Search Results (40)

Land-use changes and unsustainable agricultural practices can alter soil properties, thereby increasing soil erodibility and the risk of land degradation. This study assessed the impact of converting forest to grassland and cropland on soil erodibility in the Kolubara watershed (western Serbia) using soil samples collected at two depths (0–15 and 15–30 cm). Soil erodibility was determined using the following indicators: clay ratio (CR), soil structure stability index (SSI), mean weight diameter (MWD), soil organic carbon cementing agent index (SCAI), saturated hydraulic conductivity (Ks), the K-factor, and a comprehensive soil erodibility index (CSEI) calculated by a weighted summation method. Most soil indicators differed significantly among land uses. Forest soils exhibited the highest MWD (2.94 mm), Ks (1119.15 mm h⁻¹), and SSI (5.86), whereas the lowest values were recorded in cropland soils (1.64 mm, 29.68 mm h⁻¹, and 3.07, respectively). In contrast, cropland soils showed the highest CR (0.005) and K-factor (0.038 t ha h ha⁻¹ MJ⁻¹ mm⁻¹), while the lowest values occurred in forest soils (0.003 and 0.032 t ha h ha⁻¹ MJ⁻¹ mm⁻¹). The significantly higher CSEI in cropland (0.75) compared with forest soils (0.62) corresponded to reduced soil structural stability and lower organic matter–related indicators. Grassland soils generally showed intermediate values for most indicators. Soil depth significantly influenced only SSI and Ks. Differences in soil erodibility among land uses are closely related to soil physical and chemical properties, particularly soil organic carbon and soil structure-related properties (total porosity and bulk density). These findings emphasize the substantial impact of land-use change on soil erodibility and highlight the need to implement effective soil conservation practices to improve soil stability and mitigate erosion. Full article

Water erosion is the main driver of soil loss in semi-arid mountainous regions, particularly in Algeria. Identifying the spatial distribution of erosion is a crucial first step, providing decision-makers with essential information to develop effective mitigation strategies. The main objective of this study is to apply the Revised Universal Soil Loss Equation (RUSLE) to estimate soil loss and rank the sub-basins of the Wadi Cheliff Basin (43,750 km²). Different geographical and non-spatial data sets have been employed to develop different thematic layers of the RUSLE factors, such as rainfall erosivity factor (R), soil erodibility factor (K), topographic factor (LS), crop management factor (C), and support practice factor (P). The RUSLE empirical model indicated strong spatial variability of soil loss across the Wadi Cheliff Basin, with estimated values ranging from 0 to 50 t ha⁻¹ yr⁻¹ during October 2017–May 2018. Higher erosion rates (20–50 t ha⁻¹ yr⁻¹) were concentrated in the northern part of the basin near the Mediterranean coast, primarily due to high rainfall erosivity (800–977 MJ mm ha⁻¹ h⁻¹ yr⁻¹) and steep slopes (LS up to 29.48). In contrast, the southern part of the basin exhibited lower soil loss (0–10 t ha⁻¹ yr⁻¹), associated with lower rainfall and gentler slopes. Areas affected by extreme erosion (>50 t ha⁻¹ yr⁻¹) were very limited, representing only 0.02% in October 2017 and 0.40% in May 2018. Maximum soil loss values (224.00 t ha⁻¹ yr⁻¹ in October 2017 and 204.10 t ha⁻¹ yr⁻¹ in May 2018) indicate that high-intensity erosion is limited to specific localized hotspots, rather than being broadly distributed across the basin. Information on soil erosion patterns at the sub-basin level can guide the planning of effective conservation practices. Such information is helpful for the implementation of erosion control practices and improving overall environmental management in the basin. Full article

