Department of Internal Medicine, Reproduction and Population Medicine, Faculty of Veterinary Medicine, Ghent University, 9820 Merelbeke, Belgium; Department of Biosystems, Division of Animal and Human Health Engineering, Campus Geel, KU Leuven, 2440 Geel, Belgium.
Department of Biosystems, Division of Animal and Human Health Engineering, Campus Geel, KU Leuven, 2440 Geel, Belgium; KERMIT, Department of Data Analysis and Mathematical Modelling, Ghent University, 9000 Ghent, Belgium.
J Dairy Sci. 2024 Jan;107(1):317-330. doi: 10.3168/jds.2023-23641. Epub 2023 Sep 9.
The transition period is one of the most challenging periods in the lactation cycle of high-yielding dairy cows. It is commonly known to be associated with diminished animal welfare and economic performance of dairy farms. The development of data-driven health monitoring tools based on on-farm available milk yield development has shown potential in identifying health-perturbing events. As proof of principle, we explored the association of these milk yield residuals with the metabolic status of cows during the transition period. Over 2 yr, 117 transition periods from 99 multiparous Holstein-Friesian cows were monitored intensively. Pre- and postpartum dry matter intake was measured and blood samples were taken at regular intervals to determine β-hydroxybutyrate, nonesterified fatty acids (NEFA), insulin, glucose, fructosamine, and IGF1 concentrations. The expected milk yield in the current transition period was predicted with 2 previously developed models (nextMILK and SLMYP) using low-frequency test-day (TD) data and high-frequency milk meter (MM) data from the animal's previous lactation, respectively. The expected milk yield was subtracted from the actual production to calculate the milk yield residuals in the transition period (MRT) for both TD and MM data, yielding MRT and MRT. When the MRT is negative, the realized milk yield is lower than the predicted milk yield, in contrast, when positive, the realized milk yield exceeded the predicted milk yield. First, blood plasma analytes, dry matter intake, and MRT were compared between clinically diseased and nonclinically diseased transitions. MRT and MRT, postpartum dry matter intake and IGF1 were significantly lower for clinically diseased versus nonclinically diseased transitions, whereas β-hydroxybutyrate and NEFA concentrations were significantly higher. Next, linear models were used to link the MRT and MRT of the nonclinically diseased cows with the dry matter intake measurements and blood plasma analytes. After variable selection, a final model was constructed for MRT and MRT, resulting in an adjusted R of 0.47 and 0.73, respectively. While both final models were not identical the retained variables were similar and yielded comparable importance and direction. In summary, the most informative variables in these linear models were the dry matter intake postpartum and the lactation number. Moreover, in both models, lower and thus also more negative MRT were linked with lower dry matter intake and increasing lactation number. In the case of an increasing dry matter intake, MRT was positively associated with NEFA concentrations. Furthermore, IGF1, glucose, and insulin explained a significant part of the MRT. Results of the present study suggest that milk yield residuals at the start of a new lactation are indicative of the health and metabolic status of transitioning dairy cows in support of the development of a health monitoring tool. Future field studies including a higher number of cows from multiple herds are needed to validate these findings.
泌乳高峰期奶牛的干奶期是其泌乳周期中最具挑战性的阶段之一。众所周知,该阶段奶牛的福利和牧场的经济效益会受到影响。基于牧场可用产奶量数据开发的数据驱动型健康监测工具在识别健康干扰事件方面显示出了潜力。作为原理验证,我们探索了这些产奶量残差与奶牛在干奶期的代谢状态之间的关联。在 2 年的时间里,我们对 99 头经产荷斯坦弗里生奶牛的 117 个干奶期进行了密集监测。测量了干物质采食量,定期采集血液样本以测定 β-羟丁酸、非酯化脂肪酸(NEFA)、胰岛素、血糖、果糖胺和 IGF1 浓度。使用 2 种先前开发的模型(nextMILK 和 SLMYP),根据低频测试日(TD)数据和动物前一个泌乳期的高频奶计量器(MM)数据,预测当前干奶期的预期产奶量。用实际产量减去预期产量,计算 TD 和 MM 数据的产奶量残差(MRT),得到 MRT 和 MRT。当 MRT 为负时,实际产奶量低于预期产奶量,相反,当 MRT 为正时,实际产奶量超过预期产奶量。首先,将血液血浆分析物、干物质采食量和 MRT 与临床患病和非临床患病的过渡期进行比较。与非临床患病过渡期相比,临床患病过渡期的 MRT 和 MRT、产后干物质采食量和 IGF1 显著降低,而 β-羟丁酸和 NEFA 浓度显著升高。接下来,使用线性模型将非临床患病奶牛的 MRT 和 MRT 与干物质采食量测量值和血液血浆分析物联系起来。经过变量选择,为 MRT 和 MRT 构建了最终模型,调整后的 R 分别为 0.47 和 0.73。虽然两个最终模型不完全相同,但保留的变量相似,且具有相似的重要性和方向。总的来说,这些线性模型中最具信息性的变量是产后干物质采食量和泌乳次数。此外,在两个模型中,较低且因此更负的 MRT 与较低的干物质采食量和增加的泌乳次数相关。在干物质采食量增加的情况下,MRT 与 NEFA 浓度呈正相关。此外,IGF1、葡萄糖和胰岛素解释了 MRT 的很大一部分。本研究的结果表明,新泌乳期开始时的产奶量残差可以反映过渡奶牛的健康和代谢状态,支持开发健康监测工具。需要进行包括来自多个牛群的更多奶牛的田间研究来验证这些发现。