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255 results for “field experiment”
Incentive mechanisms and the provision of public goods: Field experiment data for testing alternative economic frameworks to supply ecosystem restoration on Virginia's Eastern Shore: 2008 data.
This dataset consists of participant responses in one of two economic experiments conducted on Virginia's Eastern Shore during 2008 and 2009 by Elizabeth C. Smith used to gauge resident preferences and willingness-to-pay for ecosystem restoration activities. This dataset was designed to be used to examine a practical method to implement an individualized pricing approach to public good provision, grounded in Lindahl's marginal benefit theory. The study's focus was on ecosystem valuation and market approaches that have potential to provide public goods, examining the potential to generate revenues for public goods from consumers. While willingness-to-pay measurement techniques have been used to assess preferences for many environmental goods, this research goes a step further to explore real money auctions that generate revenues sufficient to pay for restoration activities. The data from the field experiments conducted in coastal Virginia were used, along with laboratory experiment data, to evaluate the performance of auction mechanisms in generating revenues relative to potential (Hicksian) willingness to pay for marginal increments in public goods. The field execution of this experiment involved residents of Virginia's Eastern Shore and local public goods. This application involved half-acre increments of ecosystem restoration for sea grass habitat in coastal lagoons, plantings for migratory bird habitat, and, in some auctions, clam-based increments of water quality services, defined as delaying the harvest of clams for six months beyond normal harvest by an existing aquaculture firm. To perform these tasks, participants were provided a budget, between $90 and $150. The auctioneer described for participants the ecosystem services that may result from additional ecosystem restoration associated with each activity. The actual levels of ecosystem restoration provided were based on aggregate offers reaching a pre-determined (but unknown to the participants) provision p
Incentive mechanisms and the provision of public goods: Field experiment data for testing alternative economic frameworks to supply ecosystem restoration on Virginia's Eastern Shore: 2009 data.
This dataset consists of participant responses in one of two economic experiments conducted on Virginia's Eastern Shore during 2008 and 2009 by Elizabeth C. Smith used to gauge resident preferences and willingness-to-pay for ecosystem restoration activities. This dataset was designed to be used to examine a practical method to implement an individualized pricing approach to public good provision, grounded in Lindahl's marginal benefit theory. The study's focus was on ecosystem valuation and market approaches that have potential to provide public goods, examining the potential to generate revenues for public goods from consumers. While willingness-to-pay measurement techniques have been used to assess preferences for many environmental goods, this research goes a step further to explore real money auctions that generate revenues sufficient to pay for restoration activities. The data from the field experiments conducted in coastal Virginia were used, along with laboratory experiment data, to evaluate the performance of auction mechanisms in generating revenues relative to potential (Hicksian) willingness to pay for marginal increments in public goods. The field execution of this experiment involved residents of Virginia's Eastern Shore and local public goods. This application involved half-acre increments of ecosystem restoration for sea grass habitat in coastal lagoons, plantings for migratory bird habitat, and, in some auctions, clam-based increments of water quality services, defined as delaying the harvest of clams for six months beyond normal harvest by an existing aquaculture firm. To perform these tasks, participants were provided a budget, between $90 and $150. The auctioneer described for participants the ecosystem services that may result from additional ecosystem restoration associated with each activity. The actual levels of ecosystem restoration provided were based on aggregate offers reaching a pre-determined (but unknown to the participants) provision p
Nutrient amendment effects on phytoplankton, water chemistry, and cyanotoxins in the 2018 Large-Scale Mesocosm Experiment at the University of Kansas Field Station
This dataset includes water physicochemical parameters, phytoplankton community composition, and cyanobacteria metabolites collected during a 21-day nutrient amendment experiment conducted from 23 July to 13 August 2018 at the University of Kansas Biological Station, Lawrence, KS, United States (39.049674°N, 95.190777°W). The experiment was performed using 18 large-scale, closed-bottom fiberglass tanks (volume: 11,000 L; height: 1.25 m; diameter: 3 m). Three tanks served as ambient controls (CON), while the others received one of the following nutrient treatments: nitrogen only (280 µM) as either ammonium chloride (NH4) or sodium nitrate (NO3); nitrogen (280 µM) plus phosphorus (200 µM) as either ammonium chloride + dipotassium phosphate (NHP) or sodium nitrate + dipotassium phosphate (NOP); and phosphorus only (200 µM) as dipotassium phosphate (P). Each tank received an initial nutrient dose on Day 0.5, followed by weekly additions of 20% of the initial amendment to maintain treatment conditions. All data were quality controlled to correct basic errors and to remove measurements outside the manufacturer’s standard operational ranges.
Carbon and nitrogen isotopes and concentrations in terrestrial plants from a six-year (2006-2012) fertilization experiment at the Arctic LTER, Toolik Field Station, Alaska.
