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174 results for “Oil palm”
Insights in the structural hierarchy of statically crystallized palm oil
<p>This dataset contains all data obtained on palm oil samples and used in the publication "Insights in the structural hierarchy of statically crystallized palm oil". See paper for more information on the methods for obtaining the data.</p> <p> </p> <p>Abbreviations used:</p> <p>PO = palm oil</p> <p>PPP = tripalmitin</p> <p>I = intensity (in X-ray scattering)</p> <p>q = scattering vector (in X-ray scattering)</p> <p>SEM = Scanning Electron Microscopy</p> <p>DSC = Differential Scanning Calorimetry</p> <p>WAXS = Wide Angle X-ray Scattering</p> <p>SAXS = Small Angle X-ray Scattering</p> <p>USAXS = Ultra Small Angle X-ray Scattering</p> <p>PLM = Polarized Light Microscopy</p> <p>FC = fast cooling = 20°C/min</p> <p>SC = slow cooling = 1°C/min</p> <p>TAG = triglyceride</p> <p>FA = fatty acid</p>
Compiled database, code and raw data for the article "A Comprehensive Database of Leaf Temperature, Water, and CO2 Fluxes in Young Oil Palm Plants Across Diverse Climate Scenarios for the Evaluation of Functional-Structural Models"
<p>This dataset results from an experiment on young oil palm plants (<em>Elaeis guineensis</em>) in the Ecotron facility from CNRS in Montpellier. Four plants were put in a microcosm one by one with varying climatic conditions to investigate the effect of climate on leaf temperature, CO2, and H2O fluxes at the plant scale. The conditions were defined based on typical daily conditions from a location where it is grown (Libo, Indonesia), <em>i.e.</em>, a day with no rainfall and near-average air temperature and humidity. This base condition was then modified by adding more CO2 (400, 600 and 800ppm), less radiation (typical cloudy sky), and more or less temperature and vapour pressure deficit (± 30%).</p> <p>Find more details from the <code>README.md</code> file in the repository or from the associated <a href="https://github.com/PalmStudio/Biophysics_database_palm" target="_blank" rel="noopener">Github repository</a>.</p>
High resolution global industrial and smallholder oil palm map for 2019
<p>The dataset contains 634 100x100 km tiles, covering areas where oil palm plantations were detected. The file '<em>grid.shp</em>' contains the grid that covers the potential distribution of oil palm. The file '<em>grid_withOP.shp</em>' shows the 100x100 grid squares with presence of oil palm plantations. The classified images (‘<em>oil_palm_map</em>’ folder, in geotiff format) are the output of the convolutional neural network based on Sentinel-1 and Sentinel-2 half-year composites. The images have a spatial resolution of 10 meters and contain three classes: [1] Industrial closed-canopy oil palm plantations, [2] Smallholder closed-canopy oil palm plantations, and [3] other land covers/uses that are not closed canopy oil palm. The file ‘<em>Validation_points_GlobalOilPalmLayer_2019.shp</em>’ includes the 13,495 points that were used to validate the product. Each point includes the attribute ‘Class’, which is the labelled class assigned by visual interpretation, and the attribute ‘predClass, which reflects the predicted class by the convolutional neural network. The ‘Class’ and ‘predClass’ values are the same as the raster files: [1] Industrial closed-canopy oil palm plantations, [2] Smallholder closed-canopy oil palm plantations, and [3] other land covers/uses that are not closed canopy oil palm.</p> <p>See article for additional information:</p> <p>Descals, Adrià, et al. "High-resolution global map of smallholder and industrial closed-canopy oil palm plantations." <em>Earth System Science Data</em> 13.3 (2021): 1211-1231.</p> <p> </p> <p>Changelog v1:</p> <p>- The analysis was extended to Sri Lanka, South India, and countries in Eastern Africa where oil palm can potentially grow.</p> <p>- The validation dataset only includes the points drawn by simple random sampling and stratified random sampling in the grid cells where the IUCN industrial layer detected oil palm.</p> <p>- The 'Class' and 'predClass' values in the validation dataset were reclassified with the same values as the raster images: [1] Industrial plantations, [2] Smallholder plantations, and [3] Other land covers/uses.</p>
Annual oil palm plantation maps in Malaysia and Indonesia from 2001 to 2018
<p>This package supplements the following paper submitted to ESSD: <strong>Annual oil palm plantation maps in Malaysia and Indonesia from 2001 to 2016</strong>.<br> This dataset contains the updated version (v4) of the annual oil palm plantation maps for Malaysia and Indonesia from 2001 to 2018 at 100 resolution. </p>
ARCHIMED-φ simulation files for the simulation of Design A from the article "When architectural plasticity fails to counter the light competition imposed by planting design: an in silico approach using a functional-structural model of oil palm"; in silico Plants journal
<p>Input files for the simulation of Design A in ARCHIMED-φ from the article "When architectural plasticity fails to counter the light competition imposed by planting design: an in silico approach using a functional-structural model of oil palm"; in silico Plants journal.</p> <p>See https://archimed-platform.github.io/archimed-phys-user-doc/ for more details on the model.</p> <p>Make a simulation by opening a terminal at the root of the folder and type: `java -jar .\archimed-phys.jar .\DesignA_MockUpA_seed1_MAP_72.yml`.</p>
Fig. 6 in Pollination activity of Elaeidobius kamerunicus (Coleoptera: Curculionoidea) on oil palm on Hainan island
Fig. 6. The number of Elaeidobius kamerunicus pollinating weevils per spikelet at various hours of the 3rd day of anthesis of male inflorescences of the African oil palm. Each data point in the figure is presented as the mean ± SE.
