Short communication: Heterosis and breed effects for milk production and udder health traits in crosses between Danish Holstein,Danish Red,and Danish Jersey

During the last decade, the use of systematic crossbreeding in dairy cattle herds has increased in several countries of the world. The aim of this study was to estimate the effect of breed proportion and heterosis on milk production traits and udder health traits in dairy cattle. The study was based on records on milk yield (MY), protein yield (PY), fat yield (FY), somatic cell score (SCS), and mastitis (MAST) from 73,695 first-lactation dairy cows in 130 Danish herds applying systematic crossbreeding programs. Around 45% of the cows were crosses between Danish Holstein (DH), Danish Red (DR), or Danish Jersey (DJ), and the remaining were purebred DH, DR, or DJ. The statistical model included the fixed effects of herd-year, calving month, and calving age and an effect representing the lactation status of the cow. In addition, the model included a regression on calving interval from first to second lactation, a regression on the proportion of DH, DR, and DJ genes, and a regression on the degree of heterozygosity between DH and DR, DH and DJ, and DR and DJ. Random effects were the genetic effect of the cow and a residual. The effect of breed proportions was estimated relatively to DH. For MY, a pure DR yielded 461 kg milk less than DH, whereas a pure DJ yielded 2,259 kg milk less than a pure DH. Compared with DH, PY was 41.7 kg less for DJ, whereas PY for DR was 4.0 kg less than for DH. For FY, a DR yielded 10.6 kg less than DH, whereas there was no significant effect of breed proportion between DJ and DH. A DR cow had lower SCS (0.13) than DH, whereas DJ had higher SCS (0.14) than DH. There was no significant effect of breed proportion on MAST between the 3 breeds. Heterosis was significant in all combinations of breeds for MY, FY, and PY. Heterosis for crosses between DH and DR was 257 kg (3.2%), 11.9 kg (3.2%), and 8.9 kg (3.2%) for MY, PY, and FY, respectively. Corresponding figures for crosses between DH and DJ were 314 kg (4.4%), 14.3 kg (4.4%), and 10.4 kg (4.0%), whereas heterosis between DR and DJ was 462 kg (6.7%), 19.6 kg (6.7%), and 13.9 kg (5.4%) for MY, PY, and FY, respectively. Heterosis was only significant for SCS in the crosses between DH and DR. Heterosis effects for MAST were nonsignificant for all the crosses. The results obtained in this study demonstrate that in first lactation cows, there is a positive effect of heterosis on milk production traits, but limited effect on udder health traits.     

Genetic parameters for buffalo milk yield and milk quality traits using Bayesian inference

The availability of accurate genetic parameters for important economic traits in milking buffaloes is critical for implementation of a genetic evaluation program. In the present study, heritabilities and genetic correlations for fat (FY305), protein (PY305), and milk (MY305) yields, milk fat (%F) and protein (%P) percentages, and SCS were estimated using Bayesian methodology. A total of 4,907 lactations from 1,985 cows were used. The (co)variance components were estimated using multiple-trait analysis by Bayesian inference method, applying an animal model, through Gibbs sampling. The model included the fixed effects of contemporary groups (herd-year and calving season), number of milking (2 levels), and age of cow at calving as (co)variable (quadratic and linear effect). The additive genetic, permanent environmental, and residual effects were included as random effects in the model. The posterior means of heritability distributions for MY305, FY305, PY305, %F, P%, and SCS were 0.22, 0.21, 0.23, 0.33, 0.39, and 0.26, respectively. The genetic correlation estimates ranged from −0.13 (between %P and SCS) to 0.94 (between MY305 and PY305). The permanent environmental correlation estimates ranged from −0.38 (between MY305 and %P) to 0.97 (between MY305 and PY305). Residual and phenotypic correlation estimates ranged from −0.26 (between PY305 and SCS) to 0.97 (between MY305 and PY305) and from −0.26 (between MY305 and SCS) to 0.97 (between MY305 and PY305), respectively. Milk yield, milk components, and milk somatic cells counts have enough genetic variation for selection purposes. The genetic correlation estimates suggest that milk components and milk somatic cell counts would be only slightly affected if increasing milk yield were the selection goal. Selecting to increase FY305 or PY305 will also increase MY305, %P, and %F.     

