The objectives of this study were to estimate the prevalence and incidence of subclinical mastitis (SM) in a large population of Brazilian dairy herds and to describe how these indices changed over time. A data set comprising individual cow somatic cell counts (SCC) from 18,316 test days (TD) of 1,809 herds that participated in a Dairy Herd Improvement Association (DHIA) program between January 2011 and May 2015 was available for analysis. Only tests that had ≥10 lactating cows and that were performed at 30 ± 10-d intervals were used for analysis. The final data set included 8,285 TD from 517 herds located in 5 regions of the country. Prevalence (%) of SM was defined as the number of cows with SCC ≥200,000 cells/mL divided by the total number of tested cows on a given TD. The incidence of SM was defined as the number of cows whose SCC increased from <200,000 to ≥200,000 cells/mL over 2 consecutive TD divided by the sum of each cow's days at risk during this interval, expressed as new cases per cow month at risk. Prevalence and incidence of SM were compared among years, regions, herd size categories, and frequency of DHIA testing during the study period. The overall mean prevalence and incidence of SM including all tests performed during the study period was 46.4% and 0.17 new cases per cow month at risk, respectively. The prevalence of SM varied little from 2011 to 2015, and an increasing trend was observed over the years. Prevalence was lower in herds that performed ≥60 DHIA tests during the study period than in those that performed fewer tests and was not different among regions or herd size categories. Incidence of SM varied little over the years and was not different among the regions studied. Prevalence and incidence of SM in the 517 herds studied were high and did not improve over the years. These trends were observed across all herd size categories and regions studied. Producers who had more DHIA tests performed per herd during the study period had lower prevalence of SM. Results of this study highlight the need to establish large-scale milk quality programs in Brazil.

Our objective was to quantify the relationship between seasons of the year, milk production, and milk composition of a dairy farm based on data for 48 consecutive months, using multivariate statistical analyses. The dataset contained information on productive indexes and milk composition from the bulk tank milk, which was measured from milk samples, collected monthly and used to determine the total dry extract and defatted dry extract, lactose, fat, protein, somatic cell count, and total bacterial count. Seasons of the year and milk production/hectare were also considered. Factor, cluster, and discriminant analysis were used to study the relationships between the above-mentioned variables. A positive relationship was noted between season and total dry extract, defatted dry extract, milk fat, and protein, with higher values being observed in winter and spring. Similarly, a positive relationship was noted between season and milk production/hectare, lactose content, with an increase in milk production and lactose content in winter and spring, which was negatively related to the somatic cell count and total bacterial count. Milk production and composition varied mainly with seasons. Multivariate analyses facilitated a better understanding of the relationship between milk production and composition on this dairy farm.

Payment programs based on milk quality (PPBMQ) are used in several countries around the world as an incentive to improve milk quality. One of the principal milk parameters used in such programs is the bulk tank somatic cell count (BTSCC). In this study, using data from an average of 37,000 farms per month in Brazil where milk was analyzed, BTSCC data were divided into different payment classes based on milk quality. Then, descriptive and graphical analyses were performed. The probability of a change to a worse payment class was calculated, future BTSCC values were predicted using time series models, and financial losses due to the failure to reach the maximum bonus for the payment based on milk quality were simulated. In Brazil, the mean BTSCC has remained high in recent years, without a tendency to improve. The probability of changing to a worse payment class was strongly affected by both the BTSCC average and BTSCC standard deviation for classes 1 and 2 (1000–200,000 and 201,000–400,000 cells/mL, respectively) and only by the BTSCC average for classes 3 and 4 (401,000–500,000 and 501,000–800,000 cells/mL, respectively). The time series models indicated that at some point in the year, farms would not remain in their current class and would accrue financial losses due to payments based on milk quality. The BTSCC for Brazilian dairy farms has not recently improved. The probability of a class change to a worse class is a metric that can aid in decision-making and stimulate farmers to improve milk quality. A time series model can be used to predict the future value of the BTSCC, making it possible to estimate financial losses and to show, moreover, that financial losses occur in all classes of the PPBMQ because the farmers do not remain in the best payment class in all months.