Estimation of genetic parameters for lactational milk yields using two-dimensional random regressions on parities and days in milk in Chinese Simmental cattle

A two‐dimensional random regression model with regressions on days in milk (DIM) and parity number was applied to lactational milk yields in Chinese Simmental cattle. Random regressions were fitted for additive genetic and permanent environmental effects using a two‐dimensional polynomial on DIM and parity number. A total of 4340 lactational milk yields from Chinese Simmental cattle which calved between 1980 and early 2000 were used in this study. Variance components were estimated using Bayesian methodology via Gibbs sampling. Variances of random regression coefficients associated with all terms of the polynomials were significant. A covariance function showed that heritabilities of lactational milk yields between 200 and 400 DIM over parities varied between 0.25 and 0.45. Heritabilities of 305‐day milk yields from 1st to 6–8th parities were 0.28, 0.30, 0.32 0.32, 0.32, and 0.31, respectively. Ratios of permanent environment variances to total variances at each DIM were greater than corresponding heritabilities. Generally, genetic correlations were higher between lactational milk yields with similar DIM and parity number.     

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

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

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

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

Genetic parameters and trends for calving interval in the first three lactations of Iranian Holsteins

Calving records from the Animal Breeding Center of Iran collected from January 1987 to December 2007 and comprising 292,875 calving events of Holsteins from 1,413 dairy herds were analyzed using univariate and bivariate linear animal models to estimate heritabilities and genetic correlations for calving intervals in the first three lactations of Holstein cows. Genetic trends were obtained by regressing yearly mean estimates of breeding values on calving year. Average calving intervals were from 406 to 414 days and decreased over the parities. Heritability estimates for calving intervals varied from 0.03 to 0.04 across the parities. Also, estimates of genetic correlations between calving intervals in different parities were high and ranged from 0.67 to 0.89. The average annual phenotypic trends obtained from fitting linear regression of annual mean calving intervals at parity 1 and 2 were significant (P < 0.01), but the phenotypic trend of calving interval at parity 3 was not significant over the years. On the other hand, there was an increasing genetic trend for calving interval at parity 1, and there were decreasing genetic trends for calving intervals at parity 2 and 3 over the years (P < 0.01). The low estimates of heritability obtained in this study imply that much of the improvement in calving interval traits could be attained by improvement of production environment rather than genetic selection.     

中国荷斯坦奶牛不同地区、泌乳天数及胎次的产奶量校正系数研究

本研究通过对规模化牧场测定日数据进行分析,旨在估计不同地区305 d校正产奶量对应泌乳天数(DIM)的校正系数与胎次校正系数。利用Wood(不完全伽玛)模型对来自不同温度带9个参考群11 749头荷斯坦奶牛2010－2020年间的683 160条测定日产奶量拟合泌乳曲线,估计泌乳曲线参数,计算头胎牛和经产牛1~305 d泌乳天数对应的校正系数,分别分析参考群和验证群305 d校正产奶量和305 d实际累积奶量的差异;利用SAS 9.2中混合线性模型对1~6胎至少有前5个胎记录的牛估计305 d奶量的胎次效应值,并计算1~6胎各胎次的乘法校正系数;比较传统系数和新系数校正结果的差异。结果表明:①参考群头胎牛和经产牛中,泌乳曲线方程的拟合度R²范围分别为0.4593~0.4913和0.5796~0.6341,泌乳高峰日分别为79~85和53 d,泌乳高峰奶量分别为33.1~34.4和46.0~48.6 kg;②对于参考群,头胎牛和经产牛校正系数分别在泌乳≥90 d和泌乳≥30 d基本趋于一致,头胎牛和经产牛泌乳≥60 d的305 d校正产奶量均趋近于305 d实际累积奶量,奶量差值分别在100和200 kg以内;③对于验证群的305 d校正产奶量,头胎牛泌乳≥150 d和经产牛泌乳≥180 d的泌乳天数校正系数适用性更佳,305 d校正产奶量与305 d实际累积奶量比值高于79%;④1~6胎的胎次校正系数分别为1.2121、1.0380、1.0063、1.0000、1.0220和1.0290;⑤与传统系数相比,新泌乳天数校正系数校正效果更好,头胎牛和经产牛新系数校正的305 d奶量较305 d实际累积奶量相差分别在900和700 kg以内;利用新胎次校正系数,2~4胎产奶量可较为准确地校正到5胎成年当量。本研究结果表明,定期更新305 d校正产奶量的泌乳天数与胎次校正系数,便于准确地将不同泌乳天数、不同胎次状态下泌乳牛的产奶量调整到同一基准上,从而更好地比较奶牛个体泌乳性能的高低,为牧场管理提供参考依据。     

