Journal Articles
Permanent URI for this collectionhttps://mro.massey.ac.nz/handle/10179/7915
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Item Can we estimate herd-level prevalence of lameness in dairy cow herds kept at pasture by sampling part of the herd?(Taylor and Francis Group on behalf of the New Zealand Veterinary Association, 2025-03-26) Sapkota S; Laven RA; Müller KR; Yang DAAims: To assess whether herd-level lameness prevalence can be estimated on New Zealand dairy farms, by scoring the first, middle, or last 100 cows in the milking order. In pasture-based herds, whole herd locomotion scoring requires an assessor outside the milking parlour throughout milking. If sufficiently predictive, sampling a proportion of the herd based on milking order, could reduce the costs and time of welfare assessments. Methods: Six pasture-based, spring-calving, dairy farms in the Manawatū region of New Zealand were conveniently selected. Visits occurred at approximately 6-week intervals between October 2021 and May 2022. Cows were scored using the DairyNZ lameness score (0–3). The assessor tallied cows as they left the parlour and recorded the milking order of those with a lameness score ≥ 2. Data were analysed to determine the association between farm, visit and the proportion of lame cows in the first, middle, and last 100 cows, and the agreement between the prevalence of lame cows in those groups and from whole herd scoring. Results: Across all visits, 263 lame cows were recorded. Of these, 40.7% were in the last 100, 25.9% in the middle 100, and 14.4% in the first 100. Farm, visit and their interactions with group were all statistically significant (p < 0.001). While, overall, the last 100 cows had the highest proportion of lame cows, this pattern varied across farms and visits, Limits-of-agreement plots showed that as herd prevalence increased, agreement between the prevalence in each sample group and herd prevalence worsened. When herd prevalence exceeded 5%, only the middle 100 sampling group had a limits-of-agreement < 5%. Conclusions: Variations across farms and seasons in the proportion of lame cows in each part of the milking order lead to variations in the accuracy of predicting overall lameness from such samples. Based on limits-of-agreement, observing the middle 100 cows is likely to be the most accurate sample, but is still likely to be of limited value on New Zealand dairy farms, especially as a single, one-off measurement. Clinical relevance: On New Zealand dairy farms, locomotion scoring the middle 100 cows in the milking order as part of a welfare assessment would reduce costs and time but would not produce an accurate estimate of whole-herd lameness prevalence. However, it may be useful as a screening tool in herds routinely locomotion scoring throughout the year.Item First report of the within-farm prevalence of bovine digital dermatitis in Chinese Holstein dairy cows in Jiangsu, China: A Bayesian modelling approach(Elsevier Ltd, 2024-06) Ma X; Laven RA; Jiang P; Yang DADigital dermatitis is one of the most important causes of lameness in dairy cattle, particularly in housed, intensively-managed cattle. The number of modern intensive dairy farms in China has increased markedly in recent years; however, we lack research on digital dermatitis in Chinese dairy cattle. This preliminary study aimed to estimate the prevalence of digital dermatitis on three conveniently selected farms in Jiangsu, China. The washed hind feet of all lactating cows on all three farms were examined during milking with the aid of a mobile phone light source. True prevalence was then estimated from the apparent prevalence using a Bayesian superpopulation approach to account for the imperfect nature of identifying digital dermatitis in cows during milking. Despite none of the farms having thought it necessary to implement routine digital dermatitis monitoring or control, the disease was found on all three sampled farms. All lesions observed were either chronic M4 or M4.1 type-lesions, with no M2 lesions (i.e. acute ulcerated lesions) observed. The estimated true prevalences on the farms were 7.3% (95% credible interval [CrI]: 5.4%-9.6%), 8.3% (95%CrI: 6.3%-10.8%), and 29.8% (95%CrI: 22.9%-37.2%).
