مقالات رزومه دکتر عیسی اسفندیارپور

SWAT model is a suitable tool for simulating hydrological processes. This model requires many inputs that often cannot be measured directly and is considered one of the main sources of uncertainty in these models. The recalibration process can reduce the uncertainty in the model results by adjusting and adapting these inputs. The researches showed that calibrating a hydrological model by using the common automatic CV calibrating algorithms will not provide proper accuracy in the prediction of hydrological variables during the validation period, so PSO algorithm was used to calibrate the SWAT model. Since there is no mathematical and logical rule to determine the best combination of PSO algorithm parameters and these combinations are selected based on trial and error and among many different combinations, therefore trial and error based methods are very time-consuming and sometimes impossible. In this research, Taguchi method was used to determine the best combination of PSO algorithm parameters.

In this research, the ability to use the SWAT model to simulate monthly runoff in the Javanmardi Watershed, one of the main sub-basins of the Lordegan Watershed with an area of 380 square kilometers, was investigated. In this study, the PSO algorithm parameters, including the number of simulations (A), the number of repetitions (B), the speed calculation weight (C) and the movement parameter (D), were defined in four levels. Then, these parameters were designed and implemented according to the experiments in the L16 orthogonal array (using the Taguchi experiments design method). The performance scale used to evaluate the algorithms was RPD (Relative Percentage Deviation). Considering the variable nature of the response in this study, the S/N index "the lower the better" was used to analyze the Taguchi test results. The selection of arrays and calculations were done in Minitab 16 software.

In the sensitivity analysis stage, which was performed before the model recalibration, among the 28 parameters studied in this research, the model showed sensitivity to the changes of 22 parameters, and they were identified as variables influencing the simulation of runoff in Javanmardi Watershed. The results showed that the parameter of the runoff curve number (CN) is the most important factor and the parameters of soil apparent density in the wet state (SOL_BD) and average water usable by the plant (SOL_AWC) are among the most important factors controlling the flow rate in the study basin, respectively. Based on the results simulated by the PSO algorithm, it was found that the SWAT model has an acceptable accuracy for estimating the monthly runoff in the study area. So, in the recalibration phase, the r-factor and p-factor indices were 1.23 and 0.88, respectively, and the explanatory and Nash-Sutcliffe coefficients were 0.77 and 0.75, respectively. In the validation stage, the r-factor and p-factor indexes were 1.31 and 0.84, respectively and the explanatory and Nash-Sutcliffe coefficients were 0.72 and 0.73, respectively. In this study, the best combination resulting from the application of Taguchi method for the parameters of the number of simulations, the number of repetitions, the speed calculation weight and the appropriate parameters in the PSO algorithm were determined as 40, 100, 0.2 and 0.15 respectively (A4B4C4D3).

The results show that the SWAT model has an acceptable accuracy for estimating the monthly runoff in the Jawanmardi Watershed, and the PSO method is an effective algorithm in calibrating and determining the uncertainty of the model in this basin, and the use of the Taguchi test design method is a suitable way to determine the best combination of PSO algorithm parameters is for researchers who use this method to optimize the SWAT model.

Optimum soil water content (OSWC) for tillage is the moisture at which, if tillage is done, the maximum amount of crushing (friability) is created in the soil with minimum energy consumption. The purpose of this research was to determine the OSWC for tillage operations under different agricultural land uses in Jiroft Plain, Iran, and to evaluate the effectiveness of the decision tree model in this regard for the region. For this purpose, soil samples (90 samples) were taken intact from 0 to 20 cm depth and some of its physical and chemical characteristics were determined. To determine the soil water characteristics curve, the soil water contents at matric suctions of 10, 30, 50, 100, 300, 500, 1000 and 1500 kPa were measured using a pressure plate device. The results showed that the type of cultivation did not have a significant effect on the physical and chemical characteristics of the soil, including the optimum weighted humidity. OSWC prediction was done with decision tree. In this research, three prediction accuracy criteria, namely, coefficient of determination (R2), root mean square error (RMSE), and normalized root mean square error (NRSME) were used to evaluate the model performance. The results showed that use of decision tree for predicting the OSWC was appropriate due to having a lower NRMSE (0.017) and a high R2 (0.75). Also, the results of OSWC modeling showed organic matter as the most influencing variable, followed by the equivalent calcium carbonate percentage, electrical conductivity, apparent density, and percentage of sand and silt.

