Soil Erodibility Factor Research Articles

Study on Dynamic Changes of Soil Erosion in the North and South Mountains of Lanzhou

The North and South Mountains of Lanzhou City are the ecological protection barriers and an important part of the ecological system of Lanzhou City. This study takes the North and South Mountains as the study area, calculates the soil erosion modulus of the North and South Mountains of Lanzhou City based on the five major soil erosion factors in the RUSLE model, and analyses the spatial and temporal dynamics of soil erosion in the North and South Mountains of Lanzhou City and the soil erosion characteristics under different environmental factors. The results of the study show that: The intensity of soil erosion is dominated by slight erosion, which was distributed in the northwestern and southeastern parts of the North and South Mountains in 1995, 2000, 2005, 2010, 2015 and 2018. Under different environmental factors, the soil erosion modulus increased with elevation and then decreased; the soil erosion modulus increased with a slope; the average soil erosion modulus of grassland was the largest, followed by forest land, cultivated land, unused land, construction land, and it was the smallest for water; except for bare land, the average soil erosion modulus decreases with the increase of vegetation cover; Soil erosion modulus was the greatest in the pedocal of the North and South Mountains, and the least in the alpine soil.

The nexus between land use, land cover dynamics, and soil erosion: a case study of the Temecha watershed, upper Blue Nile basin, Ethiopia.

At the current times, soil erosion is the major problem that affects land and water resources, especially in Ethiopia's highlands. Due to the dynamics of land use land cover change, land degradation, and soil erosion increase significantly and result in a loss of fertile soil every year and lead reduction in agricultural production. This study was therefore designed to explore the land use land cover (LULC) dynamics from 1986 to 2020, to estimate mean annual soil erosion rates and identify erosion hotspot areas from 1986 to 2020, and finally, to evaluate the impacts of land use land cover change on soil loss of 1986 to 2020. For this, Landsat imageries of 4 years from 1986 to 2020 were used. Maximum likelihood supervised classification methods were used to classify LULCs. The dynamics of LULC change were used as an input for measuring soil loss by employing the combination of geospatial technologies with the revised universal soil loss equation (RUSLE). The LULC maps of 1986, 1997, 2009, and 2020 were used for identifying crop management (C) factor and conservation practice (P) factor. Rainfall erosivity factor (R), soil erodibility factor (K), and slope length and steepness factor (LS) were also used as sources of data. Based on the five factors, soil erosion intensity maps were prepared for each year. Results showed that the annual soil loss in the watershed ranged from 0 to 3938.66 t/ha/year in 1986, 0 to 4550.94 t/ha/year in 1997, 0 to 5011.21 t/ha/year in 2009, and 0 to 6953.23 t/ha/year in 2020. The annual soil loss for the entire watershed was estimated at 36.889, 42.477, 47.805, and 48.048 t/ha/year in 1986, 1997, 2009, and 2020, respectively. The mean soil loss of 1986, 1997, 2009, and 2020 was higher in cultivated land followed by shrub land, grazing land, and forest land. Mean soil loss increased from 1986 to 1997, from 1997 to 2009, and from 2009 to 2020. This is because of the expansion of agricultural land at the expense of grazing land and shrub land. Therefore, urgent soil and water conservation practices should be made in hotspot areas.

Assessment of soil loss rate using GIS–RUSLE interface in Tashat Watershed, Northwestern Ethiopia

The erosion of soil is one of the most difficult and ongoing problems caused by deforestation, improper cultivation, uncontrolled grazing, and other anthropogenic activities. As a result, assessing the level and quantity of soil erosion is essential for agricultural productivity and natural resource management. Thus, the goal of this study was to quantify soil loss rates and identify hotspot locations in the Tashat watershed, Abay basin, Ethiopia. Thematic factor maps, comprising rainfall erosivity factor (R), soil erodibility factor (K), topography factor (LS), cover and management factor (C), and conservation practices factor (P), were integrated using remote sensing data and the GIS 10.3.1 environment to estimate soil loss using RUSLE. The findings indicated that the watershed annual soil loss varies from none in the lower part to 3970.6 t ha−1 year−1 in the middle, with a mean annual soil loss of 64.2 t ha−1 year−1. The total estimated annual soil loss was 61,885,742.9 tons from the total watershed area of 48,348.4 ha. The majority of these soil erosion-affected places are geographically located in the watershed middle steepest slope portion, where Cambic Arenosols with higher soil erodibility character than other soil types in the research area predominate. Thus, sustainable soil and water conservation techniques should be implemented in the steepest middle section of the study area by respecting and acknowledging watershed logic, people, and watershed potentials.

