Genetic parameters for direct and maternal calving ease in Walloon dairy cattle based on linear and threshold models

Calving ease scores from Holstein dairy cattle in the Walloon Region of Belgium were analysed using univariate linear and threshold animal models. Variance components and derived genetic parameters were estimated from a data set including 33 155 calving records. Included in the models were season, herd and sex of calf × age of dam classes × group of calvings interaction as fixed effects, herd × year of calving, maternal permanent environment and animal direct and maternal additive genetic as random effects. Models were fitted with the genetic correlation between direct and maternal additive genetic effects either estimated or constrained to zero. Direct heritability for calving ease was approximately 8% with linear models and approximately 12% with threshold models. Maternal heritabilities were approximately 2 and 4%, respectively. Genetic correlation between direct and maternal additive effects was found to be not significantly different from zero. Models were compared in terms of goodness of fit and predictive ability. Criteria of comparison such as mean squared error, correlation between observed and predicted calving ease scores as well as between estimated breeding values were estimated from 85 118 calving records. The results provided few differences between linear and threshold models even though correlations between estimated breeding values from subsets of data for sires with progeny from linear model were 17 and 23% greater for direct and maternal genetic effects, respectively, than from threshold model. For the purpose of genetic evaluation for calving ease in Walloon Holstein dairy cattle, the linear animal model without covariance between direct and maternal additive effects was found to be the best choice.     

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.     

Bayesian inference strategies for the prediction of genetic merit using threshold models with an application to calving ease scores in Italian Piemontese cattle

First parity calving difficulty scores from Italian Piemontese cattle were analysed using a threshold mixed effects model. The model included the fixed effects of age of dam and sex of calf and their interaction and the random effects of sire, maternal grandsire, and herd‐year‐season. Covariances between sire and maternal grandsire effects were modelled using a numerator relationship matrix based on male ancestors. Field data consisted of 23 953 records collected between 1989 and 1998 from 4741 herd‐year‐seasons. Variance and covariance components were estimated using two alternative approximate marginal maximum likelihood (MML) methods, one based on expectation‐maximization (EM) and the other based on Laplacian integration. Inferences were compared to those based on three separate runs or sequences of Markov Chain Monte Carlo (MCMC) sampling in order to assess the validity of approximate MML estimates derived from data with similar size and design structure. Point estimates of direct heritability were 0.24, 0.25 and 0.26 for EM, Laplacian and MCMC (posterior mean), respectively, whereas corresponding maternal heritability estimates were 0.10, 0.11 and 0.12, respectively. The covariance between additive direct and maternal effects was found to be not different from zero based on MCMC‐derived confidence sets. The conventional joint modal estimates of sire effects and associated standard errors based on MML estimates of variance and covariance components differed little from the respective posterior means and standard deviations derived from MCMC. Therefore, there may be little need to pursue computation‐intensive MCMC methods for inference on genetic parameters and genetic merits using conventional threshold sire and maternal grandsire models for large datasets on calving ease.     

Effects of heat stress on production,somatic cell score and conception rate in Holsteins

We examined the effects of heat stress (HS) on production traits, somatic cell score (SCS) and conception rate at first insemination (CR) in Holsteins in Japan. We used a total of 228 242 records of milk, fat and protein yields, and SCS for the first three lactations, as well as of CR in heifers and in first‐ and second‐lactation cows that had calved for the first time between 2000 and 2012. Records from 47 prefectural weather stations throughout Japan were used to calculate the temperature–humidity index (THI); areas were categorized into three regional groups: no HS (THI < 72), mild HS (72 ≤ THI < 79), and moderate HS (THI ≥ 79). Trait records from the three HS‐region groups were treated as three different traits and trivariate animal models were used. The genetic correlations between milk yields from different HS groups were very high (0.91 to 0.99). Summer calving caused the greatest increase in SCS, and in the first and second lactations this increase became greater as THI increased. In cows, CR was affected by the interaction between HS group and insemination month: with summer and early autumn insemination, there was a reduction in CR, and it was much larger in the mild‐ and moderate‐HS groups than in the no‐HS group.     

