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1,425 results for “Agriculture”
Agricultural land use (raster) : National-scale crop type maps for Germany from combined time series of Sentinel-1, Sentinel-2 and Landsat data (2017 to 2021)
<p>The dataset contains maps of the main classes of agricultural land use (dominant crop types and other land use types) in Germany, which are produced annually at the Thünen Institute beginning with the year 2017 on the basis of satellite data. The maps cover the entire open landscape, i.e., the agriculturally used area (UAA) and e.g., uncultivated areas. The map was derived from time series of Sentinel-1, Sentinel-2, Landsat 8 and additional environmental data. Map production is based on the methods described in <a href="https://doi.org/10.1016/j.rse.2021.112831">Blickensdörfer et al. (2022)</a>.</p> <p>All optical satellite data were managed, pre-processed and structured in an analysis-ready data (ARD) cube using the open-source software <a href="https://force-eo.readthedocs.io/en/latest/">FORCE </a>- Framework for Operational Radiometric Correction for Environmental monitoring (Frantz, D., 2019), in which SAR and environmental data were integrated.</p> <p>The map extent covers all areas in Germany that are defined in the respective year as cropland, grassland, small woody features, heathland, peatland or unvegetated areas according to ATKIS Basis-DLM (Geobasisdaten: © GeoBasis-DE / BKG, 2020). </p> <p>Version v201:<br>Post-processing of the maps included a sieve filter as well as a ruleset for the reduction of non-plausible areas using the Basis-DLM and the digital terrain model of Germany (Geobasisdaten: © GeoBasis-DE / BKG, 2015).</p> <p>Version v202:<br>Additional post-processing was performed to detect and mask additional non-plausible areas that were not adequately covered by the first post-processing (e.g., areas with sparse vegetation, montane forests) based on the „Ökosystematlas Deutschland“ (© Statistisches Bundesamt, Deutschland, 2024). As a consequence, the current version includes a new class “Small woody features on other land”. Furthermore, the class "permanent grassland" was refined. Each pixel that was classified as "cultivated grassland" in at least five years (between 2017 and 2022) was translated to "permanent grassland" in the annual maps.</p> <p>The maps are available as cloud optimized GeoTiffs, which makes downloading the full dataset optional. All data can directly be accessed in QGIS, R, Python or any supported software of your choice using the provided URL to the datasets (right click on the respective data set --> “copy link address”). By doing so the entire map area or only the regions of interest can be accessed. QGIS legend files for data visualization can be downloaded separately.</p> <p>Class-specific accuracies for each year are provided in the respective tables. We provide this dataset "as is" without any warranty regarding the accuracy or completeness and exclude all liability. </p> <p> </p> <p><strong>References:<br></strong><br><em>Blickensdörfer, L., Schwieder, M., Pflugmacher, D., Nendel, C., Erasmi, S., & Hostert, P. (2022). Mapping of crop types and crop sequences with combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data for Germany. Remote Sensing of Environment, 269, 112831.</em></p> <p><em>BKG, Bundesamt für Kartographie und Geodäsie (2015). Digitales Geländemodell Gitterweite 10 m. DGM10. https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/dgm10.pdf (last accessed: 28. April 2022).</em></p> <p><em>BKG, Bundesamt für Kartographie und Geodäsie (2020). Digitales Basis-Landschaftsmodell. </em><br><em>https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/basis-dlm.pdf (last accessed: 28. April 2022).</em></p> <p><em>Frantz, D. (2019). FORCE—Landsat + Sentinel-2 Analysis Ready Data and Beyond. Remote Sensing, 11, 1124.</em></p> <p><em>Statistisches Bundesamt, Deutschland (2024). Ökosystematlas Deutschland <br>https://oekosystematlas-ugr.destatis.de/ (last accessed: 08.02.2024).</em></p> <p>___________________________________________________________________________<br>National-scale crop type maps for Germany from combined time series of Sentinel-1, Sentinel-2 and Landsat data (2017 to 2021) © 2024 by Schwieder, Marcel; Tetteh, Gideon Okpoti; Blickensdörfer, Lukas; Gocht, Alexander; Erasmi, Stefan; licensed under CC BY 4.0. </p> <p>Funding was provided by the German Federal Ministry of Food and Agriculture as part of the joint project “Monitoring der biologischen Vielfalt in Agrarlandschaften” (<a href="https://www.agrarmonitoring-monvia.de/en/">MonViA</a>, Monitoring of biodiversity in agricultural landscapes).</p> <p>The study was financially supported by the European Environment Agency and the European Union’s Horizon Europe Research and Innovation programme under Grant Agreement No 101060423 (LAMASUS).</p>
Disentangling the seasonal effects of agricultural intensification on birds and bats in Mediterranean olive groves
Open the record for dataset details and reuse information.
