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11 results for “aggregate carbon”
Data to support the publication "Impact of agricultural management on soil aggregates and associated organic carbon fractions: Analysis of long-term experiments in Europe"
<p><strong>Raw data:</strong> Experimental plot ids and information, mass distribution of all aggregate fractions after wet sieving, Sand content of each fraction to conduct the sand correction, mass distribution of all fractions after isolating the micro-aggregates held within the macroaggregates, yields per treatment, carbon content per fraction (raw data)</p> <p><strong>All data per plot: </strong>SOC content, MAOM and POM content of each fraction presented in the fractionation scheme included in the manuscript, together with the mass of the relative fractions. </p> <p> </p>
Grid Based Global Carbon Edge Regression Coefficients and Aggregations
<p>Grid cell based regression coefficients for predicting global biomass in the pantropics.</p> <p>To better account for the variability within a continent, we constructed 100-km grid cells throughout the pantropics. In grid cells where the majority of pixels were from forest biomes, we consider three candidate regression models to represent the relationship between biomass density and distance to forest edge. In particular, we consider:</p> <ol> <li>Asymptotic: <span class="math-tex">Biomass=θ1−θ2⋅exp(−θ3⋅Distance)</span>,</li> <li>Logarithmic: <span class="math-tex">Biomass=β0+β1ln⋅(Distance)</span> , or</li> <li>Linear: <span class="math-tex">Biomass=η0+η1⋅Distance</span></li> </ol> <p>Then, for each grid cell, the candidate with the highest R2 is used to best represent the relationship between density and distance to forest edge. Models (2) and (3) were deemed as suitable (and more simplistic) alternatives in cells where higher distances were generally not observed and as a result the forest core was not firmly established. We also note that in the vast majority of grid cells, model (1) was optimal. For each cell the magnitude and distance of the edge effect were again estimated. In cells using models (2) or (3) the forest core (<span class="math-tex">θ1</span>) was estimated as the average biomass density at the largest observed distance in the cell.</p> <p>This dataset also contains a shapefile parameter analysis of both proportion and total area of all forest landcover types (1-5).</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>
Database of physicochemical and optical properties of black carbon fractal aggregates
<p>In order to estimate the climate impact of highly absorbing black carbon (BC) aerosols, it is necessary to know their optical properties. The Lorentz-Mie theory, often used to calculate the optical properties of BC under the spherical morphological assumption, produces discrepancies when compared to measurements. In light of this, researchers are currently investigating the possibility of computing the optical properties of BC using a realistic fractal aggregate morphology. To determine the optical properties of such BC fractal aggregates, the Multiple Sphere T-Matrix method (MSTM) is used, which can take more than 24 hours for a single simulation depending on the aggregate properties. This study provides a highly accurate benchmark machine-learning algorithm that can be used to generate the optical properties of BC fractal aggregate in a fraction of a second. The machine learning algorithm was trained over an extensive database of physicochemical and optical properties of BC fractal aggregates. The extensive training data helped develop an ML algorithm that can accurately predict the optical properties of BC fractal aggregates with an average deviation of less than one percent from their actual values. Specifically, the ML algorithm provides the option to generate the optical properties in the visible spectrum using either kernel ridge regression (KRR) or artificial neural networks (ANN) for a BC fractal aggregate of desired physicochemical properties like size, morphology, and organic coating. The dataset of physicochemical and optical properties of BC fractal aggregates are provided here. The developed ML algorithm for predicting the optical properties of BC fractal aggregates (https://github.com/jaikrishnap/Machine-learning-for-prediction-of-BCFAs) is highly useful for real-world applications due to its wide parameter range, high accuracy, and low computational cost.</p> <p><strong>Contents</strong></p> <ul> <li>database_optical_properties_black_carbon_fractal_aggregtates.csv, data file, comma-separated values</li> <li>database_header.txt, metadata, text</li> </ul> <p><strong>Citation for the database: </strong></p> <p>B., Romshoo, T., Müller, B., Patil, J., Michels, T., Kloft, M., and Pöhlker, M.: Database of physicochemical and optical properties of black<br> carbon fractal aggregates, Dataset, https://doi.org/10.5281/zenodo.7523058, 2023.</p>
Mineral Protection rather than Aggregate Stability Enhanced Soil Organic Carbon Along an Elevated Gradient in Alpine Areas of Southwest China
