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519 results for “organic soil”
Divergent contributions of living roots to turnover of different soil organic carbon pools and their links to plant traits
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The main driver of soil organic carbon differs greatly between topsoil and subsoil in a grazing steppe
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Mechanisms driving the soil organic matter decomposition response to nutrient enrichment:Nutrient Network. A cross-site investigation of bottom-up control over herbaceous plant community dynamics and ecosystem function.
This experiment is one implementation of a globally distributed experiment, known as the Nutrient Network. At Cedar Creek, as in over 70 other sites in grasslands around the world, the experiment aims to describe impacts of increased nutrients (nitrogen, phosphorus, potassium, sulfur and other metals) and decreased herbivory (removal of mammals by fencing). Two overarching questions are being explored with these manipulations: 1. To what extent are plant production and diversity co-limited by multiple nutrients in herbaceous-dominated communities? 2. Under what conditions do grazers or fertilization control plant biomass, diversity, and composition? By utilizing identical protocols at diverse grassland sites around the world, NutNet aims to uncover both the generalities in ecosystem functioning, and the contingencies or differences which can obscure those common mechanisms. In addition to the standard NutNet protocol, e247 includes an additional low Nitrogen gradient (1 gram Nitrogen per meter squared per year and 5 grams Nitrogen per meter squared per year in addition to the standard 10 grams Nitrogen per meter squared per year).
Post-fire Recovery of Soil Organic Layer Carbon in Canadian Boreal Forests, 2015-2018
This dataset provides site moisture, soil organic layer thickness, soil organic carbon, nonvascular plant functional group, stand dominance, ecozone, time-after-fire, jack pine proportion, and deciduous proportion for 511 forested plots spanning ~140,000 km2 across two ecozones of the Northwest Territories, Canada (NWT). The plots were established during 2015-2018 across 41 wildfire scars and unburned areas (no burn history prior to 1965), with 317 plots in the Plains and 194 plots in the Shield regions. At each plot, two adjacent 30-m transects were established 2 m apart, running north from the plot origin. Soil organic layer (SOL) depth (cm) was measured every 3 m and the mean was taken from the 10 measurements to calculate a plot-level SOL thickness. Three soil organic layer profiles were destructively sampled at 0, 12, and 24 m using a corer that was custom designed for NWT soils. Within the transects, all stems taller than 1.37 m were identified to species to calculate tree density (stems / m2). Nonvascular plant percent cover was identified to functional group at five, 1-m2 quadrats spaced 6 m apart along the belt transect. A subset of 2,067 of 5,137 total increments from 1,803 profiles from 421 plots were analyzed for total percent C using a CHN analyzer. Time-after-fire was established using fire history records. For older plots where no known fire history is recorded, tree age was used. Data are for the period 2015-06-11 to 2018-08-24 and are provided in comma-separated values (CSV) format.
Soil Organic Carbon Stock Estimates with Uncertainty across Latin America
This dataset provides 5 x 5 km gridded estimates of soil organic carbon (SOC) across Latin America that were derived from existing point soil characterization data and compiled environmental prediction factors for SOC. This dataset is representative for the period between 1980 to 2000s corresponding with the highest density of observations available in the WoSIS system and the covariates used as prediction factors for soil organic carbon across Latin America. SOC stocks (kg/m2) were estimated for the SOC and bulk density point measurements and a spatially explicit measure of the SOC estimation error was also calculated. A modeling ensemble, using a linear combination of five statistical methods (regression Kriging, random forest, kernel weighted nearest neighbors, partial least squared regression and support vector machines) was applied to the SOC stock data at (1) country-specific and (2) regional scales to develop gridded SOC estimates (kg/m2) for all of Latin America. Uncertainty estimates are provided for the two model predictions based on independent model residuals and their full conditional response to the SOC prediction factors.
NACP Soil Organic Matter of Burned Boreal Black Spruce Forests, Alaska, 2009-2011
This data set provides organic soil layer characteristics, estimated carbon content, and soil depth measurements made at four black spruce stands in interior Alaska that had burned twice in the last 37-52 years (intermediate-interval fire events). The most recent fires occurred in 2004, 2005, and 2010. Measurements of soil depth and distance from the adventitious roots to the soil, and total organic matter are also included for unburned black spruce sites adjacent to the burned sites dominated by live, intermediate-aged (~37-52 years) black spruce trees.
