Heat stress is a growing concern in cattle production systems due to the increasing frequency and intensity of extreme weather events driven by climate change. This review synthesizes current knowledge on the multifaceted impacts of heat stress and focuses on nutritional strategies to mitigate its effects on ruminating cattle. A comprehensive literature search was conducted using PubMed, Scopus, Web of Science, and Google Scholar. Heat stress adversely affects cattle physiology, behavior, rumen function, and overall productivity, particularly in dairy animals with high metabolic activity. During heat stress episodes, changes in the microbial population have been reported; however, there is no clear consensus, as findings vary widely among studies depending on diet, feed intake, animal type and experimental design. This variability limits the ability to draw general conclusions regarding changes in the rumen microbiome driven by heat stress. In this context, dietary nutritional intervention strategies offer a practical and scalable approach to enhance thermotolerance and maintain performance under heat stress conditions. Key nutritional strategies include modifications in diet composition to reduce metabolic heat production, with some approaches carrying potential risks to animal health, e.g., increasing dietary energy density through concentrates while minimizing forage content. Supplementation with rumen-protected nutrients like amino acids, vitamins, and minerals can be used to support immune function, antioxidant capacity, and metabolic stability. Polyphenols and betaine contribute to oxidative stress reduction and gut integrity, while probiotics may be used to improve rumen fermentation and nutrient utilization. Sensor technologies, including rumen boluses and wearable devices, offer the potential to monitor physiological responses to heat stress in real time and offer opportunities for precision feeding and early intervention. Most published studies only cover short periods of heat stress, and there is a lack of in vitro models simulating rumen hyperthermia. In parallel, future research should therefore prioritize longitudinal, in vivo trials that integrate physiological, metabolic, and microbial responses to understand the long term and systemic effect of heat stress. In addition, controlled trials in commercial settings are necessary to prove the transferability of results to commercial herds. A multidisciplinary approach combining nutritional, environmental, and technological strategies is likely to play an important role in safeguarding cattle welfare and productivity in a warming climate.

In 2022, New York had over 620,000 dairy cows producing more than 7 million Mg (15 billion pounds) of milk, ranking fifth in dairy producing states in the United States. The objectives of this work were to (1) estimate total farm-gate GHG emissions and GHG emission intensity (GHGei) of 36 medium to large (>300 mature cows) commercial New York dairies, (2) determine the contribution of main GHG (on-farm methane [CH4], nitrous oxide [N2O], and carbon dioxide [CO2], plus embedded emissions [CO2 equivalents; CO2eq]) and sources (enteric fermentation, feed production, manure management, grazing, fuel, and energy) to farm-gate GHGei, and (3) identify key performance indicators (KPI) driving farm-gate GHGei. Assessments were done for 2022 using the Cool Farm Tool. Farm size ranged from 345 to 6,350 head of predominantly Holstein cows, with animal densities between 1.76 and 4.85 animal units per hectare (0.71-1.96 animal units per acre), and heifer:cow ratios between 0.02 and 0.49. Herds produced an average fat and protein corrected milk (FPCM) yield of 12.7 Mg (29,000 lb) FPCM cow-1 per year using 64% homegrown feed. Total FPCM production was 873,000 Mg (1.92 billion lb), representing ∼12% of the total New York milk production in 2022. The GHGei ranged from 0.63 to 1.06 kg CO2eq kg FPCM-1 (mean GHGei = 0.86 kg CO2eq kg FPCM-1). Methane was the biggest contributor, accounting for 60% of total GHG emissions on average, with enteric CH4 as the largest contributor (45% of total farm emissions). Among farms, feed production emissions accounted for about 25%, with ∼7% from homegrown feed production. Manure management practices accounted for about 20% of emissions and explained the largest amount of variation in GHGei among farms. Potential KPI for GHGei included manure management system, heifer:cow ratio, herd feed consumption intensity, percentage of homegrown feed, and crop nutrient source (fertilizer vs. manure). Emission intensity reflected the high proportion of good quality homegrown feed, careful nutrient management, and use of manure treatment systems (covered liquid slurry storages, anaerobic digesters) on several dairies. The influence of replacement rate and heifer:cow ratio on animal density, herd feed consumption intensity, and subsequent GHGei requires more detailed analysis. The farms in this study represent a considerable proportion of New York's 2022 FPCM production. Greater participation by smaller farms is necessary to draw conclusions for New York's dairy industry as a whole.

