The carbon balance of Africa: synthesis of recent research studies

Agroforestry systems in the mid-hills of the north-western Himalaya: A sustainable pathway to improved soil health and climate resilience

Identifying the new tree crop combinations plays an important function in transforming the low input agriculture into land units with high economic returns, increasing carbon (C) sink and nutrients storage capacity, and acting as a panacea to achieve Sustainability Development Goals (SDGs). The present study aims to evaluate various tree-crop combinations for (i) biomass production, (ii) carbon accumulation, and (iii) soil nutrient enrichment of traditional and commercially evolved eight agroforestry systems (AFSs), including agri-silvi-horticulture system, agri-silviculture system, silvi-pasture, fruit tree, fodder tree, bamboo, melia and poplar based AFSs with sole cropping system in the mid-hill zone of the north-western Himalaya. The results demonstrated that poplar based AFS accumulated a higher amount of biomass (130.87 Mg ha-1) and carbon (65.44 Mg ha-1) closely followed by melia-based AFS. The C stored in leaf litter was higher (0.66 Mg ha-1) in poplar-based AFS, however, soil C stock was maximum (114.69 Mg ha-1) under bamboo-based AFS. Overall, the Melia based AFS exhibited a higher rate of carbon dioxide mitigation (19.30 Mg ha-1 yr-1) and C-sequestration (5.26 Mg ha-1 yr-1) than other studied AFSs. Moreover, soil macro-nutrients (available N, P, K, S and Ca) were maximum under bamboo-based AFS, on the other hand, the fruit-based AFS had the higher concentrations of micro-nutrients i.e., Cu (3.05), Fe (31.10 mg g-1) and Mn (17.31 mg g-1). The soil microbial counts were higher in poplar-based AFS, whereas, the soil quality index improved significantly under bamboo based and fruit tree based AFSs. Hence, it can be concluded that the experimentally evolved AFSs represent an effective approach for boosting C-sequestration, soil fertility, regenerating the soil and sustainability of hill agriculture in the north-western Himalayas over traditional AFSs and sole cropping.

Mapping soil organic carbon distribution across South Africa's major biomes using remote sensing-topo-climatic covariates and Concrete Autoencoder-Deep neural networks

The management of soil organic carbon (SOC) stocks remains at the forefront of greenhouse gas mitigation. However, unprecedented anthropogenic disturbances emanating from continued land-use change have significantly altered SOC distribution across global biomes leading to considerable carbon losses. Consequently, understanding the spatial distribution of SOC across different biomes, particularly at larger scales, is critical for climate change policy formulation and planning. Advancements in remote sensing, availability of big data, and deep learning architecture offer great potential in large-scale SOC mapping. In this regard, this study mapped SOC distribution across South Africa's major biomes using remotely sensed-topo-climatic data and Concrete Autoencoder-Deep neural networks (CAE-DNN). From the different deep neural frameworks tested, the CAE-DNN model (developed from 26 selected covariates) achieved the best accuracy with an RMSE value of 7.91 t/ha (about 20 % of the mean). Results further showed that SOC stock correlated with general biome coverage, as the Grassland and Savanna biomes contributed the most (32.38 % and 31.28 %) to the overall SOC pool in South Africa. However, despite their smaller footprint, Forests (44.12 t/h) and the Indian Ocean Coastal Belt (43.05 t/h) biomes demonstrated the highest SOC sequestration capacity. The restoration of degraded biomes is advocated for, in order to boost SOC storage; but a balance between carbon sequestration capacity, biodiversity health, and the adequate provision of ecosystem services must be maintained. To this end, these findings provide a guideline to facilitate sustainable SOC stock management within South Africa's major biomes and indeed other regions of the world.

Sensing of Soil Organic Matter Using Laser-Induced Breakdown Spectroscopy Coupled with Optimized Self-Adaptive Calibration Strategy

Rapid quantification of soil organic matter (SOM) is a great challenge for the health assessment and fertility management of agricultural soil. Laser-induced breakdown spectroscopy (LIBS) with appropriate modeling algorithms is an alternative tool for this measurement. However, the current calibration strategy limits the prediction performance of the LIBS technique. In this study, 563 soil samples from Hetao Irrigation District in China were collected; the LIBS spectra of the soils were recorded in the wavenumber range of 288-950 nm with a resolution of 0.116 nm; a self-adaptive partial least squares regression model (SAM-PLSR) was employed to explore optimal model parameters for SOM prediction; and calibration parameters including sample selection for the calibration database, sample numbers and sample location sites were optimized. The results showed that the sample capacity around 60-80, rather than all of the samples in the soil library database, was selected for calibration from a spectral similarity re-ordered database regarding unknown samples; the model produced excellent predictions, with R² = 0.92, RPD = 3.53 and RMSEP = 1.03 g kg^-1. Both the soil variances of the target property and the spectra similarity of the soil background were the key factors for the calibration model, and the small sample set led to poor predictions due to the low variances of the target property, while negative effects were observed for the large sample set due to strong interferences from the soil background. Therefore, the specific unknown sample depended strategy, i.e., self-adaptive modelling, could be applied for fast SOM sensing using LIBS for soils in varied scales with improved robustness and accuracy.

