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13 results for “peat soils”
Mangrove soil phosphorus addition experiment from June 2013 to August 2013 at the mangrove peat soil mesocosms (FCE), Key Largo, Florida - Nutrients in Porewater, Soil and Roots
Sea levels in South Florida are conservatively predicted to rise by 0.60 m by 2060. The key mechanisms that maintain coastal peatland elevation against increasing sea level are organic matter accumulation via plant production and mineral sedimentation rates (Smoak et al. 2013). Although coastal mangrove soils are regularly inundated with seawater, little is know about the drivers of carbon sequestration (above or below ground) versus atmospheric efflux under different conditions of salinity and elevated phosphorus (P) associated with sea-level rise and storm surge. A recent study using mangrove peat soils found that seawater inundation reduced soil carbon efflux losses and salinity concentration had little effect on carbon retention or loss pathways. The next logical steps are to understand how plant-soil interactions affect above and below ground carbon processes, as well as how increases in P associated with storm surge from the Gulf of Mexico will influence physical, chemical and biological components of mangrove soils that are associated with above and belowground carbon processes. We will manipulate P in inundated peat soil mesocosms with disturbed and undisturbed red mangrove (Rhizophora mangle) seedlings to identify some of the fundamental mechanisms of soil elevation and carbon cycling given expected increases in seawater-based P availability in South Florida coastal mangroves.
Mangrove soil phosphorus addition experiment from July 2013 to August 2013 at the mangrove peat soil mesocosms (FCE), Key Largo, Florida - Nutrients in Surface Water and Aboveground Biomass
Sea levels in South Florida are conservatively predicted to rise by 0.60 m by 2060. The key mechanisms that maintain coastal peatland elevation against increasing sea level are organic matter accumulation via plant production and mineral sedimentation rates (Smoak et al. 2013). Although coastal mangrove soils are regularly inundated with seawater, little is know about the drivers of carbon sequestration (above or below ground) versus atmospheric efflux under different conditions of salinity and elevated phosphorus (P) associated with sea-level rise and storm surge. A recent study using mangrove peat soils found that seawater inundation reduced soil carbon efflux losses and salinity concentration had little effect on carbon retention or loss pathways. The next logical steps are to understand how plant-soil interactions affect above and below ground carbon processes, as well as how increases in P associated with storm surge from the Gulf of Mexico will influence physical, chemical and biological components of mangrove soils that are associated with above and belowground carbon processes. We will manipulate P in inundated peat soil mesocosms with disturbed and undisturbed red mangrove (Rhizophora mangle) seedlings to identify some of the fundamental mechanisms of soil elevation and carbon cycling given expected increases in seawater-based P availability in South Florida coastal mangroves.
PEATGRIDS: Mapping global peat thickness and carbon stock via digital soil mapping approach, dataset
