Find research datasets worth reusing

opencc-by-4.0Feb 2024View details →

Dataset to: Organic carbon stocks, quality and prediction in permafrost-affected forest soils in North Canada (CATENA) - Version 2 (Corrected)

Version update: Coordinates were not correct in previsous version and have been corrected now in version 2   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> ) Data files, variables and parameter are described in Var_names_dd_all.csv for all data on each sample and Var_names_dd_composites.csv for all data on composited samples per site and depth. DRIFT data and corresponding explenation are in Schiedung_CATENA_DRIFT_v1.1.zip.

opencc-by-4.0Nov 2021View details →

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

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. 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). Files ending with var_names are the README files.

opencc-by-sa-4.0Oct 2018View details →

Soil organic carbon stock (0–30 cm) in kg/m2 time-series 2001–2015 based on the land cover changes

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 <a href="https://gitlab.com/openlandmap/global-layers/tree/master/soil/LDN">here</a>. Antartica is not included. To access and visualize maps use:  <a href="http://www.openlandmap.org/">OpenLandMap.org</a> If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels: <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> All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention: <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>

opencc-by-sa-4.0Dec 2018View details →

Soil organic carbon stock in kg/m2 for 5 standard depth intervals (0–10, 10–30, 30–60, 60–100 and 100–200 cm) at 250 m resolution

Soil organic carbon stock in kg/m2 for 5 standard depth intervals (0–10, 10–30, 30–60, 60–100 and 100–200 cm) at 250 m resolution. To convert to t/ha multiply by 10. Derived using soil organic carbon content (<a href="https://doi.org/10.5281/zenodo.1475457">https://doi.org/10.5281/zenodo.1475457</a>), bulk density (<a href="https://doi.org/10.5281/zenodo.1475970">https://doi.org/10.5281/zenodo.1475970</a>) and coarse fragments (<a href="https://doi.org/10.5281/zenodo.2525681">https://doi.org/10.5281/zenodo.2525681</a>), predicted from point data at 6 standard depths. Depth to bed rock has been ignored, hence total stocks might be about 10–15% lower then reported. Processing steps are described in detail <a href="https://gitlab.com/openlandmap/global-layers/tree/master/soil">here</a>. Antarctica is not included. To access and visualize maps use: <a href="https://openlandmap.org">https://openlandmap.org</a> If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels: <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> All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention: <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 organic carbon content, bulk density and coarse fragments,</li> <li>m = mean value,</li> <li>250m = spatial resolution / block support: 250 m,</li> <li>b0..10cm = vertical reference: 0-10 cm layer below surface,</li> <li>1950..2017 = time reference: period 1950-2017,</li> <li>v0.2 = version number: 0.2,</li> </ul>

opencc-by-sa-4.0Oct 2018View details →

Predicted soil organic carbon stock at 30 m in t/ha for 0-100 cm depth global / update of the map of mangrove forest soil carbon

This is the 2nd update of maps produced by <a href="https://doi.org/10.1088/1748-9326/aabe1c">Sanderman et al (2018)</a>. The improvements to the <a href="https://opengeohub.github.io/spatial-prediction-eml/spatiotemporal-prediction-of-soil-organic-carbon.html">3D spatial predictions</a> include: <ul> <li> new updated global mangrove coverage map (contact Thomas Worthington), </li> <li> spatiotemporal predictions to account for differences in spectral reflectance at the time of field work, </li> <li> additional SOC points <a href="https://static-content.springer.com/esm/art%3A10.1038%2Fs41558-018-0162-5/MediaObjects/41558_2018_162_MOESM2_ESM.xlsx">published in Rovai et al. (2018)</a> used in model training (see gpkg file). </li> </ul> To open map in QGIS or similar, drag and drop the *.tif files. You can than add also the gpkg file contain the training points. Production steps (ensemble predictions using SuperLearner) are explained in detail at:  <ul> <li>R code: <a href="https://github.com/whrc/Mangrove-Soil-Carbon/">https://github.com/whrc/Mangrove-Soil-Carbon/</a> (see "R_code/GMW_mangroves_SOC_30m.R")</li> <li>Tutorial: <a href="https://envirometrix.github.io/PredictiveSoilMapping/soilmapping-using-mla.html#ensemble-predictions-using-superlearner-package">"Predictive Soil Mapping with R"</a></li> </ul> Produced for the purpose of Mangrove Restoration Potential Map funded by The Nature Conservancy and IUCN. Contact TNC: Emily Landis <<a href="mailto:elandis@TNC.ORG">elandis@TNC.ORG</a>>. Contact IUCN / University of Cambridge: Thomas Worthington <<a href="mailto:taw52@cam.ac.uk">taw52@cam.ac.uk</a>>. <ul> <li>The mangrove restoration potential map is available at: <a href="https://www.researchgate.net/deref/http%3A%2F%2Fmaps.oceanwealth.org%2Fmangrove-restoration%2F">http://maps.oceanwealth.org/mangrove-restoration/</a></li> </ul>

