Comparison of organic and conventional food and food production. Part II: Animal health and welfare in Norway

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Detection of Babesia divergens in southern Norway by using an immunofluorescence antibody test in cow sera

<p>Abstract</p> <p>Background</p> <p>The incidence of bovine babesiosis, caused by <it>Babesia divergens </it>(Apicomplexa: Piroplasmida) has decreased markedly since the 1930 s, but may re-emerge as a consequence of climate change and changes in legislation and pasturing practices. This is a potentially serious disease, with both economical and animal welfare consequences. Therefore, there is a need to survey the distribution of <it>B. divergens</it>.</p> <p>Methods</p> <p>We tested sera from 306 healthy pastured cows from 24 farms along the southern Norwegian coast by using an indirect immunofluorescence IgG antibody test (IFAT). Fractions of seropositive cows were compared by calculating 95% CI.</p> <p>Results</p> <p>The results of this test showed that 27% of the sera were positive for <it>B. divergens </it>antibodies. The fraction of antibody-positive sera that we detected showed a two-humped distribution, with a high fraction of positives being found in municipalities in the western and eastern parts of the study area, while the municipalities between these areas had few or no positive serum samples.</p> <p>Conclusions</p> <p>Neither the farmers' observations nor the Norwegian Dairy Herd Recording System give an adequate picture of the distribution of bovine babesiosis. Serological testing of cows by using IFAT is a convenient way of screening for the presence of <it>B. divergens </it>in an area.</p

Large-scale cross-sectional study of relationships between somatic cell count and milking-time test results in different milking systems

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Factors associated with milking-to-milking variability in somatic cell counts from healthy cows in an automatic milking system

Fully automated on-line analysis equipment is available for analysis of somatic cell count (SCC) at every milking in automatic milking systems. In addition to results from on-line cell counters (OCC), an array of additional cow-level and quarter-level factors considered important for udder health are recorded in these systems. However, the amount of variability in SCC that can be explained by available data is unknown, and so is the proportion of the variability that may be due to physiological or normal variability. Our aim was to increase our knowledge on OCC as an indicator for disturbances in udder health by assessing the variability in OCC in cows free from clinical mastitis. The first objective was to evaluate how much of the variability in OCC could be explained by different potential sources of variability, including intramammary infection (IMI) status (assessed by bacterial culture of quarter milk samples). The second objective was to evaluate the repeatability of the OCC sensor used in our study and the agreement between OCC values and SCC measured in a dairy herd improvement (DHI) laboratory. A longitudinal study was performed in the research herd of the Norwegian University of Life Sciences from January 5th 2016 to May 22nd 2017. Data from 62,471 milkings from 173 lactations in 129 cows were analyzed. We used ln-transformed OCC values (in 1000 cells/ml) as the outcome (lnOCC) in linear mixed models, with random intercepts at cow-level and lactation-level within cow. We were able to explain 15.0% of the variability in lnOCC with the following fixed effects: lactation stage, parity, milk yield, OCC in residual milk from the previous milking, inter-quarter difference between the highest and lowest conductivity, season, IMI status, and genetic lineage. When including the random intercepts, the degree of explanation was 55.2%. The individual variables explained only a small part of the total variability in lnOCC. We concluded that physiological or normal variability is probably responsible for a large part of the overall variability in OCC in cows without clinical mastitis. This is important to consider when using OCC data for research purposes or in decision-support tools. Sensor repeatability was evaluated by analyzing milk from the same sample multiple times. The coefficient of variation was 0.11 at an OCC level relevant for detection of subclinical mastitis. The agreement study showed a concordance correlation coefficient of 0.82 when comparing results from the OCC with results from a DHI laboratory.publishedVersio

Designing a risk-based surveillance program for Mycobacterium avium ssp. paratuberculosis in Norwegian dairy herds using multivariate statistical process control analysis

