Distribution Of Soil Erosion Research Articles

Characteristics and driving factors of spatiotemporal changes in soil erosion in the karst plateau mountainous region over 20 years.

Soil erosion is one of the main issues that endangers global ecosystems. This study explored the spatiotemporal distribution of soil erosion and its drivers in the karst plateau mountainous region. A detailed examination of topography, soil, vegetation, land use, and precipitation data from 2000 to 2020 was conducted in Bijie City using the revised universal soil loss equation model. We also explored the driving forces using a geographical detector. The findings show that between 2000 and 2020, soil erosion first decreased, followed by an increase. The southwest, south, and northern regions contained the highest intensity of soil erosion. Land use, slope, and precipitation are the primary factors influencing soil erosion, with slopes having the greatest impact. By improving our understanding of the dynamics of soil erosion and the primary variables that influence it in karst plateau mountainous environments, our findings can assist in the development of strategies and technical support for sustainable soil and water conservation.

Research on the spatiotemporal distribution and control degree of soil erosion in Dali River Basin

The Dali River basin as the representative areas of the middle reaches of the Yellow River. Accurately evaluating the degree of soil erosion control is very important for soil erosion control. In response to the problems existing in the current research on soil erosion control degree (SECD) methods, the maximum possible soil erosion modulus is introduced into the evaluation model. Thereby eliminating the uncertainty of the assumed state of the original model. This paper calculates the SECD for 2020 based on this method. The results show: (1) From the soil erosion modulus spatial distribution, it can be seen that the soil erosion intensity is stronger in the eastern and southwestern parts. (2) Based on the SECD spatial distribution, it was found that eastern and southwestern regions were well controlled. This is consistent with the actual situation. (3) As the slope increases, the area proportion of SECD within the 0.2-0.3 range in each slope zone shows an increasing trend, while that of SECD within the 0.5-1 range gradually decreases; (4) In the case of vegetation coverage greater than 80%, SECD is still concentrated between 0.3-0.5, which indicates that vegetation coverage is not the only factor affecting soil erosion control. This research provided a new perspective for the evaluation of SECD.

Characteristics of Soil Physical Properties and Spatial Distribution of Soil Erosion on Ridge-Slope Farmland in the Black Soil Areas of Northeast China

Characteristics of Soil Physical Properties and Spatial Distribution of Soil Erosion on Ridge-Slope Farmland in the Black Soil Areas of Northeast China

Predicting soil erosion risk using the revised universal soil loss equation (RUSLE) model and geo‐spatial methods

AbstractAnthropogenic activities like overgrazing, deforestation and mismanaged land use accelerate soil erosion (SE), causing nutritional and organic matter loss. In this study, we predicted the annual rate of soil loss in the Salt Range, extending south from the Pothohar plateau, Pakistan, using the Revised Universal Soil Loss Equation (RUSLE). The RUSLE model parameters and erosion probability zones were estimated using remote sensing and Geo‐Spatial methods. The annual average soil loss rates were calculated by considering five geo‐environmental factors, that is, slope length and steepness (LS), rainfall erosivity (R), cover management (C), soil erodibility (K), and conservation practice (P) range from 0–559 527, 1404–4431, 0–1, −0.14 to 1.64, and 0.2–122 respectively. This research determined that the yearly average rate of SE in the Salt Range varies from over 50 to above 350 . The distribution of land area across different SE probability zones reveals that a small portion (2.11%) is classified as High, a moderate portion (7.13%) falls under the category of Moderate, while the majority (90.7%) is classified as Low in terms of proneness towards erosion. The land devoid of vegetation and characterized by steep slopes is especially prone to SE. The Salt Range is highly vulnerable to SE risk due to climatic variations and improper land use practices. The result provides a spatial distribution of SE across the salt range, utilized for management planning processes and conservation at the policy level among decision‐makers and land‐use planners.

