BackgroundOriental theileriosis is a tick-borne, protozoan disease of cattle caused by members of the Theileria orientalis-complex. Recent outbreaks of this disease in eastern Australia have caused major concerns to the dairy and beef farming communities, but there are no published studies of the economic impact of this disease. On a farm in Victoria, Australia, we assessed whether oriental theileriosis has an impact on milk production and reproductive performance in dairy cows.MethodsBlood samples collected from all 662 cows on the farm were tested using an established molecular test. For individual cows, milk production and reproductive performance data were collected. A clinical assessment of individual cows was performed. Based on clinical findings and molecular test results, the following groups of cows were classified: group 1, with cardinal clinical signs of oriental theileriosis and molecular test-positive for T. orientalis; group 2, with mild or suspected signs of theileriosis and test-positive; group 3, with no clinical signs and test-positive; and group 4, with no clinical signs and test-negative. Milk production and reproductive performance data for groups 1, 2 and 3 were each compared with those for group 4 using linear and logistic regression analyses, respectively.ResultsAt 100 days of lactation, group 1 cows produced significantly less milk (288 l; P = 0.001), milk fat (16.8 kg; P < 0.001) and milk protein (12.6 kg; P < 0.001) compared with group 4. At this lactation point, group 2 also produced significantly less milk fat (13.6 kg; P = 0.002) and milk protein (8.6 kg; P = 0.005) than group 4. At 305 days of lactation, group 1 cows produced significantly less milk (624 l; P = 0.004), milk fat (42.9 kg; P < 0.001) and milk protein (26.0 kg; P < 0.001) compared with group 4 cows. Group 2 cows also produced significantly less milk fat (21.2 kg; P = 0.033) at this lactation point. No statistically significant difference in reproductive performance was found upon pairwise comparisons of groups 1–3 with group 4 cows.ConclusionsThe present findings demonstrate that clinical oriental theileriosis can cause significant milk production losses in dairy cattle.

This is the first report of anthelmintic-resistant O. ostertagi on Australian dairy farms. Resistance to all three available anthelmintic classes is of concern, given the high pathogenicity of this species. The study highlights the need for veterinarians and dairy farmers to be aware of the risks posed by anthelmintic resistance.

Although large herds (more than 500 cows) only represent 13% of Australian dairy farms, they represent more than 35% of the cows milked. A survey of Australian dairy farmers was conducted to assess relationships between herd size and known or proposed risk factors for adverse animal welfare outcomes in Australian dairy herds in relation to increasing scale of production. Responses from 863 Australian dairy farms (13% of Australian dairy farms) were received. Increasing herd size was associated with increases in stocking density, stock per labor unit, and grain fed per day-all of which could reasonably be hypothesized to increase the risk of adverse welfare outcomes unless carefully managed. However, increasing herd size was also associated with an increased likelihood of staff with formal and industry-based training qualifications. Herd size was not associated with reported increases in mastitis or lameness treatments. Some disease conditions, such as milk fever, gut problems, and down cows, were reported less in larger herds. Larger herds were more likely to have routine veterinary herd health visits, separate milking of the main herd and the sick herd, transition diets before calving, and written protocols for disease treatment. They were more likely to use monitoring systems such as electronic identification in the dairy, computerized records, daily milk yield or cell count monitoring, and pedometers or activity meters. Euthanasia methods were consistent between herds of varying sizes, and it was noted that less than 3% of farms make use of captive-bolt devices despite their effectiveness and ready availability. Increasing herd size was related to increased herd milking time, increased time away from the paddock, and increased distance walked. If the milking order of cows is consistent, this may result in reduced feed access for late-milking-order cows because of a difference in time away from the paddock. More than 95% of farmers believed that their cows were content most of the time, and cows were reported as well behaved on more than 90% of farms. Although the potential animal welfare issues appear to be different between herd sizes, no evidence existed for a relationship between herd size and adverse welfare outcomes in terms of reported disease or cow contentment levels.

