Phenotypic characterization of Gesha horses in southwestern Ethiopia
Abstract
Fifteen qualitative and 21 morphometric variables on a total of 394 adult horses (282 stallions and 112 mares) from three selected districts were recorded to characterize the horse populations in southwestern Ethiopia. General linear model, frequency, and multivariate analysis procedures of Statistical Analysis Software (SAS 9.0) were used to analyze the data. Sex and location significantly affected the studied traits. Stallions were larger than mares, and the Gesha horse population was the tallest, longest, and largest among the studied populations. The majority of the studied horses possess plain body colour patterns with red-coloured medium hair size. A higher frequency of white-coloured horses was observed with increasing age. Stepwise discriminant function analysis revealed that pelvic width, cannon bone length, and height at croup were the top three morphometric variables to discriminate the populations while head length, head neck circumference, chest width, cannon bone circumference, and croup length had the lowest discriminatory power. The results of discriminant function analysis showed advanced classification (76.7%) of the studied horses into their respective populations/locations. Finally, canonical discriminant function analysis categorized the horse populations into three distinct categories. The Gesha horse population was different from Masha and Telo horse populations while having a relatively higher relationship with the Masha horse population. However, the distances calculated in this study show only the relative size differences between each population. Such differences might not necessarily be due to breed (genetic) differences. Therefore, diversity studies through further genetic characterization are recommended to design conservation and breeding programmes.
Keywords
Ethiopia, Horse, Gesha, Phenotypic characterization
Introduction
Horses are among the most important livestock species in the highlands of Ethiopia. In rural areas, horses are the main source of transportation, both for humans and agricultural goods. They are used in public events including social and cultural festivals, and are the most culturally respected and highly valued domestic animals in the country in general, and in southern and southwestern Ethiopia in particular (Kefena et al., 2012). The highlands of Keffa and Sheka zones in southwest Ethiopia are also among the most benefitted areas from the indigenous horses (Kefena et al., 2012). In these areas, horses were also used for traditional racing shows.
Ethiopia is reported to possess 2.1 million horses (Central Statistical Agency, 2020). However, in terms of standard characterization and documentation, the equine sector has received little attention. Until now, only one country-wide general study by Kefena et al. (2012) was performed to phenotypically characterize the country’s horse breeds, their geographical distribution and production environments. Accordingly, eight breeds (Abyssinian, Bale, Boran, Horro, Kafa, Kundido feral horse, Ogaden/Wilwal and Selale horse) were officially reported to exist in the country (EBI, 2016; Kefena et al., 2012).
However, due to different reasons, the study by Kefena et al. (2012) did not cover or characterize three horse breeds (Boran, Kundido feral horse and Ogaden/Wilwal horses) out of the total eight breeds. Additionally, the lack of qualitative morphological data in the study, and the small sample size taken (95–106 horses per breed) can be noted as limitations of the study. Similarly, the selected sampling sites were too narrow to represent the horse populations of the area. For example, the horse populations of southwestern Ethiopia were represented by a sample from a single site (Masha district). A preliminary study by a team of livestock experts from Keffa zone hinted at the presence of an unstudied unique horse population in Gesha district.
According to the results of this preliminary study, Gesha horses are said to be typical riding horses of the Keffa zone highlands. However, in the country-wide study by Kefena et al. (2012), this population was represented by horses from the neighbouring Masha district. Therefore, further characterization studies were required to better understand the horse populations and quantify the level of relationships among them, thus providing a clear country-wide picture. Hence, the current study was designed to characterize the horse populations in southwestern Ethiopia using both quantitative morphometric measurements and qualitative morphological characteristics.
Materials and methods
Locations
This study was conducted in Keffa and Sheka zones of the Southern Nations Nationalities and Peoples Regional State (SNNPR), Ethiopia. Three locations were selected for the current study (Table 1, Figure 1). Gesha and Masha districts were sampled purposively: Gesha district (one of the ten districts in Keffa zone) is the location of the horses which were supposed to be unique and unaddressed before, while Masha district (one of the three districts in Sheka zone) is where the samples were taken for the previous country-wide study by Kefena et al. (2012). Telo district was sampled randomly from Keffa zone to study the relationship of its horses with Gesha horses.
|
Climate factors |
Telo |
Gesha |
Masha |
|
Altitude (m) |
2,436–2,451 |
1,501–3,000 |
1,700–3,000 |
|
Temperature (°C) |
17–25 |
15.1–20 |
16.7 |
|
Rainfall (mm) |
1,278 |
2,001–2,200 |
2,192 |
|
Agroecology |
Highland |
Midland and highland |
Midland and highland |
The sampling frame was defined after collecting available background information (origin, distribution, population size, and unique features) of the unstudied horse population through focus group discussions with livestock keepers and experts. Additionally, information regarding the sampling sites of the country-wide study was also taken from the reports of Kefena et al. (2012).
Data collection
Quantitative and qualitative data were recorded from a total of 394 adult horses (282 stallions and 112 mares) based on the data collection procedures outlined in FAO (2012) and the previous country-wide study by Kefena et al. (2012). Studied horses were carefully handled by their owners and trained personnel. Data were collected when the animals were calm and standing in an upright position on flat ground and early in the morning of the day before feeding and watering. To minimize measurement error, data were not taken from aggressive horses that did not stand properly. Similarly, to minimize subjectivity error, measurements and data recording were performed by the same researchers throughout the study. A centimetre-unit textile measuring tape was used for the morphometric measurements.
Twenty-one quantitative morphometric measurements (Table 2) and 15 qualitative characteristics (hair size, body colour pattern, colour of the body, head, muzzle, tail and hoof, presence/absence of stripe at dorsal body, shoulder and leg, profile of the face, back and croup, length of the tail and mane) were collected.
|
No. |
Morphometric measurements |
Explanation of the measurements |
|
1 |
Head length |
Distance from the nape to the alveolar edge of the incisors I of the upper jaw |
|
2 |
Head width |
Distance between the upper side of the eyes measured perpendicularly to the head length |
|
3 |
Ear length |
Distance from the tip of the ear to the connection point with the head |
|
4 |
Head neck circumference |
Circumference of the neck at the connection point to the head |
|
5 |
Neck length |
Distance from the highest point of the withers to the nape with the neck in a relaxed position |
|
6 |
Neck body circumference |
Circumference of the neck at the connection point with the body |
|
7 |
Chest width |
Distance between two outer points of the humeral bones from the front |
|
8 |
Shoulder depth |
Distance from the withers to the shoulder joint |
|
9 |
Thorax depth |
Distance from the withers to the sternum |
|
10 |
Thorax width |
Distance between two hypothetical vertical parallel lines drawn at the thorax sides and along the withers' height line |
|
11 |
Thorax girth |
Measured in the place of the saddle girth |
|
12 |
Cannon bone length |
Distance from the lateral tuberculum of the os metacarpale IV to the fetlock joint |
|
13 |
Cannon bone circumference |
Smallest circumference of the forelimb's cannon bone |
|
14 |
Height at wither |
Distance from the highest point of the processus spinalis of the vertebra thoracic to the floor |
|
15 |
Height at back |
Distance from the deepest point of the back to the floor |
|
16 |
Height at croup |
Distance from the croup (rump) to the floor |
|
17 |
Body length |
Distance from the most cranial point of the shoulder joint to the most caudal point of the pin bone (scapulo-ischial length) |
|
18 |
Back length |
Distance from the caudal point of the shoulder joint perpendicular to the wither to the most cranial point of the hip joint measured in the saddle place |
|
19 |
Pelvic width |
Distance between the right and left coxal tubers of the ilium |
|
20 |
Croup length |
Distance between the sacral tuber (the highest point of croup) and ischiatic tuber (most posterior point of ischium or point of buttock or seat bone) |
|
21 |
Barrel length |
Distance from the most caudal point of the scapula to the most cranial and dorsal point of the point of the hip |
The following body measure indices were calculated from morphometric measurements (adapted from Bodó and Hecker (1992); Cabral et al. (2004); Druml, Baumung, and Sölkner (2008); Bene et al. (2013)).
