How Shifting Rainfall Patterns Are Quietly Shaping Uganda’s Food Price Dynamics

Climate volatility in Uganda has only a modest but detectable influence on food prices, with rainfall shortages during growing seasons causing small, crop-specific price increases. As climate change intensifies, these weather-to-price effects are expected to strengthen, requiring more climate-sensitive forecasting tools.

A new working paper from the International Monetary Fund’s Institute for Capacity Development, drawing on data from the Uganda Bureau of Statistics, the European Centre for Medium-Range Weather Forecasts (ECMWF), and the Famine Early Warning Systems Network (FEWSNET), examines the increasingly complex relationship between climate volatility and food prices in Uganda. Authored by economists Christopher Adam and Prabhmeet Kaur Matta, the study addresses a growing concern among African central banks: how shifting weather patterns affect inflation forecasts in economies where food dominates household spending and market volatility is tightly intertwined with rainfall.

When Inflation Depends on the Weather

Uganda’s inflation story is inseparable from food. Although food accounts for about a quarter of the formal CPI basket, it represents nearly 40 percent of household consumption when home-produced crops are included. This makes Uganda exceptionally sensitive to agricultural shocks. While headline inflation has mostly stayed within the central bank’s 5 percent target over the past decade, food price inflation has been far more erratic, with dramatic spikes in 2011 and again in 2022 in the wake of the global disruptions caused by the Russia–Ukraine conflict. Prices also vary sharply across regions: Kampala’s markets behave very differently from those in Arua, Gulu, Mbale, or Mbarara, reflecting differences in production, transport costs, and market connectivity.

Yet the underlying weather patterns do not show drastic long-term shifts. ERA5 satellite data reveal a clear warming trend since the early 2000s, but aggregate rainfall has changed only mildly. Despite more frequent local floods and landslides, national rainfall averages remain relatively stable, complicating efforts to link climate volatility directly to food inflation.

Granular Mapping of Crops and Climate

To uncover the hidden linkages, the researchers built a highly detailed dataset matching specific crops, maize, beans, cassava, sorghum, millet, and matoke, to their production zones using UBOS surveys and FEWSNET trade-flow maps. They then align these zones with 31-km-resolution climate observations and construct agricultural calendars capturing planting, growing, and harvesting phases for each crop.

For each phase, they compute rainfall and temperature anomalies using several approaches: absolute and percentage deviations from long-term monthly averages, asymmetric measures distinguishing shortages from excesses, and squared deviations to detect non-linear responses. These are then combined with 14 years of monthly market price data from Uganda’s major CPI collection centres.

Do Weather Shocks Really Move Food Prices?

The first round of tests offers a reality check for policymakers: adding weather anomalies to standard inflation models improves accuracy only marginally. Even the most refined climate indicators reduce forecast error by just a few percentage points.

However, when the analysis zooms in to the commodity level and to weather conditions in the precise production regions, stronger patterns emerge. For most staples except sorghum, including season-specific weather anomalies, improves the ability to explain price movements. The impact is clearest when separating positive from negative shocks: shortages of rainfall often matter more than surpluses.

Still, many coefficients are imprecise due to high correlations across climate variables. To address this, the authors deploy an Elastic Net machine-learning technique to shrink the noise and isolate the most influential weather signals. This step reveals a consistent pattern across several crops: negative rainfall shocks during the growing season tend to raise prices, while favourable rainfall or warmth generally dampens them. For maize, a one-standard-deviation rainfall shortfall in the growing phase is associated with roughly a 2.2-percentage-point rise in monthly price inflation, an effect that is statistically modest but economically meaningful.

Preparing for a More Climate-Volatile Future

At present, Uganda’s climate volatility exerts only mild and fragile effects on food prices. This means central banks can still rely primarily on traditional forecasting tools without incurring major errors. But the authors caution that this window is narrowing. As warming intensifies, weather-to-price transmission will strengthen, and routine forecasting will need to integrate granular climate intelligence.

A major obstacle is the absence of high-frequency data on internal food trade flows, information that would reveal how local production shocks spread through Uganda’s fragmented market system. Without such data, the true pathways from rainfall to retail prices remain partially obscured.

In essence, Uganda stands at a transition point: the climate signal is strengthening, the data architecture is improving, and the economic consequences of weather volatility are becoming more visible. This study urges policymakers to invest now in climate-sensitive monitoring systems, before the pressures of climate change push food price instability into a higher and more unpredictable gear.