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63 results for “soil profile”
500-meter grid of Derived Soil Profiles (DSP) for Italy - SuoliCella500
<p>National database of Italian Soil Typological Units (STU) and corresponding Derived Soil Profiles (DSP) obtained on a 500 meters grid (1,109,672 points) by neural network. The most probable WRB Reference Soil Group (RSG), WRB Qualifiers, and USDA textural soil types were mapped on the 500 meters grid, by neural network. 18,707 Observed soil profiles and the respective 33,014 Soil Horizons were grouped into 4,472 STUs based on the combinations of Soil Region, WRB Reference Soil Group (RSG), WRB Qualifiers, and USDA textural soil types obtained on the 500 meters grid. Statistics were calculated (Mean Value, Standard Deviation Value, and Numerosity) for soil rooting depth and for the most common analytical parameters of the soil horizons (Coarse fragment content fraction; pH in water; Carbon (C) - organic; Carbonate (CO3--) - Total; Clay, Sand, and Silt fraction; Granulometry; Textural soil types). The 500 meters grid adopts EPSG 23032 (ED50 UTM-32). A reference scale of 1:250.000 may be attributed to the 500-meters grid map, on the base of the numerosity of DSP produced for the whole italian territory.</p>
ChSPD Chilean soil profile database V2
<p>ChSPD is a soil profile database for Chile. The data was compiled from different published and unpublished sources. This new soil database covers a wide range of ecosystems and climate conditions. It comprises 20 different soil physical, hydraulic, and chemical properties. Each soil property has its own number of observations, which is determined by the soil horizons surveyed and the measurements taken at each point. The ChSPD_V2 includes 19769 georeferenced records, which represent 14029 soil profiles. The properties with the most records are organic matter (15797 data points), texture distribution (clay, sand, and silt content, 4978 data points), bulk density (5088 data points), field capacity (2020), and permanent wilting point (2012). </p>
Flume Erosion Testing of Unamended and Organic Matter Amended Soil Samples Using an Acoustic Doppler Profiler, 2021
This data accompanies a publication titled "Soil Amended with Organic Matter Increases Fluvial Erosion Resistance of Cohesive Streambank Soil". Briefly, fluvial erosion testing was conducted on soil samples using an indoor flume channel. Soil samples were previously collected from the riparian zone of a river near Virginia Tech's campus in Blacksburg, VA, USA. The soil was subsequently air-dried and stored until use. Prior to erosion testing, soil samples were amended with varying amounts of organic matter (0%, 1%, and 4% OM by mass), compacted to a bulk density of 0.95 KilogramsPerCubicCentiMeters in growth containers, and allowed to mature in a greenhouse setting for 50 days prior to flume erosion testing. An Acoustic Doppler Profiler (ADP) was used to measure soil erosion and collect three-dimensional velocity data during erosion tests; raw velocity and soil depth data for each sample tested were stored in MATLAB files. Follow testing, the soil remaining from each sample was collected, stored, and analyzed for aggregate stability, soil organic matter (SOM), and extracellular polymeric substances (EPS). Additionally, soil temperature, water temperature, and volumetric water content were also measured prior to or during erosion testing. Data collected from this study, and the accompanying ADP MATLAB files, are presented here.
Soil profiles along productivity gradients in interior Alaska: Summer 2003
Boreal forests in a warmer future climate are likely to exhibit altered productivity levels, tightened fire return intervals, and increased decomposition rates to varying degrees across the landscape. This research focuses on mechanisms of soil C stabilization in P. mariana systems along gradients in stand productivity. Charred material in the soil will be quantified to understand the lasting effect of fire on the stabilization of soil C. The interaction between temperature and productivity in relation to the stabilization of soil C will be investigated by monitoring climate and soil temperatures along the productivity gradients and through laboratory incubations of soil. Research questions are addressed in three main areas of inquiry: 1) how the interaction between stand production and landscape position effect the stabilization of C throughout the soil profile, 2) how the contribution of burn residues to total C accumulation varies across the landscape, and 3) the relationship between aboveground productivity and burn residues across the landscape. The overall goal is to apply an understanding of the biophysical controls on C storage in the boreal forest to the landscape level.
Hubbard Brook Experimental Forest: Soil Profiles (Pedons), 1995-2022
This dataset documents all pedons (soil profiles) that have been located with GPS (2 meter horizontal accuracy) and have been described by genetic horizon for the Hubbard Brook Experimental Forest, within the White Mountain National Forest, NH. Soil profiles were observed between 1995 and 2022 and have been described and sampled at two levels of detail. Soil profiles in the pedon table were dug to bedrock or into the C horizon except where high boulder content limited excavation. The depth of all major soil horizons was measured and most pedons had descriptions written and physical samples collected. The horizon table contains physical observations and chemical analyses for the horizons sampled in the pedon table. Over 2500 of these horizons have had physical samples accessioned into the Hubbard Brook sample archive; the archived mass is included in the horizon table. The reconnaissance table includes pedons observed at a lower level of detail. Small pits were generally dug to a depth of 40 cm or greater, and minimal observations, as needed, were recorded to classify the soil profile by soil map unit (hpu). These data were collected to supplement the detailed observations in the previous two data tables in support of development of a spatial model of soil distribution for the entire Hubbard Brook Experimental Forest. No samples were collected from reconnaissance pits. 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.
Multiple Element Limitation in Northern Hardwood Ecosystems (MELNHE): Soil Profile Rock Characteristics by Bedrock Type in Bartlett and Hubbard Brook, 2004-2018
Soils in our northeastern forests were formed in parent materials deposited by glaciers. The direction and distance of glacial movement can be used to predict the source of glacial till at “downstream” points on the landscape (Bailey 1992). The goal of this project was to identify the rocks excavated from the soil pits in each of the plots and then to use that data to validate the glacial till model. The minority of rocks in the soil pits matched the bedrock, showing the importance of glacial movement. Additional detail on the MELNHE project, including a data table of site descriptions and a pdf file with the project description and diagram of plot configuration can be found in this data package: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-hbr&identifier=344. 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. Literature cited: Bailey, S.W., 1992. Lithologic composition and rock weathering potential of forested, glacial-till soils (Vol. 662). US Department of Agriculture, Forest Service, Northeastern Forest Experiment Station.
