53

Emissions from Fire in Sub-Saharan Africa:
the Magnitude of Sources, Their Variability and Uncertainty
Robert J SCHOLES*, Sally ARCHIBALD and Graham von MALTITZThe Council for Scientific and Industrial Research (CSIR), Natural Resources and the Environment,
PO Box 395, Pretoria 0001, South Africa*e-mail: bscholes@csir.co.za

Abstract
About half of the global gas and particle emissions to the atmosphere resulting from the burning of biomass originate from sub-Saharan Africa. There are four principal pathways: wildfires, the use of biomass
fuels for energy, burning associated with deforestation, and the burning of agricultural residues. The total
amount of biomass consumed by all four pathways is estimated from a review of many studies to be around
1400 Tg dry matter per year, with a 95% confidence interval of 1,200 to 1,600 Tg DM/y. Of this about 57% isconsumed by wildfires, 36% for domestic and industrial energy, and around 3% each as in-field combustion
associated with land use change and agricultural residue burning. While the overall emissions are relatively
well-constrained (an uncertainty of around ±14%, about the same size as the interannual variability) byconvergence between bottom-up and top down measures, the contribution from individual sources, and of
individual pyrogenic trace gases, remains much less certain.
Key words: burned area, fuel load, pyrogenic emissions

1. Introduction
Africa has been called ‘the fiery continent’ because of
the extent of vegetation which burns every year (Fig. 1).
Up to half of the global pyrogenic (fire-derived) emissions to the atmosphere have been estimated to originate
in Africa (Barbosa et al., 1999; Korontzki et al., 2003),
and these emissions have a substantial effect on theatmospheric composition both regionally and globally.
The main extent of the fires is in the seasonal savannas
north, south and east of the Congo Basin rainforests, but
fires are also prominent in montane grasslands, seasonal
wetlands and winter-rainfall thickets. Vegetation wildfires are not the only pyrogenic emission sources inAfrica. The vast majority of Africans use biomass-based
fuels for domestic and light-industrial or commercial
energy, in the form of fuel wood, charcoal or dried dung.
A portion of these biomass fuels originates in land
clearing activities, and a further portion from agricultural residues. Although the rate of land clearing(‘deforestation’) in Africa has thus far been lower thanhas been recorded in South America and South-East Asia,
the contribution of emissions from land clearing could
increase in future. Agricultural residues are largely
channelled into fodder or other uses in Africa, but in-fieldburning remains a small source of pyrogenic emissions.
The many ecological, atmospheric and social issues
associated with fire in Africa have attracted much reGlobal Environmental Research
15/2011: 53-63
printed in Japan

search over the past decades. Nevertheless, there are stillwidely divergent estimates of the area burned annually,
the amount of fuel consumed, and the quantities of gases
and aerosols emitted to the atmosphere as a consequence.
The uncertainty in the estimates has the following
three sources:
1. The burned area and the fuel load are characterised byhigh inter-annual variability, largely associated withvariation in the rainfall during the growing season
immediately prior to the dry period in which the fireoccurs. African savannas occupy a subtropical zone thatis characterised by unusually high inter-annual rainfall
variability – coefficients of variability of around 30% aretypical. Some of the variation is associated with global
climate modes such as El Niño and the Southern
Oscillation. Contrary to the widely- held notion that dry
periods are associated with more fire, both the burned
area and the fuel consumed in savannas and grasslands
are higher in fire seasons following an above-average
rainfall year (Archibald et al., 2009).
2. The inclusion of different source terms in the various
inventories. Some deal only with fires in savannas, whileothers also include fires in grasslands, shrublands, forests,
wetlands, Mediterranean thickets (fynbos) and croplands.
Most provide data for unconfined wildfires only (inreality, the overwhelming majority of ‘wild’ fires inAfrica are ignited by people, as part of formal or informal
land management practices). The emissions from the©2011 AIRIES

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R. J SCHOLES et al.

zone is culturally and ecologically more coherent thangeographic Africa as a whole. Biomass burning is a muchless widespread phenomenon in North Africa.

2. The Four Main Ways in Which Biomass is
Burned in Africa

Fig. 1 Map of average burned area fraction in sub-SaharanAfrica over twelve years, and associated uncertainty(1-σ) derived from the GFED3 dataset (Giglio et al.,2010) at 0.5 degree resolution. The unburned areaaround the equator is rainforest, too wet to burn on awidespread basis.

burning of fuel wood and the production and burning ofcharcoal are typically studied by different groups ofresearchers and published in different sets of literature.
Furthermore, they tend to be expressed on a national or
subnational basis, rather than as spatially-gridded databases.
3. There are inherent (but usually unquantified) errors inall the estimates. The sample sizes are often small and not
randomly distributed, and the parent populations are notnormally distributed, making the risk of bias high. Many
estimates depend on national statistics, which are ofhighly variable quality.
The objectives of this paper are to:
1. Critically review published estimates of the amountof biomass burned annually in Africa, considering thefour main combustion pathways; and2. Estimate their range of uncertainty and inter-annual
variability.
The paper focuses on sub-Saharan Africa including
Madagascar (SSA), an area of 25.434 million km2. This

