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Research data keyboard_double_arrow_right Dataset 2015Publisher:South African Environmental Observation Network Authors: Wim Hugo;* Technical Challenges - Technology is relatively simple and has high conversion efficiency. * Cost Challenges - Despite efficiency, levelised costs are high, due to mainly 2 factors (1) the input cost of raw material is high, and (2) operating costs are high due to feedstock (methanol) and distillation operations. Selling oilcake has a significant effect on final product cost, with a 50% oilcake internal subsidy reducing the costs by R 6,500/ t (0.65 R/kWh). This would bring production cost into line with current range of diesel prices. * Environmental Challenges - Greenhouse gas savings are significant provided land use changes are carbon neutral. Limiting cultivation to subsistence cropland should assist with this goal. * Social and Institutional Challenges - Conversion of subsistence farmers in former homeland areas, with high reliance on cattle and maize, to a cash crop with side products for own consumption and cattle feed will require significant community involvement. Cooperative farming and marketing channels need to be investigated.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2023Publisher:World Data Center for Climate (WDCC) at DKRZ Authors: Shiogama, Hideo; Abe, Manabu; Tatebe, Hiroaki;Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.ScenarioMIP.MIROC.MIROC6.ssp119' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The MIROC6 climate model, released in 2017, includes the following components: aerosol: SPRINTARS6.0, atmos: CCSR AGCM (T85; 256 x 128 longitude/latitude; 81 levels; top level 0.004 hPa), land: MATSIRO6.0, ocean: COCO4.9 (tripolar primarily 1deg; 360 x 256 longitude/latitude; 63 levels; top grid cell 0-2 m), seaIce: COCO4.9. The model was run by the JAMSTEC (Japan Agency for Marine-Earth Science and Technology, Kanagawa 236-0001, Japan), AORI (Atmosphere and Ocean Research Institute, The University of Tokyo, Chiba 277-8564, Japan), NIES (National Institute for Environmental Studies, Ibaraki 305-8506, Japan), and R-CCS (RIKEN Center for Computational Science, Hyogo 650-0047, Japan) (MIROC) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, land: 250 km, ocean: 100 km, seaIce: 100 km.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2015Publisher:Dryad Digital Repository Myers, Mark C.; Mason, James T.; Hoksch, Benjamin J.; Cambardella, Cynthia A.; Pfrimmer, Jarrett D.;Data from Myers, M.C., J.T. Mason, B.J. Hoksch, C.A. Cambardella, J.D. Pfrimmer (2015) Birds and butterflies respond to soil-induced habitat heterogeneity in experimental plantings of tallgrass prairie species managed as agroenergy crops in Iowa, USA. Journal of Applied Ecology. This Excel file includes separate sheets containing the soil, vegetation, and bird and butterfly assemblage data analyzed in the paper. Four-letter column labels for species abundances are derived from the first two letters of the genus and species names (e.g. Chondestes grammacus = "chgr"). Species lists are available from the online Supporting Information. Please contact Mark Myers (mark.myers@uni.edu) with inquiries.