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Trade-off and synergy relationships of farmland ecosystem services is not only important research topic in the agricultural ecosystem but also closely related to cropland protection and food security. However, existing researches lack analysis on trade-off and synergy relationships of farmland ecosystem services in Longdong Loess Plateau, where fragile eco-environment (e.g. severe soil erosion, scarce water resources, etc.) seriously threatens cropland resources and land productivity. In order to clarify trade-off and synergy relationships of farmland ecosystem services, Longdong Loess Plateau, one of the birthplaces of Chinese farming civilization, was selected as the study area, and six ecosystem service models, including precipitation storage, universal soil loss equation, and InVEST etc were utilized to calculate the amount of farmland ecosystem services in 2020 based on the socio-economic and resource-environmental data such as the statistical yearbooks, land use/cover, digital elevation models, normalized difference vegetation index, meteorology, soil, net primary productivity, and road POI. Meanwhile, the Pearson correlation coefficient and the bivariate global/local Moran's index were used to distinguish trade-off and synergy relationships between providing services and other services, and identify the spatial heterogeneity of the relationships among services, respectively. The results showed that: 1) The amount of food provision service, water conservation service, carbon sequestration service, soil retention service in 2020 was 4.61×106 t, 2.45×108 m³, 2.94×106 t, 6.61×109 t, respectively, and the mean value of habitat quality service was 0.26. Both providing services and cultural services shared a distribution pattern of “high in the east and west, low in the central and north”, due to the spatial heterogeneity of climate, soil, terrain and socio-economic development planning. By contrast, both adjusting services and supporting services had a "belt-like" distribution. 2) The correlation coefficient between providing services and adjusting services, supporting services, and cultural services was 0.97, 0.85, and 0.97, respectively. And the correlation coefficient between food provision service and water conservation service, carbon sequestration service, soil retention service, habitat quality service, landscape service was 0.94, 0.99, 0.93, 0.82, and 0.97, respectively. All of correlation coefficients showed that all relationships of farmland ecosystem services were synergistic in Longdong Loess Plateau. 3) Trade-off and synergy relationships among four types of services, and among each sub-service of farmland ecosystem had spatial auto-correlation, and significant spatial heterogeneity. Synergy relationships among each sub-service were clustered obviously, appeared mainly in the central and northern Longdong, while trade-off relationships among each sub-service were distributed sporadically. Therefore, it is very significant to maintain multifunctionality of farmland ecosystem in Longdong Loess Plateau, to implement zoning management strategies, to fully exploit synergistic effect of various services of farmland ecosystem, to integrally improve service functions of farmland ecosystem (especially food provision service), and further consolidate the position of "Longdong Grain Warehouse" in the new era. [ABSTRACT FROM AUTHOR]

Loess Plateau seriously suffers from severe soil erosion. The large-scale Grain for Green Project (GFGP) has been implemented since 1999. This study aims to analyze the impact of land use change driven by the GFGP on soil erosion in this area. Taking the Qingshuihe watershed (a typical watershed on the Loess Plateau) as the study area, four stages were divided into the project implementation from 2000 to 2020, according to the different main measures. The change in soil erosion intensity was estimated by applying the RUSLE (revised universal soil loss equation) model. An effect degree (ED) algorithm was proposed by using scenario simulation method. A systematic analysis was implemented on the degree of impact of land use change on soil erosion. The land use change was divided into two forms of land use conversion and modification. The impact of rainfall change was excluded from the impact process of land use change on soil erosion. The results indicated that: 1) The average erosion moduli of this watershed were 36.21, 41.02, 24.93, 23.72 and 8.24 t/(hm² ·a) in 2000, 2005, 2011, 2014 and 2020, respectively, indicating a significant decline trend in erosion intensity. The average EDs of the impacts of only land use change and only rainfall change on soil erosion for the four stages were 75.23% and 24.77%, respectively, indicating that land use change was the dominant factor on soil erosion. 2) The change of erosion intensity was directly influenced by the change of land use categories and the different measures in the regions of each land use type during conversion. By contrast, the implemented measures only directly dominated the change of erosion intensity in the regions of each land use type during modification. The average initial erosion modulus for the four stages in the modification regions of this watershed was 43.47% higher than that of conversion regions, indicating that the overall difficulty in the comprehensive soil erosion management of modification regions was greater than that of conversion regions. The average decrease of erosion modulus for the four stages in the conversion regions of this watershed was 50.80% higher than that of modification regions. By contrast, the reduction of erosion amount over the four stages in the modification regions accounted for 71.16% of the total reduction of the watershed. It inferred that the land use conversion played an important role in the decrease of erosion intensity only in the implementation regions, whereas, the land use modification posed an essential effect to reduce the erosion amount in the whole watershed, due to the larger implementation area. 3) The total variation in the grassland erosion amount over the four stages accounted for 70.51% of this watershed's total variation in the erosion amount. The variation of erosion amount in the modification regions accounted for 67.41% of the grassland's total variation. Therefore, the grassland change, particularly its modification, posed the greatest impact on soil erosion in this watershed. An attempt was made to analyze the degree and process of the impact of land use change on soil erosion. The research result can provide the scientific basis for the achievement consolidation and the high-quality development of the GFGP on the Loess Plateau. [ABSTRACT FROM AUTHOR]

Shrub willow (Salix spp.) is a potential source of biomass feedstock for bioenergy, biofuels, and other bioproducts, but, to be considered sustainable, production systems must not result in long-term degradation of soil resources. Shrub willows are expected to reduce soil loss and improve soil health over time, but data are lacking to model soil erosion from shrub willow cropping systems. Our objectives were to characterize shrub willow across a chronosequence of age classes and to use these data to parameterize the Revised Universal Soil Loss Equation (RUSLE2) and model estimated soil loss from a range of soil types, slopes, management practices, and willow cultivars. Predicted average annual soil loss from shrub willow over seven 3-year rotations ranged from 1.68 to 1.97 Mg ha−1 yr−1, below the estimated soil loss tolerance value (T = 6.7 Mg ha−1 yr−1). Steeper slopes and silty soils were more susceptible to soil loss, but soil loss estimates across the production cycle were consistently below T. However, in all cases, the first year of production resulted in predicted soil loss above T (11–17 Mg ha−1 yr−1 under typical growing conditions). Soil disturbance during site preparation and planting and the lack of cover until canopy closure in the second year of production leave the soil susceptible to erosion in the first growing season. Cover crops, if they do not interfere with the growth of the willow crop, may reduce risk to the soil and provide additional benefits. [ABSTRACT FROM AUTHOR]

Land use and management practices (LUMPs) play a critical role in regulating soil loss. The cover-management factor (C-factor) in Universal Soil Loss Equation (USLE)-type models is an important parameter for quantifying the effects of LUMPs on soil erosion. However, accurately determining the C-factor, particularly for large-scale assessments using USLE-type models, remains challenging. This study aims to address this gap by analyzing and comparing the methods used for C-factor quantification in 946 published articles, providing insights into their strengths and weaknesses. Through our analysis, we identified six main categories of methods for C-factor quantification in USLE-type modeling. Many studies have relied on empirical C-factor values for different land-use types or calculated C-factor values based on vegetation indices (VIs) in large study areas (>100 km2). However, we found that no single method could robustly estimate C-factor values for large-scale studies. For small-scale investigations, conducting experiments or consulting the existing literature proved to be more feasible. In the context of large-scale studies, employing methods based on VIs for C-factor quantification can enhance our understanding of the relationship between vegetation changes and soil erosion potential, particularly when considering spatial and spatiotemporal variations. For the global scale, we recommend the combined use of different equations. We suggest further efforts to develop C-factor datasets at large scales by synthesizing field-level experiment data and combining high-resolution satellite imagery. These efforts will facilitate the development of effective soil conservation practices, ensuring sustainable land use and environmental protection. [ABSTRACT FROM AUTHOR]

