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Clustering of breed types : Preliminary results

Clustering of breed types : Preliminary results. Anette van Dorland. ILRI, Addis Ababa, Ethiopia, 26 February 2003. Introduction. 1. Large number of unknown breed types: How different/similar are these breed types from each other ? 2. Farmers knowledge versus enumerator observation.

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Clustering of breed types : Preliminary results

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  1. Clustering of breed types: Preliminary results Anette van Dorland ILRI, Addis Ababa, Ethiopia, 26 February 2003

  2. Introduction 1. Large number of unknown breed types: How different/similar are these breed types from each other ? 2. Farmers knowledge versus enumerator observation Multivariate techniques

  3. Approach I: • Grouping of entities based on the multivariate similarities among the entities • No prior information of the formed groups available Cluster analysis Approach II: • Grouping of entities based on the multivariate similarities among the entities • Prior information of the formed groups available Discriminant analysis Introduction (cont.)

  4. Bore Hagere Mariam Liben Teltele Dire Borana Zone • Data on cattle from Borana Zone • Five woreda’s selected (see map) • Three woreda’s predominantly in lowland (Dire, Liben and Teltele) • Two woreda’s predominantly in highland (Bore and Hagere Mariam) Oromia Region Borana Zone

  5. Data and Methodology • 209 records on breed types • 26 qualitative variables on phenotypic characteristics • First step: Principal Components Analysis • Second step: Agglomerative Hierarchical Clustering (AHC) • Mahalanobis’ distance (dissimilarity) • Strong linkage as aggregation criteria

  6. Characteristic Back profile Coat colour-body Rump profile Coat colour-head Ear size Coat colour-ears Coat colour-tail Ear shape Coat colour-hoof Ear orientation Coat pattern Horn length Hair type Horn shape Horn orientation Hair size Frame size Horn spacing Dewlap size Tail length Udder size Hump size Teat size Hump shape Face profile Navel flap size Principal Components Analysis 10 principal components responsible for 64 % of the variation between the observations

  7. Contributions of the variables (%) Principal Components Analysis (cont.)

  8. Agglomerative Hierarchical Clustering:Dendrogram

  9. Cluster 1 Cluster 2 Cluster 3 Dissimilarity Dendrogram (cont.) (11 observations) (70 observations) (128 observations)

  10. Distribution of animals of cluster 1

  11. Distribution of animals of cluster 2

  12. Distribution of animals of cluster 3

  13. 40 35 30 25 % of households 20 15 10 5 0 1 2 3 4 5 6 Coat colour combination of body Coat colour of body: cluster 1

  14. 25 20 15 % of households 10 5 0 1 2 3 4 5 6 Coat colour combination of body Coat colour of body: cluster 2

  15. 25 20 15 % of households 10 5 0 1 2 3 4 5 6 Coat colour combination of body Coat colour of body: cluster 3

  16. Physical characteristics

  17. Physical characteristics (cont.)

  18. Distribution of clusters by agro-ecological zone

  19. Distribution of clusters by production system

  20. Quality of traits: Production traits

  21. Quality of traits: Adaptation traits

  22. Cluster 2 ‘Borana’ group ? ‘Guji’ group Cluster 3 Suggestion Cluster 1 Dissimilarity

  23. Breed type Arsi Borana Guji Konso Ogaden ArsixBorana BoranaxGuji Borana Zone BoranaxKonso Unknown Distribution of breed types (farmers’ knowledge)

  24. Further analysis…..

  25. Conclusions • Multivariate techniques can be used for on-farm breed characterization work by classifying the observations on individual animals into well-defined breed types/strains • Multivariate techniques can help formulating hypotheses, which can be tested using detailed genetic studies • Multivariate techniques can facilitate more focused genetic studies including molecular biology

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