The present study aimed to investigate the efficiency of silicate solubilizing bacteria in the release of elements of sepiolite. The greenhouse test was performed as factorial with completely randomized design with three repetitions. This experiment consisted of three bacterial strains (B7, B8, and D1) and pistachio (two cultivars Badami Riz Zarand and Qazvini) and four drought levels (including 90% (control), 75%, 60% and 45% of FC). The seedlings were fed with Hoagland's magnesium-free nutrient solution over a five-month growth period. The vegetative and biochemical traits of the seedlings were studied. The results showed that at the highest level of drought stress, application of strains B1 and B2 increased the rate of root Si uptake in Qazvini seedlings by 77% and 97%, respectively, and in seedlings of Badami Riz Zarand by an average of 2.75 times compared to non-drought stress conditions. Application of B7 strain increased Mg uptake in Qazvini seedlings from 6.91 mg per pot under non-drought stress conditions to 13.9 mg per pot at the highest level of drought stress. In Badami Riz Zarand, the application of the mentioned strain increased the uptake of Mg in the shoot at three stress levels of 75%, 60% and 45% of the field capacity by 74%, 67% and 66%, respectively, compared to non-drought stress conditions. Presence of these bacterial strains in symbiosis with plants will have a significant effect on the release of elements from minerals and its absorption by the plant and thus improve plant nutrition and change the mineral structure.

The date palm scale Parlatoria blanchardi (Targioni Tozzetti, 1892) (Hem.: Diaspididae) is one of the main and important pests of reducing date palm yield. The predatory coccinellid Chilocorus bipustulatus L. (Col.: Coccinellidae) is well known as the most important active natural enemy in date orchards, which feeds on scale insects, especially the date palm scale In this research, the life table parameters of C. bipustulatus were determined by feeding on the date palm scale. The experiments were carried out in growth chamber under laboratory conditions, at 25±1°C, a relative humidity of 70±5%, and a photoperiod of 16:8 h (Light: Dark). A number of 100 coccinellid eggs of the same age with less than 24 h old were studied, and the study continued until the end of the adult stage. After incubation period, the larvae were fed daily with date palm scale nymphs. The biological parameters of the coccinellid and data were analyzed based on the age–stage, two–sex life table theory. The mean and standard error of population growth parameters were calculated based on the Bootstrap method. The intrinsic rate of increase(r) (day), finite rate of increase (λ), The mean generation time (T) and the gross reproductive rate (GRR), were 0.06 ± 0.01, 1.06 ± 0.01, 56.89 ± 0.32, 34.68 ± 3.08, respectively. The results obtained from this study can make it possible to improve the efficiency of this coccinellid in integrated pest management programs against P. blanchardi by increasing its population through mass rearing and mass release.

Rheological characteristics of soils (including their deformation and flow behaviors when subjected to external stress) can provide important information on microstructural and microaggregate stability. One of the newest methods of assessing the stability of aggregates is the use of rheometric discussions in this regard. In other words, rheometry can be used to evaluate the elastic behavior of soil versus its plastic behavior in a range of aggregate deformation and thus a more quantitative interpretation of aggregate formation or stability against stress. In the last decade, Amplitude sweep test (AST) has been used to evaluate the stability of soil microstructure and its elastic behaviors. The present study was designed to determine the aggregate stability and viscoelastic behavior of the soil using the rheometric method. In the present study, physical, chemical, clay mineralogical properties and classification of dominant soil orders in Chaharmahal-va-Bakhtiari Province were studied and compared in order to determine the stability of aggregates and viscoelastic behavior of soils using the rheometric method.

Five different soil orders were described and sampled in Shahrekord, Farsan, and Koohrang cities. After air-drying and sieving of soil samples taken from the genetic horizons, the physical and chemical properties were measured using standard methods. Besides, the type of clay minerals was determined by the X-ray diffraction method. AST was used to quantify the initial rheological parameter values, including the storage (G') and loss moduli (G"), deformation limit (γL: when the material begins to irreversibly deform), deformation at flow point (γf: when the material becomes viscous), loss factor (tan δ = G''/ G') and integral z (which summarizes the overall elasticity of the material) in order to study microstructural stability and microaggregates of the five soil orders. Finally, the correlation diagram was plotted among soil properties and rheological parameters using R software. Besides, the soil was classified based on the American Soil Taxonomy (2014) and WRB (2015).

