Prognostication of total greenhouse gas emissions and economic indices for hazelnut production in Guilan province, Iran
Paper Details
Prognostication of total greenhouse gas emissions and economic indices for hazelnut production in Guilan province, Iran
Abstract
In this study, Artificial Neural Network (ANN) was developed to estimate the total GHG emissions and economic indices of hazelnut production in Guilan province of Iran. In this regard, the data collected from 120 orchardists in the studied region during plant cultivation in 2012-2013 using face-to-face questionnaires. The results indicated that total GHG emissions and hazelnut yield was 77.66 kgCO2eq. ha-1 and 450.20 kg ha-1, respectively. Based on grouping of hazelnut orchards according to three sizes level, the large size had the highest emissions and yield compare to another sizes. Moreover, the GHG ratio was 0.17 for all orchards. The economic indices including gross production value, benefit to cost ratio, productivity and net return were calculated as 1575.70 $ ha-1, 1.64, 0.47 kg $-1 and 615.34 $ ha-1, respectively. In this research, the Levenberg-Marquardt learning algorithm was applied for determination of ANN model. With respect to result, the ANN model with 7-4-4-5 structure was determined as best topology which the highest R2 and lowest RMSE showed the robust model for prediction. In the last section sensitivity analysis was done and its results demonstrated potassium had the highest sensitivity on total GHG emissions and benefit to cost ratio; while nitrogen was the most sensitive input on gross production value, productivity and net return.
Anonymous. 2013. Annual Agricultural Statistics. Ministry of Jihad-e-Agriculture of Iran. http://www.maj.ir, [in Persian].
Deh Kiani MK, Ghobadian B, Tavakoli T, Nikbakht AM, Najafi G. 2010. Application of artificial neural networks for the prediction of performance and exhaust emissions in SI engine using ethanol- gasoline blends. Energy 35(1), 65-69. http://dx.doi.org/10.1016/j.energy.2009.08.034
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.
Ghahderijani M, Pishgar Komleh SH, Keyhani A, Sefeedpari P. 2012. Energy analysis and life cycle assessment production in Iran. African Journal of Agricultural Research 8(18),1929-1939. http://dx.doi.org/10.5897/AJAR11.1197
Ghodsi R, Mirabdollah Yani R, Jalali R, Ruzbahman M. 2012. Predicting wheat production in Iran using an artificial neural networks approach. International Journal of Academic Research in Business and Social Sciences 2(2), 34-47.
Hao y, Bogdan MW. 2010. Levenberg-Marquardt training. Intelligent Systems.
Khoshnevisan B, Rafiee S, Omid M, Mousazadeh H. 2013. Environmental impact assessment of open field and greenhouse strawberry production. European Journal of Agronomy 50, 29-37. http://dx.doi.org/10.1016/j.eja.2013.05.003
Kizilaslan H. 2009. Input-output energy analysis of cherries production in Tokat Province of Turkey. Applied Energy 86, 1354-1358. http://dx.doi.org/10.1016/j.apenergy.2008.07.009
Lal R. 2004. Carbon emission from farm operations. Environment International 30(7), 981-990. http://dx.doi.org/10.1016/j.envint.2004.03.005
Mohammadi A, Rafiee S, Mohtasebi SS, Mousavi-Avval SH, Rafiee H. 2010. Energy inputs – yield relationship and cost analysis of kiwifruit production in Iran. Renewable Energy 35, 1071-1075. http://dx.doi.org/10.1016/j.renene.2009.09.004
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. http://dx.doi.org/10.1016/j.rser.2012.04.047
Nabavi-Pelesaraei A, Abdi R, Rafiee S, Mobtaker HG. 2014a. Optimization of energy required and greenhouse gas emissions analysis for orange producers using data envelopment analysis approach. Journal of Cleaner Production 65, 311-317. http://dx.doi.org/10.1016/j.jclepro.2013.08.019
Nabavi-Pelesaraei A, Kouchaki-Penchah H, Amid S. 2014b. Modeling and optimization of CO2 emissions for tangerine production using artificial neural networks and data envelopment analysis. International Journal of Biosciences 4(7), 148-158. http://dx.doi.org/10.12692/ijb/4.7.148-158
Nabavi-Pelesaraei A, Shaker-Koohi S, Dehpour MB. 2013. 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.
Najafi G, Ghobadian B, Tavakoli T, Buttsworth DR, Yusaf TF, Faizollahnejad M. 2009. Performance and exhaust emissions of a gasoline engine with ethanol blended gasoline fuels using artificial neural network. Applied Energy 86, 630-639. http://dx.doi.org/10.1016/j.apenergy.2008.09.017
Nourbakhsh H, Emam-Djomeh Z, Omid M, Mirsaeedghazi H, Moini S. 2014. Prediction of red plum juice permeate flux during membrane processing with ANN optimized using RSM. Computers and Electronics in Agriculture 102, 1-9. http://dx.doi.org/10.1016/j.compag.2013.12.017
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. http://dx.doi.org/10.1016/j.energy.2011.11.055
Smith P, Martino D, Cai Z, Gwary D, Janzen H, Kumar P, Et. al. 2008. Greenhouse gas mitigation in agriculture. Philosophical Transactions of the Royal Society Bbiological Sciences 363,789-813. http://dx.doi.org/10.1098/rstb.2007.2184
Soni P, Taewichit C, Salokhe V. 2013. Energy consumption and CO2 emissions in rainfed agricultural production systems of Northeast Thailand. Agricultural Systems 116, 25-36. http://dx.doi.org/10.1016/j.agsy.2012.12.006
Sung AH. 1998. Ranking importance of input parameters of neural networks. Expert Systems with Application 15(3-4), 405-411. http://dx.doi.org/10.1016/S0957-4174(98)00041-4
Tabatabaie SMH, 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. http://dx.doi.org/10.1016/j.renene.2012.08.077
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. http://dx.doi.org/10.5897/AJAR10.116
Zangeneh M, Omid M, Akram A. 2011. A comparative study between parametric and artificial neural networks approaches for economical assessment of potato production in Iran. Spanish Journal of Agricultural Research 9(3), 661-671. http://dx.doi.org/10.5424/sjar/20110903-371-10
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. http://dx.doi.org/10.1016/j.compag.2008.07.008
Alireza Sabzevari, Ashkan Nabavi-Pelesaraei , 2015. Prognostication of total greenhouse gas emissions and economic indices for hazelnut production in Guilan province, Iran. J. Biodiv. Environ. Sci., 6(2), 132-140.
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