Artificial Neural Networks to Estimate Nutrient Use Efficiency in Eucalypt

Background: Nutrient use efficiency (NUE) is the basis for fertilizer recommendations in eucalypt plantations in Brazil needs to be calculate individually for each nutrient and spacing. The possibility of superior performance to conventional models of regression and interpolation can be obtained by Artificial Neural Networks (ANN) enabling its use for solve complex problems. The ANN are being used in environmental science, but still studies on forest nutrition are poor. Objective: To evaluate the efficiency of NUE estimation in the Eucalyptus stem, under different spacing using ANN. Results: The nonlinear activation functions in the hidden layer generating local receptive fields were observed in all networks. Specific leaf area contributed to capture the biological realism and increased the ability of generalization of MLP's networks. Its generalization capability and connectivity allowed use only one network to perform the estimation of the stem's NUE.Conclusion: The modeling by ANN using multilayer perceptron architecture is a suitable alternative, accurate and biologically realistic to estimate the NUE by macronutrient, used in different spacings