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41 results for “crop type”
Harmonised LUCAS database classified by crop sequence type
<p>Assessing the benefits of crop diversification – a pillar of the agroecological transition – on a large scale requires a description of current crop sequences as a baseline, which is lacking at the scale of the European Union (EU). This work is based on the Harmonised LUCAS in-situ land cover and use database for field surveys from 2006 to 2018 in the European Union (doi: <a href="http://doi.org/10.2905/f85907ae-d123-471f-a44a-8cca993485a2">10.2905/f85907ae-d123-471f-a44a-8cca993485a2)</a> to fill this gap, We completed this dataset with a crop sequence type information for each point under non-perennial agricultural land cover in 2012, 2015 and 2018.</p> <p>The dataset lucas_classified.csv includes 31 159 points. Variables "point_id", "nuts0", "nuts2", "th_lat", "th_long", "LC1_2012", "LC1_2015", "LC1_2018" are inherited from the Harmonised LUCAS databse. Variables "cereals", "corn", "rapeseed", "sunflower", "pulses", "rootCrops", "forageLeg", "grassland" correspond to the temporal frequencies of respectively cereals, corn, rapeseed, sunflower, pulses, root crops, forage legumes and grassland within the 2012, 2015 and 2018 crop sequence for each point. Variable "crop_sequence_type" is the crop sequence type assigned to each point, among eight options: cereals, corn and cereals, forage legumes and cereals, pulses and cereals, rapeseed and cereals, root crops and cereals, sunflower and cereals, temporary grasslands.</p> <p>This dataset could be used to map current dominant crop sequences in the European Union, as illustrated in the map attached, and to assess the benefits of future crop diversification.</p> <p> </p>
2022 Rice Crop-type Data for Western Tanzania
<p>Rice Crop-type data from Katavi Region Tanzania was collected by the NASA Harvest Program at the University of Maryland, the Sokoine University of Agriculture, and Flamingoo Food Limited under the Optimizing Crop Yield Data Collection for Supply Chain Enhancement project (more at: https://cropanalytics.net/optimizing-yield-data/) funded by ENABLING CROP ANALYTICS AT SCALE (ECAAS) is a multi-phase initiative that aims to catalyze the development, availability, and uptake of agricultural ground and remote sensing data and applications in smallholder production systems more at (https://cropanalytics.net/)</p>
Global Crop Type Validation Data Set for ESA WorldCereal System
<p>This dataset was created by using a new IIASA tool, called “Street Imagery validation” (<a href="https://svweb.cloud.geo-wiki.org/">https://svweb.cloud.geo-wiki.org/</a>) where users could check street level images (e.g., Google Street Level images, Mapillary etc.) and identify the crop type where it is possible. The advantage of this tool is that there are plenty of georeferenced images with dates, going back in time. The disadvantage is that users need to check plenty of images where only few will clearly show cropland fields that are mature enough to be identified. To make the data collection more efficient, we provided our experts with preliminary maps of points in agricultural areas where street level images are available for the year 2021. Then, the experts checked those locations in an opportunistic way. The dataset is completely independent from all the existing maps and the reference datasets.</p> <p>There are 3 main data records uploaded:</p> <ol> <li>sv_croptype_poly.zip – an archive with a shapefile containing all the collected polygons with crop type information. Not all the polygons correspond to actual field boundaries.</li> <li>sv_croptype_validations.csv – a table with crop type observations with centroid coordinates in WGS84</li> <li>sv_worldcereal_validation.csv – a table with a subset of crop type observations used in validation of WorldCereal crop type maps for 2021.</li> </ol> <p>Fields:</p> <ul> <li>"id" – unique observation identifier;</li> <li>"imgSource" – source of imagery used for visual inspection;</li> <li>"imgLoc" – image location;</li> <li>"svImgDate" – image date;</li> <li>"imageIdKey" – image unique identifier;</li> <li>"submitedAt" – date of submission of crop type observation;</li> <li>"cropType" - crop type observation;</li> <li>"irrType" – irrigation type;</li> <li>"x", "y" – centroids of submitted polygons in WGS84.</li> </ul>