Remote Sensing Thematic Product Generation for Sustainable Development of the Geological Environment

Remote sensing thematic data products are critical for assessing and analyzing geological environments, while efficient generation of thematic products is also highly significant for achieving corresponding sustainable development goals (SDGs). Currently, remote sensing thematic product generation has problems like low levels of automation and efficiency. Addressing these challenges is imperative for advancing sustainable development within the geological environment. This paper aims to address issues related to the generation of geological environment remote sensing thematic products, sorting through the overall process of remote sensing thematic product generation, exploring algorithm encapsulation, combination, and execution under technical methods for container and workflow, and relies on the Spark distributed processing architecture to achieve efficient thematic product generation supported by multiple geological environment data processing models. Finally, taking the three SDGs of SDG6, SDG11, and SDG15 as examples, we achieved the generation of a variety of thematic products such as the interpretation of water body distribution, extraction of urban informal settlements and distribution of water and soil erosion. Meanwhile, we comparatively analyzed the efficiency of thematic product generation on different processing architectures, and the experimental results further verified the feasibility and effectiveness of our proposed solution. This research provides a programme for the automated and intelligent generation of geological environment remote sensing thematic products and effectively assists the construction of sustainable development in the geological environment.

A geospatial approach-based assessment of soil erosion impacts on the dams silting in the semi-arid region

Soil erosion significantly impacts dam functionality by leading to reservoir siltation, reducing capacity, and heightening flood risks. This study aims to map soil erosion within a Geographic Information Systems (GIS) framework to estimate the siltation of the K'sob dam and compare these estimates with bathymetric observations. Focused on one of the Hodna basin’s sub-basins, the K'sob watershed (1477 km2), the assessment utilizes the Revised Universal Soil Loss Equation (RUSLE) integrated with GIS and remote sensing data to predict the spatial distribution of soil erosion. Remote sensing data were pivotal in updating land cover parameters critical for RUSLE, enhancing the precision of our erosion predictions. Our results indicate an average annual soil erosion rate of 7.83 t/ha, with variations ranging from 0 to 224 t/ha/year. With a typical relative error of about 13% in predictions, these figures confirm the robustness of our methodology. These insights are crucial for crafting mitigation strategies in areas facing high to extreme soil loss and will assist governmental agencies in prioritizing actions and formulating effective soil erosion management policies. Future studies should explore the integration of real-time data and advanced modeling techniques to further refine these predictions and expand their applicability in similar environmental assessments.

Spatio-Temporal Variations in Soil Erosion and Its Driving Forces in the Loess Plateau from 2000 to 2050 Based on the RUSLE Model

Assessing the spatio-temporal variability and driving forces of soil erosion on the Loess Plateau is critical for ecological and environmental management. In this paper, the Revised Universal Soil Loss Equation (RUSLE) model, the patch-generating land use simulation, and the Geographical Detector are used to investigate the spatio-temporal variations of the Loess Plateau’s soil erosion from 2000 to 2050. The results showed that: (1) The primary categories of soil erosion from 2000 to 2020 were moderate, mild, and slight, and the average level of soil erosion exhibited a decreasing and then an increasing tendency during the last 20 years. (2) Soil erosion was directly impacted by changes in land use, with cropland and forest being the primary land use and land cover changes in the study region. Cropland and construction land being turned into woodland between 2000 and 2020 resulted in a significant decrease in the severity of soil erosion. Projected soil erosion is expected to increase significantly between 2020 and 2050 due to arable land being converted into construction land. (3) The key variables impacting the spatial distribution of soil erosion were LUCC (Land-Use and Land-Cover Change), NDVI (Normalized Difference Vegetation Index), and slope, and the interplay of these variables may increase their ability to explain soil erosion. Grasslands with an NDVI ranging from 0.9 to 1, rain ranging from 0.805 to 0.854 m, a slope above 35°, and a terrain elevation ranging from 1595 to 2559 m were identified as having a high risk of soil erosion. Soil erosion prevention and management efforts should focus on the ecological restoration of upland areas in the future.

