فهرست مطالب کمال نبی اللهی

The rapid, accurate, and low-cost determination of soil properties has particularly important for land planning and management. The objective of this study was to evaluate the Vis-NIR spectral reflectance of soils, as a rapid, cost-effective, and non-destructive technique, for estimating some soil properties [sand, silt, clay, pH, and calcium carbonate equivalent (CCE)] by partial least-square regression (PLSR) and bagging-PLSR methods. For this purpose, a total of 220 composite soil samples were collected from 0-20 cm depth in Ghorveh Plain, Kurdistan province, in September 2019. The selected soil properties were measured by standard laboratory methods. The proximal spectral reflectance of soil samples was also measured within the 350-2500 nm range (Vis-NIR) using a handheld spectroradiometer. Different pre-processing methods were assessed after recording the spectra. The results indicated that the R2 values for the PLSR method ranged from 0.58 to 0.76, while the bagging-PLSR produced R2 values between 0.59 and 0.74. The RMSE values obtained for sand, silt, clay, CCE, and pH were 17.43, 7.65, 7.83, 7.94, and 0.66, respectively for the PLSR, and 16.66, 7.63, 8.13, 7.71, and 0.45 for the bagging-PLSR. Based on the ratio of prediction to deviation (RPD) values, the bagging-PLSR model achieved the best performance in predicting sand and CCE. However, for clay and pH prediction, the PLSR model was the most accurate. Both the PLSR and bagging-PLSR models yielded identical predictions for silt content, with an RPD value of 1.53. Overall, the results showed that PLSR and bagging-PLSR models have acceptable accuracy for estimating the proposed properties of the soils.

Electromagnetic methods in applied geophysics are advancing rapidly. Since the TDR system has grown, its use has led to innovative applications and comparisons with other previous measurement methods. A TDR system consists of a radar (electromagnetic) receiver and generator, a transmission line, and a waveguide. The electromagnetic pulse generated from inside the conductor cable moves towards the waveguide and is tested through the waveguide into the environment under test. In the last few years, the use of the TDR system to identify water leakage situations has been expanding. In this article, by performing tests on two-strand telecommunication cables as TDR sensors, the ability and accuracy of the time domain reflectometry method in detecting leakage situations has been evaluated. In this research, the two-stranded cable was buried under GC gravel clay material, and by increasing the percentage of soil moisture stepwise at two points, the sensitivity of the TDR method to the changes in moisture around the cable was investigated. Based on the TDR waveforms, the points of reflection coefficient changes are located at the distances of 9.5-9 and 4.5 meters, which is completely consistent with the actual distance of the test points. In this research, TDR moisture meter made by soil moisture company model 6050x1 was used. The results of this research show that the TDR method has the ability to be used as a monitoring system to detect leakage in dams, dikes and other geotechnical structures.

RothC is among the most successful models in terms of simulating the impacts of climate change and management activities in natural ecosystems on local and global scales. The current research was carried out to assess and validate RothC in studying SOC content from 1990 to 2020 and defining the scenarios of maintaining the current situations (scenario 1), climate change (scenario 2), manure fertilizing till 2100 (scenario 3), and climate change with manure fertilizing till 2100 (scenario 4) in grasslands, range, and croplands in Saral Research Center, Kurdistan Province, under rotations of wheat-uncultivation and wheat-pea. The parameters of the model were determined by using the measured data from the soils sampled at two years (2018 and 2019) from Saral Research Center and the local weather data, Next, the model was validated by comparison of the predicted values with the measured SOC data. Assessment of the measured and simulated data through validation for different land uses revealed that RothC could satisfactorily predict the changes in SOC contents under different fertilizing and climate change scenarios so that root mean square error (RMSE) and the simulation efficiency were calculated as 8.92% and 74.0%, respectively. The simulation results indicated that compared to scenario1, climate change in the studied land uses will cause a decrease in SOC contents till 2100. Manure fertilizing (scenario3) would be the best scenario so that by using this strategy the grassland, with 59.83 ton SOC per hectare until the end of the current century. According to the results predicted by the model, the highest SOCrate (32.9%) and the most change in SOC sequestration (ΔSOCs) (0.16 t/h) during the next 80 years were calculated in the range under scenario 3; whereas the lowest values (-32.0% and -0.11 t/h, respectively), were achieved in croplands under rotations of wheat-uncultivation under scenario2.

