Use of a test day model for dairy goat milk yield across lactations in Germany.

Breeding value prediction for dairy goats in Germany is still based on herd mate comparison within breeding society. The objective of this study was to estimate genetic parameters for milk yield based on a test day model. For the analysis 35,308, 30,551 and 23,640 test day records from lactations 1, 2 and 3 from 5079, 4118 and 3132 animals, respectively, were used. The data between 1987 and 2003 were obtained from six German breeding societies. The multiple trait (lactations 1, 2 and 3) repeatability model (RPT) included the fixed effects of breeding society-breed-herd-year, litter size, lambing season, and days in milk of third-order Legendre polynomials nested within herd-year, and the random effects of animal additive and permanent environment. The three-trait random regression model (RR) also included the random regressions based on second-order Legendre polynomials for animal additive and permanent environmental effects. Heritability estimates in RPT were 0.27 +/- 0.02, 0.20 +/- 0.02 and 0.37 +/- 0.02 for the first, second and third lactation, respectively. The genetic correlation between the first and second lactation was 0.69, between the second and third lactation 0.79, and between the first and third lactation 0.45. Heritability estimates from the RR in the first and second lactations decreased from the beginning to the end of the lactation, with average values of 0.28 and 0.27, respectively. Estimates in the third lactation showed a maximum in the middle of lactation, averaging 0.37. Genetic correlations between the first and second lactation averaged 0.64, between the second and third lactation 0.72, and between the first and third lactation 0.46. Despite the small data set and restricted relationship structure the estimates were reasonable with the exception of estimates from the third lactation, which seemed inflated. RR could be used for genetic evaluation of dairy goats in Germany.     

Genetic evaluation of somatic cell score in dairy cattle considering first and later lactations as two different but correlated traits

A test‐day (TD) random regression model (RRM) was described for the genetic evaluation of somatic cell score (SCS) where first and later lactations were considered as two different but correlated traits. A two‐step covariance function procedure was used to estimate variance–covariances and associated genetic parameters. Analysis of estimated breeding values (EBV), ranking of top bulls and cows and some computational aspects were used to compare RRM with TD repeatability model (RPM) and lactation average model (LAM). Residuals were analysed to assess the relative fit of TD models. Comparison between RRM and RPM showed that RRM has lower mean squared error and gave better fit to the data. For young bulls and cows, the standard deviation (SD) of EBVs was highest for RRM and lowest for LAM implying efficient utilization of information on SCS, in terms of revealing more genetic variation. A much lower correlation of EBVs ranging from 0.80 to 0.92 and significant re‐ranking of top bulls and cows were observed between RRM and LAM. The lower across‐lactation correlation between RRM and LAM indicated that LAM is directed to give more weight to first lactation breeding values. The RRM, where SCS in the first and later lactations was considered as two different but correlated traits was able to make effective use of available information on young bulls and cows, and could offer an opportunity to breeders to utilize EBVs for first and later lactations.     

Autoregressive and random regression test-day models for multiple lactations in genetic evaluation of Brazilian Holstein cattle

Autoregressive (AR) and random regression (RR) models were fitted to test-day records from the first three lactations of Brazilian Holstein cattle with the objective of comparing their efficiency for national genetic evaluations. The data comprised 4,142,740 records of milk yield (MY) and somatic cell score (SCS) from 274,335 cows belonging to 2,322 herds. Although heritabilities were similar between models and traits, additive genetic variance estimates using AR were 7.0 (MY) and 22.2% (SCS) higher than those obtained from RR model. On the other hand, residual variances were lower in both traits when estimated through AR model. The rank correlation between EBV obtained from AR and RR models was 0.96 and 0.94 (MY) and 0.97 and 0.95 (SCS), respectively, for bulls (with 10 or more daughters) and cows. Estimated annual genetic gains for bulls (cows) obtained using AR were 46.11 (49.50) kg for MY and −0.019 (−0.025) score for SCS; whereas using RR these values were 47.70 (55.56) kg and −0.022 (−0.028) score. Akaike information criterion was lower for AR in both traits. Although AR model is more parsimonious, RR model assumes genetic correlations different from the unity within and across lactations. Thus, when these correlations are relatively high, these models tend to yield to similar predictions; otherwise, they will differ more and RR model would be theoretically sounder.     

