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54 results for “bulk soil”
Georgia Salt Marsh: Soil Organic Carbon, Nitrogen, Bulk Density, Moisture, and Texture
As part of project predicting soil carbon at depth from that found at the surface using remote sensing, 28 soil cores were taken from six salt marshes along the Georgia coastline. Cores were taken as deep as possible (25 – 165 cm) and sectioned into 5 cm depths. Soils were analyzed for organic carbon (SOC), total nitrogen (N), bulk density (BD), and particle size (by horizon). Stable carbon isotopes were obtained in three marshes on Sapelo Island; a subset was also analyzed for radiocarbon.
Salt River Wetlands denitrification rate, dissimilatory nitrate reduction to ammonium rate, dissolved organic carbon concentration in June 2016 as well as soil porosity and bulk density
Raw and derived data used to calculate denitrification and dissimilatory nitrate to ammonium (DNRA) from push-pull experiments with added isotopically labelled nitrate. Experiments were conducted in 2016 in the Salt River Accidental Wetlands in three different patch types: Unvegetated, dominated by Ludwigia peploides, and dominated by Typha species (T. domingensis and T. latifolia). Data include start and end of incubation concentration of nitrate, ammonium, atom percent 15N in ammonium, dissolved organic carbon, excess mass 29-N2, and excess mass 30-N2. Soil data was collected from the same patch types including soil moisture, porosity, and bulk density.
Water, Soil, Floc, Plant Total Phosphorus, Total Carbon, and Bulk Density data (FCE) from Everglades Protection Area (EPA) from 2004 to 2016
These data are a compillation of data from multiple sources including South Florida Water Management District (SFWMD) DBhydro web database, United States Environment Protection Agency Regional, Environmental Monitoring and Assessment (REMAP), Everglades Soil Mapping (ESM), and Florida Coastal Everglades Long Term Ecological Research (FCE-LTER). The matrix of these data were compiled for soil, surface water, floc, and plants where the nutrients are counted for total phosphorus, total carbon, and bulk density. When downloading the data from DBhydro, only regularly collected samples (SAMP) were included these data. As per DBhydro metadata, the regular samples were collected monthly by grab method throughout the year from 2004 to 2016 for SFWMD monitoring stations across the EPA. All flagged and field quality controlled values were excluded to avoid the duplication of data. In order to maintain the quality assurance/ quality control (QA/QC) the method detection limit for water TP was fixed at 2 µg/L by the SFWMD. This data set were used to assess the decadal trend of TP concentration in surface water and soil in EPA. Available data from 2004 to 2014 was collected for soils and from 2004 to 2016 for water to understand a decade of trends. Both Geographic Information System (GIS) and statistical data analysis were applied to determine changes in water quality and soil chemistry. These data are the basis for Shishir Sarker's Master's thesis.
iSDAsoil: soil fine-earth bulk density for Africa predicted at 30 m resolution at 0-20 and 20-50 cm depths
<p>iSDAsoil dataset soil fine-earth bulk density in 10×kg/m3 predicted at 30 m resolution for 0–20 and 20–50 cm depth intervals. Data has been projected in WGS84 coordinate system and compiled as <a href="https://gdal.org/drivers/raster/cog.html">COG</a>. Predictions have been generated using multi-scale Ensemble Machine Learning with 250 m (MODIS, PROBA-V, climatic variables and similar) and 30 m (DTM derivatives, Landsat, Sentinel-2 and similar) resolution covariates. For model training we use a pan-African compilations of soil samples and profiles (<a href="https://www.isda-africa.com/national-soil-services/">iSDA points</a>, <a href="https://www.isric.org/projects/africa-soil-profiles-database-afsp">AfSPDB</a>, <a href="https://landpotential.org/data-portal/">LandPKS</a>, and other national and regional soil datasets). Cite as:</p> <p>Hengl, T., Miller, M.A.E., Križan, J. <em>et al.</em> African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning. <em>Sci Rep</em> <strong>11, </strong>6130 (2021). <a href="https://doi.org/10.1038/s41598-021-85639-y">https://doi.org/10.1038/s41598-021-85639-y</a></p> <p>To open the maps in QGIS and/or directly compute with them, please use the <a href="https://gitlab.com/openlandmap/africa-soil-and-agronomy-data-cube"><strong>Cloud-Optimized GeoTIFF version</strong></a>.