Suffrutex grasslands in south-central Angola: belowground biomass, root structure, soil characteristics and vegetation dynamics of the ‘underground forests of Africa’

Despite its importance for carbon stocks accounting, belowground biomass (BGB) has seldom been measured due to the methodological complexity involved. In this study, we assess woody BGB and related carbon stocks, soil properties and human impact on two common suffrutex grasslands (Brachystegia- and Parinari grasslands) on the Angolan Central Plateau. Data on BGB was measured by direct destructive sampling. Soil samples were analysed for select key parameters. To investigate vegetation dynamics and human impact, we used Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) and fire data retrieved via Google Earth Engine. Mean belowground woody biomass of sandy Parinari grasslands was 17 t/ha and 44 t/ha in ferralitic Brachystegia grasslands of which 50% correspond to carbon stocks. As such, the BGB of Brachystegia grasslands almost equals the amount of aboveground biomass (AGB) of neighbouring miombo woodlands. Almost the entire woody BGB is located in the top 30 cm of the soil. Soils were extremely acid, showing a low nutrient availability. Both grassland types differed strongly in EVI and fire seasonality. The Parinari grasslands burnt almost twice as frequent as Brachystegia grasslands in a 10-year period. Our study emphasizes the high relevance of BGB in suffrutex grasslands for carbon stock accounting.

High photosynthetic capacity of Sahelian C3 and C4 plants

The semi-arid ecosystems of the African Sahel play an important role in the global carbon cycle and are among the most sensitive ecosystems to future environmental pressures. Still, basic data of photosynthetic characteristics of Sahelian vegetation are very limited, preventing us to properly understand these ecosystems and to project their response to future global changes. Here, we aim to study and quantify key leaf traits, including photosynthetic parameters and leaf nutrients (N_leaf and P_leaf), of common C₃ and C₄ Sahelian plants (trees, lianas, and grasses) at the Dahra field site (Senegal). Dahra is a reference site for grazed semi-arid Sahelian savannah ecosystems in carbon cycle studies. Within the studied species, we found that photosynthetic parameters varied considerably between functional types. We also found significant relationships between and within photosynthetic parameters and leaf traits which mostly differed in their slopes from C₃ to C₄ plants. In agreement with the leaf economic spectrum, strong relationships (R² = 0.71) were found between SLA and N_leaf whereby C₃ and C₄ plants showed very similar relationships. By comparing our data to a global dataset of plant traits, we show that measured Sahelian plants exhibit higher photosynthetic capacity (A_sat) compared to the non-Sahelian vegetation, with values that are on average a fourfold of the global average. Moreover, Sahelian C₃ plants showed photosynthetic nutrient use efficiencies that were on average roughly twice as high as global averages. We interpreted these results as the potential adaptation of Sahelian plants to short growing season lengths via an efficient nutrient allocation to optimize photosynthesis during this period. Our study provides robust estimates of key functional traits, but also traits relationships that will help to calibrate and validate vegetation models over this data-poor region.

Terrestrial Laser Scanning for Vegetation Analyses with a Special Focus on Savannas

Savannas are heterogeneous ecosystems, composed of varied spatial combinations and proportions of woody and herbaceous vegetation. Most field-based inventory and remote sensing methods fail to account for the lower stratum vegetation (i.e., shrubs and grasses), and are thus underrepresenting the carbon storage potential of savanna ecosystems. For detailed analyses at the local scale, Terrestrial Laser Scanning (TLS) has proven to be a promising remote sensing technology over the past decade. Accordingly, several review articles already exist on the use of TLS for characterizing 3D vegetation structure. However, a gap exists on the spatial concentrations of TLS studies according to biome for accurate vegetation structure estimation. A comprehensive review was conducted through a meta-analysis of 113 relevant research articles using 18 attributes. The review covered a range of aspects, including the global distribution of TLS studies, parameters retrieved from TLS point clouds and retrieval methods. The review also examined the relationship between the TLS retrieval method and the overall accuracy in parameter extraction. To date, TLS has mainly been used to characterize vegetation in temperate, boreal/taiga and tropical forests, with only little emphasis on savannas. TLS studies in the savanna focused on the extraction of very few vegetation parameters (e.g., DBH and height) and did not consider the shrub contribution to the overall Above Ground Biomass (AGB). Future work should therefore focus on developing new and adjusting existing algorithms for vegetation parameter extraction in the savanna biome, improving predictive AGB models through 3D reconstructions of savanna trees and shrubs as well as quantifying AGB change through the application of multi-temporal TLS. The integration of data from various sources and platforms e.g., TLS with airborne LiDAR is recommended for improved vegetation parameter extraction (including AGB) at larger spatial scales. The review highlights the huge potential of TLS for accurate savanna vegetation extraction by discussing TLS opportunities, challenges and potential future research in the savanna biome.

Airborne Lidar Sampling Pivotal for Accurate Regional AGB Predictions from Multispectral Images in Forest-Savanna Landscapes

Precise accounting of carbon stocks and fluxes in tropical vegetation using remote sensing approaches remains a challenging exercise, as both signal saturation and ground sampling limitations contribute to inaccurate extrapolations. Airborne LiDAR Scanning (ALS) data can be used as an intermediate level to radically increase sampling and enhance model calibration. Here we tested the potential of using ALS data for upscaling vegetation aboveground biomass (AGB) from field plots to a forest-savanna transitional landscape in the Guineo–Congolian region in Cameroon, using either a design-based approach or a model-based approach leveraging multispectral satellite imagery. Two sets of reference data were used: (1) AGB values collected from 62 0.16-ha plots distributed both in forests and savannas; and (2) an AGB map generated form ALS data. In the model-based approach, we trained Random Forest models using predictors from recent sensors of varying spectral and spatial resolutions (Spot 6/7, Landsat 8, and Sentinel 2), along with biophysical predictors derived after pre-processing into the Overland processing chain, following a forward variable selection procedure with a spatial 4-folds cross validation. The models calibrated with field plots lead to a systematic overestimation in AGB density estimates and a root mean squared prediction error (RMSPE) of up to 65 Mg.ha−1 (90%), whereas calibration with ALS lead to low bias and a drop of ~30% in RMSPE (down to 43 Mg.ha−1, 58%) with little effect of the satellite sensor used. Decomposing bias along the AGB density range, we show that multispectral images can (in some specific cases) be used for unbiased prediction at landscape scale on the basis of ALS-calibrated statistical models. However, our results also confirm that, whatever the spectral indices used and attention paid to sensor quality and pre-processing, the signal is not sufficient to warrant accurate pixelwise predictions, because of large relative RMSPE, especially above (200–250 t/ha). The design-based approach, for which average AGB density values were attributed to mapped land cover classes, proved to be a simple and reliable alternative (for landscape to region level estimations), when trained with dense ALS samples.