<p>PEATGRIDS: a dataset containing the first peat thickness and carbon stock maps estimated over peatlands area across the globe at ~1 km x ~1 km resolution. Carbon stock was calculated across all depths of the predicted peat thickness, multiplied by peat bulk density (BD) and carbon content (CC) across five depths: 0-15 cm, 15-30 cm, 30-60 cm, 60-100 cm, and 100-200 cm. Mapping effort was performed using quantile random forest regression based on remotely sensed data and environmental covariates, including topography, climate, soil properties, and land cover. The maps cover areas potentially as peatlands according to the UNEP's global peatland map obtained from the <a title="Global Peat Database" href="https://greifswaldmoor.de/global-peatland-database-en.html" target="_blank" rel="noopener">Global Peat Database</a>. We may update this dataset in the future, please consider using the latest version. </p> <p>Note: This version (2.0.1) clarifies the metric units for carbon stock per area in the previous version (2.0). </p>
Soil biological, chemical and physical parameters and herbage yield in a field experiment with organic and inorganic fertilizers on peat grassland in the Netherlands
<p>To evaluate the performance of organic and inorganic fertilizers for regeneration of ecosystem services in peat grasslands with biodiversity goals, we carried out a field experiment in the western peat district in the Netherlands. The fertilizers tested represent the current practice and potential alternatives for regenerative grassland management on drained peat.</p> <p> </p> <p><strong>Experimental setup</strong></p> <p>The field experiment (2013 – 2015) was conducted on a permanent grassland on peat soil (Terric Histosol; SOM 56 g 100 g<sup>−1</sup> and pH<sub>KCl</sub> of 4.5 in 0-10 cm) at the experimental dairy farm at Zegveld (the Netherlands). In March 2013, a randomized block experiment (six blocks) was laid out with six fertilizer treatments and a control treatment (no fertilizer: “Contr”). The fertilizer used were: conventional dairy cattle slurry manure (“Slurry”), mature compost of kitchen and garden waste (“Comp”), dairy cattle farmyard manure (“FYM”), solid fraction of the cattle slurry manure (“SFrac”, obtained by pressurized filtration), inorganic N fertilizer (“IF”; calcium ammonium nitrate, 27% N) and a combination of inorganic N fertilizer and sawdust (“IF+SD”). Plot size was 4 × 10 m; for the Slurry treatment plots were 5.2 × 10 m. Slurry was applied by slit injection, the other fertilizers were applied by hand. Target application rate was 120 kg total N ha<sup>−1</sup> yr<sup>−1</sup>, divided in two applications per year (February/March and May). This is relatively low for conventional grasslands but usual for grasslands with biodiversity goals (Kleijn et al., 2004). The amount of C<sub>total</sub> applied in Comp was taken for the rate of sawdust to be applied. All plots were fertilized with 200 kg K<sub>2</sub>O ha<sup>−1</sup> yr<sup>−1</sup> (applications in March and May) (Commissie Bemesting Grasland en Voedergewassen, 2019). Fertilizer application quantities and organic matter and nutrient inputs are provided in Fertilizer_intput.csv (dataset).</p> <p>The grassland had an history of conventional management with mainly cutting, winter grazing with sheep and a normal fertilization regime with both slurry manure and inorganic fertilizer. The normal cutting and grazing regime was continued in the first two years of the experiment; during 2015, the monitoring year, the plots were not grazed and only cut for herbage measurements.</p> <p> </p> <p><strong>Measurements</strong></p> <p>From April to October 2015, soil and aboveground measurements were carried out. Most soil parameters were measured in October. Earthworms and insect larvae are an important food source for meadow birds during the pre-breeding period in spring (Galbraith, 1989) and were therefore sampled in April. Soil moisture and penetration resistance were measured both in April and October.