A Neural Network Based Modeling and Sensitivity Analysis of Energy Inputs for Predicting Seed and Grain Corn Yields

Authors
A. Farjam 1
M. Omid 1
A. Akram 1
Z. Fazel Niari 2
1 Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Islamic Republic of Iran.
2 Agricultural Engineering Research Center of Parsabad Moghan, Ardabil Province, Islamic Republic of Iran.
Abstract
In this study, several artificial neural networks (ANNs) were developed to estimate seed and grain corn yields in Parsabad Moghan, Iran. The data was collected by a face-to-face interview method from 144 corn farms during 2011. The energy ratios for seed and grain corns were calculated as 0.89 and 2.65, respectively. Several multilayer perceptron ANNs with six neurons in the input layer and one to three hidden layers with different number of neurons in each layer and one neuron (seed or grain corn yield) in the output layer was developed and tested. Energy inputs including fertilizers, biocides, seeds, human labor, diesel fuel and machinery were considered as explanatory variables for the input layer. The best model for predicting seed and grain corn yields had 6-4-8-1 and 6-3-9-1 topologies, respectively. Model output value associated with the actual output had coefficient of determination (R2) values of 0.9998 and 0.9978 for seed and grain corn, respectively. The corresponding regression models had R2 values of 0.987 and 0.982, respectively. Sensitivity analysis showed that in seed corn production, diesel fuel and machinery, and in grain corn, diesel fuel and seeds consumption have the greatest effect on production yield.

Keywords

1. Ashofteh Beiragi, M., Ebrahimi, M., Mostafavi, K., Golbashy, M. and Khavari Khorasani, S. 2011. A Study of Morphological Basis of Corn (Zea mays L.) Yield under Drought Stress Condition Using Correlation and Path Coefficient Analysis. J. Cereals Oilseeds, 2: 32-37.
2. Azadeh, A., Ghaderi, S. F. and Sohrabkhani, S. 2008. A Simulated-based Neural Network Algorithm for Forecasting Electrical Energy Consumption in Iran. Energy Pol., 36: 2637– 2644.
3. Dai, X., Huo, Z. and Wang, H. 2011. Simulation for Response of Crop to Soil Moisture and Salinity with Artificial Neural Network. Field Crops Res., 121: 441-449.
4. Erdal, G., Esengun, K., Erdal, H. and Gunduz, O. 2007. Energy Use and Economical Analysis of Sugarbeet Production in Tokat Province of Turkey. Energy, 32: 35- 41.
5. Esengun, K., Gundoz, O. and Erdal, G. 2007. Input-output Energy Analysis in Dry Apricot Production of Turkey. Energy Convers. Manage., 48: 592-598.
6. Houshyar, E., Azadi, H., Almassi, M. and Sheikh Davoodi, M. J. 2012. Sustainable and Efficient Energy Consumption of Corn Production in Southwest Iran: Combination of Multi-fuzzy and DEA Modeling. Energy, 44: 672-681.
7. Jiu Quan, Z., Ling Xiap, Z., Ming Hua, Z. and Watson, C. 2009. Prediction of Soybean Growth and Development Using Artificial Neural Network and Statistical Models. Acta Agronomica Sinica, 35: 341-347.
8. Kaul, M., Hill, R. L. and Walthall, C. 2005. Artificial Neural Networks for Corn and Soybean Yield Prediction. Agr. Systems, 85: 1-18.
9. Khashei-Siuki A., Kouchakzadeh, M. and Ghahraman, B. 2011. Predicting Dryland Wheat Yield from Meteorological Data Using Expert System, Khorasan Province, Iran. J. Agr. Sci. Tech., 13: 627-640.
10. Kitani, O. 1999. CIGR Handbook of Agricultural Engineering. Energy and Biomass Engineering, ASAE Publication, 5: 13-24.
11. McCulloch, W.S. and Pitts, W. 1943. A Logical Calculus of the Ideas Immanent in Nervous Activity. Bullet. Mathemat. Biophysics, 5: 115–133.
12. Mohammadi, A. and Omid, M. 2010. Economical Analysis and Relation between Energy Inputs and Yield of Greenhouse Cucumber Production in Iran. Appl. Energy, 87:191–196.
13. Mohammadi, A., Rafiee, S., Mohtasebi, S. S. and Mousavi Avval S. H. 2010. Developing an Artificial Neural Network Model for Predicting Kiwifruit Production in Mazandaran Province of Iran. Agriculture Engineering Conference, 16-20 September 2010 Shanghai, China, PP. 389-395.
14. Omid, M., Baharlooei, A. and Ahmadi, H. 2009. Modeling Drying Kinetics of Pistachio Nuts with Multilayer Feed-forward Neural Network. Drying Technol., 27: 1069 -1077.
15. Ozkan, B., Fert, C. and Karadeniz F. 2007. Energy and Cost Analysis for Green House and Open-field Grape Production. Energy, 32: 1500- 1504.
16. Pahlavan, R., Omid, M. and Akram, A. 2012a. Energy Input-output Analysis and Application of Artificial Neural Networks for Predicting Greenhouse Basil Production. Energy, 37: 171-176.
17. Pahlavan, R., Omid, M. and Akram, A. 2012b. The Relationship between Energy Inputs and Crop Yield in Greenhouse Basil Production. J. Agr. S. Tech., 14: 1243-1253.
18. Pahlavan R., Omid M., and Akram A. 2012c. Application of Data Envelopment Analysis for Performance Assessment and Energy Efficiency Improvement Opportunities in Greenhouses Cucumber Production. J. Agr. Sci. Tech., 14: 1465-1475.
19. Rahman, M. M. and Bala, B. K.2010. Modeling of Jute Production Using Artificial Neural Networks. Bio Systems Engineering, 105: 350-356.
20. Thorburn, P. J., Jakku E., Webster A. J. and Everingham Y. 2011. Agricultural Decision Support Systems Facilitating Co-learning: A Case Study on Environmental Impacts of Sugarcane Production. Int. J. Agr. Sustain, 9: 322-333.
21. Yazdani, M. R., Saghafian B., Mahdian M. H. and Soltani S. 2009. Monthly Runoff Estimation Using Artificial Neural Networks. J. Agric. Sci. Tech., 11:355-362.
22. Zangeneh, M., Omid, M. and Akram, A. 2010. A Comparative Study on Energy Use and Cost Analysis of Potato Production under Different Farming Technologies in Hamadan Province of Iran. Energy, 35: 2927-2933.
23. Zangeneh, M., Omid, M. and Akram, A. 2011. A Comparative Study between Parametric and Artificial Neural Networks Approaches for Economical Assessment of Potato Production in Iran. Spanish J. Agr. Res., 9: 661-671.
Journal of Agricultural Science and Technology
Volume 16, Issue 4
July and August 2014
Pages 767-778