Animal Bioscience

Asian Australasian Association of Animal Production Societies (아세아태평양축산학회)
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Relation between body condition score and conception rate of Japanese Black cows

  • A. Setiaji (Department of Animal Science, Faculty of Animal and Agricultural Sciences, Universitas Diponegoro, Tembalang Campus) ;
  • T. Oikawa (Faculty of Agriculture, University of the Ryukyus) ;
  • D. Arakaki (Faculty of Agriculture, University of the Ryukyus)
  • Received : 2022.08.29
  • Accepted : 2023.03.02
  • Published : 2023.08.01
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Abstract

Objective: This study analyzes interactions of body condition score (BCS) with other factors and the effect of BCS on estimates of genetic paremeters of conception rate (CR) in Japanese Black cows. Methods: Factors affecting CR were analyzed through the linear mixed model, and genetic parameters of CR were estimated through the threshold animal model. Results: The interactions between BCS and each season and the number of artificial inseminations (AI) was significant (p<0.05), but that between BCS and parity showed no significance for CR. High CR was observed with BCS 3 in autumn (0.56±0.01) and BCS 4 in summer (0.56±0.02). The highest CR with BCS 3 (0.56±0.02) and BCS 4 (0.55±0.01) was observed at first AI. With BCS 5, however, the highest CR (0.55±0.08) was observed at second AI. Conclusion: The model with BCS was notably conducive to the estimation of genetic parameters because of a low deviance information criterion of heritability that, nevertheless, was slightly lower than the model without BCS.

Keywords

Acknowledgement

The authors thank the staff of Artificial Insemination Center of Northern Okinawa for their kind collaboration on data inquiry and collection.

