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http://dx.doi.org/10.5713/ajas.19.0960

Inclusion of bioclimatic variables in genetic evaluations of dairy cattle  

Negri, Renata (Department of Animal Science, Federal University of Rio Grande of Sul)
Aguilar, Ignacio (Instituto Nacional de Investigacion Agropecuaria (INIA))
Feltes, Giovani Luis (Department of Animal Science, Federal University of Rio Grande of Sul)
Machado, Juliana Dementshuk (Department of Animal Science, Federal University of Rio Grande of Sul)
Neto, Jose Braccini (Department of Animal Science, Federal University of Rio Grande of Sul)
Costa-Maia, Fabiana Martins (Department of Animal Science, Federal Technological University of Parana)
Cobuci, Jaime Araujo (Department of Animal Science, Federal University of Rio Grande of Sul)
Publication Information
Animal Bioscience / v.34, no.2, 2021 , pp. 163-171 More about this Journal
Abstract
Objective: Considering the importance of dairy farming and the negative effects of heat stress, more tolerant genotypes need to be identified. The objective of this study was to investigate the effect of heat stress via temperature-humidity index (THI) and diurnal temperature variation (DTV) in the genetic evaluations for daily milk yield of Holstein dairy cattle, using random regression models. Methods: The data comprised 94,549 test-day records of 11,294 first parity Holstein cows from Brazil, collected from 1997 to 2013, and bioclimatic data (THI and DTV) from 18 weather stations. Least square linear regression models were used to determine the THI and DTV thresholds for milk yield losses caused by heat stress. In addition to the standard model (SM, without bioclimatic variables), THI and DTV were combined in various ways and tested for different days, totaling 41 models. Results: The THI and DTV thresholds for milk yield losses was THI = 74 (-0.106 kg/d/THI) and DTV = 13 (-0.045 kg/d/DTV). The model that included THI and DTV as fixed effects, considering the two-day average, presented better fit (-2logL, Akaike information criterion, and Bayesian information criterion). The estimated breeding values (EBVs) and the reliabilities of the EBVs improved when using this model. Conclusion: Sires are re-ranking when heat stress indicators are included in the model. Genetic evaluation using the mean of two days of THI and DTV as fixed effect, improved EBVs and EBVs reliability.
Keywords
Heat Stress; Random Regression; Temperature-humidity Index; Diurnal Temperature Variation;
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1 Lough DS, Beede DL, Wilcox CJ. Effects of feed intake and thermal stress on mammary blood flow and other physiological measurements in lactating dairy cows. J Dairy Sci 1990;73:325-32. https://doi.org/10.3168/jds.S0022-0302(90)78677-8   DOI
2 Pragna P, Archana A, Aleena J, et al. Heat stress and dairy cow: impact on both milk yield and composition. Int J Dairy Sci 2017;12:1-11. https://doi.org/10.3923/ijds.2017.1.11   DOI
3 St-Pierre NR, Cobanov B, Schnitkey G. Economic losses from heat stress by US livestock industries. J Dairy Sci 2003;86(Suppl):E52-E77. https://doi.org/10.3168/jds.S0022-0302(03)74040-5   DOI
4 Costa NS, Hermuche P, Cobuci JA, et al. Georeferenced evaluation of genetic breeding value patterns in Brazilian Holstein cattle. Genet Mol Res 2014;13:9806-16. http://dx.doi.org/10.4238/2014.November.27.8   DOI
5 Zwald NR, Weigel KA, Fikse WF, Rekaya R. Identification of factors that cause genotype by environment interaction between herds of Holstein cattle in seventeen countries. J Dairy Sci 2003;86:1009-18. https://doi.org/10.3168/jds.S0022-0302(03)73684-4   DOI
6 Robertson A. The sampling variance of the genetic correlation coefficient. Biometrics 1959;15:469-85. https://doi.org/10.2307/2527750   DOI
7 Lee SH, Do CH, Choy YH, Dang CG, Mahboob A, Cho KH. Estimation of the genetic milk yield parameters of Holstein cattle under heat stress in South Korea. Asian-Australas J Anim Sci 2019;32:334-40. https://doi.org/10.5713/ajas.18.0258   DOI
8 Misztal I. Breeding and genetics symposium: resilience and lessons from studies in genetics of heat stress. J Anim Sci 2017;95:1780-7. https://doi.org/10.2527/jas.2016.0953   DOI
9 Brazilian Institute of Geography and Statistics. Agricultural census: agricultural production. 1st ed. Rio de Janeiro, Brazil: IBGE; 2017.
