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1,425 results for “Agriculture”
Supplemental data and code for "Global patterns in water flux partitioning: Irrigated and rainfed agriculture drives asymmetrical flux to vegetation over runoff"
<p>This dataset provides all data compiled and generated for the manuscript entitled "Global patterns in water flux partitioning: Irrigated and rainfed agriculture drives asymmetrical flux to vegetation over runoff" (https://doi.org/10.1016/j.oneear.2023.08.002). This includes the boundaries for 3614 hydrological catchments, the curated data used for analysis and modelling, the developed machine learning model, shapley values and area of applicability results, and data for global extrapolation</p> <p>It also contains a markdown file ('code.html') which shows how to access and use the data, and generic sample codes used to generate these results.</p> <p> </p> <p> </p> <p> </p>
Data and code for: A conceptual model-based sediment connectivity assessment for patchy agricultural catchments
<p>Authors: Pedro V G Batista, Peter Fiener, Simon Scheper, Christine Alewell</p> <p>----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>Abstract</p> <p>The accelerated sediment supply from agricultural soils to riverine and lacustrine environments leads to negative off-site consequences. In particular, the sediment connectivity from agricultural land to surface waters is strongly affected by landscape patchiness and the linear structures that separate field parcels (e.g. roads, tracks, hedges, and grass buffer strips). Understanding the interactions between these structures and sediment transfer is therefore crucial for minimising off-site erosion impacts. Although soil erosion models can be used to understand lateral sediment transport patterns, model-based connectivity assessments are hindered by the uncertainty in model structures and input data. In specific, the representation of linear landscape features in numerical soil redistribution models is often compromised by the spatial resolution of the input data and the quality of the process descriptions. Here we adapted the WaTEM/SEDEM model using high resolution spatial data (2 m x 2 m) to analyse the sediment connectivity in a very patchy mesoscale catchment (73 km<sup>2</sup>) of the Swiss Plateau. We used a global sensitivity analysis to explore model structural assumptions about how linear landscape features (dis)connect the sediment cascade, which allowed us to investigate the uncertainty in the model structure. Furthermore, we compared model simulations of hillslope sediment yields from five sub-catchments to tributary sediment loads, which were calculated with long-term water discharge and suspended sediment measurements. The sensitivity analysis revealed that the assumptions about how the road network (dis)connects the sediment transfer from field blocks to water courses had a much higher impact on modelled sediment yields than the uncertainty in model parameters. Moreover, model simulations showed a higher agreement with tributary sediment loads when the road network was assumed to directly connect sediments from hillslopes to water courses. Our results ultimately illustrate how a high-density road network combined with an effective drainage system increases sediment connectivity from hillslopes to surface waters in agricultural landscapes. This further highlights the importance of considering linear landscape features and model structural uncertainty in soil erosion and sediment connectivity research.</p> <p> </p> <p>----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>Metainformation</p> <p>This dataset includes:</p> <p>1 - The input data used for running the WaTEM/SEDEM model in the Baldegg catchment.</p> <p>2 - The discharge and sediment concentration data used for producing the sediment rating curves for the tributaries of the Lake Baldegg.</p> <p>3 - The model and sediment rating curve output data.</p> <p>4 - The R scripts for running the WaTEM/SEDEM model in the Baldegg catchment, the code for producing the sediment rating curves, and the code for summarising and analysing the model output data.</p> <p>----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>The sediment concentration and water discharge data were supplied by Robert Lovas, from the Department of Environment and Energy of the Canton of Lucerne.</p> <p>The model input data were adapted from freely available ©swisstopo geodata products:</p> <p>Swisstopo. SwissALTI3D. Das hoch aufgelöste Terrainmodell der Schweiz, 2014.</p> <p>Swisstopo. Swiss Map Vector 25 Beta, Das digitale Landschaftsmodell der Schweiz. 2018.</p> <p>Swisstopo. SwissTLM3D. Das grossmassstäbliche Topografische Landschaftsmodell der Schweiz, 2020.</p> <p>----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>For further information we refer to our preprint: https://doi.org/10.5194/hess-2021-231</p> <p> </p> <p> </p>