Improper use of land resources due to increased human food needs has led to the destruction and reduction of arable land. One way to increase production per unit area is to land suitability assessment. Land suitability assessment is the fitness of a type of land for defined use. Assessing spatial variability of land suitability class is necessary to increase production and prevent land degradation. Determining the land suitability class requires measuring soil, topography, moisture and climate properties, which are costly and time consuming. One solution to this problem is to use learning machines and auxiliary data. Learning machines are used to relate various properties with auxiliary variables to assess their spatial and temporal variability. Random forest learning machine is one of the most common and widely used learning machines. The aim of this study is to assess land suitability based on FAO land suitability framework and parametric method for three important irrigated crops of the region, including alfalfa, potato and irrigated wheat, and to predict their land suitability classes using random forest learning machine and auxiliary data.

122 soil profiles were dug, described and sampled in the Ghorveh area of Kurdistan Province (covers 6500 ha). Soil texture, acidity, organic carbon, CaCO3, gypsum, ESP, electrical conductivity and gravel were measured in all soil samples. Moreover, topography and climate data were also recorded. Environmental variables in this research were terrain attributes, land unit components map, and data of ETM+ image. To make a relationship between land suitability class and auxiliary data, random forest (RF) learning machine were applied and using cross validation method and statistic indices including overall accuracy and kappa index was validated.

The results showed that suitability class of the study area has 37, 41 and 57% N2 class, 21, 34 and 27% N1 class and 48, 19 and 16% S3 class for irrigated wheat, alfalfa and potato, respectively. The major limitations of the study area to plant the crops are included high slope, shallow soil depth, high pH and gravel.To predict land suitability class of alfalfa, potato and irrigated wheat, auxiliary variables including MRRTF index, MRVBF index, wetness index, LS factor, elevation and land unit components map were the most important. The results of this study showed that the random forest learning machine for prediction of land suitability class of irrigated wheat with 0.78, and 0.71, alfalfa with 0.75 and 0.70 and potato with 0.79 and 0.72 for overall accuracy and kappa index, respectively, had a suitable accuracy.

Topography is the most important soil forming factor and is effective in distribution of land suitability class. The study area, because of limitation of soil and topography has low to non-suitable suitability to plant these crops and it is suggested proper land improvement operations to increase production and land sustainability management. Random forest learning machine had suitable accuracy for predicting land suitability class. Therefore, it is suggested to map land suitability class learning machine techniques (such as randomized forest) and auxiliary data such as terrain attributes, land unit components map and satellite images were applied.

Soil quality is one of the most important soil properties which investigation of it's changes is essential to soil management and degradation. Quantifying soil quality using soil quality index to improve understanding of soil ecosystems is have been wieldy used. The soil quality index is calculated by measuring some soil characteristics which measuring these properties is expensive and time consuming. Therefore, one of the solutions is the use of digital soil mapping technique that can digitally predict soil properties using auxiliary data and data mining models. The purpose of this research is using a random forest model and auxiliary data for mapping the soil quality index.

Based on the geomorphology map, 17 soil profiles and 105 auger samples were taken from a depth of 0-20 cm in the Ghorveh area of Kurdistan Province ( covers 6500 ha) and soil texture, organic carbon, cation exchange capacity, electrical conductivity, pH, carbonate calcium equivalent, total nitrogen, available phosphorus, microbial respiration rate, sodium adsorption ratio (SAR), bulk density, and gravel percentage were measured and calculated then the soil additive weighted index was calculated. Environmental variables in this research were map geomorphology, terrain attributes and data of ETM+ image. Geomorphology map was prepared based on zinc method. Terrain attributes (including 10 parameters), soil adjust vegetative index index (SAVI), and normalized difference vegetative index (NDVI), and brightness index (BI) were computed and extracted using SAGA and Arc GIS software, respectively. To make a relationship between soil quality index and auxiliary data, random forest (RF) model were applied and using cross validation method and statistic criteria including coefficient of determination (R2), mean error (ME) and root mean square error (RMSE) was validated.

According to the communality (share of each soil indicator), bulk density, sand, cation exchange capacity and clay had the highest weight (≥ 0.1) and gravel and SAR had the lowest weight (≤0.05) among the soil quality properties. To predict soil quality index, auxiliary variables including slope, SAVI index, wetness index, MrVBF index, LS factor, elevation, NDVI index and geomorphology map were the most important. The results of this study showed that the random forest model with 0.65, 0.042 and 0.062 for determination of coefficient (R2), mean error (ME), and root mean square root (RMSE) had a fairly suitable accuracy for prediction of soil quality index. The soil quality index was ranged between 0.3-0.65 and its mean values in geomorphologic units with low gradient and low soil depth (Mo131, Mo141 and Hi231) were the lowest and in geomorphologic units with low slope and high soil depth (Pi111, Pi311, Pi322, Pi211 and Pi312) were the highest which these differences were statistically significant.

