Assessment and Modeling of Energy Consumption, Yield and Greenhouse Gas Emissions of Irrigated Chickpea Production in Isfahan Province

Document Type : Research Paper

Authors

1 Ph.D. Student, Department of Agricultural Machinery Engineering, Ramin Agriculture and Natural Resources University of Ahvaz, Ahvaz, Iran

2 Associate Professor, Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, University College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran

3 Assistant Professor, Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, University College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran

Abstract

This study was conducted to investigate and model the energy consumption and greenhouse gas emissions of irrigated chickpea cultivation in Isfahan province using multilayer perceptron artificial neural network (ANN). The amount of each consumed inputs in production were collected from 110 producers of chickpea randomly by a questionnaire. The total energy consumption, product yield and energy ratio in chickpea production were calculated as 33211.18 MJ/ha, 2276.36 kg/ha, and 1.02, respectively. Nitrogen fertilizer with 9808 MJ/ha had the highest amount of consumed energy. Total greenhouse gas (GHG) emissions were calculated 965.20 kg CO2eq. ha-, in which, electricity and diesel fuel had the highest amount of total GHG emissions with 36% and 34%, respectively. An ANN model with 13-7-2 topology was recognized as the best model for prediction of yield and total GHG emissions. Based on this ANN model, the values of determination coefficient in prediction of yield and total GHG emissions were determined as 0.929 and 0.979, respectively. The results of sensitivity analysis of the model showed that agricultural machinery inputs had the highest impact on yield and total GHG emissions.

Highlights

Anonymous (2013). Annual agricultural statistics, Ministry of Jihad-e-Agriculture of Iran (www.maj.ir) [In Persian].

Dyer, J.A. and Desjardins, R.L. (2006). Carbon dioxide emissions associated with the manufacturing of tractors and farm machinery in Canada. Biosystems Engineering, 93(1), 107-118.

Elhami, B., Akram, A.  and Khanali, M. (2016a). Optimization of energy consumption and environmental impacts     of chickpea production using data envelopment analysis (DEA) and multi objective genetic algorithm (MOGA) approaches. Information Processing in Agricuture, 3(3), 190-205.

Elhami, B., Akram, A., Khanali, M. and Mousavi-Avval, S.H. (2016b). Application of ANFIS and Cobb-Douglas models to predict the output energy and benefit to cost ratio of lentil and chickpea production (a case study in Iran). Energy Equipment and System, 4(2), 255-270.

FAO (2014). (www.fao.org).

Ghaderpour, O. and Rafiee, S. (2016). Analysis and Modeling of Energy and yield of reinfed Chickpea Production in Bokan County. Iranian Journal of Biosystems engineering, 47(4), 7011-720 [In Farsi].

Ghasemi-Mobtaker, H., Akram, A., Keyhani, A. and Mohammadi, A. (2012). Optimization of energy required for alfalfa production using data envelopment analysis approach. Energy for Sustainable Development, 16(2), 242-248.

Hashem, S (1993). Sensitivity analysis for feed forward artificial neural networks with differentiable activity functions. International conference on neural network, Baltimore: IEEE, 1, p. 419–429.

Harouni, S., Sheikh Davoudi, M.J. and Kiani, M. (2015). Modeling of energy consumption and amount of green house emissions in the process of sugar cane production in Raton field using artificial neural networks (a case study: Debal KHAZAEI,s agro-industry in Khuzestan province). Journal of Agricultural Machinery Mechanics 4(2), 11-19. [in Persian].

IPCC. (2007). IPCC Assessment Report 4. (www.ipcc.ch).

Khanna, T. (1990). Foundation of neural networks. Addison-Wesley Publishing Company, U.S.A.

Khoshnevisan, B., Rafiee, S., Omid, M., Yousefi, M. and Movahedi M. (2013). Modeling of energy consumption and GHG (greenhouse gas) emissions in wheat production in Esfahan province of Iran using artificial neural networks. Energy, 52, 333-338.

Khoshnevisan, B., Rafiee, S., Omid, M., Mousazadeh, H. and Rajaeifar M.A. (2014). Application of artificial neural networks for prediction of output energy and GHG emissions in potato production in Iran. Agricultural System, 123, 120-127.

Koocheki, A., Ghorbani, R., Monadi, F., Alizadeh, Y., and Moradi R. (2011). Pulses Production Systems in Term of Energy Use Efficiency and Economical Analysis in Iran. International Journal of Energy Economics and Policy 4(1), 95-106.

