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709 results for “soil carbon”
Quantitative Pit Soil Carbon and Nitrogen on Watershed 5 at the Hubbard Brook Expermental Forest, 1983-1998
We sampled soils prior to the whole-tree harvest of watershed 5 at Hubbard Brook Experimental Forest in 1983, and again in 1986, 1991, and 1998, using the quantitative soil pit method. Here we report soil mass, C and N concentrations, and loss-on-ignition for each horizon in each of the 239 soil pits excavated over the four sampling years. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
Soil carbon, soil nitrogen, and geometry of 30 playas and their catchment areas at the Jornada Basin LTER site in 2012
This data package contains soil carbon and nitrogen data at a range of soil depths from 30 ephemerally-flooded desert wetlands, or playas, in the Jornada Basin of southern New Mexico, USA. We conducted this study to assess how catchment biophysical variables control soil organic carbon and nitrogen in playas and how playas function differently than upland ecosystems. We chose 30 playas from across this Chihuahuan Desert Basin and collected 36 soil samples from four depths and nine locations. Soil cores samples were taken along two perpendicular transect lines to account for a topographic gradient from the edge of the playa to the center of the playa. At each of the nine locations, one sample was collected at four depths (0–10 cm, 10–30 cm, 30–60 cm, 60–100 cm). We measured soil organic carbon and total nitrogen concentrations in these soils using elemental combustion analysis. Using bulk density measurements for each depth range (m), we converted each soil measurement (g/g) to calculate concentrations of organic carbon and nitrogen per unit area (g/m^2) within the depth-ranges sampled. Data on the geometry of the playas and their associated catchments is also provided. This study is complete. For further information, refer to: McKenna, Owen P., and Osvaldo E. Sala. "Biophysical controls over concentration and depth distribution of soil organic carbon and nitrogen in desert playas." Journal of Geophysical Research: Biogeosciences 121, no. 12 (2016): 3019-3029. https://doi.org/10.1002/2016JG003545
Soil Total Carbon and Total Nitrogen on the Main Cropping System Experiment at the Kellogg Biological Station, Hickory Corners, MI (1989 to 2001)
Dataset Abstract Soil carbon and nitrogen are presented as % elemental carbon and nitrogen (g C or N / 100 g soil) for the sampling depth of the specific sampling date unless indicated otherwise. Samples were taken with either a 2.5 cm diameter soil corer (sampling by soil depth) or a 10 cm diameter Giddings probe (sampling by soil profile). See Baseline Soil Sampling protocol for general sampling information. For specific sampling details for a particular date, see Soil Sampling Field Log. Soil samples are sieved to 4mm and composited by plot. Soil % elemental carbon and nitrogen values are determined by sample combustion and subsequent TCD gas chromatography, using a Carlo Erba automated CHN analyzer as described in the sampling protocol. original data source http://lter.kbs.msu.edu/datasets/27
Soil Carbon and Nitrogen Deep Core Surveys at the Kellogg Biological Station, Hickory Corners, MI (2001 to 2013)
Dataset Abstract An LTER project goal is to periodically collect and analyze deep soil cores for the main site treatments and successional and forest sites. Soil is sampled to a depth of one meter, and analyzed for horizon depths, texture, moisture, and carbon and nitrogen content. original data source http://lter.kbs.msu.edu/datasets/47
Spatial Variation of Soil Carbon, Nitrogen and Phosphorus in the Luquillo Experimental Forest (LEF) (LEF_SOIL_CNP)
Hongqing Wang, a Ph.D graduate student of SUNY-ESF, with the help of many others, took soil samples in 119 locations over the entire Luquillo Experimental Forest (LEF) during the summer of 1998 and 1999. Soil organic carbon, total nitrogen and acid-extractable phosphorus were measured in the laboratory of SUNY-ESF; soil moisture and bulk density were measured at the laboratory of the El Verde Station of UPR. The geodetic coordinates (Lat., Lon.) and elevation of each sampling location were determined using a Global Positioning System (GPS Pathfinder Basic Receivers, Trimble Navigation Ltd.) in the field. Meanwhile, slope angle, aspect and topographic features (Ridge, slope I (<35 deg.), slope II (>=35 deg.), valleys) were also measured and observed in the field. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
Radiocarbon content of soil carbon and respired CO2 near T-Van from 2012 to 2015, Seasonal
To test the hypothesis that old carbon may be contributing to the carbon source strength of alpine tundra near T-Van, a chamber (growing season) and two types of subsurface gas wells (remainder of the year) were used to collect respired carbon dioxide samples for radiocarbon analysis from four locations across a soil moisture gradient near T-Van between 2012 and 2015. Near surface soil samples (~10 cm depth) from each site were additionally collected and density fractionated in order to model the contribution of various carbon pools to respired carbon fluxes on a seasonal basis through time. All samples were purified and graphitized by the INSTAAR Laboratory for AMS Radiocarbon Preparation and Research at the University of Colorado Boulder, then shipped to the Keck Carbon Cycle AMS Lab at the University of California, Irvine for analysis.
