Document Type: Original Article

Authors

1 Department of Agricultural Machinery Engineering, Faculty of Agriculture, University of Tabriz, Iran

2 Department of Agronomy and Plant Breeding, Faculty of Agriculture, University of Tabriz, Iran

3 Department of Agricultural Mechanization Engineering, Faculty of Agriculture, University of Guilan, Iran

Abstract

This paper studies the modeling and optimization of energy use and greenhouse gas emissions of eggplant production using artificial neural network and multi-objective genetic algorithm in Guilan province of Iran. Results showed that the highest share of energy consumption belongs to diesel fuel (49.24%); followed by nitrogen (33.30%). The results indicated that a total energy input of 13910.67 MJ ha-1 was consumed for eggplant production. In ANN, the Levenberg-Marquardt Algorithm was examined to finding best topology for modeling and optimization of energy inputs an GHG emissions for eggplant production. The results of ANN indicated the best topology with 12-9-9-2 structure had the highest R2, lowest RMSE and MAPE.  Also, the multi-objective optimization was done by MOGA. In this research, 42 optimal was introduced by MOGA based minimum total GHG emissions and maximum yield of eggplant production, in the studied area. Also, the results revealed that the best generation with lowest energy use was consumed about 4597 MJ per hectare. The GHG emissions of best generation was calculated as about 127 kg CO2eq. ha-1. The potential of GHG reduction by MOGA was computed as 388.48 kg CO2eq. ha-1. Also, the highest reduction of GHG emissions belongs to diesel fuel with 65.05%

