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1,648 results for “farm”
Figure 2 in Silicon derivatives induced host plant resistance against Tetranychus urticae (Acari: Tetranychidae) in eggplants farms
Figure 2. (A) Silicon leaf, total protein and phenol contents, (B) Activity of POD, CAT, and PPO of S. melongena- treated plants. Means followed by the same letter are not significantly different using Tukey's HSD Test at P <0.05. T1 = Control, T2 = OSAB 2 mL L−1, T3= OSAB 4 mL L−1, T4= Silica K 2 mL L−1, and T5 = Silica K 4 mL L−1.
Figure 1 in Silicon derivatives induced host plant resistance against Tetranychus urticae (Acari: Tetranychidae) in eggplants farms
Figure 1. Mean number ± SE of the different stages of T. urticae on S. melongena leaves 10, 30 and 50 days after spraying (DAS). Means followed by the same letter are not significantly different using Tukey's HSD at P <0.05. T1 = Control, T2 = OSAB 2 mL L−1, T3 = OSAB 4 mL L−1, T4 = Silica K 2 mL L−1, and T5 = Silica K 4 mL L−1.
Data from: Timing and probability of arrival for sea lice dispersing between salmon farms
<p>Sea lice are a threat to the health of both wild and farmed salmon and an economic burden for salmon farms. With a free-living larval stage, sea lice can disperse tens of kilometers in the ocean between salmon farms, leading to connected sea lice populations that are difficult to control in isolation. In this paper, we develop a simple analytical model for the dispersal of sea lice between two salmon farms. From the model we calculate the arrival time distribution of sea lice dispersing between farms, as well as the level of cross-infection of sea lice. We also use numerical flows from a hydrodynamic model, coupled with a particle tracking model, to directly calculate the arrival time of sea lice dispersing between two farms in the Broughton Archipelago, BC, in order to fit our analytical model and find realistic parameter estimates. Using the parametrized analytical model we show that there is often an intermediate inter-farm spacing that maximizes the level of cross-infection between farms, and that increased temperatures will lead to increased levels of cross-infection.</p>
Enriched Data of Wind Farms (EDWin)
<p>EDWin (Enriched Data of Wind Farms) is a dataset developed to provide information about global wind farms. The dataset is based on OpenStreetMap (OSM) data and has been enriched with additional variables obtained from various databases. The dataset includes two separate data sets, one for global turbines and one for wind farms. As of September 2022, this dataset contains the most recent information available.</p> <p>The datasets have the following structures:</p> <p><strong>Wind Turbine data </strong></p> <p>The data for wind turbines includes 359,947 entries and 12 columns.</p> <table> <thead> <tr> <th>Variable Name</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td>id</td> <td>Key value of the data point</td> </tr> <tr> <td>lon</td> <td>Longitude of the location</td> </tr> <tr> <td>lat</td> <td>Latitude of the location</td> </tr> <tr> <td>country</td> <td>Country where the turbine is located</td> </tr> <tr> <td>continent</td> <td>Continent where the turbine is located</td> </tr> <tr> <td>land cover</td> <td>The type of land on which the turbine is located</td> </tr> <tr> <td>landform</td> <td>The physical features of the land on which the turbine is located</td> </tr> <tr> <td>elevation</td> <td>The altitude of the turbine</td> </tr> <tr> <td>turbine spacing</td> <td>The distance between turbines in the wind farm</td> </tr> <tr> <td>WFid</td> <td>Wind Farm ID</td> </tr> <tr> <td>number of turbines</td> <td>The number of turbines in the wind farm</td> </tr> <tr> <td>shape</td> <td>The rough shape of the wind farm</td> </tr> </tbody> </table> <p> </p> <p><strong>Wind Farm data </strong></p> <p>The data for wind farms includes 20,608 entries and 11 columns.