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264 results for “soil organic carbon”
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.
Harmonized Soil Organic Carbon and Phosphorus Data for the Contiguous United States
Soil organic carbon (SOC) and soil phosphorus can strongly influence adjacent water quality by introducing nutrients into aquatic ecosystems and also altering the light environment of those ecosystems. However, national-scale data are uncommon, and even when available, they are usually not aggregated in a manner that is expeditiously merged with basin-level data. To facilitate national-scale analyses of soil data with co-located water quality data, we present aggregated SOC and soil phosphorus data for the Contiguous United States. Data are aggregated using the HydroBASINS basin shapefiles. HYBAS_ID is retained to enable merging with HydroBASINS parent datasets.
Correction Factors for Dissolved Organic Carbon Extracted from Soil in New England 2012-2013
Oxidizable dissolved organic carbon (DOC) is regularly measured in environmental samples using a colorimetric method with Mn(III)-pyrophosphate as the oxidizing agent. It is simpler to use and has a much higher throughput than the commonly used dichromate oxidation and combustion methods. Here, we demonstrate that the method often leads to an underestimation or overestimation of the concentration of common organic compounds in solutions. To our knowledge, no published study has taken this fact into account when analyzing DOC data. Hence, we compared Mn(III)-pyrophosphate-based results with measurements performed with a total organic carbon combustion analyzer for samples of organic and mineral soil horizons of two temperate deciduous forests (Harvard Forest, Hubbard Brook), of organic soil horizon of a primary growth hemlock stand (Harvard Forest), and of a peatland (Caribou Bog) located in New England, USA. The Mn(III)-pyrophosphate method consistently underestimated DOC concentration in soil extracts. We present correction factors for the different types of soil studied. By employing correction factors, we find the method can be an inexpensive, accurate, and high throughput tool to measure DOC in environmental samples.
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.
Map of Soil Organic Carbon: Region of Murcia (Spain)
This data package contain four soil organic carbon (SOC) maps resulted from the best data-model agreement of the analysis carried out in the frame of the Ph.D. Thesis ‘MODELING ORGANIC CARBON FOR QUANTIFICATION OF RESERVOIRS IN TERRESTRIAL ECOSYSTEMS AT THE NATIONAL LEVEL’ (Pilar Durante). Theses maps correspond to the estimates of SOC concentration (SOCc, g/kg) and SOC stocks (SOCs, tC/ha), and their associated spatially explicit uncertainties maps, for the Region of Murcia at 0-30 cm and 100 m spatial resolution. To achieve this, we evaluated four different digital soil mapping (DSM) approaches to estimate SOCc and SOCs for the Region of Murcia (11,313 km2), a topographic and climatic complex area in southern Iberian Peninsula, at three spatial resolutions (100m, 250m, 1000m). Using a local SOC database (255 soil profiles), we founded that a Quantile Regression Forest (QRF) approach had the best data-model agreement at 100 m spatial resolution, with the best balance of accuracy, external validation, and interpretability. The QRF model showed a mean SOCc of 12.18 g/kg with an overall uncertainty of 10.54 g/kg and an accuracy percentage of 79%; meanwhile the mean SOCs was 27,572 GgC with an uncertainty of 0.016 GgC. The analysis showed that using local environmental covariates and local soil information to predict SOC within this region resulted in a relative improvement between ~40% (for SOCc) and ~65% (for SOCs) when compared with SOC products derived from national and global databases. Our results provided evidence that large discrepancy exists between national and global estimates for reporting SOC at a local scale. Consequently, local-to-regional efforts are needed to better describe SOC spatial variability to reduce uncertainty and improve the assessment of soil resources.
Field Evidence of Carbon and Nitrogen Stabilization through Mineral Associated Organic Matter Formation in Coastal Wetland Soils from Apalachicola, Florida, collected in June, 2022.
This data set was used to observe the role of Mineral Associated Organic Matter Formation (MAOM) on biogeochemical soil properties in three coastal wetlands in Apalachicola, Florida. One wetland was restored using beneficial dredged sediment, increasing the soil's inorganic matter content. Soil samples were collected in June 2022 from this wetland and two nearby reference wetlands: one with high organic matter and the other with higher inorganic matter content. The samples were analyzed at the University of Central Florida for biogeochemical properties to determine which properties were most related to MAOM pools.
