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Revista Brasileira de Zootecnia

Print version ISSN 1516-3598On-line version ISSN 1806-9290

R. Bras. Zootec. vol.48  Viçosa  2019  Epub June 10, 2019 


Multivariate analysis of morphometry effect on race performance in Thoroughbred horses

Yavuzkan Paksoy1 

Necmettin Ünal2  *

1Yeşiloba Hippodrome, Adana, Turkey

2Ankara University, Faculty of Veterinary Medicine, Department of Animal Breeding and Husbandry, Ankara, Turkey


The objective of this study was to determine the effects of morphometric measurements on race performance (m/sec) of Thoroughbred horses. Data of morphometric measurements (withers height, rump height, chest girth, chest width, front chest width, chest depth, neck length, shoulder length, length of withers to rump, rump length, body length, head width, head length, and cannon circumference) were taken from 244 Thoroughbred horses chosen at random. A total of 2888 racing records were considered for race performance. The effects of environmental factors on morphometric measurements (stallion, gender, age, and mother age) and race performance (gender, age, mother age, year, hippodrome, race distance, racetrack, and race type) were analyzed using the least squares method. Principal component analysis (PCA) was performed for morphometric measurements, and then the factor loadings were rotated by Varimax method. Multiple linear regression analysis was applied for the significance of the obtained factors on race performance. Significant effects for stallion on all morphometric measurements, except head length and width, and for gender on withers height, cannon circumference, and head width were determined. Race performance was significantly influenced by stallion, gender, age, year, hippodrome, race distance, racetrack, and race type. After PCA, four factors with eigenvalues >1 were attained. The effects of factors on race performance were not significant, according to the results of multiple linear regression analysis. Therefore, the effects of the morphometric measurements examined on the race performance were not significant in Thoroughbred horses.

Keywords: environmental factors; morphometric measurements; PCA; flat racing


It is widely accepted that the horse was domesticated in the years 3000 BC, after dogs, sheep, goats, and cattle. After domestication, horses started to be used for various purposes, which have changed throughout history. Nowadays, horse breeding is mostly undertaken for sport and recreation purposes (Taylor and Field, 2014). Horses have been also used for transportation and farming in some countries (McManus et al., 2013; Tennah et al., 2014).

The Thoroughbred horse breed is mainly known for running, especially flat racing, but is also used in a variety of sports such as jumping, hunting, three-day eventing, dressage, and polo. Flat racing with Thoroughbred horses has a very important place in equestrian sports around the world. This breed has been improved for speed at middle - distances (1400-2400 m) (Evans et al., 1994; Taylor and Field, 2014).

Horse performance during the race is measured in various forms. In flat racing, speed (m/sec) is a measure largely used in assessing race performance. Many factors influencing race performance of Thoroughbred horses (e.g., husbandry, nutrition, exercise, race distance, racetrack, gender, handicap weight, age, mother age, and hippodrome) have been addressed in some studies (Oki et al., 1994; Barron, 1995; Ekiz and Kocak, 2007; Buxadera and Mota, 2008; Bakhtiari and Heshmat, 2009; Paksoy and Ünal, 2010; Park et al., 2011; Takahashi, 2015).

Morphometric measurements of horses are considered a criterion in determining breed characteristics and body conformation. In addition, they are important in monitoring development during the growth period and in determining the suitability for breed standards in the post-growth period. Thoroughbred horses are required to conform to the breed characteristics in various body measurements. Many studies involving morphometric measurements in different horse breeds have been carried out (Anderson and McIlwraith, 2004; Staniar et al., 2004; Gücüyener Hacan and Akçapınar, 2011; Yılmaz and Ertuğrul, 2012; Padilha et al., 2017). On the other hand, no research has been found on the impact of morphometric measurements of Thoroughbred horses on their race performance (m/sec). Only one study (Smith et al., 2006) reported that withers height, rump height, body length, and heart girth of yearling Thoroughbred foals were positively correlated with lifetime earnings and win percentage.

Principal component analysis (PCA) is the simplest of the true eigenvector-based multivariate analyses. It is most commonly used to intensify the information included in a large number of variables, strongly correlated, into a smaller set of new composite dimensions, without much loss of information. It does so by composing new uncorrelated factors that successively maximize variance (Sadek et al., 2006; Park et al., 2011; Alpar, 2013).

The purpose of this study was to evaluate the impact of morphometric measurements on race performance of Thoroughbred horses using multivariate analysis.

