The objective of this clinical trial was to evaluate the effectiveness of probiotic, prebiotic, and synbiotic supplementation on average daily weight gain (ADG), duration of diarrhea, age at incidence of diarrhea, fecal shedding of Cryptosporidium oocysts, enteric pathogens, and the odds of pneumonia in preweaning dairy heifer calves on a commercial dairy. Feeding prebiotics and probiotics may improve health and production of calves. Hence, healthy Holstein heifer calves (n = 1,801) from a large California dairy were enrolled at 4 to 12 h of age and remained in this study until weaning at 60 d of age. Calves were block randomized to 1 of 4 treatments: (1) control, (2) yeast culture enriched with mannan-oligosaccharide (prebiotic), (3) Bacillus subtilis (probiotic), and (4) combination of both products (synbiotic), which were fed in milk twice daily from enrollment until weaning. Serum total protein at enrollment and body weight at 7, 42, and 56 d of age were measured. Fecal consistency was assessed daily for the entire preweaning period. A subgroup of 200 calves had fecal samples collected at 7, 14, 21, and 42 d for microbial culture and enumeration of Cryptosporidium oocysts by direct fluorescent antibody staining. Synbiotic-treated calves had 19 g increased ADG compared with control calves for overall ADG, from 7 to 56 d. From 42 to 56 d, prebiotic-treated calves had 85 g greater ADG and synbiotic-treated calves had 78 g greater ADG than control calves. There was no difference in duration of the first diarrhea episode, hazard of diarrhea, or odds of pneumonia per calf with treatment. Probiotic-treated calves had 100 times lower fecal shedding of Cryptosporidium oocysts at 14 d and prebiotic-treated calves had fewer Escherichia coli and pathogenic E. coli at 42 d compared with control calves. Although there were no effects on duration of diarrhea or pneumonia incidence, greater ADG in the late preweaning period may reflect treatment effects on enteric pathogens during the rearing process. The decreased shedding of Cryptosporidium should reduce infectious pressure, environmental contamination, and public health risks from Cryptosporidium. Our findings suggest ADG and potential health benefits for calves fed prebiotics, probiotics, and synbiotics and can help the dairy industry make informed decisions on the use of these products in dairy production.

Intramammary infections, which cause mastitis, can increase treatment and labor costs, decrease milk production, and affect milk quality. Meters that measure quarter somatic cell count (SCC) could be used to make more informed dry cow therapy decisions. The objective of this study was to compare the RT-10 iPhone adapter (RT-10; Dairy Quality Inc., Newmarket, ON, Canada), DeLaval Cell Counter (DSCC; DeLaval, Gurnee, IL, USA), Porta Check Quick Test (PortaCheck, White City, OR, USA), California Mastitis Test (ImmuCell, Portland, ME USA), pH meter (Hanna Instruments, Smithfield, RI, USA), electrical conductivity meter (OHAUS, Parsippany, NJ, USA), and the dual laser infrared temperature thermometer (Klein Tools, Lincolnshire, IL, USA) for measuring SCC in individual Holstein mammary quarters in comparison to a reference standard, the Fourier Transform Spectrometer 600 Combi System (Combi; Bentley Instruments, Chaska, MN, USA). Meters were evaluated using 658 individual cow quarter samples and 100 bulk-tank samples to measure SCC. Individual quarter milk samples from 160 cows from four commercial dairy herds were collected just before dry off and tested within 4 h of collection. To test bulk-tank SCC, 100 bulk-tank milk samples (25 mL) were collected from UC Davis Veterinary Medicine Teaching and Research Milk Quality Lab. Meter SCC values were regressed on observed Combi SCC. Goodness of fit was then evaluated by partitioning the mean square predicted error (MSPE). For individual quarter SCC, RT-10 had the highest coefficient of determination (R² = 0.86), lowest MSPE, and highest proportion of MSPE due to random variation (96%). Both the RT-10 and DSCC had the highest sensitivity and specificity for identifying quarter SCC above and below 200,000 cells/mL. For bulk-tank SCC, DSCC had the highest coefficient of determination (R² = 0.45), lowest MSPE, and highest proportion of MSPE due to random variation (80%). The RT-10 and DSCC could be used to measure individual quarter SCC to determine which cows to treat at dry off potentially reducing antibiotic use.

