Effect of lactation number, year, and season of initiation of lactation on milk yield of cows hormonally induced into lactation and treated with recombinant bovine somatotropin

Records representing data from 1,500 barren Holstein cows over an 8-yr period from a large commercial dairy farm in northern Mexico were analyzed to determine the effects of lactation number and season and year of initiation of lactation on milk production of cows induced hormonally into lactation and treated with recombinant bovine somatotropin (rbST) throughout lactation. Peak and 305-d milk yields were also assessed as predictors of total milk yield in cows induced into lactation. A significant quadratic relationship was found between 305-d milk yield and number of lactation [7,607 ± 145 and 9,548 ± 181 kg for first- and ≥6-lactation cows, respectively; mean ± standard error of the mean (SEM)] with the highest production occurring in the fifth lactation. Total milk yields of cows with ≤2 lactations were approximately 4,500 kg less than milk yields of adult cows (the overall average ± standard milk yield was 13,544 ± 5,491 kg per lactation and the average lactation length was 454 ± 154 d). Moreover, 305-d milk production was depressed in cows induced into lactation in spring (8,804 ± 153 kg; mean ± SEM) and summer (8,724 ± 163 kg) than in fall (9,079 ± 151 kg) and winter (9,085 ± 143 kg). Partial regression coefficients for 305-d milk yield and peak milk yield indicated an increment of 157 kg of milk per lactation per 1-kg increase in peak milk yield (r² = 0.69). Neither peak milk yield (r² = 0.18) nor 305-d milk yield (r² = 0.29) was accurate for predicting total milk yield per lactation. Year, parity, and season effects had significant influence on milk yield of cows induced into lactation and treated with rbST throughout lactation, and peak milk yield can assist in the prediction of 305-d milk yield but not total milk yield. This study also showed that hormonal induction of lactation in barren high-yielding cows is a reliable, practical, and affordable technique in countries where rbST treatment and prolonged steroid administration of dairy cows are legally permitted.     

Genetic evaluation of dairy cattle with test-day models with autoregressive covariance structures and with a 305-d model

This study compared genetic evaluations from 3 test-day (TD) models with different assumptions about the environmental covariance structure for TD records and genetic evaluations from 305-d lactation records for dairy cows. Estimates of genetic values of 12,071 first-lactation Holstein cows were obtained with the 3 TD models using 106,472 TD records. The compound symmetry (CS) model was a simple test-day repeatability animal model with compound symmetry covariance structure for TD environmental effects. The AR_s and AR_e models also used TD records but with a first-order autoregressive covariance structure among short-term environmental effects or residuals, respectively. Estimates of genetic values with the TD models were also compared with those from a model using 305-d lactation records. Animals were genetically evaluated for milk, fat, and protein yields, and somatic cell score (SCS). The largest average estimates of accuracy of predicted breeding values were obtained with the AR_s model and the smallest were with the 305-d model. The 305-d model resulted in smaller estimates of correlations between average predicted breeding values of the parents and lactation records of their daughters for milk and protein yields and SCS than did the CS and AR_e models. Predicted breeding values with the 3 TD models were highly correlated (0.98 to 1.00). Predicted breeding values with 305-d lactation records were moderately correlated with those with TD models (0.71 to 0.87 for sires and 0.80 to 0.87 for cows). More genetic improvement can be achieved by using TD models to select for animals for higher milk, fat, and protein yields, and lower SCS than by using models with 305-d lactation records.     

Genetic parameters for tunisian holsteins using a test-day random regression model

Genetic parameters of milk, fat, and protein yields were estimated in the first 3 lactations for registered Tunisian Holsteins. Data included 140,187; 97,404; and 62,221 test-day production records collected on 22,538; 15,257; and 9,722 first-, second-, and third-parity cows, respectively. Records were of cows calving from 1992 to 2004 in 96 herds. (Co)variance components were estimated by Bayesian methods and a 3-trait-3-lactation random regression model. Gibbs sampling was used to obtain posterior distributions. The model included herd × test date, age × season of calving × stage of lactation [classes of 25 days in milk (DIM)], production sector × stage of lactation (classes of 5 DIM) as fixed effects, and random regression coefficients for additive genetic, permanent environmental, and herd-year of calving effects, which were defined as modified constant, linear, and quadratic Legendre coefficients. Heritability estimates for 305-d milk, fat and protein yields were moderate (0.12 to 0.18) and in the same range of parameters estimated in management systems with low to medium production levels. Heritabilities of test-day milk and protein yields for selected DIM were higher in the middle than at the beginning or the end of lactation. Inversely, heritabilities of fat yield were high at the peripheries of lactation. Genetic correlations among 305-d yield traits ranged from 0.50 to 0.86. The largest genetic correlation was observed between the first and second lactation, potentially due to the limited expression of genetic potential of superior cows in later lactations. Results suggested a lack of adaptation under the local management and climatic conditions. Results should be useful to implement a BLUP evaluation for the Tunisian cow population; however, results also indicated that further research focused on data quality might be needed.     

