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1,425 results for “Agriculture”
Conversion from forest to agriculture in the Brazilian Amazon from 1985 to 2021
<p>This file collection contains data with conversion length from forests to agriculture in the Brazilian Amazon, from 1985 to 2021. Calculations were based in the MapBiomas thematic maps. More information can be found in the repository website (<a href="https://github.com/hugotseixas/forest-agri-conversion/tree/3.0.0">https://github.com/hugotseixas/forest-agri-conversion/tree/3.0.0</a>).</p> <p>The collection is composed of six sets of data:</p> <p><strong>c_raster_mosaic</strong>: raster files of the Amazon biome with values of conversions;</p> <p><strong>c_raster_tiles</strong>: raster files of smaller raster tiles, with values of conversions;</p> <p><strong>c_tabular_dataset</strong>: a group of tables that contains data about conversions;</p> <p><strong>figures</strong>: data to create figures of the manuscript;</p> <p><strong>raw_raster_tiles</strong>: raster files of smaller raster tiles with land use and land cover classification data from MapBiomas;</p> <p><strong>validation</strong>: data used to performed validation of the conversion results.</p>
Dataset for publications "Willingness to pay for urban agriculture in Oslo" and "The Value of Urban Farming in Oslo, Norway: Community Gardens, Aquaponics and Vertical Farming"
<p>Dataset for publications “(Willingness to pay for urban agriculture in Oslo)[https://zenodo.org/record/6510590#.Y851cXbMLfs]" and “The Value of Urban Farming in Oslo, Norway: Community Gardens, Aquaponics and Vertical Farming”. The data was collected as a contingent valuation study of urban agriculture in Oslo, Norway.</p>
Global maps of agricultural expansion potential at a 300 m resolution
<p><strong>Global </strong><strong>maps of agricultural expansion potential</strong><strong> at a 300 m resolution </strong></p> <p>This repository contains data from “Global maps of agricultural expansion potential at a 300 m resolution” study.</p> <p><strong>Abstract:</strong></p> <p>The global expansion of agricultural land is a leading driver of climate change and biodiversity loss. However, the spatial resolution of current global land change models is relatively coarse, which limits environmental impact assessments. To address this issue, we developed global maps representing the potential for conversion into agricultural land at a resolution of 10 arc-seconds (approximately 300 m at the equator). We created the maps using Artificial Neural Network (ANN) models relating locations of recent past conversions (2007-2020) into one of three cropland categories (cropland only, mosaics with >50% crops, and mosaics with <50% crops) to various predictor variables reflecting topography, climate, soil and accessibility. Cross-validation of the models indicated good performance with Area Under the Curve (AUC) values of 0.88-0.93. Hindcasting of the models from 1992 to 2006 revealed a similar high performance (AUC of 0.83-0.91), indicating that our maps provide representative estimates of current agricultural conversion potential provided that the drivers underlying agricultural expansion patterns remain the same. Our maps can be used to downscale projections of global land change models to more fine-grained patterns of future agricultural expansion, which is an asset for global environmental assessments.</p> <p><strong>Data description:</strong></p> <p>We provide here raster maps of agricultural expansion potential for three categories of agriculture - (i) cropland only, (ii) mosaics with >50% crops, and (iii) mosaics with <50% crops. The source for delineating categories was the ESA CCI land cover data. ESA CCI land cover data recognizes additional categories of agricultural land, however some of them have limited spatial coverage. For that reason, we merged the rainfed cropland and irrigated cropland categories into a single category - cropland only, where a grid cell is largely dominated by crops. Rainfed croplands account for 87% of the this category, while irrigated croplands account for the remaining 13%. Mosaic categories were defined in the same way as in the ESA CCI land cover dataset. Numerical designations of these categories in the ESA CCI land cover dataset are 10, 20, 30, and 40 for rainfed, irrigated, mosaics with >50% crops, and mosaics with <50% crops, respectively.