Abstract: Heat stress is an increasingly important challenge for the performance and welfare of equine athletes, particularly in competitions conducted across diverse climatic conditions. Understanding the genetic basis of performance responses to increasing thermal load is therefore essential to support robust genetic evaluation and sustainable selection strategies. The objective of this study was to evaluate barrel racing performance of Brazilian Quarter Horses across thermal environments defined by two widely used indicators, the temperature-humidity index (THI) and the wet bulb globe temperature (WBGT), and to estimate genetic parameters associated with baseline performance and environmental sensitivity. A total of 351,993 barrel racing time (BRT) records from 13,960 Quarter Horses were analyzed. Segmented regression models were applied to least squares means to identify heat stress thresholds, while random regression models with polynomial functions were used to estimate covariance components, genetic parameters, and correlations along the thermal gradients. Distinct thermal thresholds were identified for both indices, at THI = 74.7 and WBGT = 23.6, beyond which performance deteriorated more rapidly. Heat stress conditions were observed in 31.15% and 20.71% of barrel racing events according to the THI and WBGT thresholds, respectively, emphasizing the practical relevance of the evaluated thermal gradients. Random regression models assuming continuous thermal variation provided the best overall fit to the data. Additive genetic variance remained relatively stable across thermal environments, whereas permanent environmental and residual variances increased under higher thermal load. Mean heritability under thermoneutral conditions was approximately 0.21 for THI and WBGT, declining modestly to 0.17-0.18 under extreme heat stress. Genetic correlations between thermoneutral and extreme environments remained high for both indices (at approximately 0.97), indicating strong genetic continuity of performance and no substantial reranking of estimated breeding values across the thermal gradient. Overall, THI and WBGT yielded highly consistent results in terms of thresholds, genetic parameters, and selection outcomes. Although WBGT showed slightly greater sensitivity under extreme conditions, THI offered smoother response patterns and clear operational advantages due to its simplicity and ease of calculation. No evidence of substantial genotype-by-environment interaction was detected for BRT. However, the results suggest that incorporating genetic tolerance to heat stress as a complementary selection criterion may help sustain barrel racing performance under increasingly challenging climatic conditions.
The Equine Research Bank provides access to a large database of publicly available scientific literature. Inclusion in the Research Bank does not imply endorsement of study methods or findings by Mad Barn.
This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.
Thermal stress significantly affects barrel racing performance in Brazilian Quarter Horses, with specific temperature-humidity thresholds identified that mark rapid performance decline; genetic analysis shows stable heritability across temperature gradients and suggests that selection for heat tolerance could improve sustained performance.
Focus on two environmental indices: Temperature-Humidity Index (THI) and Wet Bulb Globe Temperature (WBGT) to represent thermal environments.
Estimate genetic parameters related to baseline performance and sensitivity to thermal stress.
Support genetic evaluation and sustainable breeding strategies adapted to changing climatic conditions.
Data and Methodology
Analyzed 351,993 barrel racing time (BRT) records collected from 13,960 Brazilian Quarter Horses.
Used segmented regression models on least squares means to identify critical heat stress thresholds where performance deteriorates more rapidly.
Applied random regression models with polynomial functions to estimate genetic variance components, heritability, and correlations across thermal gradients.
Compared the effectiveness and outcomes of two thermal indices: THI and WBGT.
Key Findings: Thermal Thresholds
Identified critical heat stress thresholds at THI = 74.7 and WBGT = 23.6.
Above these thresholds, barrel racing performance declined more sharply, indicating onset of heat stress effects.
Heat stress conditions were common: 31.15% of races exceeded the THI threshold, and 20.71% exceeded the WBGT threshold.
This highlights the real-world relevance of thermal stress to competitive events.
Genetic Parameter Estimates and Heritability
Genetic additive variance remained relatively stable across the full range of thermal environments.
Permanent environmental and residual variances increased as heat stress worsened, indicating more non-genetic variability.
Heritability of barrel racing performance under thermoneutral conditions was approximately 0.21 for both THI and WBGT.
Under extreme heat stress, heritability slightly decreased to the range of 0.17 to 0.18.
High genetic correlations (~0.97) between thermoneutral and extreme heat environments suggest strong genetic consistency and negligible reranking of breeding values across thermal gradients.
Comparison of Thermal Indices (THI vs. WBGT)
Both indices produced highly consistent results for thresholds, genetic parameters, and selection implications.
WBGT showed slightly higher sensitivity to extreme heat conditions.
THI yielded smoother performance response patterns and was simpler and easier to calculate, offering operational advantages in routine genetic evaluations and management decisions.