Global agricultural ammonia emissions simulated with the ORCHIDEE land surface model : Model Ouput Data
<p>This upload includes data associated with the manuscript "Global agricultural ammonia emissions simulated with the ORCHIDEE land surface model : )" submitted to Geoscientific Model Development. The dataset includes an output file with the simulated ammonia emissions for the agricultural sector.</p> <p>The emissions (manure management and soil), manure production and soil ammonium concentrations are monthly fields from the simulation for 2007-2015.</p> <p>Additional information is given in the readme file</p>
Data to support the publication "Impact of agricultural management on soil aggregates and associated organic carbon fractions: Analysis of long-term experiments in Europe"
<p><strong>Raw data:</strong> Experimental plot ids and information, mass distribution of all aggregate fractions after wet sieving, Sand content of each fraction to conduct the sand correction, mass distribution of all fractions after isolating the micro-aggregates held within the macroaggregates, yields per treatment, carbon content per fraction (raw data)</p> <p><strong>All data per plot: </strong>SOC content, MAOM and POM content of each fraction presented in the fractionation scheme included in the manuscript, together with the mass of the relative fractions. </p> <p> </p>
High-frequency wind (u, w, v, Ts) and gas concentration measurements of CO2 and H2O over an agricultural field in Braunschweig, Germany
<p>This dataset contains high-frequency eddy covariance (EC) measurements over a flat agricultural field at the Thünen Institute in Braunschweig, Germany (52.30° N, 10.45° E).</p> <p>The data collection period spanned <strong>77 days </strong>in the year 2020 split into three files</p> <p> </p> <table> <tbody> <tr> <td>BS2020_06.rds</td> <td>June 11 to July 15</td> </tr> <tr> <td>BS2020_10.rds</td> <td>October 1 to November 10</td> </tr> <tr> <td>BS_2020_07_subset.rds</td> <td>July 11 to July 25</td> </tr> </tbody> </table> <p> </p> <p>Included in the dataset are 3D wind velocity data, recorded using a uSonic-3 Class A sonic anemometer from Metek GmbH. Additionally, the dataset provides gas concentration measurements for carbon dioxide (CO2) and water vapor (H2O), captured using an LI-7500A open-path infra-red gas analyzer from LI-COR Biosciences GmbH, Germany.</p> <p>Variables in the dataset</p> <table> <tbody> <tr> <th>Variable Name</th> <th>Description</th> <th>Units</th> </tr> </tbody> <tbody> <tr> <td>time</td> <td>Unique time stamp (POSIXct format)</td> <td>Seconds since Unix epoch</td> </tr> <tr> <td>CO2</td> <td>Wet molar density of carbon dioxide</td> <td>µmol m⁻³</td> </tr> <tr> <td>H2O</td> <td>Wet molar density of water vapor</td> <td>mmol m⁻³</td> </tr> <tr> <td>Ts</td> <td>Sonic temperature</td> <td>Kelvin</td> </tr> <tr> <td>u, v, w</td> <td>3D wind velocity components</td> <td>m s⁻¹</td> </tr> </tbody> </table> <p> </p> <p> </p> <div> <div> <div> <p>The dataset is in RDS format (version 3), compatible with R version 3.5.0 or higher.</p> <p>RDS is a binary file format native to the R programming environment</p> </div> </div> </div>
The Italian Agricultural Regions Dataset
<p>A georeferenced dataset in delineating the boundaries of the Italian Agricultural Regions (Figure_map.png) as defined by INEA (CREA) and used in the FADN/RICA database. This dataset, in a shapefile format, (RegAgr.zip) opens the possibility of conducting geographical and climatic analyses on thousands of data on farms sampled every year in Italy by the FADN/RICA network.</p> <p>The dataset is accompanied by supplementary information on the municipalities belonging to the Agronomic Regions (ARs). Specifically, for each AR, the list of statistical codes and names of administrative units (municipalities, provinces, and regions) used by ISTAT, the Italian National Institute of Statistics, is provided (attributes.zip). This way, users can easily trace the municipalities and related information that comprise each AR. Moreover, the dataset also includes the source and ancillary data used to build the dataset (SOURCE_DATA.zip).</p>
SD4EO: AI-based synthetic satellite multispectral agricultural textures in Spain (Oct 2017 - Sep 2018)