Water erosion is accelerating soil loss rates in the East Hararghe Zone due to inappropriate human activities and their complex and intertwined interactions with natural factors, particularly in sensitive agroecosystems that lack soil and water conservation (SWC) measures. Although these dynamic processes cause prolonged impacts, a comprehensive assessment of the risk of soil erosion has not yet been undertaken at the zonal level. To bridge this gap, we employed the revised universal soil loss equation (RUSLE) prediction model, along with remote sensing and geographic information systems (GIS), to estimate annual soil erosion rates, analyze the temporal-spatial patterns of erosion risk, and evaluate the potential of standard conservation practices to reduce soil loss in croplands. Total soil erosion (in millions of tonnes/year; Mt yr−1) was estimated to be 9 in 1990, 14 in 2000, 12 in 2010, and 11 in 2020, with average rates of 33, 50, 44, and 39 t ha−1 yr−1, respectively. This suggests an overall 18% increase in soil erosion from 1990 to 2020. Over 75% of the area showed a tolerable soil loss rate (<10 t ha−1 yr−1) and low susceptibility to erosion risk. A mountainous landscape in the northwest presents extremely high erosion (>120 t ha−1 yr−1), which accounts for more than 80% of soil loss, making SWC planning a priority. Analysis of land-use land-cover change (LULCC) confirmed a higher increase in soil loss for LULCC that involved conversion to croplands, with average rates of 36.4 t ha−1 yr−1 (1990–2000), 70 t ha−1 yr−1 (2000–2010), and 36 t ha−1 yr−1 (2010–2020). The results have further revealed that implementing supportive practices such as terracing, stripping, and contouring could reduce average soil erosion by approximately 87%, 65%, and 29%, respectively, compared to the baseline model's prediction. Therefore, a rigorous cost–benefit analysis is essential to design and implement optimal location-specific practices that maximize investment returns in SWC efforts and ecological restoration. However, we acknowledge the limitations of this study, associated with an empirical model that does not account for all forms of erosion, as well as reliance mainly on secondary data, which may affect the accuracy of the predicted outcomes. [ABSTRACT FROM AUTHOR]

The study area was the Hulunbuir Plateau in northeastern China, based on a natural resource element observation study. The assessment of the spatial and temporal variation of soil erosion is crucial for implementing environmental management in the fragile ecosystem of the Hulunbuir Plateau. The study provides an interesting basis for soil erosion control on the Hulunbuir Plateau and other areas with similar climatic conditions, with the aim of providing sound data to support environmental protection policies in the study area. In this study, the spatial and temporal variations in soil erosion in the region from 2000 to 2020 were quantitatively assessed using the Revised Universal Soil Loss Equation. Furthermore, the patch-generating land use simulation model predicted future soil erosion. Land use prediction data were examined using Kappa coefficients. The prediction of future land use types using CMIP6 data and natural social data in the PLUS model were used to predict soil erosion for different future scenarios. The results showed that the soil erosion rate on the Hulunbuir Plateau showed a significant increasing trend in time from 2000 to 2020. Spatially, soil erosion increases gradually from the west to the east. Soil erosion occurs mainly on grasslands, while cultivated lands show a significant increasing trend by 2020. Slope erosion occurs mainly in areas between 15° and 35°. From 2020 to 2050, soil erosion will increase significantly due to increased precipitation. The soil erosion in SSP2–4.5 is better than the other scenarios. [ABSTRACT FROM AUTHOR]

Erosion by water is the main cause of land degradation. Landscapes degraded by erosion need to be restored in many respects, and particularly in terms of ecosystem services. From an economic and management perspective, care is needed to select priority areas and determine the means to be applied to restore them. Globally, the model most commonly used to produce scenarios for the prevention of soil losses is the Revised Universal Soil Loss Equation (RUSLE). This study of the subbasin of the Sulakyurt Dam Basin in Turkey aims (1) to identify the distribution of soil losses over time and by location, and (2) to grade the priority areas for the prevention of soil losses by means of a simulation. The average potential soil losses in the area under study are estimated at 42.35 t ha−1 year−1, and the average actual losses at 39.49 t ha−1 year−1. According to the simulation, 27.61% of the study area (2782 ha) is of the highest priority for soil restoration. In our study, forests have the highest soil losses, which is contrary to the natural protection that forests provide against erosion. The high rates are due to the slope, the forest area is very steep. So it is the slope factor that outweighs the vegetation cover factor. Of the forest areas, 41.74% (1766 ha) falls within the areas of highest priority. The study serves as a guide for landscape planning and the determination of erosion risk in restoration efforts, and for identifying the methods to be adopted during the restoration work to reduce the loss of soil. [ABSTRACT FROM AUTHOR]

Background. The models currently used to predict post-fire soil erosion risks are limited by high data demands and long computation times. An alternative is to map the potential hydrological and sediment connectivity using indices to express the general properties of the burnt landscape. Aims. In this study, we aimed to answer the question: Do these tools identify post-fire sediment mobilisation hotspots? Methods. To achieve this, we assessed the spatial variability distribution of the location of soil erosion hotspots using the Index of Connectivity, Revised Universal Soil Loss Equation and the Sediment Export, and compared it with the simulation results of a more complex Landscape Evolution Model (LAPSUS model). Additionally, we evaluated statistical measures of association between the four tools. Key results. The three tools tested in this study are suitable for identifying sediment mobilisation hotspots, where the erosion rates are above the 95th percentile, and differences between their performance are small. Conclusions. The results indicate that these tools help locate extreme erosion locations in recently burnt areas. Implications. These results can be considered for post-fire and water contamination risk management, especially for fast prioritisation of areas needing emergency post-fire intervention. [ABSTRACT FROM AUTHOR]

The Agricultural Payments Agency (supervisory and registry authority) of the Slovak Republic has announced an erosion hazard layer to meet the requirements of GAEC5 (Minimising soil erosion. Limit soil erosion by putting in place suitable practical measures). If a farmer fails to observe the layer data, they will be fined and might lose access to direct subsidies (also known as direct payments). The layer that has been announced raises a number of questions and concerns amongst beneficiaries of direct subsidies and users of land parcels. For instance, with the Pastuchov land parcel, the uncertainties associated with the application of GAEC5 were raised. A comparison of the water erosion layer commitment for 2023 with the erosion calculated by the Universal Soil Loss Equation (USLE) method based on the Digital Relief Model (DRM) generated from airborne laser scanning has confirmed the uniformity in all classified categories of water erosion over a 65% land parcel area. The situation of the land user has been diminished (i.e. they have to comply with GAEC5 even when there is no reason to) over 11% of the area. The situation of the land user has improved over 24% of the area (i.e. they do not have to comply with the conditions even when there is reason to). This paper describes the problems and outlines the possibilities for the necessary adjustment of compliance with the GAEC5 conditions in Slovakia. [ABSTRACT FROM AUTHOR]

Soil conservation service (SC) is defined as the ability of terrestrial ecosystems to control soil erosion and protect soil function. A long-term and high-resolution estimation of SC is urgent for ecological assessment and land management on a large scale. Here, a 300-m resolution Chinese soil conservation dataset (CSCD) from 1992 to 2019, for the first time, is established based on the Revised Universal Soil Loss Equation (RUSLE) model. The RUSLE modelling was conducted based on five key parameters, including the rainfall erosivity (interpolation of daily rainfall), land cover management (provincial data), conservation practices (weighted by terrain and crop types), topography (30 m), and soil properties (250 m). The dataset agrees with previous measurements in all basins (R2 > 0.5) and other regional simulations. Compared with current studies, the dataset has long-term, large-scale, and relatively high-resolution characteristics. This dataset will serve as a base to open out the mechanism of SC variations in China and could help assess the ecological effects of land management policies. [ABSTRACT FROM AUTHOR]

Tells are multi-layered, archaeological mounds representing anthropogenic landforms common in arid regions. In such contexts, the preservation of the archaeological record is mined by ongoing climate changes, shift in land use, and intense human overgrazing. Such natural and human-driven factors tune the response of archaeological soils and sediments to erosion. Geomorphology offers a plethora of tools for mapping natural and anthropogenic landforms and evaluating their response to unremitting weathering, erosional and depositional processes. Here, we present a geomorphological investigation on two anthropogenic mounds in the Kurdistan Region of Iraq, with a special focus on the ongoing erosional processes mining their slope stability and threatening the preservation of the local archaeological landscape. Applying the revised universal soil loss equation model for soil loess derived from UAV imagery and implemented with geoarchaeological investigation, we assess the erosion rate along anthropogenic mounds and estimate the risk of losing archaeological deposits. We argue that a large-scale application of our approach in arid and semi-arid regions may improve our ability to (i) estimate the rate of soil and/or archaeological sediments loss, (ii) propose mitigation strategies to prevent the dismantling of the archaeological record, and (iii) schedule archaeological operations in areas of moderate to extreme erosion risk. [ABSTRACT FROM AUTHOR]

