Random regression models using different functions to model milk flow in dairy cows

Laureano, Monyka Marianna Massolini; Bignardi, Annaíza Braga; Faro, Lenira El; Cardoso, Victor José Mendes; Tonhati, Humberto; Albuquerque, Lúcia Galvão de

doi:10.4238/2014.september.12.20

Cited by 7 publications

(7 citation statements)

References 32 publications

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“…Small differences were observed for estimations of additive genetic variance and permanent environmental variance between different models of the lactation period. The trends additive genetic and permanent environmental variances are in agreement with those obtained by El Faro et al 2008, and Laureano et al (2014). In this study, minimum heritability of milk, fat and protein yields in early lactation by different functions was observed, agreeing with the results presented by Biassus et al (2011), Mohammadi, Alijani, and Daghighkia (2014) and Bohlouli and Alijani (2012).…”

Section: Discussionsupporting

confidence: 93%

“…Because heritability is low in early lactation, is obtained permanent environmental variance at this stage of lactation high and given that additive genetic variance was higher in late lactation. The results were in accordance with other reports Biassus et al (2011), and Laureano et al (2014). The analysis showed less variation in additive genetic correlation of milk yield than fat and protein yields during lactation.…”

Section: Discussionsupporting

confidence: 92%

“…The results indicated that selection for increase milk production traits at a certain point during lactation has a positive effect on any other point of the curve. These results agree with results of Cobuci, Costa, Braccini Neto, and Freitas (2011) and Laureano et al (2014). Similar genetic trends were reported by Abdullahpour, Shahrbabak, Nejati-Javaremi, and Torshizi (2010) using the 305 day measures of the traits.…”

Section: Discussionsupporting

confidence: 90%

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<b>Appropriate mathematical models to describe the lactation curves of milk production traits of Iranian Holstein dairy cattle

Naderi

2018

Acta Sci. Anim. Sci.

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The objective of this study was to compare the goodness of fit of lactation curve models; Wood, Wilmink, Linear Splines (SPL), Cubic Splines (SPC), Quadratic Splines (SPQ), Cobby and Le Du, Ali Schaeffer and Legendre Polynomial (LEG), in random regression model (RRM) for milk production traits of Iranian Holstein dairy cattle. For this purpose the records obtained from Test-day (TD) regarding milk (928513), fat (788577) and protein (653317) yields related to their first parity were used. These data collected from the years of 2003 to 2011 by the Karaj breeding center of Iran. The genetic parameters were estimated using REML method using WOMBAT software. Based on obtained results, RRM with SPL6 (6,6), SPC6 (6,6) and LEG (3,5) for milk yield, SPL6 (6,6), SPQ6 (6,6), LEG (3,5) for fat yield and SPL5 (5,5), SPQ4 (4,4) and LEG (3,4) for protein yield, were selected as better model to describe the lactation curves. The estimated heritabilities by best models were lower in the beginning lactation than other during lactation. The genetic trend of milk yields was showed an increasing during the 10 past years, which indicated Iranian Holstein dairy cattle population genetically was improved for milk yields.

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Section: Discussionsupporting

confidence: 93%

Section: Discussionsupporting

confidence: 92%

Section: Discussionsupporting

confidence: 90%

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<b>Appropriate mathematical models to describe the lactation curves of milk production traits of Iranian Holstein dairy cattle

Naderi

2018

Acta Sci. Anim. Sci.

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“…Traditionally, pedigree-based mixed models (Henderson, 1976) are used to distinguish signals of genetic merit from environmental noise. Extensions of this method include multi-trait models (Buch et al, 2011;Lopes et al, 2013;VanRaden et al, 2014) and random regression models (Karacaören et al, 2006;Mrode and Coffey, 2008;Boligon et al, 2011;Laureano et al, 2014). Methods that assist in the estimation of (co)variance components and also help to increase the accuracy of breeding values are indeed required.…”

Section: Introductionmentioning

confidence: 99%

Research Article Bayesian analyses of genetic parameters for growth traits in Nellore cattle raised on pasture.

Lopes¹,

Ferreira

Lôbo³

et al. 2017

Genet. Mol. Res.

