Genetic Parameters for Milk Yield and Lactation Persistency Using Random Regression Models in Girolando Cattle

Canaza-Cayo, Ali William; Lopes, Paulo Sávio; Silva, Marcos Vinícius Gualberto Barbosa da; Torres, Robledo de Almeida; Martins, Marta Fonseca; Arbex, Wagner; Cobuci, Jaime Araújo

doi:10.5713/ajas.14.0620

Cited by 16 publications

(12 citation statements)

References 28 publications

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“…Persistence and total production (kg/lactation) were the traits that showed the highest heritabilities. Canaza-Cayo et al (2015) estimated heritabilities for nine different persistence estimates proposed by different authors, and found results that varied from 0.18 to 0.33, corroborating with the heritability estimated for persistence in the present study (0.29). These authors also analyzed Girolando cattle, which justifies the similar results.…”

Section: Discussionsupporting

confidence: 92%

“…The heritability for total production (0.27) was higher than that found by Yilmaz et al (2011) and Pereira et al (2012), who analyzed data from Swiss and Gir cattle and reported results of 0.18 and 0.21, respectively. On the other hand, it was similar to the result of Canaza-Cayo et al (2015), who used the random regression models approach, and it was lower than the estimates found by Jakobsen et al (2002) and Cobuci et al (2006), who reported heritabilities ranging from 0.31 to 0.42 for Holstein cattle from different locations.…”

Section: Discussionsupporting

confidence: 83%

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Research Article Genomic prediction of lactation curves of Girolando cattle based on nonlinear mixed models

Teixeira¹,

Nascimento²,

Cecon³

et al. 2021

Genet. Mol. Res.

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©FUNPEC-RP www.funpecrp.com.br Genetics and Molecular Research 20 (1): gmr18691 F.R.F. Teixeira et al. 2using the Bayesian LASSO method. Then, based on the genomic estimated breeding values (GEBVs) obtained, genomic predictions of lactation curves were constructed, and the genetic parameters were calculated. Wood's equation showed the best fit among the evaluated models. Heritabilities ranged from 0.09 to 0.29 for the seven lactation variables (initial production, rates of increase and decline, lactation peak, time to peak yield, persistence and total production). The correlations among GEBVs ranged from -0.85 to 0.98. The concordances between the best animals selected according to the selected traits were greater when the correlations between GEBVs for these traits were also high. Consequently, the methodology allowed us to identify the best nonlinear model and to construct the genetic lactation curves of a Girolando cattle population, as well as to assess the differences between animals and the association between lactation variables.

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Section: Discussionsupporting

confidence: 92%

Section: Discussionsupporting

confidence: 83%

Research Article Genomic prediction of lactation curves of Girolando cattle based on nonlinear mixed models

Teixeira¹,

Nascimento²,

Cecon³

et al. 2021

Genet. Mol. Res.

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“…Residual variances were much higher in this study than permanent environmental effects whereas the reverse was reported in most of the other studies [ 41 ]. A study by Druet et al [ 40 ] reported decreasing residual and permanent environmental variances along the lactation curve in first lactation of French Holsteins.…”

Section: Discussioncontrasting

confidence: 57%

Genetic parameters for milk yield in imported Jersey and Jersey-Friesian cows using daily milk records in Sri Lanka