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2019Publisher:Smithsonian Tropical Research Institute Authors: Paton, Steven;doi: 10.25573/data.10059455.v28 , 10.25573/data.10059455.v6 , 10.25573/data.10059455.v30 , 10.25573/data.10059455.v34 , 10.25573/data.10059455.v45 , 10.25573/data.10059455.v33 , 10.25573/data.10059455 , 10.25573/data.10059455.v13 , 10.25573/data.10059455.v9 , 10.25573/data.10059455.v31 , 10.25573/data.10059455.v22 , 10.25573/data.10059455.v14 , 10.25573/data.10059455.v27 , 10.25573/data.10059455.v11 , 10.25573/data.10059455.v44 , 10.25573/data.10059455.v15 , 10.25573/data.10059455.v38 , 10.25573/data.10059455.v17 , 10.25573/data.10059455.v16 , 10.25573/data.10059455.v2 , 10.25573/data.10059455.v29 , 10.25573/data.10059455.v12 , 10.25573/data.10059455.v32 , 10.25573/data.10059455.v39 , 10.25573/data.10059455.v26 , 10.25573/data.10059455.v19 , 10.25573/data.10059455.v41 , 10.25573/data.10059455.v25 , 10.25573/data.10059455.v23 , 10.25573/data.10059455.v10 , 10.25573/data.10059455.v20 , 10.25573/data.10059455.v21 , 10.25573/data.10059455.v24 , 10.25573/data.10059455.v1 , 10.25573/data.10059455.v8 , 10.25573/data.10059455.v3 , 10.25573/data.10059455.v5 , 10.25573/data.10059455.v46 , 10.25573/data.10059455.v4 , 10.25573/data.10059455.v42 , 10.25573/data.10059455.v18 , 10.25573/data.10059455.v43 , 10.25573/data.10059455.v40 , 10.25573/data.10059455.v7
doi: 10.25573/data.10059455.v28 , 10.25573/data.10059455.v6 , 10.25573/data.10059455.v30 , 10.25573/data.10059455.v34 , 10.25573/data.10059455.v45 , 10.25573/data.10059455.v33 , 10.25573/data.10059455 , 10.25573/data.10059455.v13 , 10.25573/data.10059455.v9 , 10.25573/data.10059455.v31 , 10.25573/data.10059455.v22 , 10.25573/data.10059455.v14 , 10.25573/data.10059455.v27 , 10.25573/data.10059455.v11 , 10.25573/data.10059455.v44 , 10.25573/data.10059455.v15 , 10.25573/data.10059455.v38 , 10.25573/data.10059455.v17 , 10.25573/data.10059455.v16 , 10.25573/data.10059455.v2 , 10.25573/data.10059455.v29 , 10.25573/data.10059455.v12 , 10.25573/data.10059455.v32 , 10.25573/data.10059455.v39 , 10.25573/data.10059455.v26 , 10.25573/data.10059455.v19 , 10.25573/data.10059455.v41 , 10.25573/data.10059455.v25 , 10.25573/data.10059455.v23 , 10.25573/data.10059455.v10 , 10.25573/data.10059455.v20 , 10.25573/data.10059455.v21 , 10.25573/data.10059455.v24 , 10.25573/data.10059455.v1 , 10.25573/data.10059455.v8 , 10.25573/data.10059455.v3 , 10.25573/data.10059455.v5 , 10.25573/data.10059455.v46 , 10.25573/data.10059455.v4 , 10.25573/data.10059455.v42 , 10.25573/data.10059455.v18 , 10.25573/data.10059455.v43 , 10.25573/data.10059455.v40 , 10.25573/data.10059455.v7
Monthly and daily summary from Barro Colorado Island (BCI). Data organized in horizontal format for seasonal and inter-year comparisonsLocation 9°9'42.36"N, 79°50'15.67"WParameters: air temperature, relative humidity, wind speed and direction, precipitation, sea surface temperature, solar radiation (pyranometer), air pressure, soil moisture, runoff, potential evapotranspiration, wet/dry season starting datesLutz catchment is a 9.73ha protected watershed on BCIThe Lutz tower was built in 1972 and was originally 42m. In 2002 it was increased to 48mThe data from 48m should be considered a separate data series from the data at 42m. Wind speed is significantly higher at 48m due to the distance to the top of the canopy.The Clearing is a small, open area surrounded by forest and some buildings. Station established in 1972. Consists of a Stevenson screen with max/min thermometers and air pressure sensor. Temperature/humidity sensor, rain gauge and evaporation sensors are located at various locations around the screen.