Material and Methods

The animal material consisted of 244 Thoroughbred horses of different ages that run in races in Hippodromes organized by the Jockey Club of Turkey. The animals were randomly selected from horses housed in Adana Yeşiloba Hippodrome, in Adana city, Turkey (37°0' N and 35°19' E), in 2013. Pedigree (stallion, birth date, age, gender, and mother age) and racing (running year, hippodrome, race distance, race duration, racetrack, and race type) information of the horses was obtained from the records of the Board of High Stewards, Ministry of Food Agriculture and Livestock of Turkey, and the Jockey Club of Turkey.

All morphometric measurements (Table 1) were taken from the right side with the horse standing in a normal position inside a fixed crush.

Table 1 Descriptions of morphometric measurements examined in the research 

Morphometric measurement Definition
WH - withers height Vertical distance from the highest point of the withers to the ground
RH - rump height Vertical distance between the highest point of the sacrum and the ground
CG - chest girth Circumference around the chest from behind the scapula
CW - chest width Distance in the front side between the outer sides of the right and left articulatio humeri
FCW - front chest width Distance in the front side between the inner sides of the right and left articulatio humeri
CD - chest depth Vertical distance between the highest point of the withers and the sternum
NL - neck length Distance from the angulus mandibula to the scapula
SL - shoulder length Distance from the highest point of the withers to the caput humeri
LWR - length of withers to rump Straight distance between the end of the withers and the beginning of the rump
RL - rump length Distance from the tuber coxae to the tuber ischia
BL - body length Horizontal distance from the caput humeri to the tuber ischia
HW - head width Distance between the right and left processus lacrimalis rostralis
HL - head length Distance from the crista occipitalis to the os incisivum
CC - cannon circumference Circumference at the middle of the metacarpal bone

Of the 244 investigated animals, 159 horses that had at least three paternal half-sibs and three official racing records were selected for the statistical analysis of race performance and effects of morphometric measurements on race performance. Race performance (m/sec) was calculated based on duration and distance for each race.

The least squares mixed models including fixed effects of factors was used, as shown below:

yijklm=μ+αi+βj+γk+δl+eijklm(for morphometric measurements),

in which yijklm = dependent variable, µ = overall mean, αi = fixed effect of stallion, βj = the fixed effect of gender, γk = fixed effect of age, δl = fixed effect of mother age, and eijklm = random error.

yijklmnoprs=μ+αi+βj+γk+δl+ηm+κn+σo+τp+ωr+eijklmnoprs(for race performance),

in which yijklmnoprs = dependent variable, µ = overall mean, αi = fixed effect of stallion, βj = fixed effect of gender, γk = fixed effect of age, δl = fixed effect of mother age, ηm = fixed effect of year, κn = fixed effect of hippodrome, σo = fixed effect of race distance, τp = fixed effect of racetrack, ωr = fixed effect of race type, and eijklmnoprs = random error.

The meanings of the factors used in the mix models given above are explained below:

  • Stallion: father of horses investigated.

  • Gender: male and female horses investigated.

  • Age: ages of horses investigated = 2, 3, 4, 5, and 6+ years old.

  • Mother age: mother ages of horses investigated = 2-5, 6, 7, 8, 9, 10, 11, 12, 13-15, and 16-19 years old.

  • Year: the years of horse racing; 2001-2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, and 2013.

  • Hippodrome: cities of hippodrome where horse races took place = Adana, Bursa, İstanbul, Ankara, İzmir, Diyarbakır, Şanlıurfa, and Elazığ.

  • Race distance: between 800 and 2400 m, and run by the horses investigated from start to end points = 800-1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900, 2000, 2100, 2200, and 2400 m.

  • Racetrack: ground composition of racetrack = dirt and turf.

  • Race type: flat racing categories organized according to various characteristics. Maiden race: racing joined by horses that have never won. Handicap race: racing in which different weights are loaded on the horses, and these weights are determined by scores of official handicappers to equalize their chances of winning; handicap 13 (horses with a score between 1-50); handicap 14 (horses with a score between 1-65); handicap 15 (horses with a score between 1-75); handicap 16 (horses with a score between 30 and 85); and handicap 17 (horses with a score between 40 and 100). Condition race: racing where horses participated according to the total amount of lifetime earnings, and additional weights are loaded on horses according to the total amount of lifetime earnings. The more the number of condition race increases, the more total amount of lifetime earnings are needed to participate. Open class: racing with high-performance horses carrying the same weight.

Statistical significances among the subgroups were determined by Tukey's test at 5% significance level. Pearson's correlation coefficients were calculated among morphometric measurements.

The Kolmogorov-Simirnov normality test for normal distribution fitness, the Kaiser-Meyer-Olkin test for sample size adequacy, and Bartlett's sphericity test were applied in the morphometric measurement data. Principal component analysis for morphometric measurements were performed, and then the factor loadings were rotated by the Varimax method. The significance of the rotated factor loadings was determined using the value of 0.45, which is the limit for n = 159. Factor eigenvalues greater than 1 were accepted (Alpar, 2013; Tabachnick and Fidell, 2014). For factor analysis, the basic factor analysis equation was used, as follows:


in which Z = p×1 vector of variables, ƛ = p×m matrix of factor loadings, F = m×1 vector of factors, and e = p×1 vector of error factors.

