学术文献库

Cows are typically milked two or more times on a test day, but not all these milkings are sampled and weighed. Statistical methods have been proposed to estimate daily yields in dairy cows, centering on various yield correction factors in two broad categories. The initial approach estimated a test-day yield with doubled morning (AM) or evening (PM) yield in the AM-PM milking plans, assuming equal AM and PM milking intervals. However, AM and PM milking intervals can vary, and milk secretion rates may be different between day and night. Additive correction factors (ACF) are evaluated by the average differences between AM and PM milk yield for various milking interval classes (MIC). We show that an ACF model is equivalent to a regression model of daily yield on categorical regressor variables, and a continuous variable for AM or PM yield with a fixed regression coefficient. Similarly, a linear regression model can be implemented as an ACF model with the regression coefficient for AM or PM yield estimated from the data. Multiplicative correction factors (MCF) are ratio of daily yield to yield from single milkings, but their statistical interpretations vary. Overall, MCF were more accurate for estimating daily milk yield than ACF. MCF have biological and statistical challenges. An exponential regression model was proposed as an alternative model for estimating daily milk yield, which improved the accuracy in the present study. Characterization of ACF and MCF showed how ACF and MCF improved the accuracy compared to doubling AM or PM yield as the daily milk yield. The methods were explicitly described to estimate daily milk yield in AM and PM milking plans. Still, the principles are generally applicable to cows milked more than two times a day, and they apply similarly to the estimation of daily fat and protein yields with some necessary modifications.