Optimally achieving milk bulk tank somatic cell count thresholds

Emerging Parameters Justifying a Revised Quality Concept for Cow Milk

Milk has become a staple food product globally. Traditionally, milk quality assessment has been primarily focused on hygiene and composition to ensure its safety for consumption and processing. However, in recent years, the concept of milk quality has expanded to encompass a broader range of factors. Consumers now also consider animal welfare, environmental impact, and the presence of additional beneficial components in milk when assessing its quality. This shifting consumer demand has led to increased attention on the overall production and sourcing practices of milk. Reflecting on this trend, this review critically explores such novel quality parameters, offering insights into how such practices meet the modern consumer's holistic expectations. The multifaceted aspects of milk quality are examined, revealing the intertwined relationship between milk safety, compositional integrity, and the additional health benefits provided by milk's bioactive properties. By embracing sustainable farming practices, dairy farmers and processors are encouraged not only to fulfill but to anticipate consumer standards for premium milk quality. This comprehensive approach to milk quality underscores the necessity of adapting dairy production to address the evolving nutritional landscape and consumption patterns.

Factors influencing somatic cell counts and bacterial contamination in unpasteurized milk obtained from water buffalo in Bangladesh

Little has been published on the factors influencing the safety and quality of milk derived from water buffalo in Bangladesh. This study aims to describe the milk hygiene parameters and milk chain characteristics of unpasteurized raw milk sold to consumers in order to improve milk hygiene. A quantitative study design evaluated somatic cell counts, total bacterial counts, and specific gram-negative (Enterobacteria) and gram-positive (staphylococci) pathogens in 377 aseptically collected milk samples. Samples were collected at multiple nodes along the buffalo milk value chain: 122 bulk tank milk samples were collected at the farm level, 109 milk samples at the middlemen level, and 111 milk samples at the milk collection centers. In addition, 35 samples were taken from various milk products at the retail level. It was found that progressively increasing somatic cell counts and bacterial counts, including potential pathogens, occurred along the milk chain. A seasonal increase in spring was found, varying based on the farming system (semi-intensive versus intensive). Other factors included water purity and cleanliness of containers, mixing buffalo and cow's milk, and the location of the water buffalo milk producer (coastal or river basin). This study demonstrated how improving udder health and milk hygiene along the water buffalo milk value chain would increase the safety and quality of water buffalo milk in the study area.

A Study on the Uniform Distribution and Counting Method of Raw Cow's Milk Somatic Cells

The somatic cell count (SCC) in raw milk is an important basis for determining whether a cow is suffering from mastitis. To address the problem of an uneven distribution of somatic cells due to cell-adherent sedimentation, among other reasons, during milk sampling, which in turn results in unrepresentative somatic cell counting, a method is proposed for obtaining a uniform distribution of somatic cells and improving the counting accuracy based on a nine-cell grid microfluidic chip. Firstly, a simulation was performed to verify the uniformity of the somatic cell distribution within the chip observation cavities. Secondly, a nine-cell grid microfluidic chip was prepared and a negative-pressure injection system integrating staining and stirring was developed to ensure that the somatic cells were uniformly distributed and free from air contamination during the injection process. As well as the structure of the chip, a microscopic imaging system was developed, and the nine chip observation cavities were photographed. Finally, the somatic cells were counted and the uniformity of the somatic cell distribution was verified using image processing. The experimental results show that the standard deviation coefficient of the SCC in each group of nine images was less than 1.61%. The automatic counting accuracy of the system was between 97.07% and 99.47%. This research method lays the foundation for the detection and prevention of mastitis in cows.

Application of statistical process control for monitoring bulk tank milk somatic cell count of smallholder dairy farms

Background and Aim:

Consistency in producing raw milk with less variation in bulk tank milk somatic cell count (BMSCC) is important for dairy farmers as their profit is highly affected by it in the long run. Statistical process control (SPC) is widely used for monitoring and detecting variations in an industrial process. Published reports on the application of the SPC method to smallholder farm data are very limited. Thus, the purpose of this study was to assess the capability of the SPC method for monitoring the variation of BMSCC levels in milk samples collected from smallholder dairy farms.

Materials and Methods:

Bulk tank milk samples (n=1302) from 31 farms were collected 3 times/month for 14 consecutive months. The samples were analyzed to determine the BMSCC levels. The SPC charts, including the individual chart (I-chart) and the moving range chart (MR-chart), were created to determine the BMSCC variations, out of control points, and process signals for each farm every month. The interpretation of the SPC charts was reported to dairy cooperative authorities and veterinarians.

Results:

Based on a set of BMSCC values as well as their variance from SPC charts, a series of BMSCC data could be classified into different scenarios, including farms with high BMSCC values but with low variations or farms with low BMSCC values and variations. Out of control points and signals or alarms corresponding to the SPC rules, such as trend and shift signals, were observed in some of the selected farms. The information from SPC charts was used by authorities and veterinarians to communicate with dairy farmers to monitor and control BMSCC for each farm.

