Genetic evaluation of lactation persistency for five breeds of dairy cattle

Cows with high lactation persistency tend to produce less milk than expected at the beginning of lactation and more than expected at the end. Best prediction of lactation persistency is calculated as a function of trait-specific standard lactation curves and linear regressions of test-day deviations on days in milk. Because regression coefficients are deviations from a tipping point selected to make yield and lactation persistency phenotypically uncorrelated it should be possible to use 305-d actual yield and lactation persistency to predict yield for lactations with later endpoints. The objectives of this study were to calculate (co)variance components and breeding values for best predictions of lactation persistency of milk (PM), fat (PF), protein (PP), and somatic cell score (PSCS) in breeds other than Holstein, and to demonstrate the calculation of prediction equations for 400-d actual milk yield. Data included lactations from Ayrshire, Brown Swiss, Guernsey (GU), Jersey (JE), and Milking Shorthorn (MS) cows calving since 1997. The number of sires evaluated ranged from 86 (MS) to 3,192 (JE), and mean sire estimated breeding value for PM ranged from 0.001 (Ayrshire) to 0.10 (Brown Swiss); mean estimated breeding value for PSCS ranged from −0.01 (MS) to −0.043 (JE). Heritabilities were generally highest for PM (0.09 to 0.15) and lowest for PSCS (0.03 to 0.06), with PF and PP having intermediate values (0.07 to 0.13). Repeatabilities varied considerably between breeds, ranging from 0.08 (PSCS in GU, JE, and MS) to 0.28 (PM in GU). Genetic correlations of PM, PF, and PP with PSCS were moderate and favorable (negative), indicating that increasing lactation persistency of yield traits is associated with decreases in lactation persistency of SCS, as expected. Genetic correlations among yield and lactation persistency were low to moderate and ranged from −0.55 (PP in GU) to 0.40 (PP in MS). Prediction equations for 400-d milk yield were calculated for each breed by regression of both 305-d yield and 305-d yield and lactation persistency on 400-d yield. Goodness-of-fit was very good for both models, but the addition of lactation persistency to the model significantly improved fit in all cases. Routine genetic evaluations for lactation persistency, as well as the development of prediction equations for several lactation end-points, may provide producers with tools to better manage their herds.     

Relationships between milk yield and somatic cell score in Canadian Holsteins from simultaneous and recursive random regression models

Multiple-trait random regression animal models with simultaneous and recursive links between phenotypes for milk yield and somatic cell score (SCS) on the same test day were fitted to Canadian Holstein data. All models included fixed herd test-day effects and fixed regressions within region-age at calving-season of calving classes, and animal additive genetic and permanent environmental regressions with random coefficients. Regressions were Legendre polynomials of order 4 on a scale from 5 to 305 d in milk (DIM). Bayesian methods via Gibbs sampling were used for the estimation of model parameters. Heterogeneity of structural coefficients was modeled across (the first 3 lactations) and within (4 DIM intervals) lactation. Model comparisons in terms of Bayes factors indicated the superiority of simultaneous models over the standard multiple-trait model and recursive parameterizations. A moderate heterogeneous (both across- and within-lactation) negative effect of SCS on milk yield (from −0.36 for 116 to 265 DIM in lactation 1 to −0.81 for 5 to 45 DIM in lactation 3) and a smaller positive reciprocal effect of SCS on milk yield (from 0.007 for 5 to 45 DIM in lactation 2 to 0.023 for 46 to 115 DIM in lactation 3) were estimated in the most plausible specification. No noticeable differences among models were detected for genetic and environmental variances and genetic parameters for the first 2 regression coefficients. The curves of genetic and permanent environmental variances, heritabilities, and genetic and phenotypic correlations between milk yield and SCS on a daily basis were different for different models. Rankings of bulls and cows for 305-d milk yield, average daily SCS, and milk lactation persistency remained the same among models. No apparent benefits are expected from fitting causal phenotypic relationships between milk yield and SCS on the same test day in the random regression test-day model for genetic evaluation purposes.     