Quantifying aboveground biomass, soil organic carbon and erosion with a detailed crop map and PESERA model in the Yangtze River Basin

AbstractSoil erosion represents a primary threat to soil systems with adverse implications for ecosystem services, crop production, potable water and carbon storage. While numerous studies have quantified the spatial distribution of aboveground Biomass (AGB), soil erosion and soil organic carbon (SOC) in the Yangtze River Basin (YRB), limited attention has been given to assessing the contributions of different land use types and especially crop types to AGB, soil erosion and SOC. In most studies, cropland is taken as a land use class, while detailed crop types and rotation patterns, and their effect on soil erosion and SOC, vary significantly. In this study, we used the Metronamica model to generate a detailed crop rotation and distribution map across the YRB and subsequently employed the Pan‐European Soil Erosion Risk Assessment (PESERA) model to simulate the spatial distribution of AGB, soil erosion and SOC on a monthly basis. PESERA model simulations indicate an average soil erosion rate across the entire YRB of 7.7 ton/ha/yr, with erosion hotspots concentrated in the Sichuan Basin and the central‐southern regions. The southwestern region and western Sichuan show elevated levels of AGB and SOC, while the eastern plains display lower levels. Erosion rates are lowest in areas designated as artificial land, pasture and grassland, whereas croplands and fruit tree plantations experience the highest erosion rates. In terms of crop types, the highest erosion rates and lowest AGB are observed under fallow and potato cultivation, while the lowest erosion rates and highest AGB are found in rice‐wheat rotation fields. To the best of our knowledge, this is the first study taking detailed crop types and patterns into account while evaluating their effect at a relatively large scale (i.e., YRB). These findings can help to develop sustainable soil management and (cropping) conservation strategies.

Simulation of topographic effects on soil erosion and deposition in a small watershed of loess hilly region

AbstractTopography plays a critical role in soil migration and redistribution, but few studies have been conducted to quantify its effects on sediment deposition. In this study, we established a physical simulation and analysis framework to investigate the erosional and depositional impacts of two‐dimensional slope terrain, specifically applied to Xiannangou small watershed in the loess hilly region. The results showed that the slope gradient, slope length, and slope shape have significantly influence the distribution of soil erosion and deposition. The magnitude of the erosion/deposition rate (Yr) determines the relative intensity of slope erosion and deposition, where Yr < 0 indicates erosion and Yr > 0 signifies deposition. The erosion rate on straight slope exhibited a positive correlation with slope length, while the erosion/deposition rate on concave and convex slopes exhibited fluctuations with slope length. Soil erosion predominantly occurred along the main flow line and the middle slope, aligning with the observed distribution of gully and slope erosion in the field. The sediment deposition was primarily concentrated on the lower slope or the lowest outlet of the basin, notably in gullies and gentle slopes where the terrain slows, especially during transition from steep to gradual slopes. These results can effectively predict the relative erosion/sedimentation rate of two‐dimensional slopes, significantly contributing to a comprehensive understanding of how topography influences soil erosion and deposition. This study thoroughly considers the role of sediment deposition in the soil erosion process, providing a more accurate reflection of soil erosion/deposition in small watersheds. It addresses the existing deficiency in sediment consideration within soil erosion evaluation and supports the enhancement of soil erosion model.

Prioritization of Sub-Watershed Based on Soil Loss Estimation Using RUSLE Model

Soil erosion is one of the most crucial land degradation problems and is considered the most critical environmental hazard worldwide. The present study uses remote sensing data integrated with the geographical information system (GIS) technique and the revised universal soil loss equation (RUSLE) model for assessing the annual average soil loss of the Digaru watershed of India for 1999 and 2020. The estimated mean gross yearly soil loss from the entire watershed was 102716 t yr-1 in 1999 and 178931.6 t yr-1 in 2020. The overall average soil loss rate increased significantly between 1999 and 2020, rising from 4.73 t—ha-1yr-1 to 8.43 t—ha-1yr-1. The sub-watersheds are prioritized as high (≥ 40 t ha−1yr−1), moderate (20–40 t ha−1yr−1), and low (<20 t ha−1yr−1) based on the spatial distribution of soil erosion. Seven sub-watersheds have been grouped under low priority, followed by seven under moderate priority and one under high priority. This study demands instant attention for soil and water conservation efforts in highly eroded watershed areas.

