Relationships between characteristics of herd of bull-dams and predicting transmitting ability of young bull

Young bulls selected from Michigan herds for sampling by Select Sires Incorporated from 1975 to 1982 were identified. Data for herds from which young bulls were selected for year of young bull's birth were acquired from the Michigan DHIA. Herd average milk production, intraherd standard deviation for milk production, coefficient of variation for milk production, average sire transmitting ability of cows in the herd, and intraherd standard deviation of sire transmitting ability were computed for each herd. These herd characteristics were used in prediction of young bull transmitting ability. When considered along with a pedigree index predictor, intraherd standard deviation for milk production was significant in prediction of young bull transmitting ability, but other herd characteristics were not. Young bulls were grouped by the intraherd milk variance of the herd from which they were selected. Evaluations based on progeny tests showed young bulls selected from herds with low intraherd milk variance were genetically superior to those selected from herds with high intraherd milk variance. Predicted transmitting abilities for young bulls selected from low variance herds were less biased, but predicted transmitting abilities for those selected from high variance herds were inflated. However, the variance of biasedness was slightly lower in higher variance herds.     

Relationships of productive life evaluations with changes in evaluations for yields

The objective of this work was to investigate the relationships of productive life with changes in bull evaluations for yield traits. Two datasets were analyzed. In the first, predicted differences for change in milk yield from first to second lactation of daughters of artificial insemination (AI) Holstein bulls used widely in the southeastern United States were available from a previous study. These were correlated with predicted transmitting abilities (PTA) of productive life from May 2000 USDA sire evaluations. Based on bulls with at least 10 daughters (n = 560) the correlation of PTA productive life with predicted differences for the change in milk yield was 0.30. The correlation increased to 0.36 for bulls with at least 50 daughters (n = 319) and to 0.40 for bulls with at least 75 daughters (n = 284). The second analysis included data on 1831 AI sampled Holstein bulls evaluated by USDA between July 1989 and May 2000. Changes in PTA yields were calculated as PTA from evaluations based on first and second records of daughters minus those from first-record evaluations. Correlation analyses showed that PTA yields from first-record evaluation and changes in PTA yields were positively associated with productive life. Regression coefficients on changes in PTA yields were all positive indicating that increases in PTA for yield traits as daughters aged corresponded with longer productive life. Using changes in AI bull evaluations for yields could improve prediction of productive life for little cost.     

Comparisons of cows and herds in two progeny testing programs and two corresponding states

Data were USDA genetic evaluations of cows and DHI herd profiles from 4154 Holstein progeny-test herds from two artificial insemination organizations. 21st Century Genetics (Shawano, WI) and Genex (Ithaca, NY), and from 6361 additional herds from Minnesota and New York. We grouped herds into four categories: 21st Century Genetics herds, other Minnesota herds, Genex herds, and other New York herds. Herds were eliminated if they contributed fewer than 10 cows with genetic evaluations and birth dates from January 1989 to March 1995. Data included 83 and 74%, respectively, of first-crop daughters of 21st Century Genetics and Genex progeny-test bulls with genetic evaluations from January 1995 to February 1997. Herds were characterized by DHI profile and cow evaluation data. Daughters of progeny-test bulls with extreme production records (outside of 3 SD) relative to herd mean and variance did not appear in disproportionate numbers among the progeny of bulls likely culled or considered for further use. The two organizations appear to have selected larger, genetically superior, and better managed herds from within their respective regions for progeny-testing purposes. We were not able to predict whether a bull in the progeny-testing programs of these two organizations was going to exceed or fail to meet the pedigree prediction from characteristics of herds in which his daughters performed. Differences between parent average and daughter yield deviations for typical young sires appear to result from Mendelian segregation of genes.     

