National Soil Survey Center

Lincoln, NE, United States

National Soil Survey Center

Lincoln, NE, United States
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Margenot A.J.,University of California at Davis | Alldritt K.,University of California at Davis | Southard S.,National Soil Survey Center | O'Geen A.,University of California at Davis
Soil Science Society of America Journal | Year: 2016

Principles of soil science are highly complementary with subjects required by state and national standards and offer an opportunity to integrate science, technology, engineering, and math (STEM) education and non-STEM subjects, such as history and anthropology. However, soil science is often absent from primary education curricula. This is a missed opportunity for both primary education and the soil science community. We integrated soil science into primary education using the Soil Science Society of America (SSSA) traveling soil science exhibit Dig It! The Secrets of Soil. This effort was unique in that primary school educators, The California Museum staff, SSSA, and University of California-Davis graduate students worked to make the exhibit into an external classroom for Grades 2 through 7. The goal was to raise awareness and knowledge of soils and their importance to society and to offer example career opportunities in soil science. Grade-specific presentations and activity-based workshops were designed to address California curricula standards. These efforts identified several gaps and opportunities in soil science outreach to primary education. Participatory and observational activities were critical for conveying concepts across all educational grades. Curricula standards represented a potential constraint and corresponding entry point to facilitate the incorporation of soil science into primary education. Our experience demonstrates the ongoing impact of Dig it! The Secrets of Soil, and illustrates the potential of collaboration between academic institutions like UC-Davis and SSSA to improve soil science education. © Soil Science Society of America, 5585 Guilford Rd., Madison WI 53711 USA. All Rights reserved.

Ugarte C.M.,University of Illinois at Urbana - Champaign | Kwon H.,International Food Policy Research Institute | Andrews S.S.,National Soil Survey Center | Wander M.M.,University of Illinois at Urbana - Champaign
Journal of Soil and Water Conservation | Year: 2014

Increased understanding of the influences of management practices on soil properties and associated ecosystem function is needed to improve tools used to administer conservation programs in the United States. This study used meta-analysis to assess the influence of cropping systems (conventional, conservation with minimum tillage, conservation with no-till, and organic systems) and management practices (nitrogen [N] fertility and rotation length) on soil organic carbon (SOC). These factors are considered by tools that evaluate conservation performance and provision of ecosystem services. We also reviewed the literature to determine whether this approach could be applied to other proxy variables (erosion rates, soil erodibility factor [K values], available phosphorus [P], and nitrous oxide [N2O]). Data mining was used to populate a database with variables representing practices used by the Natural Resource Conservation Service's Conservation Measurement Tool (CMT) to determine eligibility for the Conservation Stewardship Program. Data collected from 55 peer-reviewed studies was categorized based on sampling depth (0 to 10, 0 to 15, 0 to 20, and 0 to 30 cm [0 to 3.9, 0 to 5.9, 0 to 7.8, and 0 to 11.8 in]). The magnitude of the effect estimated by meta-analysis was then compared to scores assigned to practices in the soil quality module of the CMT. Meta-analysis of data from the 0 to 20 cm (0 to 7.8 in) depth suggested that rates of SOC accrual were similar in organic systems using diversified crop rotations and conservation systems using inorganic fertility sources, increasing SOC by 9% compared to the conventional control. In comparisons at the 0 to 30 cm (0 to 11.8 in) depth, results from conservation systems using no-till and organic systems diverged, with conservation systems relying on no-till producing no gains while organic systems produced a 29% increase in SOC. While the use of organic amendments generally increased SOC, the magnitude of the effect was more modest than suggested by current CMT weighting. In addition, our results suggested that quality of manure, which is not differentiated in the CMT, influences the magnitude of the effect and that addition of wet manure may decrease SOC. A comparison of rotation length showed cropping systems with rotations of 3 years or longer were better able to increase SOC than shorter rotations. These findings suggested that the CMT generally ranks practices appropriately and shows how meta-analysis could be used to adjust credits awarded for use of reduced or no-till practices or different fertility sources. Copyright © 2014 Soil and Water Conservation Society. All rights reserved.

