Find research datasets worth reusing

openCC (other)May 2023View details →

Guatemala Soil Organic Carbon Database (0 to 30 cm, 1965-2010).

The soil organic carbon database is a harmonized and structured data set with information about soil organic carbon (SOC), soil bulk density (BD), and coarse fragments (CF). The SOC, BD, and CF data were fitted to one standard depth of 0-30 cm using a mass preservation spline implemented in R programmation language. The data was obtained from Unidad de Planificación Geográfica y Gestión de Riesgo of Ministerio de Agricultura Ganadería y Alimentación (UPGGR-MAGA), Facultad de Agronomía of Universidad de San Carlos de Guatemala (CEDIA-FAUSAC), and the World Soil Information Service (WoSIS). This database covers the entire Guatemalan territory, although its distribution does not represent all ecosystems, soil types, and coverages/land use. It contains 910 observations of SOC, 704 of them have BD information, and 8 have CF information. The entire data set has information on the year of observation, and its temporal distribution goes from 1965 to 2010. Nearly 50% is in the decade 2000-2010, and 41% is in the 2010 year. In addition, the analytical methods for quantifying SOC, BD, and CF are available in 99, 25, and 0% of the data, respectively. SOC contents range from 1.45 to 162 g*kg^-1, BD values from 0.42 to 1.69 g*cm^-3, and CF values from 0 to 21%wt.

openCC (other)Jun 2021View details →

Meta-analytical data on soil organic, particulate organic, and mineral-associated organic carbon under nitrogen fertilization, elevated atmospheric carbon dioxide, atmospheric warming, increased precipitation, drought, and their combined effects

Data were harvested from journal articles found on the Web of Science Core Collection and the ProQuest Agricultural and Environmental Database that studied soil organic matter fraction carbon responses to global changes (nitrogen fertilization, elevated atmospheric carbon dioxide, atmospheric warming, increased and decreased precipitation, and combined effects). Soil organic carbon fractions were designated as particulate organic carbon or mineral-associated organic carbon based on size and density cutoffs. Relevant metadata, including article information (authors, publication year), environmental information (soil type, climate, and land use), and experiment information (rates, methods) were also added to the dataset.

openOpenDec 2015View details →

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.

openOpenDec 2007View details →

Soil nitrogen and carbon from organic and mineral soil of 32 mature black spruce sites across interior Alaska (Sampled 2001)

Soil nitrogen and carbon was collected at 33 sites as part of a bigger study looking at the structure and function of black spruce stands in interior Alaska. These variables can be compared to any of the environmental site descriptions, GPS coordinates, soil characteristics, physical site characteristics, stand and structural characteristics, active layer, collected in the summers of 2000, 2001 for these sites

openOpenMar 2017View details →

Estimated Age of Carbon within the Soil Organic Layer for Murphy Dome study site in 2013

This file contains radiocarbon data used to estimate age of the soil organic layer for black spruce and Alaska paper birch forest in the Murphy Dome fire scar near Fairbanks, AK. All information was collected in summer 2013 and subsequently processed and analyzed in the laboratory.

openOpenJan 2020View details →

SGS-LTER Transect Study - Organic Carbon in Soils across Toposequences on the Central Plains Experimental Range, Nunn, Colorado, USA 1983-1984

This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. CPER SOC across Toposequences - Pedons and their corresponding topography were described along an 8 km transect oriented normal to the major drainages of the CPER. A total of 140 pedons representing 23 toposequences and 7 plains segments were characterized. Sampling sites were selected within toposequences according to slope position (summit, shoulder, backslope, footslope, toeslope) and within plains segments at approximate 100 m intervals. Pedons were described and sampled by genetic horizon according to the standards of the National Cooperative Soil Survey. Analyses, conducted at Colorado State University, included particle size and organic C. Bulk density was estimated empirically according to: Rawls, W.J. 1983. Estimating soil bulk density form particle size analysis and organic matter content. Soil Sci 135: 123-125. Organic C accumulation was measured along an 8 km transect at a site in the semiarid shortgrass steppe of northeastern Colorado. Specific objectives of the study were to (I) measure the quantity and distribution of organic C across toposequences, (ii) test the hypothesis that a disproportionate amount of soil organic C resides in the lowlands (as defined herein), and (iii) assess the role of geomorphic history as a determinant of contemporary rates o

opencc-by-4.0Dec 2019View details →

Diversification and Management Practices in Selected European Regions. A Data-analysis of Arable Crops Production and soil organic carbon

