Manure nitrogen (N) from cattle contributes to nitrous oxide and ammonia emissions and nitrate leaching. Measurement of manure N outputs on dairy farms is laborious, expensive, and impractical at large scales; therefore, models are needed to predict N excreted in urine and feces. Building robust prediction models requires extensive data from animals under different management systems worldwide. Thus, the study objectives were (1) to collate an international database of N excretion in feces and urine based on individual lactating dairy cow data from different continents; (2) to determine the suitability of key variables for predicting fecal, urinary, and total manure N excretion; and (3) to develop robust and reliable N excretion prediction models based on individual data from lactating dairy cows consuming various diets. A raw data set was created based on 5,483 individual cow observations, with 5,420 fecal N excretion and 3,621 urine N excretion measurements collected from 162 in vivo experiments conducted by 22 research institutes mostly located in Europe (n = 14) and North America (n = 5). A sequential approach was taken in developing models with increasing complexity by incrementally adding variables that had a significant individual effect on fecal, urinary, or total manure N excretion. Nitrogen excretion was predicted by fitting linear mixed models including experiment as a random effect. Simple models requiring dry matter intake (DMI) or N intake performed better for predicting fecal N excretion than simple models using diet nutrient composition or milk performance parameters. Simple models based on N intake performed better for urinary and total manure N excretion than those based on DMI, but simple models using milk urea N (MUN) and N intake performed even better for urinary N excretion. The full model predicting fecal N excretion had similar performance to simple models based on DMI but included several independent variables (DMI, diet crude protein content, diet neutral detergent fiber content, milk protein), depending on the location, and had root mean square prediction errors as a fraction of the observed mean values of 19.1% for intercontinental, 19.8% for European, and 17.7% for North American data sets. Complex total manure N excretion models based on N intake and MUN led to prediction errors of about 13.0% to 14.0%, which were comparable to models based on N intake alone. Intercepts and slopes of variables in optimal prediction equations developed on intercontinental, European, and North American bases differed from each other, and therefore region-specific models are preferred to predict N excretion. In conclusion, region-specific models that include information on DMI or N intake and MUN are required for good prediction of fecal, urinary, and total manure N excretion. In absence of intake data, region-specific complex equations using easily and routinely measured variables to predict fecal, urinary, or total manure N excretion may be used, but these equations have lower performance than equations based on intake.

Suppression of appetite, or hypophagia, is among the most recognizable effects of disease in livestock, with the potential to impair growth, reproduction, and lactation. The continued evolution of the field of immunology has led to a greater understanding of the immune and endocrine signaling networks underlying this conserved response to disease. Inflammatory mediators, especially including the cytokines tumor necrosis factor-α and interleukin-1β, are likely pivotal to disease-induced hypophagia, based on findings in both rodents and cattle. However, the specific mechanisms linking a cytokine surge to decreased feeding behavior are more difficult to pin down and likely include direct effects on appetite centers in the brain, alteration of gastric motility, and modulation of other endocrine factors that influence appetite and satiety. These insights into the mechanisms for disease-induced hypophagia have great relevance for management of neonatal calves, mature cows transitioning to lactation, and cows experiencing mastitis; however, it is not necessarily the case that increasing feed intake by any means possible will improve health outcomes for diseased cattle. We explore conflicting effects of hypophagia on immune responses, which may be impaired by the lack of specific substrates, versus apparent benefits for controlling the growth of some pathogens. Anti-inflammatory strategies have shown promise for promoting recovery of feed intake following some conditions but not others. Finally, we explore the potential for early disease detection through automated monitoring of feeding behavior and consider which strategies may be implemented to respond to early hypophagia.