Elevational Transects (ET): Soil temperature depth profiles from Garwood, Miers, and Taylor Valley, McMurdo Dry Valleys, Antarctica (2019-2024, ongoing)
As part of the McMurdo Dry Valleys Long Term Ecological Research (LTER) project, the Elevational Transects (ET) study was established in 1993 to investigate how elevation and topographic variation influence soil biotic communities and associated soil properties in the McMurdo Dry Valleys of Antarctica. Transects consisting of low-, mid-, and high-elevation sites were initially established in the Bonney, Hoare, and Fryxell basins of Taylor Valley, with additional transects added in Garwood and Miers Valleys during the 2012-2013 austral summer. This data package contains year-round soil temperature measurements recorded at four depths (0, 5, 10, and 20 cm) by HOBO dataloggers installed at the mid-elevation sites of each valley during the 2018–2019 austral summer.
Soil profile, climatic, physiographic, overstory and understory data in mixed and monospecific plots of Pinus sylvestris and Pinus pinaster in Spain
<p>This dataset provides valuable environmental information about a triplets’ essay of Scots pine and Maritime pine in Spain. The data characterizes the soil profile (physicochemical parameters of organic and mineral horizons), climate, physiography, understory and overstory.</p> <p>The essay, located in North-Central Spain, consists of eighteen forest plots divided in six triplets. Each triplet includes three circular plots of 15 m-radius located less than 1 km from each other: two monospecific plots dominated by <em>P. sylvestris</em> or <em>P. pinaster</em>, and one mixed plot of both species. In each plot, one pit up to 50 cm depth, one 15 m-radius overstory features inventory and ten understory 1x1 m inventories were carried out. Additionally, physiographic and climatic variables were collected per plot.</p> <p>The file contains information about the 218 environmental variables studied in the eighteen forest plots.</p> <p>Triplet: Triplet to which the plot belongs(1: Triplet 1; 2: Triplet 2; 3: Triplet 3; 4: Triplet 4; 5: Triplet 5; 6: Triplet 6).</p> <p>Stand_type: Type of stand (PS: monospecific stand of <em>Pinus sylvestris</em> L.; PP: monospecific stand of <em>Pinus pinaster</em> Ait.; MM: mixed stand of <em>Pinus sylvestris</em> L.and <em>Pinus pinaster</em> Ait.).</p> <p>Plot: Plot identification (PS01: monospecific stand of <em>Pinus sylvestris</em> L. of triplet 1; PS02: monospecific stand of <em>Pinus sylvestris</em> L. of triplet 2; PS03: monospecific stand of <em>Pinus sylvestris</em> L. of triplet 3; PS04: monospecific stand of <em>Pinus sylvestris</em> L. of triplet 4; PS05: monospecific stand of <em>Pinus sylvestris</em> L. of triplet 5; PS06: monospecific stand of <em>Pinus sylvestris</em> L. of triplet 6; MM01: mixed stand of <em>Pinus sylvestris</em> L.and <em>Pinus pinaster</em> Ait. of triplet 1; MM02: mixed stand of <em>Pinus sylvestris </em>L.and <em>Pinus pinaster</em> Ait. of triplet 2; MM03: mixed stand of <em>Pinus sylvestris</em> L.and <em>Pinus pinaster</em> Ait. of triplet 3; MM04: mixed stand of <em>Pinus sylvestris</em> L.and <em>Pinus pinaster</em> Ait. of triplet 4; MM05: mixed stand of <em>Pinus sylvestris </em>L.and <em>Pinus pinaster</em> Ait. of triplet 5; MM06: mixed stand of <em>Pinus sylvestris </em>L.and <em>Pinus pinaster Ait</em>. of triplet 6; PP01: monospecific stand of <em>Pinus pinaster</em> Ait. of triplet 1; PP02: monospecific stand of <em>Pinus pinaster</em> Ait. of triplet 2; PP03: monospecific stand of <em>Pinus pinaster </em>Ait. of triplet 3; PP04: monospecific stand of <em>Pinus pinaster</em> Ait. of triplet 4; PP05: monospecific stand of <em>Pinus pinaster</em> Ait. of triplet 5; PP06: monospecific stand of<em> Pinus pinaster </em>Ait. of triplet 6).</p> <p>Lat: Plot latitude in degrees.</p> <p>Long: Plot longitude in degrees.</p> <p>Province: Province to which the plot belongs (B: Province of Burgos; Sp: Province of Soria).</p> <p>Municipality: Municipality to which the plot belongs (M: Town of Mamolar; HP: Town of Hontoria del Pinar; N: Town of Navaleno; St: Town of Soria; CP: Town of Cabrejas del Pinar).</p> <p>Forest: Name of the forest where is located the plot (MB: Mata Blanca; MR: Mata Robledo; FP: Fuente del Pardo; PM: Pajar de la molinera; MP: Mojon Pardo; CM: Cueva de Matarubias).</p> <p>Alti: Plot elevation above sea level in m a.s.l.</p> <p>Slope: Slope (gradient) of the plot in percentage.</p> <p>Ori: Plot orientation in degrees.</p> <p>Clim: Climate classification according to Köppen classification (1936) (Cfb: Temperate without a dry season and temperate summer climate; Csb: Temperate with dry summer climate).</p> <p>XR: Accumulated rainfall in one year according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in mm.</p> <p>JR: January rainfall according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ mm</p> <p>FR: February rainfall according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in mm.</p> <p>MR: March rainfall according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in mm.</p> <p>AR: April rainfall according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in mm.</p> <p>MyR: May rainfall according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in mm.</p> <p>JnR: June rainfall according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in mm.</p> <p>JlR: July rainfall according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in mm.</p> <p>AgR: August rainfall according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in mm.</p> <p>SR: September rainfall according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in mm.