Recursive relationships between milk yield and somatic cell score of Canadian Holsteins from finite mixture random regression models

Finite mixture, multiple-trait, random regression animal models with recursive links between phenotypes for milk yield and somatic cell score (SCS) on the same test-day were applied to first lactation Canadian Holstein data. All models included fixed herd-test-day effects and fixed regressions within region-age at calving-season of calving classes, and animal additive genetic and permanent environmental regressions with random coefficients. Causal links between phenotypes for milk yield and SCS were fitted separately for records from healthy cows and cows with a putative, subclinical form of mastitis. Bayesian methods via Gibbs sampling were used for the estimation of model parameters. Bayes factors indicated superiority of the model with recursive link from milk to SCS over the reciprocal recursive model and the standard multiple-trait model. Differences between models measured by other, single-trait model comparison criteria (i.e., weighted mean squared error, squared bias, and correlation between observed and expected data) were negligible. Approximately 20% of test-day records were classified as originating from cows with mastitis in recursive mixture models. The proportion of records from cows infected with mastitis was largest at the beginning of lactation. Recursive mixture models exhibited different distributions of data from healthy and infected cows in different parts of lactation. A negative effect of milk to SCS (up to −0.15 score points for every kilogram of milk for healthy cows from 5 to 45 d in milk) was estimated for both mixture components (healthy and infected) in all stages of lactation for the most plausible model. The magnitude of this effect was stronger for healthy cows than for cows infected with mastitis. Different patterns of genetic and environmental correlations between milk and SCS for healthy and infected records were revealed, due to heterogeneity of structural coefficients between mixture components. Estimated breeding values for SCS from the best fitting model for sires of infected daughters were more related to estimated breeding values for the same trait from the regular multiple-trait model than evaluations for sires of mastitis-free cows.     

Effect of gradual or abrupt cessation of milking at dry off on milk yield and somatic cell score in the subsequent lactation

The objective of this study was to assess the effect of milk cessation method (abrupt or gradual) at dry off on milk yield and somatic cell score (SCS) up to 120 d in milk during the subsequent lactation. Data from 428 cows from 8 dairy herds in Ohio were analyzed. Abrupt cessation cows kept the farm's regular milking schedule (2 or 3 times) through dry off and gradual cessation cows were milked once daily for the final week of lactation. Milk yield and SCS were collected using Dairy Herd Improvement Association test-day records. Aseptic quarter milk samples were collected approximately 1 wk before dry off, at dry off, and within 1 wk after calving for bacterial culture to determine the presence of intramammary infections. Overall, milk cessation method was not significantly associated with either milk yield or SCS in early lactation; however, interaction between the milk cessation method and herd was highly significant. Cows producing greater amounts of milk around dry off had significantly higher SCS in the following lactation. Shorter dry periods were significantly associated with decreased milk yield in the following lactation, especially among abruptly dried off cows. Additionally, as expected, several other factors, such as parity of cows and stage of lactation, were significantly associated with both outcomes. No interactions between the milk cessation method and the other explanatory variables in the final models were significant. The results of the current study suggest that higher milk yield at dry off was associated with higher SCS in the following lactation, even though milk cessation method at the end of lactation had a varying effect on test-day milk yield and SCS in different herds during the first 120 d in milk in the following lactation. The specific herd characteristics influencing this could not be identified within this study, warranting further research.     

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

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

Random regression test-day model for clinical mastitis: Genetic parameters,model comparison,and correlations with indicator traits

