Category Archives: Bioenergy from Wood


Additivity of biomass equations means that the different equations for biomass fractions (foliage, branches, stem, bark, etc.) should add up to the same biomass as if an equation for the total aboveground biomass is used for biomass estimation to have a consistent set of models (Kozak 1970; Cunia and Briggs 1984,1985). This consis­tency between model estimates for biomass fractions and total biomass is not trivial since regressions are not perfect and even a slight bias for a model of a biomass component will affect the sum and define a deviation from an estimation of the total model. In addition, contemporaneous correlations between equations must be taken into account. If this is not done the efficiency of the model is reduced and biased parameter estimates could be the result as shown by Parresol and Thomas (1996).

For this reason different methods have been applied to ensure additivity (Parresol 1999). The most widely applied approach is a multivariate regression procedure based on the simultaneous estimation of all equations with joint-generalized least squares (Cunia and Briggs 1984, 1985), also called seemingly unrelated regression (SUR) in the more recent literature (Parresol 2001; Saint-Andre et al. 2005; Brandeis et al. 2006). Segmented regression and penalised spline approaches have also been tested to force additivity in biomass models (Goicoa et al. 2011).

Biomass Felling and Extraction Harvesting Equipment

Felling is a prerequisite of any wood based biomass supply, whether it takes place as an integrated part of traditional roundwood harvesting or as a specific biomass harvest. In this section, distinction is made on the most important types of felling technology. Felling methods range from motor-manual (chainsaws and brush-cutters) to fully mechanized systems, each with rational areas of application. Although mechanised harvesting systems have been practiced for at least three decades, motor-manual methods have been a traditional and historic part of biomass and timber procurement since the early 1950s.

The use of chainsaws and brush-cutters are at times (depending on tech­nological level of the organisation involved with bioenergy production, terrain, extraction system and biomass type and dimension) the preferred means of chang­ing the state of the standing biomass. The felling of spiny or thorny biomass places limitations on the use of motor-manual felling systems, as it is diffi­cult to approach the biomass. Multi-stemmed biomass also negatively impacts motor-manual productivity, particularly when individual stems are of small diam­eter. Typically this situation requires the use of mechanised multi-stem felling systems to overcome piece size challenges.

In developing countries the use of chainsaws in conventional FT, TL and CTL operations from which biomass residues are retrieved remains an integral part of the harvesting supply chain; i. e., felling, debranching, cross-cutting and topping, where applicable. However, productivity, worker safety and biomass product quality are marginal and modern mechanised systems for felling, debranching and cross-cutting are becoming more the rule than the exception (Pulkki 1992, 2000).

Mechanised felling (and extraction) systems are based on agricultural trac­tor units, excavators, or purpose-built forest machines, such as feller-bunchers, harvesters or forwarders. What distinguishes these machines from each other is their stability in the terrain, operator safety (ROPS, FOPS and OPS) and working capacity at the boom tip. But caution should be exercised when applying agricultural tractor/trailer systems in forwarding of biomass due to these units operating outside their intended design specifications.

In mechanised systems the felling, handling and eventual processing of the biomass is done using one of a number of specialized harvesting and/or processing heads categorized below. It is generally not necessary to use a sophisticated head for biomass harvesting as specifications on dimensions or quality are low or non­existent. However, some type of multi-stem capability is preferable. Mechanised felling equipment can be categorised as follows:

• Felling head: a felling head grasps a tree and fells it using one of a number of cutting technologies (i. e., chain saw, disc saw, shear, auger) (Fig. 6.1). The felling head is lighter and cheaper than other heads but cannot process a tree, i. e., usually is not fitted with feed rollers or debranching knives.

• Harvesting head: this head has the ability to grasp a tree, fell it, lower it in a controlled fashion/direction, and process it (i. e., debranch, measure the length and cross-cut).

• Accumulating head: this can be either a harvesting head or a felling head that has been fitted with accumulating arms which can hold multiple stems on a plate on which to rest the butt ends.

• Processing head: this head is typically fitted to an excavator or loader boom and is used for processing (i. e., debranching, cross-cutting, and in some cases debarking) FT that have been felled and gathered (e. g., at a landing). These heads do not have the capability to fell trees.

The size and type of the base machine, the crane type, forest conditions and operator skill all influence productivity. A larger base machine would be more stable and powerful when working a bigger head, or accumulating more trees, at a greater distance from the striproad (boom reach). However, smaller scale systems (e. g., agricultural tractor with crane and lightweight felling head) do and will continue to fill an important role in biomass procurement (Russell and Mortimer 2005).