Genetic and phenotypic relationships among endocrine and traditional fertility traits and production traits in Holstein-Friesian dairy cows

The objective of this study was to estimate the heritability of a number of traditional and endocrine fertility traits in addition to d-56 predicted milk yield (MY56), and the genetic and phenotypic correlations between these traits. Various fixed effects such as season, year, herd, lactation number, diet, percentage Holstein (PCH) of the cow, and occurrence of uterine infection (UI), dystocia (DYS), and retained placenta (RP) were also investigated. Data collected for 1212 lactations of 1080 postpartum (PP) Holstein-Friesian dairy cows in eight commercial farms between 1996 and 1999 included thrice weekly milk progesterone samples, calving and insemination dates, various reproductive health records, monthly/bimonthly production records, three-generation pedigrees, and PCH information. Genetic models were fitted to the data to obtain heritabilitites and correlations using ASREML. Estimates of heritability for interval to commencement of luteal activity PP (lnCLA), length of the first luteal phase PP (lnLutI) and occurrence of persistent CL type I (PCLI) were 0.16, 0.17, and 0.13, respectively. Heritabilities for pregnancy to first service (PFS), interval to first service (IFS), and MY56 were 0.14, 0.13, and 0.50, respectively. Genetic regressions of lnCLA and lnLutI on PTA of the sire for milk, fat, and protein yields, and PIN95 were investigated. Regressions of lnCLA were positive and significant on fat yield, while regressions of lnLutI on both protein yield and PIN95 were negative and significant. Genetic correlations of endocrine fertility traits (lnCLA, lnLutI, and PCLI) with MY56 were high (0.36, P < 0.05; -0.51, P < 0.05; and -0.31, P < 0.1, respectively). Percentage Holstein of the cows had no significant effect on any of the fertility parameters monitored. This work emphasizes the strong genetic correlation of fertility with production traits and, therefore, highlights the urgent requirement for selective breeding for fertility in the United Kingdom. The high heritability of endocrine fertility traits stress their potential value for inclusion in a selection index to improve fertility.     

Genetic parameters for female fertility traits and a fertility index in Spanish dairy cattle

Genetic correlations among female fertility traits (linear and binary) were estimated using 225,085 artificial insemination records from 120,713 lactations on 63,160 Holstein cows. Fertility traits were: calving interval, days open, a linear transformation of days open, days to first insemination, interval between first and last insemination, number of inseminations per service period, pregnancy within 56 and 90 d after first insemination, and success in first insemination. A bivariate animal model was implemented using Bayesian methods in the case of binary traits. Low heritabilities (0.02 to 0.06) were estimated for these fertility traits. Strong genetic correlations (0.89 to 0.99) were found among traits, except for days to first service, where the genetic correlation with other fertility traits ranged from −0.52 to −0.18 for binary traits, and from 0.50 to 0.82 for days to first service, calving interval, and days open. Four fertility indices were proposed utilizing information from insemination records; these indices combined one indicator of the beginning of the service period and one indicator of conception rate. Two additional indices used information from the milk-recording scheme, including calving interval and a linear transformation of days open. The fertility index composed of days to first service and pregnancy within 56 d achieved the highest genetic gain for reducing fertility cost, reducing days to first service, and reducing the number of inseminations per lactation ($8.60, −1.31 d, and −0.03 AI, respectively). This index achieved at least 15% higher genetic gain than obtained from indices with information from the milk recording scheme only (calving interval and days open).     

Genetic evaluation of fertility traits of dairy cattle using a multiple-trait animal model

A genetic evaluation system was developed for 5 fertility traits of dairy cattle: interval from first to successful insemination and nonreturn rate to 56 d of heifers, and interval from calving to first insemination, nonreturn rate to 56 d, and interval first to successful insemination of cows. Using the 2 interval traits of cows as components, breeding values for days open were derived. A multiple-trait animal model was applied to evaluate these fertility traits. Fertility traits of later lactations of cows were treated as repeated measurements. Genetic parameters were estimated by REML. Mixed model equations of the genetic evaluation model were solved with preconditioned conjugate gradients or the Gauss-Seidel algorithm and iteration on data techniques. Reliabilities of estimated breeding values were approximated with a multi-trait effective daughter contribution method. Daughter yield deviations and associated effective daughter contributions were calculated with a multiple trait approach. The genetic evaluation software was applied to the insemination data of dairy cattle breeds in Germany, Austria, and Luxembourg, and it was validated with various statistical methods. Genetic trends were validated. Small heritability estimates were obtained for all the fertility traits, ranging from 1% for nonreturn rate of heifers to 4% for interval calving to first insemination. Genetic and environmental correlations were low to moderate among the traits. Notably, unfavorable genetic trends were obtained in all the fertility traits. Moderate to high correlations were found between daughter yield-deviations and estimated breeding values (EBV) for Holstein bulls. Because of much lower heritabilities of the fertility traits, the correlations of daughter yield deviations with EBV were significantly lower than those from production traits and lower than the correlations from type traits and longevity. Fertility EBV were correlated unfavorably with EBV of milk production traits but favorably with udder health and longevity. Integrating fertility traits into a total merit selection index can halt or reverse the decline of fertility and improve the longevity of dairy cattle.     

