The transition from late gestation to early lactation in dairy cows involves dynamic metabolic adaptations orchestrated by homeorhetic mechanisms, including hepatic fatty acid and AA metabolism. To gain deeper understanding of these mechanisms, we evaluated changes in bloodspot acylcarnitines (AC) and free AA profiles, and conventional blood biomarkers of energy balance (BHB, nonesterified fatty acids [NEFA], glucose, insulin, IGF-1, and fructosamine) along with weekly milk composition and DMI in 2 sequential observational trials. Data were analyzed using correlation and cluster analysis, and linear mixed-effects models with and without repeated measures. Study 1, which involved 28 multiparous Holstein-Friesian cows sampled 7 d before calving and at 3, 6, 9 and 21 d after calving, revealed strong positive correlations between glycine-to-alanine ratio with BHB (r = 0.58) and NEFA (r = 0.59), though these correlations weakened in study 2. Four trajectory patterns in AC, AA, and metabolite ratios were identified. Group 1 (e.g., C5, C16, and C18) showed transient postpartum increase peaking by d 3 or 6, returning to prepartum levels by d 21. Group 2 (e.g., tyrosine, C0:(C16 + C18) exhibited transient postpartum decrease, normalizing by d 21. Group 3 (e.g., C4DC:C3, t[AC]:C0, and valine-to-phenylalanine ratio) displayed variable postpartum responses, whereas group 4 exhibited persistent differences at d 21, with elevated glycine-to-alanine and valine-to-leucine ratios but reduced methionine and ornithine-to-citrulline ratio compared with prepartum levels. Study 2, which examined intercow variations and comprised of 74 cows (83 lactations) sampled at 21 DIM, revealed 2 distinct clusters of clinically healthy cows based on longitudinal time serum BHB profiles: normal and high milk yield-hyperketonemia (HMY-HYK). The HMY-HYK cows had higher milk yield (41.6 ± 1.05 vs. 39.4 ± 0.767 kg/d), average serum BHB and NEFA (0.996 ± 0.086 vs. 0.754 ± 0.062 mmol/L and 0.498 ± 0.051 vs. 0.534 ± 0.071 mmol/L, respectively) and lower insulin (0.343 ± 0.030 vs. 0.368 ± 0.041ng/mL) compared with cows in the normal cluster. The higher milk yield, increased milk urea concentrations, and reduced bloodspot citrulline levels in the HMY-HYK cows suggest enhanced AA catabolism for gluconeogenesis and reduced activity in the ornithine-citrulline cycle. Elevated bloodspot malonylcarnitine, long-chain AC (LCAC) with C16 and C18 carbon chains, acetylcarnitine-to-free carnitine (C2:C0) ratio but lower free carnitine (C0) levels indicate efficient mitochondrial responses, potentially exporting acyl-CoA as C2 and LCAC to mitigate metabolic stress associated with elevated NEFA. In conclusion, bloodspot AC, AA, and metabolite ratios highlight time-dependent and interanimal shifts in adipose and muscle mobilization, as well as adaptive mitochondrial metabolism of NEFA and AA catabolism to support gluconeogenesis and thus, milk synthesis in early lactation.