One of the major challenges for dairy producers is to produce, harvest, and store high-quality colostrum and feed it to their replacement heifer calves. Limited published data are available in Australia regarding the relationship between colostrum management, hygiene, and quality. The objectives of this study were to investigate (1) the colostrum storage and handling practices carried out on farm; (2) the immunoglobulin concentration and bacterial composition of colostrum being fed to replacement dairy heifer calves; (3) the percentage of colostrum being fed to replacement dairy heifer calves that meet industry recommendations; and (4) risk factors for bacterial contamination of colostrum. The study was carried out on 24 dairy farms located near Rochester, Victoria, Australia. Two hundred forty colostrum samples were collected (10 samples per farm). Each farm harvested and stored first-milking colostrum under normal farm conditions. A 10-mL sample of the colostrum was collected in a sterile container immediately before feeding, and a Brix refractometer reading was taken. The samples were then frozen at -4°C and submitted for bacterial concentration analysis. Fifty-eight percent of colostrum samples met the recommended industry standard of a total plate count (TPC) of <100,000cfu/mL, and 94% of colostrum samples met the recommended industry standard of total coliform count (TCC) of 10,000cfu/mL. However, when all the current industry recommendations for TPC, TCC, and Brix refractometer percentage for colostrum quality were considered, only 23% of the samples met all standards. These findings demonstrate that a large number of calves were at risk of receiving colostrum of poor quality, with high bacterial loads that may have interfered with the acquisition of transfer of passive immunity and affected calf health. Further investigation is required to identify the farm-specific factors that may influence the level of bacterial contamination of colostrum. Recommendations as a result of this study include refrigeration of excess colostrum shortly (within 1h) after collection and thorough disinfection of the calf feeding apparatus before use.

Automated walk-over weighing systems can be used to monitor liveweights of cattle. Minimal literature exists to describe agreement between automated and static scales, and no known studies describe repeatability when used for daily measurements of dairy cows. This study establishes the repeatability of an automated walk-over cattle-weighing system, and agreement with static electronic scales, when used in a commercial dairy herd to weigh lactating cows. Forty-six lactating dairy cows from a seasonal calving, pasture-based dairy herd in southwest Victoria, Australia, were weighed once using a set of static scales and repeatedly using an automated walk-over weighing system at the exit of a rotary dairy. Substantial agreement was observed between the automated and static scales when assessed using Lin's concordance correlation coefficient. Weights measured by the automated walkover scales were within 5% of those measured by the static scales in 96% of weighings. Bland and Altman's 95% limits of agreement were -23.3 to 43.6 kg, a range of 66.9 kg. The 95% repeatability coefficient for automated weighings was 46.3 kg. Removal of a single outlier from the data set increased Lin's concordance coefficient, narrowed Bland and Altman's 95% limits of agreement to a range of 32.5 kg, and reduced the 95% repeatability coefficient to 18.7 kg. Cow misbehavior during walk-over weighing accounted for many of the larger weight discrepancies. The automated walk-over weighing system showed substantial agreement with the static scales when assessed using Lin's concordance correlation coefficient. This contrasted with limited agreement when assessed using Bland and Altman's method, largely due to poor repeatability. This suggests the automated weighing system is inadequate for detecting small liveweight differences in individual cows based on comparisons of single weights. Misbehaviors and other factors can result in the recording of spurious values on walk-over scales. Excluding outlier weights and comparing means of 7 consecutive daily weights may improve agreement sufficiently to allow meaningful assessment of small short-term changes in automated weights in individuals and groups of cows.