Whether lactose or fat is a better fuel for protein synthesis and deposition in young calves has remained controversial. Also, it remains uncertain whether bioactive factors in whole milk might influence growth compared with a milk replacer of similar macronutrient composition. We used Holstein calves (3 wk old, 46.0 kg BW) to determine effects of energy source on growth and body composition over a 35-d experiment. Calves (n = 29) were assigned either to an initial body composition group (n = 11) or to 1 of 3 treatment groups (n = 6 per group). The treatments were (1) a conventional milk replacer (CMR; 22.3% CP, 21.4% fat, and 49.1% lactose); (2) a high-fat milk replacer formulated to be similar to whole milk (HFMR; 25.4% CP, 27.4% fat, 40.1% lactose); or (3) whole milk (25.4% CP, 27.1% fat, 41.9% lactose). Reconstituted milk replacers were fed at a rate of 14% of BW for CMR and 11.65% of BW for HFMR, and whole milk was fed at 11.65% of BW. Initial intakes during wk 1 of CP and ME were similar among diets, but this condition was not maintained for the remainder of the study. Final BW and gain-to-feed ratio were not different among diets, but ADG and empty BW gain were greater for calves fed CMR. Days with fecal score ≥4 (5-point scale) were greater for calves fed HFMR than for those fed whole milk. Liver weight was greater for calves fed CMR than for those fed HFMR or milk. Final body composition did not differ among treatments except that protein percentage was lower for CMR than for HFMR and milk, and higher for HFMR than for milk. Composition of gain behaved similarly to results for whole-body composition. Efficiency of ME use for gain was similar among treatments, whereas efficiency of use of dietary CP for gain was greater for milk than for HFMR. Apparent partial efficiency of use of intake fat was greater for calves fed CMR than for those fed HFMR or milk. Concentrations of IGF-1, insulin, and total protein in plasma did not differ among diets, whereas glucose was higher for calves fed whole milk than those fed HFMR. Plasma urea-N was lower for CMR than for HFMR and milk, and lower for HFMR than for milk. Plasma NEFA were lower for CMR than for HFMR and milk, and higher for HFMR than milk. A greater nutrient supply from a high-lactose milk replacer supported greater gains of lean tissue than higher fat diets. At constant nutrient intakes, whole milk supported more efficient use of dietary CP and shortened the duration of abnormal fecal score occurrences than a milk replacer of similar composition.

Accurate prediction of intestinal digestible flows of EAA (DigFlowEAA) is a crucial step for adequately balancing EAA supply to reduce the amount of CP fed to dairy cows, without compromising either milk production or the health status of the animals. The objective of this meta-analysis was to compare the performance of 3 dairy feed evaluation systems (FES) to predict DigFlowEAA (NRC; National Academies of Sciences, Engineering and Medicine [NASEM]; and Cornell Net Carbohydrate and Protein System version 6.5.5 [CNCPS]) in relation to observed net portal appearance (NPAobs) of EAA. Although the DigFlowEAA cannot be measured directly and none of the FES were designed to estimate the net portal appearance (NPA) of EAA, NPA could be predicted (NPApred) from estimations of DigFlowEAA minus the estimated metabolic fecal protein (MFP). This NPApred, however, would not include any oxidation for EAA or synthesis for Arg, a semi-EAA, occurring during the absorption process. Intuitively, the prediction errors should be smallest for the DigFlowEAA best predicted across the 3 FES and for the EAA not oxidized by the portal-drained viscera (PDV). The dataset included 83 NPAobs treatment means from 25 studies. To avoid type I error, mean and linear biases were considered biologically relevant if statistically significant and representing >5.0% of the observed mean (%obs.mean). The NPApred of branched-chain AA (BCAA) and Thr showed a similar pattern across the 3 FES, with all overpredicted relative to NPAobs (6%obs.mean to 27%obs.mean). The NPApred for the other EAA were more variable: (1) underprediction of Arg (9%obs.mean to 20%obs.mean) with NRC and NASEM, and Met (8%obs.mean) with NRC, (2) overprediction of Lys and Phe (5%obs.mean to 11%obs.mean) with NASEM, and His, Met and Trp (8%obs.mean to 14%obs.mean) with CNCPS, and (3) linear biases for Arg (7%obs.mean) with NASEM, and for His, Lys, Met, and Phe (5%obs.mean to 14%obs.mean) with CNCPS. In our previously reported meta-analysis, the mean and linear biases between observed and predicted EAA postruminal outflow were related to the site of digesta sampling (duodenal vs. omasal) for some EAA, but it was not possible to determine which sampling site was representative of the true supply of EAA to the cows. In an effort to solve this issue, predictions of NPA (except Trp) were recalculated removing the mean and linear biases observed in duodenal and omasal studies of our previous meta-analysis (NPArec_duo and NPArec_oma). Compared with NPApred, the pattern of BCAA and Thr remained overpredicted in NPArec_duo and NPArec_oma across the 3 FES. This strongly suggests oxidation of Ile, Leu, Val, and Thr by the PDV averaging, respectively, 12% (range 6% to 18%), 16% (range 14% to 17%), 24% (range 18% to 27%), and 19% (range 13% to 23%) of NPAobs across the 3 FES. The magnitude of PDV oxidation, however, would be related to the ratio of DigFlowEAA to digestible energy intake. The negative mean biases for Arg NPApred would suggest synthesis by the PDV. For the BCAA, Thr, and Arg, because of the uncertainty on the biological quantification of the differences between NPAobs and NPApred, the relative performance of each FES could not be assessed. Assuming no oxidation of the other EAA (i.e., His, Lys, Met, and Phe) by the PDV and an adequate estimation of MFP, their NPAobs indicate that the DigFlow of (1) Met is underpredicted by NRC, (2) Lys is overpredicted by NASEM, (3) His and Met are overpredicted with CNCPS, and (4) His, Lys, Met, and Phe present a linear bias with CNCPS, supporting similar observations on the postruminal EAA outflows from our previous meta-analysis. Based on current NPAobs studies and our previous meta-analysis on postruminal outflows of EAA, sampling digesta at the duodenum appears to be more representative of the true supply of EAA to the cows than sampling at the omasum.

