Heifer fertility and its relationship with cow fertility and production traits in Holstein dairy cattle

Breeding receipts from three AI units were merged with Ontario Dairy Herd Improvement Corporation and Record of Performance production records. Data comprised 53,705 heifer, 41,253 lactation 1, 14,688 lactation 2, and 3054 lactation 3 records by daughters of 2150 sires represented in 15,877 herd-year-seasons of birth. Three measures of heifer fertility, three measures of cow fertility, and three measures of production were investigated. Measures of heifer fertility were ages at first and last breeding and number of inseminations per conception. Cow fertility traits were days from calving to first breeding, days open, and number of inseminations per conception. Production traits were breed class average milk, breed class average fat, and fat percentage. Relationships among these nine traits for the first three lactations were estimated using a maximum likelihood multiple-trait procedure. The linear mixed model for each trait included fixed effects of herd-year-season of birth and genetic groups of sire and the random effect of sire. Transformations of the data for nonnormality had no influence on the estimates of genetic and phenotypic parameters. The heritability of .12 for age at first insemination, which was higher than other heifer fertility traits, indicated that selection would result in genetic response. Genetic and phenotypic correlations between heifer fertility and cow fertility and production traits in all three lactations were not different from zero. There was no genetic antagonism between fertility and subsequent production traits.     

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.     

Genome-wide mapping of 10 calving and fertility traits in Holstein dairy cattle with special regard to chromosome 18

Over the last decades, a dramatic decrease in reproductive performance has been observed in Holstein cattle and fertility problems have become the most common reason for a cow to leave the herd. The premature removal of animals with high breeding values results in both economic and breeding losses. For efficient future Holstein breeding, the identification of loci associated with low fertility is of major interest and thus constitutes the aim of this study. To reach this aim, a genome-wide combined linkage disequilibrium and linkage analysis (cLDLA) was conducted using data on the following 10 calving and fertility traits in the form of estimated breeding values: days from first service to conception of heifers and cows, nonreturn rate on d 56 of heifers and cows, days from calving to first insemination, days open, paternal and maternal calving ease, paternal and maternal stillbirth. The animal data set contained 2,527 daughter-proven Holstein bulls from Germany that were genotyped with Illumina's BovineSNP50 BeadChip (Illumina Inc., San Diego, CA). For the cLDLA, 41,635 sliding windows of 40 adjacent single nucleotide polymorphisms (SNP) were used. At each window midpoint, a variance component analysis was executed using ASReml. The underlying mixed linear model included random quantitative trait locus (QTL) and polygenic effects. We identified 50 genome-wide significant QTL. The most significant peak was detected for direct calving ease at 59,179,424 bp on chromosome 18 (BTA18). Next, a mixed-linear model association (MLMA) analysis was conducted. A comparison of the cLDLA and MLMA results with special regard to BTA18 showed that the genome-wide most significant SNP from the MLMA was associated with the same trait and located on the same chromosome at 57,589,121 bp (i.e., about 1.5 Mb apart from the cLDLA peak). The results of 5 different cLDLA and 2 MLMA models, which included the fixed effects of either SNP or haplotypes, suggested that the cLDLA method outperformed the MLMA in accuracy and precision. The haplotype-based cLDLA method allowed for a more precise mapping and the definition of ancestral and derived QTL alleles, both of which are essential for the detection of underlying quantitative trait nucleotides.     

Genomic prediction of bull fertility in US Jersey dairy cattle

Service sire has a major effect on reproductive success in dairy cattle. Recent studies have reported accurate predictions for Holstein bull fertility using genomic data. The objective of this study was to assess the feasibility of genomic prediction of sire conception rate (SCR) in US Jersey cattle using alternative predictive models. Data set consisted of 1.5k Jersey bulls with SCR records and 95k SNP covering the entire genome. The analyses included the use of linear and Gaussian kernel-based models fitting either all the SNP or subsets of markers with presumed functional roles, such as SNP significantly associated with SCR or SNP located within or close to annotated genes. Model predictive ability was evaluated using 5-fold cross-validation with 10 replicates. The entire SNP set exhibited predictive correlations around 0.30. Interestingly, either SNP marginally associated with SCR or genic SNP achieved higher predictive abilities than their counterparts using random sets of SNP. Among alternative SNP subsets, Gaussian kernel models fitting significant SNP achieved the best performance with increases in predictive correlation up to 7% compared with the standard whole-genome approach. Notably, the use of a multi-breed reference population including the entire US Holstein SCR data set (11.5k bulls) allowed us to achieve predictive correlations up to 0.315, gaining 8% in accuracy compared with the standard model fitting a pure Jersey reference set. Overall, our findings indicate that genomic prediction of Jersey bull fertility is feasible. The use of Gaussian kernels fitting markers with relevant roles and the inclusion of Holstein records in the training set seem to be promising alternatives to the standard whole-genome approach. These results have the potential to help the dairy industry improve US Jersey sire fertility through accurate genome-guided decisions.     

