International Journal of Innovative Approaches in Agricultural Research
Abbreviation: IJIAAR | ISSN (Online): 2602-4772 | DOI: 10.29329/ijiaar

Original article    |    Open Access
International Journal of Innovative Approaches in Agricultural Research 2022, Vol. 6(2) 144-156

Comparison of Two Methods of Predicting 305-day Milk Yield for Genetic Evaluation to Design a Tunisian Holstein Reference Population

Nour Elhouda Bakrı & M'Naouer Djemali̇

pp. 144 - 156   |  DOI:

Published online: June 30, 2022  |   Number of Views: 4  |  Number of Download: 52


Various standardized milk yield prediction methods have been developed and used. The objective of this study was to compare two methods for the estimation of 305-day milk yield inthe Holstein breed, in terms of breeding values and their accuracy. Genetic evaluations of milk yield were compared using: 1) adjusted total lactation yield for days in milk, month, and age at calving (adjusted TY305) or 2) adjusted305-day milk yieldestimated by fitting test-day(TD) records to the Wood model(adjusted WY305).The method with better ability to predict standardized milk yield was used to identify a Tunisian cow reference population toward genomic evaluation of milk trait. Three datasets were used. The first data contains 380’709 TD records corresponding to 34’281 three first lactations of 20’758 cows collected between 2008 and 2018 in 33 herds. The second dataset contains 11’175 total first three lactation yields recorded between 2012 and 2017 from 6251 cows belonging to 33 herds.The third data is a pedigree file of 27’487 males and females. The predictive ability of the two methods was assessed by Spearman’s rank correlation between predicted breeding values for 305-day milk yield (PBV305) from the full dataset and reduced dataset in which the records from the last calving year were masked. The two methods were compared in terms of rank correlation between PBV305 and the percentage of selected animals in common when different selection intensities were applied based on PBV305.The average gain in accuracy was calculated and a Tunisian reference population was identified. The results showed that heritability estimates were 0.11 (±0.02) and 0.13 (±0.01) for adjusted WY305 and TY305, respectively. The highest correlation for PBVs between full data and reduced data was achieved in TY305 dataset. Rank correlations between PBV305 estimated for adjusted WY305 and TY305 were 0.67. The percentage of animals selected in common was 11% or 21%, respectively, when 1 or 5% of cows were chosen as future dams of bulls, according to PBVs. An average gain in accuracy of 15% was observed for cows when using adjusted TY305 to estimate PBVs for milk yield trait. The obtained results showed that adjustments applied to the total milk yield records could be appropriate for 305-day milk yield prediction and genetic evaluation of milk production in the Tunisian Holstein population. Based on two main designs (extreme yield and top accuracy), a total of 1000 cows were selected to form the Tunisian female reference population using adjusted TY305 records.

Keywords: Adjustments, Dairy cattle, Genetic evaluation, Milk production

How to Cite this Article

APA 6th edition
Bakri, N.E. & Djemali̇, M. (2022). Comparison of Two Methods of Predicting 305-day Milk Yield for Genetic Evaluation to Design a Tunisian Holstein Reference Population . International Journal of Innovative Approaches in Agricultural Research, 6(2), 144-156. doi: 10.29329/ijiaar.2022.451.7

Bakri, N. and Djemali̇, M. (2022). Comparison of Two Methods of Predicting 305-day Milk Yield for Genetic Evaluation to Design a Tunisian Holstein Reference Population . International Journal of Innovative Approaches in Agricultural Research, 6(2), pp. 144-156.

Chicago 16th edition
Bakri, Nour Elhouda and M'Naouer Djemali̇ (2022). "Comparison of Two Methods of Predicting 305-day Milk Yield for Genetic Evaluation to Design a Tunisian Holstein Reference Population ". International Journal of Innovative Approaches in Agricultural Research 6 (2):144-156. doi:10.29329/ijiaar.2022.451.7.