The studied pedons classified based on the American Soil Taxonomy (2014) and WRB (2015) in five different soil orders and four reference soil groups, including Entisol (Cryosol in WRB), Vertisol, Mollisol (Kastanozem in WRB), Alfisol, and Inceptisol (Calcisol in WRB). Semi-quantitative analysis of clay minerals showed that the soils have different clay minerals assemblage including kaolinite, vermiculite, smectite, illite, chlorite, and quartz. The results of the AST indicated that the deformation limit was significantly higher in subsoil horizons than topsoil horizons and was also higher in the clayey soils compared to sandy soils. The soils with “vertic soil properties” showed the high range of linear viscoelastic region (LVE) that attributed to the samples with high elasticity behavior. Soils with higher clay contents and further evolution showed higher rheological parameters, implying that these soils had more rigid microstructures incomparable with other soil orders which in turn, indicated the higher microstructural and microaggregate stability. In contrast, the horizon, which had high sand content and little soil development, had the lowest values for all properties, thus indicating a lack of micro-aggregate stability. Pearson correlation coefficient analyses revealed that integral z was influenced by soil physicochemical properties, and was higher in soils with the clay fraction dominated by expansive clay minerals and higher cation exchange capacity.

Altogether, the rheological parameters indicated that more developed soils had greater microstructural stability than their less-developed counterparts. As a result, rheological measurements may be useful for identifying the major factors that affect soil aggregation. In addition, these parameters can be used as indicators to evaluate the relative amount of soil evolution in future studies.

Tensile strength is one of the most important indicators for soil physical quality, which is equivalent to the maximum stress applied to an aggregate without any disruption. The purpose of this study was to investigate the performance of different modeling methods for estimating soil tensile strength of some Rafsanjan pistachio orchards. For this purpose, soil samples (80 samples from 0- 30 cm depth mostly sandy loam) were taken and some soil physical and chemical properties were determined. Aggregate tensile strength was also measured in different sizes. The average EC, pH, and SAR indicated that soils of the study area were saline and sodic. Multiple regression between tensile strength and other soil properties were investigated. Tensile strength modeling was also performed using multilayer perceptron neural network and decision tree. The mean squares of error and coefficient of determination were used to evaluate different modeling models. The results of model evaluation showed that the use of regression decision tree for predicting tensile strength was better than the other modeling methods because of the lowest error (R2=0.88 and RRMSE = 14%) compared to the two methods of multiple regression and the multilayer perceptron neural network. Also, the results of different tensile strength modeling showed that the percentage of clay, percentage of dispersible clay, adsorption ratio of sodium, percentage of calcium carbonate equivalent, and the percentage of organic matter are the most influential variables on tensile strength. According to the results, it seems that the most effective way to increase soil tensile strength and reduce soil bulk density in pistachio orchards is to increase the percentage of soil organic matter.

Quick and accurate estimation of soil available water as one of the most critical soil quality indices plays an essential role in agricultural water resources management. The present study estimated the least limiting water range (LLWR) for 250 soil samples taken from Khanmirza plain in Chaharmahal and Bakhtiari province. Artificial intelligence method (combining genetic algorithm (GA) with artificial neural network (ANN)) and readily available soil properties were used for this purpose. The LLWR was considered as output variable, and sand, silt and clay percentages, organic carbon content, bulk density (BD), particle density (PD), pH, electrical conductivity (EC) and equivalent calcium carbonate (CCE) were considered as input variables. From 250 data, 200 were allocated to model training and 50 to model testing. The statistical analyses showed that the artificial neural network had a reasonable estimate of LLWR with a coefficient of determination of 0.93. Finally, the combined model of artificial neural network-genetic algorithm (ANN-GA) with the highest coefficient of determination (R2 = 0.96) was identified as the most appropriate model for predicting LLWR. The two models of artificial neural network and genetic algorithm generally showed better performance than the regression equations.

