Investigating the effect of temporal,geographic, and management factors on US Holstein lactation curve parameters

We fit the Wood's lactation model to an extensive database of test-day milk production records of US Holstein cows to obtain lactation-specific parameter estimates and investigated the effects of temporal, spatial, and management factors on lactation curve parameters and 305-d milk yield. Our approach included 2 steps as follows: (1) individual animal-parity parameter estimation with nonlinear least-squares optimization of the Wood's lactation curve parameters, and (2) mixed-effects model analysis of 8,595,413 sets of parameter estimates from individual lactation curves. Further, we conducted an analysis that included all parities and a separate analysis for first lactation heifers. Results showed that parity had the most significant effect on the scale (parameter a), the rate of decay (parameter c), and the 305-d milk yield. The month of calving had the largest effect on the rate of increase (parameter b) for models fit with data from all lactations. The calving month had the most significant effect on all lactation curve parameters for first lactation models. However, age at first calving, year, and milking frequency accounted for a higher proportion of the variance than month for first lactation 305-d milk yield. All parameter estimates and 305-d milk yield increased as parity increased; parameter a and 305-d milk yield rose, and parameters b and c decreased as year and milking frequency increased. Calving month estimates parameters a, b, c, and 305-d milk yield were the lowest values for September, May, June, and July, respectively. The results also indicated the random effects of herd and cow improved model fit. Lactation curve parameter estimates from the mixed-model analysis of individual lactation curve fits describe well US Holstein lactation curves according to temporal, spatial, and management factors.     

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.     

Phenotypic relationships of common health disorders in dairy cows to lactation persistency estimated from daily milk weights

The objective of this study was to investigate the phenotypic relationship between common health disorders in dairy cows and lactation persistency, uncorrelated with 305-d yield. The relationships with peak yield and days in milk (DIM) at peak were also studied. Daily milk weights and treatment incidence records of 991 Holstein lactations from experimental dairy herds at Virginia Tech and Pennsylvania State University were used. Persistency was calculated as a function of daily yield deviations from standard lactation curves, developed separately for first (FL) and later lactations (LL), and deviations of DIM around reference dates: 128 for FL and 125 for LL. Days in milk at peak and peak yield were computed for each lactation by using Wood's function. The disease traits studied were mastitis (MAST) only during the first 100 d (MAST1), only after 100 DIM (MAST2), both before and after 100 DIM (MAST12), and at any stage of lactation (MAST1/2), as well as metritis, displaced abomasums, lameness, and metabolic diseases. Each disease was defined as a binary trait, distinguishing between lactations with at least one incidence (1) and lactations with no incidences (0). The relationships of diseases to persistency, DIM at peak, and peak yield were investigated separately for FL and LL for all disease traits except MAST12, which was investigated across parities. The relationships of persistency to probability of the diseases in the same lactation and in the next lactation were examined using odds ratios from a logistic regression model. Metritis and displaced abomasums in FL and LL were associated with significantly higher persistencies. Metabolic diseases and MAST1 in LL were significantly related to higher persistencies. The relationships of MAST2 in both FL and LL, and MAST12 across parities were significant but negative. Cows affected by MAST tended to have less persistent lactations. Most of the diseases had a significant impact on DIM at peak in LL. In LL, metritis, metabolic diseases, and displaced abomasums tended to significantly delay DIM at peak. Mastitis only after 100 DIM was associated with significantly earlier DIM at peak in LL. Increasing persistency was associated with low MAST2 and MAST1/2 in primiparous cows. None of the diseases studied was significantly related to persistency of the previous lactation.     