Crossbreeding effects on milk yield traits and calving interval in spring‐calving dairy cows

The aim of the study was to assess crossbreeding effects for 305‐day milk, fat, and protein yield and calving interval (CI) in Irish dairy cows (parities 1 to 5) calving in the spring from 2002 to 2006. Data included 188 927 records for production traits and 157 117 records for CI. The proportion of genes from North American Holstein Friesian (HO), Friesian (FR), Jersey (JE) and Montbéliarde (MO) breeds, and coefficients of expected heterosis for HO×FR, HO×JE and HO×MO crosses were calculated from the breed composition of cows’ parents. The model used to assess crossbreeding effects accounted for contemporary group, age at calving within parity, linear regression on gene proportions for FR, JE and MO, and linear regression on coefficients of expected heterosis for HO×FR, HO×JE and HO×MO, as fixed effects, and additive genetic, permanent environmental and residual as random. Breed effects for production traits were in favour of HO, while for CI were in favour of breeds other than HO. The highest heterosis estimates for production were for HO×JE, with first‐generation crosses yielding 477 kg more milk, 25.3 kg more fat, and 17.4 kg more protein than the average of the parental breeds. The highest estimate for CI was for HO×MO, with first‐generation crosses showing 10.2 days less CI than the average of the parental breeds. Results from this study indicate breed differences and specific heterosis effects for milk yield traits and fertility exist in Irish dairy population.     

Comparison of homogeneity and heterogeneity of residual variance using random regression test-day models for first lactation Japanese Holstein cows

Using a large‐scale data set that included first lactation test day records from 1975 to 2000 for Japanese Holsteins, genetic parameters for milk yield were estimated by using random regression (RR) test‐day models (TDM) with heterogeneous and homogeneous residual variances. It is necessary for the RR‐TDM to include a function that explains the shape of the lactation curve. The RR‐TDM with the LW curve, which combined Wilmink's curve and a Legendre polynomial, was used for fitting the model for milk yield. In recent years, increases in residual variance have been noted for Japanese dairy cattle. Thus, three kinds of heterogeneous residual variance over the calving year were considered: H1, H2 and HG. Linear and quadratic exponential functions for the calving year were used in H1 and H2, respectively. Residual variance of HG was divided into five groups according to calving year. Homogeneous residual variance was HO. All heterogeneous residual variances increased with calving year in an almost linear fashion. Residual variance increased over the study period. However, there is no need to consider heterogeneous residual variances in genetic evaluations, because the heterogeneity of residual variance over the years did not affect the ranking of top sires and cows.     

Bayesian estimates of genetic parameters for metritis, retained placenta, milk fever, and clinical mastitis in Holstein dairy cows via Gibbs sampling

The objective of this study was to estimate heritability and genetic correlations between the liabilities of clinical mastitis (CM), milk fever (MF), metritis (MET), and retained placenta (RP) within the first three lactations of Holstein dairy cows. The records of 57,301 dairy cows from 20 large dairy herds in Iran between January 2005 and June 2009 were analysed with univariate and bivariate threshold animal models, using Gibbs sampling methodology. The final model included the fixed class effects of herd-year, season of calving, parity of dam, the linear covariate effect of age at calving, and the random direct genetic effect of animal. Posterior means of heritability for liabilities in first, second, and third lactations were 0.06, 0.08, and 0.09, respectively, for CM; 0.10, 0.12, and 0.11, respectively, for MF; 0.09, 0.07, and 0.10, respectively, for MET, and 0.07, 0.08, and 0.08, respectively, for RP. Posterior means of genetic correlations between disease liabilities were low or moderate (from −0.01 to 0.26). The results of this study indicated the importance of health traits for considering in the selection index of Iranian Holstein dairy cows.     

