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J S Clay

Publications and source records attributed to J S Clay.

16 recordsLinked to original sources
PubMed2006

Genetic analysis of clinical mastitis data from on-farm management software using threshold models.

Producer-recorded clinical mastitis data from 77,791 cows in 418 herds were used to determine the potential for genetic improvement of mastitis resistance using data from on-farm management software programs. The following threshold sire models were applied: 1) a single-trait lactation model, where mastitis was recorded as 0 or 1 in first lactation only; 2) a 3-trait lactation model, where mastitis was recorded as 0 or 1 in each of the first 3 lactations, and 3) a 12-trait, lactation-segment model, where mastitis was recorded as 0 or 1 in each of 4 segments (0 to 50, 51 to 155, 156 to 260, and 261 to 365 d postpartum) in each of the first 3 lactations. Lactation incidence rates were 0.16, 0.20, and 0.24 in first, second, and third lactation, respectively, and incidence rates within various segments of these lactations ranged from 0.036 in late first lactation to 0.093 in early third lactation. Estimated heritability of liability to clinical mastitis ranged from 0.07 to 0.15, depending on the model and stage of lactation. Heritability estimates were higher in first lactation than in subsequent lactations, but estimates were generally similar for different segments of the same lactation. Genetic correlations between lactations from the 3-trait model ranged from 0.42 to 0.49, while correlations between segments within lactation from the 12-trait model ranged from 0.26 to 0.64. Based on the results presented herein, it appears that at least 2 segments are needed per lactation, because mastitis in early lactation is lowly correlated with mastitis in mid or late lactation. Predicted transmitting abilities of sires ranged from 0.77 to 0.89 for probability of no mastitis during the first lactation and from 0.36 to 0.59 for probability of no mastitis during the first 3 lactations. Overall, this study shows that farmer-recorded clinical mastitis data can make a valuable contribution to genetic selection programs, but additional systems for gathering and storing this information must be developed, and more extensive data recording in progeny test herds should be encouraged.

Animals↗
PubMed2005

Genetic evaluation of dairy sires for milking duration using electronically recorded milking times of their daughters.

The potential of using electronically recorded data from on-farm milking parlor and herd management software programs for genetic evaluation of dairy sires for milking duration of their daughters was assessed in the present study. Single measurements of milking duration were collected weekly from 29 herds between June 1, 2003 and April 1, 2004. These included 73,547 observations corresponding to 10,152 Holstein cows from 1551 sires. Average milking duration for a single milking in our data set was 4.5 min. Estimated heritability of milking duration was 0.17, and predicted transmitting abilities (PTA) of individual sires ranged from -0.48 min for sires with the fastest milking daughters to 0.59 min for sires with the slowest milking daughters. The correlation between PTA for milking duration and PTA for somatic cell score (SCS) was -0.15, indicating that sires whose daughters milk most quickly also tend to transmit higher SCS to their progeny. Correlations between PTA milking duration and PTA for teat placement and teat length were -0.14 and 0.20, respectively, indicating that sires that transmit wide teat placement and long teats tend to have daughters that milk slowly. Based on the results presented herein, it appears that genetic selection based on objective, electronically recorded milking times is possible. This approach would greatly improve the quality and efficiency of data collection relative to conventional evaluations of milking speed, which are based on farmer surveys. The number of herds currently equipped to routinely capture milking times is limited, but this number is increasing very rapidly. Future research should focus on refinement of data reporting and validation systems, as well as estimation of the economic value of milking duration. This trait may have an intermediate optimum, because cows that milk too slowly will disrupt parlor flow and reduce milking efficiency, but cows that milk too quickly may be at greater risk for mastitis.

Animals↗
PubMed2005

Milk urea nitrogen concentration: heritability and genetic correlations with reproductive performance and disease.

