Phenotypic variation of dairy cows’ hematic metabolites and feasibility of non-invasive monitoring of the metabolic status in the transition period - Magro, Silvia; Costa, Angela; Cavallini, Damiano; Chiarin, Elena; De Marchi, Massimo (Frontiers in Veterinary Science, 2024)
IntroductionThe incidence of metabolic diseases tends to be highest during the transition period (±3 weeks around parturition) in dairy cows due to physiological changes and the onset of lactation. Although blood profile testing allows for the monitoring of nutritional and metabolic status, conducting extensive analyses in the herd is costly and stressful for cows due to invasive procedures. Therefore, mid-infrared spectroscopy (MIR) could be seen as a valid alternative.MethodsIn the present study, we used laboratory-determined reference blood data and milk spectra of 349 Holstein cows to (i) identify the non-genetic factors affecting the variability of major blood traits in healthy cows and, subsequently, (ii) test the predictive ability of milk MIR. Cows belonged to 14 Italian commercial farms and were sampled once between 5 and 38 days in milk. For β-hydroxybutyrate (BHB), non-esterified fatty acids (NEFA), cholesterol, glucose, urea, total protein, albumin, globulin, minerals, aspartate aminotransferase, gamma-glutamyl transferase, creatine kinase, total bilirubin, and cortisol, the effects of parity, days in milk, and season were investigated using a linear model.Results and discussionThe results indicate that all fixed effects significantly affected the hematic concentration of most of the traits. Regarding MIR, the most predictable traits were BHB, NEFA, and urea, with coefficients of determination equal to 0.57, 0.62, and 0.89, respectively. These values suggest that MIR predictions of BHB and NEFA are not sufficiently accurate for precise and punctual determination of the hematic concentration, however, still the spectrum of the milk can be exploited to identify cows at risk of negative energy balance and subclinical ketosis. Finally, the predictions can be useful for herd screening, decision-making, and genetic evaluation.