فهرست مطالب علی کشاورزی

Each driving cycle designed to monitor fuel consumption, vehicle emissions, and the specific driving patterns of a region is created to facilitate better planning and enhancement of local standards. In this study, the Real Driving Emissions (RDE) method, regarded as one of the most precise techniques for determining driving cycles, was employed. To gather the necessary data, GPS from mobile phones was utilized while traveling on city buses through the streets of Kermanshah. This data collection included recording speed, acceleration, duration of constant speed, deceleration, altitude, number of braking events, and the duration of stops along the route from origin to destination. The research employed the clustering of real city data and the K-means method to derive the driving cycle for Kermanshah’s city buses. Analyzing the collected data reveals that bus drivers in Kermanshah tend to avoid rapid acceleration and high constant speeds, leading to reduced fuel consumption in the city bus cycle, indicating that this behavior is intentional and aimed at this goal.

Pandemic diseases are an integral part of the history of human societies and their long-term effects have always been considered. The outbreak of the Covid-19 disease at the end of 2019 caused economists to investigate its economic effects using different models, which were usually based on partial equilibrium. In this study, with the motivation of understanding the effect of the spread of a pandemic disease and its policy responses on economic and health conditions, the dynamic stochastic general equilibrium model and the new Keynesian perspective have been used. Examining the impulse response functions of the variables to the health shock caused by the Covid-19 outbreak indicates a decrease in employment hours, production, consumption, investment, health status and an increase in inflation. In response to these conditions, the increase in public health expenditure leads to a faster convergence of macroeconomic variables to their steady-state values. According to the results of the simulation, it is suggested that the governments use the experiences related to the first wave of the disease outbreak and equip themselves with the necessary tools to use them during the temporary social quarantine (such as the ability to conduct tests on a large part of the population). The ability to identify infected people and impose personal quarantines instead of compulsory quarantines will reduce stagnation. Another solution to control a pandemic is to vaccinate the mass population to achieve herd immunity. All of these require increased public health spending.

The evidence and results of the studies indicate the profound effects of epidemics such as the Covid-19 disease on the economy of countries. In this study, with the motivation of understanding the effect of the spread of epidemic diseases (with an emphasis on the Covid-19) on the economy and the government's policy responses to the dynamics of the macroeconomic variables of Iran, a dynamic stochastic general equilibrium (DSGE) model and the New Keynesian (NK) perspective were used. Unlike computable general equilibrium models, DSGE models are in a random environment, and since the duration of the virus's spread and its impact on the economy are unknown, it is more appropriate to use DSGE models (Yang, Zhang and Chen, 2020). In order to achieve the goals of Jazer's studies, in the first step, a DSGE model based on NK was designed and the effect of an epidemic disease on the macroeconomic variables of Iran was simulated. The designed model was quantified with a three-month (seasonal) frequency and using the data of Iran's economy (2004:2-2021:1).

The results showed that increasing the risk of health disaster by one standard deviation gradually causes a decline in health status. After that, in order to improve the health status, the quarantine hours were increased, which means an increase in investment in health. On the other hand, since more hours are allocated to quarantine, the hours of employment will decrease and subsequently the final productivity of physical capital will decrease, which is due to the complementarity of labor and capital in Cobb-Douglas production function. Finally, labor income and capital income also decrease. Therefore, production, consumption, and investment fluctuate significantly, and this comes from the optimal choice of the household in the face of this impulse. As a result, as the health status declines and consumption declines, the level of well-being declines (like the result of Yang, Zhang, and Chen, 2020). Over time, the lack of physical capital causes an increase in physical investment and working hours, and finally they slowly return to their previous stable level. In the second step, by applying a change in the AR(1) equation of public health expenditure, the effect of the government's financial reaction on the macroeconomic variables of Iran's economy in the face of the health shock was evaluated. In the base scenario, the government has no intervention in the economy and the state of fiscal inactivity is considered for the government. In another scenario, the active presence of the government, or in other words, the design of discretionary financial policy, affects the economy.

