Bulk tank somatic cell count and associated microbial quality of milk from selected dairy cattle herds in Oyo State, Nigeria

Factors affecting the microbiological quality and contamination of farm bulk milk by Staphylococcus aureus in dairy farms in Asella, Ethiopia

BACKGROUND

The determination of the microbiological quality and safety of raw milk and the associated influencing factors at the farm level is very critical given that the quality or safety of subsequent products that are further produced depends on this. Therefore, this study aimed to determine the microbiological quality and safety of bulk milk and identify associated risk factors, and assess the presence/absence of S. aureus in bulk milk with potential contaminating sources in dairy farms in Asella, Ethiopia.

RESULTS

The geometric means of bacterial counts in farm bulk milk were 5.25 log cfu/ml, 3.1 log cfu/ml and 2.97 log cfu/ml for total bacterial count (TBC), coliform count (CC) and coagulase-positive staphylococci count (CPS), respectively. Of the 50 dairy farms, 66, 88, and 32% had TBC, CC and CPS counts, respectively, that exceeded the standard international limits for raw cow's milk intended for direct human consumption. TBC tended to increase as CC increased in bulk milk (r = 0.5). In the final regression model, increased TBC, CC and the contamination of farm bulk milk by S. aureus were significantly associated with dirty barns, dirty cows and soiled udder and teats. TBC was higher during the rainy season than during the dry season. The reported practice of washing teats with warm water significantly decreased CC and CPS. The occurrence of S. aureus was significantly (p < 0.05) higher in bulk farm milk (42%) than in pooled udder milk (37.3%), teat swabs (22.5%), milkers' hand swabs (18%), bulking bucket swabs (16.7%), milking container swabs (14%), and water for cleaning of udder and milkers' hands (10%). The questionnaire survey result showed widespred raw milk consumption habits, low level of training and poor hygienic milking practices.

CONCLUSIONS

This study revealed low-quality bulk farm milk with high bacterial counts and a high occurrence of S. aureus. This indicates the potential food safety risks due to consumption of raw milk or its products. This study suggests awareness creation to dairy farmers and the public on hygienic milk production and heat treatment of milk before consumption.

Microbial Properties of Raw Milk throughout the Year and Their Relationships to Quality Parameters

Raw milk microbiota is complex and influenced by many factors that facilitate the introduction of undesirable microorganisms. Milk microbiota is closely related to the safety and quality of dairy products, and it is therefore critical to characterize the variation in the microbial composition of raw milk. In this cross-sectional study, the variation in raw milk microbiota throughout the year (n = 142) from three farms in China was analyzed using 16S rRNA amplicon sequencing, including α and β diversity, microbial composition, and the relationship between microbiota and milk quality parameters. This aimed to characterize the contamination risk of raw milk throughout the year and the changes in quality parameters caused by contamination. Collection month had a significant effect on microbial composition; microbial diversity was higher in raw milk collected in May and June, while milk collected in October and December had the lowest microbial diversity. Microbiota composition differed significantly between milk collected in January−June, July−August, and September−December (p < 0.05). Bacterial communities represented in raw milk at the phylum level mainly included Proteobacteria, Firmicutes and Bacteroidota; Pseudomonas, Acinetobacter, Streptococcus and Lactobacillus were the most common genera. Redundancy analysis (RDA) found strong correlations between microbial distribution and titratable acidity (TA), fat, and protein. Many genera were significantly correlated with TA, for example Acinetobacter (R = 0.426), Enhydrobacter (R = 0.309), Chryseobacterium (R = 0.352), Lactobacillus (R = −0.326), norank_o__DTU014 (R = −0.697), norank_f__SC-I-84 (R = −0.678), and Subgroup_10 (R = −0.721). Additionally, norank_f__ Muribaculaceae was moderately negatively correlated with fat (R = −0.476) and protein (R = −0.513). These findings provide new information on the ecology of raw milk microbiota at the farm level and contribute to the understanding of the variation in raw milk microbiota in China.

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.