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Eight Mile Lake Research Watershed, Carbon in Permafrost Experimental Heating Research (CiPEHR): soil organic carbon stocks and radiocarbon measurements, 2009 & 2022

The Carbon in Permafrost Experimental Heating Research (CiPEHR) project addresses the following questions: 1) Does ecosystem warming cause a net release of C from the ecosystem to the atmosphere?, 2) Does the decomposition of old C, that comprises the bulk of the soil C pool, influence ecosystem C loss?, and 3) How do winter and summer warming alone, and in combination, affect ecosystem C exchange? We are answering these questions using a combination of field and laboratory experiments to measure ecosystem carbon balance and radiocarbon isotope ratios at a warming experiment located in an upland tundra field site near Healy, Alaska in the foothills of the Alaska Range. This data set includes measurements of soil organic carbon stocks and radiocarbon (14C) values that are normalized to account for the effects of subsidence and ground collapse. SOC and 14C values were normalized using an equivalent ash approach described in Plaza et al. (2019) Nat Clim Change and Lathrop et al. (2025) Global Change Biology.

openOpenNov 2025View details →

opencc-by-4.0Dec 2020View details →

Soil organic carbon stocks and trends (1984-2019) predicted at 30m spatial resolution for topsoil in natural areas of South Africa

Link to scientific publication: <a href="https://doi.org/10.1016/j.scitotenv.2021.145384">https://doi.org/10.1016/j.scitotenv.2021.145384</a> Soil organic carbon (SOC) stocks (kg C m-2) are predicted over natural areas (excluding water, urban, and cultivated) of South Africa using a machine learning workflow driven by optical satellite data and other ancillary climatic, morphometric and biological covariates. The temporal scope covers 1984-2019. The spatial scope covers 0-30cm topsoil in South Africa natural land area (84% of the country). See methodology in linked publication for details. Data are provided here at 30m spatial resolution in GeoTIFF files. There is a dataset for the long-term average SOC and trend in SOC. Each dataset is split into four files (suffix *_1, *_2 etc.) covering separate regions of South Africa for ease of download. The raster files are: <ul> <li>"SOC_mean_30m..." - average of annual SOC predictions between 1984 and 2019. Values are expressed in kg C m-2</li> <li>"SOC_trend_30m..." - long-term trend in SOC derived from the Sens slope (M) across annual SOC values between 1984 and 2019. Pixel values (Y) are expressed as a percentage change over the 35 years relative to the long-term mean (X). Y = M / X * 100 * 35 years</li> </ul> NB: All files are scaled by *100 and converted to floating data point to save space. To back-convert to original values, simply divide the raster values by 100.

opencc-by-4.0Jun 2018View details →

Prediction stock of soil organic carbon in Argentina

We standardized the Stocks soil organic carbon (SOC) at 0-30 cm depth for 5,073 soil samples. We spatially predicted SOC stock (kg/m2) using regression forest and associated prediction uncertainties using quantile regression forest at 1000 m resolution. Global accuracy based on cross-validation. We obtained a RMSE 2.624 and Rsquared 0.464.

opencc-by-4.0Dec 2021View details →

Globally-gridded data for manuscript: Global stocks and capacity of mineral-associated soil organic carbon

