Cátedra de Producción de Leche, Facultad de Ciencias Agropecuarias, Universidad Nacional de Córdoba, Córdoba 5000, Argentina; Consejo Nacional de Investigaciones Científicas y Técnicas, Córdoba 5000, Argentina.
Intensive Livestock Industries, NSW Department of Primary Industries, Elizabeth Macarthur Agricultural Institute, Menangle NSW 2568, Australia.
J Dairy Sci. 2020 Sep;103(9):8189-8196. doi: 10.3168/jds.2019-17962. Epub 2020 Jun 18.
Historically, cow selection criteria were developed for conventional milking systems that have regular milking intervals (MI). However, in automatic milking systems (AMS), there is variability in MI within and between cows. These sources of variability provide an opportunity to identify cows with high daily milk yield (DY) and long MI. An extended MI (longer than 16 h in pasture-based systems) has a negative effect on DY. Cows that tolerate extended MI and maintain high DY can be considered more efficient than cows with low DY and long MI, or with high DY but short MI, thereby improving robotic system use. Knowledge of the behavior and parameters of lactation curves of cows in AMS could help farmers to identify cows with a specific lactational phenotype. The objective of this study was to identify individual cows with high DY and long MI within herds, which could reflect increased tolerance to milk accumulation under AMS. A database containing records for 773,483 milking events for one year (July 2016-June 2017) from 4 pasture-based AMS farms was used. Lactation curves within each herd were fitted using several mixed models including fixed effects for the parameters of the lactation curve and random cow effects. Predicted curves of average DY according to parity (multiparous and primiparous) were obtained. The best linear unbiased prediction of the random cow effect allowed us to categorize lactations as having either high or low milk production. The median MI of each lactation was then used to categorize cows as having either short or long MI. Daily yield at the peak of lactation, days to peak and 305-d cumulative milk production were used to compare the effect of DY and MI categories, as well as the DY × MI interaction. Milk production by multiparous and primiparous cows with high DY and long MI was between 35 and 45% higher than that of the low DY and short MI. From all lactations analyzed, the incidence of animals with high DY and long MI across farms was 7.5%. We have identified and quantified a new, AMS-specific, phenotype (the combination of a relatively higher DY with relatively longer MI) with potential to increase use of AMS units. Identifying more efficient animals should help generate new approaches for differential management and for selecting cows in AMS.
从历史上看，奶牛选择标准是为常规挤奶系统制定的，常规挤奶系统有规律的挤奶间隔（MI）。然而，在自动挤奶系统（AMS）中，奶牛的 MI 存在个体内和个体间的变化。这些变化来源为识别高日产奶量（DY）和长 MI 的奶牛提供了机会。延长 MI（在基于牧场的系统中超过 16 小时）会对 DY 产生负面影响。能够耐受延长 MI 并保持高 DY 的奶牛被认为比 DY 低、MI 长的奶牛或 DY 高但 MI 短的奶牛更有效率，从而提高机器人系统的使用效率。了解 AMS 中奶牛泌乳曲线的行为和参数可以帮助农民识别具有特定泌乳表型的奶牛。本研究的目的是在牛群中识别具有高 DY 和长 MI 的个体奶牛，这可以反映出对 AMS 下乳汁积累的耐受性增加。使用包含 4 个基于牧场的 AMS 农场 1 年（2016 年 7 月至 2017 年 6 月）773483 次挤奶记录的数据库。使用包括泌乳曲线参数的固定效应和随机奶牛效应的几个混合模型拟合每个牛群的泌乳曲线。根据胎次（经产和初产）获得平均 DY 的预测曲线。对随机奶牛效应的最佳线性无偏预测使我们能够将泌乳分类为高或低产奶量。然后，使用每个泌乳的中位数 MI 将奶牛分类为短 MI 或长 MI。泌乳高峰期的日产量、达到高峰期的天数和 305 天累计产奶量用于比较 DY 和 MI 类别以及 DY×MI 交互作用的影响。高 DY 和长 MI 的经产和初产奶牛的产奶量比低 DY 和短 MI 的奶牛高 35%至 45%。在所分析的所有泌乳中，农场之间具有高 DY 和长 MI 的动物的发生率为 7.5%。我们已经确定并量化了一种新的、特定于 AMS 的表型（相对较高的 DY 与相对较长的 MI 的组合），该表型有可能增加 AMS 单位的使用。识别更有效的动物应该有助于为差异化管理和 AMS 中的奶牛选择生成新方法。
