Using Longitudinal Assessment on Extensively Managed Ewes to Quantify Welfare Compromise and Risks
AuthorMunoz, C; Campbell, A; Barber, S; Hemsworth, P; Doyle, R
University of Melbourne Author/sBarber, Stuart; Munoz Gallardo, Carolina Andrea; Campbell, Angus; Hemsworth, Paul; Doyle, Rebecca
Melbourne School of Population and Global Health
Veterinary Clinical Sciences
Document TypeJournal Article
CitationsMunoz, C., Campbell, A., Barber, S., Hemsworth, P. & Doyle, R. (2018). Using Longitudinal Assessment on Extensively Managed Ewes to Quantify Welfare Compromise and Risks. ANIMALS, 8 (1), https://doi.org/10.3390/ani8010008.
Access StatusOpen Access
This study examined variation in the welfare of extensively managed ewes and potential welfare risks. A total of 100 Merino ewes (aged 2-4 years) were individually identified and examined at three key stages: pregnancy, lactation and weaning. Eight animal-based welfare measures were used to assess welfare: flight distance, body condition score (BCS), fleece condition, skin lesions, tail length, dag score, lameness and mastitis. Data were analysed by ANOVA and McNemar's statistics. Overall, the average BCS of the group was in agreement with industry recommendations. However, a number of animals were classified with inadequate condition (either too thin or too fat) across the three observation periods. The presence of heavy dags was greatest at mid-lactation (87%, P < 0.0001), lameness was greatest at weaning (14%, P = 0.01), clinical mastitis was 1% annually, and five ewes were lost from the study. Ewes had better health at mid-pregnancy compared to mid-lactation and weaning. The main welfare issues identified were under and over feeding, ewe mortality, lameness, ecto-parasites (flystrike) and mastitis, all of which have the potential to be reduced with improved management practices. Future welfare assessment programs must consider that significant variation in on-farm welfare will occur in extensively managed systems and this needs to be accounted for when evaluating farms.
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