| چکیده انگلیسی مقاله |
Introduction: Erodibility, which is determined by the soil's intrinsic features, is one of the most important elements in soil erosion. This factor reflects how sensitive the particles of a particular soil are to separation and transmission by erosion causes, both quantitatively and qualitatively. For measuring soil loss, the Universal Soil Loss Equation (USLE) is very useful. Sources reveal that erodibility is influenced by a variety of physical and chemical features of soil. In several soil erosion and sedimentation models, such as USLE, RUSLE, and MUSLE, one of the essential parameters is erodibility, which is represented as K. Particle size distribution, organic matter, structure, and permeability all have a role. The goal of this research was to quantify the amount of erodibility (K) in dry and semi-arid soils, as well as the physicochemical parameters that influence it. Another purpose of this research is to develop a connection that uses principal component analysis (PCA) and linear multivariate regression to estimate the quantity of soil erodibility based on effective physicochemical parameters. Methodology: The research location is 20 kilometers from Yazd city, along the Yazd-Ardakan road, on the edge of the dunes facies, which includes bare, mantled, and covered pediments. Using the stratified random sampling approach, soil samples were gathered to a depth of 10 cm within the facies in this study. The size and form of aggregates, as well as water penetration in the soil, were used to calculate soil structure codes using Wischmeier and Smith's tables. In the desert, soil permeability was assessed using double cylinders based on the ultimate infiltration rate. The hydrometer technique was used to determine the spread of soil granulation. Wet sieving and the Walkley and black methods were used to assess the proportion of extremely fine sand and organic matter, respectively. Lime was calculated by multiplying the volume of the hydrochloric acid neutralization reaction by the quantity of neutralizing agents. Statistical indicators such as mean, minimum, maximum, and standard deviation were derived at this step after computing the soil erodibility index. Principal component analysis was performed using SPSS17.0 software, and the linear multivariate regression model was utilized to predict soil erodibility index. After selecting significant components, linear multivariate regression between these components and soil erodibility was conducted concurrently. The coefficient of determination was used to assess the equation's accuracy in this investigation (R2). Results: Introduction: Erodibility, which is determined by the soil's intrinsic features, is one of the most important elements in soil erosion. This factor reflects how sensitive the particles of a particular soil are to separation and transmission by erosion causes, both quantitatively and qualitatively. For measuring soil loss, the Universal Soil Loss Equation (USLE) is very useful. The scholarly sources have revealed that erodibility is influenced by a variety of physical and chemical features of soil. In several soil erosion and sedimentation models, such as USLE, RUSLE, and MUSLE, one of the essential parameters is erodibility, which is represented as K. Particle size distribution, organic matter, structure, and permeability all have a role as well. The goal of this research was to quantify the amount of erodibility (K) in dry and semi-arid soils, as well as the physicochemical parameters that influence it. Another purpose of this research was to develop a connection that uses principal component analysis (PCA) and linear multivariate regression to estimate the quantity of soil erodibility based on effective physicochemical parameters. Methodology: The research location is 20 kilometers from Yazd city, along the Yazd-Ardakan road, on the edge of the dunes facies, which includes bare, mantled, and covered pediments. Using the stratified random sampling approach, soil samples were gathered to a depth of 10 cm within the facies in this study. The size and form of aggregates, as well as water penetration in the soil, were used to calculate soil structure codes using Wischmeier and Smith's tables. In the desert, soil permeability was assessed using double cylinders based on the ultimate infiltration rate. The hydrometer technique was used to determine the spread of soil granulation. Wet sieving and the Walkley and black methods were used to assess the proportion of extremely fine sand and organic matter, respectively. Lime was calculated by multiplying the volume of the hydrochloric acid neutralization reaction by the quantity of neutralizing agents. Statistical indicators such as mean, minimum, maximum, and standard deviation were derived at this step after computing the soil erodibility index. Principal component analysis was performed using SPSS17.0 software, and the linear multivariate regression model was utilized to predict soil erodibility index. After selecting significant components, linear multivariate regression between these components and soil erodibility was conducted concurrently. The coefficient of determination was used to assess the equation's accuracy in this investigation (R2). Results: The findings of the physical and chemical features of soil study revealed that the texture of the soil is mostly light sandy to loamy, with low organic content and calcareous. In terms of structural form, the analyzed soils were extremely fine granular and spongy, and their structural code was based on USLE (2 and 1). The permeability of the soil profile was high to extremely high (18.4 cm/h), and it was often in Class 1, 2, and in some instances Class 3 according to USLE. In the three naked, mantled, and covered pediments, the estimated erodibility indexes based on Wischmeier and Smith regression relationships were 0.0385, 0.03, and 0.0199 ton.hr/MJ.mm, respectively. According to the particular values acquired from the parameters and the percentage of variance, the top three components may be picked as the major component using principal component analysis. The first, second, and third components have correlation values of 0.88, -0.04, and 0.41, respectively, with the soil erodibility index. As a result, the first component has a stronger relationship with the soil erodibility index than the second and third ones. The percentage of sand and silt, soil permeability, and percentage of clay have a higher correlation with the soil erodibility index, respectively, and the correlation of other factors (organic matter, gravel, fine sand, and lime) is low in this component, according to the values for the given loading period. The amount of sand in the soil and its permeability are negatively correlated; whereas, the percentage of silt and clay in the soil is positively correlated. The maximum load is connected to the variables of gravel and lime in the second component, and it is related to organic matter and extremely fine sand in the third component. The effect of characteristics on soil erodibility is significant (0.001>p) and its coefficient of determination (R2) is 0.88 percent, according to an investigation of the relationship between soil erodibility and principal component values obtained from PC1, PC2, and PC3 using a linear multivariate regression model. Discussion & Conclusions: The quantity of erodibility in dry and semi-arid soils, as well as the physicochemical parameters that impact it, were investigated in this research. Using principal component analysis and linear multivariate regression, a link was found to estimate the quantity of soil erodibility based on the effective physicochemical parameters. Because of the high amount of sand in the region's soils, these soils are readily separated due to poor adhesion, but because they contain bigger particles, they resist runoff and hence create less sediment. This barrier to transfer reduces as the quantity of clay and silt in the soil increases, and consequently more sediment is transported. Furthermore, a considerable quantity of sand improves soil permeability and reduces runoff. However, when the amount of silt and clay in the soil increases as a result of surface sealing, permeability reduces and greater runoff occurs. Soil erodibility is additionally influenced by organic content, lime, gravel, and permeability. Lime has a negligible influence on soil erodibility since it contains calcium cation, which increases particle homogeneity and hence increases soil resilience to rain drops. Organic matter has a negative relationship with soil erodibility as well. The breakdown of aggregates is slowed by increasing the quantity of organic matter in the soil. As a result, as organic matter levels rise, the rate of aggregate decomposition in a particular soil falls by one-third. Similar research studies in other semi-arid and arid soils in Iran are required to provide a more reliable connection for forecasting erodibility of soils in semi-arid and arid locations. |