Global Organic Soil Carbon and Nitrogen (Zinke et al.)
This package contains worldwide soil carbon and nitrogen data for more than 3,500 soil profiles. The database was begun about 40 years ago with the collection and analyses by Zinke of soil samples from California. Additional data came from soil surveys of California, Italy and Greece, Iran, Thailand, Vietnam, various tropical Amazonian areas, U.S. forest soils,and from the soil survey literature. The main samples for laboratory analyses were collected at uniform soil depth increments and included bulk density determinations, but samples reported in the literature did not always have this uniformity. For the latter group of samples, only profiles that were samples to a meter depth or to actual depth were used; if bulk densities were not reported, then estimates were made from regressions based on organic carbon content of the soil samples associated with the profile. Methods used for analytical carbon determinations were dry combustion, 'wet combustion', or loss on ignition with adjustments made to the values obtained with the last two methods. Nitrogen was determined by the Kjeldahl method on the soil fine earth fraction and reported as total organic nitrogen. The data can be used to estimate the size of the soil's organic carbon and nitrogen pools at equilibrium with natural soil-forming factors. Most of the data are from profiles associated with natural vegetation so they constitute a baseline for evaluation of the effects that disturbance or modification to natural vegetation has on soil carbon equilibrium at either a global or regional scale. The data can also be used for understanding the range and viability of soil carbon and nitrogen pools for specific ecosystems or climatic regimes.
Stocks of Surface Soil Organic Carbon Fractions, Great Plains Region, USA, 2007-2010
This dataset provides estimates of total organic soil carbon (SOC), pyrogenic (PyC), particulate (POC), and other organic soil carbon (OOC) fractions in 473 surface layer soil samples collected from stratified-sampling locations in Colorado, Kansas, New Mexico, and Wyoming, USA. Terrain, climate, soil, fire, and land cover data used to predict and map SOC, PyC, POC, and OOC at 1 km resolution throughout the study region are also included. The estimates were derived using a best random forest regression model and cover the period 2007-05-01 to 2010-10-01.
Soil Organic Carbon Distributions in Tidal Wetlands of the Northeastern USA
This dataset provides estimates of soil organic carbon (SOC) in tidal wetlands for the northeastern United States. The data cover the period 1998-2018. Northeastern U.S. tidal wetlands and bordering areas were harmonized from government agencies [U.S. Department of Agriculture - Natural Resources Conservation Service (USDA-NRCS), National Cooperative Soil Survey (NCSS), USDA-NRCS - Rapid Carbon Assessment (RaCA), U.S. Environmental Protection Agency - National Wetland Condition and Assessment (EPA-NWCA)] and published studies. Point data for carbon stocks (in kg m-2) at four soil depths (0-5, 0-30, 0-100, and 0-200 cm) are included. SOC for the four depths was predicted for eight regional zones using regression models driven by environmental covariates. Two methods were used to estimate parameters for these models, a Random Forest (RF) Ranger method and a Quantile Regression Forest (QRF) model. The distribution of SOC was predicted for tidal wetland cover types mapped by Correll et al. (2019). Predictions and uncertainties are available at a 3 m resolution.
Soil Organic Carbon and Wetland Intrinsic Potential, Hoh River Watershed, WA, 2012-13
This dataset contains estimates of soil organic carbon stocks and wetland intrinsic potential (WIP) across the Hoh River Watershed in the Olympic Peninsula, WA, USA in 2012-2013. Estimates were derived from an equation based on wetland intrinsic potential and geology type (Stewart et al., 2023). Wetland intrinsic potential estimates the likelihood that that an area is a wetland using a random forest model built on vegetation, hydrology, and soil data (Halabisky et al., 2022). SOC estimates at 1 m and 30 cm, SOC standard deviations, and WIP are presented in Cloud-Optimized GeoTIFF (*.tif) format at 4-m resolution. Also included are 36 field observations of SOC collected from 2020-08-01 to 2022-06-29. These are contained in a comma separated (*.csv) file.