Genetic and nongenetic variation in concentration of selenium, calcium, potassium, zinc, magnesium, and phosphorus in milk of Dutch Holstein-Friesian cows

Minerals found in milk, such as Se, Ca, K, Zn, Mg, and P, contribute to several vital physiological processes. The aim of this study was to quantify the genetic variation in levels of Se, Ca, K, Zn, Mg, and P in milk and to quantify the between-herd variation in the levels of these minerals in milk. One morning milk sample from each of 1,860 Dutch Holstein-Friesian cows from 388 commercial herds in the Netherlands was used. Concentration of minerals was determined by inductively coupled plasma-atomic emission spectrometry. Variance components were estimated using an animal model with covariates for days in milk and age at first calving; fixed effects for season of calving and effect of test or proven bull; and random effects for animal, herd, and error. Heritability and proportion of phenotypic variation that can be explained by herd were estimated using univariate analysis. The intraherd heritability for Se was low (0.20) whereas herd explained 65% of the total variation in Se. Variation between herds most likely results from variation in Se content in the feed, which partly reflects variation in Se levels in the soil. Intraherd heritabilities for Ca, K, Zn, Mg, and P were moderate to high and were 0.57, 0.46, 0.41, 0.60, and 0.62, respectively. For Ca, K, Zn, Mg, and P, the proportions of phenotypic variation that could be explained by herd were low (0.13-0.24). This study shows that there are possibilities for altering the mineral composition of milk. For Ca, K, Zn, Mg, and P, there are good prospects for selective breeding whereas, for Se, measures at farm level may be more effective.     

Stability of genetic evaluations for active artificial insemination bulls

Genetic evaluations for milk, fat, and protein from 1995 through August 2003 for 17,987 Holstein bulls in active artificial insemination (AI) service were examined for changes to the November 2003 evaluation. Evaluations for active AI bulls at each of 31 evaluation dates showed mean declines to November 2003. No evidence was seen of a worsening situation over time. Bulls' early evaluations with active AI status showed much larger declines, but this overevaluation diminished and essentially disappeared after 3 yr. The bulls with first active AI evaluations since 1995 were the primary focus of the study. The influx of second-crop daughters did not appear to cause a decline in evaluations for these bulls, attesting to the successful modification to the genetic evaluation system by expanding the genetic variance of short records. Mean declines and the variation of those differences were generally similar by bull sampling organization. A change from active to inactive AI status was generally concurrent with a decline in predicted transmitting ability (PTA). Bulls coded as having standard AI sampling declined less than bulls coded as having other sampling, but the differences were much less than in previous reports. Larger increases in reliability were generally associated with greater declines in PTA, and the magnitude of these changes decreased over time (increasing evaluation number). Change in reliability underpredicted the variance of change in PTA, indicating that other important factors contribute or that the assumptions for the calculation of the expected change in PTA are not met. Declines in estimated merit over time are not sufficient to alter present genetic selection programs, but reasons for the declines continue to elude explanation.     

Use of insemination data for joint evaluation of male and female fertility in predominantly seasonal-calving dairy herds

Conception in dairy cattle is influenced by the fertility of the cow and the bull and their interaction. Despite genetic selection for female fertility in many countries, selection for male fertility is largely not practiced. The primary objective of this study was to quantify variation in male and female fertility using insemination data from predominantly seasonal-calving herds. Nonreturn rate (NRR) was derived by coding each insemination as successful (1) or failed (0) based on a minimum of at least 25 d. The NRR was treated as a trait of the bull with semen (male fertility) and the cow that is mated (female fertility). The data (805,463 cows that mated to 5,776 bulls) were used to estimate parameters using either models that only included bulls with mating data or models that fitted the genetic and permanent environmental (PE) effects of bulls and cows simultaneously. We also evaluated whether fitting genetic and PE effects of bulls as one term is better for ranking bulls based on NRR compared with a model that ignored genetic effect. The age of cows that were mated, age of the bulls with semen data, season of mating, breed of cow that mated, inbreeding of cows and bulls, and days from calving to mating date were found to have a significant effect on NRR. Only about 3% of the total variance was explained by the random effects in the model, despite fitting the genetic and PE effects of the bull and cow. The 2 components of fertility (male and fertility) were not correlated. The heritability of male fertility was low (0.001 to 0.008), and that of female fertility was also low (~0.016). The highest heritability estimate for male fertility was obtained from the model that fitted the additive genetic relationship matrix and PE component of the bull as one term. When this model was used to calculate bull solutions, the difference between bulls with at least 100 inseminations was up to 19.2% units (−9.6 to 9.6%). Bull solutions from this model were compared with bull solutions that were predicted fitting bull effects ignoring pedigree. Bull solutions that were obtained considering pedigree had (1) the highest accuracy of prediction when early insemination was used to predict yet-to-be observed insemination data of bulls, and (2) improved model stability (i.e., a higher correlation between bull solutions from 2 randomly split herds) compared with the model which fitted bull with no pedigree. For practical purposes, the model that fitted genetic and PE effect as one term can provide more accurate semen fertility values for bulls than the model without genetic effect. To conclude, insemination data from predominantly seasonal-calving herds can be used to quantify variability between bulls for male fertility, which makes their ranking on NRR feasible. Potentially this information can be used for monitoring bulls and can supplement efforts to improve herd fertility by avoiding or minimizing the use of semen from subfertile bulls.     

