No abstract

The goal of dry cow therapy (DCT) is to reduce the prevalence of intramammary infections (IMI) by eliminating existing IMI at drying off and preventing new IMI from occurring during the dry period. Due to public health concerns, however, preventive use of antibiotics has become questionable. This study evaluated selective DCT in 1,657 cows with low somatic cell count (SCC) at the last milk recording before drying off in 97 Dutch dairy herds. Low SCC was defined as <150,000 cells/mL for primiparous and <250,000 cells/ mL for multiparous cows. A split-udder design was used in which 2 quarters of each cow were treated with dry cow antibiotics and the other 2 quarters remained as untreated controls. The effect of DCT on clinical mastitis (CM), bacteriological status, SCC, and antibiotic use were determined at the quarter level using logistic regression and chi-squared tests. The incidence rate of CM was found to be 1.7 times (95% confidence interval = 1.4-2.1) higher in quarters dried off without antibiotics as compared with quarters dried off with antibiotics. Streptococcus uberis was the predominant organism causing CM in both groups. Somatic cell count at calving and 14 d in milk was significantly higher in quarters dried off without antibiotics (772,000 and 46,000 cells/mL, respectively) as compared with the quarters dried off with antibiotics (578,000 and 30,000 cells/mL, respectively). Quarters with an elevated SCC at drying off and quarters with a positive culture for major pathogens at drying off had a higher risk for an SCC above 200,000 cells/mL at 14 d in milk as compared with quarters with a low SCC at drying off and quarters with a negative culture for major pathogens at drying off. For quarters that were culture-positive for major pathogens at drying off, a trend for a higher risk on CM was also found. Selective DCT, not using DCT in cows that had a low SCC at the last milk recording before drying off, significantly increased the incidence rate of CM and SCC. The decrease in antibiotic use by drying off quarters without DCT was not compensated by an increase in antibiotic use for treating CM. Total antibiotic use related to mastitis was reduced by 85% in these quarters.

The Welfare Quality multi-criteria evaluation (WQ-ME) model aggregates scores of single welfare measures into an overall assessment for the level of animal welfare in dairy herds. It assigns herds to 4 welfare classes: unacceptable, acceptable, enhanced, or excellent. The aim of this study was to demonstrate the relative importance of single welfare measures for WQ-ME classification of a selected sample of Dutch dairy herds. Seven trained observers quantified 63 welfare measures of the Welfare Quality protocol in 183 loose housed- and 13 tethered Dutch dairy herds (herd size: 10 to 211 cows). First, values of welfare measures were compared among the 4 welfare classes, using Kruskal-Wallis and Chi-squared tests. Second, observed values of single welfare measures were replaced with a fictitious value, which was the median value of herds classified in the next highest class, to see if improvement of a single measure would enable a herd to reach a higher class. Sixteen herds were classified as unacceptable, 85 as acceptable, 78 as enhanced, and none as excellent. Classification could not be calculated for 17 herds because data were missing (15 herds) or data were deemed invalid because the stockperson disturbed behavioral observations (2 herds). Herds classified as unacceptable showed significantly more very lean cows, more severely lame cows, and more often an insufficient number of drinkers than herds classified as acceptable. Herds classified as acceptable showed significantly more cows with high somatic cell count, with lesions, that could not be approached closer than 1m, colliding with components of the stall while lying down, and lying outside the lying area, and showed fewer cows with diarrhea, more often had an insufficient number of drinkers, and scored lower for the descriptors "relaxed" and "happy" than herds classified as enhanced. Increasing the number of drinkers and reducing the percentage of cows colliding with components of the stall while lying down were the changes most effective in allowing herds classified as unacceptable and acceptable, respectively, to reach a higher class. The WQ-ME model was not very sensitive to improving single measures of good health. We concluded that a limited number of welfare measures had a strong influence on classification of dairy herds. Classification of herds based on the WQ-ME model in its current form might lead to a focus on improving these specific measures and divert attention from improving other welfare measures. The role of expert opinion and the type of algorithmic operator used in this model should be reconsidered.

