The use of data mining techniques for analysing factors affecting cow reactivity during milking

The use of data mining techniques for analysing factors affecting cow reactivity during milkingDownload file
NEJA, W., PIWCZYńSKI, D., KRĘŻEL-CZOPEK, S., SAWA, A., OZKAYA, S.THE USE OF DATA MINING TECHNIQUES FOR ANALYSING FACTORS AFFECTING COW REACTIVITY DURING MILKING

2017, 18(2), p.342 - 357, DOI: http://dx.doi.org/10.5513/JCEA01/18.2.1907

Abstract

Motor activity of 158 Polish Holstein-Friesian cows was evaluated 5 times (before and during milking in a DeLaval 2*10 milking parlour) for both the morning and evening milking, on a 5-point scale, according to the method of Budzyńska et al. (2007). The statistical analysis used multiple logistic regression and classification trees (Enterprise Miner 7.1 software which comes in with SAS package). In the evaluation of motor activity, cows that were among the first ten to enter the milking parlour were more often given a score of 3 points before (11.5%) and during milking (23.5%) compared to the other cows. Cows’ activity tended to decrease (both before and during milking) with advancing lactation. The cows’ reduced activity was accompanied by shorter teat cup attachment times and lower milk yields. The criteria calculated for the quality of models based on classification tree technique as well as logistic regression showed that similar variables were responsible for the reactivity of cows before milking (teat cup attachment time, day of lactation, number of lactation, side of the milking parlour) and during milking (day of lactation, side of the milking parlour, morning or evening milking, milk yield, number of lactation). At the same time, the applied methods showed that the determinants of the cow reactivity trait are highly complex. This complexity may be well explained using the classification tree technique.

Keywords:
behavioural reactivitydata mininglogistic regressionmilking parlour