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519 results for “organic soil”
Dataset: Effect of soil organic matter content and nutrient loading on productivity of Spartina patens (v.0.10)
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Effect of land use and soil organic matter quality on the structure and function of microbial communities in pastoral soils: implications for disease suppression
GEO Series GSE112489. Archaea; uncultured soil bacterium; Bacteria; Eukaryota. 50 samples. Type: Other.
Soil properties and their interactions on organic farms in Nebraska
<p>Sustainable soil productivity is a function of soil chemical, physical, and biological main and interaction effects that vary with inherent soil properties, topography, crop, location, and management. These effects were investigated using soil samples collected at geo-referenced points from 119 fields on 15 organically certified farms across Nebraska. Observations are reported for 46 variables. Most fields had adequate soil conditions for high productivity. The irrigated versus rainfed effects differed for several soil properties in western compared with eastern Nebraska. On average, about 80% of the soil was in water-stable aggregates (WSA) of >0.05 mm diameter with more aggregation and arbuscular mycorrhizal fungi (AMF) with pasture compared with cropland. Variation in soil microbial biomass (SMB) was more affected by variation in soil and climate properties than by management. Increased SOM for cropland in eastern Nebraska was positively related to SMB but not to AMF or the biomass ratios of saprophytic fungi (Fs) to bacteria and of actinomycetes to other bacteria. After accounting for the effect of SOM, SMB was most positively related to crop growth at the time of sampling, and availability of K and S. Mehlich-3 P was positively correlated with SMB but P and Zn availability were negatively correlated to AMF. Increased soil pH was associated with greater biomass of Fs and eukaryotes but not of bacteria. The SMB was generally less for pastures in western Nebraska compared with other field types. In conclusion, the soil properties of these organic farms were generally suited to high productivity and SMB had a strong, positive relationship to SOM.</p>
Management-induced changes in soil organic carbon and related crop yield dynamics in China's cropland
<p>Enhancing soil organic carbon (SOC) sequestration and food supply are vital for human survival when facing climate change. Site-specific best management practices (BMPs) are being promoted for adoption globally as solutions. However, how SOC and crop yield are related to each other in responding to BMPs remains unknown. Here, path analysis based on meta-analysis and machine learning was conducted to identify the effects and potential mechanisms of how the relationship between SOC and crop yield responds to site-specific BMPs in China. The results showed that BMPs could significantly enhance SOC and maintain or increase crop yield. The maximum benefits in SOC (30.6%) and crop yield (79.8%) occurred in mineral fertilizer combined with organic inputs (MOF). Specifically, the optimal SOC and crop yield would be achieved when the areas were arid, soil pH was ≥7.3, initial SOC content was ≤10 g kg<sup>-1</sup>, duration was >10 years, and the nitrogen (N) input level was 100-200 kg ha<sup>-1</sup>. Further analysis revealed that the original SOC level and crop yield change showed an inverted V-shaped structure. The association between the changes in SOC and crop yield might be linked to the positive role of the nutrient-mediated effect. The results generally suggested that improving the SOC can strongly support better crop performance. Limitations in increasing crop yield still exist due to low original SOC levels, and in regions where the excessive N inputs, inappropriate tillage or organic input is inadequate and could be diminished by optimizing BMPs in harmony with site-specific conditions.</p>
Soil properties and their interactions on organic farms in Nebraska
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The soil organic matter decomposition mechanisms in ectomycorrhizal fungi are tuned for liberating soil organic nitrogen
GEO Series GSE110485. Paxillus involutus; Laccaria bicolor. 24 samples. Type: Expression profiling by high throughput sequencing.
Soil Moisture Active Passive (SMAP) L4 Carbon Ancillary Soil Organic Carbon Restart File V001
This ancillary SMAP product contains the yearly soil organic carbon (SOC) restart file. This file contains the area density of SOC at the start of the year, which is used to calculate daily SOC based on defined deposition and decay rates.
