A. Sharifi; H. Shirani; A.A. Besalatpour; E. Esfandiarpour
Abstract
Introduction: Interrill erosion is one of the major types of erosion playing key role in the transport of fine particles of the soil, particularly in arid and semi-arid regions, which leads to the decrement of soil fertility and surface water pollution. Land-use change is one of the main ways which reflect ...
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Introduction: Interrill erosion is one of the major types of erosion playing key role in the transport of fine particles of the soil, particularly in arid and semi-arid regions, which leads to the decrement of soil fertility and surface water pollution. Land-use change is one of the main ways which reflect the interaction of human activities and the natural environment and can impact soil aggregation, aggregate stability, and erodibility. Hence, this research aimed to evaluate the susceptibility of soils under different land-use types (four types) to interrill erosion using both rainfall simulation test and soil aggregate stability indexes. The location of study area was around Jiroft city.
Materials and Methods: This study was conducted in four types of land use around Jiroft city in southern Iran, including disturbed pasture, undisturbed pasture, protected forests, and artificial forest. For each land use, 25 points were selected (A total of 100 points for all land uses). To measure soil physical and chemical properties, disturbed and undisturbed soil samples were collected from each point at a depth of 0–20 cm. The samples were transported to the laboratory where these samples were then air-dried. Some soil properties such as texture, organic carbon, electrical conductivity, soil acidity, calcium carbonate equivalent, and bulk density were measured, and available nitrogen, phosphorus, and potassium in the soil and sediment samples were also determined. Furthermore, some characteristics of soil particles, including the geometric mean diameter, geometric standard deviation, particulate organic matter, water-dispersible clay, tensile strength of soil aggregate, mean weight diameter and fractal dimension of aggregates were determined. To assess how susceptible are soils to interrill erosion, rainfall simulator was used to generate rainfall with an average intensity of 60 mm/h.
Results and Discussion: According to the results, the undisturbed pasture revealed the highest content of organic matter, particulate organic matter, clay, and tensile strength, while the minimum values of bulk density, sand percentage, and fractal dimension have been observed in this land use. For this reason, it is assumed that the aggregates of undisturbed pasture (intact rangeland) show more stability than other three land uses. The maximum and minimum values of bulk densities were observed in the protected forest (1.58 g cm-3) and undisturbed pasture (1.43 g cm-3), respectively. On the other hand, the highest value of aggregates fractal dimension, as well as minimum values for mean weight diameter and dispersible clay in the protected forest demonstrated that this land use had either no aggregate or its aggregates were very fine. As a matter of fact, lack of organic matter and insufficient clay content can be considered to be the reasons for poor aggregate stability in this land use. The highest and lowest values for tensile strength of soil aggregate were found in the undisturbed rangeland (64.82 kPa) and protected forest (34.38 kPa), respectively. The variations in the tensile strength of soil aggregate can be attributed to the changes in the contents of clay and organic matter in different land uses. Moreover, despite the maximum amount of total organic matter in the undisturbed pasture (or intact rangeland), the amount of sediment organic matter in this land use was lower than the other three land uses. It is because of the fact that most of the OM in this area was of a stable organic matter type, which was under the soil surface and was accordingly protected from surface erosion. The particle size distribution of sediment was smaller in the undisturbed pasture, whereas it was found to be larger in the protected forest. The reason can be attributed to the coarse-textured soil in the forest compared to the finer-textured soil in the undisturbed pasture (or intact rangeland). In addition, the highest sediment concentration and the highest rate of erosion were observed in the disturbed pasture. The artificial forest accounted for the minimum sediment concentration, while the artificial forest, as well as the protected forest, revealed the lowest erosion rate.
Conclusion: The results of the current research demonstrated the high rate of interrill erosion in all land uses so that the disturbed pasture and artificial forest accounted for the highest and the lowest rate of erosion (7 and 2 ton/ha) respectively. According the results, intrinsic soil characteristics such as soil texture played major role in some land uses, while for the others, the slope impact was more crucial. On the other hand, both erosion rate and sediment concentration revealed the same trend under four different land uses of the study area. Therefore, because of the fact that the highest and the lowest rate of erosion, as well as sediment concentration, were found to be in the disturbed pasture, and the artificial forest, respectively, therefore the sediment concentration can be considered to be an important index for soil erosion. Due to high rates of erosion occurring in the study areas, some measures have to be undertaken to prevent and control soil erosion in this area. To achieve this aim, preventing people from entering the vulnerable area, avoiding livestock grazing, protecting existing plants and restoration of native plants can be mentioned as efficient measures to improve conditions.
Morteza Bahmani; jahangard mohammadi; Isa Esfandiarpour Borujeni; Hamidreza Mottaghian; Keramatollah Saeidi
Abstract
Introduction: The importance and the presence of spatial variability in soil properties is inevitable, however, the understanding of causes and sources of the variability is not complete. Spatial variation of soil attributes can affect the quality and quantity of plants. Investigation of the soil variability ...
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Introduction: The importance and the presence of spatial variability in soil properties is inevitable, however, the understanding of causes and sources of the variability is not complete. Spatial variation of soil attributes can affect the quality and quantity of plants. Investigation of the soil variability at the small scale can be evaluated by classic statistics and geospatial statistics. The present study was conducted to investigate the spatial variability of yield characteristics of rose (Rosa Damasceneea Mill) and soil characteristics in two main cultivated fields of rose (Negar- Golzar) with different climatic and topographic characteristics located in Bardsir city, Kerman Province.
