GlobalFoodCrisis.com
Climate Sensitivity

Climate Yield Loss Estimator

Model yield impact from temperature anomalies and rainfall change using transparent sensitivity assumptions. Outputs include yield loss, production loss, and optional value/calorie impacts.

Planning tool only. Actual yield response depends on heat timing (flowering), soil moisture, irrigation access, and cultivar resilience.

Climate Sensitivity Yield
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Inputs Temperature + Rainfall

Enter your climate anomaly scenario

Use a temperature anomaly (ΔT) and rainfall change (%). Then apply crop sensitivity assumptions to estimate yield impact. If you have irrigation, you can reduce rainfall sensitivity.

Degrees relative to normal season (°C by default).
We convert to °C internally.
Negative = drier; positive = wetter.
Higher irrigation reduces rainfall sensitivity.
Preset fills sensitivities below; you can still edit them.
Amplifies temperature impacts if heat hits critical stages.
Example: 4% yield loss per +1°C anomaly.
Applied mainly when rainfall is lower than normal.
Prevents unrealistic outputs in extreme scenarios.
Some values were out of range and were clamped to valid limits.
Model logic: temperature loss = ΔT × temp sensitivity × timing factor. Rain loss applies when rainfall decreases; irrigation reduces that loss proportionally. Total is capped by the max loss setting.
Results Temperature Rainfall

Climate-driven yield impact

Outputs show how much of the loss comes from temperature vs rainfall, plus production and optional economic/energy impacts.

Total yield loss (%)

—

capped

Production loss

—

annual

Temperature-driven loss

—

percent points

Rainfall-driven loss

—

percent points

Component Input Effect Notes
Temperature anomaly — — ΔT × sensitivity × timing
Rainfall change — — Only applied when drier; reduced by irrigation
Irrigation damping — — Percent of rain-loss avoided

Value of lost production

—

if crop price provided

Calories lost (annual)

—

if kcal/kg provided

Narrative summary
Enter values to see a narrative summary.
Want the next upgrade? We can add a “multi-year frequency” model (e.g., 1-in-5 heat waves), link outputs to the Food Price Shock model, and add crop-stage calendars.