Distance functions of carabids in crop fields depend on functional traits, crop type and adjacent habitat: a synthesis
<p>Natural pest and weed regulation are essential for agricultural production, but the spatial distribution of natural enemies within crop fields and its drivers are mostly unknown. Using 28 datasets comprising 1,204 study sites across eight Western and Central European countries, we performed a quantitative synthesis of carabid richness, activity densities and functional traits in relation to field edges (i.e. distance functions). We show for the first time that distance functions of carabids strongly depend on carabid functional traits, crop type and, to a lesser extent, adjacent non-crop habitats. Richness of both predators and granivores and activity densities of small and granivorous species decreased towards field interiors, whereas the densities of large species increased. We found strong distance decays in maize and vegetables whereas richness and densities remained more stable in cereals, oilseed crops and legumes. We conclude that carabid assemblages in agricultural landscapes are driven by the complex interplay of crop types, adjacent non-crop habitats and further landscape parameters with great potential for targeted agroecological management. In particular, our synthesis indicates that a higher edge-interior ratio can counter the distance decay of carabid richness per field and thus likely benefits natural pest and weed regulation, hence contributing to agricultural sustainability.</p>
BSRLC+: An annual land cover dataset for the Baltic Sea Region with crop types and peat bogs at 30 m from 2000 to 2022
<p><strong>(NEW) </strong>Baltic Sea Region Land Cover <em>Urban</em> (BSRLC-U) focusing on urban built-up types now available: <a href="https://zenodo.org/records/17347941">https://zenodo.org/records/17347941 </a></p> <p><strong>Baltic Sea Region Land Cover <em>Plus </em>(BSRLC+) </strong>is annual land cover mapping (30 m) dataset in Europe from 2000 to 2022. The maps contain detailed information of 18 land cover (LC) types, including 9 crop types and 2 peat bog types.</p> <p>Input data : Optical multi-temporal remote sensing imageries (Landsat 5 (TM) / 7 (ETM+) / 8 (OLI) / 9 (OLI+) and Sentinel 2 (A / B ) from 2000 to 2022. Data is processed to surface reflectance and tiled into datacube structure using <a href="https://doi.org/10.3390/rs11091124">Framework for Operational Radiometric Correction for Environmental monitoring - FORCE.</a></p> <p>Mapping method: Maps are produced using data encoding and deep learning classification according to <a href="https://doi.org/10.1016/j.jag.2024.103867">Pham et al. 2024</a></p> <p>Validation: Maps have been rigorously validated using independent in-situ data <a href="https://doi.org/10.1038/s41597-020-00675-z">The Land Use/Cover Area frame Survey (LUCAS)</a>. </p> <p>Traing data and validation data are available: <a href="https://zenodo.org/records/11073291">https://zenodo.org/records/11073291</a></p> <p>This dataset contains:</p> <ul> <li><strong>00_preview.png</strong>: Preview map (2022) of the Baltic Sea region</li> <li><strong>BSRLC_{year}.tif</strong>: Annual map data (30 m) in GeoTIFF format (projection ETRS89 / EPSG:3035)</li> <li><strong>BSRLC_legend.xlss</strong>: Land cover codes and class names</li> <li><strong>BSRLC_qgis_style.qml</strong>: Map style to be used in QGIS</li> <li><strong>BSRLC_arcgis_style.lyrx</strong>: Map style to be used in ArcGIS</li> </ul> <p>Land cover codes (can also be found in <strong>BSRLC_legend.xlss</strong>):</p> <ul> <li>1: Built-up</li> <li>2: Bareland</li> <li>3: Water</li> <li>4: Shrubland</li> <li>5: Broadleaf forest</li> <li>6: Coniferous forest</li> <li>7: Wetland marsh</li> <li>8: Exploited peat bog</li> <li>9: Unexploited peat bog</li> <li>10: Wheat</li> <li>11: Barley</li> <li>12: Rye</li> <li>13: Oat</li> <li>14: Maize</li> <li>15: Seed crops</li> <li>16: Root crops</li> <li>17: Pulses, vegetable</li> <li>18: Grassland</li> <li>255: Nodata</li> </ul> <p> </p> <p><strong>Publication (please cite this publication if you are using the dataset):</strong></p> <ul> <li>Pham, V.