In this research, a randomized forest model was used to study the spatial variation of soil quality index in Ghorveh area of Kurdistan province. The geomorphologic conditions of the study area have affected many soil characteristics and subsequently the soil quality index in the region. The soil quality index content was the lowest in highlands of north, northwest and northeast with high slope and low soil depth. The slope was the most important auxiliary variables to predict soil quality index in the region. Based on the results of statistical indices, random forest model also had relatively accurate estimation of the soil quality index. Therefore, it is suggested to map soil properties podometric techniques (such as randomized forest) and auxiliary data such as geomorphologic map, terrain attributes, and satellite images were applied.

Soil salinity is one of the most important soil properties and it's variability investigation is essential to crop management, land degradation and environmental studies. Soil salinity is measured using electrical conductivity (EC) and estimation of soil salinity contents using experimental methods is expensive and time consuming. Therefore, the collection of information on the spatial distribution of soil salinity in n vast areas requires new inexpensive techniques. Recently, new techniques such as electromagnetic induction, visible - near infrared spectroscopy and remote sensing were applied to measure soil salinity. The purpose of this study is the estimation of soil salinity using visible- near infrared spectroscopy, electromagnetic induction, and remote sensing methods. The study area is located 20 km northeast of Ghorveh city in Kurdistan Province and covers a surface of 26000 hectares. 100 soil samples (0–30 cm depth) were collected and. soil electrical conductivity was measured in a saturated extract. Applied auxiliary data in this study were spectral information of visible - near infrared spectroscopy method, reading of electromagnetic induction method, and ETM+ data of Landsat 8. In the 100 sampling sits, horizontal and vertical readings were read using EM38 and salinity index (SI) and normalized difference vegetative index (NDVI), bright index, and Bands 1, 2, 3, 4, 5, 6 , and 7were computed and extracted using Landsat 8 ETM+ data and Arc GIS software. Moreover, the 100 samples were scanned using spectrometer (model of FieldSpec®3, ASD, FR, USA) with a spectral range of 350 to 2500 nm. To make a relationship between soil salinity and auxiliary data of the three methods, artificial neural network (ANN) model were applied. Finally, soil salinity were estimated using ANN and were validated using cross validation method. Soil salinity contents were low to high (0.23 to 14.47 dSm-1). The highest contents of soil salinity were observed in central regions (low and bare land) and the lowest contents of soil salinity were located in high and range land. Based on sensitive analysis of artificial neural network model, in remote sensing methods salinity index, NDVI index, band 7, and band 3 were the most variables to predict soil salinity. In general, the results showed the most important auxiliary variables to predict soil salinity were spectral information of visible - near infrared range, vertical reading, and remote sensing data, respectively. Soil visible - near infrared spectroscopy method to predict soil salinity had 0.94, 0.27 and 0.64, respectively for determination of coefficient (R2), mean error (ME), and root mean square root (RMSE) and was better compared to the electromagnetic induction, remote sensing although combination of three methods together had the best results to estimate soil salinity. The most important auxiliary data to predict soil salinity in the study area was spectral information of visible - near infrared range. Electromagnetic induction method also is suitable auxiliary data to predict soil salinity and it can recommend as speed, accurate and cheap method to predict soil salinity. Combination of three methods together (electromagnetic induction, visible - near infrared spectroscopy and remote sensing) had the best results to estimate soil salinity. Therefore, it is suggested to predict soil salinity, ANN model and auxiliary data such as spectral information of visible - near infrared spectroscopy method and electromagnetic induction will be applied in the future studies.