Temporal Dynamics of Soil Carbon and Nitrogen Resources Within a Grassland-Creosote Ecotone at the Sevilleta National Wildlife Refuge, New Mexico (1992-1994)
Plant communities across large portions of the southwestern United States have shifted from grassland to desert shrubland. Studies have demonstrated that soil nutrient resources become spatially more heterogeneous and are redistributed into islands of fertility with this shift in vegetation. This research addressed the additional question of whether soil resources become more temporally heterogeneous along a grassland-shrubland ecotome. Within adjacent grassland and creosotebush sites, soil profiles were described at 3 pits and samples collected for description of nutrient resources within the profile. Relative cover of plant species and bare soil were determined within each site by line transects. The top 20-cm of bare soil or soil beneath the canopy of grasses/creosotebush were collected 17 times during 1992-1994. Soil samples were analyzed for soil moisture, extractable ammonium and nitrate, nitrogen mineralization potential, microbial biomass carbon, total organic carbon, microbial respiration, dehydrogenase activity, ratio of microbial C to total C (C[mic]-to-C[org]), and microbial respiration to biomass carbon (metabolic quotient). The major differences in the structure of soils between sites were the apparent loss of a 3 to 5-cm depth of sandy surface soil at the creosotebush site and an associated increase in calcium carbonate content at a more shallow depth. Soils under plants at both sites had greater total and available nutrient resources with higher concentrations under creosotebush than under grasses. Greatest temporal variation in available soil resources was shown in soils under creosotebush. When expressed on an area basis, greater temporal variation in the total amount of available soil resources was shown in the grassland site, primarily due to greater plant cover (45% in grassland vs. 8% in creosote).
SGS-LTER Transect Study - Organic Carbon in Soils across Toposequences on the Central Plains Experimental Range, Nunn, Colorado, USA 1983-1984
This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. CPER SOC across Toposequences - Pedons and their corresponding topography were described along an 8 km transect oriented normal to the major drainages of the CPER. A total of 140 pedons representing 23 toposequences and 7 plains segments were characterized. Sampling sites were selected within toposequences according to slope position (summit, shoulder, backslope, footslope, toeslope) and within plains segments at approximate 100 m intervals. Pedons were described and sampled by genetic horizon according to the standards of the National Cooperative Soil Survey. Analyses, conducted at Colorado State University, included particle size and organic C. Bulk density was estimated empirically according to: Rawls, W.J. 1983. Estimating soil bulk density form particle size analysis and organic matter content. Soil Sci 135: 123-125. Organic C accumulation was measured along an 8 km transect at a site in the semiarid shortgrass steppe of northeastern Colorado. Specific objectives of the study were to (I) measure the quantity and distribution of organic C across toposequences, (ii) test the hypothesis that a disproportionate amount of soil organic C resides in the lowlands (as defined herein), and (iii) assess the role of geomorphic history as a determinant of contemporary rates o
Diversification and Management Practices in Selected European Regions. A Data-analysis of Arable Crops Production and soil organic carbon
<p>This data set contains a data-mining performed to assess the impact of intercropping, tillage and fertilizer type on soil organic carbon and crop yield in arable crops from four selected European pedoclimatic regions and typical cropping systems in the Atlantic, Boreal, Mediterranean North, and Mediterranean South regions. A further meta-analysis was performed with these data. </p> <p>These data correspond to the open-access articles:</p> <p>- Diversified Arable Cropping Systems and Management Schemes in Selected European Regions Have Positive Effects on Soil Organic Carbon Content. Agriculture 2019, 9, 261. https://www.mdpi.com/2077-0472/9/12/261?type=check_update&version=2</p> <p>- Diversification and Management Practices in Selected European Regions. A Data-analysis of Arable Crops Production. Agronomy 2020, 10, 297; doi:10.3390/agronomy10020297. https://www.mdpi.com/2073-4395/10/2/297</p> <p>- Deficit Drip Irrigation in Processing Tomato Production in the Mediterranean Basin: A Data Analysis for Italy. Agriculture 2019, 9, 79; doi:10.3390/agriculture9040079. https://www.mdpi.com/2077-0472/9/4/79?type=check_update&version=2</p> <p>The research and publications have been funded by he European Commission Horizon 2020 project Diverfarming [grant agreement 728003]. </p>