The objectives of this study were to estimate the heritability of milk urea nitrogen (MUN) concentration and describe the genetic relationship between MUN and reproductive performance and between MUN and diseases in Holsteins. Dairy Records Management Systems (Raleigh, NC) provided lactation data. The Danish Agricultural Advisory Center provided breeding value estimates for diseases. Infrared (IR) and wet chemistry (WC) data were analyzed separately. Heritabilities and genetic correlations for 2 different measures of MUN and reproductive performance were estimated with an animal model using ASREML. Heritabilities for MUN were estimated using all lactations combined (lactations 1 through 5) and separately for first lactation and second lactation. Genetic correlations with reproduction and health were estimated separately for parities 1 and 2. Herd-test-day or herd-year-season along with age at calving and days in milk were included as fixed effects in all models. Heritability estimates for all lactations combined were 0.15 for WC MUN and 0.22 for IR MUN. Genetic correlations between WC MUN and 2 measures of reproductive performance, days to first service, and first service conception were not different from zero. In contrast, the genetic correlation between WC MUN and days open of 0.21 in first lactation and 0.41 in second lactation indicated that higher WC MUN values were associated with increased days open. Correlations among estimated breeding values for MUN and estimated breeding values for Danish diseases identified no significant relationships. Although the results of this study indicate that heritable variation for MUN exists, the inability to identify significant genetic relationships with several measures of disease or reproductive performance appears to limit the value of MUN in selection for disease resistance and improved reproduction.

Animals↗
PubMed2004

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.

Animals↗
PubMed2004

Correlations among body condition scores from various sources, dairy form, and cow health from the United States and Denmark.

The objectives of this study were to estimate genetic correlations among body condition scores (BCS) from various sources, dairy form, and measures of cow health. Body condition score and dairy form evaluated during routine type appraisal was obtained from the Holstein Association USA, Inc. A second set of BCS was obtained from Dairy Records Managements Systems (DRMS) and was recorded by producers that use PCDART dairy management software. Disease observations were obtained from recorded veterinarian treatments in several dairy herds in the United States. Estimated breeding values for diseases in Denmark were also obtained. Genetic correlations among BCS, dairy form, and cow health traits in the United States were generated with sire models. Models included fixed effects for age, DIM, and contemporary group. Random effects included sire, permanent environment, herd-year season for health traits, and error. Predicted transmitting abilities (PTA) for BCS and dairy form were correlated with estimated breeding values for disease in Denmark. The genetic correlation estimate between BCS from DRMS and BCS from the Holstein Association USA, Inc., was 0.85. The genetic correlation estimate between BCS and a composite of all diseases in the United States was -0.79, and PTA for BCS was favorably correlated with an index of resistance to disease other than mastitis in Denmark (0.27). Dairy form was positively correlated with a composite of all diseases in the United States (0.85) and was unfavorably correlated with an index for resistance to disease other than mastitis in Denmark (-0.29). Adjustment for protein yield PTA had a minimal affect on correlations between PTA for BCS or dairy form and disease in Denmark. Selection for higher body condition or lower dairy form with continued selection for yield may slow deterioration in cow health as a correlated response to selection for increased yield.

Animals↗
PubMed2004

Genetic selection for health traits using producer-recorded data. I. Incidence rates, heritability estimates, and sire breeding values.

The objective of this study was to determine the feasibility of genetic selection for health traits in dairy cattle using data recorded in on-farm herd management software programs. Data regarding displaced abomasum (DA), ketosis (KET), mastitis (MAST), lameness (LAME), cystic ovaries (CYST), and metritis (MET) were collected between January 1, 2001 and December 31, 2003 in herds using Dairy Comp 305, DHI-Plus, or PCDART herd management software programs. All herds in this study were either participants in the Alta Genetics (Watertown, WI) Advantage progeny testing program or customers of the Dairy Records Management Systems (Raleigh, NC) processing center. Minimum lactation incidence rates were applied to ensure adequate reporting of these disorders within individual herds. After editing, DA, KET, MAST, LAME, CYST, and MET data from 75,252 (313), 52,898 (250), 105,029 (429), 50,611 (212), 65,080 (340), and 97,318 (418) cows (herds) remained for analysis. Average lactation incidence rates were 0.03, 0.10, 0.20, 0.10, 0.08, and 0.21 for DA, KET, MAST, LAME, CYST, and MET (including retained placenta), respectively. Data for each disorder were analyzed separately using a threshold sire model that included a fixed parity effect and random sire and herd-year-season of calving effects; both first lactation and all lactation analyses were carried out. Heritability estimates from first lactation (all lactation) analyses were 0.18 (0.15) for DA, 0.11 (0.06) for KET, 0.10 (0.09) for MAST, 0.07 (0.06) for LAME, 0.08 (0.05) for CYST, and 0.08 (0.07) for MET. Corresponding heritability estimates for the pooled incidence rate of all diseases between calving and 50 d postpartum were 0.12 and 0.10 for the first and all lactation analyses, respectively. Mean differences in PTA for probability of disease between the 10 best and 10 worst sires were 0.034 for DA, 0.069 for KET, 0.130 for MAST, 0.054 for LAME, 0.039 for CYST, and 0.120 for MET. Based on the results of this study, it appears that genetic selection against common health disorders using data from on-farm recording systems is possible.