Genetic characteristics of Japanese Holstein cows based on multiple-lactation random regression test-day animal models

The first three lactation curves of the Japanese Holstein cows were analyzed using a random regression (RR) test-day model with a cubic Legendre polynomial fitted to each of the three parities. The first three eigenvectors of the additive genetic RR covariance matrix explained 77.8, 10.9, and 4.2% of the total variance of the three parities and are associated mainly with the level of milk yield, the linear increase, and the concave curve, respectively. On a lactational basis, as the parity increases, the contribution of the first eigenvector to a lactational variation decreases whereas the contribution of the second eigenvector increases sharply. This means that the impact of the first eigenvector on the level of milk production decreases across parity whereas the effect of the second eigenvector on the shape of the lactation curve increases across parity. The first lactation curve was the most persistent, followed by the second and the third lactation. Persistency and days to reach peak yield decrease as the parity increases (45, 40, and 36 days for the first three parities). Daily heritabilities within lactation were lower for the first parity than for the second or the third parity. The first three lactation curves possess distinctive genetic characteristics that merit consideration when combining the proofs of the first three lactations to select for lifetime production. Within- and between-parity genetic correlations between the constant and the linear RR coefficients were all positive, suggesting that raising the level of milk production in one parity would increase the linear slope in all parities, thus improving persistency. Within- and between-parity genetic correlations between the constant and the quadratic RR coefficients were all negative, implying that increasing the level of production in one parity would deepen and/or widen the concave curve in all parities, thus decreasing persistency. The linear and quadratic RR coefficients were negatively correlated within or between parities and thus have antagonistic effects on persistency.     

Genetic parameters and single versus multi-trait evaluation of udder health traits

Abstract

Genetic parameters were estimated for lactation average somatic cell score (SCS) and clinical mastitis (CM) for the first three lactations of multiparous Finnish Ayrshire cows. A multi-trait linear sire model was used for estimation of covariance components, and the efficiencies of single- versus multi-trait multi-lactation (MT) sire evaluations were compared. Heritability of SCS and CM in the first three lactations ranged from 0.11 to 0.13 and 0.02 to 0.03, respectively. Within lactation, genetic correlations between SCS and CM ranged from 0.68 to 0.72. Within both traits, across-lactation genetic correlations were lowest between 1 and 3, and highest between 2 and 3, with estimates ranging from 0.75 to 0.86 and from 0.81 to 0.98 for CM and SCS, respectively. Residual and phenotypic correlations were low and ranged from 0.09 to 0.13 and from 0.10 to 0.13, respectively. The absolute difference between genetic and residual correlations was from 0.5 to 0.6. Within-lactation genetic correlations between traits that are much less than unity suggest a multi-trait model for genetic evaluation of mastitis resistance. Comparison of model prediction performance between single-trait (ST) and MT models using a data splitting method showed that the MT model was more stable in predicting breeding values in future records of animals. Especially, for young sires and CM, the SD of EBVs from the MT model was 14 to 23% higher than the ST model, indicating more effective use of information in terms of revealing more genetic variation.     

Application of multiple‐trait finite mixture model to test‐day records of milk yield and somatic cell score of Canadian Holsteins

Multiple‐trait (MT) finite mixture random regression (MIX) model was applied using Bayesian methods to first lactation test‐day (TD) milk yield and somatic cell score (SCS) of Canadian Holsteins, allowing for heterogeneity of distributions with respect to days in milk (DIM) in lactation. The assumption was that the associations between patterns of variation in these traits and mastitis would allow revealing the hidden structure in the data distribution because of unknown health status of cows. The MIX model assumed separate means and residual co‐variance structures for two components in four intervals of lactation, in addition to fitting the fixed effect of herd‐test‐day, and fixed and random regressions with Legendre polynomials. Results indicated that the mixture model was superior to standard MT model, as supported by the Bayes factor. Approximately 20% of TD records were classified as originated from cows with a putative, sub‐clinical form of mastitis. The proportion of records from mastitic cows was the largest at the beginning of lactation. The MIX model exhibited different distributions of data from healthy and infected cows in different parts of lactation. Records from sick cows were characterized by larger (smaller) means for SCS (milk) and larger variances. Residual, and daily genetic and environmental correlations between milk and SCS were smaller from the MIX model when compared with MT estimates. Heritabilities of both traits differed significantly among records from healthy, sick and MT model estimates. Both models fitted milk records from healthy cows relatively well. The ability of the MT model in handling SCS records, measured by model residuals, was low, but improved substantially, however, where the data were allowed to be separated into two components in the MIX parameterization. Correlations between estimated breeding values (EBV) for sires from both models were very high for cumulative milk yield (>0.99) and slightly lower (0.95 in the interval from 5 to 45 DIM) for daily SCS. EBV for SCS from MT and MIX models were weakly correlated with posterior probability of sub‐clinical mastitis on the phenotypic scale.     

