Describing temporal variation in reticuloruminal pH using continuous monitoring data

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.

Contrasting effects of high-starch and high-sugar diets on ruminal function in cattle

“…To reduce the noise caused by extraneous movements of cattle, a night-only data-frame was established using observations recorded from 21 : 00 until 07 : 00. The pH data were analysed using the method described in Denwood et al (2018) to generate a list of predicted values for each animal according to a generalized additive model fitted with a single sine-wave. For each predicted value, the residual was calculated, and the absolute value of the residual was used for the subsequent analysis.…”

Review: Rumen sensors: data and interpretation for key rumen metabolic processes

“…Moreover, to evaluate subsequent periods in which acidosis might occur, absence of interaction between initial pH level and individual response to a subsequent change in intake or diet composition has to be assumed. Similar to the individual animal approach of Villot et al (2018), Denwood et al (2018) distinguished long-term temporal pH variation by using a generalized additive model, reflecting gradual pH changes due to diet or pH sensor drift, and short-term cyclical pH variation by using a sine wave with daily frequency and a sine wave with milking frequency. Upon application of this statistical approach, they showed that deviations from a predictable daily pH rhythm were associated with decreased DMI and milk production of dairy cattle.…”

“…Such significant drift of indwelling rumen sensors that cannot be retrieved and recalibrated is a major obstacle to their use. Long-term drift may be quantified by calculating moving averages (Villot et al, 2018) or using a generalized additive model (Denwood et al, 2018) for individual animals. However, drift established in this way is confounded with any long-term, slow change in ruminal pH due to gentle, consistent variation in voluntary feed intake, feed intake behaviour or gentle dietary changes.…”

Postmortem observations on rumen wall histology and gene expression and ruminal and caecal content of beef cattle fattened on barley-based rations

“…The condition also occurs in beef cattle (Nagaraja & Titgemeyer, 2007). The diagnosis and management of SARA is difficult, as there is little consensus on diagnostic criteria and it is largely a chronic predisposition to other specific diseases, rather than being a single disease entity itself (Kleen et al, 2009, Denwood et al, 2018.…”

“…From studies with dairy cattle, it is known that some farms are more susceptible to acidosis than others, and that within herds some animals are more prone to acidosis than others (Garrett, 1996;Kleen et al, 2009;Morgante et al, 2007;Penner et al, 2009;Denwood et al, 2018). Many of the experimentally determined interactions among feeding methods, feed and animal behaviour in feedlots have been reviewed elsewhere (e.g.…”