The loss of agricultural chemicals in runoff from agricultural land is a major cause of poor surface water quality in the United State. Scientists (Natural Resources Conservation Service) developed a technique using climatic, hydrologic, and soil survey information to estimate the impact of agricultural watersheds on natural water resources. The objective of this study was to apply this technique on the Wagon Train (WT) watershed to predict loss of alkaline earth elements (calcium [Ca], magnesium [Mg], barium [Ba], and strontium [Sr]) by runoff from soils and to estimate elements loading into WT reservoir. The predicted losses of Ca, Mg, Ba, and Sr by runoff were 67.5, 19.9, 0.17, and 0.05 kg ha(-1) yr(-1) 1, respectively. These data give a total annual loss of 262.1 and 77.1 Mg of Ca and Mg, respectively, for the entire watershed and could be considered as the annual loading for WT reservoir. The total annual loss was 668 kg for Ba and 186 kg for Sr and could represent the annual loading for the reservoir. The predicted Ca, Mg, and Ba concentrations in runoff were in good agreement with those observed in water samples collected weekly from the main stream in the watershed. However, the predicted Sr concentration in runoff was much less than that observed in the stream water. Subsurface seepage into the stream might contribute to the high Sr concentration observed in the stream water. We concluded that the technique could provide a reasonable estimation of Ca, Mg, and Ba losses in runoff from agricultural watersheds and loading into surface water bodies.

There is much interest in predicting future carbon-soil degradation and that occurring today. We have National Soil Survey Laboratory data to assess some of the soil carbon degradation in the Great Basin and western Utah. For this we included data on 32 Nevada and Utah soils on Pleistocene geomorphic surfaces at elevations of 973 to 3172 m. Their mean annual precipitation (MAP) ranges from 20 to 55 cm and mean annual soil temperatures (MAST) from 5 to 12 degrees C. The MAP and MAST closely correlate with elevation (E) (r=0.96 and -0.97 respectively). Mountain big sagebrush (Artemisia tridentata Nutt. ssp. vaseyana (Rydb.) Beetle) dominates vegetation at the higher, colder elevations. Wyoming big sagebrush (Artemisia tridentata Nutt. ssp. wyomingensis Beetle and Young) and juniper (Juniperus L.) dominate at intermediate elevations. Little sagebrush (Artemisia arbuscula Nutt.) and related desert species dominate at the lower, warmer elevations. We used acid dichromate digestion and FeSO4 titration to analyze for soil organic carbon (SOC) and bulk density and coarse fragments in the soils to put the data on a volume basis. The soils are well drained and uncultivated. Accumulation of organic carbon in each pedon (OCp) is correlated to MAP and MAST (r=0.81, and -0.78 respectively). We predicted OCp from the relationship,OCp = -0.942 + 2.546*root(MAP/MAST),r(2)=0.64, S.E.=1.30, n=32. The soil OC degradation that may have occurred through the Holocene ranges from 35% at sites of the present Aridisols and Vertisols to 22% for the sites of the Mollisols and Alfisols. Eq. (1) shows that today, MAST rises of 1 to 3 degrees C would produce further OCp degradation from today's levels of 1% to 13% in Aridisols and Vertisols and 12% to 25% in the Mollisols and Alfisols respectively. It also shows that if the MAST drop of 6 degrees C predicted for the Pleistocene occurred, many of the Aridisols and Vertisols likely would have been Mollisols or Alfisols during the Pleistocene. A temperature rise of 1 degrees C in a century would likely move the Mollisol-Aridisol boundary from its present 2300 m elevation to an elevation of about 2900 m. A temperature rise of 3 degrees C in a century would likely move the Mollisol-Aridisol boundary from its present 2300 m elevation to elevations of the highest elevations in Nevada and to the middle of Idaho. Increasing the temperature by 3 degrees C will likely also increase the area affected by severe desertification in the southern Great Basin north by about 20%. (c) 2006 Elsevier B.V. All rights reserved.

