Udder health and female fertility traits are favourably correlated and support each other in multi-trait evaluations

Genetic parameters were estimated for protein yield (PY), clinical mastitis (CM), somatic cell score, number of inseminations (NI) and days from calving to first insemination (CFI) in first‐parity Swedish Red cows by series of tri‐variate linear animal models. The heritability of PY was moderate (0.34 ± 0.004), and the heritabilities of the functional traits were all low (0.014 ± 0.001–0.14 ± 0.004). The genetic correlation between CM and CFI (0.38 ± 0.05) was stronger than the correlation between CM and NI (0.05 ± 0.06), perhaps because CM and CFI usually are observed in early lactation when the cow is likely to be in negative energy balance, whereas NI generally is recorded when the cow is not in negative energy balance any more. The genetic correlation between NI and CFI was very close to zero (?0.002 ± 0.05), indicating that these two fertility traits have different genetic backgrounds. All genetic correlations between PY and the functional traits were moderate and unfavourable, ranging from 0.22 ± 0.02 to 0.47 ± 0.03. In addition, the effect of including genetic and phenotypic correlations between the trait groups milk production, udder health and female fertility on the accuracy of the selection index was quantified for a heifer, a cow and a proven bull. The difference between the accuracy obtained by multi‐trait and single‐trait evaluations was largest for the cow (0.012) and small for the heifer and the bull (0.006 and 0.004) because the phenotype of the cow for one trait could assist in predicting the Mendelian sampling term for a correlated trait.     

Genetic relationships among female fertility disorders,female fertility traits and productivity of Holstein dairy cows in the early lactation period

Fertility health disorders from the early lactation period including retained placenta (REPLA), metritis (MET), corpus luteum persistence (CLP), anoestria/acyclia (AOEAC) and ovarial cysts (OC), as well as overall disease categories (disorders during the postpartal period (DPP), ovary infertility (OINF), overall trait definition “fertility disorders” (FD)), were used to estimate genetic (co)variance components with female fertility and test‐day traits. The disease data set comprised 25,142 Holstein cows from parities 1, 2 and 3 resulting in 43,584 lactations. For disease traits, we used the binary trait definition (sick or healthy) and disease count data reflecting the sum of treatments for the same disease within lactation or within lactation periods. Statistical modelling included single and multiple trait repeatability animal models for all trait combinations within a Bayesian framework. Heritabilities for binary disease traits ranged from 0.04 (OC) to 0.10 (REPLA) and were slightly lower for the corresponding sum trait definitions. Correlations between both trait definitions were almost one, for genetic as well as for permanent environmental effects. Moderate to high genetic correlations were found among puerperal disorders DPP, REPLA and MET (0.45–0.98) and among the ovarian disorders OINF, AOEAC, CLP and OC (0.59–0.99). Genetic correlations between puerperal and ovarian disorders were close to zero, apart from the REPLA–OC association (0.55). With regard to fertility disorders and productivity in early lactation, a pronounced genetic antagonistic relationship was only identified between OC and protein yield. Genetic correlations between fertility disorders and test‐day SCS were close to zero. OINF and all diseases contributing to OINF were strongly correlated with the female fertility traits “interval from calving to first service,” “interval from service to pregnancy” and “interval from calving to pregnancy.” The strong correlations imply that fertility disorders could be included in genetic evaluations of economic fertility traits as correlated predictors. Vice versa, a breeding focus on female fertility traits will reduce genetic susceptibility to OC, CLP and AOEAC.     