Chaney N.W.,Princeton University | Wood E.F.,Princeton University | McBratney A.B.,University of Sydney | Hempel J.W.,National Soil Survey Center | And 3 more authors.
Geoderma | Year: 2016

A new complete map of soil series probabilities has been produced for the contiguous United States at a 30 m spatial resolution. This innovative database, named POLARIS, is constructed using available high-resolution geospatial environmental data and a state-of-the-art machine learning algorithm (DSMART-HPC) to remap the Soil Survey Geographic (SSURGO) database. This 9 billion grid cell database is possible using available high performance computing resources. POLARIS provides a spatially continuous, internally consistent, quantitative prediction of soil series. It offers potential solutions to the primary weaknesses in SSURGO: 1) unmapped areas are gap-filled using survey data from the surrounding regions, 2) the artificial discontinuities at political boundaries are removed, and 3) the use of high resolution environmental covariate data leads to a spatial disaggregation of the coarse polygons. The geospatial environmental covariates that have the largest role in assembling POLARIS over the contiguous United States (CONUS) are fine-scale (30 m) elevation data and coarse-scale (~. 2 km) estimates of the geographic distribution of uranium, thorium, and potassium. A preliminary validation of POLARIS using the NRCS National Soil Information System (NASIS) database shows variable performance over CONUS. In general, the best performance is obtained at grid cells where DSMART-HPC is most able to reduce the chance of misclassification. The important role of environmental covariates in limiting prediction uncertainty suggests including additional covariates is pivotal to improving POLARIS' accuracy. This database has the potential to improve the modeling of biogeochemical, water, and energy cycles in environmental models; enhance availability of data for precision agriculture; and assist hydrologic monitoring and forecasting to ensure food and water security. © 2016 Elsevier B.V.

Markewich H.W.,U.S. Geological Survey | Wysocki D.A.,National Soil Survey Center | Pavich M.J.,U.S. Geological Survey | Rutledge E.M.,University of Arkansas
Bulletin of the Geological Society of America | Year: 2011

For more than a century, the Sangamon paleosol (the Sangamon) has been an integral part of geologic and pedologic investigations in the central United States, including the Upper Mississippi and Lower Missouri River Valleys. Compositional, pedologic, micromorphologic, stratigraphic, and age data indicate that the prominent reddish paleosol developed in silt-rich deposits of the Lower Mississippi Valley, from southernmost Illinois to northwestern Mississippi, represents multiple periods of soil formation, and is wholly or in part time equivalent to the Sangamon of the central United States. Thermoluminescence data, for localities where the Sangamon developed in loess, indicate that the primary period of loess deposition was from 190 to 130 ka (oxygen isotope stage, OIS6), that loess deposition continued intermittently from 130 to 74 ka (OIS5), and that deposition was wholly or in part coeval with Loveland loess deposition in the central United States. Beryllium-10, chemical, and pedologic data indicate that in the Lower Mississippi Valley: (1) the Sangamon represents a minimum time period of 60-80 k.y.; (2) there were at least two periods of soil formation, ca. 130-90 ka and 74-58 ka (OIS4); and (3) rates of weathering and pedogenesis equaled or exceeded the net loess-accumulation rate until at least 46 ka (OIS3) and resulted in development of a paleosol in the overlying basal Roxana Silt. Along a N-S transect from southern Illinois to western Mississippi, Sangamon macroscopic characteristics as well asthe micro-morphology, chemistry, and mineralogy, suggest a regional paleoclimate during periods of soil formation that: (1) was warm to hot, with a wider range in temperature, precipitation, and evapotranspiration than present; (2) had seasonal to decadal or longer periods of drought; and (3) had down-valley (southward) trends of increasing temperature and precipitation and decreasing seasonality and variation in annualto decadal precipitation. © 2011 Geological Society of America.

