Prediction of standard lactation curves for primiparous Holstein cows by using corrected regression models

Prediction of the expected milk yield is important for the management of the primiparous cows (PPC) with a few or no data on their own milk productivity. We developed a system of regression equations for predicting milk yields in standard lactation. The models include the systematic effects of the calving season, the five-year rolling herd average of milk yield of PPC, the breeding values of the parents for milk production, and daily milk recordings. A total of 21,901 lactations of Holstein PPC were collected during the regular monthly milk recordings of cows in the Republic of Slovenia. By including daily milk recordings in the model, the coefficients of determination of regression models for the prediction of milk yield increase: without known recordings (M0) R 2 =0.80with one recording (M1) R 2 =0.82with two first consecutive recordings (M2) R 2 =0.86and with three recordings (M3) R 2 =0.89. Deviations of milk yield up to 500 kg in a standard lactation (