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85 results for “Food security”
Multiple cropping alone does not improve year-round food security among smallholders in rural India
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Data from: Perceived impact of community kitchens on the food security of Syrian refugees and kitchen workers in Lebanon: Qualitative evidence in a displacement context
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Data from: Pervasive cropland in protected areas highlight trade-offs between conservation and food security
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Food security in ASEAN countries: an overview of rice self-sufficiency amidst the COVID-19 pandemic
<p>Data for analysis in the study on <strong>Food security in ASEAN countries: an overview of rice self-sufficiency amidst the COVID-19 pandemic</strong></p>
Role of sustainable agricultural intensification in food and nutrition security of smallholder subsistence farmers: Evidence from cereal legume intercropping in Eastern Ethiopia
<p>This data set is a biological data generated through field experimentation on cereal legume intercropping. In general, the contents of the data set are type of treatment (experimental materials), site of experiment, biomass and grain yields, etc. There is no legal and ethical issues related to this data set and its process of generation. The data can be re-used with proper acknowledgement of the authors. </p>
Data and code for "Machine learning can guide food security efforts when primary data is not available"
<p>Data and code repository for the paper "Machine learning can guide food security efforts when primary data is not available" by Giulia Martini, Alberto Bracci, Lorenzo Riches, Sejal Jaiswal, Matteo Corea, Jonathan Rivers, Arif Husain, and Elisa Omodei.</p>
A POLICY BACKGROUND PAPER ON CENTRAL ASIA'S FOOD INSECURITY FOOD AND NUTRITION SECURITY FOR SUSTAINABLE DEVELOPMENT
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Farmer-mediated shifts in drought tolerance traits across an aridity gradient in an indigenous food security crop
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THE ROLE OF THE AGRICULTURAL SECTOR IN ENSURING FOOD SECURITY
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Fig 5 from: Ngoute CO, Hunter D, Lecoq M (2021) Perception and knowledge of grasshoppers among indigenous communities in tropical forest areas of southern Cameroon: Ecosystem conservation, food security, and health. Journal of Orthoptera Research 30(2): 117-130. https://doi.org/10.3897/jor.30.64266
Fig 5 Efficiency of the methods used to control pest grasshoppers: conventional methods (A) and traditional methods (B).
Fig 6 from: Ngoute CO, Hunter D, Lecoq M (2021) Perception and knowledge of grasshoppers among indigenous communities in tropical forest areas of southern Cameroon: Ecosystem conservation, food security, and health. Journal of Orthoptera Research 30(2): 117-130. https://doi.org/10.3897/jor.30.64266
Fig 6 Some grasshoppers mainly used/cited by local people: Zonocerus variegatus (pest of crops, fallowland species, use as food and to treat diseases) (A), Oxycatantops spissus (pest of crops, fallowland species, use as food and to treat diseases) (B), Atractomorpha acutipennis (pest of crops, species of forest edge and fallow, use to treat diseases) (C), Parapetasia femorata (forest species, use as indicator to characterize forest ecosystems) (D), Mazea granulosa (forest species, use as indicator to characterize forest ecosystems) (E), Gemeneta terrea (forest species, use as indicator to characterize forest ecosystems) (F).
Fig 3 from: Ngoute CO, Hunter D, Lecoq M (2021) Perception and knowledge of grasshoppers among indigenous communities in tropical forest areas of southern Cameroon: Ecosystem conservation, food security, and health. Journal of Orthoptera Research 30(2): 117-130. https://doi.org/10.3897/jor.30.64266
Fig 3 Perception of grasshoppers by local people: general perception (A), harmful effects of grasshoppers (B), and development stage of pest grasshoppers (C).