Modeling lactation curves and estimation of genetic parameters in Holstein cows using multiple‐trait random regression models

We compared the goodness of fit of three mathematical functions (including: Legendre polynomials, Lidauer‐Mäntysaari function and Wilmink function) for describing the lactation curve of primiparous Iranian Holstein cows by using multiple‐trait random regression models (MT‐RRM). Lactational submodels provided the largest daily additive genetic (AG) and permanent environmental (PE) variance estimates at the end and at the onset of lactation, respectively, as well as low genetic correlations between peripheral test‐day records. For all models, heritability estimates were highest at the end of lactation (245 to 305 days) and ranged from 0.05 to 0.26, 0.03 to 0.12 and 0.04 to 0.24 for milk, fat and protein yields, respectively. Generally, the genetic correlations between traits depend on how far apart they are or whether they are on the same day in any two traits. On average, genetic correlations between milk and fat were the lowest and those between fat and protein were intermediate, while those between milk and protein were the highest. Results from all criteria (Akaike's and Schwarz's Bayesian information criterion, and ?2*logarithm of the likelihood function) suggested that a model with 2 and 5 coefficients of Legendre polynomials for AG and PE effects, respectively, was the most adequate for fitting the data.     

Estimation of genetic parameters for somatic cell count in the first three lactations using random regression

Genetic parameters were estimated for log_e somatic cell count (LSCC) for the first three lactations of 31 236 Holstein/Friesian cows with 308 534, 236 277 and 206 729 test day yields in parities 1, 2 and 3, respectively. An animal random regression model was employed in the analyses using Gibbs sampling with each parity regarded as different traits. Linear and quadratic functions were fitted for the animal and permanent environmental effects respectively, using orthogonal polynomials. Daily heritabilities increased with days in milk (DIM) and averaged about 0.07 in all three parities. This increase in heritabilities with DIM was due to an increase in genetic variance and decreases in both permanent and residual environmental variances with DIM. Environmental effects have a large influence on LSCC in early lactation in all three parities. Within lactation, genetic correlations were highest between adjacent DIM but decreased as DIM got further apart. However, this decrease was slowest in parity one and greatest in parity three. The lowest correlation within lactation was 0.10 between DIM 7 and 305 in parity 3. Across lactations, genetic correlations were highest between parities 2 and 3, intermediate between 1 and 3, and lowest between 1 and 2. The genetic correlations computed for completed lactations were 0.69, 0.79 and 0.98 between parities 1 and 2, 1 and 3, and 2 and 3, respectively. Corresponding phenotypic correlations between parities were 0.38, 0.31 and 0.52, respectively. A test day model, accounting for these variations in heritabilities and genetic correlations, should result in a more accurate evaluation.     

Random regression models to estimate genetic parameters for test‐day milk yield in Brazilian Murrah buffaloes

The objective of this work was to estimate covariance functions for additive genetic and permanent environmental effects and, subsequently, to obtain genetic parameters for buffalo’s test‐day milk production using random regression models on Legendre polynomials (LPs). A total of 17 935 test‐day milk yield (TDMY) from 1433 first lactations of Murrah buffaloes, calving from 1985 to 2005 and belonging to 12 herds located in São Paulo state, Brazil, were analysed. Contemporary groups (CGs) were defined by herd, year and month of milk test. Residual variances were modelled through variance functions, from second to fourth order and also by a step function with 1, 4, 6, 22 and 42 classes. The model of analyses included the fixed effect of CGs, number of milking, age of cow at calving as a covariable (linear and quadratic) and the mean trend of the population. As random effects were included the additive genetic and permanent environmental effects. The additive genetic and permanent environmental random effects were modelled by LP of days in milk from quadratic to seventh degree polynomial functions. The model with additive genetic and animal permanent environmental effects adjusted by quintic and sixth order LP, respectively, and residual variance modelled through a step function with six classes was the most adequate model to describe the covariance structure of the data. Heritability estimates decreased from 0.44 (first week) to 0.18 (fourth week). Unexpected negative genetic correlation estimates were obtained between TDMY records at first weeks with records from middle to the end of lactation, being the values varied from ?0.07 (second with eighth week) to ?0.34 (1st with 42nd week). TDMY heritability estimates were moderate in the course of the lactation, suggesting that this trait could be applied as selection criteria in milking buffaloes.     

