Kamran Azizi; Shamsollah Ayoubi; Kamal Nabiollahi
Abstract
Introduction: The parent material and geology have a significant contribution to heavy metal contents and magnetic susceptibility in soils. Magnetic susceptibility is known as the extent of a material’s impact on the magnetic field, which depends on the concentration and the type of magnetic minerals ...
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Introduction: The parent material and geology have a significant contribution to heavy metal contents and magnetic susceptibility in soils. Magnetic susceptibility is known as the extent of a material’s impact on the magnetic field, which depends on the concentration and the type of magnetic minerals in the soil. Magnetic susceptibility measurement is fast, easy, economically convenient, and non-destructive. Mass magnetic susceptibility and frequency-dependent magnetic susceptibility are among the conventionally used parameters. The type of land use is among the main factors influencing magnetic susceptibility distribution in soil. Besides, soil magnetic susceptibility is affected by slope position and different soil properties such as soil organic matter and carbonates. A variety of relationships have been observed between heavy metal concentrations and magnetic susceptibility across various types of soil, parent material and climatic regimes. Generally, magnetic properties have positively correlated with the type and concentration of magnetic minerals as well as the particle size of soil fractions. Besides, magnetic susceptibility has a weak negative correlation with diamagnetic components such as quartz, gypsum, calcite, and organic materials. The main objectives of this study were to i) determine the relationships between magnetic susceptibility (MS) and concentration of some heavy metals such as copper, iron, zinc, and manganese in the surface soils and ii) explore the impacts of land use and geomorphological units on the variability of heavy metals and MS in a semi-arid region in the west of Iran.Materials and Methods: The present study was conducted in Kurdistan province located in western Iran, the area is about 110,000 ha and mean altitude of 2277m above sea level. The area has an average annual temperature of 10.20 °C and an average annual rainfall of 369.8 mm, which dominantly occurs in spring and winter. Besides, the area has soil moisture and temperature regimes of Xeric and Mesic, respectively. The lithological setting of the studied area includes reddish of sandy marls and marl sandstone, river deposits, alluvium-cultivated land, and granite. The studied area was divided into different parts in terms of geomorphology and land use. Soil sampling was done using the stratified random sampling approach. A total of 347 samples were collected from the surface layers (0-30 cm depth) of the studied area. Magnetic susceptibility at both high and low frequencies was measured using a Bartington MS2 dual-frequency sensor. The amounts of all the selected heavy metals including iron, zinc, manganese, copper, and nickel were measured using atomic absorption spectrophotometer. Soil particle sizes, acidity, SOC, CCE and electrical conductivity were measured in all soil samples. The concentration factor and Tomlinson’s Pollution Load Index were calculated. The Spearman correlation coefficient was used to examine the correlation between different parameters. The analysis of variance was used to evaluate the effects of geomorphology and land use on heavy metals and magnetic susceptibility. Spatial analysis was done conducted for some variables (Fe, Mn, Zn, Ni, Cu, and χlf) and the map of variables were created in ArcGIS v.13 software.Results and Discussion: The results showed that the positive significant correlations were observed between heavy metals and silt content and negative significant correlations were observed between heavy metals and sand content. Fine soil fraction compared with coarse fraction has the higher specific surfaces and more susceptible to attract heavy metals. Moreover, positive and significant correlations were obtained between the SOC and heavy metals across various land use types and geomorphic units. Organic matters have a high cation exchange capacity, therefore they adsorb heavy metals and hold them on their surfaces. pH and CCE showed negative and significant correlations with heavy metals and magnetic susceptibility. The positive correlation between heavy metals and magnetic susceptibility in agricultural land, piedmont, and river plains units observed. Also, PLI and CF have a positive correlation with magnetic susceptibility. The t-test showed that a significant difference between agricultural lands and non-agricultural land types and ANOVA results in various geomorphic units of the study area indicated that the magnetic susceptibility between piedmont and mountainous areas were significantly different. Conclusion: Results indicated a significant correlation between magnetic susceptibility and heavy metals. Besides, the magnetic properties of the soil are influenced by its physical and chemical properties that have large impacts on reducing or improving its magnetic field. Significant differences were observed between agricultural lands and non-agricultural lands as well as mountainous and piedmont areas that had different parent materials. These results indicate the great impact of parent materials constituting the soil on the absence or presence of diamagnetism in the region.