Effects of Dodonaea viscosa Afforestation on Soil Nutrients and Aggregate Stability in Karst Graben Basin

Dodonaea viscosa is widely cultivated in the karst graben basin and is crucial for recovering land after rocky desertification. However, the effect of long–time D. viscosa afforestation on changes in the quality of soil remains unclear. Soil nutrients and aggregate composition can be used to evaluate the beneficial effects of afforestation of D. viscosa in improving soil functional stability. In this study, soil nutrients and aggregate stability were investigated using cropland, 10–year, 20–year, and 40–year D. viscosa afforestation and secondary succession shrub. Compared to the cropland, D. viscosa afforestation significantly increased the soil water content (WC), soil organic carbon (SOC), and total nitrogen (TN) contents, with an enhanced effect observed with prolonged afforestation. Soil nutrient contents under D. viscosa afforestation rapidly reached the level of the shrub. Dodonaea viscosa afforestation promoted the formation of >2 mm aggregates and decreased the ratio of 0.053–0.25 mm aggregates, which varied with afforestation years. Compared to the cropland, the content of >0.25 mm water–stable aggregates (R>0.25), mean weight diameter (MWD), and geometric mean weight diameter (GMD) of soil increased exponentially. However, soil erodibility factor (K) and unstable aggregates index (EIt) decreased exponentially with prolonged D. viscosa afforestation, and the latter two indicators did not reach the level of the shrub. These results indicated that soil nutrients, aggregate stability, and erosion resistance increased with prolonged D. viscosa afforestation. However, the aggregate stability and erosion resistance exhibited by D. viscosa could not reach the level of secondary shrub for a long time.

Monitoring Surface Deformation Associated With Soil Erosion in Loess Plateau With an Improved Small Baseline Subset Interferometric Synthetic Aperture Radar Algorithm

As a geomorphic process persistently occurring over the earth’s surface, soil erosion is one of the most serious environmental problems in the world nowadays, seriously threatening agriculture, natural resources, and ecosystem environments. Monitoring surface deformation associated with soil erosion will help to understand the dynamics of erosion process and the erosion mechanism. In this article, an improved small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) technique is proposed to monitor the surface deformation over the wind-water erosion crisscross region in the Loess Plateau, China. In particular, a new deformation model considering both of the internal factors and external factors of soil erosion is introduced into the SBAS-InASR algorithm. The results show that the cumulative deformation is close to −200 mm during the investigated period. The deformation rate caused by precipitation is between −0.03 and 0.02 mm/mm. When the wind erosion factor changes by 1 unit, the deformation range is approximately −0.3–0.2 mm. Also, researchers found that the surface deformation is highly correlated to precipitation and wind. The root mean square errors (RMSEs) of the residual deformations estimated from the proposed model are smaller than those estimated from the conventional linear and period models, demonstrating that the proposed method is more suitable to model and analyze the surface deformation in wind-water erosion crisscross region.

Dynamic Evolution and Quantitative Attribution of Soil Erosion Based on Slope Units: A Case Study of a Karst Plateau-Gorge Area in SW China

