Journal Articles

Permanent URI for this collectionhttps://mro.massey.ac.nz/handle/10179/7915

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Author: search.filters.author.Yang DASubject: search.filters.subject.lameness

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    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 DA
    Aims: 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.
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    Evaluating Alternatives to Locomotion Scoring for Detecting Lameness in Pasture-Based Dairy Cattle in New Zealand: In-Parlour Scoring
    (MDPI (Basel, Switzerland), 2022-03-11) Werema CW; Yang DA; Laven LJ; Mueller KR; Laven RA; Kofler J
    Earlier detection followed by efficient treatment can reduce the impact of lameness. Currently, locomotion scoring (LS) is the most widely used method of early detection but has significant limitations in pasture-based cattle and is not commonly used routinely in New Zealand. Scoring in the milking parlour may be more achievable, so this study compared an in-parlour scoring (IPS) technique with LS in pasture-based dairy cows. For nine months on two dairy farms, whole herd LS (4-point 0−3 scale) was followed 24 h later by IPS, with cows being milked. Observed for shifting weight, abnormal weight distribution, swollen heel or hock joint, and overgrown hoof. Every third cow was scored. Sensitivity and specificity of individual IPS indicators and one or more, two or more or three positive indicators for detecting cows with locomotion scores ≥ 2 were calculated. Using a threshold of two or more positive indicators were optimal (sensitivity > 92% and specificity > 98%). Utilising the IPS indicators, a decision tree machine learning procedure classified cows with locomotion score class ≥2 with a true positive rate of 75% and a false positive rate of 0.2%. IPS has the potential to be an alternative to LS on pasture-based dairy farms.