Estimation of Variances and Covariances of Milk Traits by REML with an Individual Animal Model

Abstract

Records form Finnish Ayrshire cattle were used to estimate variances and covariances of milk traits by the restricted maximum likelihood (REML) method using the individual animal model (IAM). Two data sets were analyzed. The first data set consisted of 1423 sires and 16363 cows, of which 11911 had records on first lactation. The heritabilities estimated from this data set for milk yield, protein yield, protein content and fat content were 0.40, 0.31, 0.63 and 0.68, respectively. The second data set was a subset of first data set with herds with less than ten observations excluded and consisted of 1335 sires and 11262 cows with 8140 first, 5688 second and 3717 third lactation records. The heritability estimates from the second data set under a repeatability model for milk yield, protein yield, protein content and fat content were 0.30, 0.26, 0.59 and 0.66, respectively. The repeatability estimates for the same traits were 0.53, 0.51, 0.67 and 0.76, respectively. The second data set was also used to estimate genetic and phenotypic correlations among milk traits in first lactation. Both genetic and phenotypic correlations among protein yield and protein and fat content traits were small. The genetic correlation between milk yield and protein content was -0.61, between milk yield and fat content -0.50 and between protein content and fat content 0.67. Absolute values of phenotypic correlations for the same pairs of traits were somewhat smaller than respective genetic correlations.     

Genetic and Environmental Correlations Among Female Fertility Traits and Milk Production in Different Parities of Swedish Red and White Dairy Cattle

The main objective of this study was to estimate genetic correlations between fertility and production traits in first, second and third lactations as well as between fertility traits measured in the same way at different ages. The fertility traits studied were: number of inseminations per service period, number of treatments for reproductive disturbances, interval between first and last inseminations, interval between calving and first insemination, and interval between calving and last insemination. Early milk production was measured as the average of the energy-corrected milk yield at the second and third monthly testdays in a lactation. The number of records was approximately 450 000, 350 000, 180 000 and 75 000 in the heifer period, first, second, and third lactations, respectively. A linear, trivariate model that included the effects of herd-year, year, month, age and sire of the cow was applied. To reduce the effect of ongoing selection, 305-days kg protein production in first lactation was included as a variate in all of the analyses. Correlations between the herd-year effects indicated that factors of herd-year level conducive to increased production had a tendency to increase the number of inseminations as well as the number of reproductive treatments, although there was an earlier start and termination of the insemination period. Genetic correlations between fertility traits and production were in the range of 0.2-0.4, all of them unfavourable and higher at later parities. The genetic correlations between fertility traits in the heifer period and the same traits in first lactation were 0.7. Genetic correlations between the first and second lactation varied between 0.7 and 0.9, and between the second and third lactation they were all 0.9 or higher. In conclusion, fertility and production traits need to be selected for simultaneously if fertility is going to be maintained along further genetic improvement on production, and such selection should include fertility results from lactating cows.     

Genetic correlations between body condition scores and fertility in dairy cattle using bivariate random regression models

Genetic correlations between body condition score (BCS) and fertility traits in dairy cattle were estimated using bivariate random regression models. BCS was recorded by the Swiss Holstein Association on 22,075 lactating heifers (primiparous cows) from 856 sires. Fertility data during first lactation were extracted for 40,736 cows. The fertility traits were days to first service (DFS), days between first and last insemination (DFLI), calving interval (CI), number of services per conception (NSPC) and conception rate to first insemination (CRFI). A bivariate model was used to estimate genetic correlations between BCS as a longitudinal trait by random regression components, and daughter's fertility at the sire level as a single lactation measurement. Heritability of BCS was 0.17, and heritabilities for fertility traits were low (0.01-0.08). Genetic correlations between BCS and fertility over the lactation varied from: -0.45 to -0.14 for DFS; -0.75 to 0.03 for DFLI; from -0.59 to -0.02 for CI; from -0.47 to 0.33 for NSPC and from 0.08 to 0.82 for CRFI. These results show (genetic) interactions between fat reserves and reproduction along the lactation trajectory of modern dairy cows, which can be useful in genetic selection as well as in management. Maximum genetic gain in fertility from indirect selection on BCS should be based on measurements taken in mid lactation when the genetic variance for BCS is largest, and the genetic correlations between BCS and fertility is strongest.     

