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264 results for “soil organic carbon”
Soil dissolved organic carbon (DOC) machine learning model code
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Convergence in simulating global soil organic carbon by structurally different models after data assimilation
<p>This is the data for results shown in the article accepted by Global Change Biology: Convergence in simulating global soil organic carbon by structurally different models after data assimilation</p>
Spatiotemporal prediction of soil organic carbon density (SOCD) for pan-Europe (2000-2022) in 3D+T
<h2><strong>Sub-dataset: SOCD p025, 2020–2022</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 (scaled 10x). </li> </ul> <h3>Related identifiers</h3> <ul> <li><strong>SOCD mean:</strong><br> <a href="https://doi.org/10.5281/zenodo.13754343">2000-2004</a> <a href="https://doi.org/10.5281/zenodo.13771721">2004-2008</a> <a href="https://doi.org/10.5281/zenodo.13771841">2008-2012</a> <a href="https://doi.org/10.5281/zenodo.13771911">2012-2016</a> <a href="https://doi.org/10.5281/zenodo.13771967">2016-2020</a> <a href="https://doi.org/10.5281/zenodo.13772054">2020-2022</a> </li> <li><strong>SOCD p025:</strong><br> <a href="https://doi.org/10.5281/zenodo.13779539">2000-2004</a> <a href="https://doi.org/10.5281/zenodo.13774064">2004-2008</a> <a href="https://doi.org/10.5281/zenodo.13774089">2008-2012</a> <a href="https://doi.org/10.5281/zenodo.13774114">2012-2016</a> <a href="https://doi.org/10.5281/zenodo.13774167">2016-2020</a> <a href="https://doi.org/10.5281/zenodo.13774196">2020-2022</a> </li> <li><strong>SOCD p975:</strong><br> <a href="https://doi.org/10.5281/zenodo.13778472">2000-2004</a> <a href="https://doi.org/10.5281/zenodo.13773396">2004-2008</a> <a href="https://doi.org/10.5281/zenodo.13773765">2008-2012</a> <a href="https://doi.org/10.5281/zenodo.13773828">2012-2016</a> <a href="https://doi.org/10.5281/zenodo.13773953">2016-2020</a> <a href="https://doi.org/10.5281/zenodo.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>
Spatiotemporal prediction of soil organic carbon density (SOCD) for pan-Europe (2000-2022) in 3D+T
<h2><strong>Sub-dataset: SOCD mean, 2016–2020</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 (scaled 10x). </li> </ul> <h3>Related identifiers</h3> <ul> <li><strong>SOCD mean:</strong><br> <a href="https://doi.org/10.5281/zenodo.13754343">2000-2004</a> <a href="https://doi.org/10.5281/zenodo.13771721">2004-2008</a> <a href="https://doi.org/10.5281/zenodo.13771841">2008-2012</a> <a href="https://doi.org/10.5281/zenodo.13771911">2012-2016</a> <a href="https://doi.org/10.5281/zenodo.13771967">2016-2020</a> <a href="https://doi.org/10.5281/zenodo.13772054">2020-2022</a> </li> <li><strong>SOCD p025:</strong><br> <a href="https://doi.org/10.5281/zenodo.13779539">2000-2004</a> <a href="https://doi.org/10.5281/zenodo.13774064">2004-2008</a> <a href="https://doi.org/10.5281/zenodo.13774089">2008-2012</a> <a href="https://doi.org/10.5281/zenodo.13774114">2012-2016</a> <a href="https://doi.org/10.5281/zenodo.13774167">2016-2020</a> <a href="https://doi.org/10.5281/zenodo.13774196">2020-2022</a> </li> <li><strong>SOCD p975:</strong><br> <a href="https://doi.org/10.5281/zenodo.13778472">2000-2004</a> <a href="https://doi.org/10.5281/zenodo.13773396">2004-2008</a> <a href="https://doi.org/10.5281/zenodo.13773765">2008-2012</a> <a href="https://doi.org/10.5281/zenodo.13773828">2012-2016</a> <a href="https://doi.org/10.5281/zenodo.13773953">2016-2020</a> <a href="https://doi.org/10.5281/zenodo.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>
Suppression of Methanogenesis by Microbial Reduction of Iron-Organic Carbon Associations in Fully Thawed Permafrost Soil
<p>This data set contains data associated with the manuscript "Suppression of Methanogenesis by Microbial Reduction of Iron-Organic Carbon Associations in Fully Thawed Permafrost Soil". Currently under review.</p>
