BackgroundPayment 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.ResultsIn 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.ConclusionThe 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.

Mastitis is a major disease affecting the herds of dairy farmers worldwide. One of the indicators directly related to the widespread infection of this disease in herds is the bulk tank somatic cell count (BTSCC). Recent studies have shown that one of the risk factors associated with mastitis is the human factor. Therefore, understanding the influence of humans is essential to control and prevent the disease. The main goal of this study was to determine whether the motivations and barriers perceived by farmers could explain the variation in the BTSCC. This study was conducted at 75 dairy farms in southern Brazil. In the interviews with farmers, a survey based on Likert scale items was used to collect data. Structural equation models were used to explain the subjectivity in the ratio of observed variables and latent variables elucidating the possible causal relationships between the variables. The model indicated that some of the variation in the BTSCC can be explained by the farmer's behavior, which is elucidated by his/her motivations and barriers. The correlations between motivations and the BTSCC and between barriers and the BTSCC were positive. These findings suggest that variations in the BTSCC can be explained by the motivations and barriers perceived by farmers and that the Fogg Behavior Model used in this study can be used to explain how human behaviors influence mastitis control. This study also indicates that consulting companies focused on improving milk quality should pay attention to the human factor to reduce these barriers.

Total bacterial count (TBC) is a tool used to assess milk quality and is associated with not only the initial sample contamination but also the sample storage time and temperature. Several countries have reported milk samples with a high TBC, and the influence of TBC on milk preservation remains unclear. Thus, the aim of this study was to evaluate the impact of the initial bacterial contamination level on the macrocomponents and somatic cell count (SCC) of raw milk samples preserved with bronopol and maintained at two storage temperatures (7 and 25°C) for up to 12 days. Thus, we collected milk samples from 51 dairy farms, which were divided into two groups according to the initial bacterial load: low TBC (<100,000 CFU/ml) and high TBC (≥100,000 CFU/ml). We analyzed the sample composition for protein, fat, total solids, lactose, milk urea nitrogen, and the SCC. We did not observe an effect from TBC and storage time and temperature on the concentration of protein, fat, total solids, and lactose. SCC changes were not observed for samples maintained under refrigeration (7°C); however, samples maintained at room temperature (25°C) exhibited a decrease in the SCC beginning on day 6 of storage. For milk urea nitrogen, values increased when the samples were maintained at room temperature, beginning on the ninth storage day. Samples with the preservative bronopol added and maintained under refrigeration may be analyzed up to 12 days after collection, regardless of the milk microbial load.

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