Water-Land-Crop Allocation Optimization Planning Tool
This web application is designed specifically for growers in California's Central Valley who are navigating reduced and uncertain water supplies under the Sustainable Groundwater Management Act (SGMA). The tool helps users evaluate alternative common row crop mixes and water-land allocation strategies that balance profitability, water supply availability, and agronomic crop-rotation constraints.
Built on the optimization framework presented in An optimization framework for multi-year planning of land and water allocation*, this tool integrates economic, water, and agronomic constraints to support real-world decision making.
What the Tool Does:
> Identifies the most profitable row crop mix under limited water
Uses multi-objective optimization to determine combinations of crops and land allocation that maximize profit while respecting reduced water supply typical under SGMA.
> Accounts for agronomic crop-rotation rules
Ensures that solutions meet agronomic crop rotation constraints so that recommended crop mixes remain feasible over multiple years.
> Simulates multi-year planning under water scarcity
Explore how different water-availability scenarios (normal years, drought years, SGMA-restricted pumping) influence long-term profitability and crop choices.
> Evaluates trade-offs between water use and economic return
Includes crop-water production relationships, allowing users to assess strategies that rely on deficit irrigation when water supplies fall short.
> Compares alternative management strategies
Test "what-if" scenarios by changing crop mix, splitting land, or tightening water budgets, and instantly see how each scenario affects profit and water use.
Why This Matters for Central Valley Growers:
SGMA is reshaping water availability across the Central Valley. With declining groundwater allocations and increasing pressure to produce more with less, growers need tools that quantify the economic and agronomic consequences of shifting crop choices.
References:
Acknowledgements:
This work was funded by USDA NIFA Award # 2021-68012-35914.
Contact Information:
Prof. Isaya Kisekka
University of California Davis
E-mail: ikisekka@ucdavis.edu