Assessing transfer of passive immunity (TPI) is a critical management strategy to evaluate colostrum management and feeding; however, variability in hemoconcentration or serum or plasma volume in calves might influence TPI assessment. The objectives of this study were to 1) describe the variability in hemoconcentration as well as TPI in Holstein calves in New York State and 2) describe the effect of adjusting total protein (TP) for the degree of hemoconcentration by applying a sample average proportion of plasma in blood (PP) on TPI assessment. Records of TP and PP from 703 1 to 9 d of age Holstein calves from 19 commercial dairy farms were analyzed. The PP was determined by centrifugation of microhematocrit tubes and serum, and plasma TP was determined by digital refractometry. Transfer of passive immunity was categorized using unadjusted TP (uTP) as excellent = ≥ 6.2, good = 5.8–6.1, fair = 5.1–5.7, and poor = < 5.1 g/dL. Individual calf TP concentrations were adjusted to the sample average PP (aTP) and TPI categories were reassessed using aTP. The sample mean ± SD (range) PP was 68.8 ± 5.76 (50.5 to 86.0)%. The PP was lower on d 1 compared with d 7 of age. Using uTP to categorize TPI, 22 (3.1%) calves had poor, 113 (16.2%) calves had fair, 164 (23.6%) calves had good, and 397 (57.1%) calves had excellent TPI, respectively. After adjusting TP for hemoconcentration, TPI determined using aTP resulted in 52 (7.5%, + 4.4 percentage points) calves in poor, 137 (19.7%, + 3.5 percentage points) calves in fair, 122 (17.5%, - 6.1 percentage points) calves in good, and 385 (55.3%, - 1.8 percentage points) calves in excellent. The mean (range) proportion of calves with TPI determined using uTP by farm was 3.9 (0 to 16)% for poor, 19.0 (2 to 36)% for fair, 25.3 (10 to 42)% for good, 51.8 (26 to 83)% for excellent. When categorized using aTP, the proportion of calves by farm was 8.1 (0 to 21)% in poor, 20.5 (8 to 42)% in fair, 19.1 (6 to 33)% in good, 52.4 (27 to 83)% in excellent TPI. In conclusion, PP was variable in calves during the time of TPI assessment and this variability should be considered when assessing TPI at the calf- or herd-level.

Dairy cattle excreta are a valuable source of orthophosphate (Ortho-P), an inorganic form of phosphorus (P) that is readily available for microorganisms, plant growth, and development. There is, however, a growing environmental concern about the potential negative environmental impact of excessive amounts of Ortho-P excretion, which can lead to the eutrophication of water bodies. As a result, the development of mathematical equations to quantify and manage Ortho-P excretion on dairy farms could prove valuable for environmental sustainability. This study aimed to use literature data to develop empirical predictions for Ortho-P (g/kg dry matter [DM]) excretion using total P (TP [g/kg DM]) content of dairy cattle feces (Ortho-P f ) and manure (Ortho-P m ). Data sets from studies that evaluated and characterized the different forms of P in feces and manure from dairy cattle were compiled. After outlier exclusion, the final retained database for feces included 37 treatment means from 4 published papers while the manure comprised 23 treatment means from 7 published papers. A linear-mixed model was used to develop the predictive equations, incorporating the random effect of the study. A leave-one-out cross-validation procedure was used to evaluate the predictive ability of the developed models, whereby studies were regarded as folds. The fecal equation was determined as Ortho-P f (g/kg DM) = -2.447 (0.572) + 0.966 (0.083) x TP (g/kg DM) (R 2 = 0.79) and resulted in a root mean square prediction error as a percentage of mean observed value (RMSPE, %) of 32.8% and error due to random sources of 97.6%. Additionally, the manure equation was determined as Ortho-P m (g/kg) = -0.204 (0.446) + 0.590 (0.065) x TP (g/kg) (R 2 = 0.77) and had an RMSPE of 43.3% with a random error of 93.9%. Both models revealed minimal mean and slope biases on feces and manure data. Findings suggest that these sets of equations can be used to estimate excreted Ortho-P from total excreted P, helping nutritionists and farmers to understand the impact of dietary P changes on the environment. Further, these equations can be incorporated into extant models such as the Cornell Net Carbohydrate and Protein System (CNCPS) to aid in understanding and mitigating P and Ortho-P excretion from dairy cattle and to clarify the portion of P that migrates more rapidly into watersheds.

