Milk urea nitrogen concentration is higher in Brown Swiss than in Holstein dairy cows despite identical feeding

Metabolic and productive adaptive response of beef cows to successive short-nutritional challenges

This study aimed to analyze the response of lactating beef cows to repeated short nutritional challenges with their performance parameters and plasma metabolites. Multiparous lactating beef cows were subjected to three repeated nutritional challenges in the fourth month of lactation. Each challenge consisted of a 4-d feed restriction (55% of their average energy and protein requirements), followed by a 3-d refeeding period (100% requirements). Cows were classified into two groups differing in their performance (milk yield) and metabolic adaptation [non esterified fatty acids (NEFA) and β-hydroxybutyrate (BHB)] to diet changes (metabolic response, MR): High and Low MR cows, where the High MR cows showed a faster and larger response to diet changes than the Low MR cows (P < 0.001). The loss in milk yield during restriction was the smallest in challenge 1 (P < 0.001). Milk urea increased during restriction in challenges 1 and 2 (P < 0.001). The High MR cows had greater NEFA concentrations than their Low MR counterparts during restrictions, and greater BHB concentrations during the restriction of challenge 2 (P < 0.001). Restriction increased NEFA, BHB (only in the High MR cows) and urea (P < 0.01). During refeeding, both milk yield and plasma metabolites recovered basal values (P > 0.05). These results highlight the ability of beef cows to respond to and recover from successive short-term nutrient restrictions, and that despite a certain degree of sensitization of milk yield may have occurred, there were only minimal changes in the metabolic strategies triggered to cope with repeated underfeeding.

Kidney function, but not nitrogen excretion differs between Brown Swiss and Holstein dairy cows

Brown Swiss (BS) cows have greater urea concentrations in milk and blood compared with Holstein (HO) cows. We tested the hypothesis that BS and HO cows differ in kidney function and nitrogen excretion. Blood, saliva, urine, and feces were sampled in 31 multiparous BS and 46 HO cows kept under identical feeding and management conditions. Samples were collected at different lactational stages after the monthly DHIA control test-day. To test the glomerular filtration rate (GFR) and urea excretion, concentrations of creatinine and urea were measured in serum, urine, and saliva. As an additional marker to estimate GFR, we determined symmetric dimethylarginine (SDMA) in serum. Feces were analyzed for dry matter content and nitrogen concentration. Data on milk urea and protein concentrations, and daily milk yield were obtained from the monthly DHIA test-day records. The effects of breed, time, and parity number on blood, saliva, urine, feces, and milk parameters were evaluated with the GLM procedure with breed, time, and parity number as fixed effects. Differences between BS and HO were assessed by the Tukey-corrected t-test at P < 0.05. Concentrations of urea, creatinine, and SDMA in serum, were greater in BS than in HO cows (P < 0.01): 5.46 ± 0.19 vs 4.72 ± 0.13 mmol/L (urea), 105.96 ± 2.23 vs 93.07 ± 1.50 mmol/l (creatinine), and 16.78 ± 0.69 vs 13.39 ± 0.44 µg/dL (SDMA). We observed a greater urea concentration in BS cows (25.8 ± 0.7 vs 21.8 ± 0.7 mg/dL) and protein content in milk (3.70 ± 0.08 vs 3.45 ± 0.07%) than in HO cows (P < 0.01). Urea and creatinine concentrations in urine and saliva did not differ among breeds. No differences between BS and HO were observed for milk yield, fecal DM, and fecal nitrogen content. Dry matter intake and body weight were similar in BS and HO cows (P > 0.05). Despite greater urea, creatinine, and SDMA concentrations in blood as well as a higher milk urea content in BS compared with HO, respective concentrations in urine did not differ between breeds. In conclusion, our results demonstrate a lower renal GFR in BS compared with HO cows, thereby contributing to the greater plasma urea concentration in BS cows. However, estimation of nitrogen excretion via milk, urine, and feces does not entirely reflect nitrogen turnover within the animal.