Materials and Methods: In order to achieve the objectives of the present study, 100 soil and plant samples were collected from each farm. The soil samples were taken from 0 to 25 cm depth and analyzed. The measured soil properties at each location were including fragment, clay, silt, sand, and organic matter contents, CEC, calcium carbonate equivalent, EC, pH, total nitrogen, available phosphorus, and available potassium. Moreover, some plant characteristics (yield, plant height, and plant crown diameter) were measured at each point. Then, maps of soil properties and plant induces were prepared using Geoeas, Variowin, and surfer software. Descriptive statistics were applied using Statistica software (version 20). Kolmogorov-Smirnov test was also used to test the tolerance of variables distribution.
Results and Discussion: The results of Kolmogorov-Smirnov test showed that all characteristics of the plant and soil in both farms follow the normal distribution. Statistical analysis showed that coefficient of variation of soil properties was as follows: total nitrogen (54.47%) and pH (3.16%) in Negar farm, and EC (46.09%) and pH (35.3%) in Golzar farm. The variability of nutrients in both farms had similar trends, so that total nitrogen, phosphorus and potassium have the highest to lowest coefficients of variation, respectively. Analysis of variograms indicated that all of the variables in both fields have a strong and moderate spatial variability. Ranges for variograms were from 122.16m (for yield) to 218.46 m (for silt) in Negar farm and from 115.1m (for available K) to 228 m for (total nitrogen) in Golzar unit. The distribution conditions and spatial variations of the soil properties in the study area were not uniform due to variation of the range of the variograms. The results also showed that the yield characteristics of the rose with some soil characteristics have a closer spatial relationship. About this, in the Negar farm, the range of the rose flower yield was close to the clay, available potassium and calcium carbonate contents. In the Golzar farm, the range of rose flower yield was close to the range of clay, silt, fragments and available phosphorus contents. The spatial correlation ratio showed that all plant characteristics including plant yield, plant height and plant diameter had a strong spatial correlation in the Golzar farm, and all characteristics of the soil were in the medium spatial correlation. Also, in the Negar farm, the product yield characteristics were in a strong spatial correlation class, and all other characteristics were in the medium spatial correlation. Kriging maps showed that soil characteristics and product yield in the study area had spatial distribution. The similarity of the spatial distribution pattern of some variables was one of the important features that these maps showed.
Conclusion: The results of this study showed the characteristics of plant yield and soil characteristics have a moderate to strong spatial dependency even in small scales. Kriging maps illustrated that the pattern and distribution of soil properties even within a farm can be varied. However, the spatial pattern of some soil characteristics such as organic matter and total nitrogen with the spatial pattern of plant characteristics and the dimensions of the farms showed conformity. This indicates that the variability of these characteristics is mainly under the management of farmers, and in order to optimize the use of nutrients, inputs should be re-evaluated by farm managers. In general, the results of this study indicated geostatistical method can be used to recognize of control factors of plant production and use its information in order to improve management.
zohreh mosleh; Mohammad hasan Salehi; azam jafari; Abdolmohammad Mehnatkesh; Isa Esfandiarpoor Borujeni
Abstract
Introduction: There is a concern with assessment of land performance when used for specific purposes. Land evaluation analysis is considered as an interface between land resources and land use planning and management. However, the conventional soil surveys are usually not useful for providing quantitative ...
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Introduction: There is a concern with assessment of land performance when used for specific purposes. Land evaluation analysis is considered as an interface between land resources and land use planning and management. However, the conventional soil surveys are usually not useful for providing quantitative information about the spatial distribution of soil properties that are used in many environmental studies. Development of the computers and technology lead to digital and quantitative approaches have been developed. These new techniques rely on finding the relationships between soil and the auxiliary information that explain the soil forming factors or processes and finally predict soil patterns on the landscape. Different types of the machine learning approaches have been applied for digital soil mapping of soil classes, such as the logistic and multinomial logistic regressions, neural networks and classification trees. To our knowledge, most of the previous studiesapplied land suitability evaluation based on the conventional approach. Therefore, the main objective of this study was to assess the performance of digital mapping approaches for the qualitative land suitability evaluation in the Shahrekord plain of Chaharmahal-Va- Bakhtiari province.