<p>This dataset has been created as part of the deliverables for ESA’s <a title="https://eo4society.esa.int/projects/sd4eo/" href="https://eo4society.esa.int/projects/sd4eo/" target="_blank" rel="noopener">SD4EO project.</a> It consists of textures generated using a multispectral variant of a still unpublished high-order statistical constraint synthesis method for each of the following crop types:</p> <ul> <li> Barley.</li> <li> Wheat.</li> <li> Other grain leguminous.</li> <li> Peas.</li> <li> Fallow & Bare soil.</li> <li> Vetch.</li> <li> Alfalfa.</li> <li> Sunflower.</li> <li> Oats.</li> </ul> <p>The initial data was sampled from satellite images, specifically from Copernicus’ Sentinel-1 and Sentinel-2 satellites. The images were acquired over a period from October 2017 to September 2018 on the central-east region of northern Spain (Castile and León and Catalonia). From these images, the corresponding crops were extracted and used as samples for assembling large puzzles that have been applied as input reference images to generate the synthetic images that make up this dataset.</p> <p>The datasets of assembled crop field "puzzles" used as reference images combine the largest crop areas to create a square multispectral texture of the largest possible size that is a power of 2 (or nearly a power of 2). Each base image combines data from all available Sentinel-2 satellite passes for the same month and a previous monthly composition from Sentinel-1. Due to cloud masks influence, the shape and number of crops vary for each time sample, preventing the reuse of element disposition in the “puzzles” across different months. Therefore, we have a base image (puzzle) for each month and crop type, with a size dependent on the number and area of crops not covered by clouds. These base image sizes range between 256, 384, 512, 768, 1024, 1536, and 2048 pixels per side, influenced by weather conditions and crop type each year season.</p> <p>In <em>this</em> dataset, the synthetic texture sizes match the corresponding base image sizes to facilitate debugging the method implementation and enable subsequent comparisons. For crops with a base image size of 1536 pixels or larger, the generated synthetic images have been reduced to half their size to reduce computational costs and RAM requirements, thereby completing the synthesis faster. Consequently, there remains some diversity in file sizes, generally smaller for crop types with less cultivated area.</p> <p>Additionally, to increase the amount of available data, six variants have been synthesized from each base multispectral image. This number can be arbitrarily increased, as initialization with noise (random numbers) ensures the distinction among the generated data.</p> <p>File names are structured as follows:</p> <ul> <li>Prefix "HO" indicating the synthesis method</li> <li>The crop type name: <ul> <li>Barley</li> <li>Wheat</li> <li>OtherGrainLeguminous</li> <li>Peas</li> <li>FallowAndBareSoil</li> <li>Vetch</li> <li>Alfalfa</li> <li>Sunflower</li> <li>Oats</li> </ul> </li> <li>Year/Month/01 (representing the start of the month period)</li> <li>Side length of the multispectral texture in pixels (based on the highest precision instrument of Sentinel-2: 10m x 10m)</li> <li>Number of the synthesis variant</li> </ul> <p>The generation parameters for all images include:</p> <ul> <li>Normalized and weighted bands (VH band influence increased by a factor of 3 compared to others)</li> <li>4 levels of depth in the Steerable pyramid</li> <li>6 orientations in the Steerable pyramid</li> <li>14 joint statistics of the wavelet coefficients corresponding to basis functions at adjacent spatial locations, orientations, and scales. This parameter is crucial for capturing local dependencies between wavelet coefficients, essential for the visual perception of texture.</li> <li>30 iterations</li> </ul> <p>A significant effort has been made to stabilize the algorithm, and to eliminate artifacts in the generated textures, resulting in much more robust outcomes. However, in rare cases, the initial white noise distribution can be statistically unfavorable, leading to instabilities. Files have been left as generated, without correcting these effects, to make them visible despite their low frequency. Specifically, among the 657 generated multispectral textures, this phenomenon has occurred prominently in only two and is relatively noticeable in another two, leaving the rest free of this effect (affecting less than 1% of the syntheses).</p> <p>Thus, the following files can be considered partially failed syntheses:</p> <ul> <li>HO_Alfalfa_20180801_768_1.nc</li> <li>HO_FallowAndBareSoil_20180101_768_3.nc</li> <li>HO_OtherGrainLeguminous_20171201_256_4.nc</li> <li>HO_Vetch_20180301_384_3.nc</li> </ul> <p>Files are encoded in the standardized net4CDF format [<a href="https://unidata.github.io/netcdf4-python/">link</a>], each containing a single xarray with metadata corresponding to a 3D array with the synthesized texture of the indicated crop type and satellite passes for the regions of Castilla y León and Catalonia for the corresponding monthly period.