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Scaling the FOLA Approach to Strengthen Locally Led Anticipatory Action Across ASAL Counties

March 23-28, 2026 (NAIVASHA, Kenya) ICPALD, in collaboration with the University of Sussex, has been scaling the Forecasts to Local Action (FOLA) approach to strengthen locally led anticipatory action across Arid and Semi-Arid counties.

During a capacity building session of National Drought Management Authority (NDMA) officers and engagement with ASAL communities, forecasts were translated into timely, community-driven early actions and anticipatory action was institutionalized.

The six-day FOLA training was conducted under the PASSAGE Project implemented by ICPALD, with financial support from the International Development Research Centre (IDRC) /Foreign Commonwealth & Development Office (FCDO). Participants also included field monitors from nine arid counties, technical experts from University of Sussex and University of Nairobi, and ICPALD technical staff.

The forum opened with remarks from the NDMA CEO. ICPALD, remarks were delivered on behalf of Dr. Dereje Wakjira, ICPALD Director, emphasizing the need to highlight the importance of strengthening early warning systems and enhancing community-level anticipatory action.

Training sessions focused on strengthening technical capacity, improving data quality, and strengthening linkages between field monitoring and decision-making processes. Participants were introduced to the FOLA framework and the importance of integrating indigenous knowledge with scientific forecasting to support timely early action. Specific areas of training were: Participatory Co-Production and Knowledge Integration, Participatory Scenario Planning, and Forecast Integration and Impact-Based Risk Analysis.

Building on earlier FOLA pilots in cross-border clusters, this training supported the institutionalization and scaling of locally led anticipatory action within national early warning systems. It also strengthened the capacity of NDMA drought field monitors to improve data collection, interpretation of early warning indicators, and translation of forecasts into actionable anticipatory measures.

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