In practice, cows are fed by pen, but a diet is formulated to the nutrient requirements of a single cow. If the DM intake (DMI) of a pen were equal for all cows, this approach would have no error, but cows are grouped into pens on pregnancy and other management factors creating a distribution of DMI. The goal of precision feeding is to meet the requirements of individual animals to increase efficiency and reduce environmental impact but is not achieved when a group is fed as if the individuals have uniform requirements and the DMI distribution is not normal. The hypothesis of this work is that the DMI of cow pens are not normally distributed and the total DMI from the best-fit distribution shape for a cow pen will have lower percentage error to the observed DMI than a prediction of a single DMI that is fed at a uniform level and assumes a normal distribution. Our objective was to describe the distribution shape of DMI by week of lactation, and for different pen types. Pens were generated by randomly assorting cows by the week of lactation from a database into different categories of pen for size and lactation period. These pens were fitted to the best distribution type, and its parameters were used to randomly generate distribution plots that predict the total DMI for each pen. A second predictive model estimated the DMI of each pen using an empirical equation of DMI that was multiplied by the number of cows in the pen to represent feeding of a uniform DMI quantity. The percentage error for the distribution shape model was significantly lower than the empirical model with pen errors being less than 1%. The beta distribution type was the most common distribution to best represent the data of pen DMI. Describing the distribution and using it to predict a total pen DMI provides accurate estimates of feed quantity for a group. Reducing error by using the distribution of DMI for feed formulation, instead of the nutrient requirements of an individual animal can provide a precision nutrition approach to group feeding.

Prebiotics and probiotics are feed additives that can benefit the host by modulating the gut microbiome, which is crucial in digestion, immunity, and overall animal health. This study aims to evaluate the effects of supplementing prebiotics, probiotics, or synbiotics to preweaning Holstein calves on their future milk yield. This study is a retrospective analysis of milk yield records from dairy cows that were randomized at birth to 1 of 4 twice-daily treatments administered during the preweaning period: (1) control, no additive (CON), (2) prebiotic (PRE; 7 mL of Saccharomyces cerevisiae yeast culture), (3) probiotic (PRO; Bacillus subtilis and Lactobacillus plantarum, delivering ∼1 billion and 250 million cfu per head per day, respectively), or (4) synbiotic (SYN; combination of both PRE and PRO at the same dosages as the PRE and PRO treatments). The study was conducted on a dairy farm in Fresno County, California, between 2019 and 2023, involving 1,296 Holstein cows over their first 3 lactations for a total of 2,735 lactations. Monthly test day records for milk yield, fat, and protein were used to calculate ECM, standardized to 4% fat and 3.3% protein, totaling 26,464 monthly test day milk records. A 2-piece splines mixed-effect regression model evaluated the effect of treatments on ECM yield. For the first lactation, ECM yield was estimated at 28.66 kg on the first DIM, peaked at 42.1 kg, and declined to 21.34 kg by 305 DIM. For parity ≥2, ECM yield was 41.06 kg at 1 DIM, peaked at 54.2 kg, and 33.74 kg at 305 DIM. The SYN treatment increased ECM yield by 1.00 kg/d compared with CON. This increase was primarily due to an increase in milk fat yield, with 0.048 kg/d more fat produced compared with the control group. No differences in ECM yield between PRE, PRO, or CON were observed. These findings suggest that supplementing SYN during the preweaning period increased milk, milk fat, and ECM yield across lactations 1, 2, and 3.

Current rations are formulated on the requirements of a representative cow plus a correction factor. This is not precision feeding because in practice cows are fed as a pen, and within a pen, dry matter intake (DMI) among cows is not uniform and has skew or kurtosis. Since ration formulation depends on DMI as a binding constraint, rations formulated to the pen DMI could be more precise if the distribution of DMI is better described. The objectives of this study were to: 1. Develop an algorithm, the distribution shape model (DSM), to predict pen DMI based on the distribution of cow measures which could easily be estimated on a commercial dairy farm; and 2. Compare predictions of pen DMI from the DSM to predictions of pen DMI based on a representative cow using the National Academies of Sciences, Engineering, and Medicine (NASEM) equation model (NSM). Individual cow DMI from previously published studies was used to train an Extreme Gradient Boosting (XGB) algorithm to predict the distribution shape of pen DMI (Beta or Generalized Normal) from parity, week in milk, and descriptive statistics of milk yield. The NSM predicted DMI for a representative cow from pen-average inputs, and this prediction was multiplied by the number of cows, simulating the scenario where DMI is assumed to be uniform across a pen. Both models were tested by comparing predicted with observed total pen DMI using an independent testing dataset of 229,909 pens generated from previously collected cow observations. For pens classified as Beta, the mean squared error of prediction (MSEP) of pen DMI from the DSM and NSM was 5249 and 9736 kg², respectively (root mean squared error of prediction, RMSEP, 72.5 and 98.7 kg/d), with 3.0% and 80.2% due to mean bias, 5.4% and 8.3% due to regression slope not equal to 1, and 91.6% and 11.5% due to random variation. For pens classified as Generalized Normal, MSEP was 2086 and 6431 kg² (RMSEP 45.7 and 80.2 kg/d), with 0.8% and 73.2% due to mean bias, 7.6% and 4.7% due to slope not equal to 1, and 91.6% and 22.1% due to random variation. The NSM consistently overpredicted pen DMI, contributing to its higher error. Including the distribution shape in the prediction of DMI improved precision and accuracy of pen DMI prediction and should be considered when estimating the total daily DMI fed to a pen.