2.1 Wildfires
Wildfires in Africa occur predominantly in savannas(including deciduous woodlands), with minor contributions from arid shrublands, sclerophyllous thickets(fynbos in South Africa), reedy or grassy wetlands andmontane grasslands. Almost all are human-ignited, but
few within formal land management plans. In the absenceof human ignition, lightning ignition is also effective.
The first continental-scale estimates of area burned in
Sub-Saharan Africa (SSA) were produced in the 1990s.
These initial attempts used the extent of various land
cover types and expert-based estimates of the mean fire
return periods, spatially extrapolated using satellitedetected active fire observations (Hao et al., 1990;
Delmas. et al., 1991). Later attempts provided these estimates spatially (Scholes. et al., 1996; Hao & Lui, 1994),
but at coarse resolution (5 degree).
Barbosa et al. (1999) derived burned area from thevisible and near-infrared channels of the advanced very
high resolution radiometer (AVHRR), rather than thethermal channels used for active fire detection. This allowed for more accurate burned area estimation. The
5-km-resolution data were validated against a sample ofLandsat TM images (30 m resolution), and the accuracies
were estimated to be around 70%. This multi-year study
was able to assess inter-annual variability in burning.
More recent satellite-derived datasets have improved inaccuracy (see Section 3.1) and estimate that on average,
Africa as a whole contributes about 70% (~2500*103km2) to the global burned area of ~4000*103 km2 (Tansey
et al., 2008).
Relative to other parts of the world, Africa has quite alow interannual variability in burned area, around 12%(Giglio et al., 2010, see also Van der Werf et al., 2006).
This is attributed to the fact that there are always enoughsources of ignition and the grass fuel regrows very
quickly after burning (Archibald et al., 2009). The
within-year seasonal variability is high, but because thearea that burns in northern-hemisphere Africa is nearly
the same in extent as that which burns in the southernhemisphere, the high local seasonality balances out at anall-Africa scale. Because such a large area burns in Africa
every year, even this modest inter-annual variability has
an observable impact on global-scale carbon budgets(Williams et al., 2007).
The patterns of interannual variability in burned area
in African savannas are different from those displayed insystems dominated by crown-fire regimes. This is because fire spread in African savannas is limited by theamount of fuel, rather than the dryness of the fuel. Thus
burned area decreases in times of drought, and increases
in fire seasons following a period of higher rainfall

Fire in Sub-Saharan Africa(Archibald et al., 2010a; Van der Werf, 2004).
Other new insights into burning in Africa relate to thespatial pattern of fire, and the role of humans in determining it (Archibald et al., 2010b). Many parts of southern,eastern and western Africa have relatively high rural
population densities. The people use the landscape intensively for agriculture and cattle grazing, activities
which decrease and fragment the fuel load. Thus fires areunable to spread as easily as they would in less densely
populated landscapes. In the densely populated regions
the burned area consists mainly of many small fires. A
multi-year analysis reveals that it is typically the same
parts of the landscape that burn again and again, whileother patches, habitually more heavily-used, remain
unburned (Fig. 2). This observation explains why thelandscape-scale burned area fraction, as estimated byremote sensing, is typically smaller than the inverse of
the fire return time as observed on the ground, especially
if the latter estimates are provided from anecdotal
evidence rather than systematic fire scar mapping.

Fig. 2 Number of fires over an eight-year period in a heavilyutilised rural landscape in Malawi. The patches of thelandscape that burn do so repeatedly, but most of the landscape remains unburned. This means that using fire return
times to estimate burned area becomes problematic: theMean Return Interval (MRI) for fires in Malawi is 3-4 years(Archibald, 2010) which would produce a burned area
estimate of 20-30%. In reality, less than 10% burns every
year.

