فهرست مطالب وحیدرضا جلالی

The soil water curve is one of the most critical soil hydraulic characteristics. This characteristic is used to determine soil water in the field capacity point and the permanent wilting point (PWP) beside it has a vital role in the application of soil water models in the study of soil-plant-water relationships. This curve is known as the quality soil index which has an effective role in the explanation of agricultural, ecological, and environmental problems. Impressive and efficient management of soil and water resources, water flow and solute transport survey, soil pollution, and contaminant leakage into water sources are dependent upon the accurate estimation of soil water curve parameters. Moreover, this index has a functional role in applying numerical and hydrological models. On the other hand, to better identify and understand its role, different models were provided to describe this curve mathematically. The efficiency of these models depended on the accuracy of estimated parameters in the model structure that was defined. Soil water curve is known as a non-linear relationship that is used to describe the relation between soil and water content or degree of soil saturation. The soil water curve provides essential information for using irrigation methods and about soil resistance and soil mechanical properties. In this research, the performance trend of two meta-heuristic algorithms, including the differential evolution (DE) and particle swarm optimization (PSO), was studied to estimate hydraulic parameters of soil water curves based on the van Genuchten and the Brooks and Cory models in four soil texture classes; loam, silt loam, sandy loam, and sandy clay loam. Besides, this study evaluated the performance of the meta-heuristic algorithm to RETC software. This software has a non-linear square local algorithm. This study can evaluate the ability of the meta-heuristic algorithms to estimate parameters for exponential relationships and nonlinear models.

At the agricultural farm of the University of Birjand, a study was conducted to analyze soil water content in different texture classes. The research involved the random selection of four soil texture classes and the random sampling of 20 points from each class. The soil water content was measured using a sandbox and pressure plate device, covering a broad suction range of 0-15000 cm. In the first phase, soil water curve parameters were estimated for each soil texture using the van Genuchten model and the Brooks and Cory model in the RETC software. Subsequently, the Matlab desktop environment was utilized to apply meta-heuristic algorithms (DE and PSO) to estimate the soil water curve parameters based on the two models. An objective function was defined to minimize the Root Mean Square Error (RMSE) of the meta-heuristic algorithms' performance. Finally, the study compared the performance of the meta-heuristic algorithms (DE and PSO) with the RETC software in estimating soil water curve parameters based on the van Genuchten and Brooks and Cory models, using statistical indices such as RMSE and R2. The soil texture classes play a crucial role in influencing soil water content and nutrient retention, making them an essential factor in agricultural management and crop suitability. The study's findings can contribute to a better understanding of soil water dynamics and the development of improved agricultural practices.

The obtained results of the statistical indices (RMSE and R2) showed that the least value of RMSE was acquired by the differential evolution algorithm (DE) performance. The values of RMSE during the application of the DE algorithm as an estimated method based on the van Genuchten model were 0.0008, 0.0005,0.0004, and 0.0006 also based on the Brooks and Cory were 0.006, 0.006, 0.005, and 0.0005 in sandy clay loam, sandy loam, loam, and silt loam respectively. Also, the highest value of the R2 index was obtained equal to 0.995, 0.996, 0.994, and 0.994 by the utilization of the DE algorithm based on the van Genuchten model in the sandy clay loam, sandy loam, loam, and silt loam respectively. The values of RMSE by the utilization of the PSO algorithm based on the van Genuchten model were 0.0021, 0.006, 0.0057, and 0.006 in the sandy clay loam, sandy loam, loam, and silt loam classes respectively. The highest and lowest values of the RMSE and R2 indices by the application of RETC software were obtained equal to 0.017 and 0.912 (sandy clay loam), 0.01and 0.963 (sandy loam), 0.085 and 0.972 (loam), and 0.01 and 0.924 (silt loam) based on the van Genuchten model.

It could be concluded that RETC software has poor performance in the estimation of soil water curve parameters in all soil texture classes studied based on the van Genuchten and Brooks and Cory models. This trend represents the weakness of the local algorithms to solve multivariable problems where an exponential relationship exists between the variables and they are influenced by each other. On the other hand, the results show the meta-heuristic algorithms have sufficient ability to estimate parameters in multivariable problems. It could be concluded that the meta-heuristic algorithms have better performance in estimating the parameters of soil hydraulic models. The DE algorithm is the best method to estimate soil hydraulic parameters. The PSO algorithm has the nearest performance to the DE algorithm but the best performance to RETC. Finally, meta-heuristic algorithms are suitable options for estimating soil water curve parameters based on various hydraulic models.