-D., de Waard, F., Thiel, F., Bobertz, B., Hellmann, C., Nguyen, D.-V., Beer, F., Arasumani, M., Schwieder, M., Hartleib, J., Frantz, D., & van der Linden, S. (2024). An annual land cover dataset for the Baltic Sea Region with crop types and peat bogs at 30 m from 2000 to 2022. <em>Scientific Data, 11</em>, 1242, <a href="https://doi.org/10.1038/s41597-024-04062-w">https://doi.org/10.1038/s41597-024-04062-w</a></li> </ul> <p> </p> <p><strong>Other related publications:</strong></p> <ul> <li><em>Pham, V.-D., Tetteh, G., Thiel, F., Erasmi, S., Schwieder, M., Frantz, D., & van der Linden, S. (2024). Temporally transferable crop mapping with temporal encoding and deep learning augmentations. International Journal of Applied Earth Observation and Geoinformation, 129, 103867, <a href="https://doi.org/10.1016/j.jag.2024.103867">https://doi.org/10.1016/j.jag.2024.103867</a></em></li> <li><em>Frantz, D. (2019). FORCE—Landsat + Sentinel-2 Analysis Ready Data and Beyond. Remote Sensing, 11, <a href="https://doi.org/10.3390/rs11091124">https://doi.org/10.3390/rs11091124</a></em></li> </ul> <p> </p> <p><strong>Funding</strong></p> <p>This datatset is created in the frame of the Interdisciplinary Research Center for the Baltic Sea Region Research (IFZO) of University of Greifswald, Germany, and the research project Fragmented Transformations, which is funded by the German Federal Ministry of Education and Research (FKZ 01UC2102). </p>
Fig. 2 in On the proper type designation for Camelina microcarpa, a wild relative and possible progenitor of the crop species C. sativa (Brassicaceae)
Fig. 2. – Isotype of Camelina microcarpa Andrz. ex DC. [KW, Besser herbarium: KW001003104; © National Herbarium of Ukraine, Kiev]
Fig. 1 in On the proper type designation for Camelina microcarpa, a wild relative and possible progenitor of the crop species C. sativa (Brassicaceae)
Fig. 1. – Isotype of Camelina microcarpa Andrz. ex DC. [KW, Besser herbarium: KW001003103; © National Herbarium of Ukraine, Kiev]
Towards identifying industrial crop types and associated agronomies to improve biomass production from marginal lands in Europe
<p>Background: Growing industrial crops on marginal lands has been proposed as a strategy to minimize competition for arable land and food production. In the present study, eight experimental sites in three different climatic zones in Europe (Mediterranean, Atlantic and Continental), seven advanced industrial crop species [giant reed (two clones), miscanthus (<em>M</em>. × <em>giganteus</em> and two new seed-based hybrids), saccharum (one clones), switchgrass (one variety), tall wheatgrass (one variety), industrial hemp (three varieties) and willow (eleven clones)], and six marginality factors alone or in combination (dryness, unfavorable texture, stoniness, shallow soil, topsoil acidity, heavy metal and metalloid contamination) were investigated. At each site, biophysical constraints and low-input management practices were combined with prevailing climatic conditions.</p> <p>Results: The relative yield of a site-specific low-input system compared with the site-specific control was from small to large (i.e., from -99% in industrial hemp in the Mediterranean to +210% in willow in the Continental zone), due to the genotype-by-management interaction along with climatic variation between growing seasons. Genotype selection and improved knowledge on crop response to changing environmental, site-specific biophysical constraint and input application has been detected as key to profitably grow industrial crops on marginal areas.</p> <p>Conclusions: This study may act to provide hints on how to scale-up investigated cropping systems, through low-input practices, under similar environmental and soil conditions tested at each site. However, further attention to detail on the agronomy of early plant development and management in larger multi-year and multi-location field studies with commercially scalable agronomies are needed in order to validate yield performances, and thereby to inform on the best industrial crop options.</p>
GEOGLAM Best Available Crop Type Masks
