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1,425 results for “Agriculture”
CLDF dataset derived from Lee and Hasegawa's "Bayesian phylogenetic analysis supports an agricultural origin of Japonic languages" from 2011
<p>Cite the source of the dataset as:</p> <blockquote> <p>Lee, Sean and Hasegawa, Toshikazu (2011). Bayesian phylogenetic analysis supports an agricultural origin of Japonic languages. Proceedings of the Royal Society B: Biological Sciences, 278(1725), 3662–3669. doi:10.1098/rspb.2011.0518.</p> </blockquote>
Dataset on consumers' perception of different types of sustainability levies, Swiss agriculture and farmers and willingness to choose suboptimal potatoes in different settings
<p><em><span>This dataset includes survey data from 481 Swiss consumers. Data were collected in the German-speaking parts of Switzerland in February and March 2024. The survey includes three independent main parts. </span></em></p> <p><em><span>In a first part, we collected qualitative and quantitative data on participants’ perception of Swiss agriculture and farmers. Specifically, participants’ trust in crop and livestock production farmers and their perceived knowledge about production methods and their affect towards farmers was assessed. </span></em></p> <p><em><span>In a second part, we collected quantitative data on participants’ preference for different sustainability levies. For this, six different products were used (i.e., fresh/processed vegetables, dairy, and meat). For each of these six products, participants were shown four levy options from which they had to choose the one that they found most appealing. For vegetables, the options were: (A) reduction of risks related to plant protection products, (B) more support for local farmers, (C) support for environmental sustainability, and (D) sustainability projects in general. For the animal products, option (A) was an increase in animal welfare, whilst options (B), (C) and (D) were the same as for the vegetable products.</span></em></p> <p><em><span>In a third part, we collected qualitative and quantitative data on participants preferences for suboptimal or optimal potatoes. Here, a 2 × 2 experimental design (setting × information) was used. This means that participants were presented with either a supermarket or farm shop setting and with or without food waste information. Participants then chose between two potatoes: optimal potato A, suboptimal potato B, or neither. Both potatoes were equally expensive.</span></em></p>
Soil properties in agricultural systems affect microbial genomic traits
<p>Code and supplementary table </p>
Usability Evaluation of the Agriculture Product Types Ontology (APTO)
<p><strong>Recommended citation</strong>:<br><br>Soares, F. M., Saraiva, A. M., Pires, L. F., Drucker, D. P., Braghetto, K. R., Santos, L. O. B. D. S., Moreira, D. D. A., Corrêa, F. E., & Delbem, A. C. B. (2025). A novel ux-based approach for ontology evaluation: Applying tree testing to the agricultural product types ontology. <em>IEEE Access</em>, 13, <a href="https://doi.org/10.1109/ACCESS.2025.3595447">https://doi.org/10.1109/ACCESS.2025.3595447</a><br><br>In evaluating the APTO ontology, we selected tree testing as the primary UX measuring protocol. We believe tree testing is particularly suitable for ontology evaluation as it combines various metrics, such as time on task and task success, to assess how users navigate and understand a hierarchy of concepts. This method allows us to trace user paths through the ontology's structure, identifying which aspects of the modeling may be confusing or inaccurate from the user's perspective. By analyzing these user interactions, we can gain valuable insights into how the ontology's design impacts usability, ultimately guiding improvements to better align with user needs.<br><br>Update in this version: images of pietrees.<br><br><br></p>
Agriculture_2022_WAM-EPA-Ireland-2024
<p><span>2024 Excel workbook for the Agriculture sector under the With Additional Measures (WAM) scenario for 1990–2022, as compiled by the Environmental Protection Agency (Ireland) in support of Ireland's annual GHG inventory and projection submissions. The workbook also includes projected time series for data up to 2050, based on Teagasc modelling using economic projections of agricultural production.</span></p>
Morphological and physical chemical characterization of main agricultural plastics articles used for protected cultivation systems during ageing in fields, and collection practices
