Evaluation of growth performances in Malabari goats was done with body weight and major morphometric traits, viz. body height, body length and chest girth at 6, 9 and 12 months, respectively. Data pertaining on 1082 Malabari goats spread over a period of 5 years (from 2014 to 2018) were used in the study. Least squares analysis of traits was done to adjust the effect of major significant non-genetic factors. Traits were analysed by using Varimax rotated principal component analysis (PCA) with Kaiser normalization to explain growth performances. Out of twelve principal components, PCA revealed four components explained about 67.78% of total variation. The first component (PC1) explained 28.02% of total variation. It was represented by significantly positive high loading of BH9, BH12 and BH6. The second component explained 15.090% of total variance with high loading of distance between BL9, BL6 and BL12. The third component explained 12.643% of variance and showed high component loadings for CG9, CG6 and CG12. The fourth factor accounted for 12.020% of total variability with comparatively higher loading WT12, WT9 and WT6. The communality ranged from 0.562 for BL12 to 0.848 for BH9. The body weight of adult Malabari goats was predicted using stepwise multiple regression of different interdependent morphometric traits and principal components. The multiple regression model with PC1 and PC2 was most precise with coefficient of determination (R2) value 74%. Therefore, the study revealed that extracted components revealed maximum variability of growth performances in Malabari goats which could be effectively used for selection and breeding programmes.

中文翻译：

---

多元主成分分析，以评估印度的马拉巴里山羊的生长性能。

通过体重和主要形态特征对马拉巴里山羊的生长性能进行评估。分别在6、9和12个月时的身高，身长和胸围。这项研究使用了有关5年内（从2014年到2018年）分布的1082只Malabari山羊的数据。对特征进行最小二乘分析以调整主要的重要非遗传因素的影响。通过使用Varimax旋转主成分分析（PCA）和Kaiser归一化来分析性状，以解释生长表现。在十二个主要成分中，PCA显示四个成分解释了总变异的约67.78％。第一部分（PC1）解释了总变化的28.02％。它由BH9，BH12和BH6的显着正高负荷表示。第二部分说明15。BL9，BL6和BL12之间的距离负载较大时，总方差为090％。第三个分量解释了12.643％的方差，并显示了CG9，CG6和CG12的高分量负载。第四个因素占总可变性的12.020％，WT12，WT9和WT6的负载相对较高。社区范围从BL12的0.562到BH9的0.848。使用不同的相互依赖的形态特征和主要成分的逐步多元回归来预测成年马拉巴里山羊的体重。PC1和PC2的多元回归模型最精确，测定系数（R2）值为74％。因此，研究表明，提取的成分揭示了马拉巴里山羊生长性能的最大差异，可以有效地用于选择和育种计划。