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245 results for “soil datasets”
3D dataset of root soil bacteria dynamics obtained using large field of view light sheet microscope
<p>A tailor made dual-illumination light-sheet system acquired photons scattered from the plant whilst fluorescence emissions were simultaneously captured from transparent soil particles and labelled microorganisms, allowing the generation of quantitative data on samples approximately 3600 mm<sup>3</sup> in size with as good as 5 µm resolution at a rate of up to one scan every 30 minutes. The dataset shows the dynamics of Bacillus subtilis populations in the rhizosphere of lettuce plants in real time.</p> <p> </p> <p> </p>
Dataset for: Neutrons on Rails -- trans-regional monitoring of soil moisture and snow water equivalent
<p>Using the railway system for regular environmental monitoring could extend the measurement capability to trans-regional and nationwide scales. Cosmic-ray neutron detectors in trains respond to spatial patterns of water content in their environment. Three distinct real world experiments support a proof of concept for soil and snow water monitoring using trains on short and long-range tracks across Germany:</p> <ul> <li>Supplement S4: Data (raw and processed) for the train journey from Leipzig to Berlin.</li> <li>Supplement S5: Data (raw and processed) for the train journey from Dessau to Zerbst, the subsequent car-borne Rover measurements, and the TDR measurements.</li> <li>Supplement S6: Data (raw and processed) for the train journey from Garmisch-Partenkirchen to Munich to Leipzig.</li> </ul> <p>This is the dataset supplementing the corresponding GRL publication "Neutrons on Rails -- trans-regional monitoring of soil moisture and snow water equivalent", preprint available from: https://doi.org/10.1002/essoar.10507363.1</p>
Dataset for: Phosphorus mobilization from intact soil monoliths flooded under simulated summer versus spring snowmelt with intermittent freeze-thaw conditions
<p>Enhanced phosphorus (P) release from flooded, anaerobic soils have been extensively studied under summer temperatures, but not under cold temperatures with intermittent freeze-thaw events. We investigated the temperature and freeze/ thaw effects during flooding on the release of P to floodwater from soil monoliths (15-cm depth) collected from eight agricultural fields in Manitoba. Soil monoliths were flooded with reverse osmosis water and incubated for 56 d under simulated summer flooding (SSF, 22±1 ℃), or snowmelt flooding with intermittent freeze thaw (IFT, 4±1 ℃ with intermittent freezing) in triplicates. Redox potential (Eh), pore water and floodwater dissolved reactive P (DRP) concentrations, pH and concentrations of Ca, Mg, Fe and Mn were determined weekly. In seven soils, Eh decreased rapidly with days after flooding (DAF) under SSF to values < 200 mV, but not under IFT. Pore water and floodwater DRP concentrations significantly increased with DAF in all soils under SSF, and in seven soils under IFT. While DRP concentrations were consistently greater under SSF than IFT in four soils, other soils had similar concentrations at certain DAFs. Significant relationships between ion concentrations and redox status , that fitted both IFT and SSF data in most soils, suggests that similar redox-driven mechanisms are responsible for the P release; however, less P is released under IFT than SSF, since soils were not severely reduced under IFT. Substantial P release in a few soils under IFT, appeared to be unrelated to redox status, suggesting other P release mechanisms that are not redox driven.</p>
Dataset of nanoplastic transport in soil via bioturbation by Lumbricus terrestris
<p>The dataset comprises data acquired in laboratory-based process-studies with microcosms on the transport of nanoplastics (polystyrene, 256 nm diameter) in an agricultural soil via bioturbation over the course of 4 weeks. Transport of nanoplastics was monitored through the use of palladium (Pd)-doped nanoplastics (0.24 % w/w) by analyzing soil samples at specific depths (0-2 cm, 2-6 cm, 6-15 cm, 15-29 cm) after defined experimental times (7, 14, 21, 28 days). Microcosms consisted of 30 cm height, 10 cm diameter soil columns which were set up with or without earthworms and with or without nanoplastics spiked to the uppermost 2 cm. Two experimental runs were executed with the first (Exp1) designed to focus on the average transport within the soil profile over time, and the second (Exp2) focussing on the smaller scale distribution of the nanoplastics in the soil (i.e. soil matrix versus drilosphere) while monitoring earthworm burrow development over time using X-ray computed tomography. In the first experiment, earthworms were measured for their nanoplastic content after the experiment.</p> <p>The dataset made available here comprises the details of the microcosm setup for Exp1 and Exp2, details on detected nanoplastic concentrations for the average soil profile (Exp1), drilosphere and soil matrix (Exp2), as well as detected concentrations in earthworm tissue and monitoring of earthworm weight (Exp1). A selection of X-ray CT images is also provided, chosen based on the considered highest relevance. X-ray CT scans of other measurement timepoints are available upon request.</p>
Dataset on soil and soil microbial biomass carbon, nitrogen, and phosphorus stoichiometry
<p>Dataset on soil and soil microbial biomass carbon, nitrogen, and phosphorus stoichiometry. This dataset is compiled for for the scientific paper entitled "Interpreting stoichiometric homeostasis and flexibility of soil microbial biomass carbon, nitrogen, and phosphorus" (doi: 10.1016/j.ecolmodel.2022.110018).</p>
Metagenome-assembled genomes(MAGs) generated from soil dataset.