</p> <p>Global maps are provided at the spatial resolution of 10 arc-seconds (~300 meters at the equator). These files are available for three categories in the main folder with the filename prefix "Agri_potential_mosaic_*<em>"</em>. The numerical value in the file name refers to the agricultural category type (10 - cropland only, 30 - mosaics with >50% crops, and 40 - mosaics with <50% crops). In addition to the 10 arc-second layers, we provide aggregated layers with the spatial resolution of 30 arc-seconds, 5 and 10 arc-minutes, for coarse-grained applications and less computationally-intensive analyses. We provide the aggregated layer maps for the minimum, median, mean/average, and maximum values of the aggregated 10 arc-seconds values within the coarser cells. There are in total 9 files provided for each of the aggregated spatial resolutions.</p> <p><strong>Repository content:</strong></p> <p>Full resolution layers:<br> - “Agri_potential_mosaic_10.tif” is the global raster map for cropland only category at the spatial resolution of 10 arc-seconds.<br> - “Agri_potential_mosaic_30.tif” is the global raster map for mosaics with >50% crops category at the spatial resolution of 10 arc-seconds.<br> - “Agri_potential_mosaic_40.tif” is the global raster map for mosaics with <50% crops category at the spatial resolution of 10 arc-seconds.<br> - "readme.txt" is the text file with the basic description and the metadata for the repository.</p> <p>Aggregated layers:<br> This folder contains files with a different spatial resolution (30s, 5m, 10m; see argument "RESL" below).</p> <p>File names for the aggregated maps contain the following information: “Agri_potential_aggregated_RESL_TYPE_CATG.tif”</p> <p>- "RESL" is the spatial resolution of the layer. Value is either "30s", "5m", or "10m", corresponding to spatial resolution of 30 arc-second, 5 arc-minutes, and 10 arc-minutes.</p> <p>- "TYPE" is the type of aggregated values. Value is either "min", "avg", "med", or "max", corresponding to the minimum, mean, median, and maximum values of the aggregated 10 arc-seconds values within the coarser cells.</p> <p>- "CATG" is the category of agricultural land. Value is either "10", "30", or "40", where category 10 is cropland only, category 30 is mosaics with >50% crops, and category 40 is mosaics with <50% crops.</p> <p>Raster metadata:</p> <p>Driver: GTiff<br> Projection proj4string: +proj=longlat +ellps=WGS84 +no_defs</p> <p><strong>Notes on use:</strong></p> <p>Our conversion potential maps are useful for researchers and practitioners interested in downscaling projections of global land change models to a more fine-grained patterns of future agricultural expansion, or interested in assessing the locations and effects of future agricultural expansion, for example in integrated assessment modelling or biodiversity impact modelling. When coupling outputs with integrated assessment modelling, our maps need to be combined with estimates of the expected future demands for agricultural land per socio-economic region. In such a coupled approach, our global conversion potential maps can be used to spatially allocate the additional agricultural land demands. In this context, it is important to note that the modelled relationships between the agricultural conversions and our set of predictors may result in non-zero probabilities also in areas that are highly unlikely to be converted into agriculture, such as urban areas or strictly protected nature reserves. This implies that users of our maps may need to implement an additional map layer that masks areas unavailable for agricultural expansion. We also stress that our maps represent agricultural conversion potential conditional on the predictor variables that we included, implying that our maps do not capture the possible influences of other potentially relevant predictors. For example, our conversion potential models and maps do not account for permafrost, which may pose significant challenges to possible agricultural expansion to higher latitudes in response to climate change.</p>
WE3DS: An RGB-D image dataset for semantic segmentation in agriculture
<p>Here, we introduce a novel RGB-D image database (WE3DS) for semantic segmentation in crop farming. It contains 2,568 RGB-D images (color image and distance map) and hand-annotated ground-truth masks for semantic segmentation and is the first RGB-D image dataset for multi-class plant species semantic segmentation task. Images were taken under natural light conditions using an RGB-D sensor consisting of two RGB cameras in a stereo setup.</p> <p> </p> <p><strong>Please cite the original source when using this dataset.</strong></p> <p>Kitzler, F.; Barta, N.; Neugschwandtner, R.W.; Gronauer, A.; Motsch, V. WE3DS: An RGB-D Image Dataset for Semantic Segmentation in Agriculture. <em>Sensors</em> <strong>2023</strong>, <em>23</em>, 2713. <a href="https://doi.org/10.3390/s23052713">https://doi.org/10.3390/s23052713 </a></p>
