Estimation of genetic parameters for somatic cell count in the first three lactations using random regression

Genetic parameters were estimated for log_e somatic cell count (LSCC) for the first three lactations of 31 236 Holstein/Friesian cows with 308 534, 236 277 and 206 729 test day yields in parities 1, 2 and 3, respectively. An animal random regression model was employed in the analyses using Gibbs sampling with each parity regarded as different traits. Linear and quadratic functions were fitted for the animal and permanent environmental effects respectively, using orthogonal polynomials. Daily heritabilities increased with days in milk (DIM) and averaged about 0.07 in all three parities. This increase in heritabilities with DIM was due to an increase in genetic variance and decreases in both permanent and residual environmental variances with DIM. Environmental effects have a large influence on LSCC in early lactation in all three parities. Within lactation, genetic correlations were highest between adjacent DIM but decreased as DIM got further apart. However, this decrease was slowest in parity one and greatest in parity three. The lowest correlation within lactation was 0.10 between DIM 7 and 305 in parity 3. Across lactations, genetic correlations were highest between parities 2 and 3, intermediate between 1 and 3, and lowest between 1 and 2. The genetic correlations computed for completed lactations were 0.69, 0.79 and 0.98 between parities 1 and 2, 1 and 3, and 2 and 3, respectively. Corresponding phenotypic correlations between parities were 0.38, 0.31 and 0.52, respectively. A test day model, accounting for these variations in heritabilities and genetic correlations, should result in a more accurate evaluation.     

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.     

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.     

Assessing the accuracy of prediction for milk fatty acids by using a small reference population of tropical Holstein cows

Fatty acids (FA) have been related to effects on human health, sensory quality and shelf life of dairy products, cow's health and methane emission. However, despite their importance, they are not regularly measured in all dairy herds yet, which can affect the accuracy of estimated breeding values (EBV) for these traits. In this case, an alternative is to use genomic selection. Thus, the aim was to assess the use of genomic information in the genetic evaluation for milk traits in a tropical Holstein population. Monthly records (n = 36,457) of milk FA percentage, daily milk yield and quality traits from 4,203 cows as well as the genotypes of 755 of these cows for 57,368 single nucleotide polymorphisms (SNP) were used. Polygenic and genomic–polygenic models were applied for EBV prediction, and both models were compared through the EBV accuracy calculated from the prediction error and Spearman's correlation among EBV rankings. Prediction accuracy was assessed by using cross‐validation. In this case, the accuracy was the correlation between the genomic breeding values (GEBV) obtained as the sum of SNP effects and the EBV obtained in the polygenic model in each validation group. For all traits, the use of the genomic–polygenic model did not alter the animals' ranking, with correlations higher than 0.87. Nevertheless, through this model, the accuracy increased from 1.5% to 6.8% compared to the polygenic model. The correlations between GEBV and EBV varied from 0.52 to 0.68. Therefore, the use of a small group of genotyped cows in the genetic evaluation can increase the accuracy of EBV for milk FA and other traditional milk traits.     

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 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.     

Random regression test day models to estimate genetic parameters for milk yield and milk components in Philippine dairy buffaloes

Heritabilities and genetic correlations for milk production traits were estimated from first‐parity test day records on 1022 Philippine dairy buffalo cows. Traits analysed included milk (MY), fat (FY) and protein (PY) yields, and fat (Fat%) and protein (Prot%) concentrations. Varying orders of Legendre polynomials (Leg_m) as well as the Wilmink function (Wil) were used in random regression models. These various models were compared based on log likelihood, Akaike's information criterion, Bayesian information criterion and genetic variance estimates. Six residual variance classes were sufficient for MY, FY, PY and Fat%, while seven residual classes for Prot%. Multivariate analysis gave higher estimates of genetic variance and heritability compared with univariate analysis for all traits. Heritability estimates ranged from 0.25 to 0.44, 0.13 to 0.31 and 0.21 to 0.36 for MY, FY and PY, respectively. Wilmink's function was the better fitting function for additive genetic effects for all traits. It was also the preferred function for permanent environment effects for Fat% and Prot%, but for MY, FY and PY, the Leg_m was the appropriate function. Genetic correlations of MY with FY and PY were high and they were moderately negative with Fat% and Prot%. To prevent deterioration in Fat% and Prot% and improve milk quality, more weight should be applied to milk component traits.     

