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709 results for “soil carbon”
Data from: Microbial carbon use efficiency and soil organic carbon stocks across an elevational gradient in the Peruvian Andes
<p>Soils of mountain ecosystems are one of the most vulnerable ecosystems to climate change, while the ecosystem services they produce are significant and currently at risk. High altitude soils contain high C stocks, but due to difficult access to sites these areas are understudied. Moreover, how the C and N cycling is changing in response to climate change in these ecosystems, is still unclear. Microbial carbon use efficiency (CUE) and its dependency on the environmental constraints along the altitudinal gradients is one important unknown factor. Here we present results from an altitudinal gradient study (3500 to 4500 m a.s.l.) from a Polylepis forest in the Peruvian Andes. We measured the soil organic carbon (SOC) stocks and microbial metabolic CUE by <sup>13</sup>C glucose tracing and microbial resource use efficiency (CUE<sub>C</sub><sub>:</sub><sub>N</sub>) based on enzyme activity measurements. We expected to find an increase in SOC stock, microbial nutrient limitations, and lower CUE with elevation. SOC stocks depended on soil development and followed a unimodal curve that peaks at 4000 m in two of the three studied valleys. Neither <sup>13</sup>CUE nor CUE<sub>C:N</sub> changed significantly with altitude. Soil C:N ratio, β-glucosidase, chitinase, and phosphatase enzyme activities increased with elevation, but peroxidase activity decreased with elevation. We suggest that more labile organic matter left at high elevation could compensate for the increasing nutrient limitation at high elevation, resulting in no noticeable change in CUE with elevation.</p>
Protists regulate microbially-mediated organic carbon turnover in soil aggregates
<p>Soil protists, the major predator of bacteria and fungi, shape the taxonomic and functional structure of soil microbiome via trophic regulation. However, how trophic interactions between protists and their prey influence microbially mediated soil organic carbon turnover remains largely unknown. Here, we investigated the protistan communities and microbial trophic interactions across different aggregates-size fractions in agricultural soil with long-term fertilization regimes. Our results showed that aggregate sizes significantly influenced the protistan community and microbial hierarchical interactions. Bacterivores were the predominant protistan functional group and were more abundant in macroaggregates and silt + clay than in microaggregates, while omnivores showed an opposite distribution pattern. Furthermore, partial least square path modeling revealed positive impacts of omnivores on the C-decomposition genes and soil organic matter (SOM) contents, while bacterivores displayed negative impacts. Microbial trophic interactions were intensive in macroaggregates and silt + clay but were restricted in microaggregates, as indicated by the intensity of protistan-bacterial associations and network complexity and connectivity. Cercozoan taxa were consistently identified as the keystone species in SOM degradation-related ecological clusters in macroaggregates and silt + clay, indicating the critical roles of protists in SOM degradation by regulating bacterial and fungal taxa. Chemical fertilization had a positive effect on soil C sequestration through suppressing SOM degradation-related ecological clusters in macroaggregate and silt + clay. Conversely, the associations between the trophic interactions and SOM contents were decoupled in microaggregates, suggesting limited microbial contributions to SOM turnovers. Our study demonstrates the importance of protists-driven trophic interactions on soil C cycling in agricultural ecosystems.</p>
Organo-organic interactions dominantly drive soil organic carbon accrual
<p>Organo-mineral interactions have been regarded as the primary mechanism for the stabilization of soil organic carbon (SOC) over decadal to millennial timescales, and the capacity for soil carbon (C) storage has commonly been assessed based on soil mineralogical attributes, particularly mineral surface availability. However, it remains contentious whether soil C sequestration is exclusively governed by mineral vacancies, making it challenging to accurately predict SOC dynamics. Here, through a 400-day incubation experiment using <sup>13</sup>C-labeled organic materials in two contrasting soils (i.e., Mollisol and Ultisol), we show that despite the unsaturation of mineral surfaces in both soils, the newly incorporated C predominantly adheres