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1,425 results for “Agriculture”
Long-term Agricultural Experiments: Data Management Survey
<p>Results of an online survey used to guage views of researchers within the LTE community on data management issues and knowledge. The survey was broken down in to 4 main questions and can be found at the following link - further responses are still welcome: <a href="https://forms.office.com/e/8DmapwLRr8" target="_blank" rel="noopener">https://forms.office.com/e/8DmapwLRr8</a>.</p> <ul> <li>About your role</li> <li>Data management & sharing</li> <li>Describing LTEs and their data</li> <li>Challenges for data management & sharing </li> </ul> <p>At the time of publication, 55 responses had been recieved.</p> <p>The survey was developed in response to an LTE Conference Workshop held at Rothamsted Research, UK in June 2023.</p>
Normalized Difference Vegetation Index (NDVI) derived from 2019 National Agriculture Imagery Program (NAIP) data for the central Arizona region
This project calculates two vegetation indices—Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) from the National Agriculture Imagery Program (NAIP) remotely sensed imagery. The intent is to make remotely sensed variables and visualizations accessible to stakeholders and researchers studying the Phoenix metropolitan area. NDVI and SAVI are calculated from the 2019 NAIP imagery (1m resolution). This dataset extends the 2010, 2013, 2015, and 2017 NDVI and SAVI products derived from NAIP imagery (also 1m resolution). All images are cropped to the CAP study area boundary.
Soil-Adjusted Vegetation Index (SAVI) derived from 2019 National Agriculture Imagery Program (NAIP) data for the central Arizona region
This project calculates two vegetation indices—Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) from the National Agriculture Imagery Program (NAIP) remotely sensed imagery. The intent is to make remotely sensed variables and visualizations accessible to stakeholders and researchers studying the Phoenix metropolitan area. NDVI and SAVI are calculated from the 2019 NAIP imagery (1m resolution). This dataset extends the 2010, 2013, 2015, and 2017 NDVI and SAVI products derived from NAIP imagery (also 1m resolution). All images are cropped to the CAP study area boundary.
Raw data for the submitted manuscript entitled "Mapping and Disposal of Irrigation Pipes for a Sustainable Management of Agricultural Plastic Waste", authors Ileana Blanco, Giuliano Vox, Fabiana Convertino, and Evelia Schettini
<p><span>The file regards the evaluation of plastic indexes and agricultural plastic waste quantities in Apulia region due to the use of irrigation pipes. The data is used to identify the critical areas for plastic waste production due to irrigation pipes.</span></p>
Raw data for the submitted manuscript entitled "Prospective Scenarios for Addressing the Agricultural Plastic Waste Issue: Results of a Territorial Analysis"
<p><span>Agricultural activities have been positively affected by the use of plastic products, but this has resulted in the production of plastic waste and led to an increase in environmental pollution. </span><span>This file concerns plastic waste indices to different crop types and plastic products allowed quantifying and georeferencing actual plastic waste production. Two improved scenarios were considered, the first consisted of extending the lifespan of some plastics, and the second entailed the introduction of some biodegradable alternatives. </span></p>
Dataset on Weather-related disasters in agriculture in Italy - WDA
<h1><strong>Abstract</strong></h1> <p>The dataset is the supplementary material for the following journal paper:</p> <p>Pontrandolfi A, Alilla R, De Natale F, Nuti R, Parisse B, Pepe AG, Dataset on Weather-related Disasters in Agriculture (WDA) in Italy 2005–2021, Data in Brief <br><a href="https://doi.org/10.1016/j.dib.2025.111323">https://doi.org/10.1016/j.dib.2025.111323</a></p> <p>The database on Weather-related disasters in agriculture (WDA) is a part of the cloud storage which hosts the materials of the <a href="https://agrometeo.crea.gov.it/">Observatory for agricultural meteorology and climatology</a> of the Research Center for Agriculture and Environment belonging to the Council for Agricultural Research and Economics (CREA). The Observatory website has a specific section devoted to <a href="https://agrometeo.crea.gov.it/dati-e-analisi__trashed/rischio-meteorologico-in-agricoltura/">weather-related risk in agriculture</a>.</p> <p>A specific relational SQL database has been created fo data entry information from the official decrees of WDA declaration in Italy.</p> <p>From this relational SQL database, a <strong>dataset </strong>of WDA has been extracted for the period from 2005 to 2021 and here published</p> <p>The WDA dataset aims to make available useful data for weather-related risk assessment and analysis in the Italian agricultural sector.</p> <h2>Attached content:</h2> <ul> <li>pdf file "A_Description_Dataset_Weather_related_disasters_agriculture_v1.2"</li> <li>csv file "Dataset_Weather_related_disasters_agriculture_v1.2"</li> <li>csv file "DiscoveryMD_Dataset_Weather_related_disasters_agriculture_v1.2"</li> <li>xlsx file "StructuralMD_Dataset_Weather_related_disasters_agriculture_v1.2"</li> </ul>
DESIRA - inventory of digital tools for agriculture, forestry, and rural areas
<p>Inventory of digital tools for agriculture, forestry, and rural areas collected by the DESIRA consortium.</p>