Among the most serious types of land degradation, soil erosion poses a major threat to agricultural productivity, water quality, and ecosystem stability. Using a multidisciplinary approach, this study aimed to identify the spatial patterns of soil erosion and dominant drivers influencing soil loss in the Toplica River Basin in southern Serbia. Soil properties, including texture and organic matter content, were analyzed in samples collected throughout the study area, accounting for variations in altitude, soil type, and land use, to determine the erodibility factor (K). The rainfall erosivity factor (R), topographic factor (LS), and cover management factor (C) were determined using available inputs on rainfall erosivity, topography, land use, and vegetation cover. The Revised Universal Soil Loss Equation (RUSLE) was used to estimate annual soil erosion rates, and GIS tools and cartographic techniques were used to create spatial layers for each RUSLE factor and to generate a detailed erosion risk map. The results showed a mean annual soil loss of 5.45 t ha⁻¹ year⁻¹, with values ranging from 0 to 397.09 t ha⁻¹ year⁻¹, indicating considerable spatial variability. The regression modeling revealed the dominant roles of factors LS (β = 0.828), C (β = 0.731), and their interaction (LS × C, β = 0.561), followed by rainfall-related interactions (R × C, β = 0.268 and R × LS, β = 0.261). Two dominant erosion regimes were distinguished: topography-controlled erosion in mountainous regions and land-use-controlled erosion in low- to moderately sloping agricultural areas. The maps and analyses presented in this study provide a process-based framework for interpreting spatial erosion patterns, identifying critical hotspots and areas with higher erosion risk, and supporting more focused and context-aware conservation strategies. Full article

Soil erosion is a major threat to the sustainable productivity of arable lands, making the accurate prediction of soil erodibility essential for effective soil conservation planning. Soil erodibility is strongly controlled by intrinsic soil properties that regulate aggregate resistance and detachment processes under erosive forces. In this study, machine learning (ML) models, including the Multi-layer Perceptron Regressor (MLP), Random Forest (RF), Decision Tree (DT), and Extreme Gradient Boosting (XGBoost), were applied to predict the soil erodibility factor (K-factor). A comprehensive set of soil properties, including soil texture, clay ratio (CR), organic matter (OM), aggregate stability (AS), mean weight diameter (MWD), dispersion ratio (DR), modified clay ratio (MCR), and critical level of organic matter (CLOM), was analyzed using 110 soil samples collected from the northern part of Adana Province, Türkiye. The observed K-factor was calculated using the RUSLE equation, and ML-based predictions were spatially mapped using Geographic Information Systems (GISs). The mean K-factor values for arable, forest, and horticultural land uses were 0.065, 0.071, and 0.109 t h MJ⁻¹ mm⁻¹, respectively. Among the tested models, XGBoost showed the best predictive performance, with the lowest MAE (0.0051) and RMSE (0.0110) and the highest R² (0.9458). Furthermore, the XGBoost algorithm identified the CR as the most influential variable, closely followed by clay and MCR content. These results highlight the potential of ML-based approaches to support erosion risk assessment and soil management strategies at the regional scale. Full article

Accelerated water erosion is a major soil degradation process that affects soil and water quality. In Cuba, specifically, more than 40% of agricultural lands are affected by severe erosion problems. Estimating accurate erodibility values is a crucial step for the calibration and proper application of erosion models. Several equations have been developed to estimate erodibility from soil properties; however, these are often soil- or site-specific, limiting their application. This study aims to (1) identify soil properties governing the erodibility of tropical soils from western Cuba, (2) find suitable regression models to estimate erodibility from these properties, and (3) test widely applied erodibility equations. To achieve these goals, rainfall simulation experiments were conducted on runoff plots, and erosion-related physical, chemical, and mechanical soil properties were determined for 19 different soils. The main results indicated that good correlations between erodibility and certain soil properties were achieved after clustering soils based on their cation exchange capacity (CEC) values and clay content. Soils characterized by more than 30% of clay and 40 cmol+ kg⁻¹ of CEC were excluded from the main analysis. Generally, clay content controls the erodibility of these tropical soils, exhibiting an inverse relationship. However, in the excluded soils, the clay fraction showed a positive relationship with erodibility. Soil water retention at the lowest matric potentials demonstrated the strongest correlation with soil erodibility, as this variable encompasses compound information related to clay, mineralogy, and organic matter. A new regression model to estimate erodibility based solely on the volumetric water content at 1500 kPa is presented. The optimal fitted logarithmic model accounts for 64% of the predictand variability in the studied soils. When testing known erodibility models, the nomograph was found to best mimic the erodibility trend of these soils, although it exhibited marked uncertainty and underestimation biases. Full article

