Document Type: Original Article

Authors

1 Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Iran , Tehran, Iran

2 Biotechnology Group, Chemical Engineering Department, Tarbiat Modares University, Tehran, Iran

Abstract

During the past few years, modeling in agriculture has attracted considerable attention. New modeling methods including neural networks are employed in various industries, and it is necessary that their use in agriculture be also considered. This research addressed the trend of energy use in broiler farms in Alborz Province and sought to model the trend of energy consumption and production in these farms. For this purpose, 45 questionnaires were distributed among broiler producers of the province. The reported levels of energy consumption and production were 218.40 and 30.13 GJ per thousand broilers, respectively. The largest share of the energy consumed, 40%, 25%, 23% and 9%, was related to gas-oil, feed, natural gas, and electricity inputs. Indices of ratio, productivity, special energy, and net energy gain were reported to be 0.15, 0.01 kg per MJ, 76.28 MJ per kg and 188268 MJ per thousand broilers, respectively. Modeling of energy inputs and the index of energy ratio as the inputs and outputs, respectively, of various artificial neural networks indicated that the network having two hidden layers with 12 and 9 neurons in the first and second hidden layers, respectively, was the most suitable network for modeling. Results of evaluation of networks suggested that the values for the R2 and MAPE indices for the 12-9 neuron network were 0.98 and 3.078, respectively, which showed that about 98 percent of the actual data could be estimated with the help of this artificial neural network.         

Keywords

Almasi, M. Kiani, S. And Lvymy, N. 2008. Basics of agricultural mechanization. Iran: Forest Publication.
Alrwis, K.N. and Francis, E. 2003. Technical efficiency of broiler farms in the central region of Saudi Aradia. Res. Bult. 116: 5-34.
Atilgan, A. and Koknaroglu, H. 2006. Cultural energy analysis on broilers reared in different capacity poultry houses. Italian Journal of Animal Science 5: 393-400.
Berg, M.J., Tymoczkco, L.J. and Stryer L. 2002. Biochemistry. 5th edition. New York: W.H. Freeman.
Canakci, M., Topakci, M., Akinci, I. and Ozmerzi, A. 2005. Energy use pattern of some field crops and vegetable production: case study for Antalya region, Turkey. Energy Conversion and Management. 46:655-666.
Celik, L.O. 2003. Effects of dietary supplemental lcarnitine and ascorbic acid on performance, carcass composition and plasma lcarnitine concentration of broiler chicks reared under different temperature. Arch. Anim Nutr. 57: 27-38.
Chauhan, N.S., Mohapatra, P.K.J. and Pandey, K.P. 2006. Improving energy productivity in paddy production through benchmarking: an application of data envelopment analysis. Energy Conversion and Management 47:1063-1085.
Heydari, MD. 2011. Determined by measuring the energy efficiency and economic indicators poultry production units in Yazd province to help DEA and artificial neural network techniques. M.Sc. dissertation, Iran: Tehran University.
Khanna, T., 1990. Foundation of neural networks. Addison-Wesley Publishing Company, U.S.A.
Kittle, A.P. 1993. Alternate Daily Cover Materials and Subtitle-the Selection Technique Rusmar. Incorporated West Chester, PA.
Kizilaslan, H. 2009. Input-output energy analysis of cherries production in Tokat province of Turkey. Applied Energy 86: 1354-1358.
Kuchky, A. Hosseini, M. and Khazayi, H. 1997. Sustainable agricultural systems. Iran: Shiraz University Publication.
Mazandarani, A., Mahliaa, T.M.I., Chonga, W.T. and Moghavvemi, M. 2011. Fuel consumption and emission prediction by Iranian power plants until 2025. Renewable and Sustainable Energy Reviews 15: 1575-1592.
Naghibzadeh, S. Javadi, A. Rahmati, M. And Mehranzadeh, M. 2009. Process of assessing the energy consumption of poultry broiler in the northern region of Khuzestan province. 6th National Congress of Agricultural Machinery Engineering and Mechanization, Iran: University of Tehran in Karaj.
Najafianari, S. Khademolhoseini, N. Jazayry k. And Mirzade, Kh. 2008. Evaluation of Energy Efficiency in Broiler Ahvaz province. 5th National Conference on Agricultural Machinery Engineering, Ferdowsi University of Mashhad.
Overhults, D.G., Pescatore, A.J., Gates, R.S., Jacob, J.P., Miller, M. and Earnest, J. 2009. House characteristics and energy utilization in poultry houses raising large broilers. Biosystems and Agricultural Engineering. University of Kentucky, Lexington, KY. USA.
Pahlavan, R., Omid, M. and Akram, A. 2012. Energy input-output analysis and application of artificial neural networks for predicting greenhouse basil production. Energy 37:171-176.
Pishgar-Komleh S.H., Sefeedpari P. and Rafiee S., 2011. Energy and economic analysis of rice production under different farm levels in Guilan province of Iran. Energy 36: 5824-5831.
Rafiee, S., MousaviAvval, S. H., and Mohammadi, A. “Modeling and sensitivity analysis of energy inputs for apple production in Iran,” Energy 35(8), 3301–3306 (2010).
Sainz, R.D. 2003. Livestock-environment initiative fossil fuels component: Framework for calculating fossil fuel use in livestock systems. Retrieved from www.fao.org.
Sedaghat Hoseini, M. Almasi, M. Minaei, S. and Borghei, M. 2008. Design of Energy Recovery in industrial production of eggs. In National Congress of Agricultural Machinery Engineering and Mechanization. Iran: Ferdowsi University of Mashhad
Tabler G.T., Jones F.T. and Bottje W.G., 2008. Energy Use and Costs at an Applied Broiler Research Farm. Avian Advice 10(1): 4-5.
Taki, M., Ajabshirchi, Y. and Mahmoudi, A. 2012. Prediction of output energy for wheat production using artificial neural networks in Esfahan province of Iran. Journal of Agricultural Technology 8(4): 1229-1242.
Yamane, T., Elementary Sampling Theory (Prentice Hall, Inc., Engle wood Cliffs, NJ, 1967).
Zangeneh, M., Omid, M. and Akram, A. 2010. Assessment of agricultural mechanization status of potato production by means of Artificial Neural Network model. AJCS 4(5): 372-377.