Residual feed intake (RFI) is commonly used to measure feed efficiency but individual intake recording systems are needed. Feeding behavior may be used as an indicator trait for feed efficiency using less expensive precision livestock farming technologies. Our goal was to estimate genetic parameters for feeding behavior and the genetic correlations with feed efficiency in Holstein cows. Data consisted of 75,877 daily feeding behavior records of 1,328 mid-lactation Holstein cows in 31 experiments conducted from 2009 to 2020 with an automated intake recording system. Feeding behavior traits included number of feeder visits per day, number of meals per day, duration of each feeder visit, duration of each meal, total duration of feeder visits, intake per visit, intake per meal [kg of dry matter (DM)], feeding rate per visit, and feeding rate per meal (kg of DM per min). The meal criterion was estimated as 26.4 min, which means that any pair of feeder visits separated by less than 26.4 min were considered part of the same meal. The statistical model included lactation and days in milk as fixed effects, and experiment-treatment, animal, and permanent environment as random effects. Genetic parameters for feeding behavior traits were estimated using daily records and weekly averages. Estimates of heritability for daily feeding behavior traits ranged from 0.09 ± 0.02 (number of meals; mean ± standard error) to 0.23 ± 0.03 (feeding rate per meal), with repeatability estimates ranging from 0.23 ± 0.01 (number of meals) to 0.52 ± 0.02 (number of feeder visits). Estimates of heritability for weekly averages of feeding behavior traits ranged from 0.19 ± 0.04 (number of meals) to 0.32 ± 0.04 (feeding rate per visit), with repeatability estimates ranging from 0.46 ± 0.02 (duration of each meal) to 0.62 ± 0.02 (feeding rate per visit and per meal). Most of the feeding behavior measures were strongly genetically correlated, showing that with more visits or meals per day, cows spend less time in each feeder visit or meal with lower intake per visit or meal. Weekly averages for feeding behavior traits were analyzed jointly with RFI and its components. Number of meals was genetically correlated with milk energy (0.48), metabolic body weight (-0.27), and RFI (0.19). Duration of each feeder visit and meal were genetically correlated with milk energy (0.43 and 0.44, respectively). Total duration of feeder visits per day was genetically correlated with DM intake (0.29), milk energy (0.62), metabolic body weight (-0.37), and RFI (0.20). Intake per visit and meal were genetically correlated with DM intake (0.63 and 0.87), milk energy (0.47 and 0.69), metabolic body weight (0.47 and 0.68), and RFI (0.31 and 0.65). Feeding rate was genetically correlated with DM intake (0.69), metabolic body weight (0.67), RFI (0.47), and milk energy (0.21). We conclude that measures of feeding behavior could be useful indicators of dairy cow feed efficiency, and individual cows that eat at a slower rate may be more feed efficient.

Residual feed intake (RFI) is a measurement of the difference between actual and predicted feed intake when adjusted for energy sinks; more efficient cows eat less than predicted (low RFI) and inefficient cows eat more than predicted (high RFI). Data evaluating the relationship between RFI and feeding behaviors (FB) are limited in dairy cattle; therefore, the objective of this study was to determine daily and temporal FB in mid-lactation Holstein cows across a range of RFI values. Mid-lactation Holstein cows (n = 592 multiparous; 304 primiparous) were enrolled in 17 cohorts at 97 ± 26 d in milk (± standard deviation), and all cows within a cohort were fed a common diet using automated feeding bins. Cow RFI was calculated as the difference between predicted and observed dry matter intake (DMI) after accounting for parity, days in milk, milk energy, metabolic body weight and change, and experiment. The associations between RFI and FB at the level of meals and daily totals were evaluated using mixed models with the fixed effect of RFI and the random effects of cow and cohort. Daily temporal FB analyses were conducted using 2-h blocks and analyzed using mixed models with the fixed effects of RFI, time, RFI × time, and cohort, and the random effect of cow (cohort). There was a positive linear association between RFI and DMI in multiparous cows and a positive quadratic relationship in primiparous cows, where the rate of increase in DMI was less at higher RFI. Eating rate, DMI per meal, and size of the largest daily meal were positively associated with RFI. Daily temporal analysis of FB revealed an interaction between RFI and time for eating rate in multiparous and primiparous cows. The eating rate increased with greater RFI at 11 of 12 time points throughout the day, and eating rate differed across RFI between multiple time points. There tended to be an interaction between RFI and time for eating time and bin visits in multiparous cows but not primiparous cows. Overall, there was a time effect for all FB variables, where DMI, eating time and rate, and bin visits were greatest after the initial daily feeding at 1200 h, increased slightly after each milking, and reached a nadir at 0600 h (6 h before feeding). Considering the relationship between RFI and eating rate, additional efforts to determine cost-effective methods of quantifying eating rate in group-housed dairy cows is warranted. Further investigation is also warranted to determine if management strategies to alter FB, especially eating rate, can be effective in increasing feed efficiency in lactating dairy cattle.