Soil Organic Carbon Estimates and Uncertainty at 1-m Depth across Mexico, 1999-2009
This dataset provides an estimate of soil organic carbon (SOC) in the top one meter of soil across Mexico at a 90-m resolution for the period 1999-2009. Carbon estimates (kg/m2) are based on a field data collection of 2852 soil profiles by the National Institute for Statistics and Geography (INEGI). The profile data were used for the development of a predictive model along with a set of environmental covariates that were harmonized in a regular grid of 90x90 m2 across all Mexican states. The base of reference was the digital elevation model (DEM) of the INEGI at 90-m spatial resolution. A model ensemble of regression trees with a recursive elimination of variables explained 54% of the total variability using a cross-validation technique of independent samples. The error associated with the predictive model estimates of SOC is provided. A summary of the total estimated SOC per state, statistical description of the modeled SOC data, and the number of pixels modeled for each state are also provided.
ABoVE: Burn Severity of Soil Organic Matter, Northwest Territories, Canada, 2014-2015
This dataset provides maps at 30-m resolution of landscape surface burn severity (surface litter and soil organic layers) from the 2014-2015 fires in the Northwest Territories and Northern Alberta, Canada. The maps were derived from Landsat 8 Operational Land Imager/Thermal Infrared Sensor (OLI/TIRS) imagery and two separate multiple linear regression models trained with field data; one for the Plains and a second for the Shield ecoregion. Field observations were used to estimate area burned in each of five severity classes (unburned, singed, light, moderate, severely burned) in six stratified randomly selected plots of 10 x 10-m in size across a 1-ha site. Using this five class scale a burn severity index (BSI) for each 1-ha site was calculated using multiple weighted and averaged field parameters. Pre- and post-fire phenologically paired Landsat 8 images were used to model the five discrete severity classes using midpoints as breaks.
Tidal Wetlands Soil Organic Carbon and Estuarine Characteristics, USA, 1972-2015
This dataset provides a synthesis of soil organic carbon (SOC) estimates and a variety of other environmental information from tidal wetlands within estuaries in the conterminous United States for the period 1972-2015. The data were compiled from several existing data resources and include the following: soil organic carbon stock estimates, the proportion of the catchment area containing the wetlands that is barren, tidal wetland area, nontidal wetland land, open water, saltwater zone, mixed zone, agricultural, urban, forest, and wetland areas, land elevation, ocean salinity, sea surface temperature, ocean dissolved inorganic phosphorus, estuary latitude, longitude, depth, perimeter, salinity, and estuary volume, river flow, carbon, nitrogen, and phosphorus river flux, sediment organic carbon content, windspeed, mean temperature, daily and mean precipitation, frost days, and the population within each catchment. Estuaries were also classified to one of six typological categories. Coastal locations were determined by natural environmental and political divisions within the US. The data were used to investigate how tidal wetland soil organic carbon density is distributed across the continental US among various coastal locations, estuarine typologies, vegetation types, water regimes, and management regimes, and to identify whether SOC density is correlated with different environmental variables. The analytical results are not included with this dataset.
LBA Regional Organic Soil Carbon and Nitrogen Data (Zinke et al.)
The data set contains a subset of a global organic soil carbon and nitrogen data set (Zinke et al. 1986). The subset was created for the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) in South America (i.e., 10 N to 25 S, 30 to 85 W). The point data are available in three formats: a comma-delimited ASCII file (*.csv), an ESRI shapefile, and an ESRI export file (*.e00).The data for the global data set (Zinke et al. 1986) were obtained from soil surveys conducted by Zinke in 1965-1984 and from soil survey literature. The main samples for laboratory analyses were collected at uniform soil increments and included bulk density determinations. Many samples reported in the literature did not have uniform soil increments or bulk density determinations. Only soil profiles that had been sampled either to a meter in depth or to actual depth were included in this database from soil survey literature. When carbon content was known but bulk densities were absent from soil samples reported in the literature, densities were estimated by regression analysis on the basis of the relationship between organic carbon content and measured bulk density in 1800 soil profiles for which bulk densities were known.Further information can be found at ftp://daac.ornl.gov/data/lba/carbon_dynamics/Zinke_soil/comp/zinke_readme.pdf.LBA was a cooperative international research initiative led by Brazil. NASA was a lead sponsor for several experiments. LBA was designed to create the new knowledge needed to understand the climatological, ecological, biogeochemical, and hydrological functioning of Amazonia; the impact of land use change on these functions; and the interactions between Amazonia and the Earth system. More information about LBA can be found at http://www.daac.ornl.gov/LBA/misc_amazon.html.