55

2.2 Burning of agricultural residues
With some notable exceptions – such as sugar caneand cotton – in-field burning of crop residues is not awidespread practice in Africa. Crop residues are generally too valuable as forage for livestock, croplandmulches or domestic fuel to be ‘wasted’ through in-fieldburning, although accidental cropland fires do occur. In
the case of sugar cane, the dense, dry ‘slash’ is burned
prior to harvest to provide easier access for manual
harvesting, suppress hazards such as snakes, and concentrate the sugar. The practice is in decline as mechanical harvesting increases and densely-populated areas
encroach on cane fields. Cotton crop residue is burned
post-harvest in some areas to control pests.
A major review on crop residue burning was conducted by Yevich and Logan (2003). They concluded that
173 Tg/year of residues (dry matter; DM) were produced
in SSA, of which 47 Tg/y was used as biofuels and48 Tg/y was burned in the field. No uncertainty estimates
are provided, but given the sources used and the assumptions made, the uncertainties must be broad –perhaps ±50%.
The residues from plantation forests tend not to be
noted by either the agricultural sector reviews or theforestry sector reviews. Plantation forests (typically
Pinus and Eucalyptus species) generate biomass-burningemissions via three pathways. Firstly, the branches
pruned during the growing cycle and the leaves and small
branches that remain after felling are often burned toreduce the risk of uncontrollable wildfires breaking out inthe accumulated fuel. Secondly, plantation forests occasionally burn by accident or as a result of arson. Thirdly,
mill waste such as bark, sawdust and unmarketable offcuts are burned, in one of three ways: in boilers at thesawmill to generate heat and electricity; as biofuel byproducts for domestic use; or through uncontrolled
smouldering of waste dumps. Plantation forests in SSA
cover an area of 7.235 million ha (FRA, 2010), with thelargest single area in South Africa (1.763 million ha, one
third designated for pulp). In the South African case
(Muller et al., 1999), 200-230 g/m2 of residues are
generated in-plantation at the time of harvest (after a 10
to 20 year rotation) and the equivalent of a further 440 to
790 g/m2 of waste is generated at the mill. Extrapolatingthese values to all of SSA suggests a residue stream of
3.1 to 4.9 Tg/y from plantation forests. The bulk of thisplantation residue is burned as domestic or industrial
fuel.
2.3 Combustion associated with land use change(deforestation)
Most land cover change in Africa occurs in savannas
and forests, both of which cases qualify as ‘deforestation.’ Some of this change is at very-small-patch scales,or does not result in complete tree removal (‘degradation’) and is thus not easily detected using remote sensing.
Achard et al. (2002), using coarse-resolution satellite
images, estimated the net tropical humid forest deforestation rate in Africa over the period from 1990 to 1997 to

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be 0.71 million ha/y (0.36% / y), with a 95% confidence
interval ± 0.31 Mha/y. The ‘net’ deforestation consistedof 0.85 ± 0.3 Mha / y of clearing of forests (0.43% / y)balanced by 0.14 ± 0.11 Mha / y of regrowth (0.07% / y).
Achard et al. (2002) estimated that a further 0.39 ± 0.19
Mha of humid forest was degraded during the same
period (0.21% / y). Duveiller et al. (2008), for the period
1990-2000 and applying a 3% systematic sample basedon high-resolution satellite images in the Congo Riverbasin only, estimated a net deforestation rate of 0.15% / y,consisting of a deforestation rate of 0.21 ± 0.05% / y and a
regrowth rate of 0.05 ± 0.01% / y. They further estimateda degradation rate of 0.15 ± 0.03% / y and a recovery fromdegraded to dense forest of 0.6 ± 0.2% / y. The FAO’s
Forest Resources Assessment (FRA, 2001), based on
a combination of satellite-derived and country-provided
data, estimated a net deforestation rate 0.34% / y(1.2 million ha / y) for the whole of Africa for the decadeof the 1990s. Brink and Eva (2009) applied a systematic
1% sample of high-resolution images over all of Africa
and Madagascar for 1975 and 2000, to estimate a meanannual net loss rate for humid forests to be 0.64 Mha / y(which translates to a 0.18% / y rate in the Congo basin,for comparison with the above estimates). The net loss
rate in dry forests was 2.26 Mha / y and in savannas,
2.2 Mha / y.
Citing Brown (1997) for estimates of abovegroundbiomass and adding 20% for roots, Achard et al. (2002)
estimate African moist forests to have a carbon density of
17,900 gC/m2, acknowledging that the uncertainty range
could be as high as ± 60%. A series of assumptions
regarding the various fates of this biomass following
clearing (e.g., emitted as CO2 at the time of harvest, presumably mostly through burning; subsequently emitted
over a ten-year period, by decomposition of residue left
in situ; emitted from harvested material removed fromthe site (mostly domestic burning); and decreased by therate of re-accumulation of carbon on site) led to an estimate that about 57 TgC / y was emitted as a result of
deforestation (excluding degradation, and only in humid
forests) in Africa in the 1990 to 1997 period. Brink and
Eva (2009) adjusted these values to 94 TgC / y from moist
forests and 79 TgC / y from dry forests, and consideredthat the amounts resulting from savanna clearing were
‘negligible.’ The carbon content of savannas varies
greatly, depending on the rainfall, soil clay percentage
and tree cover, but we conservatively estimate
2,000 gC/m2 aboveground, which means that if the same
decay rates apply as for forests, savannas amount to a
non-negligible emission stream of 36 TgC / y. We assume
that most of this (perhaps 80%) is actually burned as
biomass fuel, and it is accounted for there.
2.4 Biomass burned for energy
Fuel wood and charcoal remain the main energy
source for the majority of households in Africa. It isestimated that 93%-94% of rural households in SSA and
58%-73% of urban households are reliant on either fuelwood or charcoal as their primary energy source (Bailis,

2005; IEA, 2006). Over 90% of urban households use
charcoal in Kenya, Tanzania, Mozambique and Zambia(IEA, 2002). Estimating the total fuel and charcoal use in
Africa remains difficult due to data constraints (Amos,
1999; IEA, 2008; Arnold et al., 2003). FAO conductednumerous fuel wood assessments during the 1970s andearly 1980s, in response to the so-called “fuel woodcrisis,” but research interest waned during the later 1980s
and 1990s (Arnold et al., 2003, 2006). There has been a
recent revival of interest in the topic, inspired by thecontribution of fuel wood to total energy use and as adriver of deforestation (Arnold et al., 2003, 2006; Zulu,
2010; World Bank, 2009). No recent synthesis has
attempted a total wood fuel use estimate. A number of
independent estimates suggest that approximately 90%
of all wood extraction in Africa, including wood extraction from timber-harvesting in tropical forests, is used forfuel wood or charcoal (FAO, 2006; FRA, 2010).