The accuracy and efficiency of the analytical and numerical models to describe water flow in soil, in unsaturated environments are affected by input data uncertainty, model structure uncertainty, and hydraulic required parameters by the model. Parameter uncertainty has an impact on the model simulation by displaying uncertainty in the simulation results. Hence, the quantitative assessment of the parameter uncertainty and its influence on the model simulation is important in reducing simulation uncertainty. The Bayesian method is a common method for uncertainty analysis that has widespread application in science and engineering to reconcile the concepts of model structure with data (assimilation of input and model outputs, and inference of the parameters). Therefore, a Markov chain Monte Carlo (MCMC) algorithm based on the Bayesian inference to improve the computational efficiency of the analysis was used. The DREAM algorithm is one of the adaptive methods, the Markov chain sampling method which is known as an effective method in used soil-water models due to searching in vast space and solving complex models with a large number of variables. In addition, one of the main problems in using Bayesian inference for hydrological models is their nonlinear relations and using them in heterogenic conditions, DREAM algorithm has been developed to use Bayesian analysis in soil-water problems. Hence, this study has taken the efficiency of the DREAM algorithm as a global optimization method and convergence parser in sampling chain paths and posterior distribution of parameters. The HYDRUS model is a hydraulic model to study the soil-water processes that include nonlinear equations. In addition, center pivot irrigation is a modern method of water management that need to study using hydraulic models under various conditions. Hence, the main purpose of this article is assessment the role of the management method and environmental prevailing conditions in the uncertainty of hydraulic parameters and model structure in estimating water flow under a center pivot irrigation system in four-year alfalfa cultivation.

The profile was dug at 120 cm depth. The soil profile was divided into three layers and two soil texture classes. The physical-chemical soil properties were studied in each layer. Assessment of soil properties stated that exists a heterogeneous layer in this soil profile. TDR was used to measure soil water content before, after, and during every irrigation period. Soil water content was measured from 10 June to 11 September 2018 consecutively. The van Genuchten-Mualem equation was used to estimate soil hydraulic parameters and describe water flow in the HYDRUS model. The HYDRUS model is coupled with the DREAM algorithm to evaluate parameter uncertainty and the model structure uncertainty based on measured soil water content data using TDR in every three categorized layers. In this article the p-factor, d-factor, and S and T indices were used to evaluate parameter uncertainty, the model structure uncertainty, and model performance.

The qualitative evaluation of soil hydraulic parameters was compiled by the posterior distributions of parameters in every three depths. The parameters had a normal distribution, the model could be recognized the value of parameters, whereas the parameters didn't have a normal distribution and had high uncertainty. The “α” parameter had high uncertainty in every three depths, in other words, in two soil texture classes, this parameter compared to other parameters had high uncertainty. Along heterogeneous soil profiles, the "α", "θs", and "n" parameters were shown high uncertainty to the Hydraulic conductivity parameter of soil saturation. The value of p-factor and d-factor were obtained equal to 83.6 and 0.13 on the soil surface and 10 and 0.14 on the subsurface soil. Reducing the p-factor index in the lower soil layers explained the overlap between measured soil water content points with estimated soil water content. So, along the soil profile could be observed high uncertainty of soil hydraulic parameters under center pivot irrigation. On the other hand, increasing the d-factor index in the sub-surface soil stated increased confidence intervals which indicate the model structure uncertainty and the poor performance of the HYDRUS model in heterogenic conditions. Also, the value of two indices of S and T were obtained 0.3 and 0.76 for the surface layer and 0.88 and 1.4 in the lower soil layers respectively. The values of S and T indices stated the ability of the DREAM algorithm to reduce parameter uncertainty and the model structure uncertainty in soil surface whereas the trend of changes in the two indices explained Asymmetry of the confidence interval with respect to the measured points and the pre-estimation of the model in the lower soil layers. Therefore, the trend of the d-factor, S and T indices showed the influence of the mathematical-physics concepts in the HYDRUS model structure in the heterogenic layer and unsaturated conditions. The research results stated the ability of the HYDRUS model in describing water flow under center pivot irrigation as a novel method of managing water sources, especially in arid and semi-arid areas. Even though, the results of the assessment indices showed decreasing model performance in the lower soil layers.

