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1,425 results for “Agriculture”
Supplementary material 3 from: Lange S, Mockford A, Burkhard B, Müller F, Diekötter T (2023) As green infrastructure, linear semi-natural habitats boost regulating ecosystem services supply in agriculturally-dominated landscapes. One Ecosystem 8: e108540. https://doi.org/10.3897/oneeco.8.e108540
LSE and the threat to erosion by water
Supplementary material 1 from: Lange S, Mockford A, Burkhard B, Müller F, Diekötter T (2023) As green infrastructure, linear semi-natural habitats boost regulating ecosystem services supply in agriculturally-dominated landscapes. One Ecosystem 8: e108540. https://doi.org/10.3897/oneeco.8.e108540
Summary statistics
Supplementary material 4 from: Lange S, Mockford A, Burkhard B, Müller F, Diekötter T (2023) As green infrastructure, linear semi-natural habitats boost regulating ecosystem services supply in agriculturally-dominated landscapes. One Ecosystem 8: e108540. https://doi.org/10.3897/oneeco.8.e108540
LSE and hydrologic soil groups
Supplementary material 2 from: Lange S, Mockford A, Burkhard B, Müller F, Diekötter T (2023) As green infrastructure, linear semi-natural habitats boost regulating ecosystem services supply in agriculturally-dominated landscapes. One Ecosystem 8: e108540. https://doi.org/10.3897/oneeco.8.e108540
LSE and landscape's slope
Insight into the impact of viruses on biogeochemical processes in agricultural soils
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Expert opinion and model of natural pest control in agricultural landscapes
<table> <tbody> <tr> <td> <div>The survey asks expert How they would estimate the capacity different land use (herbaceous semi-natural habitat, forest edge, forest core) to support the abundance of the following insect groups in the landscape: ‘complete generalists’ ‘specialized predators’, ‘parasitoids’. The score is provided on a scale from 0 (no relevant capacity) to 10 (very high relevance). For each opinion, experts provided a level of confidence: 1 'I don’t feel confident with my score', 2 'I feel fairly confident with my score” and 3: 'I feel confident with my score'. In the same way experts were asked to rate a baseline scenario of agricultural fields defined as as a conventionally managed average field (with an average field size of 3-7 ha, fertilization and pesticide application compared to the region of interest) of medium crop diversity with three functional groups over 4 years (e.g., cereal, oilseed crop, root crop). Then experts were asked how much a single change from one practices to an alternative one (e.g., conventional to organic) would affect the score they provided - 50 to - 100% = considerably worse -20 to -50%= notably better -1 to -20% = slightly worse 0 = no change +1 to 20% = slightly better +20 to 50%= notably better + 50 to 100%= considerably better +100 to 200% = extremely better/</div> <div> </div> <div>Finally experts were asked about the distance at which landscape change affect the abundance of the three group of natural enemies.</div> <div> </div> <div>The survey was conducted from April to June 2021.</div> <div> </div> <div>The scores for each practices are derived by mixed effect model and provided in the file Code_Habitats.csv. This file is used in the R model provided here to calculate natural pest control using the weighted moving window describe in Riggi et al., 2024 Ecological Indicators. NPC_Model_Riggi.R is the code of the model Fields_AOI.shp represent an example of fields with agricultural land use information CadasterEnv_AOI.tif is the land use map CADASTERENV_Label_to_change_input.csv allows the reclassification of land use into forest edge, core and herbaceous areas Code_Habitats-1.csv contrains the values associated to each support of land use for natural pest control. (2024-02-06) <div>Collapse Description [-]</div> </div> </td> </tr> <tr></tr> </tbody> </table>
Review of WorldFAIR Agricultural Plant-Pollinator Data Pilot: Plant-flower visitor interactions recorded in 49 sites in Argentina (Buenos Aires: Carlos Casares county) by Marcos Monasterolo (2013-15) and Antonio López Carretero (2016).