Raw data for the submitted manuscript entitled "Novel strategies for the determination of plastic additives derived from agricultural plastics in soil using ultrahigh-performance liquid chromatography tandem mass spectrometry (UHPLC-MS/MS)"
Open the record for dataset details and reuse information.
Data from: Analyzing the relative importance of habitat quantity and quality for boosting pollinator populations in agricultural landscapes
<p>Data and code underlying Fijen et al Analyzing the relative importance of habitat quantity and quality for boosting pollinator populations in agricultural landscapes</p>
Data in support to the paper: Comparison of Soil Water Content from SCATSAR-SWI and Cosmic Ray Neutron Sensing at four agricultural sites in Northern Italy: insights from spatial variability and representativeness by Emamalizadeh et al. (2024)
<p>The study was conducted within the 21GRD08 SoMMet project. The SoMMet project has received funding from the European Partnership on Metrology, co-financed from the European Union’s Horizon Europe Research and Innovation Programme and by the Participating States (funder name European Partnership on Metrology; funder ID 10.13039/100019599; grant no. 21GRD08 SoMMet).</p>
Agricultural plastic waste in Italy-Greece-Spain-Portugal
<p><span>Maps of the distribution of agricultural plastic waste in South Europe <span> </span>at the NUTS 2 regional level (EUROSTAT).</span></p>
Data from: Arthropod abundance is most strongly driven by crop and semi-natural habitat type rather than management in an intensive agricultural landscape in the Netherlands
<p>The dataset supporting the publication "Arthropod abundance is most strongly driven by crop and semi-natural habitat type rather than management in an intensive agricultural landscape in the Netherlands" is provided. Methods of data collection can be found in the respective publication.</p>
Soil grid dataset of agricultural sites in the Czech Republic
<p>The current dataset includes 320 topsoil samples (0–20 cm depth) collected from four agricultural sites in the Czech Republic. The samples were gathered from Přestavlky, Klučov, Nová Ves nad Popelkou, and Udrnice (80 samples from each site) in June 2021. It contains sample coordinates and some soil parameters including SOC and texture, prepared and stored in MS Excel (.xlsx) format. The data were used in STEROPES WP1 (basic local model development), WP3 (effect of texture), and WP4 (effect of vegetation and plant residues). </p>
Soil laboratory spectra of agricultural sites in the Czech Republic
<div> <p><span><span>The dataset includes VNIR-SWIR spectra of <span>dried, ground, and sieved (< 2 mm) soil samples </span>collected from three agricultural sites in the Czech Republic. The spectra were recorded in the laboratory using an ASD <span>FieldSpec</span> 3 instrument and under the standard protocol.<span> </span>The samples were gathered from Klučov, Nová Ves nad Popelkou, and Udrnice sites (80 samples from each site) in June 2021. The dataset also contains sample coordinates and some parameters including SOC and texture, prepared and stored in MS Excel (.xlsx) format. The data were used in STEROPES WP1 (basic local models development).</span></span></p> </div>
Exploring Tools for Sharing and Trading in Agricultural Machinery Rental: Insights from Gray Literature Review
<p>This spreadsheet contains data from the analysis of platforms for renting and selling agricultural machinery identified during the research. It includes overall evaluations as well as individual assessments from three evaluators, based on predefined criteria for qualitative analysis. The recorded information includes scores assigned to each evaluated site, reflecting the implementation or lack thereof of specific features, such as filter-based search, review systems, and other functionalities related to the sharing economy model in the agricultural sector. The data is organized to facilitate comparison among evaluators and overall conclusions.</p>
Osiris: a platform for advertising agricultural machinery rentals