-
Body index = (Body length/Thorax girth) x 100
-
Quadratic index = (Height at withers/Body length) x 100
-
Caliber index = (Thorax girth/Height at withers) x (Cannon circumference/Height at withers) x 1000
-
Overbuilt index = (Height at croup/Height at withers) x 100
-
Chest index = (Chest width/Thorax girth) x 100
-
Conformation index = (Thorax girth2/Height at withers)/100
Data analysis
Data entry and management were performed using Microsoft Excel© worksheet. Analysis of the quantitative traits was performed separately for stallions, mares and sex-aggregated by fitting location and age as fixed variables. UNIVARIATE procedure of Statistical Analysis Software (SAS) 9.0 was used to detect outliers and test the normality of morphometric data (SAS Institute, 2002). Data on qualitative traits were subjected to chi-square (χ2) tests of the frequency (FREQ) procedure of SAS 9.0 software. Quantitative morphometric and body measure indices data were analyzed using the general linear model (GLM) procedure of SAS 9.0 software, with adjusted Tukey-Kramer test to separate the least square means (LSM). Data analysis was performed using the following model: Yijk = μ + Si + Lj + Ak + eijk where Yijk is an observation, μ is the overall mean, Si is the fixed effect of ith sex (i = stallion, mare), Lj is the fixed effect of jth location (j = Telo, Gesha, Masha), Ak is the fixed effect of kth age (k = 4–11), and eijk is the random error attributed to the nth observation. The sex effect was removed from the class variables when the analysis was done separately for each sex.
Morphometric traits that better discriminate the horse populations from different locations were identified using the forward selection method of the stepwise discriminant function analysis (STEPDISC) procedure of SAS 9.0. The discriminant function analysis (DISCRIM) procedure of SAS 9.0. was also used to assign observations to locations and evaluate probabilities of misclassifications. Linear combination of morphometric variables that provide maximal separations between locations was performed using the canonical discriminant function analysis (CANDISC) procedure of SAS 9.0. The scored canonical variables were used to plot pairs of canonical variables to get visual interpretation of location differences. Pairwise squared Mahalanobis distances between locations were computed as: . Where is the distances between locations and , is the inverse of the covariance matrix of measured variables, and are the means of variables in the th and th populations.
Results
Morphometric measurements and body measure indices
The effect of sex on the studied morphometric variables is presented in Table 3. Most measurements were higher for stallions than mares while ear length and barrel length measurements of the mares were higher than the stallions. On the other hand, body length and back length measurements were not significantly affected by sex.
|
Traits |
Stallions |
Mares |
p-value |
|
N |
282 |
112 |
|
|
Head length |
53.2 ± 0.16 |
52.1 ± 0.23 |
< 0.0001 |
|
Head width |
21.5 ± 0.06 |
21.2 ± 0.09 |
0.0015 |
|
Ear length |
15.0 ± 0.09 |
15.5 ± 0.13 |
0.0033 |
|
Head neck circumference |
58.9 ± 0.24 |
54.7 ± 0.36 |
< 0.0001 |
|
Neck length |
59.4 ± 0.27 |
57.3 ± 0.40 |
< 0.0001 |
|
Neck body circumference |
91.4 ± 0.37 |
84.3 ± 0.55 |
< 0.0001 |
|
Chest width |
25.9 ± 0.13 |
24.3 ± 0.20 |
< 0.0001 |
|
Shoulder depth |
53.4 ± 0.18 |
51.1 ± 0.27 |
< 0.0001 |
|
Thorax depth |
61.8 ± 0.23 |
59.8 ± 0.33 |
< 0.0001 |
|
Thorax width |
34.2 ± 0.16 |
32.9 ± 0.24 |
< 0.0001 |
|
Thorax girth |
143.0 ± 0.44 |
138.6 ± 0.65 |
< 0.0001 |
|
Cannon bone length |
24.0 ± 0.09 |
23.6 ± 0.14 |
0.0066 |
|
Cannon bone circumference |
16.4 ± 0.06 |
15.6 ± 0.09 |
< 0.0001 |
|
Height at withers |
131.8 ± 0.29 |
127.8 ± 0.43 |
< 0.0001 |
|
Height at back |
129.0 ± 0.28 |
125.7 ± 0.41 |
< 0.0001 |
|
Height at croup |
132.1 ± 0.28 |
129.1 ± 0.42 |
< 0.0001 |
|
Body length |
125.0 ± 0.38 |
124.1 ± 0.56 |
0.1796 |
|
Back length |
70.0 ± 0.26 |
70.2 ± 0.38 |
0.6114 |
|
Pelvic width |
40.4 ± 0.16 |
39.5 ± 0.24 |
0.0016 |
|
Croup length |
39.7 ± 0.18 |
38.6 ± 0.27 |
0.0020 |
|
Barrel length |
67.0 ± 0.29 |
69.4 ± 0.43 |
< 0.0001 |
To have a clear picture of the differences among locations, the analysis was performed separately for both sexes. The effect of location on the morphometric measurements of the stallions is presented in Table 4. All stallions’ measurements were affected significantly by their location. Gesha stallions had significantly the highest values for most of the measurements except for cannon bone length where Telo stallions had higher values. Masha stallions had relatively higher measurement values than their counterparts from Telo district, and these populations shared more similarities. On the other hand, chest width, shoulder depth, body length and back length measurements of Telo stallions were higher than Masha stallions.
