Dairy Herd Improvement Test Research Articles

Survival Rates and Productive Herd Life of Dairy Cattle in the United States

Survival rates and productive herd life were examined for 13.8million US dairy cows that calved from January 1, 1980, through March 2, 2005. Cows that left the herd for dairy purposes or were from herds that discontinued Dairy Herd Improvement testing were excluded from any calculations to prevent underestimation of population longevity. Mean lactation length for cows without subsequently recorded lactations ranged from 205 to 235 d across breed–parity subsets and were 4 to 29 d longer for parities 2 through 7 than for parity 1. Mean survival rates were 73% to parity 2; 50% to parity 3; 32% to parity 4; and 19, 10, 5, and 2% to parities 5 through 8, respectively. The mean number of parities for Holsteins declined from 3.2 for those first calving in 1980 to 2.8 for those first calving in 1994. Mean numbers of parities for other breeds first calving in 1994 were 2.9 for Ayrshires and Brown Swiss, 2.4 for Guernseys, and 3.2 for Jerseys. Breed means for productive herd life (through parity 8) ranged from 28 to 36 mo. All regressions of mean number of parities or mean productive herd life on year were negative. The trend for decline of many of those indicators of longevity slowed or ended after the early 1990s. Between 31 (Jersey) and 39% (Guernsey) of herds were made up of first-calf heifers.

Trends in Calving Ages and Calving Intervals for Dairy Cattle Breeds in the United States

Trends since 1980 for calving age and calving interval, 2 factors that influence herd life, were examined by parity for 5 breeds of US dairy cattle. Calving data were from cows with records that passed edits for USDA genetic evaluations and were in herds that remained on Dairy Herd Improvement test. First-calf heifers calved at progressively younger ages over time, but the age decline was less for later parities because of longer calving intervals. Breed differences for calving age were evident for all parities; current mean age at first calving ranged from 24 mo for Jerseys to 28 mo for Ayrshires. Mean calving age across all parities declined over time for all breeds, primarily because of increased turnover rate, and ranged from 48 mo for Holsteins to 54 mo for Ayrshires. Across parity, annual increase in calving interval was reasonably consistent (0.90 to 1.07 d/yr) for all breeds except Jersey (0.49 d/yr). Within parity, regressions of calving interval on year were generally similar to overall breed trend. Breed means for first calving interval across time ranged from 390 d for Jerseys to 407 d for Brown Swiss.

Bulk Tank Somatic Cell Counts Analyzed by Statistical Process Control Tools to Identify and Monitor Subclinical Mastitis Incidence

The objective of this study was to examine the relationship between monthly Dairy Herd Improvement (DHI) subclinical mastitis and new infection rate estimates and daily bulk tank somatic cell count (SCC) summarized by statistical process control tools. Dairy Herd Improvement Association test-day subclinical mastitis and new infection rate estimates along with daily or every other day bulk tank SCC data were collected for 12 mo of 2003 from 275 Upper Midwest dairy herds. Herds were divided into 5 herd production categories. A linear score [LNS = ln(BTSCC/100,000)/0.693147 + 3] was calculated for each individual bulk tank SCC. For both the raw SCC and the transformed data, the mean and sigma were calculated using the statistical quality control individual measurement and moving range chart procedure of Statistical Analysis System. One hundred eighty-three herds of the 275 herds from the study data set were then randomly selected and the raw (method 1) and transformed (method 2) bulk tank SCC mean and sigma were used to develop models for predicting subclinical mastitis and new infection rate estimates. Herd production category was also included in all models as 5 dummy variables. Models were validated by calculating estimates of subclinical mastitis and new infection rates for the remaining 92 herds and plotting them against observed values of each of the dependents. Only herd production category and bulk tank SCC mean were significant and remained in the final models. High R2 values (0.83 and 0.81 for methods 1 and 2, respectively) indicated a strong correlation between the bulk tank SCC and herd's subclinical mastitis prevalence. The standard errors of the estimate were 4.02 and 4.28% for methods 1 and 2, respectively, and decreased with increasing herd production. As a case study, Shewhart Individual Measurement Charts were plotted from the bulk tank SCC to identify shifts in mastitis incidence. Four of 5 charts examined signaled a change in bulk tank SCC before the DHI test day identified the change in subclinical mastitis prevalence. It can be concluded that applying statistical process control tools to daily bulk tank SCC can be used to estimate subclinical mastitis prevalence in the herd and observe for change in the subclinical mastitis status. Single DHI test day estimates of new infection rate were insufficient to accurately describe its dynamics.