A dysregulated inflammatory response contributes to the occurrence of disorders in cows during the transition period from pregnancy to lactation. However, a detailed characterization of clinically healthy cows that exhibit an enhanced inflammatory response during this critical period remains incomplete. In this experiment, a total of 99 individual transition dairy cows and 109 observations (18 cows monitored in 2 consecutive lactations), submitted to similar transition management were involved to evaluate the relationship between elevated an inflammatory response and metabolic and oxidative status, as well as transition outcomes. Blood was taken at -7, 3, 6, 9, and 21 DIM, and concentrations of metabolic parameters (glucose, β-hydroxybutyric acid, nonesterified fatty acids [NEFA], insulin, IGF-1, and fructosamine) were analyzed. Additionally, oxidative parameters (proportion of oxidized glutathione to total glutathione in red blood cells, the activity of glutathione peroxidase [GPx] and superoxide dismutase, concentrations of malondialdehyde, and oxygen radical absorbance capacity) and acute phase proteins (APP) including haptoglobin (Hp), serum amyloid A (SAA) and albumin-to-globulin ratio (A:G) were determined in the blood at 21 DIM. The 3 APP parameters were used to group clinically healthy cows into 2 categories through k-medoids clustering (i.e., a group showing an acute phase response, APR; n = 39) and a group not showing such a response (i.e., non-APR; n = 50). Diseased cases (n = 20) were handled in a separate group. Lower SAA and Hp concentrations as well as higher A:G were observed in the non-APR group, although for Hp, differences were observed from the APR group and not from the diseased group. Only 1 of the 5 oxidative parameters differed between the groups, with the non-APR group exhibiting lower GPx activity compared with the diseased group. The non-APR group showed the highest IGF-1 levels among the 3 groups and and lower NEFA concentrations compared with the diseased groups. Cows in the diseased group also showed reduced dry matter intake and milk yield compared with clinically healthy cows, regardless of their inflammatory status. Moreover, the APR group exhibited temporarily lower activity levels compared with the non-APR group. These findings highlight that cows with a lower inflammatory status after 21 DIM exhibited better metabolic health characteristics and productive performance, as well as activity levels. Nevertheless, the detrimental effects of a higher inflammatory status in the absence of clinical symptoms are still relatively limited.

The transition period is one of the most challenging periods in the lactation cycle of high-yielding dairy cows. It is commonly known to be associated with diminished animal welfare and economic performance of dairy farms. The development of data-driven health monitoring tools based on on-farm available milk yield development has shown potential in identifying health-perturbing events. As proof of principle, we explored the association of these milk yield residuals with the metabolic status of cows during the transition period. Over 2 yr, 117 transition periods from 99 multiparous Holstein-Friesian cows were monitored intensively. Pre- and postpartum dry matter intake was measured and blood samples were taken at regular intervals to determine β-hydroxybutyrate, nonesterified fatty acids (NEFA), insulin, glucose, fructosamine, and IGF1 concentrations. The expected milk yield in the current transition period was predicted with 2 previously developed models (nextMILK and SLMYP) using low-frequency test-day (TD) data and high-frequency milk meter (MM) data from the animal's previous lactation, respectively. The expected milk yield was subtracted from the actual production to calculate the milk yield residuals in the transition period (MRT) for both TD and MM data, yielding MRTTD and MRTMM. When the MRT is negative, the realized milk yield is lower than the predicted milk yield, in contrast, when positive, the realized milk yield exceeded the predicted milk yield. First, blood plasma analytes, dry matter intake, and MRT were compared between clinically diseased and nonclinically diseased transitions. MRTTD and MRTMM, postpartum dry matter intake and IGF1 were significantly lower for clinically diseased versus nonclinically diseased transitions, whereas β-hydroxybutyrate and NEFA concentrations were significantly higher. Next, linear models were used to link the MRTTD and MRTMM of the nonclinically diseased cows with the dry matter intake measurements and blood plasma analytes. After variable selection, a final model was constructed for MRTTD and MRTMM, resulting in an adjusted R2 of 0.47 and 0.73, respectively. While both final models were not identical the retained variables were similar and yielded comparable importance and direction. In summary, the most informative variables in these linear models were the dry matter intake postpartum and the lactation number. Moreover, in both models, lower and thus also more negative MRT were linked with lower dry matter intake and increasing lactation number. In the case of an increasing dry matter intake, MRTTD was positively associated with NEFA concentrations. Furthermore, IGF1, glucose, and insulin explained a significant part of the MRT. Results of the present study suggest that milk yield residuals at the start of a new lactation are indicative of the health and metabolic status of transitioning dairy cows in support of the development of a health monitoring tool. Future field studies including a higher number of cows from multiple herds are needed to validate these findings.

