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Classification of donkey systems in Ethiopia
Girma Birhan Asteraye1, Ruth Jobling2, Wudu T Jemberu3
1Global Burden of Animal Diseases programme, United Kingdom; Institute of Infection, Veterinary and Ecological Sciences, Department of Livestock and One Health, University of Liverpool, Liverpool, United Kingdom; International Livestock Research Institute, Addis Ababa, Ethiopia; College of Veterinary Medicine and Animal Sciences, University of Gondar, Gondar, Ethiopia.
Abstract:
Working donkeys play a critical role in transportation, agriculture and household resilience in low- and middle-income countries. Other animals that are kept for production purposes, such as cattle, are often grouped into broad production system classes, such as dairy or pastoral, for comparison between and better understanding of the needs and outputs of animals within specific sectors. Despite the importance of working donkeys for sustaining livelihoods there are no systematic classifications of these populations. The aim of this study was to classify and characterise donkey systems in Ethiopia using household-level questionnaire data which included donkey ownership, husbandry, use and local environment data, through multiple factor and hierarchical cluster analysis. Household questionnaire data from 241 donkey-owning households in three districts of Ethiopia were used. Three distinct clusters of donkey ownership were identified: 'Domestic-Pastoral'; 'Domestic-Agricultural' and 'Commercial'. Differences between systems are primarily influenced by donkey purpose, environmental (agro-ecological) factors, and husbandry practices. Constraints associated with donkey ownership varied across clusters: households in the commercial system reported higher incidence of injuries and welfare concerns, in the pastoral system the main constraints were drought and feed shortage, and domestic-agricultural households reported infectious diseases as the main challenge. This new classification of donkey systems provides a framework for analysing donkey health and welfare data, enabling more context-specific needs assessments and facilitating the design of targeted interventions to improve equid health and household livelihoods.
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