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154 results for “carbon stocks”
European soil bulk density and organic carbon stock database using LUCAS Soil 2018
<p>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).</p> <p>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).</p> <p>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.</p> <p>When using the data, please cite repositories as well as the original manuscript.</p> <p>For any questions on the data, please contact Dr. Songchao Chen (chensongchao@zju.edu.cn).</p>
Data from: Microbial carbon use efficiency and soil organic carbon stocks across an elevational gradient in the Peruvian Andes
<p>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 <sup>13</sup>C glucose tracing and microbial resource use efficiency (CUE<sub>C</sub><sub>:</sub><sub>N</sub>) 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 <sup>13</sup>CUE nor CUE<sub>C:N</sub> 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.</p>
Woody debris removal modifies carbon stocks and soil properties in a fragmented tropical rainforest
<p>We examined whether and how woody debris removal for domestic fuel affects carbon storage and soil properties in an Indian rainforest. Fuelwood removal reduced aboveground carbon stocks, increased soil bulk density, and possibly reduced soil phosphorus stocks. Equitably balancing this subtle trade-off between climate-regulating and vital, widely-utilized provisioning functions, is a challenge for tropical forest research and management.</p>
High-resolution mapping of soil carbon stocks in the western Amazon
<h2>Dear Researchers and Interested Parties,</h2> <p> It is with great enthusiasm that we share our page on Zenodo, where we provide <strong>detailed maps</strong> (30 m resolution) of <strong>soil carbon stocks in Rondônia, Brazil</strong>. These maps were generated using machine learning techniques, using the Random Forest model implemented in the caret package. This initiative aims to provide a deeper and more accurate understanding of the spatial distribution of carbon in the soil, contributing significantly to environmental studies and climate change mitigation strategies in the region.</p> <h2>Available resources:</h2> <h3>High Resolution Maps:</h3> <p>We provide detailed maps of the estimates and uncertainties of soil carbon stocks at different depths (0-5; 5-15; 15-30; 30-60 and 60-100 cm). The maps include mean values (Mg ha<sup>-1</sup>), quantiles (Mg ha<sup>-1</sup>) and coefficients of variation (%), all in "tif" format, with a spatial resolution of 30 m and SAD 1969 Lambert South America projection system (<a href="https://epsg.io/102015">EPSG :102015</a>).</p> <p>The entire process was conducted in open source (R Language). The codes and database used <strong>can be found in the <a href="https://github.com/moquedace/ro_soil_carbon_stock" target="_blank" rel="noopener">GitHub repository</a></strong>, and more information about the methodology is available in the following publication:</p> <p>Moquedace, C. M., Baldi, C. G. O., Siqueira, R. G., Cardoso I. M., Souza, E. F. M., Fontes, R. L. F., Francelino, M. R., Gomes, L. C., Fernandes-Filho, E. I. High-resolution mapping of soil carbon stocks in the Western Amazon. <em>Geoderma Regional</em>, v. 36, p. e00773, 2024. DOI: <a href="https://doi.org/10.1016/j.geodrs.2024.e00773">10.1016/j.geodrs.2024.e00773</a></p> <h2>Availability objectives:</h2> <h3>Promote scientific collaborations:</h3> <p>We encourage researchers, scientists, and organizations to explore and use this data to enrich their own research and projects related to soil carbon and climate change.</p> <h3>Enhance environmental understanding:</h3> <p>By providing open access to these maps, we aim to contribute to a deeper understanding of environmental processes in Rondônia, Brazil and, by extension, enable the implementation of sustainable strategies.</p> <h3>Stimulating innovation:</h3> <p>We believe that sharing this data will stimulate innovation in modeling and spatial analysis methods, driving advances in the prediction of soil carbon stocks, especially in the Amazon.</p> <h2>Thank you in advance for your interest and collaboration. Together, we can advance knowledge and the search for sustainable solutions to important environmental challenges.</h2> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
Data from: Seed dispersal mode and habitat connectivity underpin variation in carbon stocking between Brazilian biomes