The data set describes stable carbon and nitrogen isotopes and carbon and nitrogen concentrations from an August 2012 pluck of a fertilization experiment begun in 2006. Fertilization was with nitrogen (N) and phosphorus (P). Fertilization levels included control, F2, F5, and F10, with F2 corresponding to yearly additions of 2 g/m2 N and 1 g/m2 P, F5 corresponding to yearly additions of 5 g/m2 N and 2.5 g/m2 P, and F10 corresponding to yearly additions of 10 g/m2 N and 5 g/m2 P. After harvest, plants were separated by species and then by tissue. Tissues were then dried, ground and analyzed for stable isotopes and concentrations at the University of New Hampshire stable isotope laboratory.
Block summaries of biomass, carbon, nitrogen, and phosphorus allocation among tissue types, species, and plant functional types from Arctic LTER 1981 Moist Acidic Tussock (MAT81) long-term experiment harvests: 2000 and 2015, Toolik Lake Field Station, Alaska.
A complete accounting of biomass, C, N, and P allocation both among tissue types (leaves, stems, rhizomes, roots) and among species and plant functional types from Arctic LTER 1981 Moist Acidic Tussock (MAT81) long-term experiment’s untreated control plots and plots that were fertilized annually, harvested after 20 and 35 years, near Toolik Lake Field Station, Alaska. Data are gram per meter squared summarized by block.
Infrasound array data recorded in July-August, 2019, at Mt. Etna (Italy) during the VOSSIA field experiment
<p>We present infrasound data recorded by two infrasound arrays installed at Mt. Etna (Italy) within the framework of the VOSSIA (Volcanic emissions analysis through Seismic and Infrasound Advanced monitoring) project. VOSSIA was supported by the Trans-National Access component of the EUROVOLC project (European Network of Observatories and Research Infrastructures for Volcanology, EU Horizon 2020 Research Infrastructure Project grant No 731070).</p> <p>This data repository includes continuous raw waveforms recorded by two 6-element, small-aperture, infrasound arrays during July-August, 2019. The arrays, ENEA and ENCR, were installed on Mt. Etna in proximity of the summit Nord East and South East craters, respectively. ENEA was equipped with Chaparral M60 sensors (<a href="http://chaparralphysics.com/specs/specs_model60UHP.pdf">http://chaparralphysics.com/specs/specs_model60UHP.pdf</a>), while IST2018 microphones (<a href="https://doi.org/10.1016/j.jvolgeores.2019.106668">https://doi.org/10.1016/j.jvolgeores.2019.106668</a>) were installed at ENCR. Data at both arrays were recorded with a sampling frequency of 100 Hz and 24-bit resolution using DiGOS Datacube<sup>3</sup> digitizers (<a href="https://digos.eu/seismology-and-cubes/">https://digos.eu/seismology-and-cubes</a>).</p> <p>Waveform data are provided as day-long files in MSEED format (<a href="https://ds.iris.edu/ds/nodes/dmc/data/formats/">https://ds.iris.edu/ds/nodes/dmc/data/formats/</a>).</p> <p>We also provide metadata including:</p> <p>1) Station coordinates (.csv file station_coords.csv);</p> <p>2) Instrument_response.rar: Instrument response information in different formats (individual RESP files, SEED DATALESS, xlm DATALESS)</p>
Binaural room scanning files for sound field synthesis localization experiment
<p>Binaural room scanning files that were used together with the SoundScape Renderer to perform the localization experiments described in Wierstorf [1].</p> <p>The results of the corresponding listening experiments are summarized in Fig. 5.4, see https://github.com/hagenw/phd-thesis/tree/master/05_psychoacoustics/fig5_04</p> <p>[1] H. Wierstorf, Perceptual Assessment of Sound Field Synthesis, PhD dissertation, TU Berlin, 2014.</p>
Listening test results for sound field synthesis localization experiment
<p>Result files from the the localization experiments described in section 5.1 of Wierstorf [1].</p> <p>The results are visually summarized in Fig. 5.4, see https://github.com/hagenw/phd-thesis/tree/master/05_psychoacoustics/fig5_04</p> <p>[1] H. Wierstorf, Perceptual Assessment of Sound Field Synthesis, PhD dissertation, TU Berlin, 2014.</p>
A global dataset gathering 37 field experiments involving cereal-legume intercrops and their corresponding sole crops.