Milk composition, particularly milk fatty acids, has been extensively studied as an indicator of the metabolic status of dairy cows during early lactation. In addition to milk biomarkers, on-farm sensor data also holds potential in providing insights into the metabolic health status of cows. While numerous studies have explored the collection of a wide range of sensor data from cows, the combination of milk biomarkers and on-farm sensor data remains relatively underexplored. Therefore, this study aims to identify associations between metabolic blood variables, milk variables, and various on-farm sensor data. Second, it seeks to examine the supplementary or substitutive potential of these data sources. Therefore, data from 85 lactations on metabolic status and on-farm data were collected during 3 weeks before calving up to 5 weeks after calving. Blood samples were taken on d 3, 6, 9 and 21 after calving for determination of β-hydroxybutyrate (BHB), nonesterified fatty acids (NEFA), glucose, insulin-like growth factor-1 (IGF-1), insulin, and fructosamine. Milk samples were taken during the first 3 weeks in lactation and analyzed by mid-infrared for fat, protein, lactose, urea, milk fatty acids, and BHB. Walking activity, feed intake and BCS were monitored throughout the study. Linear mixed effect models were used to study the association between blood variables and 1) milk variables (i.e., milk models); 2) on-farm data (i.e., on-farm models) consisting of activity and DMI analyzed during the dry period ([D]) and lactation ([L]) and BCS only analyzed during the dry period ([D]); and 3) the combination of both. In addition, to assess whether milk variables can clarify unexplained variation from the on-farm model and vice versa, Pearson marginal residuals from the milk and on-farm models were extracted and related to the on-farm and milk variables, respectively. The milk models had higher R2 than the on-farm models, except for IGF-1 and fructosamine. The highest marginal R2 values were found for BHB, glucose and NEFA (0.508, 0.427 and 0.303 versus 0.468, 0.358 and 0.225 for the milk models and on-farm models, respectively). Combining milk and on-farm data particularly increased R2 values of models assessing blood BHB, glucose and NEFA concentrations with the fixed effects of the milk and on-farm variables mutually having marginal R2 values of 0.608, 0.566 and 0.327 respectively. Milk C18:1 was confirmed as an important milk variable in all models, but particularly for blood NEFA prediction. On-farm data were considerably better capable of describing the IGF-1 concentration than milk data (marginal R2 of 0.192 vs. 0.086), mainly due to DMI before calving. The BCS [D] was the most important on-farm variable in relation to blood BHB and NEFA and could explain additional variation in blood BHB concentration compared with models solely based on milk variables. This study has shown that on-farm data combined with milk data can provide additional information concerning the metabolic health status of dairy cows. On-farm data are of interest to be further studied in predictive modeling, in particular since early warning predictions using milk data are highly challenging or even missing.

Lowering the dietary protein content is a promising strategy to reduce N excretions in cattle but requires an improved N utilization by the animal. Feed enzymes (e.g., exogenous α-amylase) and plant extracts (e.g., essential oils (EO)) are 2 additives which may enhance rumen function and possibly also microbial protein yield. This may increase fat and protein corrected milk yield (MY) and milk nitrogen efficiency (MNE) and thus lower N losses from dairy cows. Both types of additives were studied in an experiment including 39 Holstein cows (average ± SD: 40.7 ± 7.95 kg/d MY, 89 ± 43 d in milk (DIM), 2.7 ± 1.5 lactations, 677 ± 68.6 kg of BW) consisting of a covariate (4 weeks) and treatment period (5 weeks). During the whole experiment cows were fed a typical Benelux diet (CTRL), supplemented with concentrates to meet individual requirements for energy and metabolizable protein, which were fulfilled for 100% and 101%, respectively. The total diet was low in crude protein (15.5%) and relatively high in starch (22.6% and 6.6% rumen bypass starch). Cows were balanced for parity, DIM, MY and roughage intake and randomly assigned to one of 3 groups, receiving the following treatments in the treatment period; (1) CTRL (n = 13); (2) CTRL + 14 g/cow/d Ronozyme RumiStar (α-amylase enzyme, DSM) (AMEZ, n = 13); (3) CTRL + 2.5 g/cow/d Crina Protect (blend of EO components, DSM) (ESOL, n = 13). Animal performance, ruminal pH and enteric gas emissions were monitored throughout the experiment. During the last week of the covariate and treatment period, nitrogen balances were conducted, total-tract nutrient digestibility was determined and urinary allantoin and uric acid were determined as indicators for microbial N production. The statistical model applied to these variables contained group and DIM during treatment period as fixed effects and the values from the covariate period as covariate. Post-hoc Dunnet corrected comparisons between each treatment group and the control group were explored. The α-amylase enzyme tended to increase apparent total-tract starch digestibility and increased milk lactose concentration. The EO blend tended to increase milk yield and increased milk N output, MNE and feed efficiency. Therefore, when feeding reduced dietary protein levels, EO have potential to improve the N-use efficiency in cattle, whereas the α-amylase enzyme might increase starch digestibility and milk lactose. However, additional research is necessary to substantiate our findings.

