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.     

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.     

Effect of pregnancy on production traits of Canadian Holstein cows

Seven test-day models with different ways of accounting for the effect of pregnancy on production traits were compared by their residual variance, rank correlations of estimated breeding values of bulls and cows and number of nonpregnant cows in the top 500 for milk yield and milk persistency. Data were 22,546,696 first-parity test-day milk, fat, and protein yields and somatic cell score records of 2,677,862 Canadian Holstein heifers calved between 1988 and 2006. The first model fitted separate lactation curves to 8 days open classes and 1 curve to a nonpregnant cow class. Two other models adjusted for pregnancy by fitting the effect of month of pregnancy or stage of pregnancy. One model fitted regression on days pregnant. The remaining 3 models fitted interactions between stage of pregnancy and stage of lactation when conception occurred using either regression on days pregnant nested within days open or classes for specific stage of pregnancy and stage of lactation combination. All models were contrasted to a model without any adjustment for the effect of pregnancy. Both models that accounted for the effect of pregnancy and the model without the effect of pregnancy had similar residual variance. Adjusting for the effect of pregnancy did not cause reranking of sires for estimated breeding values for 305-d yield and persistency but influenced ranking of cows. Models that used days open for the effect of pregnancy overestimated breeding values of nonpregnant cows and cows with shorter days open. No interaction was found between stage of pregnancy and stage of lactation. Month of pregnancy and stage of pregnancy models, compared with the model without the effect of pregnancy, decreased overestimation of breeding values of nonpregnant cows and did not overestimate breeding values of cows with short days open like models fitting days open. Month of pregnancy and stage of pregnancy models are recommended for estimation of adjustment factors for the effect of pregnancy on production traits.     

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.     

Genetic parameters of feed intake, production, body weight, body condition score, and selected type traits of Holstein cows in commercial tie-stall barns

The objectives of this study were to determine the feasibility of measuring feed intake in commercial tie-stall dairies and infer genetic parameters of feed intake, yield, somatic cell score, milk urea nitrogen, body weight (BW), body condition score (BCS), and linear type traits of Holstein cows. Feed intake, BW, and BCS were measured on 970 cows in 11 Pennsylvania tie-stall herds. Historical test-day data from these cows and 739 herdmates who were contemporaries during earlier lactations were also included. Feed intake was measured by researchers once per month over a 24-h period within 7 d of 6 consecutive Dairy Herd Information test days. Feed samples from each farm were collected monthly on the same day that feed intake was measured and were used to calculate intakes of dry matter, crude protein, and net energy of lactation. Test-day records were analyzed with multiple-trait animal models, and 305-d fat-corrected milk yield, dry matter intake, crude protein intake, net energy of lactation intake, average BW, and average BCS were derived from the test-day models. The 305-d traits were also analyzed with multiple-trait animal models that included a prediction of 40-wk dry matter intake derived from National Research Council equations. Heritability estimates for 305-d intake of dry matter, crude protein, and net energy of lactation ranged from 0.15 to 0.18. Genetic correlations of predicted dry matter intake with 305-d dry matter, crude protein, and net energy of lactation intake were 0.84, 0.90, and 0.94, respectively. Genetic correlations among the 3 intake traits and fat-corrected milk yield, BW, and stature were moderate to high (0.52 to 0.63). Results indicate that feed intake measured in commercial tie-stalls once per month has sufficient accuracy to enable genetic research. High-producing and larger cows were genetically inclined to have higher feed intake. The genetic correlation between observed and predicted intakes was less than unity, indicating potential variation in feed efficiency.     

Genetic parameters of milking frequency and milk production traits in Canadian Holsteins milked by an automated milking system

Twice-a-day milking is currently the most frequently used milking schedule in Canadian dairy cattle. However, with an automated milking system (AMS), dairy cows can be milked more frequently. The objective of this study was to estimate genetic parameters for milking frequency and for production traits of cows milked within an AMS. Data were 141,927 daily records of 953 primiparous Holstein cows from 14 farms in Ontario and Quebec. Most cows visited the AMS 2 (46%) or 3 (37%) times a day. A 2-trait [daily (24-h) milking frequency and daily (24-h) milk yield] random regression daily animal model and a multiple-trait (milk, fat, protein yields, somatic cell score, and milking frequency) random regression test-day animal model were used for the estimation of (co)variance components. Both models included fixed effect of herd × test-date, fixed regressions on days in milk (DIM) nested within age at calving by season of calving, and random regressions for additive genetic and permanent environmental effects. Both fixed and random regressions were fitted with fourth-order Legendre polynomials on DIM. The number of cows in the multiple-trait test-day model was smaller compared with the daily animal model. Heritabilities from the daily model for daily (24-h) milking frequency and daily (24-h) milk yield ranged between 0.02 and 0.08 and 0.14 and 0.20, respectively. Genetic correlations between daily (24-h) milk yield and daily (24-h) milking frequency were largest at the end of lactation (0.80) and smallest in mid-lactation (0.27). Heritabilities from the test-day model for test-day milking frequency, milk, fat and protein yield, and somatic cell score were 0.14, 0.26, 0.20, 0.21, and 0.20, respectively. The genetic correlation was positive between test-day milking frequency and official test-day milk, fat, and protein yields, and negative between official test-day somatic cell score and test-day milking frequency.     