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This study was carried out to investigate (co)variance components and genetic parameters for growth traits in beef cattle using a multi-trait model by Bayesian methods. Genetic and residual (co)variances and parameters were estimated for weights at standard ages of 120 (W120), 210 (W210), 365 (W365), and 450 days (W450), and for pre- and post-weaning daily weight gain (preWWG and postWWG) in Nellore cattle. Data were collected over 16 years (1993-2009), and all animals were raised on pasture in eight farms in the North of Brazil that participate in the National Association of Breeders and Researchers. Analyses were run by the Bayesian approach using Gibbs sampler. Additive direct heritabilities for W120, W210, W365, and W450 and for preWWG and postWWG were 0.28 ± 0.013, 0.32 ± 0.002, 0.31 ± 0.002, 0.50 ± 0.026, 0.61 ± 0.047, and 0.79 ± 0.055, respectively. The estimates of maternal heritability were 0.32 ± 0.012, 0.29 ± 0.004, 0.30 ± 0.005, 0.25 ± 0.015, 0.23 ± 0.017, and 0.22 ± 0.016, respectively, for W120, W210, W365, and W450 and for preWWG and postWWG. The estimates of genetic direct additive correlation among all traits were positive and ranged from 0.25 ± 0.03 (preWWG and postWWG) to 0.99 ± 0.00 (W210 and preWWG). The moderate to high estimates of heritability and genetic correlation for weights and daily weight gains at different ages is suggestive of genetic improvement in these traits by selection at an appropriate age. Maternal genetic effects seemed to be significant across the traits. When the focus is on direct and maternal effects, W210 seems to be a good criterium for the selection of Nellore cattle considering the importance of this breed as a major breed of beef cattle not only in Northern Brazil but all regions covered by tropical pastures. As in this study the genetic correlations among all traits were high, the selection based on weaning weight might be a good choice because at this age there are two important effects (maternal and direct genetic effects). In contrast, W120 should be preferred when the objective is improving the maternal ability of the dams. Furthermore, selection for postWWG can be used if the animals show both heavier weaning weights and high growth rate after weaning because it is possible to shorten the time between weaning and slaughter based on weaning weight, postWWG, and desired weight at the time of slaughter.

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“…High-order polynomials were found to be the most adequate way of modeling changes in the mean and variance over time; however, these orders of polynomials might lead to errors in the estimates of genetic parameters, mainly due to oscillations at the extremes of the curve (Meyer, 2005). One alternative that is currently being studied to reduce the order of these polynomials and to minimize estimation problems is the application of segmented polynomials or spline functions (Laureano et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Estimation of genetic parameters for test-day milk yield in Khuzestan buffalo

Madad

Hossein-Zadeh

Shadparvar

2016

Pesq. agropec. bras.

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The objective of this work was to estimate covariance functions for additive genetic and permanent environmental effects, as well as to obtain genetic parameters for buffalo test-day milk yield using random regression models on Legendre polynomials (LPs). A total of 2,538 test-day milk yield (TDMY) records from 516 first lactation records of Khuzestan buffalo, calving from 1993 to 2009 and belonging to 150 herds located in the state of Khuzestan, Iran, were analyzed. The residual variances were modeled through a step function with 1, 5, 6, 9, and 19 classes. The additive genetic and permanent environmental random effects were modeled by LPs of days in milk using quadratic to septic polynomial functions. The model with additive genetic and animal permanent environmental effects adjusted by cubic and third order LP, respectively, and with the residual variance modeled through a step function with nine classes was the most adequate one to describe the covariance structure. The model with the highest significant log-likelihood ratio test (LRT) and with the lowest Akaike information criterion (AIC) and Bayesian information criterion (BIC) was considered to be the most appropriate one. Unexpected negative genetic correlation estimates were obtained between TDMY records of the twenty-fifth and thirty-seventh week (-0.03). Genetic correlation estimates were generally higher, close to unity, between adjacent weeks during the middle of lactation. Random regression models can be used for routine genetic evaluation of milk yield in Khuzestan buffalo.

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Random regression models using different functions to model milk flow in dairy cows

Cited by 7 publications

References 32 publications

<b>Appropriate mathematical models to describe the lactation curves of milk production traits of Iranian Holstein dairy cattle

<b>Appropriate mathematical models to describe the lactation curves of milk production traits of Iranian Holstein dairy cattle

Research Article Bayesian analyses of genetic parameters for growth traits in Nellore cattle raised on pasture.

Estimation of genetic parameters for test-day milk yield in Khuzestan buffalo

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