Prediction of detailed blood metabolic profile using milk infrared spectra and machine learning methods in dairy cattle

The adoption of preventive management decisions is crucial to dealing with metabolic impairments in dairy cattle. Various serum metabolites are known to be useful indicators of the health status of cows. In this study, we used milk Fourier-transform mid-infrared (FTIR) spectra and various machine learning (ML) algorithms to develop prediction equations for a panel of 29 blood metabolites, including those related to energy metabolism, liver function/hepatic damage, oxidative stress, inflammation/innate immunity, and minerals. For most traits, the data set comprised observations from 1,204 Holstein-Friesian dairy cows belonging to 5 herds. An exception was represented by β-hydroxybutyrate prediction, which contained observations from 2,701 multibreed cows pertaining to 33 herds. The best predictive model was developed using an automatic ML algorithm that tested various methods, including elastic net, distributed random forest, gradient boosting machine, artificial neural network, and stacking ensemble. These ML predictions were compared with partial least squares regression, the most commonly used method for FTIR prediction of blood traits. Performance of each model was evaluated using 2 cross-validation (CV) scenarios: 5-fold random (CV_r) and herd-out (CV_h). We also tested the best model's ability to classify values precisely in the 2 extreme tails, namely, the 25th (Q25) and 75th (Q75) percentiles (true-positive prediction scenario). Compared with partial least squares regression, ML algorithms achieved more accurate performance. Specifically, elastic net increased the R² value from 5% to 75% for CV_r and 2% to 139% for CV_h, whereas the stacking ensemble increased the R² value from 4% to 70% for CV_r and 4% to 150% for CV_h. Considering the best model, with the CV_r scenario, good prediction accuracies were obtained for glucose (R² = 0.81), urea (R² = 0.73), albumin (R² = 0.75), total reactive oxygen metabolites (R² = 0.79), total thiol groups (R² = 0.76), ceruloplasmin (R² = 0.74), total proteins (R² = 0.81), globulins (R² = 0.87), and Na (R² = 0.72). Good prediction accuracy in classifying extreme values was achieved for glucose (Q25 = 70.8%, Q75 = 69.9%), albumin (Q25 = 72.3%), total reactive oxygen metabolites (Q25 = 75.1%, Q75 = 74%), thiol groups (Q75 = 70.4%), total proteins (Q25 = 72.4%, Q75 = 77.2.%), globulins (Q25 = 74.8%, Q75 = 81.5%), and haptoglobin (Q75 = 74.4%). In conclusion, our study shows that FTIR spectra can be used to predict blood metabolites with relatively good accuracy, depending on trait, and are a promising tool for large-scale monitoring.

In-line near-infrared analysis of milk coupled with machine learning methods for the daily prediction of blood metabolic profile in dairy cattle

Precision livestock farming technologies are used to monitor animal health and welfare parameters continuously and in real time in order to optimize nutrition and productivity and to detect health issues at an early stage. The possibility of predicting blood metabolites from milk samples obtained during routine milking by means of infrared spectroscopy has become increasingly attractive. We developed, for the first time, prediction equations for a set of blood metabolites using diverse machine learning methods and milk near-infrared spectra collected by the AfiLab instrument. Our dataset was obtained from 385 Holstein Friesian dairy cows. Stacking ensemble and multi-layer feedforward artificial neural network outperformed the other machine learning methods tested, with a reduction in the root mean square error of between 3 and 6% in most blood parameters. We obtained moderate correlations (r) between the observed and predicted phenotypes for γ-glutamyl transferase (r = 0.58), alkaline phosphatase (0.54), haptoglobin (0.66), globulins (0.61), total reactive oxygen metabolites (0.60) and thiol groups (0.57). The AfiLab instrument has strong potential but may not yet be ready to predict the metabolic stress of dairy cows in practice. Further research is needed to find out methods that allow an improvement in accuracy of prediction equations.