https://dx.doi.org/1... arrow_drop_down Smithsonian figshareDataset . 2019License: CC BYData sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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more_vert https://dx.doi.org/1... arrow_drop_down Smithsonian figshareDataset . 2019License: CC BYData sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2021Publisher:PANGAEA Zweifel, Roman; Sterck, Frank J; Braun, Sabine; Buchmann, Nina; Eugster, Werner; Gessler, Arthur; Haeni, Matthias; Peters, Richard L; Walthert, Lorenz; Wilhelm, Micah; Ziemínska, Kasia; Etzold, Sophia;The timing of diel stem growth of mature forest trees is still largely unknown, as empirical data with high temporal resolution have not been available so far. Consequently, the effects of day-night conditions on tree growth remained uncertain. Here we present the first comprehensive field study of hourly-resolved radial stem growth of seven temperate tree species, based on 57 million underlying data points over a period of up to 8 years. We show that trees grow mainly at night, with a peak after midnight, when the vapour pressure deficit (VPD) is among the lowest. A high VPD strictly limits radial stem growth and allows little growth during daylight hours, except in the early morning. Surprisingly, trees also grow in moderately dry soil when the VPD is low. Species-specific differences in diel growth dynamics show that species able to grow earlier during the night are associated with the highest number of hours with growth per year and the largest annual growth increment. We conclude that species with the ability to overcome daily water deficits faster have greater growth potential. Furthermore, we conclude that growth is more sensitive than carbon uptake to dry air, as growth stops before stomata are known to close.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2021Publisher:Zenodo Authors: Metsaranta, Juha; Mamet, Steven; Maillet, Jay; Barr, Alan;These datasets are associated with the following paper: Metsaranta, J.M., Mamet, S.D., Maillett, J., Barr, A.G. (2021). Comparison of tree-ring and eddy covariance derived annual ecosystem production estimates for jack pine and trembling aspen forests in Saskatchewan, Canada. Agricultural and Forest Meteorology. There are two files: (1) CBMOutput.zip. This contains the hybrid biometric modelled ecosystem C stock and flux estimates. (2) StandReconstructionData.zip. This contains the field measurement data and the tree level biomass and wood volume data for the Stand Reconstruction plots used to develop the hybrid biometric modelled estimates. The data are formatted as .csv files, and an associated Microsoft Excel spreadsheet explains the data columns and provides information on the associated units of measure.
ZENODO arrow_drop_down Smithsonian figshareDataset . 2021License: CC BYData sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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visibility 24visibility views 24 download downloads 21 Powered bymore_vert ZENODO arrow_drop_down Smithsonian figshareDataset . 2021License: CC BYData sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2019Publisher:PANGAEA Fischer, Andrea; Fickert, Thomas; Schwaizer, Gabriele; Patzelt, Gernot; Groß, Günther;Monitoring of plant succession in glacier forelands so far has been restricted to field sampling. In this study, in situ vegetation sampling along a chronosequence between Little Ice Age (LIA) maximum extent and the recent glacier terminus at Jamtalferner/Silvretta (ferner is a Tyrolian toponym for glacier) is compared to time series of the Normalized Difference Vegetation Index (NDVI) calculated from 13 Landsat scenes (1985-2016). The glacier terminus positions at 16 dates between the LIA maximum and 2015 were analysed from historical maps, orthophotos and LiDAR images and used for site age determination. We sampled plots of different time since deglaciation, from very recent to approx. 150 years: after 100 years, roughly 80% of the ground is covered by plants and ground cover did not increase essentially thereafter. Species number increases from 10-20 species on young sites to 40-50 species after 100 years. The NDVI increases for all plots between 1985 and 2016, from a mean of 0.11 for 1985-1991 to 0.2 in 2009 and 0.27 in 2016. For the plots deglaciated between 1 and about 150 years, the NDVI increases with the time of exposure. As the increase in ground cover is clearly reproduced by the NDVI (R² ground cover/NDVI 0.84) - even for sparsely vegetated areas -, we see a high potential of satellite-borne NDVI to perform regional characterizations of glacier forelands for hydrological, ecological and hazard management related applications. This data collection comprises the galcier outlines, NDVIs and chronosequencing locations with diversity and ground cover data.
