S. Peaslee’s research while affiliated with Alabama Department of Conservation and Natural Resources and other places

Statistical validation of spatial predictions of soil properties requires assessment of errors against measured values. The objective of this study was to assess the size of errors in the measurement of soil pH from different sources in the United States of America national databases and implications of the size of errors for prediction, validation and management decision making under uncertain conditions. Error sources included measurement methods, laboratory conditions, pedotransfer functions, database manipulations, location accuracy, and spatial and polygon methods of interpolation. The databases consisted of measured soil pH values from the US National Cooperative Soil Survey Characterization Database (NCSS–SCDB) and estimated values from US Soil Survey Geographic (SSURGO) and State Soil Geographic (STATSGO2) databases. The degree of agreement between measurement methods ranged from poor to substantial, with Lin's concordance correlation coefficients ( ρ c ) varying from 0.83 (pH 1:1 W against 1:5 CaCl2 ) to 0.95 (pH 1:1 W against pH 1:5 W ) and root mean square error (RMSE) varying from 0.27 to 0.43. The degree of agreement between pH 1:1 W , 1:2 CaCl2 and mid‐infrared spectroscopy (MIR) ranged from poor to moderate. The RMSE for MIR was 0.40 for pH 1:1 W and 0.32 for soil pH 1:2 CaCl2 . The RMSE for between‐laboratory reproducibility varied from 0.50 (pH 1:1 W ) to 0.68 (pH 1:2 CaCl2 ) and was greater than within‐laboratory reproducibility (pH 1:1 W , 0.34; pH 1:2 CaCl2 , 0.22) and repeatability (pH 1:1 W , 0.19; pH 1:2 CaCl2 , 0.04). The RMSE for the relations for profile depth slicing (weighted mean against equal‐area spline) was 0.36. The RMSE for the relation between soil pH 1:1 W for the Global Positioning System and Public Land Survey System was 0.57. Predictions based on polygon or spatial interpolation had the largest RMSEs, 0.78 and 0.62, respectively. Soil liming recommendations based on 0.1 pH increments do not reflect error measurements or the uncertainty of spatial prediction. Although it was not possible to establish consistent trends in the size of error (progressively increasing from measurement to aggregation), its assessment can improve modelling and management at various scales. Highlights We assessed sources of errors and uncertainty for measured and spatial predictions of soil pH. The smallest error was reported for measured pH (0.06). Polygon or spatial interpolation resulted in the largest error (0.68). Differences in error size influenced rates of liming and cost.

Historically, the US National Cooperative Soil Survey used soil properties to define soil capability and function primarily for farm, forestry, and grazing land practices. The maps, which are consolidated into an official web-based database, are derived from a framework of land classification, combined soil properties (both estimated and measured), and land management classification. The mapping was originally conceived as a practical tool to provide farmers and community planners with information on the basic soil resource for economic gain. For more than 75 years, the Natural Resources Conservation Service (formerly the Soil Conservation Service) has used land capability classification as a tool for planning conservation measures and practices on farms so that the land could be used without serious deterioration from erosion or other causes. The land capability classification is one of innumerable methods of land classification based on broad interpretations of soil qualities and other site and climatic characteristics. Modern soil surveys have evolved to portray soil interpretations and soil capability both geospatially and with data analysis. As the functionality of the National Soil Survey Information System (NASIS) and Soil Survey Geographic System (SSURGO) increases, the Natural Resources Conservation Service (NRCS) is advancing its interpretation program nationally to address security issues within the context of soil capability beyond land use and land cover. Soil capability for any potential human use or ecosystem service must be assessed within the context of soil properties, either measured or estimated. Using soil security as a framework (including capability, condition, capital, connectivity, and codification), soil interpretations of the US National Cooperative Soil Survey database may be tailored to address the questions of sustainability and climate change at local, regional, and global scales and to facilitate the transfer of technology to other countries and related scientific disciplines.

