Seasonal production outlooks
Seasonal climate forecasts and current crop condition are combined in statistical or crop models to give the probability of below-normal production by region, updated as the season progresses.
The Global Centre for Risk and Innovation (GCRI)
Forecasting gives food systems lead time: seasonal production outlooks, projections of lean-season food insecurity, staple price outlooks and pest alerts. Its value lies in the lead time it gives and the skill it can demonstrate on past seasons, not in point predictions.
GCRI · Established · service level R0
Food security forecasting is the prediction of production, prices and food insecurity ahead of a season or lean period from climate forecasts, crop condition and market data. Its value lies in lead time and skill shown on past seasons. Forecasts set the triggers for anticipatory action, and agencies such as FEWS NET publish outlooks.
Forecasting and time-series analysis predict future values of variables from past observations and explanatory information. They support early warning and planning across hazards, markets and health. In food and agriculture, trade, resources and supply chains and climate and hazards, forecasts trigger early action and anticipatory finance before shocks materialise. Probabilistic forecasts allow decisions to account for uncertainty across connected systems. The canonical description is on the STEM Commons.
Seasonal climate forecasts and current crop condition are combined in statistical or crop models to give the probability of below-normal production by region, updated as the season progresses.
Time-series and structural models project staple prices in monitored markets from world prices, exchange rates, harvests and seasonality, with forecast intervals that widen with lead time.
Thresholds on forecast indicators are set and tested on past seasons, so agencies know the expected hits and false alarms before committing finance.
Each term as this page uses it. Where a standard-setting body defines the term, the wording follows that body.
| Term | Definition |
|---|---|
| Seasonal climate forecast | A probabilistic forecast of rainfall and temperature for the coming months, issued by centres such as Copernicus C3S. |
| Trigger | A pre-agreed forecast or observed threshold that releases planned action or finance when it is crossed. |
| Hindcast | A forecast run for past seasons to measure how skilful the forecasting system would have been. |
Plains, deltas and basins, the corridors that carry food to ports, and how far each division sits from one, shown for the Sahel as an example geography for this subject.
Data from the source register feeds the methods; calibration and an independent test gate the estimate and its limits; the decision stays with the competent authority.
Fig. 01Nodes carry their type, metrics and state; connectors show what feeds what; the gates are the validation steps; the timeline is one application. Design of the method's use, not a record of a run.
Datasets and records used by forecasting and time-series analysis on this platform, with the role each plays and the label its values carry. Sources are listed in the source register.
| Data | Role | Label |
|---|---|---|
| Seasonal climate forecasts (Copernicus C3S, NMME) | Forcing for outlooks | FORECAST |
| Market price time series | Price model training | OFFICIAL |
| Crop condition indicators | In-season predictors | OBSERVED |
| Past IPC and FEWS NET classifications | Verification of food insecurity projections | OFFICIAL |
Forecasts are evaluated out of sample with proper scoring rules and reliability diagrams. Rolling-origin evaluation tests performance over time.
| Check | How it is done |
|---|---|
| Hindcast verification | Skill is measured on past forecasts by lead time, region and season |
| Reliability and sharpness | Probabilities are checked for calibration as well as for resolution |
| Rolling-origin evaluation | Models are re-trained and tested through time to expose structural breaks |
| Trigger performance record | Hits, misses and false alarms are published for every trigger |
Structural breaks and regime changes undermine models trained on the past. Forecast skill typically declines rapidly with lead time.
Climate and Hazards stewards forecasting and time-series analysis for the Consortium and reviews how it is applied across platforms.
The current service level is R0: information and enquiries. An enquiry receives a reference and names the next step; applications, payments, compute allocations and registrations open only when their team, terms and review capacity exist.