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Morphological characterisation of breeds of sheep: A discriminant analysis approach

by Emmanuel Kofi Sam

Institution: Kwame Nkrumah University of Science and Technology
Department:
Degree:
Year: 2017
Keywords:
Posted: 2/1/2018
Record ID: 2163938
Full text PDF: http://dspace.knust.edu.gh:8080/jspui/handle/123456789/10025


Abstract

In this study the characterisation and separation or discrimination of three sheepbreeds (crosses, West African Dwarfs (WAD) and West African Long Legged(WALL) based on their physical traits (morphological characterisation) was investigatedextensively with the application of discriminant analysis. The study'smain objective was speci cally based on developing a variable selection criterionthat can discriminate best among the three sheep breeds as well as obtain a reliablemathematical function or equation (discriminant function) for provision ofmaximum separation among the three known sheep breeds. Data from College ofEducation, Mampong animal farms on various breeds of sheep (hybrid/crossedbreed, Sahell or WALL and Djallonke or WAD) were used. Factor Analysiswas employed as a variable selection criterion for selecting six sheep traits thatcan discriminate best among the sheep breeds. Canonical discriminant functionwas derived for the eight variable data set and was compared with the derivedQuadratic Discriminant Functions (QDF) using the six extracted sheep traits.The six variable QDF distance classi er provided maximum separation aftercross validation than the 8-variable canonical discriminant functions. The derivedmathematical functions (QDFs) were able to provide maximum separation amongthe three known sheep breeds with a correct classi cation rate of 0.86. The studyrecommended the use of a Quadratic Discriminant Function for discriminationand classi cation of breeds of animal since it gives more accurate results thanother classi ers such as Linear Discriminant Function. A thesis submitted to the Department of Mathematics,Kwame Nkrumah University of Science and Technology inpartial fufillment of the requirement for the degreeof Master of Philosophy in Mathematical Statistics, 2016 KNUST

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