Interactions between bats and agricultural insect pests worlwide
<p>This database illustrates the interactions between bats and agricultural insect pests detected conducting a systematic review in October 2022, entitled "<strong>Pest suppression by bats and management strategies to favour it: a global review</strong>", to be published in the journal Biological Reviews.</p> <p>Methodology applied:</p> <p>We compiled a comprehensive list of agricultural insect pests occurring in temperate and tropical regions. Since no more recent public documents or published lists were available, we extracted the main agricultural insect pests cited in Hill (1983, 1987). Note that species might be considered pests in certain regions while not in others, meaning that this comprehensive list will need careful review by entomologists and local or regional experts for use in agricultural management.</p> <p>We assembled a first list of 1,237 insect pest species or genera extracted from Hill (1987, 1983). We then conducted a literature search in the ISI Web of Science using the R package wosr. We searched for any indexed document containing the following terms in the topic field: "pest species name" AND "bat*", where ‘pest species name’ refers to each of the 1237 species. After the first check of the articles found, we added 562 new pest species to the first list, which were not included in Hill (1987, 1983), but were mentioned in the papers found. Thus, the updated list consisting of 1799 insect pest species was used again to perform the same literature search with the R package wosr. In addition, we also performed three literature searches including the following terms: (i) "bat" or "bats", "diet*", and "insect*"; (ii) "bat" or "bats", "predat*", and "insect*"; (iii) "bat" or "bats", "diet*", and "arthropod*". We identified a total of 1125 articles, of which we retained only those that identified bat prey at the genus or species level (N = 95).</p> <p>Predator - prey interactions were extracted from the articles reviewed and added in this data set, showing each bat species with the insect pest species it consumed, as well as the method used to confirm predation.</p>
Regional E-Atlas of the Greater Phoenix Region: Areas of significant agricultural and residential groundwater use, 1996-2000
Spatial distribution of well water usage (groundwater) for the period 1996 - 2000. These data present significant areas of agricultural and residential groundwater use during this period. This is a spatial data object with a Coordinate Reference System (CRS) of EPSG:3479 NAD83(NSRS2007) / Arizona Central (ft); https://www.spatialreference.org/ref/epsg/3479/). The coordinate reference system (CRS) associated with these data when they were constructed initially was misrepresented in early versions (<= knb-lter-cap.101.8) of this dataset. The CAP LTER has attempted to assign a CRS based on reasonable values but the accuracy of the identified CRS cannot be certain.
Land-cover mapping of the central Arizona region based on 2015 National Agriculture Imagery Program (NAIP) imagery
Detailed land-cover mapping is essential for a range of research issues addressed by sustainability science, especially for questions posed of urban areas, such as those of the Central Arizona-Phoenix Long-Term Ecological Research (CAP LTER) program. This project provides a 1-meter land-cover mapping of the CAP LTER study area (greater Phoenix metropolitan area and surrounding Sonoran desert). The mapping is generated primarily using 2015 National Agriculture Imagery Program (NAIP) four-band data, with auxiliary GIS data used to improve accuracy. Auxiliary data include the 2015 cadastral parcel data, the 2014 USGS LiDAR data (1-meter), the 2014 Microsoft/OpenStreetMap Building Footprint data, the 2015 Street TIGER/Line, and a previous (2010) NAIP-based land-cover map of the study area (https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-cap&identifier=623). Among auxiliary data, building footprints and LiDAR data significantly improved the boundary detection of above-ground objects. Post-classification, manual editing was applied to minimize classification errors. As a result, the land-cover map achieves an overall accuracy of 94 per cent. The map contains eight land cover classes, including: (1) building, (2) asphalt, (3) bare soil and concrete, (4) tree and shrub, (5) grass, (6) water, (7) active cropland, and (8) fallow. When compared to the aforementioned, previous (2010) NAIP-based land-cover map for the study area, buildings and tree canopies are classified more accurately in this 2015 land-cover map.
Normalized Difference Vegetation Index (NDVI) derived from 2021 National Agriculture Imagery Program (NAIP) data for the central Arizona region
This project calculates two vegetation indices —Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI)— from the National Agriculture Imagery Program (NAIP) remotely sensed imagery. The intent is to make remotely sensed variables and visualizations accessible to stakeholders and researchers studying the Phoenix metropolitan area. NDVI and SAVI are calculated from the 2021 NAIP imagery (1m resolution). This dataset extends the 2010, 2013, 2015, 2017, and 2019 NDVI and SAVI products derived from NAIP imagery (also 1m resolution). All images are cropped to the CAP LTER study area boundary of central Arizona, USA. The materials presented here include NDVI data with SAVI data presented in a companion dataset that is also available through the EDI.
