Prediction of Forage Maize Yield and Sensitivity Analysis of Management Parameters using Artificial Neural Network Models

The growing demand for agricultural products and the reduction of access to conventional water resources have highlighted the need for proper management of water resources in agricultural sector. On the other hand, measuring and acquiring field data problems make it necessary to employ efficient models that can accurately predict yield and sensitivity analysis of yield over various parameters. Therefore, in this research, the effects of different parameters on the yield of forage maize in the country have been investigated and the effectiveness of the artificial neural network models to predict forage maize yield has been evaluated. The parameters of irrigation water salinity, soil salinity, plant growth period, water consumption and number of irrigation of 104 farms in 8 provinces that were collected during field studies were used to develop models in the artificial neural network. Also, by estimating the water requirements of selected farms, the expected yield was estimated using the optimal selected model and compared with the measured yield in the farms. The results showed that the artificial neural network model with two hidden layers and the final structure of 1-15-19-5 can accurately estimate forage maize yield (R2=0.85). The results of the sensitivity analysis of the selected optimal model showed that the parameters of irrigation events and plant growth period were the most effective and least effective parameters on the forage maize yield, respectively. Also, the results of the modeling showed that there is a gap between the measured yield in the farms and the expected yield, and if the full irrigation requirement is applied, the forage maize yield in the studied areas can be increased by an average of 12.5 tons per hectare.