The results showed that the design of a discretionary financial policy in the form of increasing public health expenditure in the context of an epidemic has led to a faster convergence of macroeconomic variables to their stable conditions. In justifying the results, it can be stated that in the face of the outbreak of an epidemic, with the increase in public health expenditure and the subsequent increase in quarantine hours, the employment hours have decreased less. On the other hand, the increase in public health expenditure and the subsequent improvement of health leads to an increase in the productivity of the labor force through the increase in the life expectancy of a person as well as the length of working life. This has led to an increase in household income, followed by an increase in the level of consumption and investment. Also, an increase in public health expenditure leads to an improvement in health status. As a general result, the government's financial reactions in the face of the impulse of an epidemic disease lead to a faster convergence of most variables to their stable conditions in the Iranian economy.

Soil salinity is one of the most important environmental problems, especially in arid and semi-arid regions. Soil salinity is caused naturally and/or by humans. High soil salinity negatively affects crop growth and productivity and ultimately leads to land degradation. Monitoring and mapping of soil salinity Due to the serious problems of spreading this issue to regional ecology, food security and agricultural development in the early stages, it is necessary to implement an effective soil rehabilitation program to prevent and reduce soil salinity. Remote sensing science performs better than traditional methods for assessing soil salinity and offers fast and cost-effective techniques for monitoring and mapping soil salinity. Soil salinity can be identified using direct indices that are related to the properties of surface soil salts as well as indirect indices. The aim of this research is to review the challenges of selecting appropriate indices in soil salinity studies through the investigation of researches conducted in the field of soil salinity and spectral indices used in soil salinity cases, which are helpful in land management at the regional scale. It is worth noting that in this regard, the most common vegetation and salinity indices used to detect and mapping of soil salinity were discussed. Many researchers have used different remote sensing indicators to map soil salinity. Among these, BI Brightness index, SI salinity index, NDVI normalized differential vegetation index and NDSI normalized differential salinity index showed the highest correlation between data obtained from satellite images in salinity-affected soils. Choosing the most appropriate band or indices depends on the soil conditions, geographical area, climatic conditions, satellite data, physiography of the area and the type of land use.

The coronavirus outbreak in December 2019 quickly became a public health crisis, first affecting the Chinese economy and then the global economy. This issue led to the use of various models to modelling the prevalence of the virus. The present study is aimed to understand the effect of a pandemic outbreak on Iranian’s economy within the framework of a Dynamic Stochastic General Equilibrium model. To do this, after calibrating the parameters based on the quarterly information of Iran's economy during the period 1991-2016, in different scenarios and according to the persistence of health disaster risk, model simulation was performed. The results showed that the outbreak of a pandemic reduced working hours and consequently the marginal productivity of physical capital; Thus physical investment, production, and total consumption are reduced. Based on the research findings, it is recommended that the government, as a policy-maker, play a stabilizing role under pandemic disease conditions

The outbreak of the corona virus is primarily a threat to public health, but it is increasingly becoming an economic threat that requires a decisive and coordinated policy response. Following the outbreak, the country's monetary and financial authorities have taken a wide range of measures, such as closing educational centers, shopping malls, lending to the vulnerable, importing drugs, delaying loan repayments, etc. to limit the spread of the virus. The aim of this study is to investigate the effect of expansionary monetary policy during the outbreak of a pandemic disease. For this purpose, after calibrating the parameters based on the quarterly information of Iran's economy during the period 1991: 03: 01- 2021: 03: 20, in different scenarios and according to the persistence of health disaster risk, the model is simulated. The results show that the outbreak of a pandemic disease reduces working hours and consequently the marginal productivity of physical capital; thus physical investment, production, and total consumption are reduced. In addition, the implementation of an expansionary monetary policy during the outbreak reduces the severity of the recession, but is accompanied by a decline in household health. Therefore, it is recommended that public vaccination be included in the agenda as a necessary measure.

