Application of Principal Component Factor Analysis in Quantifying Size and Morphological Indices of Domestic Rabbits
Body weight and four morphostructural traits namely body length, heart girth, thigh circumference and ear length of 103 New Zealand White x Chinchilla crossbred rabbits were measured. The investigation aimed at describing objectively the interdependence among conformation traits and to predict body...
Guardado en:
| Autores principales: | , |
|---|---|
| Lenguaje: | English |
| Publicado: |
Sociedad Chilena de Anatomía
2009
|
| Materias: | |
| Acceso en línea: | http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0717-95022009000400009 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| Sumario: | Body weight and four morphostructural traits namely body length, heart girth, thigh circumference and ear length of 103 New Zealand White x Chinchilla crossbred rabbits were measured. The investigation aimed at describing objectively the interdependence among conformation traits and to predict body weight from their independent factor scores using principal component analysis. Phenotypic correlations between body weight and body dimensions were highly significant (r=0.61 0.91; P<0.01). Pairwise correlations of the body shape characters ranged from moderate to high values. From the factor analysis with varimax rotation of the intercorrelated traits, two principal components which accounted for 90.27% of the total variance were extracted. The first principal component, PC1 termed general size, had its loadings for body length, heart girth and thigh circumference and explained 74.98% of the variance. Ear length primarily determined the second principal component, PC2 which contributed to 15.29% of the generalized variance. Orthogonal body shape characters derived from the factor analysis accounted for 81.7% of the variation in body weight of rabbits. The PC-based prediction model is preferable to linear measure-based models for selecting animals for optimal balance since it combines both size and shape components into a composite index for prediction. |
|---|