Carbon dynamics, net primary productivity and human-appropriated net primary productivity across a forest-cocoa farm landscape in West Africa

Terrestrial net primary productivity (NPP) is an important metric of ecosystem functioning; however, there are little empirical data on the NPP of human-modified ecosystems, particularly smallholder, perennial crops like cocoa (Theobroma cacao), which are extensive across the tropics. Human-appropriated NPP (HANPP) is a measure of the proportion of a natural system's NPP that has either been reduced through land-use change or harvested directly and, previously, has been calculated to estimate the scale of the human impact on the biosphere. Additionally, human modification can create shifts in NPP allocation and decomposition, with concomitant impacts on the carbon cycle. This study presents the results of 3 years of intensive monitoring of forest and smallholder cocoa farms across disturbance, management intensity, distance from forest and farm age gradients. We measured among the highest reported NPP values in tropical forest, 17.57 ± 2.1 and 17.7 ± 1.6 Mg C ha^-1  year^-1 for intact and logged forest, respectively; however, the average NPP of cocoa farms was still higher, 18.8 ± 2.5 Mg C ha^-1  year^-1 , which we found was driven by cocoa pod production. We found a dramatic shift in litterfall residence times, where cocoa leaves decomposed more slowly than forest leaves and shade tree litterfall decomposed considerably faster, indicating significant changes in rates of nutrient cycling. The average HANPP value for all cocoa farms was 2.1 ± 1.1 Mg C ha^-1  year^-1 ; however, depending on the density of shade trees, it ranged from -4.6 to 5.2 Mg C ha^-1  year^-1 . Therefore, rather than being related to cocoa yield, HANPP was reduced by maintaining higher shade levels. Across our monitored farms, 18.9% of farm NPP was harvested (i.e., whole cocoa pods) and only 1.1% (i.e., cocoa beans) was removed from the system, suggesting that the scale of HANPP in smallholder cocoa agroforestry systems is relatively small.

Effects of Cropland Conversion and Climate Change on Agrosystem Carbon Balance of China’s Dryland: A Typical Watershed Study

Remarkable warm‒wet climate shifts and intensive cropland expansion strongly affected carbon (C) cycle and threaten agricultural sustainability in northwest China. In this study, we integrated a process-based ecosystem model and an empirical C bookkeeping model to investigate the coupled and isolated effects of arable land conversions and climate change (CLM) on regional C balance in a typical watershed of northwest China. Results revealed that the farmland area increased by 3367.31 km2 during 1979–2014. The combined effects of CLM with net cropland expansion enlarged the vegetation C (VEGC) and the soil organic C (SOC) stock by 2.83 and 11.83 Tg, respectively, and were strongest in 2008–2014. The conversions between desert grassland and cropland were the major driving forces for regional C balance. Cropland expansion shared equal effects on VEGC increase with CLM, but its effect on SOC increment was 53 times larger than CLM’s. VEGC was more responsive to CLM, whereas SOC gained more benefits from land management. The C sink from reclamation suffered from high water consumption and is facing great threats due to glaciers and mountain lake shrinking and groundwater overpumping. Water-saving irrigation techniques and environmentally friendly water use strategies are essential for local agricultural sustainability.

Estimating terrestrial gross primary productivity in water limited ecosystems across Africa using the Southampton Carbon Flux (SCARF) model

The amount of carbon uptake by vegetation is an important component to understand the functioning of ecosystem processes and their response/feedback to climate. Recently, a new diagnostic model called the Southampton Carbon Flux (SCARF) Model driven by remote sensing data was developed to predict terrestrial gross primary productivity (GPP) and successfully applied in temperate regions. The model is based on the concept of quantum yield of plants and improves on the previous diagnostic models by (i) using the fraction of photosynthetic active radiation absorbed by the photosynthetic pigment (FAPAR_ps) and (ii) using direct quantum yield by classifying the vegetation into C₃ or C₄ classes. In this paper, we calibrated and applied the model to evaluate GPP across various ecosystems in Africa. The performance of the model was evaluated using data from seven eddy covariance flux tower sites. Overall, the modelled GPP values showed good correlation (R>0.59, p<0.0001) with estimated flux tower GPP at most sites (except at a tropical rainforest site, R=0.38, p=0.02) in terms of their seasonality and absolute values. Mean daily GPP across the investigated period varied significantly across sites depending on the vegetation types from a minimum of 0.44gCm^-2day^-1 at the semi-arid and sub-humid savanna grassland sites to a maximum of 9.86gCm^-2day^-1 at the woodland and tropical rain forest sites. Generally, strong correlation is observed in savanna woodlands and grasslands where vegetation follows a prescribed seasonal cycle as determined by changes in canopy chlorophyll content and leaf area index. Finally, the mean annual GPP value for Africa predicted by the model was 35.25PgCyr^-1. The good performance of the SCARF model in water-limited ecosystems across Africa extends its potential for global application.