Predicting dry matter intake in Canadian Holstein dairy cattle using milk mid-infrared reflectance spectroscopy and other commonly available predictors via artificial neural networks

Dry matter intake (DMI) is a fundamental component of the animal's feed efficiency, but measuring DMI of individual cows is expensive. Mid-infrared reflectance spectroscopy (MIRS) on milk samples could be an inexpensive alternative to predict DMI. The objectives of this study were (1) to assess if milk MIRS data could improve DMI predictions of Canadian Holstein cows using artificial neural networks (ANN); (2) to investigate the ability of different ANN architectures to predict unobserved DMI; and (3) to validate the robustness of developed prediction models. A total of 7,398 milk samples from 509 dairy cows distributed over Canada, Denmark, and the United States were analyzed. Data from Denmark and the United States were used to increase the training data size and variability to improve the generalization of the prediction models over the lactation. For each milk spectra record, the corresponding weekly average DMI (kg/d), test-day milk yield (MY, kg/d), fat yield (FY, g/d), and protein yield (PY, g/d), metabolic body weight (MBW), age at calving, year of calving, season of calving, days in milk, lactation number, country, and herd were available. The weekly average DMI was predicted with various ANN architectures using 7 predictor sets, which were created by different combinations MY, FY, PY, MBW, and MIRS data. All predictor sets also included age of calving and days in milk. In addition, the classification effects of season of calving, country, and lactation number were included in all models. The explored ANN architectures consisted of 3 training algorithms (Bayesian regularization, Levenberg-Marquardt, and scaled conjugate gradient), 2 types of activation functions (hyperbolic tangent and linear), and from 1 to 10 neurons in hidden layers). In addition, partial least squares regression was also applied to predict the DMI. Models were compared using cross-validation based on leaving out 10% of records (validation A) and leaving out 10% of cows (validation B). Superior fitting statistics of models comprising MIRS information compared with the models fitting milk, fat and protein yields suggest that other unknown milk components may help explain variation in weekly average DMI. For instance, using MY, FY, PY, and MBW as predictor variables produced a predictive accuracy (r) ranging from 0.510 to 0.652 across ANN models and validation sets. Using MIRS together with MY, FY, PY, and MBW as predictors resulted in improved fitting (r = 0.679–0.777). Including MIRS data improved the weekly average DMI prediction of Canadian Holstein cows, but it seems that MIRS predicts DMI mostly through its association with milk production traits and its utility to estimate a measure of feed efficiency that accounts for the level of production, such as residual feed intake, might be limited and needs further investigation. The better predictive ability of nonlinear ANN compared with linear ANN and partial least squares regression indicated possible nonlinear relationships between weekly average DMI and the predictor variables. In general, ANN using Bayesian regularization and scaled conjugate gradient training algorithms yielded slightly better weekly average DMI predictions compared with ANN using the Levenberg-Marquardt training algorithm.     

Genetic parameters for fertility of dairy heifers and cows at different parities and relationships with production traits in first lactation