Overall Comparison of Thermochemical Conversion Technologies

A comparison of typical process yields obtained with thermochemical conversion of woody biomasses available for bioenergy production in the Southern hemisphere is presented in Table 7.2. The product yields depend on the process used and the nature of the feedstocks involved. The relative portions of cellulose, hemicelluloses and lignin in biomass feedstocks differ between hardwoods (Eucalyptus, Acacia, etc.) and softwoods (Pine, etc.) which have a significant impact on the quality of bioenergy products, further aspects of which are addressed in Chap. 8.

Determination of the Ash Content

To determine the ash content, ovendry samples are placed in a ceramic crucible and the weight of each crucible and the biomass is noted. The crucibles are then placed and in the furnace at a temperature of 575 °C for 3 h (EN 14775).

After cooling, the ash content is calculated according to Eq. 8.8:

, „ 4 mA x 100

Ash (%) = A (8.8)


mA = mass of ash, m0 = mass ofovendry sample

Table 8.5 Calorific values and ash content of typical South African wood species (Munalula and Meincken 2009; Smit 2010)


Gross CV (MJ/kg)

Ash content (%)

Acacia cyclops (Rooikrans)



Eucalyptus cladocalyx (Blue Gum)



Pinus patula (Pine)



Acacia eriloba (Camelthorn)



Vitis vinifera (Vine stumps)



Acacia saligna (Port Jackson)



Acacia mearnsii (Black Wattle)



Acacia longifolia (Long Leaved Wattle)



Casuarina cunninghamiana (Beefwood)



Pinus pinaster (Cluster pine)



Soil Conservation and Protection

The major threats to the ability of soils to sustain highly productive forest, other than nutrition-related issues discussed in Sect. 10.3.1, is soil displacement, soil erosion and soil compaction.

When viewed simplistically, soil compaction sensitivity by mechanical equip­ment is strongly related to machine mass, soil texture and soil water content at the time of impact. Coarse textured soils such as sands can be trafflced by fairly heavy loads with low risk of compaction in both wet and dry conditions (Smith et al. 1997a, b; du Toit et al. 2010; Ponder et al. 2012). Soils with very sandy textures seldom suffer from compaction problems and may even experience improved water holding capacity and sometimes improved growth following moderate compaction (Smith et al. 1997b; du Toit et al. 2010; Ponder et al. 2012). Conversely, soils with a fairly even mixture of particle size classes such as sandy loams, loams and sandy clay loams are moderately compactable when dry but strongly compactable when moist (Smith et al. 1997a, b; du Toit et al. 2010). Harvesting operations should where possible be scheduled to avoid soils with an even particle size distributions during wet conditions. Furthermore, machines travelling on plantation soils could be matched with the soils load bearing capacity, which is strongly related to texture, organic matter and initial bulk density (Ampoorter et al. 2012). Most compacting occurs in soils with the first few passes of machines over a specific area. This fact, combined with efforts to limit the spatial extent of compaction, calls for controlled vehicular movement on designated skid trails (Ampoorter et al. 2010). The effects of compaction in short-rotation bio-energy plantations can be thus minimised (a) by using designated skid trials, (b) by matching machine mass with soil texture and thus with load bearing ability, (c) by limiting harvesting operations on fine textured soils during wet conditions and (d) by retaining as much of the harvesting residue as is possible, given the harvesting system chosen.

The sustainability of bio-energy plantations will be severely compromised if erosion rates significantly exceed soil formation rates. Soil erosion is generally affected by rainfall & runoff, slope gradient and length, soil erodibility, vegetation cover and soil surface cover, soil tillage, and any other man-made support practices to limit erosion, e. g. contour banks or windbreaks.

On a site with a give soil, slope and climate, forest management practices can play a major role in limiting erosion. The single factor giving the most effective protection against wind, rain-splash and water erosion is the presence of absence of a soil cover layer, e. g. a mulch layer in agricultural fields or, the forest floor/slash layer in plantations and forests (Morgan 1995). This is echoed by several case studies where forest floor layers have been removed or destroyed by intensive fires (du Toit 2002; Miura et al. 2003; Fernandez et al. 2004). Soil loss through erosion in plantation-based systems therefore depends very strongly on management of the slash and the forest floor. If destruction of the slash/forest floor is combined with other factors that favour soil erosion, it often results in an increase in erosion by orders of magnitude. Sherry (1953,1954,1961,1964,1971) documented the effects

Table 10.3 Average soil loss over two crop cycles of short-rotation Acacia mearnsii plantations under varying management regimes

Number of erosion enhancing factors present


Soil loss (tons ha-1)


Steep slopes


Slash burning


Hoeing to control weeds



Burning on slopes


Burning and hoeing


Hoeing on slopes



Burn C Hoe on slopes


From du Toit (2002); after the work of Sherry (1953, 1954, 1961,

1964, 1971)

of three factors: slash burning, slope steepness and soil tillage (and combinations thereof) on soil erosion. The results have been re-analysed by du Toit (2002) and show an order of magnitude increase in soil erosion with the number of factors present (Table 10.3).

