Decision making method for variable-rate fertilization based on data envelopment analysis and artificial neural network

Data Envelopment Analysis(DEA) method was used to evaluate technical efficiency of input and output of the corn fertilizing unit, and to turn the inefficient unit into effective unit through projections. Then all the data were well prepared for the Artificial Neural Network(ANN) by this way. In Matlab program, BP ANN model with three layers was developed with soil nutrients and yield as inputs, and with fertilizer application rate of nitrogen, phosphorus and potassium as outputs. Automated regularization function tainbr was chosen to train the network, and the decision making model of variable-rate fertilization could be obtained by this way. The model can also consider the price factors as input and output to enlarge its function to make decision to achieve the maximum profits. In other words, the model can not only introduce an effective method to utilize all the experimental data, but also can solve the non-linear problems that cannot be solved by traditional decision method. The application shows that the model can describe the fertilizer demand property and provide optimum scheme of fertilization for every fertilizing unit. When aim yield is not higher than 9750 kg/hm², the estimation result is quite reasonable.