Find research datasets worth reusing

opencc-by-4.0May 2020View details →

Radiocarbon content of carbon dioxide, methane, dissolved organic carbon and particulate organic carbon from the northern permafrost region and other studies

The dataset includes 14C measurements of CO2, CH4, DOC and POC mostly from the northern permafrost region. Some other studies are included from sites not underlained by permafrost. The dataset focuses on 14C measurements of gaseous soil emissions and waterborne ecosystem C fluxes but the database also included C forms belowground, such as soil gases and pore water DOC.

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-zeroNov 2020View details →

Antagonistic effects of temperature and dissolved organic carbon on fish growth in California mountain lakes

Resources and temperature play major roles in determining biological production in lake ecosystems. Lakes have been warming and 'browning' over recent decades due to climate change and increased loading of terrestrial organic matter. Conflicting hypotheses and evidence have been presented about whether these changes will increase or decrease fish growth within lakes. Most studies have been conducted in low-elevation lakes where terrestrially derived carbon tends to dominate over carbon produced within lakes. Understanding how fish in high-elevation mountain lakes will respond to warming and browning is particularly needed as warming effects are magnified for mountain lakes and treeline is advancing to higher elevations. We sampled 21 trout populations in the Sierra Nevada Mountains of California to examine how body condition and individual growth rates, measured by otolith analysis, varied across independent elevational gradients in temperature and dissolved organic carbon (DOC). We found that fish grew faster at warmer temperatures and higher nitrogen (TN), but slower in high DOC lakes. Additionally, fish showed better body condition in lakes with higher TN, higher elevation and when they exhibited a more terrestrial δ13C isotopic signature. The future warming and browning of lakes will likely have antagonistic impacts on fish growth, reducing the predicted independent impact of warming and browning alone.

opencc-zeroOct 2020View details →

FIG. 6 in Trophic position of some Late Devonian-Carboniferous (Mississippian) conodonts revealed on carbon organic matter isotope signatures: a case study of the East European basin

FIG. 6. — Bivariate plot for bulk carbonate δ13Cand conodont δ13Cvalues. carb org

opencc-zeroOct 2020View details →

FIG. 4 in Trophic position of some Late Devonian-Carboniferous (Mississippian) conodonts revealed on carbon organic matter isotope signatures: a case study of the East European basin

FIG. 4. — Organic carbon isotope compositions of conodonts measured in the study.

opencc-by-4.0May 2020View details →

Predicted lake dissolved organic carbon at a global scale

The pool of dissolved organic carbon (DOC), is one of the main regulators of the ecology and biogeochemistry of inland water ecosystems, and an important loss term in the carbon budgets of land ecosystems. We used a novel machine learning technique and global databases to test if and how different environmental factors contribute to the variability of in situ DOC concentrations in lakes. In order to estimate DOC in lakes globally we predicted DOC in each lake with a surface area larger than 0.1 km2. Catchment properties and meteorological and hydrological features explained most of the variability of the lake DOC concentration, whereas lake morphometry played only a marginal role. The predicted average of the global DOC concentration in lake water was 3.88 mg L-1. The global predicted pool of DOC in lake water was 729 Tg from which 421 Tg was the share of the Caspian Sea. The results provide global-scale evidence for ecological, climate and carbon cycle models of lake ecosystems and related future prognoses.

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).

Phytoplanktonic polysaccharide-mediated sedimentation of particulate organic carbon triggers lagged methane emissions

Reservoirs act as carbon sinks when sedimentation of particulate organic carbon (POC) exceeds CO2 and CH4 emissions. Here, we study the poorly explored process where phytoplankton-derived acidic polysaccharides (APs) aggregate into particulate organic matter, promoting carbon export to sediments. This source of particulate organic carbon (POC) in sediments can mineralize to CO2 and CH4 over various timescales. Our research, centered on a Mediterranean reservoir, elucidates phenological trends of APs and POC sedimentation and identifies their predominant drivers. Our findings present synchronic sedimentation patterns of POC and APs but identify a two-week delay between POC sedimentation and CH4 emissions. Despite its eutrophic status, our data demonstrate that this reservoir acts as a carbon sink by sequestering 4.33 g C m-2 y-1, which accentuates the importance of temporal integration at multiple scales when analyzing carbon budget within reservoirs.

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.

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-4.0Feb 2024View details →

Engineering Machine Learning features to predict adsorption of carbon dioxide and nitrogen in metal-organic frameworks

This repository contains CIF files for metal-organic frameworks and Grand canonical Monte Carlo (GCMC) simulation results for the article Engineering Machine Learning features to predict adsorption of carbon dioxide and nitrogen in metal-organic frameworks by Zijun Deng and Lev Sarkisov.

opencc-by-4.0Mar 2024View details →

Dissolved organic carbon concentrations, pH and conductivity of water flowing from eroding and restored peatland catchments

Water was collected  from gullies within an eroding blanket bog. The bog is on a large high-altitude plateau blanket bog in the eastern part of the Cairngorms National Park, Scotland, UK (56.93° N, − 3.16° E, 642 m asl).  At Balmoral DOC concentrations were measured from water flowing through six v-notch weirs. Three weirs measured drainage from mini catchments that had undergone restoration and three measured drainage from  degraded mini-catchments with multiple upstream erosion gullies and bare peat. Restoration at this site included reprofiling and vegetating (turving) of peat haggs, bunding using coir logs to ‘slow the  flow’ and encourage Sphagnum growth, and mulching of areas of bare peat with locally sourced vegetation. The six V-notch weirs conforming to British Standard 3680:Part 4A:1981 (British Standards Institute, 1981) were constructed from 18 mm marine plywood. Initially 90°, 65 l s-1, V-notch plates cut from 1 mm aluminium plate were fitted. Weirs were installed at sites identified in the experimental design phase. Each weir was embedded into the peat by 20cm vertically and 20-40cm horizontally and supported by two posts embedded 60-80cm into the peat. Where required the weirs were extended to ensure that there were no leaks between the bank and the weir. A stilling well equipped with a capacitive water logger was installed at each weir. The stilling wells were manufactured from 800 mm long, 43 mm diameter, ABS waste pipe tubing. After a period of evaluation (November 2020 – May 2021) the 90°, 65 l/s, V-notch plates were replaced with 28.4°, 15 l s-1 plates in order to improve low-flow (<6cm head) accuracy. The water loggers were set to record water levels every 10 minutes. Raw data was downloaded every 6 months and processed in a Python script using the BS 3680 formula. Water samples were taken from each weir (if water was present behind the weir on the sampling day)  over a two year period from September 2021 to September 2023  After samples are delivered to the laboratory the protocol for wet chemistry analysis follows the protocol of the National Water Inventory of Scotland (NWIS) project. pH, conductivity and turbidity were then measured on all samples before a 100ml subsample was filtered through a 0.45 µm membrane. The remaining unfiltered sample is stored in a cold room and the filter membrane was retained, air dried and stored in plastic bags, for potential further analysis. The filtered water was analysed for DOC on a Skalar TOC analyser (Norcross, USA).

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).