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506 results for “crop data”
Data from: Dissecting the phenotypic components of crop plant growth and drought responses based on high-throughput image analysis
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Data from: Soil biota enhance agricultural sustainability by improving crop yield, nutrient uptake and reducing nitrogen leaching losses
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Data From: Diversifying bioenergy crops increases yield and yield stability by reducing weed abundance
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Data from: Evaluating predictive performance of statistical models explaining wild bee abundance in a mass-flowering crop
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Data from: Origins of food crops connect countries worldwide
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Data from: Legacy effects of diversity in space and time driven by winter cover crop biomass and nitrogen concentration
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Data from: Crop-to-wild hybridization in cherries – empirical evidence from Prunus fruticosa
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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.
Data for analyzing crop yield and rainfall in India
<p><strong>Description</strong></p> <p>Data used to model the nonlinear response of Kharif monsoon rice to rainfall in India.</p> <p><strong>Reference</strong></p> <p>Maiti, A., Hasan, M.K., Sannigrahi, S., Bar, S., Chakraborti, S., Mahto, S.S., Chatterjee, S., Pramanik, S., Pilla, F., Auerbach, J., Sonnentag, O., Song, C., & Zhang, Q. (2024). Optimal rainfall threshold for monsoon rice production in India varies across space and time. <em>Communications Earth & Environment</em>, 5, 302. https://doi.org/10.1038/s43247-024-01414-7</p> <p> </p>
Gentsch et al.:Soil nitrogen and water management by winter-killed catch crops. Open data and R scripts.
<p>The file contains metadata on catch crop biomass, soil water contents, and ammonia and nitrate contents in different soil depth from the CATCHY experimental field site Asendorf. R scripts are provided for statistic evaluation of the metadata, plots for publication and spatiotemporal visualization. The article to this data set ist published in the lournal SOIL from Copernicus publisher: <a href="https://soil.copernicus.org/articles/8/269/2022/">https://soil.copernicus.org/articles/8/269/2022/</a></p>
Selected data analysed in the JGR Atmosphere manuscript "Noah-MP with the generic crop growth model Gecros in the WRF model: Effects of dynamic crop growth on land-atmosphere interaction"
<p>This depository contains the simulated 3-hr data of 2m-temperature (tas), latent heat flux (hfls), sensible heat flux (hfss), soil moisture (mros) of the top 1 m, convective available potential energy (cape), convection inhibition (cin), leaf area index (lai) for the model domain, which centers Germany. Further it contains the namelist.input of the WRF simulations of the CTRL and EXP_CROP run. The simulations are described in the manuscript "Noah-MP with the generic crop growth model Gecros in the WRF model: Effects of dynamic crop growth on land-atmosphere interaction" by Warrach-Sagi et al., 2022</p>
Daily climate and rainfall data for Niger 1983-2021, for use in SARRA-O crop simulation model
<p>This dataset contains daily rainfall and climate data for Niger, that can be used as input of the <a href="https://github.com/SARRA-cropmodels/SARRA-O">SARRA-O spatialized crop simulation model</a>. This data can be directly put as input of SARRA-O model to perform computations and obtain simulation results.</p> <p>The archive contains :</p> <ul> <li>AgERA5 (doi:<a href="https://doi.org/10.24381/cds.6c68c9bb">10.24381/cds.6c68c9bb</a>) climatic data for Niger, with daily geotiff files for minimum, maximum, mean temperature (°C), reference evapotranspiration calculated with Hargraeves formula (mm), and solar radiation flux (kJ/m²) at 0.1° spatial resolution from 01/01/1981 to 31/12/2021</li> <li>TAMSAT v3.0 (doi:<a href="http://doi.org/10.1038/sdata.2017.63">10.1038/sdata.2017.63</a>) satellite rainfall estimation data for Niger (mm), with daily geotiff files at 0.0375° spatial resolution from 01/01/1983 to 31/12/2021</li> <li>CHIRPS v2.0 (doi:<a href="https://doi.org/10.1038/sdata.2015.66">10.1038/sdata.2015.66</a>) satellite rainfall estimation data for Niger (mm), with daily geotiff files at 0.05° spatial resolution from 01/01/1981 to 31/12/2022</li> </ul> <p>This data has been extracted from their original sources using the <a href="https://github.com/SARRA-cropmodels/SARRA-data-download">SARRA-data-downloader tool</a>, on June 14th and 15th, 2023.</p> <p>The applicable licences are the licences of the respective datasets.</p>
Daily climate and rainfall data for northern Cameroon 2020-2022, for use in SARRA-Py crop simulation model
<p>This dataset contains daily rainfall and climate data for north Cameroon, that can be used as input of the SARRA-Py spatialized crop simulation model. This data can be directly put as input of SARRA-O model to perform computations and obtain simulation results.</p> <p>The archive contains :</p> <ul> <li>AgERA5 (doi:<a href="https://doi.org/10.24381/cds.6c68c9bb">10.24381/cds.6c68c9bb</a>) climatic data for north Cameroon, with daily geotiff files for minimum, maximum, mean temperature (°C), reference evapotranspiration calculated with Hargraeves formula (mm), and solar radiation flux (kJ/m²/d) at 0.1° spatial resolution from 01/01/2020 to 31/12/2022</li> <li>CHIRPS v2.0 (doi:<a href="https://doi.org/10.1038/sdata.2015.66">10.1038/sdata.2015.66</a>) satellite rainfall estimation data for north Cameroon (mm), with daily geotiff files at 0.05° spatial resolution from 01/01/2020 to 31/12/2022</li> </ul> <p>This data has been extracted from their original sources using the <a href="https://github.com/SARRA-cropmodels/SARRA-data-download">SARRA-data-downloader tool</a>.</p> <p>The applicable licences are the licences of the respective datasets.</p>