</p> <p>Layer description:</p> <ul> <li>sol_db_od_m_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil bulk density mean value,</li> <li>sol_db_od_md_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil bulk density model (prediction) errors,</li> </ul> <p>Model errors were derived using bootstrapping: md is derived as standard deviation of individual learners from 5-fold cross-validation (using spatial blocking). The model 5-fold cross-validation (<a href="https://mlr.mlr-org.com/reference/makeStackedLearner.html">mlr::makeStackedLearner</a>) for this variable indicates:</p> <pre><code>Variable: db_od R-square: 0.819 Fitted values sd: 0.269 RMSE: 0.126 Random forest model: Call: stats::lm(formula = f, data = d) Residuals: Min 1Q Median 3Q Max -1.06778 -0.06450 0.00215 0.06585 0.90016 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.05538 0.04860 -1.140 0.25451 regr.ranger 0.86305 0.01577 54.733 < 2e-16 *** regr.xgboost 0.15383 0.01651 9.315 < 2e-16 *** regr.cubist 0.02039 0.01113 1.832 0.06695 . regr.nnet 0.03465 0.03710 0.934 0.35036 regr.cvglmnet -0.03021 0.01032 -2.927 0.00343 ** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.1263 on 13565 degrees of freedom Multiple R-squared: 0.8194, Adjusted R-squared: 0.8193 F-statistic: 1.231e+04 on 5 and 13565 DF, p-value: < 2.2e-16</code></pre> <p>To back-transform values (y) to kg/m-cubic use:</p> <pre><code>kg/m3 = y * 10</code></pre> <p>To submit an issue or request support please visit <a href="https://isda-africa.com/isdasoil"><strong>https://isda-africa.com/isdasoil</strong></a></p>
Soil bulk density [10x kg/m3] for continental Europe at 30 m spatial resolution for period 2000-2020: Open Soil Data Cube for Europe
<p>Predictions are based on the 3D Ensemble Machine Learning framework, as implemented in the R environment for statistical computing (Hengl & MacMillan, 2019; Hengl, et al., 2021). For each pixel we provide prediction errors as 1 standard deviation in either log or the original variable scale.</p> <p>The short description of currently available soil properties: db_od = bulk density over dry [kg/m3 ⨉ 10];</p> <p>Soil properties were predicted at fixed depths:</p> <p> Surface soil = s0..0cm,<br> Subsoil 1 = s30..30cm,<br> Subsoil 2 = s60..60cm,<br> Subsoil 3 = s100..100cm.</p> <p>To produce estimates for depth intervals e.g. 0–30 cm, 0–100 cm best use the trapezoidal rule formula.</p> <p>Periods: 2000 (2000–2003), 2004 (2004–2007), 2008 (2008–2011), 2012 (2012–2015), 2016 (2016–2019), 2020;</p> <p>The bulk density maps are also provided in 10 kg / m-cubic to reduce total data size; to convert values to kg / m-cubic multiply by 10 e.g. 120 = 1200 kg / m-cubic = 1.2 t / m-cubic.</p>
Soil bulk density (fine earth) 10 x kg / m-cubic at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution
<p>Soil bulk density (fine earth) 10 x kg / m<sup>3</sup> at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution. Processing steps are described in detail <strong><a href="https://gitlab.com/openlandmap/global-layers/tree/master/soil">here</a></strong>. Antarctica is not included.</p> <p>To access and visualize maps use: <a href="http://www.openlandmap.org/">OpenLandMap.org</a></p> <p>If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a> </li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>sol = theme: soil,</li> <li>bulkdens.fineearth = variable: soil bulk density,</li> <li>usda.4a1h = determination method: laboratory method code,</li> <li>m = mean value,</li> <li>250m = spatial resolution / block support: 250 m,</li> <li>b10..10cm = vertical reference: 10 cm depth below surface,</li> <li>1950..2017 = time reference: period 1950-2017,</li> <li>v0.2 = version number: 0.2,</li> </ul>
Chemical composition, soil water content and 16S rRNA and ITS gene copy numbers of soil aggregates and bulk soil samples
<p>This repository contains all data to reproduce the analyses presented in "Distinct microbial communities are linked to organic matter properties in millimetre-sized soil aggregates", Simon et al 2024, <em>The ISME Journal </em>(DOI: 10.1093/ismejo/wrae156).</p>
Root phytohormone levels of mangrove seedlings grown in soils of low and high bulk density
<p>These data contain information on root traits and concentrations of multiple hormones in root tissue of two mangrove species grown under low and high soil bulk density conditions. Inhibitors were used to assess the role of the phytohormone ethylene.</p> <h2>Description of the data and file structure</h2> <p>The root trait file contains total root length (cm), mean root length (cm), root dry weight (g), and root number data for two mangrove species (Avicennia marina, Rhizophora stylosa) grown under two different soil bulk densities: 0.2 and 1.0 g cm^-3. Treatment refers to the application of ethylene inhibitors (CoCl2: cobalt chloride, AIB: aminoisobutyric acid, control).</p> <p>The phytohormone file contains the root tissue concentrations (all in ng g^-1) for multiple hormones (ABA: abscisic acid, ACC: 1-aminocyclopropane-1-carboxylic acid, GAx: gibberellins, IAA: indole-3-acetic acid, iP: isopentenyl adenine, JA: jasmonic acid, SA: salicylic acid, tZ: cytokinin <em>trans</em>-zeatin) of two mangrove species (Avicennia marina, Rhizophora stylosa) grown under two different soil bulk densities: 0.2 and 1.0 g cm^-3. Treatment refers to the application of ethylene inhibitors (CoCl2: cobalt chloride, AIB: aminoisobutyric acid, control).</p> <p> </p> <p> </p>
