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.

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

Low rumination in the dairy cow is often assumed to result in reduction of saliva flow, rumen buffering, and milk fat, which is a major contributor to milk value in many pricing systems. Rumination time (RT) of individual cows can be measured with commercial rumination sensing systems, but our understanding of how daily RT (minutes per day) is related to milk fat production is limited. Our hypothesis was that between cows within a herd, greater RT would be associated with lower milk fat concentration. Data from 1,823 cows on 2 commercial dairy farms in Pennsylvania over 8 DHIA tests were analyzed for a total of 8,587 cow test-days. Rumination was measured on farm A with CowManager SensoOr ear tags (Agis Automatisering BV, Harmelen, the Netherlands) and on farm B with SCR Hi-Tag neck collars (SCR Engineers, Netanya, Israel). Rumination data were collected for 7 consecutive days leading up to each DHIA test, summed within day, and averaged across days. Data were analyzed using linear mixed models with a repeated effect of test day. Daily RT reported by commercial rumination systems varied across and within cows and was strongly influenced by a cow effect. Greater RT tended to be associated with a small decrease in milk fat concentration in farm A, but was not related to milk fat in farm B. The reason for this difference is unclear, but may be related to a potentially greater prevalence of biohydrogenation-induced milk fat depression on farm A. The significant, but small, model coefficients for milk fat and RT indicate that the relationship between these variables may not be strong enough to permit identification of cows with biohydrogenation-induced milk fat depression based on RT from commercial systems alone. Research assessing changes in rumination before, during, and after onset of altered rumen fermentation is necessary to determine whether RT could be used to identify cows with altered rumen fermentation.

Technical note: Method for improving precision of in-parlor milk meters and adjusting milk weights for stall effects.

Milk yield is a fundamental observation in most dairy experiments and is commonly determined using integrated milk meters that measure milk weight as the cow is being milked. These meters are heavily used in a harsh environment and often are not regularly calibrated, so calibration errors and mechanical problems may create artificial variation in milk weight data. Additionally, direct calibration by collection of milk in a bucket is difficult and imperfect because the use of the bucket may affect yield recorded by the milk meter. The objective of this work was to define a method to easily check parlor meter precision and adjust milk weight values for variation between individual stalls in a parlor. Because most cows are milked in a different stall at each milking, it has been proposed that stall deviations that represent the fixed effect of stall on milk weight could be statistically determined. Individual milk weights from 14 milkings across 7 d from approximately 200 cows were collected from the Penn State dairy farm, which is equipped with a double-10 herringbone parlor with an Afimilk 2000 milking system (S.A.E. Afikim, Afikim, Israel). Milk yield was measured automatically by in-line flow through milk meters (Afi 200; S.A.E. Afikim). The effect of stall on milk weight was modeled using a mixed model that included the fixed effect of stall and the random effects of day, milking time, and cow. First, stall deviations were calculated as the stall least squares means (LSM) minus the average LSM to identify malfunctioning meters requiring service (e.g., deviation exceeding 1 kg). A correction factor for each stall was then generated by dividing the LSM of each stall by the average LSM. Milk yields were then corrected by multiplying the meter weight value by the correction factor. To determine the effect of the correction, raw and corrected meter values were compared with weight of milk collected in a bucket (n = 3/stall). The corrected values had a 5% greater coefficient of determination than raw meter values (0.89 vs. 0.84) and had a lower average percent difference from the bucket milk weight compared with raw meter values (12.6% vs. 13.5%). The method was then used in 3 experiments with 121, 140, and 683 milk yield observations. In all data sets, correcting milk weights slightly improved model fit and had minimal effect on model term standard errors. However, this validation was completed in a parlor where the method was routinely used to identify stalls requiring service; the effect of stall corrections is expected to be larger in parlors without frequent monitoring. Stall deviations are expected to be due predominantly to calibration of the meter but also could be due to differences in pulsation or other stall-specific factors that result in a change in milk yield. It is important to account for these other sources of milk weight variation that are unrelated to treatment. Modeling the effect of stall is a simple, convenient, and low-cost method to monitor and improve milk meter precision and functionality and can be used to reduce artificial variation and experimental error.