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519 results for “organic soil”
Soil grid data for 4 agricultural fields in PT (ECe; soil organic carbon, pH)
<p>Soil data collected in an agricultural area with annual crops in Portugal (Lezíria Grande). The data refers to soil properties of 63 soil samples collected at a depth of 0-20 cm, considering a regular sampling grid, in four fields with varying soil salinity (field areas between 2 and 34 ha). The samples were collected at a period when the soil was bare, 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, and pH1:5. </p><p>The data may be representative of the soil conditions of the area, which is a highly productive agricultural low land, 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 (WP5 (WP5-T3) and WP2 (WP2-T3)).The aim of the collected dataset was to be able to analyze the influence of soil salinity in SOC prediction from remote sensing.</p><p>Data in the form of MS Excel files (xlsx), pictures of the soil surface in jpg. format. </p>
Supporting data for von Fromm et al. (2023) Controls on timescales of soil organic carbon persistence across sub-Saharan Africa
<p>This file contains the supporting data for<em> von Fromm et al. (2024) Controls on timescales of soil organic carbon persistence across sub-Saharan Africa, Global Change Biology</em>, <a href="https://doi.org/10.1111/gcb.17089">https://doi.org/10.1111/gcb.17089</a></p> <p>We used wet soil chemistry data from <em>Vågen et al., 2021</em> (<a href="https://doi.org/10.34725/DVN/66BFOB">https://doi.org/10.34725/DVN/66BFOB</a>). In addition, we added newly measured radiocarbon data, extracted global climate data, gross primary productivity and quantified soil mineralogy based on X-ray powder diffraction data. Turnover time for carbon (mean C age) was calculated from Δ14C (‰) values by using an one-pool model. For more details about the sampling, calculations, and units see the associated publication. To reproduce all analysis, including calculating the mean C age, please visit the author's github page: <a href="https://github.com/SophievF/AfSIS_14C">https://github.com/SophievF/AfSIS_14C</a>. </p> <p>The dataset is also part of the International Soil Radiocarbon Database (<a href="https://soilradiocarbon.org/">https://soilradiocarbon.org/</a>).</p>
European soil bulk density and organic carbon stock database using LUCAS Soil 2018
<p>We complied the European topsoil bulk density and organic carbon stock database (0-20 cm) using LUCAS Soil 2018. This database inlcudes 18,945 and 15,389 soil samples (0-20 cm) with bulk density in fine fraction (Bdfine) and soil organic cabron stock (SOCS) for the EU and UK using the best traditional pedotransfer function (T-PTF-4) and machine leanring based PTFs (Local-RFFRFS). It also contains the POINTID linked to LUCAS Soil 2018, coarse fragements in volume (coarse_vol) and coordinates (GPS_LAT, GPS_LONG). For more information, please refer to LUCAS 2018 TOPSOIL data (https://esdac.jrc.ec.europa.eu/content/lucas-2018-topsoil-data).</p> <p>This dataset is asscoated to the "European soil bulk density and organic carbon stock database using machine learning based pedotransfer function" by Chen et al. (2024).</p> <p>Manuscript citation: Chen, S., Chen, Z., Zhang, X., Luo, Z., Schillaci, C., Arrouays, D., Richer-de-Forges, A.C., Shi, Z. , 2024. European topsoil bulk density and organic carbon stock database (0-20 cm) using machine learning based pedotransfer functions. Earth System Science Data, 16, 2367–2383.</p> <p>When using the data, please cite repositories as well as the original manuscript.</p> <p>For any questions on the data, please contact Dr. Songchao Chen (chensongchao@zju.edu.cn).</p>
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>
Sorption of Colored vs Noncolored Organic Matter by Tidal Marsh Soils
<p>Raw Data for publication Sorption of Colored vs Noncolored Organic Matter by Tidal Marsh Soils<span> </span></p> <p><span> </span>Patrick J. Neale, J. Patrick Megonigal, Maria Tzortziou, Elizabeth A. Canuel, Christina R. Pondell, and Hannah Morrissette, Biogeosciences (in press)<span> </span></p> <p>https://doi.org/10.5194/egusphere-2023-2329<span> </span></p> <p>Correspondence:<span> </span>Patrick Neale (nealep@si.edu)<span> </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>
