SummaryStatistical 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 (ρc) varying from 0.83 (pH 1:1W against 1:5CaCl2) to 0.95 (pH 1:1W against pH 1:5W) and root mean square error (RMSE) varying from 0.27 to 0.43. The degree of agreement between pH 1:1W, 1:2CaCl2 and mid‐infrared spectroscopy (MIR) ranged from poor to moderate. The RMSE for MIR was 0.40 for pH 1:1W and 0.32 for soil pH 1:2CaCl2. The RMSE for between‐laboratory reproducibility varied from 0.50 (pH 1:1W) to 0.68 (pH 1:2CaCl2) and was greater than within‐laboratory reproducibility (pH 1:1W, 0.34; pH 1:2 CaCl2, 0.22) and repeatability (pH 1:1W, 0.19; pH 1:2CaCl2, 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 1:1W 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.

A long history of urbanization and industrialization has affected trace elements in New York City (NYC) soils. Selected NYC pedons were analyzed by aqua regia microwave digestion and sequential chemical extraction as follows: water soluble (WS); exchangeable (EX); specifically sorbed/carbonate bound (SS/CAR); oxide-bound (OX); organic/sulfide bound (OM/S). Soils showed a range in properties (e.g., pH 3.9 to 7.4). Sum of total extractable (SUMTE) trace elements was higher in NYC parks compared to Bronx River watershed sites. NYC surface horizons showed higher total extractable (TE) levels compared to US non-anthropogenic soils. TE levels increased over 10 year in some of the relatively undisturbed and mostly wooded park sites. Surface horizons of park sites with long-term anthropogenic inputs showed elevated TE levels vs. subsurface horizons. Conversely, some Bronx River watershed soils showed increased concentrations with depth, reflective of their formation in a thick mantle of construction debris increasing with depth and intermingled with anthrotransported soil materials. Short-range variability was evident in primary pedons and satellite samples (e.g., Pb 253 ± 143 mg/kg). Long-range variability was indicated by PbTE (348 versus 156 mg/kg) and HgTE (1 versus 0.3 mg/kg) concentrations varying several-fold in the same soil but in different geographic locations. Relative predominance of fractions: RES (37 %) > SS/CAR (22 %) > OX (20 %) > OM/S (10 %) > EX (7 %) > WS (4 %). WS and EX fractions were greatest for Hg (7 %) and Cd (14 %), respectively. RES was predominant fraction for Co, Cr, Ni, and Zn (41 to 51 %); SS/CAR for Cd and Pb (40 and 63 %); OM/S for Cu and Hg (36 and 37 %); and OX for As (59 %).

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 loss of nutrients in runoff from agricultural land is a major cause of poor surface water quality in the United State. Scientists (NRCS) developed a technique 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 (WT) watershed to predict (1) loss of water by surface runoff, (2) loss of phosphorus (P) from soils by runoff and P loading for WT reservoir. The annual loss of water by runoff was estimated at 4.32 million m(3). The USGS data for a 50-year period (1951 to 2000) indicated that the average annual inflow for WT reservoir was 4.25 million m(3). The predicted annual P loss by runoff was 844 kg and could be considered as the annual loading for WT reservoir. The predicted P concentration in the runoff water at field sites was 196 mu g/L. Phosphorus concentration observed in major streams at the beginning of spring (March) ranged from 99 mu g/L to 240 mu g/L with an average of 162 mu g/L (S.D. = 40 mu g/L), and the average P concentration in water samples taken from different locations in the reservoir was 140 mu g/L. Phosphorus uptake by algae, weeds and aquatic plants, as well as high pH in the reservoir and streams might explain the slight drop of P concentration in waters. Further, the average P concentration observed in the main stream samples for the entire rainy season (March through October), ranged between 157 and 346 mu g/L with an average of 267 mu g/L (S.D. = 65 mu g/L). Application of P fertilizers (April/May) for summer crops might explain the increase in P concentration. When factors affecting P concentration in streams are considered, the technique could provide a reasonable estimation of P concentration in stream water.