The establishment of new forest plantations requires making important decisions starting with the selection of the species to be planted, as well as the choice of an adequate planting spacing to guarantee the maximization of wood production. The aim of this study was to evaluate the performance of nine eucalypt species planted with three different spacings under the environmental conditions of the tropical dry forest of the inter-Andean valleys of Colombia. A split-plot arrangement with two replicates was used as the experimental design. The planting spacing factor (three planting spacings) was assigned to the main plots and the species factor (nine eucalypt species) was assigned to the subplots. Survival and growth were evaluated at twenty-four months of age. The study identified the Brazilian provenances of Eucalyptus camaldulensis Dehnh, Eucalyptus urophylla S.T. Blake, Eucalyptus grandis W. Hill, and Eucalyptus pellita F. Muell. and the Colombian provenance of Eucalyptus pellita F. Muell. as promising for commercial reforestation programs in areas with a water deficit in the tropical dry forest. Planting spacings of 3×2 m (1666 stems·ha-1) and 3×2.5 m (1333 stems·ha-1) maximized the production of basal area and the volume for the species evaluated. Finally, no interaction was detected between species and planting spacing factors, therefore, the species identified had a better performance regardless of the planting spacing used.
El estudio desarrolló un modelo empírico para predecir la altura dominante (H) y el índice de sitio (IS) de plantaciones de Gmelina arborea Roxb., considerando para ello variables biofísicas y de rodal. Se utilizaron datos de 160 rodales localizados en las regiones Andina, Caribe y Pacífica de Colombia. El modelo de Chapman-Richards fue seleccionado para predecir la H e IS de cada rodal. Un análisis de correlación identificó al espaciamiento relativo, la altitud y la precipitación como variables relacionadas al IS. Un modelo de regresión lineal múltiple con estas variables explicó el 70 % de la variación total observada en el IS. Estas variables incorporadas al modelo de H permitieron aumentar en 30 % y reducir en 40 y 41 % el índice de ajuste, error absoluto y error medio cuadrático, respectivamente. Los modelos desarrollados son adecuados para estimar la productividad en áreas sin historia de plantaciones forestales, adicionando flexibilidad y capacidad predictiva en un entorno cambiante.
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