Universal Soil Loss Equation Version Research Articles

The Crop Residue Removal Threshold Ensures Sustainable Agriculture in the Purple Soil Region of Sichuan, China

Sichuan, a hilly area in southwestern China, is recommended as a bioethanol production base because of its abundant crop residue resources. However, removing the crop straw for bioethanol may negatively affect soil fertility and productivity due to the local purple soil vulnerability. To explore the impact of crop residue removal on soil fertility and productivity and meet the needs of sustainable agriculture, we conducted a crop residue removal experiment by measuring the soil organic carbon (SOC), total nitrogen (TN), and total phosphorus (TP) contents, and crop yield in the purple soil region in southwest China. Soil erosion was also simulated by Revised Universal Soil Loss Equation version 2 (RUSLE 2). The results showed that soil erosion increased with the increase of the straw removal rate. Compared with 0% removal treatment, the SOC content reduced at other removal rate treatments, especially for long-term residue removal. The effect of residue removal on soil TN and TP was not consistent within one year. After two years, residue removal greater than 25% caused a decrease in TN by 1.6–3.7%, and straw removal greater than 50% caused a TP decrease by 8.5–9.3%. More than 25% of the residue removed reduced maize and canola yields, and TN and TP content. However, all crop residue removal treatments resulted in SOC content reduction and soil erosion deterioration. In conclusion, crop residue removal was not recommended due to agricultural sustainability in Sichuan, China.

Mapping of Crop Management Factor (C) for Sylhet Sadar using Remote Sensing and GIS

Sylhet Sadar suffers from excessive rainfall causing the enormous soil loss in this area. C-factor is one of the most important parameter of the Universal Soil Loss Equation (USLE) and revised version of USLE for estimating soil loss. The current study was conducted for determining the crop management factor (C) using remote sensing (RS) and geographical information system (GIS) tools. Satellite images (Landsat 7 ETM+ of 2000, 2010 and Landsat 8 OLI-TIRS of 2020) of the study area were analyzed using ArcGIS 10.5 and ENVI 5.1 software to generate maps for land use and land cover (LULC), normalized differential vegetative index (NDVI) and crop management factor (C). Results showed that the percentage of each LULC classes from 2000 to 2020 was changed over time. There was rapid growth in crop land, water bodies and settlement areas between 2000 and 2020 while the same period observed lessening in vegetation coverage. The relationship between C-factor and vegetation indices such as NDVI was identified. As NDVI and C-factor values are correlated, both lowest and highest NDVI (-0.506–0.629) and C-factor value (0.185–0.753) was found at the same year (2000). By the next 20 years in 2020, the highest NDVI value reduced to 0.373 and lowest C-factor value increased to 0.314 indicates that vegetation coverage of the study area is decreasing. To ensure proper soil management it is therefore imperative to encourage forest regeneration activities, tree planting exercises, crop rotation, conservation tillage practices and shrub growth schemes.

A review of the (Revised) Universal Soil Loss Equation ((R)USLE): with a view to increasing its global applicability and improving soil loss estimates

Abstract. Soil erosion is a major problem around the world because of its effects on soil productivity, nutrient loss, siltation in water bodies, and degradation of water quality. By understanding the driving forces behind soil erosion, we can more easily identify erosion-prone areas within a landscape to address the problem strategically. Soil erosion models have been used to assist in this task. One of the most commonly used soil erosion models is the Universal Soil Loss Equation (USLE) and its family of models: the Revised Universal Soil Loss Equation (RUSLE), the Revised Universal Soil Loss Equation version 2 (RUSLE2), and the Modified Universal Soil Loss Equation (MUSLE). This paper reviews the different sub-factors of USLE and RUSLE, and analyses how different studies around the world have adapted the equations to local conditions. We compiled these studies and equations to serve as a reference for other researchers working with (R)USLE and related approaches. Within each sub-factor section, the strengths and limitations of the different equations are discussed, and guidance is given as to which equations may be most appropriate for particular climate types, spatial resolution, and temporal scale. We investigate some of the limitations of existing (R)USLE formulations, such as uncertainty issues given the simple empirical nature of the model and many of its sub-components; uncertainty issues around data availability; and its inability to account for soil loss from gully erosion, mass wasting events, or predicting potential sediment yields to streams. Recommendations on how to overcome some of the uncertainties associated with the model are given. Several key future directions to refine it are outlined: e.g. incorporating soil loss from other types of soil erosion, estimating soil loss at sub-annual temporal scales, and compiling consistent units for the future literature to reduce confusion and errors caused by mismatching units. The potential of combining (R)USLE with the Compound Topographic Index (CTI) and sediment delivery ratio (SDR) to account for gully erosion and sediment yield to streams respectively is discussed. Overall, the aim of this paper is to review the (R)USLE and its sub-factors, and to elucidate the caveats, limitations, and recommendations for future applications of these soil erosion models. We hope these recommendations will help researchers more robustly apply (R)USLE in a range of geoclimatic regions with varying data availability, and modelling different land cover scenarios at finer spatial and temporal scales (e.g. at the field scale with different cropping options).