The objectives of this study were to estimate genetic parameters for fertility of Brown Swiss cattle, considering reproductive measures in different parities as different traits, and to estimate relationships between production traits of first lactation and fertility of heifers and first-parity and second-parity cows. Reproductive indicators were interval from parturition to first service, interval from first service to conception, interval from parturition to conception, number of inseminations to conception, conception rate at first service, and nonreturn rate at 56 d after first service. Production traits were peak milk yield (pMY), lactation milk yield, and lactation length (LL). Data included 37,546 records on heifers, and 24,098 and 15,653 records on first- and second-parity cows, respectively. Cows were reared in 2,035 herds, calved from 1999 to 2007, and were progeny of 527 AI bulls. Gibbs sampling was implemented to obtain (co)variance components using both univariate and bivariate threshold and censored linear sire models. Estimates of heritability for reproductive traits in heifers (0.016 to 0.026) were lower than those in first-parity (0.017 to 0.142) and second-parity (0.026 to 0.115) cows. Genetic correlations for fertility in first- and second-parity cows were very high (>0.920), whereas those between heifers and lactating cows were moderate (0.348 to 0.709). The latter result indicates that fertility in heifers is a different trait than fertility in lactating cows, and hence it cannot be used as robust indicator of cow fertility. Heifer fertility was not related to production traits in first lactation (genetic correlations between −0.215 and 0.251). Peak milk yield exerted a moderate and unfavorable effect on the interval from parturition to first service (genetic correlations of 0.414 and 0.353 after first and second calving, respectively), and a low and unfavorable effect on other fertility traits (genetic correlations between −0.281 and 0.295). Infertility after first calving caused a strong elongation of the lactation, and LL was negatively correlated with fertility of cows after second calving, so that LL can itself be regarded as a measure of fertility. Lactation milk yield depends on both pMY and LL, and, as such, is a cause and consequence of (in)fertility.     

Genetic correlations among body condition score, yield, and fertility in first-parity cows estimated by random regression models.

Twenty type classifiers scored body condition (BCS) of 91,738 first-parity cows from 601 sires and 5518 maternal grandsires. Fertility data during first lactation were extracted for 177,220 cows, of which 67,278 also had a BCS observation, and first-lactation 305-d milk, fat, and protein yields were added for 180,631 cows. Heritabilities and genetic correlations were estimated using a sire-maternal grandsire model. Heritability of BCS was 0.38. Heritabilities for fertility traits were low (0.01 to 0.07), but genetic standard deviations were substantial, 9 d for days to first service and calving interval, 0.25 for number of services, and 5% for first-service conception. Phenotypic correlations between fertility and yield or BCS were small (-0.15 to 0.20). Genetic correlations between yield and all fertility traits were unfavorable (0.37 to 0.74). Genetic correlations with BCS were between -0.4 and -0.6 for calving interval and days to first service. Random regression analysis (RR) showed that correlations changed with days in milk for BCS. Little agreement was found between variances and correlations from RR, and analysis including a single month (mo 1 to 10) of data for BCS, especially during early and late lactation. However, this was due to excluding data from the conventional analysis, rather than due to the polynomials used. RR and a conventional five-traits model where BCS in mo 1, 4, 7, and 10 was treated as a separate traits (plus yield or fertility) gave similar results. Thus a parsimonious random regression model gave more realistic estimates for the (co)variances than a series of bivariate analysis on subsets of the data for BCS. A higher genetic merit for yield has unfavorable effects on fertility, but the genetic correlation suggests that BCS (at some stages of lactation) might help to alleviate the unfavorable effect of selection for higher yield on fertility.     

Genetic parameters of mid-infrared methane predictions and their relationships with milk production traits in Holstein cattle

Many countries have pledged to reduce greenhouse gases. In this context, the dairy sector is one of the identified sectors to adapt production circumstances to address socio-environmental constraints due to its large carbon footprint related to CH₄ emission. This study aimed mainly to estimate (1) the genetic parameters of 2 milk mid-infrared-based CH₄ proxies [predicted daily CH₄ emission (PME, g/d), and log-transformed predicted CH₄ intensity (LMI)] and (2) their genetic correlations with milk production traits [milk (MY), fat (FY), and protein (PY) yields] from first- and second-parity Holstein cows. A total of 336,126 and 231,400 mid-infrared CH₄ phenotypes were collected from 56,957 and 34,992 first- and second-parity cows, respectively. The PME increased from the first to the second lactation (433 vs. 453 g/d) and the LMI decreased (2.93 vs. 2.86). We used 20 bivariate random regression test-day models to estimate the variance components. Moderate heritability values were observed for both CH₄ traits, and those values decreased slightly from the first to the second lactation (0.25 ± 0.01 and 0.22 ± 0.01 for PME; 0.18 ± 0.01 and 0.17 ± 0.02 for LMI). Lactation phenotypic and genetic correlations were negative between PME and MY in both first and second lactations (?0.07 vs. ?0.07 and ?0.19 vs. ?0.24, respectively). More close scrutiny revealed that relative increase of PME was lower with high MY levels even reverting to decrease, and therefore explaining the negative correlations, indicating that higher producing cows could be a mitigation option for CH₄ emission. The PME phenotypic correlations were almost equal to 0 with FY and PY for both lactations. However, the genetic correlations between PME and FY were slightly positive (0.11 and 0.12), whereas with PY the correlations were slightly negative (?0.05 and ?0.04). Both phenotypic and genetic correlations between LMI and MY or PY or FY were always relatively highly negative (from ?0.21 to ?0.88). As the genetic correlations between PME and LMI were strong (0.71 and 0.72 in first and second lactation), the selection of one trait would also strongly influence the other trait. However, in animal breeding context, PME, as a direct quantity CH₄ proxy, would be preferred to LMI, which is a ratio trait of PME with a trait already in the index. The range of PME sire estimated breeding values were 22.1 and 29.41 kg per lactation in first and second parity, respectively. Further studies must be conducted to evaluate the effect of the introduction of PME in a selection index on the other traits already included in this index, such as, for instance, fertility or longevity.     