In the context of increasing urbanization and worsening environmental pollution, nonpoint source pollution during high-frequency rainfall has become a major ecological problem that endangers residents in cities. This study takes Shenzhen as an example. On the basis of a large number of soil sample test data, and combined with relevant environmental variables, it has drawn the high-resolution, high-precision spatial distribution maps of soil attributes within the city. In addition, this paper combines the revised universal soil loss equation and the GeoDetector model to evaluate the supply capacity of nonpoint source reduction services in the city's ecological space and the main driving factors of spatial distribution characteristics for different types of land. The study found that increasing soil point density and combining environmental variables can help improve the accuracy of spatial mapping for soil attributes. The ME, MSE, ASE, RMSE, and RMSSE of spatial mapping all meet the accuracy evaluation criteria and are better than many existing studies; the spatial distribution characteristics of soil attributes and nonpoint source reduction services show significant differences among the whole city, secondary administrative regions, and different types of land; the GeoDetector results show that among the three main types of land use (forested land, industrial land, and street town residential land), topographic factors, habitat-quality factors, and ecosystem types have the greatest impact on the spatial differentiation characteristics of nonpoint source reduction services. Among climate factors, only precipitation factors have the greatest impact on the spatial differentiation characteristics of services. Facing the above factors, the q-values calculated by the GeoDetector are all higher than 10%. The results of this study can provide information for making better decisions on regional ecological system management and soil protection and on restoration work aimed at improving nonpoint source reduction services. [ABSTRACT FROM AUTHOR]

Exploring the effects of straw return and environmental factors on the spatiotemporal variation of soil organic matter (SOM) in black soil regions is essential for soil carbon sequestration research. To help address these challenging issues, new technologies have been developed and applied in soil science, such as digital soil mapping, soil remote sensing inversion, proximal soil sensing, geostatistics, spatial analysis, and machine learning. The author found that vis-NIR spectroscopy was fairly successful as a method for soil fertility class allocation for most of the soil properties, using either direct or indirect models. [Extracted from the article]

Estimating soil erosion by water is important in the management of watersheds. The growth of anthropogenic activities and climate change fluctuation in north-eastern Algeria are expected to influence land use/cover and soil sustainability. For this, the Revised Universal Soil Loss Equation (RUSLE), remote sensing (RS), geographic information system (GIS), and Machine Learning (ML) were combined to investigate the influence of Land Use Land Cover (LULC) changes on soil loss rates and its distribution in the Seybouse basin during a span of two decades between 1990 and 2010. The land use land cover classifications were categorized into six classes: water, dense forest, degraded forest, croplands, barren lands and urbanisation. The evaluation of the soil erosion distribution was based on the analyses of the LULC changes and RUSLE factors. The results reveal that the average annual soil loss based RUSLE in the basin for the years 1990, 2000, and 2010 were 12.73 t ha–1 y–1, 12.60 t ha–1 y–1, and 13.88 t ha–1 y–1 respectively. The study found that for the years 1990, 2000, and 2010 around 17, 15, and 20% areas are under the very severe and extremely severe erosion category, occurring mostly located in barren lands and degraded forests LULC classes. Overall, the results concluded that due to the impact of rainfall variability and decreased crop managements in the Seybouse basin, there was an increase in the surfaces of barren lands and degraded forests over crop lands and dense forests which resulted in the increase of erosion rates. [ABSTRACT FROM AUTHOR]

Intensifying agricultural activity associated with rapid population growth in rural western Uganda exerts immense pressure on natural resources, threatening not only soil fertility in the uplands but also water quality of the region's many small crater lakes. To assess the relative risk of excess sediment and nutrient loading to individual lakes due to (inter) rill erosion within the catchments, we used the revised universal soil loss equation (RUSLE) and sediment delivery distributed model (SEDD) to estimate soil loss and sedimentation in 75 crater-lake catchments with diverse types and intensities of land use, including 17 catchments situated partly or entirely in national parks. We found that variation in potential soil loss (Ap) among all studied catchments was strongly related to differences in mean slope within each catchment. We also found substantial seasonal variation in vegetation cover, and thus, estimated actual soil loss (Am), on both cultivated land and protected savanna grassland, whereas the vegetation cover of protected semi-deciduous tropical forest was seasonally stable. Lacking detailed field data to validate model output, we used the ratio between estimated actual soil loss (Am) and potential soil loss (Ap) to evaluate the relative influences of land-use intensity and type, as well as the impact of protective measures. Our results showed that due to their characteristically steep slopes (21% on average), all crater catchments were highly susceptible to soil loss, and because most of them were small (203 ha on average), a large portion of the eroded material was transported to and deposited in the lakes. Given the strong dependence of the local population on these crater lakes as source of water and fish protein, and on the surrounding land for crop production, increased effort by environmental planners and managers is required to safeguard or restore the long-term availability of these natural resources. Avoiding bare soil conditions by restoring natural vegetation or employing agricultural techniques that provide high vegetation cover throughout the year are likely to result in considerable improvements. [ABSTRACT FROM AUTHOR]

Erosion is an important environmental issue threatening natural resources and ecosystems, especially soil and water. Soil losses occur in many parts of the world due to erosion at different degrees, and various rehabilitation plans have been carried out to reduce these losses. However, soil protection applications are generally carried out by considering only the essential characteristics of the soil. This may decrease the chance of success of rehabilitation applications. The present study aimed to determine the soil quality index (SQI) by weighting the soil quality parameters according to the analytical hierarchy process (AHP) in the Çapakçur microcatchment (Bingöl, Türkiye) where soil loss is high. Accordingly, 428 soil samples were taken from the study area and analyzed. The soil losses in the Çapakçur watershed were calculated employing the revised universal soil loss equation (RUSLE). To determine the soil quality index, a total of 20 indicators were used, including (i) physical soil properties, (ii) chemical soil properties, and (iii) soil nutrient content. Soil quality index results are divided into classes between 1 and 5. As a result of the study, the annual total amount of soil lost from the microcatchment was calculated as 96,915.20 tons, and the yearly average amount of soil lost from the unit area was calculated as 10.14 tons ha−1. According to SQI, the largest area in the microcatchment was Class-2 (weak), with 39.49%, whereas the smallest area was 1.4% (the most suitable). However, it was determined that there was a significant negative relationship between SQI and soil erodibility. Considering the SQI distribution of the area in the planning of soil protection and erosion prevention practices in watershed rehabilitation studies may increase success. [ABSTRACT FROM AUTHOR]

In this study, two adjacent gauged catchments on the Chinese Loess Plateau were selected, in which one catchment was afforested and one was restored with natural vegetation in 1954. The distributions of soil erosion rates were estimated between 2010 and 2020 with a high spatial resolution of 2 m in the paired catchments based on the Revised Universal Soil Loss Equation model (RUSLE) and Geographic Information Systems (GIS). The results showed that the simulated soil erosion rates in 2010–2020 averaged 12.58 and 8.56 t ha−1 a−1 for the grassland and forestland catchment, respectively. Moreover, areas with high soil erosion rates (> 80 t ha−1 a−1) were mainly distributed in the topography with steep slope gradients (>45°). Comparisons between simulated soil erosion rates and observed annual sediment loads indicated that the simulation results of the grassland catchment were lower than the observed values, while it was reversed in the forestland catchment. We conclude that the RUSLE model cannot simulate the gravity erosion induced by extreme rainfall events. For the forestland catchment, insufficient streamflow and dense vegetation coverage are crucial factors resulting in hindering the movement of sediments. [ABSTRACT FROM AUTHOR]

The Cantareira System is one of the largest water supply systems in the world, supplying about half of the water consumed by 22 million inhabitants in the Metropolitan Region of São Paulo, in southeastern Brazil. In this scenario, in view of climate change, silting is a serious environmental threat and a major challenge to the sustainability of water reservoirs. Therefore, identifying the provenance of sediments is an essential tool to support soil conservation policies, slowing erosion processes and mitigating the deposition of sediments in water reservoirs. Thus, this study aimed to model soil losses—sediment production, by water erosion in the Cantareira System, based on the RUSLE model—Revised Universal Soil Loss Equation, GIS—Geographic Information System and SR—Remote Sensing. The work was conducted on data obtained from online platforms of Brazilian public institutions. The results indicate an average rate of soil loss of 13 Mg ha−1 yr−1, which corresponds to an annual loss of 3 million tons, of which 22% reaches water bodies. The data also show that: (1) in 66 % of the Cantareira System, soil losses are below the soil loss tolerance limits, and, in 34% of the region, water erosion is compromising the sustainability of water and soil resources; (2) the areas with the greatest soil losses are predominantly located in planted forests, agricultural crops and non-vegetated areas; and (3) sectors with high rates of soil loss require the adoption of conservationist practices aimed at reducing sediment production rates and thereby increasing supply and improving water quality. [ABSTRACT FROM AUTHOR]

