Department of Biosystems, Biosystems Technology Cluster, KU Leuven, Campus Geel, Kleinhoefstraat 4, 2440 Geel, Belgium; Department of Biosystems, Mechatronics, Biostatistics and Sensors division, KU Leuven, Kasteelpark Arenberg 30, 3001 Leuven, Belgium; RAFT Solutions Ltd., Mill Farm, Studley Road, Ripon HG4 2QR, United Kingdom.
Department of Reproduction, Obstetrics and Herd Health, M-team and Mastitis and Milk Quality Research Unit, Ghent University, Salisburylaan 133, 9820 Merelbeke, Belgium.
J Dairy Sci. 2021 Jan;104(1):405-418. doi: 10.3168/jds.2020-19195. Epub 2020 Nov 12.
Milk yield dynamics during perturbations reflect how cows respond to challenges. This study investigated the characteristics of 62,406 perturbations from 16,604 lactation curves of dairy cows milked with an automated milking system at 50 Belgian, Dutch, and English farms. The unperturbed lactation curve representing the theoretical milk yield dynamics was estimated with an iterative procedure fitting a model on the daily milk yield data that was not part of a perturbation. Perturbations were defined as periods of at least 5 d of negative residuals having at least 1 day that the total daily milk production was below 80% of the estimated unperturbed lactation curve. Every perturbation was characterized and split in a development and a recovery phase. Based hereon, we calculated both the characteristics of the perturbation as a whole, and the duration, slopes, and milk losses in the phases separately. A 2-way ANOVA followed by a pairwise comparison of group means was carried out to detect differences between these characteristics in different lactation stages (early, mid-early, mid-late, and late) and parities (first, second, and third or higher). On average, 3.8 ± 1.9 (mean ± standard deviation) perturbations were detected per lactation in the first 305 d after calving, corresponding to an estimated 92.1 ± 135.8 kg of milk loss. Only 1% of the lactations had no perturbations. On average, 2.3 kg of milk was lost per day in the development phase, while the recovery phase corresponded to an average increase in milk production of 1.5 kg/d, and these phases lasted an average of 10.1 and 11.6 d, respectively. Perturbation characteristics were significantly different across parity and lactation stage groups, and early and mid-early perturbations in higher parities were found to be more severe with faster development rates, slower recovery rates, and higher milk losses. The method to characterize perturbations can be used for precision phenotyping purposes that look into the response of cows to challenges or that monitor applications (e.g., to evaluate the development and recovery of diseases and how these are affected by preventive actions or treatments).
产奶量动态变化反映了奶牛对挑战的响应。本研究调查了比利时、荷兰和英国 50 个农场的 16604 头奶牛的自动挤奶系统采集的 62406 个产奶量数据中的干扰情况。使用迭代程序估计了未受干扰的泌乳曲线,该程序拟合了一个模型,该模型使用的是未受干扰的产奶量数据,不受干扰。干扰被定义为至少 5d 的负残差期,至少有 1d 的总产奶量低于估计的未受干扰的泌乳曲线的 80%。对每个干扰都进行了特征描述,并将其分为发展期和恢复期。在此基础上,我们分别计算了整个干扰的特征以及阶段的持续时间、斜率和产奶量损失。进行了 2 因素方差分析,然后对组均值进行两两比较,以检测不同泌乳阶段(早期、中早期、中晚期和晚期)和胎次(第一胎、第二胎和第三胎及以上)之间这些特征的差异。在产后 305d 内,平均每头奶牛的泌乳期检测到 3.8±1.9(平均值±标准差)个干扰,相当于估计的 92.1±135.8kg 的牛奶损失。只有 1%的泌乳期没有干扰。平均而言,发展阶段每天损失 2.3kg 的牛奶,而恢复期平均每天增加 1.5kg 的牛奶产量,这两个阶段的平均持续时间分别为 10.1 和 11.6d。干扰特征在胎次和泌乳阶段组之间存在显著差异,高胎次的早期和中早期干扰被发现发展速度更快、恢复速度更慢、产奶量损失更高。该干扰特征的描述方法可用于精确表型分析,以研究奶牛对挑战的反应或监测应用程序(例如,评估疾病的发展和恢复情况,以及这些情况如何受到预防措施或治疗的影响)。