<p>In tropical forests, about 60 to 80% of woody plant species depend on animal-plant interactions for dispersal. The dependence on animal species for dispersal makes this interaction very fragile in the face of anthropogenic changes in land use. Disrupting seed dispersal processes, principally zoochoric dispersal, could significantly alter the long-term carbon storage potential of tropical forests. An important question is how landscape structure changes tree carbon stocks in different types of tropical vegetation and how variation is mediated by the dispersal mode of animal (zoochoric) or abiotic (non-zoochoric) seeds. We focused on tree plots at 126 sites in Brazil spanning four types of forest and savanna vegetation, and calculated carbon stored in zoochoric, non-zoochoric, and large frugivore-dispersed species. Our results showed that carbon stocks in zoochoric species and non-zoochoric species differ significantly among vegetation types, with rainforests having higher stocks in zoochoric species and semideciduous seasonally dry tropical forests having higher values in non-zoochoric species. A greater area of native vegetation promotes higher proportions of carbon stocks dispersed by large frugivore species, whereas a higher mean shape index reduces this proportion. Synthesis: This study highlights that seed-dispersal type underpins the variation in carbon stocks between vegetation types and that the maintenance of habitat of large dispersers and connectivity are key for retaining carbon stocks in zoochoric species, particularly in rainforest and cerrado sensu stricto.</p>
Database of Soil Organic Carbon (SOC) stock up to 20 cm depth: A collection of studies from Uruguay
<p>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.</p> <p>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.</p> <p><strong> </strong></p> <h3>Data Table Description</h3> <p>The associated data table from this database contains the following key variables:</p> <ul> <li> <p>ID: Unique identifier for each sampling point.</p> </li> <li> <p>Latitude & Longitude: Geographic coordinates of the sampling site.</p> </li> <li> <p>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).</p> </li> <li> <p>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.</p> </li> <li> <p>Land Use: Simplified land use classification with “grassland”, “agriculture”, or “forest” categories</p> </li> <li> <p>Land Use Extended: If available, the responsible party provides a more detailed explanation of land use.</p> </li> <li> <p>Month & Year: Month and year of sample collection.</p> </li> <li> <p>SOC Method: Methodology used for SOC determination, such as Walkley-Black or dry combustion.</p> </li> <li> <p>Published: Indicates whether the data have been published, with a URL provided if available. If not published, the data are marked as unpublished.</p> </li> <li> <p>SOC Calc: The approach used for calculating SOC stock at a fixed depth, detailed in two possible methods:</p> </li> <ul> <li> <p>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.</p> </li> <li> <p>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.</p> </li> </ul> </ul> <h3>Additional Observations</h3> <ul> <li> <p>For sites listed in rows 200-216, the GPS position represents the plot´s centroid, as no specific location was reported.</p> </li> <li> <p>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.</p> </li> <li> <p>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.</p> </li> </ul> <h3> </h3> <h3>Funding:</h3> <p>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).</p> <p> </p>
GSOCS-LULCC: the Global Soil Organic Carbon Stock dataset after Land Use and Land Cover Change
<p>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.<br>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.<br>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.<br>When using the data, please cite repositories as well as the original manuscript.<br>For any questions on the data, please contact Dr. Songchao Chen (chensongchao@zju.edu.cn).</p>
Data for "Can Regenerative Agriculture increase national soil carbon stocks? Simulated country-scale adoption of reduced tillage, cover cropping, and ley-arable integration using RothC"
<p>R code and supplementary data for soil carbon simulations: "<em>Can Regenerative Agriculture increase national soil carbon stocks? Simulated country-scale adoption of reduced tillage, cover cropping, and ley-arable integration using RothC-26.3</em>"</p>
Limited increases in savanna carbon stocks over decades of fire suppression
<p>Savannas cover a fifth of the land surface and contribute a third of terrestrial net primary production, accounting for three quarters of global area burned and over half of global fire-driven carbon emissions. Fire suppression and afforestation have been proposed as tools to increase carbon sequestration in these ecosystems. A robust quantification of whole-ecosystem carbon storage in savannas is lacking, however, especially under altered fire regimes. Here, we provide the first direct estimates of whole-ecosystem carbon response to over 60 years of fire exclusion in a mesic African savanna. We found that fire suppression increased whole-ecosystem carbon storage by only 35.4 ± 12% (mean ± standard error), even though tree cover increased by 78.9 ± 29.3%, corresponding to total gains of 23.0 ± 6.1 Mg C ha<sup>-1</sup> at an average ~0.35 ± 0.09 Mg C ha<sup>-1 </sup>yr<sup>-1</sup>, more than an order of magnitude lower than previously assumed. Frequently burned savannas had substantial belowground carbon, especially in biomass and deep soils. These belowground reservoirs are not fully considered in afforestation or fire suppression schemes but may mean that the decadal sequestration potential of savannas is negligible, especially weighed against concomitant losses of biodiversity and function.</p>