Comparing Sediment Trap Data with Erosion Models for Evaluation of Forest Haul Road Stream Crossing Approaches

Abstract. Soil erosion and sediment delivery models have been developed to estimate the inherent complexities of soil erosion, but most models are not specifically modified for forest operation applications. Three erosion models, the Universal Soil Loss Equation for forestry (USLE-Forest), Revised Universal Soil Loss Equation Version 2 (RUSLE2), and Water Erosion Prediction Project (WEPP), were compared to one year of trapped sediment data for 37 forest haul road stream crossings. We assessed model performance from five variations of the three erosion models: USLE-Roadway, USLE-Soil Survey, RUSLE2, WEPP-Default, and WEPP-Modified. Each road approach was categorized into one of four levels of erosion (very low, low, moderate, and high) based on trapped erosion rate data and erosion rates reported in recent peer-reviewed literature. Model performance metrics included: (1) summary statistics and nonparametric analysis, (2) linear relationships, (3) percent agreement within erosion categories and tolerable error ranges, and (4) contingency table metrics. Sediment trap data varied from negligible ( -1 year -1 . The soil erosion models evaluated could estimate erosion within 5 Mg ha -1 year -1 for most approaches having erosion rates less than 11.2 Mg ha -1 year -1 , while models estimates varied widely for approaches that eroded at rates above 11.2 Mg ha -1 year -1 . Kruskal-Wallis nonparametric analyses revealed that only WEPP-Modified estimates were not significantly different from trapped sediment data (p ≥ 0.107). While WEPP-Modified ranked best for most model performance metrics, the time, effort, modeling expertise, and uncertainty associated with model results may discourage the use of WEPP as a forest management tool. WEPP is better suited for researchers and government agencies that have the capability to measure extensive parameter data. Additional sensitivity analysis is needed to expand default parameters for forest roads within the WEPP and USLE models.

Land leveling impact on surface runoff and soil losses: Estimation with coupled deterministic/stochastic models for a Québec agricultural field

Land leveling impact on water quality had not received much attention for fields in humid continental climate. The objectives of this study were to isolate the impact of land leveling, performed on an agricultural field (Québec, Canada) in spring 2012, on runoff and TSS load and to make recommendations to attenuate adverse environmental impacts of land leveling, if any. A total of 66 runoff events, including 22 with total suspended sediments (TSS) load estimates, from 2010 to 2014 were analyzed. To this end, deterministic models were coupled to an adaptive Metropolis-Hastings algorithm to estimate the unknown distribution of the parameters representing the most important effects, namely land leveling, tillage, and crop cover. Simulated runoff events were generated by the hydrological model SWMM version 5 while simulated TSS loads were generated by an empirical equation based on the Revised Universal Soil Loss Equation version 2 (RUSLE2). Thanks to the algorithm used, it was demonstrated that land leveling significantly decreased total runoff volume at least for the two following years. The impact on peak flow was mixed: land leveling significantly decreased peak flow for a typical stratiform rainfall event but the effect was unclear for a typical convective rainfall event. Based on 90% confidence interval, TSS load increased from 10 to 1000 times immediately after land leveling (spring 2012) compared to pre-land leveling events. The TSS load increase remained significant one year after land leveling, with TSS loads 5–20 times higher compared to pre-land leveling events. It would thus be recommended to grow crops with high ground coverage ratios coupled with cover crops during the year when land leveling is done. Sediment retention structures could also be installed at the beginning of the land leveling process to provide protection against the short term and delayed impact on water quality.