to "dirty" mineral surfaces coated with native organic matter (OM), demonstrating the crucial role of organo-organic interactions in exogenous C sequestration. Such interactions lead to multilayered C accumulation that is not constrained by mineral vacancies, a process distinct from direct organo-mineral contacts. The coverage of native OM by new C, representing the degree of organo-organic interactions, is noticeably larger in Ultisol (~14.2%) than in Mollisol (~5.8%), amounting to the net retention of exogenous C in Ultisol by 0.2–1.3 g kg<sup>−1</sup> and in Mollisol by 0.1–1.0 g kg<sup>−1</sup>. Additionally, organo-organic interactions are primarily mediated by polysaccharide-rich microbial necromass. Further evidence indicates that iron oxides can selectively preserve polysaccharide compounds, thereby promoting the organo-organic interactions. Overall, our findings provide direct empirical evidence for an overlooked but critically important pathway of C accumulation, challenging the prevailing "C saturation" concept that emphasizes the overriding role of mineral vacancies. It is estimated that, through organo-organic interactions, global Mollisols and Ultisols might sequester ~0.1–1.0 Pg C and ~0.3–1.7 Pg C per year, respectively, corresponding to the neutralization of ca. 0.5%–3.0% of soil C emissions or 5%–30% of fossil fuel combustion globally.</p>
Soil profile pits, carbon and vegetation data of an old Anogeissus grove in Mole National Park, Ghana
<p>Formation of forest islands in West Africa has been linked to anthropogenic soil improvement resulting in luxuriant tree growth in otherwise open savanna landscapes. However, there is limited understanding of how such unique ecosystems modulate soil carbon (C) dynamics and nutrient cycling. In this study, we report soil nutrient characteristics and two distinct soil organic carbon pools of Anogeissus grove (forest island) associated with abandoned village sites of the Mole National Park in the Guinea savanna or tropical continental climatic zone of Ghana, taking opportunity of a previously published study in Biotropica in 1978. We compared present-day differences in soil characteristics between the previously studied forest grove and adjoining open savanna in the Park and evaluated vegetation dynamics since first measurement in 1974. Overall, we see changes related to self-thinning and expansion of the grove on a decadal timescale. Soil organic matter and available phosphorus contents were greater in the grove and increased by 19.6 and 18.7%, respectively over time, showing persistence after four decades. Mineral associated organic carbon (MAOC) differed significantly (p<0.05) between the vegetation types, being 3.44% in the grove and 2.34% in the savanna. The grove was ca. 25% greater in particulate organic carbon (POC) content than the savanna. In both vegetation types, >55% of carbon was stabilized in the mineral fraction. Our study demonstrates long-term human impacts on soil and vegetation and offers a clear nature-based solution for climate change mitigation through sustainable land management by indigenous people towards achievement of the '4p1000' initiative.</p>
Woody debris removal modifies carbon stocks and soil properties in a fragmented tropical rainforest
<p>We examined whether and how woody debris removal for domestic fuel affects carbon storage and soil properties in an Indian rainforest. Fuelwood removal reduced aboveground carbon stocks, increased soil bulk density, and possibly reduced soil phosphorus stocks. Equitably balancing this subtle trade-off between climate-regulating and vital, widely-utilized provisioning functions, is a challenge for tropical forest research and management.</p>
Moisture and temperature effects on the radiocarbon signature of respired carbon dioxide to assess stability of soil carbon in the Tibetan Plateau
<p>Radiocarbon data set and code for the prediction of D14C values in bulk soil and respired CO2.</p> <p>Lab results for TOC, TN, TIC, Dap and physico-chemical properties of grassland and peatland soils</p> <p> </p>
High-resolution mapping of soil carbon stocks in the western Amazon