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.     

Use of test-day records beyond three hundred five days for estimation of three hundred five-day breeding values for production traits and somatic cell score of Canadian Holsteins

The Canadian Test-Day Model includes test-day (TD) records from 5 to 305 d in milk (DIM). Because 60% of Canadian Holstein cows have at least one lactation longer than 305 d, a significant number of TD records beyond 305 DIM could be included in the genetic evaluation. The aim of this study was to investigate whether TD records beyond 305 DIM could be useful for estimation of 305-d estimated breeding value (EBV) for milk, fat, and protein yields and somatic cell score. Data were 48,638,184 TD milk, fat, and protein yields and somatic cell scores from the first 3 lactations of 2,826,456 Canadian Holstein cows. All production traits were preadjusted for the effect of pregnancy. Subsets of data were created for variance-component estimation by random sampling of 50 herds. Variance components were estimated using Gibbs sampling. Full data sets were used for estimation of breeding values. Three multiple-trait, multiple-lactation random regression models with TD records up to 305 DIM (M305), 335 DIM (M335), and 365 DIM (M365) were fitted. Two additional models (M305a and M305b) used TD records up to 305 DIM and variance components previously estimated by M335 and M365, respectively. The effects common to all models were fixed effects of herd × test-date and DIM class, fixed regression on DIM nested within region × age × season class, and random regressions for additive genetic and permanent environmental effects. Legendre polynomials of order 6 and 4 were fitted for fixed and random regressions, respectively. Rapid increase of additive genetic and permanent environmental variances at extremes of lactations was observed with all 3 models. The increase of additive genetic and permanent environmental variances was at earlier DIM with M305, resulting in greater variances at 305 DIM with M305 than with M335 and M365. Model M305 had the best ability to predict TD yields from 5 through 305 DIM and less error of prediction of 305-d EBV than M335 and M365. Model M335 had smaller change of 305-d EBV of bulls over the period of 7 yr than did M305 and M365. Model M305a had the least error of prediction and change of 305-d EBV from all models. Therefore, the use of TD records of Holstein cows from 5 through 305 DIM and variance components estimated using records up to 335 DIM is recommended for the Canadian Test-Day Model.     

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: best prediction of 305-day lactation yields with regional and seasonal effects

In the United States, lactation yields are calculated using best prediction (BP), a method in which test-day (TD) data are compared with breed- and parity-specific herd lactation curves that do not account for differences among regions of the country or seasons of calving. Complete data from 538,090 lactations of 348,123 Holstein cows with lactation lengths between 250 and 500 d, records made in a single herd, at least 5 reported TD, and twice-daily milking were extracted from the national dairy database and used to construct regional and seasonal lactation curves. Herds were assigned to 1 of 7 regions of the country, individual lactations were assigned to 3-mo seasons of calving, and lactation curves for milk, fat, and protein yields were estimated by parity group for regions, seasons, and seasons within regions. Multiplicative pre-adjustment factors (MF) also were computed. The resulting lactation curves and MF were tested on a validation data set of 891,806 lactations from 400,000 Holstein cows sampled at random from the national dairy database. Mature-equivalent milk, fat, and protein yields were calculated using the standard and adjusted curves and MF, and differences between 305-d mature-equivalent yields were tested for significance. Yields calculated using 50-d intervals from 50 to 250 d in milk (DIM) and using all TD to 500 DIM allowed comparisons of predictions for records in progress (RIP). Differences in mature-equivalent milk ranged from 0 to 51 kg and were slightly larger for first-parity than for later parity cows. Milk and components yields did not differ significantly in any case. Correlations of yields for 50-d intervals with those using all TD were similar across analyses. Yields for RIP were slightly more accurate when adjusted for regional and seasonal differences.     