Genotype-by-Environment Interaction and Selection Implications
No substantial genotype-by-environment interaction was detected, meaning horse rankings for breeding value did not change significantly with thermal environment.
This strong genetic continuity simplifies selection decisions as the same horses tend to perform consistently across environments.
However, the study suggests incorporating genetic tolerance to heat stress as a complementary criterion in breeding programs.
Selection focusing on heat tolerance could sustain barrel racing performance as climate conditions become more challenging.
Practical and Scientific Contributions
Provides valuable insights into the impact of thermal stress on equine performance, specifically in barrel racing.
Offers a large-scale genomic and phenotypic analysis using advanced statistical models to elucidate environmental sensitivity.
Supports development of climate-resilient breeding programs for Quarter Horses by integrating thermal tolerance into genetic evaluations.
Highlights the practical relevance of using simple yet effective environmental indices like THI to monitor and manage heat stress in equine sports.
Cite This Article
APA
Santana ML, Bignardi AB.
(2026).
Thermal Thresholds and Genetic Sensitivity in Barrel Racing Performance of Quarter Horses Across Temperature-Humidity Indices.
J Anim Breed Genet.
https://doi.org/10.1111/jbg.70056
Grupo de Melhoramento Animal de Mato Grosso (GMAT), Instituto de Ciências Agrárias e Tecnológicas, Universidade Federal de Rondonópolis (UFR), Rondonópolis, MT, Brazil.
Bignardi, Annaiza Braga
Grupo de Melhoramento Animal de Mato Grosso (GMAT), Instituto de Ciências Agrárias e Tecnológicas, Universidade Federal de Rondonópolis (UFR), Rondonópolis, MT, Brazil.
References
This article includes 38 references
Adelman, M., and K. Thompson. 2017. “Equestrian Cultures in Global and Local Contexts.” Equestrian Cultures in Global and Local Contexts, 1–278, https://doi.org/10.1007/978‐3‐319‐55886‐8/COVER.
Arias, R. A., and T. L. Mader. 2023. “Evaluation of Four Thermal Comfort Indices and Their Relationship With Physiological Variables in Feedlot Cattle.” Animals 13: 1169. https://doi.org/10.3390/ANI13071169.
Bartolomé, E., A. Menéndez‐Buxadera, A. Molina, and M. Valera. 2018. “Plasticity Effect of Rider–Horse Interaction on Genetic Evaluations for Show Jumping Discipline in Sport Horses.” Journal of Animal Breeding and Genetics 135, no. 2: 138–148. https://doi.org/10.1111/jbg.12315.
Bernabucci, U., S. Biffani, L. Buggiotti, A. Vitali, N. Lacetera, and A. Nardone. 2014. “The Effects of Heat Stress in Italian Holstein Dairy Cattle.” Journal of Dairy Science 97, no. 1: 471–486. https://doi.org/10.3168/jds.2013‐6611.
Bernabucci, U., N. Lacetera, L. H. Baumgard, R. P. Rhoads, B. Ronchi, and A. Nardone. 2010. “Metabolic and Hormonal Acclimation to Heat Stress in Domesticated Ruminants.” Animal 4, no. 7: 1167–1183. https://doi.org/10.1017/S175173111000090X.
Binda, M. B., F. A. Teixeira, R. S. Carvalho, et al. 2016. “Effects of 3‐Barrel Racing Exercise on Electrocardiographic and on Blood Parameters of Quarter Horses.” Journal of Equine Veterinary Science 47: 71–76. https://doi.org/10.1016/J.JEVS.2016.08.001.
Bohmanova, J., I. Misztal, and J. B. Cole. 2007. “Temperature‐Humidity Indices as Indicators of Milk Production Losses due to Heat Stress.” Journal of Dairy Science 90, no. 4: 1947–1956. https://doi.org/10.3168/jds.2006‐513.
Bohmanova, J., I. Misztal, S. Tsuruta, H. D. Norman, and T. J. Lawlor. 2005. “National Genetic Evaluation of Milk Yield for Heat Tolerance of United States Holsteins.” Interbull Annual Meeting Proceedings 33: 160–162.
Brownlow, M. A., and J. X. Mizzi. 2022. “Thermoregulatory Capacity of the Thoroughbred Racehorse and Its Relationship to the Pathogenesis of Exertional Heat Illness.” Equine Veterinary Education 34, no. 4: 214–221. https://doi.org/10.1111/eve.13433.
Brownlow, M. A., and J. X. Mizzi. 2023. “An Overview of Exertional Heat Illness in Thoroughbred Racehorses: Pathophysiology, Diagnosis, and Treatment Rationale.” Animals 13: 610. https://doi.org/10.3390/ANI13040610.