“Appealing” to God’s friends (Avlia-allah) is Shrek (ascribing partner to Allah)! Why do you go to the Imams pilgrimage? These are some questions every day are being asked from Shi’a. But by whom? Why? Such suspicions have been raised by the enemies of religion and by the use of ignorant religionists such as Wahhabism and the intent to eradicate pure Islam. Their suspicion is based on two things: 1) It is wrong to put someone in the middle between yourself and God. 2) If it is possible to appeal to the living person, it is not possible to appeal to the dead. Replying to the first question: The Qur’an itself commands the use of a means to draw near to the Almighty God. It is used the word “means” generally and mentioned examples of prophets and their followers who used the appeal. Like the descendants of Prophet Jacob who made the Father forgive their sins. Replying to the second question: The friends of God are not dead, because the Qur’an states that martyrs are alive; and appealing to the God’s friends is appealing to the living people, not dead. The appealing is elixir, making the world and the hereafter of humans in the most perfect way. The appealing is worthless, and therefore has been attacked by enemies and opponents. And yet, if something was not worthwhile or influential, it would not have received the attention of both friends and foes.

Iran is one of the countries with the highest production of greenhouse gases in the world, which according to estimates is a significant part of these effects are related to agricultural activities. There are various methods for assessing the environmental impact of agricultural activities. Life cycle assessment is one of the methods for assessing the effects of sustainability that has been developed based on the product production process. Existing methods for assessing the effects of life cycle and determining the effects of agricultural activities are determined by classifying and modeling the evaluation and possible changes in soil quality indicators as a result of agricultural activities in the field. The main purpose of this study is to investigate the environmental effects of the forage maize production system using the Life Cycle Assessment (LCA) method in order to better manage and control these effects.

The study area is an educational farm of the University of Tehran with an area of 260 hectares. The required information was collected through interviews with field experts. The amount of inputs used and the emission of pollutants in several groups of effects including global warming, eutrophication, acidification, surface water poisoning and ozone depletion, classification per functional unit (one ton of forage corn) are determined and their effect on the exchange life cycle. Life cycle evaluation calculations were performed by Sima Pro software.

The results showed that (1): The most environmental degradation due to forage maize production is related to surface water pollution with a value of 1.94×10-13 kg, 1,4-DBeq that chemical fertilizers and irrigation have the most effect on this pollution 1.33×10-13 and 4.96×10-14, kg 1,4-DBeq, respectively: (2): The value of the environmental index is 2.19×10-13 points or 0.219 picopoint. It was calculated that the normalized values of the effect groups are due to the production of fodder corn, which is calculated by multiplying the total amount of contamination of each effect group by the normalization and weighting factors, specific to each effect group. The lower the value and the closer it is to zero, the less environmental impact of the product is less.

Using different methods of crop management such as the use of organic inputs, rotation, nitrogen-fixing plants, and tillage, at least based on the using of low input principles to reduce these environmental effects and also by selecting appropriate methods of irrigation yield and optimal crop yield management environmental degradation reduced these operations. The solution that can be proposed and implemented to reduce the effects of this operation is to use different methods of crop management such as the use of organic inputs, rotation, nitrogen-fixing plants, and tillage at least, based on the use of minimizing principles to reduce these environmental effects. By selecting appropriate methods for irrigation and optimal management of water consumption while increasing crop yield, the environmentally destructive effects of this operation were reduced.

The purpose of this article is to understand the effect of the outbreak of infectious diseases on the economy and also to analysis the role of government under pandemic crisis conditions. For this purpose, a dynamic stochastic general equilibrium model is used. After calibrating the parameters based on the quarterly information of Iran's economy during the period of 1991-2016, in the baseline scenario, it is assumed that the government has no involvement in the economy; In other words, the government pursues a state of fiscal passivity and shows no fiscal reaction to the change in endogenous variables after the outbreak of pandemic disease. Subsequently, in other scenarios, the government reacts fiscally to the outbreak of the pandemic, given the different conditions of production and public liability. The results of the study of fiscally active scenarios compared to the state of fiscal passivity indicate that the effect of government expenditures shock as a standard deviation on macroeconomic variables under pandemic disease conditions, has led to much less feedback.

