Revised Universal Soil Loss Equation Version 2 Research Articles

Agricultural activities lead to sediment infilling of wetlandscapes in the Canadian Prairies: Assessment of contributions by tillage, water and wind erosion

Soil erosion and sediment delivery models have been increasingly employed in studies of catchment sediment dynamics in current years. These models are also used to represent the spatial interaction of soil erosion and sediment transport processes, thereby providing spatially-distributed predictions of soil redistribution rates for agricultural landscapes. It has been widely recognized that individual soil erosion processes and their interactions contribute towards total soil erosion; however, quantifying the rates and patterns of soil erosion processes and their interactions within topographically complex landscapes is challenging. Therefore, the objective of this research was to estimate and model the relative contributions of tillage, water and wind erosion towards total soil erosion in the Canadian Prairie provinces of Manitoba and Alberta using 137Cs, a fallout radionuclide tracer, and three well-established models: (i) Tillage Erosion Model (TillEM); (ii) Revised Universal Soil Loss Equation, version 2 (RUSLE2); and (iii) Single-event Wind Erosion Evaluation Program (SWEEP). The findings indicate that the patterns of 137Cs-estimated soil erosion were closely matched with the erosion pattern predicted by TillEM and suggests that tillage erosion dominates the pattern of total soil erosion on the knolls of hummocky landscapes. Additionally, soil particle size variation within the wetland catchments reflected the modeled patterns of water and tillage erosion. Furthermore, our findings confirmed that colour coefficients are useful in identifying spatial heterogeneity of soil within wetland catchments and reflect the patterns of soil loss and gains. These results indicate that water and tillage erosion, and also their interactions, are main erosion processes in the Canadian Prairie Pothole Region (PPR), but that soil movement by tillage practices has been the predominant redistribution process.

A Review of the Science and Logic Associated with Approach Used in the Universal Soil Loss Equation Family of Models

Soil erosion caused by rain is a major factor in degrading agricultural land, and agricultural practices that conserve soil should be used to maintain the long-term sustainability of agricultural land. The Universal Soil Loss Equation (USLE) was developed in the 1960s and 1970s to predict the long-term average annual soil loss from sheet and rill erosion on field-sized areas as an aid to making management decisions to conserve soil. The USLE uses six factors to take account of the effects of climate, soil, topography, crops, and crop management, and specific actions designed to conserve soil. Although initially developed as an empirical model based on data from more than 10,000 plot years of data collected in plot experiments in the USA, the selection of the independent factors used in the model was made taking account of scientific understanding of the drivers involved in rainfall erosion. In addition, assumptions and approximations were needed to make an operational model that met the needs of the decision makers at that time. Those needs have changed over time, leading to the development of the Revised USLE (RUSLE) and a second version of that, the Revised USLE, Version 2 (RUSLE2). While the original USLE model was not designed to predict short-term variations in erosion well, these developments have involved more use of conceptualization in order to deal with the time-variant impacts of the drivers involved in rainfall erosion. The USLE family of models is based on the concept that the “unit” plot, a bare fallow area 22.1 m long on a 9% slope gradient with cultivation up and down the slope, provides a physical situation where the effect of climate and soil on rainfall erosion can be determined without the need to consider the impact of the four other factors. The science and logic associated with this approach is reviewed. The manner by which the soil erodibility factor is determined from plot data ensures that the long-term average annual soil loss for the unit plot is predicted well, even when the assumption that event soil loss is directly related to the product of event rainfall energy, and the maximum 30-min intensity is not wholly appropriate. RUSLE2 has a capacity to use CLIGEN, the weather generator used in WEPP, and so can predict soil losses based on individual storms in a similar way to WEPP. Including a direct consideration of runoff in determining event erosivity enhances the ability to predict event soil losses when runoff is known or predicted well, but similar to more process-based models, this ability is offset by the difficulty in predicting runoff well.

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.