</p> <p>The most important data structure is the 3D array, where the first two dimensions correspond to the pixel extent indicated in the file name as square textures ('x' and 'y' labels in the xarray). The third dimension denotes the spectral band of the satellite, ordered by constellation and pixel size:</p> <ul> <li>'B02' 10m (Sentinel-2)</li> <li>'B03' 10m (Sentinel-2)</li> <li>'B04' 10m (Sentinel-2)</li> <li>'B08' 10m (Sentinel-2)</li> <li>'B05' originally 20m, resampled to 10m (Sentinel-2)</li> <li>'B06' originally 20m, resampled to 10m (Sentinel-2)</li> <li>'B07' originally 20m, resampled to 10m (Sentinel-2)</li> <li>'B11' originally 20m, resampled to 10m (Sentinel-2)</li> <li>'B12' originally 20m, resampled to 10m (Sentinel-2)</li> <li>'B8A' originally 20m, resampled to 10m (Sentinel-2)</li> <li>'VH' also resampled to 10m (Sentinel-1)</li> </ul> <p>The original dynamic range is preserved in all bands, and they have been synthesized together using our multispectral algorithm variant. The new band combination may result in slightly unusual values in vegetation indices since restrictions were not considered in their transformed space, but in the latent space of the decorrelated Steerable pyramid.</p> <p>Additionally, the following metadata are stored as xarray attributes:</p> <ul> <li>"long_name": corresponding to the crop type name</li> <li>"date": the period of the original data used as the base image for synthesis</li> <li>"dataset": denotes the combination of the initial Castilla y León dataset and the extended 6 Tiles from Catalonia</li> <li>"synthetic_method": corresponds to the high-order constrained method</li> <li>"max_visible_value": a reference value to maintain the same dynamic range when comparing with base images, avoiding distortions in color space and contrast</li> </ul> <p>A total of:</p> <p><strong> 9</strong> types of crops x <strong>12</strong> months x <strong>6</strong> variants = <strong>648</strong> synthetized multispectral textures</p> <p>occupying <strong>34.5</strong>GB, have been organized and uploaded into 9 ZIP files (one per crop type) on the Zenodo website for distribution under Creative Commons Attribution 4.0 International license.</p> <p>The SD4EO Project is funded by the ESA’s FutureEO programme under contract no. 4000142334/23/I-DT and supervised by ESA Φ-lab.</p> <p> </p>
Duhumbi Agricultural Practices - Description, Audio, Video, Photos
<p>This collection of videos, audio and photo files displays Duhumbi agricultural practices as they were conducted between 2012 and 2017. Accompanying video and picture files illustrating the text files can be found at the end of this document.</p> <p>Traditionally, the people of the Chug valley depended on a mix of agriculture and animal husbandry for their livelihoods, supplemented by hunting and collection of forest produce. The Chug valley and the Sangthi valley are the only places in West Kameng district where relatively large-scale wetland rice cultivation takes place. This rice has for long been the main item in the barter trade, as well as the main item collected as tax by the erstwhile Tibetan administration and raided by the Miji.</p> <p>Agriculture has always been the main-stay of the local economy, not just in terms of self-sufficiency, but also in terms of barter trade. The agricultural produce, mainly the rice, was bartered for other food and other items, a practice that, despite increasing monetisation of the economic system, continues till date.</p> <p>This material is made freely available to everyone for informative or scientific purposes as long as the source (this DOI) / the collectors are properly credited. Please note that use of the material for commercial purposes <em><strong>of any kind</strong>, which includes conversion into commercial audio-visual media (documentaries etc.), storage and dissemination through sites that require registration & payment for access, or sites that rely on advertisement (including YouTube) </em>is <strong>not</strong> permitted without <strong>specific written consent</strong> from the speakers and their community, obtained through the collectors of the material. By downloading our material, you agree to these restrictions.</p> <p>This data set falls under the Attribution-NonCommercial-ShareAlike (CC BY-NC-SA) license. This license lets you remix, tweak, and build upon this work non-commercially, as long as you credit us and license your new creations under the identical terms. License Deed on <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">https://creativecommons.org/licenses/by-nc-sa/4.0/</a>. Legal Code on <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode">https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode</a>.</p> <p>Tim Bodt: bodttim (at) gmail (dot) com</p>
Agriculture - General: Animal Production and Health, Natural Resources and Environment 2
<p>1996-1999, entomofauna inventory in cities and countrysides on 20 different habitats in The Netherlands using pitfalls and sweeping nets Jagers op Akkerhuis G, Dimmers W (2016). Alterra (NL) - Comparison entomofauna in cities en countrysides. Version 1.1. Alterra, Wageningen UR. Occurrence dataset <a href="https://doi.org/10.15468/guxqfv">https://doi.org/10.15468/guxqfv</a> accessed via GBIF.org</p>
Agriculture - General: Animal Production and Health 6
