Australian Dairy Herds Research Articles

Feeding management and feeds on dairy farms in New South Wales and Victoria

Feeding practices in Australian dairy herds were recorded in 100 dairy herds in five districts of two states. A questionnaire about the feeding practices was completed and pasture samples were also collected, where applicable, for analysis. Data and pasture samples were collected once from each farm with visits to regions occurring at different times of the year. Diets were evaluated for nutritional adequacy using the CPM Dairy program. Average milk yield on the day of sampling was 22.8 L/day. The combination of grazed pasture with grain fed during milking was the most prevalent feeding system (54%) in all areas. This was followed by combination of pelleted grain/by-products combined with pasture grazing (25%). Only one herd in the study was not feeding any form of concentrates at the time of sampling. The estimated percentage of concentrate in the diet ranged from 25% ± 11.6 to 44% ± 12.0. Wheat, which was fed at up to 9.8 kg/cow.day DM was the most prevalent grain in all areas, except for Gippsland. The predominant sources of protein in all areas were canola meal, cottonseed meal and lupins. By-products were prevalent, with brewers grain and wheat millrun the most commonly used, fed at 2.8 and 1.6 kg/cow.day DM, respectively. Most farms (81/100) incorporated at least one type of ‘buffer’ in the ration, and limestone (67%) was the most prevalent mineral additive. Monensin and virginiamycin were fed in all areas, with a varying prevalence. Feeding or dose rates used for minerals and rumen modifiers were not always appropriate to those recommended for mineral needs or control of rumen function. This paper demonstrated that a wide variety of feeding systems are used in Australian dairy herds and provides information on nutritive characteristics of pastures.

Improved Prediction of Ovulation Time may Increase Pregnancy Rates to Artificial Insemination in Lactating Dairy Cattle

A prospective observational study was conducted in two Australian dairy herds to assess the potential for improving pregnancy rates (proportions of inseminations that result in pregnancy) to artificial insemination (AI) if the time of ovulation could be predicted with more certainty. Herd 1 calved year-round and inseminations were performed during two periods each day. Herd 2 calved during autumn-winter and inseminations were performed only after the morning milking each day. In both herds, the AI to ovulation interval of enrolled cows was determined by trans-rectal ovarian ultrasonography approximately 0, 12, 24 and 36 h after AI, and pregnancy was assessed by palpation per rectum 35-56 days after AI. Also, in Herd 1 vaginal electrical resistance (VER) measurements were taken at approximately 0, 12, 24 and 36 h after AI, and in Herd 2 cows were fitted with neck mounted activity meters that monitored cow activity count in 2-h periods. There was substantial variation in the intervals from AI to ovulation within and between herds (mean ± SD 21.2 ± 10.7, n = 102; 14.7 ± 10.4, n = 100 in herds 1 and 2, respectively). Pregnancy rates were higher for inseminations close to, but preceding, ovulation. Using combined herd data (n = 202), the highest pregnancy rate (50.8%) was observed for inseminations between 0 and 16 h before ovulation, a period in which only a modest proportion of inseminations (31.2%) occurred. In contrast, pregnancy rate was significantly lower (28.7%; risk ratio 0.6; 95% CI 0.4-1.0; p = 0.039) for inseminations between 16 and 32 h before ovulation, a period where the highest proportion of inseminations (53.2%) occurred. Thus pregnancy rates could potentially be improved if a greater proportion of inseminations were conducted shortly before ovulation. In Herd 1, mean VER during the peri-ovulatory period varied with time from ovulation. Lowest values (mean ± SEM, VER = 64.8 ± 1.2, n = 55) occurred approximately 18 h before ovulation and were significantly lower than measurements approximately 6 h before ovulation (67.4 ± 1.0; n = 73; p = 0.003). Further work is required to determine if VER can be used to identify ovulation time and hence the optimal time to inseminate in individual animals. In Herd 2 a modest proportion of inseminations (26.9%) occurred between 24 and 40 h after the onset of increased cow activity where the highest pregnancy rate (67.9%) was observed, whereas a significantly lower pregnancy rate (42.4%; risk ratio 0.6; 95% CI 0.4-0.9; p = 0.036) was observed for inseminations between 8 and 24 h after the onset of increased cow activity where the highest proportion of inseminations (56.7%) occurred. Thus cow activity monitoring may be useful to identify the optimal time to inseminate cows. Results from this study indicate that improved methods of ovulation prediction may allow better insemination timing relative to ovulation and consequently increased pregnancy rates.

