Artificial Neural Networks Application to Predict Wheat Yield Using Climatic Data

The goal of this study was to apply artificial neural networks to predict rain-fed wheat yield using meteorological data a few days to few months before harvesting. The climatic observation data used; were mean of daily minimum and maximum temperature, extreme of daily minimum and maximum temperature, sum of daily rainfall, number of rainy days, sum of daily sun hours, mean of daily wind speed, extreme of daily wind speed, mean of daily relative humidity, and sum of daily water requirements that were collected during 1990-1999 in Sararood Station for wheat phenological stages consisting; sowing, germination, emergence, 3 leaves, tillering, stem formation, heading, flowering, milk maturity, wax maturity, full maturity, separately for each growing season. Then, they arranged in a matrix whose rows form each of the statistical years and the columns are meteorological factors at each phenological stage. Finally, the obtained model had the following capabilities: Prediction of wheat yield with maximum errors of 45-60 kg/ha at least two months before full maturity stage, determination of the sensitivity of each phenological stage with respect to meteorological factors, and determination of the priority order and importance of each meteorological factor effective in plant growth and crop yield.