A stocktaking of European mid- and long-term experiments dealing with the application of external organic matter (EJP SOIL - EOM4SOIL - D 3.1.1)
<p>Extending and optimizing recycling of organic wastes in agriculture is a key element in shifting conventional agriculture towards systems adapted to both energy depletion and climate change. Long-term field experiments (LTEs) play a crucial role in assessing and modelling the effects of repeated exogenous organic matter (EOM) application to soil, which allows to formulate locally adapted recommendations of use. <br>Nevertheless, a specific database focussing on LTEs dealing with organic fertilization in Europe were missing. To close this gap, we listed LTEs dealing with repeated application of EOM from existing online databases, and collected and harmonized all available metadata. The aim of this work was threefold: (1) to facilitate connections between comparable LTEs to foster data harmonization and compilation, (2) to map the diversity of pedoclimatic contexts and experimental designs in the LTE list and, (3) to highlight current knowledge gaps and research needs. <br>Data were collected from five online databases, allowing us to describe 201 LTEs. Key characteristics such as trial name, responsible institution, location, pedoclimatic context, duration, crop type and availability of online resources are well-described in contrast to LTE goal and owner contact, experimental design, soil type, studied EOM and monitored parameters (EOM characteristics, soil and crop properties), which are more difficult to gather and harmonize. The analysis of LTE metadata highlighted first that substantial harmonization efforts are required, particularly regarding the reporting of soil, crop and EOM properties over time. Second, the survey outlines that some European regions are poorly represented in the database, which may result either from an absence of LTE or from a lack of reporting. To close this gap, we call LTE managers to complete the current database with any missing relevant LTE or additional metadata, using the editable online repository attached to this document. In the future, improvement of predictive models could contribute to provide recommendations of EOM use to uncovered situations, whether in terms of soil, climate or type of EOM. Third, long-term effects on soil properties such as changes in soil biology composition or accumulation of organic contaminants (PFAS, microplastics, antibiotics, ...) appear to be poorly documented. LTEs have a key role to play in answering these emerging questions, having the potential to provide the rationale to fix acceptable thresholds in soils and EOMs for emerging pollutants and accordingly provide the best possible guidelines for the use of EOM in agriculture. </p> <p>See related report at <a href="https://doi.org/10.5281/zenodo.14161379" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.14161379</a>.</p>
Raw data for the submitted manuscript: The Influence of Soil Organic Matter Content on the Toxicity of Pesticides to the Springtail Folsomia candida
<p>Raw data obtained from toxicity tests with the springtail Folsomia candida exposed for 28 days to chlorpyrifos, lindane, cyproconazole, carbendazim and imidacloprid in artificial soils containing 10%, 5%, 2.5% sphagnum peat, and LUFA 2.2 soil. Tests were performed following OECD guideline 232. The file includes data on springtail survival and reproduction.</p>
Raw data for the manuscript: The influence of soil organic matter content and substance lipophilicity on the toxicity of pesticides to the earthworm Eisenia andrei
<p>Raw data obtained from toxicity tests with the earthworm <em>Eisenia andrei</em> exposed for 56 days to chlorpyrifos, lindane, cyproconazole, carbendazim and imidacloprid in artificial soils containing 10%, 5%, 2.5% sphagnum peat, and LUFA 2.2 soil. Tests were performed following OECD guideline 222. The file includes data on earthworm starting and ending weights, survival, and reproduction.</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>
Soil laboratory analyses data of organic, permaculture and conventional horticultural farms of Central Hungary
<p>pH (KCl 1:2,5) [-], Arany type texture index [K<sub>A</sub>], Water soluble total salt [m/m%], CaCO<sub>3 </sub>[m/m%], Humus [m/m%], N-NO<sub>2</sub>+NO<sub>3</sub> (KCl soluble) [mg/kg], Mg (KCl soluble) [mg/kg], S (KCl soluble) [mg/kg], K<sub>2</sub>O (AL soluble) [mg/kg], Na (AL soluble) [mg/kg], P<sub>2</sub>O<sub>5</sub> (AL soluble) [mg/kg], Cu (KCl EDTA soluble) [mg/kg] , Mn (KCl EDTA soluble) [mg/kg] , Zn (KCl EDTA soluble) [mg/kg]</p>
