Soil data bases and 32 Soils from Fars province were used, and four conditions have been considered: a) Calibration the results for different Soil textural classes and validation the results for remained Soils in each Soil textural class. b) Calibration the results for UNSODA Soils and validation the results for Soils of Fars province. c) Calibration the results for Soils of Fars province and validation the results for UNSODA Soils. d) Calibration the results for about 80 percent of total Soils and validation the results for about remained 20 percent of Soils. The obtained results in condition one showed the good accuracy for estimating Soil Moisture Characteristic Curve in different Soil textural classes. Also, the comparison of conditions two, three and four indicated that the calibration results based on UNSODA Soil data bases were better for estimating Soil Moisture Characteristic Curve.

The relationship between Soil Moisture and Soil suction is called Soil Moisture Characteristic Curve, and its measurement is time-consuming and expensive. Arya et al., (1999) have derived a model for estimating Soil-Moisture Characteristic Curve based on Soil particle size distribution Curve and bulk density, which contains scaling parameter  a. There are three methods for determining a which contain constant a, linear and logistic methods with available coefficients for some Soil textures. In this study, 20 Soil samples were selected from Fars province and three equations with exponential, logarithmic and power trend (Alfa-1 to Alfa-3) were derived for estimating a based on Soil Moisture content, and another equation with linear method (Alfa-4) was obtained. Then by using another 5 Soil data sets in Fars and Boushehr provinces, the measured Soil-Moisture Characteristic Curves by combination of hanging column and pressure plate, were compared with estimated Soil-Moisture Characteristic Curve through eight different methods. These methods were: logistic, linear, Khoshnood Yazdi and Ghahraman (2004), Rezaei et al. (2005), and Alfa-1 to Alfa-4. The results indicated that the Alfa-4 method was appropraite for clay and silty clay loam textures, and the logistic method was appropriate for silty clay, loam and sandy loam textures.

Background and Objectives: Water scarcity is the most important problem in agricultural development, especially in arid areas. Therefore, the study of strategies to increase water use efficiency must be considered. Providing methods to control and reduce Soil evaporation can be a suitable way to increase water use efficiency. Soil surface Evaporation can be considered as the main part of water balance components in arid areas that cause water wastage. One of the ways to control and reduce evaporation from the Soil is to use natural amendments. Therefore, this study was conducted to investigate the effect of three minerals including bentonite, vermiculite and zeolite on the amount and intensity of evaporation from the Soil surface in a period of 4 months from Tir (July) to Mehr (October). Materials and Methods: This study was conducted as a completely randomized design (CRD) with 10 treatments and three replications at Shahrekord University. The studied treatments in this research include 3 types of natural amendments (bentonite, vermiculite and zeolite) at three levels of 0. 5, 1 and 2% with a control treatment (Soil without any amendment). These natural minerals were mixed in Soil of whole pots. The amount of evaporation was measured by measuring the amount of irrigation water of each pot and outlet water of them and based on the water balance equation. Moisture content was measured with a hygrometer at two depths of 5 and 15 cm. Finally, the Soil Moisture Characteristic Curve for each treatment by using of pressure plate was obtained and compared. Results: The results showed that among the three studied minerals, the highest reduction in Soil evaporation was related to bentonite. The two minerals vermiculite and zeolite also significantly reduced evaporation compared to the control treatment, but the difference between them was not significant. Among the studied treatments, the highest reduction effect on evaporation was observed in 2% bentonite mixed with Soil and the lowest effect was observed in 0. 5% zeolite treatment. This treatment did not show a significant difference with the control, but 2% bentonite treatment significantly reduced the rate of evaporation compared to the control by 6. 4%. The results the Soil Moisture in the treatments was the opposite of the evaporation results. In the treatments where the evaporation rate was lower, the Moisture content increased. Due to the significant effect of the three minerals on the Soil Moisture, the Soil Moisture Curve in the studied treatments has also changed compared to the control. Conclusion: The use of natural Soil amendment in addition to preventing Soil contamination, helps to improve Soil structure to reduce evaporation. Overall, due to the highest effect of using 2% bentonite on significant decreasing of Soil evaporation therefore, this Soil amendment can be recommended to control of evaporation from Soil.

Many of irrigated agriculture problems are resulting from chemical and physical composition of irrigation water. The irrigation water quality is effective on Soil Moisture Characteristic Curve by effect on Soil structure, pore size distribution and continuity of them. The aim of this study was to evaluate the effect of different water salinity in the presence of constant turbidity on the Soil Moisture Curve. The salinity treatments at five levels (1, 2, 4, 6 and 8 dS/m) with constant turbidity (200 NTU) were applied. These treatments were investigated at three depths of Soil (0 to 15, 15 to 30 and 30 to 45 cm) with a silt-loam texture with three replications in a randomized complete block design. Soil water retention Curve was determined by using pressure plate method. The results were statistically analyzed with MSTATC software. The results showed that the water percent of the Soil of S2, S3, S4, S5 treatments of irrigation water quality increased to values 13.65, 20.20, 23 and 30 percent compared to S1 treatment. Comparison of water percent of Soil at various depths showed that the depth of the second and third compared to the first decreased to 1.40 and 2 percent.

