Metabolic Clusters of Early-Lactating Dairy Cows Based on Blood ß-hydroxybutyrate Trajectories and Predicted from Milk Compounds.

High-yielding dairy cows encounter metabolic challenges in early lactation. Typically, ß-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.

Prediction of metabolic status of dairy cows in early lactation using milk fatty acids and test-day variables.

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 ß-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.

Acacia nilotica leaf meal - potential supplement to 25% dorper crosses of local sheep fed a basal diet of natural pasture hay.

This research was conducted to evaluate the replacement potential of Acacia nilotica dried leaf meal for Noug Seed Cake (NSC) to supplement low quality grass hay in the diet of crossbred sheep (25% Dorper). In doing so, four treatments were set up in such a way that 0%, 33%, 67%, and 100% NSC is replaced with dried leaf of Acacia nilotica from a conventional supplement while the treatments were kept isonitrogenous. The experiment was set up in a Randomized Complete Block Design, with initials weight used as the blocking factor. Twenty animals were allocated to the four treatments. The experiment consisted of hundred days of feeding trial followed by evaluation of carcass components at the end. In addition, the experimental feed ingredient was studied in vitro for gas production, methane (CH4) production, fractional rate of degradation (Kd), and in vitro dry matter digestibility (IVDMD). In view of chemical composition, grass hay contained lower crude protein (CP = 3.2% DM) and high cell wall contents that makes it lower quality feed. On the other hand, Acacia nilotica leaf meal was moderate in quality (CP = 14.3% DM) that can support moderate level of ruminant production. In an in vitro study, Acacia nilotica was found to have lower (P < 0.01) levels of CH4, total gas, kd, and IVDMD compared to the other feed ingredients. This indicates that tannin have an effect on Acacia nilotica feed. Partial and complete replacement of NSC with Acacia nilotica leaf meal significantly increased total dry matter intake (TDMI) (P < 0.001). Average daily gain (ADG) and dressing percentage was also higher for leaf meal supplemented groups. Hot carcass weight was in the range of 14.8-17.8 kg, which is higher than the national average carcass weight for Ethiopian sheep, and it was also found to be higher when NSC was partially (33%) as well as completely replaced by the leaf meal. In general, NSC can be replaced either partially or completely with Acacia nilotica leaf meal in the diet of 25% Dorper crosses while weight gain as well as carcass yield is promoted. The maximum inclusion level of Acacia nilotica was 61.3% or 319.2 g per day for 25% Dorper cross sheep fed natural pasture hay as a basal diet.