Application of artificial neural network for prediction of energy flow in wheat production based on mechanization development approach

Document Type : Research Paper


Department of Agricultural Economics, Qaemshahr Branch, Islamic Azad University, Qaemshahr, Mazandaran, Iran



Due to the inadequate use of limited energy resources, it is necessary to study the energy consumption patterns in agriculture. The purpose of this study was to investigate the process of energy consumption of wheat in two mechanized and semi-mechanized production systems. The statistical population included all wheat farmers in Mazandaran province in 2018-2019. Multilayer perceptron artificial neural network (MLP) was used to find the best model to predict the wheat yield. The results indicated that the average energy consumption of wheat production in Mazandaran province was 20581.46 MJ ha-1, which was higher in mechanized systems. Chemical fertilizer input by 51.64% had the highest share of energy consumption, which was higher in mechanized systems than semi-mechanized systems. The energy efficiency and energy productivity values ​​for the average production were determined to be 2.02 and 0.14 kg MJ-1, respectively, in which both indices were higher in the mechanized systems. Assessing the energy indices highlighted that energy management of wheat production in mechanized systems was better than semi-mechanized ones. In the study of energy flow modeling in mechanized systems, the model performed best with the tangent sigmoid as the activation function and nine neurons by R2 value of 0.994. In the semi-mechanized systems, the model had the best performance with logarithmic sigmoid function as the activation function and eight neurons by R2 value of 0.997.