Analysis of Factors Influencing Spatial Distribution of Soil Erosion under Diverse Subwatershed Based on Geospatial Perspective: A Case Study at Citarum Watershed, West Java, Indonesia.

The application of remote sensing data has been significant in modeling soil erosion. However, previous studies have fallen short in elucidating and lacked an understanding of the multifactor influencing erosion. This study addresses these limitations by employing the InVEST and the Geodetector models. Specifically, it aims (1) to delineate both spatial and temporal variations in soil erosion within the Citarum watershed from 2010 to 2020, (2) to identify the key drivers of soil erosion and unravel the underlying mechanisms, and (3) to identify the high-risk zones for soil erosion. Both models consider a range of natural predictors, including topography (slope factor), climate (precipitation factor), and vegetation cover (vegetation factor). In addition, they incorporate social parameters such as income per capita and population density, which interact with the watershed's position in the downstream, middle, and upper streams. The results reveal that, over a decade, the average soil erosion increased by 15.50 × 106 tons, marking a 16.65% surge. The impact of factors varies significantly across different subwatershed areas. For example, fraction vegetation cover interactions influence upper- and middle-stream regions, while the downstream area is notably affected by precipitation interactions. The high-risk erosion areas in the watershed are primarily influenced by slope, precipitation, and fractional vegetation cover. In these areas, factors causing high erosion risks include slope, precipitation, and other environmental variables categorized into strata. The study highlights the varying influential factors in different watershed areas.

Relationship between annual soil erosion and surface runoff in Wadi Hanifa sub-basins

Abstract Soil erosion is the major environmental risk that causes topsoil loss, which decreases fertility in agricultural land. Spatial estimation of soil erosion is essential for conserving land resources and for developing control plans. This study evaluated soil erosion in 17 sub-basins of the Wadi Hanifa catchment using the Gavrilović model. The soil erosion rate is categorized into three classes based on the annual volume of rainfall and runoff. About 21% of the drainage area of Wadi Hanifa is affected by erosion severity, and 33% of the area is affected by severe erosion. The correlation obtained between the annual soil erosion and annual volumes of rainfall and runoff is significant. It is concluded from the results that the drainage area of the north-western part suffers from a high soil erosion risk. The NRCS-CN and erosion predict that the Gavrilović model can be applied for estimating and analyzing the spatial distribution of soil erosion over more drainage watersheds in Saudi Arabia. Now, the GIS techniques are one of the best tools for developing the conservation and management planning processes of soil resources.

Decadal Variations in Area under Different Soil Erosion Classes using RUSLE and GIS: Case Studies of River Basins from Western and Eastern Arunachal Pradesh

Abstract This study provides a comparative evaluation of spatio-temporal distribution of soil erosion in Western (Mago Basin) and Eastern (Dibang Basin) basins of Arunachal Pradesh, India as these two basins are vulnerably exposed to soil erosion due to its topographical characteristics of mountainous steep slope and experiences heavy rainfall. The study was carried out for a ten-years period (2003 to 2014) using RUSLE model which encompasses five important factors contributing to soil erosion. Rainfall erosivity (R factor) map was calculated using Climate Prediction Center gridded precipitation. Soil map and soil samples were used to analyze soil erodibility (K factor) map. Slope length and slope steepness (LS factor) maps were computed from SRTM DEM (30 m resolution). MODIS NDVI images were used to obtain cover management (C factor) map. Landuse Landcover map was used to obtained support practice (P factor) map. Higher value in rainfall erosivity and cover management factor was observed in Mago basin which contributed to higher average annual soil loss of 17.423 t ha−1 y−1 in Mago basin and 5.461 t ha−1 y−1 in Dibang basin, whereas the other three factor values were almost the same. The spatial maps showed 56.65% of Mago basin area and 76.27% of Dibang basin area was under the class of slight erosion, with the remaining areas of moderate to severe erosion risk for both the basins. Temporal average soil erosion in Mago basin varied within moderate to very high erosion classes whereas Dibang basin erosion classes varied from slight to moderate. The temporal trend line showed that the overall soil erosion was increasing at an alarming rate for Mago basin whereas a slight increase in Dibang basin was observed.