|
Traits |
Least Square Means (LSM ± SE) |
Mean ± SE |
CV |
p-value |
||
|
Telo |
Gesha |
Masha |
||||
|
N |
94 |
136 |
52 |
|
|
|
|
Head length |
52.4 ± 0.27b |
54.2 ± 0.23a |
53.1 ± 0.36b |
53.2 ± 0.16 |
4.5 |
< 0.0001 |
|
Head width |
21.0 ± 0.11b |
21.9 ± 0.09a |
21.6 ± 0.15a |
21.5 ± 0.07 |
4.6 |
< 0.0001 |
|
Ear length |
14.5 ± 0.15b |
15.1 ± 0.13a |
15.4 ± 0.20a |
14.9 ± 0.08 |
8.9 |
0.0002 |
|
Head neck circumference |
58.1 ± 0.43b |
60.8 ± 0.36a |
57.9 ± 0.57b |
59.3 ± 0.24 |
6.4 |
< 0.0001 |
|
Neck length |
59.3 ± 0.48b |
60.6 ± 0.40a |
58.4 ± 0.63b |
59.4 ± 0.27 |
7.1 |
0.0049 |
|
Neck body circumference |
88.8 ± 0.63b |
95.9 ± 0.53a |
89.9 ± 0.83b |
92.2 ± 0.41 |
6.1 |
< 0.0001 |
|
Chest width |
26.0 ± 0.25ab |
26.7 ± 0.21a |
25.2 ± 0.33b |
26.0 ± 0.14 |
8.5 |
0.0008 |
|
Shoulder depth |
53.5 ± 0.32b |
54.6 ± 0.27a |
52.1 ± 0.42c |
53.6 ± 0.18 |
5.3 |
< 0.0001 |
|
Thorax depth |
59.6 ± 0.40c |
63.7 ± 0.33a |
61.9 ± 0.52b |
61.9 ± 0.25 |
5.7 |
< 0.0001 |
|
Thorax width |
32.8 ± 0.30c |
35.5 ± 0.25a |
34.2 ± 0.39b |
34.1 ± 0.18 |
7.7 |
< 0.0001 |
|
Thorax girth |
141.5 ± 0.78b |
149.0 ± 0.65a |
138.8 ± 1.02b |
143.6 ± 0.53 |
4.8 |
< 0.0001 |
|
Cannon bone length |
24.8 ± 0.16a |
23.5 ± 0.14b |
23.6 ± 0.21b |
24.0 ± 0.09 |
6.0 |
< 0.0001 |
|
Cannon bone circumference |
16.34 ± 0.11b |
16.9 ± 0.10a |
16.1 ± 0.15b |
16.5 ± 0.07 |
6.1 |
< 0.0001 |
|
Height at withers |
130.7 ± 0.51b |
135.2 ± 0.43a |
129.6 ± 0.67b |
132.2 ± 0.31 |
3.4 |
< 0.0001 |
|
Height at back |
127.8 ± 0.48b |
132.4 ± 0.41a |
127.0 ± 0.63b |
129.5 ± 0.30 |
3.3 |
< 0.0001 |
|
Height at croup |
131.3 ± 0.50b |
135.8 ± 0.42a |
129.6 ± 0.7b |
132.8 ± 0.31 |
3.3 |
< 0.0001 |
|
Body length |
125.3 ± 0.67b |
127.5 ± 0.56a |
122.4 ± 0.88c |
125.3 ± 0.38 |
4.7 |
< 0.0001 |
|
Back length |
70.7 ± 0.44a |
71.1 ± 0.37a |
68.2 ± 0.58b |
70.1 ± 0.25 |
5.6 |
0.0003 |
|
Pelvic width |
38.7 ± 0.29c |
42.3 ± 0.24a |
40.5 ± 0.38b |
40.5 ± 0.19 |
6.3 |
< 0.0001 |
|
Croup length |
38.7 ± 0.33b |
41.1 ± 0.28a |
39.3 ± 0.43b |
39.7 ± 0.18 |
7.3 |
< 0.0001 |
|
Barrel length |
66.1 ± 0.51b |
67.7 ± 0.43a |
67.1 ± 0.66ab |
66.8 ± 0.28 |
6.7 |
0.0287 |
The effect of location on the morphometric measurements of the mares is presented in Table 5. Most of the mares’ measurements were affected significantly by their location except ear length, neck length, chest width and barrel length. Gesha mares were the biggest and heaviest among the studied populations: their circumferences of head–neck, neck–body and thorax, and heights at withers, back and croup, and pelvic width were significantly larger than Telo or Masha.
|
Traits |
Least Square Means (LSM ± SE) |
Mean ± SE |
CV |
p-value |
||
|
Telo |
Gesha |
Masha |
||||
|
N |
29 |
47 |
36 |
|
|
|
|
Head length |
51.9 ± 0.49ab |
53.0 ± 0.38a |
51.3 ± 0.45b |
52.1 ± 0.25 |
4.9 |
0.0128 |
|
Head width |
20.6 ± 0.18b |
21.5 ± 0.14a |
21.5 ± 0.17a |
21.2 ± 0.10 |
4.4 |
0.0001 |
|
Ear length |
15.2 ± 0.27 |
15.4 ± 0.21 |
15.9 ± 0.25 |
15.5 ± 0.13 |
9.1 |
0.1536 |
|
Head neck circumference |
53.2 ± 0.70b |
56.7 ± 0.53a |
54.3 ± 0.64b |
54.9 ± 0.37 |
6.6 |
0.0002 |
|
Neck length |
56.7 ± 0.75 |
58.4 ± 0.57 |
56.5 ± 0.69 |
57.2 ± 0.38 |
6.8 |
0.0597 |
|
Neck body circumference |
81.3 ± 1.20b |
89.4 ± 0.91a |
81.7 ± 1.10b |
84.7 ± 0.71 |
7.3 |
< 0.0001 |
|
Chest width |
23.9 ± 0.32 |
24.8 ± 0.24 |
24.0 ± 0.29 |
24.3 ± 0.16 |
6.8 |
0.0535 |
|
Shoulder depth |
51.0 ± 0.54ab |
52.2 ± 0.41a |
49.9 ± 0.49b |
51.0 ± 0.30 |
5.4 |
0.0015 |
|
Thorax depth |
58.7 ± 0.63b |
61.1 ± 0.48a |
59.7 ± 0.58ab |
59.9 ± 0.34 |
5.4 |
0.0069 |
|
Thorax width |
31.4 ± 0.44b |
34.1 ± 0.34a |
33.2 ± 0.41a |
32.9 ± 0.27 |
7.0 |
< 0.0001 |
|
Thorax girth |
135.9 ± 1.26b |
144.4 ± 0.96a |
134.6 ± 1.16b |
138.6 ± 0.81 |
4.7 |
< 0.0001 |
|
Cannon bone length |
24.1 ± 0.27a |
22.9 ± 0.21b |
23.6 ± 0.25ab |
23.5 ± 0.14 |
6.0 |
0.0014 |
|
Cannon bone circumference |
15.6 ± 0.15ab |
15.9 ± 0.11a |
15.4 ± 0.13b |
15.6 ± 0.08 |
4.9 |
0.0067 |
|
Height at withers |
127.3 ± 0.81b |
130.3 ± 0.62a |
125.4 ± 0.75b |
127.9 ± 0.45 |
3.3 |
< 0.0001 |
|
Height at back |
124.8 ± 0.84b |
128.4 ± 0.64a |
123.4 ± 0.77b |
125.8 ± 0.46 |
3.5 |
< 0.0001 |
|
Height at croup |
128.3 ± 0.81b |
132.1 ± 0.61a |
126.3 ± 0.74b |
129.3 ± 0.47 |
3.2 |
< 0.0001 |
|
Body length |
124.5 ± 1.13ab |
126.6 ± 0.86a |
121.0 ± 1.04b |
124.0 ± 0.65 |
4.7 |
0.0004 |
|
Back length |
70.6 ± 0.80ab |
71.4 ± 0.61a |
68.1 ± 0.74b |
70.0 ± 0.44 |
6.0 |
0.0033 |
|
Pelvic width |
38.6 ± 0.43b |
41.4 ± 0.33a |
38.4 ± 0.40b |
39.6 ± 0.27 |
5.7 |
< 0.0001 |
|
Croup length |
38.8 ± 0.52ab |
39.6 ± 0.39a |
37.7 ± 0.47b |
38.7 ± 0.27 |
6.9 |
0.0120 |
|
Barrel length |
68.8 ± 0.89 |
70.2 ± 0.68 |
69.4 ± 0.82 |
69.3 ± 0.47 |
6.7 |
0.4347 |
The effect of location on the morphometric measurements of the studied horse populations (sex-aggregated) is presented in Table 6. All the morphometric measurements of the studied horse populations were affected significantly by their location. Significantly, the Gesha horse population had the highest values for most of the measurements except for cannon bone length, which was higher in Telo horses.