A study to investigate the effects of long-term organic and integrated fertilization on soil microbial community
GEO Series GSE104014. soil metagenome; uncultured soil microorganism. 18 samples. Type: Other.
Implications of the use of organic fertilizers for Antibiotic Resistant Gene dissemination in agricultural soils and fresh food products. A plot-scale study
GEO Series GSE179685. soil metagenome; feces metagenome; sludge metagenome; solid waste metagenome. 71 samples. Type: Other.
Metagenomic analysis revealed the microbial-mediated soil organic carbon loss under the degeneration succession in alpine meadow
GEO Series GSE93158. uncultured soil microorganism. 20 samples. Type: Other.
A global distribution of dissolved organic carbon in soil and in leaching - (database)
<p>Current global carbon (C) models are not representing the fraction of C which is displaced along the terrestrial aquatic continuum thus overestimating the land sink capacity. In order to obtain more reliable C budgets, we need to integrate the lateral transfers of C from terrestrial ecosystems through the inland water network down to the oceans, including biogeochemical transformation during transport and C exchange with the atmosphere.Representing the production and cycling of dissolved organic C (DOC) in the soil column and the leaching of DOC into the inland water network is a first major step in this development.</p> <p>In this study we used newly developed model JULES-DOCM to obtain the first global estimate of global soil DOC stock and DOC concentration, DOC concentration in runoff and DOC leaching flux.</p> <p>In this dataset model produced files are stored as netcdf files including soil DOC stocks (at Top (0-35 cm) and Total soil (0-300cm), soil DOC concentration (at Top (0-35 cm) and Bottom ( 35-300cm)), DOC leaching flux (averaged over 1980-2010) and DOC concentration in runoff (averaged over 1980-2010).</p> <p>The measured DOC collected database is enclosed as the Excel file.</p>
Mapping Soil Organic Carbon in the World's Largest Arid Mangrove Forest (Indus Delta, Pakistan): A Multi-Sensor Remote Sensing and Machine Learning Approach
<p><span>Mangrove forests play a crucial role in carbon sequestration, especially in arid regions where their ability to store carbon in soil is vital for mitigating climate change. The Indus Delta in Pakistan, the world’s largest arid mangrove forest system, lacks spatially explicit data on Soil Organic Carbon (SOC) despite its importance for conservation and carbon budgeting. This study aims to establish a baseline SOC map 2020 at 10 m spatial resolution using Sentinel-1 (Synthetic Aperture Radar) and Sentinel-2 (MultiSpectral Instrument) satellite imagery, integrated with in-situ soil sampling. SOC predictions were made using a Classification and Regression Tree (CART) machine learning model within the Google Earth Engine platform, leveraging 40 predictor variables, including spectral bands and derived indices. A total of 53 topsoil (0-10 cm) samples were collected in February 2020 across the Indus Delta, and SOC was analyzed using the Walkley-Black method. The results showed an average SOC value of 65.88 Mg C ha</span><span>⁻</span><span>¹ with substantial spatial variability, ranging from 15.06 Mg C ha</span><span>⁻</span><span>¹ to 138.03 Mg C ha</span><span>⁻</span><span>¹ with a total of 0.91 Pg C. The CART model demonstrated high accuracy, with an R² of 0.95 and an RMSE of 9.18 Mg C ha</span><span>⁻</span><span>¹. However, the region faces challenges such as seawater intrusion and salinity, which threaten its ability to sequester carbon. With the first high-resolution SOC map for the Indus Delta, this study provides valuable insights for ecosystem management, conservation planning, and carbon budgeting. These findings of this study have the potential to significantly influence initiatives like REDD+ and Blue Carbon projects, which aim to enhance carbon sequestration while addressing the ecological challenges facing Pakistan’s mangroves</span></p>
Differential effects of nitrogen addition on soil organic carbon decomposition correlate with changes in microbial C-degradation functional potentials in a Pinus tabulaeformis forest
GEO Series GSE147041. uncultured soil microorganism; Bacteria; Eukaryota; Viruses; Archaea. 16 samples. Type: Other.
