Application of milk mid-infrared spectroscopy for prediction of energy balance and associated traits in Fleckvieh and Holstein Friesian dairy cows

A negative energy balance in early lactation increases the susceptibility of dairy cows to metabolic and infectious diseases. Energy balance (EB) is therefore a valuable trait in both herd management and breeding strategies, to improve the health and efficiency of dairy cows. However, routine on-farm recording of energy intake is hardly feasible, necessitating suitable alternatives to determine EB or related traits. A potential alternative is prediction based on mid-infrared (MIR) spectral and test-day data from routine milk recording. Thus, the aim of the present study was to develop spectrometric prediction equations for EB and related traits, i.e., energy intake (EI) and dry matter intake (DMI), for Fleckvieh and Holstein Friesian dairy cows. A dataset was available comprising 64 988 daily observations of phenotypes including test-day variables and milk MIR spectra from 18 Fleckvieh and 71 Holstein Friesian cows, collected on a research farm between 2014 and 2021. Based on this dataset, quantitative prediction models were developed using different combinations of 212 selected first derivative MIR spectra and test-day variables by applying partial least squares regression analysis. An additional dataset was used for external validation by farm of the developed prediction equations, comprising 1 971 records on 16 Fleckvieh and 20 Holstein Friesian cows collected between 2017 and 2020 on another research farm. In addition to different validation scenarios, various effects, including breed, parity, and concentrate intake, were also evaluated for their impact on predictability of the traits considered. In general, prediction equations have shown to be most accurate when they included 212 MIR spectra along with parity and milk yield as predictors. The prediction equations provided moderate accuracies exhibiting correlation coefficients of 0.59 to 0.75 for EB, 0.63 to 0.71 for DMI, and 0.69 to 0.71 for EI, depending on the specific validation scenarios. The effects of breed, parity, and concentrate level showed differing impacts on the predictive capacity of the models for EB, DMI, and EI, with variations across traits. The results demonstrate potential for the generation of population-level phenotypes for EB, DMI, and EI based on routinely available MIR spectra and test-day variables. This approach would facilitate the routine recording of such indicators on a large scale for farm management and inclusion in genetic evaluation systems.