The water-level fluctuation zone (WLFZ) of the Three Gorges Reservoir (TGR) experiences seasonal submergence and exposure, resulting in soil structure degradation and intensified erosion. This study investigated how flooding duration affects root development and the erosion resistance of root–soil complexes in the WLFZ of the TGR. Two representative herbaceous species were chosen for this study: Xanthium sibiricum, an annual with a taproot system, and Cynodon dactylon, a perennial with a fibrous root system. Root traits, soil erodibility K-value, shear strength, and soil texture were measured from plant and soil samples collected at different flooding durations (145–175 m elevations). Our results showed that prolonged flooding significantly suppressed root growth, particularly in the 145–155 m zone, where root length density and root tips were markedly reduced (p < 0.05). Soil erodibility increased with flooding duration, with erodibility K-values ranging from 0.050 ± 0.002 to 0.062 ± 0.001 t·hm²·h/(MJ·mm·hm²), while shear strength declined correspondingly. Textural shifts from silty loam to silt were observed at zones experiencing extended flooding, contributing to aggregate instability and decreased internal friction angles. Notably, Cynodon dactylon demonstrated superior soil reinforcement capacity compared to Xanthium sibiricum, with its root volume and surface area significantly correlated with reduced K-values (p < 0.01) and enhanced shear strength (p < 0.001), enabling it to better prevent bank erosion under flooding conditions. These findings underscore the importance of root morphological traits in maintaining soil stability under hydrological stress and highlight the potential of perennial fibrous-rooted species for vegetation-based erosion control in fine-textured riparian zones. This study provides a theoretical basis and practical reference for ecological restoration in the WLFZ of the TGR and similar environments. Full article

Strip clearcutting and replanting are important methods for optimizing the structure of low-efficiency plantations, but their effects on soil aggregate properties remain unclear, especially in subtropical China, which experiences high levels of rainfall and high erosion risk. This study investigated changes in soil aggregate composition and stability through strip clearcutting and replanting treatments in Cunninghamia lanceolata plantations. The experimental treatments included clearcutting strips with widths of 10 m, 20 m, and 30 m and replanting with evergreen broadleaf Schima superba (SM10, SM20, and SM30) and deciduous broadleaf Liquidambar formosana (SF10, SF20, and SF30), respectively. The reserve belts were set at 15 m (S15), 30 m (S30), and 45 m (S45), with no clearcutting as the control (NT). The results indicated that soils of the treatment plots were dominated by >5 mm aggregates (57%–77%), however, lower than the control (NT) due to the clearcutting and replanting, except SF20 and S15 of reserve belts. The 20 m strip width with Liquidambar formosana replanting (SF20) demonstrated optimal soil structural stability, with significantly lower erodibility K values than the control. The content of >5 mm soil aggregates was significantly positively correlated with the mean weight diameter (MWD) and geometric mean diameter (GMD) and significantly negatively correlated with the erodibility factor (K). In contrast, the contents of the other particle sizes were significantly negatively correlated with the MWD and GMD and significantly positively correlated with the erodibility factor (K). This study demonstrates that 20 m strip clearcutting with Liquidambar formosana replanting (SF20) optimally maintains soil aggregate stability and reduces erosion risk, providing critical evidence for strip width configuration and species selection in ecological restoration of subtropical low-efficiency plantations. Full article