Samaraweera

Boerner

Cyril

et al. 2020

Asian-Australas J Anim Sci

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Objective: Estimate genetic parameters for milk yield traits using daily milk yield records from parlour data generated in an intensively managed commercial dairy farm with Jersey and Jersey-Friesian cows in Sri Lanka. Methods: Genetic parameters were estimated for first and second lactation predicted and realized 305-day milk yield using univariate animal models. Genetic parameters were also estimated for total milk yield for each 30day intervals of the first lactation using univariate animal models and for daily milk yield using random regression models fitting second-order Legendre polynomials and assuming heterogeneous residual variances. Breeding values for predicted 305-day milk yield were estimated using an animal model. Results: For the first lactation, the heritability of predicted 305-day milk yield in Jersey cows (0.08 ± 0.03) was higher than that of Jersey-Friesian cows (0.02 ± 0.01). The second lactation heritability estimates were similar to that of first lactation. The repeatability of the daily milk records was 0.28 ± 0.01 and the heritability ranged from 0.002 ± 0.05 to 0.19 ± 0.02 depending on day of milk. Pearson product-moment correlations between the bull EBVs in Australia and bull EBVs in Sri Lanka for 305-day milk yield were 0.39 in Jersey cows and-0.35 in Jersey-Friesian cows. Conclusion: The heritabilities estimated for milk yield in Jersey and Jersey-Friesian cows in Sri Lanka were low, and were associated with low additive genetic variances for the traits. Sire differences in Australia were not expressed in the tropical low-country of Sri Lanka. Therefore, genetic progress achieved by importing genetic material from Australia can be expected to be slow. This emphasizes the need for a within-country evaluation of bulls to produce locally adapted dairy cows.

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“…When trying to apply milk composition as a nutritional evaluation tool, these fluctuations should be noticed. Although several researchers have studied the genetic analysis of the persistency for milk yield and components in dairy cattle ( Cole and Null, 2009 ; Khorshidie et al, 2012 ; Canaza-Cayo et al, 2015 ), limited studies have been performed to estimate genetic parameters of persistency for milk production traits in buffaloes ( Ghavi Hossein-Zadeh et al, 2017 ). Therefore, the objective of the present study was to estimate the heritability and genetic trend of distinct persistency measures for milk fat yield and their genetic correlations with 270-day milk yield in Iranian buffaloes using random regression test-day models.…”

Section: Introductionmentioning

confidence: 99%

Genetic Analysis of Persistency for Milk Fat Yield in Iranian Buffaloes (Bubalus bubalis)

et al. 2021

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This study aimed to estimate heritabilities and genetic trends for different persistency measures for milk fat yield and their genetic correlations with 270-day milk yield in Iranian buffaloes. The records of test-day milk fat yield belonging to the first three lactations of buffaloes within 523 herds consisting of 43,818 records were got from the Animal Breeding Center and Promotion of Animal Products of Iran from 1996 to 2012. To fit the lactation curves based on a random regression test-day model, different orders of Legendre polynomial (LP) functions were selected. Three persistency measures were altered according to the specific condition of the lactation curve in buffaloes: (1) The average of estimated breeding values (EBVs) for test day fat yield from day 226 to day 270 as a deviation from the average of EBVs from day 44 to day 62 (PM1), (2) A summation of contribution for each day from day 53 to day 247 as a deviation from day 248 (PM2), and (3) The difference between EBVs for day 257 and day 80 (PM3). The estimates of heritability for PM1, PM2, and PM3 ranged from 0.20 to 0.48, from 0.36 to 0.47, and from 0.19 to 0.35 over the first three lactations, respectively. The estimate of genetic trends for different persistency measures of milk fat yield was not significant over the lactations (P > 0.05). Genetic correlation estimates between various measures of persistency were generally high over the first three lactations. Also, genetic correlations estimates between persistency measures and 270-day milk yield were mostly low and varied from 0.00 to 0.24 (between PM1 and 270-day milk yield), from −0.19 to 0.13 (between PM2 and 270-day milk yield), and from −0.02 to 0.00 (between PM1 and 270-day milk yield) over the first three lactations, respectively. Persistency measures that showed low genetic correlations with milk fat yield were considered the most suitable measures in selection schemes. Besides, medium to high heritability estimates for different persistency measures for milk fat yield indicated that relevant genetic variations detected for these characters could be regarded in outlining later genetic improvement programs of Iranian buffaloes.

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Genetic Parameters for Milk Yield and Lactation Persistency Using Random Regression Models in Girolando Cattle

Cited by 16 publications

References 28 publications

Research Article Genomic prediction of lactation curves of Girolando cattle based on nonlinear mixed models

Research Article Genomic prediction of lactation curves of Girolando cattle based on nonlinear mixed models

Genetic parameters for milk yield in imported Jersey and Jersey-Friesian cows using daily milk records in Sri Lanka

Genetic Analysis of Persistency for Milk Fat Yield in Iranian Buffaloes (Bubalus bubalis)

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