Towards modelling beef cattle management with Genetic Programming

Francesca Abbona, Leonardo Vanneschi, Marco Bona, Mario Giacobini

Research output: Contribution to journalArticle

Abstract

Among the Italian Piemontese Beef Breedings, the yearly production of calves weaned per cow, that is the calves that survive during the period of 60 days following birth, is identified as the main target expressing the performance of a farm. modeling farm dynamics in order to predict the value of this parameter is a possible solution to investigate and highlight breeding strengths, and to find alternatives to penalizing factors. The identification of such variables is a complex but solvable task, since the amount of recorded data among livestock is nowadays huge and manageable through Machine Learning techniques. Besides, the evaluation of the effectiveness of the type of management allows the breeder to consolidate the ongoing processes or, on the contrary, to adopt new management strategies. To solve this problem, we propose a Genetic Programming approach, a white-box technique suitable for big data management, and with an intrinsic ability to select important variables, providing simple models. The most frequent variables encapsulated in the models built by Genetic Programming are highlighted, and their zoological significance is investigated a posteriori, evaluating the performance of the prediction models. Moreover, two of the final expressions selected only three variables among the 48 given in input, one of which is the best performing among GP models. The expressions were then analyzed in order to propose a zootechnical interpretation of the equations. Comparisons with other common techniques, including also black-box methods, are performed, in order to evaluate the performance of different type of methods in terms of accuracy and generalization ability. The approach entailed constructive and helpful considerations to the addressed task, confirming its key-role in the zootechnical field, especially in the beef breeding management.

Original languageEnglish
Article number104205
Pages (from-to)1-12
Number of pages12
JournalLivestock Science
Volume241
Early online date7 Aug 2020
DOIs
Publication statusE-pub ahead of print - 7 Aug 2020

Keywords

  • Cattle breeding
  • Evolutionary algorithms
  • Machine learning
  • Piemontese bovines
  • Precision livestock farming

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