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

On-farm evaluation of multiparametric models to predict subacute ruminal acidosis in dairy cows

This research aimed: (i) to evaluate on-farm (FARM data) multiparametric models developed under controlled experiment (INRAE data) and based on non-invasive indicators to detect subacute ruminal acidosis (SARA) in dairy cows. We also aimed to recover high discrimination capacity, if needed, by (ii) building new models with combined INRAE and FARM data; and (iii) enriching the models increasing from 2 to 5 indicators per model. For model enrichment, we focused on indicators determinable on-farm by quick and inexpensive routine analysis. Fifteen commercial dairy farms were selected to cover a wide range of SARA risk. In each farm, four Holstein early-lactating healthy primiparous cows were selected based on their last on-farm recording of milk yield and somatic cell count analysis. Cows were equipped with a reticulo-rumen pH sensor. The pH kinetics were analysed over a subsequent 7-day period. Relative pH indicators were used to classify cows with or without SARA. Milk, blood, faeces, and urine were collected for analysis of the indicators included in the models developed by Villot et al. (2020) on INRAE data that were externally evaluated using FARM data. Then, new models based on the same indicators were developed combining INRAE and FARM data to test whether a possible loss in performance was due to a limited validity domain of model by Villot et al (2020). Finally, the models developed combining INRAE and FARM data were adapted to the on-farm application and enriched by increasing indicators from 2 to 5 per model using linear discriminant analysis and leave-one-out cross-validation. The sensitivities (true-positive rate) in external evaluation on FARM data were substantially lower than those from cross-validation by Villot et al. (2020) (range: 0.1-0.75 vs 0.79-0.96, respectively), and the specificities (true-negative rate) showed a larger range with lower minimum values (range: 0.18-1.0 vs 0.62-0.97, respectively). The sensitivities of new models developed combining INRAE and FARM data ranged from 0.63 to 0.77. Models involving blood cholesterol, β-hydroxybutyrate, haptoglobin, milk and blood urea, and models involving milk fat/protein ratio, dietary starch proportion, and milk fatty acids had the highest performances, whereas models including sieved faecal residues and urine pH had the lowest. Enriching models to three indicators per model improved sensitivity and specificity, but the inclusion of more indicators was less or not effective. Larger field trials are required to validate our results and to increase variability and validity domain of models.

Relationship between reticular pH parameters and potential on-farm indicators in the early lactation of dairy 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.

Paraclinical Changes Occurring in Dairy Cows with Spontaneous Subacute Ruminal Acidosis under Field Conditions

This study was undertaken to investigate the changes in the blood and milk biochemical parameters found in naturally occurring and long-lasting spontaneous subacute ruminal acidosis (SARA), with the aim of identifying the patterns of paraclinical changes and providing valuable data for more accurately identifying SARA in cows under field conditions. The study was conducted on a dairy herd with a history of the occurrence of SARA-associated clinical signs. Twelve cows, between 20 and 150 days in milk, were randomly selected and subsequently subjected to venous blood, milk, and ruminal fluid collection. The mean pH value of the ruminal fluid was 5.56 ± 0.32, and 58% (7/12) of the tested cows were SARA positive (ruminal pH ≤ 5.5). The albumin, calcium, and phosphorus serum concentration values were significantly lower (p < 0.05) in the SARA group than in the group of healthy cows. Serum aspartate aminotransferase (AST) and glutamate dehydrogenase (GLDH) activity were significantly higher in the SARA cows (p < 0.05) than in the group of healthy cows. The mean values of milk fat, milk protein content, and milk fat-to-protein ratio were significantly lower (p < 0.05) in the tested cows of the SARA group than in the healthy group of cows. In conclusion, the results of the current study indicate that long-term SARA triggered by a high-concentrate diet is associated with clinically significant changes in both the blood composition (hypoalbuminemia, hypocalcemia, and increased serum AST and GLDH activity) and the milk composition (decreased fat and protein percentage and milk fat-to-protein ratio). Altogether, the obtained results provide a more reliable pattern of paraclinical changes and useful insights for detecting SARA in dairy cows under field conditions.

A Novel Approach for Monitoring the Volatile Metabolome in Biological Samples from Ruminants through Miniaturized Liquid-Liquid Extraction and Multiclass Gas Chromatography Analysis

A straightforward and versatile methodology for the extraction of volatile metabolites in biological samples from ruminants for gas chromatography analysis is proposed. The methodology was applied in the determination of multiclass metabolites (short-chain fatty acids, aldehydes, alcohols, ketones, esters, phenols, and sulfides) in different analytical matrices (rumen fluid, urine, and feces) collected from Holstein cows. The 24 multiclass volatile metabolites reported in the different biological samples and their respective concentrations were critically discussed in the context of digestive physiology. Most detected compounds are derived from the rumen and lower gut fermentation of carbohydrates, proteins, and lipids or their metabolism, being consistent with the prior state of the art. The proposed method also takes advantage of the already existing tools in animal nutrition laboratories, providing a novel methodological ground that can generate relevant bioanalytical information with a significant impact on ruminant's nutritional studies.

