Wu Xiao-Lin, Wiggans George R, Norman H Duane, Miles Asha M, Van Tassell Curtis P, Baldwin Ransom L, Burchard Javier, Dürr João
Council on Dairy Cattle Breeding, Bowie, MD, United States.
Department of Animal and Dairy Sciences, University of Wisconsin, Madison, WI, United States.
Front Genet. 2022 Aug 10;13:943705. doi: 10.3389/fgene.2022.943705. eCollection 2022.
Cost-effective milking plans have been adapted to supplement the standard supervised twice-daily monthly testing scheme since the 1960s. Various methods have been proposed to estimate daily milk yields (DMY), focusing on yield correction factors. The present study evaluated the performance of existing statistical methods, including a recently proposed exponential regression model, for estimating DMY using 10-fold cross-validation in Holstein and Jersey cows. The initial approach doubled the morning (AM) or evening (PM) yield as estimated DMY in AM-PM plans, assuming equal 12-h AM and PM milking intervals. However, in reality, AM milking intervals tended to be longer than PM milking intervals. Additive correction factors (ACF) provided additive adjustments beyond twice AM or PM yields. Hence, an ACF model equivalently assumed a fixed regression coefficient or a multiplier of "2.0" for AM or PM yields. Similarly, a linear regression model was viewed as an ACF model, yet it estimated the regression coefficient for a single milk yield from the data. Multiplicative correction factors (MCF) represented daily to partial milk yield ratios. Hence, multiplying a yield from single milking by an appropriate MCF gave a DMY estimate. The exponential regression model was analogous to an exponential growth function with the yield from single milking as the initial state and the rate of change tuned by a linear function of milking interval. In the present study, all the methods had high precision in the estimates, but they differed considerably in biases. Overall, the MCF and linear regression models had smaller squared biases and greater accuracies for estimating DMY than the ACF models. The exponential regression model had the greatest accuracies and smallest squared biases. Model parameters were compared. Discretized milking interval categories led to a loss of accuracy of the estimates. Characterization of ACF and MCF revealed their similarities and dissimilarities and biases aroused by unequal milking intervals. The present study focused on estimating DMY in AM-PM milking plans. Yet, the methods and relevant principles are generally applicable to cows milked more than two times a day.
自20世纪60年代以来，具有成本效益的挤奶计划已被调整，以补充标准的每月两次的监督检测方案。人们提出了各种方法来估计日奶产量（DMY），重点是产量校正因子。本研究使用10倍交叉验证评估了现有统计方法（包括最近提出的指数回归模型）在荷斯坦奶牛和泽西奶牛中估计DMY的性能。最初的方法是在早晚挤奶计划中，将上午（AM）或晚上（PM）的产量加倍作为估计的DMY，假设上午和下午的挤奶间隔均为12小时。然而，实际上，上午的挤奶间隔往往比下午的挤奶间隔长。加法校正因子（ACF）提供了超出上午或下午产量两倍的加法调整。因此，ACF模型等效于假设上午或下午产量的固定回归系数或“2.0”的乘数。同样，线性回归模型被视为ACF模型，但它从数据中估计单一奶产量的回归系数。乘法校正因子（MCF）表示日奶产量与部分奶产量的比率。因此，将单次挤奶的产量乘以适当的MCF即可得到DMY估计值。指数回归模型类似于指数增长函数，以单次挤奶的产量作为初始状态，并通过挤奶间隔的线性函数调整变化率。在本研究中，所有方法在估计方面都具有高精度，但它们在偏差方面存在很大差异。总体而言，与ACF模型相比，MCF和线性回归模型在估计DMY时具有更小的平方偏差和更高的准确性。指数回归模型具有最高的准确性和最小的平方偏差。对模型参数进行了比较。离散的挤奶间隔类别导致估计准确性的损失。ACF和MCF的特征揭示了它们的异同以及由不相等的挤奶间隔引起的偏差。本研究重点是在早晚挤奶计划中估计DMY。然而，这些方法和相关原则通常适用于每天挤奶超过两次的奶牛。
