Clustering of breed types preliminary results
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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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Clustering of breed types preliminary results

Clustering of breed types: Preliminary results

Anette van Dorland

ILRI, Addis Ababa, Ethiopia, 26 February 2003


Introduction

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

Introduction (cont.)


Clustering of breed types preliminary results

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


Clustering of breed types preliminary results

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


Principal components analysis

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


Principal components analysis cont

Contributions of the variables (%)

Principal Components Analysis (cont.)


Agglomerative hierarchical clustering dendrogram

Agglomerative Hierarchical Clustering:Dendrogram


Dendrogram cont

Cluster 1

Cluster 2

Cluster 3

Dissimilarity

Dendrogram (cont.)

(11 observations)

(70 observations)

(128 observations)


Distribution of animals of cluster 1

Distribution of animals of cluster 1


Distribution of animals of cluster 2

Distribution of animals of cluster 2


Distribution of animals of cluster 3

Distribution of animals of cluster 3


Coat colour of body cluster 1

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


Coat colour of body cluster 2

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


Coat colour of body cluster 3

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


Physical characteristics

Physical characteristics


Physical characteristics cont

Physical characteristics (cont.)


Distribution of clusters by agro ecological zone

Distribution of clusters by agro-ecological zone


Distribution of clusters by production system

Distribution of clusters by production system


Quality of traits production traits

Quality of traits: Production traits


Quality of traits adaptation traits

Quality of traits: Adaptation traits


Suggestion

Cluster 2

‘Borana’ group

?

‘Guji’ group

Cluster 3

Suggestion

Cluster 1

Dissimilarity


Distribution of breed types farmers knowledge

Breed type

Arsi

Borana

Guji

Konso

Ogaden

ArsixBorana

BoranaxGuji

Borana Zone

BoranaxKonso

Unknown

Distribution of breed types (farmers’ knowledge)


Further analysis

Further analysis…..


Conclusions

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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