The popular Soil Survey Geographic (SSURGO) Database is available in the Web Soil Survey, but not easily used in national, regional and statewide resource planning and analysis of soils data. USDA-NRCS has added a new product designed to provide convenient access to soils information for large land areas by the simulation modeling community. The new product, called gSSURGO (g for gridded), provides detailed soil survey mapping in raster format including all traditional attributes plus “ready to map” attributes organized in statewide tiles for desktop GIS. In addition, the raster format allows GIS visualization of highly detailed soils themes for an entire state in a matter of seconds. The gSSURGO Database is derived from the official Soil Survey Geographic Database for fiscal year 2013 and was prepared by merging the traditional vector-based SSURGO digital map data and tabular data into statewide extents, adding a statewide gridded map layer derived from the vector layer, and adding a new value-added look up (valu) table database. The gSSURGO database is provided in an Environmental Systems Research Institute, Inc. (ESRI®) file geodatabase format that relates all attribute tables together to make soil queries straight forward for the user for attributes such as prime farmland, land capability class, surface pH, or depth to root restriction. The new “Ready to Map” themes in the valu1 table contain data such as (but not limited to) soil organic carbon; available water storage; National Commodity Crop Productivity Index; root-zone depth of commodity crops; available water storage within the root-zone depth; drought-vulnerable soil landscapes; and potential wetland soil landscapes. For detailed descriptions and how to obtain datasets, go to the gSSURGO website at: http://soils.usda.gov/survey/geography/ssurgo/description_gssurgo.html. The Gridded Soil Survey Geographic Database (gSSURGO) is available on the Geospatial Data Gateway for download or order placement at: http://datagateway.nrcs.usda.gov/.

The United States Department of Agriculture-Natural Resources Conservation Service (USDA-NRCS) recently revised its ground-penetrating radar (GPR) soil suitability maps (GPRSSM). These maps, which have been prepared for most areas of the USA at different scales and levels of resolution, show the relative suitability of soils for GPR soil investigations. These digital maps are based on physical and chemical properties of approximately 22,000 different soils. The smaller scale (1:250,000) Ground-Penetrating Radar Soil Suitability Map of the Conterminous United States shows the relative suitability of soils to GPR within major soil and physiographic areas. The larger scale (1:12,000 to 1:63,360) state ground-penetrating radar soil suitability maps duplicate the scale and level of detail of the original soil survey maps. GPR soil suitability maps have been used to evaluate the relative appropriateness of using GPR, select the most suitable antennas, and assess the need and level of data processing. Limitations of these maps are discussed and examples of radar records collected in soils having different GPR suitability indices are presented.

The USDA-Natural Resources Conservation Service (USDA-NRCS) has developed groundpenetrating radar (GPR) soil suitability maps (GPRSSM) for most areas of the USA at different scales and levels of resolution. These digital maps are based on the physical and chemical properties of approximately 22,000 different soils. Properties selected to prepare these maps include: soil taxonomic classification, clay content and mineralogy, electrical conductivity, sodium absorption ratio, and calcium carbonate and calcium sulfate contents. The smaller scale (1:250,000) Ground-Penetrating Radar Soil Suitability Map of the Conterminous United States shows the relative suitability of soils to GPR within major soil and physiographic areas. Larger scale (1:12,000 to 1:63,360) maps have also been prepared for individual states. These maps duplicate the scale of the original soil survey maps. GPR soil suitability maps can help evaluate the relative appropriateness of using GPR, select the most suitable antennas, and assess the need and level of data processing. Limitations of these maps are discussed and examples of radar records collected in soils having different GPR suitability indices are presented.

Poultry production in Hardy County, West Virginia, has increased considerably since the early 1990s. The Lost River basin contains the highest density of poultry facilities in the county. Most of the N-rich poultry litter produced is land applied, and concerns over water quality are widespread. The objective was to apply the Natural Resources Conservation Service exploratory technique on two watersheds (Cullers Run and Upper Cove Run) in the Lost River basin to estimate the loss of nitrate-N from soils by runoff and leaching and to predict the impact on water quality. The predicted annual nitrate-N loss by runoff was 192 Mg, whereas that by leaching was 764 Mg, and their combined amount represented the annual loading for the Lost River. The predicted averages of nitrate-N concentration in runoff and leaching water were 2.57 and 45.1 mg/L, respectively. These data would give an estimated average nitrate-N concentration of 10.4 mg/L in the Lost River. The observed nitrate-N concentration in 12 monthly samples collected from the Lost River ranged from 2.41 to 19.9 mg/L, with an average of 7.11 mg/L (S.D., 4.68 mg/L). The relatively low nitrate-N concentrations observed in the river could be attributed to assimilation by algae, weeds, and aquatic plants, as well as denitrification in stream water under anaerobic conditions. When factors affecting N concentration in streams are considered, the technique could estimate the impact on water quality. We concluded that the exploratory technique could provide a quick estimation and identify hot spots for large areas of agricultural land. Thus, lengthy and site-specific studies could be focused on certain areas of high risk.