The relationship between milk yield and the incidence of some diseases in dairy cows

Logistic regression models were used to examine the relationship between milk yield and incidence of certain disorders. Lactations (n = 2197) of 1074 Holstein-Friesian cows from 10 dairies (25 to 146 cows per dairy) in Lower Saxony were studied. The 305-d yield from the previous and current lactations served as the standards for milk yield. Eight disorder complexes were considered: retained placenta, metritis, ovarian cysts, mastitis, claw diseases, milk fever, ketosis, and displaced abomasum. Each disorder complex was modeled separately. In addition to milk yield, the influences of the lactation number, the calving season and the other disorder complexes were examined with the "herd" factor taken into account. A correlation between retained placenta, mastitis, and milk fever to milk yield during the previous lactation was found to be probable and for ketosis and displaced abomasum such a correlation was found to be possible. A connection to the yield in the current lactation was shown for ovarian cysts, claw diseases, and milk fever. No relationship to milk yield existed for metritis. An influence of the lactation number was also demonstrated in various models. Single models allowed a demonstration of the influences of both milk yield and lactation number. Limitations of the model types are discussed.     

A model for the genetic evaluation of number of clinical mastitis cases per lactation in Czech Holstein cows

Cases of mastitis from 9,550 lactations of 6,242 cows were recorded on 5 farms in the Czech Republic from 1996 to 2008. The number of clinical mastitis (CM) cases per cow adjusted to a lactation length of 305 d was analyzed with 4 linear single-trait animal models and one 3-trait model, which also included lactation mean somatic cell score (SCS) and 305-d milk yield. Factors included in the model of choice were parity, combined effect of herd and a 2-yr calving period, calving season, permanent environmental effect of the cow, and additive genetic effect of the cow. From both the single-trait and multiple-trait models, estimated heritability of number of CM cases was 0.11 (±0.015 for the multiple-trait model). Permanent environmental effects accounted for approximately one-third of the phenotypic variance. Heritability estimates for lactation mean SCS and 305-d milk yield were 0.17 ± 0.019 and 0.25 ± 0.011, respectively, and genetic correlations of these traits with number of CM cases were 0.80 ± 0.059 and 0.34 ± 0.079, respectively. Genetic evaluation of the number of CM cases in Czech Holsteins could be carried out including data from all parities using a 3-trait animal model with SCS and milk yield as additional traits.     

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.     

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.     

Milk loss and treatment costs associated with two treatment protocols for clinical mastitis in dairy cows

The objective of this study was to compare milk loss and treatment costs for cows with clinical mastitis that were given antibiotics in addition to supportive treatment or supportive treatment alone. Between January 1994 and January 1996, 116,876 daily milk records on 676 lactations were taken at the University of Illinois Dairy Research Farm. Clinical mastitis was diagnosed during 124 lactations with 25,047 daily milk records, and 1417 of the daily milk records were on days when clinical mastitis was present. Cows with clinical mastitis were randomly assigned to one of 2 treatment groups: N (supportive treatment only) or A (antibiotics in addition to supportive treatment). Extent of antibiotic and supportive treatment varied according to twice daily severity scores. Projected and actual daily milk yields were estimated utilizing a random regression test-day model, and the differences were summed over 305 d of lactation to estimate lactational milk yield loss. The actual amount of discarded milk was added to milk yield loss to determine total milk loss per lactation. A cost analysis that included milk loss and treatment costs was then performed. Cows with clinical mastitis that were given only supportive treatment lost 230 +/- 172 kg (mean +/- standard error of mean [SEM]) more milk and incurred 94 +/- 51 dollars (SEM) more cost per lactation than cows given antibiotics and supportive treatment. Cows given only supportive treatment showed a response pattern of 305-d milk yield loss and economic loss per lactation that varied 2 to 3 times as much as cows treated with antibiotics. Based on reduced milk loss, better reliability (less variable response), and lower economic loss, the addition of antibiotics to supportive treatment was more efficacious and cost effective than supportive treatment alone.     

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.     