Raw Data for D3.4: Design and performance of vacuum degasification for agricultural residues and food-waste treatment for nitrogen depletion /recovery
<p>The data contains the test results from the conducted tests at the PONDUS-N pilot plant for vacuum degasification from Circular Agronomics WP2/3. It includes 4 major test series A-D with multiple triplicate runs in each series. Analytical data of batch testing and plant parameters were recorded and put into the file to accurately describe the time course of the tests. The boundary conditions of the test runs are described in D3.4.</p>
Farmers' expectations and acceptance of new technologies involved in circular agriculture at the farm level
<table> <tbody> <tr> <td>Dataset description </td> <td>Data correspond to 149 farmers from four EU countries (Spain, Italy, Croatia, and the Czech Republic). This database contains information about farmer characteristics, farm structural characteristics, and farm management, and also contains preferences and attitudinal constructs (such as agribusiness objectives, and environmental attitudes and opinions) and variables related to questions about the potential adoption of the proposed innovations</td> </tr> <tr> <td>Collection</td> <td> Data was collected online from March to August 2021, using a survey instrument, administered to farmers face to face.</td> </tr> <tr> <td>Utility</td> <td>The data provides insight into farmer acceptance of five different circular agricultural innovations designed to reduce nutrient loss at the farm level and focused on improving environmental sustainability.</td> </tr> </tbody> </table> <p> </p> <p> </p>
CornWeed Dataset: A dataset for training maize and weed object detectors for agricultural machines
<p>There are many datasets available for training object detectors in non agricultural domains such as Autonomous Driving but these datasets fail to generalize well enough to an agricultural use-case. This dataset contains 3574 hand labelled images with bounding boxes provided in both YOLO and COCO dataset format. Additionally there are 4981 unlabelled images for testing purposes. All the images are hand labelled with bounding boxes with several labellers and reviewed. The dataset contains two class IDs namely maize and weeds. The dataset also contains a crop-row instance but has not been used in the accompanying paper but could be interesting for other future work. The images have been recorded in two resolutions i.e. 720 x 1280 and 480 x 640 covering two crop rows and single crop row respectively. </p>
Agricultural yield database Flanders
<p>Database containing the evolution of yield of agricultural crops in Flanders under ‘conventional’ farming practices, as part of the project <a href="https://www.peilimpact.be/">PEILIMPACT</a>. This database contains yield in ton/ha of the most important crops in Flanders. When available, it includes the planting and harvesting dates, and mowing times in case of grass. At the moment, the crops included in the database are maize, winter wheat, sugar beet, potato and grass. The data is collected from different research departments at ILVO and other governmental and private Flemish institutions.</p>
Urban Agriculture and Health in Africa. A Review
<p>The Excel table is raw data containing publications from the systematic literature review on urban agriculture's impacts on health and urban planning research.</p> <p>This work was totally funded by the Swiss National Science Foundation (SNF#18357) Sinergia Project – African Contribution to Global Health: Circulating Knowledge and Innovations.</p>
Initial Sample of HYPERNETS Hyperspectral Surface Reflectance Measurements for Satellite Validation from the agricultural land at Demmin, Germany
<p>The HYPERNETS project (www.hypernets.eu) aims to ensure that high-quality in situ measurements are available to support the (VNIR/SWIR) optical Copernicus products. Therefore, it established a new autonomous hyperspectral spectroradiometer (HYPSTAR® - www.hypstar.eu) dedicated to land and water surface reflectance validation with instrument-pointing capabilities. In the prototype phase, the instrument is being deployed at 24 sites covering a range of water and land types and a range of climatic and logistic conditions. This dataset provides the first published data for the HYPERNETS site in Demmin, Germany [53°52'5.80"N,13°16'6.80"E] (DEGE). It is a subset of the complete data record, consisting of the measurements withEthaturements which could be used for satellite validation. </p> <p>The provided NetCDF files are the L2A hypernets products with surface reflectances, their associated uncertainties and error-correlation information. The