The results of soil profile indicated the effect of parameter uncertainty and the model structure uncertainty in soil moisture estimation affected by management and environmental conditions. In addition, the results showed the ability of the DREAM algorithm to simultaneously evaluate the uncertainty of the parameters and the model structure in order to increase the accuracy of the HYDRUS model under the applied conditions. Also, in this study, the DREAM algorithm indicated the role of the heterogeneous layer in parameter uncertainty and its effect on the accuracy of the model performance. The DREAM algorithm is a practical and management option to evaluate the HYDRUS model during the application of the center pivot irrigation method at the farm level. So, this is an appropriate option to study the efficiency of the HYDRUS model using modern methods in agricultural practices. Moreover, to survey the efficiency of hydraulic models under the prevailing conditions could be used the ability of the DREAM algorithm based on the Markov chain.

Climate change phenomenon and warming earth as well as drought, have been reducing water sources in the world wide at the last decades, especially in arid, semi-arid regions. On the other hand, soil qualities factors including organic matter and fertility, in addition to soil salinity are the most critical issues in agricultural management and environmental conservation. Applying chemical fertilizers is one of the management methods in arid and semi-arid regions to amends decreasing water resources and supply required plant nutrients in recent decades. Although using fertilizers cause to increase production and crop yields, but meantime this management way leads to destroying the soil structures. Soil nitrate pollution is the most important challenge in agriculture and environment. The numerical models were used in predicting element concentrations under various climates, management methods, and study conditions (laboratory or field,). Estimated soil hydraulic and solute transport parameters are the most important part of applying numerical models to evaluate trends in soil element concentration changes under management methods. Hence, in this research soil hydraulic and solute transport parameters was predicated under center pivot irrigation using particle swarm optimization algorithm and the obtained results of optimization in soil water and nitrate transport along soil profile was used to survey soil pollution on the farm under four-year- alfalfa cultivation. In this study a farm with 3 hectares under center-pivot irrigation and four- year- alfalfa cultivation was chosen in an arid and semi-arid region. In the first step, a profile on the farm was drilled and sampling were done at the two depths (0-40 cm and 40-100 cm), finally soil physical-chemical properties were measured. In the second step, the particle swarm optimization algorithm linked to the HYDRUS model and parameters of soil water and nitrate transport process was predicated under management conditions. In the final step, the trend of soil water content, ammonium, and nitrate concentration changes were evaluated using the HYDRUS-PSO with the statistical indices (NSE and RMSE). The obtained results of the study soil profile showed that heterogeneous condition dominates the soil profile. Along the soil profile gravity of soil and sand content was changed in the specific. The results of the HYDRUS-PSO model were indicated different values of soil hydraulic and solute transport parameters in both studied two depths. The two indices of NSE and RMSE, were used to evaluate the calibration and validation of the HYDRUS-PSO model. The obtained results show that the HYDEUS-PSO model simulated soil water flow with NSE equal to 0.89 and RMSE equal to 0.001, ammonium transport with NSE equal to 0.98 and RMSE equal to 0.001 and nitrate transport with NSE equal to 0.94 and RMSE equal to 0.035 in the surface soil. Also, the evaluated model in the subsurface soil indicated that the HYDRUS-PSO model reasonably predicated soil water, ammonium and nitrate variation with NSE equal to 0.94, 0.96 and 0.97 respectively. Decreasing accuracy and the effectiveness of the HYDRUS-PSO model along the profile in predicting soil water content, soil ammonium, and nitrate concentrations were indicating the effects of heterogeneous condition on soil water in solute transport processes. Of course, irrigation and fertilizer methods are the other factors that influence the HYDRUS-PSO model performance. In addition, the field studies and uncontrolled conditions on the farm level are other factors to make a heterogeneous environment that causes non-uniform behavior of the model. Decreasing model performance in predicting soil water content could be related to sand content variation and the difference between the saturated hydraulic conductivity of the surface and subsurface soil. The trend changes in soil nitrate concentration in both two studied depths were indicating the amount of soil nitrate did not reach the level of toxicity and did not accumulate nitrates in the soil. Precipitation intensity, irrigation volume, and soil porosity are the most effective factors in the leaching soil nitrate issue. Based on the obtained results it could be mentioned that applied irrigating and fertilizer methods do not lead to accumulating soil nitrate. In addition, the accumulation of root systems and their extension are the other factors in the no accumulation of soil nitrate. Although, these factors had an effective role in the model performance and intensified the soil heterogenic. According to the obtained results it could be mentioned that the HYDRUS-PSO could simulate soil water and solute variations along soil profile under center pivot irrigation. Therefore, the metheuristic algorithms could use in predicting parameters as a powerful and practical tool to increase the efficiency of the numerical models in water and soil studies.