Interrelationships between herd-level reproductive performance measures based on intervals from initiation of the breeding program in year-round and seasonal calving dairy herds

In year-round calving herds, reproductive performance has traditionally been described in relation to each cow's calving date. This research described reproductive performance in year-round and seasonal calving dairy herds using herd-level measures based on interval from each cow's initiation of breeding program date, and assessed interrelationships between such measures. A large, prospective, single cohort study, implemented in 1997 and 1998, included 29,327 cows from 167 Australian dairy herds. Herd reproductive performance was described using 2 measures of primary importance to herd managers: the proportion of cows that became pregnant by 6wk after their initiation of breeding program date (6-wk pregnancy rate) and the proportion of cows that were nonpregnant 21wk after their initiation of breeding program date (21-wk nonpregnancy rate). Measures that contribute to these primary measures (secondary measures) were calculated for each herd for both the first and second 3-wk periods of each cow's breeding program; submission rates were calculated as proportions of cows that were inseminated at least once in the 3-wk period, and conception rates were calculated as the proportions of inseminations in the 3-wk period that resulted in pregnancy. The individual herd was the unit of analysis. The study results indicate that high submission rates are essential if herd reproductive performance is to be achieved. Six-week pregnancy rate was predicted to increase by 6 to 8 percentage points following a 10-percentage-point increase in submission rates in both 3-wk periods, and by 6 to 10 percentage points following a 10-percentage-point increase in conception rates. Submission rates were more variable than conception rates, indicating that managers may be able to achieve large increases in submission rates more easily than substantial increases in conception rates. However, the predicted benefits of increasing submission rates were greatest when conception rates were high and vice versa, highlighting the need to improve both submission and conception rates when both are low. The study results indicate that some herd managers can concurrently achieve high submission and conception rates.

Preliminary genetic analyses of voluntarily supplied disease data in Australian dairy herds

Health disorders data collected from Australian dairy herds were analysed to estimate incidence levels and genetic parameters for some diseases traits and to examine the consequence of selection for Australian profit ranking (the economic index in Australia) and other economic traits on incidence of some diseases. Data on disease problems including udder, leg, reproductive and ‘all disorders’ were extracted from the Australian Dairy Herd Improvement Scheme database for cows that calved in 2007 and 2008 and were analysed fitting a linear sire model. The occurrence of disease problems was as high as 0.3 if ‘all disorders’ were put into one group. Five percent of the cows that calved showed leg problems and the incidence of udder problems was ~0.13. The heritability of all disease problems ranged from 0.01 for reproductive problems to 0.03 for udder problems. The results showed that selection on milk yield traits will likely increase the frequency of all health disorders. Although selection on udder depth and somatic cell count helps to mitigate the possible genetic deterioration in udder problems, it is unlikely to avoid it totally. Therefore, more effort to collect and analyse health event data from Australia dairy herds could help to better design genetic and environmental measures to reduce the consequence of health problems.

Calculation of lifetime net income per year (LTNI/year) of Australian Holstein cows to validate the Australian profit ranking of their sires. 2. Validation of the Australian profit ranking of sires and test for genotype by environment interaction based on LTNI/year

In Australia, the Australian Dairy Herd Improvement Scheme publishes the Australian profit ranking (APR), which is an economic index combining all economic traits. The APR is used to select sires that will breed the most profitable daughters. The objectives of this study were to test the predictive power of the APR by looking at the relationship between the APR of sires and lifetime net income per year (LTNI/year) of their future daughters and to test if this prediction was accurate in different types of herds. The importance of re-ranking was also tested by considering LTNI/year of cows in different herds as different traits and estimating the genetic correlation between LTNI/year in different herd types (herds differing in calving systems, region or production level). An additional objective was to test the value of individual trait Australian breeding values (ABV) of sires to predict LTNI/year of their future daughters. The results show that overall the relationship between the APR of bulls and the LTNI/year of their daughters was as expected. That is, the regression of daughter’s LTNI/year on sire’s APR equals 0.5 as expected because cows inherit half their genes from their sire. When LTNI/year of cows was regressed on sire’s APR separately for different herd types, the regression coefficients varied from 0.36 (low production herds) to 0.62 (high production herds). The genetic correlation for LTNI/year between herd types was always high indicating little re-ranking of sires. The lowest was 0.89 between LTNI/year of cows in seasonal and year-round calving systems as well as between Regions 3 (Gippsland and Tasmania) and Region 1 (New South Wales, Queensland, South Australia and Western Australia) herds. Thus, an APR calculated across all herd types will give a good ranking of bulls in any herd type. When LTNI/year of cows was regressed on individual sire ABV the regression coefficients were approximately half the economic weight for each trait as expected. Within seasonal calving and low production herds the regression on sire’s fertility ABV was higher than expected and the regression in year-round and high production herds was less than expected. In conclusion, over all herds the sire’s APR predicts LTNI/year of future daughters as expected. Owners of low production, seasonal calving herds will get slightly less benefit from 1 unit increase in APR than owners of high production, year-round calving herds and could perhaps benefit from placing additional emphasis on the fertility ABV when selecting sires.