High-yielding dairy cows encounter metabolic challenges in early lactation. Typically, β-hydroxybutyrate (BHB), measured at a specific time point is employed to diagnose the metabolic status of cows based on a predetermined threshold. However, in early lactation, BHB is highly dynamic, and there is high interindividual variability in its time profile. This could limit the effectiveness of the single measurement and threshold-based diagnosis probably contributing to the disparities in reports linking metabolic status with productive and reproductive outcomes. This research delves into the examination of the trajectories of BHB to unveil inter-cow variations and identify latent metabolic groups. We compiled a data set from 2 observational studies involving a total of 195 lactations from multiparous Holstein Friesian cows. The data set encompasses measurements of BHB, NEFA, and insulin from blood samples collected at 3, 6, 9, and 21 d in milk (DIM), along with weekly determinations of milk composition and fatty acids (FA) proportions in milk fat. In both experiments, milk yield (MY) and feed intake were recorded daily during the first month of lactation. We explored interindividual and intraindividual variations in metabolic responses using the trajectories of blood BHB and evaluated the presence of distinct metabolic groups based on such variations. For this purpose, we employed the growth mixture model (GMM), a trajectory clustering technique. Our findings unveil novel insights into the diverse metabolic responses among cows, encompassing both trajectory patterns and the magnitude of blood BHB concentrations. Specifically, we identified 3 latent metabolic groups: the “QuiBHB” cluster (≈10%) exhibited a higher initial BHB concentration than other clusters, peaking on d 9 (average maximum BHB of 2.4 mM) and then declining by d 21; the “SloBHB” cluster (≈23%) started with a lower BHB concentration, gradually increasing until d 9, and at the highest BHB concentration at d 21 (1.6 mM serum BHB at the end of the experimental period); and the “LoBHB” cluster (≈67%) began with the lowest serum BHB concentration (serum BHB <0.75 mM), remaining relatively stable throughout the sampling period. Notably, the 3 metabolic groups exhibited significant physiological disparities, evident in blood NEFA and insulin concentrations. The QuiBHB and SloBHB cows exhibited higher NEFA and lower insulin concentrations as compared with the LoBHB cows. Interestingly, these metabolic differences extended to MY and DMI during the first month of lactation. The elevated BHB concentrations observed in QuiBHB cows were linked with lower DMI and MY as compared with SloBHB and LoBHB cows. Accordingly, these animals were considered metabolically impaired. Conversely, SloBHB cows displayed higher MY along with increased DMI, and thus the elevated BHB might be indicative of an adaptive response for these cows. The QuiBHB cows also displayed higher proportions of unsaturated FA (UFA), monounsaturated FA (MUFA), and total C18:1 FA in milk during the first week of lactation. Prediction of the QuiBHB cows using these FA and test day variables resulted in moderate predictive accuracy (ROCAUC > 0.7). Given the limited sample size for the development of prediction models, and the variation in DIM among samples in the same week, the result is indicative of the predictive potential of the model and room for model optimization. In summary, distinct metabolic groups of cows could be identified based on the trajectories of blood BHB in early lactation.