reflectance in the L2A products is the Hemispherical-directional Reflectance Factor (HDRF) defined as HDRF = π L / E where L is the directional upwelling radiance (with the field o, view of 5 degrees), and E is the (hemispherical) downwelling irradiance (i.e. including both direct solar and diffuse sky irradiance). These reflectances have dimensions of wavelength and series, where each series is a set of measurements for a given geometry (combination of viewing zenith and azimuth angle). In addition to variables for wavelength and bandwidth, the files also contain variables that provide for each series the acquisition time, viewing and solar angles, number of valid scans used, and quality flags (typically, no flags are set in the data provided in this dataset). These NetCDF files also contain further relevant metadata as attributes. See https://hypernets-processor.readthedocs.io/ for further info.</p> <p>The HYPSTAR®-XR sensor was installed on 22 July 2021 at the top of a 10m mast on an extended 5 m horizontal boom to minimise interruption of the field of view. The boom faces South at the right angle towards bare soil. The mast is located at 53.868278°N, 13.268556°E. Data are collected every 30 minutes between 9:00 and 17:00 (UTC) from different zenith and azimuth angles.</p> <p>The HYPSTAR®-XR (eXtended Range) instruments deployed at each land HYPERNETS site consist of a VNIR and a SWIR sensor and autonomously collect data between 380-1700 nm at various viewing geometries and send it to a central server for quality control and processing. The VNIR sensor spans 1330 channels between 380 and 1000 nm with an FWHM of 3 nm, and the SWIR sensor has 220 channels between 1000 and 1700 nm with an FWHM of 10 nm. The hypernets_processor (Goyens et al. 2021; De Vis et al. in prep.) automatically processes all this data into various products, including the L2A surface reflectance product provided here. All products have associated uncertainties (divided into random and systematic uncertainties, including error-correlation information) propagated using the CoMet toolkit (www.comet-toolkit.org). </p> <p>To obtain this dataset, we start from the full DEGE data record and omit all the data that do not pass all quality checks performed as part of the hypernets_processor. In addition, an additional screening procedure was also developed to remove outliers and supply the best quality data suitable for satellite validation. To remove the outliers, a sigma-clipping method is used. First, reflectances are extracted in separate 2-hour windows throughout the day (to account for BRDF differences due to different solar positions) for four different wavelengths (500, 900, 1100 and 1600 nm). Outliers in these reflectances are then identified by iteratively calculating the mean reflectance trend with time (by binning the data per maximum of 30 data points), calculating the standard deviation from this trend, and masking any data that is more than three standard deviations away from the trend. This process is repeated on the unmasked data until the standard deviation does not vary by more than 5% between two iterations. The masks for the four different wavelengths are then combined (keeping only measurements for which none of the four wavelengths is an outlier). The reflectances and associated uncertainties for any masked series (i.e. a geometry that is masked either by the sigma-clipping procedure or from the masks of the hypernets_processor) are replaced by NaNs. Any sequence that has more than half of its series masked is removed entirely. </p>
Evaluation of Agriculture Open Data Ecosystem Maturity
<p>This dataset represents an evaluation of the key elements of the agricultural open data ecosystem in Croatia: Stakeholders, Infrastructure, Data, Policy/Governance. The evaluation was made by experts. For each element, an evaluation was made according to certain criteria. The experts involved in the research were representatives of each stakeholder group, namely: Management and Support Organizations, Agriculture Producers/Farmers, Suppliers, Researchers and Scientists, Consumers/Consumer Organizations and Other Stakeholders. </p>
Data to test IDP for agriculture in developing countries