Majed Ghosairy Sabry; Kamal Ganjalipour; Kamal Nabiollahi
Abstract
Introduction: CT scan was first invented by Hounsfield in the twentieth century in 1972. But it was soon used in engineering, agriculture, biology, physics, chemistry, etc. Recently, with advances in computed tomography at the global level, the use of different generations of X-rays on a micrometer scale ...
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Introduction: CT scan was first invented by Hounsfield in the twentieth century in 1972. But it was soon used in engineering, agriculture, biology, physics, chemistry, etc. Recently, with advances in computed tomography at the global level, the use of different generations of X-rays on a micrometer scale to study some of the different phenomena in soil science has begun. Due to the lack of geotechnical and soil mechanics studies in many engineering projects, CT scan image processing method can be used as a suitable method for extracting soil particle size and other soil characteristics. The main purpose of this study: a) The use of CBCT-scan in soil science for the first time in Iran. B) Comparing the ability of CBCT-scan in terms of quality of results with conventional methods. C) Identify the best filter and binary method (threshold). Another goal of this research is to acquaint more researchers with the application of computed tomography (CT-scan) technology in soil science studies.Material and Methods: The sampling area for this study was located in Diwandareh-Saqez axis in Kurdistan province, where six disturbed and undisturbed soil samples were collected in a sandy area (12 samples in total). In disturbed samples, particle size distribution was measured by ASTM D421 method, and the porosity of the samples was measured directly using the fuzzy equations in soil mechanics. In a radiology laboratory, three-dimensional images of intact soil samples were taken using a Planmeca Promax 3D CBCT CT scanner. In this study, ImageJ software was used to process CBCT-scan images. With this software, the percentage of phases, number of particles and particle size can be calculated. One of the most important steps in image processing is generating binary images. A total of 17 global thresholding methods have been proposed for generating binary images in ImageJ software. In this study, 15 standard methods for generating binary images were examined and the best method was selected. The total pore volume and soil particle size distribution of each sample calculated by quantifying X-ray images were compared with the total pore volume and soil particle size distribution obtained in the soil science laboratory and performance of the CT scan method evaluated by statistical parameters including The results of the accuracy evaluation for the correlation coefficient, mean absolute value of deviations, mean square error, root mean square error, and mean absolute error percentage.Results and discussion: The most significant point in image processing is the image thresholding method. In this study, due to the nature of CBCT-scan images, global thresholding was preferred. From the results of image processing, it can be understood that the results of binary images with Otsu and Intermodes methods are in complete agreement with the laboratory sample. The average of total porosity of the processing image slides is 44.03%, which is approximately consistent with the calculated 45/6% for the laboratory sample. Also, the average of ineffective porosity of the samples is about 6.53%. Therefore, it can be said that the effective porosity of the samples is about 37.5%. The results of the accuracy evaluation for the correlation coefficient, mean absolute value of deviations, mean square error, root mean square error, and mean absolute error percentage were 0.98, 1.082, 1.229, 1.108 and 2.334 respectively, indicating that the use of CBCT-scan images and image processing technique can identify and evaluate the geometric properties of granular soils with acceptable accuracy. The advantages of the computed tomography method of the soil are: (1) Obtaining information from the three-dimensional structure of the soil with appropriate accuracy in a short time, (2) Non-destructiveness of this method, and (3) Accurate separation into soil phases in different energy radiations.Conclusion: Using the processes defined by the authors for image processing, this technique is well able to determine some engineering features such as particle size distribution, total porosity, effective porosity and ineffective porosity. Also, the best thresholding method for binary images and processing in ImageJ is the Ostu and Intermodes method. The accuracy of the device used in this research is 0.2 mm, in other words, spaces or grains smaller than this value cannot be identified; For this reason, in the present study, the term coarse-textured soils, which means gravel to coarse-grained sand, has been emphasized. The results of evaluating the statistical parameters testify to the accuracy and ability of this method.
