Analysis and modeling of energy and the production cost of alfalfa using multi-layer adaptive neuro-fuzzy inference system in Bukan township

Document Type : Research Paper

Authors

tehran university

Abstract

This study examines the pattern of energy consumption in the production of alfalfa, economic analysis and modeling of energy and the production cost of alfalfa in the Bukan township. Data were collected through interviews and filling specialized questionnaires. The results showed that the consumption and production total energy were 212428 and 232567 respectively. Electricity with a share of 68 percent of the input total energy was the most consumed inputs. Indicators of energy efficiency, energy efficiency, energy intensity, net energy, net income, the ratio of benefit to cost and economic efficiency were 1.09, 14.43 Mj/kg, 0.07 kg/Mj, 20139.6 Mj, 1527.14 $/ha, 2.06 and 10.17 kg/$ respectively. Values of R, RMSE and RMSE for the final ANFIS in modeling of energy efficiency were 0.97, 0.033 and 0.2, respectively and for the final ANFIS in modeling of production cost, 0.98, 0.011 and 0.1, respectively. R2 value between actual and predicted values of Production costs and energy productivity was 0.94 and 0.97 respectively

Keywords

Main Subjects


Anonymous. Statistical Center of Iran. (2010). The estimated population of each city, From http://www.amar.org.ir.
Anonymous. Department of Jihad-e-Agriculture of Iran. (2014). Annual agricultural statistics, From http://www.maj.ir/
Asgharipour, M. R., Mousavinik, S. M., & Enayat, F. F. (2016). Evaluation of energy input and greenhouse gases emissions from alfalfa production in the Sistan region, Iran. Energy Reports, 2, 135-140.
Bolandnazar E., Keyhani A. & Omid M. (2015). Modeling of energy consumption in greenhouse cucumber crop production by Adaptive Nero Fuzzy Inference System technique (ANFIS) in Jiroft region. In: 9th National Congress of Agricultural Machinery Engineering and mechanization, 22-23 April., University of Tehran, Karaj, Iran, (In Farsi).
Canakci, M., Topakci, M., Akinci, I. & Ozmerzi, A. (2005). Energy use pattern of some field crops and vegetable production: Case study for Antalya Region, Turkey. Energy Conversion and Management, 46(4), 655-666.
Canakci, M. & Akinci, I. (2006). Energy use pattern analyses of greenhouse vegetable production. Energy, 31(8), 1243-1256.
Erdal, G., Esengün, K., Erdal, H. & Gündüz, O. (2007). Energy use and economical analysis of sugar beet production in Tokat province of Turkey. Energy, 32(1), 35-41.
Farjam, A., Omid, M., Akram, A., & Fazel Niari, Z. (2014). A neural network based modeling and sensitivity analysis of energy inputs for predicting seed and grain corn yields. Journal of Agricultural Science and Technology, 16(4), 767-778.
Ghaderpour O. & Rafiee SH. (2016). Analysis, modeling of energy and yield of dryland chickpea in the Bukan township. Iran Biosystem Engeering. (In press), (In Farsi).
Ghazvineh, S., Yousefi, M. (2013). Evaluation of consumed energy and greenhouse gas emission from agroecosystems in Kermanshah province. Tech. J. Eng. Appl. Sci. 3, 349–354.
Hatirli, S. A., Ozkan, B., & Fert, C. (2005). An econometric analysis of energy input–output in Turkish agriculture. Renewable and Sustainable Energy Reviews, 9(6), 608-623.
Imanmehr A. (2015). Evaluation of efficiency and energy productivity of alfalfa production. In: 9th National Congress of Agricultural Machinery Engineering and mechanization, 22-23 April., University of Tehran, Karaj, Iran, (In Farsi).
Jang, J. S. (1993). ANFIS: adaptive-network-based fuzzy inference system. IEEE transactions on systems, man, and cybernetics, 23(3), 665-685.
Khan, S., Khan, M., Hanjra, M., & Mu, J. (2009). Pathways to reduce the environmental footprints of water and energy inputs in food production. Food policy, 34(2), 141-149.
Khoshnevisan, B., Rafiee, S., Omid, M., Mousazadeh, H. & Rajaeifar, M.A. (2014a). 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. & Mousazadeh, H. (2014b). Application of multi-layer adaptive neuro-fuzzy inference system for estimation of greenhouse strawberry yield. Measurement, 47, 903-910.
Khoshnevisan, B., Rafiee, S., Omid, M. & Mousazadeh, H. (2014c). Prediction of potato yield based on energy inputs using multi-layer adaptive neuro-fuzzy inference system. Measurement, 47, 521-530.