<p>The data has variables capturing net outward foreign direct investment per capita, gross domestic product per capita and trade openness for agriculture in developing countries. The other variables are the official exchange rate, gross secondary school enrolment in per cent and inflation measured as per cent of CPI growth. These are for the total economy. </p> <p>Net outward foreign direct investment per capita (NOFDIPC) was constructed as outward foreign direct investment less inward foreign direct investment for agriculture, forestry and fishing. The sum is divided by the population of both sexes. The foreign direct investment and population data were obtained from FAOSTAT (https://www.fao.org/faostat/en/#data/FDI; https://www.fao.org/faostat/en/#data/OA). The gross domestic product per capita (GDPPC) was computed as agricultural value added divided by the population. Agricultural value added was also obtained from FAOSTAT (https://www.fao.org/faostat/en/#data/MK). Trade openness (AGTO) was computed as agricultural exports plus imports divided by agricultural value added. The exports and imports were obtained from FAOSTAT (https://www.fao.org/faostat/en/#data/TCL). Others; official exchange rate (EXRATE), gross secondary school enrolment in per cent (HC) and inflation measured as per cent of CPI growth (INFLA) were drawn from the world development indicators database of the World Bank (https://databank.worldbank.org/source/world-development-indicators#).</p>
Dataset on Article: River ecological status is shaped by agricultural land use intensity across Europe: Establishing a typology of farming-driven freshwater impacts
<p>This repository contains raw data from the article "River ecological status is shaped by agricultural land use intensity across Europe: Establishing a typology of farming-driven freshwater impacts" which is currently under review.</p> <p>It contains data to allocate the pressures (<strong>Data_pressure_allocation.csv</strong>) and calculate the Pressure Index (<strong>Data_pressure_index.csv</strong>) for Table 1, and for the Spearman correlations for Figure 2 (<strong>Data_Spearman_correlations.csv</strong>).</p> <p> </p> <p>Also available is the Shapefile used for the different agricultural maps (Figure 1 and Figure S1-S4):</p> <p><strong>Shapefile Schürings_et_al._2023</strong> (Coordinate system: ETRS 1989 UTM Zone 32N)</p> <p><strong>Attribute description</strong></p> <p>Id - Identifier of polygons</p> <p>gridcode - Code of agricultural archetypes of Levers et al., (2018)</p> <p>M_ZHYD: Unique identifier of corresponding FEC</p> <p>mars_bt12: River types</p> <p>eco_stat_2: Ecological status</p> <p>Biogeoregi: Biogeographical Regions - AN = Northern and Highland, Temp = Temperate, Mediterranean = Mediterranean</p> <p>Cum_pressu: Agricultural pressure index</p> <p>Nitrogen: Agricultural nitrogen pressure</p> <p>Pesticides: Agricultural pesticide pressure</p> <p>Hydromorph: Agricultural hydromorphological pressure</p> <p>Water_abst: Agricultural water abstraction</p>
A 500m-Resolution Dataset of Agricultural Drought Areas in the North China Plain During 2006–2019
<p>Agricultural drought stands as a dominant natural hazard in the North China Plain, showing increasing frequency and severity over recent decades. However, limitations posed by the coarser spatiotemporal resolution of accessible agricultural drought data archives impede the practical application of regional-scale agricultural drought simulation and monitoring studies. Hence, we generated a 500 m-resolution agricultural drought areas dataset of summer-harvest crops and autumn-harvest crops spanning across the North China Plain (NCP) from 2006 to 2019, encompassing three degrees of agricultural drought area: drought-covered area, drought-damaged area, and crop failure area. The generated dataset holds the potential for effective application in generating synthetic regional drought information, support impact-based agricultural drought monitoring and prediction, and subsequently could greatly assist in optimizing agricultural water management and ensuring global food security.</p>
Not only hedgerows, but also flower fields can enhance bat activity in intensively used agricultural landscapes
<p>Agri-environment schemes (AES) have become important tools for farmland biodiversity conservation, providing suitable habitats, resources, and connectivity within agricultural landscapes. Bats are rarely studied in relation to AES effectiveness in contrast to birds, even though their presence and activity as biological control agents on insects, especially pest species, can be important for agricultural crops. While the role of hedgerows for bat occurrence and activity, as well as for their prey's diversity and abundance has been widely studied, the role of other AESs such as flower fields remains unclear. We monitored the activity of the main functional groups (edge, narrow and open space foragers) using ultrasound recorders, as well as potential prey abundances using light traps, across 35 study sites representing different AES in Central Germany from late July to September 2018. The sampled AES consisted of annual flower