Short communication: Milk ELISA status for bovine leukosis virus infection is not associated with milk production in dairy cows

The objective of this study was to assess whether the milk ELISA status for antibodies against bovine leukemia virus was associated with 305-d milk production in Canadian dairy cattle. Test results and test-day production data from 19,785 dairy cows were available for analysis. A linear mixed model was used with the estimated 305-d milk production as the outcome and lactation number, somatic cell count, calving season, days in milk, and breed as fixed effects. Herd nested in province was included as random effect. In conclusion, bovine leukemia virus antibody milk ELISA status was not associated with milk production.     

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.     

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.     

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 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.     

Mastitis and the shape of the lactation curve in Norwegian dairy cows

An investigation of the shape of the lactation curve and the mastitis incidence was conducted to identify whether management interventions of the lactation curve constitute a potential for reducing incidence of mastitis at herd level. Lactation curves were estimated to describe the variation of daily milk yield during the 305-d lactation period in Norwegian Red cows. Associations between mastitis incidence at herd level and lactation curve characteristics such as production level at onset of lactation, magnitude and time of peak milk yield, and increase and decrease of milk yield rates were studied. Data from 250,303 lactations occurring during 2005 and 2006 from 14,766 herds were obtained from the Norwegian Dairy Herd Recording System. Besides veterinary treatments, the records included information on monthly test-day milk yields. The shapes of the lactation curves at herd level were parameterized using a modified Wilmink model in two separate mixed model analyses. In the first analysis a subset of lactations with no records of veterinary treatments was used. Lactation curves from herds with high (>0·31 cases/305-d lactation) and low (<0·07 cases/305-d lactation) herd mastitis incidence rate were parameterized and compared for three separate strata of parity. The result showed that high herd mastitis incidence rate was associated with a low intercept (P<0·05), a steep slope before peak milk yield (P<0·01) and a rapid decline after peak milk yield (P<0·01). In the second analysis a subset of high-yielding lactations with veterinary treatments of mastitis only and lactations with no records of veterinary treatment were compared. This was done to investigate whether the findings at herd level were also reflected at cow level. These results showed that lactation curves from lactations with mastitis cases were associated with a steep slope before peak milk yield (P<0·05) in second and later parities and a rapid decline after peak milk yield (P<0·01) in all three parity groups.     

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.     

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.     

Genetic and phenotypic relationships among milk yield and somatic cell count before and after clinical mastitis

This paper studies whether cows with originally lower somatic cell count (SCC) are more susceptible to clinical mastitis (CM) than cows with higher somatic cell count, and evaluates the correlations between CM, SCC, and milk yield. Data were extracted from the Finnish national milk-recording database and from the health recording system. First and second lactation records of 87,861 Ayrshire cows calving between January 1998 and December 2000 were included. Traits studied were incidence of CM, test-day SCC, and test-day milk yield before and following CM. Genetic parameters were estimated using multitrait REML with a sire model. Results did not indicate that cows with genetically low SCC would be more susceptible to CM. The genetic correlation between CM in the first and second lactation was reasonably high (0.73), suggesting that susceptibility to mastitis remains similar across lactations. The genetic correlation between CM and milk yield traits was positive (from 0.38 to 0.56), confirming the genetic antagonism between production and udder health traits. The genetic correlation between SCC and milk was positive in the first lactation, but negative, or near zero in the second lactation. This indicates that breeding for lower SCC might not affect milk production in later lactations. The results of this study support the use of SCC as an indicator of mastitis and a tool for selection for mastitis resistance.     

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.     

Short communication: do Holstein lactations of varied lengths have different characteristics?