</p> <p> </p> <p><em>Soil biological parameters</em></p> <p>Earthworms and insect larvae were sampled in the top soil layer in two soil cubes (20 × 20 × 20 cm) per plot. Earthworms were hand-sorted, counted, weighed and fixed in alcohol prior to identification. Both adults and juveniles were identified to species (Sims and Gerard, 1985; Stöp-Bowitz, 1969) and classified into functional groups (Bouché, 1977). Crane flies (Tipulidae; leatherjackets) or click beetles (Elateridae; wireworms) larvae were counted.</p> <p>Phospholipid fatty acids (PLFA) were measured in October. PLFA were extracted from 4 g of fresh soil (Palojärvi, 2006), and analyzed by gas chromatography (Hewlett-Packard, USA). PLFA i15:0, a15:0, 15:0, i16:0, 16:1ω9, i17:0, a17:0, cy17:0, 18:1ω7 and cy19:0 were chosen to represent bacteria and PLFA 18:2ω6 was used as a marker of saprotrophic fungi (Hedlund, 2002). The neutral lipid fatty acid (NLFA) 16:1ω5 occurs in storage lipids of arbuscular mycorrhizal fungi (AMF) and was used as marker of AMF (Vestberg et al., 2012). PLFA i15:0, a15:0, i16:0, i17:0 and a17:0 were used as a measure of Gram-positive bacteria, and cy17:0 and cy19:0 for Gram-negative bacteria. PLFA 10Me16:0, 10Me17:0 and 10Me18:0 represented actinomycetes.</p> <p> </p> <p><em>Soil chemical parameters</em></p> <p>A soil sample from the 0−10 cm layer (c. 50 randomly taken soil cores) per experimental plot was collected in October (auger diameter 2.3 cm; Eijkelkamp grass plot sampler, Giesbeek, the Netherlands), was sieved (1 cm mesh size) and homogenized. One sub-sample was taken for analysis of hot water extractable carbon (HWC) according to Ghani et al. (2003) and one for chemical analysis. Prior to analysis of soil acidity (pH<sub>KCl</sub>), soil organic matter (SOM), total carbon (C<sub>total</sub>), total nitrogen (N<sub>total</sub>), total phosphorus (P<sub>total</sub>) and ammonium-lactate extractable P (P<sub>AL</sub>) by Eurofins Agro (Wageningen, the Netherlands), the sub sample was dried at 40°C. Soil pH<sub>KCl</sub> was measured according to NEN-ISO 10390 2005. SOM was determined by loss-on-ignition (NEN 5754 2005). C<sub>total</sub> was measured by incineration at 1150°C, and determination of the CO<sub>2</sub> produced by an infrared detector (LECO Corporation, St. Joseph, Mich., USA). For N<sub>total</sub>, evolved gasses after incineration were reduced to N<sub>2</sub> and measured with a thermal-conductivity detector (LECO Corporation, St. Joseph, Mich., USA). P<sub>total</sub> was analysed with Fleishmann acid (Houba et al., 1997). P<sub>AL</sub> is used to assess the P supply capacity of grassland soils (Reijneveld et al., 2014) and was determined according to Egnér et al. (1960) (NEN 5793).</p> <p> </p> <p><em>Soil physical parameters</em></p> <p>Soil moisture was determined in April and October in a homogenized 0−10 cm soil sample after drying at 105°C for 24 hrs. Moisture content was expressed as percentage of fresh soil weight.</p> <p>Penetration resistance was measured (April and October) with a penetrologger (Eijkelkamp, Giesbeek, the Netherlands; cone of 2.0 cm<sup>2</sup> penetration surface and 60° apex angle. Penetration resistance was expressed as an average of 7 penetrations per plot and per soil layer of 0−10, 10−20, and 20−30 cm.</p> <p>Soil structure and rooting density were assessed in October in the 0−10 cm and 10−25 cm layers. The percentage of crumbs, sub-angular blocky elements and angular blocky elements was estimated by one experienced person as described by Peerlkamp (1959) and Shepherd (2000), Root density was estimated by scoring visible roots (score 1–10; 1 for no roots and 10 for above average).