Genetic parameters for direct and maternal calving ability over parities in Piedmontese cattle

Estimates of heritabilities and genetic correlations for calving ease over parities were obtained for the Italian Piedmontese population using animal models. Field data were calving records of 50,721 first- and 44,148 second-parity females and 142,869 records of 38,213 cows of second or later parity. Calving ability was scored in five categories and analyzed using either a univariate or a bivariate linear model, treating performance over parities as different traits. The bivariate model was used to investigate the genetic relationship between first- and second- or between first- and third-parity calving ability. All models included direct and maternal genetic effects, which were assumed to be mutually correlated. (Co)variance components were estimated using restricted maximum likelihood procedures. In the univariate analyses, the heritability for direct effects was .19 +/- .01, .10 +/- .01, and .08 +/- .004 for first, second, and second and later parities, respectively. The heritability for maternal effects was .09 +/- .01, .11 +/- .01, and .05 +/- .01, respectively. All genetic correlations between direct and maternal effects were negative, ranging from -.55 to -.43. Approximated standard errors of genetic correlations between direct and maternal effects ranged from .041 to .062. For multiparous cows, the fraction of total variance due to the permanent environment was greater than the maternal heritability. With bivariate models, direct heritability for first parity was smaller than the corresponding univariate estimate, ranging from .18 to .14. Maternal heritabilities were slightly higher than the corresponding univariate estimates. Genetic correlation between first and second parity was .998 +/- .00 for direct effects and .913 +/- .01 for maternal effects. When the bivariate model analyzed first- and third-parity calving ability, genetic correlation was .907 +/- .02 for direct effects and .979 +/- .01 for maternal effects. Residual correlations were low in all bivariate analyses, ranging from .13 for analysis of first and second parity to .07 for analysis of first and third parity. In conclusion, estimates of genetic correlations for calving ease in different parities obtained in this study were very high, but variance components and heritabilities were clearly heterogeneous over parities.     

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 analysis of twinning rate and milk yield using a threshold‐linear model in Japanese Holsteins

The objective of this study was to estimate genetic parameters and breeding values for the twinning rate of the first three parities (T1, T2 and T3) and 305‐day milk yield in first lactation (MY), using a four‐trait threshold‐linear animal model in Japanese Holsteins. Data contained 1 323 946 cows calving between 1990 and 2007. Twinning was treated as a binary character: ‘single’ or ‘twin or more’. Reported T1, T2 and T3 were 0.70%, 2.87%, and 3.73%, respectively. Individual 305‐day milk yield was computed using a multiple trait prediction for cows with at least eight test‐day records. (Co)variance components were estimated via Gibbs sampling for randomly sampled subsets. Posterior means of heritabilities for T1, T2 and T3 were 0.11, 0.16 and 0.14, respectively. Genetic correlations between parities were 0.92 or greater. Genetic correlations of MY with twinning rate were not ‘significant’ (i.e. their 95% highest probability density intervals contained zeros). Multiple births at different parities were considered as the same genetic trait. The average evaluations of T1 (T2) for sires born before 1991 was 0.48% (2.25%) compared with a mean of 0.76% (3.37%) for sires born after 1992. A recent increase in the reported twinning rate reflects the positive genetic trend for sires in Japanese Holsteins.     