A photo of the typical view of organic, permaculture and conventional horticultural farms and a photo of a typical soil profile in a core sampler for each
<p>The pdf file contains an introduction slide (Slide 1) with the list of the introduced farms.</p> <p>There are 15 slides following the introductory file.</p> <p>Each slide has a photo of a horticultural farm, its code and a photo of one of its typical soil profiles.</p> <p>The photo of the profile was made of a soil core sampler that is 100 cm long.</p> <p>In some of the farms, there were multiple profiles revealed and photos were made, here we just show one of these.</p> <p>The majority of the soils are Luvisols but we have some 2 Fluvisols, 2 Chernozems and 2 Fluvisols.</p> <p>More information can be found in a published article: Szilágyi, A.; Plachi, E.; Nagy, P.; Simon, B.; Centeri, C. Assessing Earthworm Populations in Some Hungarian Horticultural Farms: Comparison of Conventional, Organic and Permaculture Farming. <em>Biol. Life Sci. Forum</em> <strong>2021</strong>, <em>2</em>, 11. https://doi.org/10.3390/BDEE2021-09416</p> <p>The purpose of the recent pdf is to provide information for an upcoming article in the journal of Diversity.</p> <p>All soil laboratory analyses have already been published for this purpose:</p> <p>https://zenodo.org/record/5717449#.YeoUYv7MJPY</p>
Decay by ectomycorrhizal fungi couples soil organic matter to nitrogen availability
<p>Interactions between soil nitrogen (N) availability, fungal community composition, and soil organic matter (SOM) regulate soil carbon (C) dynamics in many forest ecosystems, but context dependency in these relationships has precluded general predictive theory. We found that ectomycorrhizal (ECM) fungi with peroxidases decreased with increasing inorganic N availability across a natural inorganic N gradient in northern temperate forests, whereas ligninolytic fungal saprotrophs exhibited no response. Lignin-derived SOM and soil C were negatively correlated with ECM fungi with peroxidases and were positively correlated with inorganic N availability, suggesting decay of lignin-derived SOM by these ECM fungi reduced soil C storage. The correlations we observed link SOM decay in temperate forests to tradeoffs in tree N nutrition and ECM composition, and we propose SOM varies along a single continuum across temperate and boreal ecosystems depending upon how tree allocation to functionally distinct ECM taxa and environmental stress covary with soil N availability.</p>
Maps of soil organic carbon stocks in Brazil
<p>This database was created by Gustavo Vieira Veloso and Lucas Carvalho Gomes 04/06/2022. <br> Contact: gustavo.v.veloso@gmail.com and lucascarvalhogomes15@hotmail.com <br> ------------------------------------------------------------------------------------</p> <p>Maps of soil organic carbon (SOC) stocks in Brazil of the article: "Modeling and mapping soil organic carbon stocks in Brazil" (doi: 10.1016/j.geoderma.2019.01.007)</p> <p>The dataset is composed of five folders of SOC stocks maps at the standard depths (0–5, 5–15, 15–30, 30–60, and 60–100 cm). The maps are in Geotif format (EPSG 102015) with a spatial resolution of approximately 1 km and include the mean SOC stocks, standard deviation (SD), coefficient of variation (CV), 0.05 and 0.95 quantiles.</p> <p>The maps are free to use and please cite also the article:<br> Gomes, L.C., Faria, R.M., de Souza, E., Veloso, G.V., Schaefer, C.E.G., & Fernandes Filho, E.I. (2019). Modeling and mapping soil organic carbon stocks in Brazil. Geoderma, 340, 337-350.</p> <p> </p>
Positive associations of soil organic matter and crop yields across a regional network of working farms
