Journal of Energy Bioscience 2024, Vol.15, No.3, 160-170 http://bioscipublisher.com/index.php/jeb 165 Abbas et al. (2022) illustrates the energy consumption, GHG emissions, and technical efficiency of cotton production in selected districts of Punjab, Pakistan. Panel (A) shows that nonrenewable energy sources, mainly from fossil fuels, dominate with 78.95% of the total energy, whereas renewable sources contribute only 21.05%. Panel (B) highlights that diesel fuel and irrigation water are the primary sources of GHG emissions, contributing 58% and 23% respectively, emphasizing the need for more efficient fuel use and water management in cotton farming. Panel (C) presents the technical efficiency scores, with Multan (DMU5) being the most efficient district. Panel (D) illustrates the potential resource savings if other districts improve their efficiency to the level of Multan, suggesting significant room for enhancing productivity and sustainability in cotton production. 4.4 Energy-efficient machinery and equipment The adoption of energy-efficient machinery and equipment is crucial for reducing energy consumption and improving overall farm efficiency. Modern agricultural machinery equipped with advanced technologies, such as precision planting and variable rate application, can optimize input use and minimize energy wastage. Additionally, regular maintenance and proper calibration of equipment can further enhance energy efficiency and contribute to sustainable agricultural practices (Abbas et al., 2022; Maraveas, 2022). 5 Case Studies and Best Practices 5.1 Successful implementation of optimization strategies Several case studies highlight the successful implementation of optimization strategies in agricultural systems. For instance, a study conducted in northeast China demonstrated the potential of optimizing bioenergy production by considering the energy-food-water-land nexus and livestock manure under uncertainty. This approach provided decision-makers with optimal policy options and helped identify sustainability levels in agricultural systems, ultimately promoting agricultural economy while mitigating environmental side-effects (Li et al., 2020). Another example is the use of data envelopment analysis (DEA) to optimize energy consumption in rice-wheat-green gram cropping systems under conservation tillage practices. This study found that zero tillage with 0% residue retention minimized total energy input, while reduced tillage with 0% paddy straw residue and 100% N.P.K. maximized yield energy, showcasing the effectiveness of conservation tillage in energy optimization (Bhunia et al., 2021). 5.2 Comparative analysis of different regions and farming systems Comparative analysis of different regions and farming systems reveals significant variations in energy optimization practices and outcomes. In China, a multi-objective optimization model was used to balance employment, energy consumption, water use, carbon emissions, and pollutant emissions across various provinces. The study concluded that an energy-consumption-dominated industrial restructuring pathway was the most effective in achieving sustainable development goals, highlighting the importance of regional equity and policy prioritization (Wang et al., 2020) (Figure 3). In Sri Lanka, an assessment of energy balance in diversified agricultural systems showed a negative energy balance in crop production, indicating an efficient production system, while the livestock sector exhibited higher energy loss. This study underscores the need for region-specific strategies to optimize energy use in agriculture (Dhanapala et al., 2023). Wang et al. (2020) demonstrates how total outputs by province change under three different scenarios compared to a baseline. In the employment-dominated scenario (a), most regions experience increased outputs, fostering regional development. The energy-consumption-dominated scenario (b) indicates a focus on balancing production levels, promoting central provinces while reducing outputs in some northern and eastern regions. The carbon-emissions-dominated scenario (c) highlights the sacrifices required in various provinces to meet emission targets, suggesting that only a few provinces benefit. This analysis suggests that an energy consumption policy could promote regional equity, balancing industrial growth across different provinces. 5.3 Lessons learned and replicable models The lessons learned from these case studies provide valuable insights for replicable models in other regions. The multi-objective optimization model used in China can serve as a comprehensive approach for policymakers to support sustainable development policies by balancing various resource uses and emissions (Wang et al., 2020).
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