</p> <p>OR: October rainfall according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in mm.</p> <p>NR: November rainfall according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in mm.</p> <p>DR: December rainfall according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in mm.</p> <p>XT: Anual mean temperature according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in ºC.</p> <p>JT: January temperature according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in ºC.</p> <p>FT: February temperature according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in ºC.</p> <p>MT: March temperature according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in ºC.</p> <p>AT: April temperature according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in ºC.</p> <p>MyT: May temperature according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in ºC.</p> <p>JnT: June temperature according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in ºC.</p> <p>JlT: July temperature according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in ºC.</p> <p>AgT: August temperature according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in ºC.</p> <p>ST: September temperature according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in ºC.</p> <p>OT: October temperature according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in ºC.</p> <p>NT: November temperature according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in ºC.</p> <p>DT: December temperature according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in ºC.</p> <p>Par_mat: Soil parental material according to Spanish Geological Map on a 1M scale. (IGME , 2015) (SM: Sandstones and Marls).</p> <p>Geo_age: Geological age of plot according to Spanish Geological Map on a 1M scale. (IGME, 2015) (Mz: Mesozoic age).</p> <p>Soil: Soil type according to Soil-Survey-Staff (2014) (TpDx: Typic Dystroxerept; TpHx:: Typic Humixerept; AqHx:: Aquic humixerept)</p> <p>Litter_B: Total Leaf Litter Biomass in Mg/ha.</p> <p>FF_Th: Forest floor Thickness in cm.</p> <p>Fs: Percentage of Fresh to Total Leaf Litter in %.</p> <p>Fr: Percentage of Fragmented to Total Leaf Litter in %.</p> <p>Hm: Percentage of Humified to Total Leaf Litter in %.</p> <p>GH1: Fist genetic soil horizon according to Soil Survey-Staff (2014) (Ah: Mineral horizon with accumulation of organic matter. This horizon is formed at the soil surface or below an O horizon).</p> <p>GH2: Second genetic soil horizon according to Soil Survey-Staff (2014) (AB: Transition horizon between A and B. A is a mineral horizon formed at the surface or below an O horizon, B is a subsurface horizon in which the structure of the rock is obliterated; AC: Transition horizon between A and C. A is a mineral horizon formed at the surface or below an O horizon; C is a mineral horizon, excluding hard bedrock, that is little affected by pedogenetic processes; Bw: Mineral B horizon where the development of color or structure are its more important diagnostic characteristics).</p> <p>GH3: Third genetic soil horizon according to Soil Survey-Staff (2014) (Bw: Mineral B horizon where the development of color or structure are its more important diagnostic characteristics; C: Mineral horizon, excluding hard bedrock, that is little affected by pedogenetic processes; Cg: Mineral horizon in which a distinct pattern of mottling occurs that reflects alternating conditions of oxidation and reduction of sesquioxides, caused by seasonal surface waterlogging).</p> <p>Th_H1: Thickness of the first soil horizon in cm.</p> <p>Th_H2: Thickness of the second soil horizon in cm.</p> <p>Th_H3: Thickness of the third soil horizon in cm.</p> <p>moistCol_H1: Wet matrix color (Hue Value/Chroma) of the first soil horizon according to Munsell soil color chards (10YR2/1: black; 10YR2/2: very dark brown; 10YR3/1: very dark grey; 10YR3/2: very dark greyish brown; 10YR4/1: dark grey; 10YR6/3: pale brown).</p> <p>moistCol_H2: Wet matrix colour (Hue Value/Chroma) of the second soil horizon according to Munsell soil color chards (5YR5/8: yellowish red; 7.5YR4/6: strong brown; 10YR3/2: very dark greyish brown; 10YR4/1: dark grey; 10YR4/2: dark greyish brown; 10YR4/4: dark yellowish brown with chroma 4; 10YR4/6: dark yellowish brown with chroma 6; 10YR5/3: brown; 10YR5/4: yellowish brown with chroma 4; 10YR5/6: yellowish brown with chroma 6; 10YR5/8: yellowish brown with chroma 8; 10YR6/4: light yellowish brown; 10YR6/6: brownish yellow).</p> <p>moistCol_H3: Wet matrix colour (Hue Value/Chroma) of the third soil horizon according to Munsell soil color chards (5YR4/6: yellowish red; 10YR4/4: dark yellowish brown with chroma 4; 10YR4/6: dark yellowish brown with chroma 6; 10YR5/8: yellowish brown; 10YR6/1: grey).</p> <p>dryCol_H1:Dry matrix color (Hue Value/Chroma) of the first soil horizon according to Munsell soil color chards (10YR4/1: dark grey; 10YR4/2: dark greyish brown; 10YR5/1: grey with value 5; 10YR5/2: greyish brown; 10YR5/3: brown; 10YR6/1: grey with value 6; 10YR6/2: light yellowish brown; 10YR7/2: light grey).</p> <p>dryCol_H2: Dry matrix color (Hue Value/Chroma) of the second soil horizon according to Munsell soil color chards (7.5YR6/6: redish brown; 10YR4/1: dark grey; 10YR6/1: grey with value 6; 10YR6/2: light yellowish brown with chroma 2; 10YR6/3: pale brown; 10YR6/4: light yellowish brown with chroma 4; 10YR6/6: brownish yellow; 10YR7/3: very pale brown with value 7 and choma 3; 10YR7/4: very pale brown withvalue 7 and choma 4; 10YR8/4: very pale brown with value 8 and choma 4).