This data set contains a data-mining performed to assess the impact of intercropping, tillage and fertilizer type on soil organic carbon and crop yield in arable crops from four selected European pedoclimatic regions and typical cropping systems in the Atlantic, Boreal, Mediterranean North, and Mediterranean South regions. A further meta-analysis was performed with these data.  These data correspond to the open-access articles: - Diversified Arable Cropping Systems and Management Schemes in Selected European Regions Have Positive Effects on Soil Organic Carbon Content. Agriculture 2019, 9, 261. https://www.mdpi.com/2077-0472/9/12/261?type=check_update&version=2 - Diversification and Management Practices in Selected European Regions. A Data-analysis of Arable Crops Production. Agronomy 2020, 10, 297; doi:10.3390/agronomy10020297. https://www.mdpi.com/2073-4395/10/2/297 - Deficit Drip Irrigation in Processing  Tomato Production in the Mediterranean Basin: A Data Analysis for Italy. Agriculture 2019, 9, 79; doi:10.3390/agriculture9040079. https://www.mdpi.com/2077-0472/9/4/79?type=check_update&version=2 The research and publications have been funded by he European Commission Horizon 2020 project Diverfarming [grant agreement 728003].

opencc-zeroMay 2020View details →

Data from: Leaching losses of dissolved organic carbon and nitrogen from agricultural soils in the upper US Midwest

Leaching losses of dissolved organic carbon (DOC) and nitrogen (DON) from agricultural systems are important to water quality and carbon and nutrient balances but are rarely reported; the few available studies suggest linkages to litter production (DOC) and nitrogen fertilization (DON). In this study we examine the leaching of DOC, DON, NO3-, and NH4+ from no-till corn (maize) and perennial bioenergy crops (switchgrass, miscanthus, native grasses, restored prairie, and poplar) grown between 2009 and 2016 in a replicated field experiment in the upper Midwest U.S. Leaching was estimated from concentrations in soil water and modeled drainage (percolation) rates. DOC leaching rates (kg ha-1 yr-1) and volume-weighted mean concentrations (mg L-1) among cropping systems averaged 15.4 and 4.6, respectively; N fertilization had no effect and poplar lost the most DOC (21.8 and 6.9, respectively). DON leaching rates (kg ha-1 yr-1) and volume-weighted mean concentrations (mg L-1) under corn (the most heavily N-fertilized crop) averaged 4.5 and 1.0, respectively, which was higher than perennial grasses (mean: 1.5 and 0.5, respectively) and poplar (1.6 and 0.5, respectively). NO3- comprised the majority of total N leaching in all systems (59-92%). Average NO3- leaching (kg N ha-1 yr-1) under corn (35.3) was higher than perennial grasses (5.9) and poplar (7.2). NH4+ concentrations in soil water from all cropping systems were relatively low (<0.07 mg N L-1). Perennial crops leached more NO3- in the first few years after planting, and markedly less after. Among the fertilized crops, the leached N represented 14-38% of the added N over the study period; poplar lost the greatest proportion (38%) and corn was intermediate (23%). Requiring only one third or less of the N fertilization compared to corn, perennial bioenergy crops can substantially reduce N leaching and consequent movement into aquifers and surface waters.

opencc-by-4.0Oct 2020View details →

iSDAsoil: soil organic carbon for Africa predicted at 30 m resolution at 0-20 and 20-50 cm depths

iSDAsoil dataset soil organic carbon (C) log-transformed 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>, and other national and regional soil datasets). Cite as: Hengl, T., Miller, M.A.E., Križan, J. et al. African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning. Sci Rep 11, 6130 (2021). <a href="https://doi.org/10.1038/s41598-021-85639-y">https://doi.org/10.1038/s41598-021-85639-y</a> 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">Cloud-Optimized GeoTIFF version</a>. Layer description: <ul> <li>sol_log.oc_mehlich3_m_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil organic Carbon mean value,</li> <li>sol_log.oc_mehlich3_md_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil organic Carbon model (prediction) errors,</li> </ul> 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: <pre><code>Variable: log.oc R-square: 0.791 Fitted values sd: 0.716 RMSE: 0.369 Random forest model: Call: stats::lm(formula = f, data = d) Residuals: Min 1Q Median 3Q Max -3.1517 -0.1900 -0.0060 0.1793 4.2621 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.821657 0.794000 2.294 0.0218 * regr.ranger 1.047507 0.005146 203.571 <2e-16 *** regr.xgboost -0.005943 0.005340 -1.113 0.2657 regr.cubist 0.052084 0.004884 10.664 <2e-16 *** regr.nnet -0.867384 0.359213 -2.415 0.0158 * regr.cvglmnet -0.050157 0.003863 -12.984 <2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.3687 on 122457 degrees of freedom Multiple R-squared: 0.7906, Adjusted R-squared: 0.7906 F-statistic: 9.248e+04 on 5 and 122457 DF, p-value: < 2.2e-16 </code></pre> To back-transform values (y) to g/kg use the following formula: <pre><code>g/kg = expm1( y / 10 )</code></pre> To submit an issue or request support please visit <a href="https://isda-africa.com/isdasoil">https://isda-africa.com/isdasoil</a>

opencc-zeroDec 2023View details →

A meta-analysis reveals increases in soil organic carbon following the restoration and recovery of croplands in Southwest China

