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opencc-by-4.0Sep 2024View details →

Spatiotemporal prediction of soil organic carbon density (SOCD) for pan-Europe (2000-2022) in 3D+T

<h2>Sub-dataset: WPCT mean, 2000–2004</h2> <h2>Disclaimer</h2> This is the first release of pan-EU predictions of soil health indicators (the Soil Health Data Cube). Use for testing purposes only. A publication describing methods used has been submitted to PeerJ and is in review. Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or European Commision. Neither the European Union nor the granting authority can be held responsible for them. The data is provided "as is". AI4SoilHealth project consortium and its suppliers and licensors hereby disclaim all warranties of any kind, express or implied, including, without limitation, the warranties of merchantability, fitness for a particular purpose and non-infringement. Neither AI4SoilHealth project Consortium nor its suppliers and licensors, makes any warranty that the Website will be error free or that access thereto will be continuous or uninterrupted. You understand that you download from, or otherwise obtain content or services through, the Website at your own discretion and risk. <h2>Description</h2> This dataset covers pan-European areas, including Ukraine, the UK, and Turkey. This data cube could be used for applications such as soil property mapping and comprehensive soil health assessment across Europe. The dataset spans four depth ranges and multiple time periods, providing information for studies on soil organic carbon stock and dynamics. This dataset is part of the Spatiotemporal prediction of soil organic carbon density for Europe (2000-2022) in 3D+T dataset. Check the related identifiers section below to access other parts of the dataset. This data set includes: <ul> <li>Soil Organic Carbon Density (SOCD) (2000-2022, 4-year intervals): This data includes mean, p975, and p025 SOCD maps for four depth ranges (0-20cm, 20-50cm, 50-100cm, and 100-200cm) in kg/m3 (scaled 10x). </li> <li>Organic carbon content based on dry combustion weight percentage (WPCT) (2000-2022, 4-year intervals): This data includes mean, p975, and p025 WPCT maps for four depth ranges (0-20cm, 20-50cm, 50-100cm, and 100-200cm) in percentage. </li> </ul> <h3>Related identifiers</h3> <ul> <li>SOCD mean: <a href="https://zenodo.org/records/13754343">2000-2004</a> <a href="https://zenodo.org/records/13771721">2004-2008</a> <a href="https://zenodo.org/records/13771841">2008-2012</a> <a href="https://zenodo.org/records/13771911">2012-2016</a> <a href="https://zenodo.org/records/13771967">2016-2020</a> <a href="https://zenodo.org/records/13772054">2020-2022</a> </li> <li>SOCD p025: <a href="https://zenodo.org/records/13779539">2000-2004</a> <a href="https://zenodo.org/records/13774064">2004-2008</a> <a href="https://zenodo.org/records/13774089">2008-2012</a> <a href="https://zenodo.org/records/13774114">2012-2016</a> <a href="https://zenodo.org/records/13774167">2016-2020</a> <a href="https://zenodo.org/records/13774196">2020-2022</a> </li> <li>SOCD p975: <a href="https://zenodo.org/records/13778472">2000-2004</a> <a href="https://zenodo.org/records/13773396">2004-2008</a> <a href="https://zenodo.org/records/13773765">2008-2012</a> <a href="https://zenodo.org/records/13773828">2012-2016</a> <a href="https://zenodo.org/records/13773953">2016-2020</a> <a href="https://zenodo.org/records/13774003">2020-2022</a> </li> <li>WPCT mean: <a href="https://zenodo.org/records/13785010">2000-2004</a> <a href="https://zenodo.org/records/13785079">2004-2008</a> <a href="https://zenodo.org/records/13785170">2008-2012</a> <a href="https://zenodo.org/records/13785306">2012-2016</a> <a href="https://zenodo.org/records/13785419">2016-2020</a> <a href="https://zenodo.org/records/13785553">2020-2022</a> </li> <li>WPCT p025: <a href="https://zenodo.org/records/13786250">2000-2004</a> <a href="https://zenodo.org/records/13786314">2004-2008</a> <a href="https://zenodo.org/records/13786449">2008-2012</a> <a href="https://zenodo.org/records/13786565">2012-2016</a> <a href="https://zenodo.org/records/13786594">2016-2020</a> <a href="https://zenodo.org/records/13786702">2020-2022</a> </li> <li>WPCT p975: <a href="https://zenodo.org/records/13785722">2000-2004</a> <a href="https://zenodo.org/records/13785854">2004-2008</a> <a href="https://zenodo.org/records/13785917">2008-2012</a> <a href="https://zenodo.org/records/13786029">2012-2016</a> <a href="https://zenodo.org/records/13786119">2016-2020</a> <a href="https://zenodo.org/records/13786214">2020-2022</a> </li> </ul> <h3>Data Details</h3> <ul> <li>Time period: 2000–2022, in 4-year intervals (last period covers 2020–2022).</li> <li>Type of data: Spatiotemporal soil organic carbon data cube, with depth ranges and weighted percentage data for soil carbon assessments.</li> <li>How the data was collected or derived: The data was derived using machine learning models.</li> <li>Statistical methods used: Quantile Random Forest</li> <li>Limitations or exclusions in the data: The dataset does not include data for Svalbard. </li> <li>Coordinate reference system: EPSG:3035</li> <li>Bounding box (Xmin, Ymin, Xmax, Ymax): (900,000, 899,000, 7,401,000, 5,501,000)</li> <li>Spatial resolution: 30m</li> <li>Image size: 216,700P x 153,400L</li> <li>File format: Cloud Optimized Geotiff (COG) format.</li> </ul> <h3>Support</h3> If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue: GitLab Issues (tbc) <h3>Name convention</h3> To ensure consistency and ease of use across and within the projects, we follow the standard Ai4SoilHealth and Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describe important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are: <ol> <li>generic variable name: oc = organic carbon</li> <li>variable procedure combination: iso.10694.1995.mg.cm3 = ISO method 10694:1995, with values in mg/cm3 for SOCD | iso.10694.1995.wpct = ISO method 10694:1995, with values in weighted percentage of organic carbon content.</li> <li>Position in the probability distribution/variable type: m = mean | p975 = percentile 97.5 | p025 = percentile 2.5</li> <li>Spatial support: 30m</li> <li>Depth reference: b0cm..20cm = depth range from 0 to 20cm</li> <li>Time reference begin time: 20000101 = 2000-01-01</li> <li>Time reference end time: 20041231 = 2004-12-31</li> <li>Bounding box: eu = pan-Europe</li> <li>EPSG code: epsg.3035</li> <li>Version code: v20240804 = version from 2024-08-04</li> </ol>