</p> <p>dryCol_H3: Dry matrix color (Hue Value/Chroma) of the third soil horizon according to Munsell soil color chards (5YR5/6: yellowish red; 7.5YR5/6: strong brown; 10YR6/4: light yellowish brown; 10YR6/6: brownish yellow; 10YR7/4: very pale brown; 10YR8/1: white).</p> <p>Sand_H1: Percentage of sand of the first soil horizon determined by the pipette method (Van-Reeuwijk 2002) according to Soil Survey Staff (2014) in % weight/weight.</p> <p>Sand_H2: Percentage of sand of the second soil horizon determined by the pipette method (Van-Reeuwijk 2002) according to Soil Survey Staff (2014) in % weight/weight.</p> <p>Sand_H3: Percentage of sand of the third soil horizon determined by the pipette method (Van-Reeuwijk 2002) according to Soil Survey Staff (2014) in % weight/weight.</p> <p>Silt_H1: Percentage of silt of the first soil horizon determined by the pipette method (Van-Reeuwijk 2002) according to Soil Survey Staff (2014) in % weight/weight.</p> <p>Silt_H2: Percentage of silt of the second soil horizon determined by the pipette method (Van-Reeuwijk 2002) according to Soil Survey Staff (2014) in % weight/weight.</p> <p>Silt_H3: Percentage of silt of the third soil horizon determined by the pipette method (Van-Reeuwijk 2002) according to Soil Survey Staff (2014) in % weight/weight.</p> <p>Clay_H1: Percentage of clay of the first soil horizon determined by the pipette method (Van-Reeuwijk 2002) according to Soil Survey Staff (2014) in % weight/weight.</p> <p>Clay_H2: Percentage of clay of the second soil horizon determined by the pipette method (Van-Reeuwijk 2002) according to Soil Survey Staff (2014) in % weight/weight.</p> <p>Clay_H3: Percentage of clay of the third soil horizon determined by the pipette method (Van-Reeuwijk 2002) according to Soil Survey Staff (2014) in % weight/weight.</p> <p>Tex_H1: Textural class of the first soil horizon according to Soil Survey Staff (2014) (SL: Sandy Loam; L: Loam).</p> <p>Tex_H2: Textural class of the second soil horizon according to Soil Survey Staff (2014) (SL: Sandy Loam; L: Loam).</p> <p>Tex_H3: Textural class of the third soil horizon according to Soil Survey Staff (2014) (SL: Sandy Loam; L: Loam; CL: Clay loam; C: Clay).</p> <p>Stones_H1: Coarse soil material (> 2 mm) of the first soil horizon in % weight/weight.</p> <p>Stones_H2: Coarse soil material (> 2 mm) of the second soil horizon in % weight/weight.</p> <p>Stones_H3: Coarse soil material (> 2 mm) of the third soil horizon in % weight/weight.</p> <p>bD_H1: Bulk density of the first soil horizon according to (Van-Reeuwijk 2002) in g/cm<sup>3</sup>.</p> <p>bD_H2: Bulk density of the second soil horizon according to (Van-Reeuwijk 2002) in g/cm<sup>3</sup>.</p> <p>bD_H3: Bulk density of the third soil horizon according to (Van-Reeuwijk 2002) in g/cm<sup>3</sup>.</p> <p>pD_H1: Particle density of the first soil horizon according to (Van-Reeuwijk 2002) in g/cm<sup>3</sup>.</p> <p>pD_H2: Particle density of the second soil horizon according to (Van-Reeuwijk 2002) in g/cm<sup>3</sup>.</p> <p>pD_H3: Particle density of the third soil horizon according to (Van-Reeuwijk 2002) in g/cm<sup>3</sup>.</p> <p>Poro_H1: Porosity of the first soil horizon according to (Van-Reeuwijk 2002) in % vol/vol.</p> <p>Poro_H2: Porosity of the second soil horizon according to (Van-Reeuwijk 2002) in % vol/vol.</p> <p>Poro_H3: Porosity of the third soil horizon according to (Van-Reeuwijk 2002) in % vol/vol.</p> <p>pH_H1: pH (1:2.5 H2O) of the first soil horizon according to (Van-Reeuwijk 2002)</p> <p>pH_H2: pH (1:2.5 H2O) of the second soil horizon according to (Van-Reeuwijk 2002)</p> <p>pH_H3: pH (1:2.5 H2O) of the third soil horizon according to (Van-Reeuwijk 2002)</p> <p>EC_H1: Electrical conductivity of the first soil horizon according to (Van-Reeuwijk 2002) in dS/m.</p> <p>EC_H2: Electrical conductivity of the second soil horizon according to (Van-Reeuwijk 2002) in dS/m.</p> <p>EC_H3: Electrical conductivity of the third soil horizon according to (Van-Reeuwijk 2002) in dS/m.</p> <p>avP_H1: Available phosphorus of the first soil horizon according to Olsen and Sommers (1982) in mg/kg.</p> <p>avP_H2: Available phosphorus of the second soil horizon according to Olsen and Sommers (1982) in mg/kg.</p> <p>avP_H3: Available phosphorus of the third soil horizon according to Olsen and Sommers (1982) in mg/kg.</p> <p>avPstock_H1: Available phosphorus stock of the first soil horizon according to López-Marcos et al. (2019) in Mg/ha.</p> <p>avPstock_H2: Available phosphorus stock of the second soil horizon according to López-Marcos et al. (2019) in Mg/ha.</p> <p>avPstock_H3: Available phosphorus stock of the third soil horizon up to 50 cm depth according to López-Marcos et al. (2019) in Mg/ha.</p> <p>avPstock_50: Available phosphorus stock of whole soil profile up to 50 cm depth according to López-Marcos et al. (2019) in Mg/ha.</p> <p>TN_Fs: Total nitrogen of the fresh forest floor analyzed with a LECO-CHN 2000 elemental analyzer in g/kg</p> <p>TN_Fg: Total nitrogen of the fragmented forest floor analyzed with a LECO-CHN 2000 elemental analyzer in g/kg</p> <p>TN_Hm: Total nitrogen of the humified forest floor analyzed with a LECO-CHN 2000 elemental analyzer in g/kg</p> <p>TN_H1: Total nitrogen of the first soil horizon analyzed with a LECO-CHN 2000 elemental analyzer in g/kg.</p> <p>TN_H2: Total nitrogen of the second soil horizon analyzed with a LECO-CHN 2000 elemental analyzer in g/kg.</p> <p>TN_H3: Total nitrogen of the third soil horizon analyzed with a LECO-CHN 2000 elemental analyzer in g/kg.</p> <p>TNstock_H1: Total nitrogen stock of the first soil horizon according to López-Marcos et al. (2019) in Mg/ha.