Role of communal and private forestland tenure regimes in regulating forest ecosystem goods and services in Rombo district, Tanzania

This study was undertaken to compare provisioning of forest ecosystem goods and services in Manuo Hill Communal Forest and Shirima Private Forest in Rombo District, Tanzania. Fuel wood was a key forest ecosystem good and biodiversity protection was a key forest ecosystem service identified. Manuo Hill communal forest had lower endowments values in terms of number of stems (1376 stems/ha), basal area (2.6 m2/ha),  volume (7.3 m3/ha) and carbon stock (2.1 tons/ha) compared to the Shirima private forest with 2214 stems/ha, basal area of 3.2 m2/ha, volume of 11.2 m3/ha and carbon stock of 3.2 tons/ha. Only volume andcarbon stock were significantly different between the forests. Species diversity was more or less similar between the forests. Tree removals were higher in communal (1.5 m3/ha) than in private (1.0 m3/ha) but they were not significantly different. Endowments in terms of tenure rights were better in communal forest than in private. More people were entitled to fuel woodfrom communal forest (78%) than from private (32%). Environmental benefits of biodiversity protection were entitled to everybody in both forests. It was concluded that no single tenure regime can achieve all objectives of forest management. Instead, balancing between different tenures is recommended.Key words: forest ecosystems, tenure regimes, endowment and  entitlement, goods and services

Above- and belowground tree biomass models for three mangrove species in Tanzania: a nonlinear mixed effects modelling approach

International audienceAbstractKey messageTested on data from Tanzania, both existing species-specific and common biomass models developed elsewhere revealed statistically significant large prediction errors. Species-specific and common above- and belowground biomass models for three mangrove species were therefore developed. The species-specific models fitted better to data than the common models. The former models are recommended for accurate estimation of biomass stored in mangrove forests of Tanzania.ContextMangroves are essential for climate change mitigation through carbon storage and sequestration. Biomass models are important tools for quantifying biomass and carbon stock. While numerous aboveground biomass models exist, very few studies have focused on belowground biomass, and among these, mangroves of Africa are hardly or not represented.AimsThe aims of the study were to develop above- and belowground biomass models and to evaluate the predictive accuracy of existing aboveground biomass models developed for mangroves in other regions and neighboring countries when applied on data from Tanzania.MethodsData was collected through destructive sampling of 120 trees (aboveground biomass), among these 30 trees were sampled for belowground biomass. The data originated from four sites along the Tanzanian coastline covering three dominant species: Avicennia marina (Forssk.) Vierh, Sonneratia alba J. Smith, and Rhizophora mucronata Lam. The biomass models were developed through mixed modelling leading to fixed effects/common models and random effects/species-specific models.ResultsBoth the above- and belowground biomass models improved when random effects (species) were considered. Inclusion of total tree height as predictor variable, in addition to diameter at breast height alone, further improved the model predictive accuracy. The tests of existing models from other regions on our data generally showed large and significant prediction errors for aboveground tree biomass.ConclusionInclusion of random effects resulted into improved goodness of fit for both above- and belowground biomass models. Species-specific models therefore are recommended for accurate biomass estimation of mangrove forests in Tanzania for both management and ecological applications. For belowground biomass (S. alba) however, the fixed effects/common model is recommended

On the Potential of Sequential and Nonsequential Regression Models for Sentinel-1-Based Biomass Prediction in Tanzanian Miombo Forests