The Sanjiangyuan region, known as the “Chinese Water Tower”, serves as a crucial ecological zone that is highly sensitive to climate change. In recent years, rising temperatures and increased precipitation have led to permafrost melt and frequent occurrences of thermokarst landslides, exacerbating soil erosion issues. Although studies have explored the impact of freeze–thaw action (FTA) on soil properties, research on this phenomenon within the unique geomorphological unit of thermokarst landslides, formed from degrading permafrost, remains sparse. This study, set against the backdrop of temperature-induced soil landslides, combines field investigations and controlled laboratory experiments on typical thermokarst landslide bodies within the permafrost region of Sanjiangyuan to systematically investigate the effects of FTA on the properties of soils within thermokarst landslides. Furthermore, this study employs the EPIC model to establish an empirical formula for the soil erodibility (SE) factor before and after freeze–thaw cycles (FTCs). The results indicate that: (1) FTCs significantly alter soil particle composition, reducing the content of clay particles in the surface soil while increasing the content of sand particles and the median particle size, thus compromising soil structure and enhancing erodibility. (2) FTA initially significantly increases soil organic matter content (OMC); however, as the number of FTCs increases, the magnitude of these changes diminishes. The initial moisture content of the soil significantly influences the effects of FTA, with more pronounced changes in particle composition and OMC in soils with higher moisture content. (3) With an increasing number of FTCs, the SE K-value first significantly increases and then tends to stabilize, showing significant differences across the cycles (1 to 15) (p < 0.05). This study reveals that FTCs, by altering the physicochemical properties of the soil, significantly increase SE, providing a scientific basis for soil erosion control and ecological environmental protection in the Sanjiangyuan area. Full article

Global sensitivity analysis (GSA) of the revised universal soil loss equation (RUSLE) factors is in its infancy but is crucial to rank the importance of each factor in terms of its non-linear impact on the soil erosion rate. Hence, the goal of this study was to perform a GSA of each factor of RUSLE for a soil erosion assessment in southern Bahia, Brazil. To meet this goal, three non-linear topographic factor (LS factor) equations alternately implemented in RUSLE, coupled with geographic information system (GIS) software and a variogram analysis of the response surfaces (VARSs), were used. The results showed that the average soil erosion rate in the Pardo River basin was 25.02 t/ha/yr. In addition, the GSA analysis showed that the slope angle which is associated with the LS factor was the most sensitive parameter, followed by the cover management factor (C factor) and the support practices factor (P factor) (CP factors), the specific catchment area (SCA), the sheet erosion (m), the erodibility factor (K factor), the rill (n), and the erosivity factor (R factor). The novelty of this work is that the values of parameters m and n of the LS factor can substantially affect this factor and, thus, the soil loss estimation. Full article

Land use is a remarkable human-induced change that has redesigned the Earth’s surface since the beginning of civilization. Due to the combination of rugged terrain and low-income levels in rural areas, people in watershed regions often resort to overexploiting forests, agricultural land, and grasslands beyond their capacity. As a result of these spatio-temporal changes in land use, various soil properties undergo changes. This study aims to determine the changes in some physical (texture, bulk weight, particle density, total porosity), hydro-physical (water holding capacity, permeability, field capacity, wilting point), physico-chemical (organic matter, pH, electrical conductivity), and erodibility (dispersion ratio, colloid–moisture equivalent ratio, erosion ratio, clay ratio, aggregate stability and K-factor of Universal Soil Loss Equation-USLE) properties of soil depending on land use in the Deviskel Watershed in the city of Artvin in Türkiye. For this purpose, disturbed (composite) and undisturbed (cylinder) soil samples were taken from a 0 to 20 cm depth at 108 different points in the determined areas (36 from forests, 36 from agricultural areas, and 36 from grassland areas). It was determined that 15 of the 19 soil properties examined showed statistical differences depending on the change in land use. All the examined soil properties, except for clay content, particle density, dispersion ratio, and aggregate stability, were found to be statistically significantly affected by the change in land use, and the reasons behind these changes were discussed. The particle density had the lowest coefficient of variation value (15.26%) while electrical conductivity had the highest coefficient of variation value (91.25%). According to erosion tendencies, all watershed soils were found to be susceptible to erosion. The average aggregate stability was 88.52% in forest soils, 84.84% in agricultural soils, and 85.48% in grassland soils. The average USLE-K factor was determined to be 0.22 for forests, while it was determined to be 0.17 and 0.18 for agriculture and grassland areas, respectively. According to the USLE-K factor, 68.37% of the watershed was dominated by moderately erodible soils, while 31.63% consisted of highly erodible soils. Based on the colloid–moisture equivalent ratio, erosion ratio, and clay ratio, which are statistically different erodibility features, the grassland soils of the research area were found to be more susceptible to erosion than forest and agricultural soils. In terms of aggregate stability, which indicates resistance to water erosion, forest areas had higher values, while agricultural lands were more prone to erosion. Full article

