The Use of Artificial Neural Networks and a General Discriminant Analysis for Predicting Culling Reasons in Holstein-Friesian Cows Based on First-Lactation Performance Records

SIMPLE SUMMARY: Routinely collected data on the performance of dairy cows are a valuable source of information on the beginning, course, and completion of their productive life. As a result, when using sufficiently accurate methods, one can analyze and optimize the milk production process at a herd level from the breeding and economic point-of-view. In this context, it is important to have a possibility to early predict culling reasons for cows, since, in the case of finding an effective method, it would be possible to modify breeding actions and farm management practices without anticipating the end of the animals’ productive lives. Therefore, the aim of the present study was to verify whether artificial neural networks and a general discriminant analysis may be an effective tool for predicting the culling reasons in cows based on routinely collected first-lactation data. It turned out that they were most effective in predicting culling due to old age and reproductive problems. It is significant because infertility is one of the conditions that are the most difficult to eliminate in dairy herds. ABSTRACT: The aim of the present study was to verify whether artificial neural networks (ANN) may be an effective tool for predicting the culling reasons in cows based on routinely collected first-lactation records. Data on Holstein-Friesian cows culled in Poland between 2017 and 2018 were used in the present study. A general discriminant analysis (GDA) was applied as a reference method for ANN. Considering all predictive performance measures, ANN were the most effective in predicting the culling of cows due to old age (99.76–99.88% of correctly classified cases). In addition, a very high correct classification rate (99.24–99.98%) was obtained for culling the animals due to reproductive problems. It is significant because infertility is one of the conditions that are the most difficult to eliminate in dairy herds. The correct classification rate for individual culling reasons obtained with GDA (0.00–97.63%) was, in general, lower than that for multilayer perceptrons (MLP). The obtained results indicated that, in order to effectively predict the previously mentioned culling reasons, the following first-lactation parameters should be used: calving age, calving difficulty, and the characteristics of the lactation curve based on Wood’s model parameters.