Carabaño, M. J., K. Bachagha, M. Ramón, and C. Díaz. 2014. “Modeling Heat Stress Effect on Holstein Cows Under Hot and Dry Conditions: Selection Tools.” Journal of Dairy Science 97, no. 12: 7889–7904. https://doi.org/10.3168/jds.2014‐8023.
Carabaño, M. J., C. Pineda‐Quiroga, E. Ugarte, C. Díaz, and M. Ramón. 2021. “Genetic Basis of Thermotolerance in 2 Local Dairy Sheep Populations in the Iberian Peninsula.” Journal of Dairy Science 104, no. 5: 5755–5767. https://doi.org/10.3168/jds.2020‐19503.
Carabaño, M. J., M. Ramón, A. Menéndez‐Buxadera, A. Molina, and C. Díaz. 2019. “Selecting for Heat Tolerance.” Animal Frontiers 9, no. 1: 62–68. https://doi.org/10.1093/af/vfy033.
Carrara, E. R., P. S. Lopes, A. C. Z. Reis, et al. 2023. “NASA POWER Satellite Meteorological System Is a Good Tool for Obtaining Estimates of the Temperature‐Humidity Index Under Brazilian Conditions Compared to INMET Weather Stations Data.” International Journal of Biometeorology 67, no. 7: 1273–1277. https://doi.org/10.1007/s00484‐023‐02493‐5.
Da Silva, R. G., D. A. E. F. Morais, and M. M. Guilhermino. 2007. “Evaluation of Thermal Stress Indexes for Dairy Cows in Tropical Regions.” Revista Brasileira de Zootecnia 36, no. 4 SUPPLEMENT: 1192–1198. https://doi.org/10.1590/S1516‐35982007000500028.
Dolores Gómez, M., A. Menéndez‐Buxadera, M. Valera, and A. Molina. 2015. “A Reaction Norm Model Approach to Estimate the Genetic Effect of Temperature on Sportive Performance of Trotter Horses.” Journal of Animal Breeding and Genetics 132, no. 3: 256–267. https://doi.org/10.1111/jbg.12118.
Ebisuda, Y., K. Mukai, Y. Takahashi, and H. Ohmura. 2023. “Effect of High Ambient Temperature on Physiological Responses During Incremental Exercise in Thoroughbred Horses.” Comparative Exercise Physiology 19, no. 2: 159–167. https://doi.org/10.3920/CEP220018.
Freitag, G. P., L. G. F. de Lima, L. E. Kozicki, F. A. Souza, E. Nogueira, and L. B. Ribeiro. 2025. “Morphometric Characteristics of Quarter Horses in Barrel Racing Events: Impacts on Performance and Competitive Success.” Journal of Equine Veterinary Science 153: 105676. https://doi.org/10.1016/J.JEVS.2025.105676.
Gomes, C. L. N., A. M. Alves, J. D. R. Filho, et al. 2020. “Exercise Training Session‐Induced Metabolic Acidosis in Barrel Racing Horses.” Ciência Rural 50, no. 5: e20180887. https://doi.org/10.1590/0103‐8478CR20180887.
Hammami, H., J. Bormann, N. M'hamdi, H. H. Montaldo, and N. Gengler. 2013. “Evaluation of Heat Stress Effects on Production Traits and Somatic Cell Score of Holsteins in a Temperate Environment.” Journal of Dairy Science 96, no. 3: 1844–1855. https://doi.org/10.3168/jds.2012‐5947.
Klous, L., E. Siegers, J. van den Broek, et al. 2020. “Effects of Pre‐Cooling on Thermophysiological Responses in Elite Eventing Horses.” Animals 10, no. 9: 1664. https://doi.org/10.3390/ANI10091664.
Liljegren, J. C., R. A. Carhart, P. Lawday, S. Tschopp, and R. Sharp. 2008. “Modeling the Wet Bulb Globe Temperature Using Standard Meteorological Measurements.” Journal of Occupational and Environmental Hygiene 5, no. 10: 645–655. https://doi.org/10.1080/15459620802310770.
McConaghy, F. F., D. R. Hodgson, J. R. Hales, and R. J. Rose. 2002. “Thermoregulatory‐Induced Compromise of Muscle Blood Flow in Ponies During Intense Exercise in the Heat: A Contributor to the Onset of Fatigue?” Equine Veterinary Journal. Supplement 34, no. 34: 491–495. https://doi.org/10.1111/j.2042‐3306.2002.tb05471.x.
