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

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

Introduction (cont.) 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

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

 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 Data and Methodology

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

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

Agglomerative Hierarchical Clustering: Dendrogram

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

Distribution of animals of cluster 1

Distribution of animals of cluster 2

Distribution of animals of cluster 3

Coat colour of body: cluster Coat colour combination of body % of households

Coat colour of body: cluster Coat colour combination of body % of households

Coat colour of body: cluster Coat colour combination of body % of households

Physical characteristics

Physical characteristics (cont.)

Distribution of clusters by agro-ecological zone

Distribution of clusters by production system

Quality of traits: Production traits

Quality of traits: Adaptation traits

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

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

Further analysis…..

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