这项当前的研究解决了关于季节影响的知识差距,月,和THI对牛奶产量的影响,composition,体细胞计数(SCC),以及伊朗东北部地区奶牛场的细菌总数(TBC)。为此,随机选择十个奶牛群,并获得每日牛奶产量记录。在运送到乳制品加工设施后,系统地从个体牛群中收集牛奶样品,用于后续分析。包括脂肪,蛋白质,固体非脂肪(SNF),pH值,SCC,和TBC。季节的影响,月,和THI对牛奶产量的影响,composition,SCC,和TBC使用方差分析进行评估。为了解释这些影响,混合效应模型采用受限最大似然方法,将月份和THI视为固定因素。我们的调查揭示了关键牛奶参数和季节性之间值得注意的相关性,每月,和这些变化。冬季产奶量最高,脂肪,蛋白质,SNF,和pH(p<0.01),而SCC和TBC均在冬季达到最低值(p<0.01)。牛奶产量的最高值,脂肪,1月份记录pH值(p<0.01),而在3月观察到最高的蛋白质和SNF水平(p<0.01)。12月是最低的SCC和TBC值(p<0.01)。在整个光谱中,从-3.6到37.7,明显的趋势。二次回归模型占34.59%,21.33%,4.78%,20.22%,1.34%,15.42%,和13.16%的产奶量方差,脂肪,蛋白质,SNF,pH值,SCC,TBC,分别。总之,我们的发现强调了THI对牛奶生产的重大影响,composition,SCC,TBC,为乳制品管理策略提供有价值的见解。面对气候变化带来的持续挑战,这些结果为提高生产效率和坚持牛奶质量标准提供了重要指导。
This current
study addresses the knowledge gap regarding the influence of seasons, months, and THI on milk yield, composition, somatic cell counts (SCC), and total bacterial counts (TBC) of dairy farms in northeastern regions of Iran. For this purpose, ten dairy herds were randomly chosen, and daily milk production records were obtained. Milk samples were systematically collected from individual herds upon delivery to the dairy processing facility for subsequent analysis, including fat, protein, solids-not-fat (SNF), pH, SCC, and TBC. The effects of seasons, months, and THI on milk yield, composition, SCC, and TBC were assessed using an analysis of variance. To account for these effects, a mixed-effects model was utilized with a restricted maximum likelihood approach, treating month and THI as fixed factors. Our investigation revealed noteworthy correlations between key milk parameters and seasonal, monthly, and THI variations. Winter showed the highest milk yield, fat, protein, SNF, and pH (p < 0.01), whereas both SCC and TBC reached their lowest values in winter (p < 0.01). The highest values for milk yield, fat, and pH were recorded in January (p < 0.01), while the highest protein and SNF levels were observed in March (p < 0.01). December marked the lowest SCC and TBC values (p < 0.01). Across the THI spectrum, spanning from -3.6 to 37.7, distinct trends were evident. Quadratic regression models accounted for 34.59%, 21.33%, 4.78%, 20.22%, 1.34%, 15.42%, and 13.16% of the variance in milk yield, fat, protein, SNF, pH, SCC, and TBC, respectively. In conclusion, our findings underscore the significant impact of THI on milk production, composition, SCC, and TBC, offering valuable insights for dairy management strategies. In the face of persistent challenges posed by climate change, these results provide crucial guidance for enhancing production efficiency and upholding milk quality standards.