<p>Best Available Crop Specific masks (BACS) over the major production and export countries for wheat, maize, rice, and soybeans, in the context of the G20 Global Agriculture Monitoring Program, GEOGLAM. The countries covered by GEOGLAM-BACS account for a total of 84% of soy, 54% of maize, 62% of wheat, and 92% of rice production globally.</p>
Dataset from: The effects of crop type, landscape composition and agroecological practices on biodiversity and ecosystem services in tropical smallholder farms
<p>1. In the tropics, smallholder farming characterizes some of the world's most biodiverse landscapes. Agroecology as a pathway to sustainable agriculture has been proposed and implemented in sub-Saharan Africa, but the effects of agricultural practices in smallholder agriculture on biodiversity and ecosystem services are understudied. Similarly, the contribution of different landscape elements, such as shrubland or grassland cover, on biodiversity and ecosystem services to fields remains unknown.</p> <p>2. We selected 24 villages situated in landscapes with varying shrubland and grassland cover in Malawi. In each village, we assessed biodiversity of eight taxa and ecosystem services in relation to crop type, shrubland and grassland cover and the number of agroecological pest and soil management practices on smallholder's fields of different crop types (bean monoculture, maize-bean intercrop, and maize monoculture).</p> <p>3. Increasing shrubland cover altered carabid and soil bacteria communities. Carabid abundance increased in maize but decreased in intercrop and bean fields with increasing shrubland cover. Carabid abundance and richness and wasp abundance increased with soil management practices. Carabid, spider, and parasitoid abundances were higher in bean monocultures, but this was modulated by surrounding shrubland cover. Natural enemy abundances in beans were especially high in landscapes with little shrubland, possibly leading to lower bean damage in monocultures compared to intercropped fields, whereas maize monocultures had higher damage. In maize, grassland cover and pest management practices were positively related to damage. Carabid abundance was higher in fields with high bean damage and increased carabid richness in fields with high maize damage. Parasitoid abundance was negatively associated with bean damage.</p> <p>4. <em>Synthesis and application:</em> Our results suggest that maintaining biodiversity and ecosystem services on smallholder farms is not achievable with a "one size fits all" approach but should instead be adapted to the landscape context and the priorities of smallholders. Shrubland is important to maintain carabid and soil bacterial diversity, but legume cultivation beneficial to natural enemies could complement pest management in landscapes with a low shrubland cover. An increased number of agroecological soil management practices can lead to improved pest control whilst the effectiveness of agroecological pest management practices needs to be re-evaluated.</p>
Distance functions of carabids in crop fields depend on functional traits, crop type and adjacent habitat: a synthesis
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Dataset from: The effects of crop type, landscape composition and agroecological practices on biodiversity and ecosystem services in tropical smallholder farms
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Data from: Impact of crop type on biodiversity globally
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Data from: Pest control potential of adjacent agri-environment schemes varies with crop type and is shaped by landscape context and within-field position
<ol> <li>Increasing natural pest control in agricultural fields is an important aim of ecological intensification. Combined effects of landscape context and local placement of agri-environmental schemes on natural pest control and within field distance functions of natural pest control agents have rarely been addressed but might affect the distribution of biocontrol providers. Importantly, it is currently unknown whether ecosystem services provided by adjacent agri-environmental schemes (AES) are consistent for different crop types during crop rotation.