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Multistage stochastic programming modeling for farmland irrigation management under uncertainty.
Qi Li1, Guiping Hu2
1Department of Mechanical Engineering, Beijing Jiaotong University, Beijing, China.
Plos One
|June 3, 2020
Summary
Maximizing farm profit involves considering uncertainties in crop prices, water, and precipitation. Multistage stochastic programming enhances irrigation management and boosts profitability, especially with limited water resources.
Area of Science:
- Agricultural Economics
- Operations Research
- Environmental Science
Background:
- Effective farmland management and irrigation scheduling are crucial for agricultural economic productivity.
- Uncertainties in crop prices, water availability, and precipitation significantly impact farm profitability.
Purpose of the Study:
- To develop and evaluate a multistage stochastic programming model for optimizing farm profit under uncertainty.
- To assess the effectiveness of incorporating price, water, and precipitation uncertainties into irrigation scheduling decisions.
Main Methods:
- A multistage stochastic programming model was formulated to guide pre-season (seed type, plant density) and in-season (irrigation timing, volume) decisions.
- A case study on a Nebraska farm compared stochastic programming outcomes with deterministic approaches.
Main Results:
- Two-stage stochastic programming, considering corn price and water availability, yielded a 10% profit increase.
- Multistage stochastic programming, incorporating precipitation uncertainty, provided an additional 13% profit increase.
- Stochastic models significantly outperformed deterministic models, particularly under water scarcity.
Conclusions:
- Multistage stochastic programming is a valuable tool for optimizing farm-scale irrigation management.
- This approach offers a promising strategy for enhancing farm profitability by effectively managing inherent uncertainties.
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