</p> <p>TNstock_H2: Total nitrogen stock of the second soil horizon according to López-Marcos et al. (2019) in Mg/ha.</p> <p>TNstock_H3: Total nitrogen stock of the third soil horizon up to 50 cm depth according to López-Marcos et al. (2019) in Mg/ha.</p> <p>TNstock_50: Total nitrogen stock of whole soil profile up to 50 cm depth according to López-Marcos et al. (2019) in Mg/ha.</p> <p>TOC_Fs: Total organic carbon of the fresh forest floor analyzed with a LECO-CHN 2000 elemental analyzer in g/kg</p> <p>TOC_Fg: Total organic carbon of the fragmented forest floor analyzed with a LECO-CHN 2000 elemental analyzer in g/kg</p> <p>TOC_Hm: Total organic carbon of the humified forest floor analyzed with a LECO-CHN 2000 elemental analyzer in g/kg</p> <p>TOC_H1: Total organic carbon of the first soil horizon analyzed with a LECO-CHN 2000 elemental analyzer in g/kg.</p> <p>TOC_H2: Total organic carbon of the second soil horizon analyzed with a LECO-CHN 2000 elemental analyzer in g/kg.</p> <p>TOC_H3: Total organic carbon of the third soil horizon analyzed with a LECO-CHN 2000 elemental analyzer in g/kg.</p> <p>TOCstock_H1:Total organic carbon stock of the first soil horizon according to López-Marcos et al. (2019) in Mg/ha.</p> <p>TOCstock_H2: Total organic carbon stock of the second soil horizon according to López-Marcos et al. (2019) in Mg/ha.</p> <p>TOCstock_H3: Total organic carbon stock of the third soil horizon up to 50 cm depth according to López-Marcos et al. (2019) in Mg/ha.</p> <p>TOCstock_50: Total organic carbon stock of whole soil profile up to 50 cm depth according to López-Marcos et al. (2019) in Mg/ha.</p> <p>C/N_Fs: Ratio of total organic carbon to total nitrogen of the fresh forest floor </p> <p>C/N_Fg: Ratio of total organic carbon to total nitrogen of the fragmented forest floor </p> <p>C/N_Hm: Ratio of total organic carbon to total nitrogen of the humified forest floor </p> <p>C/N_H1: Ratio of total organic carbon to total nitrogen of the first soil horizon</p> <p>C/N_H2: Ratio of total organic carbon to total nitrogen of the second soil horizon</p> <p>C/N_H3: Ratio of total organic carbon to total nitrogen of the third soil horizon</p> <p>OxC_H1: Easily oxidizable carbon of the first soil horizon according to Walkley (1947) in mg/kg.</p> <p>OxC_H2: Easily oxidizable carbon of the second soil horizon according to Walkley (1947) in mg/kg.</p> <p>OxC_H3: Easily oxidizable carbon of the third soil horizon according to Walkley (1947) in mg/kg.</p> <p>OxCstock_H1: Easily oxidizable carbon stock of the first soil horizon according to López-Marcos et al. (2019) in Mg/ha.</p> <p>OxCstock_H2: Easily oxidizable carbon stock of the second soil horizon according to López-Marcos et al. (2019) in Mg/ha.</p> <p>OxCstock_H3: Easily oxidizable carbon stock of the third soil horizon up to 50 cm depth according to López-Marcos et al. (2019) in Mg/ha.</p> <p>OxCstock_50: Easily oxidizable carbon stock of whole soil profile up to 50 cm depth according to López-Marcos et al. (2019) in Mg/ha.</p> <p>CEC_H1: Cation exchange capacity of the first soil horizon according to Mehlich (1953) in cmol<sub>+</sub>/kg.</p> <p>CEC_H2: Cation exchange capacity of the second soil horizon according to Mehlich (1953) in cmol<sub>+</sub>/kg.</p> <p>CEC_H3: Cation exchange capacity of the third soil horizon according to Mehlich (1953) in cmol<sub>+</sub>/kg.</p> <p>Na<sup>+</sup>_H1: Exchangeable sodium of the first soil horizon by means of extracting with 1N ammonium acetate (pH=7) (Schollenberger and Simon 1945) in cmol<sub>+</sub>/kg.</p> <p>Na<sup>+</sup>_H2: Exchangeable sodium of the second soil horizon by means of extracting with 1N ammonium acetate (pH=7) (Schollenberger and Simon 1945) in cmol<sub>+</sub>/kg.</p> <p>Na<sup>+</sup>_H3: Exchangeable sodium of the third soil horizon by means of extracting with 1N ammonium acetate (pH=7) (Schollenberger and Simon 1945) in cmol<sub>+</sub>/kg.</p> <p>Na<sup>+</sup>stock_H1: Exchangeable sodium stock of the first soil horizon according to López-Marcos et al. (2019) in Mg/ha.</p> <p>Na<sup>+</sup>stock_H2: Exchangeable sodium stock of the second soil horizon according to López-Marcos et al. (2019) in Mg/ha.</p> <p>Na<sup>+</sup>stock_H3: Exchangeable sodium stock of the third soil horizon up to 50 cm depth according to López-Marcos et al. (2019) in Mg/ha.</p> <p>Na<sup>+</sup>stock_50: Exchangeable sodium stock of whole soil profile up to 50 cm depth according to López-Marcos et al. (2019) in Mg/ha.</p> <p>K<sup>+</sup>_H1: Exchangeable potassium of the first soil horizon by means of extracting with 1N ammonium acetate (pH=7) (Schollenberger and Simon 1945) in cmol<sub>+</sub>/kg.</p> <p>K<sup>+</sup>_H2: Exchangeable potassium of the second soil horizon by means of extracting with 1N ammonium acetate (pH=7) (Schollenberger and Simon 1945) in cmol<sub>+</sub>/kg.</p> <p>K<sup>+</sup>_H3: Exchangeable potassium of the third soil horizon by means of extracting with 1N ammonium acetate (pH=7) (Schollenberger and Simon 1945) in cmol<sub>+</sub>/kg.</p> <p>K<sup>+</sup>stock_H1: Exchangeable potassium stock of the first soil horizon according to López-Marcos et al. (2019) in Mg/ha.</p> <p>K<sup>+</sup>stock_H2: Exchangeable potassium stock of the second soil horizon according to López-Marcos et al. (2019) in Mg/ha.</p> <p>K<sup>+</sup>stock_H3: Exchangeable potassium stock of the third soil horizon up to 50 cm depth according to López-Marcos et al. (2019) in Mg/ha.</p> <p>K<sup>+</sup>stock_50: Exchangeable potassium stock of whole soil profile up to 50 cm depth according to López-Marcos et al. (2019) in Mg/ha.