Exploring the dynamics of soil erosion and identifying its driving mechanisms is key to understanding soil erosion processes, particularly in karst areas. In this study, the RUSLE model, optimized on the basis of rocky desertification factors, was used to estimate soil erosion in a karst plateau gorge area in SW China. The spatial and temporal dynamics of soil erosion in the region over the past 20 years were analyzed on the basis of slope units, while the relationship between soil erosion and elevation, slope, fractional vegetation cover (FVC), karst rocky desertification (KRD), rainfall, and land use cover/change (LUCC) was identified quantitatively by the geographical detector on the basis of spatial heterogeneity. The results showed that: (1) The no erosion area decreased from 2000 to 2020, with the highest proportion of light to medium erosion and an increasing trend of soil erosion. (2) Soil erosion conversion mainly occurred between no erosion, slight erosion, and light erosion. (3) The hotspots of erosion occurred in high slope–low elevation and high slope–high elevation units, while the coldspots of erosion occurred in low slope–low elevation units. (4) Soil erosion was positively correlated with FVC and slope, and negatively correlated with KRD. (5) The dominant factor of soil erosion changed from KRD-slope to LUCC-slope and finally to elevation-slope, while the q value of rainfall-elevation had the most significant increase throughout the study period. This study will help to advance the goal of sustainable development of soil and water conservation in karst areas.

Remote Sensing Quantitative Research on Soil Erosion in the Upper Reaches of the Minjiang River

The upper reaches of the Minjiang River are an important ecological barrier to the upper reaches of the Yangtze River and Chengdu Plain. They are also the water resources of Chengdu Plain. To protect the headwater ecosystems, it is necessary to carry out quantitative research on soil erosion in this area. This study mainly applies the USLE model to quantitatively evaluate the spatial distribution of ecosystem services on soil erosion and soil conservation. The evaluation is based on the following data: remote sensing image, meteorological radiation data, DEM, soil types, physical and chemical properties of the soil, vegetation types, and land use status. With advanced Earth observation technology, satellite remote sensing images about terrestrial vegetation and other evaluation parameters can be obtained in real time. The seasonal changes in vegetation coverage and the physical and chemical properties of soil have been fully considered. The results show that 1) the value of rainfall erosivity factor (R) is between 85.02 and 588.69 MJ•mm/(hm2•h•a), and its spatial distribution is consistent with that of annual average precipitation. 2) Soil erodibility factor (K) is between 0.12 and 0.30 (t hm2 h) •(hm2 MJ mm), showing zonal distribution, and is related to soil types. 3) Slope length and gradient factor (LS) range from 0.03 to 46.16. It is positively correlated with topographic relief. 4) Soil conservation measure factor (P) ranges from 0 to 1 and is determined by the land use and coverage. 5) Rainfall has a dominant impact on soil erosion, and the study area with violent and intense erosion reaches 17,302.17 km2, accounting for 69.81% of the total area. The soil conservation amount (T) in the study area is 283.45 million tons, and the ecosystem services are worth 434.48 million yuan. 6) RS and GIS techniques enable quick estimation. However, this assessment contains considerable uncertainty. It is still hard to reveal the physical process of soil erosion with empirical models.

Soil Erosion Process Simulation and Factor Analysis of Jihe Basin

Soil erosion is a notable contributor to carbon emissions. Distributed erosion model can be used to study erosion distribution in different land cover, identify the main influential factors, and hence guide soil and water conservation. In this study, Regional Soil Erosion model (RSEM) was used to simulate the soil erosion processes of Jihe Basin in 2015, and the Multiscale Geographically Weighted Regression (MGWR) implementation was applied to compare the erosion regression of Geographically Weighted Regression (GWR) and MGWR as well as study the impact of influential factors on sediment modulus in hillslopes. The results are as follows: (1) MGWR results indicated slope was the dominant factors affecting soil erosion at the catchment scales, where the average coefficients of slope, forest coverage, and grass coverage descended in the value of 0.90, −0.11, and −0.19, and the influences of factors operate over scales; (2) MGWR with the adoptive bandwidths performed well in the goodness of fit, t-test of variables, scales that variables operate, and interactive interpretation of soil erosion; (3) the coupling effects and scales of vegetation and topography factors are an important approach to study soil erosion at a larger scale.