Erosion-induced soil organic carbon (SOC) loss substantially affects the redistribution of global organic carbon. The Chinese Loess Plateau, the most severely eroded region on Earth, has experienced notable soil erosion mitigation over the last few decades, making it a hotspot for soil erosion studies. However, the overall rate of SOC loss and spatiotemporal evolution under changing environments remain unclear. In this study, we investigated SOC loss from 1982 to 2015 in the severely eroded Hetong region of the Chinese Loess Plateau by combining the Revised Universal Soil Loss Equation (RUSLE) model and the localized enrichment ratio function derived from field observations and attributed the changes in SOC loss to climate- and human-induced vegetation changes. The results showed that SOC loss in the Hetong region was 64.73 t·km−2·yr−1, 16.79 times higher than the global average. Over the past 34 years, SOC loss decreased by 23.84%, with a total reduction of more than 105.64 Tg C since the change-point year. Moreover, our study found that vegetation changes dominated the changes in SOC loss in the Hetong region, contributing 89.67% of the total reduction in SOC loss in the Hetong region. This study can inform carbon accounting and sustainable catchment management in regions that have experienced large-scale ecological restoration. [ABSTRACT FROM AUTHOR]

Large-scale irrigation can alter the regional water cycle process, which changes the structure and spatiotemporal distribution of local and downwind precipitation, impacting soil erosion in both the irrigated areas and the surrounding regions. However, the effects of large-scale irrigation on soil erosion in downwind vulnerable areas have not been investigated. The study used the high-resolution regional climate model (RegCM4) and the revised universal soil loss equation (RUSLE) to examine the effects of irrigation-induced precipitation in Northwest China on the frequency, distribution, and intensity of precipitation in the Yellow River Basin (YRB) under different Representative Concentration Pathways (RCPs). The response characteristics of soil erosion to the irrigation-induced precipitation effects and its relationship with slope, elevation, and land use type were analyzed as well. The results indicate that soil erosion in most regions of the YRB is below moderate, covering 84.57% of the basin. Irrigation leads to a 10% increase in summer precipitation indices (e.g., total wet-day precipitation, consecutive wet days, number of wet days with precipitation ≥ 1 mm, and number of heavy precipitation days with precipitation ≥ 12 mm) in the northwest of the basin. Irrigation also leads to a change in local circulation, resulting in reduced precipitation in the southeast of the basin, particularly under the RCP8.5 scenario. The transformation of erosion intensity between low-grade and high-grade erosion is relatively stable and small under the influence of precipitation. However, soil erosion changes display strong spatial heterogeneity, inter-annual and intra-annual fluctuations, and uncertainties. The findings of this study can be helpful for policymakers and water resource managers to better understand the impacts of large-scale irrigation on the environment and to develop sustainable water management strategies. [ABSTRACT FROM AUTHOR]

Accurate soil erosion estimations are required for both economic and ecological purposes. In sugarcane fields on Ishigaki Island, Japan, several types of cropping practices are undertaken in the same region and annual cropping practice distribution changes (ADCs) occur. ADCs suggest that the dynamics of soil erosion from sugarcane fields differ year by year. In this study, we evaluated the annual soil erosion errors derived from ADCs by reconstructing a map showing the distribution of cropping practices among sugarcane fields, calculating soil erosion using the Universal Soil Loss Equation (USLE), and determining the coefficient of variation (CoV) as an indicator of errors. The CoV was large, especially at the scale of 0.001-3 km². A comparison with previous research also indicated that the errors derived from ADCs had a larger impact on soil erosion estimations than those from the USLE properties, at least at scales smaller than 0.1 km², suggesting that an appropriate consideration of ADCs is required for accurate soil erosion estimations. [ABSTRACT FROM AUTHOR]

Life Cycle Assessment (LCA) is normally used independently of the physical and temporal location of the product, process or service under analysis. This makes LCA results more easily comparable and globally accepted. At the same time, it has drawbacks though, e.g. land use will have the same impact regardless of location. However, the use of certain terrains in high erosion risk areas as compared to others in low erosion risk areas will have a different impact on the ecosystem. The availability of airborne Light Detection and Ranging (LiDAR) data (ALS) allows a quick and accurate morphogeometric analysis of any terrain. For this reason, this article offers a methodology, based on Revised Universal Soil Loss Equation (RUSLE) method and airborne LiDAR data, for the straightforward detection of zones with high vulnerability to erosion problems. Based on these local erosion risk data, a method is developed to assess the environmental impact of land use, based on its location. In this way, the LCA methodology is incorporated to gather local data, dependent on the specific location of the activity under analysis. The methodology developed has been applied, as a case study, to a specific municipality in the high mountains of the Autonomous Community of La Rioja (Spain). [ABSTRACT FROM AUTHOR]

Soil erosion is one of the most common types of land degradation. To provide useful information for proper management, quantitative soil erosion evaluation and identifying influential factors are needed. However, rare studies have been reported on spatial modeling of soil erosion in connection with affective factors to prioritize the locality and the type of erosion control measures. Hence, the aim of this study was to (1) assess erosion-prone areas in the Talar watershed, Iran, using the revised universal soil loss equation (RUSLE) model and (2) investigate the relationship between soil erosion variability and land-use changes. Toward that, the ordinary least squares (OLS), geographically weighted regression (GWR) models, and principal component analysis (PCA) were used to analyze spatial relationships between soil erosion, land-use, and the RUSLE factors. The results of the OLS and GWR models indicated that these relationships are spatially non-stationary. GWR models had a good predictive performance than OLS with lower Akaike's Information Criterion (from 254.31 to 276.81 in OLS and from 247.87 to 269.42 in GWR) and higher adjusted R2 values (from 0.12 to 0.54 in OLS, and from 0.36 to 0.66 in GWR). Among the variables mentioned above, LS factor, P factor, forest, and irrigated land were the most influential variables in GWR models. The results of PCA showed that PC1 and PC2 explained 66.2% of the variation in soil erosion concerning land-use and the RUSLE factors. These results provided appropriate references for managers and experts properly planning the study watershed. [ABSTRACT FROM AUTHOR]

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Copyright of Bulletin of Soil & Water Conservation is the property of Bulletin of Soil & Water Conservation Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The Sianjo-anjo reservoir is used to meet the need for downstream clean water. Land activity at the Sianjoanjo reservoir watershed can potentially increase the rate of erosion and the silting of rivers and reservoirs due to sedimentation. Reservoir siltation is a crucial challenge for reservoir management because it can reduce its function and affect its service life. However, sediment yield is often overlooked in reservoir planning and environmental assessment. This study aims to predict the rate of land erosion and sediment yield, and create an erosion hazard map of the Sianjo-anjo reservoir watershed. The study used a Geographic Information System, GIS-based Universal Soil Loss Equation (USLE) method and discovered that the erosion rate of the Sianjo-anjo reservoir watershed was between 35.23 Mg·ha-1·y-1 until 455.08 Mg·ha-1·y-1, with 95.85% classified as the low level, 0.03% as moderate, and 4.12% as high. Meanwhile, the sediment yield from the Sianjo-anjo reservoir watershed was 218,812.802 Mg·y-1. USLE is vital to identify areas susceptible to erosion and crucial for reservoir sustainability. Furthermore, it is necessary to plan good sediment management. Long-term land conservation is required to maintain storage capacity and ensure effective operation of the reservoir. [ABSTRACT FROM AUTHOR]

Globally, urbanization changes land use/land cover (LULC) and alters ecosystem functions and services. Soil retention (SR) is a critical ecological service that is strongly related to LULC change. The topic of this study is assessment of LULC change on soil retention service (SRS) in a fragile seminatural-urbanized landscape of the Jajrood basin in Northern Tehran, Iran, from 2000 to 2020. To achieve the goal, the LULC maps and the other relevant datasets were imported into the Integrated Valuation of Ecosystem Services and Trade-offs tool (InVEST) using the Universal Soil Loss Equation (USLE). Calibration and validation were performed using Goodness-of-fit test for observational and modeled data. The results revealed that LULC change had both negative and positive effects on SR. The built-up area increased dramatically by about 133 percent, while the rangeland shrunk by approximately 5 % during the twenty-year, leading to an increase in soil erosion and reducing SR. On the other hand, the agricultural and gardening activities expanded by 41 %, which caused an increment in SR. Due to the outgrowth of man-made areas compared to the other land uses, the overall SR decreased by about 17,000 tons. Moreover, the result indicated that slope, elevation, and land management factors, respectively, had the highest correlation with SRS. The finding of this research can provide insight to land use planners to protect the areas with high soil erosion.