<p>This dataset contains three spreadsheets of heuristic evaluations conducted by experts and also the prototypes they used. The evaluations assess the usability and effectiveness of various systems based on established heuristic principles. Each spreadsheet includes detailed assessments, observations, and ratings provided by the experts, offering valuable insights into the user interface and experience. These evaluations are intended to support research and improvement of usability practices in the context of the evaluated systems.</p>
Communal Agricultural Prosumption Survey
<p>The data and codebook are part of the PROACT project, researching communal forms of aricultural prosumption in Switzerland, such as community supported agriculture, allotment gardens and community gardens. This particular survey was aimed at active prosumers in Swiss community supported agriculture initiatives and allotment gardens and focuses on the members' experienced during their activity. It was conducted between December 2022 and January 2023 in the German-speaking regions of Switzerland. The sampling was done through a snowball-technique, using three city wide allotment garden associations and nine community supported agriculture initatives, which in turn distributed the survey through their email lists. The particular organisations and initatives were selected on the base of specific characteristics, such as size, location and if they were a new, or well established organisation. Overall the survey was send to 3800 potential participants. </p> <p>For any quetions regarding the dataset or the codebook please contact: stefan.galley@students.unibe.ch</p>
Code and data for "Multiple planetary boundaries preclude BECCS outside of agricultural areas"
<p>This repository contains the model code, scripts, configuration files, key results and documentation for the main analysis in:</p> <p>Braun, et al. “Multiple planetary boundaries preclude BECCS outside of agricultural areas”</p> <p>It contains:</p> <p># LPJmL</p> <p>## 1_LPJmL_model_code</p> <p>## 2_configure_runs</p> <p>## 3_configurations</p> <p># R</p> <p>## 1_generate_inputs</p> <p>## 2_process_input_data</p> <p>## 3_beccs_optimization</p> <p># Results</p> <p>## 1_source_data_figures</p> <p>## 2_further_results</p> <p># README</p>
Data of Västilä & Jilbert (2024): Evaluating multiannual sedimentary nutrient retention in agricultural two-stage channels
<p>This dataset contains measured and modelled data on the flow-sediment-nutrient interactions in a two-stage (compound) agricultural channel in Sipoo, Finland. The whole dataset is analyzed by Västilä & Jilbert (2024), but parts of the data have been previously described and analyzed by <a href="https://www.tandfonline.com/doi/full/10.1080/15715124.2011.572888" target="_blank" rel="noopener">Västilä & Järvelä (2011)</a>, <a href="https://ascelibrary.org/doi/full/10.1061/%28ASCE%29HY.1943-7900.0001058" target="_blank" rel="noopener">Västilä et al. (2016)</a>, <a href="https://link.springer.com/article/10.1007%2Fs11368-017-1776-3" target="_blank" rel="noopener">Västilä & Järvelä (2018)</a> and <a href="https://www.mdpi.com/2071-1050/13/16/9349" target="_blank" rel="noopener">Västilä et al. (2021)</a>. The dataset contains three separate files: </p> <p>1) repeated elevation surveys of the cross-sectional geometry in six channel cross-sections in years 2010, 2012 and 2019 </p> <p>2) vertical distributions of total phosphorus, nitrogen, carbon and sulphur in the main channel bed, floodplain and channel bank sediments</p> <p>3) experimentally determined daily average discharges, suspended sediment loads and total phosphorus loads in years 2009-2012, as well as modelled daily average discharges and loads of suspended sediment, total phosphorus, total nitrogen and total organic carbon in years 2009-2019</p> <p>Brief metadata descriptions (read-me) are included in each Excel file. Further details are described in the original scientific publications listed below. </p> <p>Västilä, K. 2010. Cohesive sediment processes in vegetated flows: preliminary field study results. In Dittrich, A., Koll, Ka., Aberle, J. & Geisenhainer, P. (eds.), Proceedings of River Flow 2010, Fifth International Conference on Fluvial Hydraulics, 8–10 September 2010, Braunschweig, Germany, pp. 317–324. Bundesanstalt für Wasserbau, Karlsruhe. ISBN-13: 978-3939230007.