</li> <li>In this study, we assessed whether crop rotation from oilseed rape to cereals altered within-field distance functions of ground dwelling predators from adjacent agri-environmental fields along a gradient in landscape context. Additionally we recorded crop pests, predation rates, parasitoids as well as crop yields on a total of 30 study sites.</li> <li>Distance functions varied between trophic levels: Carabid richness decreased while densities of carabid beetles, staphylinid beetles as well as crop yields increased towards the field centres. Distance functions of parasitoids and pests were modulated by the amount of semi-natural habitat in the surrounding landscape, while the effects of adjacent AES were limited.</li> <li>Distance decay functions found for ground dwelling predators in oilseed rape in the previous year were not always present in cereals. Increasing distance to the field edge also increased effects of crop rotation on carabid beetle assemblages, indicating a source habitat function of field edges.</li> <li>Synthesis and applications<i>.</i> Distance functions of natural pest control are not universal and the effects of agri-environmental schemes (AES) in different adjacent crops during crop rotation vary and depends on ecological contrasts. A network of semi-natural habitats and spatially optimised AES habitats can benefit pest control in agricultural landscapes, but constraints as a result of crop type need to be addressed by annually targeted, spatially shifting AES schemes for different crops.</li> </ol> <div> </div>
Barn Swallow (Hirundo rustica) fledglings use crop habitat more frequently in relation to its availability than pasture and other habitat types
Populations of birds that forage on aerial insects have been declining across North America for several decades, but the main causes of and reasons for geographical variation in these declines remains unclear. We examined the habitat use and survival of post-fledging Barn Swallows (Hirundo rustica), near Vancouver, BC, Canada using VHF radio telemetry. We predicted that fledgling Barn Swallows hatched in higher quality natal habitat (pasture) would fledge at higher quality, stay closest to the nest, disproportionately use higher-quality habitat during the post-fledge stage and have higher survival rates in the region. Contrary to our predictions, we found that natal habitat (crop, pasture or non-agriculture) had no effect on fledgling quality or movement distance. Barn Swallow fledglings used crop habitat more frequently in relation to its availability than other habitat types, including pasture. Barn Swallows had low post-fledging survival rates (0.44; 95% CI: 0.35-0.57), which could negatively influence the population trend of the species in this region. While natal habitat had only minor effects, crop habitat appears to be important for fledgling Barn Swallows and therefore a decline in this habitat type could have further negative implications for an already declining species.
The effects of microplastics on crop variation depend on polymer types and their interactions with soil nutrient availability and weed competition
<p>Microplastics pollution of agricultural soil is a global environmental concern because of its potential risk to food security and human health. Although many studies have tested the direct effects of microplastics on growth of <em>Eruca sativa</em> Mill., little is known about whether these effects are regulated by fertilization and weed competition in field management practices.</p> <p>Here, we performed a greenhouse experiment growing <em>E. sativa</em> as target species in a three-factorial design with two levels of fertilization (low versus. high), two levels of weed competition treatments (weed competition versus no weed competition) and five levels of microplastic treatments (no microplastics, Polybutylene adipateco-terephthalate [PBAT], Polybutylene succinate [PBS], Polycaprolactone [PCL] or Polypropylene [PP]).</p> <p>Compared to the soil without microplastics, PBS and PCL reduced aboveground biomass and leaf number of the <em>E. sativa</em>. PBS also resulted in increased root allocation and thicker roots in <em>E. sativa</em>. In addition, fertilization significantly mitigated the negative effects of PBS and PCL on aboveground biomass of <em>E. sativa</em>, but weed competition significantly promoted these effects. Although fertilization alleviated the negative effect of PBS on aboveground biomass, such alleviation became weaker under weed competition than when <em>E. sativa</em> grew alone.</p> <p>The results indicate that the effects of specific polymer types on <em>E. sativa</em> growth could be regulated by fertilization, weed management, and even their interactions. Therefore, reasonable on-farm management practices may help in mitigating the negative effects of microplastics pollution on <em>E. sativa</em> growth in agricultural fields.</p>
