Genetic parameters for milk production by using random regression models with different alternatives of fixed regression modeling

Cobuci, Jaime Araújo; Costa, C. N.; Neto, José Braccini; Freitas, Ary Ferreira de

doi:10.1590/s1516-35982011000300013

Cited by 29 publications

(36 citation statements)

References 21 publications

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“…and they decreased with increasing distance between test days, in agreement with the results of other studies (Liu et al, 2001;Cobuci et al, 2011). The lowest estimates within the same lactation occurred between the 1st and 10th test day and were 0.46 and 0.48 for the first and second calving, respectively.…”

Section: Resultssupporting

confidence: 92%

“…Higher estimates were obtained for most TDMY in the second lactation (Figure 1). Similar results have been reported in different studies (Fujii and Susuki, 2006;Araujo et al, 2006;Cobuci et al, 2011).…”

Section: Resultssupporting

confidence: 91%

“…Several studies have demonstrated the existence of a genetic correlation between testday milk yields of the same lactation and between test-day yields of different lactations (Guo et al, 2002;Yang et al, 2005;Cobuci et al, 2011). However, preliminary studies have shown differences in the lactation curve between the first and second lactation, and between the second and subsequent lactations.…”

Section: Introductionmentioning

confidence: 99%

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Random regression analysis of test-day milk yields in the first and second lactations of Brazilian Gyr cows

et al. 2015

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ABSTRACT. The objective of the present study was to estimate the genetic parameters for test-day milk yields (TDMY) in the first and second lactations using random regression models (RRM) in order to contribute to the application of these models in genetic evaluation of milk yield in Gyr cattle. A total of 53,328 TDMY records from 7118 lactations of 5853 Gyr (2015) cows were analyzed. The model included the direct additive, permanent environmental, and residual random effects. In addition, contemporary group and linear and quadratic effects of the age of cows at calving were included as fixed effects. A random regression model fitting fourth-order Legendre polynomials for additive genetic and permanent environmental effects, with five classes of residual variance, was applied. In the first lactation, the heritabilities increased from early lactation (0.26) until TDMY3 (0.38), followed by a decrease until the end of lactation. In the second lactation, the estimates increased from the first (0.29) to the fifth test day (0.36), with a slight decrease thereafter, and again increased on the last two test days (0.34 and 0.41). There were positive and high genetic correlations estimated between first-lactation TDMY and the remaining TDMY of the two lactations. The moderate heritability estimates, as well as the high genetic correlations between half the first-lactation TDMY and all TDMY of the two lactations, suggest that the selection based only on first lactation TDMY is the best selection strategy to increase milk production across first and second lactations of Gyr cows.

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Section: Resultssupporting

confidence: 92%

Section: Resultssupporting

confidence: 91%

Section: Introductionmentioning

confidence: 99%

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Random regression analysis of test-day milk yields in the first and second lactations of Brazilian Gyr cows

et al. 2015

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“…In there, the W(4,4) model and L(4,4) model were estimated nearly similar heritability values. While the estimates of heritability increased in the middle of lactation was similar to the study by Takma and Akbas [12] , increased at the end of lactation was not similar to the studies by Takma and Akbas [11] and Cobuci et al [29] .…”

Section: Discussionsupporting

confidence: 81%

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

Çankaya¹,

Takma²,

Abacı³

et al. 2014

Kafkas Univ Vet Fak Derg

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SummaryThis study was conducted to compare random regression models for third order Ali Schaeffer (AS), Wilmink (W) and Legendre polynomials (L) on estimation of genetic parameters for first lactation milk yield in Jersey cows. For this aim, data used in this study were 6387 official milk yield records from monthly recording of 686 first lactations between 1996 and 2011 in Karakoy Agricultural State Farm, Samsun (Turkey). In this study, (co)variance components, heritability for first lactation test day milk yields (TDMY) and genetic correlations among these TDMYs were estimated by using DFREML statistical package under DXMRR option. To compare the models, -2LogL, Akaike's information criterion (AIC), Bayesian information criterion (BIC), Residual variances (RV) and Log likelihood values were used. Heritabilities (0.08 to 0.28), additive genetic correlations (0.68 to 0.99) and phenotypic correlations (0.21 to 0.66) were estimated by AS(4,4) random regression model which had the lowest AIC and BIC values. As a result, it was decided that the AS(4,4) random regression model can be used for management decisions and genetic evaluation of Jersey cows for milk production. Keywords: Random regression, Test day milk yield, Jersey, Genetic parameters Jersey Sığırlarında İlk Laktasyon Test Günü Süt Verimleri için Bazı Şansa Bağlı Regresyon Modellerinin Karşılaştırılması ve GenetikParametre Tahminleri ÖzetBu çalışma Jersey sığırlarında ilk laktasyon süt verimleri için genetik parametrelerinin tahmini üzerine Ali Schaeffer, Wilmink ve Legendre polinomlarının 3 farklı uyum sırasında şansa bağlı regresyon modellerini karşılaştırmak için yürütülmüştür. Bu amaçla, çalışmada Samsun Karaköy Tarım İşletmesi'ndeki 1996-2011 yılları arasındaki 686 ilk laktasyonun 6387 adet aylık süt verim kaydı kullanılmıştır. Çalışmada ilk laktasyon test günü süt verimleri (TGSV) için kovaryans bileşenleri, kalıtım dereceleri ve TGSV arasındaki genetik korelasyonlar DFREML istatistik paket programı içerisindeki DXMRR opsiyonu kullanılarak tahmin edilmiştir. Modelleri karşılaştırmak için -2LogL, Akaike bilgi kriteri (AIC), Bayesian bilgi kriteri (BIC), Hata varyansı (RV) ve Log olabilirlik değerleri kullanılmıştır. En küçük AIC ve BIC değerlerine sahip AS(4,4) modeli ile kalıtım derecesi değerleri (0.08 -0.28), eklemeli genetik korelasyonlar (0.68 -0.99) ve fenotipik korelasyonlar (0.21 -0.66) tahmin edilmiştir. Sonuç olarak, AS(4,4) modelinin Jersey sığırlarının genetik değerlendirmesi ve süt üretimi açısından işletme yönetim kararları için kullanılabilir olduğuna karar verildi.

