Relative reticulo-rumen pH indicators for subacute ruminal acidosis detection in dairy cows

Abstract

Subacute ruminal acidosis (SARA) is usually characterized by abnormal and intermittent drops in rumen pH. Nevertheless, high individual animal variability in rumen pH and the difference in measurement methods for pH data acquisition decrease the sensitivity and accuracy of pH indicators for detecting SARA in ruminants. The aim of this study was to refine rumen pH indicators in long-term SARA based on individual dairy cow reticulo-rumen pH kinetics. Animal performances and rumen parameters were studied weekly in order to validate SARA syndrome and rumen pH was continuously measured using reticulo-rumen sensors. In total, 11 primiparous dairy cows were consecutively fed two different diets for 12 successive weeks: a control diet as low-starch diet (LSD; 13% starch for 4 weeks in period 1), an acidotic diet as high-starch diet (HSD; 32% starch for 4 weeks in period 2), and again the LSD diet (3 weeks in period 3). There was a 1-week dietary transition between LSD and HSD. Commonly used absolute SARA pH indicators such as daily average, area under the curve (AUC) and time spent below pH<5.8 and pH<6 were processed from absolute (raw) daily kinetics. Then signal processing was applied to raw pH values in order to calculate relative pH indicators by filtering and normalizing data to remove inter-individual variability, sensor drift and sensor noise. Normalized AUC, times spent below NpH<-0.3 and NpH<-0.5, NpH range and NpH standard deviation were calculated. Those relative pH indicators were compared with commonly used pH indicators to assess their ability to detect SARA. This syndrome induced by HSD was confirmed by consistent expected changes in milk quality, dry matter intake and acetate : propionate ratio in the rumen, whereas the ruminal concentration of lipopolysaccharide was increased. Commonly used pH SARA indicators were not able to discriminate SARA syndrome due to high animal variability and sensor drift and noise, whereas relative pH indicators developed in this study appeared more relevant for SARA detection as assessed by receiver operating characteristic tests. This work shows that absolute pH kinetics should be corrected for drift, noise and animal variability to produce relative pH indicators that are more robust for SARA detection. These relative pH indicators could be more relevant for identifying affected animals in a herd and also for comparing SARA risk among studies.

Combinations of non-invasive indicators to detect dairy cows submitted to high-starch-diet challenge

“…At the start of the experiment, dairy cows had an average body weight of 658±37 kg (mean±SD), were 135±7 days in milk and had a milk yield of 27.5±2.3 kg/day. Two different total mixed diets (TMDs) were distributed over 12 weeks of the longitudinal experiment as described precisely by Villot et al (2018). Briefly, cows received a low-starch diet (LSD) as a control containing 13% starch (on a DM basis) for a 4-week period (P1).…”

“…Due to the lack of specific clinical signs to identify cows under SARA conditions, we performed a pilot study using a nutritional model where dairy cows were challenged with a HSD known to induce ruminal dysfunction at risk of SARA (Villot et al, 2018). Our aim was to propose an original approach by developing combination of non-invasive indicators based on multi-parametric models to detect dairy cows at risk of digestive disorders.…”

Machine learning to detect behavioural anomalies in dairy cows under subacute ruminal acidosis

“…Table 1 summarises the characteristics of the two diets. This treatment was designed to induce a SARA, which is characterised by a low pH in the rumen (Villot et al, 2018).…”

“…We monitored the ruminal pH of each cow every 15 min during the entire experiment using a specific pH sensor in the cow rumen (Farm bolus; eCow, Exeter, UK). Following the method proposed by Villot et al (2018), we normalised the ruminal pH values of each cow to take into account inter-individual variability, sensor drift and sensor noise. We considered that a cow was under SARA when the time she spent with a normalised ruminal pH (NpH) decreased by at least 0.3 for more than 50 min/d and the daily standard deviation in NpH was above 0.2 or the daily NpH range was above 0.8.…”

“…To extract the abnormal cow*days due to SARA, we kept the cow*days on which cows were detected under SARA due to abnormal ruminal pH and the following cow*day, based on the fact that ruminal pH correlates with behavioural symptoms of SARA, i.e. an animal is sick when pH goes down and recovers quickly when pH resumes normal values (Villot et al, 2018). As this study is focused on SARA, we did not use the remaining cow*days, which were days when a disturbance occurred in the barn or days considered abnormal due to another cow disorder (disease or oestrus).…”