Ground Truthing Survey Data of Crop Type in Pakistan (Rabi 2022‒Kharif 2023)
<p>The dataset comprises ground truthing survey data collected during the winter (Rabi) season of 2022–23 and the summer (Kharif) season of 2023 in Pakistan. These surveys were conducted as part of the Asian Development Bank's (ADB) initiative to support Pakistan's Ministry of National Food Security and Research (MNFSR) and provincial Crop Reporting Service (CRS) departments in adopting technology-based data collection practices. There were 43,892 data points collected during the winter (Rabi) season and 92,951 during the summer (Kharif) season. The data collected is available in the below-mentioned format.</p> <div> <table> <tbody> <tr> <td> <p><strong>Variable Name</strong></p> </td> <td> <p><strong>Description</strong></p> </td> <td> <p><strong>Data Type</strong></p> </td> <td> <p><strong>Example Values</strong></p> </td> </tr> <tr> <td> <p>ID</p> </td> <td> <p>Unique identifier for each data point</p> </td> <td> <p>Text</p> </td> <td> <p>3-324-20-2-19082023-1-1</p> </td> </tr> <tr> <td> <p>Season</p> </td> <td> <p>Season in which data was collected</p> </td> <td> <p>Text</p> </td> <td> <p>Rabi</p> </td> </tr> <tr> <td> <p>Province</p> </td> <td> <p>Name of the province where data was collected</p> </td> <td> <p>Categorical</p> </td> <td> <p>Khyber Pakhtunkhwa</p> </td> </tr> <tr> <td> <p>District</p> </td> <td> <p>Name of the district where data was collected</p> </td> <td> <p>Categorical</p> </td> <td> <p>Malakand</p> </td> </tr> <tr> <td> <p>Date</p> </td> <td> <p>Date showing when the data was collected</p> </td> <td> <p>Date</p> </td> <td> <p>19/08/2023</p> </td> </tr> <tr> <td> <p>Latitude</p> </td> <td> <p>Latitude coordinate of the data point</p> </td> <td> <p>Float</p> </td> <td> <p>34.449521</p> </td> </tr> <tr> <td> <p>Longitude</p> </td> <td> <p>Longitude coordinate of the data point</p> </td> <td> <p>Float</p> </td> <td> <p>71.907877</p> </td> </tr> <tr> <td> <p>Code</p> </td> <td> <p>Numeric code representing specific crop (e.g. Wheat is given code 1)</p> </td> <td> <p>Integer</p> </td> <td> <p>14</p> </td> </tr> <tr> <td> <p>Land</p> </td> <td> <p>Type of land</p> </td> <td> <p>Categorical</p> </td> <td> <p>Rice, Intercropping</p> </td> </tr> <tr> <td> <p>Description</p> </td> <td> <p>Detail of land type</p> </td> <td> <p>Categorical</p> </td> <td> <p>Orchard (Apple)</p> </td> </tr> <tr> <td> <p>Stage</p> </td> <td> <p>Stage of crop at the time of data collection</p> </td> <td> <p>Categorical</p> </td> <td> <p>Reproductive</p> </td> </tr> </tbody> </table> </div> <p> </p> <p> </p>
Pest species preyed upon by bats and the crop types affected by them.
<p>This database compiles the pest species consumed by bats and the crop types they attack, as part of the supplementary material of the article entitled "<strong>Pest suppression by bats and management strategies to favour it: a global review</strong>", published in the journal Biological Reviews.</p> <p>The crop types were classified into several categories this work: cereals (e.g. wheat, maize, corn, rice, barley, sorghum); forest (e.g. beech, oak, poplar, willow); fruit crops (e.g. apple, pear, apricot, strawberry, cranberry); grasses (e.g. sugarcane, turfs, pastures); legumes (e.g. pea, bean, alfalfa, soybean); ornamental (e.g. garden species); other (cotton, tea, tobacco, hop, flax, rubber tree, hemp, peppermint, jute, rapeseed, kenaf, ashwagandha, mushrooms, honeybees); stored products (e.g. stored cereals, stored tobacco, dried fruits); and vegetables (e.g. tomato, lettuce, spinach, potato, onion).</p>