Soils are the largest carbon pool in terrestrial ecosystems, which account for the greatest amount of the ‎global total terrestrial carbon stocks‏.‏‎ Accurate mapping of Soil Organic Carbon (SOC) spatial distribution ‎is a key assumption for soil resource management and environmental protection.‎‏ ‏The rapid ‎development of remote sensing and application of satellite images provide an excellent opportunity to monitor large-scale SOC storage.‎ Estimating SOC is one of the research topics that artificial neural networks are applied for this purpose in some studies, although parameter optimization is difficult. In previous studies, genetic algorithms have been used to optimize the artificial neural network initial weights and improve the prediction of the output variables. However, the effectiveness of this method in estimating the SOC by remote sensing has been less studied. In this study, the effect of genetic algorithm on artificial neural network training to predict SOC on Sentinel-2 satellite images in Arasbaran vegetation zone was investigated.

For this purpose, soil sampling was performed using stratified sampling method at 46 points at a depth of 0 to 15 cm. SOC was measured by Walky-Black titration method. In order to fit the model between the measured organic carbon in the laboratory, 9 spectral indices and three bands of satellite image, and two methods were used namely, artificial neural network and artificial neural network optimized by genetic algorithm. Cross validation was used to evaluate the models efficiently. Finally, the precision of the obtained models was evaluated with statistical indices of Root Mean Square Error (RMSE), Ratio of Performance to Deviation (RPD), Spearman's correlation coefficient (r), coefficient of determination (R2), and paired sample t-test.

The results showed that the precision of SOC estimated by artificial neural network optimized by genetic algorithm (RMSE = 1.07%, RPD = 1.89%, R2 = 0.76) was higher than artificial neural network results (RMSE =1.51%, RPD = 1.34%, R2 = 0.58). Also Spearman correlation coefficient for SOC estimated with optimized artificial neural network (r = 0.87) was higher compared to estimated SOC with artificial neural network (r = 0.76). Observed SOC was not significantly different from SOC estimated by optimized artificial neural network (p-value=0.21) while it was different from estimated SOC by artificial neural network (p-value=0.02). In addition, the results showed that TSAVI index had the highest Spearman correlation coefficient (0.565), and BI2 index had the lowest Spearman correlation coefficient (0.196) with soil organic carbon.

Generally, it was concluded that the use of genetic algorithm in the selection of artificial neural network parameters improved the performance of this modeling method in estimating soil organic carbon on Sentinel-2 satellite images in the study area. Also the performance of Sentinel-2 satellite images in estimating soil organic carbon in the study area was validated.

Digital soil mapping plays an important role in upgrading the knowledge of soil survey in line with the advances in the spatial data of infrastructure development. The main aim of this study was to provide a digital map of the soil family classes using the random forest (RF) models and boosting regression tree (BRT) in a semi-arid region of Ilam province. Environmental covariates were extracted from a digital elevation model with 30 m spatial resolution, using the SAGAGIS7.3 software. In this study area, 46 soil profiles were dug and sampled; after physico-chemical analysis, the soils were classified based on key to soil taxonomy (2014). In the studied area, three orders were recognized: Mollisols, Inceptisols, and Entisols. Based on the results of the environmental covariate data mining with variance inflation factor (VIF), some parameters including DEM, standard height and terrain ruggedness index were the most important variables. The best spatial prediction of soil classes belonged to Fine, carbonatic, thermic, Typic Haploxerolls. Also, the results showed that RF and BRT models had an overall accuracy and of 0.80, 0.64 and Kappa index 0.70, 0.55, respectively. Therefore, the RF method could serve as a reliable and accurate method to provide a reasonable prediction with a low sampling density.

In the present study, mid-term variations in soil quality were studied on the farm scale. The studied area was a research-educational farm of the Agricultural and Natural Resources Campus of University of Tehran covers 260 hectares, with thermic soil temperature and aridic soil moisture regimes, respectively. In this study, the process of improving or destruction of the integrated soil quality index at two depths (0-20 and 20-40 cm) in years of 1379 and 1396 was compared. The minimum data set was determined using the method of two-phases PCA. Definition of standard scoring functions and non-linear fuzzy membership functions were performed through programming in the MATLAB software environment. Covariance-correlation method was used for weighing the selected variables in determining Soil Quality Integrated (SQI) index. The results of this study showed that in most of the adaptive areas of the soil, the incremental and decreasing pattern of the soil quality index in the first and second depths of the previous and present studies were consistent and in some places, this pattern was almost constant. In profiles No. 1, 5, 11 of the previous research which include 27.3% of the samples, and in profiles No. 1, 5, 7, 9 and 18 of the present study which include 20.83% of the samples, the soil quality index at the first depth was less than the one in the second depth. Classification of the soil quality classes for all samples in both years revealed a reduction in soil quality index. So that the soil quality reduced from grade III (45%) and IV (54%) to grade IV (73-75%).