Cattle behaviour is fundamentally linked to the cows' health, (re)production, and welfare. The aim of this study was to present an efficient method to incorporate Ultra-Wideband (UWB) indoor location and accelerometer data for improved cattle behaviour monitoring systems. In total, 30 dairy cows were fitted with UWB Pozyx wearable tracking tags (Pozyx, Ghent, Belgium) on the upper (dorsal) side of the cow's neck. In addition to the location data, the Pozyx tag reports accelerometer data as well. The combination of both sensor data was performed in two steps. In the first step, the actual time spent in the different barn areas was calculated using location data. In the second step, accelerometer data were used to clas-sify cow behaviour using the location information of step 1 (e.g., a cow located in the cubicles cannot be classified as feeding, or drinking). A total of 156 hours of video recordings were used for the validation. For each hour of data, the total time each cow spent in each area and performing which behaviours (feed-ing, drinking, ruminating, resting, and eating concentrates) were computed using the sensors and com-pared against annotated video recordings. Bland-Altman plots for the correlation and difference between the sensors and the video recording were then computed for the performance analysis. The overall performance of locating the animals into the correct functional areas was very high. The R2 was 0.99 (P < 0.001), and the root-mean-square error (RMSE) was 1.4 min (7.5% of the total time). The best performance was obtained for the feeding and lying areas (R2 = 0.99, P < 0.001). Performance was lower in the drinking area (R2 = 0.90, P < 0.01) and the concentrate feeder (R2 = 0.85, P < 0.05). For the combined location + accelerometer data, high overall performance (all behaviours) was obtained with an R2 of 0.99 (P < 0.001) and a RMSE of 1.6 min (12% of the total time). The combination of location and accelerometer data improved the RMSE of the feeding time and ruminating time compared to the accelerometer data alone (2.6-1.4 min). Moreover, the combination of location and accelerometer enabled accurate classification of additional behaviours that are difficult to detect using the accelerom-eter alone, such as eating concentrates and drinking (R2 = 0.85 and 0.90, respectively). This study demon-strates the potential of combining accelerometer and UWB location data for the design of a robust monitoring system for dairy cattle.(c) 2023 The Author(s). Published by Elsevier B.V. on behalf of The Animal Consortium. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Metabolic and oxidative stress have been characterized as risk factors during the transition period from pregnancy to lactation. Although mutual relations between both types of stress have been suggested, they rarely have been studied concomitantly. For this, a total of 99 individual transition dairy cows (117 cases, 18 cows sampled during 2 consecutive lactations) were included in this experiment. Blood samples were taken at -7, 3, 6, 9, and 21 d relative to calving and concentrations of metabolic parameters (glucose, β-hydroxybutyric acid (BHBA), nonesterified fatty acids, insulin, insulin-like growth factor 1, and fructosamine) were determined. In the blood samples of d 21, biochemical profiles related to liver function and parameters related to oxidative status were determined. First, cases were allocated to 2 different BHBA groups (ketotic vs. nonketotic, N:n = 20:33) consisting of animals with an average postpartum BHBA concentration and at least 2 out of 4 postpartum sampling points exceeding 1.2 mmol/L or remaining below 0.8 mmol/L, respectively. Second, oxidative parameters [proportion of oxidized glutathione to total glutathione in red blood cells (%)], activity of glutathione peroxidase, and of superoxide dismutase, concentrations of malondialdehyde and oxygen radical absorbance capacity were used to perform a fuzzy C-means clustering. From this, 2 groups were obtained [i.e., lower antioxidant ability (LAA80%, n = 31) and higher antioxidant ability (HAA80%, n = 19)], with 80% referring to the cutoff value for cluster membership. Increased concentrations of malondialdehyde, decreased superoxide dismutase activity, and impaired oxygen radical absorbance capacity were observed in the ketotic group compared with the nonketotic group, and inversely, the LAA80% group showed increased concentrations of BHBA. In addition, the concentration of aspartate transaminase was higher in the LAA80% group compared with the HAA80% group. Both the ketotic and LAA80% groups showed lower dry matter intake. However, a lower milk yield was observed in the LAA80% group but not in the ketotic group. Only 1 out of 19 (5.3%) and 3 out of 31 (9.7%) cases from the HAA80% and LAA80% clusters belong to the ketotic and nonketotic group, respectively. These findings suggested that dairy cows vary in oxidative status at the beginning of the lactation, and fuzzy C-means clustering allows to classify observations with distinctive oxidative status. Dairy cows with higher antioxidant capacity in early lactation rarely develop ketosis.