He Y.,North Dakota State University | DeSutter T.,North Dakota State University | Prunty L.,North Dakota State University | Hopkins D.,North Dakota State University | And 2 more authors.
Geoderma | Year: 2012

Conducting a 1:5 soil:water extract to measure electrical conductivity (EC) is an approach to assess salinity and has been the preferred method in Australia, but not commonly used in the United States where the 1:1 soil to water ratio is preferred. The objectives of this research were to 1) compare methods of agitation for determining EC1:5 and 2) to determine optimal times for equilibration for each method across a range of salinity levels determined from EC values achieved from saturated paste extracts (ECe). Soils evaluated for this study were from north central North Dakota (USA) and had ECe values ranging from 0.96 to 21.2dSm-1. For each method, nine agitation times were used, up to 48h. The three agitation methods were shaking plus centrifuging, shaking, and stirring. Agitation methods resulted in significantly different EC1:5 values for 13 out of 20 soils across the three agitation methods, and shaking plus centrifuging was significantly different (p=0.05) from stirring for all soils. In addition, 75% of the shaking plus centrifuging soils were significantly different from shaking. Based on these results, methods were analyzed separately for optimal equilibration times. The agitation times required for the three methods to reach 95 and 98% of equilibration were a function of the level of soil salinity. For soils with ECe values below 4dSm-1, over 24h was needed to obtain both 95 and 98% of equilibration for the three methods. However, less than 3 and 8h were needed to reach 95 and 98% equilibration, respectively, across methods for soils having ECe values greater than 4dSm-1. These results indicate that investigating the effect of agitation methods and times is important to help reduce variations across EC1:5 measurements. © 2012 Elsevier B.V.

He Y.,North Dakota State University | De Sutter T.,North Dakota State University | Hopkins D.,North Dakota State University | Jia X.,North Dakota State University | Wysocki D.A.,National Soil Survey Center
Canadian Journal of Soil Science | Year: 2013

Many laboratories appraise soil salinity from measurement of electrical conductivity of 1:5 soil to water extract (EC1:5) due to its simplicity. However, the influence of salinity on plant growth is mainly based on electrical conductivity of saturated paste extract (ECe), so it is necessary to convert EC1:5 to ECe in order to assess plant response. The objectives of this research were to develop models relating EC1:5 and ECe under four different 1:5 equilibration methods: (1) shaking, (2) shaking plus centrifuging, (3) stirring, and (4) a United States Department of Agriculture-Natural Resources Conservation Service (2011) equilibration method. One hundred soil samples, which were all derived from glacial parent materials in North Dakota, USA, were selected for this study. Non-transformed, nontransformed separated, ln-transformed, and exponential models were developed between EC1:5 and ECe. Nontransformed, simple linear regression models had obvious segments for all equilibration methods and the residual distributions varied. Therefore, data were separated at EC of 4 dS m-1 and a quadratic curvilinear model was developed for relating EC1:5 and ECe (r2 values ranged from 0.87 to 0.93) when ECe values were less than 4 dS m-1. Although the linear model was significant (PB0.05), soils having ECe greater than 4 dS m-1 had r2 values less than 0.61. Across all soils, the ln-transformed model had r2 values greater than 0.85, which was greater than the non-transformed or exponential models. By comparison of r2, RMSE, and relative percentage difference, the separated curvilinear model that was established when salinity is less than 4 dS m-1, and ln-transformed models were superior at predicting ECe from EC1:5 data compared to non-transformed and exponential models. These results indicate that across all equilibration methods ECe can reliably be predicted from EC1:5 data for soils from this region.