Materials and Methods: An area in the Shahrekord plain of Chaharmahal-Va-Bakhtiari Province, Iran, across 32º13′ and 32º 23′N, and 50º 47′ and 51º 00′E was chosen. The soils in the study area have been formed on Quaternary shale and foliated clayey limestone deposits. Irrigated crops such as wheat, potato, maize and alfalfa are the main land uses in the area. According to the semi-detailed soil survey, 120 pedons with approximate distance of 750 m were excavated and soil samples were taken from different soil horizons. Soil physicochemical properties were determined. The average of soil properties was determined by considering the depth weighted coefficient up to 100 and 150 centimeters for annual and perennial crops, respectively. Qualitative land suitability evaluation for main crops of the area including wheat, maize, alfalfa and potato was determined by matching the site conditions (climatic, hydrology, vegetation and soil properties) with studied crop requirement tables presented by Givi (5). Land suitability classes were determined using parametric method. Land suitability classes reflect degree of suitability as S1 (suitable), S2 (moderately suitable), S3 (marginally suitable) and N (unsuitable). Different machine learning techniques, namely artificial neural networks (ANNs), boosted regression tree (BRT), random forest (RF) and multinomial logistic regression (MLR) were used to test the predictive power for mapping the land suitability evaluation. Terrain attributes, normalized difference vegetation index (NDVI), clay index, carbonate index, perpendicular vegetation index (PVI), geology map, existing soil map (1:50000 scale) and geomorphology map were used as auxiliary information. Finally, all of the environmental covariates were projected onto the same reference system (WGS 84 UTM 39 N) and resampled to 50 * 50 m since the soil samples were collected with approximate distance of 750 m (1:50,000 scale). According to the suggested resolutions for digital soil maps, the pixel size 50 *50 m fits to a 1:50,000 cartographic scale. Training the models was done with 80% of the data (i.e., 96 pedons) and their validation was tested by the remaining 20% of the dataset (i.e., 24 pedons) that were split randomly. The accuracy of the predicted soil classes was determined using error matrices and overall accuracy.
Results and Discussion: The results showed that climatic conditions are suitable (S1) for wheat and potato whereas the most important limiting factors for maize and alfalfa were the average of minimum temperature and average temperature, respectively. Results demonstratedthat among the studied models, random forest showed the highest performance to predict the land suitability classes and subclasses. However, different models had the same ability for prediction. In addition, the overall accuracy decreased from class to subclass for all of the crops. The terrain attributes and remote sensing indices (normalized difference vegetation index and perpendicular vegetation index) were the most important auxiliary information to predict the land suitability classes and subclasses.
Conclusion: Results suggest that the DSM approaches have enough accuracy for prediction of the land suitability classes that affecting land use management. Although digital mapping approaches increase our knowledgeabout the variation of soil properties, integrating the management of the sparse lands with different owners should be considered as the first step for optimum soil and land use management.
Saeideh Bardsirizadeh; Isa Esfandiarpour Borujeni; Ali Asghar Besalatpour; Peyman Abbaszadeh Dahaji
Abstract
Introduction: Aggregate, as the basic unit of soil structure,represents a collection of primary particles which their adherence to each other is more than their connection to environ soilparticles. Aggregate stability is a highly complex parameter influencing a wide range of soil properties, including ...
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Introduction: Aggregate, as the basic unit of soil structure,represents a collection of primary particles which their adherence to each other is more than their connection to environ soilparticles. Aggregate stability is a highly complex parameter influencing a wide range of soil properties, including carbon stabilization, soil porosity, water infiltration, aeration, compatibility, water retention, hydraulic conductivity andresistance to erosion by water and overland flows. Maintaining high stability of soil aggregate is essential for preserving soil productivity, minimizing soil erosion and degradation and thus minimizing environmental pollution as well. Nevertheless, aggregate stability is described as one of the soil properties that can serve as an indicator of soil quality.The main purpose of this study is to determine the most important component of soil aggregate (macro and/ormicro) in estimating the soil structural stability in the Rabor region, Kerman province, using geostatistical method.
Materials and Methods: Ninetysurface soil samples (0 to 10 cm) were taken on a 200 m square sampling grid in the study area for the geostatistical studies.After air drying the soil samples and passing them through a 4 mm sieve, the percentage of aggregates belong tothree parts of total, macro, and micro classes and aggregate staility were calculated in both dry and wet conditions.Some stability indices were calculated and their spatial variabilities were investigated using two variography and estimation stages methods. Finally, the kriged map of each aggregate stability indicator was produced. To determine the compatibility of kriged maps of the soil aggregates stability indices calculated for the macro and micro aggregates with aggregates stability index (i.e., AS index) calculated forthe total aggregates, the overall accuracy related to each aggregate component (i.e., macro and micro) was calculatedafter creating an error matrix.
Results and Discussion: The results showed that total aggregate stability in the dry condition and macro aggregate stability in the wet condition had the lowest and highest coefficients of variability,respectively. The highest percentage of total aggregate stability (i.e., 89.90 %)was observed in the north and southeast positions of the study areain the dryconditionwhich had the highest amount of organic matter(i.e., 2.30 %). Also, the variograms of all investigated variables were exponentially and their ranges were varied between 380 to 450 m. Although the obtainedranges were different, a sampling distance more or less equal to 420 m is reasonable to study the most of the variables in the area. This can be a good indicator to decrease the sampling tasks for monitoring of these parameters in future.An overall look at the obtained root mean square standardized error (RMSSE) values indicated a high correlation between the measured and estimated values of all investigatedvariables (except for macro aggregate stability in the wet condition). However, all variables had a strong spatial correlation class. The percentage of overall accuracy obtained from crossing the total and macro aggregate kriged maps in the dry condition (i.e., 51.75 %), was more than its percentage for similar maps in the wet condition (i.e., 32.17 %). In return, the percentage of overall accuracy obtained from crossing the total and micro aggregate kriged maps in the wet condition (i.e., 17.31 %)was greater than its percentage for the mentioned maps in the dry condition (i.e., 10.93 %). Because of macro aggregate sensitivities to the amount of pressure imposed on them (as in the wet sieving method, the aggregates are under pressure caused by water energyin addition to tensions related to mechanical motion of sieving), the conformities of above two mentioned maps were less than those in the dry sieving method.
