Sensitivity analysis of greenhouse gas emissions at farm level: case study of grain and cash crops

Sensitivity analysis is useful to downgrade/upgrade the number of inputs to limit greenhouse emissions and enhance crop yield. The primary data from the 300 rice (grain crop) and 300 cotton (cash crop) farmers were gathered in face-to-face interviews by applying a multistage random sampling technique using a well-structured pretested questionnaire. Energy use efficiency was estimated with data envelopment analysis (DEA) model, and a second-stage regression analysis was conducted by applying Cobb–Douglas production function to evaluate the influencing factors affecting. The results exhibit that chemical fertilizers, diesel fuel and water for irrigation are the major energy inputs that are accounted to be 15,721.55, 10,787.50 and 6411.08 MJ ha−1 for rice production, while for cotton diesel fuel, chemical fertilizer and water for irrigation were calculated to be 13,860.94, 12,691.10 and 4456.34 MJ ha−1, respectively. Total GHGs emissions were found to be 920.69 and 954.71 kg CO2eq ha−1 from rice and cotton productions, respectively. Energy use efficiency (1.33 and 1.53), specific energy (11.03 and 7.69 MJ ha−1), energy productivity (0.09 and 0.13 kg MJ−1) and energy gained (14,497.85 and 20,047.56 MJ ha−1) for rice and cotton crop, respectively. Moreover, the results obtained through the second-stage regression analysis revealed that excessive application of fertilizer had a negative impact on the yield of rice and cotton, while farm machinery, diesel fuel and biocides had a positive effect. We hope that these findings could help in the management of the energy budget that we believe will reduce the high emissions of GHGs to address the growing environmental hazards.