<h2>Dear Researchers and Interested Parties,</h2> <p> It is with great enthusiasm that we share our page on Zenodo, where we provide <strong>detailed maps</strong> (30 m resolution) of <strong>soil carbon stocks in Rondônia, Brazil</strong>. These maps were generated using machine learning techniques, using the Random Forest model implemented in the caret package. This initiative aims to provide a deeper and more accurate understanding of the spatial distribution of carbon in the soil, contributing significantly to environmental studies and climate change mitigation strategies in the region.</p> <h2>Available resources:</h2> <h3>High Resolution Maps:</h3> <p>We provide detailed maps of the estimates and uncertainties of soil carbon stocks at different depths (0-5; 5-15; 15-30; 30-60 and 60-100 cm). The maps include mean values (Mg ha<sup>-1</sup>), quantiles (Mg ha<sup>-1</sup>) and coefficients of variation (%), all in "tif" format, with a spatial resolution of 30 m and SAD 1969 Lambert South America projection system (<a href="https://epsg.io/102015">EPSG :102015</a>).</p> <p>The entire process was conducted in open source (R Language). The codes and database used <strong>can be found in the <a href="https://github.com/moquedace/ro_soil_carbon_stock" target="_blank" rel="noopener">GitHub repository</a></strong>, and more information about the methodology is available in the following publication:</p> <p>Moquedace, C. M., Baldi, C. G. O., Siqueira, R. G., Cardoso I. M., Souza, E. F. M., Fontes, R. L. F., Francelino, M. R., Gomes, L. C., Fernandes-Filho, E. I. High-resolution mapping of soil carbon stocks in the Western Amazon. <em>Geoderma Regional</em>, v. 36, p. e00773, 2024. DOI: <a href="https://doi.org/10.1016/j.geodrs.2024.e00773">10.1016/j.geodrs.2024.e00773</a></p> <h2>Availability objectives:</h2> <h3>Promote scientific collaborations:</h3> <p>We encourage researchers, scientists, and organizations to explore and use this data to enrich their own research and projects related to soil carbon and climate change.</p> <h3>Enhance environmental understanding:</h3> <p>By providing open access to these maps, we aim to contribute to a deeper understanding of environmental processes in Rondônia, Brazil and, by extension, enable the implementation of sustainable strategies.</p> <h3>Stimulating innovation:</h3> <p>We believe that sharing this data will stimulate innovation in modeling and spatial analysis methods, driving advances in the prediction of soil carbon stocks, especially in the Amazon.</p> <h2>Thank you in advance for your interest and collaboration. Together, we can advance knowledge and the search for sustainable solutions to important environmental challenges.</h2> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
Dataset and R-codes for Publication: "Best performances of visible-near infrared models in soils with little carbonate - a field study in Switzerland" (accepted version)
<p><span>In this upload you can find the R-codes and dataset for the Publication: </span></p> <p><span>"Best performances of visible-near infrared models in soils with little carbonate - a field study in Switzerland" by Simon Oberholzer Laura Summerauer, Markus Steffens and Chinwe Ifejika Speranza accepted in SOIL (https://doi.org/10.5194/egusphere-2023-1087)</span></p> <p><span>To reproduce the results of the manuscript, start with the R-file “ResampleandPreprocess.R” to prepare spectral data and then continue with the R-file “PLSRmodelling_GroupedCV.R” for the modelling.</span></p> <p><span>The R-files “control_train.R” and “rep_grouped_kfold_CV.R” are helper-functions for the grouped cross-validation. The file metadata.csv explains the column names in the spectral data (spcdata.RDS).</span></p>
Empirical data and model simulations of the effect of repeated hurricanes on soil carbon dynamics in a humid tropical forest
<p>Increasing hurricane frequency and intensity with climate change is likely to affect soil organic carbon (C) stocks in tropical forests. We examined the cycling of C between soil pools and with depth at the Luquillo Experimental Forest in Puerto Rico in soils over a 30-year period that spanned repeated hurricanes. We used a non-linear matrix model of soil C pools and fluxes ("soilR") and constrained the parameters with soil and litter survey data. Soil chemistry and stable and radiocarbon isotopes were measured from three soil depths across a topographic gradient in 1988 and 2018. Our results suggest that pulses and subsequent reduction of inputs caused by severe hurricanes in 1989, 1998, and two in 2017 led to faster mean transit times and younger mean ages of soil C in the particulate, occluded, and mineral-associated soil organic matter pools at 0–10 cm and 35–60 cm depths relative to a modeled control soil with constant inputs over the thirty years. Between 1988 and 2018, the occluded C stock increased, and d<sup>13</sup>C in all pools decreased, while changes in particulate and mineral-associated C were undetectable. The differences between 1988 and 2018 suggest that hurricane disturbance results in a dilution of the occluded light C pool with an influx of young, debris-deposited C, and possible microbial scavenging of old and young C in the particulate and mineral-associated pools. These effects led to a younger total soil C pool with faster mean transit times. Our results suggest that increasing frequency of intense hurricanes will speed up rates of C cycling in tropical forests, and eventually lead to net losses of C from tropical forest soils.</p>