spatial soil bulk density at OAL-UK
<p>raster containing information on the spatial distribution of soil bulk density at OAL-UK. The raster was generated following a digital soil mapping approach implemented through the random forest algorithm. More detail on the map creation can be found here: <a href="https://doi.org/10.1016/j.ecoleng.2017.04.066">https://doi.org/10.1016/j.ecoleng.2017.04.066</a></p>
Global Soil Bulk Density DataBase (GSBDDB)
<p>We complied the Global Soil Bulk Density DataBase (GSBDDB). This database inlcudes 162,470 soil samples (35,805 sampling sites) with bulk density (BD) and soil organic cabron (SOC) for the globle. Among them, 96,705 soil samples have soil particle size fractions (i.e. clay, silt and sand) as well. In addtion, this dataset also records spatial coordinates, elevation, mean annual precipitation, mean annual temperature, potential evapotranspiration and aridity index.</p> <p>This dataset is asscoated to the "Towards improved pedotransfer functions for estimating soil bulk density using the global soil bulk density database (DSBDDB)" by Chen et al. (in preparation).</p> <p>Manuscript citation: Chen, S., Dai, L, Shuai Q., Xue, J., Zhang, X., Xiao, Y., et al. Towards improved pedotransfer functions for estimating soil bulk density using the global soil bulk density database (DSBDDB). In preparation.</p> <p>When using the data, please cite repositories as well as the original manuscript.</p> <p>For any questions on the data, please contact Dr. Songchao Chen (chensongchao@zju.edu.cn).</p>
NEON Biorepository Soil Microbe Collection (Bulk Subsamples) (repackaging of occurrences published by the NEON Biorepository Data Portal)
This collection contains samples collected during periodic soil sampling and frozen at ultra-low temperatures in order to provide material for microbial sequencing or other microbial analyses (NEON sample classes: sls_soilCoreCollection_in.geneticArchiveSample1ID, sls_soilCoreCollection_in.geneticArchiveSample2ID, sls_soilCoreCollection_in.geneticArchiveSample3ID, sls_soilCoreCollection_in.geneticArchiveSample4ID, sls_soilCoreCollection_in.geneticArchiveSample5ID,sls_metagenomicsPooling_in.compositeSampleID). Archive samples are collected during each soil sampling bout and are promptly frozen. Three unique locations are sampled per plot, with ten plots per site. Bouts occur three times per year in order to capture the prevailing conditions at the site during different seasons, except in Alaska where only 1 bout is possible. Soil sampling is conducted to a maximum depth of 30 ± 1 cm, and when organic (O) and mineral (M) horizons are present within a single profile, they are separated prior to analysis and archiving. However, other sub-horizons are not separated. During the majority of bouts, only the top horizon (O if present, else M) is collected and archived. Soils are homogenized and non-soil material is removed by hand in the field, then subsamples are immediately frozen on dry ice. They are maintained in ultra-low temperature freezers until shipment to the Biorepository. See links below for NEON data products that provide physical, chemical, and biological measurements for these same soils (soil pH and moisture are always measured; chemical properties as well as microbial community composition and biomass are determined only for a subset of collection bouts). In addition, a more detailed characterization of the dominant soil types at each site, including taxonomy, texture, bulk density, and geochemical properties, occurred during the construction period of NEON through two projects. These data are available in NEON data products Soil physical and chemical
Belowground foodweb biomass and soil CN and bulk density from moist acidic tundra nutrient addition plots (since 1989, 2006) sampled July 2011.
Biomass of belowground community groups (bacteria, fungi, protozoa, nematodes, rotifers, tardigrades) determined for organic and mineral soils in moist acidic tundra. Soil carbon and nitrogen content, bulk density, and depth are included.
Belowground foodweb biomass and soil CN and bulk density from moist acidic tundra nutrient addition plots (since 2006) sampled August 2012.
Biomass of soil rotifers, tardigrades, enchytraeids, protozoa and nematode groups from organic and mineral soils in moist acidic tundra nutrient addition plots (since 2006) sampled August 2012.
