Université Paris-Saclay, INRAE, AgroParisTech, UMR GABI, 78350 Jouy-en-Josas, France; Université Paris-Saclay, INRAE, AgroParisTech, UMR MoSAR, 75005 Paris, France.
Université Paris-Saclay, INRAE, AgroParisTech, UMR GABI, 78350 Jouy-en-Josas, France.
J Dairy Sci. 2021 Jan;104(1):459-470. doi: 10.3168/jds.2020-18537. Epub 2020 Nov 6.
Livestock husbandry aims to manage the environment in which animals are reared to enable them to express their production potential. However, animals are often confronted with perturbations that affect their performance. Evaluating effects of these perturbations on animal performance could provide metrics to quantify and understand how animals cope with their environment, and therefore to better manage them. Body weight (BW) and milk yield (MY) dynamics over lactation may be used for this purpose. The goal of this study was to estimate an unperturbed performance trajectory using a differential smoothing approach on both MY and BW time series, and then to identify the perturbations and extract their phenotypic features. Daily MY and BW records from 490 primiparous Holstein cows from 33 commercial French herds were used. From the fitting procedure, estimated unperturbed performance trajectories of BW and MY were clustered into 3 groups. After the fitting procedure, 1,754 deviations were detected in the MY time series and 964 were detected in the BW time series across all cows. Overall, 425 of these deviations were detected during the same period (±10 d) in both MY and BW time series, 76 of which started at the same time. Results suggest that combining various individual dynamic measures and revealing the relationship that exists between them could be of great value in obtaining reliable estimates of resilience components in large populations.
畜牧业的目标是管理动物养殖环境,使动物能够发挥其生产潜力。然而,动物经常面临影响其性能的干扰。评估这些干扰对动物性能的影响,可以提供衡量标准来量化和理解动物如何应对其环境,从而更好地管理它们。泌乳期间的体重(BW)和产奶量(MY)动态可用于此目的。本研究的目的是使用差异平滑方法对 MY 和 BW 时间序列进行估计,以确定未受干扰的性能轨迹,然后识别干扰并提取其表型特征。使用来自 33 个法国商业奶牛场的 490 头初产荷斯坦奶牛的每日 MY 和 BW 记录。从拟合过程中,将 BW 和 MY 的估计未受干扰的性能轨迹聚类为 3 组。在拟合过程之后,在所有奶牛的 MY 时间序列中检测到 1754 个偏差,在 BW 时间序列中检测到 964 个偏差。总体而言,在 MY 和 BW 时间序列中同时检测到 425 个偏差,其中 76 个偏差同时开始。结果表明,结合各种个体动态测量值并揭示它们之间存在的关系,对于在大群体中获得可靠的弹性成分估计值可能具有重要价值。