Estimation of genetic parameters for concentrations of milk urea nitrogen

The objective of this study was to use field data collected by dairy herd improvement programs to estimate genetic parameters for concentrations of milk urea nitrogen (MUN). Edited data were 36,074 test-day records of MUN and yields of milk, fat, and protein obtained from 6102 cows in Holstein herds in Ontario, Canada. Data were divided into three sets, for the first three lactations. Two analyses were performed on data from each lactation. The first procedure used ANOVA to estimate the significance of the effects of several environmental factors on MUN. Herd-test-day effects had the most significant impact on MUN. Effects of stage of lactation were also important, and MUN levels tended to increase from the time of peak yield until the end of lactation. The second analysis used a random regression model to estimate heritabilities and genetic correlations of MUN and the yield traits. Heritability estimates for MUN in lactations one, two, and three were 0.44, 0.59, and 0.48, respectively. Heritabilities for the yield traits were of a similar magnitude. Little relationship was observed between MUN and yield. Raw phenotypic correlations were all <0.10 (absolute value). Genetic correlations with production traits were close to zero in lactations one and three and only slightly positive in lactation two. The results indicate that selection on MUN is possible, but relationships between MUN and other economically important traits such as metabolic disease and fertility are needed.     

Predicting methane emission in Canadian Holstein dairy cattle using milk mid-infrared reflectance spectroscopy and other commonly available predictors via artificial neural networks

Interest in reducing eructed CH₄ is growing, but measuring CH₄ emissions is expensive and difficult in large populations. In this study, we investigated the effectiveness of milk mid-infrared spectroscopy (MIRS) data to predict CH₄ emission in lactating Canadian Holstein cows. A total of 181 weekly average CH₄ records from 158 Canadian cows and 217 records from 44 Danish cows were used. For each milk spectra record, the corresponding weekly average CH₄ emission (g/d), test-day milk yield (MY, kg/d), fat yield (FY, g/d), and protein yield (PY, g/d) were available. The weekly average CH₄ emission was predicted using various artificial neural networks (ANN), partial least squares regression, and different sets of predictors. The ANN architectures consisted of 3 training algorithms, 1 to 10 neurons with hyperbolic tangent activation function in the hidden layer, and 1 neuron with linear (purine) activation function in the hidden layer. Random cross-validation was used to compared the predictor sets: MY (set 1); FY (set 2); PY (set 3); MY and FY (set 4); MY and PY (set 5); MY, FY, and PY (set 6); MIRS (set 7); and MY, FY, PY, and MIRS (set 8). All predictor sets also included age at calving and days in milk, in addition to country, season of calving, and lactation number as categorical effects. Using only MY (set 1), the predictive accuracy (r) ranged from 0.245 to 0.457 and the root mean square error (RMSE) ranged from 87.28 to 99.39 across all prediction models and validation sets. Replacing MY with FY (set 2; r = 0.288–0.491; RMSE = 85.94–98.04) improved the predictive accuracy, but using PY (set 3; r = 0.260–0.468; RMSE = 86.95–98.47) did not. Adding FY to MY (set 4; r = 0.272–0.469; RMSE = 87.21–100.76) led to a negligible improvement compared with sets 1 and 3, but it slightly decreased accuracy compared with set 2. Adding PY to MY (set 5; r = 0.250–0.451; RMSE = 87.66–100.94) did not improve prediction ability. Combining MY, FY, and PY (set 6; r = 0.252–0.455; RMSE = 87.74–101.93) yielded accuracy slightly lower than sets 2 and 3. Using only MIRS data (set 7; r = 0.586–0.717; RMSE = 69.09–96.20) resulted in superior accuracy compared with all previous sets. Finally, the combination of MIRS data with MY, FY, and PY (set 8; r = 0.590–0.727; RMSE = 68.02–87.78) yielded similar accuracy to set 7. Overall, sets including the MIRS data yielded significantly better predictions than the other sets. To assess the predictive ability in a new unseen herd, a limited block cross-validation was performed using 20 cows in the same Canadian herd, which yielded r = 0.229 and RMSE = 154.44, which were clearly much worse than the average r = 0.704 and RMSE = 70.83 when predictions were made by random cross-validation. These results warrant further investigation when more data become available to allow for a more comprehensive block cross-validation before applying the calibrated models for large-scale prediction of CH₄ emissions.     

