“…It is important to note here that the assessment of the genetic value of breeding animals has been applied to racehorses for many years and in many countries (Chico, 1994;Langlois, 1996;Belhajyahia et al, 2003;Langlois and Blouin, 2004;Sobczynska and Lukaszewicz, 2004;Svobodova et al, 2005;Bokor et al, 2007;Bakhtiari and Kashan, 2009;Orhan and Kaygisiz, 2010). In Algeria, no selection program has been put in place and no assessment of the genetic additive value of breeding animals based on the performance of horses has been carried out till now despite the availability of reliable performance data.…”
Section: Introductionmentioning
confidence: 99%
“…The selection of animals and the assessment of their breeding values are based on one or several measurable traits expressed during races. A great variety of performance measures have been studied for flat-racing: this includes race times Bakhtiari and Kashan, 2009;Orhan and Kaygisiz, 2010), weight handicap (Langlois, 1980;Tolley et al, 1985), the performance rate (Watanabe, 1974), the final ranking (Belhajyahia et al, 2003;Svobodova et al, 2005;Bakhtiari and Kashan, 2009) and the annual earning as well as the average earning per start (Langlois and Blouin, 1998;Belhajyahia et al, 2003;Svobodova et al, 2005). Estimates of the heritability of traits related to time have been generally low, varying from 0.01 to 0.11 (Chico, 1994;Orhan and Kaygisiz, 2010).…”
Section: Introductionmentioning
confidence: 99%
“…Estimates of the heritability of traits related to time have been generally low, varying from 0.01 to 0.11 (Chico, 1994;Orhan and Kaygisiz, 2010). Consequently, most recent studies have been centered around traits linked to earnings or rankings, for which heritability (after convenient mathematical transformations) is higher (varying from 0.10 to 0.34), (Belhajyahia et al, 2003;Svobodova et al, 2005;Bakhtiari and Kashan, 2009).…”
Section: Introductionmentioning
confidence: 99%
“…The raw measures are supposed to reflect not only the genetic potential of the horses but also the environmental conditions (non-genetic factors) in which these observations have been made. Numerous studies have shown that these non-genetic factors strongly influenced the earnings and ranking of horses in flatracing (Langlois and Blouin, 1998;Belhajyahia et al, 2003;Sobczynska and Lukaszewicz, 2004;Svobodova et al, 2005;Bokor et al, 2007;Bakhtiari and Kashan, 2009). In order to be able to estimate the genetic potential (breeding value) of the individuals, it is therefore important to determine the part played by the environment in the performances in the Algerian conditions.…”
From 1995 to 2007, flat racing data was collected for Thoroughbred and Arabian horses in Algeria. Nongenetic factors affecting racing performances have been identified and quantified using linear models. Performances are represented through the earnings and the rankings. Three traits were used: two earnings traits [the logarithm of annual virtual earnings (LAEV) and the logarithm of average annual virtual earnings per start (LAEV/S)], and one rank trait (the ranking transformed and normalised by application of the "performance rate" procedure, PERF). The results showed significant positive correlations (p < 0.001) between the three traits in the two breeds, showing that the measurements quantify similar -although different -aptitudes. The effects of sex, age, year of the performance and the interactions between age and sex and between age and year of the race turned out to be significant (p < 0.05) for the three traits LAEV, LAEV/S and PERF for the Arabian horses. However, for Thoroughbreds, the sex effect was only significant for the PERF trait and an interaction between the age and year of the performance was the only significant interaction (p < 0.001) for the LAEV trait. The effects of these nongenetic factors will be used to correct the raw measures in a future genetic evaluation.