PANGAEA - Data Publi... arrow_drop_down PANGAEA - Data Publisher for Earth and Environmental ScienceDataset . 2019License: CC BYData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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more_vert PANGAEA - Data Publi... arrow_drop_down PANGAEA - Data Publisher for Earth and Environmental ScienceDataset . 2019License: CC BYData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2006Publisher:Department of Agriculture, Forestry and Fisheries Authors: Department of Agriculture, Forestry and Fisheries;A subset of the Field Crop Boundaries data set, showing all subsistence farmland used for crop cultivation. Prepared by SAEON from data provided by DAFF.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2022Embargo end date: 11 Nov 2022Publisher:Dryad Authors: Eslamdoust, Jamshid;Plot design and harvesting Twelve sampling plots (16 m × 16 m) in three P. deltoides plantations were established based on systematic random design. To minimize edge effects, surrounding rows were not considered during sampling. The age of the stands was 18-20 years old. In each sampling plot, the DBH (diameter at breast height 1.3 m above the ground) of the individual trees was measured with a caliper in two perpendicular directions and the mean DBH determined. Tree height was measured by Haglöf-Vertex IV hypsometer. Based on the DBH and height measurements, 10 DBH classes from 15 to 42 cm (3 cm intervals) were established. The value of each DBH class represented the central value (i.e., class 15 included all DBH from 12.5 to 17.5 cm). In each DBH class, one representative tree was selected and harvested for a total of 10 P. deltoides trees. Measurements of bark percentagesThe stems of harvested trees were marked and cut into 2 m-segments. The mid-length diameter of each segment was measured outside the bark in two perpendicular directions with a caliper to determine the mean diameter. A 5 cm-thick disc was cut from the middle of each segment. A total of 123 discs were obtained and brought to the laboratory. All the discs were arranged into 2-cm wide diameter classes. The value of each disc class represents the central value (i.e., class 20 included all discs whose diameters ranged from 19.5 to 20.5 cm). Bark was separated from the wood using a peeler knife for each disc. Fresh bark and wood were weighted separately, oven-dried at 80 °C until constant weight, and the oven-dry weight measured. The bark percentage of each disc was considered as bark percentage of a 2 m-segment for fresh and dry weight. Finally, the bark percentage of the whole stem in each DBH class was calculated by adding the 2 m-segments. Bark biomass as an energy source has a high economic value. Bark content variations and production helps recognize the potential of this bioenergy source spatially before harvesting. The percentage of fresh and dry bark in Populus deltoides grown under a monoculture system was examined in the temperate region of northern Iran. Diameter at breast height (DBH) and total height data were analyzed based on an initial inventory. Ten sample trees were felled, separated into 2 m-segments, and weighted in the field. A 5-cm-thick disc from each segment was extracted for determining fresh and dry bark percentages. These were statistically significantly different in disc diameter classes and decreased with increasing disc diameters. Bark percentage of the disc classes ranged from 21.8 to 24.4% in small-sized diameters to 8.1‒9.3% in large-sized diameters. The differences between fresh and dry bark percentages depended on water content variations. Allometric power equations were fitted to data of fresh and dry bark percentages and disc diameters as well as DBH. The values of R2 ranged from 0.89 to 0.90. In addition, allometric power equations provided the best fits for relationships between total stem dry biomass, dry bark biomass, and DBH, R2 = 0.986 and 0.979 for the total stem dry biomass and stem dry bark biomass, respectively. The allometric models can be used to estimate bark percentage and bark production of P. deltoides in segments and for the whole stem for a wide range of segment diameters (8‒44 cm) and DBH (15‒45 cm).