|
Traits |
Least Square Means (LSM ± SE) |
Mean ± SE |
CV |
p-value |
||
|
Telo |
Gesha |
Masha |
||||
|
N |
123 |
183 |
88 |
|
|
|
|
Head length |
52.0 ± 0.24b |
53.6 ± 0.20a |
52.2 ± 0.27b |
52.9 ± 0.14 |
4.6 |
< 0.0001 |
|
Head width |
20.8 ± 0.10b |
21.7 ± 0.08a |
21.6 ± 0.11a |
21.4 ± 0.06 |
4.6 |
< 0.0001 |
|
Ear length |
14.8 ± 0.14b |
15.3 ± 0.11a |
15.7 ± 0.15a |
15.1 ± 0.07 |
8.9 |
< 0.0001 |
|
Head neck circumference |
55.8 ± 0.38b |
58.7 ± 0.31a |
56.0 ± 0.41b |
58.1 ± 0.23 |
6.5 |
< 0.0001 |
|
Neck length |
58.1 ± 0.42b |
59.5 ± 0.34a |
57.4 ± 0.46b |
58.8 ± 0.23 |
7.0 |
0.0004 |
|
Neck body circumference |
85.1 ± 0.58b |
92.5 ± 0.48a |
86.0 ± 0.64b |
90.1 ± 0.39 |
6.4 |
< 0.0001 |
|
Chest width |
25.0 ± 0.21b |
25.7 ± 0.17a |
24.6 ± 0.23b |
25.5 ± 0.12 |
8.1 |
< 0.0001 |
|
Shoulder depth |
52.3 ± 0.28b |
53.4 ± 0.23a |
51.0 ± 0.31c |
52.8 ± 0.17 |
5.3 |
< 0.0001 |
|
Thorax depth |
58.9 ± 0.35c |
62.6 ± 0.29a |
61.0 ± 0.39b |
61.3 ± 0.21 |
5.7 |
< 0.0001 |
|
Thorax width |
32.1 ± 0.25c |
34.8 ± 0.21a |
33.7 ± 0.28b |
33.8 ± 0.15 |
7.5 |
< 0.0001 |
|
Thorax girth |
138.9 ± 0.68b |
146.6 ± 0.56a |
136.9 ± 0.75b |
142.2 ± 0.46 |
4.8 |
< 0.0001 |
|
Cannon bone length |
24.5 ± 0.14a |
23.2 ± 0.12b |
23.6 ± 0.16b |
23.9 ± 0.08 |
6.0 |
< 0.0001 |
|
Cannon bone circumference |
15.9 ± 0.09b |
16.4 ± 0.08a |
15.7 ± 0.10b |
16.2 ± 0.06 |
5.8 |
< 0.0001 |
|
Height at withers |
128.8 ± 0.45b |
132.8 ± 0.37a |
127.7 ± 0.50b |
131.0 ± 0.28 |
3.4 |
< 0.0001 |
|
Height at back |
126.1 ± 0.43b |
130.4 ± 0.36a |
125.5 ± 0.48b |
128.4 ± 0.26 |
3.4 |
< 0.0001 |
|
Height at croup |
129.7 ± 0.44b |
134.0 ± 0.36a |
128.2 ± 0.48b |
131.8 ± 0.27 |
3.3 |
< 0.0001 |
|
Body length |
124.8 ± 0.59b |
127.0 ± 0.49a |
121.7 ± 0.65c |
124.9 ± 0.33 |
4.7 |
< 0.0001 |
|
Back length |
70.8 ± 0.40a |
71.2 ± 0.33a |
68.2 ± 0.44b |
70.1 ± 0.22 |
5.7 |
< 0.0001 |
|
Pelvic width |
38.4 ± 0.25c |
41.8 ± 0.21a |
39.6 ± 0.28b |
40.3 ± 0.16 |
6.3 |
< 0.0001 |
|
Croup length |
38.4 ± 0.29b |
40.4 ± 0.23a |
38.6 ± 0.32b |
39.4 ± 0.16 |
7.2 |
< 0.0001 |
|
Barrel length |
67.4 ± 0.45b |
69.0 ± 0.37a |
68.3 ± 0.50b |
67.5 ± 0.25 |
6.6 |
0.0113 |
Pearson correlation coefficients of the morphometric measurements of the horses (both sexes) from different locations are presented in Table 7. The majority of the traits were positively correlated. Higher positive correlation was observed between height at withers and height at back while lower positive correlation was observed between ear length and head neck circumference. Negative correlation was observed between thorax width and cannon bone length.
|
Traits |
HL |
HW |
EL |
HNC |
NL |
NBC |
CW |
SD |
TD |
TW |
TG |
CBL |
CBC |
HAW |
HAB |
HAC |
BOL |
BAL |
PW |
CL |
BRL |
|
HL |
|
0.48 |
0.16 |
0.35 |
0.22 |
0.48 |
0.41 |
0.49 |
0.51 |
0.38 |
0.55 |
0.08 |
0.41 |
0.59 |
0.56 |
0.56 |
0.39 |
0.36 |
0.42 |
0.28 |
0.29 |
|
HW |
*** |
|
0.24 |
0.37 |
0.30 |
0.44 |
0.39 |
0.41 |
0.46 |
0.44 |
0.48 |
0.06 |
0.36 |
0.42 |
0.42 |
0.41 |
0.36 |
0.30 |
0.42 |
0.35 |
0.31 |
|
EL |
** |
*** |
|
0.001 |
0.14 |
0.07 |
0.14 |
0.08 |
0.17 |
0.20 |
0.11 |
0.03 |
0.10 |
0.11 |
0.11 |
0.11 |
0.11 |
0.16 |
0.19 |
0.20 |
0.23 |
|
HNC |
*** |
*** |
NS |
|
0.34 |
0.74 |
0.49 |
0.57 |
0.46 |
0.48 |
0.67 |
0.12 |
0.53 |
0.57 |
0.52 |
0.52 |
0.43 |
0.19 |
0.49 |
0.47 |
0.11 |
|
NL |
*** |
*** |
** |
*** |
|
0.49 |
0.35 |
0.42 |
0.39 |
0.39 |
0.53 |
0.13 |
0.44 |
0.53 |
0.53 |
0.54 |
0.47 |
0.32 |
0.38 |
0.40 |
0.23 |
|
NBC |
*** |
*** |
NS |
*** |
*** |
|
0.55 |
0.66 |
0.62 |
0.56 |
0.78 |
0.06 |
0.61 |
0.70 |
0.67 |
0.67 |
0.49 |
0.31 |
0.60 |
0.52 |
0.16 |
|
CW |
*** |
*** |
** |
*** |
*** |
*** |
|
0.56 |
0.45 |
0.41 |
0.59 |
0.23 |
0.47 |
0.47 |
0.41 |
0.43 |
0.41 |
0.40 |
0.45 |
0.40 |
0.25 |
|
SD |
*** |
*** |
NS |
*** |
*** |
*** |
*** |
|
0.57 |
0.47 |
0.73 |
0.20 |
0.55 |
0.69 |
0.65 |
0.66 |
0.56 |
0.39 |
0.49 |
0.51 |
0.29 |
|
TD |
*** |
*** |
** |
*** |
*** |
*** |
*** |
*** |
|
0.56 |
0.67 |
0.06 |
0.52 |
0.62 |
0.61 |
0.59 |
0.42 |
0.32 |
0.56 |
0.44 |
0.30 |
|
TW |
*** |
*** |
*** |
*** |
*** |
*** |
*** |
*** |
*** |
|
0.66 |
-0.04 |
0.49 |
0.54 |
0.54 |
0.52 |
0.47 |
0.30 |
0.57 |
0.48 |
0.33 |
|
TG |
*** |
*** |
* |
*** |
*** |
*** |
*** |
*** |
*** |
*** |
|
0.09 |
0.66 |
0.78 |
0.76 |
0.76 |
0.67 |
0.47 |
0.72 |
0.60 |
0.36 |
|
CBL |
NS |
NS |
NS |
* |
* |
NS |
*** |
*** |
NS |
NS |
NS |
|
0.26 |
0.16 |
0.14 |
0.15 |
0.15 |
0.22 |
0.002 |
0.09 |
0.05 |
|
CBC |
*** |
*** |
NS |
*** |
*** |
*** |
*** |
*** |
*** |
*** |
*** |
*** |
|
0.63 |
0.60 |
0.61 |
0.51 |
0.39 |
0.51 |
0.44 |
0.20 |
|
HAW |
*** |
*** |
* |
*** |
*** |
*** |
*** |
*** |
*** |
*** |
*** |
** |
*** |
|
0.96 |
0.94 |
0.60 |
0.45 |
0.60 |
0.55 |
0.28 |
|
HAB |
*** |
*** |
* |
*** |
*** |
*** |
*** |
*** |
*** |
*** |
*** |
** |
*** |
*** |
|
0.93 |
0.57 |
0.43 |
0.57 |
0.54 |
0.28 |
|
HAC |
*** |
*** |
* |
*** |
*** |
*** |
*** |
*** |
*** |
*** |
*** |
** |
*** |
*** |
*** |
|
0.63 |
0.47 |
0.59 |
0.56 |
0.31 |
|
BOL |
*** |
*** |
* |
*** |
*** |
*** |
*** |
*** |
*** |
*** |
*** |
** |
*** |
*** |
*** |
*** |
|
0.51 |
0.54 |
0.50 |
0.58 |
|
BAL |
*** |
*** |
** |
** |
*** |
*** |
*** |
*** |
*** |
*** |
*** |
** |
*** |
*** |
*** |
*** |
*** |
|
0.41 |
0.29 |
0.37 |
|
PW |
*** |
*** |
** |
*** |
*** |
*** |
*** |
*** |
*** |
*** |
*** |
NS |
*** |
*** |
*** |
*** |
*** |
*** |
|
0.59 |
0.35 |
|
CL |
*** |
*** |
*** |
*** |
*** |
*** |
*** |
*** |
*** |
*** |
*** |
NS |
*** |
*** |
*** |
*** |
*** |
*** |
*** |
|
0.26 |
|
BRL |
*** |
*** |
*** |
* |
*** |
** |
*** |
*** |
*** |
*** |
*** |
NS |
*** |
*** |
*** |
*** |
*** |
*** |
*** |
*** |
|
The effect of location on body measure indices of the studied horse populations (separately for each sex) is presented in Table 8. All the body measure indices of the studied horse populations were significantly affected by sex. Similarly, most of the body measure indices were significantly affected by their location.