Spatial and temporal variations of soil characteristics occur in large and small scales. Investigating the variability of soil parameters is considered as one of the requirements for proper management of fertilizer resources in a sustainable agricultural system. Studying of these variation is very time-consuming and costly especially in large scales. In order to the fast and reliable determination of the soil properties, various interpolation techniques have been developed and applied. The most widely used interpolation technique is the different Kriging types. The copula function is one of the new interpolation techniques that are recently used in sciences such as hydrology. Thus, the aim of this research was to evaluate the spatial variation of some soil chemical properties using the copula function and comparisons with geostatistics techniques.

Sampling by regular networking was done in an area of 484 ha located in 10 km far from the west of Baft city, located in Kerman province, central Iran (latitude of 29° 15′ N and longitude of 56° 29′ E). In the studied area, three agricultural, pasture and industrial sites are located nearby. The common crops of the region are wheat, barley, alfalfa, legumes and orchards of walnuts, pomegranates, almonds and grapes. The average height of the studied area is 2270 meters above sea level, the average annual temperature of the area is 16 degrees Celsius, and the average annual precipitation of the area is 247 mm. The soil used for the experiment was collected from 0 to 20 cm depth of the field. 121 soil samples were air-dried and, some physical and chemical properties were measured. In order to fit the Copula function to the data, first the appropriate marginal distribution function should be fitted to the data. For this purpose, three tests were used: Kolmogorov-Smirnov, Anderson-Darling and Chi-Square. The mentioned tests were carried out in the EasyFit 5.5 statistical software. By fitting the best marginal distribution function, the cumulative value of the marginal distribution function is calculated for each data. After calculating the above values, detailed functions can be fitted to the data. Finally, the accuracy of each interpolation method was evaluated according to the root mean square Error (RMSE), coefficient of determination (R2), mean absolute error (MAE) and mean biass error (MBE) indices.

In all types of geostatistical methods, the first step in interpolation is to fit the semivaiogram to the measured data, so after normalizing the data and validating the models, the appropriate model was selected for fitting the semivaiogram. Among the measured parameters, Pava and Kava semivaiogram followed spherical model and the interpolation of the above variables was done on the basis of this model. Copula analysis showed that the available phosphorous and potassium variables followed from the Wakeby and gamma distribution function, respectively. Also, based on the Pearson correlation coefficient, the correlation between pairs of points was less than 2000 m and the distance more than 2000 m was known as an independent distance. Based on the validation criteria for Pava parameter, Median copula function, Average copula function, IDW, Ordinary Kriging, Disjunctive Kriging, Universal Kriging and Simple Kriging have better estimates, respectively, and in the same way, the best interpolator for Kava parameter Median copula function, Average copula function, Ordinary Kriging, Universal Kriging, Disjunctive Kriging, Simple Kriging and IDW were determined, respectively. The estimation performance based on the coefficient of determination (R2) showed that value of this coefficient for copula function for available phosphorous and potassium were 5% and 4% greater than conventional geostatistics techniques. Also, the error of estimation was less for copula function indicating the better performance of copula to estimate the mentioned soil properties

This study was performed to investigate the Feasibility study of Copula function in predicting some soil nutrients and comprising this method with widely used methods of geostatistics. Our results demonstrated that the copula function method is more capable than the classical geostatistical methods in estimating soil properties due to the non-dependence of this method on the normality of the data distribution and outlier data. Therefore, with the help of this method, having a reliable and high-quality data bank of soil characteristics, acceptable maps of other soil characteristics can be presented at various scales.