Multiple Linear Regression Analysis, using the model below, determined the importance of the effects of the obtained factors on race performance (RP):


in which a = regression constant value; FS = factor scores; b1, b2, b3, and b4 = regression coefficients of factor scores; and e = error term.

The t test was used for significance of the regression coefficients. The autocorrelation assumption was determined by the Durbin-Watson test. Statistical procedures were carried out using SPSS software (Statistical Package in Social Sciences for Windows, version 14.01).


Least squares means (Table 2) were 169.34±0.52 cm for withers height, 187.66±1.12 cm for chest girth, and 168.52±0.75 cm for body length.

Table 2 Descriptive statistics and P-values for the morphometric measurements of the horse 

Morphometric measurement Descriptive statistics of all horses (n = 244) P-value of the selected horses for race performance (n = 159)
X±Sx (cm) Min (cm) Max (cm) CV (%) X±Sx (cm) Stallion Gender Age Mother age
WH 169.15±0.23 157 178 2.11 169.34±0.52 <0.001 0.021 0.355 0.312
RH 167.46±0.23 155 177 2.16 167.65±0.54 <0.001 0.169 0.386 0.267
CG 187.22±0.46 162 204 3.86 187.66±1.12 0.032 0.390 0.555 0.852
CW 42.33±0.16 34 47 5.86 42.24±0.38 <0.001 0.469 0.227 0.356
FCW 20.34±0.10 16 24 8.01 20.10±0.25 0.038 0.606 0.552 0.769
CD 84.88±0.27 70 93 5.03 84.88±0.64 <0.001 0.302 0.282 0.524
NL 57.83±0.29 44 68 7.80 58.43±0.70 <0.001 0.158 0.600 0.671
SL 72.10±0.20 63 79 4.29 72.38±0.44 <0.001 0.211 0.171 0.741
LWR 62.50±0.26 54 72 6.45 62.43±0.56 <0.001 0.599 0.542 0.731
RL 37.57±0.17 31 43 7.13 37.22±0.36 <0.001 0.185 0.530 0.393
BL 168.17±0.33 154 178 3.04 168.52±0.75 <0.001 0.459 0.646 0.303
HW 23.88±0.05 21 25 3.02 24.00±0.13 0.116 0.029 0.381 0.552
HL 52.95±0.13 48 59 3.44 52.93±0.28 0.192 0.102 0.146 0.338
CC 20.46±0.05 19 22 3.52 20.48±0.11 0.040 0.038 0.326 0.453

WH - withers height; RH - rump height; CG - chest girth; CW - chest width; FCW - front chest width; CD - chest depth; NL - neck length; SL - shoulder length; LWR - length of withers to rump; RL - rump length; BL - body length; HW - head width; HL - head length; CC - cannon circumference; CV - coefficient of variation.

Tukey's test (P>0.05).

Age and mother age had no significant effects (P>0.05) on any of the investigated morphometric measurements, while gender was pronounced on withers height, cannon circumference, and head width (P<0.05). On the other hand, for stallion, a significant effect (P<0.05; P<0.001) occurred on all morphometric measurements except head length and width.

Least squares means for race performance were 15.29±0.06 m/sec (Table 3). Race performance was significantly influenced by stallion (P<0.01), gender (P<0.01), age (P<0.05), year (P<0.05), hippodrome (P<0.001), race distance (P<0.001), racetrack (P<0.001), and race type (P<0.001), while mother age had no marked effect on this trait.

Table 3 Effects of gender, age, mother age, year, hippodrome, race distance, racetrack, and race type on race performance of horses 