<p>The overall description of the dataset is reported in the <strong>data_report.pdf</strong> file. The methodology for data curation and tidying is published in Peer Community Journal (<a href="https://doi.org/10.24072/pcjournal.389">Mahmoud2024</a>).</p> <p>This dataset gathers the results of 37 field experiments, which involved cereal-legume intercrops and their corresponding sole crops. The field experiments were carried in 5 European countries (France, Denmark, Italy, Germany and England) from 2001 to 2017. The dataset includes:</p> <ul> <li>5 legume species , <em>i.e.</em> chickpea (<em>Cicer arietinum</em> L.), faba bean (<em>Vicia faba</em> L.), lentil (<em>Lens culinaris</em> Med.), lupin (<em>Lupinus albus</em> L.) and pea (<em>Pisum sativum</em> L.),</li> <li>3 cereal species, <em>i.e.</em> barley (<em>Hordeum vulgare</em> L.), durum wheat (<em>Triticum turgidum</em> L.) and soft wheat (<em>Triticum aestivum</em> L.), </li> <li>8 resulting intercrops, <em>i.e.</em> i) barley associated with faba bean, lupin or pea, ii) durum wheat associated with chickpea, faba bean or pea, and iii) soft wheat associated with lentil or pea. </li> </ul> <p>In total, the dataset contains 299 sole crop and 308 intercrop experimental units, one given experimental unit being defined as the unique combination of {site, year, crop management}, with the crop management including species and cultivar choice as well as agricultural interventions (sowing conditions, inputs).</p> <p>The global dataset includes four tables, all sharing a common identifier (experiment_id):</p> <ul> <li>data_trials.csv: the global features describing the experimental sites,</li> <li>data_management.csv: the agricultural management actions carried out on each of the experimental sites,</li> <li>data_traits.csv: measured plant and crop characteristics,</li> <li>data_climate.csv: climate for the experimental sites, retrieved from NASA POWER API.</li> </ul> <p>Additionally, a metadata file is provided (<strong>metadata.xlsx</strong>), describing the table to which the variables belong (variable_type, i.e. trials, management, traits or climate), their name (variable_name), their significance (description) and their unit (unit). Finally, a table including the original references related to experimental files gathered (<strong>references.xlsx</strong>) is also provided.</p> <p>Data providers and field experiments: Laurent Bedoussac, Eric Justes, Etienne-Pascal Jour- net, Christophe Naudin, Henrik Hauggaard-Nielsen, Erik Steen Jensen, Elise Pelzer, Guénaëlle Corre-Hellou, Bochra Kammoun, Loic Viguier, Romain Barillot, Antoine Couëdel, Philippe Hinsinger</p> <p>Database and management: Noémie Gaudio, Rémi Mahmoud, Pierre Casadebaig</p>
Nash's Field grassland experiment Silwood Park, UK
<p>Nash's Field is one of the field experiments of Imperial College London, Silwood Park campus and is part of The Ecological Continuity Trust (<a href="https://www.ecologicalcontinuitytrust.org/" target="_blank" rel="noopener">ECT</a>). The experiment is a long-term study that aims to understand the degree to which nutrients, soil acidity and herbivory affect grassland ecology. It is a five-factor factorial experiment replicated in two blocks of plots using a split-plot design in a neutral grassland (MG5 Cynosurus cristatus/Centaurea debeauxii, under the UK National Vegetation Classification system). Overall, the experiment contains 8 invertebrate exclusion plots (± insects and ± molluscs, 22 x 44 m), 16 vertebrate exclusion plots (± rabbits, 22 x 22 m), 32 soil acidity plots (high vs low pH, 8 x 18 m), 96 plant competition plots (± grasses, ± herbs, 6 x 8 m) and 1,152 fertilization plots (12 combinations of N, P, K and Mg, 2 x 2 m). Except for herbicides, which were used only at the start of the experiment, all treatments have been applied continuously since 1992. Data of aboveground biomass or coverage per plant species of all herbaceous plants present has been collected annually for several years from 1992.</p>
Soil respiration from a mycorrhizal and root exclusion experiment at Toolik Lake Field Station and Anaktuvuk River Burn, Alaska in 2016
Organic soil from either the Anaktuvik severe burn or Toolik Lake were collected to test of effect of removal of mycorrhizae on decompositon of tundra at Toolik Lake and the Anaktuvuk Burn IN 2016. A licor 6400 with 6400-09 soil respiration chamber was used to measure soil respiration (efflux) from the cores on a weekly basis.
Factorial experiment to test effects of food availability and temperature on slimy sculpin (Cottus cognatus) at Toolik Field Station, 2018
We used a fully factorial experiment to test effects of food availability and temperature (7.6, 12.7 and 17.4 degrees C; 50 days) on growth, consumption, respiration, and excretion of slimy sculpin (Cottus cognatus).
Eriophorum tiller length in simulated herbivory experiment in moist acidic tundra experimental plots, Arctic LTER, Toolik Field Station, North Slope Alaska, from 2018 to 2021.
Tiller length of Eriophorum vaginatum subjected to fertilization and simulated herbivory from 2018 until 2021. Plants were part of a fertilization experiment begun in 2006 and included four levels of nutrient addition. For the simulated herbivory experiment, plants were not clipped, clipped once in 2018, or clipped every year of the experiment.