Infiltration is the most important soil physical characteristic which its direct measurement is laborious, time consuming and expensive. The purpose of this study is to estimate steady infilterability rate, using Neuro-Fuzzy, Neural Network and Multivariate Linear Regression models. Consequently, steady infilterability rate, was measured using double rings in 100 points in Dehgolan region, Kurdistan Province, Iran. Soil physical (porosity, bulk density, sand, silt and clay) and topography characteristics were measured as readily available properties and used to estimate steady infilterability rate, The data were divided into two sets of terrain (70% of the data) and test (30% of the data). The models based on input type were categorized into type 1 (physical characteristics) and 2 (soil physical and topography characteristics). The results based on mean bias error (MBE), Root Mean Square Error (RMSE), Mean Error (ME), Relative Standard Error (RSE) and Relative Improvement (RI) showed that the Neuro-Fuzzy model (type 1 respectively with statistics 0.24, 2.01, 0.46, 4.04 and 46.65) (type 2 respectively with statistics -0.1, 1.24, 0.23, 1.54 and 58.62) has the most accuracy of steady infilterability rate, estimation. Also was observed using topography data as input together with soil physical characteristics can lead to improvement of the estimation accuracy of steady infilterability rate.

Soil organic carbon is one of the most important soil properties which its spatial variability is essential to crop management, land degradation and environmental studies. Investigation of variability of soil organic carbon using traditional methods is expensive and time consuming. Therefore, one of the ways to overcomethis challenge is using digital soil mapping whichcan predict soil characteristics using auxiliary data and data mining methods. Previous studies have shown that digital elevation model (DEM) and remotely sensed data are the most commonly useful ancillary data for soil organic carbon prediction. Artificial neural network (ANN) is a common technique of digital mapping. The region of Marivan in Kurdistan province is one of the forested areas inIran. In recent decades, due to population growth and the increased need for food, thisforested area has been threatened and some parts are now cultivated. Therefore, accurate mapping of soil organic carbon so as to improve land management and prevent land degradation is necessary. The purpose of this research wasusing ANN model and auxiliary data to mapsoil organic carbon. The study area is located in Kurdistan Province, Marivan (cover 20000 ha). Soil moisture and temperature regimes are Xeric and Mesic, respectively. Elevation also varies between 1280 and 1980 m. The main land use typesarecropland, forestland and wetland. The major physiographic units are piedmont plain, mountain and hills with flat to steep slopes. Using stratified random soil sampling method, 137 soil samples (for the depth of 0-30 cm) were collectedand soil organic carbon were measured. In the current study, auxiliary data were terrain attributes and ETM+ data of Landsat 7. Terrain parameters (including 15 factors), bands 1, 2, 3, 4, 5, 6, 7, brightness index (BI) and normalized difference vegetative index (NDVI) were computed and extracted using SAGA and ArcGIS software, respectively. ANN model was applied to establish a relationship between soil organic carbon and auxiliary data. Finally, soil organic carbon weremappedusing ANN and validated based oncross validation method. Three different statistics was used for evaluating the performance of model in predicting soil organic carbon, namely the coefficient of determination (R2), mean error (ME) and root mean square error (RMSE). Based on sensitive analysis of ANN model, auxiliary variables includingwetness index, index of valley bottom flatness (MrVBF), LS factor, NDVI index, and B3were the most important factors
for prediction of soil organic carbon. The quantities of R2, ME and RMSE calculated for ANN model were0.80,0.01 and 0.67, respectively.Soil organic carbon content ranged from0.26 to 8.45 % and the highest contentwasobserved in forestland with hill and mountain physiography and wetland around the lake. It is noteworthy that the differences fordifferent land uses were not statistically significant. Auxiliary data including wetness index, index of valley bottom flatness, LS factor, and B3 in different land uses had statistically significant difference (p<0.05) indicatinga closerelationship between auxiliary data and soil organic carbon. MrVBF and wetness index were lower and higher in forestland and wetland, respectively. Conversely, LS factor was higher and lower in forestland and wetland, respectively. Band 3 was lower in cropland and wetland compared to in forestland. NDVI index was also insignificantly higher in forestland compared to in cropland and wetland. In this research,ANN model was used to investigate the spatial variability of soil organic carbon in Marivan, Kurdistan province. The highest content of Soil organic carbon was foundin forestland and wetland. NDVI index was the most important auxiliary data to predict soil organic carbon within ourstudy area. According to the values of statistics, ANN accurately estimated the soil organic carbon. Therefore, employingpedometric techniques such as ANN model, auxiliary data of terrain attributes and satellite images to digitally mapsoil properties and updateold maps is recommendable. Further studies are needed to compare these results withdirect measurements of soil organic carbon.