Exploring the impact of varied background on quantification of soil carbon content using microwave and millimeter wave signal reflectance
<p>The quantification of soil carbon content is paramount to the advancement of soil carbon management practices, serving as the bedrock for the development and implementation of various carbon-negative and carbon-neutral technologies. These technologies are crucial in the battle against climate change and in enhancing soil health to boost agricultural productivity. Therefore, we employ an innovative sensor technology comprised of a compact array, including 18 pairs of radar transmitters (TX) and receivers (RX) within a microwave radar array sensor, alongside a configuration of 20 TX and 20 RX pairs in a millimeter wave radar array sensor. For this kind of setup to work, the sensors need to be carefully tested to make sure they can capture changes in the soil's carbon content over time and space.</p> <p>This dataset presents an exhaustive series of raw data aimed at evaluating the efficacy of these two sensor technologies in detecting soil carbon content. A distinctive aspect of this study is the examination of sensor performance across four divergent backgrounds—glass, metal, sponge, and wood surfaces—to ascertain the influence of background material on radar wave reflectance and, consequently, measurement accuracy. Furthermore, the investigation extends to the analysis of sensing distance as a critical parameter for classification accuracy, with the microwave radar array sensor tested across distances ranging from 1 inch to 4 inches and the millimeter wave radar sensor evaluated at intervals of 2.5 inches, 5 inches, 7.5 inches, and 10 inches. By providing a comprehensive dataset on the performance evaluation of cutting-edge sensor technologies, this study aims to facilitate further advancements in the field of soil carbon management, ultimately supporting the global effort to combat climate change through innovative and sustainable agricultural practices.</p>
Data for: Nitrogen dynamics and carbon sequestration in soil following application of digestates from one- and two-step anaerobic digestion
<p>Data set for article <span>Nitrogen dynamics and carbon sequestration in soil following application of digestates from one- and two-step anaerobic digestion (https://doi.org/10.1016/j.scitotenv.2022.158177).</span></p>
Soil grid data for agricultural fields in Spain for STEROPES (EJP Soil) project (ECe, texture, soil organic carbon, pH)
<p><span>Soil data collected in an agricultural area with vegetable crops in Spain (Campo de Cartagena). The data refers to soil properties of 141 soil samples collected at a depth of 0-10 cm, considering a regular sampling grid, in different commercial fields with varying soil salinity. The samples were collected at a period when the soil was bare, during two consecutive summers, following the harvest of the annual crops, and pictures of the soil surface were taken for eventual correction of corresponding remote sensing imaging. The data includes: soil organic carbon (SOC) (Walkley-Black method), soil water content, electric conductivity of the saturated soil paste (ECe), EC1:5, soil texture, stone content and pH1:2.5. </span></p> <p><span> </span></p> <p><span>The data may be representative of the soil conditions of the area, which is an intensive productive agricultural low land, potentially prone to the development of soil salinity as a result of the rise of saline groundwater and/or irrigation. The data can be used to establish relations between soil salinity (ECe) and other soil properties as well as build prediction models of the soil properties from remote sensing namely, for developing models for SOC prediction under the STEROPES project (WP3, WP5 and WP6).The aim of the collected dataset was to be able to analyze the influence of soil salinity in SOC prediction from remote sensing.</span></p> <p><span> </span></p> <p><span>Data in the form of MS Excel file (xlsx).</span></p> <p><span> </span></p> <p><span> </span></p>
Database of Soil Organic Carbon (SOC) stock up to 20 cm depth: A collection of studies from Uruguay
