A principal component analysis required in technical assistance guidance for chilled raw milk producers

ABSTRACT. The purpose of the present study was to evaluate the principal component analysis (PCA) to guide technical assistance regarding several dairy farms’ issues, which includes improving microbiological quality and physical-chemical composition of raw refrigerated milk. Data of monthly analysis of fat, protein, lactose, dry defatted stratum, somatic cell count, total bacterial count, milk temperature of 8,101 samples of milk from expansion tanks and production of 78 farms located in the northern region of Minas Gerais, Brazil were processed. Descriptive statistical measures and Pearson correlation coefficient were estimated involving all evaluated traits during the dry and rainy seasons. In addition, multivariate analyses were performed using PCA. The results showed that two farm sites were negatively related to milk quality in both seasons. One farm stood out positively, being able to be used as a herd management model to drive technical assistance actions. Thus, PCA is efficient in simplifying large amounts of data, allowing simpler and faster technical herd management interpretation.摘要。本研究旨在评估主成分分析（Principal Component Analysis，PCA）在指导多个奶牛场技术帮扶工作中的应用效果，帮扶内容涵盖提升冷藏生鲜乳的微生物质量与理化组成。本研究处理了巴西米纳斯吉拉斯州北部78个奶牛场的8101份储奶罐奶样与生产数据，相关检测指标包括每月测定的脂肪、蛋白质、乳糖、脱脂干物质、体细胞数、总细菌数及乳温。针对旱季与雨季的所有评估性状，研究人员计算了描述性统计量与皮尔逊相关系数，并采用PCA开展多变量分析。结果显示，有2个奶牛场在两个季节均与乳品质呈负相关；另有1个奶牛场表现优异，可作为牛群管理范本以推动技术帮扶行动落地。由此可见，PCA可有效简化海量数据，使牛群管理的技术解读更为简便快捷。