fields, mixed flower fields (with annual and biennial vegetation), perennial flower fields (sown every 5 years), hedgerows (surrounded by meadows and agricultural fields), and were compared to winter wheat (control) in a balanced design. Bat activity over hedgerows increased threefold for edge space foragers and sevenfold for narrow space foragers compared to wheat fields. Compared to wheat fields, narrow space forager activity increased fourfold over perennial flower fields, threefold over annual and twofold over mixed flower fields. This group's activity over hedgerows also increased almost threefold compared to mixed flower fields. However, the number of feeding buzzes and prey abundance did not differ significantly between AES. We detected foraging group-specific differences in bat activity between the studied AES. Thus, to promote bats in agricultural landscapes and to ensure their biological control services, it is important to establish more AES, such as hedgerows and flower fields, to increase their diversity and connectivity in intensively used agricultural landscapes.</p>
Precision agricultural data and ecosystem services: can we put the pieces together?
<ol> <li>Ecosystem services can maintain or increase crop yield in agricultural systems, but data to support management decisions is expensive and time-consuming to collect. Furthermore, relationships derived from small-scale plot data may not apply to ecosystem services operating at larger spatial scales (fields, landscapes).</li> <li>Precision yield data can be used to improve the accuracy and geographic range of ecosystem service studies, but have been underused in previous studies: out of 370 literature records, we found that less than 2% of all records were used to study biotic or landscape effects on yield. We argue that this is likely due to low data accessibility and a lack of familiarity with spatial data analysis.</li> <li>We provide examples of analysis using simulated and real precision yield data and outline two case studies of ecosystem services using precision yield data. Ecologists and agronomists should consider using precision yield data more broadly, as it can be used to test hypotheses about ecosystem services across multiple spatial scales, and could be used to inform the design of multifunctional farming landscapes.</li> </ol>
District-level Agricultural Residue Burning Emissions (DARBE) in India 2011-2020 v1.3
<p>The data provides satellite-based estimates of agricultural burned area, crop residue dry matter burned and agricultural residue burning emissions across India from 2011-2020. It also includes visualisation tools used in the emission estimation and visualisation of the data for the reader's reference.</p> <p>References: </p> <p>1. IPCC 1996, Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories: Reference Manual (Volume 3) https://www.ipcc-nggip.iges.or.jp/public/gl/invs6c.html (accessed May 04, 2023)</p> <p>2. Jain M, Mondal P, DeFries RS, Small C, Galford GL. Mapping cropping intensity of smallholder farms: A comparison of methods using multiple sensors. Remote Sens Environ 2013;134:210–23. https://doi.org/10.1016/j.rse.2013.02.029.</p> <p>3. Deshpande MV, Pillai D, Jain M. Detecting and quantifying residue burning in smallholder systems: An integrated approach using Sentinel-2 data. Int J Appl Earth Obs Geoinf 2022a;108:1–10. https://doi.org/10.1016/j.jag.2022.102761.</p> <p>4. Deshpande MV, Pillai D, Jain M. Agricultural burned area detection using an integrated approach utilizing multi spectral instrument based fire and vegetation indices from Sentinel-2 satellite. MethodsX 2022b;9. https://doi.org/10.1016/j.mex.2022.101741.</p> <p>5. Sahu SK, Mangaraj P, Beig G, Samal A, Chinmay Pradhan, Dash S, et al. Quantifying the high resolution seasonal emission of air pollutants from crop residue burning in India. Environ Pollut 2021;286:117165. https://doi.org/10.1016/j.envpol.2021.117165.</p>
Higher plant colonisation and lower resident diversity in grasslands more recently abandoned from agriculture