Modeling of soil loss and its impact factors in the Guijiang Karst River Basin in Southern China

The Guijiang Karst River Basin (GKRB) is a typical karst river basin in southern China and one of the major tributaries to the Xijiang River in the Pearl River Basin. A portion of the main stem in the upper reach of GKRB is called the Li River known for its most scenic karst landforms in the world. Soil erosion has resulted in rocky desertification in karst areas of the basin. Excessive sediment load in rivers would affect tourism navigation in the Li River and the aquatic systems in the basin. This study employed the Revised Universal Soil Loss Equation Version 2(RUSLE2) model to estimate soil loss and to evaluate the impact factors for the spatial and temporal variation of soil erosion in the basin. The GKRB RUSLE2 model was calibrated with sediment yields from nine gaging stations with the Nash–Sutcliffe efficiency coefficient (NSE) as high as 0.69. This study showed that soil erosion occurred on 54.9 % of the land areas that are primarily forest and cropland and in low hills with elevations ranging from 30 to 600 m. It also showed that the soil erosion rate is higher in areas where the degree of rocky desertification is more severe, indicating area with high soil erosion intensity often has high risk of rocky desertification. Impacted by monsoons, soil erosion in the study basin showed clear seasonal variation. The second quarter of the year had highest erosion rate, mostly due to the intensive late spring and early summer rainstorms. This study has shown that the RUSLE2 model can be used for karst river basin with reasonable accuracy. Findings from this study may help in identifying locations for flow and sediment control structures to reduce the risk of soil erosion and subsequently rocky desertification.

Accounting for the influence of runoff on event soil erodibilities associated with the EI30 index in RUSLE2

AbstractSoil erodibilities (K) associated with the EI30 index vary not only with soil properties but also with soil moisture as it varies in time and space. In Revised Universal Soil Loss Equation Version 2 (RUSLE2), temporal variations in soil erodibility in the USA are calculated using monthly precipitation and temperature as independent variables. KUM, the soil erodibility factor associated with the QREI30 index, varies independently of runoff and the product of KUM and the runoff ratio for the unit plot (QR1) provides an alternative to the temporally varying Ks currently used in predicting storm soil loss in RUSLE2. Comparisons were made between the product of QR1 and KUM and RUSLE2 Ks for representative storms at four locations representing the north to south variation in climate in the USA. Peak erosion associated with the current approach used in RUSLE2 was slightly higher at two locations and slightly lower at the other two locations. One other location, Morris, MN, provided an exception with the peak loss predicted by using the product of QR1 and KUM being 1.7 times that obtained using RUSLE2 Ks. In theory, average annual KUM values should be better related to soil properties than the average annual values of K frequently used when the average annual values of EI30 are used to predict soil loss. However, work has yet to be performed to determine how KUM varies directly with soil properties and in space and time. Copyright © 2014 John Wiley & Sons, Ltd.

An integrated model for assessment of sustainable agricultural residue removal limits for bioenergy systems

Agricultural residues have been identified as a significant potential resource for bioenergy production, but serious questions remain about the sustainability of harvesting residues. Agricultural residues play an important role in limiting soil erosion from wind and water and in maintaining soil organic carbon. Because of this, multiple factors must be considered when assessing sustainable residue harvest limits. Validated and accepted modeling tools for assessing these impacts include the Revised Universal Soil Loss Equation Version 2 (RUSLE2), the Wind Erosion Prediction System (WEPS), and the Soil Conditioning Index. Currently, these models do not work together as a single integrated model. Rather, use of these models requires manual interaction and data transfer. As a result, it is currently not feasible to use these computational tools to perform detailed sustainable agricultural residue availability assessments across large spatial domains or to consider a broad range of land management practices. This paper presents an integrated modeling strategy that couples existing datasets with the RUSLE2 water erosion, WEPS wind erosion, and Soil Conditioning Index soil carbon modeling tools to create a single integrated residue removal modeling system. This enables the exploration of the detailed sustainable residue harvest scenarios needed to establish sustainable residue availability. Using this computational tool, an assessment study of residue availability for the state of Iowa was performed. This study included all soil types in the state of Iowa, four representative crop rotation schemes, variable crop yields, three tillage management methods, and five residue removal methods. The key conclusions of this study are that under current management practices and crop yields nearly 26.5 million Mg of agricultural residue are sustainably accessible in the state of Iowa, and that through the adoption of no till practices residue removal could sustainably approach 40 million Mg. However, when considering the economics and logistics of residue harvest, yields below 2.25 Mg ha−1 are generally considered to not be viable for a commercial bioenergy system. Applying this constraint, the total agricultural residue resource available in Iowa under current management practices is 19 million Mg. Previously published results have shown residue availability from 22 million Mg to over 50 million Mg in Iowa.

