Wu Xiao-Lin, Wiggans George R, Norman H Duane, Miles Asha M, Van Tassell Curtis P, Baldwin Vi Ransom L, Burchard Javier, Durr Joao
Council on Dairy Cattle Breeding, Bowie, MD 20716.
Department of Animal and Dairy Sciences, University of Wisconsin, Madison 53706.
JDS Commun. 2022 Dec 1;4(1):40-45. doi: 10.3168/jdsc.2022-0230. eCollection 2023 Jan.
Cows are typically milked 2 or more times on a test-day, but not all these milkings are sampled and weighed. 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. Statistical methods have been proposed to estimate daily yields in dairy cows, focusing on various yield correction factors in 2 broad categories: additive correction factors (ACF) and multiplicative correction factors (MCF). The ACF are evaluated by the average differences between AM and PM milk yield for various milking interval classes, coupled with other categorical variables. 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 of 2.0. 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. The linear regression models improved the accuracy of the estimates compared with the ACF models. The MCF are ratios 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. The MCF have biological and statistical challenges. Systematic biases occurred when ACF or MCF were computed on discretized milking interval classes, leading to accuracy loss. An exponential regression model was proposed as an alternative model for estimating daily milk yields, which improved the accuracy. Characterization of ACF and MCF showed how they improved the accuracy compared with doubling AM or PM yield as the daily milk yield. All the methods performed similarly with equal AM and PM milkings. The methods were explicitly described to estimate daily milk yield in AM and PM milking plans. Still, the principles generally apply to cows milked more than 2 times a day and apply similarly to the estimation of daily fat and protein yields with some necessary modifications.
奶牛在一个测定日通常挤奶2次或更多次，但并非所有这些挤奶都进行采样和称重。最初的方法是在早晚挤奶计划中，假设早晚挤奶间隔相等，将早班（AM）或晚班（PM）产量翻倍来估计测定日产量。然而，早晚挤奶间隔可能不同，且昼夜之间的泌乳速率可能存在差异。已提出统计方法来估计奶牛的日产量，重点关注两大类不同的产量校正因子：加性校正因子（ACF）和乘性校正因子（MCF）。ACF通过不同挤奶间隔类别的早晚产奶量平均差异以及其他分类变量来评估。我们表明，ACF模型等同于以分类回归变量为自变量、早班或晚班产量为连续变量且固定回归系数为2.0的日产量回归模型。同样，线性回归模型可以实现为一个ACF模型，其中早班或晚班产量的回归系数根据数据进行估计。与ACF模型相比，线性回归模型提高了估计的准确性。MCF是日产量与单次挤奶产量的比值，但其统计解释有所不同。总体而言，MCF在估计日奶产量方面比ACF更准确。MCF存在生物学和统计学方面的挑战。当在离散的挤奶间隔类别上计算ACF或MCF时会出现系统偏差，导致准确性损失。提出了指数回归模型作为估计日奶产量的替代模型，该模型提高了准确性。对ACF和MCF的特性描述表明，与将早班或晚班产量翻倍作为日奶产量相比，它们如何提高了准确性。在早晚挤奶量相等的情况下，所有方法的表现相似。这些方法被明确描述用于估计早晚挤奶计划中的日奶产量。不过，这些原则通常适用于每天挤奶超过2次的奶牛，并且经过一些必要修改后，同样适用于日脂肪和蛋白质产量的估计。