[1] Hatirli, S.A., B. Ozkan, and C. Fert, An econometric analysis of energy input–output in Turkish agriculture. Renewable and sustainable energy reviews, 2005. 9(6): p. 608-623.
[2] Mahallati, M.N., et al., Determination of optimal strip width in strip intercropping of maize (Zea mays L.) and bean (Phaseolus vulgaris L.) in Northeast Iran. Journal of Cleaner Production, 2015. 106: p. 343-350.
[3] Chapagain, T. and A. Riseman, Barley–pea intercropping: Effects on land productivity, carbon and nitrogen transformations. Field Crops Research, 2014. 166: p. 18-25.
[4] (USDA), U.S.D.o.A., World Agricultural Production. 2017.
[5] Iran, M.o.A.o., Annual agricultural statistics. Ministry of Jihad-e-Agriculture of Iran. 2018.
[6] Nabavi-Pelesaraei, A., et al., Modeling energy consumption and greenhouse gas emissions for kiwifruit production using artificial neural networks. Journal of Cleaner Production, 2016. 133: p. 924-931.
[7] Sahabi, H., H. Feizi, and A. Karbasi, Is saffron more energy and economic efficient than wheat in crop rotation systems in northeast Iran? Sustainable Production and Consumption, 2016. 5: p. 29-35.
[8] Moghimi, M.R., B.M. Alasti, and M.H. Drafshi, Energy input-output and study on energy use efficiency for wheat production using DEA technique. International Journal of Agriculture and Crop Sciences (IJACS), 2013. 5(18): p. 2064-2070.
[9] Unakıtan, G. and B. Aydın, A comparison of energy use efficiency and economic analysis of wheat and sunflower production in Turkey: A case study in Thrace Region. Energy, 2018. 149: p. 279-285.
[10] Heydarzadeh, E., et al., Comparison of machinery and labor productivity of mechanized and semi mechanized and semi traditional wheat production systems in Mashhad. Agricultural Economics & Development, 2008. 22(1): p. 51-62
[11] Amoozad-Khalili, M., et al., Economic modeling of mechanized and semi-mechanized rainfed wheat production systems using multiple linear regression model. Information Processing in Agriculture, 2020. 7(1): p. 30-40.
[12] AghaAlikhani, M., H. Kazemi-Poshtmasari, and F. Habibzadeh, Energy use pattern in rice production: A case study from Mazandaran province, Iran. Energy Conversion and Management, 2013. 69: p. 157-162.
[13] Taheri-Rad, A., et al., Energy flow modeling and predicting the yield of Iranian paddy cultivars using artificial neural networks. Energy, 2017. 135: p. 405-412.
[14] Khoshnevisan, B., et al., Application of artificial neural networks for prediction of output energy and GHG emissions in potato production in Iran. Agricultural Systems, 2014. 123: p. 120-127.
[15] Hosseini, S., et al., Application of artificial neural network (ANN) and multiple regressions for estimating assessing the performance of dry farming wheat yield in Ghorveh region, Kurdistan province. Agricultural Research, 2007. 7(1): p. 41-54
[16] Bisheh, A.V., et al., Embedding gender factor in energy input–output analysis of paddy production systems in Mazandaran Province, Iran. Energy, Ecology and Environment, 2017. 2(3): p. 214-224.
[17] Kazemi, H., et al., Energy flow analysis for rice production in different geographical regions of Iran. Energy, 2015. 84: p. 390-396.
[18] Singh, S. and J. Mittal, Energy in production agriculture. 1992: Mittal Publications.
[19] Tabatabaie, S.M.H., et al., Energy use pattern and sensitivity analysis of energy inputs and input costs for pear production in Iran. Renewable Energy, 2013. 51: p. 7-12.
[20] Rafiee, S., S.H.M. Avval, and A. Mohammadi, Modeling and sensitivity analysis of energy inputs for apple production in Iran. Energy, 2010. 35(8): p. 3301-3306.
[21] Troujeni, M.E., et al., Sensitivity analysis of energy inputs and economic evaluation of pomegranate production in Iran. Information processing in agriculture, 2018. 5(1): p. 114-123.
[22] Royan, M., et al., Investigation of energy inputs for peach production using sensitivity analysis in Iran. Energy Conversion and Management, 2012. 64: p. 441-446.
[23] Alimagham, S.M., et al., Energy flow analysis and estimation of greenhouse gases (GHG) emissions in different scenarios of soybean production (Case study: Gorgan region, Iran). Journal of Cleaner Production, 2017. 149: p. 621-628.
[24] Hamzei, J. and M. Seyyedi, Energy use and input–output costs for sunflower production in sole and intercropping with soybean under different tillage systems. Soil and Tillage Research, 2016. 157: p. 73-82.
[25] Kordkheili, P.Q., et al., Energy consumption, input-output relationship and economic analysis for nectarine production in Sari region, Iran. International Journal of Agriculture and Crop Sciences (IJACS), 2013. 5(2): p. 125-131.
[26] Kitani, O., et al., CIGR handbook of agricultural engineering. Energy and biomass engineering, 1999. 5: p. 330.
[27] Asgharipour, M.R., S.M. Mousavinik, and F.F. Enayat, Evaluation of energy input and greenhouse gases emissions from alfalfa production in the Sistan region, Iran. Energy Reports, 2016. 2: p. 135-140.
[28] Khanali, M., et al., Investigating energy balance and carbon footprint in saffron cultivation–a case study in Iran. Journal of Cleaner Production, 2016. 115: p. 162-171.
[29] Mohammadi-Barsari, A., S. Firouzi, and H. Aminpanah, Energy-use pattern and carbon footprint of rain-fed watermelon production in Iran. Information Processing in Agriculture, 2016. 3(2): p. 69-75.
[30] Amid, S. and T. Mesri Gundoshmian, Prediction of output energies for broiler production using linear regression, ANN (MLP, RBF), and ANFIS models. Environmental Progress & Sustainable Energy, 2017. 36(2): p. 577-585.
[31] Mashaly, A.F. and A. Alazba, Thermal performance analysis of an inclined passive solar still using agricultural drainage water and artificial neural network in arid climate. Solar Energy, 2017. 153: p. 383-395.
[32] Zarini, R.L., H. Yaghoubi, and A. Akram, Energy use in citrus production of Mazandaran province in Iran. African Crop Science Journal, 2013. 21(1): p. 61-65.
[33] Kazemi, H., et al., Estimation of greenhouse gas (GHG) emission and energy use efficiency (EUE) analysis in rainfed canola production (case study: Golestan province, Iran). Energy, 2016. 116: p. 694-700.
[34] Nabavi-Pelesaraei, A., R. Abdi, and S. Rafiee, Neural network modeling of energy use and greenhouse gas emissions of watermelon production systems. Journal of the Saudi Society of Agricultural Sciences, 2016. 15(1): p. 38-47.
[35] Khoshnevisan, B., et al., Development of an intelligent system based on ANFIS for predicting wheat grain yield on the basis of energy inputs. Information processing in agriculture, 2014. 1(1): p. 14-22.
[36] Getahun, M.A., S.M. Shitote, and Z.C.A. Gariy, Artificial neural network based modelling approach for strength prediction of concrete incorporating agricultural and construction wastes. Construction and Building Materials, 2018. 190: p. 517-525.