References

  1. Sasaki Y, Uematsu M, Kitahara G, Osawa T. Reproductive performance of Japanese Black cattle: Association with herd size, season, and parity in commercial cow-calf operations. Theriogenology 2016;86:2156-61. https://doi.org/10.1016/j.theriogenology.2016.07.007 
  2. DeJarnette JM. Interactive effects of semen quality, and insemination tech- niques on conception rates of artifically inseminated cattle. Proc Natl Reprod Symp; 1994. 139 p. Pittsburg, PA, USA. 
  3. Tep B, Morita Y, Matsuyama S, et al. Seasonal changes in the reproductive performance in local cows receiving artificial insemination in the Pursat province of Cambodia. Asian-Australas J Anim Sci 2020;33:1922-9. https://doi.org/10.5713/ajas.19.0893 
  4. Setiaji A, Oikawa T. Analysis of non-genetic factors influencing reproductive traits of Japanese Black heifer. IOP Conf Ser Earth Environ Sci 2018;119:012026. https://doi.org/10.1088/1755-1315/119/1/012026
  5. Inchaisri C, Hogeveen H, Vos PLAM, van der Weijden GC, Jorritsma R. Effect of milk yield characteristics, breed, and parity on success of the first insemination in Dutch dairy cows. J Dairy Sci 2010;93:5179-87. https://doi.org/10.3168/jds.2010-3234 
  6. Barth AD. Factors affecting fertility with artificial insemination. Vet Clin North Am Food Anim Pract 1993;9:27589. https://doi.org/10.1016/S0749-0720(15)30646-0 
  7. Gearhart MA, Curtis CR, Erb HN, et al. Relationship of changes in condition score to cow health in Holsteins. J Dairy Sci 1990;73:3132-40. https://doi.org/10.3168/jds.S00220302(90)79002-9 
  8. Loeffler SH, de Vries MJ, Schukken YH. The effects of time of disease occurrence, milk yield, and body condition on fertility of dairy cows. J Dairy Sci 1999;82:2589-604. https://doi.org/10.3168/jds.S0022-0302(99)75514-1 
  9. Rutigliano HM, Santos JEP. Interrelationships among parity, body condition score (BCS), milk yield, AI protocol, and cyclicity with embryonic survival in lactating dairy cows. J Dairy Sci 2005;88:39. 
  10. Nebel RL, McGilliard ML. Interactions of high milk yield and reproductive performance in dairy cows. J Dairy Sci 1993;76:3257-68. https://doi.org/10.3168/jds.S0022-0302(93)77662-6 
  11. Sakaguchi M. Differences between body condition scores and body weight changes in postpartum dairy cows in relation to parity and reproductive indices. Can Vet J 2009;50:649-56. 
  12. Wang Y, Huo P, Sun Y, Zhang Y. Effects of body condition score changes during peripartum on the postpartum health and production performance of primiparous dairy cows. Animals 2019;9:1159. https://doi.org/10.3390/ani9121159 
  13. Wildman EE, Jones GM, Wagner PE, Boman RL, Troutt Jr HF, Lesch TN. A dairy cow body condition scoring system and its relationship to selected production characteristics. J Dairy Sci 1982;65:495-501. https://doi.org/10.3168/jds.S0022-0302(82)82223-6 
  14. Edmonson AJ, Lean IJ, Weaver LD, Farver T, Webster G. A body condition scoring chart for Holstein dairy cows. J Dairy Sci 1989;72:68-78. https://doi.org/10.3168/jds.S00220302(89)79081-0 
  15. SAS. Sas/Stat 9.3. Cary, NC, USA: SAS Inst Inc; 2011. 8640 p. 
  16. Wang CS, Rutledge JJ, Gianola D. Marginal inferences about variance components in a mixed linear model using Gibbs sampling. Genet Sel Evol 1993;25:41. https://doi.org/10.1186/1297-9686-25-1-41 
  17. Misztal I, Tsuruta S, Lourenco D, Aguilar I, Legarra A, Vitezica Z. BLUPF90 family of programs; 2015. 125 p. 
  18. Geweke J. Evaluating the accuracy of sampling-based approaches to the calculation of posterior moments. Minneapolis, MN, USA: Federal Reserve Bank of Minneapolis; 1991. 
  19. Irikura N, Uematsu M, Kitahara G, Osawa T, Sasaki Y. Effects of service number on conception rate in Japanese Black cattle. Reprod Domest Anim 2018;53:34-9. https://doi.org/10.1111/rda.13049 
  20. McFadden AM, Heuer C, Jackson R, West DM, Parkinson TJ. Reproductive performance of beef cow herds in New Zealand. NZ Vet J 2005;53:39-44. https://doi.org/10.1080/00480169.2005.36467 
  21. Mollah MFK, Gofur MR, Asaduzzaman KM, Bhuiyan MMU. Conception rate of non-descript Zebu cows and its Attributing Factors in Bangladesh. Res J Vet Sci 2015;8:42-51. https://doi.org/10.3923/rjvs.2015.42.51 
  22. Sato K, Fujita T. Elucidation of genes involved in the conception rates of cows decrease. Bull Oita Prefect Anim Ind Exp Sta 2010;39:17-9 (japanese). 
  23. Yamamoto N, Nishimura R, Gunji Y, Saneshige M, Kiriki K, Hishinuma M. Effect of post artificial insemination treatment with two different progesterone intravaginal devices on conception and synchronization of the returning estrus in Japanese Black cows. J Vet Med Sci 2018;80:1822-8. https://doi.org/10.1292/jvms.17-0094 
  24. Rae DO, Kunkle WE, Chenoweth PJ, Sand RS, Tran T. Relationship of parity and body condition score to pregnancy rates in Florida beef cattle. Theriogenology 1993;39:114352. https://doi.org/10.1016/0093-691X(93)90013-U 
  25. Uchida H, Kobayasi J, Inoue T, Suzuki K, Oikawa T. Current level of reproductive performances in Japanese Black cows. Asian-Australas J Anim Sci 2002;15:1098-102. https://doi.org/10.5713/ajas.2002.1098 
  26. Ayalon N. A review of embryonic mortality in cattle. J Reprod Fertil 1978;54:483-93. https://doi.org/10.1530/jrf.0.0540483 
  27. Roche JF. The effect of nutritional management of the dairy cow on reproductive efficiency. Anim Reprod Sci 2006;96:282-96. https://doi.org/10.1016/j.anireprosci.2006.08.007 
  28. Bage R, Gustafsson H, Forsberg M, Larsson B, Rodriguez-Martinez H. Suprabasal progesterone levels in repeat breeder heifers during the pro- and oestrous period. Theriogenology 1997:47:141. https://doi.org/10.1016/S0093-691X(96)00348-2 
  29. Selvaraju S, Agarwal SK, Karche SD, Srivastava SK, Majumdar AC, Shanker U. Fertility responses and hormonal profiles in repeat breeding cows treated with insulin. Anim Reprod Sci 2002;73:141-9. https://doi.org/10.1016/S0378-4320(02)00133-1 
  30. Abe H, Masuda Y, Suzuki M. Relationships between reproductive traits of heifers and cows and yield traits for Holsteins in Japan. J Dairy Sci 2009;92:4055-62. https://doi.org/10.3168/jds.2008-1896 
  31. Filho LASF, Sarmento JLR, Campelo JEG, De Oliveira Almeida MJ, De Sousa Junior A, Da Silva Santos NP. Genetic parameters for marbling and body score in Anglonubian goats using Bayesian inference via threshold and linear models. Asian-Australas J Anim Sci 2018;31:1407-14. https://doi.org/10.5713/ajas.17.0490