Keywords

Main Subjects

Kantharajah, AS., & Golegaonkar, PG. (2004). Somatic embryogenesis in eggplant Review. Scientia Horticulturae. 99: 107-117.
Hemmati, A., Tabatabaeefar, A., & Rajabipour, A. (2013). Comparison of energy flow and economic performance between flat land and sloping land olive orchards. Energy. 61:472-478.
IPCC. (2007). IPCC Assessment Report 4. <www.ipcc.ch>.
Dyer, J.A., Kulshreshtha, S.N., McConkey, B.G., & Desjardins, R.L. (2010). An assessment of fossil fuel energy use and CO2 emissions from farm field operations using a regional level crop and land use database for Canada. Energy. 35: 2261-2269
Khoshnevisan, B., Rafiee, S., Omid, M., & Mousazadeh, H. (2013a). Developing an artificial neural networks model for predicting output energy and GHG emission of strawberry production. International Journal of Applied Operational Research. 3(4): 43-54.
Khoshnevisan, B., Rafiee, S., Omid, M., Mousazadeh, H., & Rajaeifar, M.A. (2013b). Application of artificial neural networks for prediction of output energy and GHG emissions in potato production in Iran. Agricultural Systems. 123: 120-127.
Nabavi-Pelesaraei, A., Abdi, R., Rafiee, S., & Mobtaker, HG. (2013a). Optimization of energy required and greenhouse gas emissions analysis for orange producers using data envelopment analysis approach. Journal of Cleaner Production. http://dx.doi.org/10.1016/j.jclepro.2013.08.019.
Holland, J.H. (1975). Adaptive in Natural and Artificial Systems. Ann Arbor, MI: University of Michigan Press.
Goldberg, D.E. (1997). Genetic Algorithms, in Search, Optimization & Machine Learning. Addison
Wesley.
Bäck, T. (1996). Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms. Oxford University Press.
Hematian, A., Bakhtiari, A.A., Yaghubi, O., & Zarei-Shahamat, E. (2013). Optimization of Energy Consumption in Sugar-Beet Production Using Genetic Algorithm “A Case study in Kermanshah Province, Iran”. International journal of Agronomy and Plant Production. 4(6): 1351-1356.
Ministry of Jihad-e-Agriculture of Iran. 2012. Annual Agricultural Statistics. www.maj.ir (in Persian).
Mobtaker, HG., Keyhani, A., Mohammadi, A., Rafiee, S., & Akram, A. (2010). Sensitivity analysis of energy inputs for barley production. Agriculture, Ecosystems and Environment. 137: 367-372.
Hatirli, SA., Ozkan, B., & Fert, C. (2005). An econometric analysis of energy input-output in Turkish agriculture. Renewable and Sustainable Energy Reviews. 9: 608-623.
Mohammadshirazi, A., Akram, A., Rafiee, S., Mousavi-Avval, SH., & Bagheri Kalhor, E. An analysis of energy use and relation between energy inputs and yield in tangerine production. Renewable and Sustainable Energy Reviews. 16: 4515-4521.
Mousavi-Avval, SH., Rafiee, S., Jafari, A., & Mohammadi, A. (2011). Optimization of energy consumption for soybean production using Data Envelopment Analysis (DEA) approach. Applied Energy. 88: 3765-3772.
Rafiee, S., Mousavi-Avval, SH., & Mohammadi, A. (2010). Modeling and sensitivity analysis of energy inputs for apple production in Iran. Energy. 35: 3301-3306.
Unakitan, G., Hurma, H., & Yilmaz, F. (2010). An analysis of energy use efficiency of canola production in Turkey. Energy. 35: 3623-3627.
Nabavi-Pelesaraei, A., Abdi, R., & Rafiee, S. (2013b). Energy use pattern and sensitivity analysis of energy inputs and economical models for peanut production in Iran. International Journal of Agriculture
Kitani, O. (1999). Energy and biomass engineering. In: CIGR handbook of agricultural engineering. St. Joseph, MI: ASAE.
Dyer, JA., & Desjardins, RL. (2006). Carbon dioxide emissions associated with the manufacturing of tractors and farm machinery in Canada. Biosystems Engineering. 93(1): 107-118.
Dyer, JA., & Desjardins, RL. (2003). Simulated farm fieldwork, energy consumption and related greenhouse gas emissions in Canada. Biosystems Engineering. 85(4): 503-513.
Pishgar-Komleh, SH., Omid, M., & Heidari, MD. (2013). On the study of energy use and GHG (greenhouse gas) emissions in greenhouse cucumber production in Yazd province. Energy. 59: 63-71.
Lal, R. (2004). Carbon emission from farm operations. Environment International. 30(7): 981-990.
Najafi, G., Ghobadian, B., Tavakoli, T., Buttsworth, D.R., Yusaf, TF., & Faizollahnejad, M. (2009). Performance and exhaust emissions of a gasoline engine with ethanol blended gasoline fuels using artificial neural network. Applied Energy. 86: 630-639.
Khoshnevisan, B., Rafiee, S., Omid, M., & Mousazadeh, H. (2013c). Prediction of potato yield based on energy inputs using multi-layer adaptive neuro-fuzzy inference system. Measurement. 47: 521-530.
Zhao, Z., Chow, TL., Rees, HW., Yang, Q., Xing, Z., & Meng, FR. (2009). Predict soil texture distributions using an artificial neural network model. Computers and Electronics in Agriculture 65(1):36-48.
Zangeneh, M., Omid, M., & Akram, A. (2011). A comparative study between parametric and artificial neural networks approaches for economical assessment of potato production in Iran. Spanish Journal of
Agricultural Research. 9(3): 661-671.
Konak, A., Coit, D.W., & Smith, A.E. (2006). Multi-objective optimization using genetic algorithms: A tutorial. Reliability Engineering and System Safety. 91: 992-1007.
Uzunoz, M., Akcay, Y., & Esengun, K. (2008). Energy input-output analysis of sunflower seed (Helianthus annuus L.) oil in Turkey. Energy Sources Part B-Economics. Planning and Policy. 3: 215- 223.
Ramedani, Z., Rafiee, S., & Heidari, M.D. (2011). An investigation on energy consumption and  sensitivity analysis of soybean production farms. Energy. 36: 6340-6344.
Ghahderijani, M., Pishgar-Komleh, S.H., Keyhani, A., & Sefeedpari, P. (2013). Energy analysis and life cycle assessment of wheat production in Iran. African Journal of Agricultural Research. 8(18): 1929-39.
Rahman, MM., & Bala, BK. (2010). Modelling of jute production using artificial neural networks. Biosystems Engineering. 105(3): 350-356.
Safa, M., & Samarasinghe, S. (2011). Determination and modelling of energy consumption in wheat production using neural networks: “A case study in Canterbury province, New Zealand”. Energy. 36(8): 5140-5147.