Diversity of dairy goat lactation curves in France

A high level of production at the peak of lactation may be associated with animal health disorders, high feeding costs, and reduced milk supply throughout the year. The objective of this study was to typologize the lactation curves in French dairy goats and analyze the influence of environmental and genetic factors on these curves. The data set consisted of 2,231,720 monthly test-day records of 213,534 French Saanen and Alpine goats recorded between September 2008 and June 2012. First, principal component analysis classified the shape of the lactation curves into 3 principal components: the first component accounted for milk yield level throughout lactation, the second component accounted for lactation persistency, and the third component accounted for milk yield in mid-lactation. Then, from the principal component scores, the lactations were clustered into 5 different groups. Most lactations had a similar shape to the mean curve, except 30% of the lactations that fell into 3 clusters that had a high production level at the peak and then a different persistency according to cluster. Estimated breeding value for milk yield and home region of breeding were the factors most related to lactation production level. Month of kidding, breed, and gestation stage had the biggest effect on persistency. Month of kidding was the factor most strongly linked to mid-lactation production. A herd effect was observed on all 3 principal components.     

Genetic relationships between persistency and reproductive performance in first-lactation Canadian holsteins

The main objective of this study was to estimate genetic relationships between lactation persistency and reproductive performance in first lactation. Relationships with day in milk at peak milk yield and estimated 305-d milk yield were also investigated. The data set contained 33,312 first-lactation Canadian Holsteins with first-parity reproductive, persistency, and productive information. Reproductive performance traits included age at first insemination, nonreturn rate at 56 d after first insemination as a virgin heifer and as a first-lactation cow, calving difficulty at first calving and calving interval between first and second calving. Lactation persistency was defined as the Wilmink b parameter for milk yield and was calculated by fitting lactation curves to test day records using a multiple-trait prediction procedure. An 8-trait genetic analysis was performed using the Variance Component Estimation package (VCE 5) via Gibbs sampling to estimate genetic parameters for all traits. Heritabilities of persistency, day in milk at peak milk yield and estimated 305-d milk yield were 0.18, 0.09 and 0.45, respectively. Heritabilities of reproduction were low and ranged from 0.03 to 0.19. The highest heritability was for age at first insemination. Heifer reproductive traits were lowly genetically correlated, whereas cow reproductive traits were moderately correlated. Heifers younger than average when first inseminated and/or conceived successfully at first insemination tended to have a more persistent first lactation. First lactation was more persistent if heifers had difficulty calving (r(g) = 0.43), or conceived successfully at first insemination in first lactation (r(g) = 0.32) or had a longer interval between first and second calving (r(g) = 0.17). Estimates of genetic correlations of reproductive performance with estimated 305-d milk yield were different in magnitude, but similar in sign to those with persistency (0.02 to 0.51).     

Estimating milk, fat, and protein lactation curves with a test day model.

Test day models were used to estimate lactation curves for milk, fat, protein, fat percentage, and protein percentage and to study the influence of age, season, and herd productivity on Holstein lactation curves. Random effects of lactation within herd and fixed effects of herd test date were absorbed. Fixed effects of cow's age on test day and either DIM (57 divisions) by parity (1, 2, greater than or equal to 3) class or season of calving (winter or summer) by DIM by parity class were estimated. Lactation curves for yield traits derived from DIM solutions were flatter for first versus later lactation, even without addition of age effects. Differences between lactation curves for the two seasons were slight, suggesting that most observed seasonal differences are caused by seasonal productivity accounted for by herd test date effects. At peak, winter calving cows yielded slightly more milk of similar fat percentage but of lower protein percentage than those calving in summer. Data were also partitioned into nine subsets based on rolling herd milk and fat percentage. Lactation curves for yield traits, but not percentage traits, varied with rolling herd milk. Lactation curves for fat yield and percentage varied with rolling herd fat percentage.     

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.     

