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643 results for “cattle”
Fig. 1 in Short-term spider community monitoring after cattle removal in grazed grassland
Fig. 1. Extension of the Pampa Biome at Neotropical region. Red triangle indicates APA Ibirapuitã's localization, state of Rio Grande do Sul, Brazil. Map from ANDRADE et al., 2015.
Spatial distribution of cattle, sheep and goat density, and grazed areas for the European Union and the United Kingdom
<p>To improve the sustainability of the European livestock sector we need improved knowledge on livestock density, and also on the grazing patterns. Here we provide spatially explicit data on the distribution of cattle, sheep and goats, developed by combining agricultural and veterinary statistics, in-situ data, expert surveys and machine learning. The data allow for the differentiation between livestock that are grazing on semi-natural areas and managed grasslands, versus those that do not graze and are kept indoors. </p> <p>This dataset covers all European Union Member States and the United Kingdom, and presents the spatial distribution of cattle, sheep and goat density for approximately the year 2020. Livestock density was allocated on the Corine Land Cover data, resulting in a data-set with a 100 m resolution (EPSG: 3035 - ETRS89-extended / LAEA Europe).</p> <p>Together with the livestock density maps, we also provide spatial data on the probability for grazing, and allocated grazed and non grazed areas.</p> <p><strong>File description:</strong></p> <p>The data-set consists of the following files:</p> <p> </p> <ul> <li><strong>clc_forage_mask.tif</strong> , forage areas mask for EU, based on selected Corine Land Cover classes (not including seminatural land cover areas such as natural grasslands...). This was developed by surveying grazing, grassland and livestock experts from all EU Member States and the United Kingdom. More info in the upcoming paper and in the linked paper below (Malek et al. 2024). Values are the same as in the Corine Land Cover data.</li> <li><strong>grazing_probability.tif</strong> , grazing probability map, indicating how likely each location in the EU+UK is grazed</li> <li><strong>allocated_grazing.tif</strong> , allocated grazing map, indicating which areas are grazed and which are not</li> </ul> <p> </p> <ul> <li>cattle density maps: <ul> <li><strong>cattle_grazing.tif</strong> , cattle grazing on managed forage areas</li> <li><strong>cattle_other.tif</strong> , cattle kept indoors, receiving feed from managed forage areas</li> <li><strong>cattle_seminatural.tif</strong> , cattle grazing in semi-natural areas</li> <li><strong>cattle_mosaic_categorical.tif</strong> (with a legend file) , combined categorical map for all cattle types.</li> </ul> </li> </ul> <p> </p> <ul> <li>sheep and goat density maps: <ul> <li><strong>sheep_goat_other.tif</strong> , sheep and goat density</li> <li><strong>sheep_goat_seminatural.tif </strong>, sheep and goat grazing in seminatural areas</li> </ul> </li> </ul>
Figure 3 in Distribution and abundance of insects colonizing cattle dung in South West England
Figure 3. The proportion of the total number of adult Coleoptera (solid squares, solid line), adult Diptera (open circles, solid line), and larval Coleoptera (diamonds, dashed line) insects collected from pats of different ages.
Figure 2 in Distribution and abundance of insects colonizing cattle dung in South West England
Figure 2. The numbers of various taxa recovered from individual artificial cow pats placed out between May and November 2001; day 1 is 1 May. (A) Sylvicola punctata; (B) Chironimidae; (C) Polites lardaria; (D) Scatophaga stercoraria; (E) Chloromyia formosa; (F) Sargus spp.; (G) Cercyon lateralis; (H) Oxytelinae larvae.
Figure 1 in Distribution and abundance of insects colonizing cattle dung in South West England
Figure 1. The median seasonal occurrences (and inter-quartile ranges), and total number recovered for the insect taxa from artificial cow pats placed out between May and November 2001. Day 1 is 1 May.
