Department of Animal Breeding and Genetics, College of Veterinary Science and Animal Husbandry, Odisha University of Agriculture and Technology, Odisha, India.
Department of Veterinary Pathology, College of Veterinary Science and Animal Husbandry, OUAT, Odisha, India.
Trop Anim Health Prod. 2024 Sep 28;56(8):298. doi: 10.1007/s11250-024-04114-8.
The body conformations of 262 adult Ganjam goats were subjected to principal component analysis (PCA) with 11 morphometric variables. The results were then used to predict the mature body weight of the goats. Most of the traits were positively correlated, and the correlations were statistically significant. The three main components that the PCA recovered explained 76.12% of the variation in body morphometry overall. The first component accounted for approximately 54.74% of the overall variation and described almost all the traits except ear length and tail length, as indicated by high component loadings. The second component accounted for approximately 11.48% of the variation, mostly accounting for the variation in tail length. The principal component accounted for 9.89% and mostly explained the variation in ear length. The communalities ranged between 0.557 (horn length) and 0.848 (chest circumference) for the first three extracted components. The highest percentage of variability in chest girth was explained by the first three principal components, whereas it was the lowest for the horn length. In the context of predicting body weight through stepwise regression analysis, nine primary variables accounted for 57.3% of the total variance in body weight. Conversely, utilizing the first principal component alongside six additional principal components as independent variables resulted in capturing 56.3% of the variation in the adult live weight of goats while maintaining model comparability with other pertinent parameters. PCA was used efficiently for body weight prediction from major morphometric traits of Ganjam goats addressing the multicollinearity issue.
对 262 只成年甘贾姆山羊的体姿进行了主成分分析（PCA），使用了 11 个体尺变量。然后，利用这些结果预测了山羊的成熟体重。大多数性状呈正相关，且相关性具有统计学意义。PCA 恢复的三个主要成分解释了体尺形态总体变化的 76.12%。第一成分约占总变化的 54.74%，除了耳长和尾长外，几乎描述了所有性状，这表明成分负荷较高。第二成分约占变化的 11.48%，主要描述了尾长的变化。主成分占 9.89%，主要解释了耳长的变化。公有性在 0.557（角长）和 0.848（胸围）之间，前三个提取的成分。胸围的可变性最高，由前三个主成分解释，而角长的可变性最低。在逐步回归分析中通过预测体重，九个主要变量占体重总方差的 57.3%。相反，利用第一主成分和另外六个主成分作为自变量，可以捕捉到 56.3%的成年山羊活重变化，同时保持与其他相关参数的模型可比性。PCA 有效地用于从甘贾姆山羊的主要体型性状预测体重，解决了多重共线性问题。