In digital soil mapping, soil characteristic and classes could be extracted truly by numerical and quantitative modelling. Hence, derived rules could be fitted to similar regions for achieving ruled relations on areas without soil information which is called as extrapolation. In the present study, achieving digital soil class map of an area without adequate soil information by Random forest was tested by extrapolation at great group level. The results show overall accuracy 88% and kappa 0.77 of donor area which is able to fit over its similar region. Results of extrapolation show overall accuracy 81% and kappa 0.61 of recipient area which could show logical concordance to produce soil class map of recipient area without applying related soil samples of this area in short time and low cost. Since, extrapolation could be as an efficient way to predict soil classes of unknown areas.

Knowledge of the soils capacity to utilize them in the form of different major uses in agricultural ecosystems and natural resources requires the accurate recognition of the physico-chemical and biological properties of the soil, ensuring the sustainable use of the lands. Therefore, this research was conducted to evaluate the soil quality using quantitative models in the Kane Sorkh lands of Mehran as a part of arid and semi-arid lands of Ilam province (Iran). Soil samples were taken from the depth of 0-30 cm and weighted average for the depths of 30-100 cm of the soil profile. Physicochemical properties were measured based on the standard methods. Using principal component analysis (PCA), among a total 16 soil characteristics (TDS), eight properties were selected based on the eigen values and the correlation coefficient as the least properties affecting soil quality (MDS). Then, soil quality assessment was performed using two models of integrated quality index (IQI), Nemoro Quality Index (NQI), and each in two sets of TDS and MDS. The results showed that the top layer (0-30 cm) have a moderate quality class and the sub layer (30-100 cm of the profiles) have poor quality with severe constraints. According to the results, the most important factors limiting soil quality were organic matter deficiency, high percentage of calcium carbonate, gypsum, gravel and soil salinity. Determination coefficients (R2) for the top and sub-layer samples between the two TDS and MDS sets in both IQI and NQI models were 0.79 and 0.79, 0.95 and 0.94, respectively, indicating the reliability of using MDS set instead of TDS in both models. Therefore, the use of most effective soil properties in the soil quality assessment studies is not only time consuming but also it is cost-effective for sustainable monitoring of agricultural land use.

Outage is one of the most problematic phenomena for many people who have affected millions of people in the world. The purpose of this study was to investigate educational identity with negligence of sixth grade girl students in the Kherameh city. To this end, 100 students were selected by available sampling method. This phenomenon is one of the most important causes of failure or failure of learners. The results showed a significant and negative relationship between successful academic identity and academic procrastination and there was a positive and significant relationship between confused academic identity and late academic identity with academic lag. Considering the brief explanations and reviews provided by various approaches to neglect, and the results of data analysis, it can be concluded that today, in order to address the problem of student negligence, attention should be paid to factors associated with this phenomenon. And tried to fix them. As far as the theoretical discussion is concerned with empirical and precise studies, more promising findings are obtained.

Limitations in conventional soil identification methods and the advances made in information technology in soil science have attracted more attention to new approaches to soil mapping in order to improve the purity of soil maps. The present study was carried out in a part of Ilam province to identify and prepare soil maps of this region. At first, 46 profiles were identified. Then, based on the morphological characteristics of each profile, soil samples were taken from all genetic horizons and analyzed for chemical and physical properties. Then, the soils were classified based on the Soil Survey Staff keys (2014). A multinomial logistic regression model was used for spatial prediction of soil taxonomic classes. The geomorphometric features were extracted from digital elevation model with a resolution of 30 m2 by SAGAGIS2.2 software. The classification results of each soil control profile in the studied area showed that, in general, the soils were in three order categories: Mollisols, Inceptisols, Entisols, and six classes at the family level. The correlation between the features of digital elevation model showed that the parameters of the mid slope position, spatial solar radiation, index of moisture content, ground roughness index, surface curvature, and profile curvature had the most effect on the formation of soil family classes. The overall accuracy and Kappa index of spatial prediction map from the regression model was 60% and 0.38 at the familial level, respectively. Finally, the results of this study showed that geomorphometric variables had a significant influence on the prediction of soil classes. Therefore, it is suggested that in future studies, other covariates derived from remote sensing data should also be used to improve the quality and accuracy of soil maps.