Item n X±Sx (m/sec) Item n X±Sx (m/sec) Item n X±Sx (m/sec) Item n X±Sx (m/sec) P-value
Gender Year Race distance (m) Race type Gender 0.006
Age 0.026
Male 1499 15.331±0.061 2001-2004 66 15.086±0.108a 800-1000 27 15.667±0.141a M 563 15.100±0.062b Mother age 0.245
Female 1389 15.244±0.057 2005 83 15.306±0.101bc 1100 36 15.618±0.126a HC13 42 15.125±0.113b Year 0.033
Age 2006 74 15.216±0.09ab 1200 493 15.723±0.061a HC14 210 15.280±0.071ab Hippodrome <0.001
2007 148 15.299±0.081d 1300 242 15.665±0.071a HC15 295 15.329±0.068ab Race distance <0.001
2 462 15.184±0.053a 2008 190 15.257±0.076c 1400 505 15.584±0.062a HC16 254 15.349±0.069ab Racetrack <0.001
3 1341 15.256±0.043b 2009 229 15.341±0.067d 1500 371 15.578±0.068ab HC17 122 15.371±0.079a Race type <0.001
4 644 15.280±0.049b 2010 205 15.272±0.070cd 1600 179 15.402±0.073b C1 103 15.074±0.089b
5 260 15.347±0.062c 2011 465 15.379±0.060de 1700 97 15.149±0.086c C2 98 15.184±0.084b
6+ 181 15.374±0.072c 2012 856 15.395±0.056e 1800 67 14.800±0.096d C3 207 15.233±0.071b
Mother age 2013 572 15.328±0.059d 1900 405 15.053±0.065e C4 257 15.276±0.067ab
Hippodrome 2000 195 15.087±0.072e C5 194 15.286±0.071ab
2-5 230 15.325±0.071 2100 143 14.936±0.078e C6 129 15.358±0.080a
6 481 15.321±0.066 Adana 1136 15.194±0.055e 2200 78 14.970±0.093e C7 129 15.350±0.078a
7 368 15.297±0.072 Bursa 250 15.487±0.068b 2400 50 14.798±0.106d C8 100 15.445±0.086a
8 271 15.289±0.082 İstanbul 542 15.727±0.057a Racetrack C21-22 54 15.269±0.104ab
9 412 15.280±0.070 Ankara 602 15.410±0.057d OC 41 15.584±0.115a
10 255 15.328±0.078 İzmir 162 15.429±0.074c Dirt 1964 15.018±0.057 Total 2888 15.288±0.057
11 135 15.211±0.087 Diyarbakır 39 15.038±0.121e Turf 924 15.558±0.061
12 151 15.253±0.087 Şanlıurfa 50 15.047±0.110e
13-15 291 15.241±0.074 Elazığ 107 14.973±0.08e
16-19 294 15.332±0.074

Averages in the columns followed by different letters differed in Tukey's test (P<0.05).

n - race number; M - maiden; HC - handicap; HC13 - horses with a score between 1-50; HC14 - horses with a score between 1-65; HC15 - horses with a score between 1-75; HC16 - horses with a score between 30 and 85; HC17 - horses with a score between 40 and 100; C - conditions (C1, C2, C3, C4, C5, C6, C7, C8, C21-22) - horses that participate in these races according to the total amount of lifetime earnings. The more the number of condition race increases, the more total amount of lifetime earnings are needed to participate; OC - open class.

In general, high and significant (P<0.05; P<0.01; P<0.001) correlation coefficients among morphometric measurements in positive direction were calculated, varying from 0.117 to 0.679 (Table 4). The 14 morphometric measurements examined showed fitness to normal distribution by the Kolmogorov-Smirnov test (P>0.05). Sample size adequacy by the Kaiser-Meyer-Olkin test was 0.849. The significance level of Bartlett's sphericity test was P<0.001.

Table 4 Correlation coefficients among morphometric measurements of race horses 

Morphometric measurement RH CG CW FCW CD NL SL LWR RL BL HW HL CC
WH 0.679 0.610 0.391 0.117 0.536 0.398 0.442 0.464 0.153 0.677 0.384 0.330 0.115
P-value <0.001 <0.001 <0.001 0.045 <0.001 <0.001 <0.001 <0.001 0.041 <0.001 0.001 0.001 0.031
RH 0.384 0.430 0.142 0.454 0.307 0.402 0.320 0.128 0.532 0.303 0.414 0.126
P-value <0.001 <0.001 0.042 <0.001 <0.001 <0.001 <0.001 0.039 <0.001 0.008 0.003 0.039
CG 0.484 0.355 0.698 0.453 0.443 0.481 0.123 0.571 0.304 0.335 0.089
P-value <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 0.048 <0.001 0.006 <0.001 0.340
CW 0.344 0.617 0.334 0.323 0.342 −0.026 0.443 0.509 0.360 0.060
P-value <0.001 <0.001 <0.001 <0.001 <0.001 0.523 <0.001 <0.001 <0.001 0.552
FCW 0.430 0.346 0.327 0.063 0.046 0.433 −0.065 0.111 0.048
P-value <0.001 <0.001 <0.001 0.545 0.523 <0.001 0.475 0.039 0.593
CD 0.490 0.455 0.541 0.125 0.593 0.388 0.314 0.049
P-value <0.001 <0.001 <0.001 0.040 <0.001 <0.001 <0.001 0.560
NL 0.379 0.421 0.025 0.587 0.142 0.150 0.103
P-value <0.001 <0.001 0.645 <0.001 0.047 0.038 0.145
SL 0.338 0.074 0.327 0.182 0.140 0.109
P-value 0.001 0.320 <0.001 0.036 0.045 0.140
LWR 0.085 0.453 0.302 0.469 0.026
P-value 0.325 <0.001 0.006 <0.001 0.612
RL 0.167 0.059 0.018 −0.018
P-value 0.038 0.589 0.696 0.690
BL 0.334 0.440 0.155
P-value 0.001 <0.001 0.032
HW 0.367 −0.036
P-value <0.001 0.602
HL 0.074
P-value 0.312

