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20 results for “dairy farming”
Assessment of dairy cow welfare in small-scale farming systems dataset
<p>This database was created as preparatory work for the Scientific Opinion on the assessment of dairy cow welfare in small-scale farming systems (EFSA, 2015) to collect data for the description and the categorisation of European Small-Scale Dairy Farms (SSDF) based on size, farming system and husbandry practices and (ii) to analyse the feasibility in SSDF of animal-based measures usually used for intensive farming. The Scientific Opinion was necessary to address specific expectations of consumers on locally produced food and acceptable animal welfare conditions in the context of the EU Strategy for the protection and welfare of animals 2012-2015.</p> <p>The on-farm survey was run to collect data for welfare assessment covering Austria, France, Italy and Spain. A total of 124 farms with up to 75 cows were selected based on three criteria reflecting use of local resources or enrolment in a certification scheme: (1) the type of enterprise (ownership and workers), (2) the use of inputs in the production process, including the use of local feed and local breeds, and (3) the production type (certification schemes). From 124 dairy farms visited 119 were considered as SSDF. Among the 119 farms included in the survey as non-conventional, some of them had a very small herd size (44 had less than 25 cows and one had only 10 cows) and some of them had more animals (19 farms had between 51 and 75 dairy cows).</p> <p>The database includes 53 continuous and categorical farm descriptor variables, 23 continuous and categorical risk-factor variables and 47 animal-based measures in small-scale farms. The final data model used was based on data collection at farm/herd level, pen level and animal level.</p>
Fig. 6 Monthly anti-F in Patterns of Fasciola hepatica infection in Danish dairy cattle: implications for on-farm control of the parasite based on different diagnostic methods
Fig. 6 Monthly anti-F. hepatica antibody levels in bulk tank milk (BTM) (solid line) and average serum antibody levels of milking cows during the study period (triangle points with dashed line, error bars showing standard error of the mean) in the four farms
Fig. 5 in Patterns of Fasciola hepatica infection in Danish dairy cattle: implications for on-farm control of the parasite based on different diagnostic methods
Fig. 5 The summary of F. hepatica diagnostic test results according to farms and age during the study period (from spring 2015 to winter 2017). Colour indicates animals that were born in the same year. Coproantigen ELISA values are log-transformed (after adding a fixed constant of 1), and the cut-off defined as 1.89 (1.061 after transformation). Faecal egg counts in 5 g faeces were also log-transformed (after adding a fixed constant of 1) for the benefit of visualisation. Any post-treatment data are excluded
Fig. 4 in Patterns of Fasciola hepatica infection in Danish dairy cattle: implications for on-farm control of the parasite based on different diagnostic methods
Fig. 4 Danish climate data for the four farms for the study period (2015–2017: red) and 30 year average (1961–1990: blue). The climate in Denmark is a mixture of oceanic and continental temperate. The mean day highest and lowest temperatures of each month are shown above, while the total monthly precipitations are shown below
Fig. 3 in Patterns of Fasciola hepatica infection in Danish dairy cattle: implications for on-farm control of the parasite based on different diagnostic methods
Fig. 3 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 O1 and O2, 2015–2017
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>
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>
Supporting data and code for: Longitudinal Study on Shiga Toxin–producing Escherichia coli and Campylobacter jejuni on Finnish Dairy Farms and in Raw Milk
<p>Supporting data and code for the article: "Longitudinal Study on Shiga Toxin–producing <em>Escherichia coli</em> and <em>Campylobacter jejuni</em> on Finnish Dairy Farms and in Raw Milk".</p>
Table 1 in Patterns of Fasciola hepatica infection in Danish dairy cattle: implications for on-farm control of the parasite based on different diagnostic methods