<p>MAGs generated from soil dataset with Maxbin2, VAMB, Metabat2, SemiBin(single-sample binning) and VAMB, SemiBin(multi-sample binning).</p> <p>Single-sample binning: Maxbin2.tar.gz, Metabat2.tar.gz, VAMB.tar.gz and SemiBin(pretrain).tar.gz. </p> <p>Multi-sample binning: VAMB_multi.tar.gz and SemiBin_multi.tar.gz.</p>
Dataset for manuscript entitled: Switchgrass cropping systems affect soil carbon and nitrogen and microbial diversity and activity on marginal lands
<p class="MsoListParagraph">Switchgrass (<em>Panicum virgatum</em> L.),<span> </span>as a dedicated bioenergy crop, can provide cellulosic feedstock for biofuel production while improving or maintaining soil quality. However, comprehensive evaluations of how switchgrass cultivation and nitrogen (N) management impact soil and plant parameters remain incomplete. We conducted<span> </span>field trials in three years (2016–2018) at six locations in the North Central Great Lakes Region to evaluate the effects of cropping systems (switchgrass, restored prairie, undisturbed control) and N rates (0, 56 kg N ha<sup>-1</sup> yr<sup>-1</sup>) on biomass yield and soil physicochemical, microbial, and enzymatic parameters. Switchgrass cropping system yielded an aboveground biomass 2.9–3.3 times higher than the other two systems (Jayawardena et al., In submission) but our study found that this biomass accumulation didn't reduce soil dissolved organic C (DOC), total dissolved N (TDN), or bacterial diversity. The annual aboveground biomass removal for bioenergy feedstock, however, reduced soil microbial biomass C (MBC) and N (MBN) and bacterial richness in the 2<sup>nd</sup> and 3<sup>rd</sup> years; despite this, continuous monocropping of switchgrass improved soil TDN, inorganic N, bacterial diversity, and shoot biomass in the 2<sup>nd</sup> and/or 3<sup>rd</sup> years when compared to the 1<sup>st</sup> year. N fertilization increased aboveground biomass yield by 1.2 times and significantly increased soil TDN, MBN, and the shoot biomass of switchgrass when compared to the unfertilized control. Locations with higher C and N contents and lower C:N ratio had higher aboveground biomass, MBC, MBN, and the activity of BG, CBH, and UREA enzymes; by contrast, locations with higher pH had higher soil TDN and activity of NAG and LAP enzymes. Our research demonstrates that switchgrass cultivation could improve or maintain soil N content and N fertilization can increase plant biomass yield. The comprehensive data also can inform future biogeochemical models to successfully implement switchgrass for bioenergy production.</p>
An in situ observation dataset of soil hydraulic properties and soil moisture in a high and cold mountainous area on the northeastern Qinghai-Tibet Plateau
<p>Based on soil profile data at depths of 5 cm and 25 cm from 238 sampling sites, and on soil data from 32 soil moisture monitoring stations at depths of 5 cm, 15 cm, 25 cm, 40 cm, and 60 cm, we have compiled a soil hydraulic properties and soil moisture dataset for a high and cold mountainous area, Northeastern Qinghai-Tibet Plateau. Specifically, the soil hydraulic properties include clay, silt, sand, soil organic carbon, soil saturated hydraulic conductivity, soil water retention curve parameters (Van Genuchten model) and soil dry bulk density.</p>
Large dataset of soil organic carbon and topographic derivatives
<p><strong>Abstract</strong>: The dataset compiles 840 georeferenced SOC measurements over a 26-ha agricultural field located in southern Ontario, Canada with a sampling density of ~32 points per ha. As SOC is influenced by site topography (i.e., slope and landscape position), each point of the database was associated with a wide range of topographic derivatives. The columns include sample ID, SOC measurement, latitude, Longitude, NDVI values, as well as a set of 54 topographic derivatives (i.e., primary and secondary - see metadat.pdf attached file) with a spatial resolution of a 5 m. </p>
ChinaCropSM1km: a fine 1km daily Soil Moisture dataset for dryland wheat and maize across China during 1993–2018