In order to investigate the role of potassium and zinc fertilizers in increasing yield and reducing water consumption of wheat (Triticum aestivum L.), a field experiment was conducted in the form of a randomized complete block design with five treatments and three replications in November 2017 in Kahnooj city in Kerman province. Experimental treatments in this study include; the first treatment: control (consumption of all fertilizers except potassium based on the results of soil analysis with 7 irrigations); The second treatment: the conventionl fertilizing by the local farmers (NP) with 7 irrigations; Third treatment: first treatment + application of potassium sulfate (SOP) before planting, with 7 irrigations; Fourth treatment: first treatment + application of 33% sulfate-potassium (SOP) before planting and 67% of the remaining source of soluble potassium sulfate (SSOP) as foliar application with 7 irrigations; Fifth treatment: The first treatment + application of 33% of sulfate-potassium (SOP) before planting and 67% of the remaining source of sulfate-potassium solution containing chelate (SSOP + Zn-EDTA) as foliar application and irrigation in 5 times. The results showed that A) application of potassium and zinc fertilizers had a significant effect on yield, protein content, phosphorus, potassium and zinc concentrations and also, molar ratio of phytic acid to zinc (unfavorable nutritional index) Zn) in grain as well as water and potassium use efficiency. B) While the yield and percentage of protein in the grain in the control treatment was 4648 kg/ha and 11.68%, respectively, in the second, third, fourth and fifth treatments were 4077 and 8.57, 5402 and 69, 12.12, 6137 and 13.62, increased 6022 kg/ha and 13.85%, respectively. C) Unfavorable nutritional index (molar ratio of phytic acid to zinc) - (PA/Zn) in wheat grain in the control treatment was 46, while this ratio in the second, third, fourth and fifth treatments were 74, 33, 23, respectively. D) While the water use efficiency in the control was equal to 0.62 kg/m3, the values in the second, third, fourth and fifth treatments were 0.54, 0.72, 0.82 and 1.11 kg/m3, respectively. The efficiency of potassium fertilizer in the third treatment was 2.51 kg/kg, in the fourth treatment this value improved to 4.96 and in the fifth treatment to 4.58 kg/kg. E) Despite the reduction of water consumption from 7560 cubic meters in all four treatments to 5400 cubic meters in the fifth treatment, the yield in the fifth treatment compared to the control treatment had a 31% increase in yield, which was significant at the a percent probability level. Considering the above results, it is concluded that in soils where the concentration of potassium and zinc is less than the critical limit, it is better to use potassium and zinc fertilizers in several stages.

Splash and interrill are two important types of rain-induced erosion in agricultural lands. In many parts of the country, erosive winds can increase soil erosion rate by intensifying rain erosivity. One of the most important conservation practices to control erosion is the use of plant residue mulch. The aim of this study was to investigate the effect of different percentages of wheat straw mulch in addition to different wind velocities on soil losses due to interrill and splash using simultaneous rain and wind simulator instrument.

For this purpose, an experiment was done in a completely randomized design based on three factors including wind velocity (0, 6 and 12 m s-1 at height of 40 cm), wheat straw mulch (0 (as control), 30, 60, 90%), and soil type (namely C2mm and C4.75mm) each at three replicates. A constant rainfall intensity of 40 mm h-1 was applied on the treated soils for a period of 40 minutes. Interrill erosion as well as up- and down-ward splash materials were measured separately. Finally, the efficiency of different mulch percentages in reducing soil erosion compared to the control (bare soil) at different wind velocities was determined.

The results showed that with increasing the percentage of soil coverage, the soil loss due to interrill and splash was significantly reduced. Also, with increasing wind velocity, interrill erosion and downward splash increased, whereas upward splash decreased, significantly. The efficiency of plant residue mulch in reducing interrill erosion was ranged between 30.7 and 92.8 %, while its efficiency in controlling upward and downward splash varied from 3.3 to 81 % and 78.9 to 99.9 %, respectively. The 60 % coverage was introduced as the best mulch percentage.

The findings of this study showed that in wind-driven rains, interrill erosion rate increased with increasing wind velocity. However, it could be reduced by an appropriate coverage of plant residues. In other words, by increasing the percentage of wheat straw mulch, the soil surface is protected from the direct impact of raindrops and hence, interrill and splash erosion rates are reduced, significantly. It was concluded that the efficiency of plant residue in reducing downward splash was more than that in reducing upward splash. In addition, in the presence of plant residue, the downward splash was reduced more than interrill erosion rate. The results of this study indicate the importance of plant residue as an effective conservation agent in controlling soil erosion in agricultural lands.