Some Metrological Limitations to Rain Impact Computation in Storm Erosivity as Defined in the Revised Universal Soil Loss Equation, Version 2 (RUSLE2)

The theoretical and the instrumental metrological basis for computation of rainfall impact in storm erosivity as defined in the Revised Universal Soil Loss Equation, version 2 (RUSLE2) are considered. The present determination of rainfall erosivity is based on two factors: E, representing the rainfall kinetic energy, and I30, the maximum 30-minutes intensity for a given precipitation event. The present short review evidences some of the existing metrological limitations: (1) the non separation between the impact of falling rain and shallow flow of water; (2) the use of a non-universal semiempirical approach, (3) the absence of a clear model with respect to rain flow, runoff and soil wetting; (4) the use of a hybrid measure unit; (5) the intrinsic limitation of measuring technologies. The improvement of the existing parameter calculation techniques or a transition from a prevailing semi-empirical to a mainly physical-based approach would be desirable, even if this transformation shouldn’t affect the usability of the developed tools for practitioners.

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.

Predicting ephemeral gully erosion with RUSLER and EphGEE

Abstract. Ephemeral gully erosion is not included in predictions made with the Revised Universal Soil Loss Equation, version 2 (RUSLE2). A new distributed application called RUSLER (RUSLE2-Raster) predicts distributed soil loss and its output can be linked with the new Ephemeral Gully Erosion Estimator (EphGEE). These models were applied to a 6.3 ha research watershed near Treynor, Iowa, USA, where runoff and sediment yield were measured from 1975 to 1991. Using a 3-m raster DEM, results indicate that ephemeral gully erosion contributed about one-third of the amount of sheet and rill erosion, and that considerable deposition of sediment originating from both sources occurred within the grassed waterway. For ambient conditions, predicted annual average watershed sediment yield was 17.5 Mg ha−1 year−1, 20% greater than the measured value of 14.6 Mg ha−1 year−1.

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.

Increasing Crop Rotation Diversity Improves Agronomic, Economic, and Environmental Performance of Organic Grain Cropping Systems at the USDA‐ARS Beltsville Farming Systems Project

Increasing Crop Rotation Diversity Improves Agronomic, Economic, and Environmental Performance of Organic Grain Cropping Systems at the USDA‐ARS Beltsville Farming Systems Project

The application of the Revised Universal Soil Loss Equation, Version 2, to evaluate the impacts of alternative climate change scenarios on runoff and sediment yield

The Revised Universal Soil Loss Equation, Version 2 (RUSLE2), provides robust estimates of average annual sheet and rill erosion for one-dimensional hillslope representations. Extensive databases describing climate, soils, and management options have been devel- oped and are widely used in the United States for conservation planning. Recent RUSLE2 enhancements allow estimation of erosion and runoff from a representative sequence of run- off events that are suitable for linkage with an ephemeral gully model. This paper reviews the sensitivity of RUSLE2 erosion estimates to possible climate change scenarios, demonstrates its ability to evaluate alternative management adaptations, and compares predictions with observations of runoff and sediment yield from a 6.6 ha (16 ac) research watershed located near Treynor, Iowa. When applied to a representative hillslope profile with conventional tillage corn (Zea mays L.), increasing monthly temperature by 0.8°C (1.5°F) and rainfall depth, rain- fall erosivity density, and 10-year, 24-hour rainfall depth each by 10% cumulatively increased sheet and rill erosion by 47% and increased runoff by 33%, assuming there was no change in corn yield. If the climate changes decreased corn yield by 10%, the overall effect was to increase soil loss for conservation planning by 63%. These results demonstrate that modest and expected changes in climate will significantly increase the risk of soil erosion, and improved conservation management will be an important part of successful adaptation.

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.