Data from: Leaching losses of dissolved organic carbon and nitrogen from agricultural soils in the upper US Midwest
<p>Leaching losses of dissolved organic carbon (DOC) and nitrogen (DON) from agricultural systems are important to water quality and carbon and nutrient balances but are rarely reported; the few available studies suggest linkages to litter production (DOC) and nitrogen fertilization (DON). In this study we examine the leaching of DOC, DON, NO<sub>3</sub><sup>-</sup>, and NH<sub>4</sub><sup>+</sup> from no-till corn (maize) and perennial bioenergy crops (switchgrass, miscanthus, native grasses, restored prairie, and poplar) grown between 2009 and 2016 in a replicated field experiment in the upper Midwest U.S. Leaching was estimated from concentrations in soil water and modeled drainage (percolation) rates. DOC leaching rates (kg ha<sup>-1 </sup>yr<sup>-1</sup>) and volume-weighted mean concentrations (mg L<sup>-1</sup>) among cropping systems averaged 15.4 and 4.6, respectively; N fertilization had no effect and poplar lost the most DOC (21.8 and 6.9, respectively). DON leaching rates (kg ha<sup>-1 </sup>yr<sup>-1</sup>) and volume-weighted mean concentrations (mg L<sup>-1</sup>) under corn (the most heavily N-fertilized crop) averaged 4.5 and 1.0, respectively, which was higher than perennial grasses (mean: 1.5 and 0.5, respectively) and poplar (1.6 and 0.5, respectively). NO<sub>3</sub><sup>-</sup> comprised the majority of total N leaching in all systems (59-92%). Average NO<sub>3</sub><sup>-</sup> leaching (kg N ha<sup>-1</sup> yr<sup>-1</sup>) under corn (35.3) was higher than perennial grasses (5.9) and poplar (7.2). NH<sub>4</sub><sup>+</sup> concentrations in soil water from all cropping systems were relatively low (<0.07 mg N L<sup>-1</sup>). Perennial crops leached more NO<sub>3</sub><sup>-</sup> in the first few years after planting, and markedly less after. Among the fertilized crops, the leached N represented 14-38% of the added N over the study period; poplar lost the greatest proportion (38%) and corn was intermediate (23%). Requiring only one third or less of the N fertilization compared to corn, perennial bioenergy crops can substantially reduce N leaching and consequent movement into aquifers and surface waters.</p>
iSDAsoil: soil total Carbon for Africa predicted at 30 m resolution at 0-20 and 20-50 cm depths
<p>iSDAsoil dataset soil total carbon in permilles (g/kg) log-transformed predicted at 30 m resolution for 0–20 and 20–50 cm depth intervals. Data has been projected in WGS84 coordinate system and compiled as <a href="https://gdal.org/drivers/raster/cog.html">COG</a>. Predictions have been generated using multi-scale Ensemble Machine Learning with 250 m (MODIS, PROBA-V, climatic variables and similar) and 30 m (DTM derivatives, Landsat, Sentinel-2 and similar) resolution covariates. For model training we use a pan-African compilations of soil samples and profiles (<a href="https://www.isda-africa.com/national-soil-services/">iSDA points</a>, <a href="https://www.isric.org/projects/africa-soil-profiles-database-afsp">AfSPDB</a>, and other national and regional soil datasets). Cite as:</p> <p>Hengl, T., Miller, M.A.E., Križan, J. <em>et al.</em> African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning. <em>Sci Rep</em> <strong>11, </strong>6130 (2021). <a href="https://doi.org/10.1038/s41598-021-85639-y">https://doi.org/10.1038/s41598-021-85639-y</a></p> <p>To open the maps in QGIS and/or directly compute with them, please use the <a href="https://gitlab.com/openlandmap/africa-soil-and-agronomy-data-cube"><strong>Cloud-Optimized GeoTIFF version</strong></a>.