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Lincoln, NE, United States

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 . Source

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. Source

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. Source

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. Source

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. Source

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