Khoshnevisan, B., Rafiee, S., Omid, M. & Mousazadeh, H. (2014d). Development of an intelligent system based on ANFIS for predicting wheat grain yield on the basis of energy inputs. Information processing in agriculture, 1(1), 14-22.
Khoshnevisan, B., Rafiee, S., Iqbald, J., Shamshirbande, S., Omid, M., Anuarf, N. B., & Abdul Wahabg, A. W. (2015). A Comparative Study between Artificial Neural Networks and Adaptive Neuro-fuzzy Inference Systems for Modeling Energy Consumption in Greenhouse Tomato Production-A Case Study in Isfahan Province. Journal of Agricultural Science and Technology, 17(1), 49-62.
Kitani, O. (1999). CIGR handbook of agricultural engineering: Energy & Biomass Engineering (Vol. 5). (pp. 330). St Joseph, MI: ASAE.
Kizilaslan, H. (2009). Input–output energy analysis of cherries production in Tokat Province of Turkey. Applied Energy, 86(7), 1354-1358.
Koupahi, M. (2006). Principles of Agricultural Economics. Tehran University Press,(In Farsi).
Mobtaker, H. G., Akram, A., & Keyhani, A. (2010). Economic modeling and sensitivity analysis of the costs of inputs for alfalfa production In Iran: A case study from Hamedan province. Ozean Journal of Applied Science, 3(3).
Mobtaker, H.G., Akram, A., Keyhani, A. & Mohammadi, A. (2011). Energy consumption in alfalfa production: A comparison between two irrigation systems in Iran. African Journal of Plant Science, 5(1), 47-51.
Mohaddes, S.A. & Fahimifard, S.M. (2015). Application of Adaptive Neuro-Fuzzy Inference System (ANFIS) in Forecasting Agricultural Products Export Revenues (Case of Iran’s Agriculture Sector). Journal of Agricultural Science and Technology, 17(1), 1-10.
Mohammadi, A. & Omid, M. (2010). Economical analysis and relation between energy inputs and yield of greenhouse cucumber production in Iran. Applied Energy, 87(1), 191-196.
Naderloo, L., Alimardani, R., Omid, M., Sarmadian, F., Javadikia, P., Torabi, M.Y. & Alimardani, F. (2012). Application of ANFIS to predict crop yield based on different energy inputs. Measurement, 45(6), 1406-1413.
Ozkan, B., Akcaoz, H. & Karadeniz, F. (2004a). Energy requirement and economic analysis of citrus production in Turkey. Energy Conversion and Management, 45(11), 1821-1830.
Ozkan, B., Akcaoz, H., & Fert, C. (2004b). Energy input–output analysis in Turkish agriculture. Renewable energy, 29(1), 39-51.
Pahlavan, R., Omid, M. & Akram, A. (2012). Energy input–output analysis and application of artificial neural networks for predicting greenhouse basil production. Energy, 37(1), 171-176.
Petković, D., Pavlović, N. T., Shamshirband, S., Kiah, M. L. M., Anuar, N. B., & Idris, M. Y. I. (2014). Adaptive neuro-fuzzy estimation of optimal lens system parameters. Optics and Lasers in Engineering, 55, 84-93.
Rafiee, S., Avval, S.H.M. & Mohammadi, A. (2010). Modeling and sensitivity analysis of energy inputs for apple production in Iran. Energy, 35(8), 3301-3306.
Ramedani, Z., Omid, M., & Keyhani, A. (2012). A method based on neural networks for generating solar radiation map. International journal of energy and environment, 3(5), 775-786.
Shamshirband, S., Petković, D., Ćojbašić, Ž., Nikolić, V., Anuar, N. B., Shuib, N. L. M., ... & Akib, S. (2014). Adaptive neuro-fuzzy optimization of wind farm project net profit. Energy Conversion and Management, 80, 229-237.
Soyguder, S. & Alli, H. (2009). An expert system for the humidity and temperature control in HVAC systems using ANFIS and optimization with Fuzzy Modeling Approach. Energy and Buildings, 41(8), 814-822.
Tabatabaie, S.M.H., Rafiee, S. & Keyhani, A. (2012). Energy consumption flow and econometric models of two plum cultivars productions in Tehran province of Iran. Energy, 44(1), 211-216.
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, 87, 159-167.
Tsatsarelis, C., & Koundouras, D. (1994). Energetics of baled alfalfa hay production in northern Greece. Agriculture, ecosystems & environment, 49(2), 123-130.
Turkmen, I. (2011). Efficient impulse noise detection method with ANFIS for accurate image restoration. AEU-International Journal of Electronics and Communications, 65(2), 132-139.
Yousefi, M., & Mohammadi, A. (2011). Economical analysis and Energy use efficiency in Alfalfa production systems in Iran. Scientific Research and Essays, 6(11), 2332-2336.
Zahmatkesh, D., Amanlou, H., & Dashti, G. (2013). Economic modeling and sensitivity analysis of inputs in alfalfa production in different harvesting system. International Journal of Agriculture and Crop Sciences, 6(8), 472.