Transforming agriculture in the context of climate change is a central challenge in North Africa, the world’s most import-dependent region and a hotbed of climate change. In the face of these challenges, agroecology (AE) emerges as a response that can address both the challenges of global sustainability and local resilience. In this context, the NATAE consortium brings together high-level research institutions, international organizations and NGOs with strong experience in AE approaches and an exceptional capacity to induce transformational change by informing policy and education.
The NATAE project aims to foster the adoption of science-based, locally adapted and developed AE strategies in North Africa by creating a comprehensive and quantitative baseline on AE, providing shared understanding, multidimensional performance measures, and analyses of the potential of AE to meet consumer demand in the market.
To achieve this goal, NATAE will establish and inform a unique multi-stakeholder knowledge and capacity building community for AE in the Mediterranean, with groundbreaking results on the performance of AE practices (AEPs) in North Africa. An original, multidimensional and multi-scale assessment framework covering currently neglected dimensions, and a replicable methodological guide will be designed. An integrated modelling approach combining a biophysical modelling chain, a household/regional bioeconomic indicator modelling chain will be used to develop a unique integrated assessment of agricultural systems to assess the resilience of agricultural systems based on AEPs. Participatory approaches via living labs will be developed to develop, test and capitalise on alternative public policies and foster AE transitions. An integrated cluster of dissemination activities will test, advance and communicate a range of existing farm-to-fork agri-environmental innovations, including innovative agricultural practices, value chain innovations and food system governance innovations, advancing their respective readiness levels.