Abomasum↗
PubMed2004

Genetic selection for health traits using producer-recorded data. II. Genetic correlations, disease probabilities, and relationships with existing traits.

The objectives of this study were to calculate genetic correlations between health traits that were recorded in on-farm herd management software programs and to assess relationships between these traits and other traits that are routinely evaluated in US dairy sires. Data consisted of 272,576 lactation incidence records for displaced abomasum (DA), ketosis (KET), mastitis (MAST), lameness (LAME), cystic ovaries (CYST), and metritis (MET) from 161,622 cows in 646 herds. These data were collected between January 1, 2001 and December 31, 2003 in herds using the Dairy Comp 305, DHI-Plus, or PCDART herd management software programs. Binary incidence data for all disorders were analyzed simultaneously using a multiple-trait threshold sire model that included random sire and herd-year-season of calving effects. Although data from multiple lactations were available for some animals, our genetic analysis included only first parity records due to concerns about selection bias and improper modeling of the covariance structure. Heritability estimates for the presence or absence of each disorder during first lactation were 0.14 for DA, 0.06 for KET, 0.09 for MAST, 0.03 for LAME, 0.04 for CYST, and 0.06 for MET. Estimated genetic correlations were 0.45 between DA and KET, 0.42 between KET and CYST, 0.20 between MAST and LAME, 0.19 between KET and LAME, 0.17 between DA and CYST, 0.17 between KET and LAME, 0.17 between KET and MET, and 0.16 between LAME and CYST. All other correlations were negligible. Correlations between predicted transmitting abilities for the aforementioned health traits and existing production, type, and fitness traits were low, though it must be noted that these estimates may have been biased by low reliability of the health trait evaluations. Based on results of this study, it appears that genetic selection for health disorders recorded in on-farm software programs can be effective. These traits can be incorporated into selection indices directly, or they can be combined into composite traits, such as "reproductive disorders", "metabolic disorders", or "early lactation disorders".

Abomasum↗
PubMed2002

Heritability and correlations among body condition score loss, body condition score, production and reproductive performance.

The objectives of this study were to estimate the heritability of body condition score loss (BCSL) in early lactation and estimate genetic and phenotypic correlations among BCSL, body condition score (BCS), production, and reproductive performance. Body condition scores at calving and postpartum, mature equivalents for milk, fat and protein yield, days to first service, and services per conception were obtained from Dairy Records Management Systems in Raleigh, NC. Body condition score loss was defined as BCS at calving minus postpartum BCS. Heritabilities and correlations were estimated with a series of bivariate animal models with average-information REML. Herd-year-season effects and age at calving were included in all models. The length of the prior calving interval was included for all second lactation traits, and all nonproduction traits were analyzed with and without mature equivalent milk as a covariable. Initial correlations between BCS and BCSL were obtained using BCSL and BCS observations from the same cows. Additional genetic correlation estimates were generated through relationships between a group of cows with BCSL observations and a separate group of cows with BCS observations. Heritability estimates for BCSL ranged from 0.01 to 0.07. Genetic correlation estimates between BCSL and BCS at calving ranged from -0.15 to -0.26 in first lactation and from -0.11 to -0.48 in second lactation. Genetic correlation estimates between BCSL and postpartum BCS ranged from -0.70 to -0.99 in first lactation and from -0.56 to -0.91 in second lactation. Phenotypic correlation estimates between BCSL and BCS at calving were near 0.54, whereas phenotypic correlation estimates between BCSL and postpartum BCS were near -0.65. Genetic correlations between BCSL and yield traits ranged from 0.17 to 0.50. Genetic correlations between BCSL and days to first service ranged from 0.29 to 0.68. Selection for yield appears to increase BCSL by lowering postpartum BCS. More loss in BCS was associated with an increase in days to first service.

Animals↗
PubMed2001

Heritabilities and correlations among body condition scores, production traits, and reproductive performance.

