platform
Irrigation Strategy Optimization
A project that uses the AquaCrop crop growth simulator to optimize irrigation and field strategies.
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Decision makers
- Farmers
- Agricultural policy makers
Objectives
To optimize crop yield, water usage, and field management costs.
Deliverables
The user interface is available here: Irrigation Strategy App
Data attributes
Context
- Weather data (daily high/low temperatures, precipitation, evapotranspiration)
- Future:
- Soil features
- Crop features
Actions
- Irrigation strategy:
- Current: soil moisture thresholds
- Future: dynamic irrigation based on weather forecasts
- Field management:
- Current: mulch percentage
- Future: bunding, planting/harvesting dates, etc.
Outcomes
- Yield (tonne/ha)
- Water usage (mm)
- Mulch percentage (%)
Data
The AquaCrop simulator can be found here: AquaCrop. The Python implementation used in this project is found here: AquaCropOS
Code
The code for this project is available here: Code
Needs
List of needs:
- Contact with agricultural decision makers to validate contexts, actions, and outcomes
- What context data is available?
- Are SMT thresholds useful in practice? Would a dynamic schedule be more useful?
References
Discussion
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