</p> <table> <thead> <tr> <th>Variable Name</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td>WFid</td> <td>Wind Farm ID</td> </tr> <tr> <td>lon</td> <td>Longitude of the location (center of the wind farm)</td> </tr> <tr> <td>lat</td> <td>Latitude of the location (center of the wind farm)</td> </tr> <tr> <td>country</td> <td>Country where the wind farm is located</td> </tr> <tr> <td>continent</td> <td>Continent where the wind farm is located</td> </tr> <tr> <td>land cover</td> <td>The modal value of the land cover for the turbines in the wind farm</td> </tr> <tr> <td>landform</td> <td>The average value of the landform for the turbines in the wind farm</td> </tr> <tr> <td>elevation</td> <td>The average elevation of the turbines in the wind farm</td> </tr> <tr> <td>turbine spacing</td> <td>The average turbine spacing for the turbines in the wind farm</td> </tr> <tr> <td>number of turbines</td> <td>The number of turbines in the wind farm</td> </tr> <tr> <td>shape</td> <td>The rough shape of the wind farm</td> </tr> </tbody> </table> <p> Note that the data for "Country", "Continent", "Land Cover", "Landform", "Elevation" and "Turbine spacing" were collected turbine-specific and later added to the wind farm dataset in an aggregated form. For the categorical variables, the modulus of the respective turbine values was taken, and for numerical variables, the average was calculated. The two variables, number of turbines (i.e. wind farm size) and wind farm shape (i.e. a rough shape of the wind farm), were obtained from the wind farms data and added to the turbine dataset.<br> </p> <p><strong>Sources</strong></p> <p>[1] Open street map. <a href="https://openstreetmap.org/">https://openstreetmap.org/</a>. [Online] Accessed: 2022-10-02.</p> <p>[2] Cutler J. Cleveland, Christopher Morris, Dictionary of Energy (Second Edition), Elsevier, 2015, Pages 638-655, ISBN 9780080968117</p> <p><a href="https://doi.org/10.1016/B978-0-08-096811-7.50023-8">https://doi.org/10.1016/B978-0-08-096811-7.50023-8</a>.</p> <p>[4]<em> </em>Dunnett, S., Sorichetta, A., Taylor, G. <em>et al.</em> Harmonised global datasets of wind and solar farm locations and power. <em>Sci Data</em> <strong>7</strong>, 130 (2020).</p> <p><a href="https://doi.org/10.1038/s41597-020-0469-8">https://doi.org/10.1038/s41597-020-0469-8</a></p> <p>[5] Buchhorn, M. ; Lesiv, M. ; Tsendbazar, N. - E. ; Herold, M. ; Bertels, L. ; Smets, B. Copernicus Global Land Cover Layers-Collection 2. Remote Sensing 2020, 12 Volume 108, 1044. <a href="https://doi.org/10.3390/rs12061044">doi:10.3390/rs12061044</a></p> <p>[6] Theobald, D. M., Harrison-Atlas, D., Monahan, W. B., & Albano, C. M. (2015). Ecologically-relevant maps of landforms and physiographic diversity for climate adaptation planning. PloS one, 10(12), <a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0143619">e0143619</a></p> <p>[7] Global Multi-resolution Terrain Elevation Data 2010 courtesy of the U.S. Geological Survey</p>
Dataset for publications "Willingness to pay for urban agriculture in Oslo" and "The Value of Urban Farming in Oslo, Norway: Community Gardens, Aquaponics and Vertical Farming"
<p>Dataset for publications “(Willingness to pay for urban agriculture in Oslo)[https://zenodo.org/record/6510590#.Y851cXbMLfs]" and “The Value of Urban Farming in Oslo, Norway: Community Gardens, Aquaponics and Vertical Farming”. The data was collected as a contingent valuation study of urban agriculture in Oslo, Norway.</p>
CS9 - Mussel offshore farm technology socio-economic study in Sweden 2022
<p>The dataset contains a socio-economic analysis based on the operation of a mussel farm in Sweden. The production system has the potential to operate offshore and produce increased volumes of biomass per unit of space. The socio-economic metrics calculated include level of employment, gross value added and ripple effects of the production, at 2 different scenarios of scaled up production.</p>
Feeding indicators and bioremediation ability of the warty sea cucumber (Neostichopus grammatus) fed potential wastes from abalone (Haliotis midae) farming.