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2008Publisher:Food and Agriculture Organization of the United Nations (FAO) Authors: Food and Agriculture Organization of the United Nations (FAO);Data on cropland was obtained from the global data set produced by the UN Food and Agriculture Organisation (FAO). Data set was obtained as a raster image, and clipped to the boundaries of South Africa, before being converted to a vector layer. The BioEnergy Atlas bases its analyses on mesozones (Planning zones of approximately 50 km2, with relatively homogeneous attributes). This data set aggregates FAO Cropland to mesozones for planning purposes. The FGGD land cover occurrence maps are global raster data layers with a resolution of 5 arc-minutes. Each pixel in each map contains a value representing the percentage of the area belonging to the land
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Research data keyboard_double_arrow_right Dataset 2015Publisher:South African Environmental Observation Network Authors: Wim Hugo;* Technical Challenges - Technology is relatively simple and has high conversion efficiency. * Cost Challenges - Despite efficiency, levelised costs are high, due to mainly 2 factors (1) the input cost of raw material is high, and (2) operating costs are high due to feedstock (methanol) and distillation operations. Selling oilcake has a significant effect on final product cost, with a 50% oilcake internal subsidy reducing the costs by R 6,500/ t (0.65 R/kWh). This would bring production cost into line with current range of diesel prices. * Environmental Challenges - Greenhouse gas savings are significant provided land use changes are carbon neutral. Limiting cultivation to subsistence cropland should assist with this goal. * Social and Institutional Challenges - Conversion of subsistence farmers in former homeland areas, with high reliance on cattle and maize, to a cash crop with side products for own consumption and cattle feed will require significant community involvement. Cooperative farming and marketing channels need to be investigated.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2023Publisher:World Data Center for Climate (WDCC) at DKRZ Authors: Shiogama, Hideo; Abe, Manabu; Tatebe, Hiroaki;Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.ScenarioMIP.MIROC.MIROC6.ssp119' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The MIROC6 climate model, released in 2017, includes the following components: aerosol: SPRINTARS6.0, atmos: CCSR AGCM (T85; 256 x 128 longitude/latitude; 81 levels; top level 0.004 hPa), land: MATSIRO6.0, ocean: COCO4.9 (tripolar primarily 1deg; 360 x 256 longitude/latitude; 63 levels; top grid cell 0-2 m), seaIce: COCO4.9. The model was run by the JAMSTEC (Japan Agency for Marine-Earth Science and Technology, Kanagawa 236-0001, Japan), AORI (Atmosphere and Ocean Research Institute, The University of Tokyo, Chiba 277-8564, Japan), NIES (National Institute for Environmental Studies, Ibaraki 305-8506, Japan), and R-CCS (RIKEN Center for Computational Science, Hyogo 650-0047, Japan) (MIROC) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, land: 250 km, ocean: 100 km, seaIce: 100 km.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2015Publisher:Dryad Digital Repository Myers, Mark C.; Mason, James T.; Hoksch, Benjamin J.; Cambardella, Cynthia A.; Pfrimmer, Jarrett D.;Data from Myers, M.C., J.T. Mason, B.J. Hoksch, C.A. Cambardella, J.D. Pfrimmer (2015) Birds and butterflies respond to soil-induced habitat heterogeneity in experimental plantings of tallgrass prairie species managed as agroenergy crops in Iowa, USA. Journal of Applied Ecology. This Excel file includes separate sheets containing the soil, vegetation, and bird and butterfly assemblage data analyzed in the paper. Four-letter column labels for species abundances are derived from the first two letters of the genus and species names (e.g. Chondestes grammacus = "chgr"). Species lists are available from the online Supporting Information. Please contact Mark Myers (mark.myers@uni.edu) with inquiries.