Antimicrobial Treatment of Fresh Cows with Subclinical Gram Positive Mastitis

Subclinical mastitis is a major contributor to the bulk tank somatic cell count (SCC). Dairies with a moderate to high bulk tank SCC and a low clinical mastitis rate can expect to have a number of cows with subclinical mastitis. Using Dairy Herd Improvement (DHI) SCC results on a regular basis, subclinical mastitis can be identified as the high SCC cows. Identifying the high SCC cows is relatively easy on herds that utilize DHI testing on a regular basis. However, not all high SCC cows need treatment nor is it cost effective to treat all high SCC cows. Antimicrobial treatment should be reserved for those cows that treatment is likely to cure. It is generally regarded that these cows are relatively early in lactation with gram-positive, environmental mastitis (Staphylococcus spp and Streptococcus spp) and have not had chronic episodes of clinical mastitis. This study compared the cure rates of fresh cows with subclinical gram-positive mastitis with and without intramammary antimicrobial treatment.

Duration of Herd Participation in Dairy Herd Improvement Milk Recording in the United States

Participation in milk-recording programs that provide data for national genetic evaluations of dairy cattle in the United States is voluntary, but the effectiveness of the evaluation system increases with the number of herds that contribute data. To investigate patterns of herd participation in Dairy Herd Improvement (DHI) testing, periods of continuous testing were computed based on the year that a herd initiated or terminated testing and by geographical region. Continuous testing was defined as at least one test per 6-mo period. Some herds discontinued testing and then re-enrolled. Across all years (1960 through 2002), 65% of herds had one period of continuous testing (no testing lapse). The percentage of herds with testing lapses decreased as the number of lapses increased and as the initial test year became more recent; overall, only 1.5% of herds had more than 6 continuous testing periods. For herds that terminated DHI testing from 1960 through 2002, 64% were on continuous test for <3 yr. In general, herd frequencies decreased as continuous test period increased except for continuous testing of ≥20 yr, which increased to 13% for years 2000 to 2002. Herds with more recent termination dates had remained on continuous test longer, and one-third of herds that were still on test after June 2002 had been on test for at least 20 yr. The duration of herd participation was longest for the northeastern and mideastern United States and shortest for the southeastern United States. Multiple periods of testing with lapses of >6 mo between test periods represent a loss of data that could have enhanced the study and evaluation of genetic characteristics of US dairy cattle.

Relationships Between Milk Acetone and Clinical Metabolic Disease in Dairy Herds

Negative energy balance in the post-partum dairy cow, as indicated by elevated blood and milk ketone levels, has been linked to decreased milk production and increased metabolic disease such as clinical ketosis and displaced abomasum. If groups of cows in negative energy balance could be identified in a cost-effective manner, management could intervene in a timely fashion to correct the problem. A collaborative project involving Foss Electric and Ontario Dairy Herd Improvement (DHI) was established to evaluate a prototype milk acetone analyzer that could test DHI milk samples for the presence of acetone. The objective was to investigate relationships between test-day milk acetone and incidence of clinical ketosis (Ket), displaced abomasum (DA) and lameness (Lame). The final data set included 199 herds, which had all milking cows tested for acetone on every DHI test date from January to September 1999. Included were 60,257 test day samples from approximately 14,000 cows. These herds recorded all occurrences of DA, Ket and Lame.

