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709 results for “soil carbon”
Soil percent carbon and nitrogen:Effect of Burning Patterns on Vegetation in the Fish Lake Burn Compartments
This study examines the effects of long-term prescribed burning treatments on vegetation structure and composition, productivity, and nutrient cycling in upland oak savanna and woodland vegetation. The basis for the study is an ongoing, experimental prescribed burning program begun in 1964 at Cedar Creek, and a similar program operating since 1962 on the adjacent Helen Allison Savanna property (owned by The Nature Conservancy). These prescribed burning programs are designed to subject upland oak communities (and some old fields) to different burn frequencies and patterns of burning, with the ultimate objectives of 1) restoring and maintaining the historically important savanna and open woodland vegetation, and 2) providing information about the effects of different burning patterns on vegetation structure and composition. This study addresses the latter of these two purposes and expands on it by also investigating possible influences of fire on resource availability (nutrients, water, and light) and net primary productivity. This study represents a continuation and expansion of experiments 015 and 094.
Soil Organic Carbon balance
<p>Provisional demonstration of SOC balance in eucalyptus and sugarcane systems in Brazil</p>
The vertical distribution of soil microbial biomass carbon: A global dataset
<p>Soil microbial biomass carbon (SMBC) is important in regulating soil organic carbon (SOC) dynamics along soil profiles by mediating the decomposition and formation of SOC. The dataset is about the vertical distributions of SOC, SMBC, and soil microbial quotient (SMQ = SMBC/SOC) and their relations to environmental factors across five continents. Data are collected from literature, with a total of 289 soil profiles and 1040 observations in different soil layers compiled. The associated environment data were also collectd including climate, ecosystem types, and edaphic factors. More specifically, we develop this dataset by compiling data from 59 papers published in the Web of Sciene and the China National Knowledge Infrastructure from the year of 1970 to 2019. All the data included in this dataset meet two creteria: 1) there are at least three soil layers along a soil profile, and 2) soil MBC is measured using the fumigation extraction method. The data were obtained from tables and texts from literature directly, and the data in figures were extracted using GetData Graph digitizer software version 2.25. When climate and soil properties are not available from publications, we obtainted the data from the World Weather Information Service (https://worldweather.wmo.int/en/home.html) and SoilGrids at a spatial resolution of 250 meters (version 0.5.3, https://soilgrids.org).</p> <p>The units of all the variables are converted to the standard international units or commonly used ones and the values are converted correspondingly. For example, the value of soil organic matter (SOM) is converted to SOC using the equation (SOC = SOM × 0.58). Soil depth is calculated as the arithmetic mean value of the upper and lower boundaries for a given soil layer.</p> <p>This dataset can be used in predicting global SOC change along soil profiles using the multi-layer soil C models. It can also be used to analyse how soil microbial biomass changes with plant roots as well as the composition, structure, and functions of soil microbial communities along soil profiles at large spatial scales. This dataset offers opportunities to improve our prediction of SOC dynamics under global changes and to advance our understanding of the environmental controls.</p>
The properties of calcium carbonate-rich soils from Kacwin village (South Poland)
<p>The presented dataset contain the raw data concerning mineralogical, micromorphological and geochemical properties of the calcium carbonate-rich soils from Kacwin village (South Poland). Additionally, the selected chemical properties and particle size distribution have been shown.</p>
Supplemental information for McClelland et al. (2020). Management of cover crops in temperate climates influences soil organic carbon stocks – A meta-analysis
<p>All supplemental information for McClelland et al. (2020). Management of cover crops in temperate climates influences soil organic carbon stocks – A meta-analysis. </p>
Data from: Spatial-temporal variability and related factors of soil organic carbon in Henan province
Spatial variability and influence factors are important to evaluate soil organic carbon(SOC) and the carbon pool in large areas. In the present study, sampling was conducted from May to November 2011 in Henan province, a typical agricultural region of central China, to study the effects of soil properties and anthropogenic factors on SOC variability in cropland. Physicochemical properties of soil samples were analyzed, which were collected at 280 sites from the surface layer (at a depth of 0–20 cm), and related data about the sampling sites were also collected from the Second State Soil Survey of China (SSSSC), conducted in 1981. The main results were as follows: 1) Increasing trends in soil organic carbon density (SOCD) and soil organic carbon pool (SOCP) were obvious from 1981 to 2011, and we conclude that cropland presents great carbon sequestration potential for the future. Carbon pool ability varied with soil properties: the order of fixed carbon amount in different soil types was found to be Inceptisols > Luvisols > Semi-hydromorphic soil > Anthrosols, and the average SOCP increased significantly from 1981 to 2011. 2) Soil bulk density, pH and returning straw are the key influence factors for SOCD in the past 30 years. 3) Although random factors (returning straw) only explain 29.1% of SOCD variability, the factor should be paid more attention, because application of returning strawwas the most dominant anthropogenic factors, which can be used to improve cropland productivity and carbon sink capacity within a short period if they are properly managed in the future.