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2019Publisher:Smithsonian Tropical Research Institute Authors: Paton, Steven;doi: 10.25573/data.10059455.v28 , 10.25573/data.10059455.v6 , 10.25573/data.10059455.v30 , 10.25573/data.10059455.v34 , 10.25573/data.10059455.v45 , 10.25573/data.10059455.v33 , 10.25573/data.10059455 , 10.25573/data.10059455.v13 , 10.25573/data.10059455.v9 , 10.25573/data.10059455.v31 , 10.25573/data.10059455.v22 , 10.25573/data.10059455.v14 , 10.25573/data.10059455.v27 , 10.25573/data.10059455.v11 , 10.25573/data.10059455.v44 , 10.25573/data.10059455.v15 , 10.25573/data.10059455.v38 , 10.25573/data.10059455.v17 , 10.25573/data.10059455.v16 , 10.25573/data.10059455.v2 , 10.25573/data.10059455.v29 , 10.25573/data.10059455.v12 , 10.25573/data.10059455.v32 , 10.25573/data.10059455.v39 , 10.25573/data.10059455.v26 , 10.25573/data.10059455.v19 , 10.25573/data.10059455.v41 , 10.25573/data.10059455.v25 , 10.25573/data.10059455.v23 , 10.25573/data.10059455.v10 , 10.25573/data.10059455.v20 , 10.25573/data.10059455.v21 , 10.25573/data.10059455.v24 , 10.25573/data.10059455.v1 , 10.25573/data.10059455.v8 , 10.25573/data.10059455.v3 , 10.25573/data.10059455.v5 , 10.25573/data.10059455.v46 , 10.25573/data.10059455.v4 , 10.25573/data.10059455.v42 , 10.25573/data.10059455.v18 , 10.25573/data.10059455.v43 , 10.25573/data.10059455.v40 , 10.25573/data.10059455.v7
doi: 10.25573/data.10059455.v28 , 10.25573/data.10059455.v6 , 10.25573/data.10059455.v30 , 10.25573/data.10059455.v34 , 10.25573/data.10059455.v45 , 10.25573/data.10059455.v33 , 10.25573/data.10059455 , 10.25573/data.10059455.v13 , 10.25573/data.10059455.v9 , 10.25573/data.10059455.v31 , 10.25573/data.10059455.v22 , 10.25573/data.10059455.v14 , 10.25573/data.10059455.v27 , 10.25573/data.10059455.v11 , 10.25573/data.10059455.v44 , 10.25573/data.10059455.v15 , 10.25573/data.10059455.v38 , 10.25573/data.10059455.v17 , 10.25573/data.10059455.v16 , 10.25573/data.10059455.v2 , 10.25573/data.10059455.v29 , 10.25573/data.10059455.v12 , 10.25573/data.10059455.v32 , 10.25573/data.10059455.v39 , 10.25573/data.10059455.v26 , 10.25573/data.10059455.v19 , 10.25573/data.10059455.v41 , 10.25573/data.10059455.v25 , 10.25573/data.10059455.v23 , 10.25573/data.10059455.v10 , 10.25573/data.10059455.v20 , 10.25573/data.10059455.v21 , 10.25573/data.10059455.v24 , 10.25573/data.10059455.v1 , 10.25573/data.10059455.v8 , 10.25573/data.10059455.v3 , 10.25573/data.10059455.v5 , 10.25573/data.10059455.v46 , 10.25573/data.10059455.v4 , 10.25573/data.10059455.v42 , 10.25573/data.10059455.v18 , 10.25573/data.10059455.v43 , 10.25573/data.10059455.v40 , 10.25573/data.10059455.v7
Monthly and daily summary from Barro Colorado Island (BCI). Data organized in horizontal format for seasonal and inter-year comparisonsLocation 9°9'42.36"N, 79°50'15.67"WParameters: air temperature, relative humidity, wind speed and direction, precipitation, sea surface temperature, solar radiation (pyranometer), air pressure, soil moisture, runoff, potential evapotranspiration, wet/dry season starting datesLutz catchment is a 9.73ha protected watershed on BCIThe Lutz tower was built in 1972 and was originally 42m. In 2002 it was increased to 48mThe data from 48m should be considered a separate data series from the data at 42m. Wind speed is significantly higher at 48m due to the distance to the top of the canopy.The Clearing is a small, open area surrounded by forest and some buildings. Station established in 1972. Consists of a Stevenson screen with max/min thermometers and air pressure sensor. Temperature/humidity sensor, rain gauge and evaporation sensors are located at various locations around the screen.
