Khashei-Siuki, A., Kouchkzadeh, M., Ghahraman, B. (2011). Predicting Dryland Wheat Yield from Meteorological Data Using Expert System, Khorasan Province, Iran. , 13(4), 627-640.
A. Khashei-Siuki; M. Kouchkzadeh; B. Ghahraman. "Predicting Dryland Wheat Yield from Meteorological Data Using Expert System, Khorasan Province, Iran". , 13, 4, 2011, 627-640.
Khashei-Siuki, A., Kouchkzadeh, M., Ghahraman, B. (2011). 'Predicting Dryland Wheat Yield from Meteorological Data Using Expert System, Khorasan Province, Iran', , 13(4), pp. 627-640.
Khashei-Siuki, A., Kouchkzadeh, M., Ghahraman, B. Predicting Dryland Wheat Yield from Meteorological Data Using Expert System, Khorasan Province, Iran. , 2011; 13(4): 627-640.

Predicting Dryland Wheat Yield from Meteorological Data Using Expert System, Khorasan Province, Iran

Journal of Agricultural Science and Technology
Article 14, Volume 13, Issue 4, 2011, Pages 627-640    PDF (452.11 K)
Authors
A. Khashei-Siuki1; M. Kouchkzadeh* 1; B. Ghahraman2
1Department of Irrigation and Drainage Engineering, College of Agriculture, Tarbiat Modares University, Tehran, Islamic Republic of Iran.
2Department of Irrigation, College of Agriculture, Ferdowsi University of Mashhad, Mashhad, Islamic Republic of Iran.
Abstract
Khorasan Province is one of the most important provinces of Iran, especially as regards
agricultural products. The prediction of crop yield with available data has important
effects on socio-economic and political decisions at the regional scale. This study shows
the ability of Artificial Neural Network (ANN) technology and Adaptive Neuro-Fuzzy
Inference Systems (ANFIS) for the prediction of dryland wheat (Triticum aestivum) yield,
based on the available daily weather and yearly agricultural data. The study area is
located in Khorasan Province, north-east of Iran which has different climate zones.
Evapotranspiration, temperature (max, min, and dew temperature), precipitation, net
radiation, and daily average relative humidity for twenty-two years at nine synoptic
stations were the weather data used. The potential of ANN and Multi-Layered Preceptron
(MLP) methods were examined to predict wheat yield. ANFIS and MLP models were
compared by statistical test indices. Based on these results, ANFIS model consistently
produced more accurate statistical indices (R2= 0.67, RMSE= 151.9 kg ha-1, MAE= 130.7
kg ha-1), when temperature (max, min, and dew temperature) data were used as
independent variables for prediction of dryland wheat yield.
Keywords
artificial neural network; Prediction; ANFIS; Dryland wheat yield; Khorasan; Multi-layered preceptron
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