Construction sites rely on erosion control practices to protect bare slopes and prevent soil loss. The effectiveness of certain erosion controls is often under-evaluated if they are not a part of a product evaluation program. Furthermore, erosion controls in general are not fully understood regarding how their performance can be affected by site specific variables, such as soil variations. This study used large-scale rainfall simulators to evaluate how a commonly used erosion control on construction sites, broadcasted straw mulch, performs on three common soil types in Alabama. The study at the Auburn University, Stormwater Research Facility (AU-SRF) used the industry standard testing method and three different soil types: sand, loam, and clay in accordance with ASTM D6459-19, the standard test method for testing rolled erosion control products’ (RECPs) performance in protecting hillslopes from rainfall-induced erosion. As required by ASTM D6459-19, the rainfall simulators simulated a storm of varying 20 min increments of 2 in./h (5.08 cm/h), 4 in./h (10.16 cm/h), and 6 in./h (15.24 cm/h). A total of nine bare soil tests on the 4:1 test plots was performed with an average total soil loss of 1977 lb (897 kg), 236.2 lb (107 kg), and 114.2 lb (51.8 kg) for sand, loam, and clay, respectively. The average erodibility K-factor for each soil type is calculated to be 0.37 (sand), 0.043 (loam), and 0.013 (clay). Nine straw tests were performed on the 4:1 plots, with an average total soil loss of 44.31 lb (20.1 kg), 6.74 lb (3.1 kg), and 17.13 lb (7.8 kg) for sand, loam, and clay, respectively. Straw testing indicated substantial soil loss reduction with average cover management C-factor values under the revised universal soil loss equation (RUSLE) method of 0.021, 0.047, and 0.193 for sand, loam, and clay applications, respectively. This variation in C-factor across the three soil types indicates that the single C-factor, often reported by product manufacturers, is not adequate to imply performance. Full article

Mitigating soil erosion‘s effects have been prioritized since the early 20th century. Rainfall simulators and analytical prediction models are used to determine soil erosion susceptibility. This study used different methodologies to measure soil erodibility in two hydrographic sub-basins, the Renato and Caiabi, in the Middle and Upper Teles Pires River in Mato Grosso state, Brazil. The rainfall simulator showed a higher range of K-factor values for the Renato sub-basin of 0.0009 to 0.0086 Mg × h × (MJ × mm)⁻¹ and a lower range of K-factor values for the Caiabi sub-basin of 0.0014 to 0.0031 Mg × h × (MJ × mm)⁻¹. Soil loss equations similarly estimated a higher range of K-factor values for the Renato of 0.0008 to 0.0990 Mg × h × (MJ × mm)⁻¹ and a lower range of K-factor values for the Caiabi of 0.0014 to 0.0846 Mg × h × (MJ × mm)⁻¹. There was no significant difference at the 5% level for the K factor determined by the rainfall simulator for both sub-basins. Equations specified in Bouyoucos (1935) and Lombardi Neto and Bertoni (1975) showed significant correlation (5%) for farming systems in the Caiabi sub-basin. Indirect methodologies that performed well for correlation were equations 2 and 3 from Roloff and Denardin (1994), which use iron and aluminum as parameters. Soil erosion was most influenced by physical texture parameters of the region’s soil. Full article

Soil aggregates are important indicators of soil structure stability and quality. The black soil region of northeast China, known for its high agricultural productivity, faces significant challenges due to soil erosion. This study investigates the impact of terracing on the stability and erodibility characteristics of soil aggregates in sloped farmlands, which is crucial for this important agricultural area. Three research sites with the same basic management modes were selected along a latitudinal gradient, from the mid-temperate zone to the cold temperate zone, in the black soil region of northeast China. The Savinov method was used to analyze the differences in soil aggregate size distribution, stability characteristics, and soil erodibility between terraced and non-terraced slopes at each research site. The results showed that terracing increased the content of large soil aggregates (>0.25 mm) by 5.38–6.35%, with the increase becoming more pronounced from north to south. The improvement in soil structure varied by location and slope position, with the most significant improvement at the middle slope position. Terracing enhanced soil aggregate stability, reduced soil erodibility, and improved soil structure by increasing clay and soil organic matter (SOM) content and reducing soil bulk density (BD), promoting the conversion of small aggregates to large aggregates. Soil stability indicators such as water-stable aggregates (WSAs), mean weight diameter (MWD), and geometric mean diameter (GMD) were dominated by aggregates > 5 mm, while erodibility indicators such as fractal dimensions (Ds) and the soil erodibility factor (K values) were mainly influenced by aggregates < 0.25 mm. Terraces can improve the soil structure and stability of sloping farmland by increasing the content of large soil aggregates and enhancing overall soil quality. The benefits of these improvements increase with latitude. These findings provide critical insights for determining effective management practices for sloped farmlands in the black soil region under various site conditions. They offer scientific evidence for preventing soil erosion and improving soil quality, thus supporting the sustainable development strategy for protecting black soil and ensuring long-term agricultural productivity. Full article