https://dx.doi.org/1... arrow_drop_down Smithsonian figshareDataset . 2019License: CC BYData sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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more_vert https://dx.doi.org/1... arrow_drop_down Smithsonian figshareDataset . 2019License: CC BYData sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2021Publisher:PANGAEA Zweifel, Roman; Sterck, Frank J; Braun, Sabine; Buchmann, Nina; Eugster, Werner; Gessler, Arthur; Haeni, Matthias; Peters, Richard L; Walthert, Lorenz; Wilhelm, Micah; Ziemínska, Kasia; Etzold, Sophia;The timing of diel stem growth of mature forest trees is still largely unknown, as empirical data with high temporal resolution have not been available so far. Consequently, the effects of day-night conditions on tree growth remained uncertain. Here we present the first comprehensive field study of hourly-resolved radial stem growth of seven temperate tree species, based on 57 million underlying data points over a period of up to 8 years. We show that trees grow mainly at night, with a peak after midnight, when the vapour pressure deficit (VPD) is among the lowest. A high VPD strictly limits radial stem growth and allows little growth during daylight hours, except in the early morning. Surprisingly, trees also grow in moderately dry soil when the VPD is low. Species-specific differences in diel growth dynamics show that species able to grow earlier during the night are associated with the highest number of hours with growth per year and the largest annual growth increment. We conclude that species with the ability to overcome daily water deficits faster have greater growth potential. Furthermore, we conclude that growth is more sensitive than carbon uptake to dry air, as growth stops before stomata are known to close.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2021Publisher:Zenodo Authors: Metsaranta, Juha; Mamet, Steven; Maillet, Jay; Barr, Alan;These datasets are associated with the following paper: Metsaranta, J.M., Mamet, S.D., Maillett, J., Barr, A.G. (2021). Comparison of tree-ring and eddy covariance derived annual ecosystem production estimates for jack pine and trembling aspen forests in Saskatchewan, Canada. Agricultural and Forest Meteorology. There are two files: (1) CBMOutput.zip. This contains the hybrid biometric modelled ecosystem C stock and flux estimates. (2) StandReconstructionData.zip. This contains the field measurement data and the tree level biomass and wood volume data for the Stand Reconstruction plots used to develop the hybrid biometric modelled estimates. The data are formatted as .csv files, and an associated Microsoft Excel spreadsheet explains the data columns and provides information on the associated units of measure.
ZENODO arrow_drop_down Smithsonian figshareDataset . 2021License: CC BYData sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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visibility 24visibility views 24 download downloads 21 Powered bymore_vert ZENODO arrow_drop_down Smithsonian figshareDataset . 2021License: CC BYData sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2019Publisher:PANGAEA Fischer, Andrea; Fickert, Thomas; Schwaizer, Gabriele; Patzelt, Gernot; Groß, Günther;Monitoring of plant succession in glacier forelands so far has been restricted to field sampling. In this study, in situ vegetation sampling along a chronosequence between Little Ice Age (LIA) maximum extent and the recent glacier terminus at Jamtalferner/Silvretta (ferner is a Tyrolian toponym for glacier) is compared to time series of the Normalized Difference Vegetation Index (NDVI) calculated from 13 Landsat scenes (1985-2016). The glacier terminus positions at 16 dates between the LIA maximum and 2015 were analysed from historical maps, orthophotos and LiDAR images and used for site age determination. We sampled plots of different time since deglaciation, from very recent to approx. 150 years: after 100 years, roughly 80% of the ground is covered by plants and ground cover did not increase essentially thereafter. Species number increases from 10-20 species on young sites to 40-50 species after 100 years. The NDVI increases for all plots between 1985 and 2016, from a mean of 0.11 for 1985-1991 to 0.2 in 2009 and 0.27 in 2016. For the plots deglaciated between 1 and about 150 years, the NDVI increases with the time of exposure. As the increase in ground cover is clearly reproduced by the NDVI (R² ground cover/NDVI 0.84) - even for sparsely vegetated areas -, we see a high potential of satellite-borne NDVI to perform regional characterizations of glacier forelands for hydrological, ecological and hazard management related applications. This data collection comprises the galcier outlines, NDVIs and chronosequencing locations with diversity and ground cover data.