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.     

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.     

Analysis of genotype by environment interaction for milk yield traits in first lactation of Simmental cattle

The breeding goal for Simmental cattle is derived for intensively managed dairy farms. Its suitability for extensive farms was addressed by analysing possible genotype by environment interaction (G × E) between the management levels for first lactation milk yield traits. A first analysis was performed with the data collected from 300 000 purebred daughters of 278 second crop bulls born in Bavaria in 1993 and 1994. The farms were classified by herd‐year‐effect, using the sum of fat and protein yields into two levels of management, either with 33 or 10% quantiles, corresponding to approximately 100 000 cows and 30 000 cows, respectively. The comparison was based on ‘daughter yield’ deviations (DYD). Correlations between DYD of extensive and intensive environments were 0.90, 0.91 and 0.87 for milk, fat and protein yield (kg) for 33% quantiles, respectively. Corresponding correlations for 10% quantiles were 0.85, 0.83 and 0.77. Despite high correlations, 50 out of 149 sires showed significant differences between DYD in different environments. Bulls with higher DYD for milk yield on intensive farms were superior in all environments. For the second analysis extensive and intensive farms in northern and southern Bavaria were chosen at random. Approximately 20 000 cows in each management class were used for the estimation of genetic parameters. In both regions phenotypic and additive‐genetic variances were higher in the intensively managed herds. Likewise heritabilities were higher for fat and protein yield, but not for milk where higher heritabilities were observed in 33% quantiles. Genetic correlations between extensive and intensive environments were 0.97 and above (33% quantiles). Ten per cent quantiles led to lower genetic correlations (0.90–0.95). Although no serious re‐ranking effects of sires were evident, the scale effect and the differences in genetic parameters should be taken into consideration in practical breeding.     

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 parameters for milk fatty acids,milk yield and quality traits of a Holstein cattle population reared under tropical conditions

Information about genetic parameters is essential for selection decisions and genetic evaluation. These estimates are population specific; however, there are few studies with dairy cattle populations reared under tropical and sub‐tropical conditions. Thus, the aim was to obtain estimates of heritability and genetic correlations for milk yield and quality traits using pedigree and genomic information from a Holstein population maintained in a tropical environment. Phenotypic records (n = 36 457) of 4203 cows as well as the genotypes for 57 368 single nucleotide polymorphisms from 755 of these cows were used. Covariance components were estimated using the restricted maximum likelihood method under a mixed animal model, considering a pedigree‐based relationship matrix or a combined pedigree‐genomic matrix. High heritabilities (around 0.30) were estimated for lactose and protein content in milk whereas moderate values (between 0.19 and 0.26) were obtained for percentages of fat, saturated fatty acids and palmitic acid in milk. Genetic correlations ranging from −0.38 to −0.13 were determined between milk yield and composition traits. The smaller estimates compared to other similar studies can be due to poor environmental conditions, which may reduce genetic variability. These results highlight the importance in using genetic parameters estimated in the population under evaluation for selection decisions.     

Genetic parameters for linear type traits in the Rendena dual‐purpose breed

The aim of the study was to estimate the genetic parameters for five composite traits and 20 individual type traits on 10 735 first‐parity Rendena dual‐purpose cows. Fixed effects included in the linear animal mixed models were herd‐year‐classifier, days in milk and age at first calving; the additive genetic effect of the animal was included as a random effect. Heritability estimates varied from 0.12 (feet) to 0.52 (stature). Genetic correlations between the individual body size traits were all ≥0.69; similar strong genetic correlations existed between traits describing similar morphological characteristics (e.g. mammary system, fleshiness). Many of the body size traits were negatively genetically correlated with animal fleshiness. Genetic trends showed that genetic merit for body size increased consistently over the last 10 years, while genetic merit for fleshiness declined. These results suggest that the characteristics of the dual‐purpose Rendena cattle are becoming more like specialized milk‐producing animals. Nonetheless, sufficient genetic variation exists to halt or reverse the deterioration in fleshiness.     