Soil erodibility (K) refers to the inherent ability of soil to withstand erosion. Accurate estimation and spatial prediction of K values are vital for assessing soil erosion and managing land resources. However, as most K-value estimation models are empirical, they suffer from significant extrapolation uncertainty, and traditional studies on spatial prediction focusing on individual empirical K values have neglected to explore the spatial pattern differences between various empirical models. This work proposed a universal framework for selecting an optimal soil-erodibility map using empirical models enhanced by machine learning. Specifically, three empirical models, namely, the erosion-productivity impact calculator model (K_EPIC), the Shirazi model (K_Shirazi), and the Torri model (K_Torri) were used to estimate K values. Random Forest (RF) and Gradient-Boosting Decision Tree (GBDT) algorithms were employed to develop prediction models, which led to the creation of three K-value maps. The spatial distribution of K values and associated environmental covariates were also investigated across varying empirical models. Results showed that RF achieved the highest accuracy, with R² of K_EPIC, K_Shirazi, and K_Torri increasing by 46%, 34%, and 22%, respectively, compared to GBDT. And distinctions among environmental variables that shape the spatial patterns of empirical models have been identified. The K_EPIC and K_Shirazi are influenced by soil porosity and soil moisture. The K_Torri is more sensitive to soil moisture conditions and terrain location. More importantly, our study has highlighted disparities in the spatial patterns across the three K-value maps. Considering the data distribution, spatial distribution, and measured K values, the K_Torri model outperformed others in estimating soil erodibility in the plateau lake watershed. This study proposed a framework that aimed to create optimal soil-erodibility maps and offered a scientific and accurate K-value estimation method for the assessment of soil erosion. Full article

Differences in land use type and chronological age affect soil properties and plant community characteristics, which may influence soil structural stability and erodibility. However, knowledge on the effects of soil physicochemical properties on soil aggregate stability and erodibility at different land use years is limited. This study selected five land use types: corn field (Year 38^th-y), corn intercropped with cabbage field (Year 38^th-y + b), fruit and meridian forest (Year 6^th-jgl), naturally restored vegetation (Year 6^th-zr), and artificial forest (Year 7^th-rgl) in the karst landscape of the Chishui River Basin in Yunnan Province. We aimed to identify the influencing factors of soil stability and erodibility under different land use time series. The results indicated that the mean weight diameter (MWD), the geometric mean diameter (GMD), and soil structural stability index (SSI values) were highest in Y6^th-zr and lowest in Y7^th-rgl. Conversely, the erodibility K value was lowest in Y6^th-zr, suggesting that the soil structure in Y6^th-zr exhibited greater stability, whereas soil stability in Y7^th-rgl was lower. Redundancy and throughput analyses revealed that organic carbon and water-stable aggregates > 2.0 mm content had higher vector values. Soil bulk density, total nitrogen, organic carbon, and soil texture content were the main factors contributing to soil stability variation (0.338–0.646). Additionally, total nitrogen, organic carbon, total phosphorus, and soil texture content drove the variation in K values (0.15–1.311). Natural vegetation restoration measures can enhance soil structure to a certain extent. These findings highlight changes in soil aggregate stability and erodibility over different land use durations. The research results have important theoretical and practical significance for understanding the differences in soil erosion and soil restoration under different land use patterns in the karst landscapes of southwest China. Full article