Soil Organic Carbon Estimates for 30-cm Depth, Mexico and Conterminous USA, 1991-2011
This dataset provides two sets of gridded estimates of estimated soil organic carbon (SOC) and associated uncertainties for 0-30 cm topsoil layer in kg SOC/m2 at 250-m resolution across Mexico and the conterminous USA (CONUS). The first set of gridded SOC estimates, for the period 1991-2010, were derived using multi-source SOC field data and multiple environmental variables representative of the soil forming environment coupled with a machine learning approach (i.e., simulated annealing) and regression tree ensemble modeling for optimized SOC prediction. Predictions of gridded SOC and uncertainty based on multiple bulk density (BD) pedotransfer functions (PFTs) are also included. The second set of gridded SOC estimates, for the period 2009-2011, were derived from two fully independent validation field datasets from across both countries. Note that the same environmental variables and modeling approach used for the first set of estimates were applied to the second set to assess the models' sensitivity to multiple SOC data sources. The SOC field data for the first set of estimates are provided in this dataset and the other data sources, including the two independent validation field datasets, are referenced.
SAFARI 2000 Organic Soil Carbon and Nitrogen Data (Zinke et al.)
The data set contains a subset of the Worldwide Organic Soil Carbon and Nitrogen (Zinke et al. 1986) data set for southern Africa. The data were obtained from soil surveys by Zinke and soil survey literature. The main samples for laboratory analyses were collected at uniform soil increments and included bulk density determinations. Many samples reported in the literature did not have uniform soil increments or bulk density determinations. Only soil profiles that had been sampled either to a meter in depth or to actual depth were included in this data base from soil survey literature. In literature where bulk densities were absent, densities were estimated by regressions based on organic carbon content of the soil samples associated with the profile using 1800 soil profiles for which bulk densities were known. More information can be found at: ftp://daac.ornl.gov/data/safari2k/soils/Zinke_soil/comp/zinke_readme.pdf.
Long time-series (1980-2020) high-resolution (1km) 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 1980: 1980-1984 (five years mean soc)</p><p> </p><p> </p><p> </p>
Model output data of the paper: "Management induced changes of soil organic carbon on global croplands"
<p># Model output data of the paper: "Management induced changes of soil organic carbon on global croplands"<br> This data was prodused using the the MadRat framework and the mrsoil R-library by the R-script SOCBudget.R, which is stored together with the data. mrsoil is based on the R-libraries mrcommons, mrmagpie and mrvalidation.</p> <p>### REFERENCES<br> Dietrich J, Baumstark L, Wirth S, Giannousakis A, Rodrigues R, Bodirsky B, Kreidenweis U, Klein D (2020). _madrat: May All Data be<br> Reproducible and Transparent (MADRaT)_. doi: 10.5281/zenodo.1115490 (URL: https://doi.org/10.5281/zenodo.1115490), R package version<br> 1.86.0, <URL: https://github.com/pik-piam/madrat>.</p> <p>rstens K, Dietrich J (2020). _mrsoil: MadRat Soil Organic Carbon Budget Library_. doi: 10.5281/zenodo.4317933 (URL:<br> https://doi.org/10.5281/zenodo.4317933), R package version 1.1.0, <URL: https://github.com/pik-piam/mrsoil>.</p> <p>Bodirsky B, Karstens K, Baumstark L, Weindl I, Wang X, Mishra A, Wirth S, Stevanovic M, Steinmetz N, Kreidenweis U, Rodrigues R, Popov<br> R, Humpenoeder F, Giannousakis A, Levesque A, Klein D, Araujo E, Beier F, Oeser J, Pehl M, Leip D, Molina Bacca E, Martinelli E,<br> Schreyer F, Dietrich J (2020). _mrcommons: MadRat commons Input Data Library_. doi: 10.5281/zenodo.3822009 (URL:<br> https://doi.org/10.5281/zenodo.3822009), R package version 0.11.10, <URL: https://github.com/pik-piam/mrcommons>.