Heterobasidion annosum gene expression during saprotrophic growth on topsoil (organic layer) from mineral soil, drained and undrained peatland forests.
GEO Series GSE55290. Heterobasidion annosum. 12 samples. Type: Expression profiling by array.
Organic matter content (om) soil maps of the Upper Colorado River Basin
<p>The data here were originally posted to facilitate timely and transparent peer review. The final public data release with formal metadata is now available from at the following location:</p> <p>Nauman, T.W., and Duniway, M.C., 2020, Predictive soil property maps with prediction uncertainty at 30 meter resolution for the Colorado River Basin above Lake Mead: U.S. Geological Survey data release,<a href="http://https//doi.org/10.5066/P9SK0DO2"> https://doi.org/10.5066/P9SK0DO2</a>.</p> <p>Associated publication:</p> <p>Nauman, T. W., and Duniway, M. C., 2020, A hybrid approach for predictive soil property mapping using conventional soil survey data: Soil Science Society of America Journal, v. 84, no. 4, p. 1170-1194. <a href="https://doi.org/10.1002/saj2.20080">https://doi.org/10.1002/saj2.20080</a>.</p> <p>UPDATE: WE FOUND A RENDERING ERROR IN MANY AREAS OF THE 5 CM MAP. WE HAVE RECREATED THE MAP AND INCLUDED IN THIS VERSION OF THE REPOSITORY.</p> <p>Repository includes maps of organic matter content (% wt) as defined by United States soil survey program. </p> <p>These data are preliminary or provisional and are subject to revision. They are being provided to meet the need for timely best science. The data have not received final approval by the U.S. Geological Survey (USGS) and are provided on the condition that neither the USGS nor the U.S. Government shall be held liable for any damages resulting from the authorized or unauthorized use of the data.</p> <p>This data should be used in combination with a soil depth or depth to restriction layer map (both layers that will be released soon as part of this project) to eliminate areas mapped at deeper depths than the soil actually goes. This is a limitation of this data which will hopefully be updated in future updates. </p> <p>The creation and interpretation of this data is documented in the following article. Please note this article has not been reviewed yet and this citation will be updated as the peer review process proceeds.</p> <p>Nauman, T. W., Duniway, M. C., In Preparation. Predictive reconstruction of soil survey property maps for field scale adaptive land management. Soil Science Society of America Journal.</p> <p>File Name Details:</p> <p>ACCURACY!! Please see manuscript and Github repository (https://github.com/naumi421/SoilReconProps) for full details on accuracy. We do provide cross validation (CV) accuracy plots in this repository for both the overall sample (_CV_plots.tif). These plots compare CV predictions with observed values relative to a 1:1 line. Values plotted near the 1:1 line are more accurate. Note that values are plotted in hex-bin density scatter plots because of the large number of observations (most are >3000). Predictions are also evaluated with the U.S. soil survey laboratory database soil organic carbon (SOC) data. The SOC measurements were coverted to OM matter values using the common 1.724 conversion factor. The converted OM values are compared to predicted OM values using an accuracy plot (OM_SOC_plots.tif).</p> <p>Elements are separated by underscore (_) in the following sequence:</p> <p>property_r_depth_cm_geometry_model_additional_elements.extension</p> <p>Example: om_r_0_cm_2D_QRF_bt.tif</p> <p>Indicates soil organic matter content (om) at 0 cm depth using a 2D model (separate model for each depth) employing a quantile regression forest. This file is the raster prediction map for this model. There may be additional GIS files associated with this file (e.g. pyramids) that have the same file name, but different extensions. The _bt indicates that the map has been back transformed from ln or sqrt transformation used in modeling.</p> <p>The following elements may also exist on the end of filenames indicating other spatial files that characterize a given model's uncertainty (see below).</p> <p>_95PI_h: Indicates the layer is the upper 95% prediction interval value.</p> <p>_95PI_l: Indicates the layer is the lower 95% prediction interval value.</p> <p>_95PI_relwidth: Indicates the layer is the 95% relative prediction interval (RPI). The RPI is a standardization of the prediction interval that indicates that model is constraining uncertainty relative to the original sample. RPI values less than one represent uncertainty is being improved by the model relative to the original sample, and values less than 0.5 indicate low uncertainty in predictions. See paper listed above and also Nauman and Duniway (In revision) for more details on RPI.</p> <p>References</p> <p> Nauman, T. W., and Duniway, M. C., In Revision, Relative prediction intervals reveal larger uncertainty in 3D approaches to predictive digital soil mapping of soil properties with legacy data: Geoderma</p>
Dataset for McClelland et al. 2022. Infrequent compost applications increased plant productivity and soil organic carbon in irrigated pasture but not degraded rangeland. Agriculture, Ecosystems, and Environment.