opencc-by-4.0Dec 2021View details →

Soil biological, chemical and physical parameters and herbage yield in a field experiment with organic and inorganic fertilizers on peat grassland in the Netherlands

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.   Experimental setup The field experiment (2013 – 2015) was conducted on a permanent grassland on peat soil (Terric Histosol; SOM 56 g 100 g−1 and pHKCl 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−1 yr−1, 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 Ctotal applied in Comp was taken for the rate of sawdust to be applied. All plots were fertilized with 200 kg K2O ha−1 yr−1 (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). 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.   Measurements 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.   Soil biological parameters 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. 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.   Soil chemical parameters 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 (pHKCl), soil organic matter (SOM), total carbon (Ctotal), total nitrogen (Ntotal), total phosphorus (Ptotal) and ammonium-lactate extractable P (PAL) by Eurofins Agro (Wageningen, the Netherlands), the sub sample was dried at 40°C. Soil pHKCl was measured according to NEN-ISO 10390 2005. SOM was determined by loss-on-ignition (NEN 5754 2005). Ctotal was measured by incineration at 1150°C, and determination of the CO2 produced by an infrared detector (LECO Corporation, St. Joseph, Mich., USA). For Ntotal, evolved gasses after incineration were reduced to N2 and measured with a thermal-conductivity detector (LECO Corporation, St. Joseph, Mich., USA). Ptotal was analysed with Fleishmann acid (Houba et al., 1997). PAL 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).   Soil physical parameters 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. Penetration resistance was measured (April and October) with a penetrologger (Eijkelkamp, Giesbeek, the Netherlands; cone of 2.0 cm2 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. 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). 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−1).   Grass yield and botanical composition 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−1) and herbage N yield (kg N ha−1) were calculated. Apparent N recovery (ANR; kg N.kg N−1) was calculated as (N yield(fertilized) – N yield(non-fertilized))/(N fertilization rate) (Vellinga and André, 1999). 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).   Data files <ul> </ul>   Data_soil_grass.csv Content: Dataset with soil biological (earthworms, microbial PLFA), soil chemical, soil physical parameters, herbage dry matter and N yields, and botanical parameters. Column names and units: <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>   Fertilizer_input.csv Content: 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. Column names and units: <ul> <li>Treatment: Treatment code (see text)</li> <li>Fertilizer_fresh: Applied fertilizer in Mg.ha-1 per year (fresh weight)</li> <li>Fertilizer_DM: Applied fertilizer in Mg.ha-1 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-1 per year</li> <li>OM: Organic matter in kg.ha-1 per year</li> <li>C: Total C in kg.ha-1 per year</li> <li>Nmin: Mineral N in kg.ha-1 per year</li> <li>Norg: Organic N in kg.ha-1 per year</li> <li>P2O5: kg.ha-1 per year</li> <li>C_N_ratio: C:N ratio</li> </ul>