A Prediction of Daily Milk Yield and its Composition from the Morning or the Evening Milking Records

In the present system of Dairy Herd Improvement Test in Japan, it is necessary to measure milk yield and to take samples both in the morning and in the evening of the day in the presence of a supervisor. In the alternate test method (AT or AM-PM method), daily milk yield and composition were estimated from either the morning or the evening milk. In this study, a multiple regression equation was fitted to estimate daily milk yield and its fat, protein and Solid-Not-Fat (SNF) concentration with three or more variables in the morning milking records. When the milk yield, protein or SNF of a day was made into an objective variable and three or more independent variables relevant to morning milk were included in the equations, 0.95+ coefficient of determination was obtained. Daily milk yield and its protein and SNF were estimated with sufficient accuracy from evening milk as well as morning milk. Validations of the regression equations were performed. The coefficient of determination of the predicted values for daily milk yield, its protein and SNF from the morning milk was 0.95 +. In the same way, 0.95 or more was the coefficient of determination of the predicted values of daily milk yield, protein, and SNF from the evening milk. The differences between the predicted values and the actual measurement values of daily milk yield from morning or evening milk were distributed within 2 (kg) range. The differences in protein and SNF were less than 0.1% in many cases.

Effect of Dietary Yeast Culture on Milk Yield, Composition, and Component Yields at Commercial Dairies

A field trial was conducted to evaluate the effect of dietary yeast culture on milk yield, composition, and component yields at 11 commercial dairies in Wisconsin. Rolling herd averages ranged from 10,000 to 12,800kg per cow. Yeast culture was added to total mixed rations to allow an average daily intake of 57g over two 30-d Dairy Herd Improvement (DHI) test periods. Production data from the 30-d DHI test periods immediately before and after the yeast culture feeding period were used as the control. There were 585 cows (245 primiparous and 340 multiparous) that completed the four 30-d period feeding sequence (OFF-ON-ON-OFF). Average days in milk were similar for the yeast and control feeding periods (140.0 vs 140.5, respectively). Dietary yeast culture increased (P<0.001) actual and 150 d adjusted milk yields 0.90kg/d. Milk fat content was lower (P<0.001) for cows fed yeast culture (3.55 vs 3.65%), but milk fat yield was similar. Dietary yeast culture reduced (P<0.05) milk protein content 0.02 percentage units, but protein yield was higher (P<0.01) for cows fed yeast culture (1.17 vs 1.14 kg/d). The milk yield response was positive in 8 of 11 herds (herd by treatment interaction; P<0.001). These data indicate that under the conditions of this study the addition of yeast culture to total mixed rations for mid lactation dairy cows in high producing commercial herds increases milk and protein yield.

Relationships of milk culture status at calving with somatic cell counts and milk production of dairy heifers during early lactation on a Californian dairy

Four-quarter, composite milk samples were collected from 339 heifers calving for the first time in a large Californian dairy which consistently had low herd somatic cell counts and low prevalence of major mastitis pathogens. The milk samples were collected on average at 6.4 days post partum (range 1–17). Thirty-nine percent of the heifers were subclinically infected with coagulase-negative Staphylococcus spp. Other results were: no growth, 29%; coliform only, 16%; Streptococcus spp. only, 11%; Streptococcus spp. and coagulase-negative Staphylococcus spp. only, 4%; others, 1%. The somatic cell counts and milk production were not significantly different ( P > 0.05) between culture groups over the first five monthly Dairy Herd Improvement test periods. Somatic cell counts decreased significantly after the first test period within the Streptococcus spp. group ( P < 0.002) and Staphylococcus spp. group ( P < 0.01). Milk production was higher during most of the second and third test periods ( P < 0.03) compared with the first period. The frequency of heifers lost to analysis was not different between the culture groups. Results from this dairy indicate that subclinical infections with minor pathogens (primarily coagulasenegative Staphylococcus spp.) had no significant effect on average somatic cell counts or milk production during early to middle lactation.