Introduction Landscape represents a large portion of land/terrain that is either formed by a repetition of similar or dissimilar relief/molding types or an association of dissimilar relief/molding types (e.g., valley, piedmont, mountain, etc.). It is usually affected by a set of natural (e.g., climate, organisms, parent material, topography, time, erosion, sedimentation, etc.) and/or artificial (e.g., artifacts) factors. Soil is one of the most important components of landscape that is affected by various factors such as water and wind. Aeolian or alluvial sediments (from seasonal rivers) in arid areas cause the formation of different landforms and change the landscapes in these areas. Therefore, the study of geoforms in arid regions can lead to a better understanding of geomorphological processes and soil change in these areas. There are various methods, including soil micromorphology and clay mineralogy, to understand the alteration of landscapes and the soils change on them. The aim of this study was to investigate the physical and chemical properties, clay mineralogy and micromorphology of soils in various geomorphic units of Davaran Region, Rafsanjan. Materials and Methods Seven dominant geomorphic units (geoforms) of the region, including pediment, margin of fan and cultivated clay flat, alluvial fan, desert pavement, margin of pediment and sand sheet, active drainage, margin of fan and uncultivated clay flat were selected using Google Earth images and field studies. Nineteen pedons were excavated and described in the geomorphic units. After selecting a representative pedon in each of the geoforms, their genetic horizons were sampled. Besides, in order to conduct soil micromorphology studies, undisterbed and oriented samples were collected from selected horizons. After transferring the samples to the laboratory, their physical and chemical properties were measured using standard methods. In addition, clay mineralogy studies were performed by X-ray diffraction method and micromorphological studies were done using a petrographic microscope. Finally, soil classification was performed based on both Soil Taxonomy (2014) and WRB (2015) systems. Results and Discussion Results showed that gypsification and calcification are the dominant soil forming processes in the studied region, which have led to the formation of Gypsic and Calcic horizons. This has placed the soils in the Gypsids and Calcids suborders based on the Soil Taxonomy system and the Gypsisols and Calcisols reference soil groups according to WRB system. The representative pedon in the margin of fan and cultivated clay flat (pedon 2) geoform lacks a salic horizon based on the Soil Taxonomy; while it is in the Solonchak reference soil group of the WRB. Also, the presence of argillic horizon in the representative pedon of the margin of fan and uncultivated clay flat geoform (pedon 7) indicates presence of a more humid paleoclimate in the history of the region. The results of clay mineralogy showed that the predominant minerals in the region include chlorite, illite, kaolinite, and smectite. The illite, chlorite, and kaolinite are inherited from papent materials of the soils, and the smectite has a transformation origin (from palygorskite and illite). Addition of this mineral by aeolian or alluvial sediments could not also be neglected. The micromorphological results indicated that the soil pores were mainly chamber. The presence of carbonates and gypsum in the studied soils has caused that the b-fabric in the most horizons to be Calcitic Gypsic Crystallitic. Gypsum was observed in the form of vermicular, lenticular, interlocked gypsum plates and subhedral shapes. Other pedofeatures in the studied soils include calcite nodule and limestone. Conclusion The simultaneous presence of aeolian and alluvial sediments in the different geoforms of Davaran region has caused the formation of stratified soils. Existence of dry climate and lack of significant vegetation in the region from one hand, and the addition of different sedimentary layers at different times (which causes soil rejuvination) on the other hand, has caused that the soils of the region, in general, not to be highly developed. As a result, few differences were observed among soils in different geoforms. Comparing the results of two soil classification systems for the studied soils showed that in general there is a relatively good correlation between them. Totally, the role of climate and parent material in alteration of the studied soils is evident; so that the physical and chemical properties, clay mineralogy and micromorphology of soils in different geoforms have been affected.

Interrill erosion is one of the most important types of erosion, in which various factors such as soil, runoff, and rainfall influence its process and rate. Few studies have been conducted using artificial neural networks (ANNs) to determine the factors affecting interrill erosion in Iran. Furthermore, no research has been carried out in Jiroft on this matter. Therefore, this study was conducted to evaluate the factors influencing interrill erosion using ANNs in four different regions around Jiroft in Kerman province.

For this research, 100 soil samples were collected from two pastures and two forest land uses at depths of 0-10 cm using a random sampling method. Some physical and chemical properties were determined. The amount of interrill erosion was measured using Kamphorst rainfall simulator. Modelling was performed using feedforward multi-layer perceptrons (MLP) with the error backpropagation and Levenberg-Marquardt training algorithm along with 11 soil characteristics in two scenarios. Hill sensitivity analysis was used to investigate the significance of the input variables.

The results revealed that in the study areas, clay, silt, sand (0.05-2 mm), geometric standard deviation (σg), and geometric mean diameter (dg) of particles play a crucial role in interrill erosion while cementing agents such as organic matter (OM) and calcium carbonate equivalent (CCE) were less important. According to the results, the protected forest with high contents of sand as well as low amounts of silt, organic matter and calcium carbonate equivalent showed the lowest erosion rate (2.63 tons /ha). The R2 values for the test datasets in the scenario 1 (with input variables including soil acidity (pH), electrical conductivity (EC), bulk density (BD), organic matter, calcium carbonate equivalent, particulate organic matter (POM), sand, silt, and clay) were 0.81. Whereas the R2 values in the scenario 2 (with input variables such as pH, EC, BD, OM, CCE, POM, the dg and σg) were 0.72. In addition, root-mean-square error (RMSE) for the testing dataset in scenarios 1 and 2 were 0.77 and 1.14, respectively.

Both scenarios had almost the same accuracy in interrill erosion modeling. However, according to the values of R2 and RMSE of the data in scenario 1, this scenario showed better accuracy than scenario 2. In general, the results showed that the ANNs can estimate the amount of interrill erosion using appropriate input variables with high accuracy, and therefore it might be considered as a useful technique to estimate interrill erosion.