</p> <p>Water infiltration rate was measured in October at three spots per experimental plot in 5 of the 6 blocks (35 plots). A PVC pipe (15 cm high, 15 cm diameter) was pushed into the soil to a depth of 10 cm. 500 ml water was poured into each pipe and the infiltration time was recorded. If the infiltration time exceeded 15 min, the remaining water volume was estimated to calculate the infiltration rate (mm min<sup>−1</sup>).</p> <p> </p> <p><em>Grass yield and botanical composition</em></p> <p>Grass dry matter (DM) and N yield were determined during 2015 with a Haldrup plot harvester (J. Haldrup a/s, Løgstør, Denmark). The four harvest dates were May 15, June 29, August 19 and September 30. Fresh biomass, DM content (70°C for 24 hrs) and total N content (Kjeldahl) were determined for each harvest. Herbage DM yield (Mg DM ha<sup>−1</sup>) and herbage N yield (kg N ha<sup>−1</sup>) were calculated. Apparent N recovery (ANR; kg N.kg N<sup>−1</sup>) was calculated as (N yield<sub>(fertilized)</sub> – N yield<sub>(non-fertilized)</sub>)/(N fertilization rate) (Vellinga and André, 1999).</p> <p>In June 2015, botanical composition was measured by visually estimating the relative soil cover of the sward and the proportion of each species therein (Sikkema, 1997).</p> <p> </p> <p><strong>Data files</strong></p> <ul> </ul> <p> </p> <p><em><strong>Data_soil_grass.csv</strong></em></p> <p><em>Content:</em></p> <p>Dataset with soil biological (earthworms, microbial PLFA), soil chemical, soil physical parameters, herbage dry matter and N yields, and botanical parameters.</p> <p><em>Column names and units:</em></p> <ul> <li>plot: Experimental plot number (1-42)</li> <li>treatment: Treatment code (see text)</li> <li>block: Block number (1-6)</li> <li>EW_species_number: Earthworm - number of species</li> <li>EW_totalnumber: Earthworm - total number per m2</li> <li>EW_epigeic: Earthworm - number of epigeic adults and juveniles per m2</li> <li>EW_endogeic: Earthworm - number of endogeic adults and juveniles per m2</li> <li>EW_adults: Earthworm - number of adults per m2</li> <li>EW_juveniles: Earthworm - number of juveniles per m2</li> <li>EW_adult_epigeic: Earthworm - number of epigeic adults per m2</li> <li>EW_adult_endogeic: Earthworm - number of endogeic adults per m2</li> <li>EW_juven_epigeic: Earthworm - number of epigeic juveniles per m2</li> <li>EW_juven_endogeic: Earthworm - number of endogeic juveniles per m2</li> <li>EW_L_rubellus: Earthworm - number of L. rubellus adults and juveniles per m2</li> <li>EW_A_chlorotica: Earthworm - number of A. chlorotica adults and juveniles per m2</li> <li>EW_A_caliginosa: Earthworm - number of A. caliginosa adults and juveniles per m2</li> <li>EW_O_lacteum: Earthworm - number of O. lacteum adults and juveniles per m2</li> <li>EW_A_rosea: Earthworm - number of A. rosea adults and juveniles per m2</li> <li>EW_O_cyaenum: Earthworm - number of O. cyaneum adults and juveniles per m2</li> <li>EW_L_castaneus: Earthworm - number of L. castaneus adults and juveniles per m2</li> <li>EW_D_rubida: Earthworm - number of D. rubida adults and juveniles per m2</li> <li>EW_adult_L_rubellus: Earthworm - number of L. rubellus adults per m2</li> <li>EW_adult_A_chlorotica: Earthworm - number of A. chlorotica adults per m2</li> <li>EW_adult_A_caliginosa: Earthworm - number of A. caliginosa adults per m2</li> <li>EW_adult_O_lacteum: Earthworm - number of O. lacteum adults per