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Agrociencia

versión On-line ISSN 2521-9766versión impresa ISSN 1405-3195

Resumen

SORIANO-LUNA, María de los Á. et al. Abovegroud biomass estimation by structural component in Zacualtipán, Hidalgo, México. Agrociencia [online]. 2015, vol.49, n.4, pp.423-438. ISSN 2521-9766.

Allometric functions based on destructive samplings provided precise estimations of individual biomass and per surface unit. Forest biomass estimation represents a key process fot sustainable forest management and in the determination of forest carbon stocks (C). Prediction models of biomass were generated by structural component (stem, branches, foliage and bark) for Pinus patula and for 11 broadleaved species with ecological importance in the mountain cloud forest of Zacualtipán, Hidalgo, Mexico. A destructive sampling was made to select individuals which included the maximum interval of sizes; the sample was composed of 25 trees of P.patula and 46 trees of 11 broadleaved species. The highest proportion of the total biomass in P. patula was distributed into the stem (68.2 %), followed by branches (14.3 %), bark (9.3 %) and foliage (8.2 %). With the size of the conifer tree, the allocation of biomass in the stem and bark increased, while the proportion of biomass of the foliage decreased. The biomass models were of the form B = Exp(-β0)*(dn2*h)β1 where B is the biomass per tree (kg), dn is the diameter at breast height (cm) and h is total tree height (m). The inclusion of tree height improved the prediction capability of the models. The fit of the structural components of the tree with non-linear regression simultaneous equations is adequate, in which the sum of components is the total biomass. The statistical estimates make it possible to affirm that the equations are appropriate to be used with data of similar forests to that of the study área, to estimate the biomass components of P. patula and of broadleaved species analyzed.

Palabras llave : Pinus patula; biomass allocation; allometric equations; dry matter; managed forest; mountain cloud forest.

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