Main Cropping System Experiment Field Logs and treatment descriptions at the Kellogg Biological Station, Hickory Corners, MI (1988 to 2020)
Dataset Abstract This dataset includes information about the LTER main site treatments, agronomic practices carried out on the treatments and approved site use requests. Most long-term hypotheses associated with the KBS LTER site are being tested within the context of the main cropping systems study. This study was established on a 48 ha area on which a series of 7 different cropping systems were established in spring 1988, each replicated in one of 6 ha blocks. An eighth never-tilled successional treatment, is located 200 m off-site, replicated as four 0.06 ha plots. Cropping systems include the following treatments: T1. Conventional: standard chemical input corn/soybean/wheat rotation conventionally tilled (corn/soybean prior to 1992) T2. No-till: standard chemical input corn/soybean/wheat rotation no-tilled (corn/soybean prior to 1992) T3. Reduced input: low chemical input corn/soybean/wheat rotation conventionally tilled (ridge till prior to 1994) T4. Biologically based: zero chemical input corn/soybean wheat rotation conventionally tilled (ridge till prior to 1994) T5. Poplar: Populus clones on short-rotation (6-7 year) harvest cycle T6. Alfalfa: continuous alfalfa, replanted every 6-7 years (converted to switchgrass in 2018) T7. Early successional community: historically tilled soil T8. Mown grassland community: never-tilled soil. For specific crops in a given year see the Annual Crops Summary Table. In 1993 a series of forest sites were added to the main cropping system study to provide long-term reference points and to allow hypotheses related to substrate diversity to be tested. These include: TCF. Coniferous forest: three conifer plantations, 40-60 years old TDF. Decidious forest: three deciduous forest stands, two old-growth and one 40-60 years post-cutting TSF. Mid-successional forest: three old-field (mid-successional) sites 40+ years post-abandonment. All share a soil series with the main cropping system treatments, and are within 5 km of all other sit
Listening test results for sound field synthesis localization experiment -- head movement data
<p>This data set contains recorded head movements listeners did during several localisation tasks in the context of sound field synthesis. This is an add-on to the actual localisation results provided by [1].</p> <p>[1] Wierstorf, H. (2016). Listening test results for sound field synthesis localization experiment [Data set]. Zenodo. http://doi.org/10.5281/zenodo.55439</p>
Periodic Hydraulic Testing Dataset for "Borehole-based fracture unclogging experiment: bridging the gap between laboratory- and field-scale evidence (FRANC)"
<p>This dataset is associated with the SNSF-SPARK project “Borehole-based fracture unclogging experiment: bridging the gap between laboratory- and field-scale evidence (FRANC)”. Please read the ReadMe file for more information.</p>
Data from the field experiment on katabatic winds on a steep slope (Grand Colon, French Alps), February 2019
<p>These are the data from the field experiment described in the paper 'Katabatic winds over steep slopes: overview of a field experiment designed to investigate slope-normal velocity and near surface turbulence' by CHARRONDIERE, C., BRUN, C., COHARD, J.M., SICART, J.E., OBLIGADO, M., BIRON, R., COULAUD, C. & GUYARD, H. (2022), Boundary-Layer Meteorol. 187, 29-54.</p> <p> </p>
GATEMAN project -- D8.2 -- Field experiments raw data
<p>This dataset contains a .zip file (RawData.zip) with some of the raw files recorded during the open-field experimentation campaign in the frame of the GATEMAN project. These files are grouped for each type of validation scenario defined. The scenarios are separated in jamming and spoofing cases (JAM:Jamming, SPO:Spoofing) and classified according to the zone (Z1: Zone 1, Z2: Zone 2) and the vehicle motion (S:Static, D:Dynamic).</p> <p>Also the reference document of the deliverable D8.2 is included here.</p> <p><strong>Dataset Structure:</strong></p> <p>├── <strong>Jamming</strong><br> ├── OF-JAM-Z2-D-Test 11<br> └── Test_11.bin<br> ├── OF-JAM-Z2-D-Test 12<br> └── Test_12.bin</p> <p> ├--─ OF-JAM-Z2-D-Test 13<br> └── Test_13.bin<br> ├── GNURadio<br> └── SingleTone.grc</p> <p>├── <strong>Spoofing</strong><br> ├── OF-SPO-Z2-S-Test 1<br> └── Master_Test1.sbf<br> └── Slave_Test1.sbf</p> <p> ├── OF-SPO-Z2-S-Test 5<br> └── Master_Test5.sbf<br> └── Slave_Test5.sbf</p> <p> ├── OF-SPO-Z2-D-Test 2<br> └── Master_Test2.sbf<br> └── Slave_Test2.sbf</p> <p> ├── OF-SPO-Z2-D-Test 4<br> └── Master_Test4.sbf<br> └── Slave_Test4.sbf</p> <p> ├── OF-SPO-Z2-D-Test 6<br> └── Master_Test6.sbf<br> └── Slave_Test6.sbf</p> <p> ├── OF-SPO-Z1-D-Test 29<br> └── Master_Test29.sbf<br> └── Slave_Test29.sbf</p> <p> ├── GNURadio<br> └── Scenario1_decimated.iq<br> └── PreProcessing.grc<br> └── Signal_to_Usrp.grc</p> <p> </p>