3. Issues Relating to the Accuracy of Estimates
3.1 The area of land that burns
As the spatial resolution and sensitivity of satellite
data products improved, so too did the algorithms used to
identify burn scars, and several global burned area datasets have become available in the last decade. These
include: the 1 km L3JRC product (Tansey et al., 2008);
1 km GLOBCARBON product (Plummer et al., 2006);
the 500 m MCD45A1 product (Roy et al., 2008); as wellas the GFED3 product (Giglio et al., 2010) which integrates various satellite fire data to a 0.5 degree grid.
Validations of these products against Landsat TM data
indicate that accuracy (defined as the percentage of correctly classified pixels) has increased to over 90%. The
MODIS MCD45A1 and the GFED3 product perform
significantly better than the others – although somevegetation types (for example surface fires underneathdense tree canopies) remain problematic (Roy &Boschetti, 2009; Giglio et al., 2009 and 2010; Tsela et al.,2010).
Giglio (2010) has performed the most comprehensive
error analysis so far, and produced burned area maps withassociated uncertainty estimates by integrating several
different remotely-sensed products. Giglio’s methods forcalculating uncertainty varied depending on the type of
data, but in general uncertainty coefficients for different
parts of the globe and different months were determinedthrough accuracy assessment against high-resolution
images. These monthly uncertainties were then squared
and summed to produce an overall uncertainty estimate
for each grid cell. This method estimates uncertainties for
Africa in the order of ±3% of the area burned for eachpixel. Roy (2008) used a different method to assess theaccuracy of similar products and found coefficients ofdetermination (R2) of 0.74, indicating substantial room
for improvement in burnt area accounting. Despite thisresidual uncertainty, it appears that on average, Africa asa whole contributes about 70% (~2500*103 km2) to theglobal burned area of ~4000*103 km2.

Fire in Sub-Saharan Africa

3.2 The biomass density that can potentially burn
The amount of aboveground biomass per unit area at agiven location, the fraction of this which is potentiallycombustible because it is dry and within the reach of theflames, and the amount that actually burns given theweather at the time of the fire are three different things,
though often mixed together in the literature. Particularly
in savannas, which are the main vegetation type thatburns in Africa, over 80% of the biomass is in the form of
the stems of woody plants. Savanna fires are ground fires,
largely burning the dead dry grass and leaf litter, sometimes igniting standing or fallen dead trees, and singeing
the leaves of evergreen shrubs within the flame zone. The
tree canopies are typically leafless during the fire season
and the canopies of the few evergreen trees may bescorched, but are mostly too far above the flame zone tosustain combustion. The fires are insufficiently intense toignite living stems. The potentially-combustible fuel,which we define as the sum of the live and dead standing
grass and herbs, the shrub leaves up to a height of about
3 m, and the litter, twigs and downed logs on the groundsurface, ranges from under 20 g/m2 (below which fires
tend to fail to propagate) to over 1,000 g/m2, dependingon:• the productivity of the system, which is mostly a57function of rainfall and soil fertility;• the fraction tree cover (trees suppress grass and forb
growth);• the length of the period since the last fire; and• the fraction of the production that is consumed byherbivores or people (Scholes et al., 1996).
Estimates of fuel load in Africa that are based onmodels of primary production (e.g., Van der Werf et al.,2003, 2006) tend to give estimates that are substantiallyhigher than the average measured values. For instance,
Van der Werf et al. (2006) suggest grass biomass estimates of ~580 g/m2 for southern Africa, whereas a database of observed fuel loads in this region. Figure 3
indicates that a large portion of this landscape has fuelsloads < 400 g/m2.
Field measurements of fuel loads are labour-intensive.
Here we report on a database of 60 fuel load measurements taken across a range of savanna and woodlandenvironments in southern Africa at the beginning of thedry season. At each site many replicate samples of grasswere clipped and weighed, and litter and twigs werecollected and weighed separately. The quadrat samples
were averaged for each site to produce estimates of fine,coarse, and woody fuels, with estimation 95% confidence

Fig. 3 Biomass of grass, litter and twig fuels for different vegetation types in southern Africa in order ofgenerally increasing tree fraction cover. Data represent summaries from ~60 field measurements acrosssouthern Africa. Mean annual rainfall for the sites in each vegetation type: grassland – 740 mm,
bushveld/woodland – 720mm, dry deciduous forest – 641 mm, mopane woodland 616mm, miombo
woodland, 885 mm. Bushveld/woodland is a low(~5m tall), deciduous woodland, often dominated bytrees from the family Combretaceae. Mopane woodland is a 2-10 m tall deciduous woodland highly
dominated by Colophospermum mopane. Miombo woodland is a tall (~10 m) deciduous woodland
containing many species, but typically dominated by trees from the genus Brachystegia.

