Anette van Dorland ILRI, Addis Ababa, Ethiopia, 26 February 2003 Clustering of breed types: Preliminary results.
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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
HagereMariam
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 sizeHair type
Frame sizeDewlap sizeHump sizeHump shapeFace profile
Back profileRump profileEar sizeEar shapeEar orientationHorn lengthHorn shapeHorn orientationHorn spacingTail lengthUdder sizeTeat sizeNavel flap size
Coat colour-headCoat colour-ears
Coat colour-hoofCoat colour-tail
10 principal componentsresponsible for 64 % of the variation between the observations
Principal Components Analysis (cont.)
Characteristic PC1 PC2 PC3Body colour 1 22 1 0Body colour 2 0 0 7Head colour 20 1 0Ear colour 21 1 0Tail switch colour 5 2 10Hoof colour 9 3 4Coat colour pattern 1 1 0Hair length 2 3 13Hair type 0 1 2Frame size 2 8 0Dewlap size 1 15 0Hump size 0 10 6Hump orientation 0 0 6Face profile 1 1 17Back profile 1 2 0Rump profile 1 0 8Horn shape 0 0 1Horn orientation 0 0 2Horn spacing 1 1 7Horn length 0 3 1Ear size 0 8 0Ear shape 2 0 0Ear orientation 3 2 12Tail length 0 4 0Udder size 3 11 1Teat size 2 10 1Navel flap size 2 13 1TOTAL 100 100 100
Contributions of the variables (%)
Agglomerative Hierarchical Clustering: Dendrogram
0.0
1.0
2.0
3.0
4.0
5.0
Dissimilarity
Dendrogram
Dendrogram (cont.)
4.3
4.4
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 1
0
5
10
15
20
25
30
35
40
1 2 3 4 5 6
Coat colour combination of body
% o
f h
ou
seh
old
s
Coat colour of body: cluster 2
0
5
10
15
20
25
1 2 3 4 5 6
Coat colour combination of body
% o
f h
ou
seh
old
s
Coat colour of body: cluster 3
0
5
10
15
20
25
1 2 3 4 5 6
Coat colour combination of body
% o
f h
ou
seh
old
s
Physical characteristics
Phenotypic characteristic Cluster 1 Cluster 2 Cluster 3Frame size Short 0 31 28
Medium 45 53 53Long 55 16 19
Dewlap size Absent 0 0 2Small 9 26 46Medium 73 61 46Large 18 13 6
Hump size Absent 0 0 1Small 0 49 63Medium 91 49 35Large 9 3 1
Physical characteristics (cont.)
Phenotypic characteristic Cluster 1 Cluster 2 Cluster 3Face profile Flat 0 97 83
Convex 9 1 9Concave 91 1 8
Ear orientation Erect 40 9 11Lateral 60 56 82Drooping 0 36 6
Udder size Small 0 23 46Medium 90 57 45Large 10 20 9
Navel flap Absent 0 7 27Small 30 34 53Medium 60 50 15Large 10 9 5
Distribution of clusters by agro-ecological zone
0
10
20
30
40
50
60
70
80
Dega Weina Dega Kolla
% o
f b
ree
d t
yp
es
Cluster 1 Cluster 2 Cluster 3
Distribution of clusters by production system
01020304050607080
Crop-livestocksystem
Agro-pastoralists Pastoralists
% o
f b
ree
d t
yp
es
Cluster 1 Cluster 2 Cluster 3
Quality of traits: Production traits
0102030405060708090
100
% o
f h
ou
se
ho
lds
Cluster 1 Cluster 2 Cluster 3
Quality of traits: Adaptation traits
0
10
20
30
40
50
60
70
80
90
Diseasetolerance
Droughttolerance
Ability to walklong distances
% o
f h
ou
seh
old
s
Cluster 1 Cluster 2 Cluster 3
Suggestion
4.3
4.4
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…..
0.0
1.0
2.0
3.0
4.0
5.0
Dissimilarity
Dendrogram
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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