Data for Rasch analysis of the three Field Practice Experiences Scales
<p>Data for Rasch analysis of the three Field Practice Experiences Scales. Data rare from Danish teacher education program students. Contains the following variables:</p> <p>Variables O1 to O12 are the items for the Observed scale</p> <p>Variables P1 to P12 are the items for the Practised scale</p> <p>Variables F1 to F12 are the items to the Received feedback scale</p> <p>Gender: 1 = female, 2 = male</p> <p>Campus: 1 = campus A, 2 = campus B</p> <p>T_Progr (teacher education program): 1 = regular, 2 = other</p> <p>P_level (level of latest field practice placement): 1 = 3rd level, 2 = 2nd level, 3 = 1st level</p> <p>Age: 1 = 24 years and younger, 2 = 25 years and older</p>
Soil biological, chemical and physical parameters and herbage yield in a field experiment with organic and inorganic fertilizers on peat grassland in the Netherlands
<p>To evaluate the performance of organic and inorganic fertilizers for regeneration of ecosystem services in peat grasslands with biodiversity goals, we carried out a field experiment in the western peat district in the Netherlands. The fertilizers tested represent the current practice and potential alternatives for regenerative grassland management on drained peat.</p> <p> </p> <p><strong>Experimental setup</strong></p> <p>The field experiment (2013 – 2015) was conducted on a permanent grassland on peat soil (Terric Histosol; SOM 56 g 100 g<sup>−1</sup> and pH<sub>KCl</sub> of 4.5 in 0-10 cm) at the experimental dairy farm at Zegveld (the Netherlands). In March 2013, a randomized block experiment (six blocks) was laid out with six fertilizer treatments and a control treatment (no fertilizer: “Contr”). The fertilizer used were: conventional dairy cattle slurry manure (“Slurry”), mature compost of kitchen and garden waste (“Comp”), dairy cattle farmyard manure (“FYM”), solid fraction of the cattle slurry manure (“SFrac”, obtained by pressurized filtration), inorganic N fertilizer (“IF”; calcium ammonium nitrate, 27% N) and a combination of inorganic N fertilizer and sawdust (“IF+SD”). Plot size was 4 × 10 m; for the Slurry treatment plots were 5.2 × 10 m. Slurry was applied by slit injection, the other fertilizers were applied by hand. Target application rate was 120 kg total N ha<sup>−1</sup> yr<sup>−1</sup>, divided in two applications per year (February/March and May). This is relatively low for conventional grasslands but usual for grasslands with biodiversity goals (Kleijn et al., 2004). The amount of C<sub>total</sub> applied in Comp was taken for the rate of sawdust to be applied. All plots were fertilized with 200 kg K<sub>2</sub>O ha<sup>−1</sup> yr<sup>−1</sup> (applications in March and May) (Commissie Bemesting Grasland en Voedergewassen, 2019). Fertilizer application quantities and organic matter and nutrient inputs are provided in Fertilizer_intput.csv (dataset).</p> <p>The grassland had an history of conventional management with mainly cutting, winter grazing with sheep and a normal fertilization regime with both slurry manure and inorganic fertilizer. The normal cutting and grazing regime was continued in the first two years of the experiment; during 2015, the monitoring year, the plots were not grazed and only cut for herbage measurements.</p> <p> </p> <p><strong>Measurements</strong></p> <p>From April to October 2015, soil and aboveground measurements were carried out. Most soil parameters were measured in October. Earthworms and insect larvae are an important food source for meadow birds during the pre-breeding period in spring (Galbraith, 1989) and were therefore sampled in April. Soil moisture and penetration resistance were measured both in April and October.</p> <p> </p> <p><em>Soil biological parameters</em></p> <p>Earthworms and insect larvae were sampled in the top soil layer in two soil cubes (20 × 20 × 20 cm) per plot. Earthworms were hand-sorted, counted, weighed and fixed in alcohol prior to identification. Both adults and juveniles were identified to species (Sims and Gerard, 1985; Stöp-Bowitz, 1969) and classified into functional groups (Bouché, 1977). Crane flies (Tipulidae; leatherjackets) or click beetles (Elateridae; wireworms) larvae were counted.