Agricultural adaptation to reconcile food security and water sustainability under climate change: the case of cereals in Iran
<p>In this study, we simulate the crop yield and water footprint (WF) of major food crops of Iran on irrigated and rainfed croplands for the historical and the future climate. We assesse the effects of three agricultural adaptation strategies to climate change in terms of potential blue water savings. We then evaluate to what extent these savings can reduce unsustainable blue WF. We find that cereal production increases under climate change in both irrigated and rainfed croplands (by 2.6-3.1 and 1.4-2.3 million t y<sup>-1</sup>, respectively) due to increased yields (6.6%-78.7%). Simultaneously, the unit WF (m<sup>3</sup> t<sup>-1</sup>) tends to decrease in most scenarios. However, the annual consumptive water use increases in both irrigated and rainfed croplands (by 0.3-1.8 and 0.5-1.7 billion m<sup>3</sup> y<sup>-1</sup>, respectively). This is most noticeable in the arid regions, where consumptive water use increases by roughly 70% under climate change. Off-season cultivation is the most effective adaptation strategy to alleviate additional pressure on blue water resources, with blue water savings of 14-15 billion m<sup>3</sup> y<sup>-1</sup>. The second most effective is WF benchmarking, which results in blue water savings of 1.1-3.5 billion m<sup>3</sup> y<sup>-1</sup>. The early planting strategy is less effective, but still leads to blue water savings of 1.7-1.9 billion m<sup>3</sup> y<sup>-1</sup>. In the same order of effectiveness, these three strategies can reduce blue water scarcity and unsustainable blue water use in Iran under current conditions. However, we find that these strategies do not mitigate water scarcity in all provinces per se, nor all months of the year.</p>
Voices for Food: Food Policy Councils, Food Security and Healthy Food Choices
ClinicalTrials.gov study NCT03566095. IPD Sharing: NO. Countries: 0. Publications: 4.
Role of sustainable agricultural intensification in food and nutrition security of smallholder subsistence farmers: Evidence from cereal legume intercropping in Eastern Ethiopia
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Global Food Security-support Analysis Data (GFSAD) Cropland Extent 2015 Australia, New Zealand, China, Mongolia 30 m V001
The NASA Making Earth System Data Records for Use in Research Environments ([MEaSUREs](https://earthdata.nasa.gov/about/competitive-programs/measures)) Global Food Security-support Analysis Data (GFSAD) data product provides cropland extent data over Australia, New Zealand, China, and Mongolia for nominal year 2015 at 30 meter resolution (GFSAD30AUNZCNMOCE). The monitoring of global cropland extent is critical for policymaking and provides important baseline data that are used in many agricultural cropland studies pertaining to water sustainability and food security. The GFSAD30AUNZCNMOCE data product uses the pixel-based supervised classifier, Random Forest (RF), to retrieve cropland extent from a combination of Landsat 8 Operational Land Imager (OLI) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) data. Each GFSAD30AUNZCNMOCE GeoTIFF file contains a cropland extent layer that defines areas of cropland, non-cropland, and water bodies over a 10° by 10° area.Known Issues* Note overlapping tiles: The following tile also covers part of another tile in GFSAD30SEACE (Indonesia). Please ignore the Indonesian data in the following tile: GFSAD30AUNZCNMOCE_2015_S20E120_001_2017286154500.tif* Additional known issues, including constraints and limitations, are provided on page 22 of the ATBD.
Global Food Security-support Analysis Data (GFSAD) Cropland Extent 2015 South America product 30 m V001
The NASA Making Earth System Data Records for Use in Research Environments ([MEaSUREs](https://earthdata.nasa.gov/about/competitive-programs/measures)) Global Food Security-support Analysis Data (GFSAD) data product provides cropland extent data over South America for nominal year 2015 at 30 meter resolution (GFSAD30SACE). The monitoring of global cropland extent is critical for policymaking and provides important baseline data that are used in many agricultural cropland studies pertaining to water sustainability and food security. The GFSAD30SACE data product uses the pixel-based supervised classifier, Random Forest (RF), to retrieve cropland extent from a combination of Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), Landsat 8 Operational Land Imager (OLI) data, and elevation derived from the Shuttle Radar Topography Mission (SRTM) Version 3 data products. Each GFSAD30SACE GeoTIFF file contains a cropland extent layer that defines areas of cropland, non-cropland, and water bodies over a 10° by 10° area.Known Issues* Known issues, including constraints and limitations, are provided on page 18 of the ATBD.