The objective was to study genetic (co)variance components for binary clinical mastitis (CM), test-day protein yield, and udder health indicator traits [test-day somatic cell score (SCS) and type traits of the udder composite] in the course of lactation with random regression models (RRM). The study used a data set from selected 15 large-scale contract herds including 26,651 Holstein cows. Test-day production and CM data were recorded from 2007 to 2012 and comprised parities 1 to 3. A longitudinal CM data structure was generated by assigning CM records to adjacent official test dates. Bivariate threshold-linear RRM were applied to estimate genetic (co)variance components between longitudinal binary CM (0 = healthy; 1 = diseased) and longitudinal Gaussian distributed protein yield and SCS test-day data. Heritabilities for liability to CM (heritability ~0.15 from 0 to 305 d after calving) were slightly higher than for SCS for corresponding days in milk (DIM) in the course of lactation. Daily genetic correlations between CM and SCS were moderate to high (genetic correlation ~0.70), but substantially decreased at the very end of lactation. Genetic correlations between CM at different test days were close to 1 for adjacent test days, but were close to zero for test days far apart. Daily genetic correlations between CM and protein yield were low to moderate. For identical DIM (e.g., DIM 20, 160, and 300), genetic correlations were −0.03, 0.11, and 0.18, respectively, and disproved pronounced genetic antagonisms between udder health and productivity. Correlations between estimated breeding values (EBV) for CM from the RRM and official EBV for linear type traits of the udder composite, including EBV from 74 influential sires (sires with >60 daughters), were −0.31 for front teat placement, −0.01 for rear teat placement, −0.31 for fore udder attachment, −0.32 for udder depth, and −0.08 for teat length. Estimated breeding values for CM from the RRM were compared with EBV from a multiple-trait model and with EBV from a repeatability model. For test days covering an identical time span and on a lactation level, correlations between EBV from RRM, multiple-trait model, and repeatability model were close to 1. Most relevant results suggest the routine application of threshold RRM to binary CM to (1) allow selection of genetically superior sires for distinct stages of lactation and (2) achieve higher selection response in CM compared with selection strategies based on indicator type traits or based on the indicator-trait SCS.     

Experience with a test-day model

The Canadian Test-Day Model is a 12-trait random regression animal model in which traits are milk, fat, and protein test-day yields, and somatic cell scores on test days within each of first three lactations. Test-day records from later lactations are not used. Random regressions (genetic and permanent environmental) were based on Wilmink's three parameter function that includes an intercept, regression on days in milk, and regression on an exponential function to the power -0.05 times days in milk. The model was applied to over 22 million test-day records of over 1.4 million cows in seven dairy breeds for cows first calving since 1988. A theoretical comparison of test-day model to 305-d complete lactation animal model is given. Each animal in an analysis receives 36 additive genetic solutions (12 traits by three regression coefficients), and these are combined to give one estimated breeding value (EBV) for each of milk, fat, and protein yields, average daily somatic cell score and milk yield persistency (for bulls only). Correlation of yield EBV with previous 305-d lactation model EBV for bulls was 0.97 and for cows was 0.93 (Holsteins). A question is whether EBV for yield traits for each lactation should be combined into one overall EBV, and if so, what method to combine them. Implementation required development of new methods for approximation of reliabilities of EBV, inclusion of cows without test day records in analysis, but which were still alive and had progeny with test-day records, adjustments for heterogeneous herd-test date variances, and international comparisons. Efforts to inform the dairy industry about changes in EBV due to the model and recovering information needed to explain changes in specific animals' EBV are significant challenges. The Canadian dairy industry will require a year or more to become comfortable with the test-day model and to realize the impact it could have on selection decisions.     

Genetic evaluation of bulls and cows with single- and multiple-country test-day models

First-lactation milk yield test-day records on cows from Australia, Canada, Italy, and New Zealand were analyzed by single- and multiple-country random regression models. Models included fixed effects of herd-test day and breed composition-age at calving-season of calving by days in milk, and random regressions with Legendre polynomials of order four for animal genetic and permanent environmental effects. Milk yields in different countries were defined as genetically different traits for the purpose of multiple-trait model. Estimated breeding values of bulls and cows from single- and multiple-trait models were compared within and across countries for two traits: total milk yield in lactation and lactation persistency, defined as the linear coefficient of animal genetic curve. Correlations between single- and multiple-trait evaluations within country for total yield were higher than 0.95 for bulls and close to 1 for cows. Correlations for lactation persistency were lower than respective correlations for total yield. Between country correlations for lactation yield ranged from 0.93 to 0.96, indicating different ranking of bulls on different country scales under multiple-trait model. Lactation persistency had in general lower between-country correlations, with the highest values for Canada-Italy and Australia-New Zealand pairs, for both single- and multiple-country models. Although multiple-country random regression test-day model was computationally feasible for four countries, the same would not be true for routine international genetic evaluation in the near future.     

