Crop pattern optimization for the coordination between economy and environment considering hydrological uncertainty.

Area of Science:

• Agricultural Science
• Environmental Science
• Climate Change Adaptation

Background:

• Growing populations and economies strain land and water resources, necessitating cropping pattern optimization.
• Sustainable agriculture requires balancing economic benefits with environmental impacts.
• Climate change exacerbates the complexity of optimizing cropping patterns for economic-environmental systems.

Purpose of the Study:

• To develop an Economic-Environmental Synergistic Optimization for Cropping Pattern under Climate Change (EESO-CP-CC) model.
• To simultaneously increase economic benefits and reduce environmental pollutant emissions.
• To address the uncertainty of water supply under climate change.

Main Methods:

• Constructed a multi-objective programming model integrating non-point source pollution, water quality, and economic benefit functions.
• Employed a fuzzy method for optimization.
• Quantified water supply uncertainty using Bayesian approach and interval linear regression.

Main Results:

• The EESO-CP-CC model increased the economy-society-environment system harmony by 10.7% in the Jinxi Irrigation District.
• Pollutant emissions from corn and soybean decreased by 24.7% and 3%, respectively, with an 8% reduction in economic benefit.
• Optimized output coefficients for nitrogen and phosphorus pollutants were reduced by 20%; the model showed resilience to climate change variations.

Conclusions:

• The developed model effectively achieves synergistic economic and environmental development in cropping patterns.
• It promotes sustainable agricultural development and enhances coping capacity for water and land resources under climate change.
• Cropping pattern optimization is a viable strategy for balancing competing objectives in agricultural systems.