WH - withers height; RH - rump height; CG - chest girth; CW - chest width; FCW - front chest width; CD - chest depth; NL - neck length; SL - shoulder length; LWR - length of withers to rump; RL - rump length; BL - body length; HW - head width; HL - head length; CC - cannon circumference.

Test - Pearson's correlation method.

Four factors (FI, FII, FIII, and FIV) with eigenvalues >1 were attained as a result of the analysis with PCA. The factors of FI (general size), FII (body thickness), FIII, and FIV explained 52.46, 15.50, 8.01, and 7.21% of the total variation of 83.19%, respectively. Ten loads in FI, five in FII, two in FIII, and two in FIV were statistically significant (Table 5).

Table 5 Loadings and rotated loadings of four factors with eigenvalues >1 for the morphometric measurements of race horses 

Morphometric measurement Factor loading Rotated factor loading Common variance
WH 0.925 −0.236 −0.106 −0.239 0.9281 0.316 −0.106 −0.062 0.976
RH 0.875 −0.35 0.112 −0.404 0.8961 −0.025 −0.079 0.119 0.824
CG 0.926 0.251 0.104 0.081 0.8751 0.4771 0.019 0.005 0.994
CW 0.925 0.275 −0.089 −0.222 0.8251 0.251 −0.001 −0.093 0.752
FCW 0.468 0.684 0.407 −0.240 0.435 0.151 0.7431 0.088 0.772
CD 0.978 0.195 0.072 0.100 0.8591 0.4951 0.002 0.005 0.983
NL 0.894 0.359 −0.006 0.219 0.7771 0.5941 0.195 0.002 0.994
SL 0.749 0.371 0.104 −0.207 0.430 0.290 0.247 0.006 0.330
LWR 0.686 0.142 −0.045 0.580 0.7801 0.6251 −0.008 0.011 0.988
RL 0.295 −0.243 0.716 0.403 0.391 0.291 0.113 0.8071 0.902
BL 0.919 0.233 −0.112 0.025 0.9261 0.307 −0.011 −0.054 0.955
HW 0.569 −0.646 −0.169 0.085 0.5651 0.183 −0.5621 0.108 0.680
HL 0.579 0.189 −0.270 0.173 0.7361 0.392 −0.222 −0.126 0.761
CC 0.289 0.678 −0.449 0.207 0.199 0.5631 0.273 −0.5421 0.725
Eigenvalue 8.015 2.136 1.054 1.014 7.345 2.170 1.122 1.010 11.647
Variation (%) 57.250 15.257 7.529 7.243 52.464 15.500 8.014 7.214 83.193
Cumulative variance (%) 57.250 72.507 80.036 87.279 52.464 67.964 75.978 83.193 83.193

WH - withers height; RH - rump height; CG - chest girth; CW - chest width; FCW - front chest width; CD - chest depth; NL - neck length; SL - shoulder length; LWR - length of withers to rump; RL - rump length; BL - body length; HW - head width; HL - head length; CC - cannon circumference; FI - factor I; FII - factor II; FIII - factor III; FIV - factor IV.

Test - Principal Component Analysis and Varimax method.

1Rotated factor considered important.

The regression coefficients obtained for the factors were not significant (P>0.05) (Table 6). The Durbin-Watson test for autocorrelation yielded a value of 1.377. Correlation coefficients between race performance and regression coefficients of FI, FII, FIII, and FIV were calculated as −0.012, −0.079, 0.022, and 0.050 (P>0.05), respectively.

Table 6 Results for multiple linear regression analysis of the factors with eigenvalues >1 for the morphometric measurements of race horses 

Factor Coefficient Standard error t* P-value
FI −0.005 0.034 −0.150 0.345
FII 0.034 0.034 0.979 0.875
FIII 0.010 0.034 0.278 0.310
FIV 0.021 0.034 −0.620 0.596
R: 0.097; R2: 0.009; Corrected R2: 0.017

FI - factor I; FII - factor II; FIII - factor III; FIV - factor IV.


Test - multiple linear regression analysis.