Cytokines and tumours: problems and perspectives

Somatic cell count (SCC) is used as key indicator in mastitis screening programs typically applied in the frame of Dairy Herd Improvement (DHI) testing programs. However, mastitis is still causing tremendous economic losses to the dairy industry. Hence, new biomarkers are needed that can be used for enhanced management of mastitis on dairy farms. Besides the determination of SCC, differentiation of cells has been described to be beneficial for a more definite description of the actual udder health status of dairy cows. The aim of this study was to develop a new method for rapid and simultaneous determination of SCC and a new parameter, differential somatic cell count (DSCC), in individual cow milk samples using flow cytometry. The method is sought to be applied in central milk testing laboratories, so that existing DHI infrastructures can be used. The DSCC represents the combined proportion of polymorphonuclear leukocytes (PMN) and lymphocytes expressed in percentage. The proportion of macrophages can be calculated by subtracting DSCC from 100%. Our research revealed increasing proportions of PMN, but decreasing proportions of macrophages as SCC increased. However, lymphocytes occurred fairly constantly with low proportions across the entire SCC range. Hence, the DSCC parameter reflects the antidromic trend of PMN and macrophages. Fluorescence microscopy was used to evaluate the specificity of the new Foss DSCC method in terms of DSCC and a high correlation was found. Apart from that, the accuracy of cell differentiation using the Foss DSCC method was confirmed in a cell sorting trial. Total SCC could be determined equally well using the new method as compared with existing methods. The new method was further proven to be robust toward a range of method and milk-sample-related factors. In an initial field trial, regular DHI samples of a local dairy herd were analyzed. The DSCC values occurred in a broad range from 34 to 79% in samples with <400,000 cells/mL. Higher DSCC values (53–89%) were found in samples with >400,000 cells/mL. In conclusion, the new Foss DSCC method allows reliable, repeatable, fast, robust, and accurate determination of both DSCC and SCC at low cost. This, in turn, provides more accurate information on the actual udder health status of dairy cows. The practical application of DSCC in the frame of DHI testing programs, however, needs further investigation.

Use of Dairy Herd Improvement Testing Programs by Dairy Farmers1,2

A questionnaire designed to characterize how farmers use their dairy records was sent to 1178 Ohio dairy farmers who had been on test from 1976 through December 1983. Results from 660 (56%) usable returns were compared with herd performance measures and management information from the monthly herd reports. Monthly progress reports were the most important reason for dairy farmers’ participation in a testing program. Records were particularly important for use in sales and advertising to owners of purebred cattle and for those with higher herd averages. Farmers who used records for feeding decisions had higher herd averages, services per conception, and percent days in milk and lower age of cow at last calving and days at first service. Farmers who used records as a guide to culling had lower average age at last calving, days at first service, and calving interval and higher services per cow and culling percentage. Herds in which somatic cell count information was used had lower ages at first and last calving, higher services per conception, and higher percentage of cows with low somatic cell counts. In general, farmers making greater use of the information had higher production per cow and above average herd performance measures.

Estimation of Genetic Parameters for Stayability in Canadian Holsteins

Hazard functions were plotted and troughs were used to define Stayability traits that best coincided with the time of culling decisions. Traits evaluated were Stayability to 17, 30, 43, and 55 mo of productive life and Stayability to 42, 54, 66, and 78 mo of total life. Holstein data from Record of Performance and supervised Dairy Herd Improvement and Dairy Herd Analysis Service testing programs formed two data sets with 243,348 and 118,362 daughters of 10,723 and 4,583 sires, respectively, and were used to estimate Stayability heritabilities and correlations. Variance components were estimated by Henderson's new method using a model that included a fixed herd-year-season of first calving effect and random sire and error effects. Heritabilities for Stayability to 17, 30, 43, and 55 mo of productive life were .025, .040, .039, and .033, respectively, for the Record of Performance data and .010, .017, .023, and .033, respectively, for the Dairy Herd Improvement-Dairy Herd Analysis Service supervised data. Heritability estimates for Stayability to 42, 54, 66, and 78 mo of total life were .051, .040, .045, and .059, respectively, for the Dairy Herd Improvement-Dairy Herd Analysis Service supervised data. Genetic correlations among productive life traits were usually very high and approached unity. Genetic correlations among total life traits were lower and more variable. Most phenotypic correlations were moderately high but some were low. Stayability to 17 mo of productive life was concluded to be the best trait for incorporation into a selection program if evaluation of sires for longevity is desired.