Data from: Dryland soils in northern China sequester carbon during the early-2000s warming hiatus period
1. Drylands, covering ~45% of the Earth's terrestrial surface and supporting ~38% of the global population, play a dominant role in the trend and inter-annual variability of global land carbon (C) sink. Given that a large proportion of organic C is stored in soils, our knowledge on soil C dynamics in drylands is crucial to evaluate terrestrial C-climate feedback. However, credible understanding on this issue is still greatly limited by the lack of direct observations. 2. Here, based on a regional resampling of historical sites collected during 2002-2004, we explored the soil organic C changes in various layers over the past decade across the arid/semi-arid grasslands on the Inner Mongolian Plateau. 3. Our results revealed that the soil organic C density (SOCD) in this typical dryland increased significantly over the monitoring period, with a mean increase of 50.6 g C m-2 yr-1 or 0.8% yr-1 in the top 50 cm depth. Moreover, soil C dynamics exhibited contrasting spatial patterns between different layers: the rate of C accumulation in surface soils (0-10 cm) decreased, whereas that in deep soils (30-50 cm) exhibited an increasing trend along the aridity gradient. 4. Collectively, these findings demonstrate that dryland soils function as an important C sink, with the drier region tending to sequester C in deeper soils due to the greater root biomass allocation.
Data from: Grazer effects on soil carbon storage vary by herbivore assemblage in a semi-arid grassland
1. Accounting for 10-30% of global soil organic carbon, grassland soils potentially present a large reservoir for storing atmospheric CO2. Livestock grazing management can substantially affect grassland soil carbon (C) storage, but few controlled experiments have explored how herbivore assemblages (different herbivore species and combinations) affect soil C storage. 2. We examined effects of moderate grazing by different herbivore assemblages (no grazing; sheep grazing; cattle grazing; mixed grazing by sheep and cattle) on soil organic carbon storage in two types of grassland communities (high forbs/high diversity and low forbs/low diversity), within a semi-arid grassland with a five-year grazing history. 3. We found that herbivore assemblage generated varying effects on soil C storage and the effects were subject to grassland community types. In the low diversity community, none of three herbivore assemblages studied had obvious effects on soil C storage. In the high diversity community, however, sheep grazing significantly decreased soil C storage due to high selectivity for high quality forbs, and cattle grazing had no effects on soil C storage, while mixed grazing by sheep and cattle significantly increased soil C storage. Overall, soil C storage was highest in mixed-grazed grassland sites with high diversity. 4. Synthesis and applications. Our study suggests that explicitly incorporating grazer species and the combination of grazing livestock into grassland grazing management may help mitigate greenhouse gas emissions. Caution should be exercised when using grazer species with high food selectivity when grazing management is also aimed at climate mitigation, especially in grasslands with abundant high quality forbs and high plant diversity, as sheep grazing may reduce soil carbon (C) storage. Moreover, mixed grazing, including multiple herbivore species, may contribute to a reduction in foraging selectivity for a plant community by means of complementary foraging. It could therefore be considered as an optimal grazing management strategy to maintain and improve soil C storage.
