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英国和威尔士大型奶牛场体细胞计数动态研究。

Somatic cell count dynamics in a large sample of dairy herds in England and Wales.

机构信息

School of Veterinary Medicine and Science, University of Nottingham, Sutton Bonington Campus, Sutton Bonington, LE12 5RD, UK.

出版信息

Prev Vet Med. 2010 Aug 1;96(1-2):56-64. doi: 10.1016/j.prevetmed.2010.05.005.

Abstract

An essential reason to record and evaluate patterns of cow somatic cell count (SCC) within a dairy herd is to help in making clinical decisions on the control of mastitis. An understanding of when new infections occur and how patterns of infection influence herd bulk milk somatic cell count (BMSCC) are critical when implementing mastitis control because it enables advisors to target specific problem areas. The objective of this research was to evaluate individual cow SCC patterns in terms of their contribution to BMSCC. Data collected in 2128 herds from England and Wales between 2004 and 2006 were used. Cows were categorised as having a low, medium or high SCC based on thresholds of 100,000 cells/mL and 200,000 cells/mL. Movements between these categories in consecutive months, before or after 30 days in milk, in primiparous (heifers) and multiparous cows (cows) were used to predict BMSCC. From these categories, new variables representing different SCC patterns, were calculated and included in different models: the medium SCC category was grouped with either the low or the high category, and the denominator was either the total number of cows recorded during the herd-year or the number of cows eligible for a particular transition. Model fitting and predictions were carried out in a Bayesian framework. A random sample of 1500 herds was used for parameter estimation and the remaining 628 herds for model validation. Heifers were more likely to remain at, or to move to, a low SCC than cows. A transition threshold of 100,000 cells/mL for heifers resulted in a poorer model fit and predictive ability than a threshold of 200,000 cells/mL. A model using a single threshold of 200,000 cells/mL regardless of parity was the best to predict BMSCC. The sensitivity and specificity of this final model to correctly predict a BMSCC > 200, 000 cells/mL in the validation dataset were 86.5% and 86.8%, respectively. Important SCC patterns that influenced BMSCC were cows and heifers staying above 200,000 cells/mL for two consecutive recordings during lactation, cows moving from below to above 200,000 cells/mL across the dry period, cows remaining above 200,000 cells/mL across the dry period and heifers calving with an SCC above 200,000 cells/mL in the first month of lactation. The variation between herds in SCC transitions was evaluated and it was concluded that the performance of the top 10% of herds would be useful to provide benchmarks to evaluate dairy herd mastitis.

摘要

奶牛体细胞计数(SCC)模式的记录和评估是奶牛场临床乳腺炎控制决策的重要依据。了解新感染何时发生以及感染模式如何影响牛群的牛奶体细胞计数(BMSCC),对于实施乳腺炎控制至关重要,因为它使顾问能够针对特定的问题区域。本研究的目的是评估个体奶牛 SCC 模式对 BMSCC 的贡献。本研究使用了 2004 年至 2006 年期间在英格兰和威尔士的 2128 个牛群中收集的数据。根据 100,000 个细胞/ml 和 200,000 个细胞/ml 的阈值,将奶牛分为低、中或高 SCC 组。在产奶牛(奶牛)和初产牛(小母牛)泌乳前或泌乳后 30 天内,连续几个月的 SCC 变化情况,用于预测 BMSCC。从这些类别中,计算出不同 SCC 模式的新变量,并将其包含在不同的模型中:将中间 SCC 类别与低或高类别组合,并将分母设置为牛群年度记录的奶牛总数或符合特定转变的奶牛数量。在贝叶斯框架中进行模型拟合和预测。使用 1500 个牛群的随机样本进行参数估计,其余 628 个牛群用于模型验证。小母牛更有可能保持低 SCC 或转移到低 SCC,而奶牛则更有可能保持或转移到高 SCC。小母牛的 100,000 个细胞/ml 转换阈值导致模型拟合和预测能力不如 200,000 个细胞/ml 阈值。使用单一 200,000 个细胞/ml 阈值的模型,无论胎次如何,是预测 BMSCC 的最佳模型。该最终模型在验证数据集中正确预测 BMSCC > 200,000 个细胞/ml 的敏感性和特异性分别为 86.5%和 86.8%。影响 BMSCC 的重要 SCC 模式是奶牛和小母牛在泌乳期间连续两次记录中保持在 200,000 个细胞/ml 以上,奶牛在干奶期从低于 200,000 个细胞/ml 到高于 200,000 个细胞/ml,奶牛在干奶期保持高于 200,000 个细胞/ml,小母牛在泌乳的第一个月中 SCC 高于 200,000 个细胞/ml。评估了牛群 SCC 转变之间的差异,并得出结论,前 10%的牛群的表现将有助于提供基准,以评估奶牛乳腺炎。

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