Determining cropping patterns with emphasis on optimal energy consumption using LCA and multi-objective planning: a case study in eastern Lorestan Province, Iran

The evaluation of energy efficiency is a key principle in agroecosystem management. In this article, the optimal cropping pattern of irrigated and rainfed lands in the eastern Lorestan province is proposed using multi-objective nonlinear programming (MOP) to maximize net profit, energy efficiency, and net energy and minimize non-renewable energy and GWP. The results showed that in the MOP pattern, the indirect energy levels of irrigated and rainfed lands decreased by 1.90E+13 and 4.80E+13 J in the study area, respectively; non-renewable energy levels decreased by 3.00E+12 and 3.20E+13 J, respectively; and the GWP levels decreased by 1.00E+06 and 9.00E+05 kg CO2-eq in the region compared to the existing pattern. In the MOP pattern, the energy efficiency of irrigated and rainfed lands increased by 0.20 and 0.19, respectively, compared to the existing state. By implementing the optimal cropping pattern, energy inputs can be reduced by 1.20E+14 J and net profit increased by 968,483 USD. By using the proposed pattern, in addition to selecting the appropriate model and optimizing the use of water and land resources, effective steps can be taken for enhancing the profit and declining the energy consumption and environmental impacts.

Automated summary

In this study, a multi-objective nonlinear programming analysis was used to propose an optimal cropping pattern for irrigated and rainfed lands in eastern Lorestan province to maximize net profit and net energy while minimizing non-renewable energy and global warming potential. The results showed that implementing the proposed pattern led to improved energy efficiency and reduced energy consumption and environmental impact.