Markewich H.W.,U.S. Geological Survey | Litwin R.J.,U.S. Geological Survey | Wysocki D.A.,National Soil Survey Center | Pavich M.J.,U.S. Geological Survey
Aeolian Research | Year: 2015

Late-middle and late Pleistocene, and Holocene, inland aeolian sand and loess blanket >90,000km2 of the unglaciated eastern United States of America (USA). Deposits are most extensive in the Lower Mississippi Valley (LMV) and Atlantic Coastal Plain (ACP), areas presently lacking significant aeolian activity. They provide evidence of paleoclimate intervals when wind erosion and deposition were dominant land-altering processes. This study synthesizes available data for aeolian sand deposits in the LMV, the Eastern Gulf Coastal Plain (EGCP) and the ACP, and loess deposits in the Middle Atlantic Coastal Plain (MACP). Data indicate: (a) the most recent major aeolian activity occurred in response to and coincident with growth and decay of the Laurentide Ice Sheet (LIS); (b) by ~40ka, aeolian processes greatly influenced landscape evolution in all three regions; (c) aeolian activity peaked in OIS2; (d) OIS3 and OIS2 aeolian records are in regional agreement with paleoecological records; and (e) limited aeolian activity occurred in the Holocene (EGCP and ACP). Paleoclimate and atmospheric-circulation models (PCMs/ACMs) for the last glacial maximum (LGM) show westerly winter winds for the unglaciated eastern USA, but do not resolve documented W and SW winds in the SEACP and WNW and N winds in the MACP. The minimum areal extent of aeolian deposits in the EGCP and ACP is ~10,000km2. For the LMV, it is >80,000km2. Based on these estimates, published PCMs/ACMs likely underrepresent the areal extent of LGM aeolian activity, as well as the extent and complexity of climatic changes during this interval. © 2015 .

Elrashidi M.A.,National Soil Survey Center | Seybold C.A.,National Soil Survey Center | Delgado J.,National Soil Survey Center
Soil Science | Year: 2013

Declining surface water quality from agricultural nonpoint sources is of great concern across the Great Plains. Trends in the earth climate create abrupt changes in domestic weather (i.e., precipitation) that can alter the impact of the nonpoint sources on water quality. A 2-year (dry 2009 and wet 2010) study was conducted to assess the impact of soil C, N, and S losses by runoff on water quality of Salt Creek in the Roca watershed, Nebraska. Average dissolved nutrient concentrations in runoff were 95.4 and 94.9% of the total for the dry and wet years, respectively. The remaining nutrients in runoff were associated with sediment. Nutrient concentrations during the dry year were generally greater than those during the wet year. Average concentrations for 2009 were 63.2, 1.87, and 53.5 mg/L for C, N, and S, respectively, whereas concentrations for 2010 were 54.0, 3.0, and 16.6 mg/L, respectively. Total soil nutrient losses were greater for the wet year than those for the dry year. The dry year nutrient losses were 607, 19,978, and 441,569 metric tons for C, N, and S, respectively, whereas losses for the wet year were 1,997, 138,380, and 608,172 metric tons, respectively. These losses could be considered as the annual nutrient loadings for Salt Creek. Concentrations of C, N, and S measured in Salt Creek during the study were not expected to have any adverse effect on human/animal health or aquatic life. We concluded that greater precipitation during the wet year increased the impact on water quality and soil fertility in the Roca watershed. Copyright © 2014 by Lippincott Williams & Wilkins.

Brungard C.W.,Utah State University | Boettinger J.L.,Utah State University | Duniway M.C.,U.S. Geological Survey | Wills S.A.,National Soil Survey Center | Edwards T.C.,U.S. Geological Survey
Geoderma | Year: 2015