Genetic relationships between scrotal circumference and female reproductive traits

Records for yearling scrotal circumference (SC; n = 7,580), age at puberty in heifers (AP; n = 5,292), age at first calving (AFC; n = 4,835), and pregnancy, calving, or weaning status following the first breeding season (PR1, CR1, or WR1, respectively; n = 7,003) from 12 Bos taurus breeds collected at the Meat Animal Research Center (USDA) between 1978 and 1991 were used to estimate genetic parameters. Age at puberty (AP) was defined as age in days at first detected ovulatory estrus. Pregnancy (calving or weaning) status was scored as one for females conceiving (calving or weaning) given exposure during the breeding season and as zero otherwise. The final model for SC included fixed effects of age of dam at breeding (AD), year of breeding (Y), and breed (B) and age in days at measurement as a covariate. Fixed effects in models for AP and AFC were AD, Y, B, and month of birth. Fixed effects in models for PR1, CR1, and WR1 included AD, Y, and B. For all traits, random effects in the model were direct genetic, maternal genetic, maternal permanent environmental, and residual. Analyses for a three-trait animal model were carried out with SC, AP, and a third trait (the third trait was AFC, PR1, CR1, or WR1). A derivative-free restricted maximum likelihood algorithm was used to estimate the (co)variance components. Direct and maternal heritability estimates were 0.41 and 0.05 for SC; 0.16 and 0.03 for AP; 0.08 and 0.00 for AFC; 0.14 and 0.02 for PR1; 0.14 and 0.03 for CR1; and 0.12 and 0.01 for WR1. Genetic correlations between direct and maternal genetic effects within trait were -0.26, -0.63, -0.91, -0.79, -0.66, and -0.85 for SC, AP, AFC, PR1, CR1, and WR1, respectively. Direct genetic correlations between SC and AP and between those traits and AFC, PR1, CR1, and WR1 ranged from -0.15 (between SC and AP) to 0.23 (between AP and WR1). Estimates of heritability indicate that yearling SC should respond to direct selection better than AP, AFC, PR1, CR1, and WR1. Variation due to maternal genetic effects was small for all traits. No strong genetic correlations were detected between SC and female reproductive traits or between AP and the other female traits. These results suggest that genetic response in female reproductive traits through sire selection on yearling SC is not expected to be effective.     

宁夏地区荷斯坦牛青年牛繁殖性状遗传参数估计

旨在探究青年牛繁殖性状遗传能力群体遗传参数,评估不同配种季节间青年牛繁殖性状基因型与环境的互作效应(G×E).本研究利用宁夏地区12个牧场2007-2019年荷斯坦牛繁殖事件信息记录,计算青年牛繁殖性状包括:首配日龄(AFS)、初产日龄(AFC)、首次配种后56天不返情率(NRR56)、首末次配种间隔(IFL)、妊娠期...     

Study of the effect of genotype–environment interaction on age at first calving and production traits in Nellore cattle using multi‐trait reaction norms and Bayesian inference

This study investigated the effects of genotype–environment interaction on yearling weight, age at first calving and post‐weaning weight gain in Nellore cattle using multi‐trait reaction norm models. The environmental gradient was defined as a function of the mean yearling weight of the contemporary groups. A first‐order random regression sire model with four classes of residual variance was used in the analyses and Bayesian methods were applied to estimate the (co)variance components. The heritability estimates ranged from 0.284 to 0.547, 0.222 to 0.316 and 0.256 to 0.522 for yearling weight, age at first calving and post‐weaning weight gain, respectively. The lowest genetic correlations between environment groups for each trait were 0.38, 0.02 and 0.04 for yearling weight, age at first calving and post‐weaning weight gain, respectively. Differences in the correlation estimates were observed between traits in the same environments, with the magnitude of the estimates tending toward zero as the environment improved. The results highlight the importance of including genotype–environment interactions in genetic evaluation programs considering the differences observed between environmental groups not only in terms of heritability, but also of genetic correlations.     

Success at first insemination in Australian Angus cattle: analysis of uncertain binary responses

Field data from Australian Angus herds were used to investigate 2 methods of analyzing uncertain binary responses for success or failure at first insemination. A linear mixed model that included herd, year, and month of mating as fixed effects; unrelated service sire, additive animal, and residual as random effects; and linear and quadratic effects of age at mating as covariates was used to analyze binary data. An average gestation length (GL) derived from artificial insemination data was used to assign an insemination date to females mated to natural service sires. Females that deviated from this average GL led to uncertain binary responses. Two analyses were carried out: 1) a threshold model fitted to uncertain binary data, ignoring uncertainty (M1); and 2) a threshold model fitted to uncertain binary data, accounting for uncertainty via fuzzy logic classification (M2). There was practically no difference between point estimates obtained from M1 and M2 for service sire and herd variance; however, when uncertain binary data were analyzed ignoring uncertainty (M1), additive variance and heritability estimates were greater than with M2. Pearson correlations indicated that no major reranking would be expected for service sire effects and animal breeding values using M1 and M2. Given the results of the current study, a threshold model contemplating uncertainty is suggested for noisy binary data to avoid bias when estimating genetic parameters.     