• Probabilistic mapping Framework : Introduces a novel probabilistic method for integrating multiple sources of uncertainty—climate models, socioeconomic scenarios, and model parameterization—into spatially explicit ecosystem service assessments. • Key drivers of future Erosion : Demonstrates that land use and land cover (LULC) changes are the dominant drivers of future erosion in the Maurienne Valley, outweighing the generally reducing effects of climate change on erosion rates. • Distinct contributions of uncertainty Sources : Reveals that model parameterization drives the magnitude of erosion changes, while uncertainties in climate models and scenarios primarily influence the direction of future changes. How ecosystems will provide ecosystem services in the future given uncertain changes in climate and land use is an open question that challenges decision-making on adaptation to climate change. Prospective assessments of ecosystem services should carefully include and communicate the sources of uncertainties that affect the predictions. We used the ecosystem service of soil protection against erosion in the Maurienne Valley (French Alps) as a case study to illustrate how several sources of uncertainties can be integrated into an assessment of future ecosystem service supply. We modeled future erosion rates in the Maurienne Valley for years 2020 and 2085 using the Revised Universal Soil Loss Equation (RUSLE) and six climatic and socioeconomic scenarios. We quantified how the ecosystem service supply will be likely affected by climate and land-use change, separately and jointly. We assessed the effects of different sources of uncertainty on projected erosion rates: scenarios, climate models choice, and methods to parametrize the ecosystem service model. Land-use change increased erosion (+ 3.3 ton.ha-1.yr-1 on average, with significant increases in 81 % of the study site), while climate change contributed to a slight reduction (-0.21 ton.ha-1.yr-1 on average with significant decrease 20 % of the study site). The uncertainty of the ecosystem service model parameterization explained 93 % of the variance in erosion values. Furthermore, uncertainty linked to climate models and future scenarios contributed almost equally to the variability in the direction (positive or negative) of erosion change (41 % and 38 % respectively). The uncertainties surrounding the direction of future changes in ecosystem services come mainly from uncertainties in climate models and future scenarios rather than from uncertainties in the ecosystem service model parameters. Assessing the likelihood of future changes in ecosystem services helps prioritize locations where adaptation solutions are likely to be needed. [Display omitted] [ABSTRACT FROM AUTHOR]

• The combination of RUSLE and SDR model is suitable for typical mountainous area. • Erosion was mainly occurred in areas with slopes of 15–25°. • The erosion trend changes when NDVI is 0.67 and 0.82. • The erosion trend changes when precipitation is 550 mm. Quantifying the contributions of driving factors and analyzing dynamic changes of water erosion in mountain areas are crucial for water erosion control and sustainable soil resource utilization. In this study, the Revised Universal Soil Loss Equation (RUSLE) and Sediment Delivery Ratio (SDR) model were integrated, and the Geographically Weighted Regression (GWR) and path analysis models were used to explore the contributions and interactions of key influencing factors (precipitation, NDVI, slope, soil moisture) on water erosion in Longnan City. The results showed that the RUSLE-SDR model could simulate the water erosion process effectively in Longnan City from 2000 to 2020 (R2 = 0.821, NSE = 0.67). The spatial and seasonal distribution of water erosion intensity was consistent with precipitation, showing the characteristics of weak in northwest and strong in southeast, and summer is the most serious period of water erosion. The GWR and path analysis models revealed that vegetation and slope were the main influencing factors of water erosion, and they had a strong interaction. When NDVI was below 0.67, slope had a direct impact on water erosion; when NDVI was between 0.67 and 0.82, slope and vegetation jointly influenced water erosion; and when NDVI was above 0.82, vegetation became the dominant factor, while slope indirectly affected erosion by regulating vegetation cover. Precipitation was the main factor that influenced erosion when the rainfall was less than 550 mm, but when the rainfall exceeded 550 mm, it exhibited a strong inhibitory effect on erosion through vegetation. This study reveals water erosion's driving mechanisms in mountain areas and provides soil erosion control measures' implementation with a scientific basis and theoretical support. [ABSTRACT FROM AUTHOR]

Understanding the spatiotemporal patterns and key drivers of soil erosion in alpine grassland regions is crucial for developing effective conservation and restoration strategies in ecologically vulnerable areas. This study focuses on the source region of the Yellow River (SRYR). It evaluated the spatiotemporal trends of soil erosion intensity, as well as identified erosion-prone areas, by combining the Sediment Delivery Ratio (SDR) model and the Revised Universal Soil Loss Equation (RUSLE). The Theil-Sen trend analysis, Mann-Kendall test, and Hurst exponent were used to assess the significance and persistence of soil erosion trends. Additionally, the Geodetector was used to analyze the driving effects of natural factors and human activities on the changes in soil erosion rate. The results indicated that: (1) From 2000 to 2020, the soil erosion intensity in the SRYR exhibited an overall declining trend, despite some fluctuations, with areas of slight erosion (< 5 t/(hm2·a)) accounting for over 78 % of the region; (2) The multi-year average SDR was below 15 %, with erosion-prone areas primarily located in the central region of the SRYR and topographically complex edge regions; (3) The areas where soil erosion had significantly improved were mainly located in the western half of the SRYR. It is expected that future soil erosion conditions will continue to follow historical trends, with approximately 30 % of the area possibly continuing to improve; (4) The interaction of vegetation cover, slope, and temperature was the main natural factor affecting the interannual variation of soil erosion rates, while the impact of rainfall remained relatively stable. Currently, the influence of human activities is limited. This study revealed the spatiotemporal characteristics and driving factors of soil erosion in the SRYR, providing a scientific basis for evaluating the effectiveness of ecological protection and restoration measures and offering a reference framework for assessing soil erosion conditions in similar regions. • Assessed soil erosion vulnerability using the RUSLE-InVEST (SDR) Model. • Quantified spatiotemporal dynamics and sustainability of soil erosion trends. • Identified the drivers of soil erosion changes in the SRYR area. [ABSTRACT FROM AUTHOR]

The objective of this study was to evaluate the impact of biological practices on the prioritization of flood degree in various land use and land cover (LULC) scenarios within watershed in Iran. The study employed the index of connectivity (IC) and assessed four scenarios for sub-watershed prioritization using the C-factor of Revised Universal Soil Loss Equation (RUSLE): (a) scenario 1, which considered current biological practices, (b) scenario 2, which assumed a 25% reduction in the current LULC situation (C-factor of RUSLE), (c) scenario 3, which assumed a 50% reduction in current LULC, and (d) scenario 4, which assumed a 75% reduction in current LULC. The results revealed that in scenario 1, sub-watersheds 20 and 14 were the most susceptible to flooding, while sub-watersheds 21 and 10 showed the lowest degree of flood susceptibility. In scenario 2, due to the minimal enhancement in LULC, no significant difference was observed in relation to scenario 1, and the flooding degree and sub-watershed ranking were nearly equal in both scenarios. However, in scenarios 3 and 4, significant differences were observed in terms of flooding degree and sub-watershed ranking when compared to scenario 1. [ABSTRACT FROM AUTHOR]