Data for ms. Dairy cattle welfare – the relative effect of legislation, industry standards and labelled niche production in five European countries
<p>Repository R 1: Scores on dimension values and weight on dimension from 38 international experts. </p> <p>Repository R2: Country Benchmark scores from 38 international experts.</p>
Integrative QTL mapping and selection signatures in Groningen White Headed cattle inferred from whole-genome sequences
<p>Here, we aimed to identify and characterize genomic regions that differ between Groningen White Headed (GWH) breed and other cattle, and in particular to identify candidate genes associated with coat color and/or eye-protective phenotypes. Firstly, whole genome sequences of 170 animals from eight breeds were used to evaluate the genetic structure of the GWH in relation to other cattle breeds by carrying out principal components and model-based clustering analyses. Secondly, the candidate genomic regions were identified by integrating the findings from: a) a genome-wide association study using GWH, other white headed breeds (Hereford and Simmental), and breeds with a non-white headed phenotype (Dutch Friesian, Deep Red, Meuse-Rhine-Yssel, Dutch Belted, and Holstein Friesian); b) scans for specific signatures of selection in GWH cattle by comparison with four other Dutch traditional breeds (Dutch Friesian, Deep Red, Meuse-Rhine-Yssel and Dutch Belted) and the commercial Holstein Friesian; and c) detection of candidate genes identified via these approaches. The alignment of the filtered reads to the reference genome (ARS-UCD1.2) resulted in a mean depth of coverage of 8.7X. After variant calling, the lowest number of breed-specific variants was detected in Holstein Friesian (148,213), and the largest in Deep Red (558,909). By integrating the results, we identified five genomic regions under selection on BTA4 (70.2–71.3 Mb), BTA5 (10.0–19.7 Mb), BTA20 (10.0–19.9 and 20.0–22.7 Mb), and BTA25 (0.5–9.2 Mb). These regions contain positional and functional candidate genes associated with retinal degeneration (e.g., <em>CWC27</em> and <em>CLUAP1</em>), ultraviole<em>t</em> protection (e.g., <em>ERCC8</em>), and pigmentation (e.g. <em>PDE4D</em>) which are probably associated with the GWH specific pigmentation and/or eye-protective phenotypes, e.g. Ambilateral Circumocular Pigmentation (ACOP). Our results will assist in characterizing the molecular basis of GWH phenotypes and the biological implications of its adaptation.</p>
Data from: Measuring motivation for forage in feedlot cattle fed a high-concentrate diet using a short-term thwarting test
Open the record for dataset details and reuse information.
Data from: Measuring motivation for alfalfa hay in feedlot cattle using voluntary interaction with an aversive stimulus
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Data from: Non-additive association analysis using proxy phenotypes identifies novel cattle sydromes
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Data from: Impacts of proactive health management on cattle and horse diets and dung biodiversity in Danish rewilding areas
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Dataset for: Cattle aggregations at shared resources create potential parasite exposure hotspots for wildlife
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Data from: Long-term cattle grazing shifts the ecological state of forest soils
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Data from: Studded leather collars are very effective in protecting cattle from leopard (Panthera pardus) attacks
<p>Human-wildlife conflicts are widespread, particularly with big cats which can kill domestic livestock and create a counteraction between conservation and local livelihoods, especially near protected areas. Minimization of livestock losses caused by big cats and other predators is essential to mitigate conflicts and promote socially acceptable conservation. As big cats usually kill by throat bites, protective collars represent a potentially effective non-lethal intervention to prevent livestock depredation, yet the application and effectiveness estimation of these tools are very limited. In this study, for the first time we measured the effectiveness of studded leather collars in protecting cattle from leopard (Panthera pardus) attacks. We conducted a randomized controlled experiment during 14 months to collar 202 heads and leave uncollared 258 heads grazing in forests and belonging to 27 owners from eight villages near three protected areas in Mazandaran Province, northern Iran. Our results show that none of