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

Abstract

With the rapid growth of population and economy, shortage and mismatch of land and water resources have deepened the need for cropping pattern optimization. In the context of the sustainable development of agriculture, cropping pattern optimization should not only pursue economic benefits, but the consequent environmental effects also deserve equal attention. Meanwhile, climate change increases the complexity of balancing conflicts of economic-environmental system by cropping pattern optimization. Therefore, this paper builds a multi-objective programming model for Economic-Environmental Synergistic Optimization for Cropping Pattern under Climate Change (EESO-CP-CC) model, with the goals of economic benefit increment and environmental pollutants emission reduction. The EESO-CP-CC model couples a non-point source pollution input-output model, a one-dimensional water quality model and an economic benefit function into an integrated framework. Fuzzy method was used to solve the optimization model, and the stochastic uncertainty of water supply under climate change was quantified by the integration of Bayesian approach and interval linear regression. The model was applied to Jinxi Irrigation District (JXID) in Heilongjiang Province, northeast of China. Results show that by adjusting the acreage of rice, corn and soybean, the harmony degree of economy-society-environment system increased by 10.7% compared to the current situation, indicating that the model tends to achieve the best possible economic benefits while ensuring the environmental effects. Compared with actual cropping pattern, the pollutants emissions reduced by 24.7% and 3% from corn and soybean, respectively. However, this led to a decrease of economic benefit by 8% in exchange, showing the trade-off between environmental pollution reduction and economic benefits improvement. The output coefficients of nitrogen and phosphorus pollutants were optimized, with the optimal output reducing by 20% compared to the standard. Cropping pattern and water resources allocation vary with different climate change conditions, however, the amplitude of variation is modest, indicating that the model can cope well with the changing environment. The developed model can help achieve synergistic development of economic benefits and environmental effects, and thus promote sustainable development of irrigation areas, and improve the coping capacity of agricultural water and land under climate change, by cropping pattern optimization and planning.

Keywords: Agricultural non-point source pollution; Crop pattern; Export coefficient method; Multi-objective optimization; Uncertainty; Water quality model.