Revisiting the Relationships between Fat-to-Protein Ratio in Milk and Energy Balance in Dairy Cows of Different Parities, and at Different Stages of Lactation

Simple Summary

Data from 840 Holstein-Friesian cows (1321 lactations) were used to evaluate trends in fat-to-protein ratios in milk (FPR), and the use of FPR as an indicator of energy balance (EB). The fat-to-protein ratio was negatively related to EB, and this relationship became more negative with increased parity. Regression slopes describing linear relationships between FPR and EB differed over time, although trends were inconsistent. Similarly, ‘High’ FPR scores in milk (≥1.5) were consistently associated with a greater negative energy balance, milk yields, body weight loss, and plasma non-esterified fatty acid concentrations; however, their relationships with dry matter intake did not follow a clear trend. Although FPR can provide an indication of EB at a herd level, this analysis suggests that FPR cannot accurately predict the EB of individual cows.

Abstract

A statistical re-assessment of aggregated individual cow data was conducted to examine trends in fat-to-protein ratio in milk (FPR), and relationships between FPR and energy balance (EB, MJ of ME/day) in Holstein-Friesian dairy cows of different parities, and at different stages of lactation. The data were collected from 27 long-term production trials conducted between 1996 and 2016 at the Agri-Food and Biosciences Institute (AFBI) in Hillsborough, Northern Ireland. In total, 1321 lactations (1 to 20 weeks in milk; WIM), derived from 840 individual cows fed mainly grass silage-based diets, were included in the analysis. The energy balance was calculated daily and then averaged weekly for statistical analyses. Data were further split in 4 wk. intervals, namely, 1–4, 5–8, 9–12, 13–16, and 17–20 WIM, and both partial correlations and linear regressions (mixed models) established between the mean FPR and EB during these periods. Three FPR score categories (‘Low’ FPR, <1.0; ‘Normal’ FPR, 1.0–1.5; ‘High’ FPR, >1.5) were adopted and the performance and EB indicators within each category were compared. As expected, multiparous cows experienced a greater negative EB compared to primiparous cows, due to their higher milk production relative to DMI. Relatively minor differences in milk fat and protein content resulted in large differences in FPR curves. Second lactation cows displayed the lowest weekly FPR, and this trend was aligned with smaller BW losses and lower concentrations of non-esterified fatty acids (NEFA) until at least 8 WIM. Partial correlations between FPR and EB were negative, and ‘greatest’ in early lactation (1–4 WIM; r = −0.38 on average), and gradually decreased as lactation progressed across all parities (17–20 WIM; r = −0.14 on average). With increasing parity, daily EB values tended to become more negative per unit of FPR. In primiparous cows, regression slopes between FPR and EB differed between 1–4 and 5–8 WIM (−54.6 vs. −47.5 MJ of ME/day), while differences in second lactation cows tended towards significance (−57.2 vs. −64.4 MJ of ME/day). Irrespective of the lactation number, after 9–12 WIM, there was a consistent trend for the slope of the linear relationships between FPR and EB to decrease as lactation progressed, with this likely reflecting the decreasing milk nutrient demands of the growing calf. The incidence of ‘High’ FPR scores was greatest during 1–4 WIM, and decreased as lactation progressed. ‘High’ FPR scores were associated with increased energy-corrected milk (ECM) yields across all parities and stages of lactation, and with smaller BW gains and increasing concentrations (log transformed) of blood metabolites (non-esterified fatty acid, NEFA; beta-hydroxybutyrate, BHB) until 8 WIM. Results from the present study highlight the strong relationships between FPR in milk, physiological changes, and EB profiles during early lactation. However, while FPR can provide an indication of EB at a herd level, the large cow-to-cow variation indicates that FPR cannot be used as a robust indicator of EB at an individual cow level.

Detection of changes in the circadian rhythm of cattle in relation to disease, stress, and reproductive events

Disease and stress can disrupt the circadian rhythm of activity in animals. Sensor technologies can automatically detect variations in daily activity, but it remains difficult to detect exactly when the circadian rhythm disruption starts. Here we report a mathematical Fourier-Based Approximation with Thresholding (FBAT) method designed to detect changes in the circadian activity rhythm of cows whatever the cause of change (typically disease, stress, oestrus). We used data from an indoor positioning system that provides the time per hour spent by each cow resting, in alleys, or eating. We calculated the hourly activity level of each cow by attributing a weight to each activity. We considered 36-h time series and used Fourier transform to model the variations in activity during the first and last 24 h of these 36-h series. We then compared the Euclidian distance between the two models against a given threshold above which we considered that rhythm had changed. We tested the method on four datasets (giving a cumulative total of ~120000 cow*days) that included disease episodes (acidosis, lameness, mastitis or other infectious diseases), reproductive events (oestrus or calving) and external stimuli that can stress animals (e.g. relocation). The method obtained over 80% recall of normal days and detected 95% of abnormal rhythms due to health or reproductive events. FBAT could be implemented in precision livestock farming system monitoring tools to alert caretakers to individual animals needing specific care. The FBAT method also has the potential to detect anomalies in humans to guide healthcare intervention or in wild animals to detect disturbances. We anticipate that chronobiological studies could apply FBAT to help relate circadian rhythm anomalies to specific events.