Genetic parameters for energy balance, fat /protein ratio, body condition score and disease traits in German Holstein cows

Various health problems in dairy cows have been related to the magnitude and duration of the energy deficit post partum. Energy balance indicator traits like fat/protein ratio in milk and body condition score could be used in selection programmes to help predicting breeding values for health traits, but currently there is a lack of appropriate genetic parameters. Therefore, genetic correlations among energy balance, fat/protein ratio, and body condition score, and mastitis, claw and leg diseases, and metabolic disorders were estimated using linear and threshold models on data from 1693 primiparous cows recorded within the first 180 days in milk. Average daily energy balance, milk fat/protein ratio and body condition score were 8 MJ NEL, 1.13 and 2.94, respectively. Disease frequencies (% cows with at least one case) were 24.6% for mastitis, 9.7% for metabolic disorders and 28.2% for claw and leg diseases. Heritability estimates were 0.06, 0.30 and 0.34 for energy balance, fat/protein ratio and body condition score, respectively. For the disease traits, heritabilities ranged between 0.04 and 0.15. The genetic correlations were, in general, associated with large standard errors, but, although not significant, the results suggest that an improvement of overall health can be expected if energy balance traits are included into future breeding programmes. A low fat/protein ratio might serve as an indicator for metabolic stability and health of claw and legs. Between body condition and mastitis, a significant negative correlation of -0.40 was estimated. The study provides a new insight into the role energy balance traits can play as auxiliary traits for robustness of dairy cows. It was concluded that both, fat/protein ratio and body condition score, are potential variables to describe how well cows can adapt to the challenge of early lactation. However, the genetic parameters should be re-estimated on a more comprehensive data set.     

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

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

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.     

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.     

Modeling lactation curves and estimation of genetic parameters in Holstein cows using multiple‐trait random regression models

We compared the goodness of fit of three mathematical functions (including: Legendre polynomials, Lidauer‐Mäntysaari function and Wilmink function) for describing the lactation curve of primiparous Iranian Holstein cows by using multiple‐trait random regression models (MT‐RRM). Lactational submodels provided the largest daily additive genetic (AG) and permanent environmental (PE) variance estimates at the end and at the onset of lactation, respectively, as well as low genetic correlations between peripheral test‐day records. For all models, heritability estimates were highest at the end of lactation (245 to 305 days) and ranged from 0.05 to 0.26, 0.03 to 0.12 and 0.04 to 0.24 for milk, fat and protein yields, respectively. Generally, the genetic correlations between traits depend on how far apart they are or whether they are on the same day in any two traits. On average, genetic correlations between milk and fat were the lowest and those between fat and protein were intermediate, while those between milk and protein were the highest. Results from all criteria (Akaike's and Schwarz's Bayesian information criterion, and ?2*logarithm of the likelihood function) suggested that a model with 2 and 5 coefficients of Legendre polynomials for AG and PE effects, respectively, was the most adequate for fitting the data.     

Genetic parameters for total lactation yields of milk,fat, protein,and somatic cell score in New Zealand dairy goats

The aim of this study was to estimate genetic parameters for lactation yields of milk (MY), fat (FY), protein (PY), and somatic cell score (SCS) of New Zealand dairy goats. The analysis used 64,604 lactation records from 23,583 does, kidding between 2004 and 2017, distributed in 21 flocks and representing 915 bucks. Estimates of genetic and residual (co) variances, heritabilities, and repeatabilities were obtained using a multiple‐trait repeatability animal model. The model included the fixed effects of contemporary group (does kidding in the same flock and year), age of the doe (in years), and as covariates, kidding day, proportion of Alpine, Nubian, Toggenburg, and “unknown” breeds (Saanen was used as the base breed), and heterosis. Random effects included additive animal genetic and doe permanent environmental effects. Estimates of heritabilities were 0.25 for MY, 0.24 for FY, 0.24 for PY, and 0.21 for SCS. The phenotypic correlations between MY, FY, and PY ranged from 0.90 to 0.96, and the genetic correlations ranged from 0.81 to 0.93. These results indicate lactation yield traits exhibit useful heritable variation and that multiple trait selection for these traits could improve milk revenue produced from successive generations of New Zealand dairy goats.