Michael, P., C. R. de Cruz, N. Mohd Nor, S. Jamli, and Y. M. Goh. 2023. “Evaluation of Heat Stress Threshold and Impacts on Milk Yield in Dairy Cattle Crossbreds in a Hot and Humid Climate.” Theoretical and Applied Climatology 154, no. 1–2: 235–244. https://doi.org/10.1007/S00704‐023‐04549‐3.
Misztal, I., S. Tsuruta, D. A. L. Lourenco, et al. 2014. “Manual for BLUPF90 Family of Programs.” http://Nce.Ads.Uga.Edu/Wiki/Lib/Exe/Fetch.Php?Media=blupf90_all8.Pdf.
Monteiro, L. A., P. C. Sentelhas, and G. U. Pedra. 2018. “Assessment of NASA/POWER Satellite‐Based Weather System for Brazilian Conditions and Its Impact on Sugarcane Yield Simulation.” International Journal of Climatology 38, no. 3: 1571–1581. https://doi.org/10.1002/JOC.5282.
Morgan, K. 1998. “Thermoneutral Zone and Critical Temperatures of Horses.” Journal of Thermal Biology 23, no. 1: 59–61.
Muggeo, V. M. R. 2003. “Estimating Regression Models With Unknown Break‐Points.” Statistics in Medicine 22, no. 19: 3055–3071. https://doi.org/10.1002/SIM.1545.
Ouellet, V., I. M. Toledo, B. Dado‐Senn, G. E. Dahl, and J. Laporta. 2021. “Critical Temperature‐Humidity Index Thresholds for Dry Cows in a Subtropical Climate.” Frontiers in Animal Science 2: 706636. https://doi.org/10.3389/fanim.2021.706636.
Rashamol, V. P., V. Sejian, P. Pragna, et al. 2019. “Prediction Models, Assessment Methodologies and Biotechnological Tools to Quantify Heat Stress Response in Ruminant Livestock.” International Journal of Biometeorology 2019 63:9 63, no. 9: 1265–1281. https://doi.org/10.1007/S00484‐019‐01735‐9.
Santana, M. L., A. B. Bignardi, R. A. S. Faria, and J. A. I. V. Silva. 2026. “Genetic and Phenotypic Insights Into Thermal Load and Racing Performance of Quarter Horses.” Journal of Thermal Biology 136: 104362.
Santana, M. L., T. G. B. Franco, and A. B. Bignardi. 2025. “Genetic Evaluation of Barrel Racing Performance in Quarter Horses.” Journal of Animal Breeding and Genetics 142, no. 6: 609–616. https://doi.org/10.1111/jbg.12934.
Santana, M. L., R. J. Pereira, A. B. Bignardi, A. E. Vercesi Filho, A. Menéndez‐Buxadera, and L. El Faro. 2015. “Detrimental Effect of Selection for Milk Yield on Genetic Tolerance to Heat Stress in Purebred Zebu Cattle: Genetic Parameters and Trends.” Journal of Dairy Science 98, no. 12: 9035–9043. https://doi.org/10.3168/jds.2015‐9817.
Schroter, R. C., and D. J. Marlin. 1995. “An index of the environmental thermal load imposed on exercising horses and riders by hot weather conditions.” Equine Veterinary Journal 27: 16–22. https://doi.org/10.1111/j.2042‐3306.1995.tb05003.x.
Souza, L. A., M. M. Hunka, P. C. R. Nery, C. S. Coelho, H. E. C. C. C. Manso, and H. C. Manso Filho. 2018. “The Effect of Repeated Barrel Racing on Blood Biomarkers and Physiological Parameters in Quarter Horses.” Comparative Exercise Physiology 14, no. 1: 47–54. https://doi.org/10.3920/CEP170019.
Spiegelhalter, D. J., N. G. Best, B. P. Carlin, and A. Van Der Linde. 2002. “Bayesian Measures of Model Complexity and Fit.” Journal of the Royal Statistical Society, Series B: Statistical Methodology 64, no. 4: 583–639. https://doi.org/10.1111/1467‐9868.00353.
Stull, R. 2011. “Wet‐Bulb Temperature From Relative Humidity and Air Temperature.” Journal of Applied Meteorology and Climatology 50, no. 11: 2267–2269. https://doi.org/10.1175/JAMC‐D‐11‐0143.1.
Yan, G., H. Li, W. Zhao, and Z. Shi. 2020. “Evaluation of Thermal Indices Based on Their Relationships With Some Physiological Responses of Housed Lactating Cows Under Heat Stress.” International Journal of Biometeorology 2020 64:12 64, no. 12: 2077–2091. https://doi.org/10.1007/S00484‐020‐01999‐6.