Feasibility Assessment of a Magnetic Layer Detection Method for Field Applications

The lack of current data on the spatial distribution of soil erosion hinders the ability to predict soil erosion on slopes. To address this problem, we present a simple and sensitive soil erosion measurement method called the magnetic layer detection (MLD) method. This method can measure the changes in soil layer thickness (SLT) at a site featuring a pre-buried magnetic layer (ML) using the relationship between magnetic susceptibility and soil thickness, and then use this information to determine the erosion or accumulation at that point. To verify the adaptability and accuracy of this method, we designed three field tests. First, we conducted a simulated sediment siltation experiment using the erosion pin (EP) method as the control. This experiment demonstrated the accuracy of the MLD method for measuring SLT (Rad2 = 0.97, NSE = 88.14%, and RMSE = 1.17 mm). Next, a simulated rainfall experiment with the runoff plot (RP) method as a control was used to demonstrate the effectiveness of the MLD method under water erosion. The results demonstrated that this method can reliably estimate soil erosion on both bare plots (Rad2 = 0.83, NSE = 71.78%, and RMSE = 0.56 mm) and sparse grass plots (Rad2 = 0.90, NSE = 81.77%, and RMSE = 0.25 mm), with performance that is better than that of the traditional EP method. Finally, a soil scouring experiment, again with the RP method as a control, was designed to verify whether the MLD method could accurately measure the erosion of a slope after the soil was scoured by surface runoff. The MLD method could accurately measure the slope erosion (Rad2 = 0.91, NSE = 89.55%, and RMSE = 0.42 mm), with results superior to those of the traditional EP method. The MLD method’s results were similar to those from the laser scanner (LS) method, but more accurate and with less associated cost and data processing time. Therefore, the MLD method has potential as a reliable measurement method that can provide useful guidance for elucidating the spatial distribution of soil erosion and predicting slope soil erosion. This approach could be used to obtain raw data to quantify soil erosion on the Loess Plateau of China and beyond.

Integrated Use of Remote Sensing and GIS in estimating Soil Erosion in the Tukvar Tea Plantation Area, Darjeeling, India by RUSLE Modelling

Soil erosion is one of the major threats to food security and agricultural sustainability worldwide. Numerous factors including relief, slope, land use, land cover, rainfall pattern, soil texture, conservation techniques and anthropogenic factors, combine to cause soil erosion. The enormity and spatial distribution of soil erosion should be known for effectively assessing and mapping erosion-prone areas. In the hill region, soil loss is a significant component in decreasing stability and persistent loss causes landslides. So in order to study this, various soil erosion models have come up amongst which RUSLE has been adopted by many researchers. The goal of the current study is to forecast the projected soil loss in Tukvar tea plantations of the Darjeeling district. This study will provide an estimate of the amount and rate of erosion in the Darjeeling district's Tukvar tea plantations. This study revealed that the leading factors to soil erosion are slope factors and rainfall erosivity. The geo-coded reference of the geographic extent of soil erosion-prone areas will be useful for micro-level planning and will serve as a useful tool for managing and conserving soil.