m2</li> <li>EW_adult_A_rosea: Earthworm - number of A. rosea adults per m2</li> <li>EW_adult_O_cyaenum: Earthworm - number of O. cyaneum adults per m2</li> <li>EW_adult_L_castaneus: Earthworm - number of L. castaneus adults per m2</li> <li>EW_adult_D_rubida: Earthworm - number of D. rubida adults per m2</li> <li>EW_juven_L_rubellus: Earthworm - number of L. rubellus juveniles per m2</li> <li>EW_juven_A_chlorotica: Earthworm - number of A. chlorotica juveniles per m2</li> <li>EW_juven_A_caliginosa: Earthworm - number of A. caliginosa juveniles per m2</li> <li>EW_non_determined: Earthworm - number of non determined individuals per m2</li> <li>EW_total_biomass: Earthworm - total fresh biomass per m2</li> <li>Leatherjackets: number of leatherjackets per m2</li> <li>Wireworms: number of wireworms per m2</li> <li>TOTmicrPLFA: total microbial PLFA in nmol.g-1 dry soil</li> <li>bactPLFA: bacterial PLFA in nmol.g-1 dry soil</li> <li>saprofungPLFA: saprotrophic fungal PLFA in nmol.g-1 dry soil</li> <li>Fung_bactPLAF_ratio: ratio of fungal to bacterial PLFA</li> <li>GramPLUSplfa: gram positive PLFA in nmol.g-1 dry soil</li> <li>GramMINplfa: gram negative PLFA in nmol.g-1 dry soil</li> <li>ratioGram_PLUS_MIN: ratio of gram positive to gram negative PLFA</li> <li>AMFsporNLFA: AMF spores NLFA in nmol.g-1 dry soil</li> <li>ActinomPLFA: Actinomycetes PLFA in nmol.g-1 dry soil</li> <li>ShannonPLFA: PLFA shannon diversity index</li> <li>SOM: soil organic matter in g.100 g-1 dry soil</li> <li>Ctotal: total C in g.100 g-1 dry soil</li> <li>HWC: hot water extractable C in μg.100 g-1 dry soil</li> <li>Ntotal: total N in g.100 g-1 dry soil</li> <li>Ptotal: total P2O5 in mg.100 g-1 dry soil</li> <li>P_AL: total P-AL in mg.100 g-1 dry soil</li> <li>pH_KCl: pH-KCl</li> <li>CN_ratio: C:N ratio</li> <li>C_SOM: C:SOM ratio</li> <li>Soilmoisture_April: soil moisture content in April in g.100g-1 fresh soil</li> <li>Penetrationresistance_April_cm010: penetration resistance in April in 10-20 cm in Newton</li> <li>Penetrationresistance_April_cm1020: penetration resistance in April in 20-30 cm in Newton</li> <li>Penetrationresistance_April_cm2030: penetration resistance in April in 0-10 cm in Newton</li> <li>Soilmoisture_October: soil moisture content in October in g.100g-1 fresh soil</li> <li>Penetrationresistance_October_cm010: penetration resistance in October in 10-20 cm in Newton</li> <li>Penetrationresistance_October_cm1020: penetration resistance in October in 20-30 cm in Newton</li> <li>Penetrationresistance_October_cm2030: penetration resistance in October in 0-10 cm in Newton</li> <li>crumb_struct_cm010: percentage of crumb elements in 0-10 cm</li> <li>round_struct_cm011: percentage of sub-angular elements in 0-10 cm</li> <li>rootdensity_cm010: score (1-10) of root density in 0-10 cm</li> <li>crumb_struct_cm1025: percentage of crumb elements in 10-25 cm</li> <li>round_struct_cm1025: percentage of sub-angular elements in 10-25 cm</li> <li>sharp_struct_cm1025: percentage of angular elements in 10-25 cm</li> <li>rootdensity_cm1025: score (1-10) of root density in 10-25 cm</li> <li>water_infiltration: water infiltration rate in mm per minute</li> <li>DM_yield_year: total herbage dry matter yield in kg.ha-1 per year</li> <li>DM_yield_H1: herbage dry matter yield of harvest 1 in kg.ha-1</li> <li>DM_yield_H2: herbage dry matter yield of harvest 2 in kg.ha-1</li> <li>DM_yield_H3: herbage dry matter yield of harvest 3 in kg.ha-1</li> <li>DM_yield_H4: herbage dry matter yield of harvest 4 in kg.ha-1</li> <li>N_yield_year: total herbage N yield in kg.ha-1 per year</li> <li>N_yield_H1: herbage N yield of