Edge effects increase soil respiration without altering soil carbon stocks in temperate broadleaf forests
<p>Anthropogenic disturbance has left the world's forests highly fragmented, with a significant proportion of edge-affected area. Abiotic changes at forest edges are likely to affect forest soil carbon cycling, as higher temperatures and lower moisture availability in edge environments have well-documented effects on soil respiration. The present study sought to quantify persistent changes in soil carbon cycling in the fragmented broadleaf forests of southeastern Pennsylvania. At three sites with >80 year old forest-field edges, three 100 m transects perpendicular to the edge were established. Monthly measurements of soil respiration, temperature, and moisture were made at fixede distances along each transect throughout the growing season. Soil carbon storage from 0-20 cm depth, litter biomass, and decomposition rates were also assessed. Soil respiration was significantly higher at forest edges, relative to the interior, and this effect penetrated 60 m into the forest. Significantly elevated surface soil temperature and decreased soil moisture were also observed in edge environments. Despite elevated soil respiration at the edge, soil carbon storage, litter bssomass, and decomposition rates were invariant along edge to interior gradients. The temperature responsiveness of soil respiration was significantly higher in the forest interior (100 m), relative to locations ≤60 m from the edge. Edge effects altering elements of the soil carbon cycle were apparent in the forests of southeastern Pennsylvania, and principally manifest as increased soil respiration rates and decreased temperature responsiveness of soil respiration. Lack of variation in soil carbon pools and decomposition rates from the forest edge to interior suggests that increased soil respiration may be related to changes in root and rhizosphere respiration at the edge. These findings contribute to a growing body of evidence documenting increased soil respiration in the edge environments of temperate broadleaf forests. Discounting the alterations imposed by forest fragmentation on carbon cycling has the potential to produce misleading estimates of land-atmosphere CO<sub>2</sub> exchange and terrestrial carbon storage.</p>
Maps of soil organic carbon stocks in Brazil
<p>This database was created by Gustavo Vieira Veloso and Lucas Carvalho Gomes 04/06/2022. <br> Contact: gustavo.v.veloso@gmail.com and lucascarvalhogomes15@hotmail.com <br> ------------------------------------------------------------------------------------</p> <p>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)</p> <p>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.</p> <p>The maps are free to use and please cite also the article:<br> 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.</p> <p> </p>
Dataset for: Aboveground carbon stocks, woody and litter productivity along an elevational gradient in the Rwenzori Mountains, Uganda
<p class="MsoNormal"><span>Montane forests are characterized by high</span><span> biodiversity</span><span>, endemism and strong </span><span>elevational environmental gradients</span><span>. The latter attribute makes them also suitable as a 'natural laboratory' for studying the effects of environmental parameters on ecosystem functions. To provide better insight into the carbon cycle of Afromontane ecosystems, we used an elevational gradient approach to quantify carbon stocks, woody and litter productivity, and their constraining factors. Twenty plots were established, covering five elevations from Kibale Forest at 1250 m to 3000 m in the Rwenzori Mountains. Results revealed aboveground carbon stocks of between </span><span>185.4 <span>± 48.9 Mg C ha<sup>-1</sup></span></span><span> and </span><span>70.8 ±18.6</span><span> </span><span>Mg C ha<sup>-1</sup></span><span> at 1250-1300 m and 2700-3000 m respectively</span><span>. Aboveground</span><span> carbon tended to decrease with elevation, but this trend was not significant.<span> This was due to similarities in stem diameter combined with different effects of tree height and stem density. Similarly, woody productivity did not change with elevation, ranging from </span></span><span>8.3 ± 4.1 Mg C ha<sup>-1</sup> year<sup>-1</sup> to 3.4 ± 1.5 Mg C ha<sup>-1</sup> year<sup>-1</sup> at 2500-2600 m and 2700-3000 m respectively. </span><span>However, litter productivity decreased linearly by </span><span>0.14 ± 0.04 Mg C ha<sup>-1</sup> year<sup>-1</sup> per 100 m of elevation increase</span><span>, ranging from </span><span>4.0 ± 0.7 Mg C ha<sup>-1</sup> year<sup>-1 </sup>at 1750-1850 m to 1.2 Mg C ha<sup>-1</sup> year<sup>-1 </sup>at 2700-3000 m. Topsoil physicochemical properties varied with elevation, but showed no significant relationship with carbon stocks and woody productivity. </span><span>However, <span>litter productivity </span>increased with mean soil temperature, whereas it decreased with soil total nitrogen.</span></p>