Due to the expansion and application of technology in soil and rock mechanics, engineering geology and geotechnics, it is possible to use newer, faster and cheaper methods in these fields every day. TDR technology is one of these methods. This technology has been developed by the telecommunication and electricity industry to determine the location of cable faults. The radar is a prime example of the TDR. The basis of this technology is the sending of a short pulse of energy (electromagnetic pulse) that is reflected in the collision with some anomalies. The point location of these anomalies can be determined if the velocity and direction of the pulse can be determined. The electromagnetic pulse reaches the waveguide through the conductor cable and enters the test environment. In this research, this method was used to determine the groundwater level in the Darian dam. The TDR has the ability to detect the air-water interface, and the reported results are consistent with the measured data using traditional methods. Also in one of the situations that are prone to slide, coaxial cable was installed as TDR sensor. The results showed that the TDR recognizes the shearing and stretching zones in the cable as the result of the ground movement successfully. Finally, since manual processing of TDR data was very difficult and requires high accuracy and time, a method for computer processing of this data was provided by the authors.

Soil quality is one of the most important factors to assess soil management. Therefore, recognition of all soil quality properties such as physical, chemical and biological is essential. Forest degradation and land use change effect on soil properties variability and led to decrease soil quality factors on soil quality. Moreover, soil characteristics also are related to slope position. The region of Marivan in Kurdistan province is one of the forested areas of Zagros which in recent decades, due to population growth and the increased need for food, has been threatened and some parts are now cultivated. The aim of this research is assessing the effect of forest degradation and slope position on soil quality and evolution in west of Kurdistan Province.
Eight soil profiles in different slope position (shoulder, back slope, foot slope and toe slope) of two adjacent hill slope, under land uses of cropland and forest (uniform condition) were dug and described. Moreover, in each land use three soil samples were taken from depth 0-20 cm in each slope position. Properties of soil texture, bulk density, particle density, fine sand, organic carbon, cation exchange capacity, field capacity moisture, permanent wilting point moisture, electrical conductivity, pH, carbonate calcium equivalent, total nitrogen, available phosphorous, available potash infiltration rate, microbial respiration rate, porosity, available moisture sodium adsorption ratio (SAR) and erodibility were measured and computed.
The results showed low slope positions (toe slope and foot slope) had higher contents of clay, organic carbon, available moisture, fine sand, silt, total nitrogen, available phosphorous, available potassium, CEC and microbial respiration rate and lower contents of electrical conductivity, soil erodibility, pH and SAR compared to high slope positions. Soils formed in low slope positions had higher depth and evolution compared to high slope positions. The results also showed two land uses (cropland and forest) in relation to bulk density, porosity, silt, clay, carbonate calcium equivalent, fine sand, pH, organic carbon, total nitrogen, microbial respiration rate, infiltration, soil erodibility and available moisture had significant difference and land use change of forest land to cropland has been led to degradation of Mollisols. Therefore, soil properties are dependent to slope position and land use kind and these factors have affected soil properties and evolution.
The results showed forest degradation has led to decrease of soil quality using significant decreasing of organic carbon, microbial respiration, total nitrogen, CEC, porosity, infiltration and available moisture and significant increasing of bulk density, pH, SAR, fine sand, soil erodibility, and silt. Forest degradation and land use change also due to cultivation led to decrease organic carbon content and soil structure degradation of Mollic horizon. Therefore, Mollic horizon has converted to Ochric horizon and Entisols and Inceptisols have formed in cropland land use. Moreover, the results showed different slope position effect on bulk density, sand, silt, clay, infiltration, erodibility, available water, pH, organic carbon, carbonate calcium equivalent, microbial respiration rate, nitrogen, phosphorous, CEC and potassium and have significant difference. These results show current management of studied land effect on soil quality and led to land degradation. Therefore, soil conservation of steep area using prevention of deforestation in Marivan forests and use of land based on their capability to conserve of soil and land quality is essential.