<p>ChinaCropSM1km provides 1km gridded daily Soil Moisture for Crop drylands across China over 1993–2018, which contains four daily datasets separately by crop type and soil depth (crop-type <sub>soil-depth</sub>). This dataset is maize<sub>0-10.</sub></p> <p>*** The data file is in “.tif" format</p> <p>*** Spatial extent: maize drylands across China</p> <p>*** Soil depth: 0-10cm</p> <p>*** Temporal Resolution: Daily</p> <p>*** Pixel size: 1000 m</p> <p>*** Projection information: GCS_WGS_1984</p> <p>*** Filename convention: Year_DOY.tif</p> <p>*** Year: Values from 1993 to 2018</p>
ChinaCropSM1km: a fine 1km daily Soil Moisture dataset for dryland wheat and maize across China during 1993–2018
<p>ChinaCropSM1km provides 1km gridded daily Soil Moisture for Crop drylands across China over 1993–2018, which contains four daily datasets separately by crop type and soil depth (crop-type <sub>soil-depth</sub>). This dataset is maize<sub>10-20.</sub></p> <p>*** The data file is in “.tif" format</p> <p>*** Spatial extent: maize drylands across China</p> <p>*** Soil depth: 10-20cm</p> <p>*** Temporal Resolution: Daily</p> <p>*** Pixel size: 1000 m</p> <p>*** Projection information: GCS_WGS_1984</p> <p>*** Filename convention: Year_DOY.tif</p> <p>*** Year: Values from 1993 to 2018</p>
ChinaCropSM1km: a fine 1km daily Soil Moisture dataset for dryland wheat and maize across China during 1993–2018
<p>ChinaCropSM1km provides 1km gridded daily Soil Moisture for Crop drylands across China over 1993–2018, which contains four daily datasets separately by crop type and soil depth (crop-type <sub>soil-depth</sub>). This dataset is wheat<sub>10-20.</sub></p> <p>*** The data file is in “.tif" format</p> <p>*** Spatial extent: wheat drylands across China</p> <p>*** Soil depth: 10-20cm</p> <p>*** Temporal Resolution: Daily</p> <p>*** Pixel size: 1000 m</p> <p>*** Projection information: GCS_WGS_1984</p> <p>*** Filename convention: Year_DOY.tif</p> <p>*** Year: Values from 1993 to 2018</p>
Soil biodiversity dataset in Hong Kong during Oct 2019 - Oct 2020
<p>The dataset includes the soil biodiversity of Hong Kong during Oct 2019 - Oct 2020. The information includes the taxonomy of sampled organisms, the geographic coordinates, collection location, date of collection and DNA barcodes (with NCBI Accession number).</p>
Global soil moisture–atmosphere feedback and N2O emission dataset
<p><span>Soil moisture is essential to microbial nitrogen (N)-cycling networks in terrestrial ecosystems. Studies have found that soil moisture–atmosphere feedbacks dominate the changes in land carbon fluxes. </span><span>However, the influence of soil moisture–atmosphere feedbacks on the N fluxes changes, and the underlying mechanisms remain highly unsure, leading to uncertainties in climate projections. </span><span>To fill this gap, we utilized in situ observation coupled with gridded and remote sensing data to analyze N<sub>2</sub>O fluxes emissions globally. Here, we investigated the synergistic effects of temperature, hydroclimate on global N<sub>2</sub>O fluxes, as the result of soil moisture–atmosphere feedback impact on N fluxes. We found that soil moisture–temperature feedback dominates land N<sub>2</sub>O emissions by controlling the balance between nitrifier and denitrifier genes. The mechanism is that atmospheric water demand increases with temperature and thereby reduces soil moisture, which increases the dominant N<sub>2</sub>O production nitrifier (containing <em>amoA </em>AOB gene) and decreases the N<sub>2</sub>O consumption denitrifier (containing the<em> nosZ</em> gene), consequently will potential increasing N<sub>2</sub>O emissions. However, we find that the spatial variations of soil–water availability as a result of the nonlinear response of soil moisture to vapor pressure deficit caused by temperature are some of the greatest challenges in predicting future N<sub>2</sub>O emissions. Our data-driven assessment deepens the understanding of the impact of soil moisture-atmosphere interactions on the soil N cycle, which remains uncertain in earth system models. We suggest that the model needs to account for feedback between soil moisture and atmospheric temperature when estimating the response of the N<sub>2</sub>O emissions to climatic change globally, as well as when conducting field-scale investigations of the response of the ecosystem to warming.</span></p>