|
Traits |
Least Square Means (LSM ± SE) |
|
Mean ± SE |
p-value |
||
|
Telo |
Gesha |
Masha |
||||
|
Stallions |
|
|
|
|
|
|
|
Body index |
88.71 ± 0.40a |
85.60 ± 0.34b |
88.33 ± 0.53a |
|
87.36 ± 0.24 |
< 0.0001 |
|
Quadratic index |
104.5 ± 0.47b |
106.2 ± 0.40a |
106.0 ± 0.62ab |
|
105.7 ± 0.25 |
0.0095 |
|
Caliber index |
135.4 ± 1.16ab |
138.0 ± 0.98a |
132.8 ± 1.53b |
|
135.5 ± 0.65 |
0.0119 |
|
Overbuilt index |
100.4 ± 0.16 |
100.5 ± 0.14 |
100.0 ± 0.21 |
|
100.4 ± 0.09 |
0.2523 |
|
Chest index |
18.40 ± 0.15a |
17.89 ± 0.13b |
18.17 ± 0.20ab |
|
18.1 ± 0.08 |
0.0178 |
|
Conformation index |
1.53 ± 0.014b |
1.65 ± 0.011a |
1.49 ± 0.018b |
|
1.56 ± 0.009 |
< 0.0001 |
|
Mares |
|
|
|
|
|
|
|
Body index |
91.67 ± 0.75a |
87.75 ± 0.58b |
90.00 ± 0.69a |
|
89.57 ± 0.40 |
0.0002 |
|
Quadratic index |
102.3 ± 0.79 |
103.2 ± 0.60 |
103.9 ± 0.72 |
|
103.4 ± 0.42 |
0.3730 |
|
Caliber index |
130.7 ± 1.57 |
135.3 ± 1.20 |
131.4 ± 1.44 |
|
132.1 ± 0.89 |
0.0580 |
|
Overbuilt index |
100.8 ± 0.25 |
101.4 ± 0.19 |
100.7 ± 0.23 |
|
101.0 ± 0.13 |
0.0617 |
|
Chest index |
17.61 ± 0.21ab |
17.23 ± 0.16b |
17.88 ± 0.19a |
|
17.53 ± 0.10 |
0.0379 |
|
Conformation index |
1.45 ± 0.02b |
1.60 ± 0.02a |
1.45 ± 0.02b |
|
1.50 ± 0.01 |
< 0.0001 |
|
Both sexes |
|
|
|
|
|
|
|
Body index |
90.01 ± 0.37a |
86.71 ± 0.30b |
89.07 ± 0.41a |
|
87.99 ± 0.21 |
< 0.0001 |
|
Quadratic index |
103.3 ± 0.42b |
104.8 ± 0.34a |
105.05 ± 0.46a |
|
105.1 ± 0.22 |
0.0036 |
|
Caliber index |
133.5 ± 0.97b |
136.6 ± 0.80a |
131.9 ± 1.07b |
|
134.5 ± 0.53 |
0.0005 |
|
Overbuilt index |
100.7 ± 0.14 |
100.9 ± 0.12 |
100.4 ± 0.16 |
|
100.6 ± 0.07 |
0.0579 |
|
Chest index |
18.02 ± 0.13a |
17.55 ± 0.10b |
17.99 ± 0.14a |
|
17.95 ± 0.07 |
0.0019 |
|
Conformation index |
1.50 ± 0.01b |
1.62 ± 0.01a |
1.47 ± 0.01b |
|
1.54 ± 0.01 |
< 0.0001 |
Multivariate analysis
Stepwise discriminant function analysis revealed the order of importance of the studied morphometric variables in discriminating the horse populations (Table 9). The results were also confirmed by Wilk’s lambda test (Table 9) where all the selected variables had a highly significant (P < 0.0001) contribution in discriminating the horse populations. Pelvic width, cannon bone length and height at croup were the first three important traits used in discriminating the studied horse populations. However, some morphometric variables like head length, head neck circumference, chest width, cannon bone circumference and croup length had the lowest discriminatory power and were not used in discriminating the horse populations.
|
Step |
Variables entered |
Partial R-square |
F value |
Pr > F |
Wilks’ Lambda |
Pr < Lambda |
|
1 |
Pelvic width |
0.2214 |
55.60 |
< 0.0001 |
0.7785 |
< 0.0001 |
|
2 |
Cannon bone length |
0.1561 |
36.06 |
< 0.0001 |
0.6570 |
< 0.0001 |
|
3 |
Height at croup |
0.1362 |
30.68 |
< 0.0001 |
0.5675 |
< 0.0001 |
|
4 |
Head width |
0.0888 |
18.91 |
< 0.0001 |
0.5171 |
< 0.0001 |
|
5 |
Body length |
0.0574 |
11.79 |
< 0.0001 |
0.4874 |
< 0.0001 |
|
6 |
Ear length |
0.0500 |
10.16 |
< 0.0001 |
0.4630 |
< 0.0001 |
|
7 |
Thorax depth |
0.0381 |
7.62 |
0.0006 |
0.4454 |
< 0.0001 |
|
8 |
Shoulder depth |
0.0531 |
10.76 |
< 0.0001 |
0.4218 |
< 0.0001 |
|
9 |
Neck body circumference |
0.0393 |
7.84 |
0.0005 |
0.4052 |
< 0.0001 |
|
10 |
Back length |
0.0308 |
6.07 |
0.0025 |
0.3927 |
< 0.0001 |
|
11 |
Barrel length |
0.0336 |
6.63 |
0.0015 |
0.3795 |
< 0.0001 |
|
12 |
Thorax width |
0.0272 |
5.32 |
0.0053 |
0.3692 |
< 0.0001 |
|
13 |
Thorax girth |
0.0306 |
5.99 |
0.0028 |
0.3578 |
< 0.0001 |
|
14 |
Height at withers |
0.0191 |
3.67 |
0.0264 |
0.3510 |
< 0.0001 |
|
15 |
Height at back |
0.0227 |
4.39 |
0.0131 |
0.3430 |
< 0.0001 |
|
16 |
Neck length |
0.0200 |
3.83 |
0.0225 |
0.3362 |
< 0.0001 |
|
- |
Head length |
0.0029 |
0.55 |
0.5754 |
- |
- |
|
- |
Head neck circumference |
0.0002 |
0.05 |
0.9555 |
- |
- |
|
- |
Chest width |
0.0028 |
0.52 |
0.5947 |
- |
- |
|
- |
Cannon bone circumference |
0.0019 |
0.36 |
0.6946 |
- |
- |
|
- |
Croup length |
0.0011 |
0.20 |
0.8210 |
- |
- |
The values and significant levels of different statistical tests used in the discriminant function analysis are shown in Table 10. All the statistical tests were significant showing the appropriateness of the model used in discriminating the horse populations.