A cross-sectional study was conducted to evaluate the prevalence of extended-spectrum β-lactamase (ESBL)- and plasmid-mediated AmpC-producing Escherichia coli and associated risk factors in dairy herds. One hundred dairy herds were randomly selected and sampled to study the presence of ESBL- and AmpC-producing E. coli in slurry samples. The sensitivity of testing slurry samples for ESBL/AmpC herd status is less than 100%, especially for detecting herds with a low ESBL/AmpC prevalence. Therefore, whereas herds that tested positive for ESBL/AmpC-producing E. coli in slurry were defined as positive herds, herds with negative slurry samples were defined as unsuspected. Isolates of ESBL- and AmpC-producing E. coli were further characterized by detection and typing of their ESBL/AmpC gene. At the initial sampling, a comprehensive questionnaire was conducted at the participating farms. The farmers were asked questions about management practices potentially associated with the ESBL/AMPC herd status. Also, data on antimicrobial purchases during 2011 were acquired to evaluate whether the animal-defined daily dose of antimicrobials per year at farm level was associated with the ESBL/AmpC herd status. Multivariable logistic regression models were used to determine the association between management practices and the ESBL/AmpC herd status. Six months after the initial slurry sampling, 10 positive herds and 10 herds that had an unsuspected ESBL/AmpC herd status during the first visit were resampled. At each farm, slurry samples and feces from 24 individual cows were collected to evaluate within herd dynamics. During the first sampling, ESBL/AmpC-producing E. coli were isolated from the slurry samples collected at 41% of the herds. In total, 37 isolates were further characterized, revealing 7 different ESBL genes (bla and bla), 1 plasmid-encoded AmpC gene (bla), and 1 chromosomally encoded ampC gene (ampC type 3). The total animal-defined daily dose of antimicrobials per year at farm level was not significantly different between ESBL/AmpC-positive and unsuspected dairy herds. The use of third- and fourth-generation cephalosporins, however, was found to be associated with ESBL/AmpC status, with higher use of these antimicrobials resulting in a significant higher odds to be ESBL/AmpC-positive. Management factors that were associated with a higher odds of being ESBL/AmpC-positive were treatment of all cases of clinical mastitis with antimicrobials, a higher proportion of calves treated with antimicrobials, not applying teat sealants in all cows at dry off, and the use of a floor scraper. This last association, however, was considered a methodological effect rather than a true risk factor. On 5 of the 10 initially positive farms, no ESBL/AmpC-producing E. coli were cultured from the slurry or any of the individual cow samples collected during the second sampling. In 4 of the initially unsuspected farms, slurry or individual cow samples tested positive during the second sampling. In conclusion, ESBL/AmpC could frequently be cultured...

Routine on-farm assessment of dairy cattle welfare is time consuming and, therefore, expensive. A promising strategy to assess dairy cattle welfare more efficiently is to estimate the level of animal welfare based on herd data available in national databases. Our aim was to explore the value of routine herd data (RHD) for estimating dairy cattle welfare at the herd level. From November 2009 through March 2010, 7 trained observers collected data for 41 welfare indicators in a selected sample of 183 loose-housed and 13 tethered Dutch dairy herds (herd size: 10 to 211 cows) using the Welfare Quality protocol for cattle. For the same herds, RHD relating to identification and registration, management, milk production and composition, and fertility were extracted from several national databases. The RHD were used as potential predictors for each welfare indicator in logistic regression at the herd level. Nineteen welfare indicators were excluded from the predictions, because they showed a prevalence below 5% (15 indicators), or were already listed as RHD (4 indicators). Predictions were less accurate for 7 welfare indicators, moderately accurate for 14 indicators, and highly accurate for 1 indicator. By forcing to detect almost all herds with a welfare problem (sensitivity of at least 97.5%), specificity ranged from 0 to 81%. By forcing almost no herds to be incorrectly classified as having a welfare problem (specificity of at least 97.5%), sensitivity ranged from 0 to 67%. Overall, the best-performing prediction models were those for the indicators access to at least 2 drinkers (resource based), percentage of very lean cows, cows lying outside the supposed lying area, and cows with vulvar discharge (animal based). The most frequently included predictors in final models were percentages of on-farm mortality in different lactation stages. It was concluded that, for most welfare indicators, RHD have value for estimating dairy cattle welfare. The RHD can serve as a prescreening tool for detecting herds with a welfare problem, but this should be followed by a verification of the level of welfare in an on-farm assessment to identify false-positive herds. Consequently, the number of farm visits needed for routine welfare assessments can be reduced. The RHD also hold value for continuous monitoring of dairy cattle welfare. Prediction models developed in this study, however, should first be validated in additional field studies.