Genetic relationships of clinical mastitis,cystic ovaries,and lameness with milk yield and somatic cell score in first-lactation Canadian Holsteins

The objective of this study was to investigate the genetic relationships of the 3 most frequently reported dairy cattle diseases (clinical mastitis, cystic ovaries, and lameness) with test-day milk yield and somatic cell score (SCS) in first-lactation Canadian Holstein cows using random regression models. Health data recorded by producers were available from the National Dairy Cattle Health System in Canada. Disease traits were defined as binary traits (0 = healthy, 1 = affected) based on whether or not the cow had at least one disease case recorded within 305 d after calving. Mean frequencies of clinical mastitis, cystic ovaries, and lameness were 12.7, 8.2, and 9.1%, respectively. For genetic analyses, a Bayesian approach using Gibbs sampling was applied. Bivariate linear sire random regression model analyses were carried out between each of the 3 disease traits and test-day milk yield or SCS. Random regressions on second-degree Legendre polynomials were used to model the daily sire additive genetic and cow effects on test-day milk yield and SCS, whereas only the intercept term was fitted for disease traits. Estimated heritabilities were 0.03, 0.03, and 0.02 for clinical mastitis, cystic ovaries, and lameness, respectively. Average heritabilities for milk yield were between 0.41 and 0.49. Average heritabilities for SCS ranged from 0.10 to 0.12. The average genetic correlations between daily milk yield and clinical mastitis, cystic ovaries, and lameness were 0.40, 0.26, and 0.23, respectively; however, the last estimate was not statistically different from zero. Cows with a high genetic merit for milk yield during the lactation were more susceptible to clinical mastitis and cystic ovaries. Estimates of genetic correlations between daily milk yield and clinical mastitis were moderate throughout the lactation. The genetic correlations between daily milk yield and cystic ovaries were near zero at the beginning of lactation and were highest at mid and end lactation. The average genetic correlation between daily SCS and clinical mastitis was 0.59 and was consistent throughout the lactation. The average genetic correlation between daily SCS and cystic ovaries was near zero (−0.01), whereas a moderate, but nonsignificant, correlation of 0.27 was observed between SCS and lameness. Unfavorable genetic associations between milk yield and diseases imply that production and health traits should be considered simultaneously in genetic selection.     

Genetic analysis of milk urea nitrogen and lactose and their relationships with other production traits in Canadian Holstein cattle

The objective of this research was to estimate heritabilities of milk urea nitrogen (MUN) and lactose in the first 3 parities and their genetic relationships with milk, fat, protein, and SCS in Canadian Holsteins. Data were a random sample of complete herds (60,645 test day records of 5,022 cows from 91 herds) extracted from the edited data set, which included 892,039 test-day records of 144,622 Holstein cows from 4,570 herds. A test-day animal model with multiple-trait random regression and the Gibbs sampling method were used for parameter estimation. Regression curves were modeled using Legendre polynomials of order 4. A total of 6 separate 4-trait analyses, which included MUN, lactose, or both (yield or percentage) with different combinations of production traits (milk, fat and protein yield, fat and protein percentages, and somatic cell score) were performed. Average daily heritabilities were moderately high for MUN (from 0.384 to 0.414), lactose kilograms (from 0.466 to 0.539), and lactose percentage (from 0.478 to 0.508). Lactose yield was highly correlated with milk yield (0.979). Lactose percentage and MUN were not genetically correlated with milk yield. However, lactose percentage was significantly correlated with somatic cell score (−0.202). The MUN was correlated with fat (0.425) and protein percentages (0.20). Genetic correlations among parities were high for MUN, lactose percentage, and yield. Estimated breeding values (EBV) of bulls for MUN were correlated with fat percentage EBV (0.287) and EBV of lactose percentage were correlated with lactation persistency EBV (0.329). Correlations between lactose percentage and MUN with fertility traits were close to zero, thus diminishing the potential of using those traits as possible indicators of fertility.     