|
Statistic |
Value |
F value |
Num DF |
Den DF |
Pr > F |
|
Wilk's lambda |
0.3362 |
17.03 |
32 |
752 |
< 0.0001 |
|
Pillai's trace |
0.8298 |
16.71 |
32 |
752 |
< 0.0001 |
|
Hotelling-Lawley trace |
1.4280 |
17.35 |
32 |
668.29 |
< 0.0001 |
|
Roy's Largest Root |
0.9718 |
22.90 |
16 |
377 |
< 0.0001 |
Outputs of the canonical discrimination analysis including eigenvalues and class means under the first two canonical structures are presented in Table 11. Similarly, Table 11 also presents raw canonical coefficients used in constructing the two canonical variables (Can 1 and Can 2). Accordingly, the first canonical structure (Can 1) explained the majority (65.7%) of the total variability among the three horse populations. It also produced a greater eigenvalue and multiple correlation (0.70) between the classes (locations) and the morphometric measurements than the second canonical structure (Can 2). These results show the higher power of Can 1 compared with Can 2 in separating the horse populations from the studied locations. However, Can 2 also separated one-third of the population, which Can 1 is unable to separate. Accordingly, Can 1 separated Telo horses from the others while Can 2 separated Masha horses from the others.
|
|
Can 1 |
Can 2 |
|
Multivariate Statistics |
|
|
|
Canonical Correlation |
0.7020 |
0.5805 |
|
Eigenvalue |
0.9718 |
0.5083 |
|
Proportion |
0.6566 |
0.3434 |
|
Class (location) means |
|
|
|
Telo |
-1.4394 |
0.1662 |
|
Gesha |
0.7827 |
0.5109 |
|
Masha |
0.3841 |
-1.2949 |
|
Raw canonical coefficients |
|
|
|
Head width |
0.3332 |
-0.2810 |
|
Ear length |
0.1426 |
-0.1680 |
|
Neck length |
-0.0526 |
-0.0230 |
|
Neck body circumference |
0.0552 |
0.0332 |
|
Shoulder depth |
-0.1501 |
0.0289 |
|
Thorax depth |
0.0693 |
-0.1063 |
|
Thorax width |
0.0581 |
-0.1371 |
|
Thorax girth |
-0.0134 |
0.0875 |
|
Cannon bone length |
-0.3522 |
-0.1375 |
|
Height at withers |
-0.1633 |
-0.0871 |
|
Height at back |
0.1627 |
-0.0541 |
|
Height at croup |
0.0777 |
0.2509 |
|
Body length |
-0.0567 |
0.0267 |
|
Back length |
-0.0606 |
0.0513 |
|
Pelvic width |
0.1924 |
-0.0206 |
|
Barrel length |
0.0350 |
-0.0723 |
Discriminant function analysis classified each individual observation into a known population/location (Table 12). Accordingly, an average of 76.7% of the sampled animals were classified into their respective population/location. The highest classification of individual horses into their respective locations was observed in the Telo horse population (79.7%) with a small error rate (20.3%). On the other hand, a high error rate (26.1%) was detected in the Masha horse population. The priors (33.3%) show the chance of every individual observation to be classified into the given three populations/locations.
|
From location |
Telo |
Gesha |
Masha |
Total |
|
Telo |
98 (79.7%) |
14 (11.4%) |
11 (8.9%) |
123 (100%) |
|
Gesha |
19 (10.4%) |
140 (76.5%) |
24 (13.1%) |
183 (100%) |
|
Masha |
7 (7.9%) |
16 (18.2%) |
65 (73.9%) |
88 (100%) |
|
Total |
124 (31.5%) |
170 (43.1%) |
100 (25.4%) |
394 (100%) |
|
Error rate |
0.203 |
0.235 |
0.261 |
0.233 |
|
Priors |
0.333 |
0.333 |
0.333 |
|
Pairwise squared Mahalanobis distances between locations are shown in Table 13. All distances were significant. Gesha and Masha horse populations are closely related, while their distance from the Telo horse population is large.
|
From location |
Telo |
Gesha |
Masha |
|
Telo |
0 |
|
|
|
Gesha |
5.06*** |
0 |
|
|
Masha |
5.46*** |
3.42*** |
0 |
A plot of the first two canonical structures discriminating the studied horse populations is presented in Figure 2. Accordingly, Can 1 separates the Telo horse population from the others, while Can 2 discriminates the Masha horse population from the others. Overall, the analysis categorized the horse populations into three distinct categories. Therefore, the Gesha horse population is different from the Masha and Telo horse populations. Furthermore, the Gesha horse population has more relationship with the Masha than the Telo horse population.
Qualitative characteristics
Chi-square and Cramér’s V statistical values and level of significance for the effect of the class variables on the qualitative characteristics of the studied horse populations are presented in Table 14. All the traits were significantly affected by the location of the horse populations except body colour pattern and shoulder stripe. On the other hand, only five traits were significantly affected by the horses' sex and age. Face and back profile of the studied horse populations were found to be highly associated with location while the level of relationship of shoulder stripe with location was insignificant. A higher level of relationship between the horses' sex and age with their head colour was also observed.
|
Qualitative traits |
Location |
Sex |
Age |
||||||
|
ꭓ2 value |
Cramér's V |
Prob. |
ꭓ2 value |
Cramér's V |
Prob. |
ꭓ2 value |
Cramér's V |
Prob. |
|
|
Body colour |
43.1 |
0.234 |
*** |
6.4 |
0.127 |
NS |
95.2 |
0.201 |
*** |
|
Head colour |
34.8 |
0.210 |
* |
19.4 |
0.222 |
* |
90.5 |
0.432 |
* |
|
Muzzle colour |
37.1 |
0.217 |
*** |
5.9 |
0.122 |
NS |
42.2 |
0.164 |
* |
|
Tail colour |
23.7 |
0.173 |
** |
9.6 |
0.156 |
* |
58.1 |
0.192 |
** |
|
Hoof colour |
55.8 |
0.266 |
*** |
1.9 |
0.069 |
NS |
38.2 |
0.220 |
** |
|
Hair size |
21.3 |
0.233 |
*** |
12.4 |
0.178 |
** |
9.7 |
0.157 |
NS |
|
Body colour pattern |
8.7 |
0.105 |
NS |
0.07 |
0.014 |
NS |
8.3 |
0.103 |
NS |
|
Dorsal stripe |
16.5 |
0.205 |
** |
0.2 |
0.021 |
NS |
10.5 |
0.163 |
NS |
|
Shoulder stripe |
1.8 |
0.068 |
NS |
1.6 |
0.064 |
NS |
4.8 |
0.111 |
NS |
|
Face profile |
52.9 |
0.367 |
*** |
4.1 |
0.102 |
* |
4.3 |
0.105 |
NS |
|
Back profile |
52.8 |
0.366 |
*** |
4.0 |
0.101 |
* |
2.6 |
0.081 |
NS |
|
Tail length |
28.4 |
0.190 |
*** |
4.2 |
0.103 |
NS |
17.4 |
0.149 |
NS |
|
Mane length |
52.8 |
0.259 |
*** |
2.5 |
0.080 |
NS |
10.0 |
0.112 |
NS |
The majority of the studied horse populations possess a plain body colour pattern with red, medium hair size, and long tail and mane with a mainly black muzzle, tail and hoof (Table 16; Table 15, Figure 3). All horses had sloppy croup with the absence of leg stripe. Short hair size, convex face and straight back profiles were observed more frequently on stallions than mares. The majority of the Gesha horses had red body and head (Figure 3, C and D) while white-striped red head was also frequently observed. White body and head colour were observed more frequently on Telo horses. Around half of the horse population from Masha district had black and white hoof, which was rarely observed in the other horse populations.