Supporting globally-gridded data products for manuscript: Georgiou K., Jackson R. B., Vindu&scaron;ková O., Abramoff R. Z., Ahlström A., Feng W., Harden J. W., Pellegrini A. F. A., Polley H. W., Soong J. L., Riley W. J., Torn M. S. Global stocks and capacity of mineral-associated soil organic carbon. Nature Communications, 2022. We leveraged data from a global synthesis of soil fractionation measurements (DOI: 10.5281/zenodo.5987415) along with ancillary data on climate, vegetation, and soil characteristics to produce spatially-explicit global estimates of mineral-associated soil organic carbon stocks (MOC) and mineralogical carbon capacity (MOCmax) in non-permafrost, non-desert mineral soils. Globally-gridded datasets are given in kgC/m2 for topsoil (0-30cm) and subsoil (30-100cm) at 0.5 degree by 0.5 degree spatial resolution.

opencc-by-4.0Dec 2021View details →

Synthesis data for manuscript: Global stocks and capacity of mineral-associated soil organic carbon

Supporting synthesis data for manuscript: Georgiou K., Jackson R. B., Vindu&scaron;ková O., Abramoff R. Z., Ahlström A., Feng W., Harden J. W., Pellegrini A. F. A., Polley H. W., Soong J. L., Riley W. J., Torn M. S. Global stocks and capacity of mineral-associated soil organic carbon. Nature Communications, 2022. We performed an observational synthesis of soil fractionation data constituting 1,144 globally-distributed soil profiles from 78 studies that reported fractionation and bulk measurements of organic carbon across depths. This dataset includes measurements of mineral-associated, particulate, and bulk soil organic carbon, as well as ancillary data on edaphic, climate, and vegetation characteristics. We also performed a separate observational synthesis of soil carbon accrual from manipulation and chronosequence studies, which included changes in carbon stocks or concentrations, bulk density, experimental duration, and edaphic properties. This latter synthesis included 103 observations from 34 studies that spanned crop, pasture, grassland, and forest ecosystems across climates and soil types. Further details for both syntheses can be found in the methods and supplementary materials of the associated manuscript.

opencc-by-4.0Aug 2024View details →

Gridded spatial information on soil organic carbon content, density and stock in Hungary for 1992 and 2000