Soil erodibility is one of the most important soil properties which investigation of its spatial variability is essential to crop management, land degradation and environmental studies. Therefore, information about spatial variability of soil erodibility has important role to modeling of water erosion. Investigation of variability of soil erodibility using traditional methods is expensive and time consuming. Therefore, one of the ways to solve this challenge is using digital soil mapping that digitally can predict soil characteristics using auxiliary data and data mining models. The aim of this research is using artificial neural network (ANN) and random forest (RF) models and auxiliary data to make soil erodibility map.
Using stratified random soil sampling method 100 soil samples in depths 0-30 cm of Dehgolan soils, Kurdistan Province (covers 48710 ha) were taken and soil texture, fin sand, infiltration, soil structure and soil erodibility (using Wischmeier and Smith equation) were measured and computed. Auxiliary data in this study were terrain attributes and Landsat ETM data. Terrain parameters (include 15 parameters) and clay index (SI) and normalized difference vegetative index (NDVI) were computed and extracted using SAGA and ArcGIS10.3 software, respectively. To make a relationship between soil erodibility and auxiliary data, RF and ANN models were applied and were validated using cross validation method. Finally, soil salinity map were made using better model.
To prediction of soil erodibility, auxiliary variables include wetness index, Multi-resolution Valley Bottom Flatness (MrVBF), slope, clay index, NDVI index and B7 were the most important. The results of the study showed that two models (0.80, 0.003 and 0.021 for ANN and 0.76, 0.005 and 0.024 RF for determination of coefficient, mean error, and root mean square root, respectively) were closely matched to predict soil erodibility. Soil erodibility content ranged between 0 to 0.05 t h MJ-1mm-1 and the highest its contents were observed in southern high regions with high slope and low vegetation. In slope class > 10 % soil erodibility was higher than other slope classes. Slope class > 10 % also had the lowest contents of auxiliary data including Wetness index, MrVBF, clay index and band 7 and the highest content of NDVI index .
In this research to investigate spatial variability of soil erodibility ANN and RF models was used in Dehgolan region, Kurdistan province. Soil erodibility content was higher in slope class > 10 % compared to other slope classes. NDVI index was the most important auxiliary data to predict soil erodibility of the study area. ANN and RF also based on the results of statistics indices including determination of coefficient, mean error, and root mean square root (0.80, 0.003 and 0.021 for ANN and 0.76, 0.005 and 0.024 for RF) had accurate estimation of soil erodibility. It is suggested using pedometric techniques such as ANN model and auxiliary data of terrain attributes and satellite images to digital mapping of soil properties and updating old maps. It is suggested also direct measurement of soil erodibility and its result will be compared to this study.

Soil salinity is one of the major problems in arid and semi-arid area. In this condition, soluble salts accumulate in the soil surface and reduce yield and soil fertility. Soils survey and mapping can help to improve these soils. The investigation of variability of soil salinity using traditional methods is expensive and time consuming. Therefore, one of the ways to solve this challenge is using digital soil mapping that soil characteristics were mapped using auxiliary data. The aim of this research is using tree regression (TR) and artificial neural network (ANN) models and auxiliary data to prepare soil salinity map.
Using Hypercube soil sampling method, 100 soil samples in depths 0-30 cm of Ghorveh soils, Kurdistan Province (covers 30000 ha) were taken and soil electrical conductivity was measured. Auxiliary data in this study were terrain attributes and Landsat 8 ETM data. Terrain parameters (include 15 parameters) and salinity index (SI) and normalized difference vegetative index (NDVI) were computed and extracted using SAGA and ArcGIS software, respectively. To make a relationship between soil salinity and auxiliary data, TR and ANN models were applied and were validated using cross validation method. Finally, soil salinity map were made using better model.
To predict soil salinity, auxiliary variables include salinity index, wetness index, index of valley bottom flatness, NDVI index, Band 3, and Band 7 were the most important. The results of the study showed that ANN model (0.70, 0.036 and 0.190, respectively for determination of coefficient, mean error, and root mean square root) has more accuracy compared to TR model to predict soil salinity. Soil salinity content ranged between 0.23 to 6.93 dSm1 and the highest content of soil salinity located in central regions (lowland and bare land). In these central regions, auxiliary data include salinity index, index of valley bottom flatness, wetness index, band 7 and band 3 had the highest values and NDVI index had the lowest values.
Salinity index is the most important auxiliary data to predict soil salinity of the study area. Strong link between soil data and auxiliary data can impact on the accuracy of the model. In general, the results showed that pedometrics techniques in a wide range can be used for digital mapping of soil properties. It is suggested ANN model and auxiliary data such as terrain attributes and satellite images were applied to prepare map of soil properties in future studies.

In the recent decades, application of geostatistic for mapping salinity and sodicity of soil and investigation of their changes has developed. The purpose of this study is to use Indicator Krijing to make probability maps of soil salinity and sodicity. In order to do this, in 178 points of the study area, 356 soil samples from two depths, i.e. 0-30 and 30-60 cm, were taken in Ghorveh soils, Kurdistan Province, using hypercube method. Then, electrical conductivity, pH, Na, Ca, Mg, and Sodium Adsorption Ratio characteristics were measured. Using Indicator Krijing, probability maps of soil salinity and sodicity were prepared for both depths based on two threshold indices of 4 dS/m for salinity and 13 (mmoll-1)0.5 for SAR. Results showed that probability maps of 0-30 cm depth for soil salinity and sodicity, respectively, with 0.53 and 0.94 Kappa index, had moderate and excellent accuracy levels, while in 30-60 cm depth, with 0.64 and 0.8 Kappa index, respectively, had good and excellent accuracy levels. Central part of the area had higher probability of salinity and sodicity compared to the other parts. This part of the area had lowland physiography and somewhat unsuitable water table near the soil surface.

