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Comparative multivariate analysis of biometric traits of West African Dwarf and Red Sokoto goats

Abstract

The population structure of 302 randomly selected West African Dwarf (WAD) and Red Sokoto (RS) goats was examined using multivariate morphometric analyses. This was to make the case for conservation, rational management and genetic improvement of these two most important Nigerian goat breeds. Fifteen morphometric measurements were made on each individual animal. RS goats were superior (P < 0.05) to the WAD for the body size and skeletal proportions investigated. The phenotypic variability between the two breeds was revealed by their mutual responses in the principal components. While four principal components were extracted for WAD goats, three components were obtained for their RS counterparts with variation in the loading traits of each component for each breed. The Mahalanobis distance of 72.28 indicated a high degree of spatial racial separation in morphology between the genotypes. The Ward’s option of the cluster analysis consolidated the morphometric distinctness of the two breeds. Application of selective breeding to genetic improvement would benefit from the detected phenotypic differentiation. Other implications for management and conservation of the goats are highlighted.

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Abbreviations

AC:

Abdominal circumference

BL:

Body length

BW:

Body weight

CC:

Chest circumference

CD:

Chest depth

FL:

Foreleg length

HDL:

Hindleg length

HL:

Horn length

HW:

Head width

NC:

Neck circumference

RH:

Rump height

RL:

Rump length

RS:

Red Sokoto

RW:

Rump width

SW:

Shoulder width

WAD:

West African Dwarf

WH:

Withers height

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Correspondence to Abdulmojeed Yakubu.

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Yakubu, A., Salako, A.E. & Imumorin, I.G. Comparative multivariate analysis of biometric traits of West African Dwarf and Red Sokoto goats. Trop Anim Health Prod 43, 561–566 (2011). https://doi.org/10.1007/s11250-010-9731-y

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  • DOI: https://doi.org/10.1007/s11250-010-9731-y

Keywords

  • Nigerian goats
  • Morphology
  • Principal components
  • Cluster analysis
  • Improvement