Data from: Direct and indirect effects of nitrogen enrichment on soil organisms and carbon and nitrogen mineralization in a semi‐arid grassland
1. Semi-arid grasslands on the Mongolian Plateau are expected to experience high inputs of anthropogenic reactive nitrogen in this century. It remains unclear, however, how soil organisms and nutrient cycling are directly affected by N enrichment (i.e., without mediation by plant input to soil) vs. indirectly affected via changes in plant-related inputs to soils resulting from N enrichment. 2. To test the direct and indirect effects of N enrichment on soil organisms (bacteria, fungi, and nematodes) and their associated C and N mineralization, in 2010 we designated two subplots (with plants and without plants) in every plot of a six-level N-enrichment experiment established in 1999 in a semi-arid grassland. 3. In 2014, 4 years after subplots with and without plant were established, N enrichment had substantially altered the soil bacterial, fungal, and nematode community structures due to declines in biomass or abundance whether plants had been removed or not. N enrichment also reduced the diversity of these groups (except for fungi) and the soil C mineralization rate and induced a hump-shaped response of soil N mineralization. As expected, plant removal decreased the biomass or abundance of soil organisms and C and N mineralization rates due to declines in soil substrates or food resources. 4. Analyses of plant removal-induced changes (ratios of without- to with-plant subplots) showed that microorganisms and C and N mineralization rates were not enhanced as N enrichment increased but that nematodes were enhanced as N enrichment increased, indicating that the effects of plant removal on soil organisms and mineralization depended on trophic level and nutrient status.5. Surprisingly, there was no statistical interaction between N enrichment and plant removal for most variables, indicating that plant-related inputs did not qualitatively change the effects of N enrichment on soil organisms or mineralization. Structural equation modeling confirmed that changes in soil communities and mineralization rates were more affected by the direct effects of N enrichment (via soil acidification and increased N availability) than by plant-related indirect effects. Our results provide insight into how future changes in N-deposition and vegetation may modify below-ground communities and processes in grassland ecosystems.
Shrub encroachment decreases soil inorganic carbon stocks in Mongolian grasslands
1. Widespread shrub encroachment in global drylands may increase plant biomass and change soil organic carbon stocks of grassland ecosystems. However, the response of soil inorganic carbon (SIC), which is a major component of dryland carbon pools, to this vegetation shift remains unknown. 2. We conducted a systematic field survey in 75 pairs of shrub-encroached grassland and control plots at 25 sites in the grasslands of the Inner Mongolia Plateau to evaluate how shrub encroachment affects SIC density (SICD) in these ecosystems. 3. We found that shrub encroachment significantly reduced SICD in the upper 100 cm (3.85 vs. 4.74 kg C m-2, P < 0.05), especially in the subsurface soil (20-50 cm layer). The magnitude of SICD changes was related to the change in soil pH, shrub patch size, and initial SICD, reflecting that the reduction in SICD might be attributed to the shrub encroachment-related soil acidification. Our results also revealed that the lost SIC was mainly released into the atmosphere rather than redistributed into deeper soil layers. 4. Synthesis. We provide the first evidence for the soil acidification-induced SIC loss caused by shrub encroachment. Our findings highlight the non-negligible role of SIC dynamics in the C budget of shrub-encroached grassland ecosystems and the need to consider these dynamics in terrestrial C cycle research.
