Title of article :
Analysis and Modeling of Yield, CO2Emissions, and Energy for Basil Production in Iran using Artificial Neural Networks
Author/Authors :
Rostami, Sajad Department of Mechanical Engineering of Biosystem - Shahrekord University , Choobin, Somayeh Department of Mechanical Engineering of Biosystem - Shahrekord University , Hosseinzadeh Samani, Bahram Department of Mechanical Engineering of Biosystem - Shahrekord University , Esmaeili, Zahra Department of Mechanical Engineering of Biosystem - Shahrekord University , Zareiforoush, Hemad Department of Agricultural Mechanization Engineering - Faculty of Agricultural Sciences - University of Guilan
Abstract :
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).
Keywords :
artificial neural networks , basil , CO2 , energy flow
Journal title :
Astroparticle Physics
