Asian-Australasian Journal of Animal Sciences

  • 제32권9호
  • /
  • Pages.1349-1354
  • /
  • 2019
  • /
  • 1011-2367(pISSN)
  • /
  • 1976-5517(eISSN)
아세아태평양축산학회 (Asian Australasian Association of Animal Production Societies)
권호 표지
DOI QR코드

DOI QR Code

Different penalty methods for assessing interval from first to successful insemination in Japanese Black heifers

  • Setiaji, Asep (United Graduate School of Agricultural Sciences, Kagoshima University) ;
  • Oikawa, Takuro (United Graduate School of Agricultural Sciences, Kagoshima University)
  • 투고 : 2018.09.27
  • 심사 : 2019.01.04
  • 발행 : 2019.09.01
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Objective: The objective of this study was to determine the best approach for handling missing records of first to successful insemination (FS) in Japanese Black heifers. Methods: Of a total of 2,367 records of heifers born between 2003 and 2015 used, 206 (8.7%) of open heifers were missing. Four penalty methods based on the number of inseminations were set as follows: C1, FS average according to the number of inseminations; C2, constant number of days, 359; C3, maximum number of FS days to each insemination; and C4, average of FS at the last insemination and FS of C2. C5 was generated by adding a constant number (21 d) to the highest number of FS days in each contemporary group. The bootstrap method was used to compare among the 5 methods in terms of bias, mean squared error (MSE) and coefficient of correlation between estimated breeding value (EBV) of non-censored data and censored data. Three percentages (5%, 10%, and 15%) were investigated using the random censoring scheme. The univariate animal model was used to conduct genetic analysis. Results: Heritability of FS in non-censored data was $0.012{\pm}0.016$, slightly lower than the average estimate from the five penalty methods. C1, C2, and C3 showed lower standard errors of estimated heritability but demonstrated inconsistent results for different percentages of missing records. C4 showed moderate standard errors but more stable ones for all percentages of the missing records, whereas C5 showed the highest standard errors compared with noncensored data. The MSE in C4 heritability was $0.633{\times}10^{-4}$, $0.879{\times}10^{-4}$, $0.876{\times}10^{-4}$ and $0.866{\times}10^{-4}$ for 5%, 8.7%, 10%, and 15%, respectively, of the missing records. Thus, C4 showed the lowest and the most stable MSE of heritability; the coefficient of correlation for EBV was 0.88; 0.93 and 0.90 for heifer, sire and dam, respectively. Conclusion: C4 demonstrated the highest positive correlation with the non-censored data set and was consistent within different percentages of the missing records. We concluded that C4 was the best penalty method for missing records due to the stable value of estimated parameters and the highest coefficient of correlation.

키워드

참고문헌

  1. Cammack KM, Thomas MG, Enns RM. Reproductive traits and their heritabilities in beef cattle. Prof Anim Sci 2009;25:517-28. https://doi.org/10.15232/S1080-7446(15)30753-1
  2. 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
  3. Liu Z, Jaitner J, Reinhardt F, Pasman E, Rensing S, Reents R. Genetic evaluation of fertility traits of dairy cattle using a multiple-trait animal model. J Dairy Sci 2008;91:4333-43. https://doi.org/10.3168/jds.2008-1029
  4. Johnston DJ, Bunter KL. Days to calving in Angus cattle: genetic and environmental effects, and covariances with other traits. Livest Prod Sci 1996;45:13-22. https://doi.org/10.1016/0301-6226(95)00088-7
  5. Bormann JM, Wilson DE. Calving day and age at first calving in Angus heifers. J Anim Sci 2010;88:1947-56. https://doi.org/10.2527/jas.2009-2249
  6. SAS Institute Inc. Base/STAT SAS 9.3 User's guide. Cary, NC, USA: SAS Institute Inc.; 2011.
  7. Gilmour AR, Gogel DJ, Cullis BR, Welham SJ, Thompson R. ASReml user guide release 4.1 Structural specification. Hemel Hempstead, UK: VSN International Ltd.; 2015.
  8. Efron B, Tibshirani RJ. An introduction to the bootstrap. New York, USA: Chapman & Hall; 1993.
  9. Efron B. More efficient bootstrapping computation. J Am Stat Assoc 1990;85:79-89. https://doi.org/10.1080/01621459.1990.10475309
  10. Thai HT, Mentre F, Holford NH, Veyrat-Follet C, Comets E. A comparison of bootstrap approaches for estimating uncertainty of parameters in linear mixed-effects models. Pharm Stat 2013;12:129-40. https://doi.org/10.1002/pst.1561
  11. Reverter A, Kaiser CJ, Mallinckrodt CH. A bootstrap approach to confidence regions for genetic parameters from method R estimates. J Anim Sci 1998;76:2263-71. https://doi.org/10.2527/1998.7692263x
  12. Rao AR, Prabhakaran VT. A bootstrap method of estimating heritability from varietal trial data. Indian J Genet Plant Breed 2001;61:87-91.
  13. Liu A, Lund MS, Wang Y, et al. Variance components and correlations of female fertility traits in Chinese Holstein population. J Anim Sci Biotechnol 2017;8:56. https://doi.org/10.1186/s40104-017-0189-x
  14. Haer LCM, de. Jong G, de. Vessies PJA. Estimation of genetic parameters of fertility traits, for Virgin heifers in the Netherlands. Interbull Bull 2013;47:142-6.
  15. Fogh A, Roth A, Pedersen OM, et al. A joint Nordic model for fertility traits. Interbull Bull 2003;31:52-5.
  16. Maiti T, Ren H, Sinha S. Prediction error of small area predictors shrinking both means and variances. Scand J Stat 2014;41:775-90. https://doi.org/10.1111/sjos.12061
  17. Burnham KP, Anderson DR. Model selection and multimodel inference: a practical information-theoretic approach, 2nd edn. Colorado Cooperative Fish and Wildlife Research Unit; Fort Collins, CO, USA: Colorado State University; 2002. 299 p.
  18. Guo S-F, Gianola D, Rekaya R, Short T. Bayesian analysis of lifetime performance and prolificacy in Landrace sows using a linear mixed model with censoring. Livest Prod Sci 2001;72:243-52. https://doi.org/10.1016/S0301-6226(01)00219-6
  19. Newcom DW, Baas TJ, Stalder KJ, Schwab CR. Comparison of three models to estimate breeding values for percentage of loin intramuscular fat in Duroc swine. J Anim Sci 2005;83:750-6. https://doi.org/10.2527/2005.834750x

피인용 문헌

  1. Impact of Censored or Penalized Data in the Genetic Evaluation of Two Longevity Indicator Traits Using Random Regression Models in North American Angus Cattle vol.11, pp.3, 2019, https://doi.org/10.3390/ani11030800