Determination of energy requirements for growth depends on measuring the composition of body weight (BW) gain. Previous studies have shown that the composition of gain can be altered in young dairy calves by composition of the milk replacer diet. Here, our objective was to determine body composition and the composition of empty body gain in young calves fed increasing amounts of a milk replacer containing adequate CP. Male Holstein calves underwent an adjustment period of 14 d after birth in which they were fed whole waste milk at 10% of BW. Calves were then stratified by BW and randomly assigned to either an initial harvest group (n = 11) or to groups fed 1 of 3 milk replacer amounts and harvested after 35 d of growth. All treatments consumed the same milk replacer containing 24.8% CP (dry matter [DM] basis; from all milk proteins) and 18.9% fat, reconstituted to 12.5% solids. Treatments were milk replacer fed at 1.25% of BW (DM basis; n = 6), 1.75% of BW (n = 6), or 2.25% of BW (n = 8), adjusted weekly as calves grew. Calves fed at 1.25% or 1.75% of BW were fed twice daily and those fed 2.25% of BW were fed 3 times daily. No starter was offered. Post harvest, the bodies of calves were separated into 4 fractions: carcass; total viscera minus digesta; head, hide, feet, and tail; and blood. The sum of those 4 fractions was empty BW, which increased linearly as amount of milk replacer increased. Final heart girth and body length, but not withers height, increased linearly as intake increased. Gain:feed increased linearly with increasing milk replacer. Feeding more milk replacer increased the amounts of lean tissue and fat in the body. The percentages of water and protein in the final body decreased linearly, whereas fat percentage and energy content increased linearly as intake increased. As gain increased, the percentage of protein in gain decreased and the percentage of fat increased, resulting in an increase of energy content of EBW gain. Efficiency of energy use (retained energy:gross energy intake) increased linearly but retained energy:metabolizable energy available for growth was not different among treatments. Efficiency of protein use increased quadratically as feeding rate increased; there was no further increase at 2.25% of BW. Plasma insulin-like growth factor 1, insulin, and glucose increased linearly, whereas urea-N decreased linearly, as milk replacer intake increased. Our data document changes in body composition that affect estimates of retained energy in the bodies of calves harvested at a common age. These data are important for calculations of energy requirements for young calves.

The prepartum diet as well as individual metabolic status of the cow influences colostrum parameters. The objectives of this study were to 1) investigate the effect of increasing prepartum dietary MP supply on colostrum yield, composition, and immunoglobulin G (IgG) concentration, and 2) identify prepartum metabolic indicators associated with these outcomes. Multiparous Holstein cows (n = 96) were blocked by expected calving date and randomly assigned to 1 of 2 prepartum diets formulated to contain a control (CON; 85 g of MP/kg DM; 1,175 g of MP/d) or high (HI; 113 g of MP/kg DM; 1,603 g of MP/d) level of MP starting at 28 d before expected calving. Both prepartum diets were formulated to supply Met and Lys at an equal amount of 1.24 and 3.84 g/Mcal of metabolizable energy (ME), respectively. Metabolic indicators were determined in serum (albumin, glutamate dehydrogenase, cholesterol, aspartate transaminase, total protein, total bilirubin, and IgG) or plasma (Ca, glucose, fatty acids, BHB, and urea nitrogen) twice weekly in a subset of cows (n = 60). Colostrum was harvested at 3.6 ± 2.4 h from calving and yield as well as concentrations of IgG, fat, protein, and Ca were determined. Cows were retrospectively grouped based on the typical volume of colostrum needed for 2 colostrum meals (<6 or ≥ 6 kg), IgG concentration (<100 or ≥ 100 g/L), as well as the median concentrations of fat (<4.4 or ≥ 4.4%), protein (<16.5 or ≥ 16.5%), Ca (<0.21 or ≥ 0.21%), and total colostrum ME (<8.65 or ≥ 8.65 Mcal). Data were analyzed using mixed effects ANOVA, with repeated measures where applicable. Feeding HI tended to increase colostrum yield in cows entering parity 2 (9.4 vs. 7.2 ± 0.9 kg), but treatment did not affect yield from cows entering parity ≥3 (5.1 vs. 6.4 ± 1.0 kg). Supply of MP did not affect concentrations of IgG, fat, protein, or Ca. Cows that produced ≥ 6 kg vs. those producing <6 kg of colostrum had lower plasma concentrations of glucose. Metabolic indicators were not associated with IgG group. Colostrum fat ≥4.4% was associated with cows having lower prepartum concentrations of glucose, total protein, albumin, and aspartate transaminase activity. Colostrum protein ≥ 16.5% was associated with lower circulating serum IgG and elevated cholesterol. Elevated glucose as well as lower cholesterol and BHB concentrations were associated with colostrum Ca ≥ 0.21%. Further, higher albumin and fatty acids as well as lower glucose concentrations were associated with a greater colostrum energy output. In conclusion, increasing prepartum MP supply tended to increase colostrum yield in cows entering parity 2, but did not affect the composition or IgG concentration. The observed associations between metabolic indicators and colostrum parameters suggest that slight adjustment in metabolism during late gestation might be necessary to support colostrogenesis, but the causality of these relationships should be considered.