Data from: Relationships between plant traits, soil properties and carbon fluxes differ between monocultures and mixed communities in temperate grassland
1. The use of plant traits to predict ecosystem functions has been gaining growing attention. Aboveground plant traits, such as leaf nitrogen (N) content and specific leaf area (SLA), have been shown to strongly relate to ecosystem productivity, respiration, and nutrient cycling. Further, increasing plant functional trait diversity has been suggested as a possible mechanism to increase ecosystem carbon (C) storage. However, it is uncertain whether belowground plant traits can be predicted by aboveground traits, and if both above- and belowground traits can be used to predict soil properties and ecosystem-level functions. 2. Here, we used two adjacent field experiments in temperate grassland to investigate if above- and belowground plant traits are related, and whether relationships between plant traits, soil properties and ecosystem C fluxes (i.e., ecosystem respiration and net ecosystem exchange) measured in potted monocultures could be detected in mixed field communities. 3. We found that certain shoot traits (e.g., shoot N and C, and leaf dry matter content) were related to root traits (e.g., root N, root C:N, and root dry matter content) in monocultures, but such relationships were either weak or not detected in mixed communities. Some relationships between plant traits (i.e., shoot N, root N and/or shoot C:N) and soil properties (i.e., inorganic N availability and microbial community structure) were similar in monocultures and mixed communities, but they were more strongly linked to shoot traits in monocultures and root traits in mixed communities. Structural equation modelling showed that above- and belowground traits and soil properties improved predictions of ecosystem C fluxes in monocultures, but not in mixed communities on the basis of community-weighted mean traits. 4. Synthesis: Our results from a single grassland habitat detected relationships in monocultures between above- and belowground plant traits, and between plant traits, soil properties and ecosystem C fluxes. However, these relationships were generally weaker or different in mixed communities. Our results demonstrate that while plant traits can be used to predict certain soil properties and ecosystem functions in monocultures, they are less effective for predicting how changes in plant species composition influence ecosystem functions in mixed communities.
Data from: Litter carbon and nutrient chemistry control the magnitude of soil priming effect
1. Plant litter inputs can promote the decomposition of soil organic matter (OM) through the priming effect (PE). However, whereas leaf litter chemistry has long been identified as the primary driver of litter decomposition within biomes worldwide, little is known about how litter chemical traits influence the occurrence and strength of the PE. 2. Here, we studied the effects of 15 co-occurring C3 leaf litters of contrasting chemistry on C4 soil respiration by analyzing changes in 13C natural abundance during early and later stages of litter decomposition (up to 125 days). 3. Besides an apparent PE of 16% in the first three days, soil C respiration was increased by 24% on average with leaf litter addition in the initial stage of decomposition (426 d) and by 8% at later stages (27125 d). Most interestingly, soil PE related well to initial litter chemistry and the dominant factors influencing the magnitude of the PE changed with decomposition stage. In the early stage of decomposition, litter leachate C content and litter hemicellulose concentration were positively correlated with the strength of the PE, whereas tannin concentration was negatively associated with soil PE. Together, tannin and hemicellulose explained half of the observed variation in the PE (R2 = 0.58). In the later phase of decomposition, lignin and lignin:N ratios were negatively related to the PE, whereas Ca, K and Mg concentrations were positively related to the PE; lignin alone gave the best prediction of the PE (R2 = 0.58) at later decomposition stages. 4. Our findings provide evidence that the magnitude and direction of the PE is influenced by the chemistry of organic matter inputs and suggest that, as decomposition proceeds differently among litter of contrasting chemistry, litters can also have variable effect on soil PE through time. The predictive power of litter chemical traits on soil PE opens new perspectives for improving our mechanistic understanding of soil PE and improving our abilities to model soil C dynamics at variable scales.