|
Colour-related qualitative traits |
Location |
|
Sex |
||||
|
Telo |
Gesha |
Masha |
|
Stallions |
Mares |
||
|
Body colour |
Red |
30.1 |
50.8 |
35.2 |
|
42.2 |
37.5 |
|
|
Brown |
20.3 |
13.1 |
21.6 |
|
14.5 |
24.1 |
|
|
Gray |
16.3 |
15.8 |
20.5 |
|
18.1 |
14.3 |
|
|
White |
20.3 |
13.1 |
18.2 |
|
17.4 |
14.3 |
|
|
Tan |
0.8 |
5.5 |
4.6 |
|
3.5 |
4.5 |
|
|
Black |
9.8 |
1.1 |
0.0 |
|
3.2 |
4.4 |
|
|
Red and white |
2.4 |
0.6 |
0.0 |
|
1.1 |
0.9 |
|
Head colour |
White |
30.9 |
19.7 |
28.4 |
|
26.9 |
20.5 |
|
|
Gray |
18.7 |
12.0 |
13.6 |
|
15.3 |
12.5 |
|
|
Red |
21.1 |
26.2 |
18.2 |
|
24.5 |
18.8 |
|
|
Red with white stripe |
5.7 |
21.9 |
12.5 |
|
14.9 |
14.3 |
|
|
Black |
14.6 |
9.8 |
9.1 |
|
10.3 |
13.4 |
|
|
Black with white stripe |
0.8 |
1.6 |
4.5 |
|
1.8 |
2.7 |
|
|
Brown |
5.7 |
4.9 |
9.1 |
|
3.5 |
12.5 |
|
|
Brown with white stripe |
1.6 |
0.6 |
1.1 |
|
0.7 |
1.8 |
|
|
Tan |
0.8 |
1.1 |
0.0 |
|
1.1 |
0.0 |
|
|
Tan with white stripe |
0.0 |
2.2 |
3.4 |
|
1.1 |
3.6 |
|
Muzzle colour |
Black |
51.2 |
36.6 |
37.5 |
|
39.4 |
46.4 |
|
|
White |
26.8 |
19.7 |
12.5 |
|
22.7 |
14.3 |
|
|
Red |
10.6 |
25.7 |
18.2 |
|
19.1 |
10.6 |
|
|
Gray |
11.4 |
9.3 |
21.6 |
|
13.5 |
10.7 |
|
|
White and Black |
0.0 |
8.7 |
10.2 |
|
5.3 |
8.9 |
|
Tail colour |
Black |
52.0 |
53.0 |
36.4 |
|
48.6 |
50.0 |
|
|
Gray |
26.0 |
19.7 |
26.1 |
|
25.2 |
17.9 |
|
|
White |
13.8 |
8.7 |
12.5 |
|
11.7 |
9.8 |
|
|
Red |
4.9 |
14.8 |
12.5 |
|
11.0 |
11.6 |
|
|
Brown |
3.3 |
3.8 |
12.5 |
|
3.5 |
10.7 |
|
Hoof colour |
Black |
91.9 |
74.9 |
52.3 |
|
74.1 |
77.7 |
|
|
Black and White |
4.9 |
21.8 |
47.7 |
|
22.7 |
21.4 |
|
|
White |
3.2 |
3.3 |
0.0 |
|
3.2 |
0.9 |
|
Qualitative traits |
Location |
|
Sex |
||||
|
Telo |
Gesha |
Masha |
|
Stallions |
Mares |
||
|
Hair size |
Short |
42.3 |
43.2 |
15.9 |
|
42.2 |
23.2 |
|
|
Medium |
57.7 |
56.8 |
84.1 |
|
57.8 |
76.8 |
|
Body colour pattern |
Plain |
95.9 |
99.4 |
100 |
|
98.6 |
98.2 |
|
|
Pied |
1.6 |
0.6 |
0.0 |
|
0.7 |
0.9 |
|
|
Shaded |
2.4 |
0.0 |
0.0 |
|
0.7 |
0.9 |
|
Dorsal stripe |
Absent |
67.5 |
44.3 |
57.9 |
|
53.9 |
56.3 |
|
|
Present |
32.5 |
55.7 |
42.1 |
|
46.1 |
43.7 |
|
Shoulder stripe |
Absent |
99.2 |
99.4 |
97.7 |
|
98.6 |
100 |
|
|
Present |
0.2 |
0.6 |
2.3 |
|
1.4 |
0.0 |
|
Face profile |
Straight |
86.2 |
45.4 |
65.9 |
|
59.6 |
70.5 |
|
|
Slightly convex |
13.8 |
54.6 |
34.1 |
|
40.4 |
29.5 |
|
Back profile |
Straight |
44.7 |
76.5 |
87.5 |
|
72.0 |
61.6 |
|
|
Curved |
55.3 |
23.5 |
12.5 |
|
28.0 |
38.4 |
|
Tail length |
Short |
2.4 |
0.0 |
0.0 |
|
1.1 |
0.0 |
|
|
Medium |
40.7 |
22.4 |
14.8 |
|
28.7 |
20.5 |
|
|
Long |
56.9 |
77.6 |
85.2 |
|
70.2 |
79.5 |
|
Mane length |
Short |
4.9 |
0.0 |
0.0 |
|
2.1 |
0.0 |
|
|
Medium |
48.0 |
16.9 |
39.8 |
|
31.9 |
31.3 |
|
|
Long |
47.1 |
83.1 |
60.2 |
|
66.0 |
68.7 |
The effect of age on the colour-related qualitative characteristics of the studied horse populations is presented in Figure 4. Little effect of age on the colour-related qualitative characteristics was observed. As the age of the studied horses increased, the proportion of horses with white body colour showed a significant increase (p < 0.0001), while the proportion of the other colours decreased.
Similarly, the proportion of horses with white head colour showed a significant increase (p < 0.05) with age, while the proportion of horses with grey head colour decreased. The proportion of the others (red and red with white stripe) remained constant.
Finally, older horses also showed a higher proportion of white tail colour (p < 0.01) while the proportion of horses with black tail decreased. The proportion of the others (red and grey) remained the same.
The majority of the Gesha horses had a dorsal stripe and slightly convex face profile, which can be considered their unique characteristics (Table 9). A curved back profile was predominantly observed in Telo horses, which distinguished them from the others. A slight effect of sex on the qualitative characteristics was observed: shorter hair, a slightly convex face and a straight back profile were observed mainly in stallions.
