Relationships between milk fat and rumination time recorded by commercial rumination sensing systems

Estimates of genetic parameters for rumination time, feed efficiency, and methane production traits in first lactation Holstein cows

The large-scale recording of traits such as feed efficiency and methane emissions for use in genetic improvement programs is complex, costly, and time-consuming. Therefore, heritable traits that can be continuously recorded in dairy herds and are correlated to feed efficiency and methane emission traits could provide useful information for genetic evaluation. Rumination time has been suggested to be associated with feed efficiency, methane production (methane emission in g/day), and production traits at the phenotypic level. Therefore, the objective of this study was to investigate the genetic relationships among rumination time, feed efficiency, methane and production traits using 7,358 records from 656 first lactation Holstein cows. The estimated heritabilities were moderate for rumination time (0.45 ± 0.14), methane production (0.36 ± 0.12), milk yield (0.40 ± 0.08), fat yield (0.29 ± 0.06), protein yield (0.32 ± 0.07), and energy corrected milk (0.28 ± 0.07), while low and non-significant for feed efficiency (0.15 ± 0.07), which was defined as the residual of the multiple linear regression of DMI on ECM and MBW. A favorable negative genetic correlation was estimated between rumination time and methane production (-0.53 ± 0.24), while a positive favorable correlation was estimated between rumination time and energy corrected milk (0.49 ± 0.11). The estimated genetic correlation of rumination time with feed efficiency (-0.01 ± 0.17) was not significantly different from zero but showed a trend of a low correlation with dry matter intake (0.21 ± 0.13, P = 0.11). These results indicate that rumination time is genetically associated with methane production and milk production traits, but high standard errors indicate that further analyses should be conducted to verify these findings when more data for rumination time, methane production and feed efficiency become available.

Predicted essential fatty acid intakes for a group of dairy cows also apply at individual animal level

The ruminant requirements for essential fatty acids (EFAs), particularly linoleic acid (LA) and alpha-linolenic acid (ALA), have not been fully determined, although evidence suggests that an adequate supply of polyunsaturated fatty acids (FAs) could improve immunity and reproduction in transition cows. In previous studies, we predicted EFA intake for a group of cows based on animal characteristics and milk EFA secretions. However, to support precision livestock feeding, we need to match the nutrient requirements and intakes of each cow as closely as possible. Our group-level predictions may not be accurate enough to estimate the EFA intake of an individual cow, due to inter-individual variations in EFA digestion and metabolism related to differences in feed intake, intake patterns, and the composition and functioning of the rumen microbiota. To address this issue, here we set out to establish specific equations that predict EFA intake for an individual cow based on the difference (i.e. the residuals) between observed EFA intake and the predicted EFA intake based on our group-level equations. We studied a database of individual dairy cows (26 experiments; 503 datapoints from three research teams) and we predicted the residuals from (1) dietary and animal-related factors (i.e. full predictions) and (2) animal-related factors only (i.e. field predictions), which are considered more field-amenable. The variance of predicted LA and log ALA intake was explained to 68% by observed LA intake and 66% by observed log ALA intake, respectively. The residuals of LA intake were predicted by dietary ALA content, total FA intake, BW, milk yield and fat content in full predictions, and by BW, feeding level, milk yield and fat content, and sum of milk C4:0 to C14:0 FA in field predictions. The log residuals of ALA intake were predicted by dietary NDF and total FA contents, NDF intake, BW, milk protein, LA and ALA contents, and fat yield in full predictions, and by BW, DM intake, milk LA and ALA contents, and fat yield in field predictions. The field predictions showed a moderate loss of accuracy compared to full predictions based on RMSE of prediction (from 38 to 54 g/d for LA and from 0.090 to 0.12 log (g/d) for ALA). This work is the first to predict the EFA intake of an individual cow based on previously established group-level predictions of EFA intake adjusted for dietary and animal-related factors.

Effectiveness of the strain 919 bovine ephemeral fever virus vaccine in the face of a real-world outbreak: A field study in Israeli dairy herds

Bovine ephemeral fever virus (BEFV) is a globally spread arthropod-borne RNA virus that has significant economic impacts on the cattle industry. A live attenuated commercial BEF vaccine, based on the Australian BEFV strain 919, is widely used in Israel and other countries. A previous study has suggested the high effectiveness of this vaccine (ULTRAVAC BEF VACCINE™ from Zoetis®), but anecdotal reports of high BEF morbidity among vaccinated dairy herds in Israel casted doubt on these findings. To resolve this uncertainty, a randomized controlled field vaccine effectiveness study was conducted in Israel during a BEF outbreak which occurred in 2021. Eleven dairy herds were enrolled and monitored for BEF-associated morbidity and rumination alteration patterns using electronic monitoring tags (HR Tags, SCR® Dairy, Netanya, Israel). Four of the herds were naturally infected with BEFV during the outbreak, resulting in a total of 120 vaccinated and 311 unvaccinated subjects that were included in the effectiveness study. A mixed-effect Cox proportional hazard regression model was used to calculate the overall hazard ratio between vaccinated and unvaccinated cattle. This analysis demonstrated an average vaccine effectiveness of 60 % (95 % CI = 38 %-77 %) for preventing clinical disease. In addition, a non-statistically significant trend (p = 0.1) towards protection from mortality was observed, with no observation of mortality among the vaccinated groups compared to 2.61 % mortality (7/311) among the unvaccinated subjects. One hundred and thirty vaccinated and unvaccinated calves from affected and non-affected herds and with different status of morbidity were sampled and analysed by serum-neutralization test. The highest titers of BEFV-neutralizing antibodies were found in subjects that were both vaccinated and clinically affected, indicating a booster effect after vaccination. The results of the study provide evidence for the moderate effectiveness of the ULTRAVAC BEF VACCINE™ for the prevention of BEF.