</p> <p>Phospholipid fatty acids (PLFA) were measured in October. PLFA were extracted from 4 g of fresh soil (Palojärvi, 2006), and analyzed by gas chromatography (Hewlett-Packard, USA). PLFA i15:0, a15:0, 15:0, i16:0, 16:1ω9, i17:0, a17:0, cy17:0, 18:1ω7 and cy19:0 were chosen to represent bacteria and PLFA 18:2ω6 was used as a marker of saprotrophic fungi (Hedlund, 2002). The neutral lipid fatty acid (NLFA) 16:1ω5 occurs in storage lipids of arbuscular mycorrhizal fungi (AMF) and was used as marker of AMF (Vestberg et al., 2012). PLFA i15:0, a15:0, i16:0, i17:0 and a17:0 were used as a measure of Gram-positive bacteria, and cy17:0 and cy19:0 for Gram-negative bacteria. PLFA 10Me16:0, 10Me17:0 and 10Me18:0 represented actinomycetes.</p> <p> </p> <p><em>Soil chemical parameters</em></p> <p>A soil sample from the 0−10 cm layer (c. 50 randomly taken soil cores) per experimental plot was collected in October (auger diameter 2.3 cm; Eijkelkamp grass plot sampler, Giesbeek, the Netherlands), was sieved (1 cm mesh size) and homogenized. One sub-sample was taken for analysis of hot water extractable carbon (HWC) according to Ghani et al. (2003) and one for chemical analysis. Prior to analysis of soil acidity (pH<sub>KCl</sub>), soil organic matter (SOM), total carbon (C<sub>total</sub>), total nitrogen (N<sub>total</sub>), total phosphorus (P<sub>total</sub>) and ammonium-lactate extractable P (P<sub>AL</sub>) by Eurofins Agro (Wageningen, the Netherlands), the sub sample was dried at 40°C. Soil pH<sub>KCl</sub> was measured according to NEN-ISO 10390 2005. SOM was determined by loss-on-ignition (NEN 5754 2005). C<sub>total</sub> was measured by incineration at 1150°C, and determination of the CO<sub>2</sub> produced by an infrared detector (LECO Corporation, St. Joseph, Mich., USA). For N<sub>total</sub>, evolved gasses after incineration were reduced to N<sub>2</sub> and measured with a thermal-conductivity detector (LECO Corporation, St. Joseph, Mich., USA). P<sub>total</sub> was analysed with Fleishmann acid (Houba et al., 1997). P<sub>AL</sub> is used to assess the P supply capacity of grassland soils (Reijneveld et al., 2014) and was determined according to Egnér et al. (1960) (NEN 5793).</p> <p> </p> <p><em>Soil physical parameters</em></p> <p>Soil moisture was determined in April and October in a homogenized 0−10 cm soil sample after drying at 105°C for 24 hrs. Moisture content was expressed as percentage of fresh soil weight.</p> <p>Penetration resistance was measured (April and October) with a penetrologger (Eijkelkamp, Giesbeek, the Netherlands; cone of 2.0 cm<sup>2</sup> penetration surface and 60° apex angle. Penetration resistance was expressed as an average of 7 penetrations per plot and per soil layer of 0−10, 10−20, and 20−30 cm.</p> <p>Soil structure and rooting density were assessed in October in the 0−10 cm and 10−25 cm layers. The percentage of crumbs, sub-angular blocky elements and angular blocky elements was estimated by one experienced person as described by Peerlkamp (1959) and Shepherd (2000), Root density was estimated by scoring visible roots (score 1–10; 1 for no roots and 10 for above average).</p> <p>Water infiltration rate was measured in October at three spots per experimental plot in 5 of the 6 blocks (35 plots). A PVC pipe (15 cm high, 15 cm diameter) was pushed into the soil to a depth of 10 cm. 500 ml water was poured into each pipe and the infiltration time was recorded. If the infiltration time exceeded 15 min, the remaining water volume was estimated to calculate the infiltration rate (mm min<sup>−1</sup>).</p> <p> </p> <p><em>Grass yield and botanical composition</em></p> <p>Grass dry matter (DM) and N yield were determined during 2015 with a Haldrup plot harvester (J. Haldrup a/s, Løgstør, Denmark). The four harvest dates were May 15, June 29, August 19 and September 30. Fresh biomass, DM content (70°C for 24 hrs) and total N content (Kjeldahl) were determined for each harvest. Herbage DM yield (Mg DM ha<sup>−1</sup>) and herbage N yield (kg N ha<sup>−1</sup>) were calculated. Apparent N recovery (ANR; kg N.kg N<sup>−1</sup>) was calculated as (N yield<sub>(fertilized)</sub> – N yield<sub>(non-fertilized)</sub>)/(N fertilization rate) (Vellinga and André, 1999).</p> <p>In June 2015, botanical composition was measured by visually estimating the relative soil cover of the sward and the proportion of each species therein (Sikkema, 1997).</p> <p> </p> <p><strong>Data files</strong></p> <ul> </ul> <p> </p> <p><em><strong>Data_soil_grass.csv</strong></em></p> <p><em>Content:</em></p> <p>Dataset with soil biological (earthworms, microbial PLFA), soil chemical, soil physical parameters, herbage dry matter and N yields, and botanical parameters.