Sodium salicylate treatment in early lactation increases whole-lactation milk and milk fat yield in mature dairy cows

Multiple lines of inquiry have suggested that a high degree of inflammation in early lactation cows is associated with low productivity and increased disease incidence. In addition, some small studies have suggested that milk production increases in response to antiinflammatory treatment in the first week of lactation. Our objective was to determine if administration of sodium salicylate (SS), a nonsteroidal antiinflammatory drug (NSAID), in the first week of lactation changes whole-lactation productivity and retention in the herd. At calving, 78 cows [n = 39 primiparous (1P); n = 24 second parity (2P); n = 15 third parity or greater (3P)] were alternately assigned to either control (CON) or SS treatments for 7 d postpartum. Sodium salicylate treatment was administered via individual water bowls at a concentration of 1.95 g/L, delivering a mean of 123.3 ± 5.5 g of salicylate/d during the 7-d treatment. For the first 21 d of lactation, dry matter intake, water intake, milk yield, and health were monitored daily, and milk samples were collected twice weekly for milk component analysis. Monthly milk yield and component testing through the rest of the lactation provided data to assess long-term responses, and the effects of treatment on the risk of leaving the herd and on 305-d milk, fat, and protein yields were assessed. During the first 21 d of lactation, we observed no differences in morbidity, except for increased risk of metritis in 3P SS cows. Treatment interacted with parity to influence both 305-d milk and milk fat yields, and a tendency for an interaction was detected for 305-d milk protein yield. Milk yield was 2,469 ± 646 kg greater over the lactation in 3P SS cows compared with 3P CON cows (21% increase) and tended to decrease by 8% in 1P cows treated with SS; no effects were detected in 2P cows. Furthermore, 3P SS cows produced 130 ± 23 kg more milk fat over the lactation (30% increase), with no effects detected for 1P or 2P. Treatment with SS tended to increase 305-d milk protein yield in 3P cows by 14%, with no effects in 1P or 2P cows. A tendency for a treatment × parity interaction was also observed for the risk of leaving the herd. First-parity cows treated with SS tended to have greater risk of leaving the herd than controls (30 vs. 6% risk); however, treatment did not alter herd retention in 2P or 3P groups, and SS had no effect on the risk of leaving the herd overall. Results indicate that SS has long-term effects on lactation of mature dairy cows, particularly on fat yield, but may have negative effects for primiparous cows.     

Comparing local and commercial breeds on functional traits and profitability: the case of Reggiana dairy cattle

The objective of this study was to compare fertility, longevity, milkability, and profitability of cows from the Reggiana and Holstein breeds in northern Italy. Profitability was gauged for each breed, with consideration of economic incentive programs and alternative milk pricing scenarios. Calving to first service interval, days open, and calving interval were significantly shorter in Reggiana than in Holstein cows. Reggiana cows conceived approximately one estrus cycle before Holstein and had a calving interval 33 d shorter. Holstein cows released a significantly higher quantity of milk per unit of time (1.81 vs. 1.28 kg/min). Reggiana cows had longer expected total and productive lives than Holstein cows, by 5.8 and 10.0 mo, respectively. Replacement rate was 26% higher in the Holstein. Standard 305-d milk production was 5,360 and 7,870 kg in Reggiana and Holstein, respectively. Comparing breeds on annual milk and meat production, instead of standard 305-d milk yield, changed marginally the difference in annual profitability between the Reggiana and Holstein, from −€696 to −€679 per cow per year. Including feeding, milking, replacement, and insemination costs reduced the gap between breeds by 32%, from −€679, measured on annual milk and meat production, to −€460. These differences in profitability assumed a pricing scenario referring to milk sold to the dairy industry where protein and fat contents are valued but not the breed origin of milk. Incentive payments to farmers of endangered cattle compensated partially (22%) the lower income from Reggiana cows. When Reggiana milk production was sold as branded Parmigiano Reggiano cheese, Reggiana cows were more profitable than Holstein cows by €1,953 per cow per year.     