Predictive soil organic carbon (SOC) content, density, and stock maps, along with the associated prediction uncertainty, are provided for the years 1992 and 2000, for the entire territory of Hungary. The maps refer to the topsoils (0–30 cm) with a spatial resolution of 100⨯100 m. The uncertainty associated with the SOC property maps is expressed by the lower and upper limits of the 90% prediction interval (PI), the range of values within which the true value is expected to occur 9 times out of 10. This means that there are two maps to each SOC property map, quantifying its prediction uncertainty. It should be added that all maps have been masked with open water bodies, as these areas are not relevant for soils. For more details / to cite this dataset please use: <a href="https://doi.org/10.1038/s41597-024-04158-3">Szatmári, G., Laborczi, A., Mészáros, J., Takács, K., Benő, A., Koós, S., Bakacsi, Z., & Pásztor, L. (2024). Gridded, temporally referenced spatial information on soil organic carbon for Hungary. Scientific Data 11, 1312.</a> Custom code used for digital soil mapping and validation is available on GitHub: <a href="https://github.com/GaborSzatmari/HU-SOC-mapping" target="_blank" rel="noopener">https://github.com/GaborSzatmari/HU-SOC-mapping</a> Description of the files: The resulting maps are shared as GeoTIFF files. The coordinate reference system is the Hungarian Unified National Projection System (HD72/EOV; EPSG: 23700) (<a href="https://epsg.io/23700" target="_blank" rel="noopener">https://epsg.io/23700</a>). The table below provides further information on the published maps. Note that the first file (00_Overview.jpg) gives an overview of the SOC property maps. <table> <tbody> <tr> <td> SOC property maps </td> <td> Unit </td> <td> Year </td> <td> Filename </td> </tr> <tr> <td> SOC content map </td> <td> [g ∙ kg-1] </td> <td> 1992 </td> <td> SOCc_0_30cm_1992_pred.tif </td> </tr> <tr> <td> SOC content, lower limit of the 90% PI </td> <td> [g ∙ kg-1] </td> <td> 1992 </td> <td> SOCc_0_30cm_1992_q05.tif </td> </tr> <tr> <td> SOC content, upper limit of the 90% PI </td> <td> [g ∙ kg-1] </td> <td> 1992 </td> <td> SOCc_0_30cm_1992_q95.tif </td> </tr> <tr> <td> SOC density map </td> <td> [kg ∙ m-3] </td> <td> 1992 </td> <td> SOCd_0_30cm_1992_pred.tif </td> </tr> <tr> <td> SOC density, lower limit of the 90% PI </td> <td> [kg ∙ m-3] </td> <td> 1992 </td> <td> SOCd_0_30cm_1992_q05.tif </td> </tr> <tr> <td> SOC density, upper limit of the 90% PI </td> <td> [kg ∙ m-3] </td> <td> 1992 </td> <td> SOCd_0_30cm_1992_q95.tif </td> </tr> <tr> <td> SOC stock map </td> <td> [tons ∙ ha-1] </td> <td> 1992 </td> <td> SOCs_0_30cm_1992_pred.tif </td> </tr> <tr> <td> SOC stock, lower limit of the 90% PI </td> <td> [tons ∙ ha-1] </td> <td> 1992 </td> <td> SOCs_0_30cm_1992_q05.tif </td> </tr> <tr> <td> SOC stock, upper limit of the 90% PI </td> <td> [tons ∙ ha-1] </td> <td> 1992 </td> <td> SOCs_0_30cm_1992_q95.tif </td> </tr> <tr> <td> SOC content map </td> <td> [g ∙ kg-1] </td> <td> 2000 </td> <td> SOCc_0_30cm_2000_pred.tif </td> </tr> <tr> <td> SOC content, lower limit of the 90% PI </td> <td> [g ∙ kg-1] </td> <td> 2000 </td> <td> SOCc_0_30cm_2000_q05.tif </td> </tr> <tr> <td> SOC content, upper limit of the 90% PI </td> <td> [g ∙ kg-1] </td> <td> 2000 </td> <td> SOCc_0_30cm_2000_q95.tif </td> </tr> <tr> <td> SOC density map </td> <td> [kg ∙ m-3] </td> <td> 2000 </td> <td> SOCd_0_30cm_2000_pred.tif </td> </tr> <tr> <td> SOC density, lower limit of the 90% PI </td> <td> [kg ∙ m-3] </td> <td> 2000 </td> <td> SOCd_0_30cm_2000_q05.tif </td> </tr> <tr> <td> SOC density, upper limit of the 90% PI </td> <td> [kg ∙ m-3] </td> <td> 2000 </td> <td> SOCd_0_30cm_2000_q95.tif </td> </tr> <tr> <td> SOC stock map </td> <td> [tons ∙ ha-1] </td> <td> 2000 </td> <td> SOCs_0_30cm_2000_pred.tif </td> </tr> <tr> <td> SOC stock, lower limit of the 90% PI </td> <td> [tons ∙ ha-1] </td> <td> 2000 </td> <td> SOCs_0_30cm_2000_q05.tif </td> </tr> <tr> <td> SOC stock, upper limit of the 90% PI </td> <td> [tons ∙ ha-1] </td> <td> 2000 </td> <td> SOCs_0_30cm_2000_q95.tif </td> </tr> </tbody> </table>

opencc-by-4.0Nov 2023View details →

European soil bulk density and organic carbon stock database using LUCAS Soil 2018

We complied the European topsoil bulk density and organic carbon stock database (0-20 cm) using LUCAS Soil 2018. This database inlcudes 18,945 and 15,389 soil samples (0-20 cm) with bulk density in fine fraction (Bdfine) and soil organic cabron stock (SOCS) for the EU and UK using the best traditional pedotransfer function (T-PTF-4) and machine leanring based PTFs (Local-RFFRFS). It also contains the POINTID linked to LUCAS Soil 2018, coarse fragements in volume (coarse_vol) and coordinates (GPS_LAT, GPS_LONG). For more information, please refer to LUCAS 2018 TOPSOIL data (https://esdac.jrc.ec.europa.eu/content/lucas-2018-topsoil-data). This dataset is asscoated to the "European soil bulk density and organic carbon stock database using machine learning based pedotransfer function" by Chen et al. (2024). Manuscript citation: Chen, S., Chen, Z., Zhang, X., Luo, Z., Schillaci, C., Arrouays, D., Richer-de-Forges, A.C., Shi, Z. , 2024. European topsoil bulk density and organic carbon stock database (0-20 cm) using machine learning based pedotransfer functions. Earth System Science Data, 16, 2367–2383. When using the data, please cite repositories as well as the original manuscript. For any questions on the data, please contact Dr. Songchao Chen (chensongchao@zju.edu.cn).