Satellite passive microwaves reveal recent climate-induced carbon losses in African drylands

The African continent is facing one of the driest periods in the past three decades as well as continued deforestation. These disturbances threaten vegetation carbon (C) stocks and highlight the need for improved capabilities of monitoring large-scale aboveground carbon stock dynamics. Here we use a satellite dataset based on vegetation optical depth derived from low-frequency passive microwaves (L-VOD) to quantify annual aboveground biomass-carbon changes in sub-Saharan Africa between 2010 and 2016. L-VOD is shown not to saturate over densely vegetated areas. The overall net change in drylands (53% of the land area) was -0.05 petagrams of C per year (Pg C yr^-1) associated with drying trends, and a net change of -0.02 Pg C yr^-1 was observed in humid areas. These trends reflect a high inter-annual variability with a very dry year in 2015 (net change, -0.69 Pg C) with about half of the gross losses occurring in drylands. This study demonstrates, first, the applicability of L-VOD to monitor the dynamics of carbon loss and gain due to weather variations, and second, the importance of the highly dynamic and vulnerable carbon pool of dryland savannahs for the global carbon balance, despite the relatively low carbon stock per unit area.

High fire-derived nitrogen deposition on central African forests

Atmospheric nitrogen (N) deposition is an important determinant of N availability for natural ecosystems worldwide. Increased anthropogenic N deposition shifts the stoichiometric equilibrium of ecosystems, with direct and indirect impacts on ecosystem functioning and biogeochemical cycles. Current simulation data suggest that remote tropical forests still receive low atmospheric N deposition due to a lack of proximate industry, low rates of fossil fuel combustion, and absence of intensive agriculture. We present field-based N deposition data for forests of the central Congo Basin, and use ultrahigh-resolution mass spectrometry to characterize the organic N fraction. Additionally, we use satellite data and modeling for atmospheric N source apportionment. Our results indicate that these forests receive 18.2 kg N hectare-1 years-1 as wet deposition, with dry deposition via canopy interception adding considerably to this flux. We also show that roughly half of the N deposition is organic, which is often ignored in N deposition measurements and simulations. The source of atmospheric N is predominantly derived from intensive seasonal burning of biomass on the continent. This high N deposition has important implications for the ecology of the Congo Basin and for global biogeochemical cycles more broadly.

Evaluating the role of Pleistocene refugia, rivers and environmental variation in the diversification of central African duikers (genera Cephalophus and Philantomba)

Background

This study aims to assess the role that Pleistocene refugia, rivers and local habitat conditions may have played in the evolutionary diversification of three central African duiker species (Cephalophus dorsalis, C. callipygus and Philantomba monticola). Genetic data from geo-referenced feces were collected from a wide range of sites across Central Africa. Historical patterns of population genetic structure were assessed using a ~ 650 bp fragment of the mitochondrial control region and contemporary patterns of genetic differentiation were evaluated using 12 polymorphic microsatellite loci.

Results

Mitochondrial analyses revealed that populations of C. callipygus and P. monticola in the Gulf of Guinea refugium are distinct from other populations in west central Africa. All three species exhibit signatures of past population expansion across much of the study area consistent with a history of postglacial expansion. There was no strong evidence for a riverine barrier effect in any of the three species, suggesting that duikers can readily cross major rivers. Generalized dissimilarity models (GDM) showed that environmental variation explains most of the nuclear genetic differentiation in both C. callipygus and P. monticola. The forest-savanna transition across central Cameroon and the Plateaux Batéké region in southeastern Gabon show the highest environmentally-associated turnover in genetic variability. A pattern of genetic differentiation was also evident between the coast and forest interior that may reflect differences in precipitation and/or vegetation.

Conclusions

Findings from this study highlight the historical impact of Pleistocene fragmentation and current influence of environmental variation on genetic structure in duikers. Conservation efforts should therefore target areas that harbor as much environmentally-associated genetic variation as possible in order to maximize species’ capacity to adapt to environmental change.

Electronic supplementary material

The online version of this article (10.1186/s12862-017-1054-4) contains supplementary material, which is available to authorized users.

Improving the Prediction of African Savanna Vegetation Variables Using Time Series of MODIS Products

African savanna vegetation is subject to extensive degradation as a result of rapid climate and land use change. To better understand these changes detailed assessment of vegetation structure is needed across an extensive spatial scale and at a fine temporal resolution. Applying remote sensing techniques to savanna vegetation is challenging due to sparse cover, high background soil signal, and difficulty to differentiate between spectral signals of bare soil and dry vegetation. In this paper, we attempt to resolve these challenges by analyzing time series of four MODIS Vegetation Products (VPs): Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Leaf Area Index (LAI), and Fraction of Photosynthetically Active Radiation (FPAR) for Etosha National Park, a semiarid savanna in north-central Namibia. We create models to predict the density, cover, and biomass of the main savanna vegetation forms: grass, shrubs, and trees. To calibrate remote sensing data we developed an extensive and relatively rapid field methodology and measured herbaceous and woody vegetation during both the dry and wet seasons. We compared the efficacy of the four MODIS-derived VPs in predicting vegetation field measured variables. We then compared the optimal time span of VP time series to predict ground-measured vegetation. We found that Multiyear Partial Least Square Regression (PLSR) models were superior to single year or single date models. Our results show that NDVI-based PLSR models yield robust prediction of tree density (R2 =0.79, relative Root Mean Square Error, rRMSE=1.9%) and tree cover (R2 =0.78, rRMSE=0.3%). EVI provided the best model for shrub density (R2 =0.82) and shrub cover (R2 =0.83), but was only marginally superior over models based on other VPs. FPAR was the best predictor of vegetation biomass of trees (R2 =0.76), shrubs (R2 =0.83), and grass (R2 =0.91). Finally, we addressed an enduring challenge in the remote sensing of semiarid vegetation by examining the transferability of predictive models through space and time. Our results show that models created in the wetter part of Etosha could accurately predict trees' and shrubs' variables in the drier part of the reserve and vice versa. Moreover, our results demonstrate that models created for vegetation variables in the dry season of 2011 could be successfully applied to predict vegetation in the wet season of 2012. We conclude that extensive field data combined with multiyear time series of MODIS vegetation products can produce robust predictive models for multiple vegetation forms in the African savanna. These methods advance the monitoring of savanna vegetation dynamics and contribute to improved management and conservation of these valuable ecosystems.