<p>This dataset includes data generated upon the implementation of the ST 1.2.1 "Analysis of degradation and fragmentation of AP and transfer of MNP to soil". The activities dealt with the study of degradation and fragmentation from weathering and agricultural practices of conventional and biodegradable AP relevant for transfer of MNP to soil (during both use and end of life). In particular, the experimental data refer to characterization of biodegradable mulch films, pristine (coded M-BIO0) or subjected to photo-oxidative weathering (M-BIO192), as well as the same samples buried in soil for varying time periods, up to 353 days. The folders included contain gel permeation chromatography (GPC) and Matrix-assisted Laser Desorption Ionization (MALDI-TOF) data, which account for the change in film molecular weight upon soil burial. Furthermore, Differential Scanning Calorimetry (DSC) data and Scanning Electron Microscopy (SEM) and Water Contact Angle (WCA) images of some selected samples are also provided. The folder named MS RAW FILES.zip includes all the mass spectrometry raw data.</p>
Map of SESs/STs Bundles for Estonian agricultural land
<p>The internal EJP SOIL project SERENA contributed to the evaluation of soil multifunctionality aiming at providing assessment tools for land planning and soil policies at different scales. By co-working with relevant stakeholders, the project provided co-developed indicators and associated cookbooks to assess and map them, to report both on soil degradation, soil-based ecosystem services and their bundles, under actual conditions and for climate and land-use changes, at the regional, national, and European scales.</p> <p><span>This map is the outcome of applying a bundles cookbook developed in SERENA based on soil threats (ST) SOC loss and erosion potential and soil ecosystem service (SES) biomass production. The resulting bundles are clusters of SESs/STs where the intra-cluster variability is lower than the inter-cluster variability in the mean values of the selected SESs/STs to identify the bundles. </span><span>The generated map of SESs/STs Bundles for Estonian agricultural land is at the resolution of 100m. The input data for the cookbook was the Map of soil organic carbon loss of mineral soils in Estonia ; Soil water erosion potential in agricultural soils modelled by USLE, and primary biomass production. </span></p>
Exploring the economic, social, and environmental dimensions of community-supported agriculture in Italy (dataset)
<p>Dataset inherent to the following article:</p> <p>Medici, M., Canavari, C., Castellini, A., 2021. <em>Exploring the economic, social, and environmental dimensions of community-supported agriculture in Italy</em>, Journal of Cleaner Production, 316, 128233, DOI: <a href="https://doi.org/10.1016/j.jclepro.2021.128233">10.1016/j.jclepro.2021.128233</a></p>
Data supplement for "Global agricultural trade and land system sustainability: implications for ecosystem carbon storage, biodiversity and human nutrition"
<p>This data supplements the publication "Global agricultural trade and land system sustainability: implications for ecosystem carbon storage, biodiversity and human nutrition" by Thomas Kastner, Abhishek Chaudhary, Simone Gingrich, Alexandra Marques, U. Martin Persson, Giorgio Bidoglio, Gaëtane Le Provost, Florian Schwarzmüller, available here:</p> <p><a href="https://doi.org/10.1016/j.oneear.2021.09.006">https://doi.org/10.1016/j.oneear.2021.09.006</a></p> <p>For details, please refer to that publication.</p>
Dataset for review on innovative contracts for the promotion of biodiversity and ecosystem services in agricultural management: contract design and governance characteristics
<p>Dataset for H2020 project Contracts2.0 (GA No 818190), WP2, Task 2.1, Deliverable 2.1.</p> <p>This dataset contains the information needed to reproduce the results in:<br> Bredemeier, Birte; Herrmann, Sylvia; Sattler, Claudia; Prager, Katrin; van Bussel, Lenny, Rex, Julia (submitted): Can the greater integration of biodiversity and ecosystem services into agricultural management be achieved through innovative contract design? Submitted to Ecosystem Services.<br> <br> The Contracts2.0 project aims to develop novel contract-based approaches to incentivise farmers for the increased provision of environmental public goods alongside private goods.<br> Based on a literature review and integration of expert knowledge, we identified a comprehensive set of approaches to innovative contracts deviating from mainstream AECM contracts, and provide an overview of the variety of contracts currently being tested and experimented with. This includes different contract types like innovative Payments for Ecosystem Services (PES) approaches, value chain approaches and land tenure contracts, as well as their hybrids.</p> <p><br> We analysed 62 cases providing insights into characteristics of contract design and contract governance and the wider policy framework.</p> <p><br> The present dataset contains information on the criteria and specifications used to describe the cases studied, as well as the evaluation of each case.</p> <p><br> This information has been compiled to the best of our knowledge based on the sources available.<br> <br> For further information on the project Contracts2.0, please visit our website www.project-contracts20.eu. </p>
Data and Code from: On-farm land management strategies and production challenges in United States Organic Agricultural Systems.