The objective of this study was to analyze if maternal supply of rumen protected protein during the dry period can affect the IgG concentration and microbial composition of colostrum and the IgG absorption and fecal microbial composition in the calf. Seventy-four multiparous Holstein Friesian (HF) dairy cows were stratified per parity and randomly assigned to one of 2 different dry period diets, a diet with a low crude protein (CP) level (LP) and a diet with a high CP level (HP) by addition of rumen undegraded protein (RUP) (formaldehyde-treated soybean meal, Mervobest®, Nuscience, Drongen, Belgium). Colostrum was collected within 1 h after calving and IgG concentration was quantified by radial immunodiffusion analysis. Forty-nine calves (23 female and 26 male) were enrolled in the trial with a 2x2 factorial design, with prenatal and postnatal treatment as the 2 independent variables. This led to 4 experimental groups: LPLP, LPHP, HPLP, and HPHP, in which the first 2 letters refer to the prenatal treatment (diet of the dam) and the last 2 refer to the postnatal treatment (diet of the colostrum producing cow). Calves received 3x2L of colostrum within 2, 6 and 24h after birth. Meconium and feces were collected solely from female calves (n = 18) by digital palpation of the rectum, immediately after birth and before colostrum administration and at d 3 of age. Microbial DNA was extracted from meconium (n = 9), feces (n = 15), and colostrum (n = 49). Amplicon sequencing of the bacterial V3-V4 region of the 16S rRNA gene was performed for characterization of the bacterial communities. Colostrum IgG concentration was higher in cows that were supplemented with RUP, especially in cows entering their second lactation (least squares means (LSM) ± standard error of the mean (SEM) 61.3 ± 2.3 vs 55.2 ± 2.8 g IgG/L). Calves born out of LP cows that received colostrum from HP cows (LPHP) had a lower serum IgG level compared with HPHP and LPLP calves (LSM ± SEM 14.2 ± 1.3 vs 18.8 ± 1.2 and 20.9 ± 1.3 g IgG/L in HPHP and LPLP, respectively). The most abundant phyla in colostrum were Proteobacteria (48.2%), Firmicutes (24.8%), Bacteriodetes (9.5%), and Actinobacteria (5.0%). The most abundant phyla in calves' meconium and feces were Firmicutes (42.5 and 47.5%), Proteobacteria (21.7 and 33.7%), Bacteriodetes (16.8 and 15.7%), and Actinobacteria (2.9 and 3.1%). There was no difference in the overall microbial communities between colostrum from HP and LP cows. However, 2 genera (both members of the family Lachnospiraceae) were more abundant in colostrum from HP cows compared with LP cows. The microbial composition of meconium, feces and colostrum differed from each other. Fecal samples were more similar to each other and are characterized by a lower inter-sample diversity compared with colostrum and meconium samples. To conclude, increasing the CP level by addition of RUP in the dry period diet affected the colostrum IgG concentration and the transfer of passive immunity, but did not change the overall microbial composition of colostrum nor of meconium and feces in the calf.

Data on metabolic profiles of blood sampled at d 3, 6, 9, and 21 in lactation from 117 lactations (99 cows) were used for unsupervised k-means clustering. Blood metabolic parameters included β-hydroxybutyrate (BHB), nonesterified fatty acids, glucose, insulin-like growth factor-1 (IGF-1) and insulin. Clustering relied on the average and range of the 5 blood parameters of all 4 sampling days. The clusters were labeled as imbalanced (n = 42) and balanced (n = 72) metabolic status based on the values of the blood parameters. Various random forest models were built to predict the metabolic cluster of cows during early lactation from the milk composition. All the models were evaluated using a leave-group-out cross-validation, meaning data from a single cow were always present in either train or test data to avoid any data leakage. Features were either milk fatty acids (MFA) determined by gas chromatography (MFA [GC]) or features that could be determined during a routine dairy herd improvement (DHI) analysis, such as concentration of fat, protein, lactose, fat/protein ratio, urea, and somatic cell count (determined and reported routinely in DHI registrations), either or not in combination with MFA and BHB determined by mid-infrared (MIR), denoted as MFA [MIR] and BHB [MIR], respectively, which are routinely analyzed but not routinely reported in DHI registrations yet. Models solely based on fat, protein, lactose, fat/protein ratio, urea and somatic cell count (i.e., DHI model) were characterized by the lowest predictive performance [area under the receiver operating characteristic curve (AUCROC) = 0.69]. The combination of the features of the DHI model with BHB [MIR] and MFA [MIR] powerfully increased the predictive performance (AUCROC = 0.81). The model based on the detailed MFA profile determined by GC analysis did not outperform (AUCROC = 0.81) the model using the DHI-features in combination with BHB [MIR] and MFA [MIR]. Predictions solely based on samples at d 3 were characterized by lower performance (AUCROC DHI + BHB [MIR] + MFA [MIR] model at d 3: 0.75; AUCROC MFA [GC] model at d 3: 0.73). High predictive performance was found using samples from d 9 and 21. To conclude, overall, the DHI + BHB [MIR] + MFA [MIR] model allowed to predict metabolic status during early lactation. Accordingly, these parameters show potential for routine prediction of metabolic status.