</p> <p>Layer description:</p> <ul> <li>sol_log.c_tot_mehlich3_m_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil total Carbon mean value,</li> <li>sol_log.c_tot_mehlich3_md_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil total Carbon model (prediction) errors,</li> </ul> <p>Model errors were derived using bootstrapping: md is derived as standard deviation of individual learners from 5-fold cross-validation (using spatial blocking). The model 5-fold cross-validation (<a href="https://mlr.mlr-org.com/reference/makeStackedLearner.html">mlr::makeStackedLearner</a>) for this variable indicates:</p> <pre><code>Variable: log.c_tot R-square: 0.794 Fitted values sd: 0.571 RMSE: 0.291 Random forest model: Call: stats::lm(formula = f, data = d) Residuals: Min 1Q Median 3Q Max -2.70312 -0.16714 -0.00549 0.15691 3.01116 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.025841 0.032713 0.790 0.429570 regr.ranger 0.902240 0.008462 106.619 < 2e-16 *** regr.xgboost 0.066535 0.008145 8.169 3.18e-16 *** regr.cubist 0.145730 0.006927 21.039 < 2e-16 *** regr.nnet -0.048957 0.013466 -3.636 0.000278 *** regr.cvglmnet -0.075212 0.005556 -13.537 < 2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.291 on 50140 degrees of freedom Multiple R-squared: 0.7938, Adjusted R-squared: 0.7938 F-statistic: 3.861e+04 on 5 and 50140 DF, p-value: < 2.2e-16 </code></pre> <p>To back-transform values (y) to g/kg use the following formula:</p> <pre><code>g/kg = expm1( y / 10 )</code></pre> <p>To submit an issue or request support please visit <a href="https://isda-africa.com/isdasoil"><strong>https://isda-africa.com/isdasoil</strong></a></p>
iSDAsoil: soil organic carbon for Africa predicted at 30 m resolution at 0-20 and 20-50 cm depths
<p>iSDAsoil dataset soil organic carbon (C) log-transformed predicted at 30 m resolution for 0–20 and 20–50 cm depth intervals. Data has been projected in WGS84 coordinate system and compiled as <a href="https://gdal.org/drivers/raster/cog.html">COG</a>. Predictions have been generated using multi-scale Ensemble Machine Learning with 250 m (MODIS, PROBA-V, climatic variables and similar) and 30 m (DTM derivatives, Landsat, Sentinel-2 and similar) resolution covariates. For model training we use a pan-African compilations of soil samples and profiles (<a href="https://www.isda-africa.com/national-soil-services/">iSDA points</a>, <a href="https://www.isric.org/projects/africa-soil-profiles-database-afsp">AfSPDB</a>, and other national and regional soil datasets). Cite as:</p> <p>Hengl, T., Miller, M.A.E., Križan, J. <em>et al.</em> African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning. <em>Sci Rep</em> <strong>11, </strong>6130 (2021). <a href="https://doi.org/10.1038/s41598-021-85639-y">https://doi.org/10.1038/s41598-021-85639-y</a></p> <p>To open the maps in QGIS and/or directly compute with them, please use the <a href="https://gitlab.com/openlandmap/africa-soil-and-agronomy-data-cube"><strong>Cloud-Optimized GeoTIFF version</strong></a>.</p> <p>Layer description:</p> <ul> <li>sol_log.oc_mehlich3_m_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil organic Carbon mean value,</li> <li>sol_log.oc_mehlich3_md_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil organic Carbon model (prediction) errors,</li> </ul> <p>Model errors were derived using bootstrapping: md is derived as standard deviation of individual learners from 5-fold cross-validation (using spatial blocking). The model 5-fold cross-validation (<a href="https://mlr.mlr-org.com/reference/makeStackedLearner.html">mlr::makeStackedLearner</a>) for this variable indicates:</p> <pre><code>Variable: log.oc R-square: 0.791 Fitted values sd: 0.716 RMSE: 0.369 Random forest model: Call: stats::lm(formula = f, data = d) Residuals: Min 1Q Median 3Q Max -3.1517 -0.1900 -0.0060 0.1793 4.2621 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.821657 0.794000 2.294 0.0218 * regr.ranger 1.047507 0.005146 203.571 <2e-16 *** regr.xgboost -0.005943 0.005340 -1.113 0.2657 regr.cubist 0.052084 0.004884 10.664 <2e-16 *** regr.nnet -0.867384 0.359213 -2.415 0.0158 * regr.cvglmnet -0.050157 0.003863 -12.984 <2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.3687 on 122457 degrees of freedom Multiple R-squared: 0.7906, Adjusted R-squared: 0.7906 F-statistic: 9.248e+04 on 5 and 122457 DF, p-value: < 2.2e-16 </code></pre> <p>To back-transform values (y) to g/kg use the following formula:</p> <pre><code>g/kg = expm1( y / 10 )</code></pre> <p>To submit an issue or request support please visit <a href="https://isda-africa.com/isdasoil"><strong>https://isda-africa.com/isdasoil</strong></a></p>
Spatial heterogeneity in soil pyrogenic carbon mediates tree growth and physiology following wildfire