<p>This repository contains data and code used in:</p> <p>Isaac Mpanga, Russel Trondstad, Jessica Guo, David LeBauer, and John Omololu, 2021. On-farm land management strategies and production challenges in United States Organic Agricultural Systems. Current Research in Environmental Sustainability.</p> <p>It provides USDA Surveys of Agricultural Production from 2008-2019 to investigate state and national trends by state in organic farm area, number, and sales, as well to evaluate national trends in on-farm land-use practices and challenges facing US organic production.</p> <p>It also includes code used to transform, visualize, and analyze the data, and derived data products - notably organic farm area and sales with values imputed to correct for redacted state level measures.</p>
Visualisation Techniques for Decision-making in Agriculture
<p>This dataset is the result of a systematic review conducted to survey visualisation techniques for decision-making in agriculture.</p>
Interviews with experts on digitalisation in agriculture, forestry, and rural areas (H2020 DESIRA project, WP1)
<p>Interviews with experts on digitalisation in agriculture, forestry, and rural areas (H2020 DESIRA project, WP1).</p> <p>The scripts and the answers are provided. Two groups of experts have been interviewed: the first group with expertise in ICT, and the second group with expertise in socio-economic aspects. </p>
Meteorological variables for Agriculture: a Dataset for the Italian Area (MADIA)
<p> </p> <p>The dataset is the supplementary material for the following journal paper:</p> <p>Parisse B.*, Alilla R., Pepe A.G., De Natale F., <em>MADIA - Meteorological variables for Agriculture: a Dataset for the Italian Area,</em> Data in Brief, 46 (2023), 108843, <a href="http://doi.org/10.1016/j.dib.2022.108843">10.1016/j.dib.2022.108843</a>, (<a href="https://www.sciencedirect.com/science/article/pii/S2352340922010460">https://www.sciencedirect.com/science/article/pii/S2352340922010460</a>)</p> <ol> </ol> <p> </p> <p><strong>Abstract</strong></p> <p>The <strong>MADIA gridded dataset</strong> provides the series of the main <strong>agro-meteorological </strong>variables derived from ERA5 hourly surface data, across the Italian domain for the period <strong>1981-2022</strong>, and their respective 1981-2010 and 1991-2020 <strong>climate normals</strong>,<strong> </strong>as well as the following statistics on the 30-year dekadal values of each variable: absolute minimum and maximum, 5<sup>th</sup>, 10<sup>th</sup>, 50<sup>th</sup>, 90<sup>th</sup>, 95<sup>th</sup> percentiles. Temporal and spatial resolutions are <strong>10-daily</strong> and <strong>0.25 degrees</strong> respectively. The dataset contains time series of minimum, average and maximum air temperature, minimum and maximum air relative humidity, wind speed, solar radiation, precipitation and reference evapotranspiration according to the FAO Penman-Monteith method. The dataset is provided in both <strong>NetCDF </strong>and <strong>csv </strong>format. In addition, discovery and description metadata are provided. In order to facilitate the data reuse for computing statistics at Italian <strong>NUTS 2 and 3</strong> levels, a complementary vector file is provided which reports the cell weight in terms of fraction covered of each administrative unit considered. Another vector file is included with the <strong>ERA5 cell polygons</strong> covering the Italian country for visualizing and mapping csv data. </p> <p>A <strong>daily version of the MADIA dataset</strong> (only in csv format) is also available on Zenodo at <a href="http://doi.org/10.5281/zenodo.7621453">https://doi.org/10.5281/zenodo.7621453</a>.</p> <p>Both MADIA datasets will be periodically updated.</p> <p><strong>Attached content</strong></p> <p>A ZIP archive composed by the following folders</p> <ol> <li>nc_data: annual time series from 1981 to 2022 and climate normals (1981-2010 and 1991-2020) in NetCDF format</li> <li>csv_data: annual time series from 1981 to 2022 and climate normals (1981-2010 and 1991-2020) in csv format</li> <li>metadata: discovery and description metadata </li> <li>shp_data: two complementary vector layers with the NUTS2-3 cover fractions and the ERA5 cell polygons for Italy</li> </ol> <p><strong>Acknowledgments</strong></p> <p>This work was supported by the Italian Ministry of Agricultural, Food and Forestry Policies (AgriDigit-Agromodelli, DM n. 36502 of 20/12/2018)</p>
Stakeholder in Agricultural Data Ecosystem in Croatia
