The aim of this methodology is to find a suitable method for predicting the risk of negative energy balance (NEB) in dairy cows at the beginning of lactation using minor milk components, especially milk fat fatty acids (FA). Cows with NEB can be diagnosed in the early stages of lactation through the concentration of serum non-esterified fatty acids (NEFA). However, this method is financially, organizationally, and time-consuming, therefore it is necessary to find new diagnostic parameters based on which cows with and without NEB could be reliably classified.
Based on the obtained results, it can be stated that for reliable diagnosis of NEB in dairy cows from 3 to 35 days of lactation, a logistic regression model including milk components can be used. In all herds, C18:1 content was a significant predictor of NEB. In four out of five herds, milk fat content was also significant. In three herds, C18:0 content was also significant. In one herd, protein content and C18:1 were significant effects. Based on partial results from individual analyzed herds, a model was created for all herds together, in which fat content, C18:1 content, and C18:0 content were significant predictors. For practical use, cut-off values were proposed that divide the monitored cows into healthy and those with NEB.
The methodology is useful for farmers, especially Holstein cattle breeders, who are involved in milk recording. In 2023, 94% of all dairy cows in the Czech Republic were included in milk recording. Furthermore, the methodology is intended for cattle breeding advisors and specialists from the research and academic spheres. The values of minor milk components (including FA content) can be easily obtained for each cow with minimal costs within the framework of milk recording. Using the procedures described in the methodology, individuals with an increased risk of NEB and subsequent health disorders can be identified in a timely manner with a reasonable degree of accuracy. After verification of the equations given in the methodology on a larger amount of data, it will be possible to consider routine calculation of the NEB value for each cow included in milk recording and its publication together with other milk recording results.