Fig. 5 in Pollination activity of Elaeidobius kamerunicus (Coleoptera: Curculionoidea) on oil palm on Hainan island
Fig. 5. The number of Elaeidobius kamerunicus pollinating weevils visiting male inflorescences at various hours of the 3rd day of anthesis of male inflorescences of the African oil palm. Each data point in the figure is presented as the mean ± SE.
Fig. 2 in Pollination activity of Elaeidobius kamerunicus (Coleoptera: Curculionoidea) on oil palm on Hainan island
Fig. 2. Number of Elaeidobius kamerunicus pollinating weevils that emerged per spikelet of the inflorescences of the African oil palm each month during anthesis. Each data point is presented as the mean ± SE.
Fig. 1. Sticky trap applied for catching Elaeidobius kamerunicus, pollinating weevils. The trap was 21.7 in Pollination activity of Elaeidobius kamerunicus (Coleoptera: Curculionoidea) on oil palm on Hainan island
Fig. 1. Sticky trap applied for catching Elaeidobius kamerunicus, pollinating weevils. The trap was 21.7 cm in diameter and 26.3 cm in height. It was folded into a cylinder surrounding an Elaeis guineensis inflorescence with the sticky gel–coated surface on the outside.
Fig. 7 in Pollination activity of Elaeidobius kamerunicus (Coleoptera: Curculionoidea) on oil palm on Hainan island
Fig. 7. The number of Elaeidobius kamerunicus pollinating weevils visiting female inflorescences on each of the 6 d of the anthesis period of the female inflorescence of the African oil palm. Each data point in the figure is presented as the mean ± SE.
Data Repository for: SOCIO-ENVIRONMENTAL IMPACTS OF OIL PALM CONTRACT FARMING SCHEMES IN THE BRAZILIAN AMAZON
<p>Contract farming is arguably a pro-poor strategy to promote rural development and minimize the social impacts of large-scale agricultural expansion. Yet, little attention has been paid to its environmental impacts, particularly in tropical landscapes. This article fills this gap by linking social and environmental analysis of oil palm contract farming in the Brazilian Amazon. The analysis presented used a mix of quantitative and qualitative methods, including household surveys, remote sensing techniques, and in-depth interviews, to assess whether the scheme managed to avoid deforestation and to contribute to livelihood improvements. The results show that the Brazilian model managed to prevent the deforestation of primary forests, but achieved limited and differentiated livelihood results. The analysis suggests that the Brazilian model is more likely to work for households with an agricultural vocation and a commercial spirit in areas with an abundant availability of degraded lands, but can hardly be a solution in frontier areas or for subsistence or more dependent households. The chapter concludes with some reflections on how contract farming schemes should be designed in order to maximize livelihood gains and minimize negative environmental impacts.</p>
Figure 2 in Comparison of dung beetle communities (Coleoptera: Scarabaeidae: Scarabaeinae) in oil palm plantations and native forest in the eastern Amazon, Brazil
Figure 2 Extrapolation and rarefaction of species richness in forest and oil palm plantation dung beetle communities. Shaded area represents 95% confidence limits. This figure is in color in the electronic version.