Improved Descriptions of Herbaceous Perennial Growth and Residue Creation for RUSLE2

Earlier versions of the Revised Universal Soil Loss Equation, version 2 (RUSLE2) calculated vegetative residue production only during periods of canopy decline or in response to management operations. This resulted in underestimation of residue amounts and overestimation of soil erosion from pasture and hay lands. To solve this problem, new vegetation routines were implemented in RUSLE2. These modifications were designed to better reflect the amount of residue added by perennial vegetation during its growth and to make it is easier to model haying and grazing scenarios. The new routines were based on the assumption that all unharvested aboveground biomass growth will die after its life span is reached, and this biomass will be added to a standing residue pool. Trained specialists can define the characteristics of a vegetation assemblage in terms of total annual potential production under good management, monthly production percentages reflecting expected fertility and irrigation levels, average vegetation lifespan, maximum canopy height, cutting height for optimal yield, and the tendency of the vegetation to thicken at lower heights (form a sod) in response to repeated defoliations. Users will specify actual harvest management and an underlying model predicts plant growth responses in terms of the amount of harvested forage and the amount of above‐ and belowground residues returned to the soil. The USDA‐NRCS is developing extensive databases so that the new version of RUSLE2 will allow erosion estimates to be a factor considered as part of forage and grazing planning.

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.

Multiple corn stover removal rates for cellulosic biofuels and long-term water quality impacts

The objective of this work was to quantify the long-term runoff, percolation, erosion, and losses in nitrate-nitrogen, total phosphorus and pesticides attributed to multiple levels of corn stover (<i>Zea mays</i> L.) removal. A field-scale modeling framework, which utilized the Groundwater Loading Effects of Agricultural Management Systems/National Agricultural Pesticide Risk Analysis (GLEAMS–NAPRA) model and the Revised Universal Soil Loss Equation, Version 2 (RUSLE2), was created and applied within Indiana to evaluate water quality impacts associated with three corn stover removal rates (38%, 52.5%, and 70%), based on proposed harvest efficiencies of three multipass field collection systems, with two tillage options (fall chisel/spring disk and no-till). The model results suggest that removing corn stover at the rates 38% (2.40 to 3.19 t ha⁻¹ [1.07 to 1.42 tn ac⁻¹]), 52.5% (3.32 to 4.41 t ha⁻¹ [1.48 to 1.97 tn ac⁻¹]) and 70% (4.42 to 5.88 t ha⁻¹ [1.97 to 2.63 tn ac⁻¹]), with no-till practices, would result in statistically significant higher annual erosion losses (<i>p</i> &lt; 0.05), when compared with no residue removal management. On soils with 4% or less slopes, a 70% residue removal rate—4.4 to 5.9 t ha⁻¹ (1.96 to 2.63 tn ac⁻¹)—with no-till practices, would result in slightly lower erosion losses when compared with conventional tillage involving no residue removal. No-till systems produced higher total phosphorus losses when commercial fertilizer was surface applied as opposed to the incorporation method practiced with conventional till-age. However, levels of leached nitrate-nitrogen from no-till systems were significantly lower than losses from conventional tillage, partly due to lower mineralization processes associated with nutrient cycling. The results point to the necessity of establishing new sets of regional soil and water conservation criteria that would include harvesting unprocessed agricultural residue following corn-grain harvest to meet biofuel demands.

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 …

Conservation Planning for Construction Sites

Although conservation planning for agricultural lands has evolved to its current relatively stable form over many decades, conservation planning for construction sites is still in its infancy. This project drew on the resources of various agencies and researchers to develop a conservation planning tool specifically geared towards meeting construction site planning needs in Wisconsin, basing it on the Revised Universal Soil Loss Equation, version 2 (RUSLE2). The project began by deconstructing the planning process itself to determine possible approaches and critical elements, and used those pieces to build a rational new approach to construction site conservation planning. Although no changes were required to the erosion or sediment delivery calculations in RUSLE2, this new tool required substantial changes to the database, the interface, and how the results were presented and packaged. The lessons learned in this effort should be instructive to both a general discussion of the construction site planning process and to attempts to develop other tools that meet this need.