<p>To discover the relevant priority groups for further focus in open data ecosystem maturation, we have imposed the level of the stakeholder importance (derived from the number of query references) to the level of the stakeholder influence to the data supply (average expert assessment).</p>
Climate Change Risk Assessment Dataset for Serbian Agriculture
<p>This dataset contains the assessment of observed climate change to the agriculture in Serbia. Data are given on a lat/lon gird with 0.01° horizontal resolution in geotif format. Climatic and bioclimatic indices, as well as risk occurrence frequencies are calculated using interpolated daily observations of temperature and precipitation over Serbia in the period 1998-2017. Statistical significance of the climate indices change, change in category of viticultural indices and increase in risk occurrence is given as well. Analyzed species are: vine grape, fruits (peaches, apricots, cherries, plums, apples, pears and quinces) and crops (corn, sunflower, sugar beet and winter wheat). Calculated climatic indices are: mean annual, vegetational and summer temperature and precipitation; bioclimatic indices: Winkler index, Huglin index, Dryness index, and Cool nights index; risks: drought, frost in the beginning of vegetation, low temeperature during winter, high temperature during summer and intensive rainfall.</p>
A 500m-Resolution Agricultural Drought Area Dataset for the period 2006–2019 in the North China Plain
<p>Agricultural drought has caused huge loss in crop production in the recent decades and will continue to occur with increased frequency and severity in the upcoming future period. However, lack of reliable historic agricultural drought information at finer spatiotemporal resolutions constrains the effective formulation strategy for prevention and mitigation of agricultural drought, especially for monitoring and early/warming. Hence, we generated a 500 m-resolution agricultural drought area dataset with three degrees (drought covered, drought damaged, and crop failure) for summer-harvest crops and autumn-harvest crops encompassing the whole NCP.</p>
Research generated data supporting the article manuscript "Setting Grounds for Data Literacy in the Sector of Agriculture: Learning About and with Open Data"
<p>In the research 345 MS courses and 216 MS courses data from the ECTS catalogue (2019) of University of Zagreb Faculty of Agriculture were mapped onto the data literacy competence areas (theme) and DL competence areas sub-themes adapted ODI Data Skills Framework (2020) expanding the term “skill” to “competence” to include knowledge and attitudes. Teaching staff was interviewed in semi-structured interviews on the data literacy competences covered in their courses and open data use and teaching in their courses as well as their perceived importance for the sector of the course.</p> <p>The upload consists of the following .csv files:</p> <table> <tbody> <tr> <td>readme_DL_OD_Salamonetal.csv</td> </tr> <tr> <td>01DL_OD_Salamonetal.csv</td> </tr> <tr> <td>02DL_OD_Salamonetal.csv</td> </tr> <tr> <td>03DL_OD_Salamonetal.csv</td> </tr> <tr> <td>04DL_OD_Salamonetal.csv</td> </tr> <tr> <td>05DL_OD_Salamonetal.csv</td> </tr> <tr> <td>06DL_OD_Salamonetal.csv</td> </tr> <tr> <td>07DL_OD_Salamonetal.csv</td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p>
Measurements of soil nutrient leaching from agricultural depressions and uplands in Iowa, USA
We measured the leaching of nitrate, ammonium, and phosphorus in resin lysimeters installed along topographic transects from depressions to uplands within agricultural fields in Iowa, USA. Lysimeters were each installed for approximately one year. Crops included conventional corn and soybean, corn and soybean with a winter rye cover crop, and corn and soybean fields where depressions were planted with miscanthus. Measurements were made during 2018, 2019, and 2020, although not all transects could be measured each year. There were a total of 28 transect-years that included data from 734 individual resin lysimeters.
Organic and inorganic carbon concentration and stable isotope composition in poorly drained agricultural soils in Iowa, USA
We measured soil organic carbon (SOC) and inorganic carbon (carbonate) in samples collected along topographic gradients in agricultural fields in Iowa, USA, in 2018. We also measured stable isotopes of SOC, soil nitrogen, and carbon in respired CO2 to provide additional context for organic matter dynamics. Additional physical, chemical, and hydrologic variables were measured on these samples and in the field sites to understand mechanisms underlying patterns in soil organic and inorganic carbon.
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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.