Dipartimento di Scienze Veterinarie, Università degli Studi di Torino, Largo Paolo Braccini 2, 10095 Grugliasco, Italy.
Associazione Italiana Allevatori, Via XXIV Maggio 44/45, 00187 Roma, Italy.
Prev Vet Med. 2023 Mar;212:105834. doi: 10.1016/j.prevetmed.2022.105834. Epub 2023 Jan 6.
Test-day milk analysis has largely been used to study health and performance parameters in dairy cows. In this study, we estimated four health indicators of dairy cows using test-day data. Our purpose was to estimate (1) mastitis incidence rate, prevalence, and the probability of recovery; (2) the incidence proportion of ketosis; (3) the duration of inter-calving interval; and (4) the risk of a fresh cow being replaced, in a large cohort of dairy herds in the Piedmont region (Italy). We retrospectively analysed test day records of 261,121 lactating cows and 1315 herds during five years (2015-2020). Mastitis was defined by somatic cell count and ketosis by fat-to-protein ratio. Calving dates were used to calculate ICI and to estimate the removal of a fresh cow from the herd. Mixed-effect generalized linear models were used to adjust for unmeasured herd-level risk factors. The risk of mastitis increased by 120% with parity (Odds ratio [OR] = 2.20, confidence interval [CI]: 2.17 - 2.23), by 7% by months in milking (OR = 1.07, CI: 1.07 - 1.07), and even more if the cow was already affected during the same lactation (OR = 8.74, CI: 8.67 - 8.82). Lactose concentration on the previous test day was the best positive prognostic factor for mastitis recovery (OR = 1.12, CI: 1.08 - 1.17). Ketosis risk was the highest between 3rd and 4th lactations and itself increased the risk of having ICI longer than 440 days (OR = 1.12, CI: 1.02 - 1.22), and fresh-cow removal (OR = 1.75, CI: 1.58 - 1.93). Also, the removal of fresh cows was more likely when mastitis (OR = 1.31, CI: 1.19 - 1.45) or long ICI (OR = 1.34, CI: 1.22 - 1.48) occurred. For each health indicator, herd-level risk factors had an important role (18-56% of within-herd covariance). Our results indicate that milk analysis could be also useful for predicting mastitis, its cure rate, and ketosis. Cow-level risk factors are not enough to explain the risk of these issues. By studying a large population over a long period, this study provides an updated estimate of dairy cow health indicators in Piedmont (north-western Italy), useful for benchmarking dairy herds.
测试日牛奶分析主要用于研究奶牛的健康和性能参数。在这项研究中，我们使用测试日数据估计了奶牛的四个健康指标。我们的目的是估计：（1）乳腺炎发病率、患病率和恢复概率；（2）酮病的发病比例；（3）产犊间隔持续时间；（4）在皮埃蒙特地区（意大利）的一个大型奶牛群中，初产牛被替换的风险。我们回顾性分析了 261121 头泌乳牛和 1315 个牛群在五年（2015-2020 年）期间的测试日记录。乳腺炎通过体细胞计数定义，酮病通过脂肪-蛋白质比值定义。产犊日期用于计算 ICI，并估计初产牛从牛群中被替换的情况。使用混合效应广义线性模型调整未测量的牛群水平风险因素。随着胎次的增加，乳腺炎的风险增加了 120%（优势比[OR] = 2.20，置信区间[CI]：2.17-2.23），随着泌乳月数的增加，风险增加了 7%（OR = 1.07，CI：1.07-1.07），如果奶牛在同一泌乳期已经受到影响，风险甚至更高（OR = 8.74，CI：8.67-8.82）。前一天测试时的乳糖浓度是乳腺炎恢复的最佳正预后因素（OR = 1.12，CI：1.08-1.17）。酮病风险在第 3 至第 4 泌乳期之间最高，本身会增加 ICI 超过 440 天的风险（OR = 1.12，CI：1.02-1.22），并增加初产牛被替换的风险（OR = 1.75，CI：1.58-1.93）。此外，当发生乳腺炎（OR = 1.31，CI：1.19-1.45）或 ICI 较长（OR = 1.34，CI：1.22-1.48）时，初产牛被替换的可能性更大。对于每个健康指标，牛群水平的风险因素都起着重要作用（18-56%的群内协方差）。我们的结果表明，牛奶分析也可用于预测乳腺炎、治愈率和酮病。牛群水平的风险因素不足以解释这些问题的风险。通过对长期大量人群进行研究，本研究提供了皮埃蒙特（意大利西北部）奶牛健康指标的最新估计，可用于奶牛群的基准测试。