Mapping the spatial distribution of soil taxonomic classes is important for informing soil use and management decisions. Digital soil mapping (DSM) can quantitatively predict the spatial distribution of soil taxonomic classes. Key components of DSM are the method and the set of environmental covariates used to predict soil classes. Machine learning is a general term for a broad set of statistical modeling techniques. Many different machine learning models have been applied in the literature and there are different approaches for selecting covariates for DSM. However, there is little guidance as to which, if any, machine learning model and covariate set might be optimal for predicting soil classes across different landscapes. Our objective was to compare multiple machine learning models and covariate sets for predicting soil taxonomic classes at three geographically distinct areas in the semi-arid western United States of America (southern New Mexico, southwestern Utah, and northeastern Wyoming). All three areas were the focus of digital soil mapping studies. Sampling sites at each study area were selected using conditioned Latin hypercube sampling (cLHS). We compared models that had been used in other DSM studies, including clustering algorithms, discriminant analysis, multinomial logistic regression, neural networks, tree based methods, and support vector machine classifiers. Tested machine learning models were divided into three groups based on model complexity: simple, moderate, and complex. We also compared environmental covariates derived from digital elevation models and Landsat imagery that were divided into three different sets: 1) covariates selected a priori by soil scientists familiar with each area and used as input into cLHS, 2) the covariates in set 1 plus 113 additional covariates, and 3) covariates selected using recursive feature elimination.Overall, complex models were consistently more accurate than simple or moderately complex models. Random forests (RF) using covariates selected via recursive feature elimination was consistently the most accurate, or was among the most accurate, classifiers between study areas and between covariate sets within each study area. We recommend that for soil taxonomic class prediction, complex models and covariates selected by recursive feature elimination be used.Overall classification accuracy in each study area was largely dependent upon the number of soil taxonomic classes and the frequency distribution of pedon observations between taxonomic classes. Individual subgroup class accuracy was generally dependent upon the number of soil pedon observations in each taxonomic class. The number of soil classes is related to the inherent variability of a given area. The imbalance of soil pedon observations between classes is likely related to cLHS. Imbalanced frequency distributions of soil pedon observations between classes must be addressed to improve model accuracy. Solutions include increasing the number of soil pedon observations in classes with few observations or decreasing the number of classes. Spatial predictions using the most accurate models generally agree with expected soil-landscape relationships. Spatial prediction uncertainty was lowest in areas of relatively low relief for each study area. © 2014 Elsevier B.V.

Elrashidi M.A.,National Soil Survey Center | Seybold C.A.,National Soil Survey Center | Wysocki D.A.,National Soil Survey Center
Water, Air, and Soil Pollution | Year: 2015

Deterioration of natural water resources due to runoff from agricultural land is a major problem in the US Great Plains. Changes in earth climate can create heavy storms and alter precipitation patterns which would affect the element concentrations in runoff. A 2-year study (dry and wet years) was conducted to assess the impact of annual precipitation on element concentrations in runoff from soils and element loadings to Salt Creek in the Roca watershed, NE. Both dissolved and sediment-associated forms of five elements (Al, Fe, Mn, Cu, and Zn) were determined in runoff. The amount of dissolved element in runoff during the wet year was greater than the dry year. Except for Zn, the total amount of element associated with sediment was greater than that found in dissolved form. The Mehlich3 extraction was applied to determine the reactive fraction of element in sediment. A small fraction of element associated with sediment was in reactive form, ranging from 1 to 33 % of the total element content. The sum of both the reactive fraction of element in sediment and amount of element dissolved in water were used to calculate the total bioactive element concentration (BEC) in runoff. During the dry year, the total BEC in runoff was 424, 349, 387, 5.2, and 26.8 μg/L for Al, Fe, Mn, Cu, and Zn, respectively. The corresponding total BEC during the wet year was 622, 479, 114, 3.7, and 19.8 μg/L for Al, Fe, Mn, Cu, and Zn, respectively. Further, the bioactive element loading (BEL) into Salt Creek was greater during the wet year than the dry year. Aluminum, Fe, and Mn contributed to the greatest BEL into the surface water body while Zn and Cu had the least contribution. We concluded that greater precipitation during the wet year would increase the negative impact of runoff from soils and BEL to surface water systems in the US Great Plains. © 2015 Springer International Publishing Switzerland (outside the USA).

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