Somatic cell score genetic parameter estimates of dairy cattle in Portugal using fractional polynomials

Milk somatic cell count is an indicator trait for mastitis resistance. Genetic parameters for somatic cell score in the Portuguese Holstein-Friesian population were estimated by modeling the pattern of genetic correlation over the first 3 lactations (days in milk) with a random regression model. Data records from the first 3 lactations were from the national database of the Portuguese Holstein Association herds. Heritability estimates ranged from 0.05 at the beginning of the lactation for the 3 lactations, to 0.07 at the end of the lactation period for the first and third lactations, to 0.09 for the second lactation. This increase in the heritability values was due to an increase in the genetic variance and a decrease in the residual variances. Genetic correlations evaluated for monthly time points were high (0.65 to 0.99) for all 3 lactations, whereas phenotypic correlations were much less than the genetic correlations (0.13 to 0.62).     

Genetic parameters for clinical mastitis in the first three lactations of Dutch Holstein cattle

The first breeding value for udder health of a bull is based on the performance of his daughters in their first lactation. However, clinical mastitis (CM) is not a problem in first lactation only. Therefore, the objective of this study was to estimate genetic parameters for CM and somatic cell count (SCC) for the first three lactations of Dutch Holstein cattle. Data from 250 Dutch herds recording CM were used to quantify the genetic variation of CM in parity 1, 2, and 3, respectively. The dataset contained 35,379 lactations from 21,064 animals of different parities. Test-day SCC was available from all lactations. Somatic cell counts were log-transformed to somatic cell scores (SCS) and averaged over test-day records between 5 and 335, 5 and 150, and 151 and 335 days in milk. Variance components for CM and SCS were estimated using a sire-maternal grandsire model. The heritability for CM was approximately 3% in all parities. Genetic correlations between CM in consecutive lactations were high (0.9), but somewhat lower between parity 1 and 3 (0.6). All genetic correlations between CM and SCS were positive, implying that genetic selection on lower SCC will reduce CM-incidence. Estimated genetic correlations were stronger for SCS in the first half of lactation than in the second half of lactation. Selection indices showed that most progress could be achieved when treating CM in parity 1, 2, and 3 as different traits and by including SCS between 5 and 150 days in the udder health index.     

Estimates of covariance functions for growth of Nelore cattle applying a parametric correlation structure to model within-animal correlations

A total of 20,065 weights recorded on 3016 Nelore animals were used to estimate covariance functions for growth from birth to 630 days of age, assuming a parametric correlation structure to model within-animal correlations. The model of analysis included fixed effects of contemporary groups and age of dam as quadratic covariable. Mean trends were taken into account by a cubic regression on orthogonal polynomials of animal age. Genetic effects of the animal and its dam and maternal permanent environmental effects were modelled by random regressions on Legendre polynomials of age at recording. Changes in direct permanent environmental effect variances were modelled by a polynomial variance function, together with a parametric correlation function to account for correlations between ages. Stationary and nonstationary models were used to model within-animal correlations between different ages. Residual variances were considered homogeneous or heterogeneous, with changes modelled by a step or polynomial function of age at recording. Based on Bayesian information criterion, a model with a cubic variance function combined with a nonstationary correlation function for permanent environmental effects, with 49 parameters to be estimated, fitted best. Modelling within-animal correlations through a parametric correlation structure can describe the variation pattern adequately. Moreover, the number of parameters to be estimated can be decreased substantially compared to a model fitting random regression on Legendre polynomial of age.     

Genetic analysis of fat‐to‐protein ratio,milk yield and somatic cell score of Holstein cows in Japan in the first three lactations by using a random regression model

We estimated the genetic parameters of fat‐to‐protein ratio (FPR) and the genetic correlations between FPR and milk yield or somatic cell score in the first three lactations in dairy cows. Data included 3 079 517 test‐day records of 201 138 Holstein cows in Japan from 2006 to 2011. Genetic parameters were estimated with a multiple‐trait random regression model in which the records within and between parities were treated as separate traits. The phenotypic values of FPR increased soon after parturition and peaked at 10 to 20 days in milk, then decreased slowly in mid‐ and late lactation. Heritability estimates for FPR yielded moderate values. Genetic correlations of FPR among parities were low in early lactation. Genetic correlations between FPR and milk yield were positive and low in early lactation, but only in the first lactation. Genetic correlations between FPR and somatic cell score were positive in early lactation and decreased to become negative in mid‐ to late lactation. By using these results for genetic evaluation it should be possible to improve energy balance in dairy cows.     