L. Rasoli; K. Nabiollahi; R. Taghizadeh Mehrjardi
Abstract
Introduction Rapid population growth in developing countries implies that more food will be required to meet the demands of this population. Wheat as one of the most important grain crops in the world is a great source of food for human which is planted under a wide range of environments and its production ...
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Introduction Rapid population growth in developing countries implies that more food will be required to meet the demands of this population. Wheat as one of the most important grain crops in the world is a great source of food for human which is planted under a wide range of environments and its production influences on local food security. The production of wheat per unit area in Iran is low compared to developed countries in the world. One of the main causes for this low yield is that the suitable land for planting has not been recognized. Therefore, to overcome this problem, land suitability assessment is needed, which can help to increase crop yield. The first step in agricultural land use planning is land-suitability assessment which is often conducted to determine which type of land use is suitable for a particular location Digital mapping approach have been applied to link between soil observations and auxiliary variables to understand spatial and temporal variation in soil class and other soil properties. Little attempt has been made for using Digital mapping approach to digitally map land suitability classes Therefore, this paper applied land suitability assessment framework and digital soil mapping approach to map land suitability for rain-fed wheat in Kurdistan province. Materials and Methods The study area is located in Kurdistan Province, western Iran. It surrounds the city of Ghorveh and covers a region of 6500 ha. The climate is semi-arid whose features can be performed using a cold and rainy winter and a moderate and dry summer. The mean yearly rainfall is 369.8 mm and over 90% of the rain falls between November and March. The mean temperature (10.8℃) is relatively cool. Soil moisture and temperature regimes are Xeric and Mesic, respectively. The physiography units include piedmont, fan, hills, and mountain and slope varies from gentle to very steep. At first land unit component map was prepared by Mahler physiography method, then, 17 representative profiles in each land unit component were dug and described. 105 auger samples also were taken at three depths (0-20, 20-50 and 50-100 cm). Soil texture, acidity, organic carbon, CaCO3, gypsum, ESP, electrical conductivity and gravel were measured in all soil samples. Topography and climate data were also recorded. Numeric ratings of soil, topography and climate parameters based on land requirements of wheat were determined and land suitability index using parametric method were calculated. Then land suitability classes of wheat were determined. A set of auxiliary variables (i.e. land unit component, terrain attributes and remotely sensed data) to predict land suitability classes of rain-fed wheat. In order to generate land suitability class map, artificial neural network were applied to make relation between auxiliary variables and land suitability classes. Results and Discussion The results showed that the area has about 36.61% N2 class, 40.32% N1 class and 22.53% S3 class. The validation results of the model based on the statistical indices including root mean square error, mean error, and determination coefficient (6.56, 4.81, 0. 68, respectively), indicates that the artificial neural network model has suitable accuracy. Auxiliary data including MrVBF index, LS factor, MRRTF index, slope, Land unit component, VDCN and band 2 were the most important for prediction of wheat land suitability index in digital method. The major limitation of the study area to plant rain-fed wheat were rainfall in the flowering stage, sever slope, shallow soil depth, high pH and gravel. Therefore, to increase production and sustainable agricultural system it is suggested land improvement operations such as terracing, decreasing pH, supplementary irrigation and gathering gravel. The highest values of rain-fed land suitability index were observed in the units physiographic of river plain and plateau, while the lowest value were observed in the units physiography of mountain and hill which had high slope, shallow soil and high gravel. These results were confirmed by one-way ANOVA and Duncan tests. Conclusion Based on the results of statistics indices artificial neural network had suitable accuracy for predicting land suitability index of wheat. In general, the study area, because of limitation of sever slope, shallow soil, high pH, and gravel, has low land suitability index for rain-fed wheat. Hence, to improve land suitability of the study area and increasing its production, suitable land improvement operations is required.