Size and morphometry are extremely important traits in nearly all horse breeds including Thoroughbred, and numerous breed registries select horses on functional criteria and support the breeding of horses with body types most convenient for those particular functions. Using many body measurements from the head, neck, trunk, and limbs in a great number of horse breeds such as Thoroughbred, Shire, and Friesian in USA showed that there was a high body size variation among the horse breeds (Brooks et al., 2010).

Coefficients of morphometric measurements covered in this study were low and less than 10%, which shows that the uniformity of morphological characteristics of the breed were rather high. These findings agreed with the report by Brooks et al. (2010) in which the lowest variation for body measurements among 65 horse breeds was observed in Thoroughbred horse breed.

In terms of morphometric measurements examined, males showed higher values than females. However, the effect of gender on morphometric measurements was generally not very clear, given that gender effects were only important for withers height, cannon circumference, and head width.

There was usually a slight increase in morphometric measurements as the animals grew older, but none of these increases was statistically significant. This shows that Thoroughbred horses generally complete their growth and development at the age of two, agreeing with the statement that Thoroughbred horses are early-maturing. In fact, Thoroughbred horses start their racing life one year earlier (two years old) than Arabian horses, the other breed used in flat racing in many countries. A research by Anderson and McIlwraith (2004) found that the various body measurements of Thoroughbred horses were similar at two and three years old, despite significant increase from one to two years old. Similarity of body measurements between two- and three-year-old Thoroughbred horses (Anderson and McIlwraith, 2004) was close to the findings of the present research. On the other hand, mother age did not significantly influence morphometric measurements, which suggests that the effect of mother age remained unchanged before the age of two years.

The average withers height, one of the most important measurements, detected in this study (169.34 cm) was close to the upper limit of the range (165-170 cm) for that breed (Arpacık, 1999), but was higher than the 164.1 and 163.0 cm reported in studies conducted in Turkey (Yılmaz and Ertuğrul, 2012) and Iran (Bakhtiari and Heshmat, 2009), respectively. Withers height of the two-year-old horses in this study (168.75 cm), however, corresponded to 98.6% of this value in horses of 6+ -years old (171.12 cm), which is in line with reports describing that two-year-old horses should reach 95% of the adult value in terms of withers height (Kocher and Staniar, 2013). The average withers height of the two-year-old foals in this study was very similar to the 168.9 cm predicted for Thoroughbred foals at same age in 34 regions of England, Ireland, and the USA (Kocher and Staniar, 2013), but higher than 154.7 cm reported for the same age of Thoroughbred foals in a private enterprise in USA (Anderson and McIlwraith, 2004).

In this research, race performance was evaluated as the speed of horses (m/sec) during the activity. The general evaluation of the effects of various factors on race performance revealed that the effects of all factors were important, except for mother age. Male horses had a better race performance than females (0.087 m/sec), corroborating results of studies conducted on the same breed (Ekiz et al., 2005; Mota et al., 2005; Buxadera and Mota, 2008; Park et al., 2011). On the other hand, Oki et al. (1994) noticed that mares were faster than stallions on turf at all race distances, while on dirt, Japanese Thoroughbred stallions were faster than mares, except at 1200 m.

In the present study, race performance improved steadily with age, from the lowest, two years old, to the highest, 6+ years old. The effect of age on race performance was significant and was consistent with the findings of investigations on the same breed (Ekiz et al., 2005; Buxadera and Mota, 2008; Park et al., 2011; Takahashi, 2015). The increase in race performance with age may be related to a gradual adaptation of horses to race conditions. Young horses can exhibit behaviors that may adversely affect race performance before and during the race. Horses with poor race performance might be separated from race life at an earlier age. As a matter of fact, in a study conducted in Japan (Takahashi, 2015), it was determined that the race performance of Thoroughbred horses increased to four years old and continued high after this age. Four-year-old Brazilian Thoroughbreds were significantly faster than horses of other ages for all race distances, except for 1100 m. However, the horses older than five years showed a significantly lower performance than the other ages for all race distances, excluding the 1100 m (Mota et al., 2005). Gramm and Marksteiner (2010) observed that peak age was 4.45 years and the lowest age was two for race performance in Thoroughbred in USA. On the other hand, race performance was not significantly affected by mother age. This finding differs from those reported in a study with Thoroughbred horses in England (Barron, 1995), in which race performance was higher for horses from younger mares and reduced for horses from older mares. Furthermore, similar results of unimportant mother effects on race performance were reported in a study with Arabian horses (Köseman and Özbeyaz, 2009).

In this research, significant differences in race performance were determined among the hippodromes. This may be because high-performance horses participate more in the races in Istanbul, Ankara, Izmir, and Bursa compared with the other hippodromes. In fact, highly awarded races are organized more often in these cities.

