DAIRY CHAOS: Data driven Approach Identifying daiRY Cows affected by HeAt lOad Stress.

In intensive farming systems the facilities have a central role on both animal welfare and animal production all this paving the way of researching new housing systems and management strategies for reducing the impacts. In particular, in the dairy cattle sector, the early detection of irregular productions is fundamental for animal health and safety. On the other hand, despite the growing interest concerning the modelling and forecasting daily production data, there is lack of studies devoted to identification of anomalous data. To this regard, in this work, a data driven approach for detecting milk production and behaviour anomalies is presented and applied to three farms selected as case study. The DAIRY CHAOS procedure proposed in this paper bases on two numerical algorithms having the scope of separately detect anomalies daily data for a single cow. Both the algorithms presented hereinafter have statistical foundations and take in input daily resting time, milk yield and climate data respectively recorded by pedometer worn by the cow, automatic milking robot and a thermo-hygrometer data logger installed in each barn. The first algorithm takes into consideration three indicators, namely Relative Yield Difference, Relative Laying time Difference and Cumulative Discomfort Index. An anomaly, i.e. a deviation from a normal value, is determined, for a single cow, for a specific day, if the three conditions assessing a noticeable deviation from the normal values of the three indicators above are contemporary verified. The second algorithm, by means of a multifit procedure, introduces the concept of reliability of robust statistics and provides statistically solid, since not affected by outlier values, milk yield and laying time trends for each animal. The application, in a production context, of the procedure proposed here can result extremely useful for the identification of animals suffering heat stress and therefore can become a support to the farmer's decisions for the mitigation of the heat stress effects and a more efficient management of the animals. • A data driven approach for detecting milk production anomalies is proposed. • The DAIRY CHAOS procedure represents a robust procedure for heat stress detection. • An anomaly in the daily indicators labels the cow as suffering heat stress. [ABSTRACT FROM AUTHOR]