Planning regional-scale water-energy-food nexus system management under uncertainty: An inexact fractional programming method

In this study, an inexact fractional programming method is employed for planning the regional-scale water energy-food nexus (WEFN) system. The IFP cannot only deal with uncertainties expressed as interval parameters, but also handle conflicts among multiple decision stakeholders. The IFP approach is then applied to planning the WEFN system of Henan Province, China. An IFP-WEFN model has been established under consideration of various restrictions related to water and energy availability, as well as food demand. Solutions of the planting areas for different crops in different periods have been generated. The results suggested that there would be a significant increase for vegetable cultivation with an increasing rate of 24.4% and 30% respectively for the conservative and advantageous conditions, followed by the fruit cultivation. In comparison, the planting area of cotton would be decreased with a decreasing rate of 21.2%, and there would also be an explicit decrease for rice cultivation. These results can help generate a desired planting scheme in order to achieve a maximized unit benefit with respect to the water utilization. Comparison between the IFP-WEFN model and the ILP-WEFN model indicates that, even though a slightly lower benefit is obtained from IFP-WENF model, it can result in a higher unit benefit than the planting scheme from ILP-WEFN model. Consequently, the IFP-WEFN model can help decision-makers identify the sustainable agricultural water resources management schemes with a priority of water utilization efficiency.

Automated summary

In this study, an inexact fractional programming method is used to plan the water energy-food nexus system at a regional scale. The method is applied to Henan Province in China, and the results show that it can generate optimal planting schemes for maximizing water utilization efficiency. Additionally, the inexact fractional programming approach provides higher unit benefits compared to integer linear programming.