Genetic relationship between culling, milk production, fertility, and health traits in Norwegian red cows

First-lactation records on 836,452 daughters of 3,064 Norwegian Red sires were used to examine associations between culling in first lactation and 305-d protein yield, susceptibility to clinical mastitis, lactation mean somatic cell score (SCS), nonreturn rate within 56 d in heifers and primiparous cows, and interval from calving to first insemination. A Bayesian multivariate threshold-linear model was used for analysis. Posterior mean of heritability of liability to culling of primiparous cows was 0.04. The posterior means of the genetic correlations between culling and the other traits were −0.41 to 305-d protein yield, 0.20 to lactation mean SCS, 0.36 to clinical mastitis, 0.15 to interval from calving to first insemination, −0.11 to 56-d nonreturn as heifer, and −0.04 to 56-d nonreturn as primiparous cow. As much as 66% of the genetic variation in culling was explained by genetic variation in protein yield, clinical mastitis, interval of calving to first insemination, and 56-d nonreturn in heifers, whereas contribution from the SCS and 56-d nonreturn as primiparous cow was negligible, after taking the other traits into account. This implies that for breeds selected for a broad breeding goal, including functional traits such as health and fertility, most of the genetic variation in culling will probably be covered by other traits in the breeding goal. However, in populations where data on health and fertility is scarce or not available at all, selection against early culling may be useful in indirect selection for improved health and fertility. Regression of average sire posterior mean on birth-year of the sire indicate a genetic change equivalent to an annual decrease of the probability of culling in first-lactation Norwegian Red cattle by 0.2 percentage units. This genetic improvement is most likely a result of simultaneous selection for improved milk yield, health, and fertility over the last decades.     

Genetic relationships between persistency and reproductive performance in first-lactation Canadian holsteins

The main objective of this study was to estimate genetic relationships between lactation persistency and reproductive performance in first lactation. Relationships with day in milk at peak milk yield and estimated 305-d milk yield were also investigated. The data set contained 33,312 first-lactation Canadian Holsteins with first-parity reproductive, persistency, and productive information. Reproductive performance traits included age at first insemination, nonreturn rate at 56 d after first insemination as a virgin heifer and as a first-lactation cow, calving difficulty at first calving and calving interval between first and second calving. Lactation persistency was defined as the Wilmink b parameter for milk yield and was calculated by fitting lactation curves to test day records using a multiple-trait prediction procedure. An 8-trait genetic analysis was performed using the Variance Component Estimation package (VCE 5) via Gibbs sampling to estimate genetic parameters for all traits. Heritabilities of persistency, day in milk at peak milk yield and estimated 305-d milk yield were 0.18, 0.09 and 0.45, respectively. Heritabilities of reproduction were low and ranged from 0.03 to 0.19. The highest heritability was for age at first insemination. Heifer reproductive traits were lowly genetically correlated, whereas cow reproductive traits were moderately correlated. Heifers younger than average when first inseminated and/or conceived successfully at first insemination tended to have a more persistent first lactation. First lactation was more persistent if heifers had difficulty calving (r(g) = 0.43), or conceived successfully at first insemination in first lactation (r(g) = 0.32) or had a longer interval between first and second calving (r(g) = 0.17). Estimates of genetic correlations of reproductive performance with estimated 305-d milk yield were different in magnitude, but similar in sign to those with persistency (0.02 to 0.51).     

