Use of Body Linear Measurements to Estimate Live Weight in Communal Beef Cattle

S. Washaya, W. Bvirwa, G. Nyamushamba
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Abstract

Body measurements are important criteria in the selection of elite animals for breeding. The objective of this study was to determine the relationship, accuracy of prediction of body weight from body measurements, and identifying multicollinearity from three beef breeds.  Four classes of stock (bull, cows, steers, and heifers) were considered. Correlation, simple, and multiple linear regression models were fitted with body weight (BW) as the dependent variable and body length (BL), heart girth (HG), height at wither (HW), muzzle circumference (MC), and shank circumference (SC) as the independent variables. The BW of the animals ranged from 218 to 630 kg, the least being heifers and bulls were the heaviest. The pairwise phenotypic correlations showed a high and significant positive relationship between BW and body dimensions (r = 0.751- 0.96; P<0.01). However, negative correlations were observed between BW with BL and MC of r = -0.733 and -0.703 and -0.660, -0.650, for cows and heifers, respectively. Regressing BW on BL, HG, and HW measurements gave statistically significant (P<0.01) equations with R2 ranging from 0.60 to 0.79. Collinearity, as portrayed by high variance inflation factors (VIFs), tolerance values, and low eigenvalues, was evident in four of the variables. It was concluded that the regression model was useful in BW prediction for smallholder farms and the relationship between BW and other body measurements was influenced by breed and class of stock. It is recommended that ridge regression or principal component regression be used in cases where multicollinearity exisists.
用体线性测量法估算公养肉牛的活重
体型测量是选择优良育种动物的重要标准。本研究的目的是确定由体重测量预测体重的关系、准确性,并确定三个牛肉品种的多重共线性。考虑了四种家畜(公牛、母牛、阉牛和小母牛)。以体重(BW)为因变量,体长(BL)、胸围(HG)、腰高(HW)、口围(MC)、小腿围(SC)为自变量,拟合相关、简单和多元线性回归模型。动物体重在218 ~ 630 kg之间,最小的是母牛,最重的是公牛。两两表型相关显示体重与体型呈高度显著正相关(r = 0.751 ~ 0.96;P < 0.01)。母牛和小母牛的体重与BL、MC分别呈负相关(r = -0.733、-0.703、-0.660、-0.650)。BW与BL、HG和HW的回归方程具有统计学意义(P<0.01), R2范围为0.60 ~ 0.79。共线性,由高方差膨胀因子(vif),容差值和低特征值所描绘,在四个变量中是明显的。结果表明,该回归模型可用于小农农场的体重预测,体重与其他体重指标之间的关系受畜种和畜群类别的影响。在多重共线性存在的情况下,建议使用脊回归或主成分回归。
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