</p> <p>Ca<sup>+2</sup>_H1: Exchangeable calcium of the first soil horizon by means of extracting with 1N ammonium acetate (pH=7) (Schollenberger and Simon 1945) in cmol<sub>+</sub>/kg.</p> <p>Ca<sup>+2</sup>_H2: Exchangeable calcium of the second soil horizon by means of extracting with 1N ammonium acetate (pH=7) (Schollenberger and Simon 1945) in cmol<sub>+</sub>/kg.</p> <p>Ca<sup>+2</sup>_H3: Exchangeable calcium of the third soil horizon by means of extracting with 1N ammonium acetate (pH=7) (Schollenberger and Simon 1945) in cmol<sub>+</sub>/kg.</p> <p>Ca<sup>+2</sup>stock_H1: Exchangeable calcium stock of the first soil horizon according to López-Marcos et al. (2019) in Mg/ha.</p> <p>Ca<sup>+2</sup>stock_H2: Exchangeable calcium stock of the second soil horizon according to López-Marcos et al. (2019) in Mg/ha.</p> <p>Ca<sup>+2</sup>stock_H3: Exchangeable calcium stock of the third soil horizon up to 50 cm depth according to López-Marcos et al. (2019) in Mg/ha.</p> <p>Ca<sup>+2</sup>stock_50: Exchangeable calcium stock of whole soil profile up to 50 cm depth according to López-Marcos et al. (2019) in Mg/ha.</p> <p>Mg<sup>+2</sup>_H1: Exchangeable magnesium of the first soil horizon by means of extracting with 1N ammonium acetate (pH=7) (Schollenberger and Simon 1945) in cmol<sub>+</sub>/kg.</p> <p>Mg<sup>+2</sup>_H2: Exchangeable magnesium of the second soil horizon by means of extracting with 1N ammonium acetate (pH=7) (Schollenberger and Simon 1945) in cmol<sub>+</sub>/kg.</p> <p>Mg<sup>+2</sup>_H3: Exchangeable magnesium of the third soil horizon by means of extracting with 1N ammonium acetate (pH=7) (Schollenberger and Simon 1945) in cmol<sub>+</sub>/kg.</p> <p>Mg<sup>+2</sup>stock_H1: Exchangeable magnesium stock of the first soil horizon according to López-Marcos et al. (2019) in Mg/ha.</p> <p>Mg<sup>+2</sup>stock_H2: Exchangeable magnesium stock of the second soil horizon according to López-Marcos et al. (2019) in Mg/ha.</p> <p>Mg<sup>+2</sup>stock_H3: Exchangeable magnesium stock of the third soil horizon up to 50 cm depth according to López-Marcos et al. (2019) in Mg/ha.</p> <p>Mg<sup>+2</sup>stock_50: Exchangeable magnesium stock of whole soil profile up to 50 cm depth according to López-Marcos et al. (2019) in Mg/ha.</p> <p>SB_H1: Sum of bases of the first soil horizon by means of extracting with 1N ammonium acetate (pH=7) (Schollenberger and Simon 1945) in cmol<sub>+</sub>/kg.</p> <p>SB_H2: Sum of bases of the second soil horizon by means of extracting with 1N ammonium acetate (pH=7) (Schollenberger and Simon 1945) in cmol<sub>+</sub>/kg.</p> <p>SB_H3: Sum of bases of the third soil horizon by means of extracting with 1N ammonium acetate (pH=7) (Schollenberger and Simon 1945) in cmol<sub>+</sub>/kg.</p> <p>SBstock_H1: Sum of bases stock of the first soil horizon according to López-Marcos et al. (2019) in Mg/ha.</p> <p>SBstock_H2: Sum of bases stock of the second soil horizon according to López-Marcos et al. (2019) in Mg/ha.</p> <p>SBstock_H3: Sum of bases stock of the third soil horizon up to 50 cm depth according to López-Marcos et al. (2019) in Mg/ha.</p> <p>SBstock_50: Sum of bases stock of whole soil profile up to 50 cm depth according to López-Marcos et al. (2019) in Mg/ha.</p> <p>FC_H1: Field capacity of the first soil horizon according to Van-Reeuwijk (2002) in %.</p> <p>FC_H2: Field capacity of the second soil horizon according to Van-Reeuwijk (2002) in %.</p> <p>FC_H3: Field capacity of the third soil horizon according to Van-Reeuwijk (2002)) in %.</p> <p>PWP_H1: Permanent wilting point of the first soil horizon according to Van-Reeuwijk (2002) in %.</p> <p>PWP_H2: Permanent wilting point of the second soil horizon according to Van-Reeuwijk (2002) in %. </p> <p>PWP_H3: Permanent wilting point of the third soil horizon according to Van-Reeuwijk (2002) in %.</p> <p>AW_H1: Available water of the first soil horizon according to Van-Reeuwijk (2002) in %.</p> <p>AW_H2: Available water of the second soil horizon according to MAPA (1994) in %.</p> <p>AW_H3: Available water of the third soil horizon according to Van-Reeuwijk (2002) in %.</p> <p>WHC_H1: Water holding capacity of the first soil horizon according to López-Marcos et al. (2019) in g/cm<sup>2</sup>.</p> <p>WHC_H2: Water holding capacity of the second soil horizon according to López-Marcos et al. (2019) in g/cm<sup>2</sup>.</p> <p>WHC_H3: Water holding capacity of the third soil horizon up to 50 cm depth according to López-Marcos et al. (2019) in g/cm<sup>2</sup>.</p> <p>WHC_50: Water holding capacity of whole soil profile up to 50 cm depth according to López-Marcos et al. (2019) in g/cm<sup>2</sup>.</p> <p>Pot_veg: Potential vegetation according to Rivas-Martínez (1987) (LfQp: <em>Luzulo forsteri-Querceto pyrenaicae </em>S.; FhQp: <em>Festuco heterophyllae-Querceto pyrenaicae</em> S.; Jht: <em>Junipereto hemisphaerico-thuriferae</em> S.).</p> <p>Cur_veg: Current vegetation according to WMS service of MAPAMA(<a href="http://wms.mapama.es/sig/Biodiversidad">http://wms.mapama.es/sig/Biodiversidad</a>) (ps: <em>Pinus sylvestris</em> L.; pp: <em>Pinus pinaster</em> Ait.; pi: <em>Pinus sylvestris</em> L. and <em>Pinus pinaster</em> Ait.).</p> <p>NT: Stems per hectare of both <em>Pinus </em>species (<em>Pinus sylvestris</em> L. and <em>Pinus pinaster</em> Ait.) in trees/ha.</p> <p>NPs: Stems per hectare of <em>Pinus sylvestris</em> L. in trees/ha.</p> <p>NPp: Stems per hectare of <em>Pinus pinaster</em> Ait. in trees/ha.</p> <p>GT: Basal area per hectare of both <em>Pinus</em> species (<em>Pinus sylvestris</em> L. and <em>Pinus pinaster</em> Ait.) in m<sup>2</sup>/ha.</p> <p>GPs: Basal area per hectare of <em>Pinus sylvestris</em> L. in m<sup>2</sup>/ha.