Propriedades químicas de uma Terra Roxa Estruturada influenciadas pela cobertura vegetal de inverno e pela adubação orgânica e mineral

O presente trabalho teve por objetivo avaliar a influência da cobertura vegetal de inverno, constituída de uma associação de aveia preta (Avena strigosa Schreb) com nabo forrageiro (Raphanus sativus L.), da adubação orgânica com esterco de aves e da adubação mineral sobre propriedades químicas de uma Terra Roxa Estruturada do estado de Santa Catarina. As análises foram realizadas em amostras de solo coletadas em agosto de 1994 e janeiro de 1995, nas profundidades de 0-10, 10-20 e 20-30 cm, em um experimento iniciado em 1990. Observou-se que a cobertura vegetal de inverno mostrou-se eficiente na manutenção de nutrientes, especialmente o potássio, e dos níveis de carbono orgânico, dentro dos limites da camada arável. O uso de adubo orgânico proporcionou acúmulo de nutrientes no solo, enquanto os adubos organomineral e mineral mostraram tendência de redução, principalmente dos níveis de potássio do solo

Multivariate Geographic Clustering Using a Beowulf-Style Parallel Computer

The authors present an application of multivariate non-hierarchical statistical clustering to geographic environmental data from the 48 conterminous United States in order to produce maps of regions of ecological similarity called ecoregions. Nine input variables thought to aflect the growth of vegetation are clustered at a resolution of one square kilometer. These data represent over 7.8 million map cells in a g-dimensional data space. For the analysis, the authors built a 126-node heterogeneous cluster--aptly named the Stone SouperComputer--out of surplus PCs. The authors developed a parallel iterative statistical clustering algorithm which uses the MPI message pawing routines, employs a classical master/slave single program multiple data (SPMD) organization, performs dynamic load balancing, and provides fault tolerance. In addition to being run on the Stone Souper-Computer, the parallel algorithm was tested on other parallel platforms without code modification. Finally, the results of the geographic clustering are presented

Characterization of sediments in the Clinch River, Tennessee, using remote sensing and multi-dimensional GIS techniques

Remotely-sensed hydro-acoustic data were used as input to spatial extrapolation tools in a GIS to develop two- and three-dimensional models of sediment densities in the Clinch River arm of Watts Bar Reservoir, Tennessee. This work delineated sediment deposition zones to streamline sediment sampling and to provide a tool for estimating sediment volumes and extrapolating contaminant concentrations throughout the system. The Clinch River arm of Watts Bar Reservoir has been accumulating sediment-bound contaminants from three Department of Energy (DOE) facilities on the Oak Ridge Reservation, Tennessee. Public concern regarding human and ecological health resulted in Watts Bar Reservoir being placed on the National Priorities List for SUPERFUND. As a result, DOE initiated and is funding the Clinch River Environmental Restoration Program (CR-ERP) to perform a remedial investigation to determine the nature and extent of sediment contamination in the Watts Bar Reservoir and the Clinch River and to quantify any human or ecological health risks. The first step in characterizing Clinch River sediments was to determine the locations of deposition zones. It was also important to know the sediment type distribution within deposition zones because most sediment-bound contaminants are preferentially associated to fine particles. A dual-frequency hydro-acoustic survey was performed to determine: (1) depth to the sediment water interface, (2) depth of the sediment layer, and (3) sediment characteristics (density) with depth (approximately 0.5-foot intervals). An array of geophysical instruments was used to meet the objectives of this investigation

Interpolation of bottom bathymetry and potential erosion in a large Tennessee reservoir system using GRASS

A regularized spline with tension was used to interpolate a bathymetric bottom surface for the Watts Bar reservoir just south of Oak Ridge, TN as part of an effort to predict the spatial distribution of radionuclide contaminants. Cesium 137 was released as a by-product of the production of fissionable materials during the mid-1950s. Cesium is strongly adsorbed onto clay and silt particles in the water column, and tends to settle to the bottom. An understanding of the shape and contours of the bottom is important for understanding and prediction of the location and extent of contaminated sediments. The results of the investigations are available on the World Wide Web (WWW) at URL: http://www.esd.ornl.gov/programs/CRERP/INDEX.HTM. The Waterways Experiment Station (WES) of the US Army Corps of Engineers conducted a hydro-acoustic study of the Clinch River arm of Watts Bar Reservoir to determine the distribution, thickness, and type of bottom sediments that had accumulated since completion of Watts Bar Dam in 1942. WES has developed a rapid geophysical technique to determine material characteristics of bottom and subbottom sediments. Acoustic impedance values determined from seismic reflection data are directly related to the density and material type of the subbottom sediments. The objective was to quantify with depth the density and type of bottom and subbottom sediments up to depths of 15 ft below the bottom surface along the Clinch River and Poplar Creek, TN

