Genetic Evaluation of Barrel Racing Performance in Quarter Horses
Bibliographic record
Abstract
Barrel racing is a competitive timed rodeo event that challenges horses and riders to complete a cloverleaf pattern around three barrels in the fastest time possible. In this study, we aimed to estimate the genetic parameters of barrel racing time (BRT) and evaluate the most suitable statistical model for its analysis. We compared a repeatability model and three random regression models (RRM) to analyse the longitudinal BRT data in Brazilian Quarter Horses. A total of 356,877 BRT records from 14,108 horses that competed in various events held across Brazil between 2010 and 2024 were analysed. The cubic RRM provided the best fit to the data, and therefore, the results from this model were presented in detail. Heritability estimates for BRT varied by age (0.15-0.24), with the highest estimates observed between 36 and 54 months, suggesting that selection at younger ages could be most effective. Genetic correlations between BRT at different ages were generally strong (> 0.8). The lowest mean genetic correlation of 0.65 (0.09) was observed between BRT at 36 and 144 months of age. Thus, selecting the best-performing horses at younger ages should result in favourable genetic progress at older ages. Phenotypic trends showed an improvement in BRT over the years, although no significant genetic progress was observed, likely due to the absence of an official breeding programme and the lack of use of estimated breeding values for BRT. These findings highlight the need for a more strategic approach to genetic selection in Quarter Horses to optimise BRT performance. The substantial genetic variation identified for BRT indicates that, if properly exploited, this trait could be significantly improved in the future, ultimately enhancing competition outcomes for Brazilian Quarter Horses in barrel racing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".