Recognition of culling reasons in Polish dairy cows using data mining methods

•Data mining methods were used to recognize culling reasons in dairy cows.•It is impossible to accurately recognize culling reasons using routinely recorded data.•More specific data are required to improve the recognition ability of the models. Cow longevity and reasons for culling are one of the mo...

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Bibliographic Details
Published in:Computers and electronics in agriculture 2016-09, Vol.127, p.26-37
Main Authors: Adamczyk, Krzysztof, Zaborski, Daniel, Grzesiak, Wilhelm, Makulska, Joanna, Jagusiak, Wojciech
Format: Article
Language:English
Subjects:
Classification
Culling
Dairy cows
Data mining methods
Decisions
Electronics
Involuntary culling
Learning theory
Mathematical models
Neural networks
Recognition
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Summary:•Data mining methods were used to recognize culling reasons in dairy cows.•It is impossible to accurately recognize culling reasons using routinely recorded data.•More specific data are required to improve the recognition ability of the models. Cow longevity and reasons for culling are one of the most important research problems in the contemporary cattle breeding. Therefore, the analysis of the relationship between cow performance and involuntary disposal contributes to taking more informed decisions in herd management. The aim of the present study was to compare the efficiency of artificial neural networks (ANN) and boosted classification trees (BT) with that of linear discriminant analysis (LDA) and classification functions (CF) in recognizing culling reasons of dairy cows in Poland, based on the lifetime performance data, routinely monitored in a herd. The analyses carried out in the present study showed that the accurate recognition of different culling reasons based on predictors included in the above-mentioned models is, in general, impossible. Only BT had limited discrimination abilities, but the results obtained using this method were not much improved compared with ANN and LDA with CF. In order to predict precisely various culling reasons, more specific data are required. They could be obtained from the increasingly popular, technologically advanced, systems of real-time monitoring of animal health status (physical activity, rumination rate, etc.), dependent also on environmental conditions (e.g. temperature–humidity index).
ISSN:0168-1699
1872-7107
DOI:10.1016/j.compag.2016.05.011