opencc-by-4.0Dec 2021View details →

Germination of crop species in response to whole-soil inoculants that originate from conventional vs organic farming systems

Dataset of manuscript entitled “Germination of crop species in response to whole-soil inoculants that originate from conventional vs organic farming systems”. This manuscript includes the results of WP2 from the SOFT project (ref. 890874).

opencc-by-4.0Dec 2021View details →

Soil extracellular enzyme activity increases during the transition from conventional to organic farming

Dataset of manuscript entitled “Soil extracellular enzyme activity increases during the transition from conventional to organic farming”. This manuscript includes the results of WP1 from the SOFT project (ref. 890874).

opencc-by-4.0Dec 2018View details →

ELABORATION OF THE ITALIAN PORTION OF THE GLOBAL SOIL ORGANIC CARBON MAP (GSOCMAP)

The Global Soil Organic Carbon map (GSOCmap) published by the Food and Agriculture Organization constitutes a baseline estimation of soil organic carbon stock (CS, ton ha–1) from 0 to 30 cm, on a grid at 30 arc-seconds resolution (approximately 1 x 1 km). It has been produced for the Italian territory by the Italian Soil Partnership (ISP): a national hub of institutions dealing with soils, either academic/research institutions, and regional soil services (RSS). The RSS are the main soil data owners in Italy and play a central role in the elaboration of policies for soil management. The RSS adhering to the ISP are: Calabria, Campania, Emilia Romagna, Friuli Venezia Giulia, Liguria, Lombardia, Marche, Piemonte, Puglia, Sicilia, Toscana, and Veneto. A national soil database is maintained by the Consiglio per la Ricerca e l'Analisi dell'Economia Agraria (CREA). The RSS contributed with soil data, with mean density of 1 point per 50 square kilometres, selecting data analysed for soil organic carbon content (SOC, dag kg-1), which were representative and well distributed for the following environmental covariates: land use, geomorphology, and climate. The data were selected inbetween 1990 al 2013. This was necessary in order to exclude the effect of the new soil protection policies of the Rural Development Programme 2014-2020. For the RSS not included in the ISP, the data were selected from the national soil database. 6748 point data were finally selected. SOC values obtained with the Springer and Klee and flash combustion elemental analyser methods were retained for elaborations, because the 2 methods, were found to give statistically equivalent results. SOC values obtained with Walkey and Black method were, instead, corrected with an empirical factor of 1.3. 2292 of the 6748 point data had also measured bulk density (BD, Mg m–3). Pedotransfer functions were calibrated to estimate BD were measured BD were missing, with the following as auxiliary variables: land use, soil regions, texture, and SOC. The carbon stock (CS, ton ha–1) was calculated by multiplying: 0.3 (m) * SOC (dag kg-1) * fine earth fraction (1 - skeletal content expressed as daL m–3) * BD (Mg m–3). CS of the first 30 cm depth was calculated as depth-weighted average. A spatial statistics method was used for the CS interpolation. The following auxiliary variables were used: soil regions, soil subregions, Corine land cover 2006, lithology, soils affected by natural constrains (gleyic, histic, vertic, coarse, shallow, arenic, sodic, and acid), sand content, silt content, 30-m aster-DEM, distance from coast, distance from relieves, soil aridity index, annual mean precipitations, mean annual air temperature, soil inorganic carbon, and soil depth. For the soil region of Po valley, the land units at 1:250,000 scale were also used. The interpolation method was a general linear regression for the soil regions of Po valley, and a radial basis function for the remaining Italian territory. The 6748 point data were divided, by spatial random sampling, into 10 subsets. Ten interpolations were produced, each time leaving out 1/10 of the dataset. Average (fig. 1), standard deviation and confidence intervals of these 10 interpolations were calculated. Mean Absolute Errors (MAE) and Root Mean Squared Errors (RMSE) were respectively 25.5 and 36.4 Mg/ha. A.85 Italy Map source: Country submission Point data Number of samples: 6748 Sampling period: 1990-2013 SOC analysis method: SOC values obtained with the Springer and Klee and ’flash combustion elemental analyser’ methods were retained for elaborations. Uncorrected values obtained by the Walkey and Black method were corrected with an empirical linear equation, based on previous studies and as recommended by the Italian official methods. BD analysis method: Undisturbed sampling, core method and pit method Mapping method Mapping method details: Neural Networks and GLM, according to soil region Validation statistics: Mean Error (ME) of the prediction is 1.688 Mg/ha, MAE 25.57 Mg/ha, Root Mean Squared Error (RMSE) is 36.24 Mg/ha. Contact Data Holder: Research centre for agriculture and environment Contact: CREA Consiglio per la ricerca in agricoltura e l’analisi dell’economia agraria edoardo.costantini@crea.gov.it

