Potential for optimization of energy consumption and costs in saffron production in central Iran through data envelopment analysis and multi‐objective genetic algorithm

Abstract

AbstractTechnical management of agricultural units plays an important role in increasing the yield, energy efficiency, and decreasing the production costs. Based on that, the present study aimed to evaluate and optimize the technical and economic efficiency in Saffron farms in the 2019–20 cropping season in Iran. Required data were collected from 70 Saffron farms through interviews and questionnaires and were analyzed and compared using two optimization methods including data envelopment analysis (DEA), and multi‐objective genetic algorithm (MOGA). Based on the results related to the energy section, the total energy input was obtained as 43,578 MJ ha−1 before any optimization, while it was determined as 36,033 and 36,910 MJ ha−1 after optimization using DEA, and MOGA, respectively. Also DEA and MOGA methods improved the energy ratio index (ER) (0.002) by 50, and 159%, respectively. Results related to the economic section showed that the total production costs were mitigated from 1260 $ ha−1 to 863.5 and 1069 $ ha−1 after optimization by DEA, and MOGA, respectively. After optimization of revenue (using MOGA method), and total costs (using MOGA, and DEA), the benefit cost ratio index was improved from 1.43 to 2.09 (using DEA), and 3.3 (using MOGA). Consequently, MOGA optimization method showed better results compared to DEA in both energy and economic sections.