Short communication: Genetic analysis of dairy bull fertility from field data of Brown Swiss cattle

The aim of this study was to estimate heritability and repeatability of dairy bull fertility in Italian Brown Swiss cattle. Bull fertility indicators were calving per service and nonreturn rate at 56 d after service. Data included 124,206 inseminations carried out by 86 technicians on 28,873 heifers and cows in 1,400 herds. Services were recorded from 1999 to 2008 and were performed with semen from 306 AI Brown Swiss bulls. Data were analyzed with a threshold animal model, which included the fixed effects of parity by class of days in milk of the inseminated cow (age at insemination for heifers), year-season of insemination, and status of the service bull at the time of insemination (progeny testing or proven), and the random effects of herd, technician, additive genetic, and permanent environment of inseminated heifer/cow and service bull, and residual. Also, genetic covariance between heifer/cow and service bull effects was considered in the model. Heritability and repeatability were 0.0079 and 0.0100 for nonreturn rate at 56 d after service, and 0.0153 and 0.0202 for calving per service, respectively. The low estimates obtained in the present study indicate that selection for male fertility using field data is hardly pursuable.     

Comparison of genetic evaluations from animal model and modified contemporary comparison

Comparisons were made between characteristics of Modified Contemporary Comparison and animal model evaluations with data available for January 1989 USDA-DHIA genetic evaluations. The animal model system's requirement that cows have a valid first lactation record resulted in a decrease in cows and daughters included. New flexible comparison groups were slightly larger for small herds and much smaller for large herds, which resulted in overall smaller and more uniform-sized comparison groups. Determining the optimal method of defining management groups was not undertaken. Correlations between bull evaluations from the two procedures ranged from .92 to .95 across breeds. Increases in reliability over repeatability were substantial for bulls with limited daughter information and small for widely used bulls. Correlations between evaluations for cows born in 1985 were .92 to .96, whereas those for cows born in 1980 (old enough to have daughters affecting animal model evaluations) were lower (.90 to .93), as expected. Reliabilities for cows were .02 to .05 higher than repeatabilities. Cows with more daughters increased more in evaluation and accuracy between the two procedures and were genetically superior. Bulls and cows with more prior information, cows with higher past evaluations, and Holstein bulls with higher past evaluations tended to have larger increases in PTA. Genetic trend estimates were different for the animal model, which resulted in changes in evaluations of various magnitudes depending on breed, sex, and birth year of animal.     

Timeliness and effectiveness of progeny testing through artificial insemination

Progeny-test (PT) programs of US artificial-insemination (AI) organizations were examined to determine timeliness of sampling, PT daughter distribution, rate of return of PT bulls to widespread service, and genetic merit of PT bulls compared with AI-proven and natural-service (NS) bulls. Bull age at semen release and at birth and calving of PT daughters was documented by breed (Ayrshire, Brown Swiss, Guernsey, Holstein, Jersey, and Milking Shorthorn) for bulls that entered AI service since 1960. Mean Holstein bull age at semen release (16 mo) changed little over time, but standard deviations (SD) decreased from 4.0 mo during the 1960s to 2.4 mo during the 1990s. Most Holstein bulls (80%) had semen released by 18 mo. Mean age of Holstein bulls at birth and calving of PT daughters during the 1990s was 29 and 56 mo, respectively (a decline of 4 mo from the 1960s); SD decreased from 6 to 3 mo. Bulls of other breeds usually were older at birth and calving of PT daughters, and SD were larger. Mean Holstein bull age when 80% of PT daughters had been born declined from 36 mo during the 1960s to 31 mo during the early 1990s; for other breeds, bulls showed the same trend but at older ages. Mean Holstein bull age when 80% of PT daughters had calved declined from 65 mo during the 1960s to 59 mo during the 1990s; for other breeds, bulls were older. Percentage of herds with PT daughters has increased over time. For Holsteins, herds with five or more usable first-parity records that had PT daughters with usable records increased from 15% during 1965 to 61% during 1998; percentage of herds with from 1 to 19% PT records increased from 11 to 38%, and percentage of herds with >50% PT daughters increased from 1 to 5%. Percentage of Holstein PT bulls returned to AI service declined to about 12% for bulls with PT entry around 1990; for other breeds, 12 to 23% of most recent PT bulls were returned to service. Percentage of milking daughters that had records usable for genetic evaluation that were sired by PT bulls increased steadily from 10 to 18%, whereas percentage of daughters with usable records that were sired by NS bulls declined from 14 to 7%. Milk yield of daughters of AI-proven bulls was 107 to 200 kg greater than for daughters of PT bulls and 366 to 444 kg greater than for daughters of NS bulls for all years. More extensive and rapid sampling and increased selection intensity of PT programs have led to more rapid genetic progress. More extensive use of AI could increase US producer income by millions of dollars annually.     