Threshold-linear versus linear-linear analysis of birth weight and calving ease using an animal model: II. Comparison of models.

Several models were evaluated in terms of predictive ability for calving difficulty. Data included birth weight and calving difficulty scores provided by the American Gelbvieh Association from 26,006 calves born to first-parity cows and five simulated populations of 6,200 animals each. Included in the model were fixed age of dam x sex interaction effects, random herd-year-season effects, and random animal direct and maternal effects. Bivariate linear-threshold and linear-linear models for birth weight/calving ease and univariate threshold and linear models for calving ease were applied to the data sets. For each data set and model, one-half of calving ease records were randomly discarded. Predictive ability of the different models was defined with the mean square error (MSE) for the difference between a deleted calving ease score and its prediction obtained from the remaining data. In terms of correlation between simulated and predicted breeding values, the threshold models had a 1% advantage for direct genetic effects and 3% for maternal genetic effects. In simulation, the average MSE was .29 for linear-threshold, .32 for linear-linear, .37 for threshold, and .39 for linear model. For the field data set, the MSE was .31, .33, .39, and .40, respectively. Although the bivariate models for calving ease/birth weight were more accurate than univariate models, the threshold models showed a greater advantage under the bivariate model. For the purpose of genetic evaluation for calving difficulty in beef cattle, the use of the linear-threshold model seems justified. In dairy cattle, the evaluation for calving ease can benefit from recording birth weight.     

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.     

Genetic evaluation of calving to first insemination using natural and artificial insemination mating data

Mating and calving records for 51,084 first-parity heifers in Australian Angus herds were used to examine the relationship between probability of calving to first insemination (CFI) in artificial insemination and natural service (NS) mating data. Calving to first insemination was defined as a binary trait for both sources of data. Two Bayesian models were employed: 1) a bivariate threshold model with CFI in AI data regarded as a trait separate from CFI in NS data and 2) a univariate threshold model with CFI regarded as the same trait for both sources of data. Posterior means (SD) of additive variance in the bivariate analysis were similar: 0.049 (0.013) and 0.075 (0.021) for CFI in AI and NS data, respectively, indicating lack of heterogeneity for this parameter. A similar trend was observed for heritability in the bivariate analysis, with posterior means (SD) of 0.025 (0.007) and 0.048 (0.012) for AI and NS data, respectively. The posterior means (SD) of the additive covariance and corresponding genetic correlation between the traits were 0.048 (0.006) and 0.821 (0.138), respectively. Differences were observed between posterior means for herd-year variance: 0.843 vs. 0.280 for AI and NS data, respectively, which may reflect the higher incidence of 100% conception rates within a herd-year class (extreme category problem) in AI data. Parameter estimates under the univariate model were close to the weighted average of the corresponding parameters under the bivariate model. Posterior means (SD) for additive, herd-year, and service sire variance and heritability under the univariate model were 0.063 (0.007), 0.56 (0.029), 0.131 (0.013), and 0.036 (0.007), respectively. These results indicate that, genetically, cows with a higher probability of CFI when mated using AI also have a high probability of CFI when mated via NS. The high correlation between the two traits, along with the lack of heterogeneity for the additive variance, implies that a common additive variance could be used for AI and NS data. A single-trait analysis of CFI with heterogeneous variances for herd-year and service sire could be implemented. The low estimates of heritability indicate that response to selection for probability of calving to first insemination would be expected to be low.     