</p> <p><em>Column names and units:</em></p> <ul> <li>plot: Experimental plot number (1-42)</li> <li>treatment: Treatment code (see text)</li> <li>block: Block number (1-6)</li> <li>EW_species_number: Earthworm - number of species</li> <li>EW_totalnumber: Earthworm - total number per m2</li> <li>EW_epigeic: Earthworm - number of epigeic adults and juveniles per m2</li> <li>EW_endogeic: Earthworm - number of endogeic adults and juveniles per m2</li> <li>EW_adults: Earthworm - number of adults per m2</li> <li>EW_juveniles: Earthworm - number of juveniles per m2</li> <li>EW_adult_epigeic: Earthworm - number of epigeic adults per m2</li> <li>EW_adult_endogeic: Earthworm - number of endogeic adults per m2</li> <li>EW_juven_epigeic: Earthworm - number of epigeic juveniles per m2</li> <li>EW_juven_endogeic: Earthworm - number of endogeic juveniles per m2</li> <li>EW_L_rubellus: Earthworm - number of L. rubellus adults and juveniles per m2</li> <li>EW_A_chlorotica: Earthworm - number of A. chlorotica adults and juveniles per m2</li> <li>EW_A_caliginosa: Earthworm - number of A. caliginosa adults and juveniles per m2</li> <li>EW_O_lacteum: Earthworm - number of O. lacteum adults and juveniles per m2</li> <li>EW_A_rosea: Earthworm - number of A. rosea adults and juveniles per m2</li> <li>EW_O_cyaenum: Earthworm - number of O. cyaneum adults and juveniles per m2</li> <li>EW_L_castaneus: Earthworm - number of L. castaneus adults and juveniles per m2</li> <li>EW_D_rubida: Earthworm - number of D. rubida adults and juveniles per m2</li> <li>EW_adult_L_rubellus: Earthworm - number of L. rubellus adults per m2</li> <li>EW_adult_A_chlorotica: Earthworm - number of A. chlorotica adults per m2</li> <li>EW_adult_A_caliginosa: Earthworm - number of A. caliginosa adults per m2</li> <li>EW_adult_O_lacteum: Earthworm - number of O. lacteum adults per m2</li> <li>EW_adult_A_rosea: Earthworm - number of A. rosea adults per m2</li> <li>EW_adult_O_cyaenum: Earthworm - number of O. cyaneum adults per m2</li> <li>EW_adult_L_castaneus: Earthworm - number of L. castaneus adults per m2</li> <li>EW_adult_D_rubida: Earthworm - number of D. rubida adults per m2</li> <li>EW_juven_L_rubellus: Earthworm - number of L. rubellus juveniles per m2</li> <li>EW_juven_A_chlorotica: Earthworm - number of A. chlorotica juveniles per m2</li> <li>EW_juven_A_caliginosa: Earthworm - number of A. caliginosa juveniles per m2</li> <li>EW_non_determined: Earthworm - number of non determined individuals per m2</li> <li>EW_total_biomass: Earthworm - total fresh biomass per m2</li> <li>Leatherjackets: number of leatherjackets per m2</li> <li>Wireworms: number of wireworms per m2</li> <li>TOTmicrPLFA: total microbial PLFA in nmol.g-1 dry soil</li> <li>bactPLFA: bacterial PLFA in nmol.g-1 dry soil</li> <li>saprofungPLFA: saprotrophic fungal PLFA in nmol.g-1 dry soil</li> <li>Fung_bactPLAF_ratio: ratio of fungal to bacterial PLFA</li> <li>GramPLUSplfa: gram positive PLFA in nmol.g-1 dry soil</li> <li>GramMINplfa: gram negative PLFA in nmol.g-1 dry soil</li> <li>ratioGram_PLUS_MIN: ratio of gram positive to gram negative PLFA</li> <li>AMFsporNLFA: AMF spores NLFA in nmol.g-1 dry soil</li> <li>ActinomPLFA: Actinomycetes PLFA in nmol.g-1 dry soil</li> <li>ShannonPLFA: PLFA shannon diversity index</li> <li>SOM: soil organic matter in g.100 g-1 dry soil</li> <li>Ctotal: total C in g.100 g-1 dry soil</li> <li>HWC: hot water extractable C in μg.100 g-1 dry soil</li> <li>Ntotal: total N in g.100 g-1 dry soil</li> <li>Ptotal: total P2O5 in mg.100 g-1 dry soil</li> <li>P_AL: total P-AL in mg.100 g-1 dry soil</li> <li>pH_KCl: pH-KCl</li> <li>CN_ratio: C:N ratio</li> <li>C_SOM: C:SOM ratio</li> <li>Soilmoisture_April: soil moisture content in April in g.100g-1 fresh soil</li> <li>Penetrationresistance_April_cm010: penetration resistance in April in 10-20 cm in Newton</li> <li>Penetrationresistance_April_cm1020: penetration resistance in April in 20-30 cm in Newton</li> <li>Penetrationresistance_April_cm2030: penetration resistance in April in 0-10 cm in Newton</li> <li>Soilmoisture_October: soil moisture content in October in g.100g-1 fresh soil</li> <li>Penetrationresistance_October_cm010: penetration resistance in October in 10-20 cm in Newton</li> <li>Penetrationresistance_October_cm1020: penetration resistance in October in 20-30 cm in Newton</li> <li>Penetrationresistance_October_cm2030: penetration resistance in October in 0-10 cm in Newton</li> <li>crumb_struct_cm010: percentage of crumb elements in 0-10 cm</li> <li>round_struct_cm011: percentage of