<p>This database comprises 667 soil organic carbon (SOC) stock measurements, collected from various studies conducted across Uruguay, with sampling depths extending up to 20 cm. The database aggregates data from 14 studies, including undergraduate and postgraduate theses and research projects, sampled between 2002 and 2024. It provides comprehensive details on the geographic location of sampling sites (latitude and longitude coordinates), the date of sample collection, the methodology employed for SOC determination, the technique used to estimate SOC stock values at the specified depths, and a description of land use at the moment of sampling.</p> <p>This data collection is particularly relevant for evaluating SOC stocks modeling exercises, as it contributes to a more comprehensive understanding of carbon dynamics across various soil types, environmental conditions, land use, and land cover types. The database is available in shapefile format and is expected to serve as a valuable resource for researchers and professionals in the field.</p> <p><strong> </strong></p> <h3>Data Table Description</h3> <p>The associated data table from this database contains the following key variables:</p> <ul> <li> <p>ID: Unique identifier for each sampling point.</p> </li> <li> <p>Latitude & Longitude: Geographic coordinates of the sampling site.</p> </li> <li> <p>SOC Stock: Soil Organic Carbon (SOC) stock calculated at a fixed depth using the equation SOC stock (MgC/ha) = C × Bd × d, where C represents carbon concentration (%C), Bd is the bulk density of the soil (g/cm³), and d is the depth (cm).</p> </li> <li> <p>Depth: Depth (cm) used for SOC stock calculation. Data were harmonized to a depth of 20 cm; however, lower values are reported for sites where the sampling scheme was conducted at depths less than 20 cm or where bedrock was encountered before reaching 20 cm.</p> </li> <li> <p>Land Use: Simplified land use classification with “grassland”, “agriculture”, or “forest” categories</p> </li> <li> <p>Land Use Extended: If available, the responsible party provides a more detailed explanation of land use.</p> </li> <li> <p>Month & Year: Month and year of sample collection.</p> </li> <li> <p>SOC Method: Methodology used for SOC determination, such as Walkley-Black or dry combustion.</p> </li> <li> <p>Published: Indicates whether the data have been published, with a URL provided if available. If not published, the data are marked as unpublished.</p> </li> <li> <p>SOC Calc: The approach used for calculating SOC stock at a fixed depth, detailed in two possible methods:</p> </li> <ul> <li> <p>Real: For sampling schemes that include a segment ending at 20 cm depth, SOC stock was calculated directly up to 20 cm. This method was also applied when the maximum soil sampling depth was less than 20 cm.</p> </li> <li> <p>Spline: When the sampling depth did not include 20 cm but extended beyond it, a spline interpolation was applied. This method employs all available cumulative values and their associated depths to estimate the SOC stock at 20 cm.</p> </li> </ul> </ul> <h3>Additional Observations</h3> <ul> <li> <p>For sites listed in rows 200-216, the GPS position represents the plot´s centroid, as no specific location was reported.</p> </li> <li> <p>For sites listed in rows 344-633, sampling was conducted at 0-7.5 cm, 7.5-15 cm, and 15-30 cm depth. In sites where the total depth was 15 cm, bulk density was measured only in the first layer (0-7.5 cm), and this value was also applied to the 7.5-15 cm layer. In sites where the total depth was 20 cm, bulk density was measured in all layers and calculated for 20 cm as a weighted average.</p> </li> <li> <p>For sites listed in rows 102-134, bulk density measurements were not taken directly. Instead, data were retrieved from the "SoilGrids" website (<a href="https://soilgrids.org/">https://soilgrids.org/</a>). Bulk density values for the 0-5 cm and 5-15 cm layers were downloaded, and a weighted average was calculated before determining the SOC stock.</p> </li> </ul> <h3> </h3> <h3>Funding:</h3> <p>Funded were provided by ANII (FSDA_1_2018_1_154817, Procesos Inductivos para generación de buenas prácticas agropecuarias: Compilación y análisis de bases de datos a nivel predial; FSA_1_2022_1_175272, Evaluación multiescalar del desempeño ambiental de sistemas agropecuarios con diferente nivel de intensificación a partir de indicadores derivados de sensores remotos); INIA (FPTA-515, Indicadores de sostenibilidad ambiental para el sector agropecuario de Uruguay basados en información derivada de sensores remotos y modelos biofísicos); and IDB (URUGUAY/UR-T1277, Adopción de prácticas Agroecológicas y Huella de Carbono en la Agricultura Uruguaya).</p> <p> </p>
