Transcript OROMIA-ILRI Livestock Breed Survey

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
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
Borana Zone
 Data on cattle from Borana Zone
 Five woreda’s selected (see map)
 Three woreda’s predominantly in
Oromia Region
lowland (Dire, Liben and Teltele)
 Two woreda’s predominantly in
Bore
highland (Bore and Hagere Mariam)
Hagere
Mariam
Liben
Teltele
Dire
Borana Zone
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
10 principal components
responsible for 64 % of
the variation between the
observations
Contributions of the variables (%)
Principal
Components
Analysis (cont.)
Characteristic
Body colour 1
Body colour 2
Head colour
Ear colour
Tail switch colour
Hoof colour
Coat colour pattern
Hair length
Hair type
Frame size
Dewlap size
Hump size
Hump orientation
Face profile
Back profile
Rump profile
Horn shape
Horn orientation
Horn spacing
Horn length
Ear size
Ear shape
Ear orientation
Tail length
Udder size
Teat size
Navel flap size
TOTAL
PC1
22
0
20
21
5
9
1
2
0
2
1
0
0
1
1
1
0
0
1
0
0
2
3
0
3
2
2
100
PC2
1
0
1
1
2
3
1
3
1
8
15
10
0
1
2
0
0
0
1
3
8
0
2
4
11
10
13
100
PC3
0
7
0
0
10
4
0
13
2
0
0
6
6
17
0
8
1
2
7
1
0
0
12
0
1
1
1
100
Agglomerative Hierarchical Clustering: Dendrogram
Dendrogram
5.0
4.0
3.0
2.0
1.0
0.0
Dissimilarity
Dendrogram (cont.)
Dendrogram
Cluster 1
(11 observations)
Cluster 2
(70 observations)
Cluster 3
(128 observations)
4.4
4.3
4.2
4.1
4.0
3.9
Dissimilarity
Dissimilarity
Distribution of animals of cluster 1
Distribution of animals of cluster 2
Distribution of animals of cluster 3
Coat colour of body: cluster 1
40
% of households
35
30
25
20
15
10
5
0
1
2
3
4
5
Coat colour combination of body
6
Coat colour of body: cluster 2
% of households
25
20
15
10
5
0
1
2
3
4
5
Coat colour combination of body
6
Coat colour of body: cluster 3
% of households
25
20
15
10
5
0
1
2
3
4
5
Coat colour combination of body
6
Physical characteristics
Phenotypic characteristic
Frame size
Short
Medium
Long
Dewlap size
Absent
Small
Medium
Large
Hump size
Absent
Small
Medium
Large
Cluster 1 Cluster 2 Cluster 3
0
31
28
45
53
53
55
16
19
0
0
2
9
26
46
73
61
46
18
13
6
0
0
1
0
49
63
91
49
35
9
3
1
Physical characteristics (cont.)
Phenotypic characteristic
Face profile
Flat
Convex
Concave
Ear orientation Erect
Lateral
Drooping
Udder size
Small
Medium
Large
Navel flap
Absent
Small
Medium
Large
Cluster 1 Cluster 2 Cluster 3
0
97
83
9
1
9
91
1
8
40
9
11
60
56
82
0
36
6
0
23
46
90
57
45
10
20
9
0
7
27
30
34
53
60
50
15
10
9
5
Distribution of clusters by agro-ecological zone
% of breed types
80
70
60
50
40
30
20
10
0
Dega
Cluster 1
Weina Dega
Cluster 2
Kolla
Cluster 3
% of breed types
Distribution of clusters by production system
80
70
60
50
40
30
20
10
0
Crop-livestock
system
Cluster 1
Agro-pastoralists
Cluster 2
Pastoralists
Cluster 3
Cluster 1
Cluster 2
Lo
ng
ev
i
ty
rti
lit
y
Fe
e
ra
t
w
th
G
ro
W
or
k
M
ea
t
100
90
80
70
60
50
40
30
20
10
0
M
ilk
% of households
Quality of traits: Production traits
Cluster 3
Quality of traits: Adaptation traits
90
% of households
80
70
60
50
40
30
20
10
0
Disease
tolerance
Cluster 1
Drought
tolerance
Cluster 2
Ability to walk
long distances
Cluster 3
Suggestion
Dendrogram
Cluster 1
‘Borana’
group
Cluster
2
?
‘Guji’
group
Cluster
3
Dissimilarity
4.4
4.3
4.2
4.1
4.0
Dissimilarity
Distribution of breed types (farmers’ knowledge)
Breed type
Arsi
Borana
Guji
Konso
Ogaden
ArsixBorana
BoranaxGuji
Borana Zone
BoranaxKonso
Unknown
Further analysis…..
Dendrogram
5.0
4.0
3.0
2.0
1.0
0.0
Dissimilarity
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
biology
including
molecular