随着个人经济水平的提高,对羊肉的需求也是如此。增强肉羊的品种不仅提高了生产效率和经济效益,而且促进了肉羊养殖业的可持续增长。因此,本研究考察了天目赛诺羊的早期生长和繁殖性状,分析这些性状之间的遗传相互作用,为完善育种策略和加快该品种的遗传发展提供理论基础。调查收集了29,966个数据条目,涉及111个父亲的出生体重(BWT)和113个其他指标。数据包含来自1633个水坝的10415个BWT记录,来自1,570个水坝的12,753个断奶重量(WWT)记录,1,597个水坝的12,793个平均日收益(ADG)记录,和1499个水坝的13594个产仔数(LS)记录。利用SAS9.2软件中的GLM程序,该研究分析了羔羊BWT的非遗传影响,WWT,ADG,和LS。同时,DMU软件针对每种性状估计了各种动物模型的方差分量。采用Akaike信息准则(AIC)和似然比检验(LRT),测试了六个模型,纳入或排除母亲继承和环境影响,确定推导遗传参数的最优模型。研究结果表明,出生年份(BY),出生季度(BQ),出生类型(BT),母亲年龄(AM),出生性别(BS)对BWT产生了重大影响,WWT,和ADG(p<0.01)。此外,BQ和AM显著影响LS(p<0.01)。最准确的遗传评价模型确定了BWT的遗传力,WWT,ADG,和LS分别为0.0695、0.0849、0.0777和0.1252。
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.