Yield losses associated with clinical mastitis occurring in different weeks of lactation

The effects of the first lactational incidence of clinical mastitis (CM) on milk, fat, and protein production were studied in the Swedish Red and Swedish Holstein breeds. The data consisted of 38,535 weekly production records from 1,192 lactations (506 cows), sampled from 1987 to 2004 in one of the university's research herds. Daily yields were analyzed using a repeated-measures mixed model with an interaction between mastitis index and lactational stage, breed, parity, reproductive status, year-season of calving, and various indices for other disorders as independent variables. The indices were used to distinguish between cows with and without the studied diagnoses, as well as to indicate time (test day) in relation to day of diagnosis. Inclusion of the interaction made it possible to study the effects of CM occurring in different weeks of lactation. Primiparous and multiparous cows were analyzed separately, and the yields of nonmastitic cows were used as a reference for the production level in healthy cows. Lactational (305-d) yield losses were extrapolated from the daily estimates. High milk yield was predisposing to CM. Daily milk yield started to decline 2 to 4 wk before diagnosis. On the day of clinical onset, the milk yield of mastitic cows was reduced by 1 to 8 kg. After a case of CM, milk yield was suppressed throughout lactation. The magnitude of the yield losses was determined by the week of lactation at clinical onset. The greatest losses occurred when primiparous cows developed CM in wk 6, whereas multiparous cows experienced the greatest losses when diseased in wk 3. The 305-d milk, fat, and protein production in mastitic primiparous cows were reduced by 0 to 9, 0 to 8, and 0 to 7%, respectively. The corresponding reductions in mastitic multiparous cows were 0 to 11, 0 to 12, and 0 to 11%, respectively.     

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.     

Piecewise modeling of the associations between dry period length and milk,fat, and protein yield changes in the subsequent lactation

Our objective was to develop predictive models of 305-d mature-equivalent milk, fat, and protein yields in the subsequent lactation as continuous functions of the number of days dry (DD) in the current lactation. In this retrospective cohort study with field data, we obtained DHIA milk recording lactation records with the last DD in 2014 or 2015. Cows included had DD from 21 to 100 d. After editing, 1,030,141 records from cows in 7,044 herds remained. Three parity groups of adjacent (current, subsequent) lactations were constructed. We conducted all analyses by parity group and yield component. We first applied control models to pre-adjust the yields in the subsequent lactation for potentially confounding effects. Control models included the covariates mature-equivalent yield, days open, somatic cell score at 180 d pregnant, daily yield at 180 d pregnant, and a herd-season random effect, all observed in the current lactation. Days dry was not included. Second, we modeled residuals from control models with smooth piecewise regression models consisting of a simple linear, quadratic, and another simple linear equation depending on DD. Yield deviations were calculated as differences from predicted mature-equivalent yield at 50 DD. For validation, predictions of yield deviations from piecewise models by DD were compared with predictions from local regression for the DHIA field records and yield deviations reported in 38 experimental and field studies found in the literature. Control models reduced the average root mean squared prediction error by approximately 21%. Yield deviations were increasingly more negative for DD shorter than 50 d, indicating lower yields in the subsequent lactation. For short DD, the decrease in 305-d mature-equivalent milk yield ranged from 43 to 53 kg per DD. For mature-equivalent fat and protein yields, decreases were between 1.28 and 1.71 kg per DD, and 1.06 and 1.50 kg per DD, respectively. Yield deviations often were marginally positive and increasing for DD >50, so that the highest yield in the subsequent lactation was predicted for 100 DD. For long DD, the 305-d mature-equivalent milk yield increased at most 4.18 kg per DD. Patterns in deviations for fat and protein yield were similar to those for milk yield deviations. Predictions from piecewise models and local regressions were very similar, which supports the chosen functional form of the piecewise models. Yield deviations from field studies in the literature typically were decreasing when DD were longer, likely because of insufficient control for confounding effects. In conclusion, piecewise models of mature-equivalent milk, fat, and protein yield deviations as continuous functions of DD fit the observed data well and may be useful for decision support on the optimal dry period length for individual cows.     

Relationship between somatic cell count and milk yield in different stages of lactation