Forest Carbon Monitoring and Reporting for REDD+: What Future for Africa?

A climate change mitigation mechanism for emissions reduction from reduced deforestation and forest degradation, plus forest conservation, sustainable management of forest, and enhancement of carbon stocks (REDD+), has received an international political support in the climate change negotiations. The mechanism will require, among others, an unprecedented technical capacity for monitoring, reporting and verification of carbon emissions from the forest sector. A functional monitoring, reporting and verification requires inventories of forest area, carbon stock and changes, both for the construction of forest reference emissions level and compiling the report on the actual emissions, which are essentially lacking in developing countries, particularly in Africa. The purpose of this essay is to contribute to a better understanding of the state and prospects of forest monitoring and reporting in the context of REDD+ in Africa. We argue that monitoring and reporting capacities in Africa fall short of the stringent requirements of the methodological guidance for monitoring, reporting and verification for REDD+, and this may weaken the prospects for successfully implementing REDD+ in the continent. We presented the challenges and prospects in the national forest inventory, remote sensing and reporting infrastructures. A North-South, South-South collaboration as well as governments own investments in monitoring, reporting and verification system could help Africa leapfrog in monitoring and reporting. These could be delivered through negotiations for the transfer of technology, technical capacities, and experiences that exist among developed countries that traditionally compile forest carbon reports in the context of the Kyoto protocol.

The impact of charcoal production on forest degradation: a case study in Tete, Mozambique

Charcoal production for urban energy consumption is a main driver of forest degradation in sub Saharan Africa. Urban growth projections for the continent suggest that the relevance of this process will increase in the coming decades. Forest degradation associated to charcoal production is difficult to monitor and commonly overlooked and underrepresented in forest cover change and carbon emission estimates. We use a multitemporal dataset of very high-resolution remote sensing images to map kiln locations in a representative study area of tropical woodlands in central Mozambique. The resulting maps provided a characterization of the spatial extent and temporal dynamics of charcoal production. Using an indirect approach we combine kiln maps and field information on charcoal making to describe the magnitude and intensity of forest degradation linked to charcoal production, including aboveground biomass and carbon emissions. Our findings reveal that forest degradation associated to charcoal production in the study area is largely independent from deforestation driven by agricultural expansion and that its impact on forest cover change is in the same order of magnitude as deforestation. Our work illustrates the feasibility of using estimates of urban charcoal consumption to establish a link between urban energy demands and forest degradation. This kind of approach has potential to reduce uncertainties in forest cover change and carbon emission assessments in sub-Saharan Africa.

Sensitivity of Above-Ground Biomass Estimates to Height-Diameter Modelling in Mixed-Species West African Woodlands

It has been suggested that above-ground biomass (AGB) inventories should include tree height (H), in addition to diameter (D). As H is a difficult variable to measure, H-D models are commonly used to predict H. We tested a number of approaches for H-D modelling, including additive terms which increased the complexity of the model, and observed how differences in tree-level predictions of H propagated to plot-level AGB estimations. We were especially interested in detecting whether the choice of method can lead to bias. The compared approaches listed in the order of increasing complexity were: (B0) AGB estimations from D-only; (B1) involving also H obtained from a fixed-effects H-D model; (B2) involving also species; (B3) including also between-plot variability as random effects; and (B4) involving multilevel nested random effects for grouping plots in clusters. In light of the results, the modelling approach affected the AGB estimation significantly in some cases, although differences were negligible for some of the alternatives. The most important differences were found between including H or not in the AGB estimation. We observed that AGB predictions without H information were very sensitive to the environmental stress parameter (E), which can induce a critical bias. Regarding the H-D modelling, the most relevant effect was found when species was included as an additive term. We presented a two-step methodology, which succeeded in identifying the species for which the general H-D relation was relevant to modify. Based on the results, our final choice was the single-level mixed-effects model (B3), which accounts for the species but also for the plot random effects reflecting site-specific factors such as soil properties and degree of disturbance.

Carbon dioxide fluxes from a degraded woodland in West Africa and their responses to main environmental factors

BACKGROUND

In West Africa, natural ecosystems such as woodlands are the main source for energy, building poles and livestock fodder. They probably behave like net carbon sinks, but there are only few studies focusing on their carbon exchange with the atmosphere. Here, we have analyzed CO₂ fluxes measured for 17 months by an eddy-covariance system over a degraded woodland in northern Benin. Specially, temporal evolution of the fluxes and their relationships with the main environmental factors were investigated between the seasons.

RESULTS

This study shows a clear response of CO₂ absorption to photosynthetic photon flux density (Q_p), but it varies according to the seasons. After a significant and long dry period, the ecosystem respiration (R) has increased immediately to the first significant rains. No clear dependency of ecosystem respiration on temperature has been observed. The degraded woodlands are probably the "carbon neutral" at the annual scale. The net ecosystem exchange (NEE) was negative during wet season and positive during dry season, and its annual accumulation was equal to +29 ± 16 g C m^-2. The ecosystem appears to be more efficient in the morning and during the wet season than in the afternoon and during the dry season.