Preweaning milk replacer intake and effects on long-term productivity of dairy calves

The preweaning management of dairy calves over the last 30 yr has focused on mortality, early weaning, and rumen development. Recent studies suggest that nutrient intake from milk or milk replacer during the preweaning period alters the phenotypic expression for milk yield. The objective of this study was to investigate the relationship between nutrient intake from milk replacer and pre- and postweaning growth rate with lactation performance in the Cornell dairy herd and a commercial dairy farm. The analysis was conducted using traditional 305-d first-lactation milk yield and residual lactation yield estimates from a test-day model (TDM) to analyze the lactation records over multiple lactations. The overall objective of the calf nutrition program in both herds was to double the birth weight of calves by weaning through increased milk replacer and starter intake. First-lactation 305-d milk yield and residuals from the TDM were generated from 1,244 and 624 heifers from the Cornell herd and from the commercial farm, respectively. The TDM was used to generate lactation residuals after accounting for the effects of test day, calving season, days in milk, days pregnant, lactation number, and year. In addition, lactation residuals were generated for cattle with multiple lactations to determine if the effect of preweaning nutrition could be associated with lifetime milk yield. Factors such as preweaning average daily gain (ADG), energy intake from milk replacer as a multiple of maintenance, and other growth outcomes and management variables were regressed on TDM milk yield data. In the Cornell herd, preweaning ADG, ranged from 0.10 to 1.58 kg, and was significantly correlated with first-lactation yield; for every 1 kg of preweaning ADG, heifers, on average, produced 850 kg more milk during their first lactation and 235 kg more milk for every Mcal of metabolizable energy intake above maintenance. In the commercial herd, for every 1 kg of preweaning ADG, milk yield increased by 1,113 kg in the first lactation and further, every 1 kg of prepubertal ADG was associated with a 3,281 kg increase in first-lactation milk yield. Among the 2 herds, preweaning ADG accounted for 22% of the variation in first-lactation milk yield as analyzed with the TDM. These results indicate that increased growth rate before weaning results in some form of epigenetic programming that is yet to be understood, but has positive effects on lactation milk yield. This analysis identifies nutrition and management of the preweaned calf as major environmental factors influencing the expression of the genetic capacity of the animal for milk yield.     

Dairy management practices associated with incidence rate of clinical mastitis in low somatic cell score herds in France

An epidemiological prospective study was carried out in French dairy herds with Holstein, Montbéliarde, or Normande cows and with low herd somatic cell scores. The objective was to identify dairy management practices associated with herd incidence rate of clinical mastitis. The studied herds were selected on a national basis, clinical cases were recorded through a standardized system, and a stable dairy management system existed. In the surveyed herds, mean milk yield was 7420 kg/cow per yr and mean milk somatic cell score was 2.04 (132,000 cells/mL). Overdispersion Poisson models were performed to investigate risk factors for mastitis incidence rate. From the final model, the herds with the following characteristics had lower incidence rates of clinical mastitis: 1) culling of cows with more than 3 cases of clinical mastitis within a lactation; 2) more than 2 person-years assigned to dairy herd management; 3) balanced concentrate in the cow basal diet. Moreover, herds with the following characteristics had higher incidence rates of clinical mastitis: 1) milking cows loose-housed in a straw yard; 2) no mastitis therapy performed when a single clot was observed in the milk; 3) clusters rinsed using water or soapy water after milking a cow with high somatic cell count; 4) 305-d milk yield >7435 kg; 5) herd located in the South region; 6) herd located in the North region; 7) cows with at least 1 nonfunctional quarter; and 8) premilking holding area with a slippery surface. The underlying mechanisms of some highlighted risk factors, such as milk production level and dietary management practices, should be investigated more thoroughly through international collaboration.     

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.     