The association between somatic cell count (SCC) and daily milk yield in different stages of lactation was investigated in cows free of clinical mastitis (CM). Data were recorded between 1989 and 2004 in a research herd, and consisted of weekly test-day (TD) records from 1,155 lactations of Swedish Holstein and Swedish Red cows. The main data set (data set A) containing 36,117 records excluded TD affected by CM. In this data set, the geometric mean SCC was 55,000 and 95,000 cells/mL in primiparous and multiparous cows, respectively. A subset of data set A (data set B), containing 27,753 records excluding all TD sampled in lactations affected by CM, was created to investigate the effect of subclinical mastitis (SCM) in lactations free of CM. Daily milk yields were analyzed using a mixed linear model with lactation stage; linear, quadratic and cubic regressions of log₂-transformed and centered SCC nested within lactation stage; weeks in lactation; TD season; parity; breed; pregnancy status; year-season of calving; calving, reproductive, metabolic and claw disorders; and housing system as fixed effects. A random regression was included to further improve the modeling of the lactation curve. Primiparous and multiparous cows were analyzed separately. The magnitude of daily milk loss associated with increased SCC depended on stage of lactation and parity, and was most extensive in late lactation irrespective of parity. In data set A, daily milk loss at an SCC of 500,000 cells/mL ranged from 0.7 to 2.0 kg (3 to 9%) in primiparous cows, depending on stage of lactation. In multiparous cows, corresponding loss was 1.1 to 3.7 kg (4 to 18%). Regression coefficients of primiparous cows estimated from data set B were consistent with those obtained from data set A, whereas data set B generated more negative regression coefficients of multiparous cows suggesting a higher milk loss associated with increased SCC in lactations in which the cow did not develop CM. The 305-d milk loss in the average lactation affected with SCM was 155 kg of milk (2%) in primiparous cows and 445 kg of milk (5%) in multiparous cows. It was concluded that multiparous cows in late lactation can be expected to be responsible for the majority of the herd-level production loss caused by SCM, and that preventive measures need to focus on reducing the incidence of SCM in such cows.     

Genomic prediction of lactation curves for milk,fat, protein,and somatic cell score in Holstein cattle

Application of random regression models (RRM) in a 2-step genomic prediction might be a feasible way to select young animals based on the complete pattern of the lactation curve. In this context, the prediction reliability and bias of genomic estimated breeding value (GEBV) for milk, fat, and protein yields and somatic cell score over days in milk (DIM) using a 2-step genomic approach were investigated. In addition, the effect of including cows in the training and validation populations was investigated. Estimated breeding values for each DIM (from 5 to 305 d) from the first 3 lactations of Holstein animals were deregressed and used as pseudophenotypes in the second step. Individual additive genomic random regression coefficients for each trait were predicted using RRM and genomic best linear unbiased prediction and further used to derive GEBV for each DIM. Theoretical reliabilities of GEBV obtained by the RRM were slightly higher than theoretical reliabilities obtained by the accumulated yield up to 305 d (P305). However, validation reliabilities estimated for GEBV using P305 were higher than for GEBV using RRM. For all traits, higher theoretical and validation reliabilities were estimated when incorporating genomic information. Less biased GEBV estimates were found when using RRM compared with P305, and different validation reliability and bias patterns for GEBV over time were observed across traits and lactations. Including cows in the training population increased the theoretical reliabilities and bias of GEBV; nonetheless, the inclusion of cows in the validation population does not seem to affect the regression coefficients and the theoretical reliabilities. In summary, the use of RRM in 2-step genomic prediction produced fairly accurate GEBV over the entire lactation curve for all analyzed traits. Thus, selecting young animals based on the pattern of lactation curves seems to be a feasible alternative in genomic selection of Holstein cattle for milk production traits.     

Predicting Milk Yield of Holstein Cows from 306 to 395 Days in Milk

Prediction equations were determined to estimate daily milk yield from 306 to 395 d in milk for forecasting herd milk sales from Holstein cows in lactation >305 d. Data were test day milk weights for 65,322 primiparous and 119,220 pluriparous lactations of > 305 d from the Southern US. A forecast model was developed using same lactation 305 d milk yield (in classes of 500 kg increments) that gave similar predicted daily yields as models utilizing last sample milk weight information. This model has the advantage of early forecasting of later milk using projected 305-d yields.Reduced forecast models ignoring days pregnant, yield class, or both accounted for 95, 68, and 59%, and 91, 67, and 56% as much variation in daily milk as the full model for the primiparous and pluriparous cows. Percentage of 305-d milk yielded in mo 11, 12, and 13, depending on 305-d yield class, ranged from 7.1 to 7.0%, 6.2 to 6.0%, and 5.4 to 5.0%, and 5.4 to 5.0%, 4.3 to 3.9%, and 3.3 to 3.0% for first parity and pluriparous cows calving in winter and 125 d open. Cows not calving in winter or with more than 125 d open yielded more milk in extended lactation. These percentages are larger than generally assumed in studies of days open, thus indicating that cost of days open may have been overestimated.     

