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427 results for “Dairy”
Fig. 1 in Patterns of Fasciola hepatica infection in Danish dairy cattle: implications for on-farm control of the parasite based on different diagnostic methods
Fig. 1 Map of Denmark, showing the regions and locations of the four farms that participated in the study
Fig. 2 in Patterns of Fasciola hepatica infection in Danish dairy cattle: implications for on-farm control of the parasite based on different diagnostic methods
Fig. 2 Schematic map and Gantt chart of grazing periods (grey shaded, time of sampling; green shaded, grazing; pasture areas are indicated by capital letters), pasture characteristics (refer to the common map legend) and treatment against Fasciola hepatica on farms C1 and C2, 2015–2017
Data for a dairy farm microgrid solution
<p><strong>Load data </strong>(Farm_load_kW_data.txt)</p> <p>A pre-determined hourly data series of electricity consumption (one year). The consumption was considered independent from the microgrid operating state – network connected or islanded operation. Load was not controlled in order to obtain longer islanded operation capability, nor to minimize power exchange with the network in normal state. The dairy farm case was with about 180 cows and corresponding electricity consumption of approximately 261 MWh/a. The farm data series was created based on data from similar size farms. Daily consumption profile was based on diurnal consumption data of a large cowhouse in a winter day, and the variation from day to day throughout the year was approximated by creating sliding data series based on monthly electricity consumption. The dataset was then suitably scaled for the specified annual consumption.</p> <p><strong>PV generation data </strong>(PV_pu_data.txt)</p> <p>An hourly PV production data series for one year was created for a specific location (in Finland) based on MERRA-2 time series data on radiation [1] and air temperature [2]. The daily average radiation and temperature were scaled to match monthly values from PVGIS database [3,4]. PV panel generation (in per units) was calculated considering location and temperature, and selected panel tilt given by PVGIS ‘optimal inclination angle’. The PV generation data series was then scaled appropriately for the selected PV capacity in the case study.</p> <p> </p> <p><strong>References</strong></p> <ol> <li>Global Modeling and Assimilation Office (GMAO) (2015), MERRA-2 tavg1_2d_rad_Nx: 2d,1-Hourly, Time-Averaged, Single-Level, Assimilation, Radiation Diagnostics V5.12.4, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC).</li> <li>Global Modeling and Assimilation Office (GMAO) (2015), MERRA-2 tavg1_2d_flx_Nx: 2d,1-Hourly, Time- Averaged, Single-Level, Assimilation, Surface Flux Diagnostics V5.12.4, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC).</li> <li>Huld, T., Müller, R. & Gambardella, A. A new solar radition database for estimating PV performance in Europe and Africa. Sol. Energy 86, 1803–1815 (2012).</li> <li>European Communities (2012), PVGIS interactive application. Available: http://re.jrc.ec.europa.eu/pvgis/apps4/pvest.php#.</li> </ol>
Data and R-code on a cross-sectional study of factors associated with lameness in dairy cows housed in freestall and compost-bedded pack dairy farms in southern Brazil
<p>The data correspond to a cross-sectional study designed to investigate factors associated with lameness in dairy cows on intensive farms in southern Brazil.<br> Farms: 38 freestall and 12 compost-bedded pack visited once in 2016. All lactating cows (n = 13,716) were examined and body condition score (BCS) and gait score were assessed. Additionally, some variables were collected through inspection of facilities and using data from an interview with farmers on routine herd management practices.<br> Additional information is provided in the published paper ("Factors associated with lameness prevalence in lactating cows housed in freestall and compost-bedded pack dairy farms in southern Brazil" https://doi.org/10.1016/j.prevetmed.2019.104773)</p>
Electrodialysis with bipolar membranes for reagents recovery from dairy wastewater
<p>Poster presented in IEX24 regarding reagents recovery from dairy wastewater by using electrodialysis with bipolar membranes</p>
Supplementary data to publication "Abomasal infusion of essential fatty acids and conjugated linoleic acid during late pregnancy and early lactation affects immunohematological and oxidative stress markers in dairy cows"
<p>Supplementary data to publication "Abomasal infusion of essential fatty acids and conjugated linoleic acid during late pregnancy and early lactation affects immunohematological and oxidative stress markers in dairy cows" in Journal of Dairy Science; DOI: <a href="https://doi.org/10.3168/jds.2022-22514">https://doi.org/10.3168/jds.2022-22514</a></p>