Behavioral Fingerprinting: Acceleration Sensors for Identifying Changes in Livestock Health

During disease or toxin challenges, the behavioral activities of grazing animals alter in response to adverse situations, potentially providing an indicator of their welfare status. Behavioral changes such as feeding behavior, rumination and physical behavior as well as expressive behavior, can serve as indicators of animal health and welfare. Sometimes behavioral changes are subtle and occur gradually, often missed by infrequent visual monitoring until the condition becomes acute. There is growing popularity in the use of sensors for monitoring animal health. Acceleration sensors have been designed to attach to ears, jaws, noses, collars and legs to detect the behavioral changes of cattle and sheep. So far, some automated acceleration sensors with high accuracies have been found to have the capacity to remotely monitor the behavioral patterns of cattle and sheep. These acceleration sensors have the potential to identify behavioral patterns of farm animals for monitoring changes in behavior which can indicate a deterioration in health. Here, we review the current automated accelerometer systems and the evidence they can detect behavioral patterns of animals for the application of potential directions and future solutions for automatically monitoring and the early detection of health concerns in grazing animals.

Is greater milk production associated with dairy cows who have a greater probability of ruminating while lying down?

The objective of this study was to determine whether associations exist between position while ruminating (lying vs. standing) and milk and component production in dairy cows. Data from 30 lactating Holstein cows were assembled from 2 studies in which cows were milked by automated milking system (AMS) and fed a partial mixed ration (PMR) in feed bins that recorded intake behavior. Rumination and lying behavior were monitored using automated neck- and leg-based sensors, respectively. Each cow was monitored over 2 separate 2-wk treatment periods. To estimate position while ruminating for each 2-h period of the day for each cow, a conditional probability was calculated to determine the probability that any rumination time and lying time were occurring at the same time in any 2-h period. These probabilities (RwL), and all behavioral data, were summarized per cow per 2-h interval, and then averaged per day and per 2-wk period, along with milk yield and component data. Cows averaged (mean ± standard deviation) 1.9 ± 1.1 lactations and 85.5 ± 55.2 d in milk, and weighed 668.5 ± 96.0 kg. Data included rumination time (557.7 ± 41.1 min/d), lying time (703.9 ± 65.3 min/d), idle standing time (520.1 ± 83.2 min/d), PMR feeding time (204.7 ± 48.5 min/d), PMR dry matter intake (DMI; 21.8 ± 4.6 kg/d), AMS pellet provision (4.6 ± 1.6 kg/d), total DMI (26.4 ± 4.5 kg/d), milk yield (42.4 ± 7.2 kg/d), milk fat content (3.75 ± 0.51%), and milk protein content (3.21 ± 0.32%). Greater rumination time and lying time were associated with greater RwL probability (mean = 0.19 ± 0.02; range = 0.14 to 0.23). The RwL probability tended to be positively associated with total DMI and milk fat content, was associated with milk protein content, but was not associated with any measures of milk yield. The results indicate that in a free-traffic AMS, cows who have greater probability of ruminating while lying down spend more time ruminating and lying, and tend to consume more total dry matter and produce milk with greater components.

Meta-analysis of rumination behavior and its relationship with milk and milk fat production, rumen pH, and total-tract digestibility in lactating dairy cows

Time spent ruminating is affected by diet and affects the rumen environment. The objective of the current study was to conduct a meta-regression to characterize the variation in rumination time and its relationship with milk and milk fat yields and variables mechanistically associated with milk fat synthesis, including rumen pH and total-tract digestibility. The analysis included 130 journal articles published between 1986 and 2018 that reported 479 treatment means from lactating Holsteins cows during established lactation. Milk yield averaged 34.3 kg/d (range 14.2-52.1 kg/d), milk fat averaged 3.47% (range 2.20-4.60%), and rumen pH averaged 6.1 (range 5.3-7.0). Rumination observation systems were categorized into 6 groups, but there was little difference in average rumination time among systems. The total time spent ruminating averaged 444 min/d (range 151-638 d) and occurred in 13.8 bouts/d (range 7.8-17.4 bouts/d) that averaged 32.7 min (range 20.0-48.1 min). Bivariate regressions were modeled to include the random effect of study, and correlations were evaluated through the partial R² that excluded variation accounted for by the random effect. Rumination time was quadratically increased with increasing milk fat yield (partial R² = 0.27) and milk fat percent (partial R² = 0.17). Rumination was also increased with increasing milk yield, dry matter intake, and rumen pH, and was quadratically related to dietary neutral detergent fiber (NDF) and total-tract NDF digestibility (partial R² = 0.10-0.27). Similar relationships were observed for rumination per unit of dry matter and NDF intake. The best-fit multivariate model predicting total rumination time included milk yield, milk fat yield, and concentration and accounted for 37% of the variation. Total-tract digestibility was available for 217 treatment means; when included in the model, the partial R² increased to 0.41. Last, principal component analysis was conducted to explore the relationship among variables. The first 2 principal components in the broad analyses explained 36.7% of the 39 variables evaluated, which included rumination bouts and time spent ruminating. In conclusion, rumination time was related to milk fat across a large number of studies, although it explained only a limited amount of the variation in milk fat.

