Incidence of clinical mastitis was studied in 274 herds grouped in three categories by bulk milk somatic cell count (SCC). Mean incidence rate of clinical mastitis was 0.278, 0.257, and 0.252 cases per 365 cow-days at risk in herds with low (< or = 150,000), medium (150,000 to 250,000), and high (250,000 to 400,000 cells/ml) bulk milk SCC, respectively. The incidence rate of clinical mastitis was not different among the three categories. Variance in the incidence of clinical mastitis among herds increased as bulk milk SCC decreased. Clinical mastitis caused by Gram-negative pathogens, such as Escherichia coli, Klebsiella spp., or Pseudomonas spp., occurred more often in herds with a low bulk milk SCC. Clinical mastitis caused by Staphylococcus aureus, Streptococcus dysgalactiae, and Streptococcus agalactiae occurred more often in herds with a high bulk milk SCC. Systemic signs of illness caused by clinical mastitis occurred more often in herds with a low bulk milk SCC. Both overall culling rate and culling rate for clinical mastitis were not different among groups catergorized by bulk milk SCC. In herds with a high bulk milk SCC, however, more cows that produced milk with a high SCC were culled. In herds with a low bulk milk SCC, more cows were culled for teat lesions, milkability, udder shape, fertility, and character than were cows in herds with a high bulk milk SCC. In herds with a low bulk milk SCC, cows were also culled more for export and production reasons.

The objective of this study was to determine the factors affecting somatic cell count (SCC), to estimate variance components of these factors, and to calculate and evaluate the thresholds for intramammary infection based on SCC. The infection status from 22,467 quarter milk samples from 544 cows in seven herds was determined. Infections status was the most important factor affecting SCC. The increase in SCC was more pronounced for major pathogens than for minor pathogens. Even after adjustment for infection status, the interaction between stage of lactation and parity was significant. For culture-negative samples within a lactation, the shape of the SCC curve was inversely related to the shape of the milk production curve. The shape of the SCC curve was flat for first lactation cows compared with the shape of the SCC curve for cows in subsequent lactations. The effect of clinical mastitis on SCC was significant. The use of SCC thresholds for specific parities and stages of lactation to detect intramammary infection improved quality parameters only slightly over a fixed threshold of 200,000 cells/ml.

Good udder health is not only important for the dairy farmer but, because of increasing interest of consumers in the way dairy products are produced, also for the dairy production chain as a whole. An important role of veterinarians is in advising on production diseases such as mastitis. A large part of this advice is given around the planning of management to maintain or improve the udder health status of a farm. Mastitis is a costly disease, due to losses (a reduction of output due to mastitis) and expenditure (additional inputs to reduce the level of mastitis). Worldwide, published estimates of the economic losses of clinical mastitis range from €61 to €97 per cow on a farm, with large differences between farms, e.g. in The Netherlands, losses due to clinical and subclinical mastitis varied between €17 and €198 per cow per year. Moreover, farmers tended to underestimate these costs. This indicates that for a large proportion of farms there are many avoidable losses. In order to provide good support to farmers' decision-making, it is important to describe the mastitis setting not only in terms of disease, e.g. incidence of clinical mastitis, but also in monetary terms; and to make good decisions, it is necessary to provide the dairy farmer with information on the additional expenditure and reduced losses associated with alternative decisions. Six out of 18 preventive measures were shown to have a positive nett benefit, viz blanket use of dry-cow therapy, keeping cows standing after milking, back-flushing of the milk cluster after milking a cow with clinical mastitis, application of a treatment protocol, washing dirty udders, and the use of milkers' gloves. For those measures that included a large amount of routine labour or investment, the reduced losses did not outweigh the additional expenditure. The advisor cannot expect that measures that are cost-effective are always implemented. Reasons for this are the objectives of the dairy farmer can be other than maximisation of profit, resources to improve the mastitis situation compete with other fields of management, risk involved with the decision, economic behaviour of the dairy farmer, and valuation of the cost factors by the dairy farmer. For all decision-makers this means that, although financial incentives do have an effect on the management of mastitis, it is not always sufficient to show the economic benefits of improved management to induce an improvement of management of mastitis.