  1. Ben Gara. A.. Rekik. B.. & Bouallègue. M. (2006). Genetic parameters and evaluation of the Tunisian dairy cattle population for milk yield by Bayesian and BLUP analyses. Livestock Science. 100(2‑3). 142‑149. [Google Scholar] [Crossref] 
  2. Bohmanova. J.. Miglior. F.. Jamrozik. J.. Misztal. I..& Sullivan. P. G. (2008). Comparison of Random Regression Models with Legendre Polynomials and Linear Splines for Production Traits and Somatic Cell Score of Canadian Holstein Cows. Journal of Dairy Science.91(9). 3627‑3638. [Google Scholar] [Crossref] 
  3. Boldman. K.G.. Kriese. L.A.. Van Vleck. L.D.. Van Tassell. C.P.. Kachman. S.D. (1995). A Manual for Use of MTDFREML. A Set of Programs to Obtain Estimates of Variances and Covariances. Agricultural Research Service: Washington. DC. USA. [Google Scholar]
  4. Boujenane. I..& Hilal. B. (2012). Genetic and non genetic effects for lactation curve traits in Holstein-Friesian cows. Archives Animal Breeding.55(5). 450‑457. [Google Scholar] [Crossref] 
  5. Brody. S.. Ragsdale. A. C..& Turner. C. W. (1923). The Rate of Decline of Milk Secretion with The Advance of The Period of Lactation. Journal of General Physiology.5(4). 441‑444. [Google Scholar] [Crossref] 
  6. Carta. A..Sanna. S. R.. Rosati. A..& Casu. S. (1998). Milk yield adjustments for milking length and age-parity-lambing month interaction in Sarda dairy sheep. Annales de Zootechnie.47(1). 59‑66. [Google Scholar] [Crossref] 
  7. Djemali. M..& Berger.P. J. (1992). Yield and Reproduction Characteristics of Friesian Cattle Under North African Conditions. Journal of Dairy Science.75(12). 3568‑3575. [Google Scholar] [Crossref] 
  8. Dongre. V..& Gandhi. R. (2014). Genetic and phenotypic parameters of fortnightly test day and first lactation 305-day or less milk yield in Sahiwal cattle. International Journal of Livestock Research.4(3). 17. [Google Scholar] [Crossref] 
  9. Elahi Torshizi. M. (2016). Effects of season and age at first calving on genetic and phenotypic characteristics of lactation curve parameters in Holstein cows. Journal of Animal Science and Technology.58(1). 8. [Google Scholar] [Crossref] 
  10. Garrick. D. J.. Taylor. J. F..& Fernando. R. L. (2009). Deregressing estimated breeding values and weighting information for genomic regression analyses. Genetics Selection Evolution.41(1). 55. [Google Scholar] [Crossref] 
  11. Jakobsen. J. H.. Madsen. P.. Jensen. J.. Pedersen. J.. Christensen. L. G..& Sorensen. D. A. (2002). Genetic Parameters for Milk Production and Persistency for Danish Holsteins Estimated in Random Regression Models using REML. Journal of Dairy Science.85(6). 1607‑1616. [Google Scholar] [Crossref] 
  12. Kong. L..Li. J.. Li. R.. Zhao. X.. Ma. Y.. Sun. S.. Huang.J.. Ju. Z.. Hou. M..& Zhong. J. (2018). Estimation of 305-day milk yield from test-day records of Chinese Holstein cattle. Journal of Applied Animal Research.46(1). 791‑797. [Google Scholar] [Crossref] 
  13. Macciotta. N. P. P.. Vicario. D..& Cappio-Borlino. A. (2005). Detection of Different Shapes of Lactation Curve for Milk Yield in Dairy Cattle by Empirical Mathematical Models. Journal of Dairy Science.88(3). 1178‑1191. [Google Scholar] [Crossref] 
  14. Perez.B. C.. Balieiro. J. C. C..Carvalheiro. R.. Tirelo. F.. Oliveira Junior.G. A.. Dementshuk. J. M.. Eler. J. P.. Ferraz. J. B. S..& Ventura.R. V. (2019). Accounting for population structure in selective cow genotyping strategies. Journal of Animal Breeding and Genetics.136(1). 23‑39. [Google Scholar] [Crossref] 
  15. R Core Team (2017). R: A language and environment forstatistical computing. R Foundation for Statistical Computing.Vienna. Austria. URL [Google Scholar]
  16. Rekaya. R.. Carabaño. M. J..& Toro. M. A. (2000). Bayesian Analysis of Lactation Curves of Holstein-Friesian Cattle Using a Nonlinear Model. Journal of Dairy Science.83(11). 2691‑2701. [Google Scholar] [Crossref] 
  17. SAS Institute Inc. 2002 [Google Scholar]
  18. Santos. D. J. A.. Peixoto. M. G. C. D.. Aspilcueta Borquis. R. R.. Panetto. J. C. C..El Faro. L..& Tonhati. H. (2014). Predicting breeding values for milk yield of Guzerá (Bos indicus) cows using random regression models. Livestock Science.167. 41‑50. [Google Scholar] [Crossref] 
  19. Schaeffer. L. R.. Jamrozik. J.. Kistemaker. G. J..& Van Doormaal. J. (2000). Experience with a Test-Day Model. Journal of Dairy Science.83(5). 1135‑1144. [Google Scholar] [Crossref] 
  20. Tekerli. M.. Akinci. Z.. Dogan. I..& Akcan. A. (2000). Factors Affecting the Shape of Lactation Curves of Holstein Cows from the Balikesir Province of Turkey. Journal of Dairy Science.83(6). 1381‑1386. [Google Scholar] [Crossref] 
  21. Wayne Kellogg. D.. Scott Urquhart. N..& Ortega. A. J. (1977). Estimating Holstein Lactation Curves with a Gamma Curve. Journal of Dairy Science.60(8). 1308‑1315. [Google Scholar] [Crossref] 
  22. Wood. P. D. P. (1976). Algebraic models of the lactation curves for milk. fat and protein production. with estimates of seasonal variation. Animal Science.22(1). 35‑40. [Google Scholar] [Crossref] 
  23. Zar. J. H. (2005). Spearman Rank Correlation: Overview. 9. [Google Scholar]