Long time-series (2020-2100) high-resolution (1km) multi-scenario and multi-depth soil organic carbon dataset in China
<p>unit: kg C m-2 (soil oganic carbon density)</p><p>0100: denote 0-100 cm</p><p>020: denote 0-20 cm</p><p>Example 2020: 2020-2024 (five years mean soc)</p>
Supplemental Data for "Soil organic carbon change can reduce the climate benefits of biofuel produced from forest residues"
<p>The files contain the code and supplementary data for the article.</p>
Fluxes and concentrations of dissolved organic carbon in soils
<p>Dissolved organic carbon (DOC) in soil solution plays roles in soil C storage and biogeochemical cycles. Factors regulating fluxes and concentrations of DOC still remain unclear. To identify the factors regulating fluxes and concentrations of DOC in the soil profiles, we compiled the data of site information [Country, Region or state, Coordinates, Vegetation, Mean annual air temperature (ºC), Climate type, Vegetation type, Mycorrhiza type, Soil (USDA, Soil Taxonomy)], soil properties [Litter pH (H<sub>2</sub>O), Soil pH (H<sub>2</sub>O), Soil C/N ratio, Clay (%), Al<sub>o</sub>+1/2Fe<sub>o</sub> (g kg<sup>-1</sup>), O horizon C stock (Mg C ha<sup>-1</sup>), Mineral soil C stock (Mg C ha<sup>-1</sup>)], fluxes and concentrations of DOC [Throughfall DOC flux (kg C ha<sup>-1</sup> yr<sup>-1</sup>), DOC flux at the bottom of the O horizon (kg C ha<sup>-1</sup> yr<sup>-1</sup>), DOC flux at the bottom of the B horizon (kg C ha<sup>-1</sup> yr<sup>-1</sup>), DOC concentration at the bottom of the O horizon (mg C L<sup>-1</sup>), DOC concentration at the bottom of the B horizon (mg C L<sup>-1</sup>), DOC/Dissolved organic N (DON) (O horizon), DOC/DON (B horizon), Precipitation (mm yr<sup>-1</sup>), Water flux at the bottom of the O horizon (mm y<sup>r-1</sup>), Water flux at the bottom of B horizon (mm yr<sup>-1</sup>)], plant litter properties [Litterfall C input (Mg C ha<sup>-1</sup> yr<sup>-1</sup>), C/N ratio in litter, Lignin content in litter (%), Lignin/N ratio in litter, Root litter C input (Mg C ha<sup>-1</sup> yr<sup>-1</sup>)], and DOC retention in mineral soil (%), DOC flux relative to C input (%), Contribution of DOC to C input in mineral soil (%), and Turnover time of mineral soil C (yr)].</p>
Patterns and drivers of soil organic carbon fractions and persistence in coastal wetlands in China
Open the record for dataset details and reuse information.
Effect of soil stoichiometry mediated by vegetation on carbon emissions in a large wetland under hydrological stress
Open the record for dataset details and reuse information.
A Millimeter-Scale Change in Leaf Litter Placement Within Soil-Water Interfaces Alters Carbon Dioxide and Methane Emission
Open the record for dataset details and reuse information.
Dataset for Prolonged storage of bound organic carbon in wetland but not upland soils: A 13C and 14C perspective
Open the record for dataset details and reuse information.
Spatiotemporal prediction of soil organic carbon density (SOCD) for pan-Europe (2000-2022) in 3D+T
<h2><strong>Sub-dataset: SOCD mean, 2000-2004</strong></h2> <h2>Disclaimer</h2> <p>This is the first release of pan-EU predictions of soil health indicators (the Soil Health Data Cube). Use for testing purposes only. A publication describing methods used has been submitted to PeerJ and is in review. Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or European Commision. Neither the European Union nor the granting authority can be held responsible for them. The data is provided "as is". AI4SoilHealth project consortium and its suppliers and licensors hereby disclaim all warranties of any kind, express or implied, including, without limitation, the warranties of merchantability, fitness for a particular purpose and non-infringement. Neither AI4SoilHealth project Consortium nor its suppliers and licensors, makes any warranty that the Website will be error free or that access thereto will be continuous or uninterrupted. You understand that you download from, or otherwise obtain content or services through, the Website at your own discretion and risk.</p> <h2>Description</h2> <p>This dataset covers pan-European areas, including Ukraine, the UK, and Turkey. This data cube could be used for applications such as soil property mapping and comprehensive soil health assessment across Europe. The dataset spans four depth ranges and multiple time periods, providing information for studies on soil organic carbon stock and dynamics.