中国荷斯坦牛乳房性状、体型总分与产奶性状的相关分析

研究利用武汉市58头公牛的971头女儿2006—2007年间的体型性状鉴定记录及2001—2007年间的生产性能测定记录,配合动物模型,采用REML方法进行参数估计,探讨了乳房性状、体型总分与产奶性状之间的关系。结果表明:乳房性状与产奶性状之间的表型相关较小。后乳房宽度与产奶性状之间存在强的遗传正相关(0.44～0.89)。后乳房高度与305 d产奶量(0.27)、305 d乳脂量(0.16)存在遗传正相关,而与305 d乳蛋白量(-0.32)存在遗传负相关。前乳房附着与产奶性状基本不存在相关。悬韧带与305 d产奶量存在遗传正相关(0.79)。乳房深度与305 d产奶量存在遗传负相关(-0.20)。体型总分与305 d产奶量、305 d乳脂量、305 d乳蛋白量存在较强的遗传正相关,故加强乳房性状和体型总分的选择对提高奶牛的生产性能有益。     

Estimation of genetic parameters for milk and fertility traits within and between low,medium and high dairy production systems in Kenya to account for genotype-by-environment interaction

Dairy records from the Dairy Recording Service of Kenya were classified into low, medium and high production systems based on mean 305-day milk yield using the K-means clustering method. Milk and fertility records were then analysed to develop genetic evaluation systems accounting for genotype-by-environment interaction between the production systems. Data comprised 26,638 lactation yield, 3,505 fat yield, 9,235 age at first calving and 17,870 calving interval records from 12,631 cows which were descendants of 2,554 sires and 8,433 dams. An animal model was used to estimate variance components, genetic correlations and breeding values for the production systems. Variance components increased with production means, apart from genetic group variances, which decreased from the low to the high production system. Moderate heritabilities were estimated for milk traits (0.21–0.27) and fat traits (0.11–0.38). Low heritabilities were estimated for lactation length (0.04–0.10) and calving interval (0.03–0.06). Moderate heritabilities (0.25–0.26) were estimated for age at first calving, except under the high production system (0.05). Within production systems, lactation milk yield, 305-day milk yield and lactation length had high positive genetic correlations (0.52–0.96), while lactation milk yield and lactation length with age at first calving had negative genetic correlations. Milk yield and calving interval were positively correlated except under the low production system. The genetic correlations for lactation milk yield and 305-day milk yield between low and medium (0.48 ± 0.20 and 0.46 ± 0.21) and low and high production systems’ (0.74 ± 0.15 and 0.62 ± 0.17) were significantly lower than one. Milk yield in the low production system is, therefore, a genetically different trait. The low genetic correlations between the three production systems for most milk production and fertility traits suggested that sires should be selected based on progeny performance in the targeted production system.     

Genetic and phenotypic parameters and annual trends for milk production and fertility traits of the Sahiwal cattle in semi arid Kenya

Data comprising 7211 lactation records of 2894 cows were used to estimate genetic and phenotypic parameters for milk production (lactation milk yield, LMY and lactation length, LL) and fertility (calving interval, CI; number of services per conception, NSC and age at first calving, AFC) traits. Genetic, environmental and phenotypic trends were also estimated. Variance components were estimated using univariate, bivariate and trivariate animal models on based restricted maximum likelihood procedures. Univariate models were used for each trait, while bivariate models were used to estimate genetic and phenotypic correlations between milk production and fertility traits and between LMY, LL, CI and NSC within each lactation. Trivariate models were used in the analysis of LMY, LL, CI and NSC in the first three lactations. Heritability estimates from the univariate model were 0.16, 0.07, 0.03, 0.04 and 0.01 for LMY, LL, CI, AFC and NSC, respectively. The heritability estimates from trivariate analysis were higher for milk production traits than those from univariate analyses. Genetic correlations were high and undesirable between milk production and fertility traits, while phenotypic correlations were correspondingly low. Genetic trends were close to zero for all traits, while environmental and phenotypic trends fluctuated over the study period.     

