1 PhD student of Agricultural Economics, University of Tehran, Karaj, Iran

2 Assistant Professor of Islamic Azad University, Rasht Branch, Iran


In the present study Iran’s rice imports trend is forecasted, using artificial neural networks and econometric methods, during 2009 to 2013, and their results are compared. The results showed that feet forward neural network leading with less forecast error and had better performance in comparison to econometric techniques and also, other methods of neural networks, such as Recurrent networks and Multilayer perceptron networks. Moreover, the results showed that the amount of rice import has ascending growth rate in 2009-2013 and maximum growth occurs in 2009-2010 years, which was equal to 25.72 percent. Increasing rice import caused a lot of exchange to exit out of the country and also, irreparable damage in domestic production, both in terms of price and quantity. Considering mentioned conditions, economic policy makers should seek ways to reduce increasing trend of rice import; and more investment and planning for domestic rice producers.


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