Understanding cow evaluations in univariate and multivariate animal and random regression models

The relationship between cow evaluations from a 305-d lactation yield animal model [i.e., lactation model (LM)] and a random regression model (RRM) were studied using the first-lactation milk yield of 2,477,807 Holstein heifers. In the LM analysis, 2 values of heritability were used, 0.35 (LM1-H) or 0.57 (LM2-H), the latter being equal to that used in the random regression model for the analysis of the Holstein test-day records (RRM-H). The relative weights on parent average (PA) and yield deviations (YD) were computed and studied to understand factors contributing to reranking of cows’ predicted transmitting abilities (PTA) from the various models. The degree of relatedness and inbreeding were calculated for the top 2,000 cows from the various models. Analyses of Jersey milk yield in the first 3 parities was implemented using 305-d lactation yield multivariate animal (MLM-J) and random regression models (MRRM-J). The ability of both models using only first-parity yield records to predict evaluations in second and third parities when records for these later parities were excluded was studied in a sample of cows. The correlations of cow PTA between LM1-H or LM2-H and RRM-H were 0.91 and 0.92, respectively, in the Holstein data. The data sets used were identical in this case for all models in terms of number of cows and yield records. The correlations were slightly lower at 0.89, 0.87, and 0.88 for parities 1, 2, and 3 in the Jersey analyses, where the data sets were not identical. The relative weights on PA and YD were 0.28 (0.11) and 0.72 (0.89), respectively, from the LM2-H (RRM-H). The RRM-H placed more emphasis on YD and therefore on Mendelian sampling deviations. Thus, the top 2,000 cows from the RRM-H were less related and inbred. The average additive genetic relationship was 22% greater in the LM2-H and average inbreeding coefficients were 0.68 and 0.43% for the LM2-H and RRM-H, respectively. When records were initially available in the first parity, the MRRM-J predicted PTA in parities 2 and 3 with about 2 to 7% greater accuracy compared with the MLM-J.     

Feeding and management of dairy heifers for optimal lifetime productivity

A total of 433 Holstein heifer calves were fed two different energy amounts from 6 wk of age to breeding weight to determine the effect of early nutrition and age at first calving on lifetime performance. A control group of 182 heifers was fed according to the Beltsville growth standard. A second group of 251 heifers was accelerated in growth by providing more energy during early development. Both groups of heifers were bred at a minimum weight of 340 kg. Average ages at first calving for control and accelerated heifers were 24.6 mo and 22.2 mo with corresponding 305-d first lactation unadjusted milk production values of 6985 and 6729 kg and unadjusted milk fat yields of 222 and 216 kg. Average 305-d milk production values through subsequent lactations for control and accelerated animals, were lactation 2, 7790, 7842 kg; lactation 3, 8200, 8330 kg; lactation 4, 9481, 9134 kg; lactation 5, 9865, 9588 kg; lactation 6, 9515, 10,108; lactation 7, 9661, 10,112. Average total milk yields over five lactations were 42,321 and 41,623 kg. Percentages of cows remaining in the herd after five lactations were 19 and 18 for control and accelerated animals. Reproductive problems, mastitis, and deaths accounted for 70% of cows leaving the herd and did not differ between treatment groups.     

Modeling extended lactations of Holsteins

Modeling extended lactations for the US Holsteins is useful because a majority (>55%) of the cows in the present population produce lactations longer than 305 d. In this study, 9 empirical and mechanistic models were compared for their suitability for modeling 305-d and 999-d lactations of US Holsteins. A pooled data set of 4,266,597 test-day yields from 427,657 (305-d complete) lactation records from the AIPL-USDA database was used for model fitting. The empirical models included Wood (WD), Wilmink (WIL), Rook (RK), monophasic (MONO), diphasic (DIPH), and lactation persistency (LPM) functions; Dijkstra (DJ), Pollott (POL), and new-multiphasic (MULT) models comprised the mechanistic counterparts. Each model was separately tested on 305-d (>280 days in milk) and 999-d (>800 days in milk) lactations for cows in first parity and those in third and greater parities. All models were found to produce a significant fit for all 4 scenarios (2 parity groups and 2 lactation lengths). However, the resulting parameter estimates for the 4 scenarios were different. All models except MONO, DIPH, and LPM yielded residuals with absolute values smaller than 2 kg for the entire period of the 305-d lactations. For the extended lactations, the prediction errors were larger. However, the RK, DJ, POL, and MULT models were able to predict daily yield within a ± 3 kg range for the entire 999-d period. The POL and MULT models (having 6 and 12 parameters, respectively) produced the lowest mean square error and Bayesian information criteria values, although the differences from the other models were small. Conversely, POL and MULT were often associated with poor convergence and highly correlated, unreliable, or biologically atypical parameter estimates. Considering the computational problems of large mechanistic models and the relative predictive ability of the other models, smaller models such as RK, DJ, and WD were recommended as sufficient for modeling extended lactations unless mechanistic details on the extended curves are needed. The recommended models were also satisfactory in describing fat and protein yields of 305-d and 999-d lactations of all parities.     

