A bi-level multiobjective stochastic approach for supporting environment-friendly agricultural planting strategy...

Area of Science:

• Agricultural Economics
• Environmental Management
• Operations Research

Background:

• Optimizing irrigation water use efficiency is critical for sustainable agriculture, especially in arid regions.
• Balancing economic profitability with environmental protection in agricultural production presents complex challenges.
• Existing models often struggle to account for decision-maker hierarchies and stochastic environmental factors.

Purpose of the Study:

• To develop an integrated framework for improving irrigation water use efficiency and minimizing pollution in agricultural production.
• To create a bi-level multiobjective stochastic approach that incorporates regional knowledge, socioeconomic factors, and environmental impacts.
• To provide a decision-making tool for profitable and eco-friendly agricultural planting strategies.

Main Methods:

• Integration of Analytic Hierarchy Process (AHP) and Entropy (EW) methods for quantifying qualitative data.
• Application of Partial Least Squares Regression (PLS) to analyze relationships between water use and agronomic inputs.
• Development of a bi-level multiobjective stochastic programming (BMSP) model to handle multiple objectives and uncertainty.

Main Results:

• The BMSP model successfully improved irrigation water use efficiency and reduced CO2 emissions.
• The approach demonstrated the ability to expand ecosystem service values through optimized planting strategies.
• Case study in the Heihe River basin confirmed the model's efficacy in providing profitable and environmentally sound strategies.

Conclusions:

• The developed bi-level multiobjective stochastic approach offers a robust method for optimizing agricultural production in water-scarce regions.
• The BMSP model effectively addresses leader-follower decision-making dynamics and stochastic elements like runoff.
• This framework is highly applicable to arid and semi-arid regions facing similar challenges in agricultural planning and sustainable development.