The loss of nutrients in runoff and leaching water from agricultural land is a major cause of poor water quality in the United States. Scientists (NRCS) developed a technique to estimate the impact of agricultural watersheds on natural water resources. The objectives were to apply the technique on Wagon Train (WT) watershed in Nebraska to predict: (i) loss of water by surface runoff and subsurface leaching, (ii) loss of nitrate-N from soils by runoff and leaching, and (iii) nitrate-N loading for WT reservoir. The annual loss of water was estimated at 4.32 million m(3) for runoff and 0.98 million m(3) for leaching. The observed annual inflow for WT reservoir was 4.25 million m(3). The predicted annual nitrate-N loss by runoff was about 7.0 Mg and could be considered the annual loading for the reservoir. The predicted nitrate-N loss by leaching was 7.73 Mg, however, the fate was not clear. The estimated average nitrate-N concentration in runoff and leaching water at field sites was 1.63 and 7.88 mg/L, respectively. The observed nitrate-N concentration in water samples taken from 12 major streams ranged between 0.37 and 1.56 mg/L with an average of 0.90 mg/L. Nitrogen uptake by algae, weeds, and aquatic plants and emission of gaseous nitrogen oxides from fresh water under reducing conditions might explain the lower nitrate-N concentration. No attempt was made to monitor the nitrate-N concentration in soil leachate or groundwater. When factors affecting N concentration in streams are considered, the technique could provide a reasonable estimation of N concentration in stream water. We concluded that the technique could be applied to estimate the loss of nitrate-N by runoff and leaching from soils and the impact on surface waters.

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.

Application of manure on agricultural land can introduce considerable amounts of phosphorus (P) to natural water resources. The objectives of this study were to evaluate the effect of dairy manure application on 1) P released from surface soil by rainfall, 2) P removed from surface soil by runoff, and 3) soil P available for plants. A technique implementing a Soil Survey Laboratory method and USDA Runoff Model was applied on four Texas and three Utah soils. The application of manure (100Mg/ha) considerably increased the amount of P released from the surface soil by rainfall, but there was no significant change in the pattern of P release (phosphorus release characteristics). Manure application increased both the runoff and available P for soils. For the Blanket soil (Texas), P released from surface soil by rainfall increased from 1.06 to 30.8 kg/ha/yr. The runoff P (kg/ha/yr) increased from 0.18 to 5.15 for fallow, from 0.16 to 4.71 for cropland, and from 0.13 to 3.88 for grassland. Soil P available for plants increased from 0.88, 0.90, and 0.93 to 25.7, 26.1. and 26.9kg/ha/yr for fallow, cropland, and grassland, respectively. Similar effects of manure application were noticed for other Texas and Utah soils. The data suggest that manure could provide substantial amounts of available P for crop production in these soils. However, irrigated cropland amended annually with manure could contribute to nonpoint source pollution of surface freshwater bodies. The technique provides a tool to quantify the impact of manure application to agricultural land on water resources.

Nitrate-nitrogen (N) loss from agricultural land to natural water resources is an issue for both crop production and water quality. The objective of this study was to develop a technique to evaluate and map nitrate-N loss by surface runoff and subsurface leaching for agricultural land. The technique implemented water loss calculated by the National Resources Conservation Service (NRCS) runoff equation and a percolation model to predict nitrate-N loss for soils with different types of land cover. The technique was applied on agricultural land in Lancaster County, Nebraska, which covers 221,000 ha near the eastern edge of the Great Plains. The Soil Survey Report was used to identify 11 major soil series that comprise 83% of acreage in the county. Predicted nitrate runoff loss from soils ranged from 0.84 to 6.20 kg/ha/y for fallow, 0.83 to 5.97 kg/ha/y for cropland, and 0.80 to 5.29 kg/ha/y for grassland. For most soils nitrate loss by leaching was greater than that by runoff. The average loss predicted by leaching was 8.75, 7.01, and 3.73 kg/ha/y for fallow, cropland, and grassland, respectively. Nitrate concentration predicted in runoff water from three crop-covered soils exceeded the threshold of 10 mg/L, while most soils generated leaching water with nitrate exceeding 10 mg/L. The county-level average of nitrate predicted in runoff (6.55 mg/L) and leaching water (11.8 mg/L) emphasized a need of nutrient management plan to reduce N loss from cropland. The Geographical Information Systems (GIS) were applied to map water loss and nitrate risk potential (NRP) for surface and groundwater contamination in the county.

