Describing temporal variation in reticuloruminal pH using continuous monitoring data
Describing temporal variation in reticuloruminal pH using continuous monitoring data
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DOI：
10.3168/jds.2017-12828
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发表时间：
2018-01-01
影响因子：
3.5
通讯作者：
Jonsson, N. N.
中科院分区：
文献类型：
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作者：
Denwood, M. J.;Kleen, J. L.;Jonsson, N. N.
网状腔pH值与奶牛的亚临床疾病有关，因此人们对确定低于给定阈值的pH值非常感兴趣。相对较新的pH值连续监测数据的可用性为描述pH随时间的正常模式提供了新的机会，并使用比简单阈值更敏感和更具体的方法将这些模式与异常模式区分开来。我们对来自13个农场的93只动物的连续监测数据进行了一系列统计模型的拟合，以表征动物内部和动物之间的正常变化。我们使用数据的一个子集将正常模式的偏差与单一畜群中24头奶牛的生产率联系起来。我们的研究结果显示，尽管同一农场的动物往往表现出更一致的模式，但动物之间的pH值特征存在很大差异。有强有力的证据表明，所有动物的昼夜变化都是可预测的，并且高达70%的观察到的pH变化可以用一个简单的统计模型来解释。对于24只可获得生产信息的动物，生产力（通过产奶量和干物质摄入量来衡量）与生产力观察前2天的预期pH日模式偏差之间也存在很强的关联。相比之下，生产力与低于阈值pH值的观测值之间没有关联。我们得出结论，统计模型可以用来解释观测到的pH值变异性的很大一部分，未来使用连续监测的pH值数据的工作应该关注与可预测模式的偏差，而不是低于任意pH值的观测频率。
Reticuloruminal pH has been linked to subclinical disease in dairy cattle, leading to considerable interest in identifying pH observations below a given threshold. The relatively recent availability of continuously monitored data from pH boluses gives new opportunities for characterizing the normal patterns of pH over time and distinguishing these from abnormal patterns using more sensitive and specific methods than simple thresholds. We fitted a series of statistical models to continuously monitored data from 93 animals on 13 farms to characterize normal variation within and between animals. We used a subset of the data to relate deviations from the normal pattern to the productivity of 24 dairy cows from a single herd. Our findings show substantial variation in pH characteristics between animals, although animals within the same farm tended to show more consistent patterns. There was strong evidence for a predictable diurnal variation in all animals, and up to 70% of the observed variation in pH could be explained using a simple statistical model. For the 24 animals with available production information, there was also a strong association between productivity (as measured by both milk yield and dry matter intake) and deviations from the expected diurnal pattern of pH 2 d before the productivity observation. In contrast, there was no association between productivity and the occurrence of observations below a threshold pH. We conclude that statistical models can be used to account for a substantial proportion of the observed variability in pH and that future work with continuously monitored pH data should focus on deviations from a predictable pattern rather than the frequency of observations below an arbitrary pH threshold.