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R. J SCHOLES et al.

ranges at a site level of around ± 10%. The data aresummarised by vegetation type to give an indication of
the variability across the region (Fig. 3).
3.3 Combustion completenessCombustion completeness is the fraction of thepotentially combustible fuel that actually burns. It
depends on the moisture content of the fuels (Hely et al.,2003) and the intensity of the fire, and varies for each fueltype (Fig. 4). Combustion completeness ranges fromnearly 100% in dry standing dead grass, to under 50% forslightly damp litter layers. It is not independent of fuelload, since the more fuel that becomes entrained in thefire, the more energy is released, igniting the wetter andcoarser fuels and increasing the height range exposed to
fire.
Monitoring of the energy released by fires provides an
independent way of estimating the amount of fuel combusted. The instantaneous Fire Radiative Power (FRP)
can be measured using the thermal channels of polarorbiting earth observation satellites. Integrating thissignal over the fire duration requires the use of coarserresolution geostationary weather satellites, and yields
Fire Radiative Energy (FRE), which is proportional toamount of fuel combusted (Kaufman et al., 1998;
Wooster et al., 2003). The constant of proportionality
differs somewhat from the energy content of the fuel,because corrections need to be made for the failure todetect small fires (<100 MW) and the attenuation ofsome of the radiated energy by the atmosphere, including
complete blocking in the presence of clouds. Since thetotal amount of fuel combusted is the product of the areaburned, the fuel load and fire completeness, the fuel loads
can be calculated if the burned area is known and thecompleteness is estimated.
Using this logic, Roberts et al. (2005) estimated thatfor several large regions in southern Africa, the fuelcombusted in forest, woodland (savanna), and open
savanna or grassland fires was 490 ± 150, 270 ± 200 ,and 150 ± 90 g/m2 (± 1 SD), respectively. Ellicott et al.

Fig. 4 Combustion completeness in different categories of fuelobserved in savanna fires in southern Africa (data from
Shea et al., 1996). The fireline intensity is the rate ofenergy release along the burning edge of a fire.

(2009), using the FRE approach, assessed the biomass
burned in Africa to be between 716 to 881 Tg DM/y,which translates to an average combusted fuel load of
279 to 344 g/m2. These values are consistent with themeasured fuel loads (taking into account combustion
completeness, Shea et al., 1996; and Fig. 4 in this paper)and the lower range of modelled fuel loads (Scholes et al.,1996).
3.4 The amounts and sources of wood used as fuelHousehold fuel use patterns vary substantially across
Africa. The BEST 1994 study (Amous, 1999) found theuse rate to be 1.4 Mg per capita in tropical southern
Africa, while in West Sahelian Africa (where fuel woodis less available) 0.7 Mg per capita was used, and in
North Africa only 0.2 Mg per capita (Amos, 1999). Bailis
et al. (2005) found national per capita fuel wood consumption to range from less than 0.2 to over 1.5 Mg per
capita in individual SSA countries. In urban areas, a shift
from fuel wood to charcoal has been widely documented.
For instance, it was found that in Bakoma and Mali, theproportion of charcoal-using households rose from under
5% in 1975 to close to 60% by 2000, as users swopped
from wood to charcoal (Girard, 2002) In Dar-es-Salaam,
Tanzania, urban charcoal use rose from 47% in 2001 to
71% in 2007 (World Bank, 2009). Considering the rapid
urbanisation in most African countries, this represents ahuge growth rate in total urban charcoal consumption.
Households typically use more than one energy source,with the more expensive fuels used for lighting, andbiomass-derived fuels used for cooking. Charcoal remains the principle energy source for the poor in most
sub-Sahara African cities, though its use is relatively rarein South Africa, Botswana, Namibia and Zimbabwe.
Some countries, such as Tanzania and Kenya, haveattempted to ban charcoal from cities, but this has beenineffective and simply created an illegal charcoal trade,making the collection of charcoal use statistics moredifficult (Mugo & Ong, 2006).
It is commonly accepted that the transition fromtraditional to modern fuels is strongly related to the levelof economic development (Reddy, 1982; Cooke et al.,2001). This suggests that fuel wood and charcoal dependence is likely to be a feature of African energy systems for the foreseeable future, especially given recent
hikes in fossil fuel prices (Miranda et al., 2010). Together
with an estimated annual population growth rate of 2.5%
in SSA (PRB, 2009), this suggests that total fuel woodand charcoal use will increase rather than decrease in the
future (IEA, 2006; Arnold et al., 2006; Broadhead et al.,
2001; World Bank, 2009).
Charcoal production in Africa tends to be performed
in primitive earth-mound kilns, achieving an 8% - 15%
energy conversion efficiency. It is acknowledged thatskilled charcoal makers may achieve a substantially better efficiency (Kalumiana & Shakachite, 2003; Yigard,
2002; Pereira, 2001; Paddon, 1988). Green wood is typically used in the kilns, reducing charcoaling efficiency
and increasing the emissions of volatile organic carbon