Effect of Parity, Stage of Lactation, and Intramammary Infection on Concentration of Somatic Cells and Cytoplasmic Particles in Goat Milk

Aseptic foremilk samples (about 15ml) were collected biweekly for four samplings and then monthly throughout lactation from 35 does in three herds on Dairy Herd Improvement test. Bacteriology, direct microscopic somatic cell counts, and cytospin differential cell counts were performed on all samples. Milk yield was obtained from monthly Dairy Herd Improvement records. Thirty-nine percent of udder halves were infected, and coagulase negative staphylococci were the most frequent isolates (201 of 205). Somatic cell counts were influenced by lactation number, stage of lactation, and intramammary infection. Also, infection of one udder half increased somatic cells in the corresponding uninfected mammary half of the same animal. Cytoplasmic particles were not affected by stage of lactation or by intramammary infection but were greater for goats in first lactation. Milk yield was influenced by lactation number, stage of lactation, and intramammary infection.

Grouping Lactating Cows by Nutritional Requirements for Feeding

A method (cluster) of grouping cows simultaneously by requirements of crude protein percentage and megacalories net energy per kilogram expected dry matter intake was developed and compared to grouping by test day milk, fat-corrected milk, and dairy merit (fat-corrected milk/body weight.75). One year of monthly Dairy Herd Improvement test day records from 80 Holstein herds chosen from four herd sizes yielded 110,264 records from 941 herd test days. Cows in each herd on each test day were ranked and allocated by each of the four methods to two groups of equal size. The cluster method used standardized concentrations of protein and energy to minimize variation within groups. It grouped 25, 22, and 15% of the cows differently from milk, fat-corrected milk, and dairy merit methods. Displacement of cows in rank within herd averaged 17, 15, and 11% of possible misranking. Cows in first lactation less than 46 days were placed in the high group with a frequency of 98, 49, 48, and 64% for cluster, milk, fat-corrected milk, and dairy merit methods. Intraclass correlations among cows in the same group were .66, 49, .54, and .61 for protein percentage and .61, .28, .34, and .42 for net energy concentration. Average variation of net energy concentration in the low cluster groups was half that in the low groups from other methods. The cluster procedure was effective for grouping cows homogeneously by nutritional requirements.

Factors Affecting Milk Yield and Reproductive Performance

Dairy Herd Improvement testing records of 201 dairy herds of sizes from under 100 to over 1000 cows and herd average milk yields from under 5,000 to over 10,000kg were studied. Average days to first postpartum breeding tended to be less in herds of over 500 cows. Herds of 300 to 600 cows had highest production per cow. Herds with higher average yields averaged shorter intervals to first postpartum breeding and fewer days open. Days open included farrow cows arbitrarily assigned 305 days open.Number of breedings changed little as herd yield increased, but days open for highest producing herds averaged one estrous period shorter than for low producing herds, suggesting better detection of estrus.For the individual cow, high yield or associated factors have a small but real antagonistic association with reproductive efficiency. Days to first breeding, to last breeding, and days open increased .27, .80, and .61, and number of breedings increased .014 for each 100kg increase in 180-day yield of fat-corrected milk. The record averages of high-producing herds indicate this antagonism may be over shawdowed by good management; effective estrus detection probably is a major factor.

Components of Variance for Milk and Fat Yields in Dairy Goats

Age-season adjusted records from five breeds of dairy goats on Dairy Herd Improvement test from 1965 to 1976 were used to estimate components of variance for milk yield, fat yield, and fat percentage. The data were 6,452 Alpine, 1,730 LaMancha, 6,897 Nubian, 2,759 Saanen, and 4,007 Toggenberg lactation records. Components of variance were estimated by Henderson's Method 1 with a four-way model that included random effects of herd, year-season, sire, and doe. Herd effects accounted for 22 to 31% of the total variation in milk and fat yields and 15 to 25% of the variation in fat percentage. These effects were large enough to indicate that they must be considered in genetic evaluations of milk and fat production. Sire components of variance were 8 to 10% of the total variation in milk yield, fat yield, and fat percentage. Does accounted for 16 to 25% of total variation in milk yield, fat yield, and fat percentage. Repeatabilities of milk yield, fat yield, and fat percentage varied from .39 to .55 whereas heritabil-ities also within herd-year-season ranged from .48 to .62. Heritabilities larger than repeatabilities indicate that confounding may have inflated sire effects. Phenotypic and genetic correlations between milk and fat yields averaged .94 and .86. Milk yield and fat percentage were correlated negatively both phenotypically and genetically, whereas genetic correlations between fat yield and fat percentage averaged .18.