Raw data for "Plot-scale variability of organic carbon in temperate agricultural soils - Implications for soil monitoring"
<p>This dataset is the raw data that belongs to a peer-reviewed study on the small-distance variability of soil organic carbon in agricultural soils in Germany. It consists of three different files. The first file gives the coordinates of the 16 soil cores that were taken at each of the 16 sites (eight cropland and eight grassland sites). The second file gives the soil properties measured at each individual core (n=16 per site) and the third file the soil properties measured at each indivdual soil profile (n=6 per site).</p>
Temperature Controls the Relation between Soil Organic Carbon and Microbial Carbon Use Efficiency
<p>This is the dataset for the manuscript entitled "Temperature controls the relation between soil organic carbon and microbial carbon use efficiency".</p>
Data associated with "The importance of terrain and climate for predicting soil organic carbon is highly variable across local to continental scales"
<p>The zipped folder contains the processed soil datasets including covariates, soil depths, and SOC concentrations for training the deep learning models described in the paper "The importance of terrain and climate for predicting soil organic carbon is highly variable across local to continental scales". </p> <p>"soil_profile/" contains a table including the geolocations of all the soil profiles in this study. "patch_data/" and "point_data/" contain the covariates to feed the models with patch input and point input respectively. "depth/" contains the upper and lower depths of the soil samples. "y/" contains the target variable - SOC concentration of the soil samples. The data files with suffix "_1" is a small subset of their counterparts without "_1" (10 % in sample size) used for model hyperparameters tuning.</p>
Data from: Impacts of organic matter amendments on urban soil carbon and soil quality: A meta-analysis
<p>Organic matter amendment application is an important avenue of beneficial waste diversion and is used to improve soil quality in agricultural and urban settings. In urban regions, amendments are used to support local food production, maintain vegetation for landscaping and recreational use, and reclaim disturbed soils. Urban regions generate large quantities of wasted organic resources for potential application aiding in creating a circular nutrient economy. There is a growing interest in understanding the effects of amendments such as compost, biosolids, and biochar on soil properties in agricultural settings. Gaps remain, however, in assessing their effects in urban land uses. We conducted a literature review to assess the effects of compost, biochar, and biosolids on soil carbon and soil quality of urban soils managed for gardening, landscaping, recreation, and reclamation. Application of organic matter amendments led to an average increase of 3.6 units of soil organic matter% (SOM%). Compost and biochar improved SOM% the most, by 3.1 and 6.5 units of SOM%, respectively. Biosolids resulted in the smallest increase in SOM% but had greater nutrient benefits than other amendments. Parameters related to chemical and physical soil quality improved with the application of amendments. Gaps in the literature remain, such as assessing urban gardens, soil to depths greater than 30 cm, and the persistence of SOM in amended soils. This meta-analysis proposes that organic matter amendments are a powerful means to improve soil quality in urban regions, provide vital cobenefits to surrounding communities, and increase soil carbon storage.</p>
Soil organic carbon formation efficiency from straw/stover and manure input and its drivers: Estimates from long-term data in global croplands