harvest 1 in kg.ha-1</li> <li>N_yield_H2: herbage N yield of harvest 2 in kg.ha-1</li> <li>N_yield_H3: herbage N yield of harvest 3 in kg.ha-1</li> <li>N_yield_H4: herbage N yield of harvest 4 in kg.ha-1</li> <li>DMperc_yield_year: herbage dry matter content (per year; weighed average over the 4 harvests) in g.100g-1 fresh weight</li> <li>DMperc_yield_H1: herbage dry matter content of harvest 1 in g.100g-1 fresh weight</li> <li>DMperc_yield_H2: herbage dry matter content of harvest 2 in g.100g-1 fresh weight</li> <li>DMperc_yield_H3: herbage dry matter content of harvest 3 in g.100g-1 fresh weight</li> <li>DMperc_yield_H4: herbage dry matter content of harvest 4 in g.100g-1 fresh weight</li> <li>Ncontent_yield_year: herbage N content (per year; weighed average over the 4 harvests) in g.kg-1 dry matter</li> <li>Ncontent_yield_H1: herbage N content of harvest 1 in g.kg-1 dry matter</li> <li>Ncontent_yield_H2: herbage N content of harvest 2 in g.kg-1 dry matter</li> <li>Ncontent_yield_H3: herbage N content of harvest 3 in g.kg-1 dry matter</li> <li>Ncontent_yield_H4: herbage N content of harvest 4 in g.kg-1 dry matter</li> <li>fresh_yield_H1: herbvage fresh yield of harvest 1 in Mg.ha-1</li> <li>ANR: apparent N recovery in kg N.kg N-1</li> <li>productive_grasses: cover percentage of L. perenne and P trivialis</li> <li>monocotyledons: cover percentage of monocotyledons</li> <li>dicotyledons: cover percentage of dicotyledons</li> <li>plant_species: number of plant species</li> <li>monocot_species: number of monocotyledon species</li> <li>dicot_species: number of dicotyledon species</li> <li>Lolium_perenne: plant cover %</li> <li>Poa_trivialis: plant cover %</li> <li>Phleum_pratense: plant cover %</li> <li>Elytrigia_repens: plant cover %</li> <li>Poa_annua: plant cover %</li> <li>Agrostis_stolonifera: plant cover %</li> <li>Holcus_lanatus: plant cover %</li> <li>Alopecurus_pratensis: plant cover %</li> <li>Alopecurus_geniculatus: plant cover %</li> <li>Trifolium_repens: plant cover %</li> <li>Taraxacum_officinale: plant cover %</li> <li>Ranunculus_arvensis: plant cover %</li> <li>Rumex_obtusifolius: plant cover %</li> <li>Rumex_crispus: plant cover %</li> <li>Ranunculus_acris: plant cover %</li> <li>Stellaria_media: plant cover %</li> <li>Cardamine_pratensis: plant cover %</li> <li>Bellis_perennis: plant cover %</li> <li>Rumex_acetosa: plant cover %</li> <li>Ranunculus_sceleratus: plant cover %</li> <li>Polygonum_aviculare: plant cover %</li> <li>Capsella_bursa-pastoris: plant cover %</li> <li>Glechoma_hederacea: plant cover %</li> <li>Geranium_molle: plant cover %</li> </ul> <p> </p> <p><em><strong>Fertilizer_input.csv</strong></em></p> <p><em>Content:</em></p> <p>Application quantities of fertilizers and ash, organic matter, C and mineral inputs, and fertilizer C:N ratio. Total N input is the sum of mineral N (Nmin) and organic N (Norg). Average values per hectare and per year over the years 2013−2015.</p> <p><em>Column names and units:</em></p> <ul> <li>Treatment: Treatment code (see text)</li> <li>Fertilizer_fresh: Applied fertilizer in Mg.ha<sup>-1</sup> per year (fresh weight)</li> <li>Fertilizer_DM: Applied fertilizer in Mg.ha<sup>-1</sup> per year (dry matter weight); for IF+SD this is the sum of 2.72 Mg sawdust + 0.45 Mg N fertilizer</li> <li>Ash: Mineral fraction in kg.ha<sup>-1</sup> per year</li> <li>OM: Organic matter in kg.ha<sup>-1</sup> per year</li> <li>C: Total C in kg.ha<sup>-1</sup> per year</li> <li>Nmin: Mineral N in kg.ha<sup>-1</sup> per year</li> <li>Norg: Organic N in kg.ha<sup>-1</sup> per year</li> <li>P2O5: kg.ha<sup>-1</sup> per year</li> <li>C_N_ratio: C:N ratio</li> </ul>
