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.

Recently, many changes have been implemented in Dutch dairy herds. Herd sizes have increased and antimicrobial use has been reduced. Certain types of antimicrobials can only be used in specific circumstances, and the preventive use of antimicrobials in dry cows is prohibited. The aim of this study was to quantify clinical mastitis (CM), subclinical mastitis (SCM), and risk factors associated with CM in Dutch dairy herds in 2013, in the context of these changes. For this study, 240 dairy herds were randomly selected from farms that participated in test-day milk recording, used a conventional milking system, and agreed to participate in the study. Eventually, 233 Dutch dairy farmers had complete records of CM in their herds in 2013 and 224 of these farmers completed a questionnaire on management factors potentially associated with CM. All participating farmers gave consent to use their routinely collected herd data such as test-day records and cow identification and registration data. Clinical and subclinical mastitis incidence rate (CMI and SCMI, respectively) per 100 cows per year, subclinical mastitis prevalence, and average bulk tank milk somatic cell count were obtained for 2013. The risk factor analysis was conducted using a generalized linear model with a log link function and a negative binomial distribution on herd level in Stata 13.1. A median CMI of 28.6 per 100 cows at risk per year, SCMI of 70.1 per 100 cows at risk per year, SCM prevalence of 15.8%, and bulk tank milk somatic cell count of 171 × 10(3) cells/mL were observed in 2013. Factors that were significantly associated with a higher CMI were cleaning slatted floors only once per day compared with more than 4 times a day (i.e., mechanical), a higher percentage of Holstein Friesian cows present in the herd, treating less than 50% of the cows with CM with antimicrobials, postmilking teat disinfection, and treatment of cows with elevated somatic cell count with antimicrobials. The results of this study indicated that udder health had not deteriorated compared with udder health in previous Dutch studies where herd sizes were somewhat smaller and before the restrictions in antimicrobial use. Several of the risk factors that were found can be influenced by the farmer and can prevent the occurrence of CMI. Still, when cases of CM occur, treatment with antimicrobials might be necessary to cure the CM case and is beneficial for the overall udder health in the herd.

The aim of this study was to evaluate whether it was possible to (1) estimate the clinical mastitis incidence rate (CMI) for all Dutch dairy herds and (2) to detect farms with a high CMI based on routinely collected herd data. For this study, 240 dairy farms with a conventional milking system that participated in the milk recording program every 4 to 6 wk were randomly selected and agreed to participate. From the initial 240 herds, data of clinical mastitis (CM) registrations and routinely collected herd data of 227 herds were complete and could be used for analysis. Routinely collected herd data consisted of identification and registration records, antimicrobial usage, test-day records from the milk recording program, bulk tank milk (BTM) somatic cell count data and results of diagnostic tests on BTM samples. For each of the 227 herds, the CMI per 100 cows per year was calculated per quarter of the year and was combined with the available herd data. Two models were developed to predict the CMI for all dairy herds and to detect individual herds that belonged to the 25% herds with the highest CMI. Records of 156 (67%) herds were used for development of the models and the remaining 71 (33%) were used for validation. The model that estimated the CMI in all herds consisted of 11 explanatory variables. The observed and predicted averages of the validation herds were not significantly different. The model estimated a CMI per 100 cows per year of 32.5 cases (95% confidence interval=30.2-34.8), whereas the farmers registered 33.4 cases (95% confidence interval=29.5-37.4). The model that aimed at detecting individual herds with a high CMI contained 6 explanatory variables and could correctly classify 77% of all validation herds at the quarter-year level. The most important variables in the model were antibiotic usage for treating CM and BTM somatic cell count. In conclusion, models based on routinely collected herd data gave an accurate prediction of CMI for all Dutch dairy herds and could detect individual dairy herds with a high CMI. With these models it is possible to periodically monitor CMI both at the herd and at the national level, which is valuable for monitoring purposes and can motivate farmers to continuously improve udder health in their herds.

In the Dutch national surveillance system, an increasing number of reports were received in the summer of 2017 from farmers about unusual behavior of their cows. The cows were grouping during the day in summer in one part of the barn and did not move for several hours, which, according to the farmers, led to reduced food and water intake and lying time and resulted in decreased milk production and increased risk of lameness. Many farmers perceived magnetic fields from, for instance, high-voltage lines, automated milking systems, or solar panels as possible causes for the behavior of their cows. Our aim for the study was to study potential factors such as magnetic fields and other factors such as barn climate and insect burden for adverse grouping behavior of dairy cows in the barn. For each case herd, 2 control herds were selected in the same postal area code. A case was a herd in which cattle grouped at least on 7 occasions in a month for several hours. In a control herd, the cows were in the barn during the same time period as in the matching case herd but did not show adverse grouping behavior. A questionnaire was administered by telephone in 31 case herds and 62 control herds. The questionnaire gathered information on behavior of the cows and potential risk factors. In addition, data on the distance of the herd to high-voltage lines was obtained. From a total of 74 variables, all variables with a P-value ≤0.10 were included in full multivariable logistic regression model. Backward selection was carried out at P ≤ 0.10. The grouping behavior of the cows started in most herds in June, was seen only during the day, and lasted mostly 6 to 8 h, with cows often grouped in the northern part of the barn. Identified risk factors appeared to be recently constructed barns, measured stray voltage in barns, and presence of fans in barns. Given the cross-sectional design of the case-control study, causality for these risk factors leading to adverse behavior of the cows could not be proven. Dissemination of the results to farmers hopefully results in measures that can prevent the unusual grouping behavior of cows.