<p><span>The supporting data for raw data, geographic location of the experimental sites, grid-level maps showing the predicted NCE (%) of global cropland</span></p>
Soil organic carbon formation efficiency from straw/stover and manure input and its drivers: Estimates from long-term data in global croplands
<p>In-situ observations collected from publications, grid-level maps showing the predicted NCE (%) of global cropland and data-driven model codes</p>
Soil organic carbon formation efficiency from straw/stover and manure input and its drivers: Estimates from long-term data in global croplands
<p>In-situ observations collected from publications, grid-level maps showing the predicted NCE (%) of global cropland and data-driven model codes</p>
Dataset for "Towards an ecosystem capacity to stabilise organic carbon in soils"
<p>This dataset includes the data that was used in the Global Change Biology publication "Towards an ecosystem capacity to stabilise organic carbon in soils" by Poeplau et al.. It contains two xlsx files, with dataset_full.xlsx including all sites with soil properties that were used in the first part of the manuscript. It is a combined dataset from several open source datasets with a total of 1396 individual sites. The file modelled_converged.xlsx includes the RothC model results of a total of 587 sites, for which modelling was possible and a convergence of measured and modelled data was reached. Both files include two sheets, one with a short explanation of the variable names and one data sheet. </p>
Soil organic carbon formation efficiency from straw/stover and manure input and its drivers: Estimates from long-term data in global croplands
<p>Field observation data collected from publications, the references from the main text and data sources , grid-level maps showing prediction of global cultivated land NCE(%) and data-driven model codes </p>
Rare taxa drives soil organic carbon accumulation in sagebrush desert grassland under grazing exclusion
Open the record for dataset details and reuse information.
Data from: The microbially-mediated soil organic carbon loss under degenerative succession in an alpine meadow
Land-cover change has long been recognized as having marked effect on the amount of soil organic carbon (SOC). However, the microbially-mediated processes and mechanisms on SOC are still unclear. In this study, the soil samples in a degenerative succession from alpine meadow to alpine steppe meadow in the Qinghai-Tibetan Plateau were analyzed using high-throughput technologies, including Illumina sequencing and GeoChip functional gene arrays. The soil microbial community structure and diversity were significantly (P < 0.05) different between alpine meadow and alpine steppe meadow, the microbial ɑ-diversity in alpine steppe meadow was significantly (P < 0.01) higher than in alpine meadow. Molecular ecological network analysis indicated that the microbial community structure in alpine steppe meadow was more complex and tighter than in the alpine meadow. The relative abundance of soil microbial labile carbon degradation genes (e.g., pectin and hemicellulose) was significantly higher in alpine steppe meadow than in alpine meadow, but the relative abundance of soil recalcitrant carbon degradation genes (e.g. chitin and lignin) showed the opposite tendency. The Biolog Ecoplate experiment showed that microbially-mediated soil carbon utilization was more active in alpine steppe meadow than in alpine meadow. Consequently, more soil labile carbon might be decomposed in alpine steppe meadow than in alpine meadow. Therefore, the degenerative succession of alpine meadow because of climate change or anthropogenic activities would most likely decreased SOC and nutrients medicated by changing soil microbial community structure and their functional potentials for carbon decomposition.