CONCLUSIONS

This study shows diurnal and seasonal contrasted variations in the CO₂ fluxes in relation to the alternation between dry and wet seasons. The Nangatchori site is close to the equilibrium state according to its carbon exchanges with the atmosphere. The length of the observation period was too short to justify the hypothesis about the "carbon neutrality" of the degraded woodlands at the annual scale in West Africa. Besides, the annual net ecosystem exchange depends on the intensity of disturbances due to the site management system. Further research works are needed to define a woodland management policy that might keep these ecosystems as carbon sinks.

Comparison between remote sensing and a dynamic vegetation model for estimating terrestrial primary production of Africa

BACKGROUND

Africa is an important part of the global carbon cycle. It is also a continent facing potential problems due to increasing resource demand in combination with climate change-induced changes in resource supply. Quantifying the pools and fluxes constituting the terrestrial African carbon cycle is a challenge, because of uncertainties in meteorological driver data, lack of validation data, and potentially uncertain representation of important processes in major ecosystems. In this paper, terrestrial primary production estimates derived from remote sensing and a dynamic vegetation model are compared and quantified for major African land cover types.

RESULTS

Continental gross primary production estimates derived from remote sensing were higher than corresponding estimates derived from a dynamic vegetation model. However, estimates of continental net primary production from remote sensing were lower than corresponding estimates from the dynamic vegetation model. Variation was found among land cover classes, and the largest differences in gross primary production were found in the evergreen broadleaf forest. Average carbon use efficiency (NPP/GPP) was 0.58 for the vegetation model and 0.46 for the remote sensing method. Validation versus in situ data of aboveground net primary production revealed significant positive relationships for both methods. A combination of the remote sensing method with the dynamic vegetation model did not strongly affect this relationship.

CONCLUSION

Observed significant differences in estimated vegetation productivity may have several causes, including model design and temperature sensitivity. Differences in carbon use efficiency reflect underlying model assumptions. Integrating the realistic process representation of dynamic vegetation models with the high resolution observational strength of remote sensing may support realistic estimation of components of the carbon cycle and enhance resource monitoring, providing suitable validation data is available.

Will Passive Protection Save Congo Forests?

Central Africa’s tropical forests are among the world’s largest carbon reserves. Historically, they have experienced low rates of deforestation. Pressures to clear land are increasing due to development of infrastructure and livelihoods, foreign investment in agriculture, and shifting land use management, particularly in the Democratic Republic of Congo (DRC). The DRC contains the greatest area of intact African forests. These store approximately 22 billion tons of carbon in aboveground live biomass, yet only 10% are protected. Can the status quo of passive protection — forest management that is low or nonexistent — ensure the preservation of this forest and its carbon? We have developed the SimCongo model to simulate changes in land cover and land use based on theorized policy scenarios from 2010 to 2050. Three scenarios were examined: the first (Historical Trends) assumes passive forest protection; the next (Conservation) posits active protection of forests and activation of the national REDD+ action plan, and the last (Agricultural Development) assumes increased agricultural activities in forested land with concomitant increased deforestation. SimCongo is a cellular automata model based on Bayesian statistical methods tailored for the DRC, built with the Dinamica-EGO platform. The model is parameterized and validated with deforestation observations from the past and runs the scenarios from 2010 through 2050 with a yearly time step. We estimate the Historical Trends trajectory will result in average emissions of 139 million t CO₂ year^-1 by the 2040s, a 15% increase over current emissions. The Conservation scenario would result in 58% less clearing than Historical Trends and would conserve carbon-dense forest and woodland savanna areas. The Agricultural Development scenario leads to emissions of 212 million t CO₂ year^-1 by the 2040s. These scenarios are heuristic examples of policy’s influence on forest conservation and carbon storage. Our results suggest that 1) passive protection of the DRC’s forest and woodland savanna is insufficient to reduce deforestation; and 2): enactment of a REDD+ plan or similar conservation measure is needed to actively protect Congo forests, their unique ecology, and their important role in the global carbon cycle.

Estimating the consequences of fire exclusion for food crop production, soil fertility, and fallow recovery in shifting cultivation landscapes in the humid tropics

In the Congo Basin, smallholder farmers practice slash-and-burn shifting cultivation. Yet, deliberate burning might no longer be sustainable under reduced fallow scenarios. We synthesized data from the Forest Margins Benchmark Area (FMBA), comprising 1.54 million hectares (ha), in southern Cameroon and assessed the impact of fire exclusion on yield, labor inputs, soil fertility, ecosystem carbon stocks, and fallow recovery indicators in two common field types (plantain and maize) under both current and reduced fallow scenarios. While we could not distinguish between impacts of standard farmer burning practice and fire exclusion treatments for the current fallow scenario, we concluded that fire exclusion would lead to higher yields, higher ecosystem carbon stocks as well as potentially faster fallow recovery under the reduced fallow scenario. While its implementation would increase labor requirements, we estimated increased revenues of 421 and 388 US$ ha(-1) for plantain and maize, respectively. Applied to the FMBA, and assuming a 6-year reduced fallow scenario, fire exclusion in plantain fields would potentially retain 240,464 Mg more ecosystem carbon, comprising topsoil carbon plus tree biomass carbon, than standard farmer practice. Results demonstrate a potential "win-win scenario" where yield benefits, albeit modest, and conservation benefits can be obtained simultaneously. This could be considered as a transitional phase towards higher input use and thus higher yielding systems.