GSOCS-LULCC: the Global Soil Organic Carbon Stock dataset after Land Use and Land Cover Change
<p>We complied the Global Soil Organic Carbon Stock dataset after Land Use and Land Cover Change (GSOCS-LULCC) from 632 papers documented in Web of Science till the June 2024. This database comprises 1,187 sites with 5,805 records at multiple sample depths.<br>This dataset (in csv formats) is associated to the "GSOCS-LULCC: the Global Soil Organic Carbon Stock dataset after Land Use and Land Cover Change" by Chen et al. (2025). The README file includes the full explanation of all the columns.<br>Manuscript citation: Chen, S., Shuai, Q., Arrouays, D., Chen, Z., Dai, L., Hong, Y., Hu, B., Huang, Y., Ji, W., Li, S., Liang, Z., Ma, Y., Richer-de-Forges, A.C., Schillaci, C., Su, Y., Teng, H., Wang, N., Wang, X., Wang, Y., Wang, Z., Wang, Z., Xu, D., Xue, J., Ye, S., Zhang, X., Zhou, Y., Zhu, P., Shi, Z. , 2025. GSOCS-LULCC: the Global Soil Organic Carbon Stock dataset after Land Use and Land Cover Change. In preparation.<br>When using the data, please cite repositories as well as the original manuscript.<br>For any questions on the data, please contact Dr. Songchao Chen (chensongchao@zju.edu.cn).</p>
Data from: Reindeer control over subarctic treeline alters soil fungal communities with potential consequences for soil carbon storage
<p>Here we present the data and R script from "<em>Reindeer control over subarctic treeline alters soil fungal communities with potential consequences for soil carbon storage</em>" by Henni Ylänne, Rieke L. Madsen, Carles Castaño, Daniel B. Metcalfe and Karina E. Clemmensen (Global Change Biology, 2021). In this study we reported the impacts of grazing regime and mountain birch vicinity on the abundance, diversity and community composition of the soil fungal community, and explored how the soil fungal communities relate to vegetation and, ultimately, to the observed variation in soil carbon stocks. The study was conducted at a treeline ecotone in northern Fennoscandia, where two adjacent 65-year-old grazing regimes with or without summer grazing by reindeer (<i>Rangifer tarandus</i>) could be compared. As these grazing regimes differ in the abundance of mountain birch (<i>Betula pubescens</i> ssp. <i>czerepanovii</i>), the own effects of mountain birch vicinity on the fungal communities were assessed by collecting soil samples directly underneath and > 3 m away from mountain birches.</p> <p>This dataset includes data of soil properties, soil carbon and nitrogen stocks, vegetation community composition (as deterred from plant reads captured by the high-throughput sequencing of ITS2 region), fungal abundance (ITS2 copy numbers), fungal diversity and the taxonomic and functional composition of the soil fungal communities.</p>
Deepened snow cover mitigates soil carbon loss from intensive land use in a semi-arid temperate grassland
<p>Carbon (C) loss due to soil erosion is a major issue in semi-arid grasslands. The extent of soil erosion is determined by soil properties and vegetation structure, especially during the non-growing season. In many Inner Mongolian grasslands, intensive land use, such as overgrazing and mowing, has severely reduced plant cover and damaged soil structure, which has exacerbated soil C loss by erosion. At the same time, increasing winter snowfall due to climate change is stimulating plant growth and altering plant composition. However, we do not know how changes in winter snow cover interact with land-use practices to regulate soil C loss due to erosion.</p> <p>Here, we conducted a six-year snow manipulation experiment under different land-use practices (control; moderately mowed, MM; heavily mowed, HM) to measure net changes in soil depth, soil C, plant biomass, and vegetation structure.</p> <p>After six years, soil C loss under ambient snow was three times greater in the MM and four times greater in the HM treatment compared with controls during non-growing season. However, deepened winter snow alleviated erosion-induced soil C loss by 14%, 47%, 16% in the controls, MM and HM treatments, respectively.</p> <p>The severity of soil C loss declined with increasing aboveground biomass (AGB), surface root biomass and vegetation structure. Vegetation structure and AGB explained more of the variation in soil C loss than surface root biomass, possibly because a complex canopy and plant cover increases overall surface roughness, thereby reducing soil C loss. Intensified land use reduced AGB, surface root biomass and vegetation structure, but deepened snow increased overall surface roughness by promoting AGB. Hence, our study demonstrates that deepened snow can alleviate soil C loss due to land use practices by promoting AGB.</p>
