Food Convergence Innovation for Africa (FCI4Africa)

The Food Convergence Innovation Approach

At the core of the project sits the Food Convergence Innovation (FCI) theory of change, a framework developed over more than a decade by a research network led by McGill University. FCI treats food system transformation as a multi-scale, multi-actor process that integrates agricultural, economic, environmental, and social health outcomes rather than optimizing any single dimension in isolation.

The approach combines human creativity, agency, empathy, and trust with digital-powered tools (AI and machine learning for data synthesis, IoT for real-time monitoring, blockchain for transparent transactions, and cloud computing for cross-border collaboration) to enable convergence across sectors, geographies, and jurisdictions.

WP1 Objectives (McGill Lead)

McGill University leads Work Package 1, "Climate-Neutral, Fair and Just Food Systems," contributing 150 person-months (the largest single-partner allocation in the consortium) and co-leading activities in WP2, WP3, and WP5. WP1 pursues four objectives:

• Understand and validate the activities and identify missing reporting indicators for measuring the sustainability of African food systems subject to intra- or interregional trade.
• Determine systemic tipping points capable of unlocking transformation in selected food systems.
• Leverage tipping points and FCI concepts to design and validate solutions that accelerate transformation.
• Design and develop a decision analytics tool that promotes the diffusion of innovations and supports sustainable, fair, and just intra-African and inter-African to Europe trade.

Technical Workstreams

Extended value chain assessment (Task 1.1).

The lab leads desk studies and in-depth literature reviews drawing on FAO Food Balance Sheets, CAADP reports, and national databases to map the regulatory architecture, actor interactions, and production volumes of selected commodity chains. Field validation with key informants refines the analysis. Validation data feeds into SynthEco, an open-source ecosystem that uses statistically representative synthetic populations derived from census data to cluster health, behavioural, and environmental variables at fine geospatial granularity. Extending SynthEco with African data is a novel contribution of this project.

Tipping point identification (Task 1.2, co-lead with Wageningen University).

The lab identifies positive tipping points (fortification, aflatoxin-free commodities, traceability labels, and local best practices) situated in their structure-agency contexts. Drawing on institutional analysis frameworks, we assess which points are most amenable to transformation and where agency from leadership actors can accelerate change.

FCI solution design (Task 1.3, lead).

The lab anchors solution co-design in the convergent space where stakeholder values overlap with the values underlying multilevel regulatory frameworks. We apply and further develop the "value in situ" method (Sethamo et al., 2022) to elicit shared values across businesses, communities, and civil society organizations, then design interventions that are technically sound and institutionally legitimate.

Extended sustainability assessment (Task 1.4, lead).

The lab conducts a baseline assessment of each selected commodity chain and compares it to the FCI solutions from Task 1.3 using environmental life cycle assessment and systems thinking. The assessment spans climate neutrality, land use, biodiversity, fairness, justness, and efficient food supply at local, regional, intra-country, and inter-continental scales, accounting for positive and negative feedback among actors, drivers, enablers, and barriers.

Interactive decision analytics tool (Task 1.5, lead).

The lab translates the assessment framework into an interactive online platform that supports food system analysis and predicts the impact of FCI solutions on sustainability indicators. The tool is built on environmental life cycle models and systems thinking algorithms and will be accompanied by training workshops, manuals, and video resources so that stakeholders in the selected value chains can adopt it for analysis and solution design.

Cross-Work-Package Contributions

WP2 (Trade modelling and NTM impact assessment): contributions to GTAP-based computable general equilibrium modelling of EU-AU agricultural trade under non-tariff measures, systems dynamics models linking NTMs to nutrition, health, and environmental outcomes, and policy recommendations for AU countries. McGill leads or co-leads eight of the twelve WP2 tasks.

WP3 (Digitization and traceability): lead on Task 3.1, prioritizing commodities for traceability and claim-verification methods, reviewing existing and prospective legislative and private requirements (EUDR, CSDD, organic, TSD) and identifying analytical methods for product-trait combinations including cocoa, peanut, palm oil, and bananas.

WP5 (Use case analytics): supports use case partners with decision analytics for factory diagnostics, trade of fortified commodities, and fair and just trade scaling, feeding real-world use case data back into the WP1 framework.

Expected Outputs from McGill-led Work

• D1.1 Value chain performance report (M10): baseline vs. extended assessment with nutrient maps, knowledge transfer, and sustainability indicators.
• D1.2 Tipping points analysis report (M22): technical report on positive tipping points for food system transformation.
• D1.3 Food Convergence Innovation solutions (M24): design strategies positioning selected systems toward a convergence economy.
• D1.4 Sustainability evaluation of FCI solutions (M24): impact evaluation against climate, biodiversity, and social indicators.
• D1.5 Decision analytics tool (M30): deployed interactive tool for predicting FCI solution impacts on climate-neutral, fair, and just food supply.

Why This Matters

The WP1 decision analytics tool is the analytical backbone of the entire project. It converts the tipping-point and convergence-innovation theory into a usable instrument that trade modellers (WP2), traceability developers (WP3), knowledge platform builders (WP4), and use case implementers (WP5) can plug into their workflows. By embedding environmental life cycle assessment, systems thinking, and shared-value elicitation into a single decision-support platform, the lab’s contribution ensures that the project’s outputs are not just evidence-based but convergence-based, attuned to the interconnected realities of African food systems rather than optimizing for any one dimension at the expense of others.