Data for : EU Green Deal improves food system sustainability with unequal economic impacts on consumers and producers of crops and livestock
<p>Data for an article in Communications Earth Environment - soler et al-SM3 file presents the economic model used to simulate the impacts of changes in agricultural practices, food waste and consumers' diets in the framemork of the European Green Deal proposed by the European Commission. It displays the data used to calibrate the economic model, the coefficients used to calculate the non-market environmental and nutritional impacts, and the detailed results of simulated scenarios. It provides the list of variables and parameters used in the model. The data sources used for the calibration process are detailed, as well as the status of each variable (endogenous vs exogenous). The model calibration process aims at determining (reproducing) the baseline scenario that represents the current situation; it checks that production, consumption, and price estimates calculated with the economic model are equal to those currently observed. </p> <p>soler et al-SM4 provides sensibility tests for different parameter sets</p>
Common sowing and harvesting data of Flanders' crops
<p>This dataset contains the most common sowing and harvesting dates of crops cultivated on arable fields in Flanders.</p> <p> </p> <p>For access to the dataset contact Greet Ruysschaert at greet.ruysschaert@ilvo.vlaanderen.be</p>
2022 & 2023 Hyper Yielding Crops Paddock Data
<p>This dataset contains agronomic benchmarking data from the 2022 and 2023 GRDC Hyper Yielding Crops (HYC) Awards, which aimed to drive adoption and scale up learnings from the HYC project by engaging growers across high-yielding regions of southern Australia. Over 330 wheat and barley paddocks were assessed across SA, TAS, VIC, and WA, with detailed data collected on yield, grain quality, crop inputs, management practices, soil characteristics, and yield components. Data are presented in Excel files, organised by site ID, location, crop type, and variety, and include assessments such as sowing and harvest details, fertiliser and chemical use, soil nutrients, and grain characteristics. The dataset is self-describing, with assessment descriptions and units detailed in a ‘Read Me’ tab. Data were collected through grower engagement and regional project officers, with analysis conducted by FAR Australia and data managed through the HYC portal developed by CeRDI. Access to this dataset is subject to agreement by data co-owners.</p>
2022 Hyper Yielding Crops Data
<p>The GRDC Hyper Yielding Crops (HYC) project aims to push the boundaries of crop productivity and profitability in high-yielding regions across southern Australia. The 2022 dataset captures raw trial data from five states—Western Australia, South Australia, Victoria, New South Wales, and Tasmania—covering wheat, barley, and canola, depending on the location. Data are organised by site, crop type, and trial ID, and are presented in Excel spreadsheets containing original measurements and derived calculations (e.g., plant density), with each sheet including a key to units, codes, and abbreviations. The dataset is accompanied by a PDF report detailing experimental design, treatments, and environmental conditions such as soil and climate. Trial assessments were conducted using GDM ARM software, and metadata such as crop growth stage, assessment descriptions, and notes are provided within the spreadsheets. This research was made possible through the support of GRDC and contributions from growers and research partners, including FAR Australia, CSIRO, DPIRD, SFS, CeRDI, and several regional farming groups.</p>
2023 Hyper Yielding Crops Data
<p>This dataset contains raw trial data from the 2023/24 GRDC Hyper Yielding Crops (HYC) project, which aims to improve productivity and profitability in high-yielding cropping regions across southern Australia. Trials were conducted across five states—WA, SA, VIC, TAS, and NSW—focusing on wheat, barley, and canola. The dataset covers: barley trials in SA, TAS, VIC, and WA; canola trials in NSW, SA, VIC, and WA; and wheat trials in all five states.</p> <p>Data are organised by crop type, site, and trial ID, with corresponding trial details and analysis provided in the included PDF results report. Each Excel file captures original data and related calculations (e.g., plant density from counts), with consistent formatting across trials. Assessments were conducted using GDM ARM trial management software, and each sheet includes a key describing units, codes, and abbreviations. The accompanying report outlines the experimental design, treatment summaries, and environmental factors (e.g., soil and climate) relevant to data interpretation.</p> <p>The dataset is self-describing, with metadata such as crop growth stage, assessment dates, and explanatory notes embedded in the spreadsheet headers. This research was made possible by the support of GRDC, collaborating growers, and delivery partners including FAR Australia, CSIRO, DPIRD (WA), Brill Ag, SFS, Techcrop, CeRDI, MFMG, Riverine Plains Inc., and Stirling to Coast Farmers.</p>
Data from: Management-induced micro-ecosystems in crop fields alter the functional trait composition of arable plant communities
<p>Data used for RLQ analysis. Include trait table, environment table and species table. </p><p>Traits/species table: <br>EQUAR, PLAMA and SPRAR were removed due to few observations. CHEPR and TAROF were removed due to lack of traits data.</p><p>Environmental table:<br>MD (mechanical disturbance) is a combination or location and a row-hoeing coefficient that assumes the impact of the hoe decrease with increasing distance from the centre of the hoe tine. cr hoed twice and ic hoed once both got a value of 2 and to distinguish them the latter was changed manually to 2.5</p>
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.