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Research Article

volume 52 issue 12 (december 2018) : 1675-1679,   Doi: 10.18805/ijar.B-3432

Modeling lactation curve for genetic evaluation of Murrah buffaloes

S
Saroj Kumar Sahoo
A
Avtar Singh
S
Shakti Kant Dash
G
G.S. Ambhore
1ICAR-National Dairy Research Institute, Karnal-132 001, Haryana, India.
Cite article:- Sahoo Kumar Saroj, Singh Avtar, Dash Kant Shakti, Ambhore G.S. (2017). Modeling lactation curve for genetic evaluation of Murrah buffaloes. Indian Journal of Animal Research. 52(12): 1675-1679. doi: 10.18805/ijar.B-3432.

ABSTRACT

In this study, four lactation curve models viz. Gamma-type function (GF), Mixed log function (MLF), Exponential function (EF) and Polynomial regression function (PRF) were fitted to predict the weekly test-day milk yields pertaining to 961 Murrah buffaloes progeny of 101 sires spread over a period of 36 years (1977-2012) maintained at ICAR-NDRI, Karnal. The PRF was the best fit with highest R2 (99.3%) and lowest RMSE (0.3%) which was used subsequently to estimate the first lactation 305-day or less milk yield (FL305DMY) from weekly test-day milk yields. The sires were evaluated using four linear models viz. Least Squares(LSQ), Simple Regressed Least Squares (SRLS), Sire (BLUP-SM) and animal (BLUP-AM) models from actual as well as PRF predicted FL305DMYs. BLUP-AM was the most efficient, among the sire evaluation models, in predicting the breeding values. Sire evaluation on the basis of predicted data (using PRF) was similar to that on the basis of actual data indicating that the best fitted lactation curve function can be used as an alternative for early animal evaluation and reduction of generation interval.

REFERENCES

  1. Ali, T. E. and Schaeffer, L. R. (1987). Accounting for covariances among test day milk yields in dairy cows. Can. J. Anim. Sci. 67(3): 637-644.
  2. Aziz, M.A., Shalaby, N.A., El-Shafie, O.M., Mahdy, A.T., Nishida, A. (2006). Comparison between the shapes of lactation curve of Egyptian buffalo milk yield estimated by the incomplete gamma function and a new model. Livest. Res. Rural Dev. 18(5):59.
  3. Banik, S. and Gandhi, R. S. (2006). Animal model versus conventional methods of sire evaluation in Sahiwal cattle. Asian-Aust. J. Anim. Sci. 19(9): 1225-1228.
  4. Catillo G., Macciotta N.P.P., Carretta A., Cappio Borlino A. (2002). Effects of age and calving eeason on lactation curves of milk production traits in Italian water buffaloes. J. Dairy Sci. 85 (5):1298-1306.
  5. Dimauro, C., Catillo, G., Bacciu, N., Macciotta, N.P.P. (2005). Fit of different linear models to the lactation curve of Italian water buffalo. Italian J. Anim. Sci. 4:22-24.
  6. Dongre, V.B. and Gandhi, R.S. (2014). Study on sire evaluation methods in Sahiwal cattle. Indian J. Vet. & Anim. Sci. Res., 43 (3):174 – 179.
  7. Guo, Z. and Swalve, H. H. (1995). Comparison of different lactation curve sub-models in test day models. Proc. Interbull Mtg. Vienna, Austria Interbull Bull.No.16. 
  8. Harvey, W. R. (1979). Accuracy of simplified regressed least-squares (SRLS) vs. BLUP methods for ranking sires. Indian J. Anim. Genet. Breed. 1(1): 7-19.
  9. Harvey, W. R. (1990). User’s guide for LSMLMW, mixed model least-squares and maximum likelihood computer program, Ohio State Univ., Columbus, Mimeo.
  10. Henderson, C. R. (1975). Use of relationships among sires to increase accuracy of sire evaluation. J. Dairy Sci. 58(11): 1731-1738. 
  11. Katneni, V.K. (2007). Studies on genetic persistency of milk production in Murrah buffaloes. Ph.D. Thesis, NDRI (Deemed University), Karnal, Haryana, India.
  12. Kumar, S. (2007). A comparative study of lactation curve models for prediction of lactation milk yield in Murrah buffaloes. M.V.Sc. Thesis, NDRI (Deemed University), Karnal, Haryana, India.
  13. Kumar, V., Chakravarty, A. K., Patil, C. S., Shivahre, P. R., Gupta, A. (2015). Comparison among different conventional methods of sire evaluation in Murrah buffalo. Indian J. Anim. Sci 85(6): 670-673.
  14. Macciotta, N., Vicario, D., Cappio-Borlino, A. (2005). Detection of different shapes of lactation curve for milk yield in dairy cattle by empirical mathematical models. J. Dairy Sci. 88: 1166– 1177.
  15. Meyer, K. (2007). WOMBAT- A tool for mixed model analyses in quantitative genetics by REML. J. Zhejiang Univ. Sci. 8(11): 815-821.
  16. Rana, J.S. (2008). Genetic evaluation of Murrah buffalo sires based on part lactation and test day milk yield records. Ph.D. Thesis submitted to Ch. Charan Singh University, Meerut (UP).
  17. Singh, M., Singh, A., Gupta, A.K., Chakravarty, A.K., Prakash, V., Singh, A., Sahoo, S.K. (2014). Relative effectiveness of best linear unbiased prediction-animal model vis-à-vis other sire evaluation methods for genetic evaluation of Murrah sires. Indian J. Dairy Sci. 67(6):510-514.
  18. Wood, P. D. P. (1967). Algebraic model of lactation curve in cattle. Nature 216: 164-165. doi:10.1038/216164a0. 
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    Research Article

    volume 52 issue 12 (december 2018) : 1675-1679,   Doi: 10.18805/ijar.B-3432

    Modeling lactation curve for genetic evaluation of Murrah buffaloes

    S
    Saroj Kumar Sahoo
    A
    Avtar Singh
    S
    Shakti Kant Dash
    G
    G.S. Ambhore
    1ICAR-National Dairy Research Institute, Karnal-132 001, Haryana, India.
    Cite article:- Sahoo Kumar Saroj, Singh Avtar, Dash Kant Shakti, Ambhore G.S. (2017). Modeling lactation curve for genetic evaluation of Murrah buffaloes. Indian Journal of Animal Research. 52(12): 1675-1679. doi: 10.18805/ijar.B-3432.