Threshold-linear estimation of genetic parameters for farrowing mortality, litter size, and test performance of Large White sows

Up to 109,447 records of 49,656 Large White sows were used to evaluate the genetic relationship between number of pigs born dead (BD) and number born alive (BA) in first and later parities. Performance data (n = 30,832) for ultrasound backfat (BF) at the end of the test and days to reach 113.5 kg (AD) were used to estimate their relationships with BD and BA at first parity in a four-trait threshold-linear analysis (TL). Effects were year-farm, contemporary group (CG: farm-farrowing year-farrowing month) and animal additive genetic. At first parity, estimates of heritability were 0.09, 0.09, 0.37, and 0.31 for BA, BD, AD, and BF, respectively. The estimate of genetic correlation between BD and litter size was -0.04 (BD-BA). Corresponding values with test traits were both -0.14 (BD-AD, BD-BF). Estimates of genetic correlation between BA and performance traits were 0.08 (BA-AD) and 0.05 (BA-BF). The two test traits were moderately negatively correlated (-0.22). For later parities, a six-trait (BD, BA in three parities) TL model was implemented. The estimates of additive genetic variances and heritability increased with parity for BD and BA. Estimates of heritabilities were: 0.09, 0.10, and 0.11 for BD, and 0.09, 0.12, and 0.12 for BA in parities one to three, respectively. Estimates of genetic correlations between different parities were high (0.91 to 0.96) for BD, and slightly lower (0.74 to 0.95) for BA. Genetic correlations between BD and BA were low and positive (0.02 to 0.17) for BA in Parities 1 and 2, but negative (-0.04 to -0.10) for BA in Parity 3. Selection for increased litter size should have little effect on farrowing piglet mortality. Intense selection for faster growth and increased leanness should increase farrowing piglet mortality of first-parity sows. A repeatability model with a simple correction for the heterogeneity of variances over parities could be implemented to select against farrowing mortality. The genetic components of perinatal piglet mortality are independent of the ones for litter size in the first parity, and they show an undesirable, but not strong, genetic association in second parity.     

Population parameters for birth and ewe fleece weight at different parities in Baluchi sheep