Multiple perspective accountings of cropland soil erosion in China reveal its complex connection with socioeconomic activities

Cropland soil erosion is a major threat to global agricultural sustainability. Compared with the well-studied natural factors of soil erosion, socioeconomic factors have just begun to be considered. The major obstacle is that the soil erosion of different crops has not been accounted for and connected to socioeconomic activities. This study adopted the revised universal soil loss equation (RUSLE) and the environmentally extended multiregional input-output (EE-MRIO) model to account for soil erosion on cropland from the production, consumption, and income perspectives at the provincial and sectoral levels in China. The results showed the major responsible agents in a synthetic view. On the production side, the Yunnan-Kweichow Plateau and surrounding hills of the Sichuan Basin, the Chinese grain bases located in northeastern and central China, and the Loess Plateau were the major regions that caused the most cropland soil erosion. On the consumption side, the national final demand for Sichuan, Yunnan, Shandong, Henan, and other provinces with high production-based cropland soil erosion (PBE) values per se pulled remarkable cropland soil erosion. On the income side, the crucial provinces into which the national primary input enabled notable cropland soil erosion were still those areas with serious cropland soil erosion. From the industrial perspective, the direct effects of human primary inputs into agriculture and the demand for agricultural products played the dominant role. In addition, Cereal and BOP (beans, oil and potatoes) were the most important among the agricultural sectors. The effects of the nonagricultural sectors, especially the manufacture of foods and tobacco (MFT) and the chemical industry (CI), on cropland soil erosion also cannot be ignored. Our results indicated that accounting from multiple perspectives could inform the better management of cropland soil erosion from a more comprehensive perspective. To the best of our knowledge, this study is the first to consider the accounting of cropland soil erosion in China from multiple perspectives. Tailored packages of measures should be established based on different patterns of areas with serious cropland soil erosion.

SOIL EROSION RISK ESTIMATION BY USING SEMI EMPIRICAL RUSLE MODEL: A CASE STUDY OF NUN WATERSHED, UTTARAKHAND

Day by day numerous population pressure on land and due industrialization there is a vigorous increase of temperature increase in atmosphere, acid rain along with Deforestation definitely degrade the quality the of land. It should have to evaluate the land for estimate the quality of soil and find out the nutrition status as well as the soil health. The present study was employed with in Geographic Information System (GIS) environment to predict erosion risk following semi empirical Revised Soil Loss Erosion Model (RUSLE). The physiographic soil map was prepared by visual interpretation of satellite image LISS-4 from which soil erodibility factor is derived. The Digital Elevation Model (DEM) derived from Contour map, used as the base map for the topographic related analysis. In the model LS factor was derived from the DEM. C and P factor derived from the LULC map. In the watershed 15.90 % under very high erosion, 5.49 % high erosion,7.03 % under moderate high erosion, 8.54 % under moderate erosion,11.69 % under low erosion, and 51.36 % under very low erosion.

Comparison of the uncertainty of soil organic carbon stocks in different land uses

This study was performed to compare the uncertainty spatial distribution of soil organic carbon (SOC) stocks in different land uses. To achieve this goal, 210 soil samples were collected in 1140 km2 land areas, and soil carbon, pH, EC, soil texture, and mineralogy were determined. Then, SOC stocks and soil erodibility factor were calculated. This data set was combined with the environmental variables data for modeling and mapping of SOC stocks using the random forest model. Accordingly, the uncertainty https://ezproxy.ir/science/article/pii/S0016706114002006 of SOCof SOC stocks was calculated. The mean uncertainty of SOC stocks was compared in dry farming, riverbed, gardens, irrigated farming, and grasslands. Finally, the response surface methodology was utilized to analyze the effect of soil properties on the uncertainty of SOC stocks. The results showed that the R2, RMSE, MAE, and concordance (Pc) for modeling SOC stocks by the random forest model were 0.81, 0.44 (kg.m−2), 0.34 (kg.m−2), and 0.83, respectively. The mean comparisons of uncertainty among the different land uses showed that the highest uncertainty was related to dry farming. The response surface methodology in dry farming showed that interactive effects between soil erodibility factor, sand, and EC were the most effective factors to address the uncertainty of SOC stocks.