Soil is known as a dynamic media so it easily degrade with inapplicable usage so with increasing in degradation of this limited source, the world’s food safety would be in danger. Thus, applicable and sustainable usage of agricultural lands are become an essential and inevitable agenda. Therefore, the aim of this study is to Digital soil mapping using decision tree for agricultural land suitability, In order to constitute management programs for sustainable use of agricultural lands. For this aim, samples were collected based on cLHS and after some laboratory experiments, modeling and digital soil mapping were created by Random Forest Model. Also, agricultural land suitability for dominant crops were investigated by parametric method. The results showed that the land evaluation for irrigated wheat with surface irrigation 75.27% of the total area S2 class and 24.73% of the land in the class S3, respectively. In assessing the suitability of land for Maize irrigation, 14.78% of the land in classes S1, S2 84.82 of class and 0.39% of the land in the class S3, respectively. Results for alfalfa irrigation land evaluation showed that 11.10 percent of the land in classes S1, 88.49% in the S2 class and 0.4% of the class S3, respectively

Today, there is great demand for accurate soil information to determine the relationship between soil and landscape, and easy updating of soil maps has increasingly gained importance. Digital soil mapping techniques can be cost-effective solutions to obtain information dealing with the soil types over large areas. The objective of this study was to provide a digital soil map in the Abyek region of Qazvin province using random forest model for management purposes and sustainable land use planning. To this end, soil samples were collected based on cLHS. After the laboratory analysis, using random forest model and auxiliary variables derived from a digital elevation model with a spatial resolution of 30 m and Landsat 8 imagery, the modeling and preparation of regional soil maps were performed. Out of 7261 ha, the dominant soil was classified as Loamy-skeletal, mixed, superactive, thermic Typic Calcixerepts. The results showed that soil modeling using random forest algorithm could accurately predict (Kappa coefficient ~ 0. 83) soil classes in the region.

Data mining enables generalization of data of soil to remote areas and which is able to up/down scale of data in wide ranges of level that facilitate the decision-making process of executives. Cation Exchange Capacity (CEC) is one of the most important parameters in soil database and shows the ability of a soil to retention of minerals and pollutants. Due to low organic matter and specific mineralogy of soils in arid and semi-arid regions, measurement of CEC is time consuming and expensive. The objective of this study was to evaluate Coactive Neuro-Fuzzy Inference System (CANFIS) in prediction of CEC in soils of arid and semi-arid regions. A total of 85 soil samples from target area were selected among 440 soil sample database (available reference database) with a ratio of 1:5. Correlation test was conducted to assess the co-linearity of independent variables. Forward regression model was used to determine the most important and influential input parameters on the output results. The results indicated the reliability and high performance of the CANFIS approach in estimation of CEC using easily measurable characteristics, organic material, and satellite images.

Determination of soil fertility is very important to measure fertilization. Regardless of the level of soil fertility، the improper use of chemical fertilizers، not only the quantity and quality of agricultural products would not rise up، but also it makes imposed additional costs، disturb the balance of nutrients in the soil and environmental issues are also raised. Therefore، the assessment and mapping of soil fertility is essential. In this research، the fuzzy technique and the Analytic Hierarchy Process (AHP) were used for mapping soil fertility into Geographic Information System (GIS). The amount of available soil phosphorus and potassium، total nitrogen، soil organic carbon، pH and cation exchange capacity (CEC) obtained form 77 surface soil samples were used as the input data. In the first stage، the interpolation of data was done by Inverse Distance Weighting (IDW) model into GIS. Then a fuzzy membership function was defined for each factor according to Sys guideline. Finally، the map of soil fertility was prepared by using AHP technique into Expert Choice software with an inconsistency ratio of 0. 07. The results showed that soil organic carbon has the greatest impact on the soil fertility in the study area and for wheat cultivation، most area were classified into group of moderate suitable.