</p> <p>Västilä, K. & Jilbert, T. 2024 Evaluating multiannual sedimentary nutrient retention in agricultural two-stage channels. Scientific Reports, doi: 10.1038/s41598-024-84956-2.</p> <p>Västilä, K. & Järvelä, J. 2011 Environmentally preferable two-stage drainage channels: considerations for cohesive sediments and conveyance. International Journal of River Basin Management, 9(3–4): 171–180. doi: 10.1080/15715124.2011.572888. </p> <p>Västilä, K. & Järvelä, J. 2018 Characterizing natural riparian vegetation for modeling of flow and suspended sediment transport. Journal of Soils and Sediments, 18(10): 3114–3130. doi: 10.1007/s11368-017-1776-3.</p> <p>Västilä, K., Järvelä, J., and Koivusalo, H. 2016 Flow–vegetation–sediment interaction in a cohesive compound channel. Journal of Hydraulic Engineering, 142(1): 04015034. doi:10.1061/(ASCE)HY.1943-7900.0001058.</p> <p>Västilä, K., Väisänen, S., Koskiaho, J., Lehtoranta, V., Karttunen, K., Kuussaari, M., Järvelä, J., Koikkalainen, K. 2021 Agricultural Water Management Using Two-Stage Channels: Performance and Policy Recommendations Based on Northern European Experiences. Sustainability, 13(16), 9349. doi: 10.3390/su13169349</p>
Databases and scripts for Calvo, et al. Assessing the effect of glacier runoff changes on basin runoff and agricultural production in the Indus, Amu Darya, and Tarim Interior Basins.
<p>This repository has the necessary script and databases to reproduce the figures and tables presented in Calvo et al. (in press) Assessing the effect of glacier runoff changes on basin runoff and agricultural production in the Indus, Amu Darya, and Tarim Interior Basins. Earth´s Future. Wiley.</p>
Agronomic and Environmental Performance of Lemnaminor Cultivated on Agricultural Wastewater Streams—A Practical Approach
<p>This study investigated the potential of Lemna minor to valorise agricultural wastewater in protein-rich feed material in order to meet the growing demand for animal feed protein and reduce the excess of nutrients in certain European regions. For this purpose, three pilot-scale systems were monitored for 175 days under outdoor conditions in Flanders. The systems were fed with the effluent of aquaculture (pikeperch production—PP), a mixture of diluted pig manure wastewater (PM), and a synthetic medium (SM). PM showed the highest productivity (6.1 ± 2.5 g DW m<sup>−2</sup> d<sup>−1</sup>) and N uptake (327 ± 107 mg N m<sup>−2</sup> d<sup>−1</sup>). PP yielded a similar productivity and both wastewaters resulted in higher productivities than SM. Furthermore, all media showed similar P uptake rates (65–70 P m<sup>−2</sup> d<sup>−1</sup>). Finally, duckweed had a beneficial amino acid composition for humans (essential amino acid index = 1.1), broilers and pigs. This study also showed that the growing medium had more influence on the productivity of duckweed than on its amino acid composition or protein content, with the latter being only slightly affected by the different media studied. Overall, these results demonstrate that duckweed can effectively remove nutrients from agriculture wastewaters while producing quality protein.</p>
Large-scale homogenization of soil bacterial communities in response to agricultural practices in paddy fields, China
<div>This dataset contains data from 257 sites in four typical rice-growing regions across a 4,000-km transect in China, including geographic location data and environment factors data.</div> <div> <br> The study was conducted across a 4,000-km transect of China's rice-growing areas, from Heilong Jiang province to Yunnan province (100°55′ E to 134°08′° E, 22°46′ N to 48°02′ N, Table S1). Four typical rice-growing regions were selected along this transect: Sanjiang Plain (modern mechanical farming), Taihu Plain (mechanical plus minor manual farming), Lianghu Plain (manual plus minor mechanical farming), and Hani Terrace (traditional manual farming).