Abstract:
Worldwide, forests are an important carbon sink and thus are
key to mitigate the effects of climate change. Mountain moist evergreen forests in Mozambique
are threatened by agricultural expansion, uncontrolled logging, and firewood collection, thus
compromising their role in carbon sequestration. There is lack of local tools for above-ground
biomass (AGB) estimation of mountain moist evergreen forest, hence carbon emissions from
deforestation and forest degradation are not adequately known. This study aimed to develop
biomass allometric equations (BAE) and biomass expansion factor (BEF) for the estimation of
total above-ground carbon stock in mountain moist evergreen forest. The destructive method
was used, whereby 39 trees were felled and measured for diameter at breast height (DBH), total
height and the commercial height. We determined the wood basic density, the total dry weight
and merchantable timber volume by Smalian’s formula. Six biomass allometric models were
fitted using non-linear least square regression. The BEF was determined based on the
relationship between bole stem dry weight and total dry weight of the tree. To estimate the mean
AGB of the forest, a forest inventory was conducted using 27 temporary square plots. The
applicability of Marzoli’s volume equation was compared with Smalian’s volume equation in
order to check whether Marzoli’s volume from national forest inventory can be used to predict
AGB using BEF. The best model was the power model with only DBH as predictor variable,
which provided an estimated mean AGB of 291 ± 141 Mg ha−1 (mean ± 95% confidence level).
The mean wood basic density of sampled trees was 0.715 ± 0.182 g cm−3. The average BEF was
of 2.05 ± 0.15 and the estimated mean AGB of 387 ± 126 Mg ha−1. The BAE from miombo
woodland within the vicinity of the study area underestimates the AGB for all sampled trees.
Chave et al.’s pantropical equation of moist forest did not fit to the Moribane Forest Reserve,
while Brown’s equation of moist forest had a good fit to the Moribane Forest Reserve, having
generated 1.2% of bias, very close to that generated by the selected model of this study. BEF
showed to be reliable when combined with stand mean volume from Marzoli’s National Forestry
Inventory equation. The BAE and the BEF function developed in this study can be used to
estimate the AGB of the mountain moist evergreen forests at Moribane Forest Reserve in
Mozambique. However, the use of the biomass allometric model should be preferable when
DBH information is available.