Meta analysis carbon stocks Technosols allory update 2022
<p>This dataset contain the data published before 2021 resulting from the search equation "TS = (Technosol* AND (Organic carbon OR Organic matter)" in the Web of Science (WOS) database. Update from Allory 2022: <a href="https://doi.org/10.24396/ORDAR-60">https://doi.org/10.24396/ORDAR-60</a>.</p>
Climate and mycorrhizae mediate the relationship of tree species diversity and carbon stocks in subtropical forests
<p><span>1. </span><span>It is increasingly being recognized that tree species diversity has positive effects on forest ecosystem carbon (C) stock. However, at broad spatial scales this relationship may depend on climate conditions and species mycorrhizal associations.</span></p> <p><span>2. </span><span>Here, observations from 667 forest plots in subtropical China, were used to investigate the effects of species diversity, mean annual precipitation (MAP), mean annual temperature (MAT) and mycorrhizal type (arbuscular or ectomycorrhizal) on the forest C stock</span> <span>and its components (tree C stock, shrub layer C stock, herb layer C stock, litter layer C stock, root C stock and soil C stock).</span></p> <p><span>3. </span><span>We found positive effect of tree species diversity on total forest C stock.</span><span> MAP had positive effects on total forest C stock and its components, while MAT had consistently negative effects on total forest C stock and most of its components.</span><span> Different levels of MAP and MAT </span><span>did modulate the strength of effect of species diversity on forest C stock and its components. In addition, species diversity, MAT and MAP showed a significant positive relationship with arbuscular mycorrhiza-associated tree C stock but had no or negative relationship with ectomycorrhiza-associated tree C stock.</span></p> <p><span>4. </span><span>Synthesis.</span><span> Our results indicate that maintaining high level of species diversity may support the buffering of negative effects resulting from climate warming. Furthermore,</span> <span>under climate warming the specific C stock of AM trees can increase, which can potentially promote forest C stock. Taken together, our study suggests that afforestation policies should consider not only tree species diversity to increase forest C stock but also the effects of different tree mycorrhizal types.</span></p>
Agroforestry carbon stocks and greenhouse gas emission rates in central Alberta, Canada
<p>Agroforestry systems (AFS) contribute to carbon (C) sequestration and reduction in greenhouse gas emissions from agricultural lands. However, previously understudied differences among AFS may underestimate their climate change mitigation potential. In this 3-year field study, we assessed various C stocks and greenhouse gas emissions across two common AFS (hedgerows and shelterbelts) and their component land uses: perennial vegetated areas with and without trees (woodland and grassland, respectively), newly planted saplings in grassland, and adjacent annual cropland in central Alberta, Canada. Between 2018 and 2020 (~April–October), nitrous oxide emissions were 89% lower under perennial vegetation relative to the cropland (0.02 and 0.18 g N m−2 year−1, respectively). In 2020, heterotrophic respiration in the woodland was 53% lower in shelterbelts relative to hedgerows (279 and 600 g C m−2 year−1, respectively). Within the woodland, deadwood C stock was particularly important in hedgerows (35 Mg C ha−1 or 7% of ecosystem C) relative to shelterbelts (2 Mg C ha−1 or < 1% of ecosystem C), and likely affected C cycling differences between the woodland types by enhancing soil labile C and microbial biomass in hedgerows. Deadwood C stock was positively correlated with annual heterotrophic respiration and total (to ~100 cm depth) soil organic C, water-soluble organic C, and microbial biomass C. Total ecosystem C was 1.90–2.55 times greater within the woodland than all other land uses, with 176, 234, 237, and 449 Mg C ha−1 found in the cropland, grassland, planted saplings treatment, and woodland, respectively. Shelterbelt and hedgerow woodlands contained 2.09 and 3.03 times more C, respectively, than adjacent cropland. Our findings emphasize the importance of AFS for fostering C sequestration and reducing greenhouse gas emissions and, in particular, retaining hedgerows (legacy woodland) and their associated deadwood across temperate agroecosystems to help mitigate climate change.</p>
Effects of land clearing for agriculture on soil organic carbon stocks in drylands: A meta-analysis
<p><span>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 </span><span>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.</span></p>
Data from: Socio-ecological drivers of long-term ecosystem carbon stock trend: An assessment with the LUCCA model of the French case
Open the record for dataset details and reuse information.