Conclusions: In general, the soil aggregates stability depends strongly on the amount of pressure imposed on them. Besides, the study of spatial variability of macro and micro aggregate stabilities and relative effects of each on the soil structure stability can be useful for choosing proper land management activities in future studies. According to theeffect of aggregation on nutrient cycling, capture, storage and water movement, and also other soil characteristics affecting plant growth and sustainable agriculture on one hand, and the effect of organic matter on aggregation on the other hand, it can be concluded thatall human activities that have a role in reducing or removing organic matter from the soil (e.g., grazing, deforestation, and intensive cultivation etc.) may reduce soil aggregate stability and finally can jeopardize human life in a near future.
zohreh mosleh; mohammad hassan salehi; azam jafari; Isa Esfandiarpoor Borujeni
Abstract
Introduction: Effective and sustainable soil management requires knowledge about the spatial patterns of soil variation and soil surveys are important and useful sources of data that can be used. Prior knowledge about the spatial distribution of the soils is the first essential step for this aim but ...
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Introduction: Effective and sustainable soil management requires knowledge about the spatial patterns of soil variation and soil surveys are important and useful sources of data that can be used. Prior knowledge about the spatial distribution of the soils is the first essential step for this aim but this requires the collection of large amounts of soil information. However, the conventional soil surveys are usually not useful for providing quantitative information about the spatial distribution of soil properties that are used in many environmental studies. Recently, by the rapid development of the computers and technology together with the availability of new types of remote sensing data and digital elevation models (DEMs), digital and quantitative approaches have been developed. These new techniques relies on finding the relationships between soil properties or classes and the auxiliary information that explain the soil forming factors or processes and finally predict soil patterns on the landscape. Different types of the machine learning approaches have been applied for digital soil mapping of soil classes, such as the logistic and multinomial logistic regressions, neural networks and classification trees. In reality, soils are physical outcomes of the interactions happening among the geology, climate, hydrology and geomorphic processes. Diversity is a way of measuring soil variation. Ibanez (9) first introduced ecological diversity indices as measures of diversity. Application of the diversity indices in soil science have considerably increased in recent years. Taxonomic diversity has been evaluated in the most previous researches whereas comparing the ability of different soil mapping approaches based on these indices was rarely considered. Therefore, the main objective of this study was to compare the ability of the conventional and digital soil maps to explain the soil variability using diversity indices in the Shahrekord plain of Chaharmahal-Va- Bakhtiari province.
Materials and Methods: The soils in the study area have been formed on Quaternary shale and foliated clayey limestone deposits. Irrigated crops such as wheat, barley and alfalfa are the main land uses in the area. According to the semi-detailed soil survey, 120 pedons with approximate distance of 750 m were excavated and described according to the “field book for describing and sampling soils”. Soil samples were taken from different genetic horizons and soil physicochemical properties were determined. Based on the pedons description and soil analytical data, pedons were classified according to the Soil Taxonomy (ST) up to subgroup level. Using aerial photo interpretation, geology map, google earth image and field observations primary soil map was created. With considering the taxonomic level, the representative pedons were determined and soil map was prepared. Multinomial logistic regression was used to predict soil classes at great group and subgroup levels. The map units that have the highest frequency were selected as indicator to calculate diversity indices in the conventional soil map at each taxonomic level. The selected map units were overlay to digital soil map and further diversity indices were calculated. Diversity indices including the Shannon’s diversity, evenness and richness index. In order to know whether the means of Shannon’s diversity for two approaches are significantly different, means comparison was done.
Results and Discussion: The results confirmed that the Shannon's diversity index was higher in the digital soil map than the conventional soil map for most soil map units. At great group and subgroup levels, a significant difference was observed for the Shannon's diversity index at 0.05 and 0.001 probability levels, respectively. Comparing the conventional and the digital soil maps showed the numbers of soil map units with significant difference regarding the Shannon's diversity index decreased from great group to the subgroup level. Although the conventional soil map did not show a good efficiency to explain the soil variability in this region considering more soil information to select the representative pedons at subgroup level in the conventional soil mapping could increase the ability of this approach.
Conclusion: A significant difference for the Shannon's diversity index between the conventional and the digital soil maps demonstrated that conventional soil mapping has not enough ability to explain the soil variability. It is recommended to test the effect of soil mapping approaches on explanation of the soil variability in other areas. Despite the deficiencies of traditional soil survey, it is still difficult to state about their replacement by digital methods.
zohreh mosleh; mohammad hassan salehi; azam jafari; Isa Esfandiarpoor Borujeni
Abstract
Introduction: Soil classification generally aims to establish a taxonomy based on breaking the soil continuum into homogeneous groups that can highlight the essential differences in soil properties and functions between classes.The two most widely used modern soil classification schemes are Soil Taxonomy ...
