Indian Journal of Animal Research
SCOPUSWeb of Science
  • Year: 2015
  • Volume: 49
  • Issue: 1

Growth modeling and factors affecting growth traits in Madras red Sheep

  • Author:
  • R. Ganesan1,, P. Dhanavanthan, D. Balasubramanyam2, P. Kumarasamy3, Kiruthika
  • Total Page Count: 6
  • Page Number: 20 to 25

1Department of Animal Genetics and Breeding, Rajiv Gandhi Institute of Veterinary Education and Research, Kurumbapet, Puducherry – 605 006. India

2Center for Animal Production Studies, Tamil Nadu Veterinary and Animal Sciences University, Chennai-600 051, India

3Department of Bioinformatics & ARIS Cell, Madras Veterinary College, Chennai-600 007, India

Department of Statistics, Pondicherry University, Puducherry-605 014, India

*Correponding author's email: ashwinarvind@sify.com

Online published on 21 February, 2015.

Abstract

Sheep rearing is an important livelihood for a large number of small and marginal farmers in India. Growth is a trait of interest in domestic animals. Information about growth model parameters is very useful for selection studies. In this paper, the growth of Madras Red sheep, a native breed of Tamil Nadu state in India, is studied through nonlinear growth models using data on average monthly body weight measurements of 1424 sheep from birth to 2 years of age born during 1996 to 2010. Sex of lamb, dam weight at lambing and year of birth were the significant non-genetic factors affecting body weight at birth, 3rd, 6th, 9th and 12th month. The birth weight and 9th month weight showed significant difference between the seasons. Average daily weight gain showed significant difference between sex of lamb, season of birth and year of birth while it showed significant difference between dam weight at lambing only for the period 3–6 months. Brody's growth model described the average monthly growth remarkably well in males and females suggesting its usefulness in growth monitoring of Madras Red sheep.

Keywords

Average daily gain, Body weight, Growth curve, Madras Red sheep, Nonlinear regression