<table> <tbody> <tr> <td>Pyrogenic carbon (PyC) is a ubiquitous legacy of wildfire in terrestrial soils, yet how it affects the growth and function of regenerating plants has received little research attention.<br> <br> We examined responses to a natural gradient of PyC deposition five years following a severe fire in a northern boreal forest, based on measurements of growth (height, basal area increment, and leader extension), physiological performance (Fv/Fm), and foliar nutrition (foliar C, N, P, K, Mg) of Pinus banksiana Lamb. We determined the concentration of PyC, expressed as a dosage (t·ha-1), in mineral soils collected from the rhizospheres of each sapling and used it as an independent factor to model trait responses to increasing PyC levels, in conjunction with measurements of soil physio-chemical properties (pH, EC, VOC, Ash, N, P, K, Ca, and Mg).<br> <br> Quantification and spatial analysis of PyC reveals heterogeneous deposition across the landscape with fine-grained patchiness at scales <0.5 m. In response to this heterogeneity, phenotypic and nutritional adjustments followed dose-dependent response patterns. Beneficial effects of PyC on sapling growth occurred to an optimum point of ~30-60 t·ha-1, while declining patterns were found for trees in dosages exceeding 100 t·ha-1. Some traits were positively and negatively related to soil K and N, respectively, and shared strong negative associations with soil pH and volatile matter.<br> <br> Synthesis. This study supports the longstanding hypothesis that soil PyC enhances growth and physiological function of fire-adapted plants, but indicates that responses are highly dosage-dependent, with natural levels of PyC deposition commonly exceeding an optimum point. These results also suggest that the main mechanisms for observed responses to PyC include: i) enhanced supply of base cations, ii) immobilization of N, and iii) pronounced liming. Future changes in climate are expected to increase fire frequency, particularly in circumpolar boreal forests. We predict shifts in PyC to frequently exceed the threshold resulting in reduced plant growth and ultimately ecosystem productivity.</td> <td> </td> <td> </td> </tr> </tbody> </table>
Data from: Soil carbon response to woody plant encroachment: Importance of spatial heterogeneity and deep soil storage
1. Recent global trends of increasing woody plant abundance in grass-dominated ecosystems may substantially enhance soil organic carbon (SOC) storage and could represent a strong carbon (C) sink in the terrestrial environment. However, few studies have quantitatively addressed the influence of spatial heterogeneity of vegetation and soil properties on SOC storage at the landscape scale. In addition, most studies assessing SOC response to woody encroachment consider only surface soils, and have not explicitly assessed the extent to which deeper portions of the soil profile may be sequestering C. 2. We quantified the direction, magnitude, and pattern of spatial heterogeneity of SOC in the upper 1.2 m of the profile following woody encroachment via spatially-specific intensive soil sampling across a landscape in a subtropical savanna in the Rio Grande Plains, USA, that has undergone woody proliferation during the past century. 3. Increased SOC accumulation following woody encroachment was observed to considerable depth, albeit at reduced magnitudes in deeper portions of the profile. Overall, woody clusters and groves accumulated 12.87 and 18.67 Mg C ha-1 more SOC compared to grasslands to a depth of 1.2 m. 4. Woody encroachment significantly altered the pattern of spatial heterogeneity of SOC to a depth of 5 cm, with marginal effect at 5-15 cm, and no significant impact on soils below 15 cm. Fine root density explained greater variability of SOC in the upper 15 cm, while a combination of fine root density and soil clay content accounted for more of the variation in SOC in soils below 15 cm across this landscape. 5. Synthesis: Substantial SOC sequestration can occur in deeper portions of the soil profile following woody encroachment. Furthermore, vegetation patterns and soil properties influenced the spatial heterogeneity and uncertainty of SOC in this landscape, highlighting the need for spatially specific sampling that can characterize this variability and enable scaling and modeling. Given the geographic extent of woody encroachment on a global scale, this undocumented deep soil C sequestration suggests this vegetation change may play a more significant role in regional and global C sequestration than previously thought.
