This study gives an overview of the performance and accuracy of devices used for the fast measurement of β-hydroxybutyrate (BHBA) in blood for the on-farm indication of subclinical ketosis. Data were collected on ten dairy farms. In each farm, blood samples were taken from ten cows on four test days (2, 4, 9 and 11), resulting in 400 samples. The reference method was the BHBA concentration in blood serum (BHBALAB). Four different devices that measure BHBA in whole blood were tested. The thresholds applied for identifying subclinical ketosis were ≥1.0, ≥1.2 and ≥1.4 mmol/L in blood serum. The BHBALAB was assigned in three classes: low—≤0.9 mmol/L; high—>0.9 mmol/L; and total—all values unclassified. Due to initial negative effects on the health and performance of cows with BHBA levels ≥0.9 mmol/L, this cut-off was chosen. The Passing–Bablok regression revealed different constant as well as absolute biases for each device in the aforementioned classes. The area under the receiver operating characteristics curve indicated highly accurate results, with 94–97% accuracy levels. As an overall conclusion, the performance of the devices was good and supports their use by farmers for the detection of subclinical ketotic cows in their herds.

The aim of this work was to develop an innovative multivariate plausibility assessment (MPA) algorithm in order to differentiate between ‘physiologically normal’, ‘physiologically extreme’ and ‘implausible’ observations in simultaneously recorded data. The underlying concept is based on the fact that different measurable parameters are often physiologically linked. If physiologically extreme observations occur due to disease, incident or hormonal cycles, usually more than one measurable trait is affected. In contrast, extreme values of a single trait are most likely implausible if all other traits show values in a normal range. For demonstration purposes, the MPA was applied on a time series data set which was collected on 100 cows in 10 commercial dairy farms. Continuous measurements comprised climate data, intra-reticular pH and temperature, jaw movement and locomotion behavior. Non-continuous measurements included milk yield, milk components, milk mid-infrared spectra and blood parameters. After the application of the MPA, in particular the pH data showed the most implausible observations with approximately 5% of the measured values. The other traits showed implausible values up to 2.5%. The MPA showed the ability to improve the data quality for downstream analyses by detecting implausible observations and to discover physiologically extreme conditions even within complex data structures. At this stage, the MPA is not a fully developed and validated management tool, but rather corresponds to a basic concept for future works, which can be extended and modified as required.

Subacute ruminal acidosis (SARA) represents one of the most important nutritional disorders in high-producing dairy farms. The determination of ruminal pH is a key factor for the diagnosis of SARA. However, measuring ruminal pH in the field is not practicable. Therefore, indicators that reflect the ruminal pH are in demand. The main objective of this study was to examine the relationship between the milk fat-to-protein ratio (FPR) and ruminal pH parameters (daily mean pH, daily time with pH < 5.8, and pH range) on a meta-analytical level including 47 studies with 189 treatment means. Besides the FPR, it was examined how a stepwise extension of further indicators (milk yield, rumination time, and dietary starch and structure effectiveness) can improve the prediction of ruminal pH parameters. Significant associations between milk FPR and ruminal pH parameters were found. The inclusion of further on-farm indicators improved the prediction of daily mean ruminal pH up to Rm2 = 0.46 and time with pH < 5.8 up to Rm2=0.58. Still, a considerable part of variability was explained by the random factor study. Additional information (dietary PUFA content) may improve the models in further investigations.

A routine monitoring for SARA on the individual level could support the minimization of economic losses and the ensuring of animal welfare in dairy cows. The objectives of this study were (1) to develop a SARA risk score (SRS) by combining information from different data acquisition systems to generate an integrative indicator trait, (2) the investigation of associations of the SRS with feed analysis data, blood characteristics, performance data, and milk composition, including the fatty acid (FA) profile, (3) the development of a milk mid-infrared (MIR) spectra-based prediction equation for this novel reference trait SRS, and (4) its application to an external data set consisting of MIR data of test day records to investigate the association between the MIR-based predictions of the SRS and the milk FA profile. The primary data set, which was used for the objectives (1) to (3), consisted of data collected from 10 commercial farms with a total of 100 Holstein cows in early lactation. The data comprised barn climate parameters, pH and temperature logging from intrareticular measurement boluses, as well as jaw movement and locomotion behavior recordings of noseband-sensor halters and pedometers. Further sampling and data collection included feed samples, blood samples, milk performance, and milk samples, whereof the latter were used to get the milk MIR spectra and to estimate the main milk components, the milk FA profile, and the lactoferrin content. Because all measurements were characterized by different temporal resolutions, the data preparation consisted of an aggregation into values on a daily basis and merging it into one data set. For the development of the SRS, a total of 7 traits were selected, which were derived from measurements of pH and temperature in the reticulum, chewing behavior, and milk yield. After adjustment for fixed effects and standardization, these 7 traits were combined into the SRS using a linear combination and directional weights based on current knowledge derived from literature studies. The secondary data set was used for objective (4) and consisted of test day records of the entire herds, including performance data, milk MIR spectra and MIR-predicted FA. At farm level, it could be shown that diets with higher proportions of concentrated feed resulted in both lower daily mean pH and higher SRS values. On the individual level, an increased SRS could be associated with a modified FA profile (e.g., lower levels of short-and medium-chain FA, higher levels of C17:0, odd-and branched-chain FA). Furthermore, a milk MIR-based partial least squares regression model with a moderate predictability was established for the SRS. This work provides the basis for the development of routine SARA monitoring and demonstrates the high potential of milk composition-based assessment of the health status of lactating cows.

Subacute ruminal acidosis (SARA) is an important nutritional disorder affecting animal welfare and economy of milk production. Definitions rely on ruminal pH but due to limitations of its measurement, indicators reflecting low pH are highly desirable. The aim of this study was to investigate the relationship between reticular pH and 18 on‐farm indicators in milk, blood, faeces, urine and chewing behaviour in early lactating dairy cows. Ten farms were visited for 3 weeks and in total samples of 100 cows (10 per farm) were taken. The statistics and graphical visualization were performed using Pearson correlation and linear regression models on an animal individual level as well as with linear mixed models. Eight indicators (milk fat, fat‐to‐protein ratio, rumination time, feed intake time, rumination frequency, rumination boluses, lying time and faecal pH) were statistically significant associated with the daily animal individual reticular pH average. However, none of the models including the potential explanatory variables explained more than 5% of the pH variations. The study confirms the necessity of pH measurement to detect SARA risk animals in early lactation dairy cows.