Mortality in goats limits production and productivity, particularly in smallholder and extensive grazing systems, where resources for health interventions and management practices are often limited. This study investigates risk factors for goat mortality using Cox proportional hazards regression modelling at the Papua New Guinea (PNG) University of Natural Resources and Environment (PNGUNRE) breeding farm from 2020 to 2023. A non-parametric survival analysis and multivariable Cox regression were used to assess biological and clinical variables. The mean mortality rate was 32%. Health status and severity of clinical symptoms significantly influenced mortality rates. Bucks had a 91% higher risk of death compared to does (LR ꭓ² = 0.04; p = 0.84), while kids faced significantly higher risks (LR ꭓ² = 68.82; p < 0.001). Goats with body conditions of 3 and 4 were 8 and 17 times more likely to survive than extremely thin ones (p = 0.01). Overall body condition significantly impacted mortality risk (LR ꭓ², p < 0.001). Poor FAMACHA anaemic scores (D and E) increased the hazard of death (p < 0.001), while healthy goats had a 17-fold higher survival chance (HR = 14.97, p = 0.08). Our evaluation indicated that the causes of goat mortality were multifactorial and involved complex interactions among various factors. These findings support developing strategies to enhance goat health, welfare, and productivity in breeding farm settings. Management practices should meet the specific needs of different sex and class groups, with special attention to breeding does, kids, male goats, body condition monitoring, and anaemic conditions. Regular training for farmers on modern husbandry practices and data recording could also improve farm management. Additionally, enhanced veterinary services and resource allocation are crucial for reducing mortality rates. This work provides a template for improving goat farm health management strategies in PNG, emphasizing a comprehensive and practical approach.
Key words: Goat mortality, Papua New Guinea, Risk factors, Small ruminant, Survival analysis
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