Irrigation water salinity is one of the growing problems in the path of agricultural and horticultural production that researchers must determine different solutions to this problem and provide it to farmers. In this regard, silicon and selenium due to their positive role, both structurally and as cofactors of enzymes related to the metabolism of various antioxidants can play an effective role in increasing plant tolerance to salinity stress. For this purpose, a factorial experiment was conducted in a completely randomized design including four salinity levels and three fertilizer sources of silicon and selenium compounds in normal and nano size in Shirvan Faculty of Agriculture, Bojnourd University. Statistical analysis showed that the main effect of salinity stress, as expected, had a significant reducing effects on all studied traits (except essential oil). In these variables, although the main effect of fertilizer type was not statistically significant, but at different levels of silicon and selenium consumption, an increasing trend of these variables was observed, indicating the positive effect of simultaneous use of these two elements in reducing the effect of salinity. Also the main effects of salinity and fertilizer sources on leaf length and width factors, fresh and dry weight of stem, number of sub-branches, number of leaves and leaf area index were significant. In all these indicators, the application of silicon and selenium elements in nano size has always been more effective than the normal size of these elements, which should be considered as a management strategy in salinity stress conditions.

Iran is located in arid and semi-arid belt of the earth and also because of drought, climate changes and mismanagement of water use; its freshwater resources are declining. Therefore, the need to increase water productivity is obviously rational. New methods of irrigation can be mentioned to increase the efficiency of water resources management. Management and application of these new methods of irrigation also requires studying the process of soil moisture changes and its availability to the plant. The purpose of this study was to evaluate the performance of HYDRUS-1D hydrological model at two different depths using inverse solution method in relation to pedo-transfer functions in four-year alfalfa farm which irrigated with center pivot irrigation system. Therefore in this study, the Particle Swarm Optimization (PSO) algorithm (as inverse solution method) as well as three parametric functions including Rosetta, Gorbani Dashtaki and Homaee, and Sepaskhah and Bondar were used to estimate soil hydraulic parameters in simulating soil movement and moisture distribution in HYDRUS-1D hydrological model. So, among the three parametric functions, the best function was selected and then the efficiency of the inverse solution method was compared to the parametric method in the process of simulating the unsaturated flow with the HYDRUS model. The results indicated the acceptable ability of PSO algorithm to estimate soil moisture characteristic curve and its hydraulic parameters. Also by evaluating the statistical indices, it was shown that by linking HYDRUS model with PSO algorithm, this model was efficient to estimate the trend of soil moisture changes. The best model performance was obtained in the soil upper layer with E=0.89, d=0.94 and R2=0.98.

Spatial and temporal variations of soil characteristics occur in large and small scales. Study of these variations is very time-consuming and costly especially in large scales. In order to the fast and reliable determination of soil properties, various interpolation techniques have been developed and applied. The most widely used interpolation techniques in various sciences is the Kriging types. The copula function is one of the new interpolation techniques that are widely used in sciences such as hydrology. Thus, the aim of this study was to evaluate the spatial variations of some soil physical properties using copula function and to compare with geostatistics techniques.

Sampling by regular networking was done in an area of 484 ha located in 10 km from the west of Baft city, Kerman province and finally, 121 surface soil samples were collected. After air drying, the apparent bulk density was determined using the Hunk, then the soil samples were passed through a 2 mm sieve to determine the percentage of sand. To interpolate, four functions of the Archimedean copula including the Clayton, Frank, Gumbel and Joe functions, and geostatistics techniques including simple, ordinary, universal and disjunctive Kriging and the Inverse Distance Weighting (IDW) method were used. The results were analyzed using Root Mean Square Error (RMSE), determination coefficient (R2), Mean Absolute Error (MAE), and Mean Bias Error (MBE).

Based on the descriptive statistics, soil bulk density and soil sand followed a normal and skewed distribution, respectively. In order to fit the copula function, the distribution functions of the studied variables were firstly determined. The results showed that the sand and bulk density followed the Frechet (3P) and Wakeby distribution functions, respectively. Also, based on the Pearson correlation coefficient, the correlation between pairs of points was determined in distances less than 2000 m and distances more than 2000 m were known as an independent distance. The estimation efficiency based on the determination coefficient (R2) showed that value of determination coefficient for copula function for the sand variable, 6% and for bulk density 8%, more than conventional geostatistics techniques were obtained. Also, the estimation error of copula function was minimum that indicate good performance of copula function to estimate the spatial variation of soil physical properties.