opencc-by-4.0Jun 2023View details →

Seasonal controls override forest harvesting effects on the composition of dissolved organic matter mobilized from boreal forest soil organic horizons

Dataset comprised of nutrient fluxes (DOC, TDN, NH4, TDN and SRP), optical parameters related to DOM composition (SUVA, spectral slopes and slope ratio), pH, and other nutrient and elemental ratios for passive pan lysimeters installed across terrestrial sites in Pynn's Brook, Newfoundland.

openCC0Aug 2022View details →

Nevada Desert FACE Facility Soil Organic Carbon Data

This data set is the result of soils analysis from the Nevada Desert Free-Air CO2 Enrichment Facility (NDFF) experiment in the Mojave Desert and reports soil organic carbon (%C) and delta 13C stable isotope values. These soils were collected at the end of the NDFF experiment in 2007 and stored at Cornell University until analysis in 2018. Soils were harvested from 6 cover types (5 perennial vegetation covers and unvegetated interspace soils) from 0-100 cm in the soil profile in 20 cm increments. Soils were pretreated for inorganic carbon removal using an acid fumigation technique with HCl. Bulk density from NDFF plots is provided (kg soil* ha ^ -1) so that SOC stocks may be calculated. These data provide the basis for a publication challenging the prevailing idea that arid ecosystems will increase soil organic carbon stocks under long term elevated CO2.

openCC (other)Dec 2022View details →

Organic and inorganic carbon concentration and stable isotope composition in poorly drained agricultural soils in Iowa, USA

We measured soil organic carbon (SOC) and inorganic carbon (carbonate) in samples collected along topographic gradients in agricultural fields in Iowa, USA, in 2018. We also measured stable isotopes of SOC, soil nitrogen, and carbon in respired CO2 to provide additional context for organic matter dynamics. Additional physical, chemical, and hydrologic variables were measured on these samples and in the field sites to understand mechanisms underlying patterns in soil organic and inorganic carbon.

openCC (other)Jun 2023View details →

Effects of drying temperature on potential carbon mineralization and water-extractable organic carbon in Iowa cropland and riparian buffer soils

Measuring carbon dioxide (CO2) produced after re-wetting a previously dried soil is an increasingly popular soil health assay, but there is disagreement on the optimal soil drying temperature. We tested whether soil drying temperature impacts water-extractable organic carbon (WEOC) and soil CO2 emissions (potential carbon mineralization) following rewetting of dried soil. Samples were collected at four sites in north-central Iowa, US, and each site had soils planted to corn/soybean or perennial vegetation. The dataset includes measurements of WEOC prior to the incubation experiment, and measurements of CO2 flux and its stable carbon isotope ratio over the course of a 28-day incubation. The manuscript describing these data is under review in Geoderma.

openCC (other)May 2024View details →

Fungal traits associated with soil organic matter formation, Harvard Forest, Petersham MA, 2020-2023