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>
Dairy-Milkyield-Taenikon
<p>These data files have been used as input for the models described and evaluated in "Can meteorological data improve the short term prediction of individual milk yield in dairy cows?", accepted by the Journal of Dairy Science on February 23, 2023. Each file corresponds to one of the seven data subsets (for both breeds) used in the modeling part.</p> <p>DOI: https://doi.org/10.3168/jds.2022-22980</p>
Data from: Development and application of a novel approach to scoring ear tag wounds in dairy calves
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Data from: Wound healing and pain sensitivity following caustic paste disbudding in dairy calves
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Data from: Response to novel feed in dairy calves is affected by prior hay provision and presentation method
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Data from: Behavioral changes in the first 3 weeks after disbudding in dairy calves
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Data from: Limit feeding TMR exacerbates intersucking in year-old dairy heifers
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Data from: Providing long hay in a novel pipe feeder or a bucket reduces some, but not all abnormal oral behaviors in milk-fed dairy calves
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Dairy cow dry matter intake for multiverse analysis and Bradley-Terry modeling
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Environmental and behavioral sensor data to identify heat stress in dairy cows
Heat stress impairs the health and performance of dairy cows, yet only a few studies have investigated the diversity of cattle behavioral responses to heat waves. This research was conducted on an Italian Holstein dairy farm equipped with precision livestock farming sensors to assess potential different behavioral patterns of the animals. Three heat waves, defined as at least five consecutive days with mean daily temperature-humidity index higher than 72, were recorded in the farm area during the summer of 2021. Individual daily milk yield data of 102 cows were used to identify ‘heat-sensitive’ animals, meaning the cows that, under a given heat wave, experienced a milk yield drop that was not linked with other health events (e.g., mastitis). Milk yield drops were detected as perturbations of the lactation curve estimated by iteratively using Wood’s equation. Individual daily minutes of lying, chewing, and activity were retrieved from ear-tag-based accelerometer sensors. Semi-parametric generalized estimating equations models were used to assess behavioral deviations of heat-sensitive cows from the herd means under heat stress conditions. Heat waves were associated with an overall increase in the herd’s chewing and activity times, along with an overall decrease of lying time. Heat-sensitive cows spent approximately 15 min/d more chewing and performing activities (p < 0.05). The findings of this research suggest that the information provided by high-frequency sensor data could assist farmers in identifying cows for which personalized interventions to alleviate heat stress are needed.
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>
REFLOW: Phosphorus recovery for fertilisers from Dairy Waste - Introduction Video
<p>Introductory Video outlining the REFLOW interdisciplinary cross-sectoral European Training Network.</p>
Staphylococcus aureus isolated from ruminants with mastitis in northern Greece dairy herds: genetic relatedness and phenotypic and genotypic characterization
<p>Figure S1: Dendrogram of SmaI PFGE pulsotypes (P) and characteristics of the 162 S. aureus isolates.</p>
Initial soil conditions outweigh management in a cool-season dairy farm's carbon sequestration potential
<p>Data used in the manuscript "Initial soil conditions outweigh management in a cool-season dairy farm’s carbon sequestration potential" (<a href="http://dx.doi.org/10.1016/j.scitotenv.2021.152195">10.1016/j.scitotenv.2021.152195</a>)</p> <p> </p> <p>Soil samples, gas fluxes, and biomass samples measured at the Organic Dairy Research Farm at the University of New Hampshire. Soil samples were in two sets, a spatially explicit set from 0 - 15 cm depth, and less spatially explicit samples taken at 10 cm increments. Soils were sampled for soil carbon and nitrogen content. Gas fluxes were measured using the chamber method with carbon dioxide and nitrous oxide gases measured on gas chromatographs with the change over time used to measure the gas flux rates. Forage biomass was measured by collecting biomass in 1 m2 plots. Please see the manuscript for more details on sampling.</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.