The relationship between Soil Moisture and Soil matric suction is called Soil Moisture Characteristic Curve, which its measurement is time-consuming and expensive. One of the estimation methods of Soil Moisture Characteristic Curve is using the Soil particle size distribution Curve and bulk density which contains a scaling parameter (a). In this study, 10 Soil samples were selected from Marvdasht region in Fars province, and Soil texture and Soil Moisture Characteristic Curve of each Soil were measured. Then, the Soil particle size distribution Curve of each Soil was estimated based on Fool admand and Sepaskhah model (FS) and Fool admand and Mansurimodel (FM), and also eight methods were used for determining the scaling parameter including linear procedure with constant void ratio (N), logistic procedure with constant void ratio (G), linear procedure with local voidratio (LN), logistic procedure with local void ratio (LG), Alfa-1 (A1), Alfa-2 (A2), Alfa-3 (A3) and Alfa-4 (A4). There fore, the Soil Moisture Characteristic Curve of each Soil was estimated with 16 different methods, and all ofthem were compared with measured Soil Moisture Characteristic Curve data. For this purpose, Standard error (SE), geometric mean error ratio (GMER) and geometric standard deviation of the error ratio (GSDER) were used. The results sowed that the FM model for estimating the Soil particle size distribution Curve and then estimation the Soil Moisture Characteristic Curve was better than the FS model. In general, the results in dicatedthat the procedures of FM-A1, FM-A2, FM-A3, FM-N and FM-LN were appropriate for estimating the Soil Moisture Characteristic Curve.

Quantitative knowledge of Soil hydraulic properties such as the Soil Moisture Characteristics Curve (SMC) is crucial for flow and transport modeling supporting hydrologic and agricultural engineering. However, many laboratory and field methods are currently available for direct measurement of the Soil hydraulic properties but, most or all of direct methods are too time consuming and costly. Thus developing of physically-based methods for predicting SMC is essential. In this study, an analytical method was developed to estimate Brooks-Cory model parameters using horizontal infiltration data. The new method was compared with Wang et al (2002) method. Sixteen Soils with wide range of hydraulic properties were used to test the new method. The results showed that the new method estimates n and hd parameters smaller than those experimental values. Although, results showed that the new method properly predicts the measured SMC data. High coefficient of determination (R2=0.93) and low root mean square error (RMSE =0.03) confirmed the accurate predictability of new method. Mean RMSE of Wang et al (2002) method was 0.049. Therefore, results indicated that the new method is more accurate than Wang et al (2002) method for predicting Soil Moisture Characteristics Curve. The sensitivity analysis indicated that, for a given Soil, the accurately estimation of SMC depends mainly on sorptivity parameter.

Soil particle size distribution and bulk density are used for estimating Soil-Moisture Characteristic Curve. In this model, Soil particle size distribution Curve is divided into a number of segments, each with a specific particle radius and cumulative percentage of the particles greater than that radius. Using these data, Soil-Moisture Characteristic Curve is estimated. In the model a scale factor, a, is used which may be considered as a constant, or obtained by logistic or linear procedures. For most conditions, Soil particle size distribution Curve is not available, but only the percentages of clay, silt and sand could be obtained using Soil textural data. In this situation, at first a precise Soil particle size distribution must be developed, based on which the Soil-Moisture Characteristic Curve can be predicted. According to the previous studies, using particle radius of 999 ¥m is more appropriate than radius 125 ¥m. Also, adjusted coefficients for estimating Soil particle size distribution Curve for radii 1 to 20 ¥m was obtained. In this study, using the Soil textural data of 19 different Soils from UNSODA database, Soil-Moisture Characteristic Curve of each was estimated with logistic and linear methods based on initial and adjusted Soil particle size distribution estimation. The estimated values were compared with the measured data. The results indicated that for most Soils, using the combination of logistic and adjusted particle size distribution estimation procedures is more appropriate than the previous methods.

The common method of puddling is using a conventional tiller which requires long time of Soil operations.In this study, the effects of tillage equipment on Moisture Characteristic Curve of a paddy Soil were investigated. The treatments included tillage equipment (T1: conventional tiller, T2: rotary puddler, T3: cone puddler, T4: tractor mounted rotivator) and number of puddlings (P1: puddling once, P2: puddling twice, P3: three times’puddling and P4: four times’puddling). The results showed that at saturation point, tractor mounted rotivator presented the highest Moisture content. At field capacity and permanent wilting point, the cone puddler showed the highest Moisture value. The two newly made units held more Moisture and saved water. In different tillage equipment, increasing the number of puddling reduced Soil Moisture. Available Moisture in the Soil without tillage (control) was less than in Soils under rotary puddler and cone puddler.