With the rapid advancement of urbanization and industrialization, intense land utilization in mountainous areas has led to ecological and environmental issues, and also causing a gradual degradation of ecosystem services. Investigating the spatiotemporal characteristics of human activities and land use on ecosystem services in mountainous areas holds significant practical importance for local ecological conservation and land management. This study utilizes human activity and land use models to evaluate the complex relationship between humans and land in the northeastern Guizhou, employs the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) and the Modified Universal Soil Loss Equation (RUSLE) models to conduct an in-depth investigation into the spatiotemporal distribution of soil conservation (SC), water yield (WY), carbon sequestration (CS), habitat quality (HQ), and landscape aesthetics (LA). Besides, we utilized the Pearson correlation coefficient and spatial bivariate models to scrutinize the spatial heterogeneity and interactions among these ecosystem services. The results revealed a significant escalation in the intensity of human activity and land use within the study area, with values rising from 0.483 and 316 in 2000 to 0.695 and 351 in 2020, respectively. For 2000–2020, the added value of SC and WY experienced a change of 1.179 t‧hm−2 and 118.503 mm respectively, while the CS, HQ, and LA have largely remained stable. The spatial distribution of ecosystem service functions exhibited distinct patterns of trade-offs and synergies. The ecosystem's multifunctional benefits were predominantly concentrated in the northern sector of the study area, with the maximum migration distance of class IV hotspots recorded at 70.431 km. Over time, the impact of human activities and land use on ecosystem services has been escalating. Studying the interactions between human activities and land use and their impact on ecosystem services can help us to develop land development policies and ecological management strategies. [Display omitted] • This study proposes a framework for the sustainable development of ecosystem services under the human activities in mountainous areas. • This study explores the interactive effects of human activities and land use on ecosystem service trade-offs and synergies. • The agglomeration of ecosystem services and large spatial migration trajectories exhibit noticeable disparities. [ABSTRACT FROM AUTHOR]

This study proposes a comprehensive concept and a novel approach to examine and assess the impact of climate change on soil degradation within the Loukkos watershed. The main aims of this study are to quantify and predict the potential rate of soil erosion, monitor its changes, and determine the sediment yield (SY) from 1999 to 2040 more accurately. This is achieved using the Revised Universal Soil Loss Equation (RUSLE), which accounts for variations in rainfall erosivity and soil cover, along with the linear mixed-effects model and Cellular Automata-Markov forecasting models and the Sediment Delivery Ratio (SDR). Our approach is based on data provided by remote sensing as well as daily rainfall data recorded at ten stations and multi-temporal time series satellite data for the reference period of 1999–2019. The results of the study revealed that soil erosion rates in the Loukkos basin range from 0 to 3797.66 t. ha − 1. year − 1 between 1999 and 2040, with an average loss of 111.51 t. ha − 1. year − 1 and a SDR of 21.2 %. The average SY estimated by RUSLE/SDR is 6.81 Mm3, almost equal to the value measured at the El Makhazine dam reservoir by the Loukkos Hydraulic Basin Agency (ABHL). We obtained detailed and reliable estimates showing that the average soil loss in 1999 was 100.64 t. ha − 1. year − 1 ; in 2009, it was 138.27 t. ha − 1. year − 1 , in 2019, it was 110.98 t. ha − 1. year − 1 , and it is projected to be 103.50 t. ha − 1. year − 1 in 2029 and 104.16 t. ha − 1. year − 1 in 2040. This study will provide valuable concepts and insights into soil degradation in the study area, serving as a fundamental reference for researchers in this field. Additionally, it will be an essential guide for regional policymakers in planning soil erosion control strategies and watershed management. • Predict soil erosion changes from 1999 to 2040 using advanced models. • Provides reliable soil loss projections for future climate scenarios. • Novel approach to assess climate change impact on soil degradation. • Combines RUSLE, SDR, LME, and CA-Markov models for soil erosion prediction. • Soil erosion rates in Loukkos range from 0 to 3797.66 t/ha/year (1999–2040). [ABSTRACT FROM AUTHOR]

Soil erosion is a significant environmental problem that can have devastating impacts on ecosystems and the sustainability of agricultural lands, including landscape degradation and biodiversity loss. Estimating erosion rates requires the installation of measurement devices such as sediment traps and runoff collectors or the use of erosion plots considering soil texture and vegetation cover. However, these procedures have significant spatial and temporal limitations, in addition to high implementation costs. The objective of this study was to analyze soil erosion in Ecuadorian basins using the Revised Universal Soil Loss Equation (RUSLE) based on Geographic Information System (GIS) and high-resolution satellite data from 2001 to 2020. The methodology involved delineating and evaluating the environmental characteristics of Ecuadorian basins, precipitation data, soil physical and chemical properties, identification of the Normalized Difference Vegetation Index (NDVI), land use, Digital Elevation Model (DEM) selection and assigning values based on slope raster. Basin delineation was obtained from regional literature; precipitation data from GPM-IMERG web servers; sand, clay, silt, and Soil Organic Carbon (SOC) content from SoilGrids web servers; DEM from SRTM database; spatial-temporal distribution of NDVI and land use from MODIS database; and P Factor value ranges based on slope from global literature. Recorded erosion rates ranged from 0.28 t/ha year to 2373.85 t/ha year, with the highest values identified in the Andes Mountains due to steep slopes. The main driver of soil erosion in Ecuadorian basins was the R-Factor, followed by LS and C. Land use variations indicated a loss of 9771 km2 of forests and 80 km2 of cultivated lands between 2001 and 2020, while semi-natural vegetation increased by 9851 km2. These results provide relevant information to assist land managers in making decisions to address this natural phenomenon. • First estimate of soil erosion rates on a national scale in Ecuador using RUSLE. • R was the RUSLE-Factor with greatest influence on the generation of erosion rates. • Soil erosion rates have been decreasing slightly in recent years. • Almost 10,000 km2 of forests have been lost between 2001 and 2020. [ABSTRACT FROM AUTHOR]

Preventing water erosion is crucial for maintaining ecosystems and ensuring food security, necessitating a comprehensive understanding of the spatial and temporal patterns of water erosion and its underlying drivers. In the context of global warming, analyzing the impacts of land use dynamics and climate change on water erosion contributes to effective land management and sustainability of both industry and agriculture. This study aims to analyze the spatial distribution of water erosion in the western Songnen Plain from 1990 to 2020 using the Revised Universal Soil Loss Equation (RUSLE), with a focus on assessing the impacts of land use and climate on water erosion. The results revealed a 7.1 % increase in the area experiencing water erosion above light levels in the western Songnen Plain. The hotspots for water erosion were located in the southeast and northwest of the study area. The rapid expansion of farmland and land salinization, leading to reduced vegetation cover and soil property deterioration, were the main causes of intensified water erosion in the region before 2000. Although water erosion was slightly alleviated after 2000, the further expansion of farmland, the worsened water erosion intensity in alkaline land and frequent extreme weather still posed serious threats to water erosion in the study area. Water erosion was positively correlated with temperature and dry/wet alternation events, including frequency, duration, and severity. In addition, land use type was the main factor influencing the heterogeneous distribution of water erosion in the western Songnen Plain, whose interaction with dry/wet alternation events had the strongest explanatory power. Therefore, this study calls for the implementation of soil and water conservation measures to mitigate the impacts of land cultivation, salinization, and climate change on water erosion. [Display omitted] • The spatial-temporal patterns of water erosion were explored. • Rapid expansion of farmland and salinization exacerbated water erosion. • The connection between dry/wet alternating events and water erosion was evaluated. • The effects of land use and climate change on water erosion were revealed. [ABSTRACT FROM AUTHOR]

Intense precipitation events pose growing threats to forest infrastructure causing flooding, and soil erosion and deposition, creating bottlenecks at road-stream crossing structures (RSCS). We describe a hillslope-scale ensemble hydro-geomorphological vulnerability assessment integrating geospatial Streambank Erosion Vulnerability Assessment (SBEVA), Modified Revised Soil Loss Equation (MRUSLE), and process-based Water Erosion Prediction Project (WEPP) model into an ensemble hydro-geomorphologic vulnerability index (EHVI) for USDA Forest Service (USFS) managed 194 road-culverts at the Hubbard Brook Experimental Forest (HBR-EF) in New Hampshire, USA. The results revealed that five and one culvert with diameters of 0.46m and 0.61m, respectively, have extreme EHVI values between 4 and 5, and fifteen and three culverts with diameters of 0.46m and 0.61m, respectively, have severe EHVI values between 3 and 4, some of which were previously identified as hydrologically vulnerable (undersized) to floods. This knowledge will inform USFS efforts to improve the resilience of the RSCS and protect aquatic habitats. [Display omitted] • Hydro-geomorphologic risk management using ensemble vulnerability assessment. • Streambank and hillslope scale soil erosion potential on forested headwater catchments were studied. • Used empirical, conceptual, and continuous process-based models in the analysis. • 194 road culverts were assessed for hydro-geomorphologic risk of siltation and clogging. • Culverts with diameter of 0.46 and 0.61 m are found to be susceptible to hydro-geomorphologic risk during flooding. [ABSTRACT FROM AUTHOR]

