Climate Resilience Through AI: From Forecast to Action
Published:
Climate change poses existential threats to global food security. While AI-powered climate models excel at prediction, a critical gap remains: translating forecasts into farmer actions. Our research addresses this through a complete causal chain from data to validated outcomes.
The Missing Link in Climate AI
Current systems provide forecasts but fail farmers because:
- Predictions aren’t translated into actionable recommendations
- Farmers lack technical expertise to interpret complex models
- No feedback loop validates intervention effectiveness
Our Complete Causal Chain
| Step | Component | Implementation | Output |
|---|---|---|---|
| 1 | Data Collection | IoT + ERA5 | Real-time farm state |
| 2 | Prediction | SEACLID + LLM | 48-72 hr scenarios |
| 3 | Decision | LLM + RAG | Actionable advice |
| 4 | Execution | IMS-ARS | Automated adjustment |
| 5 | Validation | Yield comparison | Quantified benefit |
Example: Heat Stress Response
Scenario: +5°C heat wave predicted for 3 days during rice flowering
Traditional Approach: Farmer notices wilting, reacts too late
Our AI Response:
- Climate Analyst Agent queries ERA5, identifies flowering-stage risk
- LLM generates recommendation: “Increase irrigation frequency; apply foliar spray; delay fertilization”
- Automated drip system adjusts; farmer receives SMS in local language
- Yield comparison shows +8% preservation vs. conventional response
LLM Safety Protocols
Agricultural AI must prevent harmful recommendations:
| Risk Level | Action Type | Safeguard |
|---|---|---|
| Low | Information queries | Direct response |
| Medium | Irrigation ±20% | Auto-execute with notification |
| High | Large changes | Mandatory farmer approval |
| Critical | Pesticides, harvest | Agronomist review required |
Agronomic Feasibility Bounds
We implement hard limits to prevent hallucinations:
- Nitrogen: 0-200 kg N/ha (rejects 500 kg/ha suggestions)
- Irrigation: 0-100 mm/day
- Temperature setpoints: 15-35°C
Future Research: Foundation Models for Agriculture
We’re fine-tuning LLaMA-3.1-8B on 5 million agricultural tokens from FAO, IRRI, and extension manuals to create domain-specific climate advisors that speak the language of farmers.
The goal: every smallholder farmer equipped with AI-powered climate resilience.
Aligned with SDG 2 (Zero Hunger) and SDG 13 (Climate Action)
