Collaborative Learning and Joint Creation Workshop on Agroecological Transitions in Ethiopia

In April 2024, the Private Sector Incentives, and Investments (PSii) project of the Agroecological Transitions program convened a two-day collaborative learning and joint creation workshop at the Swiss Inn Nexus Hotel in Addis Ababa, Ethiopia. Building upon Ethiopia’s Agroecological policy framework, 2030 Vision, the National Food System Transformation pathway and related policies, this workshop brought together a diversity of actors with the specific objectives of co-creating key building blocks and shared knowledge on incentives and investments that can support agroecological strategies as well as facilitating engagement and cross-pollination between public and private sector actors for agroecological transitions.

The workshop brought together a diverse group of stakeholders, including government bodies, universities, regional organizations, and a consortium of NGOs working on the agroecological transition at the production level. These participants engaged in discussions and debates about the different barriers and successes in the Ethiopian food and agricultural system, particularly the wheat-based production system.

Some of the key questions that were raised and discussed during the workshop include:

What new perspectives or approaches are being introduced that have not been discussed before?
Should the focus remain on smallholder agriculture, or is a transition to a different model necessary? If a transition is needed, how can it be achieved in an environmentally and holistically sustainable manner?
What kind of evidence or data have been generated so far to support the proposed approaches?
What metrics have been used to quantify the effectiveness of wheat cluster farming as an agroecological practice?
It is crucial to understand the realities on the ground and how the transition can be implemented by examining the available resources and options that can be easily customized for efficient adoption.
Agroecology is context-specific, and there is no one-size-fits-all solution. Instead, recommendations should be based on the specific context and conditions.