Figure 1 in Comparison of dung beetle communities (Coleoptera: Scarabaeidae: Scarabaeinae) in oil palm plantations and native forest in the eastern Amazon, Brazil
Figure 1 Location of the study area in the Brazilian Amazon, in the state of Pará. The right map represents the study area and the spatial distribution of 10 transects (red lines) in forest and oil palm habitats. Green and orange areas indicate primary forest and oil palm plantations, respectively (modified from Mendes-Oliveira et al., 2017). This figure is in color in the electronic version.
Fig. 1 in Native enemies of Strategus aloeus (Coleoptera: Scarabaeidae) in oil palm plantations in Colombia
Fig. 1. Natural enemies of Strategus aloeus. Metarhizium anisopliae infecting S. aloeus in: A. larvae, B. pupae, and C. adult of Phileurus didymus, D. searching for prey and E. preying on 3rd stage larvae of S. aloeus.
Soil microbial communities from tropical forest and oil palm
<div> <h1>Description</h1> <p>A study examining the impact of selective logging and forest conversion to oil palm on soil microbial community composition. Soil samples were collected from old growth forest, selectively logged forest and oil palm plantations. Soil bacterial, protistan and fungal community composition were measured and summarised by calculating richness. </p> <h1>Projects</h1> <p> This dataset was collected as part of the following projects: </p><ul> <li><a href="https://safeproject.net/projects/project_view/124">https://safeproject.net/projects/project_view/124</a> </li> </ul> <p></p> <h1>Funding</h1> <p> These data were collected as part of research funded by: </p> <ul> <li>UK NERC-funded Biodiversity And Land-use Impacts on Tropical Ecosystem Function (BALI) consortium (Standard grant , NE/K016377/1 ) </li> </ul> <p></p> <p>This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.</p> <h1>Permits</h1> <p>These data were collected under permit from the following authorities:</p> <ul> <li>Sabah Biodiversity Centre ( Research licence JKM/MBS.1000-2/2 JLD.5 (20))</li> <li>Sabah Biodiversity Centre ( Export licence JKM/MBS.1000-2/3 JLD.2 (70))</li> </ul> <p></p> <h1>Files</h1> <p>This dataset consists of 1 file: SAFE_Dataset_Richness.xlsx</p> <h2>SAFE_Dataset_Richness.xlsx</h2> <p>This file contains dataset metadata and 1 data tables:</p> <h3>Soil_Microbial_Communities</h3> <ul> <li>Worksheet: Soil_Microbial_Communities</li> <li>Description: Summary richness statistics from bacterial 16S, protistan 18S and fungal ITS biomarker microbial sequencing from DNA extracted from soils</li> <li>Number of fields: 10</li> <li>Number of data rows: 225</li> <ul> <li>PlotName: Plot name corresponding to the GEM Carbon plot where soils were sampled (type: id)</li> <li>ForestType: Old-growth, Logged or Oil palm (type: categorical)</li> <li>ForestPlotsCode: Plot name as listed in ForestPlots database (type: id)</li> <li>Replicate: replicate identifier for which soil core taken from each subplot (type: replicate)</li> <li>location_name: Name of subplot where soils were collected (type: location)</li> <li>Bacteria_Richness: Number of observed bacterial taxa from sequencing of 16S marker genes from soil samples (type: numeric)</li> <li>Protist_Richness: Number of observed protistan taxa from sequencing of 18S marker genes from soil samples (type: numeric)</li> <li>Fungal_Richness: Number of observed fungal taxa from sequencing of 16S marker genes from soil samples (type: numeric)</li> <li>EcM_Fungal_Richness: Number of observed Ectomycorrhizal fungal taxa from sequencing of ITS marker genes from soil samples (type: numeric)</li> <li>AMF_Fungal_Richness: Number of observed Arbuscular Mycorrhizal fungal taxa from sequencing of ITS marker genes from soil samples (type: numeric)</li> </ul> </ul> <h1>Extents</h1> <ul> <li>Date range: 2014-10-01 to 2018-09-01</li> <li>Latitudinal extent: 4.64° to 4.954°</li> <li>Longitudinal extent: 116.95° to 117.796°</li> </ul> </div>
Global oil palm extent and planting year from 1990 to 2021