The calving intervals of Holstein cows in many countries have been increasing in length over recent years. Many reports of lactation characteristics refer only to a standard 305-d lactation. This paper attempts to describe the characteristics of lactations of different lengths using a large database of nationally recorded data. Three different lactation lengths were studied: 305 d (the traditional annual calving interval), 370 d (the current UK national average), and 440 d (equivalent to an 18-mo calving interval). Test-day milk yield; fat, protein, and lactose composition; and somatic cell counts were analyzed. Characteristics of each of 29,838 lactations were calculated using a biological model of lactation and were analyzed to identify which factors affected them. Maximum secretion potential was the only trait not affected by lactation length. Day of peak yield, peak yield, persistency, and total milk yield all increased with lactation length, whereas relative cell death rate and the rate of milk increase in early lactation both decreased as lactation length increased. For most weeks of lactation, the 3 lactation-length groups differed from each other in most traits, following the order of their lengths; for example, milk yield was always higher for lactations of 440 d and lower for those of 305 d. The post-peak trends in all traits were found to continue in longer lactations. Thus, daily milk production and lactose percentage continued to decrease as lactations became longer, whereas fat percentage, protein percentage, and somatic cell count continued to increase. Pregnancy was found to affect all traits, leading to an accentuation of these trends in late lactation. However, the effect of pregnancy depended on the yield at about the fourth month of pregnancy. Lactations of 305, 370, and 440 d all had different characteristics and not solely due to increasing length.     

Random herd curves in a test-day model for milk, fat, and protein production of dairy cattle in The Netherlands

With random regression models, genetic parameters of test-day milk production records of dairy cattle can be estimated directly from the data. However, several researchers that used this method have reported unrealistically high variances at the borders of the lactation trajectory and low genetic correlations between beginning and end of lactation. Recently, it has been proposed to include herd-specific regression curves in the random regression model. The objective was to study the effect of including random herd curves on estimated genetic parameters. Genetic parameters were estimated with 2 models; both included random regressions for the additive genetic and permanent environmental effect, whereas the second model also included a random regression effect for herd x 2-yr period of calving. All random regressions were modeled with fourth-order Legendre polynomials. Bayesian techniques with Gibbs sampling were used to estimate all parameters. The data set comprised 857,255 test-day milk, fat, and protein records from lactations 1, 2, and 3 of 43,990 Holstein cows from 544 herds. Genetic variances estimated by the second model were lower in the first 100 d and at the end of the lactation, especially in lactations 2 and 3. Genetic correlations between d 50 and the end of lactation were around 0.25 higher in the second model and were consistent with studies where lactation stages are modeled as different traits. Subsequently, estimated heritabilities for persistency were up to 0.14 lower in the second model. It is suggested to include herd curves in a random regression model when estimating genetic parameters of test-day production traits in dairy cattle.     

Genomic prediction of milk-production traits and somatic cell score using single-step genomic best linear unbiased predictor with random regression test-day model in Thai dairy cattle

Cow genotypes are expected to improve the accuracy of genomic estimated breeding values (GEBV) for young bulls in relatively small populations such as Thai Holstein-Friesian crossbred dairy cattle in Thailand. The objective of this study was to investigate the effect of cow genotypes on the predictive ability and individual accuracies of GEBV for young dairy bulls in Thailand. Test-day data included milk yield (n = 170,666), milk component traits (fat yield, protein yield, total solids yield, fat percentage, protein percentage, and total solids percentage; n = 160,526), and somatic cell score (n = 82,378) from 23,201, 82,378, and 13,737 (for milk yield, milk component traits, and SCS, respectively) cows calving between 1993 and 2017, respectively. Pedigree information included 51,128; 48,834; and 32,743 animals for milk yield, milk component traits, and somatic cell score, respectively. Additionally, 876, 868, and 632 pedigreed animals (for milk yield, milk component traits, and SCS, respectively) were genotyped (152 bulls and 724 cows), respectively, using Illumina Bovine SNP50 BeadChip. We cut off the data in the last 6 yr, and the validation animals were defined as genotyped bulls with no daughters in the truncated set. We calculated GEBV using a single-step random regression test-day model (SS-RR-TDM), in comparison with estimated breed value (EBV) based on the pedigree-based model used as the official method in Thailand (RR-TDM). Individual accuracies of GEBV were obtained by inverting the coefficient matrix of the mixed model equations, whereas validation accuracies were measured by the Pearson correlation between deregressed EBV from the full data set and (G)EBV predicted with the reduced data set. When only bull genotypes were used, on average, SS-RR-TDM increased individual accuracies by 0.22 and validation accuracies by 0.07, compared with RR-TDM. With cow genotypes, the additional increase was 0.02 for individual accuracies and 0.06 for validation accuracies. The inflation of GEBV tended to be reduced using cow genotypes. Genomic evaluation by SS-RR-TDM is feasible to select young bulls for the longitudinal traits in Thai dairy cattle, and the accuracy of selection is expected to be increased with more genotypes. Genomic selection using the SS-RR-TDM should be implemented in the routine genetic evaluation of the Thai dairy cattle population. The genetic evaluation should consider including genotypes of both sires and cows.     

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.