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Sporthorse performance testing in eventing by own and progeny performance
49-56Views:125The aim of the study was to evaluate the Hungarian Sporthorse population based on eventing competition performance. The database contained the results of 792 horses and 449 riders between 2000 and 2006. The eventing results were gathered from Hungary and other European countries. Blom transformed ranks were used to evaluate the sport performance.Three models were fitted to the Blom scores. Evaluating all the competition categories at the same time weighted Blom scores were used according to the difficulty of the category. The linear mixed model included fixed effects for age, sex, breeder, owner, location, year; and random effects for animal and rider. Horses from the database were judged by their own performance, and stallions were investigated by performance of their progenies on the basis of descriptive statistics of Blom scores and weighted Blom scores. Breeding values of eventing performance were predicted. To improve the reliability of breeding values, more progenies should be
used in eventing competitions. -
Evaluation of Hungarian Sporthorse mare performance tests
83-87Views:76Results of the Hungarian Sporthorse mare performance tests were evaluated. Data from the period of 1993-2009 were used, covering
scores of 618 3-year-old and 310 4-year-old mares, 109 of them were tested at both ages. Seventeen traits were scored on the tests, which
covered ten conformational, three free jumping performance and four movement analyses traits, respectively. Breeding value estimation was
based on BLUP animal model. Test year, age and owner were included in the model as fixed effects. Variance components were estimated
with VCE-6 software package. Heritabilities ranged from 0.32 (frame) to 0.50 (saddle region) for conformation traits, from 0.39 (jumping
style) to 0.49 (jumping ability and jumping skill) for free jumping traits and from 0.20 (walk) to 0.48 (canter) for movement analysis traits.
Breeding value indexes were constructed for each trait group. Conformation index was computed based on the weighted scores of the
breeding values of conformational traits. The conformational score scales were used as weightings. Free jumping and movement indexes
contain the proper breeding values with equal weights. A total index was also constructed using conformation index, two times the free
jumping index and two times the movement index. Each breeding values and breeding value indexes were presented with the mean 100 and
standard deviation of 20 for the easier understanding. -
Random regression models for genetic evaluation of performance of the Hungarian show-jumping horse population
87-91Views:125The aim of the study was to estimate genetic parameters for show-jumping competition performance using random regression model. Show-jumping competition results collected between 1996 and 2009 were analyzed. The database contained 272 951 starts of 8020 horses. Identity number and gender of the horse, rider, competition date, the level of the competition and placing were recorded in the database. Competition levels were categorized into five groups. Weighted – competition level used – square root transformed placing was used to measure performance of horses. The random regression model included fixed effects for gender, year and place of competition, and random effects for rider, animal and permanent environment.
Later performance of show-jumping horses measured with weighted square root ranks is less influenced by rider and permanent environmental effects than performance at the beginning of a horse’s sporting career. Heritability increased continuously from 6.3 years of age (2296 age in days), values were in the range of 0.07 and 0.37. Higher heritability was found in later ages. Weak genetic and phenotypic correlation was found between the early 4–5–6 years of age and older (7, 8, 8+) age classes. From 8.5 years of age (3132 days old) there were strong genetic and phenotypic correlations between neighboring age groups. For the same age classes moderate and strong genetic and phenotypic correlation was found. Genetic correlation between 13.5 years of age and older horses was very strong.
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Evaluation of Hungarian show-jumping results using different measurement variables
81-85Views:145The aim of the study was to compare different fitted models for show-jumping results of sporthorses and to estimate heritability and repeatability value. Show-jumping competition results collected between 1996 and 2011 were analyzed. The database contained 358 342 starts of 10 199 horses. Identity number, name and gender of the horse, rider, competition year, the level and location of the competition and placing were recorded in the database. To measure performance of horses, placing, number of starters and competition level were used. Competitions were categorized into five groups based on their difficulty level. The used repeatability animal model included fixed effects for age, gender, competition place, year of competition (and competition level in case of non-weighted measurement variables), and random effects for rider, animal and permanent environment effect. Variance components were estimated with VCE-6 software package. The goodness-of-fit of the models was low and moderate. Heritability and repeatability values were low for each measurement variables. The best goodness-of-fit model the weighted square root of placing resulted the highest heritability and repeatability value h2=0.074 and R=0.296.
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Analysis of show-jumping results with different measure variables
77-81Views:114The aim of this paper is to estimate heritabilities and to compare different fitted models for Hungarian Sporthorse showjumping results. Our analysis is based on the show-jumping results between 1996 and 2004. The repeatability animal model for the evaluation of the test results included the fixed effects of gender, breeder, rider, age, year of competition, type of competition, height of fence and number of starters. Variance and covariance components were estimated with VCE-5 software package. Fitting of the models were evaluated with log-likelihood values and Akaike’s information criterion (AIC). Heritability was low in all cases.
The lowest goodness-of-fit model was height of fence-error score and the best-fitting genetic model based on AIC was model using cotangent transformation.