Peripartum rumen-protected choline (RPC) supplementation is beneficial for cow health and production, yet the optimal dose is unknown. In vivo and in vitro supplementation of choline modulates hepatic lipid, glucose, and methyl donor metabolism. The objective of this experiment was to determine the effects of increasing the dose of prepartum RPC supplementation on milk production and blood biomarkers. Pregnant multiparous Holstein cows (n = 116) were randomly assigned to one of 4 prepartum choline treatments that were fed from -21 d relative to calving (DRTC) until calving. From calving until +21 DRTC, cows were fed diets targeting 0 g/d choline ion (control, CTL) or the recommended dose (15 g/d choline ion; RD) of the same RPC product that they were fed prepartum. The resulting treatments targeted: (1) 0 g/d pre- and postpartum [0.0 ± 0.000 choline ion, percent of dry matter (%DM); CTL]; (2) 15 g/d pre- and postpartum of choline ion from an established product (prepartum: 0.10 ± 0.004 choline ion, %DM; postpartum: 0.05 ± 0.004 choline ion, %DM; ReaShure, Balchem Corp.; RPC1_RD▸RD); (3) 15 g/d pre- and postpartum of choline ion from a concentrated RPC prototype (prepartum: 0.09 ± 0.004 choline ion, %DM; postpartum: 0.05 ± 0.003 choline ion, %DM; RPC2, Balchem Corp.; RPC2_RD▸RD); or (4) 22 g/d prepartum and 15 g/d postpartum from RPC2 [prepartum: 0.13 ± 0.005 choline ion, %DM; postpartum: 0.05 ± 0.003 choline ion, %DM; high prepartum dose (HD), RPC2_HD▸RD]. Treatments were mixed into a total mixed ration, and cows had ad libitum access via a roughage intake control system (Hokofarm Group). From calving to +21 DRTC, all cows were fed a common base diet and treatments were mixed into the total mixed ration (supplementation period, SP). Thereafter, all cows were fed a common diet (0 g/d choline ion) until +100 DRTC (postsupplementation period, postSP). Milk yield was recorded daily and composition analyzed weekly. Blood samples were obtained via tail vessel upon enrollment, approximately every other day from -7 to +21 DRTC, and at +56 and +100 DRTC. Feeding any RPC treatment reduced prepartum dry matter intake compared with CTL. During the SP, no evidence for a treatment effect on energy-corrected milk (ECM) yield was found, but during the postSP, RPC1_RD▸RD and RPC2_RD▸RD treatments tended to increase ECM, protein, and fat yields. During the postSP, the RPC1_RD▸RD and RPC2_RD▸RD treatments tended to increase, and RPC2_HD▸RD increased, the de novo proportion of total milk fatty acids. During the early lactation SP, RPC2_HD▸RD tended to increase plasma fatty acids and β-hydroxybutyrate concentrations, and RPC1_RD▸RD and RPC2_RD▸RD reduced blood urea nitrogen concentrations compared with CTL. The RPC2_HD▸RD treatment reduced early lactation serum lipopolysaccharide binding protein compared with CTL. Overall, peripartum RPC supplementation at the recommended dose tended to increase ECM yield postSP, but no evidence was seen of an additional benefit on milk production with an increased prepartum dose of choline ion. The effects of RPC on metabolic and inflammatory biomarkers support the potential for RPC supplementation to affect transition cow metabolism and health and may support the production gains observed.

α-1-Acid glycoprotein (AGP) is an acute-phase protein that may suppress dry matter intake (DMI), potentially by acting on the leptin receptor in the hypothalamus. Our objectives were to characterize plasma AGP concentration and associations with DMI during the transition period, and to determine the utility of AGP to identify or predict cows with low DMI. Plasma samples (n = 2,086) from 434 Holstein cows in 6 studies were analyzed on d -21, -13 ± 2, -3, 1, 3, 7 ± 1, 14 ± 1, and 21 ± 1 relative to parturition. A commercially available ELISA kit specific for bovine AGP was validated, and 2 internal controls were analyzed on each plate with interplate variation of 15.0 and 17.3%, respectively. Bivariate analysis was used to assess the relationship between AGP and DMI. For significant associations, treatment(study) was added to the model, and quadratic associations were included in the model if significant. Plasma AGP concentration (±SEM) increased from 213 ± 37.3 μg/mL on d -3 to 445 ± 60.0 μg/mL on d 14. On d 3, AGP was associated negatively with DMI in a quadratic manner for wk 1 and wk 2 and linearly for wk 3. Day 7 AGP was associated negatively with DMI in a quadratic manner for wk 2 and linearly for wk 3. Similarly, d 14 AGP was negatively associated with DMI for wk 3 and wk 4. As d 3 AGP concentration increased over the interquartile range, a calculated 1.4 (8.5%), 0.5 (2.7%), and 0.4 (1.9%) kg/d reduction in predicted DMI was detected during wk 1, 2, and 3, respectively. Using bivariate analysis, d 3 AGP explained 10% of the variation in DMI during wk 1. We explored the clinical utility of d 3 AGP to diagnose low DMI, defined as wk 1 DMI >1 standard deviation below the mean. Receiver operating characteristic analysis identified a threshold of 480.9 μg/mL, providing 76% specificity and 48% sensitivity (area under the curve = 0.60). Limited associations occurred between AGP and blood biomarkers; however, AGP was associated with plasma haptoglobin concentration postpartum and incidence of displaced abomasum, retained placenta, and metritis. These results demonstrate a negative association between plasma AGP concentration and DMI in early-postpartum dairy cows, although its diagnostic performance was marginal. Further investigation into whether AGP directly suppresses DMI in dairy cattle is warranted.