Discussion
Morphometric measurements
The studied morphometric measurements produced reliable information to characterize and differentiate the three horse populations phenotypically. Besides studying the main effect (location), the effects of age and sex were also analyzed to see if they could cause a significant difference. The effect of age was not significant, which might be due to the nature of the sampling, which included adult horses only. On the other hand, sex significantly affected the studied traits. Stallions had higher values than mares on most morphometric measurements, in line with Rensch’s rule (Rensch, 1950). According to Rensch (1950), males of a given species are usually larger than females. Such differences between stallions and mares may be ascribed to levels of testosterone secreted by stallions, which leads to larger muscle mass and skeletal development (Baneh & Hafezian, 2009). Similar results were also reported by Kefena et al. (2012), Ghezelsoflou, Hamidi, and Gharahveysi (2018) and Sadek, Al-Aboud, and Ashmawy (2006) on Ethiopian, Iranian Turkoman and Arabian horses, respectively.
According to Kefena et al. (2012), Selale horses (the tallest and typical riding horses in Ethiopia) had values of 131.2 ± 0.4, 125.6 ± 0.4, and 131.7 ± 0.5cm for heights at withers, back and croup, respectively. The current study revealed that Gesha horses are the tallest horses in Ethiopia with a value of 132.8 ± 0.37, 130.4 ± 0.36, and 134.0 ± 0.36cm for heights at withers, back and croup, respectively (Table 6). However, these values were much lower than the reports of Zechner et al. (2001) for Lipizzan horses studied in different locations in Europe, and Ghezelsoflou et al. (2018) for Iranian Turkoman horses in Iran. The tall and big body of the Gesha horse population in Ethiopia indicates that they can be categorized as typical saddle horses. This is in line with the study by Kristjansson et al. (2016) in Iceland, which showed a higher riding ability as the horses’ height increased. Traditionally, Gesha horses, which are known for their aggressiveness, are also known and recognized as typical riding horses.
The barrel and neck lengths, and cannon bone length and circumference for all the populations from the current study are comparable with the reports of Kefena et al. (2012) on all Ethiopian horse populations. The body length of Gesha horses (127.0 ± 0.49cm) is lower than the reports of Kefena et al. (2012) for all Ethiopian horse populations. On the other hand, the head and back lengths of Gesha horses (53.6 ± 0.20 and 71.2 ± 0.33cm, respectively) is higher than all Ethiopian horse populations (Kefena et al., 2012). Such wide disagreement might be due to differences in points of measurement. The thorax girth of Gesha horses (146.6 ± 0.56cm) is comparable with Selale (146.6 ± 0.8cm), Bale (145.3 ± 0.7cm), and Horro horses (145.5 ± 0.6cm) while it was higher than Abyssinian horses (140.4 ± 0.5cm) and lower than Keffa horses (152.6 ± 0.7cm) (Kefena et al., 2012).
Body measure indices
The body index shows the length of the animal. A long animal is best suited for speed, a short animal for strength (Torres & Jardim, 1981). Long animals have a body index value greater than 90, while a value less than 85 indicates that the animal is short (Torres et al., 1981). According to Table 8, the Telo and Masha mares were categorized as long horses. However, in reality, Gesha stallions are known for their speed.
The caliber index, which shows the overall size of the horse, increases with age and size (Kaps, Curik, & Baban, 2005). Kaps et al. (2005) observed its increase from 119.1 to 135 in Lipizzan horses from 6 to 36 months of age. The current findings show the comparably big size of Gesha stallions.
The overbuilt index of a horse indicates the proportion of its height at withers and at croup. A horse with downhill conformation (height at croup higher than height at withers) is indicated as the best riding horse by Padilha et al. (2017), since stronger muscles in the hind limbs and taller hind limbs indicate greater power for jumping and the ability to give a solo performance. In line with the current findings, Mcmanus et al. (2005) in Campeiro horses, Rezende et al. (2014) in Brazilian sport horses and Mariz et al. (2015) in Quarter horses reported a slightly downhill conformation. However, uphill conformation was reported as an important characteristic by Lucena, Vianna, Neto, Filho, and Diniz (2015) in Marchador horses and Kristjansson et al. (2016) in Icelandic horses.
According to (Torres et al., 1981), a riding horse must have a conformation index value of 2.1125. A value above this threshold shows the suitability of a horse for work. The conformation index values found in the current study were between 1.47 and 1.65 (Table 8), with Gesha stallions having the highest conformation index values among the studied populations.
Multivariate analysis
Stepwise discriminant function analysis selected and ranked the morphometric variables according to their importance in discriminating the studied horse populations. The inclusion of height at croup and body length within the top five discriminatory variables is comparable with the reports of Kefena et al. (2012), who classified them among the top four variables to discriminate Ethiopian horse populations. The results of discriminant function analysis showed an advanced classification (76.7%) of the studied horses into their respective populations/locations. This high value shows the dissimilarity among the studied populations. Canonical discriminant function analysis revealed the higher power of Can 1 than Can 2 to separate the horse populations. This shows the separation of Gesha and Masha horses from Telo horses while differences also occur between Gesha and Masha populations. However, the distances showed only the relative size differences between each population. Such differences might not necessarily be due to breed (genetic) differences (Zechner et al., 2001). Therefore, a diversity study through further genetic characterization is recommended to design conservation and breeding programmes.
Qualitative characteristics
Besides their aggressiveness and top-riding ability, the examined qualitative characteristics clearly differentiated the Gesha horse population from the other studied populations. The majority of Gesha horses possess red body colour, red and white-striped red head colour, striped dorsal body, slightly convex face and long mane while some similarities were observed with the adjacent Masha horses. A slight effect of sex and age on the qualitative characteristics was observed. Shorter hair, a slightly convex face and a straight-back profile were observed predominantly in stallions than mares.
The current study revealed the level of relationship between age and body colour. As age advanced, the proportion of horses with white (body, head and tail) colour increased while the proportion of horses with grey and brown colours decrease, which might be due to the progressive depigmentation of the coat's hairs (Locke, Penedo, Bricker, Millon, & Murray, 2002). At birth, grey horses may have any colour but over time, white hairs begin to appear and become gradually more dominant as white hairs become intermixed with hairs of other colours. At a later age, most horses of this type ultimately become completely white, though some retain intermixed light and dark hairs (Locke et al., 2002). This is due to the presence of a greying allele of the KIT gene, which inhibits the hair follicles from producing melanin. The coat takes on a 'dappled' pattern that increasingly becomes white. However, grey horses with a totally white coat can be distinguished from white horses by their underlying black skin, particularly around the eyes, muzzle, and genital area (Locke et al., 2002).
Conclusion
The studied phenotypic traits (morphometric measurements and qualitative characteristics) had produced reliable information in characterizing and differentiating Gesha, Masha and Telo horse populations. Gesha horses were the tallest, longest and largest among the studied horse populations. Besides their size, the most important characteristics of Gesha horses are their aggressiveness, top-riding ability, red-dominated body colour, white-striped red head colour and slightly convex face. These results were also supported by the multivariate analysis, which differentiated the Gesha horse population from the Masha and Telo horse populations, and showed a relatively higher relationship with Masha horses. Further genetic characterization is recommended to confirm the above results and design conservation and breeding programmes.
Acknowledgments
The authors are highly indebted to the Ethiopian Biodiversity Institute (EBI) for covering all the budget needs of the work. Our special appreciation also goes to the animal owners for providing their animals for this work for free. We also take this opportunity to thank the animal science experts and development agents in the districts for their endless help during data collection. A special word also goes to our friend and work partner Mr Tadesse Hunduma for mapping the study area.
Author contributions
All authors contributed to the study conception and design. Material preparation and data collection were performed by Amine Mustefa, Aweke Engdawork, and Seble Sinke. Amine Mustefa performed the data analysis and wrote the first draft of the manuscript. All authors commented on previous versions of the manuscript, and read and approved the final manuscript.
Conflict of interest statement
The authors declare that they have no conflict of interest.