The dairy industry in the developed world has undergone profound changes over recent decades. In this paper, we present an overview of some of the most important recent changes in the dairy industry that affect health and welfare of dairy cows, as well as the science associated with these changes. Additionally, knowledge gaps are identified where research is needed to guide the dairy industry through changes that are occurring now or that we expect will occur in the future. The number of farms has decreased considerably, whereas herd size has increased. As a result, an increasing number of dairy farms depend on hired (nonfamily) labor. Regular professional communication and establishment of farm-specific protocols are essential to minimize human errors and ensure consistency of practices. Average milk production per cow has increased, partly because of improvements in nutrition and management but also because of genetic selection for milk production. Adoption of new technologies (e.g., automated calf feeders, cow activity monitors, and automated milking systems) is accelerating. However, utilization of the data and action lists that these systems generate for health and welfare of livestock is still largely unrealized, and more training of dairy farmers, their employees, and their advisors is necessary. Concurrently, to remain competitive and to preserve their social license to operate, farmers are increasingly required to adopt increased standards for food safety and biosecurity, become less reliant on the use of antimicrobials and hormones, and provide assurances regarding animal welfare. Partly because of increasing herd size but also in response to animal welfare regulations in some countries, the proportion of dairy herds housed in tiestalls has decreased considerably. Although in some countries access to pasture is regulated, in countries that traditionally practiced seasonal grazing, fewer farmers let their dairy cows graze in the summer. The proportion of organic dairy farms has increased globally and, given the pressure to decrease the use of antimicrobials and hormones, conventional farms may be able to learn from well-managed organic farms. The possibilities of using milk for disease diagnostics and monitoring are considerable, and dairy herd improvement associations will continue to expand the number of tests offered to diagnose diseases and pregnancy. Genetic and genomic selection for increased resistance to disease offers substantial potential but requires collection of additional phenotypic data. There is every expectation that changes in the dairy industry will be further accentuated and additional novel technologies and different management practices will be adopted in the future.

A model to calculate the economic losses of mastitis on an average Dutch dairy farm was developed and used as base for a tool for farmers and advisors to calculate farm-specific economic losses of mastitis. The economic losses of a clinical case in a default situation were calculated as euro210, varying from euro164 to euro235 depending on the month of lactation. The total economic losses of mastitis (subclinical and clinical) per cow present in a default situation varied between euro65 and euro182/cow per year depending on the bulk tank somatic cell count. The tool was used to measure perception of the total economic losses of mastitis on the farm and the farmers' assessment of the cost factors of mastitis on 78 dairy farms, of which 64 were used for further analyses. Most farmers (72%) expected their economic losses to be lower than those revealed by our calculation made with their farm information. Underestimating the economic losses of mastitis can be regarded as a general problem in the dairy sector. The average economic losses assessed by the farmers were euro78/cow per year, but a large variation was given, euro17-198/cow per year. Although the average assessment of the farmers of the different cost factors is close to the default value, there is much variation. To improve the adoption rate of advice and lower the incidence of mastitis, it is important to show the farmers the economic losses of mastitis on their farm. The tool described in this paper can play a role in that process.

