تحلیل جریان انرژی تولید انگور در خراسان شمالی به روش شبکه عصبی مصنوعی

Amid, S., & Mesri-Gundoshmian, T. (2017). Prediction of output energies for broiler production using linear regression, ANN (MLP, RBF), and ANFIS models. Environmental Progress & Sustainable Energy, 36 (2) 577–585.

D’Agostino, R.B. (1986). Tests for the normal distribution. Goodness-of-fit techniques, 68, p.576.

Einset, J., & Dratt, C. (1975). Grape. In: Janick, J., and Moore, J. N. (Eds.), Advances in Fruit Breeding, (pp. 130-153).Purdu University Oress, West Lafayette.

Emadi, B., Nikkhah, A., Khojastehpour, M., & Payman, S. H. (2015). Effect of farm size on energy consumption and input costs of peanut production in Guilan province of Iran. Journal of Agricultural Machinery, 5(1) 217-228.  (In Farsi)

Erdal, G., Esengun, K., Erdal, H., & Gunduz, O. (2007). Energy use and economical analysis of sugar beet production in Tokat province of Turkey. Energy, 3235–41.

Firouzi, S., Nikkhah, A. and Rosentrater, K.A. (2017). An integrated analysis of non‐renewable energy use, GHG emissions, carbon efficiency of groundnut sole cropping and groundnut‐bean intercropping agro‐ecosystems. Environmental Progress & Sustainable Energy. DOI 10.1002/ep

Firouzi, S., Nikkhah, A., Khojastehpour, M. and M Holden, N. (2016). Energy use efficiency, GHG emissions, and carbon efficiency of paddy rice production in Iran. Energy Equipment and Systems, 4(2), 169-176.

Ghaffari-Gharebagh, A., Aghkhani, M. H., & Emadi, B. (2014). Energy and economic analysis of grape production in the Urmia city with study of effect of planting system and garden size. In: Proceedings of  8th National Congress on Agr. Machinery Eng. (Biosystem) & Mechanization of Iran. 28-29 Jan., Mashhad, Iran (In Farsi).

Hosseini, S.M.T., Sioseh Mardeh, A., Fathi, P., & Siosehmardeh, M. (2007). Application of artificial neural network (ANN) and multiple regressions for estimating assessing the performance of dry farming wheat yield in Ghorveh region, Kurdistan province. Agriculture Research, 7 (1):41-54.

Khojastehpour, M., Nikkhah, A. and Hashemabadi, D. (2015). A comparative study of energy use and greenhouse gas emissions of canola production. International Journal of Agricultural Management and Development, 5(1), 51-58.

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

Khoshroo, A., Mulwa, R., Emrouznejad, A., & Arabi, B. (2013). A non-parametric Data Envelopment Analysis approach for improving energy efficiency of grape production. Energy, 63 189-194.

Mamarabadi, M., & Rohani, A. (2017). Clustering of fungal hexosaminidase enzymes based on free alignment method using MLP neural network. Neural Computing & Applications, 28 1-11.

Mandal, K.G., Saha, K.P., Ghosh, P.K., Hati, K.M., & Bandyopadhyay, K.K. (2002). Bioenergy and economic analysis of soybean-based crop production systems in central India. Biomass and Bioenergy. 23 33–45

Mardani, A., & Taghavifar, H. (2016). An overview on energy inputs and environmental emissions of grape production in West Azerbayjan of Iran. Renewable and Sustainable Energy Reviews, 54 918-924.

Mohammadi, A., Rafiee, S., Mohtasebi, S.S., & Rafiee, H. (2010). Energy inputs – yield relationship and cost anal‌ysis of kiwifruit production in Iran. Renewable Energy. 35 1071-1075.

Nabavi-Pelesaraei, A., Abdi, R., & Rafiee, S. (2016). 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.

Ozkan, B, R. Ceylan, F., & H. Kizilay. (2011). Comparison of energy inputs in glasshouse double crop (fall and summer crops) tomato production. Renewable Energy, 361639-1644.

Ozkan, B., Fert, C. & Karadeniz, C.F. (2007). Energy and cost analysis for greenhouse and open-field grape production. Energy, 32 (8) 1500-1504.

Pishgar-Komleh, S. H., Omid, M., & Heidari, M.D. (2013). On the study of energy use and GHG (greenhouse gas) emissions in greenhouse cucumber production in Yazd province. Energy, 59 (15) 63–71.

Rajabi-Hamedani, S.R., Keyhani, A., & Alimardani, R. (2011). Energy use patterns and econometric models of grape production in Hamadan province of Iran. Energy, 36 (11) 6345-6351.

Sefeedpari, P., Shokoohi, Z., & Behzadifar, Y. (2014). Energy use and carbon dioxide emission analysis in sugarcane farms: a survey on Haft-Tappeh Sugarcane Agro-Industrial Company in Iran. Journal of Cleaner Production, 83 (15 212–219.

Singh, J. (2002). On farm energy use pattern in different cropping systems in Haryana, India. MSc. thesis, International Institute of Management University of Flensburg; Sustainable energy systems and management. Flensburg, Germany.

Singh, S., & Mittal, J. P. (1992). Energy in Production Agriculture. New Delhi: Mittal Publications.

Singleton, V. L., & Esau, P. (1969). Phenolic Substances in Grapes and Wine and Their Significance. NewYork: Academic Press.

Snedecor, G.W., & Cochran W.G. (1989). Statistical methods. Iowa City: Iowa State University Press.

Soltanali, H., Emadi, B., Rohani, A., Khojastehpour, M. and Nikkhah, A. (2016). Optimization of Energy Consumption in Milk Production Units through Integration of Data Envelopment Analysis Approach and Sensitivity Analysis. Iranian Journal of Applied Animal Science, 6(1), 15-23.

Soltani, A., Rajabi, M. H., Zeinali, E., & Soltani, E. (2013). Energy inputs and greenhouse gases emissions in wheat production in Gorgan, Iran. Energy, 50 54-61.

Taghavifar, H. & 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, 87159-167.

Taheri-Rad, A., Khojastehpour, M., Rohani, A., Khoramdel, S. and Nikkhah, A. (2017). Energy flow modeling and predicting the yield of Iranian paddy cultivars using artificial neural networks. Energy, 135, 405-412.