An annual pattern of milk composition has been well recognized in dairy cattle, with the highest milk fat and protein concentration observed during the winter and lowest occurring in the summer; however, rhythms of milk yield and composition have not been well quantified. Cosinor rhythmometry is commonly used to model repeating daily and annual rhythms and allows determination of the amplitude (peak to mean), acrophase (time at peak), and period (time between peaks) of the rhythm. The objective of this study was to use cosinor rhythmometry to characterize the annual rhythms of milk yield and milk fat and protein concentration and yield using both national milk market and cow-level data. First, 10 yr of monthly average milk butterfat and protein concentration for each Federal Milk Marketing Order were obtained from the US Department of Agriculture Agricultural Marketing Service database. Fat and protein concentration fit a cosine function with a 12-mo period in all milk markets. We noted an interaction between milk marketing order and milk fat and protein concentration. The acrophase (time at peak) of the fat concentration rhythm ranged from December 4 to January 19 in all regions, whereas the rhythm of protein concentration peaked between December 27 and January 6. The amplitude (peak to mean) of the annual rhythm ranged from 0.07 to 0.14 percentage points for milk fat and from 0.08 to 0.12 percentage points for milk protein. The amplitude of the milk fat rhythm generally was lower in southern markets and higher in northern markets. Second, the annual rhythm of milk yield and milk fat and protein yield and concentration were analyzed in monthly test day data from 1,684 cows from 11 tiestall herds in Pennsylvania. Fat and protein concentration fit an annual rhythm in all herds, whereas milk and milk fat and protein yield only fit rhythms in 8 of the 11 herds. On average, milk yield peaked in April, fat and protein yield peaked in February, fat concentration peaked in January, and protein concentration peaked in December. Amplitudes of milk, fat, and protein yield averaged 0.82 kg, 55.3 g, and 30.4 g, respectively. Milk fat and protein concentration had average amplitudes of 0.12 and 0.07, respectively, similar to the results of the milk market data. Generally, milk yield and milk components fit annual rhythm regardless of parity or diacylglycerol O-acyltransferase 1 (DGAT1) K232A polymorphism, with only cows of the low-frequency AA genotype (5.2% of total cows) failing to fit rhythm of milk yield. In conclusion, the yearly rhythms of milk yield and fat and protein concentration and yield consistently occur regardless of region, herd, parity, or DGAT1 genotype and supports generation by a conserved endogenous annual rhythm.

The timing of feed intake can alter circadian rhythms of peripheral tissues. Milk synthesis displays a daily rhythm across several species, but the effect of feeding time on these rhythms is poorly characterised. The objective of this experiment was to determine if the time of feed intake modifies the daily patterns of milk synthesis, plasma metabolites and body temperature in dairy cows. Sixteen lactating Holstein dairy cows were randomly assigned to one of the two treatment sequences in a cross-over design with 17 d periods. Treatments included day-restricted feeding (DRF; feed available from 07.00 to 23.00 hours) and night-restricted feeding (NRF; feed available from 19.00 to 11.00 hours). Cows were milked every 6 h on the last 7 d of each period, and blood samples were collected to represent every 4 h over the day. Peak milk yield was shifted from morning in DRF to evening in NRF, while milk fat, protein and lactose concentration peaked in the evening in DRF and the morning in NRF. Plasma glucose, insulin, NEFA and urea nitrogen concentration fit daily rhythms in all treatments. Night feeding increased the amplitude of glucose, insulin and NEFA rhythms and shifted the daily rhythms by 8 to 12 h (P < 0·05). Night feeding also phase-delayed the rhythm of core body temperature and DRF increased its amplitude. Altering the time of feed availability shifts the daily rhythms of milk synthesis and plasma hormone and metabolite concentrations and body temperature, suggesting that these rhythms may be entrained by food intake.

Dual-flow continuous-culture fermenters are commonly used to study rumen fermentation in vitro. Research using culture-based and oligonucleotide techniques have shown that certain microbial populations within fermenters may be maintained at abundances similar to those observed in vivo. In this study, the bacterial and archaeal communities in the rumen of dairy cattle and in a dual-flow continuous culture fermentation system were compared using high-throughput amplicon sequencing targeting the V4 hypervariable region of 16S rRNA. We hypothesized that the in vitro system harbored a comparable bacterial and archaeal community to that observed in the rumen. Members of the Bacteroidetes and Firmicutes made up the two most abundant phyla in the rumen, inoculum and fermenters and did not differ among sample types (P > 0.10). Similarly, Prevotellaceae, the most abundant family in all three sample types, did not differ based on source (P = 0.80). However, beta diversity analyses revealed that bacterial and archaeal communities differed between fermenters and rumen samples (P < 0.001), but fermenter bacterial and archaeal communities stabilized by day 4 of each period. While the overall bacterial and archaeal community differs between natural rumens and in vitro fermenter systems, several prominent taxa were maintained at similar relative abundances, suggesting that fermenters may provide a suitable environment in which to study shifts among the predominant members of the microbial community.

