Assessment of a Monthly Data Structure for Growth and Yield Projections from Early to Harvest Age in Hybrid Eucalypt Stands

Whole-stand Models (WSM) have always been fitted with permanent plot data organised in a sequential age-matched database, i.e., i and i+1, where = 1, 2, ... N measurements. The objectives of this study were (1) to evaluate the statistical efficiency monthly distributed structure by fitting models Clutter (1963), Buckman (1962) version modified A. L. da Silva et al. (2006), deep learning, (2) possibility gaining accuracy yield projections made from an early age harvest eucalypt stands. Three alternatives for organizing analyzed. first is paired measurement ages, In second, all possible intervals each considered, ii+1; i, i+2; ...; iN; ..., N-1, N. third has month (j), interval one month, j, j+1; j+1, j+2; j+M-1, M, M stand months. This shows that consistency depend on organization data, except model. A better alternative increasing assumptions forecast based using learning method.