The response of organic carbon fractions in sediment to rainfall characteristics and slope lengths: Based on four-years on-site monitoring data

The effect of soil erosion processes on the properties of sediment and organic carbon fractions is not well understood under complex erosion environments, such as various rainfall characteristics and transport distances, which leads to a gap in knowledge regarding the role of soil erosion in the global carbon cycle. This study investigated the impact of different rainfall characteristics (patterns and parameters) and slope lengths (5 m ∼ 20 m) on the sediment yield, particle size distribution (PSD) and properties of organic carbon fractions on China’s Loess Plateau. The results show that runoff was highest under rainfall pattern A (extreme rainfall), while the sediment concentration peaked under rainfall pattern B (heavy rainfall), with significant correlation with slope length, rainfall intensity, kinetic energy, rainfall erosivity and precipitation (p less than 0.05). Silt- and clay-sized fractions were transported as primary particles and enriched under rainfall patterns B and C with no differences between slope lengths. The enrichment ratio (ER) of sediment, effective and ultimate particles were primarily influenced by rainfall intensity-related parameters. In contrast, the aggregation ratio (AR) tended to be influenced by rainfall kinetic energy. The content and loss of organic carbon fractions correlated to rainfall parameters and slope length, although no significant difference was observed between different rainfall patterns and slope lengths. Lost organic carbon fractions were predominantly Mineral-associated organic carbon (MOC), with great enrichment under rainfall pattern C and rapid depletion as the slope length increased. Structural equation models (SEM) indicated the effect of rainfall characteristics and slope length on organic carbon loss was mainly due to the regulation of soil loss and particle size distribution of sediment in selective transport erosion processes. We highlighted the need to consider the effect of soil erosion and particle size distribution comprehensively on the carbon cycle from the perspective of organic carbon fractions to describe the potential effects of soil erosion on the carbon budget quantitatively, especially in complex environmental conditions with consideration of scale effect.

Comparison of Sampling and Grid Methods for Regional Soil Erosion Assessment

To control soil erosion, the intensity, area, and distribution of regional soil erosion must be determined to accurately plan and implement corresponding soil conservation measures. Therefore, regional soil erosion assessment has received extensive attention worldwide. At present, a sampling survey approach and full-coverage grid-based calculation are mainly applied in regional soil erosion assessment. The quantitative evaluation of the entire region depends on the quality of the data source. Furthermore, owing to the greatness of the evaluation object, the difficulty of data acquisition, the high cost, and poor usability, the present approach is bound to be at the expense of data accuracy, spatial resolution, time resolution, etc. The sampling survey approach can obtain high-precision data of soil erosion factors. Therefore, it can accurately quantify soil erosion in a field investigation unit. However, the sampling method, sampling density, and extrapolation methods have a significant impact on regional soil erosion assessments. This study considers the case of Baiquan County in the rolling hills of Northeast China as an example. Regional soil erosion evaluation using sampling survey and grid computing were compared. The impact of the data source accuracy on the soil erosion assessment was also quantitatively evaluated. The results of grid method showed a phenomenon of large rates of soil erosion and the ratio of the soil erosion area (the share of areas above the mild level), which were overestimated by 20% and 6%, respectively. A digital elevation model (DEM) with a resolution of 30 m can be used for soil erosion evaluation in plain areas, but that with the same resolution in hilly areas has insufficient calculation accuracy and provides large errors. The grid method can be adopted when land use and soil conservation measures are accurate. Otherwise, the sampling method is recommended. Interpolation of the ratio of the soil erosion area in the survey unit based on land use can better evaluate regional soil erosion.

Soil erosion assessment by RUSLE model using remote sensing and GIS in an arid zone