Wind erosion is an important cause of land degradation and desertification in arid and semi-arid regions of the world. This phenomenon occurs more severely in dry and bare soils. During wind erosion, soil particles are transported by three mechanisms known as creeping, saltation and suspension, which result in numerous on- and off-site damages. Wind erosion as a natural phenomenon, is affected by many factors. Our understanding of wind erosion is commonly constrained by the multiplicity and complexity of factors in this process. In recent years, solving pattern recognition and optimization problems with metaheuristic algorithms has received considerable attention among researchers. Genetic algorithms is a search technique inspired by the process of natural selection, which is well applied to multimodal, non-linear, and non-derivable objective functions. In the current research, for identifying the most important parameters affecting wind erosion rate, two GA-ANN and NSGA-II hybrid algorithms were developed using genetic algorithm and artificial neural networks. In order to prepare a suitable and reliable data set; after designing a grid sampling strategy, soil samples were collected from 51 study sites in the Narmashir plain, Kerman and then some soil parameters were measured. In addition, wind erosion rate was determined at each study site using a portable wind tunnel device. Based on the GA-ANN algorithm results, gravel coverage, sand, clay, aggregate stability, surface crust, moisture, and organic matter were identified as the main determinant features affecting spatial variation of wind erosion rate. However, the selected feature subset by NSGA-II algorithm included gravel coverage, sand, aggregate stability, surface crust, and moisture. The calculated error function for the GA-ANN algorithm performance was 3.58%. It was 1.70% for the NSGA-II algorithm performance. According to the results, both algorithms had acceptable performance to achieve the purpose of the present study. Therefore, the algorithms developed in this study can be applied to identify the most important parameters affecting wind erosion rate in other areas with similar challenges.

Aggregate stability of soils informs about their relative strengths against erosive forces and mechanical disruption. In this research, a hybrid Genetic Algorithm-Artificial Neural Network method was used to select the best subset of features affecting the mean weight diameter (MWD. In addition, the ability of ANNs and multiple linear regression (MLR) for quantifying the relationship between the MWD index and some soil properties was assessed. After the modeling process, the importance of the selected features in relation to spatial variability of aggregate stability was investigated. In order to prepare a suitable data set; MWD index and some soil features were measured in collected soils from 90 sampling points. Feature selection results showed that six soil features including clay, sand, organic matter, calcium carbonate, electrical conductivity, and sodium adsorption ratio had the greatest effect on the aggregates stability of the studied soils. According to the MWD modeling results, the obtained values of coefficient of determination (R2), mean absolute error percentage (MAEP), and root mean square error (RMSE) for the ANN model performance were 0.94, 21.39, and 0.07% respectively. These findings indicated that the developed ANN model was able to predict the complex and nonlinear relationships between the MWD index and the soil properties selected by the algorithm. Based on the sensitivity analysis results, calcium carbonate equivalent, sand particles, and organic matter were identified as key factors in estimating aggregate stability. Overall, this study provides a robust framework for the prediction of aggregate stability and identifying the most determinant parameters influencing it in arid and semi-arid soils that could be applied to other regions with similar challenges.

Due to the relationship between crop yield, and soil characteristics and land topography, knowledge and awareness of these characteristics is necessary to achieve sustainable development in agriculture. This study was performed to evaluate and determine the relationships between Rose yield (Rosa Damasceneea Mill) with soil properties and land topography by using Multivariate Linear Regression models (MLR), Artificial Neural Network (ANN) and Structural Equation Modeling (SEM) in Bardsir City, Kerman Province. For this purpose, soil sampling and crop yield were performed in the form of a regular grid pattern. Besides, some topographic features of the land were calculated using digital elevation model (DEM) of the region, and to implement the conceptual models, three theoretical models were designed and tested. The results showed that MLR and ANN models were able to justify 68 and 87 % of the yield variability, respectively, which indicates the higher accuracy of ANN model than MLR in yield estimation. The results of SEM illustrated that Rose yield is mainly controlled by soil chemical properties, topographic features, and soil physical properties, respectively. Different scenarios for SEM showed that simpler models with fewer hidden structures could have a better fitting. Therefore, the first conceptual model of this method with the values of root mean square error, goodness of fit index and comparative fit index of 0.033, 0.88 and 0.94, respectively, was selected as the best model. The overall results showed that the ANN model was more efficient than MLR in yield prediction due to consideration of the nonlinear relationship between crop yield and the factors affecting it. In addition to the ability of the ANN model to estimate crop yield, the SEM also showed that the latter method can provide more explanations about the relationships and simultaneous interactions between variables. In general, the application of SEM method, relying on the capabilities of this method, can improve the yield of various crops.

