Quantifying the effect of heat stress on daily milk yield and monitoring dynamic changes using an adaptive dynamic model
Quantifying the effect of heat stress on daily milk yield and monitoring dynamic changes using an adaptive dynamic model
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DOI：
10.3168/jds.2010-4139
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发表时间：
2011-09-01
影响因子：
3.5
通讯作者：
Lansink, A. G. J. M. Oude
中科院分区：
文献类型：
--
作者：
Andre, G.;Engel, B.;Lansink, A. G. J. M. Oude
自动化和机器人的使用越来越多地在奶牛养殖中使用，并产生大量实时数据。这些信息为精准畜牧业的新管理理念提供了基础。 2003年至2006年，荷兰6个实验研究农场收集了牛群平均日产奶量的时间序列。这些时间序列采用贝叶斯方法的自适应动态模型进行分析，以量化热应激的影响。热应激的影响是根据发生热应激的临界温度、热应激期的持续时间以及由此导致的产奶量损失来量化的。此外，还监测水平和趋势的动态变化，包括每周模式的估计。监测包括检测潜在异常值和其他恶化情况。自适应动态模型与数据拟合良好；预测的均方根误差范围为每天 0.55 至 0.99 公斤牛奶。潜在异常值和恶化信号的百分比范围为 5.5% 至 9.7%。用于时间序列分析和监控的贝叶斯程序为过程控制提供了有用的工具。对每日产奶量水平和趋势的在线估计（仅基于过去和现在）和回顾性估计（随后根据所有数据确定）显示出几乎每年的周期，这与产犊模式一致：大多数奶牛在冬季和早春产犊，而不是夏季和秋季。就工作日影响而言，估计的每周模式可能与具体的管理行动有关，例如放牧期间更换牧场。对于热应激的影响，预计出现热应激的平均估计临界温度为 17.8 +/- 0.56 摄氏度。热应激期的估计持续时间为 5.5 +/- 1.03 天，每年每头奶牛的估计损失为 31.4 +/- 12.2 千克牛奶。针对农场的估计有助于确定影响热应激影响的管理因素，如放牧、住房和饲养。通过改变这些因素可以减少热应激的影响。
Automation and use of robots are increasingly being used within dairy farming and result in large amounts of real time data. This information provides a base for the new management concept of precision livestock farming. From 2003 to 2006, time series of herd mean daily milk yield were collected on 6 experimental research farms in the Netherlands. These time series were analyzed with an adaptive dynamic model following a Bayesian method to quantify the effect of heat stress. The effect of heat stress was quantified in terms of critical temperature above which heat stress occurred, duration of heat stress periods, and resulting loss in milk yield. In addition, dynamic changes in level and trend were monitored, including the estimation of a weekly pattern. Monitoring comprised detection of potential outliers and other deteriorations. The adaptive dynamic model fitted the data well; the root mean squared error of the forecasts ranged from 0.55 to 0.99 kg of milk/d. The percentages of potential outliers and signals for deteriorations ranged from 5.5 to 9.7%. The Bayesian procedure for time series analysis and monitoring provided a useful tool for process control. Online estimates (based on past and present only) and retrospective estimates (determined afterward from all data) of level and trend in daily milk yield showed an almost yearly cycle that was in agreement with the calving pattern: most cows calved in winter and early spring versus summer and autumn. Estimated weekly patterns in terms of weekday effects could be related to specific management actions, such as change of pasture during grazing. For the effect of heat stress, the mean estimated critical temperature above which heat stress was expected was 17.8 +/- 0.56 degrees C. The estimated duration of the heat stress periods was 5.5 +/- 1.03 d, and the estimated loss was 31.4 +/- 12.2 kg of milk/cow per year. Farm-specific estimates are helpful to identify management factors like grazing, housing and feeding, that affect the impact of heat stress. The effect of heat stress can be decreased by modifying these factors.