Agreement of predicted 305-day milk yields relative to actual 305-day milk weight yields

The objectives of this study were to compare the multiple trait prediction (MTP) model estimate of 305-d lactation yield with the 305-d daily milk yield data from on-farm automated meters and software and to examine the accuracy of electronic identification (ID). Twenty-four-hour milk and component yields are calculated by using milk weights and samples collected 8 to 10 times/yr by Dairy Herd Improvement (DHI) organizations. Daily milk weights were collected from cows on 20 Canadian farms that used parlor milking systems with electronic ID and that were enrolled in a regular DHI program. A total of 10,175 DHI test days from 1,103 cows with complete 305-d lactation yields were entered into the MTP model, and lactation yields were predicted. Test days were grouped into first, second, and third and greater lactations and within each lactation group, days in milk were categorized in 3 stages (5 to 60, 61 to 120, and 120 to 305 d in milk) for a total of 9 classes. Agreement analysis was used to compare the 305-d sum of daily milk to the MTP 305-d lactation yield predictions by using inputs from test days throughout the lactations. Results indicated that the MTP model overestimated lactation yields across all parity groups, ranging from 310 to 1,552 kg in parity 1, 640 to 2,000 kg in parity 2, and 567 to 1,476 kg in parity 3 and greater. A preliminary examination of electronic ID accuracy was conducted on 4 farms. Two electronic ID systems were examined for cow ID accuracy by verifying the ID number appearing in the parlor with the corresponding ear tag number. There were no ID errors on 3 of 4 farms tested and only a very small number of errors (3/80) on the fourth farm, indicating that the electronic ID systems used in milking parlors identify cows accurately.     

Heritability of gross feed efficiency and associations with yield, intake, residual intake, body weight, and body condition score in 11 commercial Pennsylvania tie stalls

The objectives of this study were to calculate the heritability of feed efficiency and residual feed intake, and examine the relationships between feed efficiency and other traits of productive and economic importance. Intake and body measurement data were collected monthly on 970 cows in 11 tie-stall herds for 6 consecutive mo. Measures of efficiency for this study were: dry matter intake efficiency (DMIE), defined as 305-d fat-corrected milk (FCM)/305-d DMI, net energy for lactation efficiency (NE_LE), defined as 305-d FCM/05-d NE_L intake, and crude protein efficiency (CPE), defined as 305-d true protein yield/305-d CP intake. Residual feed intake (RFI) was calculated by regressing daily DMI on daily milk, fat, and protein yields, body weight (BW), daily body condition score (BCS) gain or loss, the interaction between BW and BCS gain or loss, and days in milk (DIM). Data were analyzed with 3- and 4-trait animal models and included 305-d FCM or protein yield, DM, NE_L, or CP intake, BW, BCS, BCS change between DIM 1 and 60, milk urea nitrogen, somatic cell score, RFI, or an alternative efficiency measure. Data were analyzed with and without significant covariates for BCS and BCS change between DIM 1 and 60. The average DMIE, NE_LE, and CPE were 1.61, 0.98, and 0.32, respectively. Heritability of gross feed efficiency was 0.14 for DMIE, 0.18 for NE_LE, and 0.21 for CPE, and heritability of RFI was 0.01. Body weight and BCS had high and negative correlations with the efficiency traits (−0.64 to −0.70), indicating that larger and fatter cows were less feed efficient than smaller and thinner cows. When BCS covariates were included in the model, cows identified as being highly efficient produced 2.3 kg/d less FCM in early lactation due to less early lactation loss of BCS. Results from this study suggest that selection for higher yield and lower BW will increase feed efficiency, and that body tissue mobilization should be considered.