The fact that the differences in horse race performance between the 800 and 1500 m distance were not significant indicated that races up to 1500 m did not negatively impact performance. After 1500 m, the increase in race distance affected race performance negatively. It is possible that the number of turns on the racetrack had an influence on race performance, because races up to 1500 m include a single turn, while racetracks from 1600 to 2400 m include two turns. A research conducted on Thoroughbred horses in Japan reported that the number of turns was changeable (one or two) according to race distance, and turns induced a decrease in race performance regardless of the composition of the racetrack (Takahashi, 2015).

Horses running in turf racetrack performed better than those running in dirt racetrack, with 0.54 m/sec, and this result was consistent with literature reports (Arpacık, 1999; Buxadera and Mota, 2008; Park et al., 2011; Takahashi, 2015).

Race type also affected race performance, and the lowest was obtained in the Maiden and Condition 1 racings. Horses that have never been ranked first participated in maiden racing, while horses competing for the first time were involved in Condition 1 racing. Therefore, race performance was expected to be low in these races. On the other hand, the highest race performance was achieved in open-class racing. High-performance horses are known to have participated in open-class racing and being highly awarded.

Withers height, rump height, and chest girth were positively and significantly correlated with the other measurements. These results are in agreement with previous reports on Thoroughbred horses (Brooks et al., 2010), Brazilian Sport horses (Padilha et al., 2017), and Arabian horses (Sadek et al., 2006).

All measurements loaded in a positive direction for FI, indicating a positive correlation among all 14 measurements, and therefore head, neck, trunk, and limbs increase or decrease in size coordinately. Factor I was represented by significantly positive high loadings on withers height, body length, chest girth, chest width, chest depth, rump height, neck length, length of withers to rump, head length, and head width. Factor I explained more than half of the variation. Therefore, this factor seemed to represent general size. For FII, all measurements, except rump height, loaded in a positive direction. Factor II was highly loaded for chest girth, cannon circumference, chest depth, neck length, and length of withers to rump, and this factor seemed to represent body thickness. While half of the measurements in FIII were loaded in negative direction, this factor included significantly positive high loadings for cannon circumference and head width. The top contributors to FIV included cannon circumference (negative direction) and rump length (positive direction). Both FIII and FIV had only two significant loadings. It has been reported that two factors were obtained using the data collected from the head, neck, trunk, and limbs in 65 horse breeds including Thoroughbred using 35 body measurements (Brooks et al., 2010). Miserani et al. (2002) described only two factors for 15 linear measurements in Pantaneiro horses, while Sadek et al. (2006) reported that three extracted factors for each gender (mares and stallions) were determined in Arabian horses using 13 body measurements. These may be due to the differences among breeds or the type of investigated measurements.

Lower common variance for shoulder length and head width might indicate that these measurements were less effective to the variation compared with the other measurements in Thoroughbred horses.

According to the results of multiple regression analysis, the effects of the four factors on race performance obtained in this study were not significant, and only a very small portion of the variation of race performance can be explained by the examined morphometric measurements.


The effects of the examined morphometric measurements on race performance are not significant in Thoroughbred horses.


This study was prepared from the first author's PhD thesis.


Alpar, R. 2013. Applied multivariate statistical methods. 4th ed. Detay Publishing, Turkey. [ Links ]

Anderson, T. M. and McIlwraith C. W. 2004. Longitudinal development of equine conformation from weanling to age 3 years in the Thoroughbred. Equine Veterinary Journal 36:563-570. ]

Arpacık, R. 1999. Horse breeding. 3rd ed. Sahin Printing House, Turkey. [ Links ]

Bakhtiari, J. and Heshmat, G. 2009. Estimation of genetic parameters of conformation traits in Iranian Thoroughbred horses. Livestock Science 123:116-120. ]

Barron, J. K. 1995. The effect of maternal age and parity on the racing performance of Throughbred horses. Equine Veterinary Journal 27:73-75. ]

Brooks, S. A.; Makvandi-Nejad, S.; Chu, E.; Allen, J. J.; Streeter, C.; Gu, E.; McCleery, B.; Murphy, B. A.; Bellone, R. and Sutter, N. B. 2010. Morphological variation in the horse: defining complex traits of body size and shape. Animal Genetics 41(Suppl. 2):159-165. ]

Buxadera, A. M. and Mota, M. D. S. 2008. Variance component estimations for race performance of Thoroughbred horses in Brazil by random regression model. Livestock Science 117:298-307. ]

Ekiz, B. and Koçak, Ö. 2007. Estimates of genetic parameters for racing times of Thoroughbred horses. Turkish Journal of Veterinary and Animal Science 31:1-5. [ Links ]

Ekiz, B.; Koçak, Ö. and Yılmaz, A. 2005. Phenotypic and genetic parameter estimates for racing traits of Thoroughbred horse in Turkey. Archiv Tierzucht 48:121-129. [ Links ]