Proper plant nutrition is one of the most important factors in improvement of crop quality and quantity. Potassium is one of the essential macronutrients for plant growth. Understanding the different forms of potassium is important in evaluation the situation of this element in soil fertility and plant nutrition. Incorrect understanding of the potassium dynamics in soil off leads to improper management of soil fertility. Soil potassium exists in four forms: soluble, exchangeable, non-exchangeable and structural that their availability for plant are different. Soluble and exchangeable forms are widely used for determination of the potassium availability for plants. In addition to the exchangeable potassium, non-exchangeable potassium plays an important role in plant nutrition. This study was done to evaluate the different chemical forms of potassium and effect of pistachio seedlings planting on changes of their forms in some calcareous soils of Rafsanjan. For determination of the chemical forms of their forms potassium in 20 calcareous soils samples of Rafsanjan before and after planting, 1 M NH4OAc, boiling 1 M HNO3 anddistilled water were sample used. Results indicated that there was no significant difference between the soluble potassium before and after the planting. However, amount of exchangeable potassium increased from 239 mg kg-1 soilbefore planting to 270 mg kg-1 soilafter planting and non-exchangeable potassium decreased from 683 mg kg-1 soilbefore planting to 570 mg kg-1 soilafter planting. Multiple regression equations indicated that the different forms of potassium in soils were correlated with the physical and chemical properties of the soil such as clay content.

The main purpose of this research was to study the effect of soil physicochemical properties on aggregate stability and magnetic susceptibility in two aeolian geomorphic units (desert pavement and aeolian clay flat) of Davarn plain in Rafsanjan. After taking 50 samples from the surface soil (0 to 10 cm) of desert pavement and 43 samples of aeolian clay flat, the magnetic susceptibility, mean weight diameter of aggregates and some of their most important physical and chemical characteristics were determined. Results showed that the mean magnetic susceptibility in desert pavement was 842.53 and in clay flat was 632.13 (*10-8 m3 kg-1) which is very high compared to those in soils of Iran and its main reason could be attributed to the parent material of the area. The low mean frequency-dependent magnetic susceptibility in the both study units also confirms this issue. The mean weight diameter of aggregates in the desert pavement varied from 1.20 to 1.93 mm and in the clay flats from 0.54 to 1.80 mm. The findings of this research showed a direct correlation between iron forms (Fed and Feo) with aggregate stability in the both of studied geoforms; however, the level of this correlation was high and significant for Fed. The results also indicated that there is a negative correlation between magnetic susceptibility with electrical conductivity (EC), organic matter (OM) and calcium carbonate equivalent (CCE) in the both of studied units. A positive and significant correlation was seen between total silt, total sand and its fractions with magnetic susceptibility in both geomorphic units. In addition, a negative correlation was seen between the mean weight diameter of aggregates and magnetic susceptibility, which its possible reason can be the presence of a large amounts of sand resulted from aeolian losses and its adverse effects on aggregate stability. The results of regression model indicated that dithionate iron and sand had a significant effect on the magnetic susceptibility; and sand, clay and electrical conductivity had a significant effect on aggregate stability.

Use of the curve gradient of the Soil Water Retention Curves (SWRC) in the inflection point (S Index) is one of the main indices for assessing the soil quality for management objectives in agricultural and garden lands. In this study Anneling Simulated – artificial neural network (SA-ANN) hybrid algorithm was used to identify the most effective soil features on estimation of S Index in Jiroft plain. For this purpose, 350 disturbed and undisturbed soils samples were collected from the agricultural and garden lands and then some physical and chemical soil properties including Sand, Silt, Clay percent, Electrical Conductivity at saturation, Bulk Density, total porosity, Organic Mater, and percent of equal Calcium Carbonate were measured. Moreover, the soil moisture amount was determined within the suctions of 0, 10, 30, 50, 100, 300, 500, 1000, 1500 KP using pressure plate. Then, the determinant features influencing the modeling of S Index were derived using SA-ANN hybrid algorithm. The results indicated that modeling precision increased by reducing the input variables. According to the sensitivity analysis, the Bulk Density had the highest sensitivity coefficient (sensitivity coefficient=0.5) and was identified as the determinant feature for modeling the S Index. So, since increasing the number of features does not necessarily increase the accuracy of modeling, reducing input features is due to cost reduction and time-consuming research.

The assessment of soil quality for agricultural land is essential for the economic successes and sustainability of the environment in developing countries. Currently, many types of methods with different factors are used to evaluate soil quality. Soil quality evaluation is an indicator of sustainable agriculture and optimized application of utilization of natural resources. Moreover, the Bardsir city is one of the most important regions of Rose (Rosa Damasceneea Mill) in Kerman province and Iran. In this research, soil quality were evaluated in a part of the cultivated land of Rose using integrated soil quality index (IQI) and Nemoro soil quality index (NQI) in combination with two of data sets, total data set (TDS) and minimum data set (MDS) for 0-25 and 25-50 cm depth.