Mortality, Culling by Sixty Days in Milk, and Production Profiles in High- and Low-Survival Pennsylvania Herds

The objectives were to describe culling patterns and reasons for culling across lactation, estimate mortality and the proportion of cows leaving from 21 d before an expected calving date through 60 d in milk (DIM; CULL60) for Pennsylvania (PA) dairy herds, and to describe production measures for herds with high and low mortality and CULL60. Weekly culling frequencies and reasons for culling from 3 wk before a reported expected calving date through ≥100 wk of lactation were calculated for all PA cows with at least 1 Dairy Herd Improvement test in 2005. It was estimated that at least 5.0% of PA dairy cows died in 2005, and that at least 7.6% were culled by 60 DIM. The majority of cows exiting the herd by 60 DIM either died (35.1%) or had a disposal code of injury/other (29.9%). A total of 137,951 test-day records from 20,864 cows in herds with high mortality (>8.0%) and CULL60 (>12.0%) and 136,906 test-day records from 12,993 cows in herds with low mortality (<1.4%) and CULL60 (<2.9%) were retained to describe differences among herds with high and low survival. Least squares means for weekly milk yield, fat and protein percentages, and somatic cell score (SCS) were estimated with a model that included fixed effects for herd environment (high or low survival) and week nested within herd environment and lactation; random effects were cow, herd-test-day, and error. Cows from herds with high mortality and CULL60 produced more milk in lactations 1 (+1.9 ± 0.15 kg/d) and 2 (+0.9 ± 0.16 kg/d), but less in lactations 4 (−0.7 ± 0.22 kg/d), 5 (−1.4 ± 0.29 kg/d), and ≥6 (−0.7 ± 0.32 kg/d) and had higher SCS (+0.24 ± 0.02), more change in early-lactation fat percentage (−1.77% vs. −1.59%), and a greater frequency of fat-protein inversions (3.6 ± 0.3%). There is an opportunity to manipulate management practices to reduce mortality and early-lactation culling rates, which will improve cow welfare and the efficiency of dairy production by capturing a greater proportion of potential lactation milk yield, increasing cow salvage values, and reducing replacement costs.     

Heritability estimates associated with alternative definitions of mastitis and correlations with somatic cell score and yield

The objectives of this study were to compare alternative mastitis definitions and to estimate genetic correlations of producer-recorded mastitis with somatic cell score (SCS) and yield. Cow health events and lactation records from June 2002 through October 2007 were provided by Dairy Records Management Systems (Raleigh, NC). First- through fifth-lactation records from cows calving between 20 and 120 mo of age and that calved in a herd-year with at least 1% of cows with a clinical mastitis event were retained. The edited data contained 118,516 lactation records and 1,072,741 test-day records of 64,893 cows. Mastitis occurrence (1 = at least one mastitis event during lactation or test-day interval, 0 = no mastitis events), number of mastitis events during lactation, SCS, and yield were analyzed with animal models (single trait) or sire-maternal grandsire models (multiple trait) in ASREML. Comparisons were made among models assuming a normal distribution, a binary distribution, or Poisson distribution (for total episodes). The overall incidence of clinical mastitis was 15.4%; and heritability estimates ranged from 0.73% (test-day interval mastitis with a linear model) to 11.07% (number of mastitis episodes with a Poisson model). Increased mastitis incidence was genetically correlated with higher SCS (range 0.66 to 0.88) and was generally correlated with higher yield (range −0.03 to 0.40), particularly during first lactation (0.04 to 0.40). Significant genetic variation exists for clinical mastitis; and health events recorded by producers could be used to generate genetic evaluations for cow health. Sires ranked similarly for daughter mastitis susceptibility regardless of how mastitis was defined; however, test-day interval mastitis and a total count of mastitis episodes per lactation allow a higher proportion of mastitis treatments to be included in the genetic analysis.     