Global Food Security Support Analysis Data (GFSAD) Crop Dominance 2010 Global 1 km V001
The NASA Making Earth System Data Records for Use in Research Environments ([MEaSUREs](https://earthdata.nasa.gov/about/competitive-programs/measures)) Global Food Security Support Analysis Data (GFSAD) Crop Dominance Global 1 kilometer (km) dataset was created using multiple input data including: Advanced Very High Resolution Radiometer (AVHRR), Satellite Probatoire d'Observation de la Terre (SPOT) vegetation, and Moderate Resolution Imaging Spectrometer (MODIS) remote sensing data; crop type data, secondary elevation data; 50-year precipitation and 20-year temperature data; reference sub-meter to 5 meter resolution ground data; and country statistic data.The GFSAD1KCD data were produced for nominal 2010 by overlaying the five dominant crops of the world produced by Ramankutty et al. (2008), Monfreda et al. (2008), and Portman et al. (2009) over the remote sensing derived global irrigated and rainfed cropland area map of the International Water Management Institute (IWMI; Thenkabail et al., 2009a, 2009b, 2011, Biradar et al., 2009) to ultimately create eight classes of crop dominance. The GFSAD1KCD nominal 2010 product is based on data ranging from years 2007 through 2012.Known Issues* See Section 3.0 of the GFSAD 1 km User Guide.
Global Food Security Support Analysis Data (GFSAD) Crop Mask 2010 Global 1 km V001
The NASA Making Earth System Data Records for Use in Research Environments ([MEaSUREs](https://earthdata.nasa.gov/about/competitive-programs/measures)) Global Food Security Support Analysis Data (GFSAD) Crop Mask Global 1 kilometer (km) dataset was created using multiple input data including: remote sensing such as Landsat, Advanced Very High Resolution Radiometer (AVHRR), Satellite Probatoire d'Observation de la Terre (SPOT) vegetation and Moderate Resolution Imaging Spectrometer (MODIS); secondary elevation data; climate 50-year precipitation and 20-year temperature data; reference submeter to 5 meter resolution ground data and country statistics data.The GFSAD1KCM provides spatial distribution of a disaggregated five class global cropland extent map derived for nominal 2010 at 1 km based on four major studies: Thenkabail et al. (2009a, 2011), Pittman et al. (2010), Yu et al. (2013), and Friedl et al. (2010). The GFSAD1KCM nominal 2010 product is based on data ranging from years 2007 through 2012.Known Issues* See Section 3.0 of the GFSAD 1 km User Guide.
Global Food Security-support Analysis Data (GFSAD) Cropland Extent 2015 South Asia, Afghanistan, and Iran product 30 m V001
The NASA Making Earth System Data Records for Use in Research Environments ([MEaSUREs](https://earthdata.nasa.gov/about/competitive-programs/measures)) Global Food Security-support Analysis Data (GFSAD) data product provides cropland extent data over South Asia, Afghanistan, and Iran for nominal year 2015 at 30 meter resolution (GFSAD30SAAFGIRCE). The monitoring of global cropland extent is critical for policymaking and provides important baseline data that are used in many agricultural cropland studies pertaining to water sustainability and food security. The GFSAD30SAAFGIRCE data product uses the pixel-based supervised classifier, Random Forest (RF), to retrieve cropland extent from a combination of Landsat 8 Operational Land Imager (OLI) and elevation derived from the Shuttle Radar Topography Mission (SRTM) Version 3 data products. Each GFSAD30SAAFGIRCE GeoTIFF file contains a cropland extent layer that defines areas of cropland, non-cropland, and water bodies over a 10° by 10° area.Known Issues* Known issues, including constraints and limitations, are provided on page 18 of the ATBD.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.