The influence of diet composition on the degree of adipose and lean muscle mobilization and concentrations of circulating AA has been demonstrated during the transition period. Altering the MP supply might offer a strategy to control tissue mobilization and increase circulating AA availability, but the optimum supply of MP fed pre- and postpartum remains unknown. We investigated the effect of increasing the MP supply in the prepartum, postpartum, or both diets on plasma AA concentrations and ultrasound and circulating indicators of tissue mobilization. Multiparous Holstein cows (n = 96) were assigned to 1 of 4 treatment groups at 28 d before expected calving following a randomized block design. Prepartum diets were formulated to contain either a control (CON; 85 g of MP/kg DM; 1,175 g of MP/d) or high (HI; 113 g of MP/kg DM; 1,603 g of MP/d) level of estimated MP. From calving to 21 DIM, fresh diets were formulated to contain either a control (CON; 104 g of MP/kg DM; 2,044 g of MP/d) or high (HI; 131 g of MP/kg DM; 2,685 g of MP/d) level of estimated MP. To control the potential confounding effect of Met and Lys supply, diets were formulated to supply an equal amount at 1.24 and 3.84 g/Mcal of ME in both prepartum diets and 1.15 and 3.16 g/Mcal of ME in both postpartum diets, respectively. The combination of a pre- and postpartum diet resulted in 4 treatment groups: (1) CON-CON (CC; n = 23), (2) CON-HI (CH; n = 24), (3) HI-CON (HC; n = 22), and (4) HI-HI (HH; n = 23). A common lactation diet (113 g of MP/kg DM; 2,956 g of MP/d) was fed from 22 DIM to the end of the observation period at 42 DIM. Transcutaneous ultrasonography was used to determine the longissimus dorsi muscle diameter and backfat thickness. Concentrations of plasma AA, 3-methylhistidine (3MH), and creatinine were determined on a subset of cows (n = 60) using ultra-high-performance liquid chromatography and MS. Treatment did not affect the longissimus dorsi muscle diameter from -14 to 21 d relative to calving, but the diameter was greater in CH compared with HH at 40 DIM. Backfat thickness and the ratio of 3MH to creatinine did not differ by treatment. Concentrations of EAA were greater at -13 d relative to calving in HH compared with CC and CH and at -6 d relative to calving EAA concentrations were higher in HC compared with CC. Cows fed the HI diet postpartum had elevated EAA concentrations at 6 and 20 DIM compared with cows fed the CON postpartum diet but EAA concentration did not differ at 40 DIM. Total NEAA concentrations were higher in CH compared with HC and HH at -6 d relative to calving, but NEAA concentration did not differ by treatment at -13, 6, 20, or 40 d relative to calving. In conclusion, increasing the supply of MP fed prepartum, postpartum, or both had minimal effects on tissue mobilization but influenced concentrations of plasma AA.

Quantification of potassium (K) excretion in dairy cattle is important to understand the environmental impact of dairy farming. To improve and monitor the environmental impact of dairy cows, there is a need for a simple, inexpensive, and less laborious method to quantify K excretion on dairy farms. The adoption of empirical mathematical models has been shown to be a promising tool to address this issue. Thus, the current study aimed to develop empirical predictive models for K excretion in dairy cattle from urine and feces that can help evaluate efficiency and monitor the environmental impact of milk production. To develop urine K (K-Ur, g/d) and fecal K (K-Fa, g/d) excretion prediction models, published literature that involved 45 and 54 treatment means from 10 and 14 studies, respectively, were used. Some studies reported either urinary or fecal K excretion or both, but in total, treatment means used to develop the models were from 17 studies. The linear mixed models were fitted with the fixed effect of K intake, DMI, dietary K content, urine volume, milk yield, and water intake, and the random effect of study weighted according to the number of observations. Leave-one-study out cross-validation was used to evaluate the performance of the proposed models and the best model was based on the lowest root mean square prediction error as a percentage of the observed mean values (RMSPE%) and highest concordance correlation coefficient (CCC). As expected, most daily K excretion was through urine (202.5 +/- 92.1 g/d) than through feces (43.5 +/- 21.0 g/d), and among the proposed models, the model including dietary K concentration showed poor predictive ability for both K-Ur and K-Fa with the lowest CCC values (-0.15 and -0.02, respectively) and systematic bias. The model developed using DMI to predict K-Fa excretion showed reasonable accuracy, as indicated by RMSPE, CCC, and R-marginal(2) of 46.6%, 0.42, and 48%, respectively. Among the proposed models for K-Ur and K-Fa, the model with K intake demonstrated better predictive performance, showing minimal systematic bias and random errors due to data variability of >92%. While these proposed models suggested that reducing K intake can lead to a decrease in K excretion, it is important to ensure that dairy cows receive adequate amounts of this nutrient to maintain optimal health and productivity, especially during periods of heat stress.