The loss of nutrients in runoff from soils treated with heavy manure application is a major cause of poor surface water quality in the United States. Poultry production in the Hardy County, West Virginia, has increased considerably since the early 1990s. The Lost River basin contains the highest density of poultry houses in the county. Most of phosphorus (P)-rich manure produced is land applied, and concerns over water quality impacts are widespread. The objectives of this study were to apply the Natural Resources Conservation Service technique on two watersheds (Cullers Run and Upper Cove Run) in the Lost River basin to predict the loss of water and P from soils by runoff and to estimate the impact on water quality. The predicted average runoff was 4374 m3/ha per year, and agreed with the observed average runoff of 4267 m3/ha per year. This gives an annual runoff of 74.6 million m3 for the two watersheds. The average P loss by runoff was 0.57, 1.98, and 5.51 kg/ha per year from soils under forest, pasture, and crop, respectively. The high P loss by runoff was probably associated with application of P fertilizer or poultry manure to cropped soils. The total annual loss of P from soils by runoff was estimated at 16,435 kg. The predicted P concentration varied widely in runoff water generated from different soils and land covers. The average P concentration in runoff water was 133, 432, and 1146 μg/L for forestland, pastureland, and cropland, respectively. The predicted average P concentration in runoff was 224 μg/L for the two watersheds. However, the observed P concentration was very low (1.3-13.3 μg/L) in the monthly water samples (January-December 2006) collected from the Lost River, where the pH ranged between 7.6 and 8.4. The average pH in soils was 4.22, 5.42, and 6.15 for forestland, pastureland, and cropland, respectively. Changing the pH of runoff water from acidic (soils) to the alkaline range in the Lost River could precipitate calcium phosphates and decrease P concentration in water. The technique predicted P concentration in runoff at the edge of field. The increase in water pH as well as P removal by aquatic weeds and algae could be the cause of the low P concentration observed in the Lost River.

The loss of agricultural chemicals in runoff from agricultural land is a major cause of poor surface water quality in the United States. A technique using climatic, hydrologic, and soil survey information was developed to estimate the impact of agricultural watersheds on natural water resources. The objectives of this study were to apply this technique on the Wagon Train watershed to predict loss of eight elements (Al, Fe, Si, Cd, Cu, Ni, Pb, and Zn) by runoff from soils and to estimate elements loading into Wagon Train reservoir. The predicted losses of Al, Fe, and Si by runoff were 25.3, 13.7, and 28.9 kg ha-1 year-1, respectively. The corresponding values for Cd, Cu, Ni, Pb, and Zn were much smaller at 0.61, 52.0, 21.3, 1.40, and 37.4 g ha-1 year-1, respectively. These data give a total annual loss (from the entire watershed) of 98.3, 53.2, and 112 Mg for Al, Fe, and Si, respectively. The total annual loss was 2.4, 202, 82.7, 5.4, and 147 kg for Cd, Cu, Ni, Pb, and Zn, respectively. The predicted Cd, Cu, and Pb concentrations in runoff were in reasonable agreement with the concentrations observed in the main stream in the watershed. However, the predicted concentration for other elements (Al, Fe, Si, Ni, and Zn) investigated in runoff was greater than that observed in the stream water. Elements uptake by algae, weeds, and aquatic plants and/or precipitation due to high pH in water might explain the lower element concentrations. We concluded that the technique could provide an estimation of elements loss in runoff from agricultural watersheds. The loading into surface water bodies could be predicted for Cd, Cu, and Pb. For other elements (Al, Fe, Si, Ni, and Zn), the loading could be estimated when factors affecting element concentration in streams are considered.