Abstract
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.
Highlights
From a physiological standpoint, nutrition for IONE M.P
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
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
Summary
Nutrition for IONE M.P. HAYGERT-VELHO et al.2018). The animals have daily nutritional requirements that need to be attended (Fox et al 2004, Lanzas et al 2007, Tylutki et al 2008) to avoid a reduction in consumption and production, as well as changes in milk composition (Fagan et al 2010, Ganche et al 2014, Hristov et al 2005). It is accepted that many factors influence the production and composition of milk, and a systematic view of the production chain is necessary from agricultural science professionals, as unilateral models do not correctly represent the dynamics of dairy production. According to Mele et al (2016), multivariate factor analysis is an important tool in the study of the influence of different production environments and to produce milk and dairy products according to the needs and/or desires of consumers. An example is cluster analysis, which allows grouping and discriminating between groups, where the Euclidean distances, a measure of similarity, define that the means of nearer observations are in the same group, while the most distant are in separate groups (Lebart et al 2000)
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