collared cattle and nine uncollared cattle were lost to leopard depredation, meaning that collars caused a zero relative risk of damage and a perfect 100% damage reduction. Most losses occurred in summer and autumn due to lush vegetation attracting more cattle, long daytime allowing movements deep into leopard habitats, and dense cover favoring leopard hunts from ambush. Losses were recorded in only six owners and four villages, suggesting local rarity and patchy distribution of leopards. We suggest that collars can be successfully applied to cattle freely grazing in habitats of leopards or other felids for a long time and thus remaining persistently exposed to depredation. As grazing cattle are usually not supervised by shepherds or dogs, collars can be the only practical protection tool. Production and sales of collars can become a sustainable small-scale business for farmers to further boost conservation and rural livelihoods.</p>
Data & Code: Artificial eyespots on cattle reduce predation by large carnivores
<p>Data and code used in article on: artificial eyespots on cattle reduce predation by large carnivores.</p>
Beef database for prediction of main environmental challenges to resilience and efficiency in cattle production systems at regional resolution
<p>The beef database comprises average values for a wide range of variables (110 or 119), available in 4 worksheets, and is further differentiated into All Beef, Breeders and Fatteners: BasicFarmType (18 rows), DetailedFarmType (75 rows), ClimateClass+BasicFarmType (270 rows), NUTS+BasicFarmType (2074 rows). Data are omitted when the sample size (n) is below 15, as per the confidentiality agreement under FADN data use rules.</p> <p>A combined farm characterisation database was constructed using two major data sources, the Farm Accountancy Data Network (FADN), and the Gridded Agro-Meteorological Data in Europe (AGRI4CAST). The database initially constructed was further enhanced through the addition of forage and crop yield data from the Food and Agriculture Organization of the United Nations (FAO) and the International Institute for Applied Systems Analysis (IIASA) developed Agro-Ecological Zones (AEZ) methodology database (FAO, 2012). The data was processed and is presented in D1.2 as two databases (dairy and beef), as averages for a wide range of variables at basic or detailed farm types, and at NUTS2 regional scale.</p> <p>Detailed FADN data (anonymised individual farm data) was requested for all ruminant and mixed farm types, over 10 years and the most recent data available at request (2011-2013) was utilised for the analysis. Following receipt of the data (~250k farms) this has been compiled into two consistent datasets, one for dairy (141,961) farms and one for beef farms (54,417). Each dataset comprises some values directly from the FADN data, but also a large number of calculated variables, to identify dairy or beef enterprise performance at per animal, per output product unit or per hectare. These values were calculated according to the respective dairy and beef enterprise allocation methodologies described by FADN. Further economic and structural variables have been calculated as necessary, as described in GenTORE D1.1 (Quiédeville et al., 2019).</p> <p>For each farm within the dataset, the structural, production and economic data from the FADN data is supplemented with the addition of meteorological data. The daily meteorological data was downloaded from the AGRI4STAT database web portal at a NUTS2 scale. For each NUTS2 region data was available for a number of weather stations. This large dataset was processed through scripts in STATA software to generate annual values for a wide range of climatic variables, including Temperature Humidity Index (THI), and indicators of drought and seasonality of weather. Furthermore, the altitude values per weather station allowed for a sub-grouping of weather station data by altitude zone (aligned with values available in the FADN dataset).</p> <p>Using a Latent Class Analysis process, the meteorological data was analysed to identify consistent environmental regions in Europe. Selected climatic variables, together with altitude zone, were utilised to statistically identify differing zones, and to classify each NUTS2 region to a zone, resulting in 6 lowland zones and 3 upland zones (above 600m) The LCA process enhanced an earlier method of manually overlaying the Metzger et al. (20054) pedo-climatic zone allocation, but closely correlates. Therefore for each farm in the dairy and beef datasets, meteorological and environmental zone data was allocated on a NUTS2 by altitude zone basis and this dataset has been subsequently assessed and submitted as papers; Quiédeville et al., (submitted May 2020) and Grovermann et al. (submitted May 2020).