Although a better understanding and quantitative knowledge of digital elevation model scale will help to improve soil predictions, the influence of pixel size has not been investigated in detail. The aim of this study was to investigate the role of spatial scale on soil clay content prediction by empirically testing the interaction between pixel resolution and window size with regression tree model. In two different areas in terms of their geomorphology and soil (area 1, Maybod located in Yazd province covered 400 km2; area 2, Iasokand located in Kurdistan province covered 400km2), 120 surface soil samples (0-30 cm) were taken and their clay contents were measured. From 121 digital elevation models representing different scales, 22 attribute were extracted and used for soil clay content prediction. Results showed that Maybod area had the minimum RMSE (9.0%) and maximum R2 (0.47) and dependence of tree model on pixel size was significant for clay prediction[H1] ; however, in Iasokand area, the minimum RMSE (5.65%) and maximum R2 (0.77) were obtained and window size was significant for clay prediction.

Non-scientific and unconscious changes in land use have negative impacts on ýthe soil physical and chemical properties. In this research, the effect of land use ýchanges on some soil physical and chemical properties was investigated. For this propose, three sites in three ýýýadjacent land use ýtypes including pasture, forest andþ þagriculturalþ þlands were randomly selected in region of Baneh, ýýýýýKurdistan ýprovince. Disturbed and undisturbed soil samples were collected from three sites at each of the different land use types from depths of 0–15 and 15–30 cm ýand were analysed in the laboratoryý. The results indicated that land use changes have no significant effects on the percentage of sand, silt and clay. Change in the land use type caused increase in bulk density and decrease in total porosity, soil hydraulic conductivity and aggregate stability. The ýmost percentage of coarse aggregates (0.25-8 mm) and fine aggregates (0.053-0.25 mm) was found in forest ýand ýagricultural land, respectively. Land use change caused increase in electrical conductivity and ýsoil reaction which increases with depth too. The results also indicated that land use change significantly decreases the amount of soil organic carbon. The average of soil organic carbon in forest, agricultural land and pastures were 4.99, 2.26 and 1.79%, respectively. In general, the results of this research showed that changes in land use has adverse effects on the soil physical and chemical properties that can result in reducing soil quality and increasing its degradation.