Soil organic carbon and nutrient dynamics in response to anaerobic digestate application to farm fields, Eastern Iowa, 2011-2023
This dataset documents a long-term, field-scale study of anaerobic digestate application on commercial croplands in eastern Iowa, USA. It includes detailed records of digestate composition, application rates, and timing, as well as soil test results collected over a 12-year period (2011–2023) from 14 agricultural fields. The dataset supports analysis of soil organic carbon (SOC), nutrient dynamics, and isotopic composition in response to digestate inputs. It contains 421 georeferenced soil samples, digestate nutrient profiles, field management histories, and spatial boundaries. The data were collected as part of a collaborative effort between researchers at Iowa State University and Sievers Family Farms to evaluate the agronomic and environmental implications of integrating anaerobic digestion into row crop and livestock systems.
NEON distributed initial soil characterization dataset (DP1.10047.001) modified for statistical analysis of organic carbon and extractable metals in Hall and Thompson (2021)
We compiled National Ecological Observatory Network (NEON) datasets related to the initial distributed soil sampling effort and subsetted them (removed samples with missing values for certain variables, and several samples with extreme values) for use in statistical analyses to describe relationships between soil organic carbon (SOC) and metals measured in several soil chemical extractions. The NEON provisional data products we used were DP1.10047.001 and DP1.10008.001, which were subsequently combined by NEON as a single data product DP1.10047.001, “Soil physical and chemical properties, distributed initial characterization”. These datasets were used for the analyses reported in a manuscript by Hall and Thompson (2021) in the Soil Science Society of America Journal.
Model estimates of runoff, dissolved organic carbon, soil temperature and moisture for Elson Lagoon watershed, Alaska, 1981-2020
This dataset contains model estimates of dissolved organic carbon (DOC) yield (mg C/m^2) and runoff (mm), for surface and subsurface flows, soil temperature (degree C), and soil moisture (% of soil volume) for grid cells spanning the Elson Lagoon watershed in northwest Alaska. Daily air temperature, precipitation, and wind speed data from Utqiagvik airport were used for meteorological forcings for the daily simulation by the Permafrost Water Balance Model (PWBM) from 1981 to 2020. The DOC and runoff data files are organized by grid cell and month. The soil temperature and soil moisture files are organized by grid cell and day of year (DOY), and contain values for the first eight model soil layers, with centers of the layers at 1, 3, 8, 13, 23, 33, 45, 55 cm depth. The estimates are most useful for analyses of the dynamics of the watershed’s surface and subsurface runoff and DOC yield. Leachate DOC concentrations can be obtained using the gridded runoff and yield values. A manuscript describing the data and associated analysis has been accepted for publication in Environmental Research Letters (Rawlins et al., 2021).
Data on ground ice, organic carbon and soluble cations in tundra permafrost and active-layer soils near Lac de Gras in the Slave Geological Province, N.W.T., Canada
<p>Data and computer code for producing figures for the manuscript:</p> <p>Subedi, R., Kokelj, S. V., and Gruber, S.: Ground ice, organic carbon and soluble cations <br> in tundra permafrost soils and sediments near a Laurentide ice divide in the Slave <br> Geological Province, N.W.T., Canada. The Cryosphere, accepted for publication in October 2020. </p> <p>Discussion paper and final version: https://doi.org/10.5194/tc-2020-33</p> <p> </p> <p>==========================================================================================<br> CONTENT OF DIRECTORIES<br> ==========================================================================================<br> -– data [input data to produce plots]<br> |–– BoreholesMeta.csv<br> |–– brackets_photos_ice.csv<br> |–– brackets_photos_thawed.csv<br> |–– Lac_de_Gras_permafrost_20200612.csv<br> |–– NordicanaD<br> <br> |–– ds_000582159 [authoritative copy at doi: 10.5885/45558XD-EBDE74B80CE146C6]<br> |–– Cored_Drill_TCR.csv<br> |–– Cored_Drill_TCR.csv_ReadMe.txt<br> <br> |–– ds_000582163 [authoritative copy at doi: 10.5885/45558XD-EBDE74B80CE146C6]<br> |–– Cored_Drill_Logs.csv_ReadMe.txt<br> |–– Cored_Drill_Logs.csv</p> <p>–– plot [R scripts write plots into this subdirectory]</p> <p>–– src [R scripts to generate plots]<br> |–– Combined_Plots.R [produces Figures 3–6]<br> |–– Eskers.R [helper function called by Combined_Plots.R]<br> |–– Organics.R [helper function called by Combined_Plots.R]<br> |–– plot_boreholes_DD_single.R [produces Figures S3]<br> |–– plot_boreholes_DD.R [produces raw Figure S2 for further graphic processing]<br> |–– Till.R [helper function called by Combined_Plots.R]<br> |–– Valley.R [helper function called by Combined_Plots.R]</p> <p><br> ==========================================================================================<br> RUNNING SCRIPTS<br> ==========================================================================================</p> <p>Adjust the variable 'path' in these scrips, then run: <br> Combined_Plots.R<br> plot_boreholes_DD_single.R<br> plot_boreholes_DD.R </p> <p>Tested with R version 3.6.3 (2020-02-29) -- "Holding the Windsock"</p> <p> </p> <p>==========================================================================================<br> REFRERENCE<br> ==========================================================================================<br> Please note that the data contained in data/NordicanaD is published as Gruber et al. (2018)<br> and only included here for convenience. The full reference for the authoritative copy is: <br> <br> Gruber, S., Brown, N., Stewart-Jones, E., Karunaratne, K., Riddick, J., Peart, C., <br> Subedi, R., Kokelj, S. 2018. Drill logs, visible ice content and core photos from 2015 <br> surficial drilling in the Canadian Shield tundra near Lac de Gras, Northwest Territories, <br> Canada, v. 1.0 (2015-2015). Nordicana D38, doi: 10.5885/45558XD-EBDE74B80CE146C6. <br> http://www.cen.ulaval.ca/nordicanad/dpage.aspx?doi=45558XD-EBDE74B80CE146C6 </p>
Dataset to: Organic carbon stocks, quality and prediction in permafrost-affected forest soils in North Canada (CATENA) - Version 2 (Corrected)
<p><strong>Version update: Coordinates were not correct in previsous version and have been corrected now in version 2</strong></p> <p> </p> <p>Dataset to the manuscript: Schiedung et al. (2022, Catena) Organic carbon stocks, quality and prediction in permafrost-affected forest soils in North Canada ( <a href="https://doi.org/10.1016/j.catena.2022.106194">https://doi.org/10.1016/j.catena.2022.106194</a> )</p> <p>Data files, variables and parameter are described in <em>Var_names_dd_all.csv</em> for all data on each sample and <em>Var_names_dd_composites.csv </em>for all data on composited samples per site and depth. DRIFT data and corresponding explenation are in <em>Schiedung_CATENA_DRIFT_v1.1.zip.</em></p> <p> </p> <p><strong> </strong></p>
Dataset to manuscript: Soil organic carbon stocks and quality in small-scale tropical, sub-humid and semi-arid watersheds under shrubland and dry deciduous forest in southwestern India
<p>Raw data to the manuscript entitled "Soil organic carbon stocks and quality in small-scale tropical, sub-humid and semi-arid watersheds under shrubland and dry deciduous forest in southwestern India" by Severin-Luca Bellè, Jean Riotte, Muddu Sekhar, Laurent Ruiz, Marcus Schiedung and Samuel Abiven.</p> <p>Data files include all raw data of soil cores (20211111_Raw_data.zip), data measured on composited samples (20211111_Composite_data.zip) and DRIFT spectra (20211111_DRIFT_data.zip).</p> <p>Files ending with var_names are the README files.</p>
Data to support the publication "Soil Water Retention as Affected by Management Induced Changes of Soil Organic Carbon: Analysis of Long-Term Experiments in Europe", https://doi.org/10.3390/land10121362
<p>Soil organic carbon content and water content at the different pressure points, as measured by Ioanna Panagea for the publication "Soil Water Retention as Affected by Management Induced Changes of Soil Organic Carbon: Analysis of Long-Term Experiments in Europe", https://doi.org/10.3390/land10121362 from the the long term experiments belonging in some of the SoilCare project partners. </p>
Soil organic carbon content [g/kg] 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: log organic carbon [g/kg] to back-transform use exp(x/10)-1;</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>To back-transform the log.oc maps use formula: exp(x/10)-1. These are examples of back-transformed values:</p> <p> log.oc = 15 → 0.3% SOC;<br> log.oc = 20 → 0.6% SOC;<br> log.oc = 25 → 1.1% SOC;<br> log.oc = 30 → 1.9% SOC;<br> log.oc = 35 → 3.2% SOC;<br> log.oc = 40 → 5.3% SOC;<br> log.oc = 50 → 14.8% SOC;</p>
Soil Organic Carbon Content estimations over the Lithuanian pilot area (2022)
<p>In the context of the EU-funded project DIONE (No. 870378), Soil Organic Carbon Content (SOC) estimations have been released as outputs of novel machine learning algorithms which combined the point measurements (i.e. soil properties detected by the Soil Scanning Systems) with temporal EO multispectral imagery and other ancillary variables, enabling end-users, and for the DIONE case, the national paying agency of Lithuania (National Paying Agency - NPA) to mine meaningful information about overall soil health and the effects applied agricultural practices.<br> The dataset is delivered in a single-banded GeoTIFF file (DIONE_SOC_estimations_LT_2022.tif- EPSG:4326) containing the SOC content (SOC %) labelled as Band 1.</p>