<p>Raw data files accompanying the published article "Infrequent compost applications increased plant productivity and soil organic carbon in irrigated pasture but not degraded rangeland" in <em>Agriculture, Ecosystems, and Environment</em>. <a href="https://authors.elsevier.com/a/1etJPcA-Ik6yb">https://authors.elsevier.com/a/1etJPcA-Ik6yb</a></p> <p>Units for response variables in .csv files are as follows. Please reach out to scm229@cornell.edu with any questions about using the files or the data within.</p> <p>--</p> <p>Aboveground biomass: total (Mg ha-1), carbon (Mg C ha-1), nitrogen (kg N ha-1)</p> <p>Bulk density: g cm-3</p> <p>Respiration (Rs): micro mol m-2 s-1</p> <p>Roots: Mg C ha-1</p> <p>Soil C and N: organic and inorganic carbon (Mg C ha-1), nitrogen (Mg N ha-1)</p> <p> </p>
Particulate organic carbon controlled the upper limit of soil organic carbon in natural alpine ecosystems of northeast Qinghai-Tibet Plateau
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Phosphorus addition decreases microbial residual contribution to soil organic carbon pool in a tropical coastal forest
<p>This is the data supporting the study of 'Phosphorus addition decreases microbial residual contribution to soil organic carbon pool in a tropical coastal forest'. Data in the excel sheet were used for the figures and tables in the article. </p>
Contribution of wheat and maize to soil organic carbon in a wheat-maize cropping system: a field and laboratory study
<ol> <li>Retention of crop biomass is widely recommended to improve soil organic carbon (SOC). However, the magnitude of contribution of aboveground residues and belowground roots from C3 and C4 crops to SOC is unclear.</li> <li>Data from a 10-year field experiment and a 60-day laboratory incubation were synthesized to identify the respective contribution of C3 (e.g., wheat) and C4 (e.g., maize) residues and roots to SOC, as well as its underlying mechanisms under no-till (NT) using <sup>13</sup>C labelling trace in wheat-maize rotations.</li> <li>The field experiment showed that residue retention significantly increased SOC accumulation, and SOC derived from wheat was 126.0% higher than that from maize. Conversion to NT promoted SOC derived from wheat and thus accumulated 17.6% higher SOC stock compared with plow tillage (PT) under residue returning at 0-20 cm soil depth (<em>P</em><0.05). The data from laboratory incubation revealed the mechanisms that lower priming effects at 0-10 cm depth decreased total mineralization by 91.8% after inputs of wheat residues and roots compared with that of maize residues and roots, especially under NT compared with PT. Priming effects were negatively correlated with enzyme activities associated with the C recycle, SOC, and total nitrogen (TN) contents (<em>P</em><0.01). NT increased enzyme activities, SOC, and TN contents and thus reduced priming effects and improved residual carbon.</li> <li><em>Synthesis and applications.</em> These results suggested that wheat may contribute more to SOC accumulation than maize, and carbon increment efficiency in farmland could be enhanced by considering the crucial roles of C3 crops in SOC accumulation. NT practice sustains the benefits of C3 crops to SOC sequestration in the upper soil depths.</li> </ol>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
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.