Threshold-linear analysis of measures of fertility in artificial insemination data and days to calving in beef cattle

Mating and calving records for 47,533 first-calf heifers in Australian Angus herds were used to examine the relationship between days to calving (DC) and two measures of fertility in AI data: 1) calving to first insemination (CFI) and 2) calving success (CS). Calving to first insemination and calving success were defined as binary traits. A threshold-linear Bayesian model was employed for both analyses: 1) DC and CFI and 2) DC and CS. Posterior means (SD) of additive covariance and corresponding genetic correlation between the DC and CFI were -0.62 d (0.19 d) and -0.66 (0.12), respectively. The corresponding point estimates between the DC and CS were -0.70 d (0.14 d) and -0.73 (0.06), respectively. These genetic correlations indicate a strong, negative relationship between DC and both measures of fertility in AI data. Selecting for animals with shorter DC intervals genetically will lead to correlated increases in both CS and CFI. Posterior means (SD) for additive and residual variance and heritability for DC for the DC-CFI analysis were 23.5 d2 (4.1 d2), 363.2 d2 (4.8 d2), and 0.06 (0.01), respectively. The corresponding parameter estimates for the DC-CS analysis were very similar. Posterior means (SD) for additive, herd-year and service sire variance and heritability for CFI were 0.04 (0.01), 0.06 (0.06), 0.14 (0.16), and 0.03 (0.01), respectively. Posterior means (SD) for additive, herd-year, and service sire variance and heritability for CS were 0.04 (0.01), 0.07 (0.07), 0.14 (0.16), and 0.03 (0.01), respectively. The similarity of the parameter estimates for CFI and CS suggest that either trait could be used as a measure of fertility in AI data. However, the definition of CFI allows the identification of animals that not only record a calving event, but calve to their first insemination, and the value of this trait would be even greater in a more complete dataset than that used in this study. The magnitude of the correlations between DC and CS-CFI suggest that it may be possible to use a multitrait approach in the evaluation of AI and natural service data, and to report one genetic value that could be used for selection purposes.     

Genetic evaluation of Australian dairy cattle for somatic cell scores using multi-trait random regression test-day model

Summary A multi-trait (MT) random regression (RR) test day (TD) model has been developed for genetic evaluation of somatic cell scores for Australian dairy cattle, where first, second and third lactations were considered as three different but correlated traits. The model includes herd-test-day, year-season, age at calving, heterosis and lactation curves modelled with Legendre polynomials as fixed effects, and random genetic and permanent environmental effects modelled with Legendre polynomials. Residual variance varied across the lactation trajectory. The genetic parameters were estimated using asreml . The heritability estimates ranged from 0.05 to 0.16. The genetic correlations between lactations and between test days within lactations were consistent with most of the published results. Preconditioned conjugate gradient algorithm with iteration on data was implemented for solving the system of equations. For reliability approximation, the method of Tier and Meyer was used. The genetic evaluation system was validated with Interbull validation method III by comparing proofs from a complete evaluation with those from an evaluation based on a data set excluding the most recent 4 years. The genetic trend estimate was in the allowed range and correlations between the two sets of proofs were very high. Additionally, the RR model was compared to the previous test day model. The correlations of proofs between both models were high (0.97) for bulls with high reliabilities. The correlations of bulls decreased with increasing incompleteness of daughter performance information. The correlations between the breeding values from two consecutive runs were high ranging from 0.97 to 0.99. The MT RR TD model was able to make effective use of available information on young bulls and cows, and could offer an opportunity to breeders to utilize estimated breeding values for first and later lactations.     

Genetic evaluation of calf and heifer survival in Iranian Holstein cattle using linear and threshold models

Calf and heifer survival are important traits in dairy cattle affecting profitability. This study was carried out to estimate genetic parameters of survival traits in female calves at different age periods, until nearly the first calving. Records of 49 583 female calves born during 1998 and 2009 were considered in five age periods as days 1–30, 31–180, 181–365, 366–760 and full period (day 1–760). Genetic components were estimated based on linear and threshold sire models and linear animal models. The models included both fixed effects (month of birth, dam's parity number, calving ease and twin/single) and random effects (herd‐year, genetic effect of sire or animal and residual). Rates of death were 2.21, 3.37, 1.97, 4.14 and 12.4% for the above periods, respectively. Heritability estimates were very low ranging from 0.48 to 3.04, 0.62 to 3.51 and 0.50 to 4.24% for linear sire model, animal model and threshold sire model, respectively. Rank correlations between random effects of sires obtained with linear and threshold sire models and with linear animal and sire models were 0.82–0.95 and 0.61–0.83, respectively. The estimated genetic correlations between the five different periods were moderate and only significant for 31–180 and 181–365 (r_g = 0.59), 31–180 and 366–760 (r_g = 0.52), and 181–365 and 366–760 (r_g = 0.42). The low genetic correlations in current study would suggest that survival at different periods may be affected by the same genes with different expression or by different genes. Even though the additive genetic variations of survival traits were small, it might be possible to improve these traits by traditional or genomic selection.     