A genome-wide association study of tick burden and milk composition in cattle

To study the genetic basis of tick burden and milk production and their interrelationship, we collected a sample of 1961 cattle with multiple tick counts from northern Australia of which 973 had dairy production data in the Australian Dairy Herd Information Service database. We calculated heritabilities, genetic and phenotypic correlations for these traits and showed a negative relationship between tick counts and milk and milk component yield. Tests of polymorphisms of four genes associated with milk yield, ABCG2, DGAT1, GHR and PRLR, showed no statistically significant effect on tick burden but highly significant associations to milk component yield in these data and we confirmed separate effects for GHR and PRLR on bovine chromosome 20. To begin to identify some of the molecular genetic bases for these traits, we genotyped a sample of 189 of these cattle for 7397 single nucleotide polymorphisms in a genome-wide association study. Although the allele effects for adjusted milk fat and protein yield were highly correlated (r = 0.66), the correlations of allele effects of these milk component yields and tick burden were small (|r| ≤ 0.10). These results agree in general with the phenotypic correlations between tick counts and milk component yield and suggest that selection on markers for tick burden or milk component yield may have no undesirable effect on the other trait.

Evaluation of a Neck Mounted 2-Hourly Activity Meter System for Detecting Cows About to Ovulate in Two Paddock-Based Australian Dairy Herds

Two studies were conducted to assess the performance of a commercially available neck-mounted activity meter to detect cows about to ovulate in two paddock-based Holstein-Friesian dairy herds. The activity monitoring system recorded cow activity count in 2-hourly periods. Study I investigated the ability of the system to detect cow ovulatory periods in dairy herds managed in two different Australian environments and breeding systems using five activity alert algorithms. Herd 1 consisted of approximately 130 milking cows calving year-round in a sub-tropical environment and kept in a single dry lot paddock. Herd 2 consisted of approximately 400 milking cows calving seasonally in a temperate climate and fed pasture by rotation through multiple grazing paddocks. Ovulatory periods and non-ovulatory days were identified using milk progesterone monitoring alone or in combination with ovarian ultrasonography; using these 'gold standards' 141 and 135 ovulatory periods were identified in 64 and 135 cows in Herds 1 and 2 respectively. Sensitivity of the activity monitoring system for detecting cow ovulatory periods ranged from 79.4% to 94.1%, specificity from 90.0% to 98.2% and positive predictive value from 35.8% to 75.8%. Study II investigated the ability of the activity meter system to predict the timing of ovulations in paddock-based pasture-fed dairy cattle (Herd 2). The time of ovulation was estimated by repeat trans-rectal ovarian ultrasonography at approximately 0, 12, 24 and 36 h after artificial insemination (AI). The mean times (± SD) from onset and end of increased activity to ovulation were 33.4 ± 12.4 and 17.3 ± 12.8 h respectively (n = 94). Fifty per cent of cows (n = 47) ovulated within the 8-h period between 30 to 38 hs after the onset of increased activity, 76.6% (n = 72) within the 16 h between 24 to 40 h, 85.1% (n = 80) within the 24 h between 18 and 42 h and 90.4% (n = 85) within the 32 h from 19 to 51 h after the onset of increased activity. Results from these studies show that in paddock-based dairy cows in two diverse management systems, this neck-mounted activity meter system detects high proportions of cows that are about to ovulate and provides a useful indication of when ovulation is likely to occur. However, the specificities and positive predictive values using the algorithms assessed may be lower than desirable.

