<b>Machine learning forecasting of teak (</b><b><i>Tectona grandis </i></b><b>L.f.) growth parameters in Ghanaian plantations: a plot-level grouped-split analysis with ablation and sensitivity analysis</b>

This study built and tested a multilayer perceptron (GrowthMLP) that forecasts total height and stem volume of Ghana teak from stand, soil, and topographic covariates. Tree-level records (n = 4,995) were extracted using Ghana-calibrated allometric and yield relationships and merged with soil and topographic covariates from two published chronosequence studies. Several tree records were extracted for the same published plot and therefore share identical or near-identical stand and site covariates.