Frequent burning causes large losses of carbon from deep soil layers in a temperate savanna
<p>1. Fire activity is changing dramatically across the globe, with uncertain effects on ecosystem processes, especially belowground. Fire‐driven losses of soil carbon (C) are often assumed to occur primarily in the upper soil layers because the repeated combustion of aboveground biomass limits organic matter inputs into surface soil. However, C losses from deeper soil may occur if frequent burning reduces root biomass inputs of C into deep soil layers or stimulates losses of C via leaching and priming.</p> <p>2. To assess the effects of fire on soil C, we sampled 12 plots in a 51‐year‐long fire frequency manipulation experiment in a temperate oak savanna, where variation in prescribed burning frequency has created a gradient in vegetation structure from closed‐canopy forest in unburned plots to open‐canopy savanna in frequently burned plots.</p> <p>3. Soil C stocks were non‐linearly related to fire frequency, with soil C peaking in savanna plots burned at an intermediate fire frequency and declining in the most frequently burned plots. Losses from deep soil pools were significant, with the absolute difference between intermediately burned plots versus. most frequently burned plots more than doubling when the full 1 m sample was considered rather than the top 0–20 cm alone (losses of 98.5 MgC ha<sup>‐1</sup> (−76%) and 42.3 MgC ha<sup>‐1</sup> (−68%) in the full 1 m and 0–20 cm layers, respectively). Compared to unburned forested plots, the most frequently burned plots had 65.8 MgC ha<sup>‐1</sup> (−58%) less C in the full 1 m sample. Root biomass below the top 20 cm also declined by 39% with more frequent burning. Concurrent fire‐driven losses of nitrogen and gains in calcium and phosphorus suggest that burning may increase nitrogen limitation and play a key role in the calcium and phosphorus cycles in temperate savannas.</p> <p>4. <i>Synthesis</i>: Our results illustrate that fire‐driven losses in soil C and root biomass in deep soil layers may be critical factors regulating the net effect of shifting fire regimes on ecosystem C in forest‐savanna transitions. Projected changes in soil C with shifting fire frequencies in savannas may be 50% too low if they only consider changes in the topsoil.</p>
A meta-analysis reveals increases in soil organic carbon following the restoration and recovery of croplands in Southwest China
<p>In China, the Grain for Green Program (GGP) is an ambitious project to convert croplands into natural vegetation, but exactly how changes in vegetation translate into changes in soil organic carbon remains less clear. Here we conducted a meta-analysis using 734 observations to explore the effects of land recovery on the soil organic carbon and nutrients in 4 provinces in Southwest China. Following GGP, the soil organic carbon content (SOCc) and soil organic carbon storage (SOCs) increased by 33.73% and 22.39%, respectively. Likewise, soil nitrogen increased, while phosphorus decreased. Outcomes were heterogeneous, however, depending on variation in soil and environmental characteristics. Both the regional land use and cover change indicated by landscape type transfer matrix and net primary production from 2000 to 2020 further confirmed that GGP promoted the forest area (2.95%) and regional mean net primary production (52.94%). Our findings suggest that GGP could enhance soil and vegetation carbon sequestration in Southwest China and help to develop carbon neutral strategy.</p>
Grain size and clay mineralogy data of the Esplugafreda sequence (Spain) with stable carbon, oxygen and clumped isotope data of soil carbonates
<p>This datasets contains 1) grain size distribution data of mudstone paleosols of the Esplugafreda sequence (Esplugafreda and Claret Formations) measured by laser diffraction, and 2) clay mineralogy measured by powder X-ray diffraction, as well as 3) stable carbon, oxygen and clumped (D47) isotope compositions of soil carbonates. The Esplugafreda sequence is found in the Tremp-Graus Basin in the southern forefront of the Pyrenees and consists of continental sediments formed in a coastal alluvial setting during the late Paleocene and early Eocene.</p> <p>In addition, a proxy dataset of late Paleocene and PETM continental temperatures of the northern hemisphere is also included.</p>
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>
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.