opencc-zeroDec 2023View details →

Data from: Microbial carbon use efficiency and soil organic carbon stocks across an elevational gradient in the Peruvian Andes

Soils of mountain ecosystems are one of the most vulnerable ecosystems to climate change, while the ecosystem services they produce are significant and currently at risk. High altitude soils contain high C stocks, but due to difficult access to sites these areas are understudied. Moreover, how the C and N cycling is changing in response to climate change in these ecosystems, is still unclear. Microbial carbon use efficiency (CUE) and its dependency on the environmental constraints along the altitudinal gradients is one important unknown factor. Here we present results from an altitudinal gradient study (3500 to 4500 m a.s.l.) from a Polylepis forest in the Peruvian Andes. We measured the soil organic carbon (SOC) stocks and microbial metabolic CUE by 13C glucose tracing and microbial resource use efficiency (CUEC:N) based on enzyme activity measurements. We expected to find an increase in SOC stock, microbial nutrient limitations, and lower CUE with elevation. SOC stocks depended on soil development and followed a unimodal curve that peaks at 4000 m in two of the three studied valleys. Neither 13CUE nor CUEC:N changed significantly with altitude. Soil C:N ratio, β-glucosidase, chitinase, and phosphatase enzyme activities increased with elevation, but peroxidase activity decreased with elevation. We suggest that more labile organic matter left at high elevation could compensate for the increasing nutrient limitation at high elevation, resulting in no noticeable change in CUE with elevation.

opencc-by-4.0Nov 2024View details →

Database of Soil Organic Carbon (SOC) stock up to 20 cm depth: A collection of studies from Uruguay