Genetic analysis of milk production traits of polish black and white cattle using large-scale random regression test-day models

Genetic parameters for milk, fat, and protein yield and persistency in the first 3 lactations of Polish Black and White cattle were estimated. A multiple-lactation model was applied with random herd-test-day effect, fixed regressions for herd-year and age-season of calving, and random regressions for the additive genetic and permanent environmental effects. Three data sets with slightly different edits on minimal number of days in milk and the size of herd-year class were used. Each subset included more than 0.5 million test-day records and more than 58,000 cows. Estimates of covariance components and genetic parameters for each trait were obtained by Bayesian methods using the Gibbs sampler. Due to the large size and a good structure of the data, no differences in estimates were found when additional criteria for record selection were applied. More than 95% of the genetic variance for all traits and lactations was explained by the first 2 principal components, which were associated with the mean yield and lactation persistency. Heritabilities of 305-d milk yield in the first 3 lactations (0.18, 0.16, 0.17) were lower than those for fat (0.12, 0.11, 0.12) and protein (0.13, 0.14, 0.15). Estimates of daily heritabilities increased in general with days in milk for all traits and lactations, with no apparent abnormalities at the beginning or end of lactation. Genetic correlations between yields in different lactations ranged from 0.74 (fat yield in lactations 1 and 3) to 0.89 (milk yield in lactations 2 and 3). Persistency of lactation was defined as the linear regression coefficient of the lactation curve. Heritability of persistency increased with lactation number for all traits and genetic correlations between persistency in different lactations were smaller than those for 305d yield. Persistency was not genetically correlated with the total yield in lactation.     

Short communication: associations between blood calcium status at calving and milk yield in dairy cows

The purpose of the present study was to estimate the effect of total blood plasma calcium (TBPCC) concentration at calving on milk yield in dairy cows. Data originated from 153 dairy cows in 27 herds from a single veterinary practice. For each cow, data included calcium concentration in a blood sample taken within 12 h postpartum, monthly test-day milk yield until 300 d in milk, calving date, parity, breed, and herd. The TBPCC ranged from 0.69 to 2.73 mmol/L, with a mean value of 1.80 mmol/L. The statistical analysis adjusted for the fixed effects of parity and lactation stage, random effects of herd and cow, and the correlation between repeated measures of test-day milk yield. The results showed that TBPCC at calving was not significantly related to fat- and protein-corrected milk yield at any lactation period. The present study indicates that hypocalcemia (low TBPCC) at calving is not an important risk factor for decreased milk yield.     

Factors affecting the shape of lactation curves of Holstein cows from the Balikesir Province of Turkey

The shape of the lactation curve for 475 Turkish Holsteins was estimated by fitting a gamma function to daily milk yields from monthly recording of 754 lactations. Lactation curve traits that were analyzed included a scaling factor associated with yield at the beginning of lactation, the inclining and declining slopes before and after peak yield, DIM at peak yield, and peak and lactation yields. Persistency of lactation yield was measured from 1) the gamma function, 2) the coefficient of variation for monthly test-day yields, and 3) the ratio of lactation yield to peak yield. The log-transformed gamma function explained 71% of variation in daily yield. Effects of farm operation, calving year, calving season, parity, and service period were significant for the various lactation curve traits. Peak and lactation yields were higher for cows that calved in fall and winter, and persistency was higher for cows that calved in summer and fall. Peak and lactation yields were lower, but persistency was higher during first lactation. Repeatability estimates were moderate for peak (0.26) and lactation (0.34) yields and lower (0.06 to 0.20) for other lactation curve traits.     