A national programme for mastitis control in Australia: Countdown Downunder

In 1998, Countdown Downunder, Australia's national mastitis and cell count control programme, was created. With funding from the country's leading dairy organisation, Dairy Australia, this programme was originally intended to run for three years but is now in its tenth year. As it was the first time Australia had attempted a national approach to mastitis control on the farm, the first three years of the programme were largely concerned with the development of resources to be used by farmers and service providers. The second three years were devoted to training with both groups. Since that time, Countdown Downunder has entered into a second resource development phase. The goal of the programme was to achieve a reduction in the bulk milk somatic cell count from the Australian dairy herd. To achieve this, the programme had to develop resources with clear and consistent messages around mastitis and somatic cell count control on farms. It was determined that progress toward the goals would be made more rapidly if service providers were trained in the use of these resources prior to farmers. This paper reviews the Countdown Downunder programme from 1998 to 2007.

Effect of data collection methods on the availability of calving ease, fertility and herd health data for evaluating Australian dairy cattle

There is concern in the Australian dairy industry that the fertility, calving ease and disease resistance of cows is declining and that this decline is, at least in part, a genetic change. Improvement in these traits might be achieved through better herd management and genetic selection. Both these strategies are dependant on the availability of suitable data. The Australian Dairy Herd Improvement Scheme publishes estimated breeding values for fertility, calving ease and somatic cell count. However, the accuracy of the estimated breeding values is limited by the amount and quality of data collected. This paper reports on a project conducted to identify a more efficient system for collecting non-production data, with the hypothesis that quantity and quality of data collected would improve if farmers used electronic data collection methods instead of ‘traditional’ methods, such as writing in a notebook. Of 78 farmers involved in the trial, 51 used a PALM handheld (PALM group), 18 wrote data on paper and later entered it in their farm computer (PC group) and nine submitted a paper record to their data processing centres for entry into the centres’ computers (PAPER group). Data collected from these 78 trial herds during the trial period (2002–04) were compared to data collected from 88 similar non-trial farms, which kept records on PC or paper. The ratio of number of events (health, calving ease or fertility) recorded to number of calvings was considered as a measure of level of recording. The results showed that, after adjusting for location and level of recording before the trial started, the PALM group collected significantly more calving ease, pregnancy test and other fertility data per calving than farmers who were not involved in the trial and PAPER and PC groups. The number of records collected by the PALM group increased from 0.13 pregnancy tests in 2001 to 0.36 per calving in 2004, whereas there was little change in the amount of data collected by the other groups. Similarly, the number of calving ease records increased from 0.26 in 2001 to 0.33 in 2004 and the number of heats recorded increased from 0.02 in 2001 to 0.12 in 2004. This increase in data capture among farmers using the PALM was partly due to an increase in the number of farmers who submitted any data at all. For instance, of the PALM group, 86% sent data on calving ease and 61% on pregnancy, as compared to those from the PC and PAPER groups (below 57%) or those who were not involved in the trial (below 44%). When farmers who at least submitted one record of each type of data are considered, farmers in the PALM group still submitted significantly more fertility event data than those who were not involved in the trial and those in the PAPER group. The quality of the data did not appear to be affected by the data collection methods, though the completeness of the mating data was better in PALM and PC users. The use of electronic data entry on farms would increase the amount of data available for the calculation of estimated breeding values and hence the accuracy of these values for fertility, calving ease and health traits.

The use of a hand-held conductivity meter for the diagnosis of subclinical mastitis in dairy cows during late lactation

AIM: To evaluate the accuracy of a hand-held electrical conductivity meter for the detection of subclinical mastitis in an Australian dairy herd in late lactation. METHODS: A hand-held conductivity meter was evaluated during late lactation in a herd of dairy cows that had a high prevalence of Streptococcus agalactiae and Staphylococcus aureus infection. The ability of the conductivity meter to accurately discriminate between uninfected quarters and those infected with major pathogens was assessed, using bacteriology as the definitive test for infection status. Milk samples for bacteriology, and electrical conductivity measurements, were collected from 233 quarters from 59 cows. The ability of the device to identify infected and uninfected cows was also assessed. Sensitivity and specificity were calculated for a range of threshold values, using absolute values, the range of quarter values within a cow and the ratio of quarter values within a cow. RESULTS: Electrical conductivity was higher in infected quarters than uninfected quarters, but the degree of overlap between the populations was substantial, even when quarters were assessed as case-control pairs from the same cow. Use of the device according to the manufacturer's recommendations for diagnostic criteria of mastitic quarters had a sensitivity of 51% and a specificity of 71%. The ability of the device to correctly diagnose the infection status of cows was dependent on both the analytical method and the threshold value used. Applying the manufacturer's recommended criteria to the diagnosis of the status of cows, the sensitivity of the test was 91% and the specificity 17%. CONCLUSION: The variability between individual quarters and between cows in this study was such that the use of this device to measure the conductivity of milk was not a reliable method by which to diagnose subclinical mastitis in cows in late lactation. CLINICAL RELEVANCE: Interpretation of results from handheld conductivity meters when used for the diagnosis of subclinical mastitis in dairy cows needs to be made with care. In some circumstances the results obtained do not accurately reflect the bacteriological status of either individual quarters, or cows.