This database comprises 667 soil organic carbon (SOC) stock measurements, collected from various studies conducted across Uruguay, with sampling depths extending up to 20 cm. The database aggregates data from 14 studies, including undergraduate and postgraduate theses and research projects, sampled between 2002 and 2024. It provides comprehensive details on the geographic location of sampling sites (latitude and longitude coordinates), the date of sample collection, the methodology employed for SOC determination, the technique used to estimate SOC stock values at the specified depths, and a description of land use at the moment of sampling. This data collection is particularly relevant for evaluating SOC stocks modeling exercises, as it contributes to a more comprehensive understanding of carbon dynamics across various soil types, environmental conditions, land use, and land cover types. The database is available in shapefile format and is expected to serve as a valuable resource for researchers and professionals in the field.   <h3>Data Table Description</h3> The associated data table from this database contains the following key variables: <ul> <li> ID: Unique identifier for each sampling point. </li> <li> Latitude & Longitude: Geographic coordinates of the sampling site. </li> <li> SOC Stock: Soil Organic Carbon (SOC) stock calculated at a fixed depth using the equation SOC stock (MgC/ha) = C × Bd × d, where C represents carbon concentration (%C), Bd is the bulk density of the soil (g/cm³), and d is the depth (cm). </li> <li> Depth: Depth (cm) used for SOC stock calculation. Data were harmonized to a depth of 20 cm; however, lower values are reported for sites where the sampling scheme was conducted at depths less than 20 cm or where bedrock was encountered before reaching 20 cm. </li> <li> Land Use: Simplified land use classification with “grassland”, “agriculture”, or “forest” categories </li> <li> Land Use Extended: If available, the responsible party provides a more detailed explanation of land use. </li> <li> Month & Year: Month and year of sample collection. </li> <li> SOC Method: Methodology used for SOC determination, such as Walkley-Black or dry combustion. </li> <li> Published: Indicates whether the data have been published, with a URL provided if available. If not published, the data are marked as unpublished. </li> <li> SOC Calc: The approach used for calculating SOC stock at a fixed depth, detailed in two possible methods: </li> <ul> <li> Real: For sampling schemes that include a segment ending at 20 cm depth, SOC stock was calculated directly up to 20 cm. This method was also applied when the maximum soil sampling depth was less than 20 cm. </li> <li> Spline: When the sampling depth did not include 20 cm but extended beyond it, a spline interpolation was applied. This method employs all available cumulative values and their associated depths to estimate the SOC stock at 20 cm. </li> </ul> </ul> <h3>Additional Observations</h3> <ul> <li> For sites listed in rows 200-216, the GPS position represents the plot´s centroid, as no specific location was reported. </li> <li> For sites listed in rows 344-633, sampling was conducted at 0-7.5 cm, 7.5-15 cm, and 15-30 cm depth. In sites where the total depth was 15 cm, bulk density was measured only in the first layer (0-7.5 cm), and this value was also applied to the 7.5-15 cm layer. In sites where the total depth was 20 cm, bulk density was measured in all layers and calculated for 20 cm as a weighted average. </li> <li> For sites listed in rows 102-134, bulk density measurements were not taken directly. Instead, data were retrieved from the "SoilGrids" website (<a href="https://soilgrids.org/">https://soilgrids.org/</a>). Bulk density values for the 0-5 cm and 5-15 cm layers were downloaded, and a weighted average was calculated before determining the SOC stock. </li> </ul> <h3> </h3> <h3>Funding:</h3> Funded were provided by ANII (FSDA_1_2018_1_154817, Procesos Inductivos para generación de buenas prácticas agropecuarias: Compilación y análisis de bases de datos a nivel predial; FSA_1_2022_1_175272, Evaluación multiescalar del desempeño ambiental de sistemas agropecuarios con diferente nivel de intensificación a partir de indicadores derivados de sensores remotos); INIA (FPTA-515, Indicadores de sostenibilidad ambiental para el sector agropecuario de Uruguay basados en información derivada de sensores remotos y modelos biofísicos); and IDB (URUGUAY/UR-T1277, Adopción de prácticas Agroecológicas y Huella de Carbono en la Agricultura Uruguaya).

opencc-by-4.0Jul 2024View details →

GSOCS-LULCC: the Global Soil Organic Carbon Stock dataset after Land Use and Land Cover Change

We complied the Global Soil Organic Carbon Stock dataset after Land Use and Land Cover Change (GSOCS-LULCC) from 632 papers documented in Web of Science till the June 2024. This database comprises 1,187 sites with 5,805 records at multiple sample depths. This dataset (in csv formats) is associated to the "GSOCS-LULCC: the Global Soil Organic Carbon Stock dataset after Land Use and Land Cover Change" by Chen et al. (2025). The README file includes the full explanation of all the columns. Manuscript citation: Chen, S., Shuai, Q., Arrouays, D., Chen, Z., Dai, L., Hong, Y., Hu, B., Huang, Y., Ji, W., Li, S., Liang, Z., Ma, Y., Richer-de-Forges, A.C., Schillaci, C., Su, Y., Teng, H., Wang, N., Wang, X., Wang, Y., Wang, Z., Wang, Z., Xu, D., Xue, J., Ye, S., Zhang, X., Zhou, Y., Zhu, P., Shi, Z. , 2025. GSOCS-LULCC: the Global Soil Organic Carbon Stock dataset after Land Use and Land Cover Change. In preparation. When using the data, please cite repositories as well as the original manuscript. For any questions on the data, please contact Dr. Songchao Chen (chensongchao@zju.edu.cn).