Deadwood carbon stocks under logging and fragmentation impacts
<b>Description: </b><p>Wood density estimates of deadwood across decomposition classes</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/23"><b>Deadwood carbon stocks under logging and fragmentation impacts</b></a></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=79">here</a></p><p><b>Data worksheets: </b>There are 1 data worksheets in this dataset:</p><ol><li><p><b>Wood density estimates</b> (Worksheet WoodDensity)</p><p>Dimensions: 418 rows by 23 columns</p><p>Description: Estimates of wood density for deadwood samples</p><p>Fields: </p><ul><li><b>Site</b>: Specific locations where deadwood samples were collected (Field type: Location)</li><li><b>Location</b>: SAFE Project sample block where deadwood samples were collected from (Field type: Location)</li><li><b>Block</b>: SAFE Project sample block where deadwood samples were collected from (Field type: ID)</li><li><b>Samples</b>: Unique identifier for each deadwood item sampled from in the field (Field type: Replicate)</li><li><b>DecayClass</b>: Decomposition level of the deadwood sample (Field type: Ordered Categorical)</li><li><b>SubS</b>: Replicate number. Multiple samples were analysed from a single 'Samples' (Field type: Replicate)</li><li><b>DeadPiece</b>: Type of the deadwood item in the field that the sample for analysis was extracted from (Field type: Categorical)</li><li><b>SpeciesID</b>: Identity of the deadwood item in the field that the sample for analysis was extracted from (Field type: Taxa)</li><li><b>D_upper_cm</b>: Diameter at one end of the deadwood item in the field that the sample for analysis was extracted from (Field type: Numeric)</li><li><b>D_lower_cm</b>: Diameter at other end of the deadwood item in the field that the sample for analysis was extracted from (Field type: Numeric)</li><li><b>Length_cm</b>: Length of the deadwood item in the field that the sample for analysis was extracted from (Field type: Numeric)</li><li><b>Type</b>: Shape of the deadwood sample being analysed (Field type: Categorical)</li><li><b>MassFresh _ g</b>: Wet weight of the deadwood sample (Field type: Numeric)</li><li><b>L1_mm</b>: Dimension measurement (Field type: Numeric)</li><li><b>L2_mm</b>: Dimension measurement (Field type: Numeric)</li><li><b>L3_mm</b>: Dimension measurement (Field type: Numeric)</li><li><b>V_fresh_ml</b>: Volume of the deadwood sample (Field type: Numeric)</li><li><b>MassDry_g</b>: Dry weight of the deadwood sample (Field type: Numeric)</li><li><b>V_dry_ml</b>: Volume of the deadwood sample (Field type: Numeric)</li><li><b>V_Piece_cm3</b>: Volume of the deadwood sample (Field type: Numeric)</li><li><b>V_Piece_cm3_set5</b>: Volume of the deadwood sample (Field type: Numeric)</li><li><b>Density</b>: Wood density (Field type: Numeric)</li></ul><br></li></ol><p><b>Date range: </b>2012-01-01 to 2013-12-31</p><p><b>Latitudinal extent: </b>4.6880 to 4.7436</p><p><b>Longitudinal extent: </b>117.5346 to 117.6290</p><p><b>Taxonomic coverage: </b><br> All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.</p><div>Plantae<br> - [Kayu malam]<br> - [Laran]<br> - [Sadaman egari]<br> - [Sedaman]<br> - Tracheophyta<br> -  - Magnoliopsida<br> -  -  - Malpighiales<br> -  -  -  - Euphorbiaceae<br> -  -  -  -  - <i>Macaranga</i><br></div><p></p>
Data from: Is salvage logging effectively dampening bark beetle outbreaks and preserving forest carbon stocks?
<p>The published dataset contains the result of the paper titled <em><strong>Is salvage logging effectively dampening bark beetle outbreaks and preserving forest carbon stocks? </strong></em></p> <p>The first sheet contains time averages for the 104years simulation period for several variables driven by different climate conditions and salvaging intensities. Other sheets shows data for the Appendices: time series for reference climate and one climate model scenario, time averages of salvaged and removed C and finally the time series of number of frost days.</p> <p>See more details about target area, and iLand model:</p> <p>https://www.sciencedirect.com/science/article/pii/S0168192318302946</p> <p>http://iland.boku.ac.at/startpage</p> <p> </p> <p>contact: Laura Dobor; dobor.laura@gmail.com</p>
The legacy of one hundred years of climate change for organic carbon stocks in global agricultural topsoils - full dataset
<p>This zip folder contains a txt and a shp file with predicted soil organic carbon stocks for a total of 931149 points on agricultural land across the globe at three different timepoints. The initial value (2018) for the scenarios c (constant carbon input) and v (variable carbon input) was derived from the FAO GSP Global SOC map published in 2018. the values in 1969 and 1919 are the results of backwards modelling with RothC model to estimate past climate change effects on SOC stocks. Details can be found in the publication " The legacy of one hundred years of climate change for organic carbon stocks in global agricultural topsoils" as published in Scientific Reports.</p>
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Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
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