Allometric models for prediction of above- and belowground biomass of trees in the miombo woodlands of Tanzania

Abstract

Miombo woodland is a significant forest type occupying about 9% of the African land area and forms a dominant vegetation type in many southeastern African countries including Tanzania. Quantification of the amount of carbon stored in forests presently is an important component in the implementation of the emerging carbon credit market mechanisms. This calls for appropriate allometric models predicting biomass which currently are scarce. The aim of this study was to develop above- and belowground allometric general and site-specific models for trees in miombo woodland. The data were collected from four sites in Tanzania and covers a wide range of conditions and tree sizes (diameters at breast height from 1.1 to 110 cm). Above- and belowground biomass models were developed from 167 and 80 sample trees, respectively. The model fitting showed that large parts of the variation (up to 97%) in biomass were explained by diameter at breast height and tree height. Since including tree height only marginally increased the explanation of the biomass variation (from 95% to 96–97% for aboveground biomass), the general recommendation is to apply the models with diameter at breast height only as an independent variable. The results also showed that the general models can be applied over a wide range of conditions in Tanzania. The comparison with previously developed models revealed that these models can probably also be applied for miombo woodland elsewhere in southeastern Africa if not used beyond the tree size range of the model data.

Description

Forest Ecology and Management, 2013; 310 (2013): 87–101

Keywords

Destructive sampling, Miombo woodland, Biomass, Root-shoot ratio

Citation

8.1.11 Mugasha, W.A., Eid, T., Bollandsås, O.M., Malimbwi, R.E., Chamshama, S.A.O., Zahabu, E. and Katani, J.Z. (2013). Allometric models for prediction of above-and belowground biomass of trees in the miombo woodlands of Tanzania. Forest Ecology and Management, 310: 87-101