NRCS scientists developed a cost-effective technique utilizing the soil survey database to estimate the environmental impact of agricultural watersheds on natural water resources. The objectives of this study were to apply this technique on Wagon Train (WT) watershed to predict: i) loss of water by surface runoff, ii) loss of phosphorus (P) from soils by runoff, and iii) P loading for WT reservoir, and iv) to validate the technique. The annual loss of water by runoff was estimated at 4.32 million m3. USGS data for a 50-year period (1951-2000) indicated that the average annual inflow for WT reservoir was 4.25 million m3. The predicted annual P loss by runoff was 846 kg and could be considered as the annual loading for WT reservoir. The estimated average P concentration in the runoff water was 196 µg/L. The average P concentration in water samples taken from five locations in the reservoir was 140 µg/L. The concentration of P in water samples collected from 12 major streams ranged between 99 and 240 µg/L with an average of 162 µg/L. Phosphorus uptake by weeds and aquatic plants in the streams and reservoir might explain the slight drop of P concentration in waters. Also, the high pH (≈ 8.5) measured in water from the streams and reservoir compared to soils (≈ 6.0) might explain this drop in P concentration. We concluded that the NRCS technique could be applied to predict the impact of P loss from an agricultural watershed on surface waters.

Silt-to sand-size clay aggregates are absent or relatively rare in unweathered glacial loesses, but dominant in parna where they resist dispersion in particle size analysis (PSA). There are reports that some aggregates in glacial loesses also resist dispersion. However, one would expect freeze-dried aggregates to have mostly edge-to-face orientation of the platy clays and to be easily dispersed in PSA. We test this hypothesis in this paper. We selected Midwest and Alaskan soils formed in late-Pleistocene loess derived from glacial outwash and floodplains beyond the late-Pleistocene glacial boundary. Analytical methods are those in use by the National Soil Survey Laboratory. Soil A and B horizons dispersed well as shown by the 1.5 MPa water to clay ratios of < 0.6. In the C horizon, silt-size, rounded, compound particles (aggregates) were few to common and randomly distributed. A few were volcanic glass. Other aggregates consisted of carbonates or layer silicates. Layer silicate aggregates dispersed well in PSA, as hypothesised; Fe and carbonate cemented aggregates did not. Most of these dispersed when given an ultrasonic dispersion treatment.

Using soil tests to estimate phosphorus (P) released from agricultural soil by runoff had limited success because P loss is a function of source and transport parameters. There are good procedures applying these parameters, but they are lengthy, expensive and demand numerous laboratory and field data. The objective is to develop reliable exploratory technique to estimate runoff P for agricultural land. Various forms of P like moisture are held by soil particles at different energy levels. Kinetic energy exerted by raindrops on surface soil plays a major role in releasing P. The Soil Survey Laboratory suggests an anion exchange resin (AER) method to determine P release characteristics (PRC) for soils. In this method, different levels of energy are applied by water on soil particles when soil suspension is shaken for various periods. Understanding the relationship between shaking and rainfall energy enabled us to use the AER method to predict P released by rainfall. USDA/NRCS (SCS) runoff equation is applied to determine the relationship between rainfall and runoff for agricultural watersheds. Soil hydrology, rainfall, and type of vegetation are parameters utilized by the runoff model. We propose a technique implementing the AER method and runoff equation to estimate runoff P for agricultural land. The estimated runoff P for 24 soils investigated ranged between 0.09 and 8.3 (fallow), 0.06 and 7.5 (cropland), and 0 and 6.0 kg P/ha/y for grassland. Field studies on different benchmark soils of the United States are in progress to estimate runoff P by using rainfall simulators. These data could be used to verify and calibrate the technique.

Few existing extractions such as Mehlich3 and ammonium bicarbonate‐DTPA (ABDTPA) can be used as a multi‐element soil test. A multi‐element extraction is attractive to scientists and soil testing laboratories because it eliminates the need for multiple extractions and allows simultaneous measurement of elements by using the Inductively Coupled Plasma (ICP). The objective of this study was to evaluate Mehlich3 and ABDTPA for simultaneous measurement of 15 elements in 30 acidic and 20 alkaline U.S. soils from 21 states. Widely‐accepted, and conventional soil tests (Bray1 and Olsen for phosphorus (P); NH4OAc for calcium (Ca), magnesium (Mg), potassium (K), and sodium (Na); diluted HCl and DTPA for aluminum (Al), cadmium (Cd), cobalt (Co), chromium (Cr), Copper (Cu), iron (Fe), manganese (Mn), nickel (Ni), lead (Pb), and zinc (Zn) were employed for the evaluation. Single and multiple regression analysis were applied to investigate the relationship between Mehlich3 or ABDTPA and the respective soil test. The results can be summarized: 1) Mehlich3 provided a good measurement for labile P in all soils while ABDTPA provided reliable results for alkaline soils; 2) Mehlich3 was a suitable extract for Ca, Mg, K, and Na for all soils while ABDTPA could not be used for Ca; 3) Mehlich3 or ABDTPA was an appropriate extract for Al, Cd, Cu, Fe, Mn, Ni, Pb, and Zn in all soils. The fact that most soils investigated had trace amounts of Co, and Cr hindered their evaluation. Accordingly, Mehlich3 extraction could be recommended for simultaneous measurement of at least 13 elements in soils.