Spatial variation of soil properties is significantly influenced by numerous environmental factors such as landscape features, including position, topography, slope gradient and aspect, parent material, climate and vegetation. Soil properties vary spatially in south- and north-facing hill slopes. This factor (different slope aspects) can affect the distribution of soil organic matter, the presence or absence of a layer, pH, nutrient levels, soil mineralogical and micromorphological properties. Topographic factors such as the orientation of the hill slope and the steepness of the slope affect microclimate, vegetation establishment, water movement and erosion. Aspect and slope control the movement of water and materials in a hill slope and contribute to differences in soil properties. Temperature, precipitation and climate vary with elevation and influence pedogenic processes. Accelerated rates of weathering and soil development were found to occur in soils on south-facing slopes. Slopes with a south aspect are dominated by stone and bare soil patches, while slopes with a north aspect are dominated by biotic components. Northern slopes have higher productivity and species diversity compared to Southern slopes. Slope aspect has a significant effect on the composition, species richness, structure and density of plant communities, differed significantly between North- and South- facing slopes. In the present study, the effects of two slope aspects on some soil properties and soil evolution was investigated in Northern Rostam Abad region in the Guilan Province. Five profiles in Southern hill slope(South-facing hill slopes) and five profiles in Northern hill slopes(North-facing hill slopes) with 40% slope and same parent material (basaltic andesite) and same plant cover were dug. The elevation of two slope aspects was 240 meters from the sea level. Average annual temperatures and precipitation are16 degrees centigrade and 1359 mm, respectively. Thus, the soil moisture and temperature regimes are udic and thermic, respectively. The physical and chemical analysis were carried out on soil samples including particle size distribution, bulk density, pH, organic carbon, total nitrogen, available phosphor and cation exchange capacity. This study was done in a completely randomized design several observational with five replications. The total of 34 soil samples were collected in the two slope aspect of the profile and all samples were tested and statistical analyzed. For the micromorphological study, thin sections were prepared from undisturbed samples. The samples were impregnated with polyester resin and later sectioned. The thin sections were prepared and analyzed in petrographic microscope equipped with polarized light. The results of multivariable analysis of variance (MANOVA) and Hotteling's T2 showed that there is significant different in soil properties between two hill slopes(p≤0.01). Also, the results of t-test showed the values of pH, content of sand, sand to clay ratio and available phosphorous significantly was higher in Southern hill slope in comparison with Northern hill slope(p≤0.01). Whereas, clay content and cation exchange capacity significantly were higher in Northern hill slope in comparison with Southern hill slope(p≤0.05). Also observed micromorphological studies showed biological activity was stronger in Northern hill slope in comparison with Southern hill slope. Properties observed in thin sections of Northern slope aspect include fungal hyphae, spherical and ellipsoid excrement of microorganisms in root residual (related to oribatid mites) which indicated stronger biology in Northern slope aspect soils as compare to Southern slope aspect soils. Also, more accumulates* of clay inside voids, nodules, fragmented of coating of well-oriented, micro laminated, reddish-brown clay, chamber voids in Northern slope soils toward Southern slope soils were observed. B-fabricobserved in Northern hill slope soils is stipple speckled in surface horizons and in subsurface horizons is grano-striated and stipple speckled and b-fabric observed in Southern hill slopes soils in surface horizons and subsurface horizons is stipple speckled. Higher content of clay, Cation exchange capacity, Accumulation of clay in pores, Fragments of clay coating (papule), chamber pores, Fe/Mn oxide nodule and micro-laminations in Northern hill slope and higher values of pH, higher content of sand, sand to clay ratio and available phosphorous, lithorelict in Southern hill slope showed that weathering was higher in Northern hill slope in comparison with Southern hill slope. Generally, Southern hill slope had less developed soils (Entisols and Udorthents great group) and Northern hill slope had high developed soils (Alfisols and Hapludalfs great group).

In order to investigate the sources of arsenic contamination and assessing the role of geomorphic properties on its distribution in Bijar area, 83 profiles were selected based on the prepared geopedology map and 227 soil and 20 water samples were collected. Routine physical and chemical characteristics, iron oxides and soil arsenic contents were measured in 227 samples. Arsenic probability risk was computed using indicator Kriging (IK) to assess the arsenic (As) contamination potential. Topsoil arsenic concentration showed significant correlations at the level 0.01 with clay (r=0.77), sand (r= -0.45), silt (r= -0.48), CEC (r=0.65), and Fe oxides (r=0.65) statistically. Subsoil arsenic concentrations showed similar correlations with soil properties. Significant differences in clay, silt, sand, Fe2O3 percentages and CEC in arsenic source landforms with Fe- rich rock and mud stone have led to their higher arsenic concentration compared to the other landforms. Arsenic content of water resources located at higher altitudes were lower than EPA and WHO guideline level, while the opposite was true (2-130 times) about the landforms located at lower altitudes. The distribution of high arsenic areas rasterized by IK confirmed that, from the arsenic sources (arsenic source landforms with Fe- rich rock and mud stone), the arsenic is spread to lower positions by erosion-deposition processes or soluble form. Soils with such concentrations of arsenic may be hazardous for human being and ecosystems.

In order to determine the effect of different tillage methods on grain yield and yield components of two wheat cultivars an experiment was conducted in Sanandaj in during 2005 and 2006-2007. Experiment was carried out in a split plot design based on RCBD with four replications. The three methods of tillage, including moldboard plow, chisel plow and no tillage were arranged as main plots, and two cultivars (Sardari and Azar 2) as the subplot. Also, the effect of different tillage methods on soil physical properties was evaluated with complete randomized blocks. The results showed that different tillage methods had significant effect on bulk density, porosity and soil cone index. The greatest bulk density and acone index was attained in chisel plow and no tillage system, respectively. The tillage methods and cultivars had a significant effect on yield and yield components. Sardary cultivars produced more grain (1624 kgha-1) than Azar2, statistically. The greater yield was attained in chisel plow than other tillage methods. Chisel plow improved soil water and soil physical properties. Using chisel plow resulted in maximum 1000 seed weight and maximum grain number in spike. Tillage methods and the cultivars had a significant effect on plant height and biomass.