Association between genomic daughter pregnancy rates and reproductive parameters in Holstein dairy cattle

The objective of this observational study was to evaluate the relationship between genomic daughter pregnancy rate (GDPR) with reproduction parameters such as pregnancy at first artificial insemination (AI), pregnancy per AI, and pregnancy losses (PL). A total of 12,949 events from 3,499 Holstein cows were included. Cows were enrolled as nulliparous (n = 1,220), primiparous (n = 1,314), or multiparous (n = 965). Cows were bred either after a timed AI protocol, timed embryo transfer (ET), or spontaneous estrus. Most lactating cows were bred following a timed AI protocol based on estradiol and progesterone, and most nulliparous were artificially inseminated following estrus detection. Hair samples were collected from the tail switch and cows were genotyped using a SNP platform (Clarifide, Zoetis). Cows that were bred by timed AI were evaluated for estrous behavior using tail chalk. Tail chalk was applied on the head of the tail 2 d before timed AI and the chalk was evaluated at AI (no estrus: 100% of chalk remaining or ≥50% of chalk remaining; Estrus: <50% of chalk remaining). Pregnancy diagnosis was performed at d 32 and 60 after AI using ultrasonography, and the presence of a heartbeat was considered a positive diagnosis. Pregnancy loss was defined as a pregnant cow on d 32 that was nonpregnant on d 60. As GDPR increased, the odds of pregnancy at first AI increased [odds ratio (OR) = 1.28, 95% CI = 1.20–1.35], the odds of pregnancy per AI increased (OR = 1.31, 95% CI = 1.25–1.36), and the odds of PL decreased (OR = 0.66, 95% CI = 0.60–0.72). Most cows that were bred on the day of the timed AI demonstrated estrus (n = 6,075; 92.9%). The odds of demonstrating estrus on the day of timed AI increased as GDPR increased (OR = 1.31, 95% CI = 1.17–1.48). There was no interaction between GDPR and parity or breeding management for pregnancy at first AI, pregnancy per AI, and PL. In conclusion, the odds of pregnancy at first AI and pregnancy per AI increased as GDPR increased. Moreover, the odds of PL increased as GDPR decreased. Greater GDPR was also associated with greater occurrence of estrus on the day of timed AI. These results suggest that selecting for higher GDPR could result in better reproductive performance, but this would need to be assessed with additional research.     

Genetic analysis of fertility traits in Israeli dairy cattle

First through third parity lactation records of 91,770 Israeli Holsteins inseminated between 1980 and 1986 were evaluated by univariate mixed model methodology for fertility and production traits. The analytical model included the effects of herd-year-season, group of sires, sire, cow, and residual. Sire, cow, and residual were random: all other effects were fixed. Sires were assumed to be unrelated. Variance components were computed separately for first and second parity by Henderson's method 3. First parity heritabilities were .035 for conception status [1/number of inseminations to conception], .048 for days from calving to first breeding, and .135 for milk production. Corresponding second parity heritabilities were .022, .031, and .125. First parity genetic correlations were -.02 between conception status and milk, .27 between days to first breeding and milk, and -.03 between the two fertility traits. All environmental correlations, and all second parity genetic correlations among these traits, were between -.2 and .2. Genetic trends, estimated as twice the regression of the evaluation of the cow's sire on calving date, were 1% for conception status, .1 for days to first breeding, and 154 kg milk/yr. Thus, there was no indication of an adverse genetic relationship between fertility and milk production in this population.     