Genetic relationships of fertility traits with test‐day milk yield and fat‐to‐protein ratio in tropical smallholder dairy farms

The test‐day milk fat‐to‐protein ratio (TD‐FPR) could serve as a measure of energy balance status and might be used as a criterion to improve metabolic stability and fertility through genetic selection. Therefore, genetic parameters for fertility traits, test‐day milk yield (TD‐MY) and TD‐FPR, as well as, their relationships during different stages of lactation, were estimated on data collected from 25 968 primiparous Thai dairy crossbred cows. Gibbs sampling algorithms were implemented to obtain (co)variance components using both univariate linear and threshold animal models and bivariate linear‐linear and linear‐threshold animal models with random regression. Average TD‐MY and TD‐FPR were 12.60 and 1.15. Heritability estimates for TD‐MY, TD‐FPR and selected fertility traits ranged from 0.31 to 0.58, 0.17 to 0.19 and 0.02 to 0.05, respectively. Genetic correlations among TD‐FPR and TD‐MY, TD‐FPR and fertility traits, and TD‐MY and fertility traits ranged from 0.05 to ‐0.44, from ‐0.98 to 0.98 and ‐0.22 to 0.79, respectively. Selection for lower TD‐FPR would decrease numbers of inseminations per conception and increase conception at first service and pregnancy within 90 days. In addition, cow selection based only on high milk production has strong effects to prolong days to first service, days open and calving interval.     

Comparison of threshold vs linear and animal vs sire models for predicting direct and maternal genetic effects on calving difficulty in beef cattle

This study compared the accuracy of several models for obtaining genetic evaluations of calving difficulty. The models were univariate threshold animal (TAM), threshold sire-maternal grandsire (TSM), linear animal (LAM), and linear sire-maternal grandsire (LSM) models and bivariate threshold-linear animal (TLAM), threshold-linear sire-maternal grandsire (TLSM), linear-linear animal (LLAM), and linear-linear sire-maternal grandsire (LLSM) models for calving difficulty and birth weight. Data were obtained from the American Gelbvieh Association and included 84,420 first-parity records of both calving difficulty and birth weight. Calving difficulty scores were distributed as 73.4% in the first category (no assistance), 18.7% in the second, 6.3% in the third, and 1.6% in the fourth. Included in the animal models were fixed sex of calf by age of dam subclasses, random herd-year-season effects, and random animal direct and maternal breeding values. Sire-maternal grandsire models were similar to the animal models, with animal and maternal effects replaced by sire and maternal grandsire effects. Models were compared using a data splitting technique based on the correlation of estimated breeding values from two samples, with one-half of the calving difficulty records discarded randomly in the first sample and the remaining calving difficulty records discarded in the second sample. Reported correlations are averages of 10 replicates. The results obtained using animal models confirmed the slight advantage of TAM over LAM (0.69 vs 0.63) and TLAM over LLAM (0.90 vs 0.86). Bivariate analyses greatly improved the accuracy of genetic prediction of direct effects on calving difficulty relative to univariate analyses. Similar ranking of the models was found for maternal effects, but smaller correlations were obtained for bivariate models. For sire-maternal grandsire models, no differences between sire or maternal grandsire correlations were observed for TLSM compared to LLSM, and small differences were observed between TSM and LSM. The threshold model offered advantages over the linear model in animal models but not in sire-maternal grandsire models. For genetic evaluation of calving difficulty in beef cattle, the threshold-linear animal model seems to be the best choice for predicting both direct and maternal effects.