Comparing Sediment Trap Data with the USLE-Forest, RUSLE2, and WEPP-Road Erosion Models for Evaluation of Bladed Skid Trail BMPs

Three erosion models, the Universal Soil Loss Equation for Forestry (USLE-Forest), the Revised Universal Soil Loss Equation Version 2 (RUSLE2), and the Water Erosion Prediction Project for Forest Roads (WEPP-Road), were compared to sediment trap data for bladed skid trail best management practices (BMPs). The bladed skid trail BMPs evaluated were: (1) water bar only (control treatment); (2) water bar + lime, fertilizer, and grass seed (seed treatment); (3) seed + straw mulch (mulch treatment); (4) control + piled hardwood slash (hardwood slash treatment); and (5) control + piled pine slash (pine slash treatment). This study used three erosion models to evaluate the BMPs while also using linear regression, model efficiency (NSE), and percent bias (PBIAS) to compare the prediction accuracy and applicability of the models to monthly erosion collected in sediment traps from six replications of the five treatments. Results showed significant treatment differences due to the BMPs, with the control treatment being the most erosive, followed generally by the seed, hardwood slash, pine slash, and mulch treatments. Model predictions indicated that all models were suitable for ranking erosion rates for the skid trail closure treatments for simple hazard or BMP ratings. However, the older and simpler USLE-Forest and RUSLE2 models had satisfactory NSE and PBIAS values, whereas WEPP-Road did not. Results indicate that WEPP-Road needs additional enhancement with regard to skid trail parameters before it can be effectively used for erosion prediction on bladed skid trails.

Application of Revised Universal Soil Loss Equation to the Design of Construction Site Sedimentation Basins

A method is presented to estimate the sediment volume and associated frequency of sediment dredging for sedimentation basins (SBs) by applying the Revised Universal Soil Loss Equation Version 2 (RUSLE2) with additional site-specific information. Sedimentation basins in Pennsylvania are currently designed according to the standards of allocating 2000 cubic feet (57 m3) of sediment storage volume per acre of drainage basin as suggested by Pennsylvania Department of Environmental Protection. A procedure for calculating sediment storage volume and sediment dredging frequency concomitant with soil loss from the drainage basin is currently not available. Inadequate provisions for sediment volume lead to accumulation of sediment in the basin, decreasing particle removal and increasing outlet total suspended solids concentration. RUSLE2 was applied to estimate sediment delivery into four SBs at a Pennsylvania Department of Transportation I-99 construction site. RUSLE2 estimate was higher than the field-measured value by roughly 30%. Field observations indicated that the basins were filled with sediment beyond the sediment zone, and basin effluent sampling showed peaks in sediment concentration. Hence, the RUSLE2 estimate appears reasonable, accounting for soil loss from the basin. Furthermore, RUSLE2 analysis explained that the basis for observed high turbidities in one of the basins was a result of increased sediment delivery to the basin, due to physical configurations and the types of grassy areas associated with that basin. Results show that to control sediment resuspension and sediment loss in the basin, the design for sediment volume of a basin must be calculated individually, accounting for sediment delivery, soil particle size, and precipitation events. RUSLE2 has been used here to show how a soil loss calculation tool can be used in organizing these data for design purposes.

Beyond T: Guiding sustainable soil management

S cientists, conservation advisors, and producers recognize the need for a more comprehensive and integrated approach to soil management that (1) considers the multiple production and ecological functions soil provides, (2) evaluates multiple factors of soil degradation, (3) provides standards or thresholds for managing soil to sustain its multiple production and ecological functions, and (4) results in more comprehensive recommendations for soil management and conservation. The Soil and Water Conservation Society undertook a project to help accelerate the development of more comprehensive soil assessment, management, and planning tools. This article summarizes the results of an expert consultation held in Nebraska City, Nebraska, May 22-24, 2007, to recommend actions to move toward more comprehensive soil assessment, management, and planning tools. ### MORE COMPREHENSIVE SYSTEM NEEDED Soil conservation standards and tools that enable a more comprehensive assessment of management and conservation systems on multiple production and environmental endpoints are needed to meet the conservation challenges we face today. The most widely used soil conservation standard in the United States is the Soil Loss Tolerance Standard (T). The most widely used soil conservation planning tools in the United States are the Revised Universal Soil Loss Equation version 2 (RUSLE2) and the Wind …