<p>The amount of soil organic matter (SOM) is considered a key indicator of soil properties associated with higher fertility. Despite the ubiquity of assumptions surrounding SOM's contributions to soil functioning, we lack quantitative relationships between SOM and yield outcomes on working farms. We quantified the relationship between SOM and yields of corn (<i>Zea mays </i>L.) and silage for a dataset of 170 fields arrayed across 49 farms in a network of growers based in Wisconsin and Minnesota, USA. As SOM concentrations increase, so do yields, though gains start to level off around 4% SOM. When examining the relationship between yield and soil health indicators representative of biologically active carbon pools, we found that mineralizable carbon (min-C) has a stronger relationship with yield than permanganate oxidizable C (POXC). Mineral fertilizer, manure, and SOM had relationships of similar magnitude with yield, highlighting that SOM in combination with exogenous inputs likely plays an important role in driving agricultural productivity in this region. An SOM by crop rotation interaction indicated that the impact of SOM on crop yields varied depending on rotation (continuous corn versus corn in rotation). That is, continuous corn had lower yields than corn in rotation despite higher SOM concentrations. Our findings provide insight into the relationship between indicators of soil health, farm management, and crop yields for a set of working farms and lend support to the goals of soil health initiatives that rest on building SOM in agricultural soils to improve agricultural outcomes.</p>
Data from: Decipher soil organic carbon dynamics and driving forces across China using machine learning
<p><span><span>The dynamics of soil organic carbon (SOC) play a critical role in modulating global warming. However, the long-term spatiotemporal changes of SOC at large scale and the impacts of driving forces remain unclear. In this study, we investigated the dynamics of SOC in different soil layers across China through the 1980s to 2010s using a machine learning approach and quantified the impacts of the key factors based on factorial simulation experiments. Our results showed that the latest (2000-2014) SOC stock in the first meter soil (SOC<sub>100</sub>) was 80.68 ± 3.49 Pg C, of which 42.6% was stored in the top 20 cm, sequestrating carbon with a rate of 30.80 </span><span>± 12.37</span><span> g C m<sup>-2</sup> yr<sup>-1</sup> since the 1980s. Our experiments focusing on the recent two periods (2000s and 2010s) revealed that climate change exerted the largest relative contributions to SOC dynamics in both layers and warming or drying can result in SOC loss. However, the influence of climate change weakened with soil depth, while the opposite for vegetation growth. </span><span>Relationships between SOC and forest canopy height further confirmed this strengthened impact of vegetation with soil depth, and highlighted the carbon sink function of deep soil in mature forest. Moreover, our estimates suggested that SOC dynamics in 71% of topsoil were controlled by climate change and its coupled influence with environmental variation (CE). Meanwhile CE and the combined influence of climate change and vegetation growth dominated the SOC dynamics in 82.05% of the first meter soil. </span><span>Additionally, the national cropland topsoil organic carbon increased with a rate of 23.6 </span><span>± 7.6 </span><span>g C m<sup>-2</sup> yr<sup>-1</sup> since the 1980s, and the widely applied nitrogenous fertilizer was a key stimulus. </span><span>Overall, our study extended the knowledge about the dynamics of SOC and deepened our understanding about the impacts of the primary factors.</span></span></p>
Data from: Loamy sand soil approaches organic carbon saturation after 37 years of conservation tillage
<p>This is digital research data corresponding to a published manuscript, Loamy sand soil approaches organic carbon saturation after 37 years of conservation tillage. Conservation tillage is reported to increase soil organic carbon (SOC) and total nitrogen (TN) contents, but long-term (>30 yr) field results quantifying the responses in Coastal Plain Ultisols are sparse. The distribution, accumulation, and topsoil storage of SOC and TN after 37 yr of crop production using conventional (CvT) or conservation tillage (CnT) on a Norfolk loamy sand (fine-loamy, kaolinitic, thermic, Typic Kandiudults) were quantified. Soil samples were collected annually from the 0−5-, 5−10-, and 10−15-cm depth increments beneath corn (Zea mays L.), soybean [Glycine max (L.) Merr.], and cotton (Gossypium hirsutum L.) crops.</p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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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.