Abstract
This study was realized to evaluate the monthly production, composition and quality of milk (total and defatted dry extract, lactose, fat and protein) and their relation to somatic cell count (SCC) and total bacterial count (TBC) using multivariate statistical analyses. The data are from a dairy farm for the period of two years (from January 2015 to December 2016). The SCC and TBC variables were transformed to somatic cell score (SCS) and log10 (LogTBC). Factor analysis, discriminant analysis and cluster analysis were used. Through factor analysis, it was found two factors that together explained 69.5% of the total data variation. The first factor represented the inverse relationship between lactose versus fat and protein content, while the second factor represented the inverse relationship among monthly milk yield versus SCS and LogTBC. The discriminant analysis identified that lactose and protein contents and SCS were the variables that had the greatest participation in the separation of the groups formed by the cluster analysis. The groups differed mainly by the monthly production of milk, composition and SCS. Finally, there are important multivariate relations between the variables milk production, composition and quality.
Highlights
The milk composition enables to monitor the feeding of the dairy herd, aiming to correct failures that might compromise the production of lactose, fat and protein (Dias et al, 2017; Fagan, Jobim, Calixto Júnior, Silva, & Santos, 2010)
It enables the monitoring of total bacterial counts (TBC) and somatic cell counts (SCC), which are indicators that can be used to evaluate the hygienic management during milking and the health of the mammary gland of lactating cows (Busanello et al, 2017a; Paixão et al, 2017)
In the factorial analysis of the monthly production, composition, SCC and TBC of the milk samples taken from the bulk tank in the evaluated smallholder dairy farm, 69.5% of the total variance was explained by the first two factors, with a KMO of 52.2 (Table 3)
Summary
The milk composition enables to monitor the feeding of the dairy herd, aiming to correct failures that might compromise the production of lactose, fat and protein (Dias et al, 2017; Fagan, Jobim, Calixto Júnior, Silva, & Santos, 2010). The hygiene practices adopted during milking, besides contributing with the maintenance of the microbiological quality of the milk, collaborates in the health of the mammary gland In this way, the discharge of the first three jets at the beginning of milking, which is directly associated with the SCC and TBC, helps in the prevention and identification of the occurrence of mastitis (Belage, Dufour, Bauman, Jones-Bitton, & Kelton, 2017; Eckstein et al, 2016). Considering the factors related to milk production and composition, which include the physiological status of lactating cows, feeding, hygiene conditions at the time of milking and the health of the mammary gland, the use of multivariate analysis is appropriate. The reason for that is because both factorial and cluster analysis allow to evaluate better the interactions between the various factors involved in milk production and composition, when compared to conventional (univariate) analysis that can measure the effect of only one or two factors singly (Macciotta, Cecchinato, Mele, & Bittante, 2012)
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