Estimates of genetic parameters and eigenvector indices for milk production of Holstein cows

The objectives of the present study were to estimate genetic parameters of monthly test-day milk yield (TDMY) of the first lactation of Brazilian Holstein cows using random regression (RR), and to compare the genetic gains for milk production and persistency, derived from RR models, using eigenvector indices and selection indices that did not consider eigenvectors. The data set contained monthly TDMY of 3,543 first lactations of Brazilian Holstein cows calving between 1994 and 2011. The RR model included the fixed effect of the contemporary group (herd-month-year of test days), the covariate calving age (linear and quadratic effects), and a fourth-order regression on Legendre orthogonal polynomials of days in milk (DIM) to model the population-based mean curve. Additive genetic and nongenetic animal effects were fit as RR with 4 classes of residual variance random effect. Eigenvector indices based on the additive genetic RR covariance matrix were used to evaluate the genetic gains of milk yield and persistency compared with the traditional selection index (selection index based on breeding values of milk yield until 305 DIM). The heritability estimates for monthly TDMY ranged from 0.12 ± 0.04 to 0.31 ± 0.04. The estimates of additive genetic and nongenetic animal effects correlation were close to 1 at adjacent monthly TDMY, with a tendency to diminish as the time between DIM classes increased. The first eigenvector was related to the increase of the genetic response of the milk yield and the second eigenvector was related to the increase of the genetic gains of the persistency but it contributed to decrease the genetic gains for total milk yield. Therefore, using this eigenvector to improve persistency will not contribute to change the shape of genetic curve pattern. If the breeding goal is to improve milk production and persistency, complete sequential eigenvector indices (selection indices composite with all eigenvectors) could be used with higher economic values for persistency. However, if the breeding goal is to improve only milk yield, the traditional selection index is indicated.     

Modeling extended lactations of dairy cows

Nine mathematical models were compared for their ability to predict daily milk yields (n = 294,986) in standard 305-d and extended lactations of dairy cows of Costa Rica. Lactations were classified by parity (first and later), lactation length (9 to 10, 11 to 12, 13 to 14, 15 to 16, and 16 to 17 mo), and calving to conception interval (1 to 2, 3 to 4, 5 to 6, 7 to 8, and 9 to 10 mo). Of the nine models, the diphasic model and lactation persistency model resulted in the best goodness of fit as measured by adjusted coefficient of determination, residual standard deviation, and Durbin-Watson coefficient. All other models showed less accuracy and positively correlated residuals. In extended lactations, models were also fitted using only test-day records before 305 d, which resulted in a different ranking. The diphasic model showed the best prediction of milk yield in standard and extended lactations. We concluded that the diphasic model provided accurate estimates of milk yield for standard and extended lactations. Interpretation of parameters deserves further attention because of the large variation observed. As expected, the calving to conception interval was found to have a negative effect on milk yield for cows with a standard lactation length. In extended lactations, these negative effects of pregnancy on milk yield were not observed.     

Short communication: Jersey × Holstein crossbreds compared with pure Holsteins for production, mastitis, and body measurements during the first 3 lactations

Jersey (JE) × Holstein (HO) crossbred cows (n = 76) were compared with pure HO cows (n = 73) for 305-d milk, fat, and protein production, somatic cell score (SCS), clinical mastitis, lifetime production, and body measurements during their first 3 lactations. Cows were in 2 research herds at the University of Minnesota and calved from September 2003 to June 2008. Best prediction was used to determine actual production for 305-d lactations as well as lifetime production (to 1,220 d in the herd after first calving) from test-day observations. During first lactation, JE × HO cows and pure HO cows were not significantly different for fat plus protein production; however, JE × HO cows had significantly lower fat plus protein production during second (−25 kg) and third (−51 kg) lactation than pure HO cows. Nevertheless, JE × HO cows were not significantly different from pure HO cows for lifetime production or lifetime SCS. The JE × HO cows were not significantly different from pure HO cows for SCS and clinical mastitis during first and second lactations; however, JE × HO cows tended to have higher SCS (3.79) than pure HO cows (3.40), but significantly lower (−23.4%) clinical mastitis during third lactation. The JE × HO cows had significantly less hip height, smaller heart girth, less thurl width, and less pin width than pure HO cows during the first 3 lactations. Furthermore, JE × HO cows had significantly less udder clearance from the ground and significantly greater distance between the front teats than pure HO cows during their first 3 lactations.     