Lowering the dietary protein content can reduce N excretions and NH3 emissions from manure and increase milk N efficiency of dairy cows. However, milk yield (MY) and composition can be compromised due to AA deficiency. Methionine and Lys are known as first limiting EAA for dairy cows, and recently His is also mentioned as limiting, especially in grass-based or low-protein diets. To examine this, a trial was conducted with a 3-wk pre-experimental adaptation period (diet 16.5% crude protein), followed by a depletion period of 4 wk, in which 39 cows (average ± standard deviation: 116 ± 29.3 d in milk, 1.8 ± 1.2 lactations, 638 ± 73.2 kg of body weight, and 32.7 ± 5.75 kg MY/d) received a low-protein diet (CTRL) (14.5% crude protein). Then, taking into account parity, His plasma concentration, and MY, cows were randomly assigned to 1 of 3 treatment groups during the rumen-protected (RP) AA period of 7 wk; (1) CTRL; (2) CTRL + RP-Met + RP-Lys (MetLys); (3) CTRL + RP-Met + RP-Lys + RP-His (MetLysHis). Products were dosed, assuming requirements for digestible (d) Met, dLys, and dHis being, respectively, 2.4%, 7.0%, and 2.4% of intestinal digestible protein. In the cross-back period of 5 wk, all cows received the CTRL diet. During the last week of each period, a N balance was conducted by collecting total urine and spot samples of feces. Total feces production was calculated using the inert marker TiO2. Statistical analysis was performed with a linear mixed model with cow as random effect and data of the last week of the pre-experimental period used as covariate for the animal performance variables. No effect of supplementing RP-Met and RP-Lys nor RP-Met, RP-Lys, and RP-His on feed intake, milk performance, or milk N efficiency was observed. However, the plasma AA profile indicated additional supply of dMet, dLys, and dHis. Nevertheless, evaluation of the AA uptake relative to the cow's requirements showed that most EAA (exclusive Arg and Thr) were limiting over the whole experiment. Only dHis was sufficiently supplemented during the RP-AA period due to an overestimation of the diet's dMet and dLys supply in the beginning of the trial. The numerically increased milk urea N and urinary N excretion when RP-Met, RP-Lys, and RP-His were added to the low-protein diet suggest an increased catabolism of the excess His.

Early lactation metabolic imbalance is an important physiological change affecting the health, production, and reproduction of dairy cows. The aims of this study were (1) to evaluate the potential of test-day (TD) variables with or without milk fatty acids (FA) content to classify metabolically imbalanced cows and (2) to evaluate the robustness of the metabolic classification with external data. A data set was compiled from 3 experiments containing plasma β-hydroxybutyrate, nonesterified FA, glucose, insulin-like growth factor-I, FA proportions in milk fat, and TD variables collected from 244 lactations in wk 2 after calving. Based on the plasma metabolites, 3 metabolic clusters were identified using fuzzy c-means clustering and the probabilistic membership value of each cow to the 3 clusters was determined. Comparing the mean concentration of the plasma metabolites, the clusters were differentiated into metabolically imbalanced, moderately impacted, and balanced. Following this, the 2 metabolic status groups identified were imbalanced cows (n = 42), which were separated from what we refer to as "others" (n = 202) based on the membership value of each cow for the imbalanced cluster using a threshold of 0.5. The following 2 FA data sets were composed: (1) FA (groups) having high prediction accuracy by Fourier-transform infrared spectroscopy and, thus, have practical significance, and (2) FA (groups) formerly identified as associated with metabolic changes in early lactation. Metabolic status prediction models were built using FA alone or combined with TD variables as predictors of metabolic groups. Comparison was made among models and external evaluations were performed using an independent data set of 115 lactations. The area under the receiver operating characteristics curve of the models was between 75 and 91%, indicating their moderate to high accuracy as a diagnostic test for metabolic imbalance. The addition of FA groups to the TD models enhanced the accuracy of the models. Models with FA and TD variables showed high sensitivities (80-88%). Specificities of these models (73-79%) were also moderate and acceptable. The accuracy of the FA models on the external data set was high (area under the receiver operating characteristics curve between 76 and 84). The persistently good performance of models with Fourier-transform infrared spectroscopy-quantifiable FA on the external data set showed their robustness and potential for routine screening of metabolically imbalanced cows in early lactation.