Tree species richness and soil organic carbon stock
<p class="MsoNormal"><span>Recently, the perspectives for the stronger persistence of soil organic carbon (SOC) caused by the higher molecular diversity of organic compounds were proposed. Therefore, the effects of tree species richness and composition on the diversity of molecular components of SOC need to be explored. In this study, we collected data on tree species diversity and composition, SOC concentration, chemical composition, litter and fine root properties, and examined the relationships between the richness, composition and functional diversity of tree species, and the evenness of SOC chemical compositions at a molecular level by <sup>13</sup>C nuclear magnetic resonance, across six natural forest types encompassing a diversity gradient, ranging from cold temperate to tropical forests. Across the range, tree species richness correlated to the evenness of SOC chemical components through tree species composition. The negative correlation of evenness of SOC chemical components with tree species composition and the positive correlation of evenness of SOC chemical components with tree functional diversity were found. The positive correlation of the evenness of SOC chemical components with indicator tree species. These findings suggest that the indicator tree species conservation might be preferable to simply increasing tree species richness, for enhancing the potential resistance of SOC to decomposition.</span></p>
Data for: Cover crop functional types differentially alter the content and composition of soil organic carbon in particulate and mineral-associated fractions
<p>Cover crops (CCs) can increase soil organic carbon (SOC) sequestration by providing additional OC residues, recruiting beneficial soil microbiota, and improving soil aggregation and structure. The various CC species that belong to distinct plant functional types (PFTs) may differentially impact SOC formation and stabilization. Biogeochemical theory suggests that selection of PFTs with distinct litter quality (C:N ratio) should influence the pathways and magnitude of SOC sequestration. Yet, we lack knowledge on the effect of CCs from different PFTs on the quantity and composition of physiochemical pools of SOC. We sampled soils under monocultures of three CC PFTs (legume [crimson clover]; grass [triticale]; and brassica [canola]) and a mixture of these three species, from a long-term CC experiment in Pennsylvania, USA. We measured C content in bulk soil and C content and composition in contrasting physical fractions: particulate organic matter, POM; and mineral-associated organic matter, MAOM. The bulk SOC content was higher in all CC treatments compared to the fallow. Compared to the legume, monocultures of grass and brassica with lower litter quality (wider C:N) had higher proportion of plant-derived C in POM, indicating selective preservation of complex structural plant compounds. In contrast, soils under legumes had greater accumulation of microbial-derived C in MAOM. Our results for the first time, revealed that the mixture contributed to a higher concentration of plant-derived compounds in POM relative to the legume, and a greater accumulation of microbial-derived C in MAOM compared to monocultures of grass and brassica. Mixtures with all three PFTs can thus increase the short- and long-term SOC persistence balancing the contrasting effects on the chemistries in POM and MAOM imposed by monoculture CC PFTs. Thus, despite different cumulative C inputs in CC treatments from different PFTs, the total SOC stocks did not vary between CC PFTs, rather PFTs impacted whether C accumulated in POM or MAOM fractions. This highlights that CCs of different PFTs may shift the dominant SOC formation pathways (POM vs. MAOM), subsequently impacting short- and long-term SOC stabilization and stocks. Our work provides a strong applied field test of biogeochemical theory linking litter quality to pathways of C accrual in soil.</p>
Supplemental data for "Uncertainty in Land Use Obscures Global Soil Organic Carbon Stock Estimates"
<p>These are supporting data for the manuscript: "Uncertainty in Land Use Obscures Global Soil Organic Carbon Stock Estimates." They include data for the figures showing the spatial dynamics of cropland and LULCC-induced SOC loss etc, including Fig.3, Fig.6, Fig.9, and Fig.10.</p>
Nine years of warming and nitrogen addition in the Tibetan grassland promoted loss of soil organic carbon but did not alter the bulk change of chemical structure
<p>Understanding the changes in soil organic carbon (SOC) storage and chemical stabilization dynamics is important for accurately predicting ecosystem C sequestration and/or potential C loss, but the relevant information, especially for the intervention of environmental controls on grassland soil is limited in Tibetan plateau regions. Here we used a 9-year two-way factorial experiment involving warming with open top chambers (+1.80 °C in the daytime and +0.77 °C in the nighttime at the soil surface) and multilevel nitrogen (N) enrichment treatments (0, 5, 10, and 15 g m<sup>-2</sup> year<sup>-1</sup>) in the Tibetan plateau to investigate the changes in SOC pool size and chemical structure. 9-year warming treatment significantly decreased SOC stock in the Tibetan grassland. We observed decreasing SOC concentrations which may be related to changes in the C degrading enzymes. Surprisingly, the SOC molecular structure remained unchanged in all N enrichment and warmed plots, suggesting that both treatments had affected all forms of SOC, from simple and complex polymeric in a similar way. Our results suggest that long-term warming stimulates soil C loss but no preference in SOC loss with different chemical structure.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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OpenNeuro
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