<p>This repository contains a 10-m global oil palm extent layer for 2021 and a 30-m oil palm planting year layer from 1990 to 2021. The oil palm extent layer was produced using a convolutional neural network that identified industrial and smallholder plantations in Sentinel-1 data. The oil palm planting year was developed using a methodology specifically designed to detect the early stages of oil palm development in the Landsat time series. </p> <p>The repository contains the following data:</p> <p>- Grid_OilPalm2016-2021.shp: shapefile that delineates the 609 grid cells of 100 x 100 km where oil palm was found.</p> <p>- GlobalOilPalm_OP-extent.zip: 609 raster tiles of 100x100 km in geotiff format. The raster files show the results of the deep learning classification at a spatial resolution of 10 meters. The classes are the following:</p> <p>[0] Other land covers that are not oil palm.</p> <p>[1] Industrial oil palm plantations</p> <p>[2] Smallholder oil palm plantations.</p> <p>- GlobalOilPalm_YoP.zip: 609 raster tiles of 100x100 km in geotiff format. The raster files depict the year of oil palm plantation. The raster files have a spatial resolution of 30 meters.</p> <p>- Validation_points_GlobalOP2016-2021.shp: shapefile that contains the 18,812 points used to validate the global oil palm extent 2016–2021 and the oil palm age layer. Each point includes the attribute ‘Class’, which is the class assigned by visual interpretation of sub-meter resolution images, and the attributes ‘OP2016-2021’ and ‘OP2019’, which show the mapped classes in the oil palm extent 2016–2021 (this dataset) and the global oil palm layer 2019 (Descals et al., 2021), respectively. These attributes contain the following class values:</p> <p>[0] Other land covers that are not oil palm.</p> <p>[1] Industrial oil palm plantations.</p> <p>[2] Smallholder oil palm plantations.</p> <p>The oil palm extent and the planting year can be visualized at: https://ee-globaloilpalm.projects.earthengine.app/view/global-oil-palm-planting-year-1990-2021. This web map allows for the inspection of Landsat time series and the visualization of historical satellite images for a given oil palm plantation.</p> <p>If you are interested in obtaining the GeoTIFF file for a specific region, please refer to this Google Earth Engine script, which shows the IDs of each tile:<br>https://code.earthengine.google.com/284cf1dc974295c57fe55bccf5acd83e </p> <p>Changelog v1.2: The validation dataset includes 1,000 additional points generated using stratified random sampling: 300 points for the class ‘smallholder oil palm’ and 700 points for ‘industrial oil palm’. The shapefile contains a new attribute, 'Sampling_type', which specifies whether the points were generated with simple or stratified random sampling. </p>
Linked collectors and determiners for: A newspecies ofplanthopper in thegenus Cobacella (Hemiptera: Auchenorrhyncha Derbidae) from oil palm (Elaeis guineensis) in Costa Rica.
Natural history specimen data linked to collectors and determiners held within, "A newspecies ofplanthopper in thegenus Cobacella (Hemiptera: Auchenorrhyncha Derbidae) from oil palm (Elaeis guineensis) in Costa Rica". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/d20ba705-70c7-42e2-bf6f-9b07bd0b325f">https://bionomia.net/dataset/d20ba705-70c7-42e2-bf6f-9b07bd0b325f</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/d20ba705-70c7-42e2-bf6f-9b07bd0b325f">https://gbif.org/dataset/d20ba705-70c7-42e2-bf6f-9b07bd0b325f</a>. Formatted as a Frictionless Data package.
Figure 1 in Field application of six commercial essential oils against Date Palm mite, Phyllotetranychus aegypticus (Acari: Tenuipalpidae) in Egypt
Figure 1. The relationship (regression modeling at P = 0.05) between X (temperature recorded in spring, summer, and autumn), and Y (predicted value which representing mite populations before and after oil treatments). The line is the linear model, significant part till temperature ≤ 27 (which represents spring and autumn treatments). While the circle in the end part, temperature> 27.50 (representing effect of oil in summer) is not significant, P = 0.202ns.
Figure 5 in Field application of six commercial essential oils against Date Palm mite, Phyllotetranychus aegypticus (Acari: Tenuipalpidae) in Egypt
Figure 5. Independent-samples test of ordered alternatives (Jonckheere-Terpstra Test) on the relation between tested commercial essential oils of thyme, spearmint, jasmine, banana, camphor and clove across the temperature recorded in the field application 2018, at 0.05 significance level.
Figure 4 in Field application of six commercial essential oils against Date Palm mite, Phyllotetranychus aegypticus (Acari: Tenuipalpidae) in Egypt
Figure 4. Independent-samples median (Kolmogorov-Smirnov Test) resulted the distribution and the median of tested commercial essential oils of thyme, spearmint, jasmine, banana, camphor and clove across the three categories of season (spring, summer, and autumn), at 0.05 significance level.
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