This study derives regression models for aboveground biomass (AGB) estimation in miombo woodlands of Tanzania that utilize the high availability and low cost of Sentinel-1 data. The limited forest canopy penetration of C-band SAR sensors along with the sparseness of available ground truth restricts their usefulness in traditional AGB regression models. Therefore, we propose to use AGB predictions based on airborne laser scanning (ALS) data as a surrogate response variable for SAR data. This dramatically increases the available training data and opens for flexible regression models that capture fine-scale AGB dynamics. This becomes a sequential modeling approach, where the first regression stage has linked in situ data to ALS data and produced the AGB prediction map; we perform the subsequent stage, where this map is related to Sentinel-1 data.We develop a traditional, parametric regression model and alternative nonparametric models for this stage. The latter uses a conditional generative adversarial network (cGAN) to translate Sentinel-1 images into ALS-based AGB prediction maps. The convolution filters in the neural networks make them contextual. We compare the sequential models to traditional, nonsequential regression models, all trained on limited AGB ground reference data. Results show that our newly proposed nonsequential Sentinel-1-based regression model performs better quantitatively than the sequential models, but achieves less sensitivity to fine-scale AGB dynamics. The contextual cGAN-based sequential models best reproduce the distribution of ALS-based AGB predictions. They also reach a lower RMSE against in situ AGB data than the parametric sequential model, indicating a potential for further development

Charcoal Supply In Dar Es Salaam City, Tanzania

In Tanzania, charcoal is the primary source of energy particularly in urban areas. Dar es Salaam, being the largest urban center in the country, is also the largest consumer of charcoal. Assuming that all charcoal transported in the city is consumed, an investigation to estimate the amount of charcoal supplied daily was undertaken through monitoring at checkpoints the amount of charcoal transported daily to the city of Dar es Salaam. The study reveals that on average about 6,000 bags of charcoal are transported daily to the city. The figure may be an underestimation by four fold as most charcoal enters the city unrecorded. The highest amount of charcoal comes from North-West (34 %) and South (31 %) of Dar es Salaam. Open trucks transport the highest amount of charcoal (88 %) into the city. However, bicycles are the most frequent means of charcoal transportation constituting on average about 64 % of all individuals engaged daily in charcoal transportation. Though there are some new vehicles, the greatest percentage of vehicles involved in charcoal transportation are old (mainly registered in the 1980's). Most of the charcoal is transported during morning hours (56 %). Most of the charcoal transported to the city is for commercial use. The revenues from charcoal transportation taxes contribute a significant amount of money to both Local and Central Governments. If properly collected and used, they can effectively contribute to the development of the country and sustainable management of the catchment areas for charcoal. TJFNC Vol. 75 2004: pp. 108-11

Use of local and global maps of forest canopy height and aboveground biomass to enhance local estimates of biomass in miombo woodlands in Tanzania

Abstract Field surveys are often a primary source of aboveground biomass (AGB) data, but plot-based estimates of parameters related to AGB are often not sufficiently precise, particularly not in tropical countries. Remotely sensed data may complement field data and thus help to increase the precision of estimates and circumvent some of the problems with missing sample observations in inaccessible areas. Here, we report the results of a study conducted in a 15,867 km² area in the dry miombo woodlands of Tanzania, to quantify the contribution of existing canopy height and biomass maps to improving the precision of canopy height and AGB estimates locally. A local and a global height map and three global biomass maps, and a probability sample of 513 inventory plots were subject to analysis. Model-assisted sampling estimators were used to estimate mean height and AGB across the study area using the original maps and then with the maps calibrated with local inventory plots. Large systematic map errors – positive or negative – were found for all the maps, with systematic errors as great as 60–70 %. The maps contributed nothing or even negatively to the precision of mean height and mean AGB estimates. However, after being calibrated locally, the maps contributed substantially to increasing the precision of both mean height and mean AGB estimates, with relative efficiencies (variance of the field-based estimates relative to the variance of the map-assisted estimates) of 1.3–2.7 for the overall estimates. The study, although focused on a relatively small area of dry tropical forests, illustrates the potential strengths and weaknesses of existing global forest height and biomass maps based on remotely sensed data and universal prediction models. Our results suggest that the use of regional or local inventory data for calibration can substantially increase the precision of map-based estimates and their applications in assessing forest carbon stocks for emission reduction programs and policy and financial decisions

A data support infrastructure for Clean Development Mechanism forestry implementation: an inventory perspective from Cameroon