The impact of 3 strategies for incorporating polled genetics into a dairy cattle breeding program on the overall herd genetic merit

Dehorning in cattle has been associated with behavioral, physiological, and neuroendocrine responses indicative of pain. Unaddressed, the pain associated with a routine production procedure could contribute to a negative public perception of livestock production practices. Alternative considerations of dehorning include the selection of polled cattle within herds, thereby avoiding pain and production loss. As polledness results from an autosomal dominant pattern of inheritance, genetic selection for polled cattle could reduce the prevalence of the horned trait. Herein we discuss 3 strategies to incorporate polled genetics into a cow herd and the estimated impact on the overall genetic merit of the herd. Furthermore, the availability and genetic merit of polled artificial insemination bulls in the United States is summarized. Both Holstein and Jersey dairy bulls registered with the National Association of Animal Breeders from December 2010 through April 2013 were queried. Polled bulls were identified as either being homozygous (PP) or heterozygous (Pp) and the average net merit (NM) predicted transmitting ability (PTA) of each sire group was calculated. The percentage of polled calves born each year over a 10-yr period was calculated for the following 3 scenarios: (A) various percentages of horned cows were randomly mated to Pp bulls, (B) various percentages of horned cows were preferentially mated to Pp bulls, and (C) horned cows were selectively mated to PP bulls, heterozygous cows to Pp bulls, and homozygous polled cows to horned bulls. Additionally, the change in NM PTA of the cow herd was calculated over the same period. The highest percentage of polled animals (87%) was achieved in scenario C. An evaluation of the herd NM PTA highlights the trade-offs associated with increasing polled genetics. Given the current genetic merit of horned and polled bulls, increasing the percentage of polled calves will decrease the NM PTA in Holstein, but may have minimal impact in Jersey herds. Decisions regarding selective breeding to increase polled genetics will need to be evaluated in the context of production objectives, cost of dehorning, and impact on overall genetic merit.     

Pregnancy rates and milk production in natural service and artificially inseminated dairy herds in Florida and Georgia

Effects of artificial insemination (AI) and natural service (NS) breeding systems on pregnancy rates (PR) by stage of lactation, season, and changes in milk production over time were examined using lactation and herd DHIA records of Holstein cows in dairy herds located in Florida and Georgia. The reported genetic profile of service sires of the herd was used to determine the percentage of cows bred to natural service bulls (%NS). Two seasons were considered: winter (November-April) and summer (May-October) from 1995 to 2002 (16 periods). Herd-periods were assigned 1 of 3 breeding systems: AI (0 to 10% NS), mixed (11 to 89% NS) and NS (90 to 100% NS). Seventy percent of the herds used NS bulls as a component of their breeding system during the study period. The PR during winter (17.9%) was greater than that during summer (9.0%). During winter, PR for AI herds (17.9%) did not differ from that for mixed (17.8%) and NS herds (18.0%). During summer, PR for AI herds (8.1%) was slightly less than that for mixed (9.3%) and NS herds (9.8%). During winter, PR for cows at 71 to 91 d, 92 to 112 d, and 113 to 133 d in milk were 1.4% lower for mixed herds compared with AI and NS. Pregnancy rate for NS herds was 2.6% lower during late lactation compared with AI and mixed herds. During summer, PR for cows at 71 to 91 and 92 to 112 d in milk were 2.6 and 1.8% greater, respectively, for NS herds compared with AI. However, from 260 to 364 d in milk, PR for NS herds was less than that for AI and mixed herds. No significant interaction was detected between breeding system and lactation number. Rolling herd average milk production during the study period was less in the NS herds (7180 kg) compared with AI (8513 kg) and mixed herds (8176 kg), but the annual change in milk production was not different among breeding systems. The results indicated that use of NS bulls did not result in meaningful disadvantages in terms of PR and changes in milk production over time.     

