Analysis and Modeling of Yield, CO2 Emissions, and Energy for Basil Production in Iran using Artificial Neural Networks

The present study attempts to investigate the potential relationship between input energies, performance production of greenhouse basil, and greenhouse gases emitted from this product. The data were collected from 24 greenhouses using a questionnaire and verbal interaction with farmers. Results of the study showed that the total input energy and total output energy for basil production were 119,852.9 MJ/ha and 61,040 MJ/ha, respectively. The highest rate of energy consumption was related to electricity (52,200 MJ/ha), followed by plastic (23,220 MJ/ha) and chemical fertilizers (13,894 MJ/ha). The energy and productivity indices were estimated at 0.45 and 0.21, respectively, which indicated that the efficiency of energy in the agricultural sector was low. In addition, it was found that the pure energy index and total greenhouse gases emitted from basil production were equal to -722,706.9 and 9,595.6 kg (CO2), respectively. The highest emission of greenhouse gases was attributed to electricity (2,216 kg/CO2). Results of modeling proved that artificial neural networks can predict basil performance and CO2 emissions with a high degree of accuracy (R2=0.99 and MSE= 0.00023).