title: Quantifying the effect of heat stress on daily milk yield and monitoring dynamic changes using an adaptive dynamic model.
abstract: 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°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.
authors.affiliation: Livestock Research, Wageningen University and Research Centre, Lelystad, the Netherlands. Geert.Andre@wur.nl
authors.forename: G
authors.initials: G
authors.lastname: André
authors.forename: B
authors.initials: B
authors.lastname: Engel
authors.forename: P B M
authors.initials: PB
authors.lastname: Berentsen
authors.forename: Th V
authors.initials: TV
authors.lastname: Vellinga
authors.forename: A G J M Oude
authors.initials: AG
authors.lastname: Lansink
mesh_terms: D000222:Adaptation, Physiological / Q000502:physiology; D000818:Animals; D001499:Bayes Theorem; D002417:Cattle / Q000502:physiology*; D003612:Dairying / Q000379:methods; D005260:Female; D018869:Heat-Shock Response / Q000502:physiology*; D007774:Lactation / Q000502:physiology*; D008892:Milk / Q000378:metabolism; D008954:Models, Biological; D013696:Temperature