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

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.

Estimation of Dairy Cow Survival in the First Three Lactations for Different Culling Reasons Using the Kaplan–Meier Method

“…Cows have increased chances of remaining in the herd if they are healthy, reproduce regularly, have functional feet, legs, and udders, and produce enough milk [ 15 ]. Thus, knowledge of the association between various indicators monitored at the individual animal and herd levels with cow lifespans and culling reasons is essential to predict cow longevity and support optimal decisions in herd management [ 31 ]. The survival and cluster analyses performed in the present study showed some similarities between individual culling categories, which may indicate their direct connection.…”

“…Considering the above, the analysis of longevity and survivability of dairy cows in terms of breeding and production aspects has been a top research topic in recent years. Special attention has been paid to the selection of methods precisely determining the relationships among variables [ 28 , 29 , 30 , 31 ]. It appears that survival analysis is one of them.…”

Artificial Neural Networks- An Introduction and Application in Animal Breeding and Production: A Review

“…In a study conducted on Holstein-Friesian cows culled in Poland between 2017 and 2018 it was found that ANN were the most effective in predicting the culling of cows due to old age (99.76-99.88% of correctly classified cases) and 99.24-99.98% due to reproductive problems. It is significant because infertility is one of the conditions that are the most difficult to eliminate in dairy herds (Adamczyk et al 2021).…”

Mathematical modeling for genomic selection in Serbian dairy cattle

“…According to 11 developed ANN performances, the optimal numbers of neurons in the hidden layer for Fat%, Protein%, Cheese Merit, Fluid Merit, LIV, SCE, HCR, CCR, Daughter Stillbirth, Sire Stillbirth and GL calculation were: 11 to obtain the highest values of r 2 (during the training cycle r 2 for output variables were: 0.951; 0.947; 0.989; 0.985; 0.902; 0.887; 0.676; 0.953; 0.590; 0.647 and 0.444, respectively), Table 1. The obtained ANN model for prediction of output variables (Fat%, Protein%, Cheese Merit, Fluid Merit, LIV, SCE, HCR, CCR, Daughter Stillbirth, Sire Stillbirth, and GL) was complex (276 weights-biases) because of the high nonlinearity of the observed system (MONTGOMERY, 1984;ADAMCZYK et al, 2021).…”