The objectives of this study were to estimate the heritability of body condition scores (BCS) from producer and consultant-recorded data and to describe the genetic and phenotypic relationships among BCS, production traits, and reproductive performance. Body condition scores were available at calving, postpartum, first service, pregnancy check, before dry off, and at dry off from the Dairy Records Management Systems in Raleigh, NC, through the PCDART program. Heritabilities, genetic correlations, and phenotypic correlations were estimated assuming an animal model using average information REML. Herd-year-season effects and age at calving were included in all models. Prior calving interval was included in models for second and third lactations. Analyses that included reproductive traits were conducted with and without mature equivalent milk as a covariable. Heritability estimates for BCS ranged from 0.09 at dry-off to 0.15 at postpartum in first lactation. Heritability estimates ranged from 0.07 before dry-off to 0.20 at pregnancy check in second lactation and from 0.08 before dry-off to 0.19 at first service in third lactation. Genetic correlations between adjacent BCS within first lactation were greater than 0.96 with the exception of calving and postpartum (0.74). In second lactation, adjacent genetic correlations were 1.0 with the exception of calving and postpartum (0.84). Genetic correlations across lactations were greater than 0.77. Phenotypic correlations between scoring periods were highest for adjacent scoring periods and when BCS was lowest. Phenotypic correlations were lower than genetic correlations, i.e., less than 0.70. Higher BCS during the lactation were negatively related to production, both genetically and phenotypically, but the relationship was moderate. Higher BCS were favorably related genetically to reproductive performance during the lactation.

Animals↗
PubMed2001

Computing mating bull fertility from DHI nonreturn data.

Animal model methodology was used to compute yearly measures of relative fertility of Holstein AI mating bulls based upon 70-d nonreturn of first breedings as reported to U.S. DHIA from 1988 through 1997. Estimated Relative Conception Rates (ERCR) were computed for bulls with a minimum of 50 first breedings in a single year using variance ratios 45.5 for mating bull, 45.5 for animal genetic effects, and 31 for permanent environment. The model assumed repeatability across lactations of 0.05 and included fixed effects of herd-year-month bred and classes of parity, early lactation energy-corrected milk and days open when bred. Estimates of fertility were greater for breedings to cows that were young, had low early lactation production, and were in late stages of lactation. ERCR were expressed as difference in nonreturn from the average AI mating bull of herdmates. Values ranged from -18 to +13. For ERCR computed from a minimum of 1000 breedings, 90% were within four units of zero. Early ERCR computed from a few breedings in a single year were tested for ability to predict later ERCR computed from a minimum of 1000 different breedings. Early ERCR computed from 300 or more matings accurately predicted later independent ERCR. For yearly estimates each based upon a minimum of 1000 breedings, 8% changed more than three units, and 4% declined more than three units. Correlations between ERCR and predicted transmitting abilities protein and type production index were significant but accounted for little variance. Correlations between ERCR and other traits were not significant.

Age Factors↗
PubMed2001

Relationship of somatic cell score with fertility measures.

Dairy Herd Improvement data from 284,450 cows in 37 states were used to examine the relationship of test-day somatic cell score, herd, calving year, parity, lactation stage, and calving ease score with fertility measures (rate of nonreturn to estrus by 70 d after first service, days to first service, and days open) for US Holsteins and Jerseys. Factors other than somatic cell score were examined to ensure that the estimation of the effect of somatic cell score was independent of other effects. Nonreturn rates were highest during April and May and lowest during June. Parity had a large effect on nonreturn rate, which was 6 to 7% higher for first parity than for sixth parity and later. Effect of lactation stage at first service on nonreturn rate was large; nonreturn rate increased by 8 to 13% from early to late lactation. Effect of calving ease score on nonreturn rate also was large: a 7% decline in nonreturn rate from score 1 to 5. For Holsteins, a small linear regression was found for nonreturn rate on preceding test-day somatic cell score, but this relationship was not significant for Jerseys. The magnitude of the effect of somatic cell score on fertility traits does not warrant postponing first service when somatic cell score is high.

Animals↗
PubMed1999

Comparison of test interval and best prediction methods for estimation of lactation yield from monthly, a.m.-p.m., and trimonthly testing.

A method with best prediction properties that condenses information from all test days into measures of lactation yield and persistency has been proposed as a possible replacement for the test interval method and projection factors. The proposed method uses previously established correlations between individual test days and includes inversion of a matrix for each lactation. Milk weights that were representative of monthly, a.m.-p.m., and trimonthly test plans were examined to compare the accuracy of best prediction and test interval methods for estimating lactation yield. Individual milk weights or daily yields of 658 Canadian cows in 17 herds were selected to correspond to test intervals for 100,000 US cows. For a.m.-p.m. testing, the initial milk weight that was credited was selected randomly from the a.m. or p.m. milking and was alternated thereafter. Trimonthly credits were from one of the first three designated test day weights, selected randomly, and each third designated test weight thereafter. Correlations between 305-d actual lactation yield and lactation estimates by the test interval method were 0.97, 0.96, and 0.93 for monthly, a.m.-p.m., and trimonthly testing, respectively. Corresponding correlations for the best prediction method were 0.97, 0.97, and 0.93. Standard deviations of differences between estimated and 305-d actual yields for monthly, a.m.-p.m., and trimonthly testing were 373, 400, and 546 kg, respectively, for best prediction regressed on herd mean, which was a reduction in estimation error of 4, 6, and 10% over the test interval method. The advantage of best prediction was moderate if two milk weights were recorded monthly and was larger if testing was less frequent. Advantages also were found for fat and protein yields estimated by multitrait best prediction for records with reduced component sampling.

