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Open Access Research Article

Applications of Multivariate Multiple Linear Regression Models to Predict Body Weight of Local Chickens from Biometrical Traits in Ethiopia

Kefelegn Kebede1*, Hailemichael Nigussie2 and Negassi Amaha1

1School of Animal and Range Sciences, Haramaya University, Ethiopia

2Department of Animal Production and Technology, Adigrat University, Ethiopia

Corresponding Author

Received Date: February 10, 2021;  Published Date: March 4, 2021


The study aimed at assessing variability among biometrical traits, deducing components that describe these traits, and predicting body weight from both original and orthogonal traits using regression models. Body weight and nine biometrical traits namely, comb height, comb length, keel length, wattle length, body length, back length, breast circumference, wingspan, and shank length were measured on 720 (237 males and 483 females) randomly selected and extensively managed chickens. Phenotypic correlations among body weight and biometrical traits were positive and very highly significant (r = 0.51-0.93; P<0.0001). In the varimax rotation principal component (PC) factor analysis, two factors were extracted which accounted for 88.9% of the total variation. PC1 loaded heavily on comb length, wattle length, comb height, wingspan, shank length, and body length while PC2 loaded heavily on breast circumference and back length. When utilized as predictors in regression analyses, the interdependent biometrical traits had accounted for 82% variation in body weight. However, multicollinearity problem was existent in this estimation. Utilizing the extracted two factor scores as predictors, on the other hand, had positive significant effects on body weight, accounting for 78% variation in body weight. The use of factor scores provided better and reliable prediction of body weight as multicollinearity problem was handled and eliminated. The results found could be used as selection criteria for improving body weight of local chickens.

Keywords: Biometrical traits; Chicken; Principal Component Factor Analysis.

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