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Introduction: Soil classification generally aims to establish a taxonomy based on breaking the soil continuum into homogeneous groups that can highlight the essential differences in soil properties and functions between classes.The two most widely used modern soil classification schemes are Soil Taxonomy (ST) and World Reference Base for Soil Resources (WRB).With the development of computers and technology, digital and quantitative approaches have been developed. These new techniques that include the spatial prediction of soil properties or classes, relies on finding the relationships between soil and the auxiliary information that explain the soil forming factors or processes and finally predict soil patterns on the landscape. These approaches are commonly referred to as digital soil mapping (DSM) (14). A key component of any DSM mapping activity is the method used to define the relationship between soil observation and auxiliary information (4). Several types of machine learning approaches have been applied for digital soil mapping of soil classes, such as logistic and multinomial logistic regressions (10,12), random forests (15), neural networks (3,13) and classification trees (22,4). Many decisions about the soil use and management are based on the soil differences that cannot be captured by higher taxonomic levels (i.e., order, suborder and great group) (4). In low relief areas such as plains, it is expected that the soil forming factors are more homogenous and auxiliary information explaining soil forming factors may have low variation and cannot show the soil variability.
Materials and Methods: The study area is located in the Shahrekord plain of Chaharmahal-Va-Bakhtiari province. According tothe semi-detailed soil survey (16), 120 pedons with approximate distance of 750 m were excavated and described according to the “field book for describing and sampling soils” (19). Soil samples were taken from different genetic horizons, air dried and grounded. Soil physicochemical properties were determined. Based on the pedon description and soil analytical data, pedons were classified according to the ST (20) and WRB (11). Terrain attributes, remote sensing indices, geology, soil and geomorphology map were considered as auxiliary information. All of the auxiliary information were projected onto the same reference system (WGS 84 UTM 39N) and resampled to 50×50 m according to the suggested resolution for digital soil maps (14). Four modeling techniques (multinomial logistic regression (MLR), artificial neural networks (ANNs), boosted regression tree (BRT) and random forest (RF)) were used for each taxonomic level to identify the relationship between soil classes and auxiliary information in each classification system. The models were trained with 80 percent of the data (i.e., 96 pedons) and their validation was tested by remaining 20 percent of the dataset (i.e., 24 pedons) that split randomly. The accuracy of the predicted soil classes was determined by using overall accuracy and Brier score.For each classification system, the model with the highest OA and the lowest BS values were considered as the most accurate model for each taxonomic level.
Results and Discussion: The results confirmed that ST showedmore accessory soil properties compared to WRB. The ST described the cation-exchange activity, soil depth classes, temperature and moisture regime. The different models had the same ability for prediction of soil classes across all taxonomic levels based on ST. Among the studied models, MLR had the highest performance to predict soil classes based on WRB. For all the studied models and both classification system, OA values showed a decreasing trend with increasing the taxonomic levels. Predicted soil classes based on the ST had the higher accuracy. Different models selected different auxiliary information to predict soil classes. For most of the models and both classification systems, the terrain attributes were the most important auxiliary information at each taxonomic level.
Conclusion: Results demonstrated that although ST showed more accessory soil properties compared to WRB, the DSM approaches have not enough accuracy for prediction of the soil classes at lower taxonomic levels. More investigations are needed in this issue to make a firm conclusion whether DSM approaches are appropriate for prediction of soil classes at the levels that are important for soil management. Prediction accuracy of soil classes can be influenced by the target taxonomic level and classification system, soil spatial variability in the study area, soil diversity, sampling density and the type of auxiliary information.
Ali Asghar Besalatpour; Hossein Shirani; Isa Esfandiarpour Borujeni
Abstract
Introduction: Soil aggregate stability is a key factor in soil resistivity to mechanical stresses, including the impacts of rainfall and surface runoff, and thus to water erosion (Canasveras et al., 2010). Various indicators have been proposed to characterize and quantify soil aggregate stability, for ...
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Introduction: Soil aggregate stability is a key factor in soil resistivity to mechanical stresses, including the impacts of rainfall and surface runoff, and thus to water erosion (Canasveras et al., 2010). Various indicators have been proposed to characterize and quantify soil aggregate stability, for example percentage of water-stable aggregates (WSA), mean weight diameter (MWD), geometric mean diameter (GMD) of aggregates, and water-dispersible clay (WDC) content (Calero et al., 2008). Unfortunately, the experimental methods available to determine these indicators are laborious, time-consuming and difficult to standardize (Canasveras et al., 2010). Therefore, it would be advantageous if aggregate stability could be predicted indirectly from more easily available data (Besalatpour et al., 2014). The main objective of this study is to investigate the potential use of support vector machines (SVMs) method for estimating soil aggregate stability (as quantified by GMD) as compared to multiple linear regression approach.
Materials and Methods: The study area was part of the Bazoft watershed (31° 37′ to 32° 39′ N and 49° 34′ to 50° 32′ E), which is located in the Northern part of the Karun river basin in central Iran. A total of 160 soil samples were collected from the top 5 cm of soil surface. Some easily available characteristics including topographic, vegetation, and soil properties were used as inputs. Soil organic matter (SOM) content was determined by the Walkley-Black method (Nelson & Sommers, 1986). Particle size distribution in the soil samples (clay, silt, sand, fine sand, and very fine sand) were measured using the procedure described by Gee & Bauder (1986) and calcium carbonate equivalent (CCE) content was determined by the back-titration method (Nelson, 1982). The modified Kemper & Rosenau (1986) method was used to determine wet-aggregate stability (GMD). The topographic attributes of elevation, slope, and aspect were characterized using a 20-m by 20-m digital elevation model (DEM). The data set was divided into two subsets of training and testing. The training subset was randomly chosen from 70% of the total set of the data and the remaining samples (30% of the data) were used as the testing set. The correlation coefficient (r), mean square error (MSE), and error percentage (ERROR%) between the measured and the predicted GMD values were used to evaluate the performance of the models.