Soil-Adjusted Vegetation Index (SAVI) derived from 2021 National Agriculture Imagery Program (NAIP) data for the central Arizona region
This project calculates two vegetation indices —Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI)— from the National Agriculture Imagery Program (NAIP) remotely sensed imagery. The intent is to make remotely sensed variables and visualizations accessible to stakeholders and researchers studying the Phoenix metropolitan area. NDVI and SAVI are calculated from the 2021 NAIP imagery (1m resolution). This dataset extends the 2010, 2013, 2015, 2017, and 2019 NDVI and SAVI products derived from NAIP imagery (also 1m resolution). All images are cropped to the CAP LTER study area boundary of central Arizona, USA. The materials presented here include SAVI data with NDVI data presented in a companion dataset that is also available through the EDI.
Agronomic Yields in Row Crop Agriculture at the Kellogg Biological Station, Hickory Corners, MI (1989 to 2021)
Dataset AbstractThis data set contains information about agronomic yields for the Main Cropping System Experiment which include treatments 1-4 (corn – wheat – soybean rotations) and after 1994 treatment 6 (alfalfa). Agronomic yields are measured during normal crop harvest; yields are determined by machine harvesters appropriate to each crop as described in the Agronomic protocol.original data source http://lter.kbs.msu.edu/datasets/23
North Temperate Lakes LTER: Patterns of Soil Phosphorus Across an Urbanizing Agricultural Landscape 2000 - 2001
Understanding the magnitude and location of soil phosphorus (P) accumulation in watersheds is a critical step toward managing runoff of this pollutant to aquatic ecosystems. Here, we examined the usefulness of urban-rural gradients (URGs), an emerging paradigm in urban ecology, for predicting soil P concentrations across a rapidly urbanizing agricultural watershed in southern Wisconsin. We compared several measures of an urban-rural gradient to predictors of soil P such as soil type, slope, topography, land use, land cover, and fertilizer and manure use. Most of the factors that were expected to drive differences in soil P concentrations were not found to be good predictors of soil P; while there were several significant relationships, most explained only a small proportion of the variation. There was a significant relationship between soil P concentration and each of the urban-rural gradients, but these relationships explained only a small amount of the variation in soil P concentrations. Soil P concentration, unlike some other ecosystem properties, is not well predicted by urban-rural gradients Additional Chemical Analyses: These additional analyses were done to provide comparisons to Bray-1 P. Specifically, we wanted to know whether, in Dane County, there was a consistent relationship between total P and Bray-1 P. For sample sites on private property, specific site location information, such as GPS coordinates, is not included in these datasets. If you have a need for this information, please get in touch with the contact person listed above Number of sites: 334; 20 of these sites with additional chem analyses
Data set and code supporting Marshall et al. 2020. No room to roam: King Cobras reduce movement in agriculture.
<p>Data and code used in the publication:</p> <p>Marshall, B.M., Crane, M., Silva, I., Strine, C.T., Jones, M.D., Hodges, C.W., Suwanwaree, P., Artchawakom, T., Waengsothorn, S., Goode, M. (2020). No room to roam: King Cobras reduce movement in agriculture. <em>Mov Ecol</em> <strong>8, </strong>33 (2020). https://doi.org/10.1186/s40462-020-00219-5</p> <p>Marshall, B.M., Crane, M., Silva, I., Strine, C.T., Jones, M.D., Hodges, C.W., Suwanwaree, P., Artchawakom, T., Waengsothorn, S., Goode, M. (2020). No room to roam: King Cobras reduce movement in agriculture. bioRxiv 2020.03.24.006676; doi: https://doi.org/10.1101/2020.03.24.006676</p> <p>Including: telemetry data, habitat shapefile and derived rasters, ISSF and JAGS model specification and results, code to reproduce analysis and generate figures. </p>
Data from: Spatial and host-related variation in prevalence and population density of wheat curl mite (Aceria tosichella) cryptic genotypes in agricultural landscapes
<p><strong>Filename: coord.csv</strong></p> <p>Names of the sampling locations and their geographic coordinates.</p> <ol> <li>Name - sampling locality identifier</li> <li>Lat - latitude</li> <li>Long - longitude</li> </ol> <p> </p> <p><strong>Filename: lineages.csv</strong></p> <ol> <li>id.sample - sample identifier</li> <li>host - host species (Arrela=<em>Arrhenantherum elatius</em>, Avesat=<em>Avena sativa</em>, Broine=<em>Bromus inermis</em>, Elyres=<em>Elymus repens</em>, Horvul=<em>Hordeum vulgaris</em>, Seccer=<em>Secale cereale</em>, Triaes=<em>Triticum aestivum</em>, Tririm=<em>Triticale rimpaui</em></li> <li>x, y - geodetic coordinates</li> <li>stems - no. of stems in a sample</li> <li>leaves - no. of leaves in a sample</li> <li>MT.01 to MT.27 - no. of mites belonging to each genetic lineage</li> </ol>
Database of water, agriculture and economic development in Huang-Huai-ai region of China