</p> <p>GPp: Basal area per hectare of <em>Pinus pinaster</em> Ait. in m<sup>2</sup>/ha.</p> <p>%PS: Percentage of basal area of <em>Pinus sylvestris</em> L. from total basal area</p> <p>%PP: Percentage of basal area of <em>Pinus pinaster</em> Ait. from total basal area</p> <p>dgT: Quadratic mean diameter of both <em>Pinus </em>species (<em>Pinus sylvestris</em> L. and <em>Pinus pinaster</em> Ait.) in cm.</p> <p>dgPs: Quadratic mean diameter of <em>Pinus sylvestris</em> L. in cm.</p> <p>dgPp: Quadratic mean diameter of <em>Pinus pinaster</em> Ait. in cm.</p> <p>HoT: Dominant height of both <em>Pinus</em> species (<em>Pinus sylvestris</em> L. and <em>Pinus pinaster</em> Ait.) in cm.</p> <p>HoPs: Dominant height of <em>Pinus sylvestris</em> L. in m.</p> <p>HoPp: Dominant height of <em>Pinus pinaster</em> Ait. in m.</p> <p>AgePs: Normal age of <em>Pinus sylvestris</em> L. in years.</p> <p>AgePp: Normal age of <em>Pinus pinaster</em> Ait. In years.</p> <p>SIPs: Site index of <em>Pinus sylvestris</em> L. related at age 100 for total plot according to Rojo and Montero (1999)</p> <p>SIPp: Site index of <em>Pinus pinaster</em> Ait. related at age 100 for total plot according to Bravo-Oviedo et al. (2007)</p> <p>Litter_cov: Cover of leaf litter in %.</p> <p>Vasc: Cover of understory vascular plants in %.</p> <p>Bryo: Cover of understory bryophytes in %.</p> <p>Under_sp: More abundant specie of understory vegetation (Aica: <em>Aira caryophyllea</em> L.; Aruv: <em>Arctostaphylos uva-ursi</em> (L.) Spreng.; Cavu: <em>Calluna vulgaris</em> (L.) Hull; Erar: <em>Erica arborea </em>L.; Erau: <em>Erica australis</em> L.; Pipi: <em>Pinus pinaster</em> Aiton. (seedlings/saplings); Pisy: <em>Pinus sylvestris</em> L. (seedlings/saplings; Ptaq: <em>Pteridium aquilinum</em> (L.) Kuhn)</p> <p>Aqu: Understory cover of family Aquifoliaceae in %.</p> <p>Aste: Understory cover of family Asteraceae in %.</p> <p>Cari: Understory cover of family Cariophyllaceae in %.</p> <p>Cist: Understory cover of family Cistaceae in %.</p> <p>Cupr: Understory cover of family Cupresaceae in %.</p> <p>Eric: Understory cover of family Ericaceae in %.</p> <p>Faba: Understory cover of family Fabaceae in %.</p> <p>Faga: Understory cover of family Fagaceae in %.</p> <p>Junc: Understory cover of family Juncaceae in %.</p> <p>Lili: Understory cover of family Liliaceae in %.</p> <p>Pina: Understory cover of family Pinaceae in %.</p> <p>Poac: Understory cover of family Poaceae in %.</p> <p>Poli: Understory cover of family Poligalaceae in %.</p> <p>Rosa: Understory cover of family Rosaceae in %.</p> <p>Rubi: Understory cover of family Rubiaceae in %.</p> <p>Scro: Understory cover of family Scrofulariaceae in %.</p> <p>Viol: Understory cover of family Violaceae in %.</p> <p>Xant: Understory cover of family Xanthorrhoeaceae in %.</p>
Global patterns of soil organic carbon distribution in the 20–100 cm soil profile for different ecosystems: A global meta-analysis
<p><span><span> </span></span><span>The file named <span>“</span>Rawdata.xlsx<span>”</span> contains data sourced from the literature.<span> The file name is “GE_β.tif<span>”</span><span>,</span></span></span><span><span> GE represents</span></span><span> global ecosystems, which including cropland (CL), grassland (GL), and forestland (FL). “FL_β.tif” represents the spatial distribution of β for forestland at 20-100 cm depth. The file name is “GE_d_SOCD.tif”, where SOCD represents soil organic carbon density, d represents soil depth, for example, “FL_20-100_SOCD.tif” represents the spatial distribution of SOCD for forestland at 20-100 cm depth.</span></p>
Nitrogen cycling at treeline. IV. Soil Profile Descriptions
We studied spatial and temporal patterns of nitrogen pools and fluxes in soils at treeline and forested sites within three mountain ranges across a 785 km transect in Alaska during 2001- 2002. We measured pools of soil mineral (ammonium and nitrate) and organic (amino acid and microbial biomass) nitrogen, in situ rates of net mineralization, net nitrification, net amino acid production, and decomposition, as well as soil carbon turnover in a laboratory incubation experiment. A complete characterization of the study can be found in Loomis et al. (2006).
Cone Pond Watershed: Soil Profiles (Pedons), 1988-2023
Cone Pond Watershed: Soil Profiles (Pedons), 1988-2023 This dataset documents pedons (soil profiles) sampled at Cone Pond Watershed, Thornton, New Hampshire. Since the early 1980’s, Cone Pond, on the Pemigewasset District of the White Mountain National Forest, has been an active research satellite site to the nearby Hubbard Brook Experimental Forest. In 1988, intensive monitoring began as part of a comprehensive watershed scale ecosystem study. A weir was built on the main inlet stream, just above its mouth at Cone Pond, to monitor streamflow. Two rain gages were installed to monitor atmospheric deposition and multiple studies of major ecosystem components were initiated. Soil profiles documented in this dataset were sampled between 1988 and 2003. All descriptive profile and horizon data as well as chemical analyses of samples of genetic horizons are included in this dataset. Samples from 123 of these horizons have been accessioned into the Hubbard Brook physical sample archive; the archived mass of each is included in the horizon table. In addition to these pedons sampled in detail, a number of reconnaissance observations, made at a lower level of detail, and without sampling, were made in 2023 in order to validate a hydropedologic soil model created at Hubbard Brook. These data are included in a separate table. 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.