A longitudinal study of Shiga-toxigenic Escherichia coli (STEC) prevalence in three Australian dairy herds

Over a 12 month period, 588 cattle faecal samples and 147 farm environmental samples from three dairy farms in southeast Queensland were examined for the presence of Shiga-toxigenic Escherichia coli (STEC). Samples were screened for Shiga toxin gene ( stx) using PCR. Samples positive for stx were filtered onto hydrophobic grid membrane filters and STEC identified and isolated using colony hybridisation with a stx-specific DNA probe. Serotyping was performed to identify serogroups commonly associated with human infection or enterohaemorrhagic Escherichia coli (EHEC). Shiga-toxigenic Escherichia coli were isolated from 16.7% of cattle faecal samples and 4.1% of environmental samples. Of cattle STEC isolates, 10.2% serotyped as E. coli O26:H11 and 11.2% serotyped as E. coli O157:H7, and the E. coli O26:H11 and E. coli O157:H7 prevalences in the cattle samples were 1.7 and 1.9%, respectively. Prevalences for STEC and EHEC in dairy cattle faeces were similar to those derived in surveys within the northern and southern hemispheres. Calves at weaning were identified as the cattle group most likely to be shedding STEC, E. coli O26 or E. coli O157. In concurrence with previous studies, it appears that cattle, and in particular 1–14-week-old weanling calves, are the primary reservoir for STEC and EHEC on the dairy farm.

Effects of Monensin on the Reproduction, Health, and Milk Production of Dairy Cows

A randomized clinical trial including 1109 cows from 12 Australian dairy herds was used to evaluate the effects of monensin on the health (n = >686 cows), production (n = 915 cows), and reproduction (n = >908 cows) of dairy cows. Cows were allocated to a treatment group receiving a slow-release intraruminal bolus containing 32g of sodium monensin that was administered 40 d before and 50 d following the anticipated calving date or to a control group. Treatment did not significantly alter any reproductive outcome; 54.5% of cows treated with monensin and 58.2% of control cows were pregnant at first service, and days to conception were lower for cows treated with monensin. The hazard rate (0.95) was not significant for these cows. The percentage of cows pregnant was 83.8 for cows treated with monensin and 83.3 for control cows, and days to first estrus (hazard rate = 1.04) and first service (hazard rate = 1.04) were not significantly higher for treated cows.Treatment with monensin did not significantly alter the risk of any disease. The incidence of retained fetal membranes, pyometra, lameness, abortion, and infectious diseases was not significantly lower for cows in the treatment group, and the incidence of mastitis was not significantly higher for cows in the treatment group. Monensin significantly increased milk production by 0.75 L/d per cow and tended to increase milk fat and protein yields but had no significant effect on milk fat or milk protein percentages. Changes in the production of milk and milk constituents were consistent throughout lactation.

Reproductive performance in South Australian dairy herds.

Herd breeding records (161) were examined from a random 10% sample of dairy farms (96) in South Australia with more than 40 cows and suitable breeding records for 1988 and/or 1989. Of these farms, 53 (55.2%) had a year-round calving pattern, 42 (43.8%) claimed to be seasonal and one was changing from a year-round to a seasonal pattern. Only 14.6% of farmers observed oestrous behaviour outside milking times and 18.8% used tailpaint. Overall, the average proportion of unobserved heats was estimated to be 32%. Artificial insemination (AI) was carried out on 85.4% of farms (16.7% used AI alone and 68.7% also used bulls) of which 32.9% used commercial technicians and 67.1% were do-it-yourself operators. Overall, the average 30-day submission rate was 59.9% and the average 49-day non-return to service rate was 58.9%. Investigation into the probable causes of suboptimal reproductive performance gave a tentative diagnosis of inefficient oestrous detection, deferral of service, inadequate nutrition, poor AI technique, insufficient bulls, and venereal/other diseases in 69.1, 32.1, 27.2, 28.4, 13.6 and 7.4% of records, respectively.