Application of principal component analysis for predicting body weight of Ethiopian indigenous chicken populations

Under smallholder management conditions, where weighing scale is not readily available, body weight (BW) can be predicted from morphometric measurements using multiple regression. However, the statistical interpretation of the regression parameters estimated by least squares is difficult due to multicollinearity problems. Thus, principal component analysis (PCA) was used to predict BW from body measurements, including body length (BL), chest circumference (CC), shank length (SL), and shank circumference (SC) of Ethiopian indigenous chicken populations reared by smallholder farmers. The effectiveness of this technique was also compared with the traditional multiple regression analysis (MRA).

Measurements were taken from 134 male and 487 female chickens. The whole dataset was partitioned into two portions, namely, training and testing datasets, for model comparison and validation purposes. The training dataset, which consisted of 75% of the dataset, was used to develop the model, and the testing dataset, which consisted of 25% of the dataset, was used to validate the model.

The PCA results revealed that the variables for body measurements were represented by PC1 and PC2 in male birds and PC1, PC2, and PC3 in female birds. Regression models developed using scores derived from these PCs explained 88 and 69% of total variation in BW in male and female birds, respectively. Compared with traditional MRA, regression models generated using the PCA procedure were more accurate in predicting BW.

Thus, the results of the present study could not only be used for predicting BW of Ethiopian indigenous chickens but also in their genetic selection programs.