Soil microbes are a major source of organic residues that accumulate as soil organic matter (SOM), the largest terrestrial reservoir of carbon on Earth. As such, there is growing interest in determining the microbial traits that drive SOM formation and stabilization; however, whether certain microbial traits consistently predict SOM accumulation across different functional pools (e.g., total vs. stable SOM) is unresolved. To address these uncertainties, we incubated individual species of fungi in SOM-free model soils, allowing us to directly relate the physiological, morphological, and biochemical traits of fungi to their SOM formation potentials. We find that the formation of different SOM functional pools is associated with distinct fungal traits, and that ‘multifunctional’ species with intermediate investment across this key grouping of traits (namely, carbon use efficiency, growth rate, turnover rate, and biomass protein and phenol contents) promote SOM formation, functional complexity, and stability. Our results highlight the limitations of categorical trait-based frameworks that describe binary (high/low) trade-offs between microbial traits, instead emphasizing the importance of synergies among microbial traits for the formation of functionally complex SOM.

openOpenDec 2015View details →

Soil aggregate size distribution and particulate organic matter content from Arctic LTER moist acidic tundra nutrient addition plots, Toolik Field Station, Alaska, sampled July 2011.

Soil aggregate size distribution, aggregate carbon and nitrogen, and light fraction carbon were determined for mineral soils in moist acidic tundra. Soil was sampled in control, and N+P plots of the Arctic LTER Moist Acidic Tundra plots established in 1989 and 2006.

Photo-oxidation and photomineralization apparent quantum yield dataset for dissolved organic carbon leached from permafrost soils collected from the North Slope of Alaska, July 2018.

Dissolved organic carbon (DOC) was leached from permafrost soils near the Toolik Field Station in the Alaskan Arctic and then characterized for its photochemical properties. Oxygen (O2) consumed from photo-oxidation of permafrost DOC was measured as a function of sunlight wavelength, defined as the apparent quantum yield spectrum of photo-oxidation (O2 consumed per mol photon absorbed by DOC). Carbon dioxide (CO2) produced from photomineralization of permafrost DOC was measured as a function of sunlight wavelength, defined as the apparent quantum yield spectrum of photomineralization (CO2 produced per mol photon absorbed by DOC).

openOpenMar 2003View details →

Tanana River Floodplain Dissolved Organic Nitrogen (DON) Budget, extracted soil protein content

Ammonium, Nitrate, and Amino Acid concentrations in .5M K2SO4 extracted T0 Tanana Floodplain soils Ammonium, Nitrate, and Amino Acid concentrations in .5M K2SO4 extracted T30 Tanana Floodplain soils Sodium Bicarbonate Extracted Tanana River Floodplain Soil Protein Concentrations, 2001 Plot locations for the Tanana River Floodplain DON buget study. GPS coordinates for the Tanana River Floodplain Dissolved Organic Nitrogen (DON) Budget Study plots All values given are in micrograms of bovine serum albumin equivalents per gram dry weight. Site designations in parenthesis are specific to the study and represent individual transects,each within a unique stand of the following standtypes: W=willow,A=alder,BP=balsam poplar,4=white spruce,5=black spruce. Months: 6=June,7=July,etc.

openOpenDec 2015View details →

Post-fire succession in 1994 Hajdukovich Creek burn: Measurements of soil organic layer depth

This dataset contains soil organic layer depth measurements collected in July 2009 from the 1994 Hajdukovich Creek Burn.

openOpenDec 2020View details →

Measurement of Fuel load (organic biomass) Quantities of Organic Soils, Understory Plants and Trees at Sites within the Bonanza Creek LTER Regional Site Network in Interior Alaska, 2019

This dataset contains fuel load measurements for a subset of the BNZ LTER's RSN sites (n = 28). The data for each site separted by 16 fuel types (or plant functional types) including organic soil layers (fibric, mesic), vascular understory (evergreen shrub, short deciduous shrub, graminoid, forb, tall deciduous shrub, tree seedling/sapling, dead-downed wood), nonvascular understory (feather moss, Sphagnum moss, colonizer moss, lichen) and trees (evergreen tree, deciduous tree).

openCC0Oct 2021View details →

Soils Organic Matter: Biodiversity II: Effects of Plant Biodiversity on Population and Ecosystem

Biodiversity II (E120) is designed to determine how the number of plant species affects the dynamics of ecological processes at the population, community, and ecosystem levels. By experimentally manipulating the number of species and the kinds of species, the amount of plant growth and the change from year to year, that result can be examined. Plots are large (9m x 9m actively maintained) and well-replicated, allowing responses of plant pathogens, insect herbivores, seed predators, soil parameters, invasive plant species and other variables to also be studied. Plots were seeded in May 1994 to have 1, 2, 4, 8, or 16 species, with roughly 30 replicates of each diversity level. The species composition of each plot was chosen by random draw from a pool of 18 grassland perennials that included four warm-season (C4) grasses, four cool-season (C3) grasses, four legumes, four non-legume forbs, and two woody species. All species occur in monoculture allowing comparison of responses of each species in monoculture to combinations of these same species. The experiment was established in 1994 by the lead investigators David Tilman, Peter Reich, Johannes Knops, and David Wedin. Experiment 120 is similar to Experiment 123, but it uses larger plots to provide a large capacity for long-term subexperiments.