Soils Bulk Density: Biodiversity II: Effects of Plant Biodiversity on Population and Ecosystem
Biodiversity II (E120) is designed to determine how the number of plant species affects the dynamics of ecological processes at the population, community, and ecosystem levels. By experimentally manipulating the number of species and the kinds of species, the amount of plant growth and the change from year to year, that result can be examined. Plots are large (9m x 9m actively maintained) and well-replicated, allowing responses of plant pathogens, insect herbivores, seed predators, soil parameters, invasive plant species and other variables to also be studied. Plots were seeded in May 1994 to have 1, 2, 4, 8, or 16 species, with roughly 30 replicates of each diversity level. The species composition of each plot was chosen by random draw from a pool of 18 grassland perennials that included four warm-season (C4) grasses, four cool-season (C3) grasses, four legumes, four non-legume forbs, and two woody species. All species occur in monoculture allowing comparison of responses of each species in monoculture to combinations of these same species. The experiment was established in 1994 by the lead investigators David Tilman, Peter Reich, Johannes Knops, and David Wedin. Experiment 120 is similar to Experiment 123, but it uses larger plots to provide a large capacity for long-term subexperiments.
Measurements of Soil Bulk Density at the Coweeta Terrestrial Gradient Sites
This project is part of a larger examination of site productivity along an elevational gradient. Forest floor weights, %C, and %N were measured at each of the five terrestrial gradient plots located along an elevational gradient at Coweeta Hydrologic Lab, Otto, NC.
Soil bulk density measurements from 9 Hillslope Project sites in Macon County, North Carolina, within the Upper Little Tennessee River Basin
Soil bulk density samples were collected for the soil chemistry studies at the hillslope plots in Macon County, North Carolina. There were 9 hillslope sites representing a gradient of development, including forested, valley agriculture, and mountain housing developments. Sites were sampled 9/2013-11/2013. Samples were obtained from 4 plots in each site at 3 soil depths (0-10 cm, 10-30 cm, and 30+ cm).
Bacterial community composition of bulk soil from date palm (Phoenix dactylifera) farm depend on irrigation water salinity
<p>Non-saline and saline ground water irrigation is extensively used in the arid regions of United Arab Emirates (UAE) for date palm (<em>Phoenix</em> <em>dactylifera</em>) cultivation without knowing its effect on bulk soil bacterial communities. Bulk soil acts as a supply base for microbes and nutrients that are accessed by date palm roots. We collected soil samples from date farms across UAE and performed V3-V4 16s rRNA metabarcoding analysis to understand how bulk soil bacterial diversity and communities respond to irrigation water sources (non-saline and saline groundwater irrigation). There was no significant variation in bulk bacterial diversity (Shannon diversity, richness as well as evenness). But bulk bacterial communities differed between irrigation water sources and irrigation water electrical conductivity was the significant factor that explained a part of community variation. Out of total 5089 OTUs, saline bulk soil harbored only 21.3% of total OTUs compared to 31.5% OTUs in non-saline bulk soil, while 47.15% OTUs shared between both types of irrigation. Proteobacteria abundance was higher in saline bulk soil, while Actinobacteriota abundance was enhanced in non-saline bulk soil. Similar selection was observed at genus level, wherein saline bulk soil showed increase in abundance of <em>Subgroup_10, Nitrospira </em>and<em> Mycobacterium</em>, whereas <em>Microvirga, Ammoniphilus, Nitrospira</em> and <em>Lysinibacillus </em>were elevated in non-saline bulk soil. Saline (<em>Novibacillus</em> and <em>Bauldea</em>) and non-saline bulk soil (<em>Microvirga</em>, <em>Marmoricola</em>, <em>Domibacillus</em>, <em>Oceanobacillus</em>, <em>Bhargavaea</em> and <em>Solirubrobacter</em>) showed significant selection of indicator taxa (P < 0.05). This indicate that bacterial communities colonizing bulk soil differ depending on irrigation water source and it is affected by irrigation water EC.</p>
Figure 3 in Organic farming and moderate tillage change the dominance and spatial structure of soil Collembola communities but have little effects on bulk abundance and species richness
Figure 3. Abundance, number of species and Berger-Parker index in samples in different management types and fields. Colors show fields. Boxplots show data distribution (n = 81 per field), horizontal lines represent the medians.
Saline groundwater irrigation affects the date palm bulk soil associated fungal guild community and enhances the pathotroph abundance
<p><strong><em> Exploring the Influence of Saline Groundwater Irrigation on Soil Fungal Biodiversity in Date Palm (Phoenix dactylifera) Bulk Soil</em></strong></p>
Arctic LTER 1991: Percent moisture, bulk density, percent loss on ignition and percent organic carbon were measured for peat collected from soils in the Imnavait Creek watershed.
Percent moisture, bulk density, percent loss on ignition and percent organic carbon were measured for peat collected from soils in the Imnavait Creek watershed.
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