<h3>Abstract</h3> <p>Life on Earth is sustained by complex interactions between organisms and their environment. These biotic interactions can be captured in datasets and published digitally. We describe a review process of such an openly accessible digital interactions dataset of known origin, and discuss their outcome. The dataset under review (aka globalbioticinteractions/gonzalez-vaquero2023) has size 1.95MiB and contains 2471 interactions with 1 unique types of associations (e.g., flowersVisitedBy) between 77 primary taxa (e.g., Hirschfeldia incana) and 141 associated taxa (e.g., Palpada). The report includes detailed summaries of interactions data as well as a taxonomic review from multiple perspectives.</p> <p>Please note that the pilot contributors have opted to shared their review and metadata only. This means that data appendices in the data review are not openly available. The data associated are published in the restricted review data publication at </p> <table> <tbody> <tr> <td><strong>filename</strong></td> <td><strong>description</strong></td> </tr> <tr> <td>index.pdf</td> <td>data review report as pdf</td> </tr> <tr> <td>index.docx</td> <td>data review report as docx</td> </tr> <tr> <td>index.md</td> <td>data review report as markdown</td> </tr> <tr> <td>index.html</td> <td>data review report as html</td> </tr> </tbody> </table>
Supplementary material 3 from: Mulema J, Phiri S, Bbebe N, Chandipo R, Chijikwa M, Chimutingiza H, Kachapulula P, Kankuma Mwanda F, Matimelo M, Mazimba-Sikazwe E, Mfune S, Mkulama M, Moonga M, Mphande W, Mufwaya M, Mulenga R, Mweemba B, Ndalamei Mabote D, Nkunika P, Nthenga I, Tembo M, Chowa J, Odunga S, Opisa S, Kasoma C, Charles L, Makale F, Rwomushana I, Phiri NA (2024) Rapid risk assessment of plant pathogenic bacteria and protists likely to threaten agriculture, biodiversity and forestry in Zambia. NeoBiota 91: 145-178. https://doi.org/10.3897/neobiota.91.113801
Plant pathogenic bacteria assessment for Zambia
Supplementary material 4 from: Mulema J, Phiri S, Bbebe N, Chandipo R, Chijikwa M, Chimutingiza H, Kachapulula P, Kankuma Mwanda F, Matimelo M, Mazimba-Sikazwe E, Mfune S, Mkulama M, Moonga M, Mphande W, Mufwaya M, Mulenga R, Mweemba B, Ndalamei Mabote D, Nkunika P, Nthenga I, Tembo M, Chowa J, Odunga S, Opisa S, Kasoma C, Charles L, Makale F, Rwomushana I, Phiri NA (2024) Rapid risk assessment of plant pathogenic bacteria and protists likely to threaten agriculture, biodiversity and forestry in Zambia. NeoBiota 91: 145-178. https://doi.org/10.3897/neobiota.91.113801
Plant pathogenic protist assessment for Zambia
Supplementary material 2 from: Mulema J, Phiri S, Bbebe N, Chandipo R, Chijikwa M, Chimutingiza H, Kachapulula P, Kankuma Mwanda F, Matimelo M, Mazimba-Sikazwe E, Mfune S, Mkulama M, Moonga M, Mphande W, Mufwaya M, Mulenga R, Mweemba B, Ndalamei Mabote D, Nkunika P, Nthenga I, Tembo M, Chowa J, Odunga S, Opisa S, Kasoma C, Charles L, Makale F, Rwomushana I, Phiri NA (2024) Rapid risk assessment of plant pathogenic bacteria and protists likely to threaten agriculture, biodiversity and forestry in Zambia. NeoBiota 91: 145-178. https://doi.org/10.3897/neobiota.91.113801
Guidelines for scoring species
Supplementary material 5 from: Mulema J, Phiri S, Bbebe N, Chandipo R, Chijikwa M, Chimutingiza H, Kachapulula P, Kankuma Mwanda F, Matimelo M, Mazimba-Sikazwe E, Mfune S, Mkulama M, Moonga M, Mphande W, Mufwaya M, Mulenga R, Mweemba B, Ndalamei Mabote D, Nkunika P, Nthenga I, Tembo M, Chowa J, Odunga S, Opisa S, Kasoma C, Charles L, Makale F, Rwomushana I, Phiri NA (2024) Rapid risk assessment of plant pathogenic bacteria and protists likely to threaten agriculture, biodiversity and forestry in Zambia. NeoBiota 91: 145-178. https://doi.org/10.3897/neobiota.91.113801
Assessment for vector species
Supplementary material 1 from: Mulema J, Phiri S, Bbebe N, Chandipo R, Chijikwa M, Chimutingiza H, Kachapulula P, Kankuma Mwanda F, Matimelo M, Mazimba-Sikazwe E, Mfune S, Mkulama M, Moonga M, Mphande W, Mufwaya M, Mulenga R, Mweemba B, Ndalamei Mabote D, Nkunika P, Nthenga I, Tembo M, Chowa J, Odunga S, Opisa S, Kasoma C, Charles L, Makale F, Rwomushana I, Phiri NA (2024) Rapid risk assessment of plant pathogenic bacteria and protists likely to threaten agriculture, biodiversity and forestry in Zambia. NeoBiota 91: 145-178. https://doi.org/10.3897/neobiota.91.113801