</p> <p>The GAEZ forage and crop yield data was downloaded from the GAEZ data portal as baseline and two future climate prediction periods: Baseline (1961-2000), 2020s (2011-2040), and 2050s (2041-2070), for the Hadley CM3 model and IPCC scenario A (the most extreme scenario). See: <a href="http://www.fao.org/nr/gaez/about-data-portal/agro-climatic-resources/en/">http://www.fao.org/nr/gaez/about-data-portal/agro-climatic-resources/en/#</a>). A zonal statistics was applied to the GAEZ layers to aggregate the data to NUT2 region and altitude zone (0-300m, 300-600m, 600m+) with raster package in R. The result is an average yield<a href="#_ftn1">[1]</a> for varying forages and crops for each altitude zone in each nuts2, for both the baseline and the future climate scenario. This data allows further analysis of the future impacts on cattle farming at both a regional scale, but also by farm type or system, which may be affected differently (Moakes et al. in preparation).</p> <p>All variable processing from FADN data is shown in the Annex, as performed in Stata software.</p> <p> </p> <p><a href="#_ftnref1">[1]</a> The mean was performed on non-zero yield pixels in order to exclude non-suitable areas from average.</p>
Dairy database for prediction of main environmental challenges to resilience and efficiency in cattle production systems at regional resolution
<p>The dairy database comprises average values for a wide range of variables (110 or 119), available in 4 worksheets: BasicFarmType (18 rows), DetailedFarmType (10 rows), ClimateClass+BasicFarmType (100 rows), NUTS+DetailedFarmType (1452 rows). Data are omitted when the sample size (n) is below 15, as per the confidentiality agreement under FADN data use rules.</p> <p>A combined farm characterisation database was constructed using two major data sources, the Farm Accountancy Data Network (FADN), and the Gridded Agro-Meteorological Data in Europe (AGRI4CAST). The database initially constructed was further enhanced through the addition of forage and crop yield data from the Food and Agriculture Organization of the United Nations (FAO) and the International Institute for Applied Systems Analysis (IIASA) developed Agro-Ecological Zones (AEZ) methodology database (FAO, 2012). The data was processed and is presented in D1.2 as two databases (dairy and beef), as averages for a wide range of variables at basic or detailed farm types, and at NUTS2 regional scale.</p> <p>Detailed FADN data (anonymised individual farm data) was requested for all ruminant and mixed farm types, over 10 years and the most recent data available at request (2011-2013) was utilised for the analysis. Following receipt of the data (~250k farms) this has been compiled into two consistent datasets, one for dairy (141,961) farms and one for beef farms (54,417). Each dataset comprises some values directly from the FADN data, but also a large number of calculated variables, to identify dairy or beef enterprise performance at per animal, per output product unit or per hectare. These values were calculated according to the respective dairy and beef enterprise allocation methodologies described by FADN. Further economic and structural variables have been calculated as necessary, as described in GenTORE D1.1 (Quiédeville et al., 2019).</p> <p>For each farm within the dataset, the structural, production and economic data from the FADN data is supplemented with the addition of meteorological data. The daily meteorological data was downloaded from the AGRI4STAT database web portal at a NUTS2 scale. For each NUTS2 region data was available for a number of weather stations. This large dataset was processed through scripts in STATA software to generate annual values for a wide range of climatic variables, including Temperature Humidity Index (THI), and indicators of drought and seasonality of weather. Furthermore, the altitude values per weather station allowed for a sub-grouping of weather station data by altitude zone (aligned with values available in the FADN dataset).