In China, the Grain for Green Program (GGP) is an ambitious project to convert croplands into natural vegetation, but exactly how changes in vegetation translate into changes in soil organic carbon remains less clear. Here we conducted a meta-analysis using 734 observations to explore the effects of land recovery on the soil organic carbon and nutrients in 4 provinces in Southwest China. Following GGP, the soil organic carbon content (SOCc) and soil organic carbon storage (SOCs) increased by 33.73% and 22.39%, respectively. Likewise, soil nitrogen increased, while phosphorus decreased. Outcomes were heterogeneous, however, depending on variation in soil and environmental characteristics. Both the regional land use and cover change indicated by landscape type transfer matrix and net primary production from 2000 to 2020 further confirmed that GGP promoted the forest area (2.95%) and regional mean net primary production (52.94%). Our findings suggest that GGP could enhance soil and vegetation carbon sequestration in Southwest China and help to develop carbon neutral strategy.

opencc-by-4.0Nov 2023View details →

Soil grid data for 4 agricultural fields in PT (ECe; soil organic carbon, pH)

Soil data collected in an agricultural area with annual crops in Portugal (Lezíria Grande). The data refers to soil properties of 63 soil samples collected at a depth of 0-20 cm, considering a regular sampling grid, in four fields with varying soil salinity (field areas between 2 and 34 ha). The samples were collected at a period when the soil was bare, following the harvest of the annual crops, and pictures of the soil surface were taken for eventual correction of corresponding remote sensing imaging. The data includes: soil organic carbon (SOC) (Walkley-Black method), soil water content, electric conductivity of the saturated soil paste (ECe), EC1:5, and  pH1:5. The data may be representative of the soil conditions of the area, which is a highly productive agricultural low land, prone to the development of soil salinity as a result of the rise of saline groundwater and/or irrigation. The data can be used to establish relations between soil salinity (ECe) and other soil properties as well as build prediction models of the soil properties from remote sensing namely, for developing models for SOC prediction under the STEROPES project (WP5 (WP5-T3) and WP2 (WP2-T3)).The aim of the collected dataset was to be able to analyze the influence of soil salinity in SOC prediction from remote sensing.Data in the form of MS Excel files (xlsx), pictures of the soil surface in jpg. format.

opencc-by-4.0Nov 2023View details →

Supporting data for von Fromm et al. (2023) Controls on timescales of soil organic carbon persistence across sub-Saharan Africa

This file contains the supporting data for von Fromm et al. (2024) Controls on timescales of soil organic carbon persistence across sub-Saharan Africa, Global Change Biology, <a href="https://doi.org/10.1111/gcb.17089">https://doi.org/10.1111/gcb.17089</a> We used wet soil chemistry data from Vågen et al., 2021 (<a href="https://doi.org/10.34725/DVN/66BFOB">https://doi.org/10.34725/DVN/66BFOB</a>). In addition, we added newly measured radiocarbon data, extracted global climate data, gross primary productivity and quantified soil mineralogy based on X-ray powder diffraction data. Turnover time for carbon (mean C age) was calculated from Δ14C (&permil;) values by using an one-pool model. For more details about the sampling, calculations, and units see the associated publication. To reproduce all analysis, including calculating the mean C age, please visit the author's github page: <a href="https://github.com/SophievF/AfSIS_14C">https://github.com/SophievF/AfSIS_14C</a>.  The dataset is also part of the International Soil Radiocarbon Database (<a href="https://soilradiocarbon.org/">https://soilradiocarbon.org/</a>).