Fire in Australian savannas: from leaf to landscape

Savanna ecosystems comprise 22% of the global terrestrial surface and 25% of Australia (almost 1.9 million km2) and provide significant ecosystem services through carbon and water cycles and the maintenance of biodiversity. The current structure, composition and distribution of Australian savannas have coevolved with fire, yet remain driven by the dynamic constraints of their bioclimatic niche. Fire in Australian savannas influences both the biophysical and biogeochemical processes at multiple scales from leaf to landscape. Here, we present the latest emission estimates from Australian savanna biomass burning and their contribution to global greenhouse gas budgets. We then review our understanding of the impacts of fire on ecosystem function and local surface water and heat balances, which in turn influence regional climate. We show how savanna fires are coupled to the global climate through the carbon cycle and fire regimes. We present new research that climate change is likely to alter the structure and function of savannas through shifts in moisture availability and increases in atmospheric carbon dioxide, in turn altering fire regimes with further feedbacks to climate. We explore opportunities to reduce net greenhouse gas emissions from savanna ecosystems through changes in savanna fire management.

Perturbations in the carbon budget of the tropics

The carbon budget of the tropics has been perturbed as a result of human influences. Here, we attempt to construct a 'bottom-up' analysis of the biological components of the budget as they are affected by human activities. There are major uncertainties in the extent and carbon content of different vegetation types, the rates of land-use change and forest degradation, but recent developments in satellite remote sensing have gone far towards reducing these uncertainties. Stocks of carbon as biomass in tropical forests and woodlands add up to 271 ± 16 Pg with an even greater quantity of carbon as soil organic matter. Carbon loss from deforestation, degradation, harvesting and peat fires is estimated as 2.01 ± 1.1 Pg annum(-1); while carbon gain from forest and woodland growth is 1.85 ± 0.09 Pg annum(-1). We conclude that tropical lands are on average a small carbon source to the atmosphere, a result that is consistent with the 'top-down' result from measurements in the atmosphere. If they were to be conserved, they would be a substantial carbon sink. Release of carbon as carbon dioxide from fossil fuel burning in the tropics is 0.74 Pg annum(-1) or 0.57 MgC person(-1) annum(-1) , much lower than the corresponding figures from developed regions of the world.

Spatial optimization of carbon-stocking projects across Africa integrating stocking potential with co-benefits and feasibility

Carbon offset projects through forestation are employed within the emissions trading framework to store carbon. Yet, information about the potential of landscapes to stock carbon, essential to the design of offset projects, is often lacking. Here, based on data on vegetation carbon, climate and soil, we quantify the potential for carbon storage in woody vegetation across tropical Africa. The ability of offset projects to produce co-benefits for ecosystems and people is then quantified. When co-benefits such as biodiversity conservation are considered, the top-ranked sites are sometimes different to sites selected purely for their carbon-stocking potential, although they still possess up to 92% of the latter carbon-stocking potential. This work provides the first continental-scale assessment of which areas may provide the greatest direct and indirect benefits from carbon storage reforestation projects at the smallest costs and risks, providing crucial information for prioritization of investments in carbon storage projects.

Disproportionate Contribution of Riparian Inputs to Organic Carbon Pools in Freshwater Systems

A lack of appropriate proxies has traditionally hampered our ability to distinguish riverine organic carbon (OC) sources at the landscape scale. However, the dissection of C₄ grasslands by C₃-enriched riparian vegetation, and the distinct carbon stable isotope signature (δ¹³C) of these two photosynthetic pathways, provides a unique setting to assess the relative contribution of riparian and more distant sources to riverine C pools. Here, we compared δ¹³C signatures of bulk sub-basin vegetation (δ¹³C_VEG) with those of riverine OC pools for a wide range of sites within two contrasting river basins in Madagascar. Although C₃-derived carbon dominated in the eastern Rianala catchment, consistent with the dominant vegetation, we found that in the C₄-dominated Betsiboka basin, riverine OC is disproportionately sourced from the C₃-enriched riparian fringe, irrespective of climatic season, even though δ¹³C_VEG estimates suggest as much as 96% of vegetation cover in some Betsiboka sub-basins may be accounted for by C₄ biomass. For example, δ¹³C values for river bed OC were on average 6.9 ± 2.7‰ depleted in ¹³C compared to paired estimates of δ¹³C_VEG. The disconnection of the wider C₄-dominated basin is considered the primary driver of the under-representation of C₄-derived C within riverine OC pools in the Betsiboka basin, although combustion of grassland biomass by fire is likely a subsidiary constraint on the quantity of terrestrial organic matter available for export to these streams and rivers. Our findings carry implications for the use of sedimentary δ¹³C signatures as proxies for past forest-grassland distribution and climate, as the C₄ component may be considerably underestimated due to its disconnection from riverine OC pools.

Changes in biocrust cover drive carbon cycle responses to climate change in drylands

Dryland ecosystems account for ca. 27% of global soil organic carbon (C) reserves, yet it is largely unknown how climate change will impact C cycling and storage in these areas. In drylands, soil C concentrates at the surface, making it particularly sensitive to the activity of organisms inhabiting the soil uppermost levels, such as communities dominated by lichens, mosses, bacteria and fungi (biocrusts). We conducted a full factorial warming and rainfall exclusion experiment at two semiarid sites in Spain to show how an average increase of air temperature of 2-3 °C promoted a drastic reduction in biocrust cover (ca. 44% in 4 years). Warming significantly increased soil CO2 efflux, and reduced soil net CO2 uptake, in biocrust-dominated microsites. Losses of biocrust cover with warming through time were paralleled by increases in recalcitrant C sources, such as aromatic compounds, and in the abundance of fungi relative to bacteria. The dramatic reduction in biocrust cover with warming will lessen the capacity of drylands to sequester atmospheric CO2 . This decrease may act synergistically with other warming-induced effects, such as the increase in soil CO2 efflux and the changes in microbial communities to alter C cycling in drylands, and to reduce soil C stocks in the mid to long term.