Kamran Azizi; Kamal Nabiollahi; Masoud Davari
Abstract
Introduction Soil salinity and alkalization are recognized worldwide as a major threat to agriculture, particularly in arid and semi-arid regions. To manage these soils a lot of data are needed and laboratory measurement is costly and time-consuming. Therefore, indirect methods that are cheap, fast and ...
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Introduction Soil salinity and alkalization are recognized worldwide as a major threat to agriculture, particularly in arid and semi-arid regions. To manage these soils a lot of data are needed and laboratory measurement is costly and time-consuming. Therefore, indirect methods that are cheap, fast and easy to access are one of the research priorities. One of these methods is visible near infrared diffuse reflectance spectroscopy. Visible and near infrared diffuse reflectance spectroscopy is a time and cost-effective approach that has been successfully used for characterizing soil properties. Materials and Methods The study area is located in Kurdistan Province, about 20 km northeast of Ghorveh city, west of Iran, and covers 260 km2. Average annual precipitation and temperature are 369.8mm and 10.8 °C, respectively. Soil moisture and temperature regimes are Xeric and Mesic, respectively. In the study area, 100 soil samples were collected (0–30 cm depth). The main land use types consist of cropland and rangeland. The soil samples were air-dried at room temperature and then, passed through a 2mm sieve. EC, pH, SAR, OC, CaCO3 and ΔMWD were measured. Sodium Adsorption Ratio (SAR) was calculated using results from the saturated paste extracts of sodium, calcium, and magnesium. The stability aggregate was measured using the difference between distributions of particle size in dry and wet sieve methods. Spectral analysis of soil samples was done using a spectrophotometric instrument with a wavelength of 350 to 2500 nm and recorded using RS3 software. After recording the spectra, different preprocessing methods were evaluated. Two models of multiple linear regression and artificial neural network were used to predict soil properties using spectral data. Results and Discussion The soil salinity of the study area ranged between low and high. The highest amount of salinity was observed in the center, south and southwest of the study area and the least amount of salinity was observed in northwest, southeast, northeast and north. The maximum amounts of acidity and sodium adsorption ratio showed that the central part of the study area has saline and sodium soils. The results showed that the best method for preprocessing of spectral data is the 1st Derivative + Savitzky-Golay filter + Mean center + SNV. The Pearson correlation coefficient between the soil properties and the spectral reflection values for each wavelength in the range of 2450-400 nm showed that there is a relatively high correlation between the measured characteristics and the spectral values of the soil. The results showed that the correlation coefficient can be positive or negative. The maximum positive correlation coefficients for electrical conductivity, soil acidity, sodium adsorption, organic carbon, calcium carbonate and aggregate stability at the wavelengths 1229, 2397, 2399, 1298, 2090, 2014, and two spectra 2257 and 660 were 0.45**, 0.43**, 0.46**, 0.61**, 0.53** and 0.40**, respectively. The maximum negative correlation coefficients for electrical conductivity, soil acidity, sodium adsorption ratio, organic carbon, calcium carbonate and aggregate stability at the wavelengths 630, 2289, 630, 1904, 1379 and 2107 were -0.47**, -0.42**, -0.44**, -0.46**, -0.55** and -0.44**, respectively. Based on the determination coefficient statistic, artificial neural network model (0.88, 0.25, 0.59, 0.68, 0.52 and 0.48 to electrical conductivity, PH, SAR, calcium carbonate and aggregate stability, respectively) had better results compared to the multiple linear regression model (0.45, 0.13, 0.23, 0.66, 0.48 and 0.28 to electrical conductivity, PH, SAR, calcium carbonate and aggregate stability, respectively). Conclusion In this study, visible near infrared diffuse reflectance spectroscopy was evaluated to estimate some properties of salt-affected soils. After recording the spectral data, the continuity curve and pre-processing of spectral data were performed. The results showed that the best method for pre-processing of spectral data is the first derivative + Savitzky filter and Glair + Mid filter + Normal standard variable. Multiple linear regression and artificial neural network models were used to estimate some properties of salt-affected soils (EC, pH, SAR, OC, CaCO3 and ΔMWD) using spectral data. Based on the statistics of mean error, root mean squared error, and correlation coefficient, the artificial neural network model had better results in estimateing the properties of salt-affected soils compared to the multiple linear regression model. Therefore, based on these findings it is suggested that soil spectral data be used as an indirect method to the estimate soil properties.
