Animal (Aug 2024)
Milk components as potential indicators of energy status in early lactation Holstein dairy cows from two farms
Abstract
Negative energy balance (NEB) is a serious problem in most dairy cows. It occurs most frequently after calving, when cows are unable to consume sufficient DM to meet their energy requirements during early lactation. During NEB, the breakdown of fat stores releases non−esterified fatty acids (NEFAs) into the bloodstream. High blood concentrations of NEFAs cause health problems such as ketosis, fatty liver syndrome, and enhanced susceptibility to infections. These issues may substantially increase premature culling from the herd. Serum NEFA concentrations are often used as a direct marker of energy metabolism. However, because the direct measurement of serum NEFAs is difficult under commercial conditions, alternative indicators, such as milk components, have been increasingly investigated for their use in estimating energy balance. The objectives of this study were to (1) evaluate the relationships between serum NEFA concentrations and selected milk components in cows from two farms during the first 5 weeks of lactation, and to (2) develop a model valid for both herds for predicting serum NEFA concentrations using milk components. A total of 121 lactating Holstein cows from two different farms were included in the experiment. Blood samples were collected for NEFA analysis on days 7 (± 3), 14 (± 3), 21 (± 3), and 35 (± 3) after calving. Composite milk samples were collected during afternoon milking on the same days as blood sampling. Concentrations of fat, protein, lactose, and milk fatty acids (FAs) were determined using Fourier-transform IR spectroscopy analysis. The strongest correlations (r > 0.43) were recorded between serum NEFAs and milk long-chain FAs, monounsaturated FAs, C18:0, and C18:1 within each farm and for both farms combined. Two prediction models for serum log(NEFA) using milk components as predictors were developed by stepwise regression. The prediction model with the best fit (R2 = 0.52) included days in milk, fat-to-protein ratio, and C18:1, C18:12 and C14:0 expressed as g/100 g of milk fat. An essential finding is that, despite different concentrations of NEFAs, and of most milk components observed in the evaluated herds, there were no significant interactions between farm and any of the FAs, so the same regression coefficients could be used for the prediction models in both farms. Validation of these findings in a greater number of herds would allow for the use of milk FAs to identify energy−imbalanced cows in herds under different farm conditions.