Breeding practices on Illinois Holstein farms

Questionnaires requesting breeding information were mailed to 1,148 Illinois Holstein herds in the Dairy Herd Improvement program. A total of 591 questionnaires (51%) were returned. Dairy producers with herds producing over 7,100 kg of milk returned 64% of their questionnaires whereas 34% of dairy producers with herds producing less than 5,900 kg returned their questionnaires. State average and standard errors were: 23 +/- 1 bulls used per 100 cows, 78 +/- 2% dairy producers select the bulls, 96 +/- 1% herds use artificial insemination, 2.1 +/- .1 artificial insemination organizations per herd, 29 +/- 2% herds participate in young sire testing programs, 87 +/- 1% farmers consider calving ease indicators in mating heifers, and 17 +/- 2% farmers consider calving ease indicators in mating cows. Breeding practices positively associated with increasing rolling herd average milk production were number of bulls per herd and per 100 cows, self as bull selector, use of artificial insemination, number of artificial insemination organizations, and participation in young sire testing programs. Three breeding practices were negatively associated with increasing rolling herd average milk production: artificial-insemination technician as bull selector and consideration of calving ease for mating heifers and cows. Dairy producers also were asked to rate the emphasis placed on traits in bull and cow selection. For bull selection, udder conformation and Predicted Difference milk were most important. In cow selection, milk production, followed by udder conformation, feet and legs, and fat percentage, was the most important trait.     

Variances of and correlations among progeny tests for reproductive traits of cows sired by AI bulls

Estimates of daughter fertility were computed using first artificial insemination (AI) breedings reported to the US Dairy Herd Improvement Association (DHIA) from 1995 through 1997. An animal model was used to compute estimated breeding values (EBV) of daughter groups with fixed effects of herd-year-month bred and classes of early lactation energy-corrected milk, days in milk (DIM) when bred, and parity. Standard deviations and ranges of bull EBV for daughter fertility for DIM were 9.1 and -31 to 18; standard deviations and ranges of bull EBV for daughter fertility for nonreturn were 3.8 and -11 to 10. Correlations were computed for EBV for daughter fertility with EBV for mating bull fertility and with predicted transmitting abilities (PTA) for milk, somatic cell score (SCS), and productive life for bulls (213) with minimums of 200 matings and 100 progeny with reproductive traits. None of the correlations among EBV for reproductive traits differed from 0.0. Correlations of EBV for daughter fertility with PTA for productive life were significantly positive. PTA for yield traits were not correlated with EBV for daughter differences in nonreturn or DIM. Very low correlations of EBV for daughter reproductive traits with PTA for yield indicate that, in order to improve daughter fertility, fertility must be incorporated in sire selection decisions.     

Evaluating sire selection practices using lifetime net income functions

Dairy farmers do not take full advantage of opportunities available for genetic improvement through use of artificial insemination, perhaps because economic advantages of good sire selection may not be fully recognized or understood. This study was undertaken to document differences between use of AI and non-AI bulls and to develop prediction equations to compare lifetime economic merit of future progeny from alternative sire selection policies. We describe the use of two methods of measuring lifetime economic merit, with and without adjustment for opportunity cost of a postponed replacement. Comparison of lifetime relative net income adjusted for opportunity cost on groups of cows sired by different kinds of bulls showed that daughters of proven AI bulls generated $148 and $120 more lifetime net income under fluid and manufactured milk market conditions than daughters of non-AI bulls. Daughters of proven AI bulls produced $60 more than daughters of AI young sires in progeny testing programs at the time of daughter conception. We developed prediction equations from combinations of genetic evaluations for production, productive life, SCS, and linear type traits on sires to predict lifetime relative net income of progeny produced from alternative sire selection strategies. Prediction equations explained 14 to 18% of variation in relative net income (not adjusted for opportunity cost), but herd and year of first freshening accounted for considerably more variation than did genetic evaluations on the sire of the cow. Finally, two independent data sets were used to develop and test predictions of lifetime relative net income adjusted for opportunity cost using genetic evaluations based on the eight traits included in the Merit indexes for the sire of each cow. Prediction equations from odd numbered herds were used to predict lifetime economic merit in even numbered herds and vice versa. Coefficients of determination ranged from 0.088 to 0.103 and averaged 0.004 higher than prediction equations with Net or Fluid Merit. Accuracy of predictions showed that Net and Fluid Merit were robust and useful indexes that accurately identified bulls whose daughters generated highest lifetime economic merit.     