A GIS/Simulation Framework for Assessing Change in Water Yield over Large Spatial Scales

Recent legislation to,initiate vegetation management in the Central Sierra hydrologic region of California includes a focus on corresponding changes in water yield. This served as the impetus for developing a combined geographic information system (GIS) and simulation assessment framework. Using the existing vegetation density condition, together with proposed rules for thinning to reduce fire risk, a set of simulation model inputs were generated for examining the impact of the thinning scenario on water yield. The approach allows results to be expressed as the mean and standard deviation of change in water yield for each 1 km2 map cell that is treated. Values for groups of cells are aggregated for typical watershed units using area-weighted averaging. Wet, dry and average precipitation years were simulated over a large region. Where snow plays an important role in hydrologic processes, the simulated change in water yield was less than 0.5% of expected annual runoff for a typical water shed. Such small changes would be undetectable in the field using conventional stream flow analysis. These results suggest that use of water yield increases to help justify forest-thinning activities or offset their cost will be difficult

Quantitative Representativeness and Constituency of the Long-Term Agroecosystem Research Network and Analysis of Complementarity with Existing Ecological Networks

Studies conducted at sites across ecological research networks usually strive to scale their results to larger areas, trying to reach conclusions that are valid throughout larger enclosing regions. Network representativeness and constituency can show how well conditions at sampling locations represent conditions also found elsewhere and can be used to help scale-up results over larger regions. Multivariate statistical methods have been used to design networks and select sites that optimize regional representation, thereby maximizing the value of datasets and research. However, in networks created from already established sites, an immediate challenge is to understand how well existing sites represent the range of environments in the whole area of interest. We performed an analysis to show how well sites in the USDA Long-Term Agroecosystem Research (LTAR) Network represent all agricultural working lands within the conterminous United States (CONUS). Our analysis of 18 LTAR sites, based on 15 climatic and edaphic characteristics, produced maps of representativeness and constituency. Representativeness of the LTAR sites was quantified through an exhaustive pairwise Euclidean distance calculation in multivariate space, between the locations of experiments within each LTAR site and every 1 km cell across the CONUS. Network representativeness is from the perspective of all CONUS locations, but we also considered the perspective from each LTAR site. For every LTAR site, we identified the region that is best represented by that particular site—its constituency—as the set of 1 km grid locations best represented by the environmental drivers at that particular LTAR site. Representativeness shows how well the combination of characteristics at each CONUS location was represented by the LTAR sites’ environments, while constituency shows which LTAR site was the closest match for each location. LTAR representativeness was good across most of the CONUS. Representativeness for croplands was higher than for grazinglands, probably because croplands have more specific environmental criteria. Constituencies resemble ecoregions but have their environmental conditions “centered” on those at particular existing LTAR sites. Constituency of LTAR sites can be used to prioritize the locations of experimental research at or even within particular sites, or to identify the extents that can likely be included when generalizing knowledge across larger regions of the CONUS. Sites with a large constituency have generalist environments, while those with smaller constituency areas have more specialized environmental combinations. These “specialist” sites are the best representatives for smaller, more unusual areas. The potential of sharing complementary sites from the Long-Term Ecological Research (LTER) Network and the National Ecological Observatory Network (NEON) to boost representativeness was also explored. LTAR network representativeness would benefit from borrowing several NEON sites and the Sevilleta LTER site. Later network additions must include such specialist sites that are targeted to represent unique missing environments. While this analysis exhaustively considered principal environmental characteristics related to production on working lands, we did not consider the focal agronomic systems under study, or their socio-economic context. © 2023, This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.Open access articleThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]