</p> <p>This dataset is part of the Spatiotemporal prediction of soil organic carbon density for Europe (2000-2022) in 3D+T dataset. Check the related identifiers section below to access other parts of the dataset.</p> <p>This data set includes:</p> <ul> <li><strong>Soil Organic Carbon Density (SOCD) (2000-2022, 4-year intervals):</strong><br>This data includes mean, p975, and p025 SOCD maps for four depth ranges (0-20cm, 20-50cm, 50-100cm, and 100-200cm) in kg/m<sup>3</sup> (scaled 10x).</li> <li><strong>Organic carbon content based on dry combustion weight percentage (WPCT) (2000-2022, 4-year intervals):</strong><br>This data includes mean, p975, and p025 WPCT maps for four depth ranges (0-20cm, 20-50cm, 50-100cm, and 100-200cm) in percentage.</li> </ul> <h3>Related identifiers</h3> <ul> <li><strong>SOCD mean:</strong><br><a href="https://zenodo.org/records/13754343">2000-2004</a> <a href="https://zenodo.org/records/13771721">2004-2008</a> <a href="https://zenodo.org/records/13771841">2008-2012</a> <a href="https://zenodo.org/records/13771911">2012-2016</a> <a href="https://zenodo.org/records/13771967">2016-2020</a> <a href="https://zenodo.org/records/13772054">2020-2022</a></li> <li><strong>SOCD p025:</strong><br><a href="https://zenodo.org/records/13779539">2000-2004</a> <a href="https://zenodo.org/records/13774064">2004-2008</a> <a href="https://zenodo.org/records/13774089">2008-2012</a> <a href="https://zenodo.org/records/13774114">2012-2016</a> <a href="https://zenodo.org/records/13774167">2016-2020</a> <a href="https://zenodo.org/records/13774196">2020-2022</a></li> <li><strong>SOCD p975:</strong><br><a href="https://zenodo.org/records/13778472">2000-2004</a> <a href="https://zenodo.org/records/13773396">2004-2008</a> <a href="https://zenodo.org/records/13773765">2008-2012</a> <a href="https://zenodo.org/records/13773828">2012-2016</a> <a href="https://zenodo.org/records/13773953">2016-2020</a> <a href="https://zenodo.org/records/13774003">2020-2022</a></li> </ul> <h3>Data Details</h3> <ul> <li><strong>Time period:</strong> 2000–2022, in 4-year intervals (last period covers 2020–2022).</li> <li><strong>Type of data:</strong> Spatiotemporal soil organic carbon data cube, with depth ranges and weighted percentage data for soil carbon assessments.</li> <li><strong>How the data was collected or derived:</strong> The data was derived using machine learning models.</li> <li><strong>Statistical methods used:</strong> Quantile Random Forest</li> <li><strong>Limitations or exclusions in the data:</strong> The dataset does not include data for Svalbard.</li> <li><strong>Coordinate reference system:</strong> EPSG:3035</li> <li><strong>Bounding box (Xmin, Ymin, Xmax, Ymax):</strong> (900,000, 899,000, 7,401,000, 5,501,000)</li> <li><strong>Spatial resolution:</strong> 30m</li> <li><strong>Image size:</strong> 216,700P x 153,400L</li> <li><strong>File format:</strong> Cloud Optimized Geotiff (COG) format.</li> </ul> <h3>Support</h3> <p>If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue: GitLab Issues (tbc)</p> <h3>Name convention</h3> <p>To ensure consistency and ease of use across and within the projects, we follow the standard Ai4SoilHealth and Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describe important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are:</p> <ol> <li><strong>generic variable name:</strong> oc = organic carbon</li> <li><strong>variable procedure combination:</strong> iso.10694.1995.mg.cm3 = ISO method 10694:1995, with values in mg/cm<sup>3</sup> for SOCD | iso.10694.1995.wpct = ISO method 10694:1995, with values in weighted percentage of organic carbon content.</li> <li><strong>Position in the probability distribution/variable type:</strong> m = mean | p975 = percentile 97.5 | p025 = percentile 2.5</li> <li><strong>Spatial support:</strong> 30m</li> <li><strong>Depth reference:</strong> b0cm..20cm = depth range from 0 to 20cm</li> <li><strong>Time reference begin time:</strong> 20000101 = 2000-01-01</li> <li><strong>Time reference end time:</strong> 20041231 = 2004-12-31</li> <li><strong>Bounding box:</strong> eu = pan-Europe</li> <li><strong>EPSG code:</strong> epsg.3035</li> <li><strong>Version code:</strong> v20240804 = version from 2024-08-04</li> </ol>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.