Genetic analysis of an artificial insemination progeny test program

The success of the progeny test (PT) program from one Spanish artificial insemination (AI) organization was evaluated. The annual genetic trend for the organization was compared with PT programs from other countries. The relationships among parents' estimated breeding values (EBV) and PT results for sons were also studied. Estimated breeding values for type and production traits were obtained from international genetic evaluations from February 2004. The annual genetic gain of the Spanish PT program was similar to that of other international programs. The Spanish AI organization graduated 13% of its sampled bulls, and 52% of primiparous cows were daughters of Spanish bulls (32% from proven bulls and 20% from sampling bulls).Correlations between EBV for PT bulls and their pedigree indices (0.52 to 0.70) were slightly lower than correlations between EBV for PT bulls and their parent averages (0.63 to 0.73). Both young and mature cows contributed to genetic progress. Success of PT bulls (defined by number of second-crop daughters) depended mainly on their EBV for final score, protein yield, and the type-production index. Significant correlations of sire EBV were found for final score and type-production index with the number of second-crop daughters (0.22 and 0.17). Likewise, significant correlations of dam EBV for final score and type-production index with the number of second crop daughters were found (0.25 and 0.18). Final score and protein yield were the main factors in success of a PT bull. The type-production index for PT bulls was not important for success unless it was 2.5 standard deviations above average. The PT bulls with low EBV for type-production index were used as proven bulls when they had higher EBV either for protein or final score.     

Selection of bull dams for production and functional traits in an open nucleus herd

The aim of this study was to compare different scenarios for bull dam selection in a nucleus herd. A deterministic simulation study using selection index methodology was undertaken. In the scenarios studied, differing amounts of information on functional traits were available when bull dams were selected, and the resulting genetic responses in these traits were compared. Field-recorded fertility traits used in the scenarios were available as progeny test results of artificial insemination bulls: these included pregnant at first insemination (PFI), interval between calving and first insemination (CFI), and cases of reproductive disorders (RD). Similarly, field-recorded cases of clinical mastitis (CM), lactation somatic cell score (LSCS), and protein yield (PY) were included for pedigree selection. In the scenarios, heat intensity score and progesterone levels were treated as new indicator traits of fertility recorded in the nucleus herd. Traits CFI and LSCS were assumed to be better recorded with higher heritability in the nucleus herd than in ordinary herds. Economic weights currently used in Nordic Cattle Genetic Evaluation (NAV) were adapted and used in the scenarios. The results showed that these weights, if used in multiple trait genetic evaluation, would lead to undesirable genetic changes in functional traits for the bull dam selection path in a nucleus environment. More frequent recording of additional traits failed to improve selection for functional traits, as did more frequent recording of ordinary traits. Restriction index methodology was used to derive the bull dam total weights that gave no unfavorable response (i.e., zero genetic change) in traits PFI, CFI, and CM. When summarized over lactations, the new bull dam total weights, when additional records from nucleus were used, had to be 12 to 23 times higher for fertility, and 3 times higher for mastitis, than the presently used NAV weights, if these traits were to remain unchanged through the bull dam selection path. Thus, nucleus herd selection of bull dams is questionable for low heritability traits that are already recorded in the field.     

Estimates of genetic trend in an artificial insemination progeny test program and their association with herd characteristics.