Soil properties and crop yield in fruit orchards under Mediterranean conditions in terms of intercropping, tillage and fertilizer type
<p>This data set contains a data-mining performed to assess the impact of intercropping, tillage and fertilizer type on soil and crop yield in fruit orchards under Mediterranean conditions by a further meta-analysis of the data. </p> <p>These data correspond to the open-access article "The impact of intercropping, tillage and fertilizer type on soil and crop yield in fruit orchards under Mediterranean conditions: A meta-analysis of field studies" published in Agricultural Systems. (<a href="https://doi.org/10.1016/j.agsy.2019.102736">https://doi.org/10.1016/j.agsy.2019.102736</a>), funded by he European Commission Horizon 2020 project Diverfarming [grant agreement 728003]. Raúl Zornoza acknowledges the financial support from the Spanish Ministry of Science, Innovation and Universities through the “Ramón y Cajal” Program [RYC-2015-18758].. </p> <p> </p>
Probabilistic Data Generating Process-based Crop Type Map for the EU 2010-2020
<h3>General Description</h3> <p>This dataset consists of probabilistic crop type maps for the EU-28 for the years 2010-2020 that distinguish 28 crop types at 1km resolution (EPSG:3035). The maps were generated using the Data Generating Process-based procedure developed by Baumert, Heckelei and Storm (2024) [<em><span><a href="https://doi.org/10.1016/j.ecoinf.2024.102836">https://doi.org/10.1016/j.ecoinf.2024.102836</a></span></em>]. We refer to this paper for details on the generation and validation of the maps. The code used to create the maps including a detailed list of the input data can be found here: <a href="https://github.com/JoBaumert/Probabilistic_Crop_Mapping_EU">GitHub - JoBaumert/Probabilistic_Crop_Mapping_EU</a> . </p> <h3>Downloadable Data</h3> <p>The file “EU_expected_crop_shares.zip” consists of 11 raster files, one for each year from 2010 – 2020. The raster files indicate the expected shares for each of the 28 distinguished crop types in a grid cell for the entire EU-28 (see readme.txt contained in the zipped folder). Note that this raster file does not contain uncertainty information.</p> <p>The other 28 zip files contain the entire crop map ensemble (i.e., including uncertainty information), each for one of the EU countries and the United Kingdom. Each of those zip files contain 11 raster files, one for each year from 2010 – 2020. Each raster file has 2830 bands: the first two bands indicate the weight of the cell (proportional to the utilized agricultural area in a cell) and the estimated number of agricultural fields in a cell, respectively. The next 28 bands indicate the expected shares for each of the 28 crops in the respective cell. The remaining 2800 bands compose the crop type map ensemble, i.e., 100 simulated crop shares for each of the 28 crops. The zipped country folder also includes a csv file named “bands” that describes which band refers to which crop. Note that all crop shares were multiplied by 1000 when writing them to the raster files (saving them as integers requires less storage capacity), i.e., if a crop share is 0.325 or 32.5% it will appear as 325 in the raster files. </p> <p>The distinguished crops are (with abbreviation used in "bands.csv"):</p> <ul> <li>Apples and other fruits, nuts and berries (APPL+OFRU)</li> <li>Barley (BARL)</li> <li>Citrus fruits (CITR)</li> <li>Durum wheat (DWHE)</li> <li>Flowers and ornamental plants (FLOW)</li> <li>Grassland (GRAS)</li> <li>Maize (both green maize as well as grain maize, LMAIZ)</li> <li>Rape and turnip (LRAPE)</li> <li>Nurseries (NURS)</li> <li>Oats (OATS)</li> <li>Other cereals (OCER)</li> <li>Other permanent crops (OCRO)</li> <li>Other forage plants (OFAR)</li> <li>Other industrial plants (OIND)</li> <li>Olives (OLIVGR)</li> <li>Rice (PARI)</li> <li>Potatoes (POTA)</li> <li>Pulses (PULS)</li> <li>Fodder roots and brassicas (ROOF)</li> <li>Rye (RYEM)</li> <li>Soybeans (SOYA)</li> <li>Sugar beets (SUGB)</li> <li>Sunflowers (SUNF)</li> <li>Soft/common wheat (SWHE)</li> <li>Other oilseeds and fibre crops (TEXT)</li> <li>Tobacco (TOBA)</li> <li>Fresh vegetables, melons, strawberries (TOMA+OVEG)</li> <li>Vineyards (VINY)</li> </ul> <p> </p> <p> </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.