No abstract

Animal welfare assessments were conducted on 50 Australian pasture-based dairy farms of varying herd sizes: 16 small (<300 cows), 15 medium-sized (300-500 cows), 11 large (501-750 cows), and 10 very large (751+ cows). A protocol based on elements of Welfare Quality adapted for Australian conditions was developed to assess the broad categories of good feeding, housing, health, and appropriate behavior. Farm records, body condition scores, integument injuries, fecal plaques, avoidance distance of humans, and fecal pat scoring for acidosis assessment were undertaken. The mean maximum kilograms of grain fed per day significantly increased with herd size, from 5.2 ± 0.38 (small), 7.7 ± 0.29 (medium-sized), 8.8 ± 0.45 (large), to 10.1 ± 0.80 kg (very large). Acidosis was not related to herd size based on either farm records or fecal pat scoring. All cows had access to water for more than 12 h in a 24-h period. More larger farms had water points on the farm tracks or at the dairy. Very large farms (90%) were more likely than others (36-39%) to provide water suitable for human consumption. Integument lesions were not related to herd size and were uncommon; 56 and 84% of farms had no cows with lesions or hairless areas, respectively, and no farm had >6% integument lesions. Heat stress is an important welfare risk in Australia. All farms had some form of cooling strategy; shade in all paddocks was more common on smaller farms (>90%) than others (<75%). Sprinklers were more common on large or very large farms (>80%) than others (<65%). Mastitis and lameness were the most common health conditions, followed by dystocia, downer cows, and gastrointestinal diseases. Prevalence of lameness, mastitis, downer cows, dystocia, and gastrointestinal disease were not related to farm size. Larger farms were more likely to have electronic infrastructure to monitor or electronically draft cows for inspection. We found wide variation in the avoidance distance of humans, but this was not related to farm size. Larger farms had longer walking distances to pasture and longer time away from pasture, which could affect the time available for behaviors such as lying down. Animal welfare risks differ on Australian farms compared with housed cattle. As animal welfare is multidimensional, both animal-and resource-based indicators can be useful. Animal-based indicators have strengths in that, when measured accurately, they genuinely reflect the outcome being measured, but they also have weaknesses in that the point-estimate of a disease prevalence on a given day may not be representative of other times of year or differences in case definition may exist when farm records are used. Similarly, resource-based indicators have strengths in that they may be applicable to longer periods, but weaknesses because the fact a resource is present does not guarantee it is being used. Identifying the major risks to animal welfare on individual farms and ensuring a plan is in place to effectively manage them should be an important element of any on-farm animal welfar...

In large Australian pasture-based dairy herds, it is common for the time taken to milk a herd of cows to be up to 4 h. Cows are collected from the paddock as a group, wait in turn in the dairy yard to be milked, and then return individually to the paddock or feed pad immediately after leaving the milking parlor. In such herds, we previously found a consistent milking order, resulting in some cows being regularly away from pasture for several hours per day more than others. Increased time away from pasture may affect the time budgets of cows because of decreased opportunity for grazing or lying down. Lying behavior is a high-priority behavior for cows, and the duration of lying has been used as an important measure of their welfare. We applied activity monitors for 7 d to 15 cows toward the beginning and 15 cows toward the end of the milking order in 10 dairy herds milking 500 to 730 cows as a single group to understand the effect of extra time spent in the dairy on lying behavior. Study cows typically produced 6,000 to 8,000 L in a 300-d lactation on rotary dairy platforms with 40 to 80 units, being fed 2.5 to 6 kg of grain mix in the milking parlor daily, with the rest of the diet being supplied as pasture or forage provided in the pasture or close to the exit of the dairy. Over the 10 farms, 1,948 cow-days were available for analysis. The furthest paddocks on each farm were 1.8 to 3.5 km walking distance from the dairy. A wide range of steps were taken each day, ranging from 1,705 to 15,075 (mean = 5,916). The main predictor of the number of steps was the farm on which the cows were located. Cows that spent less than an hour waiting to be milked (and would be unlikely to have their ability to lie down affected by the milking process) laid down for a mean of 9.8 h/d. Steps walked and delay in the dairy waiting to be milked were both significantly associated with lying time, but the effect was not large. A regression model accounting for the waiting time at the dairy, steps taken, cow age, and farm was used to investigate the relationship with daily lying time. For every 1,000 steps, lying time reduced by 0.49 h; however, the number of steps explained only 1% of the variation in lying time. For every hour increase in waiting time at the dairy, lying decreased by approximately 14 min, but this explained only 14% of the variation in lying. We concluded that milking time durations of 2 to 4 h, common in large Australian pasture-based dairy herds, did not significantly affect the time budget for lying of individual cows in our study herds. Whereas the effect of long milking times does not appear to be a major risk to animal welfare in terms of lying time, the effect on cow health and production warrants further investigation.