Comparison of Two Systems for Group Feeding Dairy Cows ,

Jersey cows on a one-group feeding system (Treatment I) were compared to those on a two-group system (Treatment II) over 3 yr. Those on Treatment I were group-fed concentrates at 7.3kg per cow per day during the entire lactation regardless of yield. Cows on Treatment II were allotted to high and low subgroups on production of fat-corrected milk on a single day (Dairy Herd Improvement test day). Both subgroups were group-fed concentrates at the same rate (1kg per 2.25kg fat-corrected milk). Silage and hay allowances were constant for both groups throughout lactation. Except for gradual adjustment to concentrate feeding beginning 2 wk prepartum, nonlactating cows were allowed roughage only. The groups did not differ significantly in actual milk production, fat-corrected-milk production, body weight change, health, or reproductive performance. Milk fat percentage was higher for cows on the one-group system.

Milk Production Response to Shifting Cows Between Intraherd Groups

Seven dairy herds, each divided into two or more groups of lactating cows, were studied for effects of shifting between groups on production. Herds averaged 140 cows and from 4858 to 7037kg milk. Treatments were shifting 2, 4, 6, 8, 10, 12, or 14% of milking cows between production groups within 1 wk after regular Dairy Herd Improvement test. Milk production, butterfat percent, days in milk, cow age, and body weight were obtained from test. Second test-day milk weights were normally recorded third and fourth milkings following cow shift.Multivariate analysis of 6731 cow observations showed differences in actual and percent changes in milk after shifting for herds, months, treatments, groups, group by treatment interaction, days in milk, and butterfat percent Milk decreased .53±3.11kg or 2.28% between test days. Cows shifted declined .99kg while those remaining in the high group declined .33kg. Initially production decreased most in those herds that began shifting at the start of this study. Decreased losses in subsequent months indicated possible adaptation.

Use of the Fossomatic Somatic Cell Counts in a Mastitis Control Program

The Fossomatic determination of somatic cells present in individual cow milk samples was instituted in the Virginia Dairy Herd Improvement testing program in June, 1976. During the first 6 months the average count was 449,060 cells/ml for 113,735 observations. The average percentage of cows according to ranges in cell counts was: under 150,000, 47.9%; 150–400,000, 25.8%; 400–800,000, 12.5%; 800–1,500,000, 7.2%; and over 1,500,000, 6.6%. The percentage of cows with less than 150,000 cells/ml was reduced for September, 1976. On a herd basis, 66% averaged less than 500,000 cells/ml, 30% between 500 to 1,000,000, and 4% exceeded 1,000,000. The percentage of herds with less than 500,000 cells/ml decreased from June to September, 1976 but increased in October. Quarters infected with primary pathogens were estimated by Westgarth's equation to be 10.4% resulting in a calculated 5% reduction in total herd milk production. Procedures are described which would assist dairymen in the interpretation of somatic cell counts.

Regression of Daughter and Herdmate Milk Yield on Genetic Value of the Herdmates’ Sires

The effect that average genetic value of the herdmates’ sires has on daughter deviation from herdmate average and consequently upon sire summaries was examined. Lactation records from a national sample of herds from five breeds on official Dairy Herd Improvement testing were used. Regressions within sire, within year of calving of a) daughter yield, b) various herdmate averages, and c) daughter deviations from herdmate averages on average Predicted Differences of the herdmates’ sires were calculated.As average Predicted Difference for herdmates’ sires in artificial insemination (AI) increased, so did first lactation daughters’ yield (b = .35 to .73) and AI herdmate average (b = 1.34 to 1.78), but the daughters’ deviation from AI herdmate average decreased (b = −.72 to −1.14). Standard errors showed that these latter regressions were not different from minus one. Regression using artificial insemination contemporaries in both first and second lactation gave results similar to those expected. These results indicate that differences between bulls in the genetic value of herdmates affect present sire summary procedures and cause some misrankings among bulls. These regressions provide evidence that adjusting for the average genetic value of the herdmates’ sires would significantly increase the accuracy of herdmate comparison sire summaries.