Using spectral reflectance and random forest method for modeling soil surface changes induced by simulated rainfall - datasets
<p>Using spectral reflectance and random forest method for modeling soil surface changes induced by simulated rainfall - datasets</p> <p>The impact of simulated rainfall on the soil surface roughness of different soil types with various initial surface states and the differences between their spectral characteristics were studied under laboratory conditions. The soil samples were collected from a horizon of fields near Poznań, western Poland. The physical and physicochemical properties of each soil sample were determined. Then, the part of the soil materials, consisting of natural aggregates, were used to form three soil surface roughness. </p> <p>An explanation of the table column names in the “soils properties.csv” file:</p> <p> </p> <ul> <li> <p>“textural classification” - Name of the granulometric group. Soil texture was determined by the hydrometer method according to standard PN-R-04032.</p> </li> <li> <p>“sand” - Sand content in the soil sample in %.</p> </li> <li> <p>“silt” – Silt content in the soil sample in %.</p> </li> <li> <p>“clay” – Clay content in the soil sample in %.</p> </li> <li> <p>pHH2O” - The pH of the soil sample determined in water. The soil pH was determined by the potentiometry method.</p> </li> <li> <p>“pHKCl” – The pH of the soil sample determined in KCl. The soil pH was determined by the potentiometry method.</p> </li> <li> <p>“SOC” – Organic matter content in soil was determined by oxidation titration using K2Cr2O7 with H2SO4 on the block mineralization.</p> </li> </ul> <p> </p> <p>An explanation of the table column names in the “rainfall doses.csv” file:</p> <p> </p> <ul> <li> <p>“rainfall simulation” - Rainfall simulation number.</p> </li> <li> <p>“rainfall dose” - One-time amount of rainfall dose expressed in millimeters.</p> </li> <li> <p>“accumulated rainfall” – Summation of rainfall after each successive dose expressed in millimeters.</p> </li> </ul> <p> </p> <p>An explanation of the table column names in the “soil measurements” file:</p> <p> </p> <ul> <li> <p>“textural classification” - Name of the granulometric group. Soil texture was determined by the hydrometer method according to standard PN-R-04032.</p> </li> <li> <p>“rainfall simulation” - Rainfall simulation number.</p> </li> <li> <p> “reflectance” - The amount of radiation reflected from the soil surface under the influence of successive rainfalls and expressed in nanometres. </p> </li> <li> <p>“roughness state” - The size of the roughness: R1 is the lowest soil roughness state, R2 represents medium soil roughness, and R3 represents the greatest roughness.</p> </li> <li> <p>“T3D” - Tortuosity index is a surface roughness index. It was calculated from DEM (Digital Elevation Model). It expresses the ratio between the true surface of DEM and its flat horizontal area.</p> </li> <li> <p>“HSD” - Height Standard Deviation is the second surface roughness index. It was calculated from DEM and expressed in millimeters. </p> </li> </ul> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p><br> </p> <p> </p>
soil nutrient balance(NPK) dataset of Rwanda
<p>This soil nutrient balance dataset contains raw, processed and analyzed data of soil nutrient depletion workflow developed by Uwiragiye et al.[1]. Soil nutrient (NPK) balance were predicted only in covers cropland of Rwanda (Fig.1). This dataset was developed from freely available data from different sources (Table 1). The main folder which contain all data is called ’’Data”. This main folder has five sub folders (soil nutrient flows, soil erosion and sediments, environmental covariates, NPK balance modelling results and code used for soil nutrient balance modelling)</p> <p>This is a dataset developed from available data for soil nutrient modelling in humid areas : A published paper </p> <p>Y. Uwiragiye, M. Junior Yannick Ngaba, M. Zhao, A.S. Elrys, G.B.M. Heuvelink, J. Zhou, Modelling and mapping soil nutrient depletion in humid highlands of East Africa using ensemble machine learning: A case study from Rwanda, CATENA. 217 (2022) 106499. https://doi.org/https://doi.org/10.1016/j.catena.2022.106499.</p>
