摘要
目的:选育工作中以主成分为单位首先进行集团选择,再根据对主成分贡献率较大的单一性状进行筛选,以提高选育效率。根据雷州山羊体重和体尺性状之间的相关性进行主成分分析,可以为雷州山羊的选种提供重要参考依据。方法:该试验选取体重相近且健康状况良好的雷州山羊公羔60只,采用放牧+补饲方式饲养至60日龄断奶,并进行体重、体高、体斜长等11个体尺指的测定及主成分分析。结果:雷州山羊公羔的腰角宽变异系数最大,有较大的选择潜力;体重与管围的相关系数为0.484,达到显著正相关的水平(P<0.05)。体重与体尺性状可区分为相对独立的4个主成分:躯体因子、匀称因子、特征类因子以及宽度因子。第一主成分的贡献率为31.727%,其中胸深、额宽、体斜长以及肢高的特征向量系数较大,分别为0.432、0.413、0.384以及0.361。结论:胸深、额宽、体斜长以及肢高是雷州山羊公羔的主要性状指标。
Objective:In livestock breeding,initial selection of groups based on the principal component enhances the quality of certain traits.Utilizing the principal component with the highest contribution rate for single trait screening can elevate breeding efficiency.Methods:Sixty healthy,similarly-weighted newborn male Leizhou lambs were chosen and fed by free grazing with additional feed until 60 days of age.Eleven metrics,including body weight,height,and length,were assessed using grazing and supplementary feeding.Principal component analysis was performed on these measurements to offer data for the effective evaluation of appearance and informed breeding of Leizhou goats during the weaning phase.Results:Leizhou male lambs exhibited the highest variation in waist angle width,indicating high selection potential.A significant positive correlation of 0.484(P<0.05)was found between body weight and tube circumference.The traits of Leizhou male lambs can be categorized into four primary components:body,symmetry,characteristic,and width.The primary component had a 31.727%contribution,with eigenvector coefficients for chest depth,frontal width,body skew length,and limb height being 0.432,0.413,0.384,and 0.361,respectively.Conclusion:The main traits influencing male Leizhou lambs are chest depth,frontal width,body skew length,and limb height.
作者
张仕唐
黄艳霞
尹福泉
高振华
ZHANG Shitang;HUANG Yanxia;YIN Fuquan;GAO Zhenhua(Guangdong Ocean University,Zhanjiang Guangdong 524088,China)
出处
《现代畜牧科技》
2024年第1期22-26,共5页
Modern Animal Husbandry Science & Technology
基金
广东省现代草牧业(羊)产业技术体系项目(2019KJ127)。
关键词
雷州山羊
体重体尺性状
主成分分析
Leizhou goat
body weight and body size traits
principal component analysis
