STRAIN CLASSIFICATION OF DONKEYS IN NORTHWEST NIGERIA USING CANONICAL DISCRIMINANT ANALYSIS ON QUANTITATIVE TRAITS
Keywords:
Strain classification, Donkeys, Nigeria, Canonical Discriminant Analysis and Quantitative Traits.Abstract
Metric traits were used to determine the relationship among Red (Auraki), Black (Duni), White (Fari), Brown (Idabari) and Brown-white (Idabari-fari) donkeys. A total of 700 donkeys were used for the study. Metric measures taken were head length, head width, ear length, neck length, neck circumference, shoulder width, height at withers, heart girth, body length and tail length. Data obtained were subjected to General Linear Model Procedure of SAS to determine the relationship
among donkey strains. Canonical discriminant analysis (CANDISC procedure), was used to perform uni-and multivariate analysis to derive canonical variables (CAN), which was used to match the donkey strain groups until reached the satisfactory number of clusters (genetic groups) and to show the clustering groups among these four donkey strains. The canonical coefficients and a scatter diagram for visual interpretation of the different groups were also generated during the canonical discriminant analysis. Among the all the donkey strains, Black (Duni) strain had the highest trait loading in CAN 1 for HL (0.61), HW (0.71), NC (0.91), SW (0.71), HG (0.84) and BL (0.82). The four strains that is, red, black, white and brown donkeys were clustered or separated into a separate group while the brown-white were separated into another distinct group. The results of the study showed that Black donkeys had more canonical weight on the generalized canonical component followed by red and white donkeys. There is need to screen and conserve the four basic donkey strains to arrest further genetic erosion and dilution in these strains.
References
Asai, K. (2010). The Role of Head Up Display in Computer Assisted Instruction. Human-Computer Interaction, New Developments, 31-48.
Bodelid, P., & Oscarsen, A. (2002). Wearable Computing: An Introduction. Wearable Computing.
Chittaro, L. (2003). Human-Computer Interaction with Mobild Devices and Services. Springer Science & Business Media, (pp. 61-75). Udine, Italy.
Fitbit Device. (2014). Retrieved from Stroke Survivors Tattler.
ICNIRP. (1998). Guidelines for limiting exposure to time-varying electric, magnetic and electromagnetic fields (up to 300GHz). International Commission on Non-Ionizing Radiation Protection. Health Phys, 494-522.
IEEE. (2005). IEEE Standard fro Safety Levels with respect to human exposure to radiofrequency electromagnetic fields, 3kHz to 300 GHz. In International Committee on Electromagnetic Safety, IEEE Standard. New York.
Moulton, J. (2010). World Wide SAR Test Standards. San Marcos.
New Health Advisor. (2015, August 19). Fitbit Sleep Tracking. Retrieved from New Health Advisor.
Randewich, N. (2014, January 6). Intel shows off wearable gadgets as chipmaker expands beyond PCs. Las Vegas, Nevada.
Sean. (2016). The top 10 best wireless Earbuds in the market. Retrieved from The Wire Realm: http://www.wirerealm.com/guides/top-10-best-wireless-earbuds
Starner, T., Mann, S., Rhodes, B., Levine, J.-r., Healey, J., Kirsch, D., . . . Pentland, A. (1997). Augumented Reality Through Wearable Computing. Presence, 6(4), 386-398.
Stone, W. T. (2013). Google Glass and Wearable Technology: A New Generation of Security Concerns. Retrieved from Tufts University.
WHO. (2007). Extremely Low Frequency Fields, Environmental Health Criteria 238. Geneva, Switzerland.
Published
How to Cite
Issue
Section
FUDMA Journal of Sciences