Soil Organic Carbon Content estimations over the Cypriot pilot area (2022)
<p>In the context of the EU-funded project DIONE (No. 870378), Soil Organic Carbon Content (SOC) estimations have been released as outputs of novel machine learning algorithms which combined the point measurements (i.e. soil properties detected by the Soil Scanning Systems) with temporal EO multispectral imagery and other ancillary variables, enabling end-users, and for the DIONE case, the national paying agency of Cyprus (Cyprus Agricultural Payments Organisation - CAPO) to mine meaningful information about overall soil health and the effects applied agricultural practices.<br> The dataset is delivered in a single-banded GeoTIFF file (DIONE_SOC_estimations_CY_2022.tif- EPSG:4326) containing the SOC content (SOC %) labelled as Band 1.<br> </p>
Soil Organic Carbon Content estimations over the Cypriot pilot area (2021)
<p>In the context of the EU-funded project DIONE (No. 870378), Soil Organic Carbon Content (SOC) estimations have been released as outputs of novel machine learning algorithms which combined the point measurements (i.e. soil properties detected by the Soil Scanning Systems) with temporal EO multispectral imagery and other ancillary variables, enabling end-users, and for the DIONE case, the national paying agency of Cyprus (Cyprus Agricultural Payments Organisation - CAPO) to mine meaningful information about overall soil health and the effects applied agricultural practices at a parcel level.</p> <p>The dataset is delivered in a shapefile format (DIONE_SOC_estimations_CY_2021.shp - EPSG: 4326) containing the SOC content (SOC %) labeled as SOC.</p>
Soil Organic Carbon Content estimations over the Lithuanian pilot area (2021)
<p>In the context of the EU-funded project DIONE (No. 870378), Soil Organic Carbon Content (SOC) estimations have been released as outputs of novel machine learning algorithms which combined the point measurements (i.e. soil properties detected by the Soil Scanning Systems) with temporal EO multispectral imagery and other ancillary variables, enabling end-users, and for the DIONE case, the national paying agency of Lithuania (National Paying Agency - NPA) to mine meaningful information about overall soil health and the effects applied agricultural practices at a parcel level.</p> <p>The dataset is delivered in a shapefile format (DIONE_SOC_estimations_LT_2021.shp - EPSG:3346) containing the SOC content (SOC %) labeled as SOC.</p>
Soil organic carbon content in x 5 g / kg at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution
<p>Soil organic carbon content in × 5 g / kg (to convert to % divide by 2) at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution. The maps are provided using Byte type to significantly reduce file size. Predicted from a global compilation of soil points. Also available for download: soil organic stock maps in in kg / m<sup>2</sup> (<a href="https://doi.org/10.5281/zenodo.1475453">https://doi.org/10.5281/zenodo.1475453</a>) and bulk density maps in kg / m<sup>3</sup> (<a href="https://doi.org/10.5281/zenodo.1475970">https://doi.org/10.5281/zenodo.1475970</a>). 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>organic.carbon = variable: soil organic carbon content in x 5 g / kg,</li> <li>usda.6a1c = 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>
Soil organic carbon stock (0–30 cm) in kg/m2 time-series 2001–2015 based on the land cover changes
<p>Estimated SOC loss based on the European Space Agency (ESA) Climate Change Initiative (ESACCI-LC) land cover maps 2001–2015. This only shows estimated SOC loss (in kg/m2) as a result of change in land use / land cover (assuming standard change factors based on the literature and IPCC reports). Methodology produced for the purpose of the Land Degradation Neutrality (UNCCD) project. Processing steps are described in detail <strong><a href="https://gitlab.com/openlandmap/global-layers/tree/master/soil/LDN">here</a></strong>. Antartica 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>organic.carbon.stock = variable: soil organic carbon stock in kg/m2,</li> <li>msa.kgm2 = determination method: derived from carbon content, bulk density and coarse fragments,</li> <li>m = mean value,</li> <li>250m = spatial resolution / block support: 250 m,</li> <li>b0..30cm = vertical reference: standard layer 0-30 cm below surface,</li> <li>2014 = time reference: year 2014,</li> <li>v0.2 = version number: 0.2,</li> </ul>
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