Data for "Can Regenerative Agriculture increase national soil carbon stocks? Simulated country-scale adoption of reduced tillage, cover cropping, and ley-arable integration using RothC"
<p>R code and supplementary data for soil carbon simulations: "<em>Can Regenerative Agriculture increase national soil carbon stocks? Simulated country-scale adoption of reduced tillage, cover cropping, and ley-arable integration using RothC-26.3</em>"</p>
Initial soil conditions outweigh management in a cool-season dairy farm's carbon sequestration potential
<p>Data used in the manuscript "Initial soil conditions outweigh management in a cool-season dairy farm’s carbon sequestration potential" (<a href="http://dx.doi.org/10.1016/j.scitotenv.2021.152195">10.1016/j.scitotenv.2021.152195</a>)</p> <p> </p> <p>Soil samples, gas fluxes, and biomass samples measured at the Organic Dairy Research Farm at the University of New Hampshire. Soil samples were in two sets, a spatially explicit set from 0 - 15 cm depth, and less spatially explicit samples taken at 10 cm increments. Soils were sampled for soil carbon and nitrogen content. Gas fluxes were measured using the chamber method with carbon dioxide and nitrous oxide gases measured on gas chromatographs with the change over time used to measure the gas flux rates. Forage biomass was measured by collecting biomass in 1 m2 plots. Please see the manuscript for more details on sampling.</p>
Carbon sequestration of a forested wetland receiving nutrient inputs - soil, tree and greenhouse gas data
<p><span><span><span><span><span><span><span><span><span><span><span>Here we describe a pilot wetland carbon project located 30 km west of New Orleans where measurements were taken in 2013 and 2018, and applied to the carbon offset methodology, "Restoration of Degraded Deltaic Wetlands of the Mississippi Delta" ("the ACR Methodology") published by the American Carbon Registry (ACR). Baseline emissions were modeled using values derived from scientific literature. Results indicate net sequestration rate of 619,727 tons carbon dioxide equivalent (CO<sub>2</sub>e) over the 40 year project duration, which equates to 16,527 t CO2-e/yr, if wetland greenhouse gases (GHGs) are included, and 200,143 t CO<sub>2</sub>e over 40 years, or 5,003 t CO2-e/yr, if wetland greenhouse gasses were conservatively omitted. A kriging exercise was carried out that modeled the tree and soil pools, which resulted in net sequestration of 723,375 t CO2-e over 40 years (annual mean 18,084 t CO2-e/yr) with greenhouse gases, and 262,472 t CO2-e over 40 years (annual mean rate 6,560 t CO2-e/yr) if greenhouse gases were omitted. Unfortunately, the project was withdrawn, prohibiting the issuance and eventual transaction of carbon credits, due to very large uncertainty estimates mostly associated with GHG emissions and the kriging approach as in situ sampling could not be conducted as required by the methodology.</span></span></span></span></span></span></span></span></span></span></span></p>
Links between boreal forest management, soil fungal communities and belowground carbon sequestration
<p>Forest management has a potential to alter belowground carbon storage. However, the underlying mechanisms, and the relative importance of carbon input and decomposition in regulation of soil carbon dynamics are poorly understood.</p> <p>We examined whether interactive effects of forest fertilization and thinning on carbon stocks in the topsoil of boreal forests were linked to changes in fungal community composition, biomass, and enzyme activities, in a long-term fertilization and thinning experiment distributed across 29 Pinus sylvestris forests along a 1300 km latitudinal transect in Sweden.</p> <p>Nitrogen fertilization increased fungal biomass, particularly towards the north and mainly by promoting root associated Ascomycetes, but the response was moderated by thinning. Fungal biomass correlated positively with carbon stocks in the organic topsoil. However, ectomycorrhizal Cortinarius species were reduced in abundance by fertilization and correlated negatively with carbon stocks.</p> <p>Plausibly, increased soil carbon stocks after fertilization are linked to increased input of carbon in the form of root-associated mycelium combined with loss of ectomycorrhizal decomposers within the genus Cortinarius. These fungal responses to fertilization may mediate a natural climate solution by promoting carbon sequestration in the organic topsoil, but the effect of fertilization may also be undesired from a biodiversity perspective.</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.