A genomic assessment of the correlation between milk production traits and claw and udder health traits in Holstein dairy cattle

Claw diseases and mastitis represent the most important disease traits in dairy cattle with increasing incidences and a frequently mentioned connection to milk yield. Yet, many studies aimed to detect the genetic background of both trait complexes via fine-mapping of quantitative trait loci. However, little is known about genomic regions that simultaneously affect milk production and disease traits. For this purpose, several tools to detect local genetic correlations have been developed. In this study, we attempted a detailed analysis of milk production and disease traits as well as their interrelationship using a sample of 34,497 50K genotyped German Holstein cows with milk production and claw and udder disease traits records. We performed a pedigree-based quantitative genetic analysis to estimate heritabilities and genetic correlations. Additionally, we generated GWAS summary statistics, paying special attention to genomic inflation, and used these data to identify shared genomic regions, which affect various trait combinations. The heritability on the liability scale of the disease traits was low, between 0.02 for laminitis and 0.19 for interdigital hyperplasia. The heritabilities for milk production traits were higher (between 0.27 for milk energy yield and 0.48 for fat-protein ratio). Global genetic correlations indicate the shared genetic effect between milk production and disease traits on a whole genome level. Most of these estimates were not significantly different from zero, only mastitis showed a positive one to milk (0.18) and milk energy yield (0.13), as well as a negative one to fat-protein ratio (?0.07). The genomic analysis revealed significant SNPs for milk production traits that were enriched on Bos taurus autosome 5, 6, and 14. For digital dermatitis, we found significant hits, predominantly on Bos taurus autosome 5, 10, 22, and 23, whereas we did not find significantly trait-associated SNPs for the other disease traits. Our results confirm the known genetic background of disease and milk production traits. We further detected 13 regions that harbor strong concordant effects on a trait combination of milk production and disease traits. This detailed investigation of genetic correlations reveals additional knowledge about the localization of regions with shared genetic effects on these trait complexes, which in turn enables a better understanding of the underlying biological pathways and putatively the utilization for a more precise design of breeding schemes.     

Estimation of quantitative trait loci parameters for milk production traits in German Holstein dairy cattle population

The main objective of this study was to estimate the proportion of total genetic variance attributed to a quantitative trait locus (QTL) on Bos taurus autosome 6 (BTA6) for milk production traits in the German Holstein dairy cattle population. The analyzed chromosomal region on BTA6 spanned approximately 70 cM, and contained 6 microsatellite markers. Milk production data were obtained from routine genetic evaluation for 4500 genotyped German Holstein bulls. Technical aspects related to the estimation of model parameters for a large data set from routine genotype recording were outlined. A fixed QTL model and a random QTL model were introduced to incorporate marker information into parameter estimation and genetic evaluation. Estimated QTL variances, expressed as the ratio of QTL to polygenic variances, were 0.04, 0.03, and 0.07 for milk yield; 0.06, 0.08, and 0.14 for fat yield; and 0.04, 0.04, and 0.11 for protein yield, in the first 3 parities, respectively. The estimated QTL positions, expressed as distances from the leftmost marker DIK82, were 18, 31, and 17 cM for milk yield; 25, 17, and 9 cM for fat yield; and 16, 30, and 17 cM for protein yield in the 3 respective parities. Because the data for the parameter estimation well represented the current population of active German Holstein bulls, the QTL parameter estimates have been used in routine marker-assisted genetic evaluation for German Holsteins.     

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 analysis of the Israeli Holstein dairy cattle population for production and nonproduction traits with a multitrait animal model

Milk, fat, and protein production, somatic cell score (SCS), and female fertility in the Israeli Holstein dairy cattle population were analyzed using a multitrait animal model (AM) with parities 1 through 5 as separate traits. Female fertility was measured as the inverse of the number of inseminations to conception in percent. Variance components were estimated using both the repeatability AM and multitrait AM. The multitrait heritabilities for individual parities were greater than the heritabilities from the repeatability AM, and heritabilities decreased with an increase in parity number. Heritabilities were higher for production traits, lower for SCS, and lowest for female fertility. The genetic correlations were higher than the environmental correlations. Genetic correlations between parities decreased with an increase in the difference in parity number, but all were greater than 0.5. The environmental correlations were higher for production traits, lower for SCS, and close to zero for female fertility. In the analysis of the complete milk recorded population, genetic trends from the repeatability and multitrait models were very similar. The genetic trend for SCS was economically unfavorable until 1993, and favorable since then. The genetic trend for female fertility was close to zero, but the annual environmental trend was -0.2%. The multitrait lactation model is an attractive compromise between repeatability lactation models, which do not account for maturing trends across parities, and test-day models, which are much more demanding computationally.     

