Energy input-output analysis and application of artificial neural networks for predicting greenhouse basil production

Energy

Volumn 37, Issue 1, 2012, Pages 171-176

  Pahlavan, Reza ^a   Omid, Mahmoud ^a   Akram, Asadollah ^a  

^a UNIVERSITY OF TEHRAN   (Iran)

Author keywords

Artificial neural networks; Basil yield; Energy ratio; Input energy; Prediction; Sensitivity analysis

Indexed keywords

DIESEL FUELS; FERTILIZERS; FORECASTING; GREENHOUSES; MANURES; NEURAL NETWORKS; NEURONS; SENSITIVITY ANALYSIS;

BASIL YIELD; BEST MODEL; CHEMICAL FERTILIZERS; COEFFICIENT OF DETERMINATION; ENERGY RATIO; HIDDEN LAYERS; HUMAN LABOR; INPUT ENERGY; INPUT LAYERS; INPUT OUTPUT ANALYSIS; MULTI LAYER PERCEPTRON; OPTIMAL MODEL; OUTPUT LAYER; PRODUCTION YIELD; RANDOM METHODS; YIELD VALUE;

TOPOLOGY;

AGRICULTURAL MODELING; ARTIFICIAL NEURAL NETWORK; CROP PRODUCTION; CROP YIELD; DATA ACQUISITION; ENERGY USE; FERTILIZER APPLICATION; GREENHOUSE ECOSYSTEM; INPUT-OUTPUT ANALYSIS; OPTIMIZATION; PREDICTION;

IRAN;

OCIMUM;

EID: 84975883509     PISSN: 03605442     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.energy.2011.11.055     Document Type: Article

References (38)