Application of robust procedures for estimation of breeding values in multiple-trait random regression test-day model

Robust procedures for estimation of breeding values were applied to multiple‐trait random regression test‐day (TD) model to reduce the influence of outliers on inferences. Robust estimation methods consisted of correcting selected observations (defined as outliers) in the process of solving mixed‐model equations in such a way that ‘new’ observations gave residuals (actual observation minus predicted) within k residual standard deviations for a given day in milk in 305‐day lactation. Data were 980 503 TD records on 63 346 Canadian Jersey cows. Milk, fat, protein and somatic cell score in the first three lactations were analysed jointly in the model that included fixed herd‐TD effect and regressions within region–age–season of calving, and regressions with random coefficients for animal genetic and permanent environmental effects. All regressions were orthogonal polynomials of order 4. Robust procedures for k = 1.5, 2.0, 2.5, 2.75 and 3.0 were contrasted with the regular best linear unbiased prediction (BLUP) method in terms of numbers and distributions of outliers, and estimated breeding values (EBV) of animals. Distributions of outliers were similar across traits and lactations. Early days in milk (from 5 to 15) were associated with larger frequency of outliers compared with the remaining part of lactation. Several, computationally simple, robust methods (for k > 2.0) reduced the influence of outlier observations in the model and improved the overall model performance. Differences in rankings of animals from robust evaluations were small compared with the regular BLUP method. No clear associations between changes in EBV (rankings) of top animals from different methods and the occurrence of outliers were detected.     

Genetic associations between milk fat‐to‐protein ratio,milk production and fertility in the first two lactations of Thai Holsteins dairy cattle

The aims of this study were to estimate, simultaneously, the genetic parameters of test‐day milk fat‐to‐protein ratio (FPR), test‐day milk yield (MY), and days‐open (DO) in the first two lactations of Thai Holsteins. A total of 76 194 test‐day production records collected from 8874 cows with 8674 DO records between 2001 and 2011 from different lactations were treated as separated traits. The estimates of heritability for test‐day FPR in the first lactation showed an increasing trend, whereas the estimates in the second lactation showed a U‐shape trend. Genetic correlations for FPR‐DO and MY‐DO showed a decreasing trend along days in milk (DIM) in both lactations, whereas genetic correlations for FPR‐MY increased along DIM in the first lactation but decreased in the second lactation. Genetic correlations of FPR between consecutive DIM were moderate to high, which showed the effectiveness of simultaneous analyses. Selection of FPR in the early stage has no adverse effect on MY and DO for the first lactation but has a negative effect on MY and positive effect on DO for the second lactation. This study showed that genetic improvement of the energy balance using FPR, MY and DO with multi‐trait test day model could be applied in a Thailand dairy cattle breeding program.     

Genetic parameters for mastitis incidence and its indicators based on somatic cell score for Holsteins in Hokkaido,Japan

The objectives of this study were to estimate the heritability of mastitis incidence and genetic correlations between the mastitis and the somatic cell score (SCS) statistics, and to compare the practicability between different models. We used test‐day records with the mastitis incidence and SCS collected from Holstein cows calving from 1988 to 2015 in Hokkaido, Japan. As indicators of mastitis, the average SCS (avSCS), the standard deviation of SCS (sdSCS), and the maximum SCS (maxSCS) were calculated using test‐day records up to the first 305 days in milk within a lactation. We compared a four‐trait repeatability animal model (MTRP) with a four‐trait multiple‐lactation animal model (MTML). The heritability for mastitis was equal to or lower than 0.05 in all the models. Genetic correlations between lactations with MTML within the same trait were positive and close to 1. With MTRP, the estimated genetic correlations of the mastitis incidence with avSCS, sdSCS, and maxSCS were 0.66, 0.79, and 0.82, respectively. A joint evaluation with SCS statistics is expected to give an extra reliability for mastitis because of high and positive genetic correlations among the traits.     

Genetic trends for fertility,udder health and protein yield in Swedish red cattle estimated with different models

The aim of this study was to estimate and compare genetic trends in Swedish Red cattle using a full multiple-trait (MT) model and trait-group-wise models for female fertility, udder health and protein yield. Field data for maiden heifers from 1989 and cows with a first and second lactation between 1990 and 2007 were included. (Co)variance components were estimated prior to prediction of breeding values. The estimated genetic trends were clearly favourable for protein yield and udder conformation, and in most cases neutral to favourable for clinical mastitis and calving to first insemination. In maiden heifers, the trends were neutral for number of inseminations per service period. Unfavourable genetic trends were estimated for number of inseminations in the first two lactations, but the trends seemed less unfavourable from evaluations within trait groups compared with when using the full MT model. Excluding maiden heifer data affected genetic trends less than using trait-group-wise analyses instead of a full MT model. Unfavourable genetic trends in functional traits may be missed unless the traits are evaluated in a MT model including traits under strong selection.     

