Optimization, Sensitivity Analysis, and Global Warming Potential Assessment of the Canola Production System in Mehran County

Efficient energy management and reduction of environmental impacts are essential for sustainable agricultural production. This study aimed to optimize energy consumption, evaluate economic performance, and assess greenhouse gas emissions and global warming potential (GWP) of the canola production system in Mehran County. Data were collected through questionnaires and face-to-face interviews with farmers, and all inputs and outputs were converted into energy equivalents. Energy indices and economic indicators were calculated, and the Cobb–Douglas production function was applied to analyze input sensitivity. Technical, pure technical, and scale efficiencies were estimated using Data Envelopment Analysis (DEA). Results showed that diesel fuel and electricity were the main contributors to greenhouse gas emissions. Input optimization reduced CO₂, N₂O, and CH₄ emissions and significantly decreased GWP, while improving energy efficiency, economic viability, and overall sustainability of the production system.
Introduction
Canola (Brassica napus L.) is an important oilseed crop widely used for edible oil production, animal feed, and industrial applications. Expansion of canola cultivation has increased the consumption of energy-intensive inputs such as fossil fuels, chemical fertilizers, electricity, and agricultural machinery. Although these inputs improve yield, their excessive use raises production costs and intensifies environmental impacts, particularly greenhouse gas emissions and global warming potential. Evaluating energy consumption patterns and improving input efficiency are therefore essential for enhancing productivity and long-term sustainability. The Cobb–Douglas production function is commonly used to analyze input–output relationships and returns to scale, while Data Envelopment Analysis (DEA) provides an effective tool for measuring technical efficiency and identifying input-saving potentials. In addition, assessing greenhouse gas emissions associated with agricultural inputs is critical for mitigating climate change. Accordingly, this study integrates energy, economic, efficiency, and environmental analyses to evaluate and optimize the canola production system in Mehran County.
Materials and Methods
Data were collected from 32 canola-producing farmers in Mehran County using questionnaires and face-to-face interviews. Quantities of all inputs and outputs were converted into energy equivalents based on standard coefficients. Energy indices, including energy efficiency, energy productivity, energy intensity, and net energy, were calculated to assess system performance and resource use efficiency. Economic indicators such as total production cost, gross income, net profit, and benefit-to-cost ratio were estimated to evaluate economic viability. Sensitivity analysis and the Cobb–Douglas production function were employed to determine the contribution and elasticity of different inputs to crop yield. Technical efficiency, pure technical efficiency, and scale efficiency were estimated using Data Envelopment Analysis (DEA) under constant and variable returns to scale. Greenhouse gas emissions (CO₂, N₂O, and CH₄) related to fuel, fertilizer, and electricity consumption were calculated, and global warming potential (GWP) was estimated to assess the environmental effects of input optimization.
 
Results and Discussion
The results indicated that diesel fuel and electricity consumption accounted for the largest share of greenhouse gas emissions and global warming potential in the canola production system. Optimization of input use, particularly through reducing excessive labor, nitrogen fertilizer, and electricity consumption and improving diesel fuel and phosphorus fertilizer management, led to a substantial reduction in CO₂, N₂O, and CH₄ emissions. Consequently, the GWP of the production system decreased significantly, demonstrating the effectiveness of targeted energy management strategies. DEA results revealed that a considerable number of farms operated below the efficiency frontier due to technical or scale inefficiencies. Adjusting input use enabled these farms to achieve optimal efficiency levels and resulted in potential energy savings of approximately 15–35% without reducing yield. These findings confirm that efficiency improvement can simultaneously enhance economic performance and environmental sustainability.
Conclusion
This study evaluated energy consumption, economic performance, and environmental impacts of the canola production system in Mehran County. Diesel fuel and electricity were identified as the major contributors to greenhouse gas emissions and global warming potential, emphasizing the importance of managing energy-intensive inputs. Optimizing input use specially reducing excessive labor, nitrogen fertilizer, and electricity consumption while improving diesel fuel and phosphorus fertilizer management significantly reduced CO₂, N₂O, and CH₄ emissions and lowered GWP. DEA results showed that many farms were technically or scale inefficient, and appropriate input adjustments could save 15–35% of energy without compromising yield. Sensitivity analysis using the Cobb–Douglas production function further confirmed the overuse of some inputs and the potential for yield improvement through optimal input allocation. Overall, integrating energy, economic, efficiency, and environmental analyses provides a practical and robust framework for improving the sustainability of mechanized canola production systems.