African tropical rainforest net carbon dioxide fluxes in the twentieth century

The African humid tropical biome constitutes the second largest rainforest region, significantly impacts global carbon cycling and climate, and has undergone major changes in functioning owing to climate and land-use change over the past century. We assess changes and trends in CO₂ fluxes from 1901 to 2010 using nine land surface models forced with common driving data, and depict the inter-model variability as the uncertainty in fluxes. The biome is estimated to be a natural (no disturbance) net carbon sink (−0.02 kg C m⁻² yr⁻¹ or −0.04 Pg C yr⁻¹, p < 0.05) with increasing strength fourfold in the second half of the century. The models were in close agreement on net CO₂ flux at the beginning of the century (σ₁₉₀₁ = 0.02 kg C m⁻² yr⁻¹), but diverged exponentially throughout the century (σ₂₀₁₀ = 0.03 kg C m⁻² yr⁻¹). The increasing uncertainty is due to differences in sensitivity to increasing atmospheric CO₂, but not increasing water stress, despite a decrease in precipitation and increase in air temperature. However, the largest uncertainties were associated with the most extreme drought events of the century. These results highlight the need to constrain modelled CO₂ fluxes with increasing atmospheric CO₂ concentrations and extreme climatic events, as the uncertainties will only amplify in the next century.

Impact of grazing intensity on seasonal variations in soil organic carbon and soil CO2 efflux in two semiarid grasslands in southern Botswana

Biological soil crusts (BSCs) are an important source of organic carbon, and affect a range of ecosystem functions in arid and semiarid environments. Yet the impact of grazing disturbance on crust properties and soil CO(2) efflux remain poorly studied, particularly in African ecosystems. The effects of burial under wind-blown sand, disaggregation and removal of BSCs on seasonal variations in soil CO(2) efflux, soil organic carbon, chlorophyll a and scytonemin were investigated at two sites in the Kalahari of southern Botswana. Field experiments were employed to isolate CO(2) efflux originating from BSCs in order to estimate the C exchange within the crust. Organic carbon was not evenly distributed through the soil profile but concentrated in the BSC. Soil CO(2) efflux was higher in Kalahari Sand than in calcrete soils, but rates varied significantly with seasonal changes in moisture and temperature. BSCs at both sites were a small net sink of C to the soil. Soil CO(2) efflux was significantly higher in sand soils where the BSC was removed, and on calcrete where the BSC was buried under sand. The BSC removal and burial under sand also significantly reduced chlorophyll a, organic carbon and scytonemin. Disaggregation of the soil crust, however, led to increases in chlorophyll a and organic carbon. The data confirm the importance of BSCs for C cycling in drylands and indicate intensive grazing, which destroys BSCs through trampling and burial, will adversely affect C sequestration and storage. Managed grazing, where soil surfaces are only lightly disturbed, would help maintain a positive carbon balance in African drylands.

Rate of belowground carbon allocation differs with successional habit of two afromontane trees

BACKGROUND

Anthropogenic disturbance of old-growth tropical forests increases the abundance of early successional tree species at the cost of late successional ones. Quantifying differences in terms of carbon allocation and the proportion of recently fixed carbon in soil CO(2) efflux is crucial for addressing the carbon footprint of creeping degradation.

METHODOLOGY

We compared the carbon allocation pattern of the late successional gymnosperm Podocarpus falcatus (Thunb.) Mirb. and the early successional (gap filling) angiosperm Croton macrostachyus Hochst. es Del. in an Ethiopian Afromontane forest by whole tree (13)CO(2) pulse labeling. Over a one-year period we monitored the temporal resolution of the label in the foliage, the phloem sap, the arbuscular mycorrhiza, and in soil-derived CO(2). Further, we quantified the overall losses of assimilated (13)C with soil CO(2) efflux.

PRINCIPAL FINDINGS

(13)C in leaves of C. macrostachyus declined more rapidly with a larger size of a fast pool (64% vs. 50% of the assimilated carbon), having a shorter mean residence time (14 h vs. 55 h) as in leaves of P. falcatus. Phloem sap velocity was about 4 times higher for C. macrostachyus. Likewise, the label appeared earlier in the arbuscular mycorrhiza of C. macrostachyus and in the soil CO(2) efflux as in case of P. falcatus (24 h vs. 72 h). Within one year soil CO(2) efflux amounted to a loss of 32% of assimilated carbon for the gap filling tree and to 15% for the late successional one.

CONCLUSIONS

Our results showed clear differences in carbon allocation patterns between tree species, although we caution that this experiment was unreplicated. A shift in tree species composition of tropical montane forests (e.g., by degradation) accelerates carbon allocation belowground and increases respiratory carbon losses by the autotrophic community. If ongoing disturbance keeps early successional species in dominance, the larger allocation to fast cycling compartments may deplete soil organic carbon in the long run.

Greenhouse gases in the Earth system: setting the agenda to 2030

What do we need to know about greenhouse gases? Over the next 20 years, how should scientists study the role of greenhouse gases in the Earth system and the changes that are taking place? These questions were addressed at a Royal Society scientific Discussion Meeting in London on 22-23 February 2010, with over 300 participants.