10 Ravagnolo O, Misztal I. Genetic component of heat stress in dairy cattle, parameter estimation. J Dairy Sci 2000;83:2126-30. https://doi.org/10.3168/jds.S0022-0302(00)75095-8   DOI
11 Bernabucci U, Biffani S, Buggiotti L, Vitali A, Lacetera N, Nardone A. The effects of heat stress in Italian Holstein dairy cattle. J Dairy Sci 2014;97:471-86. https://doi.org/10.3168/jds.2013-6611   DOI
12 Bouraoui R, Lahmar M, Majdoub A, Djemali M, Belyea R. The relationship of temperature-humidity index with milk production of dairy cows in a Mediterranean climate. Anim Res 2002;51:479-91. https://doi.org/10.1051/animres:2002036   DOI
13 Bohmanova J, Misztal I, Cole JB. Temperature-humidity indices as indicators of milk production losses due to heat stress. J Dairy Sci 2007;90:1947-56. https://doi.org/10.3168/jds.2006-513   DOI
14 Aguilar I, Misztal I, Tsuruta S. Genetic components of heat stress for dairy cattle with multiple lactations. J Dairy Sci 2009;92:5702-11. https://doi.org/10.3168/jds.2008-1928   DOI
15 Picinin LCA, Bordigon-Luiz MT, Cerqueira MMOP, et al. Effect of seasonal conditions and milk management practices on bulk milk quality in Minas Gerais State - Brazil. Arq Bras Med Vet Zootec 2019;71:1355-63. http://dx.doi.org/10.1590/1678-4162-10063   DOI
16 Alvares CA, Stape JL, Sentelhas PC, Goncalves JLM, Sparovek G. Koppen's climate classification map for Brazil. Meteorol Z 2013;22:711-28. https://doi.org/10.1127/0941-2948/2013/0507   DOI
17 National Research Council. A guide to environmental research on animals. Washington, DC, USA: National Academies; 1971.
18 Wilmink JBM. Adjustment of test-day milk, fat and protein yield for age, season and stage of lactation. Livest Prod Sci 1987;16:335-48. https://doi.org/10.1016/0301-6226(87)90003-0   DOI
19 Yano M, Shimadzu H, Endo T. Modelling temperature effects on milk production: a study on Holstein cows at a Japanese farm. Springerplus 2014;3:129. https://doi.org/10.1186/2193-1801-3-129   DOI
20 Misztal I, Tsuruta S, Strabel T, Auvray B, Druet T, Lee DH. BLUPF90 and related programs (BGF90). In: Proceedings of the 7th World Congress on Genetics Applied to Livestock Production 2002; 2002 Aug 19-23: Montpellier, France. pp. 743-4.
21 Johnson HD, Ragsdale AC, Berry IL, Shanklin MD. Effect of various temperature-humidity combinations on milk production of Holstein cattle. Columbia, MO, USA: University of Missouri; 1962.
22 Ravagnolo O, Misztal I, Hoogenboom G. Genetic component of heat stress in dairy cattle, development of heat index function. J Dairy Sci 2000;83:2120-5. https://doi.org/10.3168/jds.S0022-0302(00)75094-6   DOI
23 Sae-tiao T, Koonawootrittriron S, Suwanasopee T, Elzo MA. 508 Trend for diurnal temperature variation and relative humidity and their impact on milk yield of dairy cattle in tropical climates. J Anim Sci 2017;95(Suppl 4):248. https://doi.org/10.2527/asasann.2017.508   DOI
24 West JW. Effects of heat-stress on production in dairy cattle. J Dairy Sci 2003;86:2131-44. https://doi.org/10.3168/jds.S0022-0302(03)73803-X   DOI
25 Christison GI, Johnson HD. Cortisol turnover in heat-stressed cows. J Anim Sci 1972;35:1005-10. https://doi.org/10.2527/jas1972.3551005x   DOI