Overlapping chromosomal regions for fertility traits and production traits in the Danish Holstein population

Before implementing selection based on quantitative trait loci (QTL) for fertility, it is important to determine the existence of correlated effects between the fertility QTL and QTL with effects on production traits. When a QTL is detected for a trait that is a composite of subtraits, it is of interest to validate which of the subtraits are affected by the QTL. Phenotypic and marker data were collected from 34 grandsire families from the Danish Holstein population. First, the trait data for “fertility treatments” were separated into their underlying subtraits: uterine infections, antibiotics placed in the placenta, and abortions. In addition, retained placenta was selected for analysis because it is related to uterine infections. A genome scan was performed using 416 microsatellite markers for the fertility treatment subtraits and retained placenta, and an additional genome scan for milk production traits conditional on the QTL regions for the subtraits and retained placenta was conducted. Second, we selected 24 genomic regions harboring QTL for fertility traits from a previous study. A QTL scan for milk production traits conditional on the selected regions was conducted. We found that 16 selected genomic regions containing a QTL for fertility (including the fertility treatment subtraits and retained placenta) also harbored QTL for milk yield or milk composition traits. Furthermore, 12 QTL regions corresponding to 9 different fertility traits (including the fertility treatment subtraits) did not harbor a QTL for milk production or milk composition traits; that is, the region was specific for the fertility trait. The genome scan for the fertility treatment subtraits did not correspond to the QTL found for fertility treatments. No QTL were detected for the subtrait abortion, however genome scans for retained placenta revealed 4 different QTL.     

Quantitative trait loci affecting fertility and calving traits in Swedish dairy cattle

Impaired fertility is the main reason for involuntary culling of dairy cows in Sweden. The objective of this study was to map quantitative trait loci (QTL) influencing fertility and calving traits in the Swedish dairy cattle population. The traits analyzed were number of inseminations, 56-d nonreturn rate, interval from calving to first insemination, fertility treatments, heat intensity score, stillbirth, and calving performance. A genome scan covering 20 bovine chromosomes was performed using 145 microsatellite markers. The mapping population consisted of 10 sires and their 417 sons in a granddaughter design. Nine of the sires were of the Swedish Red Breed, and one was a Swedish Holstein. Least squares regression was used to map loci affecting the analyzed traits, and permutation tests were used to set significance thresholds. Cofactors were used in the analyses of individual chromosomes to adjust for QTL found on other chromosomes. The use of cofactors increased both the number of QTL found and the significance level. In the initial analysis, we found 13 suggestive QTL that were mapped to chromosomes 6, 7, 9, 11, 13, 15, 20, and 29. When cofactors were included, 30 QTL were detected on chromosomes 1, 3, 4, 18, 19, 22, and 25, in addition to the 8 previously mentioned chromosomes. Some of the results from the cofactor analysis may be false positives and require further validation. In conclusion, we were able to map several QTL affecting fertility and calving traits in Swedish dairy cattle.     

Evaluation of bull fertility in Italian Brown Swiss dairy cattle using cow field data

