Green Resilience
Research & development

Applied research for practical climate resilience.

Green Resilience develops climate-risk models, indicators, and decision-support tools that connect scientific insight to operational action.

Research focus

Our R&D work focuses on turning weather, satellite, agronomic, operational, and climate data into practical tools for anticipation, adaptation, and resilience.

Research areas

Multi-hazard agricultural risk

Understanding how drought, heat, excess water, disease pressure, and crop development stages interact to affect production risk.

Crop disease forecasting

Developing weather-dependent indicators to anticipate conditions favourable to crop diseases.

Satellite and weather-data fusion

Combining forecasts, observations, satellite indicators, and field context to monitor evolving climate risks.

AI-supported recommendations

Using AI and structured rules to translate complex risk signals into clear recommendations, while maintaining expert oversight and guardrails.

Operational impact modelling

Connecting climate hazards to impacts on logistics, safety, productivity, maintenance, and continuity.

Strategic and financial risk analysis

Supporting organisations in understanding how climate risk affects planning, investment, insurance, and resilience decisions.

From research to product

Each step is designed so that scientific insight ends as something a decision-maker can act on.

  1. 1Research insight
  2. 2Indicator design
  3. 3Platform logic
  4. 4Alerts & recommendations
  5. 5Reports & decisions
Model to operations

From model logic to operational use

Green Resilience R&D is designed to move from scientific indicators to decision-support outputs that real users can act on.

  1. 01

    Define the decision

    Examples: spray, delay, irrigate, monitor, escalate, close access, schedule maintenance, stress-test assets.

  2. 02

    Identify relevant hazards

    Rainfall, heat, wind, humidity, soil moisture, drought, flood, disease-conducive conditions, lightning, visibility, compound risk.

  3. 03

    Configure thresholds

    Thresholds can reflect agronomic logic, operational constraints, safety rules, SOPs, or partner-defined tolerances.

  4. 04

    Generate risk scores and windows

    Risk models convert indicators into severity bands, operational windows, and advisory outputs.

  5. 05

    Deliver and validate

    Outputs are delivered through dashboards, alerts, APIs, WhatsApp, SMS, reports, or partner workflows, then refined through feedback and local validation.

Risk score building blocks

How risk scores are built

Risk scores combine hazards, thresholds, persistence, compound conditions, and operational context.

Threshold exceedance

Examples: wind above a defined limit, rainfall above a defined amount, heat index above a safety threshold.

Rolling windows and persistence

Examples: rainfall accumulation over 6, 12, 24, or 72 hours; multi-day heat stress; dry spell duration.

Compound triggers

Examples: wind plus low humidity for drift or dust risk; rain plus antecedent wetness for access risk; temperature plus humidity for disease pressure.

Operational windows

Examples: spray windows, access windows, safe work windows, maintenance windows, irrigation windows.

Severity bands and escalation

Low, moderate, high, and critical categories with configurable recipients and escalation pathways.

Decision-grade outputs

Go / caution / no-go guidance with short rationale, traceability, and lead-time framing.

Low
Moderate
High
Critical
Responsible decision intelligence

Responsible decision intelligence

Green Resilience should not provide overconfident recommendations where data is incomplete, ambiguous, outside supported scope, or insufficient. The system should distinguish between weather alerts, probable operational or agronomic implications, and confirmed field-level diagnoses.

Clear recommendation

Where confidence is high, the system provides concise and actionable guidance.

Cautious guidance

Where uncertainty exists, the system explains the main limiting factor.

Request for more information

Where context is missing, the system asks rather than guesses.

Human escalation

Where local observation or professional judgement is required, the system recommends escalation.

Collaboration and partnerships

We collaborate with research, development, and industry partners. Logos will be added once authorised.

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Logos and references shown on this page are placeholders. Real partner and client materials added once authorised.

Publications and outputs

A curated list of outputs will be published here. The categories below are placeholders.

Publications
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Technical notes
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White papers
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Research collaborations
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Conference presentations
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Interested in research collaboration?

Green Resilience is open to partnerships that connect applied climate science, operational decision-making, and resilience outcomes.