The measurement of pH in the reticulorumen in combination with a time-pH threshold has been widely applied in research to diagnose subacute ruminal acidosis. However, other pH metrics also have biological value. In this study, 44 animals were monitored during the transition period using reticuloruminal pH boluses. Traditional and more complex pH characteristics were calculated to characterize the reticuloruminal pH profile: time pH <6, slope of a logistic cumulative pH curve (β0), and deviations [squared error (SqEr)] from pH predictions based on a harmonic static model. In this study, we aimed to examine the associations between those pH metrics and metabolic health parameters, feed intake, and activity. Finally, to describe the reticuloruminal pH dynamically, we also constructed a dynamic linear model. The results of this model were studied in relation to feed intake. All pH parameters were mutually correlated (particularly β0 and SqEr; mean Pearson correlation of -0.52). pH patterns, rather than time pH <6, were associated with metabolic health and feed intake: high variation in daily pH (β0 parameter) was reflected in higher blood concentrations of nonesterified fatty acids. Moreover, pH deviations of the harmonic model were negatively associated with feed intake and rumination behavior. This research confirms the biological importance of pH metrics focusing on pH variation and pH deviations and provides deeper insight into its associations with metabolic health status, feed intake, and activity during early lactation.

Both ruminal microbial structure and functionality might play a role in inter-individual variation in susceptibility for subacute rumen acidosis (SARA) observed in dairy cows. The aims of this study were to determine whether differences between cows with distinct SARA susceptibility were reflected in distinct (1) ruminal microbial communities, (2) salivary bacterial communities, and (3) fermentative capacity of ruminal microbiota assessed in vitro. To test this hypothesis, rumen samples were collected via an esophageal tube on 21 d postpartum from 38 multiparous Holstein cows, which were classified into 4 groups differing in median and mean time of reticular pH below 6 as well as area under the curve of pH below 6.0. During the 21 d postpartum, all cows within a group fulfilled following criteria: susceptible (S, n = 10; mean or median ≥180 min/d), moderately susceptible (MS, n = 7; 60 min/d < mean time of pH below 6 < 180 min/d, and median time of pH below 6 <180 min/d), moderately unsusceptible (MU, n = 11; 10 min/d < mean < 60 min/d, and median time of pH below 6 ≤30 min/d), or unsusceptible (U, n = 10; median = 0 min/d, and mean <10 min/d). Groups did not differ in total daily dry matter intake nor in total, roughage, or concentrate intake during daily 6-h time intervals. Rumen bacterial α-diversity did not differ among groups, but β-diversity varied and bacterial 16S rRNA gene copy numbers were lower in S compared with U cows. The relative abundance of genera Streptococcus, Sharpea, Prevotellaceae_YAB2003, Succinivibrionaceae_UCG-001, Ruminococcus, and Ruminococcaceae_UCG-001 were higher in S compared with U cows. In contrast, Lachnospiraceae_ND3007 and Oscillospiraceae_V9D2013 were more abundant in U cows. Although pH-associated, inter-animal differences were also observed in the salivary bacteria, common differences in ruminal and salivary bacterial genera were limited. The functionality of the rumen microbiota was evaluated in vitro through exposure of the microbial inoculum of S and U cows to an anaerobic buffer at pH 5.8 and 6.8, in the presence of sterile supernatant of their own and of dry cows' rumen fluid (2 × 2 design). Generally, the S inoculum produced more volatile fatty acids, except at low pH with dry cows' supernatant, where volatile fatty acid production was completely impaired and lactate accumulation was highest. Compared with the microbes of U cows, microbes of S cows showed less fermentative activity in situations with 2 stress factors (low pH and an unfamiliar environment, i.e., rumen fluid supernatant of dry cows).