The objective of this study was to investigate the effect of increasing MP supply in the prepartum, postpartum, or both diets on intake, performance, and metabolic indicators. Multiparous Holstein cows (n = 96) were assigned to 1 of 4 treatment groups at 28 d before expected calving following a randomized block design. Prepartum diets were formulated to contain either a control (CON; 85 g of MP/kg DM) or high (HI; 113 g of MP/kg DM) level of estimated MP. From calving to 21 DIM, diets were formulated to contain either a CON (104 g of MP/kg DM) or HI (131 g of MP/kg DM) level of estimated MP. To control the potential confounding effect of Met and Lys supply, diets were formulated to supply an equal amount at 1.24 and 3.84 g/Mcal of ME in both prepartum diets and 1.15 and 3.16 g/Mcal of ME in both postpartum diets, respectively. The combination of a pre- and postpartum diet resulted in 4 treatment groups: (1) CON-CON (CC; n = 23), (2) CON-HI (CH; n = 24), (3) HI-CON (HC; n = 22), and (4) HI-HI (HH; n = 23). A common lactation diet (113 g of MP/kg DM) was fed from 22 DIM to the end of the observation period at 42 DIM. Milk yield and DMI were collected daily, and plasma metabolic indicators (BHB, fatty acids [NEFA], PUN, and glucose) were determined twice weekly from -28 to 28 d relative to calving and once weekly from 29 to 42 DIM. Samples with BHB >= 1.2 mmol/L between 3 and 10 DIM were considered hyperketonemia events. Milk composition was determined weekly. Milk yield during 1 to 21 DIM was greater in HH (44.7 f 1.0 kg/d) compared with CC (39.2 f 1.0 kg/d) and HC (38.0 f 1.0 kg/d) and milk yield in CH (42.4 f 0.9 kg/d) was greater than HC, respectively. From 22 to 42 DIM, milk yield was greater in CH (53.3f 1.0 kg/d) and HH (54.1 f 1.0 kg/d) compared with CC (49.6 f 1.0 kg/d) and HC (49.3 f 1.0 kg/d). Dry matter intake (% of BW) and concentrations of milk protein, fat, and total solids were not affected by treatment. Prepartum concentrations of PUN were greater in HI compared with CON. From 1 to 21 DIM, PUN concentrations were greater in CH and HH compared with CC and HC. From 1 to 21 DIM, concentrations of glucose were lower in HH compared with HC, and BHB were greater in CH and HH compared with HC. Concentrations of NEFA, as well as the number of hyperketonemia events did not differ by treatment during this time. From 22 to 42 DIM, concentrations of NEFA were greater in HH compared with HC and concentrations of BHB were greater in CH and HH compared with HC. Overall, feeding CH or HH increased lactation performance without altering intake or hyperketonemia events. Results from this study support formulating a fresh diet to reduce the negative MP balance during early lactation.

The use of zwitterionic-hydrophilic interaction liquid chromatography (Z-HILIC) columns for analysis of underivatized analytes has allowed simpler sample preparation of bovine plasma for sensitive and selective analysis, when coupled with mass spectrometry (MS). The objective of this study was to evaluate and validate this analytical technique to measure AA and metabolites in bovine plasma at 2 deproteinization times. A robust method using Z-HILIC coupled to a triple quadrupole MS (TQMS) was evaluated and validated to quantitatively analyze 19 AA using isotope dilution and 8 AA metabolites qualitatively in bovine deproteinized plasma. The timing of deproteinization was investigated to determine if plasma should be deproteinized upon collection (on-site) or immediately before analysis (in-lab). Analytes were separated using a Z-HILIC column in a 21 min run and analyzed with a TQMS in positive electrospray ionization for identification and quantification. The method was validated for standard curve linearity, limits of detection (LOD) and quantification (LOQ), intra- and interday precision (% coefficient of variation; CV), recovery (%), and freeze-thaw stability (% CV) after 1 mo. Coefficients of determination (R2) were over 0.993, and LOD and LOQ were below measured values for all AA. The CV for the intraday and interday precision were below 18%, except for cystine (Cys2) and Orn in-lab. Recoveries on-site and in-lab ranged from 75% to 120% for all analytes except Cys2 in-lab. Most analytes were stable after 1 mo of freezing regardless of deproteinization timing, CV <25%, except for hydroxyproline (Hyp). The concentration of Cys2 was affected by deproteinization in-lab compared with on-site, and even though Glu and Hyp were different between the 2 deproteinization timings, the concentrations between the 2 timings were within the standard deviation.

Adequate prediction of postruminal outflows of essential AA (EAA) is the starting point of balancing rations for EAA in dairy cows. The objective of this meta-analysis was to compare the performance of 3 dairy feed evaluation systems (National Research Council [NRC], Cornell Net Protein and Carbohydrate System version 6.5.5 [CNCPS], and National Academies of Sciences, Engineering and Medicine [NASEM]) to predict EAA outflows (Trp was not tested). The data set included a total of 354 treatment means from 70 duodenal and 24 omasal studies. To avoid Type I error, mean and linear biases were considered of concern if statistically significant and representing >5.0% of the observed mean. Analyses were conducted on raw observed values and on observations adjusted for the random effect of study. The analysis on raw data indicates the ability of the feed evaluation system to predict absolute values whereas the analysis on adjusted values indicates its ability to predict responses of EAA outflows to dietary changes. For the prediction of absolute values (based on raw data), NRC underpredicted outflows of all EAA, from 5.3% to 8.6% of the observed mean (%(obs.mean)) except for Leu, Lys, and Val; NASEM overpredicted Lys (10.8%(obs.mean)); and CNCPS overpredicted Arg, His, Lys, Met, and Val (5.2 to 26.0%(obs.mean)). No EAA had a linear bias of concern with NASEM, followed by NRC for His (6.8%(obs.mean)), and CNCPS for all EAA (5.6 to 12.2%(obs.mean)) except Leu, Phe, and Thr. In contrast, for the prediction of responses to dietary changes (based on adjusted data), NRC had 2 EAA presenting a linear bias of concern, followed by NASEM and CNCPS with 4 and 6 EAA, respectively. Predictions of His showed a linear bias of concern (5.3 to 9.6%(obs.mean)) with the 3 feed evaluation systems. Measured chemistry of crude protein and EAA were reported for 1 or more feed ingredients of the ration in 36% of the studies, and resulted in decreased linear biases in the 3 feed evaluation systems. The difference in mean biases of Met outflows was systematically positive when comparing omasal versus duodenal studies. Predictions of Met outflows with NRC had a higher concordance correlation coefficient in duodenal (used to develop NRC equations) versus omasal studies, whereas the opposite was observed with CNCPS, the latter showing the lowest mean bias for Met in omasal sampling studies. The 30% difference in Met mean biases between sampling sites appeared related to a similar difference found for observed Met versus nonammonia nitrogen outflows between duodenal and omasal studies, which is independent of predictions. In conclusion, NRC and NASEM yielded accurate predictions of EAA outflows, with a small superiority of NASEM to predict absolute values, and slight superiority of NRC to predict the responses to dietary changes. In comparison, CNCPS may present mean and linear biases of concern for many EAA. Moreover, it remains to determine which sampling site is more representative of the true supply of EAA to the cows.