Results and Discussion: The description statistics showed that there was little variability in the sample distributions of the variables used in this study to develop the GMD prediction models, indicating that their values were all normally distributed. The constructed SVM model had better performance in predicting GMD compared to the traditional multiple linear regression model. The obtained MSE and r values for the developed SVM model for soil aggregate stability prediction were 0.005 and 0.86, respectively. The obtained ERROR% value for soil aggregate stability prediction using the SVM model was 10.7% while it was 15.7% for the regression model. The scatter plot figures also showed that the SVM model was more accurate in GMD estimation than the MLR model, since the predicted GMD values were closer in agreement with the measured values for most of the samples. The worse performance of the MLR model might be due to the larger amount of data that is required for developing a sustainable regression model compared to intelligent systems. Furthermore, only the linear effects of the predictors on the dependent variable can be extracted by linear models while in many cases the effects may not be linear in nature. Meanwhile, the SVM model is suitable for modelling nonlinear relationships and its major advantage is that the method can be developed without knowing the exact form of the analytical function on which the model should be built. All these indicate that the SVM approach would be a better choice for predicting soil aggregate stability.
Conclusion: The pixel-scale soil aggregate stability predicted that using the developed SVM and MLR models demonstrates the usefulness of incorporating topographic and vegetation information along with the soil properties as predictors. However, the SVM model achieved more accuracy in predicting soil aggregate stability compared to the MLR model. Therefore, it appears that support vector machines can be used for prediction of some soil physical properties such as geometric mean diameter of soil aggregates in the study area. Furthermore, despite the high predictive accuracy of the SVM method compared to the MLR technique which was confirmed by the obtained results in the current study, the advantages of the SVM method such as its intrinsic effectiveness with respect to traditional prediction methods, less effort in setting up the control parameters for architecture design, the possibility of solving the learning problem according to constrained quadratic programming methods, etc., should motivate soil scientists to work on it further in the future.
sh. jorkesh; Mohammad hasan Salehi; I. Esfandiarpour
Abstract
One of the most important soil contaminants are heavy metals. Chemical analysis of the samples can be used to evaluate the contamination but these methods are expensive and time consuming. Thus, for rapid evaluation, other techniques such as magnetic susceptibility are considered. The aim of this study ...
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One of the most important soil contaminants are heavy metals. Chemical analysis of the samples can be used to evaluate the contamination but these methods are expensive and time consuming. Thus, for rapid evaluation, other techniques such as magnetic susceptibility are considered. The aim of this study was to compare the spatial distribution of magnetic susceptibilityand cadmium, lead, nickel and copper in soil series of Isfahan, Khomeinishahr and Zayanderood in Lenjan at region, Isfahan province. Estimation of heavy metals via pedotransfer functions using magnetic susceptibility was also investigated. Total concentration of Cd, Pb, Ni and Cu in soil samples was determines and the magnetic susceptibility of the samples was also measured. Results showed magnetic susceptibility does not have high accuracy for estimation of heavy metals contents in the soils of this region. On the other hand, similar trends of continuous maps for heavy metals and magnetic susceptibility suggest that magnetic susceptibility can be a good indicator for trend of soil contamination in this area.
Ali reza Karimi; Isa Esfandiarpour Borujeni
Abstract
Soil maps are the common sources of soil information for land evaluation and land use planning. The objective of this study was to evaluate the capability of conventional and geostatistical methods for mapping selected physical (sand, silt and clay) and chemical (carbonate calcium equivalent and pH) ...
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Soil maps are the common sources of soil information for land evaluation and land use planning. The objective of this study was to evaluate the capability of conventional and geostatistical methods for mapping selected physical (sand, silt and clay) and chemical (carbonate calcium equivalent and pH) soil properties. Based on interpretation of aerial photographs, satellite images and field observations, five geopedologic map units were identified in an area of about 12 km2 in southern Jiroft. 100 surface soil samples (0-20 cm) were taken from a regular grid of 500 × 250 m. The results indicated that geopedological map units were significantly different in at least one soil property. Differences of characteristics between units are resulting differences in geomorphic processes. Continuous soil maps prepared by the ordinary kriging also revealed continuous variations of characteristics in accordance with the changes in geomorphic processes. However, variations between units obviously recognizable in the boundary of units were not revealed by the geostatistical method. Based on results of this study, the conventional method is proposed for large areas (small scale maps) and geostatisticals method for small areas (large scale maps) are proposed for soil mapping.
I. Esfandiarpour Borujeni
Abstract
Soil salinity and sodicity are considered as the important factors limiting the plants growth. This study was conducted to assess the influence of the sample size on the accuracy of estimation of soil salinity and sodicity status, made by ordinary kriging, inverse distance weighting and spline estimators ...
