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Optimum prediction and forecasting of wheat demand in Iran

Author

Listed:
  • Reza Babazadeh
  • Meisam Shamsi
  • Fatemeh Shafipour

Abstract

Wheat is the staple food source in most countries and is grown in bad climatic conditions such as cold areas. Wheat contains about 55% carbohydrates and 20% calories. Optimum prediction of wheat demand would help policy makers to take optimum strategic decisions about the amount of domestic wheat production, import, and export for mid and long terms. In this study, firstly, the factors affecting demand for wheat are identified according to market analysis. Then, artificial neural network (ANN) method is employed for optimum forecasting of wheat demand in Iran. Different regression methods are used to justify the efficiency of the ANN model. The mean absolute percentage error (MAPE) of the ANN method is achieved equal to 4.64% which shows about 95% precision of the ANN method. According to acquired results, the ANN method could be efficiently applied for wheat demand prediction in order to take appropriate related strategic decisions.

Suggested Citation

  • Reza Babazadeh & Meisam Shamsi & Fatemeh Shafipour, 2021. "Optimum prediction and forecasting of wheat demand in Iran," International Journal of Applied Management Science, Inderscience Enterprises Ltd, vol. 13(2), pages 141-151.
    • Handle: RePEc:ids:injams:v:13:y:2021:i:2:p:141-151
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