</p> <p>Using a Latent Class Analysis process, the meteorological data was analysed to identify consistent environmental regions in Europe. Selected climatic variables, together with altitude zone, were utilised to statistically identify differing zones, and to classify each NUTS2 region to a zone, resulting in 6 lowland zones and 3 upland zones (above 600m) The LCA process enhanced an earlier method of manually overlaying the Metzger et al. (20054) pedo-climatic zone allocation, but closely correlates. Therefore for each farm in the dairy and beef datasets, meteorological and environmental zone data was allocated on a NUTS2 by altitude zone basis and this dataset has been subsequently assessed and submitted as papers; Quiédeville et al., (submitted May 2020) and Grovermann et al. (submitted May 2020).</p> <p>The GAEZ forage and crop yield data was downloaded from the GAEZ data portal as baseline and two future climate prediction periods: Baseline (1961-2000), 2020s (2011-2040), and 2050s (2041-2070), for the Hadley CM3 model and IPCC scenario A (the most extreme scenario). See: <a href="http://www.fao.org/nr/gaez/about-data-portal/agro-climatic-resources/en/">http://www.fao.org/nr/gaez/about-data-portal/agro-climatic-resources/en/#</a>). A zonal statistics was applied to the GAEZ layers to aggregate the data to NUT2 region and altitude zone (0-300m, 300-600m, 600m+) with raster package in R. The result is an average yield<a href="#_ftn1">[1]</a> for varying forages and crops for each altitude zone in each nuts2, for both the baseline and the future climate scenario. This data allows further analysis of the future impacts on cattle farming at both a regional scale, but also by farm type or system, which may be affected differently (Moakes et al. in preparation).</p> <p>All variable processing from FADN data is shown in the Annex, as performed in Stata software.</p> <p> </p> <p><a href="#_ftnref1">[1]</a> The mean was performed on non-zero yield pixels in order to exclude non-suitable areas from average.</p>
Molecular characterisation of Crimean-Congo haemorrhagic fever virus detected in African blue ticks feeding on cattle in a Ugandan abattoir
<p>CCHFV multiple sequence alignments and maximum likelihood phylogeny treefiles.</p>
Data from: Knowledge co-production with traditional herders on cattle grazing behaviour for better management of species-rich grasslands
The research gap between rangeland/livestock science and conservation biology/vegetation ecology has led to a lack of evidence needed for grazing-related conservation management. Connecting scientific understanding with traditional ecological knowledge of local livestock keepers could help bridge this research and knowledge gap. 1. We studied the grazing behaviour (plant selection and avoidance) of beef cattle (ca. 33 000 bites) on species-rich lowland pastures in Central Europe and traditional herding practices. We also did >450 outdoor interviews with traditional herders about livestock behaviour, herders' decisions to modify grazing behaviour, and effects of modified grazing on pasture vegetation. 2. We found that cattle grazing on species-rich pastures displayed at least 10 different behavioural elements as they encountered 117 forage species from highly desired to rejected. The small discrimination error suggests that cattle recognize all listed plants 'by species'. 3. We also found that herders had broad knowledge of grazing desire and they consciously aimed to modify desire by slowing, stopping or redirecting the herd. Modifications were aimed at increasing grazing intensity in less desired patches and decreasing grazing selectivity in heterogenous swards. 4. Synthesis and applications: These traditional herd management practices have significant conservation benefits, such as avoiding under- and overgrazing, and targeted removal of pasture weeds, litter and enchroaching bushes, tall competitive plants and invasive species. We argue that knowledge co-production with traditional herders who belong to another knowledge system could help connect isolated scientific disciplines especially if ecologists and rangeland scientists work closely with traditional herders, co-designing research projects and working together in data collection, analysis and interpretation. Stronger links between these disciplines could help develop evidence-based, specific conservation management practices while herders could contribute with their practical experiences and with real world testing of new management techniques.04-May-2020
Cattle Trough Warwick Gardens
A London Metropolitan Drinking Fountain Association cattle trough at the north end and in the centre of Warwick Gardens (road), Kensington, London. 383 photos taken in May 2022 with a Sony a7R III and processed in Reality Capture. Source: Objaverse 1.0 / Sketchfab
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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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.