Artigo Acesso aberto

Estimates of genetic parameters of growth traits in Madras Red sheep

2013; Volume: 3; Issue: 5 Linguagem: Inglês

10.9790/2380-0356973

ISSN

2319-2380

Autores

Rajesh Ganesan,

Tópico(s)

Agricultural Systems and Practices

Resumo

Sheep rearing is an important livelihood for a large number of small and marginal farmers in India as sheep make significant contribution to income generation, supply of animal source of food and serve as financial security to the resource-poor rural households.Growth is an economic trait of interest in domestic animals.The present study was aimed at evaluating growth performance and estimating the genetic parameters for growth traits of Madras Red sheep, a native breed of Tamilnadu state in India, under organized farm conditions.Monthly body weight measurements of 1424 sheep from birth to 2 years of age born during the years 1996 to 2010 were utilized for the analysis.Using least squares analysis, the effects of non-genetic factors namely, sex of lamb, season of birth (Main season(Oct.-Mar.)and off season(Apr.-Sep.)),dam weight at lambing (<25, 25-29.99,30-34.99,≥ 35 kg) and year of birth (1996-2000, 2001-2005, 2006-2010) on the observed birth weight and body weight at 3 rd , 6 th , 9 th and 12 th months and also on the average daily weight gain during 0-3, 3-6, 6-9 and 9-12 months were studied.The body weight at birth, 3 rd , 6 th , 9 th and 12 th month showed significant difference between sex of lamb, dam weight at lambing and year of birth (P < 0.01).The body weight, except at birth and 9 th month showed significant difference between the seasons (P < 0.01).Average daily weight gains showed significant difference between sex of lamb, season of birth and year of birth while it showed significant difference between dam weights at lambing only for the period 3-6 months.Heritability and genetic correlation estimated using paternal half-sib analysis for body weights at various ages and average daily weight gains for different growth periods indicated scope for improvement through selection.

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