ABSTRACT In this research, we used the Revised Universal Soil Loss Equation (RUSLE) and Geographical Information System (GIS) to predict the annual rate of soil loss in the District Chakwal of Pakistan. The parameters of the RUSLE model were estimated using remote sensing data, and the erosion probability zones were determined using GIS. The estimated length slope (), crop management (), rainfall erosivity (), soil erodibility (), and support practice () range from 0–68,227, 0–66.61%, 0–0.58, 495.99–648.68 0.15–0.25 , and 1 respectively. The results indicate that the estimated total annual potential soil loss of approximately 4,67,064.25 is comparable with the measured sediment loss of 11,631 during the water year 2020. The predicted soil erosion rate due to an increase in agricultural area is approximately 164,249.31 . In this study, we also used Landsat imagery to rapidly achieve actual land use classification. Meanwhile, 38.13% of the region was threatened by very high soil erosion, where the quantity of soil erosion ranged from 365487.35 . Integrating GIS and remote sensing with the RUSLE model helped researchers achieve their final objectives. Land-use planners and decision-makers use the result's spatial distribution of soil erosion in District Chakwal for conservation and management planning.

Spatial Estimation of Soil Erosion Using RUSLE Model: A Case Study of Sangareddy Telangna State, India

Soil erosion is a major environmental challenge that affects agricultural productivity, water quality, and land management. To address this issue, accurate estimation and spatial mapping of soil erosion rates are essential for effective soil conservation planning and management. This study focuses on utilizing the Revised Universal Soil Loss Equation (RUSLE) model to estimate and map soil erosion rates in the Sangareddy district of Telangana State, India. The RUSLE model integrates several factors contributing to soil erosion, including rainfall erosivity, soil erodibility, slope length and steepness, cover management factor, and conservation practices. These factors were derived from geospatial datasets, including rainfall records, soil characteristics, topographic data, land use/land cover maps, and conservation practices information. By applying the RUSLE model, the study estimates the average annual soil erosion rates across the Sangareddy district. The spatial distribution of soil erosion is then mapped using geographic information system (GIS) techniques, providing valuable insights into the area’s most susceptible to erosion. These findings can inform decision-makers and land managers in prioritizing and implementing appropriate soil conservation strategies to mitigate erosion and protect valuable soil resources. The spatial estimation of soil erosion using the RUSLE model provides a scientific basis for land-use planning, conservation program implementation, and policy formulation to promote sustainable land management practices. It highlights the need for effective erosion control measures, such as contour farming, terracing, afforestation, and appropriate land use practices, in order to minimize soil erosion and preserve the soil quality in Sangareddy and similar regions.

Landscape pattern analysis using GIS and remote sensing to diagnose soil erosion and nutrient availability in two agroecological zones of Southern Mali

BackgroundSoil is a basic natural resource for the existence of life on earth, and its health is a major concern for rural livelihoods. Poor soil health is directly associated with reduced agricultural land productivity in many sub-Saharan countries, such as Mali. Agricultural land is subjected to immense degradation and the loss of important soil nutrients due to soil erosion. The objective of the study was to diagnose the spatial distribution of soil erosion and soil nutrient variations under different land use in two agroecological zones of Southern Mali using the Geographical Information System (GIS) software, the empirically derived relationship of the Revised Universal Soil Loss Equation, in-situ soil data measurement and satellite products. The soil erosion effect on agricultural land productivity was discussed to highlight the usefulness of soil and water conservation practices in Southern Mali.ResultsThe results of the land use and land cover change analysis from 2015 to 2019 revealed significant area reductions in water bodies, bare land, and savanna woodland for the benefit of increased natural vegetation and agricultural land. There was significant variation in the annual soil loss under the different land use conditions. Despite recordings of the lowest soil erosion rates in the majority of the landscape (71%) as a result of field-based soil and water conservation practices, the highest rates of erosion were seen in agricultural fields, resulting in a reduction in agricultural land area and a loss of nutrients that are useful for plant growth. Spatial nutrient modelling and mapping revealed a high deficiency and significant variations (p < 0.05) in nitrogen (N), phosphorus (P), potassium (K), and carbon (C) in all land use and land cover types for the two agroecologies.ConclusionsThe study highlighted the inadequacies of existing field-based soil and water conservation practices to reduce soil erosion and improve landscape management practices. The findings of the study can inform land management planners and other development actors to strategize and prioritize landscape-based intervention practices and protect catchment areas from severe erosion for the enhanced productivity of agricultural fields.