Agro-socio-ecological systems are a crucial link connecting urbanization, agricultural development and environmental evolution. However, there is no effective research on realizing regional collaborative development and environmental governance of the agricultural social-system collaborative governance model, especially spatial differentiation governance. In this study, the region with the most severe soil erosion in the world was selected as the research area. We used socio-economic statistical data and remote sensing data, combined with the Revised Universal Soil Loss Equation, to provide analytical evidence in different sub-regions. Results showed that the soil erosion rate of the Loess Plateau has dropped significantly during the past three decades, with an average decreasing rate of 0.46 t ha−1 yr−2 or 2.33%. Large-scale investments in ecological infrastructure (e.g., check dams) increase agro-socio-ecological productivity by reducing soil erosion. The productivity of social-ecological systems increased significantly, especially after 2000, with the grain production capacity and gross primary productivity rising by 84.28% and 18.06% from 1990 to 2017. The decrease in grain yield caused by the ecological return of upland cropland and the occupation of high-quality cropland will be raised to a better level. Large-scale investment in ecological infrastructure enabled farmers to obtain ecological compensation, and agricultural income resulting from land productivity improvement brought about by infrastructure investment. This study further confirms the dual benefits of ecological infrastructure in controlling erosion and improving land productivity, and provides sustainable support for achieving coordinated economic growth and ecological environmental protection through effective management of agricultural ecosystems. • Large-scale ecological infrastructure controls soil erosion through water conservation and farmland protection. • Regions reliant on resource development can achieve growth through collaborative governance of agricultural social-ecosystems. • Enhancing agricultural social-ecosystem productivity mitigates prime farmland loss from rapid urbanization. • Urbanization and ecological infrastructures promoted a win-win for ecology and production. [ABSTRACT FROM AUTHOR]

The watershed area of the Mangla Reservoir spans across the Himalayan region of India and Pakistan, primarily consisting of the Jhelum River basin. The area is rugged with highly elevated, hilly terrain and relatively thin vegetation cover, which significantly increases the river's sediment output, especially during the monsoon season, leading to a decline in the reservoir's storage capacity. This work assesses the soil erosion risk in the Jhelum River watershed (Azad Jammu and Kashmir (AJ&K), Pakistan) using the Revised Universal Soil Loss Equation of (RUSLE). The RUSLE components, including the conservation support or erosion control practice factor (P), soil erodibility factor (K), slope length and slope steepness factor (LS), rainfall erosivity factor (R), and crop cover factor (C), were integrated to compute soil erosion. Soil erosion risk and intensity maps were generated by computing the RUSLE parameters, which were then integrated with physical factors such as terrain units, elevation, slope, and land uses/cover to examine how these factors affect the spatial patterns of soil erosion loss. The 2021 rainfall data were utilized to compute the rainfall erosivity factor (R), and the soil erodibility (K) map was created using the world surface soil map prepared by the Food and Agriculture Organization (FAO). The slope length and slope steepness factor (LS) were generated in the highly rough terrain using Shuttle Radar Topography Mission Digital Elevation Model (SRTM DEM). The analysis revealed that the primary land use in the watershed was cultivated land, accounting for 27% of the area, and slopes of 30% or higher were present across two-thirds of the watershed. By multiplying the five variables, the study determined that the annual average soil loss was 23.47 t ha−1 yr−1. In areas with dense mixed forest cover, soil erosion rates ranged from 0.23 t ha−1 yr−1 to 25 t ha−1 yr−1. The findings indicated that 55.18% of the research area has a low erosion risk, 18.62% has a medium erosion risk, 13.66% has a high risk, and 11.6% has a very high erosion risk. The study's findings will provide guidelines to policy/decision makers for better management of the Mangla watershed. [ABSTRACT FROM AUTHOR]

Soil erosion is a natural occurrence in landforms but is a major threat that causes essential soil nutrients loss and it is hazardous to agriculture practices as depletion of soil makes the land less productive. The major factors influencing soil erosion are rainfall erosivity factor (R), soil erodibility factor (K), length and steepness factor (LS), cover management factor (C) and support practice factor (P). In most of the earlier research studies, the estimation of soil erosion has been done with Revised Universal Soil Loss Equation (RUSLE) but accuracy in analysis of C and P factors has not been considered. The main objective of the study is to explore the influence of different LULC classifier in determining the rate of erosion using RUSLE model. This study utilizes five classifiers such as Maximum Likelihood Classifier (MLC), Random Trees Classifier (RTC), Support Vector Machine Classifier (SVM) and Artificial Neural Network (ANN) and Mahalanobis Distance (MD) to generate the C and P factor. The Accuracy assessment, a statistical method is used to validate the classified Land Use / Land Cover (LULC). The obtained results show that the respective annual mean soil losses based on MLC, MD, ANN, SVM, RTC are 1.95, 2.08, 1.71, 2.19 and 1.85 tons−1 ha−1 yr−1. The estimated annual mean soil loss by using different classifiers is validated using a statistical method Receiver operating curve / Area under Curve. The accuracy level of RTC is 0.800 of Area Under the Curve (AUC) which is higher compared to conventional classifiers like MLC, MDC, ANN, SVM with the values 0.793, 0.707, 0.693, 0.793 of AUC, respectively. RTC, a Machine learning-based classifier, is observed to be more accurate than the conventional classifiers. The insights obtained from the present study will be very useful for land use planning and management and to undertake soil and water conservation measures. [ABSTRACT FROM AUTHOR]

Excessive sand mining to meet the growing demands of the construction industry has resulted in the degradation of rivers, lowering of groundwater table, saltwater intrusion into coastal aquifers, and damages to the biodiversity of the riverine ecosystem, besides endangering bridges and hydraulic structures at several locations. Methods presently being employed to assess the amount of sand that can be mined from rivers lack a scientific basis and are seldom supported by systematic studies on the behaviour of riverine systems. Sand audit reports shall be prepared based on hydrologic, hydraulic, and sediment transport modelling of rivers, and shall take into account relevant environmental and ecological considerations. These reports shall clearly present details pertaining to the annual sediment yield of the river basin, reaches of the river/ locations from which sand can be extracted without disturbing the river sediment equilibrium, and the volume of sediment that can be extracted from these locations. Sand mining from rivers is a major environmental issue in Kerala and the urgent need for regulating sand extraction based on a scientific assessment of the amount of sand available is widely recognized. It is in this context that this work aimed at developing a framework to perform sand auditing was taken up. The methodology is demonstrated by applying it to the Chaliyar River in Kerala. Rainfall-runoff and soil erosion modelling, and sediment routing were performed in HEC-HMS. Soil erosion from the catchment was modelled using the Modified Universal Soil Loss Equation (MUSLE) and the sediment was routed through the river by the uniform equilibrium method. Hydraulic and sediment transport modelling was then performed in HEC-RAS. Values of the model performance criteria for both HEC-RAS and HEC-HMS indicate that these models performed reasonably well. Based on the analysis, a sand audit report and sand volume map were prepared. [ABSTRACT FROM AUTHOR]