Fire in Sub-Saharan Africa

compounds. Stove efficiency can also greatly alter theamount of charcoal needed to prepare a meal. Traditional
charcoal stoves are estimated to have an efficiency of
about 15%, while improved stoves reach 35%. By
comparison, cooking on the traditional “three stone fireplace” has 5% - 10% efficiency (WEC, 1999). The higher
efficiency of charcoal stoves when used for cooking cancompensate for the inefficiencies during the charcoaling
process. Though there have been many initiatives topromote improved charcoal stoves, it is probable thatmost households still use inefficient traditional hearths
and stoves.
Charcoal production and fuel wood collection havedifferent impacts on forested areas. During charcoal
production, areas are totally cleared of all woody
vegetation, including large trees. In contrast, rural fuelwood collection tends to target dead wood or smaller
trees (Mugo & Ong, 2006).
The FAO bases its estimates of wood use in SSA
either on data supplied by national forestry departments,
or on models based on estimates of population and consumption patterns (Arnold et al., 2003). Because wood iscollected directly by the household or through informal
trade, and charcoal is produced and traded through theinformal sector, governments tend to have poor data on
the quantity of wood and charcoal used (Arnold et al.,
2003; Mugo & Ong, 2006). In some countries permits are
required for charcoal production, and the countries reporton the permits issued, but in many cases there is an illicit
trade that may far exceed the formal trade. The wood andcharcoal trade is huge, with some studies suggesting that
fuel wood and charcoal trade represents the single biggest market for a rural (including agricultural) commodity in countries such as the Congo, Kenya, Tanzania andMalawi (Sepp, undated; World Bank, 2009; Schure,2009).
An alternative approach to quantifying the production
and trade in fuel wood is to do detailed household
fuel-use studies and to extrapolate this data to the59national or regional level. This gives a more rigorous
estimate, but is an expensive and methodologically complex process. Ideally, household fuel-use should betracked over an entire year to understand seasonal variations, and over the entire country to establish spatial
variations. Few countries have the resources to do this.
These consumption-side studies suggest that fuel woodand charcoal use may be far higher than the supply-side
studies indicate. For instance, a study of charcoal use in
Tanzania suggested that total charcoal use was one million tons of charcoal, requiring 30 million m3 of wood.
This is approximately twice the FOA estimate for charcoal use in this country.
In 1999 FAO (Amous, 1999) attempted to consolidate
available Africa-wide fuel wood use estimates based on
FAO, IEA and other data sources (see summary data in
Table 1). This study highlights many of the uncertainties
surrounding the FAO and other available data sources.
Updated FAO data are available for 2005 (FAO, 2010),
but remain based on estimates submitted by countries andare likely to suffer from the same data problems identified by Amous (1999). IEA produces annual estimates of
total biomass usage, expressed in million tonnes oil
equivalent (Mtoe). Since a tonne of oil equivalent represents 41.868 GJ of energy, and the energy content of fuelwood is about 17 MJ/kg, 1 Mtoe is equivalent to about
2,460 kg of dry fuelwood. The IEA estimate is derived
from a number of data sources and its origin is poorly
documented (Amous, 1999). The IEA 2002 world energy
outlook estimated that Africa in year 2000 had a total fuelwood demand of 254 Mtoe (i.e., 625 Tg wood), of which
71% was for residential energy use. Bailis et al. (2005),
using a combination of FAO and IEA data, suggest thatthe household sector of SSA used 470 Tg of fuel wood in
2000. Other studies have suggested that household wood
fuel use contributes about 80% of total wood fuel use(Amous, 1999). As can be seen from Table 2, estimates of
total wood fuel demand range widely among studies, but
are in the region of 500 Tg/y in recent years, with an

Table 1 Burned area estimates for sub-Saharan Africa.
Burned area SH(km2x103)

Burned area NH(km2x103)

Burned area ALL(km2x103)

mean

range

mean

range

mean

range

1

1684

--

--

--

--

--

5km pixel

8

1541

SPOT burn scars

1km pixel

1

960

--

--

--

--

--

Regressed against Landsat estimates: onaverage ~40% of the variance not explained

2004

ATSR-2 burn scars

1km pixel

1

--

--

--

--

1210*

--

Regressed against Landsat estimates (by Roy,
2009): 49% of the variance not explained

Giglio2006

Calibrated MODIS
active fires

4810(753-840)

Ito

2007

VIRS active fires

0.5 degree

8

2300

--

Roy

2009

MODIS burn scars

500m pixel

81171 (915-1292)

GiglioMODIS burn scars,
2010 VIRS active fires, ASTR
active fires

0.5 degree

12Uncertainty estimates based on Landsat1252 (1126-1531) 1310 (1144-1524) 2562 (2317- 3018) accuracies and summed over time period: ~2%
per 0.5 degree pixel ~ 321 km2x103 in total

ReferenceYear

Scholes

1996

Calibrated AVHRR
active fires

5 degree

Barbosa

1999

AVHRR burn scars

Silva

2003

Simon

Method

Scale

No of
years

3931

(1005-1918) 2390

1359 (1255-1532) 2163--

SH = Southern Hemisphere, NH = Northern Hemisphere, ALL = sub-Saharan Africa* High errors of omission reported----(2564-4894)