<p>This data contains Background, Dominant plant and their biomass, Environmental variables, Aggregate stability, Fe/Al oxides, Mass of soil density fractions, Carbon contetn in each density fraction, Mass of aggregates, Carbon content in each aggregate class size, Ratio of carbon content in each soil density and aggregate fractions and Enzyme avtivity of our investigated sites. Total 46 factors were given.</p>
Protists regulate microbially-mediated organic carbon turnover in soil aggregates
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Crop Diversification Effects on Soil Aggregation and Aggregate-Associated Carbon and Nitrogen in Short-Term Rainfed Olive Groves under SemiaridMediterranean Conditions
<p>Soil particle aggregation and their associated carbon (C) and nitrogen (N) content can<br> provide valuable diagnostic indicators of changes in soil properties in response to the implementation<br> of different agricultural management practices. In this sense, there is limited knowledge regarding the<br> impact of intercropping on soil organic carbon (SOC) and total nitrogen (TN) pools in aggregates. This<br> study aimed to evaluate the short-term effect (4 years) of three crop diversifications in rainfed olive<br> orchards on soil aggregation, SOC and TN concentration and SOC stocks (SOC-S) under semi-arid<br> Mediterranean conditions. Olive orchards were diversified with Crocus sativus (D-S), Vicia sativa and<br> Avena sativa in rotation (D-O) and Lavandula x intermedia (D-L) and compared with monocropping<br> system (CT). Soil samples were collected at two depths (0–10 and 10–30 cm) and analysed for soil<br> aggregate mass, SOC and TN content in aggregate-size fractions obtained by the wet-sieving method.<br> Changes caused by crop diversifications on SOC-S were also determined. Overall, after 4 years,<br> a reduction in aggregation values was observed. However, D-S increased the macroaggregates<br> (>250 m) percentage, Mean Weigh Diameter values, and Geometric Mean Value in the 0–10 cm.<br> Across treatments, aggregate-associated C in 0–10 cm was higher in the D-S treatment, while in<br> the 10–30 cm soil layer, the greatest values were found in CT. Regarding the SOC-S, after 4 years,<br> significant losses were recorded under CT management in 0–10 cm (1.21 Mg ha1) and 10–30 cm<br> (0.84 Mg ha1), while D-O and D-L showed similar values to those obtained at the beginning of the<br> study. The highest increases in SOC-S were found in D-S, with an increase of 5.88% in the 0–10 cm<br> and 14.47% in the 10–30 cm. Our results showed the high potential of the diversified cropping system<br> to increase soil stability and SOC sequestration.</p>
Data from: Impacts of long-term tillage and fertilization on soil carbon stock and aggregate stability in tropical agriculture
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Data from: Dispersion, sedimentation, and aggregation of multi-walled carbon nanotubes as affected by single and binary mixed surfactants
Two commonly used dispersants octyl phenol ethoxylate (Triton X-100) and sodium dodecyl sulfate (SDS) were employed to explore the effects of single or mixed surfactants on the dispersion, sedimentation, and aggregation of multi-walled carbon nanotubes (MWCNTs). Nonionic surfactant TX100 showed much superior capability to anionic surfactant SDS in dispersing MWCNTs due to the benzene ring structure in its tail group. The addition of SDS reduced the adsorption of TX100 on the surface of MWCNTs and the consequent suspension of MWCNTs. The dispersing ability of TX100-SDS binary mixture was between those of individual SDS and TX100. The introduction of SDS greatly retarded the sedimentation and aggregation of suspended MWCNTs. The critical coagulation concentration (CCC) values of suspended MWCNTs dispersed by TX100 (2000 mg/L), SDS (2000 mg/L), and TX100-SDS (2000 mg/L of each component) were 48.6, 398, and 324 mM, respectively, for the Na+ treatments. The CCC values were much lower for Ca2+ treatments, which were 30.4 and 32.1 mM, respectively, for MWCNT dispersed by TX100 and TX100-SDS mixture. Overall, these results demonstrated that although the introduction of SDS did not improve the ability of TX100 in suspending MWCNTs, the suspensions exhibited more stable properties than those dispersed by TX100 alone. Our findings have important implications on the design of surfactant mixtures and the prediction on the behavior and fate of MWCNTs in water environment.
Data from: Dispersion, sedimentation, and aggregation of multi-walled carbon nanotubes as affected by single and binary mixed surfactants
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Effects of Hemoperitoneum Driven Carbon Monoxide on Post-Traumatic Stability and Platelet Aggregation
ClinicalTrials.gov study NCT01594034. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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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.