    ABSTRACT

    In this study, four lactation curve models viz. Gamma-type function (GF), Mixed log function (MLF), Exponential function (EF) and Polynomial regression function (PRF) were fitted to predict the weekly test-day milk yields pertaining to 961 Murrah buffaloes progeny of 101 sires spread over a period of 36 years (1977-2012) maintained at ICAR-NDRI, Karnal. The PRF was the best fit with highest R2 (99.3%) and lowest RMSE (0.3%) which was used subsequently to estimate the first lactation 305-day or less milk yield (FL305DMY) from weekly test-day milk yields. The sires were evaluated using four linear models viz. Least Squares(LSQ), Simple Regressed Least Squares (SRLS), Sire (BLUP-SM) and animal (BLUP-AM) models from actual as well as PRF predicted FL305DMYs. BLUP-AM was the most efficient, among the sire evaluation models, in predicting the breeding values. Sire evaluation on the basis of predicted data (using PRF) was similar to that on the basis of actual data indicating that the best fitted lactation curve function can be used as an alternative for early animal evaluation and reduction of generation interval.

    REFERENCES

    1. Ali, T. E. and Schaeffer, L. R. (1987). Accounting for covariances among test day milk yields in dairy cows. Can. J. Anim. Sci. 67(3): 637-644.
    2. Aziz, M.A., Shalaby, N.A., El-Shafie, O.M., Mahdy, A.T., Nishida, A. (2006). Comparison between the shapes of lactation curve of Egyptian buffalo milk yield estimated by the incomplete gamma function and a new model. Livest. Res. Rural Dev. 18(5):59.
    3. Banik, S. and Gandhi, R. S. (2006). Animal model versus conventional methods of sire evaluation in Sahiwal cattle. Asian-Aust. J. Anim. Sci. 19(9): 1225-1228.
    4. Catillo G., Macciotta N.P.P., Carretta A., Cappio Borlino A. (2002). Effects of age and calving eeason on lactation curves of milk production traits in Italian water buffaloes. J. Dairy Sci. 85 (5):1298-1306.
    5. Dimauro, C., Catillo, G., Bacciu, N., Macciotta, N.P.P. (2005). Fit of different linear models to the lactation curve of Italian water buffalo. Italian J. Anim. Sci. 4:22-24.
    6. Dongre, V.B. and Gandhi, R.S. (2014). Study on sire evaluation methods in Sahiwal cattle. Indian J. Vet. & Anim. Sci. Res., 43 (3):174 – 179.
    7. Guo, Z. and Swalve, H. H. (1995). Comparison of different lactation curve sub-models in test day models. Proc. Interbull Mtg. Vienna, Austria Interbull Bull.No.16. 
    8. Harvey, W. R. (1979). Accuracy of simplified regressed least-squares (SRLS) vs. BLUP methods for ranking sires. Indian J. Anim. Genet. Breed. 1(1): 7-19.
    9. Harvey, W. R. (1990). User’s guide for LSMLMW, mixed model least-squares and maximum likelihood computer program, Ohio State Univ., Columbus, Mimeo.
    10. Henderson, C. R. (1975). Use of relationships among sires to increase accuracy of sire evaluation. J. Dairy Sci. 58(11): 1731-1738. 
    11. Katneni, V.K. (2007). Studies on genetic persistency of milk production in Murrah buffaloes. Ph.D. Thesis, NDRI (Deemed University), Karnal, Haryana, India.
    12. Kumar, S. (2007). A comparative study of lactation curve models for prediction of lactation milk yield in Murrah buffaloes. M.V.Sc. Thesis, NDRI (Deemed University), Karnal, Haryana, India.
    13. Kumar, V., Chakravarty, A. K., Patil, C. S., Shivahre, P. R., Gupta, A. (2015). Comparison among different conventional methods of sire evaluation in Murrah buffalo. Indian J. Anim. Sci 85(6): 670-673.
    14. Macciotta, N., Vicario, D., Cappio-Borlino, A. (2005). Detection of different shapes of lactation curve for milk yield in dairy cattle by empirical mathematical models. J. Dairy Sci. 88: 1166– 1177.
    15. Meyer, K. (2007). WOMBAT- A tool for mixed model analyses in quantitative genetics by REML. J. Zhejiang Univ. Sci. 8(11): 815-821.
    16. Rana, J.S. (2008). Genetic evaluation of Murrah buffalo sires based on part lactation and test day milk yield records. Ph.D. Thesis submitted to Ch. Charan Singh University, Meerut (UP).
    17. Singh, M., Singh, A., Gupta, A.K., Chakravarty, A.K., Prakash, V., Singh, A., Sahoo, S.K. (2014). Relative effectiveness of best linear unbiased prediction-animal model vis-à-vis other sire evaluation methods for genetic evaluation of Murrah sires. Indian J. Dairy Sci. 67(6):510-514.
    18. Wood, P. D. P. (1967). Algebraic model of lactation curve in cattle. Nature 216: 164-165. doi:10.1038/216164a0. 
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