Prediction of yield and economic indices for tangerine production using artificial neural networks based on energy consumption

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Research Paper 01/05/2014
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Prediction of yield and economic indices for tangerine production using artificial neural networks based on energy consumption

Ashkan Nabavi-Pelesaraei, Farhad Fatehi, Asghar Mahmoudi
Int. J. Agron. Agri. Res.4( 5), 57-64, May 2014.
Certificate: IJAAR 2014 [Generate Certificate]

Abstract

Determination of suitable model for forecasting of yield and economic indices of tangerine production in Guilan province of Iran using artificial neural network (ANN) was the main aim of this study. For this purpose, the energy consumption for three groups size of tangerine orchards were found from 60 questionnaires. The results revealed the average total energy use and yield of tangerine production were 27873 MJ ha-1 and 25740 kg ha-1, respectively. In the next step, the economic indices were calculated for tangerine orchards. Accordingly, benefit to cost ratio, productivity, net return and energy intensiveness were calculated as 1.37, 3.42 kg $-1, 2777.82 $ ha-1, 2.71 $ ha-1, respectively. In this study, a back propagation algorithm was used for training of ANN model and Levenberg-Marquardt was learning algorithm. The best topology had the 10-8-5 structure. Moreover, the R2 of best structure was found 0.971, 0.954, 0.983, 0.991 and 0.973 for tangerine yield, benefit to cost ratio, productivity, net return and energy intensiveness, respectively. In the last section of this research, sensitivity analysis was done and results illustrated the highest sensitivity rate of tangerine yield, the benefit to cost ratio, productivity, net return and energy intensiveness was belonged to farmyard manure, insecticide, insecticide, phosphate and diesel fuel, respectively.

VIEWS 1

Anonymous. 2013. Annual Agricultural Statistics. Ministry of Jihad-e-Agriculture of Iran. http://www.maj.ir, [in Persian].

Farjam A, Omid M, Akram A, Niari ZF. 2014. A neural network based modeling of energy inputs for predicting economic indices in seed and grain corn production. Elixir Agriculture 66, 20478-20481.

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

Hertz E. Energy utilization in fruit production in Chile. Agricultural Mechanization in Asia, Africa, and Latin America 29(2), 17-20.

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

Khoshnevisan B, Rafiee S, Omid M, Yousefi M, Movahedi M. 2013a. 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. 2013b. Environmental impact assessment of open field and greenhouse strawberry production. European Journal of Agronomy 50, 29-37.

Kitani O. 1999. Energy and biomass engineering. In: CIGR handbook of agricultural engineering. St. Joseph, MI: ASAE.

Kizilaslan H. 2009. Input-output energy analysis of cherries production in Tokat Province of Turkey. Applied Energy 86, 1354-1358.

Mandal KG, Saha KP, Gosh PL, Hati KM, Bandyopadhyay KK. 2002. Bioenergy and economic analyses of soybean-based crop production systems in central India. Biomass Bioenergy 23, 337-345.

Mobtaker HG, Akram A, Keyhani A. 2012. Energy use and sensitivity analysis of energy inputs for alfalfa production in Iran. Energy for Sustainable Development 16, 84-89.

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.

Mohammadi A, Omid M. 2010. Economical analysis and relation between energy inputs and yield of greenhouse cucumber production in Iran. Applied Energy 87, 191-196.

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

Mohammadi A, Tabatabaeefar A, Shahan S, Rafiee S, Keyhani A. 2008. Energy use and economical analysis of potato production in Iran a case study: Ardabil Province. Energy Conversion and Management 49, 3566-3570.

Mohammadshirazi A, Akram A, Rafiee S, Mousavi-Avval SH, Bagheri Kalhor E. 2012. An analysis of energy use and relation between energy inputs and yield in tangerine production. Renewable and Sustainable Energy Reviews 16, 4515-4521.

Nabavi-Pelesaraei A, Abdi R, Rafiee S, Mobtaker HG. 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, Shaker-Koohi S, Dehpour M.B. 2013a. Modeling and optimization of energy inputs and greenhouse gas emissions for eggplant production using artificial neural network and multi-objective genetic algorithm. International Journal of Advanced Biological and Biomedical Research 1(11), 1478-1489.

Nabavi-Pelesaraei A, Sadeghzadeh A, Payman MH, Mobtaker HG. 2013b. Energy flow modeling, economic and sensitivity analysis of eggplant production in Guilan province of Iran. International Journal of Agriculture and Crop Sciences 5(24), 3006-3015.

Ozkan B, Akcaoz H, Karadeniz F. 2004. Energy requirement and economic analysis of citrus production in Turkey. Energy Conversion and Management 45, 1821-1830.

Penjor T, Yamamoto M, Uehara M, Ide M, Matsumoto N, Matsumoto R, Nagano Y. 2013. Phylogenetic relationships of citrus and its relatives based on matK Gene Sequences. Plos one 8(4), 1-13.

Qasemi-Kordkheili P, Nabavi-Pelesaraei A. 2014. Optimization of energy required and potential of greenhouse gas emissions reductions for nectarine production using data envelopment analysis approach. International Journal of Energy and Environment 5(2), 207-218.

Rafiee S, Mousavi-Avval SH, Mohammadi A. 2010. Modeling and sensitivity analysis of energy inputs for apple production in Iran. Energy 35, 3301-3306.

Rahimi-Ajdadi F, Abbaspour-Gilandeh Y. 2011. Artificial Neural Network and stepwise multiple range regression methods for prediction of tractor fuel consumption. Measurement 44, 2104-2111.

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.

Strapatsa AV, George D, Nanos A, Tsatsarelis Constantinos A. 2006. Energy flow for integrated apple production in Greece. Agriculture, Ecosystems and Environment 116, 176-180.

Tabatabaie S.M.H, Rafiee S, Keyhani A, Heidari M.D. 2013. Energy use pattern and sensitivity analysis of energy inputs and input costs for pear production in Iran. Renewable Energy 51, 7-12.

Taki M, Mahmoudi A, Mobtaker H.G, Rahbari H. 2012. Energy consumption and modeling of output energy with multilayer feed-forward neural network for corn silage in Iran. CIGR Journal 14(4), 93-101.

Unakitan G, Hurma H, Yilmaz F. 2010. An analysis of energy use efficiency of canola production in Turkey. Energy 35, 3623-3627.

Warren S. 1994. Neural networks and statistical models. Proceedings of the Nineteenth Annual SAS Users Group International Conference, April.

Zangeneh M, Omid M, Akram A. 2010. Assessment of machinery energy ratio in potato production by means of artificial neural network. African Journal of Agricultural Research 5(10), 993-998.

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.