-In this article the use of somatic cell counts for monitoring udder health and milk quality is discussed. Somatic cell count dynamics at quarter, cow, herd and population level are discussed and illustrated with examples. Quarter and cow somatic cell counts directly represent the inflammatory status of the mammary gland. Herd and population somatic cell count are related to the inflammatory process in individual cows but much more reflect the udder health status of the herd and the quality of the raw milk in the herd and the population. Application of monitoring tools in herd health management are illustrated using a case study. Understanding infection dynamics requires precise longitudinal data. Monitoring tools are required to find the areas of risk in the herd. It is inevitable that more complete udder health programs and monitoring systems are to be developed and implemented. These programs are necessarily dynamic and complex. Implementation of complete udder health programs should be accompanied by research efforts to further fine-tune these complete udder health control and monitoring programs. somatic cell count / mastitis / milk quality / monitoring / epidemiology

Our objective was to estimate the effects of the first occurrence of pathogen-specific clinical mastitis (CM) on milk yield in 3071 dairy cows in 2 New York State farms. The pathogens studied were Streptococcus spp.,Staphylococcus aureus, Staphylococcus spp., Escherichia coli, Klebsiella spp., Arcanobacterium pyogenes, other pathogens grouped together, and "no pathogen isolated." Data were collected from October 1999 to July 2001. Milk samples were collected from cows showing signs of CM and were sent to the Quality Milk Production Services laboratory at Cornell University for microbiological culture. The SAS statistical procedure PROC MIXED, with an autoregressive covariance structure, was used to quantify the effect of CM and several other control variables (herd, calving season, parity, month of lactation, J-5 vaccination status, and other diseases) on weekly milk yield. Separate models were fitted for primipara and multipara, because of the different shapes of their lactation curves. To observe effects of mastitis, milk weights were divided into several periods both pre- and postdiagnosis, according to when they were measured in relation to disease occurrence. Another category contained cows without the type of CM being modeled. Because all pathogens were modeled simultaneously, a control cow was one without CM. Among primipara, Staph. aureus, E. coli, Klebsiella spp., and "no pathogen isolated" caused the greatest losses. Milk yield generally began to drop 1 or 2 wk before diagnosis; the greatest loss occurred immediately following diagnosis. Mastitic cows often never recovered their potential yield. Among older cows, Streptococcus spp., Staph. aureus, A. pyogenes, E. coli, and Klebsiella spp. caused the most significant losses. Many multipara that developed CM were actually higher producers before diagnosis than their nonmastitic herd-mates. As in primipara, milk yield in multipara often began to decline shortly before diagnosis; the greatest loss occurred immediately following diagnosis. Milk loss persisted until at least 70 d after diagnosis for Streptococcus spp., Klebsiella spp., and A. pyogenes. The tendency for higher producing cows to contract CM may mask its impact on cow health and production. These findings provide dairy producers with more information on which pathogen-specific CM cases should receive treatment and how to manage these cows, thereby reducing CM impact on cow well being and profitability.

Milk samples were collected from 108,312 dairy cows during 1601 farm visits made between January 1991 and June 1995. The herd visits were made by personnel from the Central Laboratory of the Quality Milk Promotion Services at Cornell University (Ithaca, NY) to farms located in central New York and northern Pennsylvania. Dairy Herd Improvement Association records were available for 32,978 cows in 327 herds. Intramammary infections, as defined by positive milk cultures, were present in 48.5% of all cows and in 36.3% of cows in herds enrolled in the Dairy Herd Improvement Association. Over 75% of the intramammary infections were caused by Streptococcus agalactiae, Streptococcus spp. other than Strep. agalactiae, Staphylococcus aureus, and coagulase-negative staphylococci. Mean days in milk at the time of diagnosis, linear score of the somatic cell count, cost of milk loss per lactation, and milk production effects were calculated for 24 etiologic agents of bovine mastitis.

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Twenty-one quarters of seven cows were experimentally infected with Staphylococcus aureus (ATCC 29740) to study the shedding pattern in quarter milk samples. Of 991 consecutive quarter milk samples collected from infected quarters during the trial, 745 were positive for S. aureus by bacteriological culture. The sensitivity of a single quarter milk sample to determine infection status of a quarter was 74.5% based on the mean of each gland's recovery pattern. Sensitivity of bacterial culture increased to 94% and 98% by including a second and a third consecutive sample. Because S. aureus is shed in a cyclical manner from mammary glands, consecutive samples would be advisable for accurate diagnosis of infected quarters.

The objective of this study was to estimate the cost of generic clinical mastitis (CM) in high-yielding dairy cows given optimal decisions concerning handling of CM cases. A specially structured optimization and simulation model that included a detailed representation of repeated episodes of CM was used to study the effects of various factors on the cost of CM. The basic scenario was based on data from 5 large herds in New York State. In the basic scenario, 92% of the CM cases were recommended to be treated. The average cost of CM per cow and year in these herds was $71. The average cost of a CM case was $179. It was composed of $115 because of milk yield losses, $14 because of increased mortality, and $50 because of treatment-associated costs. The estimated cost of CM was highly dependent on cow traits: it was highest ($403) in cows with high expected future net returns (e.g., young, high-milk-yielding cows), and was lowest ($3) in cows that were recommended to be culled for reasons other than mastitis. The cost per case of CM was 18% higher with a 20% increase in milk price and 17% lower with a 20% decrease in milk price. The cost per case of CM was affected little by a 20% change in replacement cost or pregnancy rate. Changes in CM incidence, however, resulted from changes in these factors, thus affecting whole-farm profitability. The detailed results obtained from this insemination and replacement optimization model can assist farmers in making CM treatment decisions.

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The objective of this study was to estimate the milk production losses associated with clinical mastitis using mixed linear models and correlation structures that have not been available previously. Data used included computer-recorded daily milk yields and detailed and accurate recordings of clinical mastitis cases. Two commercial Holstein dairy farms in New York State participated in the study, one with 650 lactating cows and another that began the study with 830 lactating cows and increased to 1120 cows by the end of the study. Cows on both farms were housed in free stall barns and milked 3 times daily in milking parlors. Electrical conductivity was used as a diagnostic aid for clinical mastitis on both farms. Date of clinical onset was recorded for every episode of clinical mastitis as well as for 8 other diseases defined using standardized case definitions (dystocia, milk fever, retained placenta, metritis, ketosis, displaced abomasum, lameness, and cystic ovarian disease) during the study period of October 1, 1999 to July 31, 2001. The mixed linear model for explaining variation in the outcome variable daily milk yield relative to non-mastitic herdmates found the terms for all 9 diseases studied, including clinical mastitis, significant. The model with an autoregressive correlation structure was preferred based on -2 * log likelihood, Akaike's information criterion, and Bayesian information criterion as well as savings in degrees of freedom. Separate analyses were run for first lactation cows and for second-plus lactation cows because their lactation curves were shaped differently. Adjusting for the effects of the other 8 diseases, milk production loss from clinical mastitis during the whole lactation was estimated as approximately 598 kg for second-plus lactation cows. However, cows that contracted mastitis had a daily production advantage of 2.6 kg over their herdmates until they contracted the disease. When compared with this potentially higher milk production, the total loss from clinical mastitis was estimated as 1181 kg.