<p>1. Rates of species colonisation and extirpation are increasing in plant communities worldwide. Colonisation could potentially help compensate for, or compound, resident diversity loss that results from global environmental change. 2. We use a multifactorial seed addition grassland experiment to examine relationships between plant colonisation, resident species diversity and key community assembly factors over three years. By manipulating colonist seed rate, imposing disturbance and examining abundance and diversity impacts of 14 formerly absent sown colonists in communities that varied in successional stage and time since agricultural abandonment, we were able to disentangle effects of global change factors (species introduction, novel disturbance and land use change) that are usually confounded. 3. Evidence suggested that cover abundance of sown colonists was most strongly influenced by successional stage of recipient communities, though number of growing seasons was also important for the group of seven colonists with resource conservative "slow" life history traits. Colonist type, seed rate and disturbance had weaker relationships with colonist cover. 4. Factors affecting sown colonist cover were highly conditional. A negative relationship between plot-level disturbance and colonist cover in early successional communities meant that, despite a positive relationship in late succession, colonisation was negatively related to disturbance overall, defying theoretical expectations. 5. Non-sown resident diversity was negatively related to colonist cover and positively related to successional stage. Resource acquisitive colonists with "fast" life history traits appeared to limit cover of "slow colonists" when the two groups were sown together, likely reflecting niche pre-emption. 6. Communities at earlier stages of succession had lower resident diversity and experienced higher levels of colonisation than communities at later stages of succession. Elevated colonisation and lower resident diversity both appeared to be symptoms of human-induced land use change. However, results suggested that resource competition from plant colonists may also limit resident diversity in grasslands abandoned from agriculture more recently. Synthesis: Our findings point to the importance of resource availability and competition on plant colonisation and colonist impacts on residents. Although colonisation is potentially a source of biodiversity in the short-term, our results suggest that plant colonists that reach high abundance may be a further threat to resident plant diversity in secondary grasslands recovering from a recent history of agriculture.</p>
Potassium fertilization effects on cereal yield and soil organic carbon in agricultural ecosystems at the global scale
<p>This dataset includes the raw data of a global meta-analysis study on the responses of cereal yield and soil organic carbon to potassium fertilization in agricultural ecosystems.</p>
Environmental DNA highlights the influence of salinity and agricultural run-off on coastal fish assemblages in the Great Barrier Reef region
<p class="MsoNormal">Agricultural run-off in Australia's Mackay-Whitsunday region is a major source of nutrient and pesticide pollution to the coastal and inshore ecosystems of the Great Barrier Reef. While the effects of run-off are well documented for the region's coral and seagrass habitats, the ecological impacts on estuaries, the direct recipients of run-off, are less known. This is particularly true for fish communities, which are shaped by the physico-chemical properties of the coastal waterways that vary greatly in tropical regions. To address this knowledge gap, we used environmental DNA (eDNA) metabarcoding to examine teleost and elasmobranch fish assemblages at four locations (three estuaries and a harbour) subjected to varying levels of agricultural run-off during a wet and dry season. Pesticide and nutrient concentrations were markedly lower during the sampled dry season. With the influx of freshwater and agricultural run-off during the wet season, teleost and elasmobranch taxa richness significantly decreased in all three estuaries, along with pronounced changes in fish community composition which were largely associated with environmental variables (particularly salinity). In contrast, the nearby Mackay Harbour exhibited a far more stable community structure, with no marked changes in fish assemblages observed between the sampled seasons. Within the wet season, differing compositions of fish communities were observed among the four sampled locations, with this variation being significantly correlated with environmental variables (salinity, chlorophyll, DOC) and contaminants from agricultural run-off, i.e., nutrients (nitrogen and phosphorus) and pesticides. Historically contaminated and relatively unimpacted estuaries each demonstrated distinct fish communities, reflecting their associated catchment use. Our findings emphasise that while seasonal effects (e.g., changes in salinity) play a key role in shaping the community structure of estuarine fish in this region, agricultural contaminants (nutrients and pesticides) are also important contributors in some systems.</p>
ScienceDex guides
Understand access before you commit
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.