In vitro DM disappearance (IVDMD) and gas production can be used to rapidly estimate apparent total tract digestibility of DM and GE in feed ingredients used in swine diets. However, the accuracy of the system in estimating ME among sources feed ingredients with high content of dietary fiber is not clear. Objectives of this study were 1) to measure IVDMD of feed ingredients with high insoluble fiber content and determine and compare in vitro gas production kinetics from fiber fermentation among wheat straw (WS; 16 sources; 69.0–83.4% NDF), soybean hulls (SBH; 16 sources; 60.9–67.7% NDF), and corn distiller's dried grains with solubles (DDGS; 16 sources; 28.8–44.0% NDF); and 2) to estimate ME contributions resulting from gas production of DDGS. Each 2-g sample was hydrolyzed for 2 h with pepsin and for a subsequent 4 h with pancreatin. Hydrolyzed residues were filtered, washed, dried, weighed, pooled within the same sample, and used for subsequent fermentation using swine fecal inocula. Volume of gas produced was recorded at 11 time points during 72 h of incubation. Parameters of gas production kinetics were calculated using a nonlinear monophasic model, and differences among ingredients were compared using a mixed model. The IVDMD from simulated gastric and small intestinal hydrolysis (IVDMD_h) in DDGS (55.7%) was greater (P < 0.05) than that in SBH (19.7%), which was greater (P < 0.05) than that in WS (14.5%). In vitro DM digestibility from simulated large intestine fermentation (IVDMD_f) of SBH (68.5%) was greater (P < 0.05) than that of DDGS (52.7%), which was greater than that of WS (41.8%). In vitro DM digestibility from simulated total tract digestion (IVDMD_t) was greatest (P < 0.01) in DDGS (79.2%) followed by SBH (74.8%), and both were greater than that in WS (50.2%). The asymptotic gas production (mL/g substrate) was greater (P < 0.05) for SBH (293) than for DDGS (208) and WS (53). There were differences (P < 0.01) in IVDMD_h among sources of WS, SBH, and DDGS, whereas IVDMD_f and IVDMD_t were different (P < 0.01) among sources of SBH but not among sources of DDGS or WS. There were no differences in asymptotic gas production among sources of WS, SBH, or DDGS. In conclusion, the modified 3-step procedure allowed for characterizing the variability of DM digestibility and asymptotic gas production resulting from residue fermentation among WS, SBH, and DDGS and among sources of each ingredient.

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.

The annual rhythms of milk and milk component yields are not well described and are important to dairy management. Recent analysis of federal milk marketing orders in the United States observed that the amplitude and time at peak (acrophase) of the rhythms of milk fat and protein concentration differ among regions, but the rhythms of milk and milk component yields are not well described. Our objective was to determine the annual rhythms of milk and milk component production from 4 US regions at the herd level and examine potential environmental factors entraining these rhythms. Monthly Dairy Herd Improvement Association records of all available herds in Pennsylvania (PA), Minnesota (MN), Texas (TX), and Florida (FL) from the years 2003 to 2016 were obtained from Dairy Records Managements Systems. Milk yield, fat and protein yield, and fat and protein concentration were fit to the linear form of the cosine function with a 12-mo period using a linear mixed effects model. Additionally, the fit of models containing either the cosine function or environmental temperature were compared using an F-test. Milk yield and fat and protein yields and concentrations fit a cosine function in all 4 states, indicating an annual rhythm. The amplitude (peak to mean) of the rhythm of milk yield varied by state and was lower in PA (1.2 kg) and MN (1.2 kg) compared with TX (3.1 kg) and FL (3.3 kg). Fat and protein yields similarly showed greater amplitudes in the southern versus northern states. The amplitudes of the rhythms of fat and protein concentration were opposite by region, with greater amplitudes occurring in MN and PA than in TX and FL. The acrophases of milk yield and milk fat and protein yields and concentrations also varied by state, but all peaked between October and March. An annual rhythm fit the data better than changes in environmental temperature for all responses in all states, except for fat and protein concentrations in FL, which exhibited lower amplitude seasonal rhythms. The yearly pattern of milk yield closely followed the fixed yearly pattern of the day to day changes in day length, whereas the rhythms of milk fat and protein concentrations followed the yearly pattern of absolute day length. Results suggest that the region of the United States in which a herd is located affects their annual rhythms of production, with a greater yearly variation in milk, fat, and protein yields occurring in the southern United States. The consistency of annual rhythms across years and herds allowed development of regression equations to adjust expectations across the year to account for the annual rhythm.