Predicting dry matter intake in Pelibuey sheep using machine learning methods

Camacho-Perez E., Tirink C., Garcia-Herrera R., Piñeiro-Vazquez Á. T., Casanova-Lugo F., Canul-Solis J. R., ...More

Heliyon, vol.11, no.2, 2025 (SCI-Expanded) identifier

  • Publication Type: Article / Article
  • Volume: 11 Issue: 2
  • Publication Date: 2025
  • Doi Number: 10.1016/j.heliyon.2025.e41913
  • Journal Name: Heliyon
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, CAB Abstracts, Food Science & Technology Abstracts, Veterinary Science Database, Directory of Open Access Journals
  • Keywords: Dry matter intake, Hair sheep, Machine learning
  • Yozgat Bozok University Affiliated: Yes

Abstract

This study determined to predict the dry matter intake (DMI) in growing male Pelibuey sheep by using 3 different machine learning methods. Individual data was obtained from 130 animals whose average body weight (ABW) was 23 ± 6 kg and the DMI was 1.04 ± 0.27 kg/d from an experiment conducted under tropical conditions. To create the database, the following data were recorded: % concentrate in the diet (CON), initial body weight (IBW, kg), final BW (FBW, kg), mean metabolic BW (MBW0.75, kg0.75), daily weight gain (ADG, g/d), crude protein (CP) and neutral detergent fibre (NDF). Multivariate Adaptive Regression Splines (MARS), Classification and Regression Tree (CART), and Support Vector Regression (SVR) were used for the development of a predictive algorithm. The determination coefficient was determined over 0.90 for the MARS algorithm. Overall, the MARS algorithm was a reliable predictive model for DMI prediction in the Pelibuey sheep.