ZUSAMMENFASSUNG: Populationsparameter für Geburts- und Vlie?gewicht von Baluchi Schafen Das Datenmarterial stammt von zwei Herden einer Schafzuchtstation in NO Iran aus den Jahren 1966-1989. Die Tiere waren unselektiert und stammten aus zuf?llig verteilten Paarungen. Es wurden Geburtsgewicht und Vlie?gewicht bei verschiedenen Altersstufen erhoben und Varianzkomponenten mittels Restringierter Maximaler Likelihood mit einem bivariaten Tiermodell mit fixen Wirkungen von Jahr, Geschlecht, Geburtstyp und Parit?t sowie Zufallswirkungen für additiven Genotyp des Lammes (direkt) und des Mutterschafes (maternal), gemeinsamer Umwelt (ausgenommen Vlie?gewicht) und Resteinflu? gesch?tzt. Direkte und maternale genetische Korrelationen zwischen Leistungen verschiedener Parit?ten wurden berechnet. In Herde 1 scheinen Varianzen und Heritabilit?tswerte für Lammgewicht bis Parit?t 5 zuzunehmen, kaum aber in Herde 2. Die durchschnittlichen Heritabilit?tswerte, direkt, maternal und gesamt waren 0.12, 0.11 und 0.26, die genetische Korrelation zwischen direkten und maternalen Wirkungen 0.42. Bei Vlie?gewicht waren in Herde 1 keine Ver?nderungen der Varianzen und Heritabilit?tswerte mit Alter zu erkennen, aber bei Herde 2 nahmen ph?notypische und Umweltvarianz mit Alter leicht zu. Durchschnittliche direkte, maternale und Gesamtheritabilit?t waren 0.19, 0.04 und 0.22, die genetische Korrelation zwischen direkten und maternalen Wirkungen geringgradig positiv in Herde 1, aber mit Alter zunehmend negativ in Herde 2. Die genetischen Korrelationen für direkte Wirkungen auf Geburtsgewicht waren hoch zwischen Parit?ten 1 bis 5, aber niedriger bei Parit?t 6 und jene zwischen maternal bedingten Wirkungen zeigten ?hnliche Trends. In Herde 2 waren Werte mit Parit?t 6 ?hnlich wie die zwischen den übrigen Parit?ten. Die die Vlie?gewichte betreffenden direkt genetischen Korrelationen zwischen Parit?ten waren in beiden Herden ?hnlich (0.73-0.92), jene, die maternale Wirkungen betreffen, deutlich geringer, besonders soweit sie Parit?ten 5 und 6 betroffen haben und zeigten besonders bei Herde 2 starke Schwankungen (-0.54 bis 0.74). SUMMARY: Direct and maternal performance of ewes at different parities were examined in Baluchi sheep. The data set was collected during the period 1966-1989 from two flocks at a sheep breeding station in the north-east of Iran. The animals included in the data set were unselected and randomly mated. The traits analysed were birth weight of lamb and fleece weight at different parities of the ewe. Variance components were estimated using Restricted Maximum Likelihood with a bivariate animal model including fixed effects of year, sex, type of birth and parity, and random effects of additive genotype of lamb (direct genetic effect), additive genotype of ewe (maternal genetic effect) and common environment (excluded for ewe fleece weight), and random residual effect. Direct and maternal genetic correlations between different parities were estimated. There was evidence of increasing phenotypic and genetic variances and heritabilities from parity 5 for birth weight of lamb in flock 1, but only evidence of a slightly increasing age trend for the environmental and phenotypic variance in flock 2. The average heritabilities over flocks and parities were 0.12, 0.11 and 0.26 for the direct, maternal and total heritability, respectively, while the average genetic correlation between direct and maternal effects for this trait was 0.42. There were no indications of any age changes in variances or heritabilities for ewe fleece weight in flock 1, but indications of slightly increasing age trends for the environmental and phenotypic variance. The average heritabilities over flocks and parities were 0.19, 0.04 and 0.22 for the direct, maternal and total heritability, respectively, while the average genetic correlation between direct and maternal effects was slightly positive in flock 1 but increasingly negative with age of the ewe in flock 2. Direct genetic correlations between parities 1-5 were very high for birth weight of lambs (on average 0.96) in contrast to the markedly lower correlations of parities 1-5 with parity 6 (on average 0.67) in flock 1 with a similar pattern for the maternal genetic correlations. In flock 2, these correlations were also high but without the marked decrease between parities 1-5 with parity 6 that was found in flock 1. Direct genetic correlations between the various parities for ewe fleece weight were similar for the two flocks, ranging from 0.73 to 0.92 and without any obvious differences between the various combinations of parities. However, the maternal were markedly lower than the direct genetic correlations, especially for the combinations of parity 5 and 6 with the earlier parities, and most pronounced in flock 2 fluctuating from -0.54 to 0.79. To obtain reliable estimates of breeding values for birth weight of lamb, it is recommended that the prediction should include not only earlier but also later parities (ages) of the ewe.     

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

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

Genetic 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.     

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

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

Genetic parameter estimation for number born alive at different parities in Landrace and Large White pigs

We estimated genetic parameters for number born alive (NBA) from the first to the seventh parities in Landrace and Large White pigs using three models. Analyzing 55,160 farrowing records for 12,677 Landrace dams and 43,839 for 10,405 Large White dams, we used a single‐trait animal model to estimate the heritability of NBA at each parity and a two‐trait animal model and a single‐trait random regression model to estimate the genetic correlations between parities. Heritability estimates of NBA at each parity ranged from 0.08 to 0.13 for Landrace and from 0.05 to 0.16 for Large White. Estimated genetic correlations between parities in all cases were positive. Genetic correlations between the first and second parities were slightly lower than those between other neighboring parities. Genetic correlations between more distant parities tended to be lower, in some cases <0.8. The results indicate the necessity to investigate the applicability of evaluating NBA at different parities as different traits (e.g., the first and later parities), although a repeatability model might still be reasonable.     