Days to calving in artificially inseminated beef cows: Comparison of potential traits

Four fertility traits were compared for artificially inseminated (AI) beef cows: A) for cows that calved to the AI sire (from either the initial or follow-up inseminations that season), the number days from initial AI to calving; B) for cows calving either by AI or to a backup bull, the number days from initial AI to calving; C) As trait B for cows that calved, otherwise the maximum of trait B for the contemporary group plus a penalty of 21 days; D) Define the ‘start date’ for a contemporary group as the date the first cow in the group was AI'd. For cows that calved, trait D was the number of days from the ‘start date’ to calving, otherwise the maximum of trait D for cows in the group that calved plus a penalty of 21 days.The vast majority of cows received only one insemination in a season, so trait A resembled gestation length and had estimated heritability of 12%. Traits B, C and D had estimated heritabilities of 3.2%, 3.5% and 5.2% respectively; estimated genetic correlations of traits A–D with naturally mated days to calving were 0.48, 0.60, 0.80 and 0.74 respectively. Trait D is therefore the recommended female fertility trait for AI cows. It has a similar frequency distribution to days to calving from natural mating and should be included in a joint analysis with days to calving of naturally mated cows.     

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

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

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

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

Fertility traits in spring-calving Aberdeen Angus cattle. 1. Model development and genetic parameters

Calving records (n = 6,763) obtained from first, second, and third parities of 3,442 spring-calving, Uruguayan Aberdeen Angus cows were used to estimate heritabilities and genetic correlations for the linear trait calving day (CD) and the binary trait calving success (CS), using models that considered CD and CS at 3 calving opportunities as separate traits. Three approaches were defined to handle the CD observations on animals that failed to calve: 1) the cows were assigned a penalty value of 21 d beyond the last observed CD record within contemporary group (PEN); 2) the censored CD values were randomly obtained from a truncated normal distribution (CEN); and 3) the CD records were treated as missing, and the parameters were estimated in a joint threshold-linear analysis including CS traits (TLMISS). The models included the effects of contemporary group (herd x year of calving x mating management), age at calving (3 levels), physiological status at mating (nonlactating or lactating), animal additive genetic effects, and residual. Estimates of heritability for CD traits in the PEN and CEN data sets ranged from 0.20 to 0.31, with greater values in the first calving opportunity. Genetic correlations were positive and medium to high in magnitude, 0.57 to 0.59 in the PEN data set and 0.38 to 0.91 in the CEN data set. In the TLMISS data set, heritabilities ranged from 0.19 to 0.23 for CD and 0.37 to 0.42 for CS. Genetic correlations between CD traits varied between 0.82 and 0.88; between CS traits, genetic correlations varied between 0.56 and 0.80. Negative (genetically favorable), medium to high genetic correlations (-0.54 to -0.91) were estimated between CD and CS traits, suggesting that CD could be used as an indicator trait for CS. Data recording must improve in quality for practical applications in genetic evaluation for fertility traits.     

Genetic correlations between production with disease resistance and fertility in dairy cattle and consequences for total merit selection

Twelve years of data from progeny‐test results of 1486 Swedish Red and White (SRB) and 756 Swedish Black and White (SLB) AI bulls were analysed to provide estimates of genetic correlations between yield of protein and three health‐ and fertility traits. For both breeds, the correlations were unfavourable (r_G= —0.13 to —0.37). The effects of negative genetic correlations (compared to a situation with genetic correlations with zero values) on the b‐values in the total merit index of bulls were rather small but the effects on the estimated genetic gain were large. The effect of not including health and fertility traits in the index, although included in the breeding goal, resulted in a 9–10% reduced accuracy of estimated breeding values for total merit and thus a corresponding loss in total gain.     