Biogeochemial Soil Dataset for Old Growth and Disturbed Forest Sites at Mount Kilimanjaro
<p>This dataset presents biogeochemical data (pH, CNS, soil texture, bulk density, extractable metals, cation exchange capacity) from 21 profiles at Mount Kilimanjaro, Tanzania. The data is part of Schrumpf's dissertation ("Biogeochemical Investigations in Old Growth and Disturbed Forest Sites at Mount Kilimanjaro").</p> <p>Exact sampling locations (WSG84: long/lat) are reported for each profile. Analyses of soil material were carried out on air dried samples (30°C) of the fraction < 2 mm. Soil pH was determined using H20, and 1 M KCl at a soil solution ratio of 1 (m) : 2.5 (v) and a standard combined electrode with integrated temperature probe (WTW SenTix 41 pH 330). Total C and total N contents were analyzed on ball-milled samples using a total element analyzer (Elementar Vario EL). Exchangeable cations (K, Ca,Mg, Na, Al) were extracted by an unbuffered 0.5 M NH4Cl solution (Trüby & Aldinger 1989) and measured by atomic absorption (Varian SpectrAA 400). The ECEC was calculated as the sum of exchangeable base cations and exchangeable Al. At ten profiles in ten main study area between 2100 and 2330 m, Fe and Al in metal-humus complexes (Fep, Alp) were extracted by shaking 2g of soil in 0.1 M sodium pyrophosphate (pH 10) for 16h, followed by centrifugation, after addition of CaCl2. Fe, Al and Si in ferrihydrite, allophane, imogolite and metal humus complexes (Feo, Alo, Sio) were determined in all soils using a 0.2 M oxalate solution (pH 3) (Schwertmann 1964). The cold dithionite-citrate buffer (DCB) method (Holmgren 1967) was used to analyze the amounts of Fe and Al (Fed and Ald) in crystalline oxides and fractions from metal-humus complexes and amorphous constituents. Bulk density was determined on three undisturbed soil cores with a volume of 100 cm-3, which were taken from each soil horizon.</p>
Contrasting Responses of Particulate and Mineral-Associated Organic Carbon to Afforestation Potentially Obscure Soil Carbon Accumulation [Dataset]
<p><span>This is the data repository for the manuscript “Contrasting Responses of Particulate and Mineral-Associated Organic Carbon to Afforestation Potentially Obscure Soil Carbon Accumulation” submitted to <em>Global Biogeochemical Cycles</em>.</span></p>
Partitioning of water and CO2 fluxes at NEON sites into soil and plant components: a five-year dataset for spatial and temporal analysis
<p>This dataset includes estimates of transpiration, evaporation, soil respiration, and plant net photosynthesis obtained using five partitioning approaches. Flux components are available at 47 NEON sites over a period of five years. Additional meteorological inputs and water-use efficiency data are also included.</p>
Dataset for Modelling of infiltration rates in soils derived from contrasting parent materials using Horton's model
<p>This dataset was initially generated as part of a broader project investigating infiltration and various soil properties in soils derived from three parent materials in Akwa Ibom State, Nigeria: Alluvial Clay, Coastal Plain Sands, and Sandstone. Utin and Oguike (2018) initially utilized this data to evaluate Philip's and Kostiakov's models in these soils. Subsequently, the infiltration data were employed as secondary data to assess the performance of Horton's model in predicting infiltration rates for the three soil types.</p>
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Allen Brain Atlas
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OpenNeuro
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