Individual lactation records from Holstein cows in 3449 herds participating in an AI stud's young sire sampling program from 1971 to 1987 were used to characterize the sampling program and to estimate genetic merit and trend. Average genetic merit of cows in sampling program herds was consistently superior to the average genetic merit of cows in the US population. Genetic trend of sires of first-crop cows was 58 kg of milk and 1.5 kg of fat/yr from 1971 to 1978 and 176 kg of milk and 5.5 kg of fat/yr from 1979 to 1987. The average genetic merit of sires of first-crop cows born after 1983 was equivalent to or exceeded the genetic level of sires of other cows in the herd. Within-herd-year means and standard deviations of yield, genetic evaluation, and management traits (herd-year characteristics) were computed for a subset of 341 herds contributing first-crop daughters for at least 10 yr. The average of each herd-year characteristic during 10 or more years was used to predict within-herd genetic trend. Herd characteristics explained up to 51% of differences in within-herd genetic trends. Average sire genetic merit of daughters other than first-crop daughters accounted for up to 80% of the explained differences. Other herd characteristics suggested that herds with larger within-herd standard deviation milk yields, a larger number of young sires represented, younger cows, and greater percentage of cows sired by AI sires made greater genetic improvement. Results indicated that the average genetic merit of cows and the rate of within-herd genetic improvement are higher in herds that participate in a young sire sampling program.     

Genetic parameters for fertility of dairy bulls

Genetic parameters for male fertility and fertility ratings of AI bulls were obtained by analyzing 298,013 service records of cows with successive calving records. Cows were mated to 746 service bulls, which were progeny of 126 sires. The model for variance component estimation accounted for fixed effects of herd-year-seasons, sire of the service bull, age of mates, and random effects of service bull and residual error. Estimates of variances of service bulls and residual error components for bull fertility indicated almost 10% of the phenotypic variation for fertility is among AI bulls. Best linear unbiased prediction of fertility ratings of individual bulls with inclusion of sire and maternal grandsire relationships on these data permitted the evaluation of 886 AI bulls for bull fertility. Heritability for bull fertility computed as twice the regression of son on sire was .158. Differences in fertility ratings of AI bulls ranged from -.29 to .19. Prediction of fertility of young AI bulls and more accurate rating of proven bulls might be useful to the industry.     

Milk Yield,Sire Selection Profitability,and Selection Error Costs

Objectives were to determine effect of herd milk yield on profitability of sire selection and cost of selection errors by ignoring this information. Net present values of semen were calculated for alternative herd averages for milk, selection policies, conception rates to first service, and generations of descendants in the financial planning horizon for real interest of 3%. Predicted Differences for milk, fat, and type score and retail semen prices were for the 440 AI Holstein sires active and available for purchase after the July 1985 USDA Sire Summary. Herd averages for milk yield were low (<5500 kg), average (6500 to 6999 kg), and high (>9499 kg). Optimal sires for low yield herds were 1.1 to 1.6 SD more profitable when used in average yield herds and they were 3.0 to 4.5 SD more profitable when used in herds with highest average milk. Cost of sire selection errors was inconsequential when information about herd average milk yield for either policy was ignored. However, the 20 optimal sires selected either for high or low herds but used instead in the opposite environment were .5 SD less profitable than correct choices for the 3 to 1 selection policy. Ten of the 20 most profitable bulls differed between these herd situations. Thus, accurate use of herd average milk yield information can reduce the cost of sire selection errors for selection policies including type score.     

Chlamydophila species in dairy farms: Polymerase chain reaction prevalence, disease association, and risk factors identified in a cross-sectional study in western Germany

The prevalence of Chlamydophila spp. was determined in a cross-sectional study carried out in 2007 using 100 randomly selected dairy herds in the western part of Germany. Ten dairy cows per herd were sampled in herds with fewer than 100 cows; in bigger herds, 10% of the cows were sampled. For the detection of Chlamydophila spp., vaginal swabs from early lactating dairy cows were analyzed using an established highly sensitive genus-specific real-time PCR. In consideration of the discontinuous shedding of the pathogen, a herd was classified as positive if at least 1 animal per herd tested positive for Chlamydophila spp. By use of these methods and definitions, 61% of the dairy herds and 13.5% of the cows were detected as PCR-positive for Chlamydophila spp., which is indicative for ongoing infections. To compare herd health and herd performance between herds testing positive or negative and to identify risk factors for the presence of Chlamydophila spp., a questionnaire was designed to evaluate farm characteristics and management practices. In addition, the performance recordings of the state dairy recording organization were used for these purposes. Milk yield, number of lactations, and calving to first-service interval were lower in herds testing positive for Chlamydophila spp. compared with negative herds. For all these variables, there was no interaction between Chlamydophila status and lactation number. Replacement of animals from outside sources, use of breeding bulls, lack of separate calving pens, and low scores for cleanliness of beddings, walkways, and cows were identified as the main risk factors for Chlamydophila spp.