Few studies of soil geochemistry over large geographic areas exist, especially studies encompassing data from major pedogenic horizons that evaluate both native concentrations of elements and anthropogenically contaminated soils. In this study, pedons (n = 486) were analyzed for trace (Cd, Co, Cr, Cu, Hg, Mn, Ni, Pb, Zn) and major (Al, Ca, Fe, K, Mg, Na, P, Si, Ti, Zr) elements, as well as other soil properties. The objectives were to (i) determine the concentration range of selected elements in a variety of U.S. soils with and without known anthropogenic additions, (ii) illustrate the association of elemental source and content by assessing trace elemental content for several selected pedons, and (iii) evaluate relationships among and between elements and other soil properties. Trace element concentrations in the non-anthropogenic dataset (NAD) were in the order Mn > (Zn, Cr, Ni, Cu) > (Pb, Co) > (Cd, Hg), with greatest mean total concentrations for the Andisol order. Geometric means by horizon indicate that trace elements are concentrated in surface and/or B horizons over C horizons. Median values for trace elements are significantly higher in surface horizons of the anthropogenic dataset (AD) over the NAD. Total Al, Fe, cation exchange capacity (CEC), organic C, pH, and clay exhibit significant correlations (0.56, 0.74, 0.50, 0.31, 0.16, and 0.30, respectively) with total trace element concentrations of all horizons of the NAD. Manganese shows the best inter-element correlation (0.33) with these associated total concentrations. Total Fe has one of the strongest relationships, explaining 55 and 30% of the variation in total trace element concentrations for all horizons in the NAD and AD, respectively.

Twenty-one benchmark soils of the United States, including surface and subsurface horizons and satellites, from the Water Erosion Prediction Project (WEPP) were analyzed for phosphorus (P), using methods that include total (TP), water-soluble (WP), Bray 1 (BP), Mehlich No. 3 (MP), Olsen (OLP), New Zealand P Retention (NZP), organic (OP), anion exchange resin (AEP), and acid oxalate (Po). Objectives of this study were to determine relationships among soil P test values and other soil properties. Knowledge and understanding of these relationships are important to researchers when evaluating soil P data sets for use in predictive models for agronomic, soil genesis, or environmental purposes. Important relationships that were developed, using simple or multiple linear regression models, among P methods and other soil properties, e.g., organic carbon (OC), total N (TN), dithionite-citrate extractable iron and aluminum (Fed, Ald), and clay are as follows:

In response to national and international concerns about soil and water quality, personnel at the United States Department of Agriculture, Natural Resources Conservation Service (USDA-NRCS), Soil Survey Laboratory are developing standard procedures to determine trace metals in soils. This paper illustrates how trace metal studies, complemented by total analysis, increase the use and value of the modem National Cooperative Soil Survey. Examples include anthropogenic metal contamination (Pb, As, Hg, Cd, Cu, Zn) from a copper smelter in Deer Lodge Valley, Montana; nutrient deficiencies (Ca, N, P, K) and metal enrichment (Co, Cr, Fe, Ni) in serpentinitic soils in Klamath Mountains, Oregon; naturally-elevated metal concentration (As, Pb, Hg) in hydrothermally active soils in Yellowstone National Park; total P and Fe as measures of weathering, chelation, and translocation of parent material in chronosequence study on Mendenhall Glacier in southeast Alaska; and metal determinations (Ni, Cr, Pb, Cd, Zn, Cu) to provide interpretations for land use in New York City. Native metal concentrations in soils vary widely, determined by geologic origin and pedogenic processes. Studies of soils examine landscape distribution of soils and often include metal partitioning to determine specific fractions. Soils with high natural concentrations of metals or those contaminated through atmospheric deposition, waste application, or surface/ground water are common. Total and bioavailable metal concentrations must be determined because of their impact on land use. In general, knowledge limited to total metal concentrations can be misleading. Application and interpretation of trace metal data for soil surveys are method-dependent and caution must be exercised in its application in the soil survey.