Serve Moradi; Kamal Nabiollahi; Syed Mohammad taher hossaini
Abstract
Introduction Soil quality is the capacity of soil function to sustain plant and animal productivities, to maintain or enhance water and air quality and to support human health. Slope position and deforestation are known to influence soil quality and assessing the soil quality degradation is important ...
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Introduction Soil quality is the capacity of soil function to sustain plant and animal productivities, to maintain or enhance water and air quality and to support human health. Slope position and deforestation are known to influence soil quality and assessing the soil quality degradation is important to soil management. Soil-quality indices are a common and easy way to quantify soil quality; they can improve understanding of soil ecosystems and allow more efficient soil management Two soil indicator selection approaches, total data sets and minimum data sets have been widely used to evaluate soil quality. The region of Marivan in Kurdistan province is one of the forested areas of Zagros which has been threatened due to population growth and increasing demand for food and, some parts are now under agriculture land use. Based on the present reports, deforestation and cultivation on the sloping areas have started almost 30 years ago. The aim of this research was to assess the effect of forest degradation and slope position on soil quality index. Materials and Methods The study area is located in Kurdistan Province, about 10 km northeast of Marivan city, west Iran (46°24΄ 46°40΄E, 35°42΄ 35°50΄N). Two adjacent sites were selected, consisting of a natural forest and deforested cultivated land on a hill slope. Average annual precipitation and temperature are 813 mm and 13.8 °C, respectively. Soil moisture and temperature regimes are Xeric and Mesic, respectively. Forests of the study area are relative intensive and their main forest vegetation is oak. In this study, 24 soil samples (0–20 cm depth) were taken from four slope positions (shoulder, back slope, foot slope and toe slope) of forest and adjacent deforested cultivated soils. Eight profiles (on each slope positions of both land uses) were also described. Fifteen soil properties: pH, electrical conductivity, sodium adsorption ratio, organic carbon, cation exchange capacity, carbonat calcium equivalent, soil erodibility, soil porosity, mean weight diameter of aggregates, available water, soil microbial respiration, available phosphorous, available potassium, total nitrogen, bulk density, were measured for 24 soil samples (0–20 cm depth). These Fifteen soil properties were applied as the total data set. Then, seven soil properties were selected as minimum data set using principle component analysis. Weight and score of each property were found using communality and scoring function (including more is better, low is better and optimum) and finally weighted additive and nemoro soil quality indices was computed. Results and Discussion: Seven soil properties (including soil organic carbon, cation exchange capacity, bulk density, soil erodibility, plant available water content, available potassium and total nitrogen) were selected as total data set using principle component analysis. The soils formed in low slope positions had higher depth and evolution compared to high slope positions. The results also showed land use change of forest land to cropland has led to degradation of Mollisols. The results showed that the mean values for weighted additive and nemoro soil quality indices in the deforested were significantly lower compared to forest. The mean values for weighted additive and nemoro soil quality indices in the shoulder were significantly lower compared to other slope position. significantly Strong Pearson correlation coefficients (0.98) were obtained between computed weighted additive soil quality index using total data set and a minimum data set. Conclusion: The results showed that forest degradation in the Marivan region led to a decrease in weighted additive and nemoro soil quality indices through a significant reduction of organic carbon, microbial respiration, total nitrogen, CEC, soil porosity and available moisture and significant increasing of bulk density, pH, SAR and soil erodibility. Forest degradation and land use change also due to cultivation led to decrease in the 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 positions affect weighted additive soil quality index and mark significant difference. The results also showed that using the weighted additive soil quality index and minimum data set method can adequately represent total data set (R2=0.98) and thus reduce the time and cost involved in evaluating soil quality. Slope positions and where forest was converted to agriculture were characterized by low values of weighted additive soil quality index, suggesting a recovery of soil quality through changing to sustainable practices.