The genetic relationship between calving interval, body condition score and linear type and management traits in registered Holsteins

The trend to poorer fertility in dairy cattle with rising genetic merit for production over the last decade suggests that breeding goals need to be broadened to include fertility. This requires reliable estimates of genetic (co)variances for fertility and other traits of economic importance. In the United Kingdom at present, reliable information on calving dates and hence calving intervals are available for most dairy cows. Data in this study consisted of 44,672 records from first lactation heifers on condition score, linear type score, and management traits in addition to 19,042 calving interval records. Animal model REML was used to estimate (co)variance components. Genetic correlations of body condition score (BCS) and angularity with calving interval were -0.40 and 0.47, respectively, thus cows that are thinner and more angular have longer calving intervals. Genetic correlations between calving interval and milk, fat, and protein yields were between 0.56 and 0.61. Records of phenotypic calving interval were regressed on sire breeding values for BCS estimated from records taken at different months of lactation and breeding values for BCS change. Genetic correlations inferred from these regressions showed that BCS recorded 1 mo after calving had the largest genetic correlation with calving interval in first lactation cows. It may be possible to combine information on calving interval, BCS, and angularity into an index to predict genetic merit for fertility.     

Random regression analysis of milk yield and milk composition in the first and second lactations of Murciano-Granadina goats

Records from the milk recording scheme of Spanish Murciano-Granadina goats were studied to estimate genetic (co)variance components and breeding values throughout the first and second lactations. The data used consisted of 49,696 monthly test-day records of milk (MY), protein (PY), fat (FY), and dry matter (DMY) yields from 5,163 goats, distributed in 20 herds, offspring of 2,086 does and 206 bucks. These records were analyzed by 2-trait random regression models (RRM) and a repeatability test-day model (RTDM). At the middle of lactation, heritability estimates for MY, DMY, and FY obtained with RTDM were larger than those estimated with RRM, and the opposite was true for PY. The RRM estimates of heritability for MY, FY, and PY were very similar throughout the trajectories of both lactations. Heritability estimates for DMY decreased through the lactation period. The genetic correlations between the first and second lactation records estimated for all traits by RRM were positive and ranged from 0.43 to 0.80 throughout the lactation curves. The correlation between BV estimated with RTDM and RRM was 0.742 for MY and 0.664 for DMY. The RRM could be a useful alternative to RTDM for the prediction of BV in this breed.     

Relationships between fertility and production in Holstein dairy cattle in different lactations

Record of Performance and Dairy Herd Improvement Corporation production records of Ontario Holstein cows were merged with breeding receipts of three Ontario AI units from September 1981 through December 1985. Relationships between fertility and production in the first three lactations were investigated for 97,368 daughters of 3806 sires in 22,768 herd-hear-seasons of calving. Fertility traits were days from calving to first insemination, number of inseminations per conception, and days open. Production traits were age and month of calving adjusted 305-d milk and fat yields and fat percentage. Multiple-trait maximum likelihood was used to estimate variances and covariances. Heritabilities for the first three lactations were .18, .18, and .19 for milk yield; .20, .19, and .19 for fat yield; and .58, .52, and .48 for fat percentage. Heritabilities of fertility traits ranged from .03 to .06. Genetic and phenotypic correlations between fertility and production traits in all three lactations were essentially 0. Genetic correlations between different lactation production traits ranged from .2 to .65. Repeatabilities of fertility traits ranged from .05 to .16 in different lactations. Repeatabilities for production traits in different lactations ranged from .51 to .77. Genetic and phenotypic correlations between fertility and production in the subsequent lactation and between production and subsequent lactation fertility were also very low or zero.     

Genetic association of clinical mastitis with test-day somatic cell score and milk yield during first lactation of Finnish Ayrshire cows

In this study the genetic association during lactation of 2 clinical mastitis (CM) traits: CM1 (7 d before to 30 d after calving) and CM2 (31 to 300 d after calving) with test-day somatic cell score (SCS) and milk yield (MY) was assessed using multitrait random regression sire models. The data analyzed were from 27,557 first-lactation Finnish Ayrshire cows. Random regressions on second- and third-order Legendre polynomials were used to model the daily genetic and permanent environmental variances of test-day SCS and MY, respectively, while only the intercept term was fitted for CM. Results showed that genetic correlations between CM and the test-day traits varied during lactation. Genetic correlations between CM1 and CM2 and test-day SCS during lactation varied from 0.41 to 0.77 and from 0.34 to 0.71, respectively. Genetic correlations of test-day MY with CM1 and CM2 ranged from 0.13 to 0.51 and from 0.49 to 0.66, respectively. Correlations between CM1 and SCS were strongest during early lactation, whereas correlations between CM2 and SCS were strongest in late lactation. Genetic correlations lower than unity indicate that CM and SCS measure different aspects of the trait mastitis. Milk yield in early lactation was more strongly correlated with both CM1 and CM2 than milk yield in later lactation. This suggests that selection for higher lactation MY through selection on increased milk yield in early lactation will have a more deleterious effect on genetic resistance to mastitis than selection for higher yield in late lactation. The approach used in this study for the estimation of the genetic associations between test-day and CM traits could be used to combine information from traits with different data structures, such as test-day SCS and CM traits in a multitrait random regression model for the genetic evaluation of udder health.     