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

Abstract

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

Genetic parameters for calving difficulty, stillbirth, and birth weight for Hereford and Charolais at first and later parities

The aim of this study was to estimate direct and maternal genetic parameters for calving difficulty score, stillbirth, and birth weight at first and later parities for Charolais and Hereford cattle in Sweden. Calving traits have long been recorded for pure-bred beef cattle in Sweden, but only birth weight has been used in the selection in order to avoid calving difficulties. Linear animal model analyses included records on birth weight for 60,309 Charolais and 30,789 Hereford calves born from 1980 to 1999, and calving traits for 74,538 Charolais and 37,077 Hereford calves born from 1980 to 2001. The frequencies of difficult calvings and stillbirths were approximately 6% at first and 1 to 2% at later parities for both breeds. Fewer than half the stillborn calves were born from difficult calvings. Heritabilities estimated for birth weight in different univariate and bivariate analyses for Charolais and Hereford calves born at first and later parities ranged from 0.44 to 0.51 for direct effects and 0.06 to 0.15 for maternal effects. Heritabilities on the observable scale for calving difficulty score of Charolais and Hereford, scored in three classes, ranged from 0.11 to 0.16 for direct and 0.07 to 0.12 for maternal effects at first parity, and lower at later parities. All estimated heritabilities for stillbirth were very low (0.002 to 0.016 on the observable scale). Direct-maternal genetic correlations were negative, with few exceptions. Genetic correlations between the traits and between parities within traits were generally moderate to high and positive. Calving difficulty score should be included in the genetic evaluation of beef breeds in Sweden, whereas progeny groups in Swedish beef populations are too small for stillbirth to be considered directly.     

Comparison of two models to estimate genetic parameters for number of born alive in pigs

The performance of the two‐trait animal model that regards the first parity and later parities as two different traits in estimating genetic parameters for number of born alive (NBA) was examined using real and simulated data. Genetic parameters for NBA were estimated in purebred Landrace and Large White pigs using a single‐trait repeatability model (Model 1) that regards all parities as the same trait and a two‐trait animal model (Model 2) that regards the first and the later parities as different traits. For Model 2, the permanent environmental effect was fitted to only the records of the later parities. Heritability for NBA estimated using Model 1 was 0.12 for Landrace and 0.11 for Large White. Estimated heritability for NBA of the first parity and the later parities was 0.21 and 0.16, respectively, for Landrace; 0.18 and 0.16, respectively, for Large White obtained using Model 2, and higher than those in both breeds obtained using Model 1. Further results based on data simulated using the Monte Carlo method suggest that estimated additive genetic variance could be more biased using Model 2 than Model 1.     

Cumulative risk of bovine mastitis treatments in Denmark, Finland, Norway and Sweden

Data from the national dairy cow recording systems during 1997 were used to calculate lactation-specific cumulative risk of mastitis treatments and cumulative risk of removal from the herds in Denmark, Finland Norway and Sweden. Sweden had the lowest risk of recorded mastitis treatments during 305 days of lactation and Norway had the highest risk. The incidence risk of recorded mastitis treatments during 305 days of lactation in Denmark, Finland, Norway and Sweden was 0.177, 0.139, 0.215 and 0.127 for first parity cows and 0.228, 0.215, 0.358 and 0.204 for parities higher than three, respectively. The risk of a first parity cow being treated for mastitis was almost 3 times higher at calving in Norway than in Sweden. The period with the highest risk for mastitis treatments was from 2 days before calving until 14 days after calving and the highest risk for removal was from calving to 10 days after calving in all countries. The study clearly demonstrated differences in bovine mastitis treatment patterns among the Nordic countries. The most important findings were the differences in treatment risks during different lactations within each country, as well as differences in strategies with respect to the time during lactation mastitis was treated.     

Lactation curves of friesian-bunaji crosses in NigeriaCourves de lactation de croisées frisonne-bunaji au NigeriaLaktationskurven von friesian-bunaji rinderkreuzungen in Nigeria

The validity of the gamma function to describe lactation curves of $\text{1}\text{2}$ and $\text{3}\text{4}$ bred Friesian-Bunaji crosses was investigated. The function explained 71% of the variation in yield in lactations which differed in duration, parity and season of calving. The effect of these variables on the components of the lactation curve was analysed by least-squares procedures. The goodness of fit of the function did not differ between classes of varying duration of lactation; short lactations, however, in addition to a lower persistence, also showed a low level of production. Lactation curves of first parity were flatter and also had a greatly reduced level of production compared to higher parities.Multiplying factors for estimating total lactation yield from part records were obtained from the fitted curves. The accuracy of prediction was greater when separate factors were used for each class of lactation length. The usefulness of part records in progeny testing is also discussed.     

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.