In the late 1980s, the USDA Agricultural Research Service, along with other federal agencies and multiple universities, collaborated to develop a new physically based soil erosion model, the Water Erosion Prediction Project (WEPP) Model. The WEPP model was intended to replace the Universal Soil Loss Equation and was to include estimates for upland runoff and erosion, sediment delivery to first order channels, and runoff and sediment routing through a downstream channel network. The WEPP technology estimated erosion from raindrop splash and sheet flow (interrill erosion) and concentrated channel flow (rill erosion). To make these erosion estimates, WEPP required new soil erodibility values for interrill erodibility (Ki). rill erodibility (Kr), and critical shear (𝜏𝜏 c) for concentrated flow erosion. The WEPP Core team determined that they needed to estimate these three erodibility values from measurable soil properties for a wide range of soil conditions. To develop relationships between WEPP soil erodibility variables and other soil properties, a field study was carried out using rainfall and runoff simulation to measure the three erodibility values for 36 soils. Sites were identified on croplands from Washington to Georgia and Maine to California, USA for erodibility measurement. Concurrently, the USDA Soil Conservation Service (SCS) carried out detailed soil surveys and laboratory analyses for all sites to provide a large database of soil physical, chemical, and engineering properties. Correlation and regression analyses were carried out to develop relationships between SCS measurable soil properties and WEPP soil erodibility values. This article provides a summary of the field procedures, data analyses, and subsequent predictive equations that were developed. The predictive equations that were finalized in the WEPP User Summary used sand, very fine sand, clay, and organic carbon contents to predict cropland soil erodibility, but the Coefficient of Determination (r²) values were 0.55 or less. More complex predictive equations were developed with soil physical, chemical, mineralogical, and geomorphic properties, with r² values up to 0.81. Most of the better predictive equations included terms for soil texture and clay mineralogy, often with additional chemical properties. A set of simplified erodibility equations using only the readily available properties of soil texture, organic carbon, cation exchange capacity, slope steepness, and taxonomic order were derived for use within the WEPP Model, with r² values greater than 0.5 for all three equations for estimating soil erodibility from measurable soil properties. [ABSTRACT FROM AUTHOR]

Soil erosion due to anthropogenic interventions is an emerging threat to the south-eastern coastal districts of Bangladesh. In the recent decade, land degradation intensified this process in this region due to the settlement of the Forcibly Displaced Myanmar Nationals popularly known as 'Rohingya', who fled from Myanmar in 2017. To control soil erosion, it is necessary to identify the soil erosion locations and their intensity. The present study has attempted to quantify the regional soil loss (from 2015 to 2020) using the Revised Universal Soil Loss Equation model. In this method, soil erosion risk assessments are carried out by integration of different physiographic parameters in the spatial dimension. Results showed that the year 2015 and 2020 witnessed mean soil erosion at a rate of around 58.2 and 59 t ha− 1 yr− 1, respectively in the Cox's Bazar district in which 20–21% of the study area was highly subjected to soil erosion. But in the Rohingya camps mean soil erosion for these periods is 59 and 78 t ha− 1 yr− 1, respectively with a cumulative increase of 32%. The sub-districts adjacent to the Rohingya refugee camps (Ramu, Ukhia, and Teknaf) have experienced intense erosion compared to the others. From 2015 to 2020, a large increase of about 4.54 (6%) t ha− 1 yr− 1 is observed in the Palongkhali union whereas a maximum decrease of about − 8.08 (23%) t ha− 1 yr− 1 is observed in the Pokkhali union. In Rohingya camps, the worst soil erosion-affected areas are the camps 8E, 10, 14, 15, 16, and 17 having more than 100 t ha− 1 yr− 1 mean soil loss. The geographic detector method is used to identify the influence of different factors on soil erosion. Land use change is found to be the major contributing factor to soil erosion increase from 2015 to 2020. The spatial variations are significantly controlled by slope and elevation factors. This work can help to understand the dynamics of soil erosion and the knowledge can be used for conservation planning in the Cox's Bazar and Rohingya camp areas. [ABSTRACT FROM AUTHOR]

Most agro-tourism areas are located in the upstream area of the watershed, in the highlands, steep slopes, and the air temperature is peaceful and calm. One of them is in the tourism area in the Penet upstream watershed, Bali, Indonesia. Agro-tourism, in addition to providing economic benefits to agricultural products, also from tourist visits to the panorama and landscape of agricultural land. Land use that is not wise and not following conservation rules can cause erosion. This study aims to investigate soil erosion rate and plans for environmental-based conservation. The method used to quantify erosion uses Universal Soil Loss Equation (USLE) with a geographic information system (GIS) approach. This research integrates field surveys and soil analysis in the laboratory. The result showed that the erosion rate in agro-tourism areas ranges from 0.32 t ha-1 yr-1 (very light) to 1,535.34 t ha-1 yr-1 (very heavy). The agro-tourism areas affected by erosion with heavy to very heavy categories are the villages of Antapan, Bangli, Apuan, Angseri, and Candikuning. Conservation actions can be taken, including improving plant management factors with dense vegetation, increasing land management actions by constructing bench terraces, and planting parallel to contour lines. It is recommended for further researchers to model erosion by integrating remote sensing data and geographic information systems in order to complement data that cannot be obtained in the field or the laboratory. [ABSTRACT FROM AUTHOR]

Land abandonment is among the most complex la nd use change processes driven by a multiplicity of anthropogenic and natural factors, such as agricultural over-exploitation, implementation of agricultural policies, socio-economic and climatic aspects. Therefore, it is necessary to deepen the effects of land abandonment based on methodologies that are as multidisciplinary as possible. Environmental and social problems related to abandonment include soil erosion and environmental degradation. Approaches combining GIS (Geographic Information System), remote sensing, and image analysis techniques allow for assessments and predictions based on integrating theoretical models with advanced geospatial and geostatistical models. One of the most widely used models for soil erosion estimation is the Revised Universal Soil Loss Equation (RUSLE). The present work developed a model using remote sensing and GIS tools to investigate some factors of the RUSLE equation to evaluate the adverse effects of soil erosion in areas covered by arable crops and subsequently abandoned. To identify potentially degraded areas, two factors of the RUSLE were related: the C Factor describing the vegetation cover of the soil and the A Factor representing the amount of potential soil erosion. Through statistical correlation analysis with the RUSLE factors, based on the deviations from the average erosion values and mapping of the areas of vegetation degradation relating to arable land, the areas identified and mapped are susceptible to soil degradation. [ABSTRACT FROM AUTHOR]

Soil erosion often leads to land degradation, agricultural production reduction, and environmental deterioration, which seriously restricts the sustainable development of regions. Clarifying the driving factors of soil erosion is the premise of preventing soil erosion. Given the lack of current research on the driving factors/force changes of soil erosion in different regions or under different erosion intensity grades, this paper pioneered to use machine learning methods to address this problem. Firstly, the widely used (Revised) Universal Soil Loss Equation ((R)USLE) framework was applied to simulate the spatial distribution of soil erosion. Then, the K-fold algorithm was used to evaluate the accuracy and stability of five machine learning algorithms for fitting soil erosion. The random forest (RF) method performed best, with average accuracy reaching 86.35%. Then, the Permutation Importance (PI) and the Partial Dependence Plot (PDP) methods based on RF were introduced to quantitatively analyze the main driving factors under different geological conditions and the driving force changes of each factor under different erosion intensity grades, respectively. Results showed that the main drivers of soil erosion in Chongqing and Guizhou were cover management factors (PI: 0.4672, 0.4788), while that in Sichuan was slope length and slope factor (PI: 0.6165). Under different erosion intensity grades, the driving force of each factor shows nonlinear and complex inhibitory or promoting effects with factor value changing. These findings can provide scientific guidance for the refined management of soil erosion, which is significant for halting or reversing land degradation and achieving sustainable use of land resources. [ABSTRACT FROM AUTHOR]

Soil erosion is an important environmental problem that can have various negative consequences, such as land degradation, which affects sustainable development and agricultural production, especially in developing countries like Tunisia. Moreover, soil erosion is a major problem around the world because of its effects on soil fertility by nutriment loss and siltation in water bodies. Apart from this, soil erosion by water is the most serious type of land loss in several regions both locally and globally. This study evaluated regional soil erosion risk through the derivation of appropriate factors, using the Revised Universal Soil Loss Equation (RUSLE), which was applied to establish a soil erosion risk map of the whole Tunisian territory and to identify the vulnerable areas of the country. The RUSLE model considers all the factors playing a major role in erosion processes, namely the erodibility of soils, topography, land use, rainfall erosivity, and anti-erosion farming practices. The equation is, thus, implemented under the Geographic Information System (GIS) "Arc GIS Desktop". The results indicated that Tunisia has a serious risk of soil water erosion, showing that 6.43% of the total area of the country is affected by a very high soil loss rate, estimated at more than 30 t/ha/year, and 4.20% is affected by high mean annual soil losses, ranging from 20 to 30 t/ha/year. The most eroded areas were identified in the southwestern, central, and western parts of the country. The spatial erosion map can be used as a decision support document to guide decision-makers towards better land management and provide the opportunity to develop management strategies for soil erosion prevention and control on the global scale of Tunisia. [ABSTRACT FROM AUTHOR]