A Review on Soil Erodibility Studies in Malaysia

Studies on soil erodibility in Malaysia were critically reviewed. Soil erodibility is the only factor of Universal Soil Loss Equation (USLE), which requires laboratory work and analysis complexity to estimate soil loss. Therefore, the main objective is to review soil erodibility studies to enhance understanding of Malaysia’s soil erosion impacts. These studies were summarized in their application, purpose, value, utilization method/approach, and study location. On the other hand, a summarization of what, why, where, and how the soil erodibility was used was analyzed. Therefore, the importance of soil erodibility as input for environmental management and conservation practices can be addressed. For a large-scale area, the soil erodibility factor will be analyzed as one of the USLE variables in estimating the erosion rate. As for a small-scale area, this factor is an adaption of the ROM Scale, which use to identify the slopes or riverbanks prone to landslides induced by erosion. However, the determination of this factor requires extensive artistry, time, and cost. These would be an obstacle to the holistic assessment of soil erosion impacts since only a little soil sampling will be analyzed. Therefore, the simplified version for the determination of soil erodibility without any requirement of laboratory works will be an extra mile, especially for an area that does not have any information on soil series.

Assessment of soil erosion hazard and factors affecting farmers’ adoption of soil and water management measure: A case study from Upper Domba Watershed, Southern Ethiopia

Soil erosion is a serious and continuous environmental problem in the highlands of Ethiopia, particularly, in the study watershed. The purpose of the study was to assess potential annual soil loss and factors affecting the adoption of soil and water conservation technologies in the Domba watershed. In the study, rainfall data, satellite imageries, and digital soil map were used to determine the RUSLE factors. In addition, household data was used to assess contributing factors to erosion hazards in the area. Furthermore, Revised Universal Soil Loss Equation along with Remote Sensing Techniques, Geographical Information System, multiple regression model was used in analysing the data to find out the contributing factors for the severe soil erosion in the study area. The study result revealed that the estimated annual soil loss of the watershed was ranging between 0 to 95 t ha -1 y -1. Degraded mountain ranges of Sule and Gana kare-Woyza ridges contributed majority (more than 82%) soil loss in the watershed. This part of the watershed was categorized under severe erosion intensity class and levelled in priority list for intervention measure. The study further showed that there exists a strong positive relationship (r = 0.874) between adoption of improved SWC measures and the independent variables used in the study at 0.05 significant level. Among these variables, plot area, plot distance to residence and perception of erosion problem significantly but negatively influences adoption of improved SWC practices. The study further showed that above 77.6% of the variance of adoption of SWC measures were explained by eleven variables used in the study. Therefore, to revert the severity of soil erosion, both government and non-government institutions should enhance timely and proper management measure in the study watershed.

Determination of soil erodibility (K) factor derived from different geologic formations of Akwa Ibom State

The onsite and offsite effect of erosion has caused it to be considered as a global issue. For erosion to occur there must be soil erodibility and rainfall erosivity. Different geologic formations give rise to different types of soil and this has affect their responses to an erosive event. Five (5) geologic formations were identified within the study area. This study assessed the erodibility of soils from different geologic formations. Soil samples were taken at a depth of 0-10, 10-20, 20-30 cm for standard laboratory analysis. The fine sand, silt, clay, organic matter and permeability of the soils obtained from the different geologic formations were used to determine their erodibility indices using Wischmeier and Smith model. The results obtained shows that Alluvium geologic formation has an average K factor of 0.150 tons MJ-1 hmm-1. Ameki had an average K factor of 0.131 MJmmha-1hr-1yr. Benin had an average K factor of 0.130 tons MJ-1 hmm-1. Imo had an average K factor of 0.147 tons MJ-1 hmm-1. Ogwashi had an average K factor K of 0.127 tons MJ-1 hmm-1. The results show that soils within the study area are vulnerable to erosion.

Towards robust smart data-driven soil erodibility index prediction under different scenarios

