Principal Component Analysis of Body Measurements of Yankassa Sheep in Anyigba, Kogi State, Nigeria

Adejoh Christiana Ojonegecha, Musa Abdulraheem Arome, Okoh Joseph Joseph, Okolo Freedom Atokolo, Emmanuel Amanabo Theophilus, Efienokwu Jude
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Abstract

Yankasa sheep play a vital role in food security and the livelihood of smallholder farmers. This study aimed to evaluate the relationship amongst body measurements. A total of 126 extensively reared Yankasa rams, between 15.5 – 28.3 months of age, were randomly selected for the study. Data on body measurements were collected and subjected to correlation, principal component (PC), and step-wise multiple regression analyses. We found that mean body measures ranged from 11.2cm for scrotal circumference (SC) to 71.9cm for chest girth (CG), and the coefficient of variation ranged from 10.7%for height at withers (HW) to 30.3%forBW. All body measures, except ear length, were significantly (P<0.01) associated with BW. All body measures, except ear length, were significantly (P<0.01) associated with BW. Of all body measures, CG, rump width (RW), and neck circumference (NC) were the most associated with BW, with correlation coefficients of 0.83, 0.8, and 0.79, respectively, while neck length, ear width, and tail length were the least associated with correlation coefficients of 0.21, 0.33, and 0.46. Three principal components from the factor analysis of body measurements explained about 64% of the total variance. Regression models using original morphometric traits as predictors explained up to 80% of the variation in body weight, while PC explained up to 75%. This study shows that body measurements, such as CG, RW, and NC, could serve as markers for BW in Yankasa sheep.
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尼日利亚科吉州Anyigba Yankassa羊身体测量的主成分分析
洋卡萨羊在粮食安全和小农生计方面发挥着至关重要的作用。这项研究旨在评估身体测量之间的关系。选取15.5 ~ 28.3月龄的粗养洋卡公羊126只进行研究。收集身体测量数据并进行相关、主成分(PC)和逐步多元回归分析。我们发现平均体长从阴囊围(SC)的11.2cm到胸围(CG)的71.9cm不等,变异系数从肩高(HW)的10.7%到体重的30.3%不等。除耳长外,所有体型指标与体重均显著相关(P<0.01)。除耳长外,所有体型指标与体重均显著相关(P<0.01)。体长、臀宽和颈围与体重的相关系数分别为0.83、0.8和0.79,颈长、耳宽和尾长与体重的相关系数最小,分别为0.21、0.33和0.46。来自身体测量因子分析的三个主成分解释了大约64%的总方差。使用原始形态特征作为预测因子的回归模型解释了高达80%的体重变化,而PC解释了高达75%。本研究表明,体重测量如CG、RW和NC可作为羊体重的指标。
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