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
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.