Clean Development Mechanism (CDM) forestry project development requires highly multi-disciplinary and multiple-source information that can be complex, cumbersome and costly to acquire. Yet developing countries in which CDM projects are created and implemented are often data poor environments and unable to meet such complex information requirements. Using Cameroon as an example, the present paper explores the structure of an enabling host country data support infrastructure for CDM forestry implementation, and also assesses the supply potential of current forestry information. Results include a conceptual data model of CDM project data needs; the list of meso- and macro-level data and information requirements (Demand analysis); and an inventory of relevant data available in Cameroon (Supply analysis). From a comparison of demand and supply, we confirm that data availability and the relevant infrastructure for data or information generation is inadequate for supporting carbon forestry at the micro, meso and macro-levels in Cameroon. The results suggest that current CDM afforestation and reforestation information demands are almost impenetrable for local communities in host countries and pose a number of cross-scale barriers to project adoption. More importantly, we identify proactive regulatory, institutional and capacity building policy strategies for forest data management improvements that could enhance biosphere carbon management uptake in poor countries. CDM forestry information research needs are also highlighted

Scenarios of land use and land cover change and their multiple impacts on natural capital in Tanzania

REDD+ (reducing emissions from deforestation, and forest degradation, plus the conservation of forest carbon stocks, sustainable management of forests, and enhancement of forest carbon stocks, in developing countries) requires information on land use and land cover changes (LULCC) and carbon emissions trends from the past to the present and into the future. Here we use the results of participatory scenario development in Tanzania, to assess the potential interacting impacts on carbon stock, biodiversity and water yield of alternative scenarios where REDD+ is effectively implemented or not by 2025, the green economy (GE) and the business as usual (BAU) respectively. Under the BAU scenario, land use and land cover changes causes 296 MtC national stock loss by 2025, reduces the extent of suitable habitats for endemic and rare species, mainly in encroached protected mountain forests, and produce changes of water yields. In the GE scenario, national stock loss decreases to 133 MtC. In this scenario, consistent LULCC impacts occur within small forest patches with high carbon density, water catchment capacity and biodiversity richness. Opportunities for maximising carbon emissions reductions nationally are largely related to sustainable woodland management but also contain trade-offs with biodiversity conservation and changes in water availability

Aboveground forest biomass varies across continents, ecological zones and successional stages: refined IPCC default values for tropical and subtropical forests

For monitoring and reporting forest carbon stocks and fluxes, many countries in the tropics and subtropics rely on default values of forest aboveground biomass (AGB) from the Intergovernmental Panel on Climate Change (IPCC) guidelines for National Greenhouse Gas (GHG) Inventories. Default IPCC forest AGB values originated from 2006, and are relatively crude estimates of average values per continent and ecological zone. The 2006 default values were based on limited plot data available at the time, methods for their derivation were not fully clear, and no distinction between successional stages was made. As part of the 2019 Refinement to the 2006 IPCC Guidelines for GHG Inventories, we updated the default AGB values for tropical and subtropical forests based on AGB data from >25 000 plots in natural forests and a global AGB map where no plot data were available. We calculated refined AGB default values per continent, ecological zone, and successional stage, and provided a measure of uncertainty. AGB in tropical and subtropical forests varies by an order of magnitude across continents, ecological zones, and successional stage. Our refined default values generally reflect the climatic gradients in the tropics, with more AGB in wetter areas. AGB is generally higher in old-growth than in secondary forests, and higher in older secondary (regrowth >20 years old and degraded/logged forests) than in young secondary forests (20 years old). While refined default values for tropical old-growth forest are largely similar to the previous 2006 default values, the new default values are 4.0-7.7-fold lower for young secondary forests. Thus, the refined values will strongly alter estimated carbon stocks and fluxes, and emphasize the critical importance of old-growth forest conservation. We provide a reproducible approach to facilitate future refinements and encourage targeted efforts to establish permanent plots in areas with data gaps