Evans, J. W.; Borton, A.; Hintz, H. and Van Vleck, L. D. 1994. The horse. 2nd ed. W.H. Freeman and Company, USA. [ Links ]

Gramm, M. and Marksteiner, R. 2010. The effect of age on Thoroughbred racing performance. Journal Equine Science 21:73-78. [ Links ]

Gücüyener Hacan, Ö. and Akçapınar, H. 2011. Some phenotypic and genetic parameters of Purebred Turkish Arabian horses raised in different stud farms. Journal of Lalahan Livestock Institute 51:55-70. [ Links ]

Kocher, A. and Staniar, W. B. 2013. The pattern of thoroughbred growth is affected by a foal's birthdate. Livestock Science 154:204-214. ]

Köseman, A. and Özbeyaz, C. 2009. Some phenotypic and genetic parameters of racing performance in Arabian horses. Veterinary Journal of Ankara University 56:219-224. [ Links ]

McManus, C.; Santos, S. A.; Dallago, B. S. L.; Paiva, S. R.; Martins, R. F. S.; Braccini Neto, J.; Marques, P. R. and Abreu, U. G. P. 2013. Evaluation of conservation program for the Pantaneiro horse in Brazil. Revista Brasileira de Zootecnia 42:404-413. ]

Miserani, M. G.; McManus, C.; Santos, S. A.; Silva, J. A.; Mariante, A. S.; Abreu, U. G. P.; Mazza, M. C. and Sereno, J. R. B. 2002. Variance analyses for biometric measures of the Pantaneiro horse in Brazil. Archivos de Zootecnia 51:113-120. [ Links ]

Mota, M. D.; Abrahão A. R. and Oliveira, H. N. 2005. Genetic and environmental parameters for racing time at different distances in Brazilian Thoroughbreds. Journal of Animal Breeding Genetics 122:393-399. ]

Oki, H.; Sasaki, Y. and Wilham, R. L. 1994. Genetics of racing performance in the Japanese Thoroughbred horse: II. Environmental variation of racing time on turf and dirt tracks and the influence of gender, age, and weight carried on racing time. Journal of Animal Breeding and Genetics 111:128-137. ]

Padilha, F. G. F.; Andrade, A. M.; Fonseca, A. B. M.; Godoi, F. N.; Almeida, F. Q. and Ferreira, A. M. R. 2017. Morphometric measurements and animal-performance indices in a study of racial forms of Brazilian Sport Horses undergoing training for eventing. Revista Brasileira de Zootecnia 46:25-32. ]

Paksoy, Y. and Ünal, N. 2010. The factors affecting horse racing performance. Journal of Lalahan Livestock Research Institute 50:91-101. [ Links ]

Park, J.; Lee, J.; Oh, S.; Lee, J. W.; Oh, H. and Kim, H. 2011. Principal components analysis applied to genetic evaluation of racing performance of Thoroughbred race horses in Korea. Livestock Science 135:293-299. ]

Sadek, M. H.; Al-Aboud, A. Z. and Ashmawy, A. A. 2006. Factor analysis of body measurements in Arabian horses. Journal of Animal Breeding and Genetics 123:369-377. ]

Smith, A. M.; Staniar, W. B. and Splan, R. K. 2006. Associations between yearling body measurements and career racing performance in Thoroughbred racehorses. Journal of Equine Veterinary Science 26:212-214. ]

Staniar, W. B.; Kronfeld, D. S.; Hoffman, R. M.; Wilson J. A. and Harris P. A. 2004. Weight prediction from linear measures of growing Thoroughbreds. Equine Veterinary Journal 36:149-154. ]

Tabachnick, B. G. and Fidell, L. S. 2014. Using multivariate statistics. 6th ed. Pearson Education Limited, England. [ Links ]

Takahashi, T. 2015. The effect of age on the racing speed of Thoroughbred racehorses. Journal of Equine Science 26:43-48. ]

Taylor, R. E. and Field T. G. 2014. Scientific animal production. 10th ed. Pearson Education Limited, England. [ Links ]

Tennah, S.; Farnir, F.; Kafidi N.; Nsangou, I. N.; Leroy, P. and Antoine-Moussiaux N. 2014. Selective breeding of Arabian and Thoroughbred racehorses in Algeria: perceptions, objectives and practices of owners-breeders. Revista Brasileira de Zootecnia 43:188-196. ]

Yılmaz, O. and Ertuğrul, M. 2012. Some morphological traits of Thoroughbred horses in Turkey. AgroLife Scientific Journal 1:1-5. [ Links ]

Received: March 06, 2018; Accepted: April 10, 2019


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