PANGAEA - Data Publi... arrow_drop_down PANGAEA - Data Publisher for Earth and Environmental ScienceDataset . 2019License: CC BYData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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more_vert PANGAEA - Data Publi... arrow_drop_down PANGAEA - Data Publisher for Earth and Environmental ScienceDataset . 2019License: CC BYData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2006Publisher:Department of Agriculture, Forestry and Fisheries Authors: Department of Agriculture, Forestry and Fisheries;A subset of the Field Crop Boundaries data set, showing all subsistence farmland used for crop cultivation. Prepared by SAEON from data provided by DAFF.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2022Embargo end date: 11 Nov 2022Publisher:Dryad Authors: Eslamdoust, Jamshid;Plot design and harvesting Twelve sampling plots (16 m × 16 m) in three P. deltoides plantations were established based on systematic random design. To minimize edge effects, surrounding rows were not considered during sampling. The age of the stands was 18-20 years old. In each sampling plot, the DBH (diameter at breast height 1.3 m above the ground) of the individual trees was measured with a caliper in two perpendicular directions and the mean DBH determined. Tree height was measured by Haglöf-Vertex IV hypsometer. Based on the DBH and height measurements, 10 DBH classes from 15 to 42 cm (3 cm intervals) were established. The value of each DBH class represented the central value (i.e., class 15 included all DBH from 12.5 to 17.5 cm). In each DBH class, one representative tree was selected and harvested for a total of 10 P. deltoides trees. Measurements of bark percentagesThe stems of harvested trees were marked and cut into 2 m-segments. The mid-length diameter of each segment was measured outside the bark in two perpendicular directions with a caliper to determine the mean diameter. A 5 cm-thick disc was cut from the middle of each segment. A total of 123 discs were obtained and brought to the laboratory. All the discs were arranged into 2-cm wide diameter classes. The value of each disc class represents the central value (i.e., class 20 included all discs whose diameters ranged from 19.5 to 20.5 cm). Bark was separated from the wood using a peeler knife for each disc. Fresh bark and wood were weighted separately, oven-dried at 80 °C until constant weight, and the oven-dry weight measured. The bark percentage of each disc was considered as bark percentage of a 2 m-segment for fresh and dry weight. Finally, the bark percentage of the whole stem in each DBH class was calculated by adding the 2 m-segments. Bark biomass as an energy source has a high economic value. Bark content variations and production helps recognize the potential of this bioenergy source spatially before harvesting. The percentage of fresh and dry bark in Populus deltoides grown under a monoculture system was examined in the temperate region of northern Iran. Diameter at breast height (DBH) and total height data were analyzed based on an initial inventory. Ten sample trees were felled, separated into 2 m-segments, and weighted in the field. A 5-cm-thick disc from each segment was extracted for determining fresh and dry bark percentages. These were statistically significantly different in disc diameter classes and decreased with increasing disc diameters. Bark percentage of the disc classes ranged from 21.8 to 24.4% in small-sized diameters to 8.1‒9.3% in large-sized diameters. The differences between fresh and dry bark percentages depended on water content variations. Allometric power equations were fitted to data of fresh and dry bark percentages and disc diameters as well as DBH. The values of R2 ranged from 0.89 to 0.90. In addition, allometric power equations provided the best fits for relationships between total stem dry biomass, dry bark biomass, and DBH, R2 = 0.986 and 0.979 for the total stem dry biomass and stem dry bark biomass, respectively. The allometric models can be used to estimate bark percentage and bark production of P. deltoides in segments and for the whole stem for a wide range of segment diameters (8‒44 cm) and DBH (15‒45 cm).
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2008Publisher:Food and Agriculture Organization of the United Nations (FAO) Authors: Food and Agriculture Organization of the United Nations (FAO);Data on cropland was obtained from the global data set produced by the UN Food and Agriculture Organisation (FAO). Data set was obtained as a raster image, and clipped to the boundaries of South Africa, before being converted to a vector layer. The BioEnergy Atlas bases its analyses on mesozones (Planning zones of approximately 50 km2, with relatively homogeneous attributes). This data set aggregates FAO Cropland to mesozones for planning purposes. The FGGD land cover occurrence maps are global raster data layers with a resolution of 5 arc-minutes. Each pixel in each map contains a value representing the percentage of the area belonging to the land
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