van Schaik G, Green L E, Guzmán D, Esparza H, Tadich N
Instituto de Medicina Preventiva, Facultad de Ciencias Veterinarias, Universidad Austral de Chile, Casilla 567, Valdivia, Chile.
Prev Vet Med. 2005 Jan;67(1):1-17. doi: 10.1016/j.prevetmed.2004.10.002. Epub 2004 Dec 19.
We investigated the principal management factors that influenced bulk milk somatic cell count (BMSCC) and total bacterial count (TBC) of smallholder dairy farms in the 10th region of Chile. One hundred and fifty smallholder milk producers were selected randomly from 42 milk collection centres (MCCs). In April and May of 2002, all farms were visited and a detailed interview questionnaire on dairy-cow management related to milk quality was conducted. In addition, the BMSCC and TBC results from the previous 2 months' fortnightly tests were obtained from the MCCs. The mean BMSCC and TBC were used as the dependent variables in the analyses and were normalised by a natural-logarithm transformation (LN). All independent management variables were categorised into binary outcomes and present (=1) was compared with absent (=0). Biserial correlations were calculated between the LNBMSCC or LNTBC and the management factors of the smallholder farms. Management factors with correlations with P</=0.20 were entered into the linear multiple regression models and backward elimination was used to exclude non-significant (P>0.05) factors. A random MCC effect was included in the models to investigate the importance of clustering of herds within MCC. In the null model for mean LNTBC, the random effect of MCCs was highly significant. It was explained by: milk collected once a day or less compared with collection twice a day, not cleaning the bucket after milking mastitic cows versus cleaning the bucket and cooling milk in a vat of water versus not cooling milk or using ice or a bulk tank to cool milk. Other factors that increased the LNTBC were a waiting yard with a soil or gravel floor versus concrete, use of plastic buckets for milking instead of metal, not feeding California mastitis test (CMT)-positive milk to calves and cows of dual-purpose breed. The final model explained 35% of the variance. The model predicted that a herd that complied with all the management practices had a mean predicted TBC of 105 colony forming units (cfu)/ml, whereas a herd that did not comply with any of these management factors had a predicted TBC of 59 x 10(9)cfu/ml. The model of mean LNBMSCC explained 18% of the variance; the random effect of MCC was not significant. Management factors that decreased the mean LNBMSCC were: using the CMT for 1 year versus using the test for more than 1 year or not at all, absence of a concrete waiting yard, not filtering the milk or using filters other than a plastic sieve to filter the milk, milking cows with mastitis last, and sometimes or always examining the udder before milking. A herd that complied with all of these management factors had a BMSCC of approximately 46,166 cells/ml, whereas a herd that did not comply with any of the management practices above had a mean BMSCC of 2 x 10(6)cells/ml.
我们调查了影响智利第10大区小农户奶牛场原料奶体细胞计数(BMSCC)和总细菌数(TBC)的主要管理因素。从42个牛奶收集中心(MCC)中随机挑选了150个小农户牛奶生产商。2002年4月和5月,走访了所有农场,并就与牛奶质量相关的奶牛管理进行了详细的访谈问卷调查。此外,从MCC获取了前两个月每两周检测一次的BMSCC和TBC结果。分析中以BMSCC和TBC的均值作为因变量,并通过自然对数变换(LN)进行标准化。所有独立管理变量被分类为二元结果,将存在(=1)与不存在(=0)进行比较。计算了LN BMSCC或LN TBC与小农户农场管理因素之间的双列相关。与P≤0.20相关的管理因素被纳入线性多元回归模型,并采用向后消除法排除不显著(P>0.05)的因素。模型中纳入了随机的MCC效应,以研究MCC内畜群聚集的重要性。在平均LN TBC的空模型中,MCC的随机效应非常显著。其原因如下:与每天挤奶两次相比,每天挤奶一次或更少;挤完患乳腺炎奶牛后不清洗奶桶,而不是清洗奶桶并将牛奶在水桶中冷却,与不冷却牛奶或使用冰块或奶罐冷却牛奶相比。其他增加LN TBC的因素包括:有泥土或砾石地面而非混凝土地面的待挤区;使用塑料桶而非金属桶挤奶;不给犊牛和兼用品种奶牛喂加利福尼亚乳腺炎检测(CMT)呈阳性的牛奶。最终模型解释了35%的方差。该模型预测,符合所有管理措施的畜群预计平均TBC为105菌落形成单位(cfu)/毫升,而不符合任何这些管理因素的畜群预计TBC为59×10⁹cfu/毫升。平均LN BMSCC模型解释了18%的方差;MCC的随机效应不显著。降低平均LN BMSCC的管理因素包括:使用CMT检测1年,与使用该检测超过1年或根本不使用相比;没有混凝土待挤区;不过滤牛奶或使用除塑料筛以外的过滤器过滤牛奶;最后挤患乳腺炎的奶牛;以及有时或总是在挤奶前检查乳房。符合所有这些管理因素的畜群BMSCC约为46,166个细胞/毫升,而不符合上述任何管理措施的畜群平均BMSCC为2×10⁶个细胞/毫升。