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Soil salinity and sodicity are considered as the important factors limiting the plants growth. This study was conducted to assess the influence of the sample size on the accuracy of estimation of soil salinity and sodicity status, made by ordinary kriging, inverse distance weighting and spline estimators in Eslamieh area, Rafsanjan city. First, electrical conductivity (EC) and sodium adsorption ratio (SAR) were measured for 100 observation points, collected from three depths using a regular grid sampling pattern with an interval of 500 meter. These properties were mapped using aforesaid estimators. Then, random omission of 20, 40 and 60 samples from the total primary dataset (100 samples), was done and in each new situation, EC and SAR were mapped again. At the end of all 10 stages used to omit the samples, the index of standardized root mean square error (RMSE%) was measured for each estimator. Finally, the obtained contents of RMSE% were statistically compared using Friedman and Wilcoxon tests. The results showed using relatively high number of samples (all 100 observation points), three analyzed estimators have no significant difference (95% confidence level). In the cases of lower sample sizes, Friedman test revealed a significant difference among the estimators; whereas using Wilcoxon test, as a supplementary procedure, no significant difference was observed between the results obtained from ordinary kriging and inverse distance weighting. Hence, thanks to the relatively good precision, ease of processing and lower required sampling points, the inverse distance weighting estimator is recommended for future studies in the studied area.
Z. Rashidi Koochi; Isa Esfandiarpour Borujeni; A. Abbaspoor
Abstract
Geopedology which is a systematic approach to analyze the influence of the geomorphic levels on soil mapping, makes it possible to generalize the soil survey results in one geomorphic unit to the other similar units of a studied area that resulted in reducing the time and costs of soil survey. The main ...
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Geopedology which is a systematic approach to analyze the influence of the geomorphic levels on soil mapping, makes it possible to generalize the soil survey results in one geomorphic unit to the other similar units of a studied area that resulted in reducing the time and costs of soil survey. The main question, in this regard, is to what extent the soil data generalization can be valid? To answer this question the effect of landform's kind on the reliability of the geopedology approach was studied in an area of about 1500 ha on the east of Damavand. For this purpose, the geomorphic units were determined by interpreting the 1:55000 scale air-photos of the study area. In the next step, two similar delineations were selected in the piedmont landscape and two similar ones were selected in the hill-land landscape as well. Then, according to semi-detail soil surveys, some pedons were studiedin each of similar delineations. Through the description and sampling of all of pedons and through physical and chemical analyses on all of the pedons’ master horizons, the pedons were classified up to the family level according to Soil Taxonomy system. The results indicated a significant effect of landform's kind on the results of geopedology approach. Although the kind of map unit for similar landforms was the same; the similarity was lower among the pedons in the hill-land landscape than the piedmont in all levels of Soil Taxonomy system. It was probably due to the more slope variations in the hill-land (compared to piedmont areas), and as a result, a greater spatial variation of the nature of exist soils on this landscape.
Z. Rashidi Koochi; I. Esfandiarpoor Borujeni; A. Abbaspour; A. Kamali; A.A. Naderi
Abstract
Geopedology is a systematic approach to analyze the geomorphic levels for soil mappingwhich organizes natural terrains in a hierarchical order in accordance with their scale. Hereon, the effect of mapping scale and kinds of soil classification system on the reliability of the geopedologic approach was ...
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Geopedology is a systematic approach to analyze the geomorphic levels for soil mappingwhich organizes natural terrains in a hierarchical order in accordance with their scale. Hereon, the effect of mapping scale and kinds of soil classification system on the reliability of the geopedologic approach was investigated. In view of that, after air-photos interpretation (1:55,000 and 1:40,000 scales) of an area (1500 ha) on the east of Damavand, two similar delineations (named A and B) were selected on the geoform map. Then, some pedons were dug in the similargeoforms based on semi-detailed soil surveys. The pedons were classified up to the family level according to Soil Taxonomy and up to the subunit level (including suffix and prefix qualifiers) based on WRB soil classification systems. This was the conducted when description and sampling from all genetic horizons and physical and chemical analyses had been already done. The results showed that mapping scale affected the results of the geopedologic approach significantly, so that relative similarity in all taxonomic levels was lower in 1:40,000 scale than the 1:55,000 scale for all studied pedons. Besides, Soil Taxonomywas more adaptable than WRB in predicting the relative similarity among soils in the same geoforms. On the whole, the geopedologic approach is still not able to estimate and determine the complete variability of soils and define their chaotic nature precisely, and the performance of this approach is limited to semi-detailed surveys and smaller ones.
M. Bagheri-Bodaghabadi; M.H. Salehi; J. Mohammadi; N. Toomanian; I. Esfandiarpour Borujeni
Abstract
Abstract
Limitations of traditional (conventional) soil surveys and improvement of information technology have lead soil surveyors to invent new methods which are generally called digital soil mapping (DSM). The aim of these methods is the prediction of soil classes or soil properties based on easily-available ...
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Abstract
Limitations of traditional (conventional) soil surveys and improvement of information technology have lead soil surveyors to invent new methods which are generally called digital soil mapping (DSM). The aim of these methods is the prediction of soil classes or soil properties based on easily-available or measuring environmental variables. The objective of this investigation is to study the efficiency of digital elevation model and its derivates for soil mapping using Soli-Land Inference Model (SoLIM) and credibility of its results in the Borujen area, Chaharmahal-va-Bakhtiari province. Eighteen terrain attributes including height, slope (angle), aspect, curvature, minimum curvature, maximum curvature, tangent curvature, profile curvature, planform curvature, flow direction, flow accumulation, direct radiation, diffuse duration, diffuse radiation, area solar radiation, power index, sediment index and wetness index, were derived from the DEM. These derivates as well as three dominant soil subgroups and seven soil families of the region were used to construct the input data matrix of the model. Results showed an accuracy of 65% and 40% for interpolation and extrapolation of the soils at subgroup level, respectively. The accuracy decreased to half when soil families were considered for credibility of the model. Because of using crisp limitations in American Soil Taxonomy system, assessing soil survey results can be miss-leading partially, whereas using SoLIM model shows well the reality of the soils in the field.
