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40 results for “dairy cattle”
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 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 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>
Effect of using mycotoxin-detoxifying agents in dairy cattle feed on natural whey starter biodiversity
<p><strong>Supplemental Figure S1</strong>: p-values for alpha diversity index in the milk whey microbiota between treatments and controls. The control group for BD1 (ctr_BD1) was taken as reference class in the model. </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>
Sequence-based genome-wide association study of individual milk mid-infrared wavenumbers in mixed-breed dairy cattle
<p>Fourier-transform mid-infrared (FT-MIR) spectroscopy provides a high-throughput and inexpensive method for predicting milk composition and other novel traits from milk samples. Whilst there have been many genome-wide association studies (GWAS) conducted on FT-MIR predicted traits, there have been few GWAS for individual FT-MIR wavenumbers. Here we examine associations between genomic regions and individual FT-MIR wavenumber phenotypes within a population of 38,085 mixed-breed New Zealand dairy cattle with imputed whole-genome sequence. GWAS were conducted for each of 895 individual FT-MIR wavenumber phenotypes and three FT-MIR predicted milk composition traits, and gene annotation and mammary tissue gene expression datasets were employed to identify candidate causative genes and variants. This resulted in the identification of 38 co-locating, co-segregating expression QTL (eQTL), and 31 protein-sequence mutations for FT-MIR wavenumber phenotypes, the latter including a null mutation in <i>ABO</i> that has a potential role in changing milk oligosaccharide profiles. For the candidate causative genes implicated in these analyses, the strength of association between relevant loci and each wavenumber across the mid-infrared spectrum revealed shared association patterns for groups of genomically-distant loci, highlighting clusters of loci linked through their biological roles in lactation and their presumed impacts on the chemical composition of milk.</p>
Sequence-based genome-wide association study of individual milk mid-infrared wavenumbers in mixed-breed dairy cattle
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Fine mapping highlights ITGAL and MUS81 loss-of-function mutations modulating recessive impacts in dairy cattle
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Predicting disease in transition dairy cattle based on behaviors measured before calving
<p>Data set and code for the article "Predicting disease in transition dairy cattle based on behaviors measured before calving"</p>
Data from: Role of ambient pressure in self-heating torrefaction of dairy cattle manure
<p><span><span><span><span><span><span><span><span><span><span><span>This paper describes the role of ambient pressure in self-heating torrefaction of livestock manure. We explored the initiating temperatures required to cause self-heating of wet dairy cattle manure at different ambient pressures (0.1, 0.4, 0.7, and 1.0 MPa). Then, we conducted proximate, elemental, and calorific analyses of biochar torrefied at 210, 250, and 290 °C. The results showed that self-heating was induced at 155 °C or higher for 0.1 MPa and at 115 °C or lower for 0.4 MPa or higher. The decrease of the initiating temperature at elevated pressure was due not only to more oxygen, but also to the retention of moisture that can promote chemical oxidation of manure. Biochar yields decreased with increasing torrefaction temperature and pressure, and the yield difference at 0.1 and 1.0 MPa was more substantial at lower temperatures: a 29.8, 16.4, and 9.4% difference at 210, 250, and 290 °C, respectively. Proximate and elemental analyses showed that elevated pressure promotes devolatilization, deoxygenation, and coalification compared to atmospheric pressure; its impact, however, was less at higher temperatures as the torrefaction temperature became more dominant. Calorific analysis revealed that elevated pressure can increase the higher heating value (HHV) on a dry and ash-free basis at 210 °C because of the increase in carbon content, but its impact is limited at 250 and 290 °C. Meanwhile, the HHV on a dry basis exhibited the opposite trend due primarily to an enlargement of ash content. The present study revealed that ambient pressure considerably affects the initiating temperature of self-heating and the chemical properties of biochar at a low torrefaction temperature.</span></span></span></span></span></span></span></span></span></span></span></p>
Data from: Signatures of positive selection in African Butana and Kenana dairy zebu cattle
Butana and Kenana are two types of zebu cattle found in Sudan. They are unique amongst African indigenous zebu cattle because of their high milk production. Aiming to understand their genome structure, we genotyped 25 individuals from each breed using the Illumina BovineHD Genotyping BeadChip. Genetic structure analysis shows that both breeds have an admixed genome composed of an even proportion of indicine (0.75 ± 0.03 in Butana, 0.76 ± 0.006 in Kenana) and taurine (0.23 ± 0.009 in Butana, 0.24 ± 0.006 in Kenana) ancestries. We also observe a proportion of 0.02 to 0.12 of European taurine ancestry in ten individuals of Butana that were sampled from cattle herds in Tamboul area suggesting local crossbreeding with exotic breeds. Signatures of selection analyses (iHS and Rsb) reveal 87 and 61 candidate positive selection regions in Butana and Kenana, respectively. These regions span genes and quantitative trait loci (QTL) associated with biological pathways that are important for adaptation to marginal environments (e.g., immunity, reproduction and heat tolerance). Trypanotolerance QTL are intersecting candidate regions in Kenana cattle indicating selection pressure acting on them, which might be associated with an unexplored level of trypanotolerance in this cattle breed. Several dairy traits QTL are overlapping the identified candidate regions in these two zebu cattle breeds. Our findings underline the potential to improve dairy production in the semi-arid pastoral areas of Africa through breeding improvement strategy of indigenous local breeds.
Predicting methane emission in Canadian Holstein dairy cattle using milk mid-infrared reflectance spectroscopy and other commonly available predictors via artificial neural networks
<p>Supplementary Tables - Version 2</p>
Predicting dry matter intake in Canadian Holstein dairy cattle using milk mid-infrared reflectance spectroscopy and other commonly available predictors via artificial neural networks
<p>Supplementary Tables</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>
Data from: Role of ambient pressure in self-heating torrefaction of dairy cattle manure
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