Selecting for lactation curve and milk yield in dairy cattle

Knowledge of genetic relationships between characteristics of lactation curves and lactation yields is essential for joint selection for both. An equation, yt = atbexp(-ct), was chosen to depict individual lactation curves for 5,927 first lactations by Holsteins in 557 herds in Michigan Dairy Herd Improvement where yt is daily milk yield at day t in lactation, a is yield at time zero, b is ascent to peak, and c is decline after peak. Genetic correlations for 305-day milk yield with initial production (a), ascent to peak (b), descent after peak (c), and peak yield were -.37, .40, 0, and .91. From empirical results from applied selection indexes, selecting for both increase of ascent to peak and peak yield did not decrease 305-day milk substantially. Rankings of sires on these indexes were similar to their rankings on milk yield alone. Attempts to decrease peak yield and increase persistency decreased milk yield greatly.     

Prediction of daily milk, fat, and protein production by a random regression test-day model

Test-day genetic evaluation models have many advantages compared with those based on 305-d lactations; however, the possible use of test-day model (TDM) results for herd management purposes has not been emphasized. The aim of this paper was to study the ability of a TDM to predict production for the next test day and for the entire lactation. Predictions of future production and detection of outliers are important factors for herd management (e.g., detection of health and management problems and compliance with quota). Because it is not possible to predict the herd-test-day (HTD) effect per se, the fixed HTD effect was split into 3 new effects: a fixed herd-test month-period effect, a fixed herd-year effect, and a random HTD effect. These new effects allow the prediction of future production for improvement of herd management. Predicted test-day yields were compared with observed yields, and the mean prediction error computed across herds was found to be close to zero. Predictions of performance records at the herd level were even more precise. Discarding herds enrolled in milk recording for <1 yr and animals with very few tests in the evaluation file improved correlations between predicted and observed yields at the next test day (correlation of 0.864 for milk in first-lactation cows as compared with a correlation of 0.821 with no records eliminated). Correlations with the observed 305-d production ranged from 0.575 to 1 for predictions based on 0 to 10 test-day records, respectively. Similar results were found for second and third lactation records for milk and milk components. These findings demonstrate the predictive ability of a TDM.     

Best prediction of yields for long lactations

Lactation records of any reasonable length now can be processed with the selection index method known as best prediction (BP). Previous prediction programs were limited to the 305-d standard used since 1935. Best prediction was implemented in 1998 to calculate lactation records in USDA genetic evaluations, replacing the test interval method used since 1969 to calculate lactation records. Best prediction is more complex but also more accurate, particularly when testing is less frequent. Programs were reorganized to output better graphics, give users simpler access to options, and provide additional output, such as BP of daily yields. Test-day data for 6 breeds were extracted from the national dairy database, and lactation lengths were required to be ≥500 d (Ayrshire, Milking Shorthorn) or ≥800 d (all others). Average yield and SD at any day in milk (DIM) were estimated by fitting 3-parameter Wood's curves (milk, fat, protein) and 4-parameter exponential functions (somatic cell score) to means and SD of 15- (≤300 DIM) and 30-d (>300 DIM) intervals. Correlations among TD yields were estimated using an autoregressive matrix to account for biological changes and an identity matrix to model daily measurement error. Autoregressive parameters (r) were estimated separately for first (r = 0.998) and later parities (r = 0.995). These r values were slightly larger than previous estimates due to the inclusion of the identity matrix. Correlations between traits were modified so that correlations between somatic cell score and other traits may be nonzero. The new lactation curves and correlation functions were validated by extracting TD data from the national database, estimating 305-d yields using the original and new programs, and correlating those results. Daily BP of yield were validated using daily milk weights from on-farm meters in university research herds. Correlations ranged from 0.900 to 0.988 for 305-d milk yield. High correlations ranged from 0.844 to 0.988 for daily yields, although correlations were as low as 0.015 on d 1 of lactation, which may be due to calving-related disorders that are not accounted for by BP. Correlations between 305-d yield calculated using 50-d intervals from 50 to 250 DIM and 305-yield calculated using all TD to 500 DIM increased as TD data accumulated. Many cows can profitably produce for >305 DIM, and the revised program provides a flexible tool to model these records.     

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.