Describing temporal variation in reticuloruminal pH using continuous monitoring data

M. J. Denwood*, J. L. Kleen, D. B. Jensen, N. N. Jonsson

*Corresponding author for this work
    11 Citations (Scopus)

    Abstract

    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.

    Original languageEnglish
    JournalJournal of Dairy Science
    Volume101
    Issue number1
    Pages (from-to)233-245
    Number of pages13
    ISSN0022-0302
    DOIs
    Publication statusPublished - 2018

    Keywords

    • acidosis
    • remote sensing data
    • reticuloruminal pH
    • statistical model

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