Physics-informed neural networks (PINNs) with unsaturated water flow models for inverse analysis of soil hydraulic parameters of layered soil profiles
<p>Information about the spatial distribution of soil hydraulic parameters is necessary for the accurate prediction of soil water flow and coupled movement of chemicals and heat at the field scale using a process-based model. Physics-informed neural networks (PINNs), which can provide physical constraints in deep learning to obtain a mesh-free solution, can be used to inversely estimate the soil hydraulic parameters from less and noisy training data. Previous studies using PINNs have successfully estimated soil hydraulic parameters for homogeneous soil but estimating such parameters of layered soil profiles where the interface depth and the parameters are unknown still has some difficulties. The objective of this study was to develop PINNs to inversely estimate the distribution of soil hydraulic parameters, such as saturated hydraulic conductivity and <em>α</em> and <em>n</em>, of the Mualem-van Genuchten model directly within layered soil profiles by predicting changes in pressure head from training data based on simulation results at given depths during infiltration. The impact of factors affecting PINNs performance, such as the weights assigned to each component of the loss function, the time range used in error computations, and the number of samples used to assess physical constraint was investigated. By assigning a larger weight to the physical constraint and excluding the earlier stage of infiltration in the loss function, the changes in pressure head and the three soil hydraulic parameter distributions within the layered soil profiles were successfully estimated. The developed PINNs can be further applied to more complex soils and can be improved.</p>
Georeferenced Spanish Soil Profile Database (SODES)
<p>The Georeferenced Spanish Soil Profile Database (SODES) contains soil information and data values for 1683 georereferenced soil profiles, compiled from an extensive bibliographic review. SODES allows the characterisation of the different peninsular Spanish soil types and is a suitable tool for assessments and decision making purposes dealing with different issues.</p>
Soil temperature profiles, measured using a coil-shaped fiber-optic distributed temperature sensor
<p>Measurements of soil temperature temperature profile, by reference sensors and a coil-shaped fiber optic distributed temperature sensor.</p> <p>Retrieved at the Speulderbos measurement site, 52.251048 N, 5.690061 E.</p> <p> </p> <p>A full description can be found in:</p> <p>Schilperoort, B. (2022). <em>Heat Exchange in a Conifer Canopy: A Deep Look using Fiber Optic Sensors</em> [Delft University of Technology]. https://doi.org/10.4233/uuid:6d18abba-a418-4870-ab19-c195364b654b</p>
Physics-informed neural networks (PINNs) with unsaturated water flow models for inverse analysis of soil hydraulic parameters of layered soil profiles
Open the record for dataset details and reuse information.
Profiles of 0-50 cm soil CO2 and N2O concentrations collected in the CPCRW from 1998-2002
This table contains concentrations (ppmv) of CO2 and N2O measured at 5, 10, 20, 30, 40, and 50 cm depths below the soil surface in closed-canopy black spruce and mixed hardwood sites (@ 3 replicate sites) in the Caribou Poker Creeks Research Watershed. Samples were taken at weekly or bi-weekly intervals from two profiles in each site during growing seasons from June 1, 1999 through September 17, 2002. This period brackets the Frostfire burn of July 1999; because the fire missed the planned burn sites in mixed hardwoods, the mixed hardwood plots were moved (reflected in the site numbering in the database).
Eight Mile Lake Research Watershed, Thaw Gradient: Growing season soil profile CO2 production at 10, 20, 30, and 40 cm, 2005-2007.
This dataset contains CO2 concentrations, diffusion coefficient, and soil CO2 fluxes at each depth interval and soil CO2 production at each replicate. The data were collected during the growing season as well as shoulder seasons.
Hubbard Brook Experimental Forest: Soil Profile Maps and Horizon Thicknesses on Watershed 5, 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 horizon thicknesses and present hand drawn maps of the sides of a subset of the 239 sampling pits excavated over the four sampling years. Note that U.S. standard soil horizon nomenclature changed between 1983 and 1986. In nearly all cases, the 1983 horizon designations have the following equivalencies: A2 = E, Bhir = Bs, Bir = Bs1, B23 and B23+ = Bs2. 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. An analysis of these data has been published in: Johnson, C.E., A.H. Johnson, T.G. Huntington, and T.G. Siccama. 1991. Whole-tree clear-cutting effects on soil horizons and organic-matter pools. Soil Science Society of America Journal. 55:497-502. https://doi.org/10.2136/sssaj1991.03615995005500020034x
Soil water and chloride concentration profiles in 20 playas at the Jornada Basin LTER site in 2014
This data package contains soil water and chloride content data from cores collected in 20 playas in the Jornada Basin of southern New Mexico, USA. The objectives of this work were to assess the rates and controls of groundwater recharge through playas in the Southwestern USA. The twenty playas were cored to 5 meter depth and we used the chloride mass balance (CMB) approach to empirically estimate groundwater recharge beneath playa surfaces. The measured variables are gravimetric soil moisture (kg water/kg soil), soil bulk density (kg/m3), volumetric water content (m3/m3), and porewater chloride (mg/L). All data were taken from playa ecosystems. All measurements were taken in June 2014. This study is complete. For further information, refer to: McKenna, Owen P., and Osvaldo E. Sala. "Groundwater recharge in desert playas: current rates and future effects of climate change." Environmental Research Letters 13, no. 1 (2018): 014025. https://doi.org/10.1088/1748-9326/aa9eb6
Soil inoculation alters leaf metabolic profiles in genetically identical plants
<p> Abiotic and biotic properties of soil can influence growth and chemical composition of plants. Although it is well-known that soil microbial composition can vary greatly spatially, how this variation affects plant chemical composition is poorly understood. We grew genetically identical <em>Jacobaea vulgaris</em> in sterilized soil inoculated with live soil collected from four natural grasslands and in 100% sterilized soil. Within each grassland we sampled eight plots, totalling 32 different inocula. Two samples per plot were collected, leading to three levels of spatial variation: within plot, between and within grasslands. The leaf metabolome was analysed with <sup>1</sup>H Nuclear magnetic resonance spectroscopy (NMR) to investigate if inoculation altered the metabolome of plants and how this varied between and within grasslands. Inoculation led to changes in metabolomics profiles of J. vulgaris in two out of four sites. Plants grown in sterilized and inoculated soils differed in concentrations of malic acid, tyrosine, trehalose and two pyrrolizidine alkaloids (PA). Metabolomes of plants grown in inoculated soils from different sites varied in glucose, malic acid, trehalose, tyrosine and in one PA. The metabolome of plants grown in soils with inocula from the same site was more similar than with inocula from distant sites. We show that soil influences leaf metabolomes. Performance of aboveground insects often depends on chemical composition of plants. Hence our results imply that soil microbial communities, via affecting aboveground plant metabolomes, can impact aboveground plant-insect food chains but that it is difficult to make general predictions due to spatial variation in soil microbiomes.</p> <div> <div> <div class="msocomtxt"> </div> </div> </div>
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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
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International Brain Laboratory public data
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OpenNeuro
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