Using Principal Component Factor Scores in Multiple Linear Regression Models to Predict Body Weight of Indigenous Chickens from Morphometric Traits in Bench Maji Zone, Southwestern Ethiopia
Downloads
Background: There is a rich genetic resource base of indigenous chickens in Ethiopia. However, the productivity of indigenous chicken in the country and their generic base has not been explored sufficiently through characterization using multivariate analysis.
Objective: The objectives of this study were to understand complex interrelations among morphometric traits of indigenous chickens and predict body weight using principal component (PC) factor analysis.
Materials and Methods: A total of 660 (180 males and 480 females) randomly selected chickens of age six months and above were used for the study. Data were collected on body weight and morphometric traits.
Results: In factor solution of the PCA with varimax rotation of the transformation matrix, two principal components (PCs) were extracted (PC1 and PC2) explaining 75.76% of the total variation in the original variables. PC1 had the largest share (62.43%) of the total variance and had its loadings on comb length, wattle length, wingspan, comb height, shank length, and keel length while the PC2 shared only 13.33% of the total variance with positive loadings on body length, back length, and neck length. Prediction model based on PC factor scores accounted for 48% of the variation in the body weight and was more valid than the inter-dependent based models (which accounted for 49% of the variation in the body weight) as it removed multi-collinearity which was present as inter-dependent traits were used in the model.
Conclusion: According to the findings of this study, body weight can be estimated more accurately from PC factor scores than inter-dependent original morphometric traits (i.e. comb height, comb length, wattle length, neck length, back length, body length, wingspan, shank length, and keel length) and the results obtained could be used by chicken producers and researchers for selection, management purposes and estimating market values of the chickens, since weight is the pivotal point on which animal production thrives.
Copyright (c) 2021 Kefelegn Kebede, Getachew Bekele, Negassi Ameha
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
- I am authorized by my co-authors to enter into these arrangements.
- I warrant, on behalf of myself and my co-authors, that:
- the article is original, has not been formally published in any other peer-reviewed journal, is not under consideration by any other journal and does not infringe any existing copyright or any other third party rights;
- I am/we are the sole author(s) of the article and have full authority to enter into this agreement and in granting rights to Springer are not in breach of any other obligation;
- the article contains nothing that is unlawful, libellous, or which would, if published, constitute a breach of contract or of confidence or of commitment given to secrecy;
- I/we have taken due care to ensure the integrity of the article. To my/our - and currently accepted scientific - knowledge all statements contained in it purporting to be facts are true and any formula or instruction contained in the article will not, if followed accurately, cause any injury, illness or damage to the user.
- I, and all co-authors, agree that the article, if editorially accepted for publication, shall be licensed under the Creative Commons Attribution License 4.0. If the law requires that the article be published in the public domain, I/we will notify Springer at the time of submission, and in such cases the article shall be released under the Creative Commons 1.0 Public Domain Dedication waiver. For the avoidance of doubt it is stated that sections 1 and 2 of this license agreement shall apply and prevail regardless of whether the article is published under Creative Commons Attribution License 4.0 or the Creative Commons 1.0 Public Domain Dedication waiver.
- I, and all co-authors, agree that, if the article is editorially accepted for publication in Haramaya Journals, data included in the article shall be made available under the Creative Commons 1.0 Public Domain Dedication waiver, unless otherwise stated. For the avoidance of doubt it is stated that sections 1, 2, and 3 of this license agreement shall apply and prevail.
