Predicting milk mid-infrared spectra from first-parity Holstein cows using a test-day mixed model with the perspective of herd management

摘要

The use of test-day models to model milk mid-infrared(MIR) spectra for genetic purposes has already been explored;however, little attention has been given to theiruse to predict milk MIR spectra for management purposes.The aim of this paper was to study the ability ofa test-day mixed model to predict milk MIR spectra formanagement purposes. A data set containing 467,496test-day observations from 53,781 Holstein dairy cowsin first lactation was used for model building. Principalcomponent analysis was implemented on the selected311 MIR spectral wavenumbers to reduce the numberof traits for modeling; 12 principal components (PC)were retained, explaining approximately 96% of thetotal spectral variation. Each of the retained PC wasmodeled using a single trait test-day mixed model. Themodel solutions were used to compute the predictedscores of each PC, followed by a back-transformationto obtain the 311 predicted MIR spectral wavenumbers.Four new data sets, containing altogether 122,032records, were used to test the ability of the model topredict milk MIR spectra in 4 distinct scenarios withdifferent levels of information about the cows. The averagecorrelation between observed and predicted valuesof each spectral wavenumber was 0.85 for the modelingdata set and ranged from 0.36 to 0.62 for the scenarios.Correlations between milk fat, protein, and lactosecontents predicted from the observed spectra and fromthe modeled spectra ranged from 0.83 to 0.89 for themodeling set and from 0.32 to 0.73 for the scenarios.Our results demonstrated a moderate but promisingability to predict milk MIR spectra using a test-daymixed model. Current and future MIR traits predictionequations could be applied on the modeled spectra topredict all MIR traits in different situations instead ofdeveloping one test-day model separately for each trait.Modeling MIR spectra would benefit farmers for cowand herd management, for instance through predictionof future records or comparison between observed andexpected wavenumbers or MIR traits for the detectionof health and management problems. Potential resultingtools could be incorporated into milk recordingsystems.