<p><b>Table 1</b> Summary of data used as inclusion criteria for the 4 farms in the study</p><table><tbody><tr><th>Farm</th><th>Year</th><th>No. of heifers</th><th>No. of cows</th><th>Total no. of cattle</th><th>Liver condemnation (%)</th><th>BTM ELISA value (S/P%)a</th></tr></tbody><tbody><tr><th>C1</th><td>2011</td><td>72.5</td><td>176.5</td><td>314</td><td>6.2</td><td><i>–</i></td></tr><tr><th></th><td>2012</td><td>65.5</td><td>184.5</td><td>303</td><td>21.3</td><td><i>–</i></td></tr><tr><th></th><td>2013</td><td>65</td><td>187.5</td><td>315</td><td>30.0</td><td><i>–</i></td></tr><tr><th></th><td>2014</td><td>63</td><td>183.5</td><td>312</td><td>18.6</td><td>179.3</td></tr><tr><th>C2</th><td>2011</td><td>103</td><td>135</td><td>292</td><td>8.3</td><td><i>–</i></td></tr><tr><th></th><td>2012</td><td>98.5</td><td>145</td><td>300</td><td>11.9</td><td><i>–</i></td></tr><tr><th></th><td>2013</td><td>105.5</td><td>144</td><td>314</td><td>16.1</td><td><i>–</i></td></tr><tr><th></th><td>2014</td><td>111</td><td>149</td><td>331</td><td>19.4</td><td>181.2</td></tr><tr><th>O1</th><td>2011</td><td>145</td><td>172</td><td>367</td><td>2.6</td><td><i>–</i></td></tr><tr><th></th><td>2012</td><td>141</td><td>168</td><td>362</td><td>7.6</td><td><i>–</i></td></tr><tr><th></th><td>2013</td><td>124</td><td>174.5</td><td>354</td><td>33.3</td><td><i>–</i></td></tr><tr><th></th><td>2014</td><td>172</td><td>183</td><td>425</td><td>23.3</td><td>221.4</td></tr><tr><th>O2</th><td>2011</td><td>90.5</td><td>113.5</td><td>251</td><td>18.1</td><td><i>–</i></td></tr><tr><th></th><td>2012</td><td>97.5</td><td>124</td><td>275</td><td>32.6</td><td><i>–</i></td></tr><tr><th></th><td>2013</td><td>111</td><td>131.5</td><td>282</td><td>27.7</td><td><i>–</i></td></tr><tr><th></th><td>2014</td><td>113</td><td>133.5</td><td>285</td><td>38.1</td><td>206.9</td></tr></tbody></table><p><sup>a</sup> by IDEXX ELISA test (cut-off is 30 and ≥ 150S/P% is considered high)</p>
Appendix from: Understanding challenges and strengths in the post-dairy farm surplus calf value chain: An interview study
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Measuring deposition of ammonia emitted by a dairy farm using biomonitors
<p>Raw data for bachelor project where biomonitors were used to measure nitrogen deposition around a dairy farm.</p>
Supplemental material: The microbiota of ensiled forages and of bulk tank milk on dairy cattle farms in northern Sweden - a case study
<p>Supplemental Figures.</p>
Optimizing Sustainable Dairy Farming: A Techno-Economic Analysis of Graphene Ranch Restoration
<p> </p> <p>This dataset, titled Standardized Dairy Farm Cost Output Table, contains financial and production information related to the establishment and operation of a proposed dairy farm. It includes initial investment costs such as stock cows, fixed assets, and working capital, as well as production capacity and budget projections over multiple years.</p> <p>Key sections include:<br>Initial Cost of Investment: Covers items like stock cows and fixed assets.<br>Production Capacity: Provides figures on dairy farm output over the years.<br>Budget Projections: Displays financial allocations and expected expenditures over different time periods.</p> <p>The dataset consists of multiple columns spanning projected years and various financial indicators to help estimate the cost-output relationship in dairy farming operations.</p>
Isotopic signatures of methane emissions from dairy farms in California's San Joaquin Valley
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Fig. 7 in Patterns of Fasciola hepatica infection in Danish dairy cattle: implications for on-farm control of the parasite based on different diagnostic methods
Fig. 7 Results of generalised additive mixed models (GAMM) showing the relative effects of animal age, season of sampling, and date of the sampling (for longer-term temporal trends) within the studied farms. Each combination of farm and diagnostic test was modelled independently. The estimates are shown using solid lines and shaded areas indicate 95% confidence intervals. The y-axis is on the natural logarithm scale
The Effect of Vaccination with Live Attenuated Neethling Lumpy Skin Disease Vaccine on Milk Production and Mortality—an Analysis of 77 Dairy Farms in Israel
<p>This is the R script, full data and data for figures 3 and 4 of the article "The Effect of Vaccination with Live Attenuated Neethling Lumpy Skin Disease Vaccine on Milk Production and Mortality—an Analysis of 77 Dairy Farms in Israel"</p>
Chemical and Microbiological Exposure of Women on Dairy Cattle Farms
ClinicalTrials.gov study NCT06709989. IPD Sharing: NO. Countries: 1. Publications: 0.
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