<p> Data set contains data used to estimate the consumption rate, faecal production rate, assimilation efficiency, growth and bioremediation ability of<em> Neostichopus grammatus</em> fed abalone waste, <em>Ulva lactuca</em> mixed with sand, pellet and sand.</p>
Database 1 - First-year samplings in the CodeRe-farm pilot sites in Greece
<p>This database contains the results of the first year of AWIN assessments and general data recordings in the Greek goat pilots participating in the Code: Re-farm research project.</p>
ipaast - agrivation collaboration EMI survey Manor Farm Field 70
<p>These data were collected as part of the ipaast-Agrivation collaboration to develop survey workflows that produce data compatible with common applications across archaeological, agricultural, and environmental domains. The project is described in the report at: <a href="http://ipaast-czo.glasgow.ac.uk/index.php/ipaast-agrivation-ltd-collaboration/">https://ipaast-czo.glasgow.ac.uk/index.php/ipaast-agrivation-ltd-collaboration/</a>. The ipaast project is funded by the British Academy Award KF5210407.</p>
Dataset from: The effects of crop type, landscape composition and agroecological practices on biodiversity and ecosystem services in tropical smallholder farms
<p>1. In the tropics, smallholder farming characterizes some of the world's most biodiverse landscapes. Agroecology as a pathway to sustainable agriculture has been proposed and implemented in sub-Saharan Africa, but the effects of agricultural practices in smallholder agriculture on biodiversity and ecosystem services are understudied. Similarly, the contribution of different landscape elements, such as shrubland or grassland cover, on biodiversity and ecosystem services to fields remains unknown.</p> <p>2. We selected 24 villages situated in landscapes with varying shrubland and grassland cover in Malawi. In each village, we assessed biodiversity of eight taxa and ecosystem services in relation to crop type, shrubland and grassland cover and the number of agroecological pest and soil management practices on smallholder's fields of different crop types (bean monoculture, maize-bean intercrop, and maize monoculture).</p> <p>3. Increasing shrubland cover altered carabid and soil bacteria communities. Carabid abundance increased in maize but decreased in intercrop and bean fields with increasing shrubland cover. Carabid abundance and richness and wasp abundance increased with soil management practices. Carabid, spider, and parasitoid abundances were higher in bean monocultures, but this was modulated by surrounding shrubland cover. Natural enemy abundances in beans were especially high in landscapes with little shrubland, possibly leading to lower bean damage in monocultures compared to intercropped fields, whereas maize monocultures had higher damage. In maize, grassland cover and pest management practices were positively related to damage. Carabid abundance was higher in fields with high bean damage and increased carabid richness in fields with high maize damage. Parasitoid abundance was negatively associated with bean damage.</p> <p>4. <em>Synthesis and application:</em> Our results suggest that maintaining biodiversity and ecosystem services on smallholder farms is not achievable with a "one size fits all" approach but should instead be adapted to the landscape context and the priorities of smallholders. Shrubland is important to maintain carabid and soil bacterial diversity, but legume cultivation beneficial to natural enemies could complement pest management in landscapes with a low shrubland cover. An increased number of agroecological soil management practices can lead to improved pest control whilst the effectiveness of agroecological pest management practices needs to be re-evaluated.</p>
Birkbeck node deployment plan for Crichton Royal Farm
<p>CSV file containing the planned deployment positions of nodal seismic sensors for PhD project.</p> <p>File contains the following fields:</p> <ol> <li><strong>UniqueID</strong> - a unique numerical identifier for each node</li> <li><strong>Line</strong> - seismic line number</li> <li><strong>Point</strong> - point identifier for each node along the line. Points ending in 01 are vertical component; 02 are horizontal North-South; 03 are horizontal East-West</li> <li><strong>Longitude</strong> - decimal degrees</li> <li><strong>Latitude</strong> - decimal degrees</li> <li><strong>Easting</strong> - UTM Easting, metres</li> <li><strong>Northing</strong> - UTM Northing, metres</li> <li><strong>ZoneNum</strong> - UTM Zone Number</li> <li><strong>ZoneLetter</strong> - UTM Zone Letter</li> </ol>
FIG. 18. — A-C in First in situ middle Pliocene cercopithecoid fossils from the Palaeokarst System of Bolt's Farm (South Africa)
FIG. 18. — A-C, Left lower molar of Papionina indet., specimen BPB 14: A, buccal view; B, lingual view; C, occlusal view; D-F, right lower fourth premolar (p/4) of Papionina indet., specimen BPB 15: D, buccal view; E, lingual view; F, occlusal view; G, H, lower left deciduous first incisor (di/1) of Papionina indet., specimen BPB 27: G, labial view; H, lingual view. Scale bar: 10 mm.