In this research, a farm with an area of 30 hectares was selected in Bardsir city, Kerman province. Then, 100 locations were sampled to determine soil characteristics (0-25 and 25-50 cm), then in each location, the yield was determined. Among the total characteristics of soil quality, the most important of characteristics were determined Using principal component analysis (PCA). The results showed that in the topsoil and subsoil, minimum data set were organic matter, sand, Mn, calcium carbonate equivalent, Zn, Cu and sand, K, calcium carbonate equivalent, Zn, P, fragments and Mn, respectively.

The results showed that at the 0-25 cm, the correlation between IQITDS and IQIMDS and between NQITDS and NQIMDS were 0.85 and 0.79, respectively. Also, at the depth of 25-50 cm, the correlation between IQITDS and IQIMDS and between NQITDS and NQIMDS were 0.75 and 0.77, respectively. The geostatistical analysis of soil quality indices showed that all of the studied soil parameters and Rose yield have spherical model and strong to medium spatial structure. The range of variograms is varied from 119.33 m for IQITDS in the subsoil to 151.8 m for NQITDS in the topsoil. Besides, the range of variogram for Rose yield was 122.16 m. The correlation between kriging maps of Rose yield and soil quality indices showed that the highest correlation was found between the yield and the IQITDS index at both studied depths. The results of correlation between soil quality indices and Rose yield showed that the IQITDS index has a higher correlation with yield than other indices.

These results demonstrated that the IQI index, especially in the TDS set, has a better performance for assessing the soil quality in the study area. Because of the relatively good correlation between this set and MDS, the TDS set is better to determine soil quality indices; this result may be attributed to use MDS to determine the soil quality indices with proper accuracy. However, if the purpose of soil quality assessment is to achieve the optimal yield, the use of IQITDS index, due to its greater correlation with the Rose yield, has a better performance.

Soil maps have considerable significance as basic maps in many environmental and natural resources studies. Digital soil maps are based on the relationship between environmental variables and soil properties. The main purpose of this research was to analyze error and uncertainty of digital soil classes predicted at different taxonomic levels of Soil Taxonomy system using an artificial neural network. One hundred and twenty soil profiles were described and sampled based on a regular grid scheme in Shahrekord plain. Two groups of soil properties (qualitative and quantitative) and auxiliary parameters (including geologic map, landform map, landform-phase map, traditional soil map, normalized difference vegetation index, and some derivatives of digital elevation model) were used to estimate soil classes. After preparing the soil properties maps and checking their accuracy, these maps were used along with auxiliary parameters for estimating soil classes using an artificial neural network model in the R software. Finally, the accuracy and uncertainty of the model were evaluated by overall accuracy and confusion index, respectively. Results showed that the entry of more details in the soils classification at the lower levels of the Soil Taxonomy system, while increasing the number of classes, leads to decreasing the overall accuracy and increasing uncertainty. It is noticeable that the artificial neural network model has a good accuracy up to the great group level through the acceptable level of overall accuracy (i.e., 75 %), hence it has a high degree of uncertainty. Therefore, the accuracy of the model could not be effective in its selection trough the modeling process; however, paying attention to its uncertainty is also very important along with the model error. Accordingly, we suggest using the other methods of soft computing for modeling in plain areas or in low relief regions.