All data from horizon scanning for Zambia
Impact of Digital Inclusive Finance on Agricultural Total Factor Productivity in Zhejiang Province from the Perspective of Integrated Development of Rural Industries
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Step Change -CSI 5 – Off-Grid Renewable Energy in Agriculture in Uganda – Infographic
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STUDYING THE PROPERTIES OF TRANSMISSION OILS USED IN AGRICULTURAL MACHINERY
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Data from: Evaluating the Agricultural Production Systems sIMulator (APSIM) wheat module for California
<p>Context: Computer-based crop simulation models are important tools for agricultural research and management. The Agricultural Production Systems sIMulator (APSIM) is commonly used around the world, but has not been widely validated in North America.</p> <p>Aims: The objective of this work was to evaluate the reliability of APSIM for simulating wheat production in California, and to identify future research needs, by using pre-existing data from state-wide variety trials.</p> <p>Methods: Environmental and management data from three seasons of state-wide wheat variety trials, were used to parameterize the APSIM-Wheat module (version 7.10 r4220). Simulated yield and protein data were compared to and actual field data to test the reliability of the APSIM simulations.</p> <p>Key results: The most reliable simulation of grain yield had a root mean square error of 1040 kg/ha and normalised root mean square error of 16 % relative to actual field data.</p> <p>Conclusions: The accuracy of the simulations was comparable to other tests of the APSIM-Wheat module in environments where it has not been previously calibrated, but was too low to be considered reliable. The lack of reliability was due to the poor representation of local Californian wheat genotypes, as well as inaccuracy of management and environmental data.</p> <p>Implications: APSIM could be a valuable tool for wheat research and management in California, but our work shows the current model is unreliable. Further research is needed to generate field data needed for model calibration.</p>
MARKETING SYSTEM AND POSSIBILITIES OF ITS APPLICATION IN AGRICULTURAL ENTERPRISES.
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Raw data for the submitted manuscript: Response of the terrestrial crustacean Porcellio scaber and the mealworm Tenebrio molitor to agricultural microplastics exposure: comparison of nondegradable and biodegradable fossil-based mulching films
<p>We uploaded two datasets on the response of terrestrial crustacean Porcellio scaber and the mealworm Tenebrio molitor to agricultural microplastics exposed in soil for 3 weeks and 4 weeks, respectively. </p> <p>a) <strong>Dataset</strong> "Response of the terrestrial crustacean Porcellio scaber to agricultural microplastics in soil" contains data on: electron transfer system activity, haemocyte viability, and share of hyalinocytes, semigranulocytes and granulocytes in haemolymph. </p> <p>b) <strong>Dataset</strong> "Response of the mealworm Tenebrio molitor to agricultural microplastics in soil" contains data on: larval moult and growth and animal survival </p> <p>These datasets are linked to publication entitled: Response of the terrestrial crustacean Porcellio scaber and the mealworm Tenebrio molitor to agricultural microplastics exposure: comparison of nondegradable and biodegradable fossil-based mulching films. Methods are described in detail in publication. Manuscript under review. </p>
Raw data for the submitted manuscript: Multigenerational effects of agricultural microplastics on the mealworm Tenebrio molitor
<p>Dataset "Multigenerational effects of agricultural microplastics on the mealworm Tenebrio molitor" contains data on the moult, growth. development and survival of mealworms exposed to microplastics in food over two generations. Datasets are part of publication entitled: Multigenerational effects of agricultural microplastics on the mealworm Tenebrio molitor which is currently under review. </p> <p> </p>
Data Set for Asymmetric Effects of Government Investment on Private Agricultural Spending in Iraq: A Nonlinear ARDL Approach
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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.