FIG. 22. — A, B in First in situ middle Pliocene cercopithecoid fossils from the Palaeokarst System of Bolt's Farm (South Africa)
FIG. 22. — A, B, Distal part of the ascending ramus of a right mandible of Cercopithecidae indet., specimen BPB 8: A, lateral view; B, distal view; C, D, distal part of the ascending ramus of a right mandible of Cercopithecidae indet., specimen BPB 26: C, lateral view; D, medial view; E, F, right intermediate manual phalanx of Cercopithecidae indet., specimen BPB 19: E, dorsal view; F, volar view. Scale bar: 10 mm.
FIG. 6 in First in situ middle Pliocene cercopithecoid fossils from the Palaeokarst System of Bolt's Farm (South Africa)
FIG. 6. — Teeth of Parapapio broomi Jones, 1937, specimen BPB 1: A, occlusal view of the right tooth row P4/-M3/; B, oblique-lingual view of M2/ and M3/. Scale bar: 10 mm.
FIG. 19 in First in situ middle Pliocene cercopithecoid fossils from the Palaeokarst System of Bolt's Farm (South Africa)
FIG. 19. — Bivariate plots MD × BL (mesio-distal length × maximum bucco-lingual breadth in mm) of lower fourth premolars (p/4) of different taxa of fossil Papionines from South Africa with some from East Africa (data from Freedman 1957, 1965; Frost 2001; Frost & Delson 2002; this study) (Appendix 7).
FIG. 5 in First in situ middle Pliocene cercopithecoid fossils from the Palaeokarst System of Bolt's Farm (South Africa)
FIG. 5. — Skull of Parapapio broomi Jones, 1937, specimen BPB 1: A, fronto-right lateral view (as preserved naturally); B, frontal view; C, right lateral view (of face); D, dorsal view; E, inferior view. Scale bar: 10 mm.
FIG. 13 in First in situ middle Pliocene cercopithecoid fossils from the Palaeokarst System of Bolt's Farm (South Africa)
FIG. 13. — Bivariate plots MD × BL (mesio-distal length × maximum bucco-lingual breadth in mm) of lower third molars (m/3) of different taxa of fossil Papionines from South Africa with some from East Africa (data from Freedman 1957, 1965; Frost 2001; Frost & Delson 2002; this study) (Appendix 6).
FIG. 9 in First in situ middle Pliocene cercopithecoid fossils from the Palaeokarst System of Bolt's Farm (South Africa)
FIG. 9. — Bivariate plots MD × BL (mesio-distal length × maximum bucco-lingual breadth in mm) of upper second molars (M2/) of different taxa of fossil Papionines from South Africa with some from East Africa (data from Freedman 1957, 1965; Frost 2001; Frost & Delson 2002; this study) (Appendix 3).
FIG. 17 in First in situ middle Pliocene cercopithecoid fossils from the Palaeokarst System of Bolt's Farm (South Africa)
FIG. 17. — Part of the left mandible of Parapapio broomi Jones, 1937, specimen BPB 30:A, buccal view;B, lingual view;C, occlusal view of m/3. Scale bar:10 mm.
FIG. 4 in First in situ middle Pliocene cercopithecoid fossils from the Palaeokarst System of Bolt's Farm (South Africa)
FIG. 4. — Preliminary observations concerning the breccia infilling of the "Parapapio spot" at the locus BPB: A, synthetic log of the locus BPB showing the stratigraphic position of Parapapio fossils within Unit 2; B, geological details of the "Parapapio spot" evidencing that the original bedding has been partly deformed by soft sediment deformation features at its topmost part.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.