<p>This dataset contains the digitized treatments in Plazi based on the original journal article Viana, Jéssica Herzog, Ribeiro-Costa, Cibele Stramare (2013): Review of the largest species group of the New World seed beetle genus Sennius Bridwell (Coleoptera: Chrysomelidae), with host plant associations. Zootaxa 3736 (5): 501-535, DOI: <a href="http://dx.doi.org/10.11646/">http://dx.doi.org/10.11646/</a></p>
Agriculture - General: Animal Production and Health, Natural Resources and Environment 3
<p>1996-1999, entomofauna inventory in cities and countrysides on 20 different habitats in The Netherlands using pitfalls and sweeping nets Jagers op Akkerhuis G, Dimmers W (2016). Alterra (NL) - Comparison entomofauna in cities en countrysides. Version 1.1. Alterra, Wageningen UR. Occurrence dataset <a href="https://doi.org/10.15468/guxqfv">https://doi.org/10.15468/guxqfv</a> accessed via GBIF.org</p>
Agriculture - General: Animal Production and Health 7
<p>This dataset contains the digitized treatments in Plazi based on the original journal article Johnson, Robert A., Cover, Stefan P. (2015): A taxonomic revision of the seed-harvester ant genus Pogonomyrmex (Hymenoptera: Formicidae) on Hispaniola. Zootaxa 3972 (2): 231-249, DOI: <a href="http://dx.doi.org/10.11646/">http://dx.doi.org/10.11646/</a></p>
Agriculture - General: biological diversity 6
<p>A database of tree of biological, cultural, ecological or historical interest because of their age, size or condition. National Biodiversity Data Centre (2016). Heritage Trees of Ireland. Occurrence dataset <a href="https://doi.org/10.15468/9athfc">https://doi.org/10.15468/9athfc</a> accessed via GBIF.org</p>
Agriculture - General: biological diversity 4
<p>The records in this dataset are general marine and coastal records of different taxonomic groups submitted to the National Biodiversity Data Centre. National Biodiversity Data Centre (2018). Coastal and Marine Species Database. Occurrence dataset <a href="https://doi.org/10.15468/oynwkx">https://doi.org/10.15468/oynwkx</a> accessed via GBIF.org</p>
Agriculture - General: Animal Production and Health, Natural Resources and Environment
<p>1996 till 2000, entomofauna inventory on clay digged off riversides and on reference site in The Netherlands using pyramidtraps Faber J, Dimmers W (2016). Alterra (NL) - Entomofauna inventory in riverside grasslands. Version 1.1. Alterra, Wageningen UR. Occurrence dataset <a href="https://doi.org/10.15468/hlhr1r">https://doi.org/10.15468/hlhr1r</a> accessed via GBIF.org</p>
Agriculture - General: biological diversity 3
<p>Data on the distribution of Irish CWR species. Data on key ITPGRA species were compiled in 2010 from the National Parks and Wildlife Service, the National Herbarium, and the National Vegetation Database. The database also includes recent CWR data collected under projects funded by DAFM (Genetic Heritage Ireland 2009-2010; and the National Biodiversity Data Centre 2011 & 2012). National Biodiversity Data Centre (2016). Irish Crop Wild Relative Database. Occurrence dataset <a href="https://doi.org/10.15468/lohime">https://doi.org/10.15468/lohime</a> accessed via GBIF.org</p>
Agriculture - General: Animal Production and Health, Natural Resources and Environment 7
<p>Spring 2010 and Summer 2011, microarthropod fauna inventory for foodweb analysis in five European countries using soil cores and tullgren extraction</p> <p>Bloem J, Dimmers W (2016). Alterra (NL) - Microarthropods inventory in European countries. Version 1.1. Alterra, Wageningen UR. Occurrence dataset <a href="https://doi.org/10.15468/zkqto2">https://doi.org/10.15468/zkqto2</a> accessed via GBIF.org</p>
Agriculture - General: Animal Production and Health 5
<p>This dataset contains the digitized treatments in Plazi based on the original journal article Michelsen, Verner (2012): Revision of the European Delia pruinosa species group (Diptera: Anthomyiidae) feeding as larvae in seed capsules of Silene L. (Caryophyllaceae). Zootaxa 3434: 31-48, DOI: 10.5281/zenodo.282060</p>
Agriculture - General: biological diversity
<p>The records in this dataset are general records of different taxonomic groups submitted to the National Biodiversity Data Centre. This provides a temporary facility to store and make available data submitted to the Centre, until such time as subsets of the data can be added to a recognised national database. National Biodiversity Data Centre (2016). General Biodiversity Records from Ireland. Occurrence dataset <a href="https://doi.org/10.15468/w8q1jm">https://doi.org/10.15468/w8q1jm</a> accessed via GBIF.org</p>
Agriculture - General 4
<p>Witt A, Beale T (2018). CABI Africa Invasive and Alien Species data. CABI (Centre for Agriculture and Biosciences International). Occurrence dataset <a href="https://doi.org/10.15468/pkgevu">https://doi.org/10.15468/pkgevu</a> accessed via GBIF.org</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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International Brain Laboratory public data
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OpenNeuro
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