Modelling CO2 emissions of cultivated and rewetted peat soils with SWAP-ANIMO - Dataset
<p><span>Three locations in Europe (wet river valley (Denmark), coastal peatland (The Netherlands) and broad river floodplain (Switzerland)) were selected for which two to three years of measurements of hydrological variables and CO2 exchange fluxes were available for some period between 2015 and 2023. The hydrology, grass growth and CO2 fluxes of these sites were modelled with the SWAP-ANIMO model using the available measurement period for model input and calibration. Model simulations were used to improve the understanding of the hydrological drivers of each site and to obtain estimates of the different pools contributing to the measured CO2 fluxes using a period of 10 years (2014-2023). Rewetting was considered either by calibration on direct measurements of an actual rewetting measure (Denmark, The Netherlands) or extrapolation of the reference simulation (Switzerland). Also, the potential impact of climate change on the rate of peat oxidation was modelled for these sites for both the reference and rewetting measure. </span></p> <p><span>The dataset contains the relevant detailed, daily model output of the 10 year simulation period and aggregated, yearly model output of the scenario simulations which are detailed in the corresponding report (van de Craats et al., 2024), available at <a href="https://doi.org/10.5281/zenodo.14041243">https://doi.org/10.5281/zenodo.14041243</a>.</span></p>
Text-fig. 3 Pollen diagram from the locality of Bohutín. 0-0.01 m – sandy soil mixed with humus, slightly clayey, sample B11; 0.05 m – grey-blue strongly sandy clay, sample B10; 0.10-0.25 m – brown-grey sandy clay with plant remains and mixed with a small amount of peat, sample B9, sample B8 (0.15 m), sample B7 (0.20 m), sample B6 (0.25 m); 0.30-0.35 m – dark sandy clay mixed with peat and plant remains, sample B5, sample B4 (0.35 m); 0.40 m – grey strongly sandy clay mixed with peat, sample B3; 0.45-0.50 m – grey-blue strongly sandy clay, sample B2, sample B1 (0.50 m). in Reconstruction Of Vegetation Development On The Floodplain Of The Litavka River In The Holocene (Central Bohemia, Brdy Mts.)
Text-fig. 3 Pollen diagram from the locality of Bohutín. 0-0.01 m – sandy soil mixed with humus, slightly clayey, sample B11; 0.05 m – grey-blue strongly sandy clay, sample B10; 0.10-0.25 m – brown-grey sandy clay with plant remains and mixed with a small amount of peat, sample B9, sample B8 (0.15 m), sample B7 (0.20 m), sample B6 (0.25 m); 0.30-0.35 m – dark sandy clay mixed with peat and plant remains, sample B5, sample B4 (0.35 m); 0.40 m – grey strongly sandy clay mixed with peat, sample B3; 0.45-0.50 m – grey-blue strongly sandy clay, sample B2, sample B1 (0.50 m).
Arctic LTER 1991: Percent moisture, bulk density, percent loss on ignition and percent organic carbon were measured for peat collected from soils in the Imnavait Creek watershed.
Percent moisture, bulk density, percent loss on ignition and percent organic carbon were measured for peat collected from soils in the Imnavait Creek watershed.
Peat cores were collected along the Dalton Highway for the analysis of soil properties, 13C and percent of modern age, North Slope, Alaska 1989.
Peat cores were collected along the Dalton Highway in 1989 and analysed for percent moisture, percent organic carbon, bulk densitey, del C-13, and radiocarbon content at varying depth intevals throughout the core. Samples were collected to the mineral zone and kept in cold storage until analysis. Samples were collected from 12 sites.