Geum and Kobresia soil inorganic and organic property data for Saddle and North of Tvan, 1993.

This study was initiated to examine bulk density and organic matter content differences between soils where Geum (Acomastylis) rossii and Kobresia myosuroides were present. A subset of samples were also measured for total C, N, and P. Paired plots were randomly selected in locations where Kobresia and Geum populations were adjacent to one another. Soil samples were collected from 35 plots, 22 and 13 of which had southerly and northerly aspects, respectively. Soil cores were removed with 3.5-cm interior diameter PVC pipe that was driven into the tundra by use of a rubber mallet. The minimum depth of individual cores was 9 cm, and these depths were recorded at the time of removal (15 July, 19 July, 22 July, and 9 August 1993).

openCC (other)Oct 2019View details →

Krummholz island soil inorganic and organic property data for East of Tvan, 1995 - 1996.

Previous work has shown that passage of Engelmann spruce (Picea engelmannii) and subalpine fir (Abies lasiocarpa) tree islands across tundra lowers the soil carbon and nitrogen storage capacity of the top 15cm of soil (A horizon) (Pauker and Seastedt 1996). This study shows that levels of KCl extractable ammonium and percent organic matter were also significantly higher in the A horizon of undisturbed tundra sites compared with soils underneath or immediately adjacent to (windward or leeward) the krummholz. The response of soil KCl extractable NO3- also showed this trend but was not statistically significant. Holtmeier and Broll (1992) suggested that the depletion of organics and nutrients following the passage of tree island may be associated with a reduced clay content. We analyzed a subsample of these soils for cation exchange capacity (CEC) and texture. We did not find significantly lower clay content in soils under or adjacent to krummholz compared with those from undisturbed tundra. In fact, percent clay was greater in krummholz and windward sites than in tundra sites. The percent clay of windward sites was significantly greater in windward soils than either krummholz or tundra soils. Clearly, depletion of organics following the passage of tree islands does not appear to be associated with a depleted clay content. We found that CEC was very highly significantly correlated with percent organic content (using percent organic data only from the subset of soils on which CEC was measured). Therefore, the CEC content of these soils would appear to be strongly associated with the organic content but not with the clay content. Percent soil moisture was significantly higher directly underneath the krummholz compared with the other sites. There was no effect of treatment on pH. We also investigated whether the organic matter lost in association with krummholz colonization (i.e. 1m from the tree) was replenished as tundra vegetation recolonized in the wake of the tree is

Soil inorganic and organic property data for subalpine forest, treeline, and alpine zone, 1999.

This study was initiated to examine the nitrogen content of three montane soils: subalpine, treeline and alpine; and to determine if the differences in soil nitrogen content were attributed to plant community and elevation. Soil organic matter, soil carbon, bulk density, pH and soil moisture were also measured for each site. Soil samples were collected from 64 total plots [22 subalpine,15 treeline and 27 alpine sites]. The subalpine site plots included aspen, fir, lodgepole, spruce and meadow vegetation cover. The treeline site plots included fir, spruce and meadow vegetation cover. The alpine site plots included dry meadow and mesic meadow fertilization (control, N, P, NP) plots. Soil cores were removed with 3.5-cm interior diameter PVC pipe that was driven into the soil by use of a rubber mallet. The minimum depth of individual cores was 10 cm. Cores were taken at each site three times over the period between 29 June 1999 and 29 July 1999.

Baseline soil inorganic and organic property data for Saddle snowfence, 1993.

Soil cores were collected during the construction of the 100+year snowfence on the Niwot Ridge Saddle in the autumn of 1993. Organic matter determinations were made on the samples in January 1994. The samples were also measured for total phosphorus, nitrogen, and carbon. These 1993 samples were representative of the baseline (pre-snowfence) soil conditions.