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-zeroDec 2023View details →

Protists regulate microbially-mediated organic carbon turnover in soil aggregates

Soil protists, the major predator of bacteria and fungi, shape the taxonomic and functional structure of soil microbiome via trophic regulation. However, how trophic interactions between protists and their prey influence microbially mediated soil organic carbon turnover remains largely unknown. Here, we investigated the protistan communities and microbial trophic interactions across different aggregates-size fractions in agricultural soil with long-term fertilization regimes. Our results showed that aggregate sizes significantly influenced the protistan community and microbial hierarchical interactions. Bacterivores were the predominant protistan functional group and were more abundant in macroaggregates and silt + clay than in microaggregates, while omnivores showed an opposite distribution pattern. Furthermore, partial least square path modeling revealed positive impacts of omnivores on the C-decomposition genes and soil organic matter (SOM) contents, while bacterivores displayed negative impacts. Microbial trophic interactions were intensive in macroaggregates and silt + clay but were restricted in microaggregates, as indicated by the intensity of protistan-bacterial associations and network complexity and connectivity. Cercozoan taxa were consistently identified as the keystone species in SOM degradation-related ecological clusters in macroaggregates and silt + clay, indicating the critical roles of protists in SOM degradation by regulating bacterial and fungal taxa. Chemical fertilization had a positive effect on soil C sequestration through suppressing SOM degradation-related ecological clusters in macroaggregate and silt + clay. Conversely, the associations between the trophic interactions and SOM contents were decoupled in microaggregates, suggesting limited microbial contributions to SOM turnovers. Our study demonstrates the importance of protists-driven trophic interactions on soil C cycling in agricultural ecosystems.

opencc-zeroJan 2024View details →

Organo-organic interactions dominantly drive soil organic carbon accrual

Organo-mineral interactions have been regarded as the primary mechanism for the stabilization of soil organic carbon (SOC) over decadal to millennial timescales, and the capacity for soil carbon (C) storage has commonly been assessed based on soil mineralogical attributes, particularly mineral surface availability. However, it remains contentious whether soil C sequestration is exclusively governed by mineral vacancies, making it challenging to accurately predict SOC dynamics. Here, through a 400-day incubation experiment using 13C-labeled organic materials in two contrasting soils (i.e., Mollisol and Ultisol), we show that despite the unsaturation of mineral surfaces in both soils, the newly incorporated C predominantly adheres to "dirty" mineral surfaces coated with native organic matter (OM), demonstrating the crucial role of organo-organic interactions in exogenous C sequestration. Such interactions lead to multilayered C accumulation that is not constrained by mineral vacancies, a process distinct from direct organo-mineral contacts. The coverage of native OM by new C, representing the degree of organo-organic interactions, is noticeably larger in Ultisol (~14.2%) than in Mollisol (~5.8%), amounting to the net retention of exogenous C in Ultisol by 0.2–1.3 g kg−1 and in Mollisol by 0.1–1.0 g kg−1. Additionally, organo-organic interactions are primarily mediated by polysaccharide-rich microbial necromass. Further evidence indicates that iron oxides can selectively preserve polysaccharide compounds, thereby promoting the organo-organic interactions. Overall, our findings provide direct empirical evidence for an overlooked but critically important pathway of C accumulation, challenging the prevailing "C saturation" concept that emphasizes the overriding role of mineral vacancies. It is estimated that, through organo-organic interactions, global Mollisols and Ultisols might sequester ~0.1–1.0 Pg C and ~0.3–1.7 Pg C per year, respectively, corresponding to the neutralization of ca. 0.5%–3.0% of soil C emissions or 5%–30% of fossil fuel combustion globally.

opencc-by-4.0Nov 2024View details →

Soil grid data for agricultural fields in Spain for STEROPES (EJP Soil) project (ECe, texture, soil organic carbon, pH)

Soil data collected in an agricultural area with vegetable crops in Spain (Campo de Cartagena). The data refers to soil properties of 141 soil samples collected at a depth of 0-10 cm, considering a regular sampling grid, in different commercial fields with varying soil salinity. The samples were collected at a period when the soil was bare, during two consecutive summers, following the harvest of the annual crops, and pictures of the soil surface were taken for eventual correction of corresponding remote sensing imaging. The data includes: soil organic carbon (SOC) (Walkley-Black method), soil water content, electric conductivity of the saturated soil paste (ECe), EC1:5, soil texture, stone content and pH1:2.5.   The data may be representative of the soil conditions of the area, which is an intensive productive agricultural low land, potentially prone to the development of soil salinity as a result of the rise of saline groundwater and/or irrigation. The data can be used to establish relations between soil salinity (ECe) and other soil properties as well as build prediction models of the soil properties from remote sensing namely, for developing models for SOC prediction under the STEROPES project (WP3, WP5 and WP6).The aim of the collected dataset was to be able to analyze the influence of soil salinity in SOC prediction from remote sensing.   Data in the form of MS Excel file (xlsx).

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).