Lai, R. (2004). Carbon emission from farm operations. Environment International, 30(7), 981-990

Lu, M., AbouRizk, S. and Hermann U. (2001). Sensitivity analysis of neural networks in spool fabrication productivity studies. Journal of Computing in Civil Engineering, 15, 299–308.

Nabavi-Pelesaraei, A., Abdi, R., Rafiee, S. and Ghasemi-Mobtaker, H. (2014). Optimization of energy required and greenhouse gas emissions analysis for orange producers using data envelopment analysis approach. Journal of Cleaner Production, 65, 311-317.

Nabavi-Pelesaraei, A., Abdi, A. and Rafiee, S. (2016a). Neural network modeling of energy use and greenhouse gas emissions of watermelon production systems. Journal of the Saudi Society of Agricultural Sciences, 15(1), 38-47.

Nabavi-Pelesaraei, A., Rafiee, S., Hosseinzadeh-Bandbafha, H. and Shamshirband, S. (2016b). Modeling energy consumption and greenhouse gas emissions for kiwifruit production using artificial neural networks. Journal of Cleaner Production, 133, 924–931.

Nourani, V. and Sayyah Fard, M. (2012) Sensitivity analysis of the artificial neural network outputs in simulation of the evaporation process at different climatologic regimes. Advances in Engineering Software, 47, 127–146.

Pahlavan, R., Omid, M., and Akram, A. (2012). Energy input-output analysis and application of artificial neural networks for predicting greenhouse basil production. Energy, 37(1), 171-176.

Pishgar-Komleh, S.H., Keyhani, A., Mostofi-Sarkari, M.R. and Jafari, A. (2012). Energy and economic analysis of different seed corn harvesting systems in Iran. Energy, 43(1), 469-476.

Rahimyan, B. (2015). Determination of economic, energy and environmental indicators of some crops (sugar beet, wheat and pea) in West Azarbaijan Province (Boukan Region) using computional intelligence techniques. Msc. dissertation, Unevercity of Tehran [In Farsi].

Safa, M. and Samarasinghe, S. (2011). Determination and modeling of energy consumption in wheat production using neural networks: “A case study in Canter bury province, New Zealand”. Energy, 36(8), 5140-5147.

Salami, P. and Ahmadi, H. (2010). Energy inputs and outputs in a chickpea production system in Kurdestan, Iran. African Crop Science Journal, 18(2), 51-57.

Sohrabi, Y., Heidari, G. and Esmailpoor, B. (2008). Effect of salinity on growth and yield of Desi and Kabuli Chickpea cultivars. Pakistan Journal of Biological Sciences, 11(4), 664-667.

 Taghavifar, H. and Mardani, A. (2015). Prognostication of energy consumption and greenhouse gas (GHG) emissions analysis of apple production in West Azarbayjan of Iran using artificial neural network. Journal of Cleaner Production, 87, 159-167.

 

 

 

 

 

 

 

 

 

               

 