Economic weights of production and functional traits for Holstein‐Friesian cattle in Hungary

A bio‐economic model was used to estimate economic values of 15 milk production, functional, growth and carcass traits for Hungarian Holstein‐Friesian cattle. The calculations were carried out for the situation in Hungary from 2000 to 2007, assuming no production quotas. The marginal economic values were defined as partial derivatives of the profit function with respect to each trait in a production system with dairy cow herds and with sales of surplus male calves. The economic weights for maternal and direct components of traits were calculated multiplying the marginal economic values by the number of discounted expression summed over a 25‐year investment period for 2‐year‐old bulls (candidates for selection). The standardized economic weight (economic weight × genetic standard deviation) of the trait or trait component expressed as percentage of the sum of the standardized economic weights for all traits and trait components represented the relative economic importance of this trait or trait component. The highest relative economic importance was obtained for milk yield (25%), followed by productive lifetime of cows (23%), protein yield and the direct component of a cow’s total conception rate (9% each), the maternal effect of the total conception rate of cows and the somatic cell score (approximately 7% each), fat yield (5%) and mature weight of cows and daily gain in rearing of calves (approximately 4% each). Other functional traits (clinical mastitis incidence, calving difficulty score, total conception rate of heifers and calf mortality) reached a relative economic importance between 0.5% and 2%. Birth weight and dressing percentage were least important (<0.5%). Based on these results, the inclusion of productive lifetime and cow fertility in the breeding programme for Holstein‐Friesian cattle in Hungary is advisable.     

Genetic relationships of alternative somatic cell count traits with milk yield,composition and udder type traits in Italian Jersey cows

The aim of this study was to estimate genetic associations between alternative somatic cell count (SCC) traits and milk yield, composition and udder type traits in Italian Jersey cows. Alternative SCC traits were test‐day (TD) somatic cell score (SCS) averaged over early lactation (SCS_150), standard deviation of SCS of the entire lactation (SCS_SD), a binary trait indicating absence or presence of at least one TD SCC >400,000 cells/ml in the lactation (Infection) and the ratio of the number of TD SCC >400,000 cells/ml to total number of TD in the lactation (Severity). Heritabilities of SCC traits, including lactation‐mean SCS (SCS_LM), ranged from 0.038 to 0.136. Genetic correlations between SCC traits were moderate to strong, with very few exceptions. Unfavourable genetic associations between milk yield and SCS_SD and Infection indicated that high‐producing cows were more susceptible to variation in SCC than low‐producing animals. Cows with deep udders, loose attachments, weak ligaments and long teats were more susceptible to an increase of SCC in milk. Overall, results suggest that alternative SCC traits can be exploited to improve cow's resistance to mastitis in Italian Jersey breed.     

Estimation of genetic and phenotypic relationships between age at first calving and productive performance in Iranian Holsteins

Calving records from the Animal Breeding Center of Iran collected from January 1990 to December 2007 and comprising 207,106 first calving events of Holsteins from 2,506 herds were analysed using univariate and bivariate linear sire models to estimate heritabilities and genetic correlations between age at first calving (AFC) and productive performance. Average AFC was 26.48 months in this study. The peak in the frequency distribution of AFC clearly exists coinciding with cows calving for the first time at approximately 25 months of age. Heritability estimate for AFC was 0.34 which was greater than the corresponding values for productive traits. The heritability estimates were low to medium for productive traits which ranged from 0.17 to 0.26 for cows in their first calvings. Except for fat and protein percentages of milk, phenotypic and genetic correlations between AFC and productive performance traits were low to moderately negative. Range of genetic correlations between productive traits was −0.53 to 0.99. Reduction of age at first calving appeared to have a negative effect on first lactation protein and fat percentages; however, it had positive effects on milk yield, fat yield, protein yield and their mature equivalents. It seems that reducing age at first calving to 24–25 months is probably more profitable than reducing age at first calving to an earlier time in Iranian conditions.     