Uncertainty

Estimated at 30%
Tested against Landsat data: ~30%
misclassification

Uncertainty estimates based on Landsat(2051-2372) accuracies and summed over time period: ~3%
per 0.5 degree pixel-Regressed against Landsat estimates: 25% of
the variance not explained

60

R. J SCHOLES et al.

annual growth rate of around 1.5% and an uncertainty
range of around 62 Tg/y (±15%). These predictions areprobably conservative given the rapid increases incharcoal use that have been observed in a number ofurban centres recently (World Bank, 2009).
3.5 The emissions which result from biomassburning
The emissions from biomass burning, which include a
range of gases (some of which are important greenhousegases such as CO2 and CH4, and others are reactive andcontribute to the formation of tropospheric ozone) andfine particles (also called as aerosols), have been widely
studied in Africa, amongst others in the SAFARI 94 andTable 2 Summary of Africa-wide wood fuel use estimates.
Charcoal and fuel wood data are combined to give total
wood fuel. A charcoal production efficiency of 165 kg
charcoal per m3 fuel wood is assumed (Amous, 1999),
and a wood density of 700 kg/m3.
SourceYear

FAO (in Amous, 1999)
BEST (in Amous, 1999)
WEIS (in Amous, 1999)
FAO (in Amous, 1999)
IEA (2002)
FAO (Broadhead et al. 2001)
Bailis (2005) (household use only)4
FAO (2010)
FAO (Broadhead et al. 2001)

1990
1994
1996
1999
1999
2000
2000
2005
2010

Fuel use
Mt (Tg)
317.1
451.2
418.1
371.4
495.6
452.0
470.0
429.5
493.0

Table 3 The quantity of biomass consumed by fire annually in
sub-Saharan Africa (including Madagascar), nominallyfor the year 2000.
SourceBiomass consumed(Tg/y)%
Uncertaintyb% Interannual
variation

10

5.4bGrowing at
1.5%/yBest lower upper
Wildfires
Wood as fuelLand usechange(deforestation –on-site burning
only
Agriculturalresidues(in-field
burning)
Total

800d

716d

881d

500

435

565

15

74

60 (areaestimate
uncertainty
about 30%and biomass
30%)

46a

50c

18

35

75

30

1396 1204 1595

14

Apparently
stable in 1980s
and 1990s
Smallinterannual
variation,
possibly
growing
5, increasing
trend

Notes
a. The total biomass involved is about ten times higher than this, but muchis lost through decomposition rather than fire (we assume 50%), and ofthat which burns, we assume that 80% is already reflected in the ‘wood
as fuel’ category.
b. Calculated as 0.5*(upper-lower) /best*100.
c. The Yevich and Logan estimate, with their estimate of what fraction isburned in-field, plus an additional 2 Tg/y for plantation residues,
estimated by this paper.
d. Ellicott et al., 2009 estimate, based on observed Fire Radiative Power.
Estimates based on modelled fuel loads are typically in the 2200 to
2700 Tg DM/y range, but are in poor agreement with observed fuelloads.

SAFARI 2000 campaigns in southern Africa and the
DECAFE and EXPRESSO campaigns in West Africa.
Andeae and Merlet (2001) provide a comprehensive
synthesis of emission factors from Africa and elsewhere;
Marufu et al. (2000) quantify emissions from domestic
fires; Bertschi et al. (2003) provide information forsmouldering logs; and Janhall et al. (2009) update theinformation regarding particles. The emissions are typically initially determined as changes in the molar ratios
of various constituents of the atmosphere relative to theirconcentration in uncontaminated air. The ‘delta molar
ratios’ are then converted to ‘emission factors’ (EF, g/kg
DM fuel burned) for more convenient application. The
latter step requires a number of assumptions regarding
the chemistry of the fuels and the combustion conditions.
The uncertainly range is small for CO2 (perhaps ±5%)but is suggested to be up to ±30% for CH4 , CO, NOx,
N2O, non-methane hydrocarbons and particulate matter(Langmann et al., 2009), but can be reduced throughseveral strategies. The carbon content of dry biomass isfairly consistently between 0.41 (grass) and 0.45 (wood).
The emission factors for products of incomplete oxidation (such as methane and non-methane hydrocarbons
and aerosols) tend to have a strong negative linear
relationship with the ‘Modified Combustion Efficiency’(MCE, ΔCO2/(ΔCO2+ΔCO)), which approaches a valueof 1.0 for the flaming combustion typical of fine, dry
fuels such as grass, and may be as low as 0.7 for smouldering wood or peat (Wooster, 2011). If it is possible toestimate the MCE from the fuel type and combustion
conditions, much of the variability in emission factors
can be removed (and, by definition, all of the uncertainty
in the emission factors for CO2 and CO, if the MCE estimate is perfect). Emissions of nitrogenous and sulphur-containing gases scale linearly with the N and S
content of the fuel, so if these can be estimated, theuncertainties in the NOx, N2O and SOx emission factors
can be greatly reduced.