Correlations among first and second lactation milk yield and calving interval

Estimates of genetic correlations were .17 between first lactation milk yield and concurrent calving interval, .10 between second lactation milk yield and first calving interval, and .82 between first and second milk yields. Corresponding phenotypic correlations were .27, .16, and .58. Heritability estimates were .27 and .25 for first and second lactations and .15 for calving interval. Estimates were averages of two samples of 15 New York State herds averaging 144 AI-sired Holstein cows and 30 sires. Milk yields were 305-d, mature equivalent. Calving interval was days between first and second freshening. First milk records without a second freshening were included. Multiple-trait animal model included separate herd-year-season effects for first and second milk yields and calving interval. Numerator relationships among animals within herd, except for daughter-dam relationships, were included. The REML with the expectation-maximization algorithm was used to estimate (co)variance matrices among genetic values and environmental effects for the three traits. Results indicate a need to adjust milk records for the phenotypic effects of current and previous calving interval. The genetic association, however, between fertility and milk yield appears small. Genetic improvement of 450 kg of milk yield may result in 2 added d to first calving interval.     

Genetic parameters for atypical reproductive patterns in dairy cows estimated from in-line milk progesterone profiles

Our aim was to estimate genetic parameters of atypical reproductive patterns and estimate their genetic correlation with milk production and classical fertility traits for commercial dairy cows. In contrast with classical fertility traits, atypical reproductive patterns based on in-line milk progesterone profiles might have higher heritability and lower genetic correlation with milk production. We had in-line milk progesterone profiles available for 12,046 cycles in 4,170 lactations of 2,589 primiparous and multiparous cows (mainly Holstein Friesian) from 14 herds. Based on progesterone profiles, 5 types of atypical reproductive patterns in a lactation were defined: delayed ovulation types I and II, persistent corpus luteum types I and II, and late embryo mortality. These atypical patterns were detected in 14% (persistent corpus luteum type II) to 21% (persistent corpus luteum type I) of lactations. In 47% of lactations, at least 1 atypical pattern was detected. Threshold model heritabilities for atypical reproduction patterns ranged between 0.03 and 0.14 and for most traits were slightly higher compared with classical fertility traits. The genetic correlation between milk yield and calving interval was 0.56, whereas genetic correlations between milk yield and atypical reproductive patterns ranged between ?0.02 and 0.33. Although most of these correlations between milk yield and atypical reproductive patterns are still unfavorable, they are lower compared with the correlations between classical fertility traits and milk yield. Therefore selection against atypical reproductive patterns may relax some constraints in current dairy breeding programs, to enhance genetic progress in both fertility and milk yield at a steady pace. However, as long as the target trait for fertility is calving interval, atypical reproductive patterns will not add additional value to the breeding goal in the near future due to the low number of available records.     

Estimation of genetic parameters for production and reproduction in Finnish Ayrshire cattle

Records of AI-sired cows born between 1978 and 1982 were used to form two composite production and reproduction data sets. First (second) consisted of 35,568 (26,443) first lactations of daughters of 270 (237) sires. Traits were FCM, heifer, and first parity nonreturn rates, days between calving and first insemination, and days open, with means 5075 (5280) kg, .62 (.62), .44 (.49), 81 (81) d and 110 (111) d. (Co)variance components were estimated by REML with an expectation maximization algorithm. Sire model included age, month, herd-year effects, and relationships among sires. Records on animals with observations missing on some traits were included. Estimates of heritabilities, averaged over data sets, were nonreturn rates for heifers and for cows, .02; FCM, .32; days to first insemination, .19; and days open, .10. Genetic correlations between first parity fertility and yield were unfavorable; the highest, .43, was between FCM and days open. Heifer nonreturn rate had a .09 correlation with production and a .26 correlation with cow nonreturn rate. Phenotypic correlations were in the same direction as genetic correlations but were smaller in magnitude. Results suggest that selection only for production would cause deterioration in level of fertility. When economical, AI sires should be evaluated for daughter fertility. A multi-trait model including milk production, days open and relationships among bulls is recommended for genetic evaluation.