The Soil and Water Hub is jointly developed by USDA Agricultural Research Service (USDA-ARS) and Texas A&M AgriLife Research, part of The Texas A&M University System. Modeling dataset resources are available for download for use with software tools Agricultural Policy/Environmental eXtender Model (APEX), Soil and Water Assessment Tool (SWAT), ArcSWAT, and related Conservation practices. Resources in this dataset:Resource Title: Website Pointer to Soil and Water Hub Modeling Datasets. File Name: Web Page, url: https://soilandwaterhub.brc.tamus.edu/Home/Download Modeling datasets for APEX, SWAT, ArcSWAT, and Conservation

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SWIFT (Small Watershed Nutrient Forecasting Tool), a component of the USDA/ARS Soil and Water Hub, is a web-based tool that allows the rapid estimation of sediment and nutrient loads from small watersheds for a given ecoregion in the US.

SWIFT can be used to estimate loads given relatively common information such as watershed size, landuse, and location. Estimated loads are provided as a distribution to illustrate the relative uncertainty inherent in the methods used. The web interface supports charting of precipitation, runoff, water yield, sediment, phosphorus, and nitrogen forecasts by ecoregion, watershed, and landuse (rangeland, cropland, urban, forest, pasture/hay) across the contiguous United States as annual averages.

SWIFT is based on the concept of export coefficients and delivery ratios. SWIFT uses export coefficients for major landuse categories in the US derived from SWAT (Soil and Water Assessment Tool) predictions. Delivery components are based on data derived from the CEAP (Conservation Effects and Assessment Project).

SWIFT (Small Watershed Nutrient Forecasting Tool), a component of the USDA/ARS Soil and Water Hub, is a web-based tool that allows the rapid estimation of sediment and nutrient loads from small watersheds for a given ecoregion in the US.

SWIFT can be used to estimate loads given relatively common information such as watershed size, landuse, and location. Estimated loads are provided as a distribution to illustrate the relative uncertainty inherent in the methods used. The web interface supports charting of precipitation, runoff, water yield, sediment, phosphorus, and nitrogen forecasts by ecoregion, watershed, and landuse (rangeland, cropland, urban, forest, pasture/hay) across the contiguous United States as annual averages.

SWIFT is based on the concept of export coefficients and delivery ratios. SWIFT uses export coefficients for major landuse categories in the US derived from SWAT (Soil and Water Assessment Tool) predictions. Delivery components are based on data derived from the CEAP (Conservation Effects and Assessment Project).

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SNAP (Soil Nutrient Assessment Program), a component of the USDA/ARS Soil and Water Hub, is a web-based tool that provides an estimate of plant-available nutrients that the soil naturally provides.

Soil test fertilizer recommendations have long been predicated upon response curves generated from fertility trials across the country. These response curves have been compared to relative yield which provide probability ranges for a response to varying fertilizer inputs. Category responses include very low, low, adequate, high or very high inversely related to probability of a response to various inputs of nitrogen, phosphate, and potassium (N, P, and K).

New soil test methods, increases in computing power and access to the internet have enabled development of an interactive tool that is based on plant available NPK from both the inorganic fraction and organic pool of the soil. The new methods provide an estimate of plant available nutrients that the soil naturally provides, which has largely been ignored for decades.

Since we have access to large datasets we can calculate the amounts of NPK required growing crops in lbs NPK per bu of the desired crop. For example, it requires 100 lbs of N, 50 lbs P2O5, 50 lbs K2O to grow 100 bu corn. These are the base numbers from which we subtract the soil test data after converting from the analytical ppm to Lbs P2O5 or lbs K2O. This is a straight subtraction. It also eliminates the need for "calibration data" since the soil tests reflect the soils inherent fertility. Using the example above, of 100, 50, 50 of N, P, and K required and soil test results of 25, 35, 45 then the fertilizer needed would be 75 N, 15 P2O5 and 5 K2O. This is a simple approach that doesn't get lost in relative yield-crop response curves that have been used for decades from differing geographical areas.

This tool will include current fertilizer prices, soil test inputs, and crop based county averages for the last 15 years that will predict the chances of making the yield goal the user inputs compared to historical yield data for their county and calculate the fertilizer cost with and without soil testing compared to user input yield goal and county average. This tool will allow the user via the internet to produce a more straightforward approach to realistically planning next year's fertilizer inputs and associated cost. It will also show the benefits of soil testing for increased fertilizer efficiency and reduced environmental impact.

SNAP (Soil Nutrient Assessment Program), a component of the USDA/ARS Soil and Water Hub, is a web-based tool that provides an estimate of plant-available nutrients that the soil naturally provides.

Soil test fertilizer recommendations have long been predicated upon response curves generated from fertility trials across the country. These response curves have been compared to relative yield which provide probability ranges for a response to varying fertilizer inputs. Category responses include very low, low, adequate, high or very high inversely related to probability of a response to various inputs of nitrogen, phosphate, and potassium (N, P, and K).

New soil test methods, increases in computing power and access to the internet have enabled development of an interactive tool that is based on plant available NPK from both the inorganic fraction and organic pool of the soil. The new methods provide an estimate of plant available nutrients that the soil naturally provides, which has largely been ignored for decades.

Since we have access to large datasets we can calculate the amounts of NPK required growing crops in lbs NPK per bu of the desired crop. For example, it requires 100 lbs of N, 50 lbs P2O5, 50 lbs K2O to grow 100 bu corn. These are the base numbers from which we subtract the soil test data after converting from the analytical ppm to Lbs P2O5 or lbs K2O. This is a straight subtraction. It also eliminates the need for "calibration data" since the soil tests reflect the soils inherent fertility. Using the example above, of 100, 50, 50 of N, P, and K required and soil test results of 25, 35, 45 then the fertilizer needed would be 75 N, 15 P2O5 and 5 K2O. This is a simple approach that doesn't get lost in relative yield-crop response curves that have been used for decades from differing geographical areas.

This tool will include current fertilizer prices, soil test inputs, and crop based county averages for the last 15 years that will predict the chances of making the yield goal the user inputs compared to historical yield data for their county and calculate the fertilizer cost with and without soil testing compared to user input yield goal and county average. This tool will allow the user via the internet to produce a more straightforward approach to realistically planning next year's fertilizer inputs and associated cost. It will also show the benefits of soil testing for increased fertilizer efficiency and reduced environmental impact.

NLET (National Load Estimating Tool), a component of the USDA/ARS Soil and Water Hub, is a web-based tool for estimating pollutant loads in watersheds across the contiguous United States. This tool helps visualize the effects of land use patterns, cultivated crops, and conservation practices through graphical representation. Visualizations illustrate baseline and scenario land-use, crops, conservation, runoff, sediment, nitrogen, and phosphorus, and load differences at 50th percentile.

NLET implements an export coefficient approach for predicting the pollutant loads. NLET also addresses the need for a user-friendly, reliable and cost-effective watershed modeling tool.

NLET utilizes the D3.js library for creating an open-source JavaScript and data-driven charts, as well as Mapbox and OpenStreetMap for the Leaflet library, another open-source JavaScript library used for displaying the locations of Hydrologic Unit Catalog (HUC).