</p> <p>Karstens K, Dietrich J, Chen D, Windisch M, Alves M, Beier F, v. Jeetze P, Mishra A, Humpenoeder F (2020). mrmagpie: madrat based MAgPIE Input Data Library. doi: 10.5281/zenodo.4319612 (URL: https://doi.org/10.5281/zenodo.4319612), R package version 0.31.0, <URL: https://github.com/pik-piam/mrmagpie>.</p> <p>Bodirsky B, Wirth S, Karstens K, Humpenoeder F, Stevanovic M, Mishra A, Biewald A, Weindl I, Chen D, Molina Bacca E, Kreidenweis U, W. Yalew A, Humpenoeder<br> F, Wang X, Dietrich J (2020). _mrvalidation: madrat data preparation for validation purposes_. doi: 10.5281/zenodo.4317826 (URL:<br> https://doi.org/10.5281/zenodo.4317826), R package version 2.5.0, <URL: https://github.com/pik-piam/mrvalidation>.</p> <p>## LICENSE<br> This data is open-source: you can redistribute it and/or modify it under the terms of the **CC Attribution 4.0 International** as published by the Creative Commons Corporation at https://creativecommons.org/licenses/by/4.0/legalcode.</p> <p>## CONTACT<br> karstens@pik-potsdam.de</p>
Dataset: Effect of soil organic matter content and nutrient loading on productivity of Spartina patens (v.0.10)
<p class="MsoNormal">River sediment diversions in the Mississippi River Delta have been planned as a keystone strategy for wetland restoration in coastal Louisiana. The introduction of mineral sediment and dissolved nutrients present in Mississippi River water could drastically alter the abiotic environment of the surrounding wetlands, and the effects on wetland vegetation are unclear. In this study, 50 sods were transplanted from a <em>Spartina patens-</em>dominated brackish marsh into a greenhouse and grown in either mineral or organic soil in combination with one of five levels of nutrient enrichment. The primary component of the mineral soil was silt collected from the bank of the Mississippi River, while that of the organic soil was peat collected from the aforementioned <em>S. patens</em> marsh. Nutrient treatments were based on a range of Mississippi River diversion discharge rates of nitrate, phosphate, sulfate, potassium, and iron as well as a control with no nutrient addition. After one summer, we found that total porewater conductivity increased with nutrient loading under both soil types. Under higher nutrient treatments the concentrations of porewater sulfide were elevated to phytotoxic levels, but this effect was only observed in the organic soil treatment. Nitrate and ammonium porewater concentrations were affected by both nutrient loading and soil type; however, nitrate was higher in the mineral soil while ammonium was higher in the organic soil. Porewater phosphorous and potassium concentrations were elevated in organic soil conditions and higher nutrient loading, while porewater iron was higher under mineral soil and lower nutrient loading. Aboveground standing crop, belowground biomass accumulation (i.e., ingrowth), and soil shear strength were also sampled at the end of the growing season with the goal of examining their relationships with nutrient and sediment enrichment and porewater chemistry.</p>
Dataset for: Calcium promotes persistent soil organic matter by altering microbial transformation of plant litter
<p>Dataset and code for the publication:</p> <p>Shabtai, I.A., Wilhelm, R.C., Schweizer, S.A. <em>et al.</em> Calcium promotes persistent soil organic matter by altering microbial transformation of plant litter. <em>Nat Commun</em> <strong>14</strong>, 6609 (2023). https://doi.org/10.1038/s41467-023-42291-6<br><br></p>
ScienceDex guides
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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.