The use of zwitterionic-hydrophilic interaction liquid chromatography (Z-HILIC) columns for analysis of underivatized analytes have allowed simpler sample preparation of bovine plasma for sensitive and selective analysis, when coupled with mass spectrometry (MS). The objective of this study was to evaluate and validate this analytical technique to measure AA and metabolites in bovine plasma at 2 deproteinization times. A robust method using Z-HILIC coupled to a triple quadrupole MS (TQMS) was evaluated and validated to quantitatively analyze 19 AA using isotope dilution and 8 AA metabolites qualitatively in bovine deproteinized plasma. The timing of deproteinization was investigated to determine if plasma should be deproteinized upon collection (on-site) or immediately before analysis (in-lab). Analytes were separated using a Z-HILIC column in a 21 min run and analyzed with a TQMS in positive electrospray ionization for identification and quantification. The method was validated for standard curve linearity, limits of detection (LODs) and quantification (LOQs), intra- and inter-day precision (%CV), recovery (%), and freeze-thaw stability (%CV) after one month. Coefficients of determination (R2) were over 0.993, and LODs and LOQs were below measured values for all AA. The CV for the intraday and interday precision were below 18%, except for cystine (Cys2) and Orn in-lab. Recoveries on-site and in-lab ranged from 75% to 120% for all analytes except Cys2 in-lab. Most analyte were stable after one month of freezing regardless of deproteinization timing, CV <25%, except for hydroxyproline (Hyp). The concentration of Cys2 was affected by deproteinization in-lab compared with on-site (P < 0.001), and even though Glu and Hyp were different between the 2 deproteinization timings (P < 0.01), the concentrations between the 2 timings were within the standard deviation.

Greenhouse gas emissions, such as enteric methane (CH4) from ruminant livestock, have been linked to global warming. Thus, easily applicable CH4 management strategies, including the inclusion of dietary additives, should be in place. The objectives of the current study were to: (i) compile a database of animal records that supplemented monensin and investigate the effect of monensin on CH4 emissions; (ii) identify the principal dietary, animal, and lactation performance input variables that predict enteric CH4 production (g/d) and yield (g/kg of dry matter intake DMI); (iii) develop empirical models that predict CH4 production and yield in dairy cattle; and (iv) evaluate the newly developed models and published models in the literature. A significant reduction in CH4 production and yield of 5.4% and 4.0%, respectively, was found with a monensin supplementation of ≤24 mg/kg DM. However, no robust models were developed from the monensin database because of inadequate observations under the current paper's inclusion/exclusion criteria. Thus, further long-term in vivo studies of monensin supplementation at ≤24 mg/kg DMI in dairy cattle on CH4 emissions specifically beyond 21 days of feeding are reported to ensure the monensin effects on the enteric CH4 are needed. In order to explore CH4 predictions independent of monensin, additional studies were added to the database. Subsequently, dairy cattle CH4 production prediction models were developed using a database generated from 18 in vivo studies, which included 61 treatment means from the combined data of lactating and non-lactating cows (COM) with a subset of 48 treatment means for lactating cows (LAC database). A leave-one-out cross-validation of the derived models showed that a DMI-only predictor model had a similar root mean square prediction error as a percentage of the mean observed value (RMSPE, %) on the COM and LAC database of 14.7 and 14.1%, respectively, and it was the key predictor of CH4 production. All databases observed an improvement in prediction abilities in CH4 production with DMI in the models along with dietary forage proportion inclusion and the quadratic term of dietary forage proportion. For the COM database, the CH4 yield was best predicted by the dietary forage proportion only, while the LAC database was for dietary forage proportion, milk fat, and protein yields. The best newly developed models showed improved predictions of CH4 emission compared to other published equations. Our results indicate that the inclusion of dietary composition along with DMI can provide an improved CH4 production prediction in dairy cattle.

