Classification of Daily Body Weight Gains in Beef Calves Using Decision Trees, Artificial Neural Networks, and Logistic Regression
by
, ,
Wilhelm Grzesiak
1,
Daniel Zaborski
1,*,
Renata Pilarczyk
1,
Jerzy Wójcik
1 and
Krzysztof Adamczyk
2 1
Department of Ruminants Science, West Pomeranian University of Technology, Klemensa Janickiego 29, 71-270 Szczecin, Poland
2
Department of Genetics, Animal Breeding and Ethology, University of Agriculture in Krakow, al. Mickiewicza 24/28, 30-059 Kraków, Poland
*
Author to whom correspondence should be addressed.
Animals 2023, 13(12), 1956; https://doi.org/10.3390/ani13121956
Submission received: 17 April 2023
/
Revised: 7 June 2023
/
Accepted: 9 June 2023
/
Published: 11 June 2023
(This article belongs to the Special Issue Data-Mining Methods Applied to Livestock Management)
Simple Summary
In the management of beef cattle, it can be useful to divide individuals based on a specific trait value (below and above average). This in turn allows for focusing on a larger group of animals with the aim of improving, e.g., their growth rate or obtaining a more uniform group in terms of a given trait. Classifying calves into less (below average) and more (above average) efficient growth creates an opportunity for producers to direct their efforts towards the “worse” animals and improve their performance through adjustments in nutrition, animal grouping, or reorganization of work. In this study, models were developed based on data from a beef farm. They were used to classify beef calves into poorer and better growth groups. In order to obtain more input data, predictions were made for the third calf. Among the analyzed models, random forest was the most effective. The most significant factors influencing daily body weight gains were also identified and discussed in the present study. The results demonstrate that machine learning models can be useful for classifying calves based on their growth rates. However, it is necessary to maintain proper breeding documentation from which the predictors can be obtained.
Abstract
The aim of the present study was to compare the predictive performance of decision trees, artificial neural networks, and logistic regression used for the classification of daily body weight gains in beef calves. A total of 680 pure-breed Simmental and 373 Limousin cows from the largest farm in the West Pomeranian Province, whose calves were fattened between 2014 and 2016, were included in the study. Pre-weaning daily body weight gains were divided into two categories: A—equal to or lower than the weighted mean for each breed and sex and B—higher than the mean. Models were developed separately for each breed. Sensitivity, specificity, accuracy, and area under the curve on a test set for the best model (random forest) were 0.83, 0.67, 0.76, and 0.82 and 0.68, 0.86, 0.78, and 0.81 for the Limousin and Simmental breeds, respectively. The most important predictors were daily weight gains of the dam when she was a calf, daily weight gains of the first calf, sex of the third calf, milk yield at first lactation, birth weight of the third calf, dam birth weight, dam hip height, and second calving season. The selected machine learning models can be used quite effectively for the classification of calves based on their daily weight gains.