4. Summary and Perspectives
Table 3 combines, in our opinion, the best estimatesof biomass combustion from each of the four pathways
discussed in the preceding sections (wildfires, domestic
and industrial biofuels, land-use-change fires and burning of agricultural residues) along with our evidenceguided judgement of the likely range and notes on thetrend or interannual variability. The given range is our
expert judgement of the ‘likely feasible range,’ in otherwords, excluding outlier or outdated studies. Although
not strictly statistically-derived, it is conceptually closer
to a 95% confidence interval than either an absolute
range or a standard deviation.
Table 3 shows that wildfires dominate the carbon
emissions from Africa, being nearly twice as large a
consumer of biomass as the next largest category, which
is biofuel use. The land use change and agricultural residue categories are both quite small assuming that muchof this material actually ends up as domestic fuels, and isFire in Sub-Saharan Africa

therefore already accounted for under that category.
The situation for individual trace gases may differ
substantially from that for carbon as a whole, since thechemical profile of emissions resulting from wildfires(especially those in savannas and grasslands) is quite
different from the profile resulting from charcoal manufacture or the burning of charcoal and wood in enclosed
stoves. In general, the grass-dominated wildfires produce
a higher fraction of highly-oxygenated products, such as
CO2, and less partially-oxidated products such as CO,
CH4 and non-methane hydrocarbons and aerosols than dofires consuming woody fuels, or those in confined spaces
such as kilns, stoves or hearths. Thus the regional budgets for CO, tropospheric ozone and smoke particles islikely to be more evenly split between wildfires anddomestic biofuels than the quantity of fuel consumed
suggests.
The pathways of emission also differ with respect towhether they constitute net CO2 emissions or not. In
general, it is assumed that the emissions of CO2 fromwildfires and burning of agricultural residues in SSA do
not represent a net emission, because the CO2 is taken up
again by the regrowth of the vegetation or crop within ayear or two. Emissions from net land use change, on theother hand, are regarded as net CO2 emissions. To theextent that the domestic and industrial biofuel usedoriginates from biomass made available through land use
transformations (and we suggest that at least half of itdoes), the biofuel emissions also represent net emissions.
A way to check the validity of bottom-up pyrogenic
emission estimates is to compare them with regional
budgets of atmospheric trace gases and aerosols generated by fires. Candidate pyrogenic proxies are carbon
monoxide, smoke aerosols (particularly black carbon)
and tropospheric ozone. Studies of this sort remain few
and not yet convergent (Novellie et al., 1998; Arellano
et al., 2004; Petron et al., 2004; Ito & Penner 2005; Itoet al., 2007). The inferred emission values for SSA are in
the region of 239 ± 160 TgCO/y (±SD) (Petron et al.,2004).
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63
Robert J SCHOLESDr RJ (Bob) SCHOLES is a systems ecologist,
employed by the Council for Scientific andIndustrial Research in South Africa. He has overthree decades of research experience on the topics
of savanna ecology, biogeochemistry, global environmental change and biodiversity and is widely
published in these fields. He has been involved in several high-profile
environmental assessments, including the Intergovernmental Panel on
Climate Change (he is a Convening Lead Author in the current assessment)
and the Millennium Ecosystem Assessment, where he co-chaired the Status
and Trends working group. He is a Co-Chair of Diversitas, the international
biodiversity research coordinating body, and Chair of the Group on Earth
Observation Biodiversity Observation Network (GEO BON). He is a
Fellow of the CSIR, the South African Academy and the Royal Society ofSouth Africa, and serves on the editorial board of several journals.

Sally ARCHIBALD
Sally ARCHIBALD is a South African ecologist
with interests in fire ecology, savannah vegetation
dynamics and the behaviour of complex systems.
She works at the CSIR in a research group whichuses systems theory, modelling and advanced
earth observation techniques to understand the
dynamics of southern African ecosystems under human use.

Graham Paul von MALTITZ
Mr von MALTITZ is a systems ecologist and ruraldevelopment specialist, employed by CSIR South
Africa since 1989. He obtained his MSc from the
University of the Witwatersrand in 1990 and is
working toward a PhD through the Nelson
Mandela Metropolitan University. Specialising inlarge projects involving the interface between humans and natural resource
management, he has worked extensively in the savanna, forest and grassland biomes of southern Africa, focusing particularly on natural resource
use within communal areas. Much of this research has been linked to policy
formation. He coordinated the drafting of the South African National
Action Plan for preventing desertification, developed some of the earlymodels on fuelwood supply and demand in South Africa, represented South
Africa on the Committee for Science and Technology (CST) at the UNCCD
CoP, and now sits on the national coordinating body for the UNCCD.
Recently, his interest has extended into sustainability concerns related tobiofuel production, and he has become the South African coordinator for
two large European Union funded projects considering the potential
impacts of biofuel development within developing countries. Mr vonMaltitz has published numerous peer reviewed scientific articles,
conference proceedings and technical project reports.

(Received 5 April 2011, Accepted 26 August 2011)