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<p>Human–elephant conflict (HEC) represents one of the most persistent socio-ecological challenges in tropical landscapes, where expanding agriculture and fragmented habitats intensify encounters between wildlife and people. This study integrates Participatory Rural Appraisal (PRA), Traditional Ecological Knowledge (TEK), and Design-Based Research (DBR) to co-design early warning systems (EWS) for conflict mitigation in Odisha, India. Using digitized PRA tools, communities mapped seasonal and spatial patterns of elephant movement, crop damage, and risk hotspots, combining local knowledge with geospatial analysis. Iterative feedback and community validation sessions ensured contextual accuracy and stakeholder ownership. The resulting framework identifies priority locations and temporal windows for deploying AI-enabled camera-based EWS, linking ecological data with social acceptability. Findings highlight how participatory technology design can strengthen adaptive conservation governance by embedding community knowledge into decision-making innovation. The approach has broad implications for locally led, socially grounded conservation strategies across diverse human–wildlife conflict landscapes.</p>

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Participatory Co-Design for Human–Elephant Coexistence: Integrating Local Knowledge into Early Warning System Planning in Odisha

  • 期刊论文

  • 发表日期：

    2026年3月31日

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  • Balu M. Menon,
  • Tarek Rashed,
  • Lekshmi Venkitesh P,
  • Vasundharan Nidhi,
  • Rao R. Bhavani

摘要

Human–elephant conflict (HEC) represents one of the most persistent socio-ecological challenges in tropical landscapes, where expanding agriculture and fragmented habitats intensify encounters between wildlife and people. This study integrates Participatory Rural Appraisal (PRA), Traditional Ecological Knowledge (TEK), and Design-Based Research (DBR) to co-design early warning systems (EWS) for conflict mitigation in Odisha, India. Using digitized PRA tools, communities mapped seasonal and spatial patterns of elephant movement, crop damage, and risk hotspots, combining local knowledge with geospatial analysis. Iterative feedback and community validation sessions ensured contextual accuracy and stakeholder ownership. The resulting framework identifies priority locations and temporal windows for deploying AI-enabled camera-based EWS, linking ecological data with social acceptability. Findings highlight how participatory technology design can strengthen adaptive conservation governance by embedding community knowledge into decision-making innovation. The approach has broad implications for locally led, socially grounded conservation strategies across diverse human–wildlife conflict landscapes.