National and regional aboveground biomass (AGB) estimates are generally computed based on standing stem volume estimates from forest inventories and default biomass expansion factors (BEFs). AGB estimates are converted to estimates of belowground biomass (BGB) using default root-to-shoot ratios (R/S). Thus, BEFs and R/S are not estimated in ordinary forest inventories, which results in uncertainty in estimates of AGB and BGB. Here, we measured BEF and R/S values (including uncertainty) for different components of Lebombo ironwood (Androstachys johnsonii Prain) trees and assessed their dependence on tree size. The BEF values of tree components were unrelated or weakly related to tree size, and R/S was independent of tree size. BEF values varied from 0.02 for foliage to 1.31 Mg m−3 for whole tree; measurement uncertainty (SE) varied from 2.9% for stem BEF to 10.6% for whole-tree BEF. The belowground, aboveground, and whole-tree BEF-based biomass densities were 30 ± 2.3 (SE = 3.89%), 121 ± 7.84 (SE = 3.23%), and 151 ± 9.87 Mg ha−1(SE = 3.27%), respectively. R/S was 0.24 with an uncertainty of 3.4%. Based on the finding of independence or weak dependence of BEF on tree size, we concluded that, for A. johnsonii, constant component BEF values can be accurately used within the interval of harvested tree sizes.

Coffee ( Coffea L.) agroforestry systems (CAFS) and wooded grasslands (WG) have been pointed out as having high soil organic carbon (SOC) storage potential compared to monoculture systems. Studies analyzing the response of soil bulk density (BD) and SOC to the conversion of WG to slash‐and‐burn agriculture (SBA) and to CAFS are lacking in southern Africa. This study was conducted in the buffer zone of Gorongosa National Park (Mozambique), where depth profiles of BD and SOC were estimated to 0‐ to 100‐cm soil depth in WG, SBA, and CAFS sites, with coffee shrubs aged 3, 5, and 8 years after planting. The stratification ratio (SR) was used as an indicator of soil quality and recovery from disturbance. BD and SOC stocks varied significantly among land use systems and coffee ages only in the surface soil layer (0–20 cm). SOC stocks of the surface soil and SR increased with increasing coffee age. Compared to SBA, significant increases in SOC stocks were only observed 5 years after implementation of CAFS. WG conversion to SBA did not alter SOC stocks in any soil layer; however, it led to decreased SR. Surface SOC stocks were 25.6 and 33.7 Mg ha ⁻¹ in WG and SBA, and 28.0, 41.9, and 61.1 Mg ha ⁻¹ in 3‐ and 5‐ and 8‐year‐old CAFS (mean SOC accumulation of 6.65 Mg ha ⁻¹  year ⁻¹ ). This study reveals that CAFS have the potential to increase belowground carbon sequestration when compared to SBA and WG over comparable soils, making it a practical option for climate change mitigation.

The belowground component of the trees is still poorly known because it needs labour- and time-intensive in situ measurements. However, belowground biomass (BGB) constitutes a significant share of the total forest biomass. I analysed the BGB allocation patterns, fitted models for estimating root components and root system biomasses, and called attention for its possible use in predicting anchoring functions of the different root components. More than half and almost one third of BGB is allocated to the lateral roots and to the root collar, respectively. More than 80% of the BGB is found at a depth range of 9.6–61.2 cm. As the tree size increased, the proportion of BGB allocated to taproots decreased and that allocated to lateral roots increased. All independent models performed almost equally, with the predictors explaining, on average, 98% of the variation in the BGB. It was hypothesised that BGB allocation patterns are a response of the anchoring functions of the tap and lateral roots and therefore, root component biomass models can be used as a methodology to predict anchoring functions of the different root components. Based on the fact that all models performed almost equally, the models using either diameter at breast height (DBH) exclusively as a predictor should be preferred, as tree height is difficult to measure. Models using the root collar diameter (RCD) only should be preferred when the tree is found cut down, as sometimes the RCD is affected by root buttress. Given the large sample size, the validation results, and the coverage of a wide geographical, soil and climatic range, the models fitted can be applied in all A. johnsonii stands in Mozambique.

Secondary Miombo woodlands and forest plantations occupy increasing areas in Mozambique, the former due to anthropogenic activities. Plantations, mainly species ofEucalyptusandPinus, are being established on sites previously covered by secondary Miombo woodlands. This affects the evolution, cycle and spatiotemporal patterns of carbon (C) storage and stocks in forest ecosystems. The estimation of C storage, which is indispensable for formulating climate change policies on sequestrating CO₂, requires tools such as biomass models and biomass conversion and expansion factors (BCEF). In Mozambique, these tools are needed for both indigenous forests and plantations. The objective of this study is to fit species-specific allometric biomass models and BCEF for exotic and indigenous tree species. To incorporate efficient inter-species variability, biomass equations were fitted using nonlinear mixed-effects models. All tree component biomass models had good predictability; however, better predictive accuracy and ability was observed for the 2-predictors biomass model with tree height as a second predictor. The majority of the variability in BCEF was explained by the variation in tree species. Miombo species had larger crown biomass per unit of stem diameter and stored larger amounts of biomass per stem volume. However, due to relatively rapid growth, larger stem diameters, heights, and stand density, the plantations stored more biomass per tree and per unit area.

Biomass regression equations are claimed to yield the most accurate biomass estimates than biomass expansion factors (BEFs). Yet, national and regional biomass estimates are generally calculated based on BEFs, especially when using national forest inventory data. Comparison of regression equations based and BEF-based biomass estimates are scarce. Thus, this study was intended to compare these two commonly used methods for estimating tree and forest biomass with regard to errors and biases. The data were collected in 2012 and 2014. In 2012, a two-phase sampling design was used to fit tree component biomass regression models and determine tree BEFs. In 2014, additional trees were felled outside sampling plots to estimate the biases associated with regression equation based and BEF-based biomass estimates; those estimates were then compared in terms of the following sources of error: plot selection and variability, biomass model, model parameter estimates, and residual variability around model prediction. The regression equation based below-, aboveground and whole tree biomass stocks were, approximately, 7.7, 8.5 and 8.3 % larger than the BEF-based ones. For the whole tree biomass stock, the percentage of the total error attributed to first phase (random plot selection and variability) was 90 and 88 % for regression- and BEF-based estimates, respectively, being the remaining attributed to biomass models (regression and BEF models, respectively). The percent bias of regression equation based and BEF-based biomass estimates for the whole tree biomass stock were −2.7 and 5.4 %, respectively. The errors due to model parameter estimates, those due to residual variability around model prediction, and the percentage of the total error attributed to biomass model were larger for BEF models (than for regression models), except for stem and stem wood components. The regression equation based biomass stocks were found to be slightly larger, associated with relatively smaller errors and least biased than the BEF-based ones. For stem and stem wood, the percentages of their total errors (as total variance) attributed to BEF model were considerably smaller than those attributed to biomass regression equations.