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“…In fact, Germany, Canada, United Kingdom and Italy have already adopted random regression models in their national genetic evaluations, using Legendre polynomials of third, fourth or fifth orders (Muir et al, 2007;Yamazaki et al, 2013). Costa et al (2008), Biassus et al (2011), and Cobuci et al (2011) studied the use of random regression models with Legendre polynomials, in order to determine the best order for genetic evaluation of Holstein cattle in Brazil, and to substitute the current 305-day lactation model. However, there are few studies on milk and its components, as fat and protein, in tropical countries as Brazil, using random regression models in the genetic evaluations of Holstein breed (Costa et al, 2008;Biassus et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Reliability of breeding values between random regression and 305-day lactation models

Padilha

Cobuci

Daltro

et al. 2016

Pesq. agropec. bras.

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-The objective of this work was to verify the gain in reliability of estimated breeding values (EBVs), when random regression models are applied instead of conventional 305-day lactation models, using fat and protein yield records of Brazilian Holstein cattle for future genetic evaluations. Data set contained 262,426 test-day fat and protein yield records, and 30,228 fat and protein lactation records at 305 days from first lactation. Single trait random regression models using Legendre polynomials and single trait lactation models were applied. Heritability for 305-day yield from lactation models was 0.24 (fat) and 0.17 (protein), and from random regression models was 0.20 (fat) and 0.21 (protein). Spearman correlations of EBVs, between lactation models and random regression models, for 305-day yield, ranged from 0.86 to 0.97 and 0.86 to 0.98 (bulls), and from 0.80 to 0.89 and 0.81 to 0.86 (cows), for fat and protein, respectively. Average increase in reliability of EBVs for 305-day yield of bulls ranged from 2 to 16% (fat) and from 4 to 26% (protein), and average reliability of cows ranged from 24 to 38% (fat and protein), which is higher than in the lactation models. Random regression models using Legendre polynomials will improve genetic evaluations of Brazilian Holstein cattle due to the reliability increase of EBVs, in comparison with 305-day lactation models.Index terms: breeding value, correlation, Legendre polynomials, reliability. Confiabilidade de valores genéticos entre modelos de regressão aleatória e de lactação aos 305 diasResumo -O objetivo deste trabalho foi verificar o ganho em confiabilidade dos valores genéticos estimados (VGE), quando os modelos de regressão aleatória são aplicados em lugar dos modelos convencionais de lactação até os 305 dias, tendo-se utilizado registros de produção de gordura e proteína de bovinos da raça Holandesa no Brasil, para futuras avaliações genéticas. O conjunto de dados continha 262.426 registros de produção de gordura e proteína no dia do controle e 30.228 registros de produção acumulada de gordura e proteína até os 305 dias de lactação. Aplicaram-se modelos de regressão aleatória unicaracterística, com os polinômios de Legendre, e modelos unicaracterística de lactação aos 305 dias. A herdabilidade estimada para as produções até os 305 dias pelos modelos de lactação foi 0,24 (gordura) e 0,17 (proteína), e, pelos modelos de regressão aleatória, foi 0,20 (gordura) e 0,21 (proteína). Correlações de Spearman dos VGEs entre os modelos de lactação e de regressão aleatória, para a produção aos 305 dias, variaram de 0,86 a 0,97 e 0,86 a 0,98 (touros) e de 0,80 a 0,89 e 0,81 a 0,86 (vacas), quanto à gordura e à proteína, respectivamente. O aumento médio em confiabilidade dos VGEs para a produção aos 305 dias variou de 2 a 16% (gordura) e de 4 a 26% (proteína), em touros, e variou de 24 a 38% (gordura e proteína) em vacas, o que é superior àquela obtida pelos modelos de lactação. Os modelos de regressão aleatória com uso dos polinômios de Legendre melhorarão a av...

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Genetic parameters for milk production by using random regression models with different alternatives of fixed regression modeling

Cited by 29 publications

References 21 publications

Random regression analysis of test-day milk yields in the first and second lactations of Brazilian Gyr cows

Random regression analysis of test-day milk yields in the first and second lactations of Brazilian Gyr cows

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

Reliability of breeding values between random regression and 305-day lactation models

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