Keywords: SoLIM, Fuzzy logic, Digital soil mapping, Digital elevation model
I. Esfandiarpour Borujeni; M.H. Farpoor; A. Kamali
Abstract
Abstract
Soil classification is a simple tool which is useful to improve human knowledge and to transfer the experience and technology obtained from landscape. The objective of the present research is to compare the efficiency of Soil Taxonomy and WRB in saline soils of different areas in Kerman province. ...
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Abstract
Soil classification is a simple tool which is useful to improve human knowledge and to transfer the experience and technology obtained from landscape. The objective of the present research is to compare the efficiency of Soil Taxonomy and WRB in saline soils of different areas in Kerman province. Soil climatic units of the province were separated and based on the area covered by each climatic unit, several pedons were studied. Finally, 12 saline pedons were selected. Results showed that WRB can better express field conditions from both horizontal and vertical dimensions for classification of saline soils of arid areas due to various qualifiers used and more flexibility of this system in reflecting effective properties in soil nomenclature. Besides, adding new "Calcic Natrisalids" and "Calcic Petrosalids" subgroups to Soil Taxonomy from one hand, and "Petrogypsic", "Hypergypsic", "Episalic", "Endosalic", and "Aquic" qualifiers to prefix qualifiers of Solonchaks reference group of WRB from the other hand, can better correlate subgroups of Soil Taxonomy with second level classes of WRB.
Keywords: Soil Taxonomy, WRB, Saline soils, Soil correlation, Kerman province
S. Sanjari; M.H. Farpoor; M. Karimiam Eghbal; I. Esfandiarpour Borujeni
Abstract
Abstract
Geomorphology and soil genesis and its development are closely related. Besides, soil-landscape studies provide a better understanding of soil forming processes. The objectives of the present research include soil genesis studies, micromorphology and clay mineralogy of soils related to geomorphic ...
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Abstract
Geomorphology and soil genesis and its development are closely related. Besides, soil-landscape studies provide a better understanding of soil forming processes. The objectives of the present research include soil genesis studies, micromorphology and clay mineralogy of soils related to geomorphic surfaces in Jiroft area. Soil temperature and moisture regimes of the area are hyperthermic and aridic respectively. Alluvial fan, mantled pediment, intermediate surfaces, alluvial plain, and lowland landforms were identified. Each landform was divided into different surfaces due to geomorphic stability. One representative pedon was studied and sampled on each geomorphic surface. Routine physicochemical, clay mineralogy, and micromorphology analysis were performed on soil samples. The results showed that electrical conductivity, pH, and SAR contents increased from mantled pediments toward lowland positions. Besides, fine soil textures were found in downward positions. Chlorite, illite, palygorskite, smectite, and kaolinite clay minerals were found. Moving down toward alluvial plain, palygorskite stability decreased due to high water table, that is why smectite is the dominant clay mineral in alluvial plain. Source of palygorskite in mantled pediment, intermediate surfaces, and alluvial plain is inherited, pedogenic, and detrital respectively. Clay coating and infillings in Btn and Btk horizons of stable and unstable surfaces were investigated during thin section observations. Besides, calcite coating and infilling were found in Btk horizon at stable geomorphic surfaces. Results of the present research show that difference in soil characteristics is highly affected by geomorphology.
Keywords: Geomorphology, Palygorskite, Clay and calcite coating, Jiroft
I. Esfandiarpour Borujeni; N. Toomanian; M.H. Salehi; J. Mohammadi
Abstract
Abstract
Geopedology is a systematic approach of geomorphic analysis for soil mapping which focuses the field operation mainly on sample area. The purpose of this study is to determine the credibility of generalization of the results of geopedological approach for similar landforms in the Borujen region, ...
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Abstract
Geopedology is a systematic approach of geomorphic analysis for soil mapping which focuses the field operation mainly on sample area. The purpose of this study is to determine the credibility of generalization of the results of geopedological approach for similar landforms in the Borujen region, using diversity and similarity indices in a soil taxonomic hierarchical structure. After a primary interpretation of the study area on air photos (1:20000 scale), the largest delineation of Pi111 geomorphic unit was selected and 19 pedons with an approximate 125 m interval were excavated, described and sampled. The credibility of generalizing the results of the geopedological approach for the studied unit was tested by comparison with 15 pedons in a similar unit outside the sample area, named the validation area. Results showed that as the category decreases from order to soil family, the Shannon's diversity index increases in both the sample and validation areas. A significant difference at 95% confidence level was observed for pedodiversity mean values of two areas at family level. Soil diversity also remains high through the soil taxonomic hierarchy when we change the understanding level and consider the horizon/genetic diversity in both the sample and validation areas. Jaccard index and proportional similarity also indicated that up to subgroup level, the geopedological approach can be used for generalization of the similar geomorphic unit results and it does not have a good efficiency for lower soil taxonomic levels (family and series). Therefore, the use of landform phases and also phases of soil families and/or series for each of landform phases is recommended to increase the accuracy of geopedological results.
Key words: Geopedology, Pedodiversity, Similarity index, Sample area, Validation area