Experiment
Defra Digital Twin
Build living digital representations of farms, landscapes or regulatory systems that stay connected to real-world evidence and can support simulation.
01
Problem to address
Administrative records tell Defra what was submitted or processed, but policy and operational decisions concern changing real-world systems such as landscapes, material flows and disease spread.
02
What the evidence tells us
Farmers ask whether an intervention will work here. Strategy asks where to target interventions. Field officers reconcile what is recorded with what is on the ground. That supports a living picture of place, but not yet a full digital twin.
03
The proposition to explore
The idea
Create bounded living digital representations of farms, landscapes or regulatory systems that combine current state, history, observations and models to explore change.
04
Hypothesis
If Defra progressively connects state, history, interventions and observations at the right real-world scale, it can move from retrospective reporting toward understanding and exploring system change.
05
How it could work
Start with a bounded real-world object such as a catchment. Maintain current state and history from authoritative and observational sources, then attach models that can explore how conditions may change under interventions.
06
Example: bring it to life
A landscape twin connects soil, hydrology, weather, satellite imagery, interventions, farmer observations and ecological surveys to explore where peat restoration is most likely to establish successfully.
This can be done by understanding the past to simulate possible futures.
AI and analytical models could allow Defra to ask:
“What is likely to happen if we do X?”
For example:
“If we offer this environmental intervention to farms in this catchment, what is the likely effect on water quality, biodiversity, farmer participation and public expenditure over ten years?”
Or:
“If packaging fees change in this way, how might producer behaviour, material choice and recycling volumes respond?”
Or:
“If an animal disease appears here, where is it most likely to spread next?”
07
AI opportunity
AI enables interrogation, synthesis, model selection and explanation. The twin itself also depends on connected data, real-world observations, temporal state and validated predictive/simulation models.
08
Potential value
- Better spatial targeting
- Shared operational/policy picture
- Foundation for simulation and adaptive management
- Potential reduction in blanket interventions
09
Reduce uncertainty without overcommitting
Smallest meaningful experiment
Do not “build a Defra twin”. Pick one question and one geography. Create the minimum digital representation needed to test whether a twin adds more decision value than a normal analytical model.
10
Open questions and risks
What we still need to learn
- Very broad scope can become a technology programme without a decision use-case
- Model quality and data freshness are critical
- Complex governance across data owners