sub-angular elements in 0-10 cm</li> <li>rootdensity_cm010: score (1-10) of root density in 0-10 cm</li> <li>crumb_struct_cm1025: percentage of crumb elements in 10-25 cm</li> <li>round_struct_cm1025: percentage of sub-angular elements in 10-25 cm</li> <li>sharp_struct_cm1025: percentage of angular elements in 10-25 cm</li> <li>rootdensity_cm1025: score (1-10) of root density in 10-25 cm</li> <li>water_infiltration: water infiltration rate in mm per minute</li> <li>DM_yield_year: total herbage dry matter yield in kg.ha-1 per year</li> <li>DM_yield_H1: herbage dry matter yield of harvest 1 in kg.ha-1</li> <li>DM_yield_H2: herbage dry matter yield of harvest 2 in kg.ha-1</li> <li>DM_yield_H3: herbage dry matter yield of harvest 3 in kg.ha-1</li> <li>DM_yield_H4: herbage dry matter yield of harvest 4 in kg.ha-1</li> <li>N_yield_year: total herbage N yield in kg.ha-1 per year</li> <li>N_yield_H1: herbage N yield of harvest 1 in kg.ha-1</li> <li>N_yield_H2: herbage N yield of harvest 2 in kg.ha-1</li> <li>N_yield_H3: herbage N yield of harvest 3 in kg.ha-1</li> <li>N_yield_H4: herbage N yield of harvest 4 in kg.ha-1</li> <li>DMperc_yield_year: herbage dry matter content (per year; weighed average over the 4 harvests) in g.100g-1 fresh weight</li> <li>DMperc_yield_H1: herbage dry matter content of harvest 1 in g.100g-1 fresh weight</li> <li>DMperc_yield_H2: herbage dry matter content of harvest 2 in g.100g-1 fresh weight</li> <li>DMperc_yield_H3: herbage dry matter content of harvest 3 in g.100g-1 fresh weight</li> <li>DMperc_yield_H4: herbage dry matter content of harvest 4 in g.100g-1 fresh weight</li> <li>Ncontent_yield_year: herbage N content (per year; weighed average over the 4 harvests) in g.kg-1 dry matter</li> <li>Ncontent_yield_H1: herbage N content of harvest 1 in g.kg-1 dry matter</li> <li>Ncontent_yield_H2: herbage N content of harvest 2 in g.kg-1 dry matter</li> <li>Ncontent_yield_H3: herbage N content of harvest 3 in g.kg-1 dry matter</li> <li>Ncontent_yield_H4: herbage N content of harvest 4 in g.kg-1 dry matter</li> <li>fresh_yield_H1: herbvage fresh yield of harvest 1 in Mg.ha-1</li> <li>ANR: apparent N recovery in kg N.kg N-1</li> <li>productive_grasses: cover percentage of L. perenne and P trivialis</li> <li>monocotyledons: cover percentage of monocotyledons</li> <li>dicotyledons: cover percentage of dicotyledons</li> <li>plant_species: number of plant species</li> <li>monocot_species: number of monocotyledon species</li> <li>dicot_species: number of dicotyledon species</li> <li>Lolium_perenne: plant cover %</li> <li>Poa_trivialis: plant cover %</li> <li>Phleum_pratense: plant cover %</li> <li>Elytrigia_repens: plant cover %</li> <li>Poa_annua: plant cover %</li> <li>Agrostis_stolonifera: plant cover %</li> <li>Holcus_lanatus: plant cover %</li> <li>Alopecurus_pratensis: plant cover %</li> <li>Alopecurus_geniculatus: plant cover %</li> <li>Trifolium_repens: plant cover %</li> <li>Taraxacum_officinale: plant cover %</li> <li>Ranunculus_arvensis: plant cover %</li> <li>Rumex_obtusifolius: plant cover %</li> <li>Rumex_crispus: plant cover %</li> <li>Ranunculus_acris: plant cover %</li> <li>Stellaria_media: plant cover %</li> <li>Cardamine_pratensis: plant cover %</li> <li>Bellis_perennis: plant cover %</li> <li>Rumex_acetosa: plant cover %</li> <li>Ranunculus_sceleratus: plant cover %</li> <li>Polygonum_aviculare: plant cover %</li> <li>Capsella_bursa-pastoris: plant cover %</li> <li>Glechoma_hederacea: plant cover %</li> <li>Geranium_molle: plant cover %</li> </ul> <p> </p> <p><em><strong>Fertilizer_input.csv</strong></em></p> <p><em>Content:</em></p> <p>Application quantities of fertilizers and ash, organic matter, C and mineral inputs, and fertilizer C:N ratio. Total N input is the sum of mineral N (Nmin) and organic N (Norg). Average values per hectare and per year over the years 2013−2015.</p> <p><em>Column names and units:</em></p> <ul> <li>Treatment: Treatment code (see text)</li> <li>Fertilizer_fresh: Applied fertilizer in Mg.ha<sup>-1</sup> per year (fresh weight)</li> <li>Fertilizer_DM: Applied fertilizer in Mg.ha<sup>-1</sup> per year (dry matter weight); for IF+SD this is the sum of 2.72 Mg sawdust + 0.45 Mg N fertilizer</li> <li>Ash: Mineral fraction in kg.ha<sup>-1</sup> per year</li> <li>OM: Organic matter in kg.ha<sup>-1</sup> per year</li> <li>C: Total C in kg.ha<sup>-1</sup> per year</li> <li>Nmin: Mineral N in kg.ha<sup>-1</sup> per year</li> <li>Norg: Organic N in kg.ha<sup>-1</sup> per year</li> <li>P2O5: kg.ha<sup>-1</sup> per year</li> <li>C_N_ratio: C:N ratio</li> </ul>
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