“…It is important to note here that the assessment of the genetic value of breeding animals has been applied to racehorses for many years and in many countries (Chico, 1994;Langlois, 1996;Belhajyahia et al, 2003;Langlois and Blouin, 2004;Sobczynska and Lukaszewicz, 2004;Svobodova et al, 2005;Bokor et al, 2007;Bakhtiari and Kashan, 2009;Orhan and Kaygisiz, 2010). In Algeria, no selection program has been put in place and no assessment of the genetic additive value of breeding animals based on the performance of horses has been carried out till now despite the availability of reliable performance data.…”
Section: Introductionmentioning
confidence: 99%
“…The selection of animals and the assessment of their breeding values are based on one or several measurable traits expressed during races. A great variety of performance measures have been studied for flat-racing: this includes race times Bakhtiari and Kashan, 2009;Orhan and Kaygisiz, 2010), weight handicap (Langlois, 1980;Tolley et al, 1985), the performance rate (Watanabe, 1974), the final ranking (Belhajyahia et al, 2003;Svobodova et al, 2005;Bakhtiari and Kashan, 2009) and the annual earning as well as the average earning per start (Langlois and Blouin, 1998;Belhajyahia et al, 2003;Svobodova et al, 2005). Estimates of the heritability of traits related to time have been generally low, varying from 0.01 to 0.11 (Chico, 1994;Orhan and Kaygisiz, 2010).…”
Section: Introductionmentioning
confidence: 99%
“…Estimates of the heritability of traits related to time have been generally low, varying from 0.01 to 0.11 (Chico, 1994;Orhan and Kaygisiz, 2010). Consequently, most recent studies have been centered around traits linked to earnings or rankings, for which heritability (after convenient mathematical transformations) is higher (varying from 0.10 to 0.34), (Belhajyahia et al, 2003;Svobodova et al, 2005;Bakhtiari and Kashan, 2009).…”
Section: Introductionmentioning
confidence: 99%
“…The raw measures are supposed to reflect not only the genetic potential of the horses but also the environmental conditions (non-genetic factors) in which these observations have been made. Numerous studies have shown that these non-genetic factors strongly influenced the earnings and ranking of horses in flatracing (Langlois and Blouin, 1998;Belhajyahia et al, 2003;Sobczynska and Lukaszewicz, 2004;Svobodova et al, 2005;Bokor et al, 2007;Bakhtiari and Kashan, 2009). In order to be able to estimate the genetic potential (breeding value) of the individuals, it is therefore important to determine the part played by the environment in the performances in the Algerian conditions.…”
From 1995 to 2007, flat racing data was collected for Thoroughbred and Arabian horses in Algeria. Nongenetic factors affecting racing performances have been identified and quantified using linear models. Performances are represented through the earnings and the rankings. Three traits were used: two earnings traits [the logarithm of annual virtual earnings (LAEV) and the logarithm of average annual virtual earnings per start (LAEV/S)], and one rank trait (the ranking transformed and normalised by application of the "performance rate" procedure, PERF). The results showed significant positive correlations (p < 0.001) between the three traits in the two breeds, showing that the measurements quantify similar -although different -aptitudes. The effects of sex, age, year of the performance and the interactions between age and sex and between age and year of the race turned out to be significant (p < 0.05) for the three traits LAEV, LAEV/S and PERF for the Arabian horses. However, for Thoroughbreds, the sex effect was only significant for the PERF trait and an interaction between the age and year of the performance was the only significant interaction (p < 0.001) for the LAEV trait. The effects of these nongenetic factors will be used to correct the raw measures in a future genetic evaluation.
“…There are many previous studies which estimate genetic parameters and evaluate fixed effects relative to racing performance (Lee et al, 1995;Park and Lee, 1999;Bakhtiari and Kashan, 2009). However, since those literatures focused on estimating the breeding values using animal mixed models, the proper use of statistical analysis has not been applied extensively to identifying the linkage of offspring with their parents for specific characteristics in individuals of differing phenotypes that most affect the progeny's racing performance.…”
In this study, we suggest an objective standard in selection of candidate horse mates. Korea Racing Authority provided racing records and pedigree information of 44 sires and 954 dams. The datasets were used to predict Racing Indices represented by the averages of earnings earned by offspring for each dam and sire that indicate the racing performance of its domestic progeny. Proportion of wins and second places to the number of taken races and the mean of distances for the won races of a sire were significant factors in linear model with minimum prediction errors. For dam, those factors were the average of earned money per race, number of outstanding broodmares in pedigree, and the comparable index which indicates the relative affinity with its mate. We can use the resultant model for a horse mate by choosing one of the candidates with the largest predicted value for hypothetical offspring.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.