Conclusion:

This study showed that the SPC method can be used to monitor the variation of BMSCC in milk sampled from smallholder farms. Moreover, information obtained from the SPC charts can serve as a guideline for dairy farmers, dairy cooperative boards, and veterinarians to manage somatic cell counts in bulk tanks from smallholder dairy farms.

Measuring farmers' dynamic technical and udder health management inefficiencies: The case of Wisconsin dairy farms

This study measures the dynamic technical and udder health management inefficiencies of a sample of Wisconsin dairy farms. Udder health management inefficiency is defined as a farmer's failure to achieve lower levels of milk somatic cell counts compared with those of the best-practice farmers within the sample. The study proposes the treatment of somatic cell count as an undesirable output. We measured inefficiency using a dynamic directional distance function that accounts simultaneously for the expansion of desirable outputs and investments in capital assets, and contraction of undesirable output and variable inputs. In a second step, a bootstrap truncated regression was used to analyze factors that cause differences in dynamic technical and udder health management inefficiencies. Results showed that the sample farmers had considerably higher udder health management inefficiency scores than technical inefficiency scores. The results of the second-stage analysis indicated that technical inefficiency was influenced by summer precipitation and farmers' financial characteristics, and was regionally heterogeneous. Udder health management inefficiency was affected by summer temperature and nonfarm income. By ranking farms in this study in terms of technical and udder health management inefficiency, we allowed inefficient farms to compare their performance with that of their efficient peers, and thus identify targets for production and udder health management improvement efforts. Finally, although our study focused on farmers' performances with respect to udder health management, the proposed modeling framework can be applied to the management of other animal diseases and welfare conditions.

Identification of the association between FABP4 gene polymorphisms and milk production traits in Sfakia sheep

The aim of this study was to estimate the effect of variation in the fatty acid binding protein 4 gene (FABP4) on milk production traits in Greek Sfakia sheep. Polymerase chain reaction – single-stranded conformational polymorphism (PCR-SSCP) analysis was used to genotype a total of 374 Sfakia ewes for two regions of FABP4 located around exon 2–intron 2 (Region 1) and exon 3–intron 3 (Region 2). Each month, for a period of 6 months, milk samples were collected from the ewes to measure total milk yield, fat content, protein content, lactose content, non-fat solid content, pH, and somatic cell count (SCC). A general linear model was used to test the association between the variation observed in FABP4 and milk production traits. Four gene variants (A1–A4) were found in Region 1 and two variants (C1–C2) were found in Region 2. In the first region, the FABP4 genotype significantly affected (P<0.05) non-fat solid levels, fat content, and SCC. The presence of the A2 variant was significantly associated (P<0.05) with decreased SCC, while the presence of A4 was significantly associated with decreased milk yield (P<0.01), increased non-fat solid content (P<0.05), decreased fat content (P<0.01), increased lactose content (P<0.05), and increased pH (P<0.05). In the second region, FABP4 genotype had an effect (P<0.05) on protein content and the presence of the C2 variant was associated (P<0.05) with increased protein content, decreased SCC, and lower pH. The results suggest an association between variation in ovine FABP4 and milk production traits in Greek Sfakia sheep. Nevertheless, further analyses in independent sheep populations of increased size will strengthen these findings.

Milk losses associated with somatic cell counts by parity and stage of lactation

The reduction of milk production caused by subclinical mastitis in dairy cows was evaluated through the regression of test-day milk yield on log-transformed somatic cell counts (LnSCC). Official test-day records (n = 1,688,054) of Holstein cows (n = 87,695) were obtained from 719 herds from January 2010 to December 2015. Editing was performed to ensure both reliability and consistency for the statistical analysis, and the final data set comprised 232,937 test-day records from 31,692 Holstein cows in 243 herds. A segmented regression was fitted to estimate the cutoff point in the LnSCC scale where milk yield started to be affected by mastitis. The statistical model used to explain daily milk yield included the effect of herd as a random effect and days in milk and LnSCC as fixed effects regressions, and analyses were performed by parity and stage of lactation. The cutoff point where milk yield starts to be affected by changes in LnSCC was estimated to be around 2.52 (the average of all estimates of approximately 12,400 cells/mL) for Holsteins cows from Brazilian herds. For first-lactation cows, milk losses per unit increase of LnSCC had estimates around 0.68 kg/d in the beginning of the lactation [5 to 19 d in milk (DIM)], 0.55 kg/d in mid-lactation (110 to 124 DIM), and 0.97 kg/d at the end of the lactation (289 to 304 DIM). For second-lactation cows, milk losses per unit increase of LnSCC had estimates around 1.47 kg/d in the beginning of the lactation (5 to 19 DIM), 1.09 kg/d in mid-lactation (110 to 124 DIM), and 2.45 kg/d at the end of the lactation (289 to 304 DIM). For third-lactation cows, milk losses per unit increase of LnSCC had estimates around 2.22 kg/d in the beginning of the lactation (5 to 19 DIM), 1.13 kg/d in mid-lactation (140 to 154 DIM), and 2.65 kg/d at the end of the lactation (289 to 304 DIM). Daily milk losses caused by increased LnSCC were dependent on parity and stage of lactation, and these factors should be considered when estimating losses associated with subclinical mastitis.