Fourier transformed infrared reflectance (FTIR) spectra of peat soils collected from the top and bottom of peatland erosion gullies
<p>Peat soil was randomly collected from gullies within two eroding blanket bogs. Balmoral (BAM) is on a large high-altitude plateau blanket bog in the eastern part of the Cairngorms National Park, Scotland, UK (56.93° N, − 3.16° E, 642 m asl) and Glensaugh (GSA) is an upland livestock farm with sections of and blanket bog peatland in the Grampian foothills (56.55° N, 2.33° E, 412 m asl). Both sites have undergone extensive degradation and peat erosion, and both have, in some parts, recently undergone restoration practices, including bunding and reprofiling.</p> <p>Peat samples were collected at Glensaugh and Balmoral as follows. At Glensaugh, peat at the top 1 cm of exposed gully sides (approximately 10-20cm from the vegetated surface) and at the gully bottom were taken, air dried and passed on for FTIR analysis. These gullies correspond to four erosion pin measurement areas and their corresponding peat sediment trap areas at Glensaugh. At Balmoral, the same approach was taken except six ‘gully top’ and ‘gully bottom’ sites were randomly selected and not geographically paired in the same way at Glensaugh.</p> <p>Samples were air dried and finely ball milled, prior to FTIR analysis. FTIR spectra were recorded using a Bruker Vertex 70 FTIR spectrometer (Bruker, Ettlingen, Germany) and OPUS 7.2 software. To record the FTIR spectra, each of the samples were placed, in turn, on a Diamond Attenuated Total Reflectance (DATR) sampling accessory, with a single reflectance system. Data points in the range of 4000-400 cm-1 were recorded with a resolution of 4 cm-1 and average of 64 scans. A spectrum of the empty sampling accessory, with the same resolution and number of scans, was recorded as the background spectrum before each measurement. </p> <p>Since the penetration depth for the DATR accessory is different for each wavelength and is directly proportional to the wavelength of the incident light (The higher the wavenumber the lower the penetration), an ATR correction was applied to the spectra to correct this effect, using the OPUS software. No correction was required for water vapour and CO2 as the spectrometer is continuously purged with dry air.</p> <p> </p> <p>In the dataset, columns correspond to the following:</p> <p>Site: Balmoral or Glensaugh</p> <p>Gully Position: Top or bottom</p> <p>Gully Number: Replicate gullies within the site</p> <p>Sample date: Date</p> <p>Sample ID: Unique identifier</p> <p>Remaining columns: Reflectance at a given wavelength</p>
Supplemental Dataset for Article: Non-invasive measurements on production, release, and vertical distribution of biogenic gas content in large peat soil columns
<p>Supplemental dataset for a journal article in preparation for publication:</p> <p><strong>Structure controls gas migration: Non-invasive measurements on production, release, and vertical distribution of biogenic gas content in large peat soil columns</strong></p> <p>William Wright<sup>1*</sup> and Xavier Comas<sup>1</sup></p> <p><sup>1</sup>Department of Geosciences, Florida Atlantic University, Davie, Florida, 33314, USA</p> <p>*Corresponding author (wwrigh19@gmail.com)</p>
Data from: The dominant detritus-feeding invertebrate in arctic peat soils derives its essential amino acids from gut symbionts
Open the record for dataset details and reuse information.
Results for "Evaluating the impact of peat soils and snow schemes on simulated active layer thickness at pan-Arctic permafrost sites"
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Global and regional distribution of brGDGTs MBT5ME prxies in soil and peats across elevation and latitudinal transects
<p>The supplementary tables present data on the distribution of brGDGT proxies, environmental and soil parameters measured in soil samples collected across the western Himalaya, as well as brGDGT proxy distributions and environmental parameters for global latitudinal soils, global elevation soils, and global peat samples.</p>
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