The evaluation and application of all remote sensing, surface and subsurface methods of exploration are areas of concern with engineering geologists as well as geotechnical engineers. Also of interest is the application, evaluation, and correlation of all existing and proposed earth material classifications, surveys, and associated survey techniques. The paper provides a brief review of the dramatic evolvement of conventional methods of exploring and classifying materials as well as some of the promising new technologies that have been developed but have not yet been adopted as conventional; methods.

Fuzzy set logic is used to express the risk in soil interpretation ratings. Uncertainty inherent in the definition of estimated sets of properties used to characterize a given map unit is described with the help of fuzzy sets. Threshold levels of soil properties (where no adverse consequences can be expected) are used to formulate a continuum for assessment. Different types of adverse consequences may be considered. Here we describe economic, noneconomic private, and non-economic social consequences. The relative consequences are functions of the deviation between the estimated and threshold soil properties characterizing a given rating. We assumed that the consequence functions estimated by an expert group are exact. The methodology can be extended to uncertain consequence functions and is applied to soil suitability ratings in four map units in Saunders County, Nebraska. The integrated risk index calculated with the methodology can help to select a cost-effective solution to management considerations.

The United States National Cooperative Soil Survey Program has prepared soil maps for much of the country. Both field and laboratory data are used to design map units and provide supporting information for scientific documentation and predictions of soil behavior. Coordination of mapping, sampling site selection, and sample collection in this program contributes to the quality assurance process for laboratory characterization. Requisites to successful laboratory analysis of soils occur long before the sample is analyzed. In the field, these requisites include site selection, descriptions of site and soil pedon, and careful sample collection. A complete description of the sampling site not only provides a context for the various soil properties determined, but it is also a useful tool in the evaluation and interpretation of the soil analytical results. Landscape, landform, and pedon documentation of the sampling site serves as a link in a continuum of analytical data, sampled horizon, pedon, landscape, and overall soil survey area.

The risk a decision maker takes when using soils data to implement a practice or make an interpretive decision is dependent on the quality of the data, the tools for decisionmaking, the amount of pertinent data, how well the soil forming processes are understood, and how well such processes can be conveyed in an understandable way. Fuzzy logic is a tool that can be used to characterize uncertainty in soils information so that a risk-based method of soil interpretations can be implied. A methodology is presented to demonstrate how fuzzy soil interpretations provide a realistic approach to decisionmaking for risk-based soil interpretations. Map units from a soil survey are used to demonstrate the appropriateness of soils for septic tank filter fields and tillage.

We prepared sets of <2 mm soil samples, distributed them to soil scientists, and asked them to determine the dry and moist Munsell color of each soil. We observed that soil scientists agreed on the same color chip for a single color component (hue, value, or chroma) 71% of the time, and there was an average of 52% agreement for all three color components. The standard deviation (SD) varied from 0.45 (value-moist) to 0.68 (chroma-moist) with an average SD of 0.57. Regression equations were computed that compared the mean soil color with the nearest color chip noted by an individual soil scientist, and the coefficient of simple determination (r 2 ) ranged from 0.49 (chroma-moist) to 0.79 (value-moist and dry). When “in-between” colors were estimated the r 2 improved and ranged from 0.70 (chroma-moist) to 0.95 (value-moist). A detailed evaluation was made of data from a commercial tristimulus colorimeter, and results were compared to colors described by soil scientists. The r 2 ranged from 0.88 (chroma-moist) to 0.96 (value-dry); however the slopes and intercepts were different. Commercial colorimeters have great potential as tools for measuring soil colors, but field colors by soil scientists are not identical to instrumental data. They differ because the sensor, light source, and angle of light refraction are different for each color measurement method. The inherent complexity of color identification by humans vs. instrumental measurements is also a contributing factor.

AbstractThere are undefined particle‐size distributions that are within the definition of sandy loams but not within the definitions of any of the subclasses of sandy loams according to the 1981 draft of chapter 4 of the Soil Survey Manual. These undefined particle‐size distributions can be accommodated by appending the following phrase to the sandy loam subclass definition: “or less than 15 percent very coarse, coarse and medium sand and less than 30 percent either fine or very fine sand.”