Random regression model estimation of genetic parameters for show‐jumping results of Hungarian Sporthorses

The aim of this study was to estimate the genetic parameters for show‐jumping competition performance of Hungarian Sporthorses using a random regression model. There were 21 210 records from 739 horses collected in Hungary between 1996 and 2004. Performance was expressed as shifted Blom normalized ranks and as the difference between fence height and fault points. The random regression model (RRM) included fixed effects for sex, year, location, and obstacle height and random effects for animal, rider and permanent environment. Regressions for the random effects in the RRM were modelled with Legendre polynomials from first to fifth order of fit. The model focused on performance of horses from 4 to 11 years of age, with heterogeneous residual variances considered. The heritabilities were low to moderate for both variables. Genetic and phenotypic correlations between different ages decreased with increasing distance between the ages.     

Two-step and random regression analyses of weight gain of station-tested beef bulls

Our objectives were to compare a two-step model and a joint procedure via random regression model for evaluating weight gain of beef bulls, weighed every 28 d on 140-d test, and to estimate genetic, environmental, and phenotypic parameters. Two-step analysis consisted of fitting fixed linear regressions to weights of each bull to determine weight gain on test. In the second step, gain on test was analyzed by a mixed model that included fixed effects of breed, test group, and starting age and random effects of weaning herd-year group and animal (additive genetic). The random regression model included the same effects as the two-step mixed-model analysis with an additional random animal permanent environment effect. Fourth-order Legendre polynomials of days on test were fitted for all fixed and random effects in the random regression model, except for breed. Breed effects and residual variances varied for each measurement period. Variance components and EBV for gain were obtained from the covariance function and estimates of random regression coefficients for weight, respectively. Random regression heritability estimates for gain on test increased over time, being maximum at end of test (0.38) and equal to two-step estimate. Permanent environment variance ratio estimates also increased over time and were greater than heritability estimates. Estimate of weaning herd-year variance ratio was approximately constant over time, being equal to 0.07 at end of test and similar to two-step estimate. Genetic correlations between gain through different periods on test given by random regression model were high (from 0.81, between 28 and 140-d gain on test, to 0.99, between 112 and 140-d gain on test). Genetic correlations between gain on discrete 28-d intervals were moderate to high (e.g., 0.49 and 0.99 between the last 28 d on test and the first and fourth 28 d, respectively). Rank correlations between EBV for 140-d gain by the two procedures were 0.98, 0.84, and 0.73 for all bulls and the 5% and 1% of bulls with highest random regression EBV, respectively. Results indicated that the two procedures rank top bulls quite differently for 140-d gain on test. Random regression model accounted for changes over time of genetic and environmental effects on the test weight gain curve of the bulls. Use of 112-d instead of a 140-d test provided similar ranking of bulls on the basis of EBV for gain on test.     

Genetic analysis of the rates of conception using a longitudinal threshold model with random regression in dairy crossbreeding within a tropical environment

This study was designed to: (i) estimate genetic parameters and breeding values for conception rates (CR) using the repeatability threshold model (RP‐THM) and random regression threshold models (RR‐THM); and (ii) compare covariance functions for modeling the additive genetic (AG) and permanent environmental (PE) effects in the RR‐THM. The CR was defined as the outcome of an insemination. A data set of 130 592 first‐lactation insemination records of 55 789 Thai dairy cows, calving between 1996 and 2011, was used in the analyses. All models included fixed effects of year × month of insemination, breed × day in milk to insemination class and age at calving. The random effects consisted of herd × year interaction, service sire, PE, AG and residual. Variance components were estimated using a Bayesian method via Gibbs sampling. Heritability estimates of CR ranged from 0.032 to 0.067, 0.037 to 0.165 and 0.045 to 0.218 for RR‐THM with the second, third and fourth‐order of Legendre polynomials, respectively. The heritability estimated from RP‐THM was 0.056. Model comparisons based on goodness of fit, predictive abilities, predicted service results of animal, and pattern of genetic parameter estimates, indicated that the model which fit the desired outcome of insemination was the RR‐THM with two regression coefficients.