<p>The database of water, agriculture and economic development contains 61 prefecture-level cities in the Huang-Huai-Hai region from 2010 to 2019.</p> <p>Firstly, we summarize the city-level agricultural dataset from the Provincial Bureau of Statistics, which contains the annual agricultural output, total planting area, labor, fertilizer, and machinery of each prefecture-level city. </p> <p>Secondly, we collect agricultural output (total land value per hectare) as the output and four main types of inputs: labor, fertilizer, machinery, and agricultural water consumption.</p> <p>Thirdly, we also collect city-level unbalanced panel data from the Water Resources Bulletin database, which contains annual data on agricultural water consumption, groundwater supply, precipitation, and groundwater resources.</p>
Swiss public's acceptance and sustainability perceptions of food produced with chemical, digital and mechanical weed control measures and the influence of information source on technology perception in agriculture
<p><span>This data was obtained from an online survey conducted with the Swiss public from the two biggest language regions (German and French) in Switzerland. The survey was conducted in February 2023. Participants were recruited through a professional panel provider and quotas were used for age, gender and language region. The final sample contained </span><span>542 respondents. </span><span>In the first part of the survey, respondents provided basic sociodemographic information. In the second part, their sustainability perceptions regarding four different weed management practices (full-surface spraying, hoeing machine, spot spraying and precise spraying) were investigated. Respondents were then assigned to one of five information source groups, in which information on a hoeing and a milking robot was presented, using 5 different information sources (male/female farmer, male/female scientist, no source). Technology perception was assessed using several questions and aspects. Finally, respondents answered several questions assessing their attitudes towards the perception of farmers, food technology neophobia, chemophobia and the importance of naturalness. The survey can be used and adapted to different contents, aiming to investigate public perception of smart farming technologies and the influence of information sources on technology perception. </span></p>
History, Adoption and Key impacts of precision agriculture
<p>Precision agriculture technologies have revolutionized modern farming practices, offering innovative solutions to optimize crop production, minimize resource use, and enhance environmental sustainability. This research paper explores the historical evolution, adoption trends, and importance of precision agriculture technologies in contemporary agriculture.</p>
Sustainable Agricultural Pathways in Europe (SIPATH) – land use data
<p>The published land use data were part of the project “What is Sustainable Intensification? Operationalizing Sustainable Agricultural Pathways in Europe (SIPATH)”, which was funded by the Swiss National Science Foundation (grant no. CRSII5_183493). The overall objective of this project was to assess short-term trajectories in agriculture land use and landscape structure over a period of 20 years for 16 individual study sites located in 11 countries, ranging from the Mediterranean to the boreal zone. The following datasets illustrate land use data that were generated for the different study sites at two points in time between 2000 and 2020, depending on data availability. The data collection process encompassed land use mapping through visual image interpretation of orthorectified aerial photographs using geographic information systems (ESRI ArcGIS Pro). Land use digitalization was conducted by two scientists in Switzerland, who were in contact with the respective project partners in the study countries to exchange expert knowledge. Minimal mapping unit was 25m<sup>2</sup> for areal elements. Land cover was classified following the European Nature Information System (EUNIS) habitat classification (EEA 2019). The broadest habitat classes were systematically mapped in the study sites, with each covering an area of approximately 25 km². While EUNIS focuses on habitat types, the study, however, targeted the intensity of agricultural land-use, several EUNIS classes were complemented with levels of land-use intensities. This was applied for grassland, olive groves and fruit orchards. The spatial resolution of the orthophotos ranged from 25cm to 2m. Accordingly, there were situations in which the spatial resolution was insufficient to accurately determine the land use. In such cases, either orthophotos from about the same year were consulted, possibly showing different phenological stages, or land use statistics and expert judgement based on local expert knowledge of the respective study area were applied.</p> <p> </p> <p>Reference:</p> <p>EEA, 2019. EUNIS habitat classification 2007 (Revised descriptions 2012) amended 2019. Copenhagen (<a href="https://www.eea.europa.eu/ds_resolveuid/27788ca43d9e4f2e9477b15df88d20be" target="_blank" rel="noopener">Permalink</a>)</p>
ScienceDex guides
Understand access before you commit
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.