Soil erosion is a major cause of damage to agricultural lands in many parts of the world and is of particular concern in semiarid parts of Iran. We use five machine learning techniques—Random Forest (RF), M5P, Reduced Error Pruning Tree (REPTree), Gaussian Processes (GP), and Pace Regression (PR)—under two scenarios to predict soil erodibility in the Dehgolan region, Kurdistan Province, Iran. Our models are based on a variety of soil properties, including soil texture, structure, permeability, bulk density, aggregates, organic matter, and chemical constituents. We checked the validity of the models with statistical metrics, including the coefficient of determination (R2), mean absolute error (MAE), root mean squared error (RMSE), T-tests, Taylor diagrams, and box plots. All five algorithms show a positive correlation between the soil erodibility factor (K) and silt, sand, fine sand, bulk density, and infiltration. The GP model has the highest prediction accuracy (R2 = 0.843, MAE = 0.0044, RMSE = 0.0050). It outperformed the RF (R2 = 0.812, MAE = 0.0050, RMSE = 0.0061), PR, (R2 = 0.794, MAE = 0.0037, RMSE = 0.0052), M5P (R2 = 0.781, MAE = 0.0043, RMSE = 0.0053), and REPTree (R2 = 0.752, MAE = 0.0045, RMSE = 0.0056) algorithms and thus is a useful complement to studies aimed at predicting soil erodibility in areas with similar climate and soil characteristics.

Estimating the Best Exponent and the Best Combination of the Exponent and Topographic Factor of the Modified Universal Soil Loss Equation under the Hydro-Climatic Conditions of Ethiopia

The effect of the topographic factor of the Modified Universal Soil Equation (MUSLE) on soil erosion and sediment yield is not clear. Except for the coefficient, soil erodibility, cover, and conservation practice factors of the MUSLE, an individual effect of the exponents and topographic factors of the MUSLE on soil erosion and sediment yield can be seen by applying the model at different watersheds. A primary objective of this paper is to estimate the best exponents and topographic factors of the MUSLE under the hydro-climatic conditions of Ethiopia. For the sake of the calibration procedure, the main factors of the MUSLE that directly affect the soil erosion process, such as cover, conservation practice, soil erodibility, and topographic factors, are estimated based on past experiences from the literature and comparative approaches, whereas the parameters that do not directly affect the erosion process or that have no direct physical meaning (i.e., coefficient a and exponent b) are estimated through calibration. We verified that the best exponent of the MUSLE is 1 irrespective of the topographic factor, which results in the maximum performance of the MUSLE (i.e., approximately 100%). The best exponent that corresponds to the best equation of the topographic factor is 0.57; in this case, the performance of the model is greater than or equal to 80% for all watersheds under our consideration. We expect the same for other watersheds of Ethiopia, while for other exponents and topographic factors, the performance of the model decreases. Therefore, for the conditions of Ethiopia, the original exponent of the MUSLE is changed from 0.56 to 0.57, and the best equations of the topographic factor are provided in this paper.

Digital mapping of soil erodibility factor in northwestern Iran using machine learning models.

Understanding the spatial distribution of soil erodibility factor (K-factor) at the district scale is essential for managing water erosion risk. In this research, we performed to predict the low and high classes of K-factor in the northwest of Iran. Based on this, soil sampling was performed at 64 points using the grid sampling method with 1km spacing. To calculate the K-factor, the distribution of particle size and organic carbon (OC) were determined. In addition, 21 terrain attributes were calculated by Digital Elevation Model (DEM) to add value to the soil data. Then, K-factor was modeled using Random Forest (RF) and Artificial Neural Network (ANN) models. In the next step, a non-linear Multiple Logistic Regression (NMLR) was used to obtain low and high classes of K-factor. The results showed that the performance of RF is superior to ANN with a high coefficient of determination [R2 = 0.85] and good accuracy [RMSE = 0.003 (Mg ha h/ha MJ mm)]. Therefore, the RF was employed for predicting the K-factor spatial distribution. Finally, using the NMLR model, the study area was divided into low and high classes of K-factor with good correlation [R2 Cox and Snell = 0.78, R2 Nagelkerke = 0.65]. The areas of these two classes were 60.4% for low class and 39.6% for the high class of K-factor. Based on these results, it was concluded that the resultant map of low and high classes of K-factor could be used by farmers and managers for managing soil water erosion risks in the study area.