Unraveling the Relationship between Milk Yield and Quality at the Test Day with Rumination Time Recorded by a PLF Technology

Simple Summary

Precision livestock farming, by real time monitoring of dairy cows, has the potential to generate a huge amount of data to be used for farm management purposes, as well as in breeding programs. Daily rumination time (RT) recorded by commercial systems is promising in this context because it may be related to individual milk yield and composition. However, it is necessary to assess the ability of sensor data to be used in a predictive model, but also to evaluate and standardize the correct phenotypes, and how they are related to individual variability rather than from other sources. RT data and milk test day (TD) records collected from 691 cows, monitored for thirteen months, were analyzed for the already mentioned goals and to better characterize the effect of high-, medium- and low-level daily RT on milk yield and composition. Our results showed that “animal” in a farm major contributed to the RT total variability, confirming a possible use in breeding program. The higher RT class reported the best productive performance for milk and each solid yield, in spite of a small reduction in their contents, and appears to be related to a higher degree of saturation in the fatty acid profile.

Abstract

The study aimed to estimate the components of rumination time (RT) variability recorded by a neck collar sensor and the relationship between RT and milk composition. Milk test day (TD) and RT data were collected from 691 cows in three farms. Daily RT data of each animal were averaged for 3, 7, and 10 days preceding the TD date (RT_D). Variance component analysis of RT_D, considering the effects of farm, cow, parity, TD date, and lactation phase, showed that a farm, followed by a cow, had major contributions to the total variability. The RT₁₀ variable best performed on TD milk yield and quality records across models by a multi-model inference approach and was adopted to study its relationship with milk traits, by linear mixed models, through a 3-level stratification: low (LRT₁₀ ≤ 8 h/day), medium (8 h/day < MRT₁₀ ≤ 9 h/day), and high (HRT₁₀ > 9 h/day) RT. Cows with HRT₁₀ had greater milk, fat, protein, casein, and lactose daily yield, and lower fat, protein, casein contents, and fat to protein ratio compared to MRT₁₀ and LRT₁₀. Higher percentages of saturated fatty acid and lower unsaturated and monounsaturated fatty acid were found in HRT₁₀, with respect to LRT₁₀ and MRT₁₀ observations.

Determination of relationships between rumination and milk fat concentration and fatty acid profile using data from commercial rumination sensing systems

Milk fat production is highly influenced by nutrition and rumen fermentation. Rumination is an essential part of the ruminant digestive process and can serve as an indicator of rumen fermentation. The objective of this research was to quantify variation in rumination time between and within dairy herds and test for relationships between rumination time and milk fat production and fatty acid (FA) profile as a proxy of rumen fermentation. Our hypothesis was that rumination may indicate disruptions to rumen fermentation and that cows that spent less time ruminating would have lower milk fat due to these rumen disruptions. Data were collected from 1,733 Holstein cows on 5 commercial dairy farms (4 in Pennsylvania and 1 in New York) of 200 to 700 head using 1 of 2 commercially-available rumination sensing systems, CowManager SensOor ear tags (Agis Automatisering BV) or SCR model HR-LDn neck collars (SCR Engineers). Rumination data were collected for 7 consecutive days leading up to a DHIA test, summed within day, then averaged to obtain mean daily minutes of rumination time. Milk samples from the DHIA test were analyzed for fat content by mid-infrared spectroscopy and for milk FA profile by gas chromatography. Rumination data were analyzed using multiple linear regression models. Rumination time was related to concentration of specific odd- and branched-chain and trans FA in milk but was not directly related to milk fat concentration. Rumination time also did not contribute to models predicting milk fat concentration after accounting for other cow-level variables. There was a linear relationship between trans-10 C18:1 and rumination time that was positive after accounting for the effect of farm (partial R² of 2.97% across all data, 4.24% in SCR data, and 2.22% in CowManager data). Although rumination time was not related directly to milk fat, it was associated with differences in trans and odd- and branched-chain FA that have been demonstrated to change during subacute ruminal acidosis or biohydrogenation-induced milk fat depression, which may affect milk fat and other production variables. These associations suggest that further investigation into using rumination data from commercial systems to predict or identify the presence of these conditions is warranted.