Short communication: Genetic parameters for mastitis and its predictors in Canadian Holsteins

The objective of this study was to estimate genetic parameters for mastitis and its predictors [mean somatic cell score (SCS) in early lactation, standard deviation of SCS, excessive test-day somatic cell count (SCC), udder depth (UD), fore udder attachment (FUA), and body condition score (BCS)]. Mastitis data recorded by producers were available from the national dairy cattle health system in Canada. Mastitis was defined as a binary variable based on whether or not the cow had at least 1 mastitis case in the period from calving to 305 d after calving. A Bayesian analysis using Gibbs sampling was applied. Threshold liability models were applied for binary traits (mastitis and excessive test-day SCC), and linear models were used for other normally distributed traits. For mastitis, a heritability of 0.07 was obtained. Heritability estimates for mean SCS in early lactation, standard deviation of SCS, excessive test-day SCC, UD, FUA, and BCS were 0.10, 0.04, 0.06, 0.41, 0.21, and 0.18, respectively. Mastitis was highly correlated with mean SCS in early lactation (0.63), standard deviation of SCS (0.74), and excessive test-day SCC (0.76). Moderate genetic correlations of −0.36, −0.24, and −0.28 were found between mastitis and UD, FUA, and BCS, respectively. As much as 72% of the genetic variation in mastitis resistance was explained by all the indirect predictor traits, whereas the most commonly used indirect measures of mastitis resistance (SCS in early lactation, UD, and FUA) explained together only 46% of the genetic variation in mastitis resistance. A combination of mean and standard deviation of SCS seem to be more successful in improving udder health than the traditional indirect measures. The results of the present study highlight that although routine cow SCC is the best measurement to monitor udder health, it cannot explain all the genetic variation in mastitis resistance and, therefore, direct information on mastitis resistance can be expected to yield to a more accurate genetic evaluation for this trait.     

Effects of continuous milking during the dry period or once daily milking in the first 4 weeks of lactation on metabolism and productivity of dairy cows

The objective was to compare the effects of 3 management systems in high-yielding dairy cows on metabolic profiles and milk production. Thirty-six multiparous Brown Swiss cows were randomly assigned to 1 of 3 treatment groups (n = 12 cows/group): the control (C) group, in which cows were dried off 56 d before calving and milked twice daily throughout next lactation (305 d); the once daily milking (ODM) group, in which cows were dried off 56 d before calving and milked once daily for the first 4 wk of lactation and twice daily for the remaining lactation; and the continuous milking (CM) group, in which cows were milked twice daily until calving and also during the subsequent lactation. Serum glucose concentrations decreased between wk 1 and 4 exclusively in C cows. Serum concentrations of NEFA and BHBA in the first 4 wk of lactation were highest in C cows compared with ODM and CM cows. Decreased backfat thickness during early lactation and reduction of body condition score were markedly more pronounced in C cows compared with ODM and CM cows. Mean lactational milk yield of C cows [11,310 ± 601 kg of energy-corrected milk (ECM)/305 d] was approximately 16% higher compared with ODM cows (9,531 ± 477 kg of ECM/305 d) and CM cows (9,447 ± 310 kg of ECM/305 d). The lactation curve of CM cows compared with C cows was characterized by a similar time of peak yield (wk 3), a reduced peak yield, and no obvious differences in persistency. Mean percentage of milk protein was significantly higher for CM cows (3.91%) compared with C cows (3.52%). In contrast, once daily milking was accompanied by a reduced and significantly delayed peak yield (wk 8) compared with the control treatment, whereas persistency was better and milk protein (3.79%) was higher in ODM cows than in C cows. In conclusion, continuous milking and once daily milking, targeting the interval before or after calving, respectively, substantially reduced the metabolic challenge of fresh cows and improved milk protein percentage. Continuous milking and once daily milking increased milk protein percentage markedly; furthermore, once daily milking during the first 4 wk of lactation improved the lactation curve.