Genetic parameters of milk production traits and fatty acid contents in milk for Holstein cows in parity 1 – 3

The objective of this study was to estimate genetic parameters of milk, fat, and protein yields, fat and protein contents, somatic cell count, and 17 groups and individual milk fatty acid (FA) contents predicted by mid‐infrared spectrometry for first‐, second‐ and third‐parity Holstein cows. Edited data included records collected in the Walloon region of Belgium from 37 768 cows in parity 1, 22 566 cows in parity 2 and 8221 in parity 3. A total of 69 (23 traits for three parities) single‐trait random regression animal test‐day models were run. Approximate genetic correlations among traits were inferred from pairwise regressions among estimated breeding values of cow having observations. Heritability and genetic correlation estimates from this study reflected the origins of FA: de novo synthetized or originating from the diet and the body fat mobilization. Averaged daily heritabilities of FA contents in milk ranged between 0.18 and 0.47. Average daily genetic correlations (averaged across days in milk and parities) among groups and individual FA contents in milk ranged between 0.31 and 0.99. The genetic variability of FAs in combination with the moderate to high heritabilities indicated that FA contents in milk could be changed by genetic selection; however, desirable direction of change in these traits remains unclear and should be defined with respect to all issues of importance related to milk FA.     

Genetic association between milk yield,stayability, and mastitis in Holstein cows under tropical conditions

The objective of this study was to estimate genetic parameters for milk yield, stayability, and the occurrence of clinical mastitis in Holstein cows, as well as studying the genetic relationship between them, in order to provide subsidies for the genetic evaluation of these traits. Records from 5,090 Holstein cows with calving varying from 1991 to 2010, were used in the analysis. Two standard multivariate analyses were carried out, one containing the trait of accumulated 305-day milk yields in the first lactation (MY1), stayability (STAY) until the third lactation, and clinical mastitis (CM), as well as the other traits, considering accumulated 305-day milk yields (Y305), STAY, and CM, including the first three lactations as repeated measures for Y305 and CM. The covariance components were obtained by a Bayesian approach. The heritability estimates obtained by multivariate analysis with MY1 were 0.19, 0.28, and 0.13 for MY1, STAY, and CM, respectively, whereas using the multivariate analysis with the Y305, the estimates were 0.19, 0.31, and 0.14, respectively. The genetic correlations between MY1 and STAY, MY1 and CM, and STAY and CM, respectively, were 0.38, 0.12, and ?0.49. The genetic correlations between Y305 and STAY, Y305 and CM, and STAY and CM, respectively, were 0.66, ?0.25, and ?0.52.     

Genetic Correlations Between Beef Traits Measured on Young Performance Test Bulls and Daughter Milk Yield

In a breeding programme where young potential breeding bulls are reared on performance test stations, selection based on own results can be carried out before test inseminations. Both beef and milk production traits are included in the total merit index used for selection, and estimates of genetic and phenotypic parameters of these traits are therefore of interest for an optimal construction of such indices. Data on first lactation milk records from the field and beef records of potential dairy breeding bulls from the Danish performance test stations were analysed in bivariate animal-sire models using the AI-REML algorithm. Genetic correlations of 0.16, 0.25 and 0.43 between feed intake capacity and protein yield were obtained for Red Danish (RD), Danish Black and White (DBW) and Danish Jersey (DJ), respectively. These correlations were significantly different from zero for the two populations (DBW and DJ). Genetic correlations around zero between feed efficiency and protein yield were obtained for all three populations. Genetic correlations of 0.44, 0.19 and 0.47 between average daily gain and protein yield were obtained for RD, DBW and DJ, respectively. The genetic correlations between protein yield and muscle area was close to zero for DBW, while it was -0.31 for RD. Selection index calculations indicate that indices composed of different beef performance traits can be used as early predictors for milk yield. Selection on such an index could increase the breeding value of the young bulls for milk production traits by 0.8-2.0% of the population mean.