A Comparison of Some Random Regression Models for First Lactation Test Day Milk Yields in Jersey Cows and Estimating of Genetic Parameters

Çankaya, Soner; Takma, Çiğdem; Abacı, Samet Hasan; Ülker, Mehmet

doi:10.9775/kvfd.2013.9234

Cited by 1 publication

(2 citation statements)

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“…Similarly, Biassus et al (2010) compared models fitted by LP from third to sixth orders, which showed that the differences between RV of models decreased from 14% to 5% when the polynomial orders increased. Çankaya et al (2014) compared models fitted from second to fourth orders and the differences between RV of models decreased from 24% to 10%. Residual variance values presented by Takma and Akbas (2009) decreased from 30% to 7% when adjusted models from second to sixth orders.…”

Section: Discussionmentioning

confidence: 99%

“…Several studies have shown that there may be divergences between breeding values estimated by LM and RRM ( Lidauer et al, 2003 ; Melo et al, 2007 ) as well as between parameters obtained by fitting RRM using different covariance functions ( Kim et al, 2009 ; Çankaya et al, 2014 ). Legendre polynomials (LP) have been the preferred function to fit RRM, but there is not a consensus in literature about the best order to use ( Biassus et al, 2010 ; Çankaya et al, 2014 ; Aliloo et al, 2014 ). Canada, Italy and United Kingdom are already using a fourth or fifth order LP to fit RRM in their national genetic evaluations ( Muir et al, 2007 ).…”

Section: Introductionmentioning

confidence: 99%

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Random Regression Models Are Suitable to Substitute the Traditional 305-Day Lactation Model in Genetic Evaluations of Holstein Cattle in Brazil

Padilha

Cobuci

Costa

et al. 2015

Asian Australas. J. Anim. Sci

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The aim of this study was to compare two random regression models (RRM) fitted by fourth (RRM4) and fifth-order Legendre polynomials (RRM5) with a lactation model (LM) for evaluating Holstein cattle in Brazil. Two datasets with the same animals were prepared for this study. To apply test-day RRM and LMs, 262,426 test day records and 30,228 lactation records covering 305 days were prepared, respectively. The lowest values of Akaike’s information criterion, Bayesian information criterion, and estimates of the maximum of the likelihood function (−2LogL) were for RRM4. Heritability for 305-day milk yield (305MY) was 0.23 (RRM4), 0.24 (RRM5), and 0.21 (LM). Heritability, additive genetic and permanent environmental variances of test days on days in milk was from 0.16 to 0.27, from 3.76 to 6.88 and from 11.12 to 20.21, respectively. Additive genetic correlations between test days ranged from 0.20 to 0.99. Permanent environmental correlations between test days were between 0.07 and 0.99. Standard deviations of average estimated breeding values (EBVs) for 305MY from RRM4 and RRM5 were from 11% to 30% higher for bulls and around 28% higher for cows than that in LM. Rank correlations between RRM EBVs and LM EBVs were between 0.86 to 0.96 for bulls and 0.80 to 0.87 for cows. Average percentage of gain in reliability of EBVs for 305-day yield increased from 4% to 17% for bulls and from 23% to 24% for cows when reliability of EBVs from RRM models was compared to those from LM model. Random regression model fitted by fourth order Legendre polynomials is recommended for genetic evaluations of Brazilian Holstein cattle because of the higher reliability in the estimation of breeding values.

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Section: Discussionmentioning

confidence: 99%