Dairy bull fertility is traditionally evaluated using semen production and quality traits; however, these attributes explain only part of the differences observed in fertility among bulls. Alternatively, bull fertility can be directly evaluated using cow field data. The main objective of this study was to investigate bull fertility in the Italian Brown Swiss dairy cattle population using confirmed pregnancy records. The data set included a total of 397,926 breeding records from 1,228 bulls and 129,858 lactating cows between first and fifth lactation from 2000 to 2019. We first evaluated cow pregnancy success, including factors related to the bull under evaluation, such as bull age, bull inbreeding, and AI organization, and factors associated with the cow that receives the dose of semen, including herd-year-season, cow age, parity, and milk yield. We then estimated sire conception rate using only factors related to the bull. Model predictive ability was evaluated using 10-fold cross-validation with 10 replicates. Interestingly, our analyses revealed that there is a substantial variation in conception rate among Brown Swiss bulls, with more than 20% conception rate difference between high-fertility and low-fertility bulls. We also showed that the prediction of bull fertility is feasible as our cross-validation analyses achieved predictive correlations equal to 0.30 for sire conception rate. Improving reproduction performance is one of the major challenges of the dairy industry worldwide, and for this, it is essential to have accurate predictions of service sire fertility. This study represents the foundation for the development of novel tools that will allow dairy producers, breeders, and artificial insemination companies to make enhanced management and selection decisions on Brown Swiss male fertility.     

Genome-wide association study for lactation persistency,female fertility,longevity, and lifetime profit index traits in Holstein dairy cattle

Female fertility in Holstein cattle can decline when intense genetic selection is placed on milk production. One approach to improving fertility is to identify the genomic regions and variants affecting fertility traits and then incorporate this knowledge into selection decisions. The objectives of this study were to identify or refine the positions of the genomic regions associated with lactation persistency, female fertility traits (age at first service, cow first service to conception, heifer and cow nonreturn rates), longevity traits (herd life, indirect herd life, and direct herd life), and lifetime profit index in the North American Holstein dairy cattle population. A genome-wide association study was performed for each trait, using a single SNP (single nucleotide polymorphism) regression mixed linear model and imputed high-density panel (777k) genotypes. No associations were identified for fertility traits. Several peak regions were detected for lifetime profit index, lactation persistency, and longevity. The results overlap with previous findings and identify some novel regions for lactation persistency. Previously proposed causative and candidate genes supported by this work include DGAT1, GRINA, and CPSF1, whereas new candidate genes are SLC2A4RG and THRB. Thus, the chromosomal regions identified in this study not only confirm several previous findings but also highlight new regions that may contribute to genetic variation in lactation persistency and longevity-associated traits in dairy cattle.     

Association of lipid-related genes implicated in conceptus elongation with female fertility traits in dairy cattle

Elongation of the preimplantation conceptus is a requirement for pregnancy success in ruminants, and failures in this process are highly associated with subfertility in dairy cattle. Identifying genetic markers that are related to early conceptus development and survival and utilizing these markers in selective breeding can improve the reproductive efficiency of dairy herds. Here, we evaluated the association of 1,679 SNP markers within or close to 183 candidate genes involved in lipid metabolism of the elongating conceptus with different fertility traits in US Holstein cattle. A total of 27,371 bulls with predicted transmitting ability records for daughter pregnancy rate, cow conception rate, and heifer conception rate were used as the discovery population. The associations found in the discovery population were validated using 2 female populations (1,122 heifers and 2,138 lactating cows) each with 4 fertility traits, including success to first insemination, number of services per conception, age at first conception for heifers, or days open for cows. Marker effects were estimated using a linear mixed model with SNP genotype as a linear covariate and a random polygenic effect. After multiple testing correction, 39 SNP flagging 27 candidate genes were associated with at least one fertility trait in the discovery population. Of these 39 markers, 3 SNP were validated in the heifer population and 4 SNP were validated in the cow population. The 3 SNP validated in heifers are located within or near genes CAT, MYOF, and RBP4, and the 4 SNP validated in lactating cows are located within or close to genes CHKA, GNAI1, and HMOX2. These validated genes seem to be relevant for reducing pregnancy losses, and the SNP within these genes are excellent candidates for inclusion in genomic tests to improve reproductive performance in dairy cattle.     

Genetic parameters for health traits and their relationship to different persistency traits in German Holstein dairy cattle

Data from 3,200 Holstein cows from 3 commercial dairy farms in Germany were used to estimate heritabilities and breeding values for liability to udder diseases (UD), fertility diseases (FD), metabolic diseases (MD), and claw and leg diseases (CLD) using single-trait threshold sire models. A total of 92,722 medical treatments recorded from 1998 to 2003 were included in the analysis. Approximate genetic correlations between persistency of milk yield, fat yield, protein yield, and persistency of milk energy yield and liability to the health traits were calculated based on correlations between EBV. Posterior means of heritability of liability ranged from 0.05 to 0.08 for UD, from 0.04 to 0.07 for FD, from 0.08 to 0.12 for MD, and from 0.04 to 0.07 for CLD. Approximate genetic correlations of the disease traits with the persistency traits were favorable, except for MD in all lactations, which were unfavorable, and UD, which were around zero. Highest correlations in the range of 0.13 to 0.46 were found between the different persistency traits and CLD.     