Current analytical methods for amino acid (AA) analysis in ruminant nutrition are time-consuming and expensive. This study aimed to develop a method for AA analysis that is faster, more efficient, rugged, and accessible. Four representative matrixes were selected for method development and validation: milk, tissue, feed, and soy flour standard reference material from National Institute of Standards and Technology. Acid and alkaline hydrolysis were used to analyze 18 AA. Separation of AA was performed using a Z-HILIC column in an 18-min run coupled to a triple quadrupole LC/MS system in positive and negative electrospray ionization for identification and quantitation. The method was evaluated for recovery, precision, calibration curve linearity, and limits of detection (LODs) and limits of quantitation (LOQs) and applied to other feed samples. Good quantitation results were achieved for all AA, with coefficients of determination (R2) over 0.995; LODs at 0.2-28.2 and LOQs at 0.7-94.1 ng/mL; intraday and interday precision <14.9% relative standard deviation; blank recovery between 75.6 and 116.2%; and sample recovery between 75.6 and 118.0%. Overall, AA concentrations were similar to literature values, and there was a tendency for higher N recovery as AA. In conclusion, an efficient and robust method was validated to routinely analyze AA for appropriate characterization in diet formulation for dairy cattle.

Improving the ability of diet formulation models to more accurately predict AA supply while appropriately describing requirements for lactating dairy cattle provides an opportunity to improve animal productivity, reduce feed costs, and reduce N intake. The goal of this study was to evaluate the sensitivity of a new version of the Cornell Net Carbohydrate and Protein System (CNCPS) to formulate diets for rumen N, Met, and all essential AA (EAA). Sixty-four high-producing dairy cattle were randomly assigned to 1 of the 4 following diets in a 14-wk longitudinal study: (1) limited metabolizable protein (MP), Met, and rumen N (Base), (2) adequate Met but limited MP and rumen N (Base + M), (3) adequate Met and rumen N, but limited MP (Base + MU), and (4) adequate MP, rumen N, and balanced for all EAA (Positive). All diets were balanced to exceed requirements for ME relative to maintenance and production, assuming a nonpregnant, 650-kg animal producing 40 kg of milk at 3.05% true protein and 4.0% fat. Dietary MP was 97.2, 97.5, 102.3, and 114.1 g/kg of dry matter intake for the Base, Base + M, Base + MU, and Positive diets, respectively. Differences were observed for dry matter intake and milk yield (24.1 to 24.7 and 39.4 to 41.1 kg/d, among treatments). Energy corrected milk, fat, and true protein yield were greater (2.9, 0.13, and 0.08 kg/d, respectively) in cows fed the Positive compared with the Base diet. Using the updated CNCPS, cattle fed the Base, Base + M, and Base + MU diets were predicted to have a negative MP balance (-231, -310, and -142 g/d, respectively), whereas cattle fed the Positive diet consumed 33 g of MP/d excess to ME supply. Bacterial growth was predicted to be depressed by 16 and 17% relative to adequate N supply for the Base and Base + M diets, respectively, which corresponded with the measured lower apparent total-tract NDF degradation. The study demonstrates that improvements in lactation performances can be achieved when rumen N and Met are properly supplied and further improved when EAA supply are balanced relative to requirements. Formulation using the revised CNCPS provided predictions for these diets, which were sensitive to changes in rumen N, Met, all EAA, and by extension MP supply.

Postpartum cows experience a nadir in energy and AA deficit early postpartum. At the same time, cows are challenged with inflammatory stimuli and often show heightened immune responsiveness, further increasing their metabolic needs during this critical time. This study investigated the response to a systemic inflammatory stimulus after a 4-d intravenous (IV) AA infusion designed to ameliorate the estimated metabolizable protein (MP) deficit in postpartum cows. Our objectives were to (1) describe the production and metabolic responses to early postpartum IV AA infusion, (2) determine the metabolic and hormonal responses to an acute IV lipopolysaccharide (LPS) challenge in early postpartum cows, and (3) compare these metabolic and hormonal responses between IV AA treated and control cows. Cows (n = 14, 4 ± 1 d in milk) were continuously IV infused for 4 d in a matched-pair randomized controlled design and received IV AA (IVAA) or 0.9% NaCl (CTRL). Treatment with IV AA consisted of 1 g/kg of BW per day of combined essential AA (EAA) and nonessential AA (NEAA). After infusion ended, cows were challenged IV with LPS (0.0625 µg/kg of BW over 1 h), and serial blood samples were collected to quantify AA, metabolite, and hormone concentrations. Amino acid infusion increased plasma EAA and NEAA concentrations and ameliorated the estimated MP deficit but not the metabolizable energy deficit in IVAA cows. Patterns of dry matter intake during infusion were different between groups. Milk yield and milk protein content and yield were unaffected, but IV AA was associated with increased milk fat content and yield of both de novo and preformed fatty acids. Before LPS infusion, plasma EAA and NEAA concentrations were greater in IVAA compared with CTRL. During LPS challenge, plasma AA concentrations decreased to a greater degree in IVAA than CTRL. Glucagon concentrations were greater and glucose concentrations lower in IVAA during challenge; however, previous AA infusion did not affect the time-dependent changes in concentrations of energy metabolites or glucoregulatory hormones. Plasma urea nitrogen concentration increased in both treatments following challenge, although the temporal pattern depended on treatment. Effects of AA infusion on milk fat response were pronounced and likely due to a combination of increased lipolysis and de novo milk fat synthesis. Despite differences in circulating concentrations of nutrients and hormones before challenge, metabolic responses to systemic inflammation did not differ between the 2 treatments. We conclude that AA infusion changed metabolic status and milk fat but did not appear to alter the metabolic response to subsequent systemic inflammation.