The results of study showed that copula function, especially the median copula, have the better performance for estimation the studied soil properties. One of the most important reasons for this superiority is the ability to fit the marginal distribution function on the data in copula, while it is not possible in geostatistics techniques. Other reasons include the ability to express the correlation between the data at different intervals and the lack of sensitivity to outlier data in copula relative to conventional geostatistics techniques. Due to the skewness nature of soil data, as well as the need for more accurate analysis and interpretation of actual soil data, copula functions can be widely used to estimate of soil properties.

Maize in response to salinity stress exhibits different responses during different growth stages and reproductive growth stage is the most sensitive stage of plant growth. The objective of the present study was to evaluate the ability of salinity simulator models to estimate maize (SC704) yield, during reproductive growth stage. Process-physical models including Maas and Hoffman, van Genuchten and Hoffman, Dirksen et al. and Homaee et al. models were used to access this objective. In order to create real growth conditions in saline soils, natural saline water of Nough lake in Rafsanjan, Kerman province, Iran, with electrical conductivity of 42.6 dS.m-1, was used and diluted with normal fresh water for preparation of salinity treatments studied in this experiment. Treatments used in this study were five salinity levels of 1, 2, 4, 6 and 8 dS/m with a non-saline water (as check treatment) that were studied in three replications. The results of modified efficiency coefficient (E'), modified agreement index (d') and coefficient of residual mass (CRM) statistics showed that the van Genuchten and Hoffman model with the highest accuracy (E'=0.87, d'=0.91) was the most accurate model for shoot height, while for shoot dry weight, the Nonlinear model of Homaee et al. (with the accuracy of E'=0.86 and d'=0.90) and for final grain yield, the Maas and Hoffman model (with the accuracy of E'=0.94 and d'=0.96), had the most accuracy and the best estimate. In total, the results of this study showed that salinity simulation models had good ability to estimate maize yield under salinity stress and better management of allocation of low quality water resources in different stages of plant growth. In other words, the four models of Maas and Hoffman, van Genuchten and Hoffman, Dirksen et al. and Homaee et al. can be effective tools in using saline water sources with varying degrees of salinity to achieve optimal maize production. Therefore, by precisely determining the optimal model for each growth stage and accepting the risk of yield loss due to salinity of irrigation water, low-quality water resources can also be considerably utilized in maize yield production.

The soil moisture characteristic curve (SMCC) is a key concept in the modeling process of physical and hydrological studies of soil that plays a critical role in soil and water management. At the same time, the accuracy of the models used to describe the SMCC is affected by the trend of its parameters changes. The uncertainty analysis of hydraulic parameters of SMCC plays an important role in the modeling process, determining the model input parameters and evaluating the performance of the models. Therefore, the objective of this study was to evaluate the application of the GLUE simulation method, which is based on Monte Carlo simulation method, to estimate the uncertainty of alpha and n variables with constant assumption of other SMCC parameters, in three models of vanGenuchten, vanGenuchten-Mualem and vanGenuchten-Burden.

Initially, two soil samples were taken from Shahid Bahonar University of Kerman field and the SMCC for both soil samples (sandy loam and silty clay loam textures) was plotted on the basis of all three models using the Pressure Plate data and RETC Software and their moisture curve parameters were derived. Then, using the GLUE method, the uncertainty of alpha and n parameters in all three models were investigated. In addition, based on GLUE performance, the intrinsic uncertainty of each of the three models was evaluated for each of soil textures.

The posterior distribution for each of the studied hydraulic parameters were obtained for three models in each textural class. The 95% confidence interval of SMCC simulations was obtained for all three models in two texture classes as the main output of this study. To quantify the uncertainty of the models, four uncertainty assessment indices were calculated and evaluated. Based on the evaluation indices, the best models for silty clay loam and loamy sand were the vanGenuchten-Mualem model (PCI = 85.71, d-factor 0.2013, S = 0.079, T = 0.4642) and the vanGenuchten model, (PCI = 28.75, d-factor = 0.0766, S = 0.6453, T = 1.1034) respectively.

The results of the posterior distributions diagrams showed that the alpha and n hydraulic variables were less identifiable in the calibration process and could not determine the optimal range for them, therefore, these two variables play a major role in the uncertainty of soil moisture curve. Also the uncertainty analysis of all three models showed that GLUE method was able to estimate soil moisture curve points so that the moisture curve obtained by RETC software for all three models was within 95% confidence level. The presence of about 85% of the soil moisture curve points for silty clay loam texture within the 95% confidence interval indicates the high capability of the GLUE method.