One of the ways to express physical behavior of soil is to describe the soil structure. Since physical soil could not be measured directly, therefore, the special properties defined as indicators, are used to express the soil physical quality. of different indicators, S-index is used to express the physical quality of the soil which is the slope of the soil water retention curve at inflection point. It seems that little research has been done on the effect of landuse on the S-index, then The purpose of this study was to compare the results of S-index of three different soil water retention curve models of the moisture characteristic of the soil (Van Genuchten, Brooks-Corey and Grant-Groenevelt Models) in 5 different land uses in Jiroft city. For this reason, 350 disturbed and 350 undisturbed soil samples of different landuse (citrus, palm, arable, forest and combinatory) were analyzed. Some of the physical and chemical features of the soil, including sand, silt, and clay percentage, the electronic conductivity at saturation, Bulk Density, the percentage of the total porosity and the percentage of the Organic Matter were measured. Also, the amount of the soil moisture in suctions 0, 10, 30, 50, 100, 300, 500, 1000 and 1500 KPa was determined using pressure plates. The results showed in the study region the best physical quality of the soil was seen in palm land use. All landuses, except forest, had high physical quality. High value of S index in citrus, palm, and arable land uses can indicate better soil management at the start of farming. In combinatory land use, due to Simultaneous cropping of alfalfa and palm, alfalfa causes more organic matter to be returned to the soil and as a result, the soil structure improves. Which is displayed at high value of S index. In general, The S index was higher in landuses where the soil had better structure or management operations were improved. because of the distinction between the soils with closely physical quality, Van Genuchten model was better and more appropriate than the other two models in assessing the physical quality. In general, despite the slight differences in citrus, palm and cultivate landuses, palm landuse had a higher mean of S index. So that their values in Van Genuchten, Brooks-Corey and Grant-Groenevelt models were 0.06, 0.06 and 0.04 respectively. The results of this study showed that the Van Genuchten model accuracy was better in estimating and evaluating the soil quality due to the greater differentiation between the soils with close physical quality. Because the fitting of Soil water characteristic curve had a higher R2 (R2=0.92). However, due to low differences with two other models, in particular the Brooks and Corey model (R2 =0.87), It seems that, in different landuses, a suitable model for assessing soil quality varies. Therefore, it is imperative that this index and other indicators of soil physical quality assessment in different climates are further examined.

Potassium is a very important plant nutrient not only because of its large demand, but also because of its important physiological and biological functions in plant. Therefore, understanding of soil potassium status can lead to better use of potassium fertilizers. This study was done to evaluate 10 extractants for estimating available potassium for pistachio in 28 calcareous soils of Rafsanjan in a completely randomized design with three replications in greenhouse conditions in Vali-e-Asr university of Rafsanjan in 2014. Potassium was extracted by 1 M NH4OAc, 0.01 M CaCl2,2 M NaCl, 0.1 M HNO3, boiling 1 M HNO3, distilled water, 1M NaOAc, Mehlich1, Mehlich3 and DTPA-NH4HcO3.The results showed that extraction of K decreased in the order: boiling 1 M HNO3> 2 M NaCl > 1M NaOAc > Mehlich3> 1 M NH4OAc > 0.1 M HNO3> DTPA-NH4HcO3> Mehlich1> 0.01 M CaCl2> distilled water. 1M NaOAc was selected as the most suitable extractant for potassium in calcareous soils of Rafsanjan on its high correlation with leaf and shoot K concentration. Multiple regression equations indicated that potassium extracted from the soil dependent to different physical and chemical properties of soil such as amount of clay and cation exchange capacity.

The mobility, bioavailability and environmental fate of heavy metal ions in soil are controlled by sorption onto soil minerals and solid organic matter. The sorption is strongly affected by the presence of various low-molecular-weight organic acids. The main objective of this study was to investigate the effects of oxalic acid on sorption of Cd on clay fraction of a soil.
The sorption of Cd on clay was studied as a function of pH and time of equilibrium (Cd concentration: 0.045 mM) in the range of 6-9.5, and as a function of metal concentration (Cd concentration in the range of 0.013-0.089 mM) in the absence and presence of oxalic acid with a fixed concentration of 500 μM at room temperature (23 ± 2 °C). The amounts of Cd sorbed on the adsorbent were calculated from the difference between the metal concentration in the blank and equilibrium concentration of Cd in the solutions.
The results of pH-dependent experiments showed that the oxalic acid decreased sorption of Cd onto the sorbent at all solution pHs. Experimental and modeling data from our kinetic reveal that the pseudo-second-order kinetic model gave the best fit. The Langmuir sorption model describes the interaction between Cd and the clay materials better than the Freundlich model. The value of Langmuir (KL) bonding constant was greater in suspensions without oxalic acid (26.6 Lg-1) as compared to suspensions containing this ligand (15.2 Lg-1). Also, the value of Freundlich (KF) bonding constants was greater in suspensions without oxalic acid (1.2 Lg-1) as compared to suspensions containing this ligand (1.8 Lg-1). These values are indication of low tendency of Cd for sorption on the clay surfaces in the presence of oxalic acid. In order to interpret the effect of oxalate on the sorption behavior of Cd onto the sorbent, knowledge of Cd speciation is essential. Species activities were estimated using the PHREEQC code. In the absence of oxalate, the predominant Cd(II) species is Cd2 over the pH range 3.0–9.0. In the presence of oxalate, Cd2 and Cd-Oxalate species were the major solution Cd(II) species at pH ≤10 . The formation of soluble complex of Cd-oxalate may be responsible for the inhibition of Cd sorption by the sorbent.
In general, our results demonstrate that the oxalate could have negative effects on Cd sorption to clay fraction of soils. Thus, it may play an important role in Cd availability and transport in soil.