Animals↗
PubMed1985

Differences in modified contemporary comparison sire evaluations from first and later lactations by breed.

Sire evaluations from three sets of daughter records, first records only (first), later records after firsts (later), and all records (all) by Modified Contemporary Comparison procedures were used to examine differences in first and later lactation evaluations by breed. January 1984 evaluations for milk for 767 Ayrshires, 3,175 Guernsey, 29,498 Holstein, 3,530 Jersey, and 984 Brown Swiss bulls with 10 or more daughters in each set were used. Average differences between evaluations (later minus first) were 36, 0, 6, 35, and 35 kg milk for Ayrshire, Guernsey, Holstein, Jersey, and Brown Swiss bulls. Standard deviations of the difference were 114, 94, 142, 91, and 134 kg, showing considerable sire-to-sire variation in difference. Correlations between evaluations based on first and later records were .84 to .86 for all breeds except .92 for Jerseys. Percent of first lactations culled was correlated .20, .18, .16, .16, and .19 with difference for Ayrshire, Guernsey, Holstein, Jersey, and Brown Swiss, indicating that culling produced larger differences between evaluations for first and later lactations in favor of later evaluations. Prediction of sire evaluation from later records was enhanced by knowledge of sire's age in addition to first evaluation for Guernsey, Holstein and Jersey sires. In these breeds, for a constant first evaluation, and percent culled in first lactation, younger bulls had higher evaluations from later records. This study showed important differences between evaluations from first and later records for all breeds.

Animals↗
PubMed1983

Selecting groups of sires by computer to maximize herd breeding goals.

A computerized linear program, MAXBULL, selects sires for dairymen to obtain maximum Predicted Difference milk while maintaining other goals. Goals are minimum average Predicted Differences fat percent and type, six individual type traits, and maximum average semen price. Individuals traits for bulls are coded +1, 0, -1 according to evaluations from breed associations. Dairymen restrict bulls offered to the program by specifying unacceptable repeatability, Predicted Differences, and price. They further control breed, stud organizations, minimum bull numbers, use of sires with low repeatability (less than 50%) and without type proofs, and maximum use of specific bulls and sons of sires. Goals for individual type are specified as the percentage of cows in the herd needing improvement in each trait. The solution contains at least that percentage of semen from sires coded +1 in each trait. The dairyman receives a count of bulls surviving each of his edits, a reiteration of his goals, a list of recommended units and all information for each sire, averages for all traits weighted by the units recommended, and the expected increase in Predicted Difference milk from relaxing each goal one unit. MAXBULL has led to individualized breeding programs characterized by reasonable goals, balance in emphasis among traits, and stability in goals over time.

Animals↗
PubMed1983

Breeding programs of dairymen selecting Holstein sires by computer.

A computerized linear program (MAXBULL) selects groups of sires to attain or exceed goals of dairymen and maximize weighted average Predicted Difference for milk. Goals for 196 dairymen with Holsteins in the spring of 1982 averaged -.05 Predicted Difference fat percent, .85 Difference type, $17.52 per unit, and percents net superiority 29, 35, 36, 27, 33, and 46 for descriptive traits udder support, rear udder, fore udder, teats, hind legs, and feet. Percent net superiority is the percentage of units from sires having a significant number of superior daughters in a trait, reduced by the percentage of units from sires having a significant number of inferior daughters. Associated Predicted Difference milk, fat, and dollars were 789 kg, 25 kg, and $215. All goals exceeded averages of artificial insemination sires available, and Predicted Difference dollars exceeded Virginia service sire averages by $44 and the 90th percentile sire by $6. Predicted Difference fat percent was the most frequently binding production trait, whereas udder support was the most frequently binding type trait. Predicted Difference fat percent and price were most influential on maximum Predicted Difference milk. Breeding programs of dairymen using bulls from on organization differed from those using bulls from several organizations.

Animals↗