</div> <div> <br> Field sampling was conducted during July and August in 2014 and 2015. Soil samples were collected from 178 flooded paddy fields and 79 surrounding non-paddy areas across four typical rice-growing regions of China. At each site, one homogenized sample was obtained, which was then separated into two parts. One part, which was obtained for DNA extraction, was placed into a sterile plastic tube then immediately placed in liquid nitrogen for short-term transportation. After shipping to the laboratory, these tubes were stored at −80 °C. The second part of the soil sample was placed into a plastic bag and stored at 4.0 °C for determining the soil physicochemical properties.</div> <div> <br> Main results of the experiments are that: (1) Distance–decay patterns of bacterial communities in paddy fields revealed reduced β-diversity compared to surrounding natural habitats. (2) Modern rice farming practices (plowing with machines) caused stronger homogenization of soil bacterial communities than traditional farming (plowing by hand). Among the four paddy regions, plowing by hand retained the highest soil bacterial β-diversity. (3) Moreover, a significant inverse correlation was observed between bacterial β-diversity and the agricultural mechanization level. (4) Among multiple environmental factors, dramatic spatial homogenization of soil physicochemical properties, particularly soil nutrient contents, and reduced dispersal limitation caused by modern farming activities both strongly predict a reduction of bacterial β-diversity in modern paddy fields.</div>
Raw data for "Plot-scale variability of organic carbon in temperate agricultural soils - Implications for soil monitoring"
<p>This dataset is the raw data that belongs to a peer-reviewed study on the small-distance variability of soil organic carbon in agricultural soils in Germany. It consists of three different files. The first file gives the coordinates of the 16 soil cores that were taken at each of the 16 sites (eight cropland and eight grassland sites). The second file gives the soil properties measured at each individual core (n=16 per site) and the third file the soil properties measured at each indivdual soil profile (n=6 per site).</p>
The Dataset of the PhD thesis titled "Operationalizing Values in Mobile Applications: A Mixed-Methods Empirical Study on Agriculture Apps for Bangladeshi Female Farmers"
<p>This package includes the survey (PVQ) questionnaire, demographic questions, focus groups questionnaire, interview questionnaire, and member checking summary used in this thesis.</p>
Steering microbiomes by organic amendments towards climate-smart agricultural soils
<p>We steered the soil microbiome via applications of organic residues (mix of cover crop residues, sewage sludge + compost, and digestate + compost) to enhance multiple ecosystem services in line with climate-smart agriculture. Our result highlights the potential to reduce greenhouse gases (GHG) emissions from agricultural soils by the application of specific organic amendments (especially digestate + compost). Unexpectedly, also the addition of mineral fertilizer in our mesocosms led to similar combined GHG emissions than one of the specific organic amendments. However, the application of organic amendments has the potential to increase soil C, which is not the case when using mineral fertilizer. While GHG emissions from cover crop residues were significantly higher compared to mineral fertilizer and the other organic amendments, crop growth was promoted. Furthermore, all organic amendments induced a shift in the diversity and abundances of key microbial groups. We show that organic amendments have the potential to not only lower GHG emissions by modifying the microbial community abundance and composition, but also favour crop growth-promoting microorganisms. This modulation of the microbial community by organic amendments bears the potential to turn soils into more climate-smart soils in comparison to the more conventional use of mineral fertilizers.</p>
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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.