Abstract:
In this study, several artificial neural networks (ANNs) were developed to estimate
seed and grain corn yields in Parsabad Moghan, Iran. The data was collected by a face-toface
interview method from 144 corn farms during 2011. The energy ratios for seed and
grain corns were calculated as 0.89 and 2.65, respectively. Several multilayer perceptron
ANNs with six neurons in the input layer and one to three hidden layers with different
number of neurons in each layer and one neuron (seed or grain corn yield) in the output
layer was developed and tested. Energy inputs including fertilizers, biocides, seeds,
human labor, diesel fuel and machinery were considered as explanatory variables for the
input layer. The best model for predicting seed and grain corn yields had 6-4-8-1 and 6-3-
9-1 topologies, respectively. Model output value associated with the actual output had
coefficient of determination (R2
) values of 0.9998 and 0.9978 for seed and grain corn,
respectively. The corresponding regression models had R
2
values of 0.987 and 0.982,
respectively. Sensitivity analysis showed that in seed corn production, diesel fuel and
machinery, and in grain corn, diesel fuel and seeds consumption have the greatest effect
on production yield.
Keywords: Artificial neural networks, Corn production, Energy input, Regression,
Sensitivity analysis.