In the course of the present study, the results of parametric approach are compared with those obtained through fuzzy approach using AHP as well as multivariate regression methods for comparisons and investigating the efficiency of the fuzzy method as applied in land suitability assessments. The correlation coefficients between land index and observed yield in the study area through fuzzy approach plus AHP (r=0.91*) and multivariate regression methods (r=0.88*) are more than those in the parametric approach (r=0.85*). Although, the fuzzy set approach offers a higher accuracy and better results for land suitability assessment, the most critical issue in its application to land suitability assessment remains as the choice of membership functions, cross-over points and weight values as regards different land characteristics.

In the present study، Analytic Hierarchy Process method was used for weighting different assessed criteria for land suitability of irrigated wheat and the results obtained from fuzzy methods compared with those from conventional methods. Comparison of fuzzy and parametric land indices showed that fuzzy approach has increased the land indices in all of land units. The correlation coefficient between land index and observed yield in the study area for fuzzy approach based on AHP method and parametric approach were 0. 91 and 0. 87، respectively. Although، the fuzzy set approach offers the higher accuracy for land suitability assessment، the most critical issue in its application to land suitability assessment remains the choice of membership functions، cross-over points and weight values for different land characteristics.

Investigation of soil hydraulic properties like soil moisture retention curve and unsaturated hydraulic conductivity plays an important role in study of environmental researches in which their spatial and temporal variability led to development of indirect methods in prediction of these soil characteristics. Therefore، in this study indirect methods have been used in order to estimate surface fractal dimension to predict soil moisture curve. One parameter linear and nonlinear regressions were developed and compared to artificial neural networks by using readily available parameters like soil clay content، water content at permanent wilting point، cation exchange capacity and soil porosity. In the training step of regression analysis and neural networks، 97 measured soil samples and in the testing step 24 rest of soil samples with Petersen et al. (1996) database were used. The calculated values of RSE and RMSE showed that neural networks with seven neurons in the hidden layer are able to estimate surface fractal dimension from the easily available parameters more accurate than the other models.

Evaluation of land qualitative suitability of gypsiferous soils for the important agronomic productions of the bardsir area in Kerman province Abstract Bardsir is one of the important agronomic productions areas in Kerman province، which some positions of it are extremely affected by gypsum. This research was performed to study of properties of gypsiferous soils and evaluation of land qualitative suitability for agronomic productions of the area At first، the land maps were studied، then 35 farms were chosen، they had gypsic horizen and were scattered in the total plane. Then one profile in each farm was excavated and described then catch the samples from all horizons. The total gypsum characteristics، using of agriculture instruments and applying of different inputs were studied. Land characteristics for example: salinity، acidity، Sodium Absorption Ratio (SAR)، Cation Exchange Capacity (CEC)، gypsum، lime، texture، structure and climate were used to classification of land suitability and land utilization types like of wheat، barely، alfalfa، maize، sugarbeet and potato were studied. The mean while plant and climate tables were gathered by SYS (1991) and givi (1376). The results show that the maximum and average of soil gypsum was 31% and 12%، it is one of the most production limitations. According to 2003 soil taxonomy، the gypsic and salic diagnostic horizon in aridic order was classified. Parent materials، climate، microrelief (topography)، physiography، and pendant shapes effect the gypsum formation in the studied erea. Acicular crystals and missilium were separated. Investigation of the land qualitative suitability results show that the change range of classes According to Simple limitation and parametric approaches were (S2 to N2) and (S1 to N2) for wheat، barely: (S1 to N2) and (S1 to N2); potato: (S3 to N2) and (S3 to N2); maize: (S2 to N2) and (S2 to N2); for sugarbeet (S2 to N2) and (S2 to N2); alfalfa: (S1 to N2) and (S2 to N2) respectively which the best and worst position were for barely and potato respectively. The researches show that the medium yield of different production in gypsic and non_gypsic soils is different.