Between January and April 2007, 424 calves under 22 days of age from 108 Dutch dairy herds were sampled to estimate the prevalence of non-normal faeces ('custard-like'-yellowish-coloured with custard consistency or diarrhoea: watery-like faeces) and the shedding of enteropathogens Escherichia coli K99 (E. coli), Coronavirus, Cryptosporidium parvum (C. parvum), Rotavirus and Clostridium perfringens (Cl. perfringens). In addition, information was collected on animal characteristics and herd-management practices. The probability of detecting each one of five enteropathogens given a calf with 'custard-like' faeces or diarrhoea was estimated using Bayes' rule and was based on the predicted probabilities from a multinominal model including each of five enteropathogens as independent variables. In addition, putative risk factors for the presence of each of five enteropathogens were analysed using logistic regression models with random herd effects. Fifty-seven percent of calves had faeces of normal colour (brownish) and consistency (firm), 23.8% (95%CI: 19.8-28.2%) had 'custard-like' faeces and 19.1% (95%CI: 15.5-23.2%) had diarrhoea. E. coli was the least detected enteropathogen (2.6% (95%CI: 1.3-4.6%) of calves, 9% (95%CI: 5-16%) of herds) and Cl. perfringens was most detected (54.0% (95%CI: 49.1-58.8%) of calves, 85% (95%CI: 77-91%) of herds). E. coli and Coronavirus were detected incidentally in only one or two calves per herd, whereas C. parvum and Cl. perfringens were frequently detected in up to four calves per herd. For calves with 'custard-like' faeces, the probability of detecting Rotavirus from a calf in its first week of age was 0.31 whereas for a calf in its second week, there was a 0.66 probability of detecting C. parvum. The probabilities of detecting E. coli, Rotavirus and C. parvum in calves with diarrhoea in their first week of age were 0.10, 0.20 and 0.43, respectively. In calves with diarrhoea between 1 and 2 weeks of age, the probability of detecting enteropathogens was 0.43 for C. parvum. None of the tested enteropathogens were related to 'custard-like' faeces or diarrhoea in the third week of age. Putative risk factors for E. coli, Coronavirus and C. parvum included the presence of peer-calves shedding Coronavirus, C. parvum or Rotavirus, respectively. Additionally, managerial risk factors such as non-optimal hygienic housing (for Coronavirus) and the routine use of antibiotics for diarrhoeic calves (for C. parvum) were found. No animal or managerial factors were associated with shedding of Cl. perfringens.

The prevention and control of endemic pathogens within and between farms often depends on the adoption of best management practices. However, farmers regularly do not adopt recommended measures or do not enroll in voluntary disease control programs. This indicates that a more comprehensive understanding of the influences and extension tools that affect farmers' management decisions is necessary. Based on a review of relevant published literature, we developed recommendations to support policy-makers, industry representatives, researchers, veterinarians, and other stakeholders when motivating farmers to adopt best management practices, and to facilitate the development and implementation of voluntary prevention and control programs for livestock diseases. Farmers will make management decisions based on their unique circumstances, agricultural contexts, beliefs, and goals. Providing them with rational but universal arguments might not always be sufficient to motivate on-farm change. Implementation of recommended management practices is more likely if farmers acknowledge the existence of a problem and their responsibility to take action. The perceived feasibility and effectiveness of the recommended management strategy and sufficient technical knowledge further increase the likelihood of adequate adoption. Farmers will also weigh the expected advantages of a proposed change against the expected disadvantages, and these considerations often include internal drivers such as pride or the desire to conform with perceived standards. Extension tools and farmers' social referents (e.g., veterinarians, peers) not only provide technical information but also influence these standards. Whereas mass media have the potential to deliver information to a broad audience, more personal approaches such as participatory group learning or individual communication with farm advisors can enable the tailoring of recommendations to farmers' situations. Approaches that appeal to farmers' internal motivators or that unconsciously elicit the desired behavior will increase the success of the intervention. Collaboration among stakeholders, assisted by social scientists and communication specialists, is necessary to provide a context that facilitates on-farm change and transfers consistent messages across extension tools in the most effective way.

Management practices associated with bulk milk somatic cell counts (SCC) were studied for 201 dairy herds grouped into three categories according to bulk milk SCC. The cumulative production of fat-corrected milk over 305 d of lactation and category for bulk milk SCC were highly correlated; herds within the low category had the highest milk production. Differences in bulk milk SCC among the categories were well explained by the management practices studied. This correlation was not only true for the difference between the high (250,000 to 400,000) and low (< or = 150,000) categories for bulk milk SCC but also for the difference between the medium (150,000 to 250,000) and low categories and the high and medium categories. Management practices that are known to be important for herds in the high category for bulk milk SCC, such as dry cow treatment, milking technique, postmilking teat disinfection, and antibiotic treatment of clinical mastitis, were also found to be important in the explanation of the difference between herds in the medium and low categories for bulk milk SCC. More attention was paid to hygiene for herds in the low category than for herds in the medium or high category. Supplementation of the diet with minerals occurred more frequently for cows in the low category for bulk milk SCC than for cows in the medium and high categories.