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Reproductive system morphometry and productive parameters of beef heifers according to morphological classification.

Anna Carolina de Carvalho RibeiroMarco Roberto Bourg de MelloMarina Mortati Dias BarberoDanielle de OliveiraSamuel Rodrigues Bonamichi do CoutoRondineli Pavezzi Barbero
Published in: Tropical animal health and production (2024)
The aim of the present study was to analyze the effects of visual morphological classification based on body structure, maturity, and musculosity on the morphometry of the reproduction system and productive parameters of beef heifers. The study was conducted for 84 days, during winter season in Brazil. Thirty non-pregnant Nellore heifers, with an average body weight 338 kg and average age 18 months, were used for the study. The heifers were housed in collective pens and fed corn silage (70%) and concentrate (30%) twice a day. All heifers were weighed weekly without prior fasting. Morphological evaluation of body structure, maturity, and musculosity was conducted in the third-middle of the experimental period by a trained evaluator, adopting a comparative methodology of visual evaluation on a scale from 1 to 6, and the heifers were classified according to the visual assessment. The effects of visual classification, time, and interactions were analyzed, considering significant effects was detected (p < 0.05). The significant (p < 0.05) results obtained were compared using Tukey test. Morphological classification did not influence the morphometry of the reproductive system or implied on time interactions. Body structure, maturity, and musculosity had no effect on dry matter intake, body weight gain, feed efficiency, and carcass dressing. However, body weight and feed efficiency were influenced by the experimental time. For both variables, significant variations were detected after 63 days of finishing in the feedlot (p < 0.001), indicating that maximum productive efficiency of feedlot-finished Nellore beef heifers was achieved at a mean body weight of 424 kg.
Keyphrases
  • body weight
  • weight gain
  • machine learning
  • deep learning
  • body mass index
  • physical activity
  • pregnant women
  • body composition
  • resistance training