The future of food demand at the local level: from national to subnational

How to ensure that food systems meet changing demand in the years ahead? While global and national projections of food demand have improved significantly over the past decades, they often do not simulate where within countries that demand is projected to occur. 

Yet food policy decisions are rarely made at the national average level; they are implemented locally.

A new study, Spatially Downscaling of Future Food Demand for Policy Planning, introduces a novel approach to bridge this gap by translating global food demand projections into subnational insights that can directly inform policy planning.

Why location matters in food demand

Food demand is not evenly distributed. It varies widely within countries due to differences in income, urbanization, demographics, and access to markets. Global economic models can project how much food will be needed overall, but they cannot fully capture local variation in diets and consumption patterns. As a result, national averages can be misleading for policymakers trying to design targeted interventions.

Our research shows that:

• Subnational food demand trends can move in the opposite direction of national projections
• Dietary transitions occur unevenly across regions
• Local conditions strongly shape future demand

These findings highlight a fundamental challenge: planning food systems without local detail risks misallocating resources and missing vulnerable populations.

A new approach: Downscaling global food demand

To address this gap, we developed a method to “downscale” national food demand projections to the subnational level.

Our approach combines:

• Global economic model outputs from IMPACT model
• High-resolution spatial data
• Machine learning techniques
• Matching methods based on similarity

This allows us to estimate how future food demand for 62 commodities may evolve across nearly 3,000 subnational regions worldwide.

Rather than treating countries as homogeneous units, the method reveals how global drivers (such as income growth and population change), translate into local consumption patterns.

The results reveal that national results may hide major local differences

Even when national demand appears stable, local patterns can vary significantly. For example, a country’s average demand for a maize may decline. Yet specific regions within that country may experience increases in maize demand. This means that relying on national-level statistics can lead to policy blind spots, particularly in regions facing rising demand pressures.

We also found that dietary change is uneven. Across many countries, diets are becoming more diverse over time. However, this transition is not uniform. Regions with higher income and better market access tend to diversify faster. Gains in dietary diversity are frequently driven by areas that were already better off

In Kenya, for instance, projected improvements in dietary diversity are concentrated in central and western counties, while northern regions remain less diversified.

We also see that global trends play out differently at the local level. Mega drivers such as urbanization, income growth, and demographic change affect regions in different ways. Urban areas show rising demand for vegetables, fruits, and animal products. These dynamics create complex, localized demand patterns that are key for policy making, but national models alone cannot capture.

Why this matters for policy

Understanding subnational food demand is not just a theoretical exercise. It has direct implications for policy design, including targeting food security interventions. Moreover, policymakers can identify hotspots of rising demand and prioritize investments where they are most needed. Regions with increasing demand but poor connectivity may face higher prices and supply shortages, requiring investments in roads and transport, storage facilities, market access, and supporting local production and supply chains

In some areas, boosting local production may be more effective; in others, imports or interregional trade may be the better solution.

Policies aimed at improving nutrition must account for where dietary transitions are lagging, not just national averages.

A step toward more localized food policy

Our work represents an important step toward making food demand projections more actionable. By linking global models with local realities, this approach helps bridge the gap between macro-level analysis and local decision-making; Enables more targeted, efficient, and equitable policies; Provides insights even in data-scarce regions

At the same time, it is important to recognize limitations. These are downscaled projections, not exact local forecasts, and uncertainty remains—especially in sparsely populated areas.

Looking ahead, as food systems face increasing pressure from population growth, climate change, and economic transitions, the need for granular, location-specific insights will only grow.