特克塞尔×哈萨克羊F1代体重和体尺相关分析

Correlation Analyses between Body Weight and BodySize of F1(Te xel × Kazakh)

本试验旨在探讨特克塞尔×哈萨克羊F1代体重和体尺的相关关系,为通过体尺指标估计体重提供科学的理论依据。选取测定记录中6月龄特哈杂交F1代公羔62只、母羔62只,共计124只。测量其体重(Y)、体高(X1)、胸宽(X2)、胸深(X3)、体斜长(X4)、胸围(X5)、单腿围(X6)、管围(X7),运用SPSS19.0软件对特哈杂交F1代羔羊的体重和体尺进行相关分析、逐步线性回归分析。结果表明,特哈杂交F1代的体重和体高、胸宽、胸深、体斜长、胸围、单腿围、管围均呈极显著正相关(P<0.01),体斜长与体重相关程度最高,胸宽与体重相关程度最低,相关系数分别为0.70、0.41。体高与单腿围的相关性未达到显著水平,胸宽与体高和管围之间为负相关,其余各体尺指标间均达到极显著正相关(P<0.01)。经逐步回归分析,将体斜长、单腿围、管围纳入回归方程。最优回归方程为:Y=0.780X4+1.005X6+4.125X7-87.391。回归方程的决定系数R2=0.64,拟合程度较高。

In this study,the scientific theoretical basis for estimating weight through size index was provided based on the correlation between body weight and body size of F1(Texel × Kazakh).In the measurement records,The total of 6months old 124 F1(Texel × Kazakh)were used,included 62 male lambs and 62 female lambs.Weight(Y),body height(X1),chest width(X2),chest depth(X3),body oblique length(X4),chest circumference(X5),single leg circumference(X6)and tube circumference(X7)were measured,Correlation and stepwise linear regression analysis of weight and body size of the F1 lambs of the Texel × Kazakh cross breed were used SPSS19.0 software.The results showed that the weight and body height,chest width,chest depth,body oblique length,chest circumference,single-leg circumference,and tube circumference of the T1 hybrid F1 generation had significant positive correlations(P<0.01).The correlation between body oblique length and weight was the highest(0.70),and the correlation between chest width and weight was the lowest(0.41).The correlation between single leg circumference and weight was not significant,Chest width is negatively correlated with body height and tube circumference,the other body size indicators reached extremely significant positive correlations(<0.01).Stepwise regression analysis contaned the body oblique length,single leg circumference and tube circumference trait.The optimal regression equation is:Y=0.780X4+1.005X6+4.125X7-87.391.The determination coefficient R2 of the regression equation is 0.64,and the level of fitting was higher.