opencc-by-4.0Dec 2021View details →

Maps of soil organic carbon stocks in Brazil

This database was created by Gustavo Vieira Veloso and Lucas Carvalho Gomes 04/06/2022.  Contact: gustavo.v.veloso@gmail.com and lucascarvalhogomes15@hotmail.com  ------------------------------------------------------------------------------------ Maps of soil organic carbon (SOC) stocks in Brazil of the article:  "Modeling and mapping soil organic carbon stocks in Brazil" (doi: 10.1016/j.geoderma.2019.01.007) The dataset is composed of five folders of SOC stocks maps at the standard depths (0–5, 5–15, 15–30, 30–60, and 60–100 cm). The maps are in Geotif format (EPSG 102015) with a spatial resolution of approximately 1 km and include the mean SOC stocks, standard deviation (SD),  coefficient of variation (CV), 0.05 and 0.95 quantiles. The maps are free to use and please cite also the article: Gomes, L.C., Faria, R.M., de Souza, E., Veloso, G.V., Schaefer, C.E.G., & Fernandes Filho, E.I. (2019). Modeling and mapping soil organic carbon stocks in Brazil. Geoderma, 340, 337-350.

opencc-zeroOct 2022View details →

Effects of land clearing for agriculture on soil organic carbon stocks in drylands: A meta-analysis

To improve our understanding of clearing natural ecosystems for cropland on soil organic carbon stocks in drylands, we searched for related peer-reviewed research papers published from 1980 to 2022 on the Web of Science (<a href="https://www.webofscience.com">https://www.webofscience.com</a>) and the Scopus Database (<a href="https://www.scopus.com">https://www.scopus.com</a>) (accessed on 30th April 2022). Then, we screened papers for integrity, relevance, and scientific merit under the following criteria: (1) We made sure all studies were independent and based on field-measured data; (2) Each study had to report paired SOC stocks of cropland and adjacent natural ecosystems with the same or a similar suite of environmental factors; (3) Studies need to explicitly present results on SOC stocks or concentrations for certain depths and areas; (4) Studies have specified the types of natural ecosystems that were converted to cropland, which are used as criteria for defining CNEC types. Finally, we winnowed results to a total of 159 scientific journal articles, comprising 242 sites with 1379 paired soil layer observations from 601 paired soil profiles.

opencc-zeroMay 2023View details →

Drivers of soil organic carbon stock during tropical forest succession

Soil organic matter contributes to productivity in terrestrial ecosystems and contains more carbon than is found in the atmosphere. Yet, there is little understanding of soil organic carbon (SOC) sequestration processes during tropical forest succession, particularly after land abandonment from agriculture practices. Here we used vegetation and environmental data from two large-scale surveys covering a total landscape area of 20,000 ha in Southeast Asia to investigate the effects of plant species diversity, functional trait diversity, phylogenetic diversity, aboveground biomass, and environmental factors on SOC sequestration during forest succession. We found that functional trait diversity plays an important role in determining SOC sequestration across successional trajectories. Increases in SOC carbon storage were associated with indirect positive effects of species diversity and succession age via functional trait diversity, but phylogenetic diversity and aboveground biomass showed no significant relationship with SOC stock. Furthermore, the effects of soil properties and functional trait diversity on SOC carbon storage shift across elevation. Synthesis: Our results suggest that reforestation and restoration management practices that implement a trait-based approach by combining long-lived and short-lived species (conservative and acquisitive traits) to increase plant functional diversity could enhance SOC sequestration for climate change mitigation and adaptation efforts, as well as accelerate recovery of healthy soils.

opencc-zeroJul 2023View details →

Cropland management impacts on soil organic carbon stock changes in US croplands from 1990 to 2015

This geospatial dataset represents soil organic carbon stock changes estimated from a counterfactual analysis of climate-smart soil management practices that were adopted in U.S. croplands between 1990 and 2015. The counterfactual scenarios are relative to historical cropland management implemented in the U.S. for the temporal domain of this study. These data provide a large-scale overview of the carbon stock changes in US cropland agricultural soils associated with conservation tillage, manure amendments, cover crops terminated with cultivation, cover crop terminated with herbicide, hay and pasture in rotation with annual crops, set-aside/Conservation Reserve Program lands. Data were generated using the DayCent ecosystem model driven by cropping histories in the USDA National Resources Inventory (NRI) and associated agricultural management data. The average annual stock change was calculated for each management practice to determine the impact. Average rates of annual stock changes on a per-hectare basis (averaged from 1990 to 2015) are presented as a gridded dataset. Data are in a GeoTIFF format on a 5 km grid.