Effect of synchronized breeding on genetic evaluations of fertility traits in dairy cattle

Estrus detection has become more difficult over the years due to decreases in the estrus expression of high-producing dairy cows, and increased herd sizes and animal density. Through the use of hormonal synchronization protocols, also known as timed artificial insemination (TAI) protocols, it is possible to alleviate some of the challenges associated with estrus detection. However, TAI masks cows' fertility performance, resulting in an unfair comparison of treated animals and innately fertile animals. Consequently, genetically inferior and superior cows show similar phenotypes, making it difficult to distinguish between them. As genetic programs rely on the collection of accurate phenotypic data, phenotypes collected on treated animals likely add bias to genetic evaluations. In this study, to assess the effect of TAI, the rank correlation of bulls for a given trait using only TAI records were compared with the same trait using only heat detection records. A total of 270,434 records from 192,539 animals split across heifers, first and second parity cows were analyzed for the traits: calving to first service, first service to conception, and days open. Results showed large reranking across all traits and parities between bulls compared based on either having only TAI records or only heat detection records, suggesting that a bias does indeed exist. Large reranking was also observed for both the heat detection and TAI groups among the top 100 bulls in the control group, which included all records. Furthermore, breeding method was added to the model to assess its effect on bull ranking. However, there were only minor changes in the rank correlations between scenario groups. Therefore, more complex methods to account for the apparent bias created by TAI should be investigated; for this, the method by which these data are collected needs to be improved through creating a standardized way of recording breeding codes. Though the results of this study suggest the presence of bias within current fertility evaluations, additional research is required to confirm the findings of this study, including looking at high-reliability bulls specifically, to determine if the levels of reranking remain. Future studies should also aim to understand the potential genetic differences between the fertility traits split via management technology, possibly in a multiple-trait analysis.     

Relationship between reproduction traits and functional longevity in canadian dairy cattle

The aim of this study was to use survival analysis to assess the relationship between reproduction traits and functional longevity of Canadian dairy cattle. Data consisted of 1,702,857; 67,470; and 33,190 Holstein, Ayrshire, and Jersey cows, respectively. Functional longevity was defined as the number of days from first calving to culling, death, or censoring; adjusted for the effect of milk yield. The reproduction traits included calving traits (calving ease, calf size, and calf survival) and female fertility traits (number of services, days from calving to first service, days from first service to conception, and days open). The statistical model was a Weibull proportional hazards model and included the fixed effects of stage of lactation, season of production, the annual change in herd size, and type of milk recording supervision, age at first calving, effects of milk, fat, and protein yields calculated as within herd-year-parity deviations for each reproduction trait. Herd-year-season of calving and sire were included as random effects. Analysis was performed separately for each reproductive trait. Significant associations between reproduction traits and longevity were observed in all breeds. Increased risk of culling was observed for cows that required hard pull, calved small calves, or dead calves. Moreover, cows that require more services per conception, a longer interval between first service to conception, an interval between calving to first service greater than 90 d, and increased days open were at greater risk of being culled.     

Applied genetic evaluations for production and functional traits in dairy cattle

The objective of this study was to review the current status of genetic evaluation systems for production and functional traits as practiced in different Interbull member countries and to discuss that status in relation to research results and potential improvements. Thirty-one countries provided information. Substantial variation was evident for number of traits considered per country, trait definition, genetic evaluation procedure within trait, effects included, and how these were treated in genetic evaluation models. All countries lacked genetic evaluations for one or more economically important traits. Improvement in the genetic evaluation models, especially for many functional traits, could be achieved by closing the gaps between research and practice. More detailed and up to date information about national genetic evaluation systems for traits in different countries is available at www.interbull.org. Female fertility and workability traits were considered in many countries and could be next in line for international genetic evaluations.