Assessment of Land Use and Land Cover Changes on Soil Erosion Using Remote Sensing, GIS and RUSLE Model: A Case Study of Battambang Province, Cambodia

Soil erosion causes land degradation which negatively impacts not only natural resources but also livelihoods of people due to low agricultural productivity. Cambodia is prone to soil erosion due to poor agricultural practices. In this research we use Battambang province as a case study to quantify impact of land use and land cover change (LULC) on soil erosion. This study assessed the impact from LULC changes to soil erosion. LULC change maps were analyzed based on Landsat satellite imagery of 1998, 2008, and 2018, computed in QGIS 6.2.9, while the soil erosion loss was estimated by the integration of remote sensing, GIS tools, and Revised Universal Soil Loss Equation (RUSLE) model. The results showed that the area of agricultural land of Battambang province significantly increased from 44.50% in 1998 to 61.11% in 2008 and 68.40% in 2018. The forest cover significantly decreased from 29.82% in 1998 to 6.18% in 2018. Various soil erosion factors were estimated using LULC and slope. Based on that, the mean soil loss was 2.92 t/ha.yr in 1998, 4.20 t/ha.yr in 2008, and 4.98 t/ha.yr in 2018. Whereas the total annual soil loss was 3.49 million tons in 1998, 5.03 million tons in 2008, and 5.93 million tons in 2018. The annual soil loss at the agricultural land dramatically increased from 190,9347.9 tons (54%) in 1998 to 3,543,659 tons (70.43%) in 2008 and to 4,267,439 tons (71.91%) in 2018 due to agricultural land expansion and agricultural practices. These losses were directly correlated with LULC, especially agricultural land expansion and forest cover decline. Our results highlight the need to develop appropriate land use and crop management practices to decrease land degradation and soil erosion. These data are useful to bring about public awareness of land degradation and alert local citizens, researchers, policy makers, and actors towards land rehabilitation to bring the area of land back to a state which is safe for increasing biodiversity and agricultural productivity. Measures to reduce or prevent soil erosion and the use of conservation agriculture practices, along with water and soil conservation, management, agroforestry practices, vegetation cover restoration, the creation of slope terraces, and the use of direct sowing mulch-based cropping systems should be considered.

QUANTITATIFICATION OF SOIL EROSION USING CAUSATIVE FACTORS IN THE GIS ENVIRONMENT FOR SYLHET DISTRICT, BANGLADESH

The aim of this study was to enumerate the potential soil erosion in the Sylhet district of Bangladesh using the Revised Universal Soil Loss Equation (RUSLE) approach in the software-based windows. Using the rainfall erosivity factor (R), the soil erodibility factor (K), the cover management factor (C), the topographic factor (LS) and the support practices factor (P), the RUSLE formula quantified the possible soil loss rate. The soil erosion rate was classified into six classes based on the erosion severity. The result shows that the annual soil deterioration rate differs from 0 to 799.67 t/ha/year. However, almost about 97.71% of the total surface area is under low potential (54.1%) and moderate (43.61%) erosion risk. Rather about 2.29% area of Sylhet district was under average, high, very high and critic erosion risk. Result indicates that the north-eastern and central zone are highly vulnerable to soil erosion due to steep slope and P factor.

Evaluating the Changes in Soil Erosion for Micro Watersheds of Sabarkantha and Aravalli Districts of Gujarat, India

Soil erosion is the most critical environmental problem in the semi-arid region of the sabarkantha-Aravalli districts of Gujarat state. The present study is carried out on micro-watersheds of Mazum and Vatrak tributaries of the Sabarmati river basin of Gujarat state, west India, having an area of 8638 sq. km. In this paper, the Universal Soil Loss Equation (USLE) model has been used to quantify soil loss in micro watersheds. Five essential parameters such as Runoff-rainfall erosivity factor (R), soil erodibility Factor (K), slope length and steepness (LS), cropping management factor (C), and support practice factor (P) have been used to estimate the soil loss amount in the study area. All of these factors have been calculated using various data sources and data preparation methods. The soil erodibility (K) factor in the study area ranged from 0.46 to 0.93. All essential parameters have been calculated for before-after watershed scenarios. Changes in soil erosion have been estimated after the implementation of micro-watershed development projects under various government schemes. The average annual predicted soil loss ranges between 45 and 230 t/ha/y before implementation and 14.50 and 138 t/ha/y after implementation. Low decreases in soil loss areas (111%) in soil erosion was found to have a good provision of land treatment with watershed interventions along the watersheds on the main course of the Mazum and Watrak rivers.