1. Chalchat J.C., Ozcan M.M. Comparative essential oil composition of flowers, leaves and stems of basil (Ocimum Basilicum L.) used as herb. Food Chem 2008, 110:501-503.
2. Nguyen P.M., Kwee E.M., Niemeyer E.M. Potassium rate alters the antioxidant capacity and phenolic concentration of basil (Ocimum basilicum L.) leaves. Food Chem 2010, 123:1235-1241.
3. Food and Agriculture Organization (FAO) 2008. http://www.fao.org.
4. Anonymous Iran annual agricultural statistics 2008, Ministry of Jihad-e-Agriculture of Iran. http://www.maj.ir.
5. Alam M.S., Alam M.R., Islam K.K. Energy flow in agriculture: Bangladesh. Am J Environ Sci 2005, 1(3):213-220.
6. Esengun K., Gunduz O., Erdal G. Input-output energy analysis in dry apricot production of Turkey. Energy Convers Manage 2007, 48:592-598.
7. Uhlin H. Why energy productivity is increasing: an input-output analysis of Swedish agriculture. Agric Syst 1998, 56(4):443-465.
8. Geoffrey A.P. Economic forecasting in agriculture. Int J Forecasting 1994, 10(1):81-135.
9. Malca J., Freire F. Energy and environmental benefits of rapeseed oil replacing diesel. Int J Green Energy 2010, 6:287-301.
10. Singh S. Astudy on technical efficiency of wheat cultivation in Haryana. Agric Econ Res Rev 2007, 20:127-136.
11. Heidari M.D., Omid M. Energy use patterns and econometric models of major greenhouse vegetable productions in Iran. Energy 2011, 36:220-225.
12. Omid M., Ghojabeige F., Ahmadi H., Delshad M. Energy use pattern and benchmarking of selected greenhouses in Iran using data envelopment analysis. Energy Convers Manage 2011, 52(1):153-162.
13. Zhang G., Patuwo B.E., Hu M.Y. Forecasting with artificial neural networks: the state of the art. Int J Forecasting 1998, 14:35-62.
14. Kalogirou S.A. Artificial neural networks in renewable energy systems applications: a review. Renew Sust Energy Rev 2001, 5:373-401.
15. Diamantopoulou M.J. Artificial neural networks as an alternative tool in pine bark volume estimation. Comput Electron Agric 2005, 48:235-244.
16. Grzesiak W., Blaszczyk P., Lacroix R. Methods of predicting milk yield in dairy cows-predictive capabilities of Wood's lactation curve and artificial neural networks. Comput Electron Agric 2006, 54:69-83.
17. Ermis K., Midilli A., Dincer I., Rosen M.A. Artificial neural network analysis of world green energy use. Energy Policy 2007, 35:1731-1743.
18. Rahman M.M., Bala B.K. Modelling of jute production using artificial neural networks. Biosystems Eng 2010, 105:350-356.
19. Zangeneh M., Omid M., Akram A. Acomparative study between parametric and artificial neural networks approaches for economical assessment of potato production in Iran. Span J Agric Res 2011, 9(3):661-671.
20. Bayramoglu Z., Gundogmus E. Cost efficiency on organic farming: a comparison between organic and conventional raisin-producing households in Turkey. Span J Agric Res 2008, 6(1):3-11.
21. Mandal K.G., Saha K.P., Ghosh P.K., Hati K.M., Bandyopadhyay K.K. Bioenergy and economic analysis of soybean-based crop production systems in central India. Biomass Bioenergy 2002, 23(5):337-345.
22. Ozkan B., Fert C., Karadeniz F. Energy and cost analysis for greenhouse and open-field grape production. Energy 2007, 32:1500-1504.
23. Liu Y., Høgh-Jensen H., Egelyng H., Langer V. Energy efficiency of organic pear production in greenhouses in China. Renew Agric Food Syst 2010, 25(3):196-203.
24. Contreras A., Díaz G., Gallardo L., Loaiza R. Energy ratio analysis of genetically optimized potato for ethanol production in the Chilean market. Span J Agric Res 2010, 8(3):559-569.
25. Nassiri S.M., Singh S. Study on energy use efficiency for paddy crop using data envelopment analysis (DEA) technique. Appl Energy 2009, 86:1320-1325.
26. Ozkan B., Kurklu A., Akcaoz H. An input-output energy analysis in greenhouse vegetable production: a case study for Antalya region of Turkey. Biomass Bioenergy 2004, 26:189-195.
27. Chaudhary V.P., Gangwar B., Pandey D.K., Gangwar K.S. Energy auditing of diversified rice-wheat cropping systems in Indo-gangetic plains. Energy 2009, 34:1091-1096.
28. FAO FAO food and nutrition paper 77. Food energy - methods of analysis and conversion factors 2003.
29. Zangeneh M., Omid M., Akram A. Acomparative study on energy use and cost analysis of potato production under different farming technologies in Hamadan province of Iran. Energy 2010, 35:2927-2933.
30. Erdal G., Esengun K., Erdal H., Gunduz O. Energy use and economical analysis of sugar beet production in Tokat province of Turkey. Energy 2007, 32:35-41.
31. Avami A., Boroushaki M. Energy consumption forecasting of Iran using recurrent neural networks. Energy Sources Part B Economics Planning Policy 2011, 6:339-347.
32. Omid M., Baharlooei A., Ahmadi H. Modeling drying kinetics of pistachio nuts with multilayer feed-forward neural network. Drying Technol 2009, 27(10):1069-1077.
33. Azadeh A., Ghaderi S.F., Sohrabkhani S. Annual electricity consumption forecasting by neural network in high energy consuming industrial sectors. Energy Convers Manage 2008, 49:2272-2278.
34. Cigizoglu H.K., Kisi O. Methods to improve the neural network performance in suspended sediment estimation. JHydrol 2006, 317:221-238.
35. Neurosolutions for excel, neurodimension, Inc 2011, http://www.neurosolutions.com.
36. Canakci M., Akinci I. Energy use pattern analyses of greenhouse vegetable production. Energy 2006, 31:1243-1256.
37. Mohammadi A., Omid M. Economical analysis and relation between energy inputs and yield of greenhouse cucumber production in Iran. Appl Energy 2010, 87:191-196.
38. Mohammadi A., Rafiee S., Mohtasebi S.S., Mousavi Avval S.H., Rafiee H. Developing an artificial neural network model for predicting kiwifruit production in Mazandaran province of Iran. Proceedings Int Agric Eng Conf 2010. Sept. 16-20 2010, 389-395. Shanghai, China.