Economic values of reproductive and growth traits in Chinese Yiling sheep

Li, Jihua; Liu, Guiqiong; Jiang, Xunping; Shen, Yumeng; Sun, Ling; Chen, Yan; Wang, Xu; Yang, Shiping; Yang, Han

doi:10.1007/s11250-023-03810-1

Cited by 1 publication

(1 citation statement)

References 30 publications

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“…In recent years, the study of sheep growth and reproductive traits has intensified, as these factors significantly influence farm profitability (2)(3)(4)(5)(6)(7). Animal growth varies over time, displaying a pattern of steady increase, although growth rates differ among individuals.…”

Section: Introductionmentioning

confidence: 99%

Comparison of different animal models for estimating genetic parameters for early growth traits and reproductive traits in Tianmu Sainuo sheep

Liu,

Lu,

Tang

et al. 2024

Front. Vet. Sci.

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As the economic level of individuals rises, so too does the demand for mutton. Enhancing the breeds of mutton sheep not only boosts production efficiency and economic benefits but also fosters the sustainable growth of the mutton sheep breeding industry. Thus, this study examines the early growth and reproductive traits of Tianmu Sainuo sheep, analyzing the genetic interactions among these traits to furnish a theoretical foundation for refining breeding strategies and expediting the genetic advancement of this breed. The investigation compiled 29,966 data entries, involving 111 sires for birth weight (BWT) and 113 for other metrics. The data encompassed 10,415 BWT records from 1,633 dams, 12,753 weaning weight (WWT) records from 1,570 dams, 12,793 average daily gain (ADG) records from 1,597 dams, and 13,594 litter size (LS) records from 1,499 dams. Utilizing the GLM procedure in SAS 9.2 software, the study analyzed the non-genetic influences on lamb BWT, WWT, ADG, and LS. Concurrently, DMU software estimated the variance components across various animal models for each trait. Employing the Akaike Information Criterion (AIC) and likelihood ratio test (LRT), six models were tested, incorporating or excluding maternal inheritance and environmental impacts, to identify the optimal model for deriving genetic parameters. The findings reveal that birth year (BY), birth quarter (BQ), birth type (BT), age of mother (AM), and birth sex (BS) exerted significant impacts on BWT, WWT, and ADG (p < 0.01). Additionally, BQ and AM significantly influenced LS (p < 0.01). The most accurate genetic evaluation model determined the heritability of BWT, WWT, ADG, and LS to be 0.0695, 0.0849, 0.0777, and 0.1252, respectively.

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