Genetic parameters and evaluations from single- and multiple-trait analysis of dairy cow fertility and milk production

The objective of this study was to describe the genetic and phenotypic relationship between milk urea nitrogen (MUN) and reproductive traits in Iranian Holstein dairy cows. Test-day MUN data obtained from 57 301 dairy cows on 20 large dairy herds in Iran between January 2005 and June 2009. Genetic parameters for MUN and reproductive traits were estimated with a five-trait model using ASREML program. Random regression test-day models were used to estimate heritabilities separately for MUN from first, second and third lactations. Regression curves were modeled using Legendre polynomials of order 3. Herd-year-season along with age at calving was included as fixed effects in all models for reproductive traits. Heritabilities for MUN and reproductive traits were estimated separately for first lactation, second lactation and third lactation. The estimated heritabilities for MUN varied from 0.18 to 0.22. The heritability estimate was low for reproductive traits, which ranged from 0.02 to 0.06 for different traits and across parities. Except for days open, phenotypic and genetic correlations of MUN with reproductive performance traits were close to zero. Genetic correlations between MUN and days open were 0.23, 0.35 and 0.45 in first, second and third lactation, respectively. However, the phenotypic correlation between MUN at different parities was moderate (0.28 to 0.35), but the genetic correlation between MUN at different parities was high and ranged from 0.84 to 0.97. This study shows a limited application of MUN for use in selection programs to improve reproductive performance.

Section: Genetic and Phenotypic Correlationssupporting

confidence: 92%

“…Weigel and Rekaya, 2000;Ranberg et al, 2003). Genetic correlations among various fertility traits generally agreed with previous findings (Kadarmideen et al, 2003;Liu et al, 2008). Consistent with our results, Kadarmideen et al (2003) reported genetic correlations among fertility traits were generally high.…”

Section: Genetic and Phenotypic Correlationssupporting

confidence: 92%

Genetic relationship between milk urea nitrogen and reproductive performance in Holstein dairy cows

Hossein-Zadeh

Ardalan

2011

“…However, the predictive reliability of estimating breeding value by using fertility traits might be increased by using a multiple-trait model that includes milk yields (Kadarmideen et al, 2003;Sun et al, 2009;Sewalem et al, 2010). Therefore, establishing genetic correlations between various fertility traits and milk yields is likely to provide important information for the genetic evaluation of female fertility.…”

Section: Introductionmentioning

confidence: 99%

Relationships between conception rate in Holstein heifers and cows and milk yield at various stages of lactation

Hagiya

Terawaki

Yamazaki

et al. 2013

We investigated the relationships between conception rates (CRs) at first service in Japanese Holstein heifers (i.e. animals that had not yet had their first calf) and cows and their test-day (TD) milk yields. Data included records of artificial insemination (AI) for heifers and cows that had calved for the first time between 2000 and 2008 and their TD milk yields at 6 through 305 days in milk (DIM) from first through third lactations. CR was defined as a binary trait for which first AI was a failure or success. A threshold-linear animal model was applied to estimate genetic correlations between CRs of heifers or cows and TD milk yield at various lactation stages. Two-trait genetic analyses were performed for every combination of CR and TD milk yield by using the Bayesian method with Gibbs sampling. The posterior means of the heritabilities of CR were 0.031 for heifers, 0.034 for first-lactation cows and 0.028 for second-lactation cows. Heritabilities for TD milk yield increased from 0.324 to 0.433 with increasing DIM but decreased slightly after 210 DIM during first lactation. These heritabilities from the second and third lactations were higher during late stages of lactation than during early stages. Posterior means of the genetic correlations between heifer CR and all TD yields were positive (range, 0.082 to 0.287), but those between CR of cows and milk yields during first or second lactation were negative (range, 20.121 to 20.250). Therefore, during every stage of lactation, selection in the direction of increasing milk yield may reduce CR in cows. The genetic relationships between CR and lactation curve shape were quite weak, because the genetic correlations between CR and TD milk yield were constant during the lactation period.

“…Bias in genetic evaluation of female fertility traits via selection on milk production traits has already been addressed (Kadarmideen et al, 2003;Sewalem and Kistemaker, 2008;Mrode and Coffey, 2009;Sun et al, 2010). The bias for female fertility traits is not just limited to selection on a correlated trait.…”

Section: Introductionmentioning

confidence: 99%

“…The bias for female fertility traits is not just limited to selection on a correlated trait. There are also management and preferential treatment biases in favour of animals with higher genetic merit for milk yield (Kadarmideen et al, 2003;Biffani et al, 2005). Many of these problems are well described by Kadarmideen et al (2003).…”

Section: Introductionmentioning

confidence: 99%

Multiple-trait multiple-country genetic evaluation of Holstein bulls for female fertility and milk production traits

Nilforooshan

Jakobsen

Fikse

et al. 2014

The aim of this study was to investigate the effect of including milk yield data in the international genetic evaluation of female fertility traits to reduce or eliminate a possible bias because of across-country selection for milk yield. Data included two female fertility traits from Great Britain, Italy and the Netherlands, together with milk yield data from the same countries and from the United States, because the genetic trends in other countries may be influenced by selection decisions on bulls in the United States. Potentially, female fertility data had been corrected nationally for within-country selection and management biases for milk yield. Using a multiple-trait multiple across-country evaluation (MT-MACE) for the analysis of female fertility traits with milk yield, acrosscountry selection patterns both for female fertility and milk yield can be considered simultaneously. Four analyses were performed; one single-trait multiple across-country evaluation analysis including only milk yield data, one MT-MACE analysis including only female fertility traits, and one MT-MACE analysis including both female fertility and milk yield traits. An additional MT-MACE analysis was performed including both female fertility and milk yield traits, but excluding the United States. By including milk yield traits to the analysis, female fertility reliabilities increased, but not for all bulls in all the countries by trait combinations. The presence of milk yield traits in the analysis did not considerably change the genetic correlations, genetic trends or bull rankings of female fertility traits. Even though the predicted genetic merits of female fertility traits hardly changed by including milk yield traits to the analysis, the change was not equally distributed to the whole data. The number of bulls in common between the two sets of Top 100 bulls for each trait in the two analyses of female fertility traits, with and without the four milk yield traits and their rank correlations were low, not necessarily because of the absence of the US milk yield data. The joint international genetic evaluation of female fertility traits with milk yield is recommended to make use of information on several female fertility traits from different countries simultaneously, to consider selection decisions for milk yield in the genetic evaluation of female fertility traits for obtaining more accurate estimating breeding values (EBV) and to acquire female fertility EBV for bulls evaluated for milk yield, but not for female fertility.