Keywords

Main Subjects

References

Anonymous (2013). Annual agricultural statistics, Ministry of Jihad-e-Agriculture of Iran (www.maj.ir) [In Persian].
Dyer, J.A. and Desjardins, R.L. (2006). Carbon dioxide emissions associated with the manufacturing of tractors and farm machinery in Canada. Biosystems Engineering, 93(1), 107-118.
Elhami, B., Akram, A.  and Khanali, M. (2016a). Optimization of energy consumption and environmental impacts     of chickpea production using data envelopment analysis (DEA) and multi objective genetic algorithm (MOGA) approaches. Information Processing in Agricuture, 3(3), 190-205.
Elhami, B., Akram, A., Khanali, M. and Mousavi-Avval, S.H. (2016b). Application of ANFIS and Cobb-Douglas models to predict the output energy and benefit to cost ratio of lentil and chickpea production (a case study in Iran). Energy Equipment and System, 4(2), 255-270.
FAO (2014). (www.fao.org).
Ghaderpour, O. and Rafiee, S. (2016). Analysis and Modeling of Energy and yield of reinfed Chickpea Production in Bokan County. Iranian Journal of Biosystems engineering, 47(4), 7011-720 [In Farsi].
Ghasemi-Mobtaker, H., Akram, A., Keyhani, A. and Mohammadi, A. (2012). Optimization of energy required for alfalfa production using data envelopment analysis approach. Energy for Sustainable Development, 16(2), 242-248.
Hashem, S (1993). Sensitivity analysis for feed forward artificial neural networks with differentiable activity functions. International conference on neural network, Baltimore: IEEE, 1, p. 419–429.
Harouni, S., Sheikh Davoudi, M.J. and Kiani, M. (2015). Modeling of energy consumption and amount of green house emissions in the process of sugar cane production in Raton field using artificial neural networks (a case study: Debal KHAZAEI,s agro-industry in Khuzestan province). Journal of Agricultural Machinery Mechanics 4(2), 11-19. [in Persian].
IPCC. (2007). IPCC Assessment Report 4. (www.ipcc.ch).
Khanna, T. (1990). Foundation of neural networks. Addison-Wesley Publishing Company, U.S.A.
Khoshnevisan, B., Rafiee, S., Omid, M., Yousefi, M. and Movahedi M. (2013). Modeling of energy consumption and GHG (greenhouse gas) emissions in wheat production in Esfahan province of Iran using artificial neural networks. Energy, 52, 333-338.
Khoshnevisan, B., Rafiee, S., Omid, M., Mousazadeh, H. and Rajaeifar M.A. (2014). Application of artificial neural networks for prediction of output energy and GHG emissions in potato production in Iran. Agricultural System, 123, 120-127.
Koocheki, A., Ghorbani, R., Monadi, F., Alizadeh, Y., and Moradi R. (2011). Pulses Production Systems in Term of Energy Use Efficiency and Economical Analysis in Iran. International Journal of Energy Economics and Policy 4(1), 95-106.
Lai, R. (2004). Carbon emission from farm operations. Environment International, 30(7), 981-990
Lu, M., AbouRizk, S. and Hermann U. (2001). Sensitivity analysis of neural networks in spool fabrication productivity studies. Journal of Computing in Civil Engineering, 15, 299–308.
Nabavi-Pelesaraei, A., Abdi, R., Rafiee, S. and Ghasemi-Mobtaker, H. (2014). Optimization of energy required and greenhouse gas emissions analysis for orange producers using data envelopment analysis approach. Journal of Cleaner Production, 65, 311-317.
Nabavi-Pelesaraei, A., Abdi, A. and Rafiee, S. (2016a). Neural network modeling of energy use and greenhouse gas emissions of watermelon production systems. Journal of the Saudi Society of Agricultural Sciences, 15(1), 38-47.
Nabavi-Pelesaraei, A., Rafiee, S., Hosseinzadeh-Bandbafha, H. and Shamshirband, S. (2016b). Modeling energy consumption and greenhouse gas emissions for kiwifruit production using artificial neural networks. Journal of Cleaner Production, 133, 924–931.
Nourani, V. and Sayyah Fard, M. (2012) Sensitivity analysis of the artificial neural network outputs in simulation of the evaporation process at different climatologic regimes. Advances in Engineering Software, 47, 127–146.
Pahlavan, R., Omid, M., and Akram, A. (2012). Energy input-output analysis and application of artificial neural networks for predicting greenhouse basil production. Energy, 37(1), 171-176.
Pishgar-Komleh, S.H., Keyhani, A., Mostofi-Sarkari, M.R. and Jafari, A. (2012). Energy and economic analysis of different seed corn harvesting systems in Iran. Energy, 43(1), 469-476.
Rahimyan, B. (2015). Determination of economic, energy and environmental indicators of some crops (sugar beet, wheat and pea) in West Azarbaijan Province (Boukan Region) using computional intelligence techniques. Msc. dissertation, Unevercity of Tehran [In Farsi].
Safa, M. and Samarasinghe, S. (2011). Determination and modeling of energy consumption in wheat production using neural networks: “A case study in Canter bury province, New Zealand”. Energy, 36(8), 5140-5147.
Salami, P. and Ahmadi, H. (2010). Energy inputs and outputs in a chickpea production system in Kurdestan, Iran. African Crop Science Journal, 18(2), 51-57.
Sohrabi, Y., Heidari, G. and Esmailpoor, B. (2008). Effect of salinity on growth and yield of Desi and Kabuli Chickpea cultivars. Pakistan Journal of Biological Sciences, 11(4), 664-667.
 Taghavifar, H. and Mardani, A. (2015). Prognostication of energy consumption and greenhouse gas (GHG) emissions analysis of apple production in West Azarbayjan of Iran using artificial neural network. Journal of Cleaner Production, 87, 159-167.
 
 
 
 
 
 
 
 
               
 
Iranian Journal of Biosystems Engineering
Volume 49, Issue 3
November 2018
Pages 409-421

Files

History

  • Receive Date: 03 October 2017
  • Revise Date: 01 January 2018
  • Accept Date: 03 February 2018

Share

How to cite

Statistics