ARTIFICIAL NEURAL NETWORKS (ANN) FOR HEIGHT ESTIMATION IN A MIXED-SPECIES PLANTATION OF Eucalyptus globulus LABILL AND Acacia mearnsii DE WILD

The objective of this study is to evaluate the fit of Artificial Neural Networks (ANN) for height estimation and evaluation of the effects of consortium in a mixed-species plantation of Eucalyptus globulus (E) and Acacia mearnsii (A). The experiment was installed in 2005, on two farms in the municipality of Piratini-RS, where was planted the species Eucalyptus globulus (E) and Acacia mearnsii (A), in monoculture and mixed in simple lines (50%E:50%A-SL), and double lines (50%E:50%A-DL). The training and evaluation of the networks were made in R-project with the package neuralnet. All ANNs, from the simplest to the most complex, showed high values for Rx177;y and low for Syx, BIAS and RMSE, with superior results in ANN 3, 4, and 6, which demonstrates that the information of DBHmin, DBHmean, and DBHmax were important stand attributes. Furthermore, the ANNs were able to capture the different growth patterns shown by the species in the different forms of consortiums, therefore is indicated for the height estimation in monocultures and mixed plantations of Eucalyptus globulus and Acacia mearnsii, and only one ANN would be necessary to represent the entire population.