Data envelopment analysis and multi-objective genetic algorithm based optimization of energy consumption and greenhouse gas emissions in rice-wheat system

This study seeks to optimize energy use and reduce greenhouse gas emissions in rice-wheat cropping systems across Chhattisgarh, Bihar, and Punjab, India, using Data Envelopment Analysis and a multi-objective genetic algorithm. Energy inputs, including labour, machinery, diesel, fertilizers, herbicides, pesticides, fungicides, and irrigation, were evaluated across 65 farms. The average energy input was 39,706 ± 4877 MJ ha⁻1, while the average output was 140,961 MJ ha⁻1. The highest energy expenditures were attributed to nitrogen (15,995 ± 2973 MJ ha⁻1), diesel (5978 ± 358 MJ ha⁻1), and machinery (4438 ± 141 MJ ha⁻1). Data envelopment analysis results indicated that 26.15 % of farms operated at technical efficiency, with an average technical efficiency score of 0.833 and scale efficiency of 0.838. The Banker, Charnes, and Cooper model suggested an optimal energy input of 24,635 MJ ha⁻1. multi-objective genetic algorithm further optimized energy use, achieving a reduction of 13,440 MJ ha−1 compared to data envelopment analysis results alone. Conventional farming systems emitted 67,410 kg CO2-eq ha⁻1, while optimized farms achieved a reduction of 34 kg CO2-eq ha⁻1. These findings highlight the potential for substantial energy savings and greenhouse gas reductions through optimized input management, promoting more sustainable agricultural practices by minimizing reliance on chemical fertilizers, diesel, and machinery in rice-wheat systems. [Display omitted] •Energy input and output data collected from 270 farmers.•Average energy input for rice-wheat systems was 39,706 MJ ha−1.•Following DEA optimized DMUs average 15268 MJ ha−1 energy could be saved.•MOGA optimization cut energy use by 13,440 MJ ha−1 compared to DEA results.•Efficient farms emitted 34 kg CO2-eq ha−1 less than inefficient farms.