Surveillance programs for animal diseases are critical to early disease detection and risk estimation and to documenting a population’s disease status at a given time. The aim of this study was to describe a risk-based surveillance program for detecting Mycobacterium avium ssp. paratuberculosis (MAP) infection in Norwegian dairy cattle. The included risk factors for detecting MAP were purchase of cattle, combined cattle and goat farming, and location of the cattle farm in counties containing goats with MAP. The risk indicators included production data [culling of animals >3 yr of age, carcass conformation of animals >3 yr of age, milk production decrease in older lactating cows (lactations 3, 4, and 5)], and clinical data (diarrhea, enteritis, or both, in animals >3 yr of age). Except for combined cattle and goat farming and cattle farm location, all data were collected at the cow level and summarized at the herd level. Predefined risk factors and risk indicators were extracted from different national databases and combined in a multivariate statistical process control to obtain a risk assessment for each herd. The ordinary Hotelling’s T2 statistic was applied as a multivariate, standardized measure of difference between the current observed state and the average state of the risk factors for a given herd. To make the analysis more robust and adapt it to the slowly developing nature of MAP, monthly risk calculations were based on data accumulated during a 24-mo period. Monitoring of these variables was performed to identify outliers that may indicate deviance in one or more of the underlying processes. The highest-ranked herds were scattered all over Norway and clustered in high-density dairy cattle farm areas. The resulting rankings of herds are being used in the national surveillance program for MAP in 2014 to increase the sensitivity of the ongoing surveillance program in which 5 fecal samples for bacteriological examination are collected from 25 dairy herds. The use of multivariate statistical process control for selection of herds will be beneficial when a diagnostic test suitable for mass screening is available and validated on the Norwegian cattle population, thus making it possible to increase the number of sampled herds.publishedVersio

Large-scale cross-sectional study of relationships between somatic cell count and milking-time test results in different milking systems

publishedVersio

Factors associated with milking-to-milking variability in somatic cell counts from healthy cows in an automatic milking system

Fully automated on-line analysis equipment is available for analysis of somatic cell count (SCC) at every milking in automatic milking systems. In addition to results from on-line cell counters (OCC), an array of additional cow-level and quarter-level factors considered important for udder health are recorded in these systems. However, the amount of variability in SCC that can be explained by available data is unknown, and so is the proportion of the variability that may be due to physiological or normal variability. Our aim was to increase our knowledge on OCC as an indicator for disturbances in udder health by assessing the variability in OCC in cows free from clinical mastitis. The first objective was to evaluate how much of the variability in OCC could be explained by different potential sources of variability, including intramammary infection (IMI) status (assessed by bacterial culture of quarter milk samples). The second objective was to evaluate the repeatability of the OCC sensor used in our study and the agreement between OCC values and SCC measured in a dairy herd improvement (DHI) laboratory. A longitudinal study was performed in the research herd of the Norwegian University of Life Sciences from January 5th 2016 to May 22nd 2017. Data from 62,471 milkings from 173 lactations in 129 cows were analyzed. We used ln-transformed OCC values (in 1000 cells/ml) as the outcome (lnOCC) in linear mixed models, with random intercepts at cow-level and lactation-level within cow. We were able to explain 15.0% of the variability in lnOCC with the following fixed effects: lactation stage, parity, milk yield, OCC in residual milk from the previous milking, inter-quarter difference between the highest and lowest conductivity, season, IMI status, and genetic lineage. When including the random intercepts, the degree of explanation was 55.2%. The individual variables explained only a small part of the total variability in lnOCC. We concluded that physiological or normal variability is probably responsible for a large part of the overall variability in OCC in cows without clinical mastitis. This is important to consider when using OCC data for research purposes or in decision-support tools. Sensor repeatability was evaluated by analyzing milk from the same sample multiple times. The coefficient of variation was 0.11 at an OCC level relevant for detection of subclinical mastitis. The agreement study showed a concordance correlation coefficient of 0.82 when comparing results from the OCC with results from a DHI laboratory

Estimation of the probability of freedom from Bovine virus diarrhoea virus in Norway using scenario tree modelling

Disease caused by Bovine virus diarrhoea virus (BVDV) is notifiable in Norway. An eradication programme started in 1992. The number of herds with restrictions decreased from 2950 in 1994 to zero at the end of 2006. From 2007, the aim of the programme has been surveillance in order to document freedom from the infection. To estimate the probability of freedom from BVDV infection in the Norwegian cattle population by the end of 2011, a scenario tree model of the surveillance program during the years 2007-2011 was used. Three surveillance system components (SSCs) were included in the model: dairy, beef suckler sampled at farms (2007-2010) and beef suckler sampled at slaughterhouses (2011). The design prevalence was set to 0.2% at herd level and to 30% at within-herd level for the whole cattle population. The median probability of freedom from BVDV in Norway at the end of 2011 was 0.996; (0.995-0.997, credibility interval). The results from the scenario tree model support that the Norwegian cattle population is free from BVDV. The highest estimate of the annual sensitivity for the beef suckling SSCs originated from the surveillance at the slaughterhouses in 2011. The change to sampling at the slaughterhouse level further increased the sensitivity of the surveillance.publishedVersio