Comparative Evaluations of Abnormal Behaviour, Panting, Lamb Mortality and Fleece Cleanliness in Kenguri Sheep under Intensive and Extensive Rearing Systems, Yadgir District, India
Kanakaraja M. G., Sagar M., Niranjan D., Shivappa Nayaka, Hemanth P.
Journal of Scientific Research and Reports · pp. 321–327 · Published 21 Feb 2024
10.9734/jsrr/2024/v30i31882Abstract
The important welfare issues of Kenguri sheep faced during group welfare assessment were analyzed during the study. Total of 20 each intensive and extensive Kenguri sheep farms having the herd size ranging from 35 to 300 and 25 to 1480, respectively were cited for group welfare assessment in Yadgir (i.e., Northern Eastern dry agro-climatic zone) district of Karnataka state, India. Krishi Vigyan Kendra, Kawadimatti, Yadgir, UAS, Raichur. Here each selected herd raised up in both intensive and extensive rearing systems was assessed for 20 minutes, during the months of January and February of the year 2022. The group level welfare indicators such as abnormal behaviour (stereotypy), panting, lamb mortality and fleece cleanliness were evaluated as per Animal Welfare Indicators assessment protocol for sheep. The overall first group level welfare assessment was significant in between the intensive and extensive rearing systems. The comparative welfare study among total of 40 sheep farms revealed that, the stereotypy observed was 4.88 and 1.24%, panting was 1.71 and 3.66%, mild heat stress was 2.18 and 6.49%, lamb mortality was 30.06 and 26.47% in lambs born alive and 0.89 and 2.03%, in lambs born dead. Losses upto weaning was 3.40 and 4.45% and minimum ewes mated was 45.73 and 42.48%. Minimum lambs reared was 30.48 and 26.52%; Fleece cleanliness was very wet that is 2.19 and 3.47%, and filthiness is 0.14 and 1.11% of animals in both intensive and extensive rearing systems, respectively. This study may boost the quality of animal assessment for getting better value-added products under the frame work of welfare status in farming community in the future.
Cited by 2
Ricard Parés, Pol Llonch, Manel López-Béjar · Livestock Science · 2024
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