In non-planted areas, road design, construction and maintenance is critically important to minimize erosion because cuttings and road construction will lay the soil bare and poor road drain maintenance may cause the water flow to be concentrated in certain areas.

Aerial Photo and Satellite Image-Based Estimates of Stand Parameters

Aerial photography has been the basis of resource assessments since the start of human flight, but more intensely since the Second World War. Space photography, on the other hand, has been in use since the 1970s. Orthocorrected photographs, i. e., where the effects of terrain or topographical variations have been removed, can be used as a planimetric source of information, as all 3D distortions are removed. It is the use of photogrammetry that is of particular interest in biomass estimates. Not only can areas of the resource be mapped, but a height value can be estimated for standing biomass via stereo-photogrammetry.

Stereo-photogrammetry provides a measure of parallax, which can be interpreted as the positional difference between the base and the top of an object, due to changes in viewing geometry. By collecting points on the ground surface (terrain) and the

Подпись: Fig. 2.5 Illustration of a Digital Terrain Model (DTM) and Digital Surface Model (DSM) and LiDAR terms, as referred to in this text

top of all objects on the terrain (surface) and performing an interpolation on these points, digital terrain models (DTM) and digital surface models (DSM) are products of the digital photogrammetry process.

Subtraction of a DTM from a DSM results in the height of an object above the terrain at each chosen point, as shown in Fig. 2.5. The digital orthophoto, which is also a product of the digital photogrammetry process, also provides a basis of measuring crown diameters. Although trained analysts can estimate individual tree sizes, which are useful for landscape-level analyses, this process must be automated to increase analyst efficiency. Automatic extraction of crown area, DBH, and biomass at the tree level is a relatively new type of image-analysis capability and associated techniques, which rely on delineating a tree crown from background soil and vegetation, and adjacent trees. A GIS layer of polygons, where the polygon area represents the crown area, can then be produced (Maltamo et al. 2003). Crown diameters are related to stem diameters for a given species and site, which means that both height and DBH could be estimated from the products of photogrammetry, with accuracy depending on the scale or resolution of the photography and the natural variability of the variables in the population.

Images that are suitable as input to the digital photogrammetry process range from satellite products to unmanned aerial vehicle (UAV) photography, which is becoming increasingly popular. For instance, individual tree canopies were mapped from 0.1 m aerial imagery to estimate carbon in a tropical forest in Belize, while canopy structure attributes can be estimated from images with resolution better than 4 m (Chambers et al. 2007; Greenberg et al. 2005). Figure 2.6 shows a graphical representation of the interaction between object size, in this case a tree canopy, and spatial resolution (pixel size) of some common satellite sensors. It is important to note, however, that (i) there is a trade-off between spatial resolution and temporal resolution (revisit time), where sensors with high spatial resolutions, e. g., IKONOS can take years to revisit the same spot on Earth; (ii) higher spatial resolutions generally imply lower spectral resolutions (broader bands); and (iii) inventory models that are developed at one spatial resolution, e. g., high spatial resolution IKONOS imagery, should ideally be coupled to and scaled with lower spatial resolution sensors, such as the Landsat suite of sensors. This approach effectively enables a tree-to-stand-to-landscape type inventory approach.


Fig. 2.6 Illustration of spatial resolution of different satellite sensors in relation to tree crown (adapted from Katsch 1999)

Stocking and Basal Area

The available literature on Southern African woodland indicates that stem density vary significantly from one woodland type to another and also whether the woodland is a regrowth or not. Density ranged from 837 to 1,131 stems ha_1 in old growth Kalahari woodlands of the undifferentiated woodland while in Miombo old growth, the stocking was reported to be 2,434-2,773 stems ha_1 on average (Syampungani 2008). Higher variations in stocking (stems/ha) have also been observed in mesic, semiarid and arid localities in central lowveld of the South African savannas; Shackleton and Scholes (2011) recorded higher values in mesic (18,530 stems ha_1) compared to semi-arid (3,996 stems ha_1) and arid (3,978 stems ha_1) localities. A comparison of regrowth stand densities between Kalahari woodlands and miombo woodlands indicated a significant variation from 1,131 to 6,685 stems ha_1 in

Table 4.2 Biomass related parameters of Southern Africa woodlands

Range of variables

Vegetation type


Density (stems ha-1)


Re-growth (miombo)

Syampungani et al. (2010), Strang (1974)


Uneven aged mature woodland (miombo)

Syampungani (2008)


Uneven aged mature woodland (South African Central lowveld)

Shackleton and Scholes (2011)


Uneven aged mature woodland (Kalahari)

Timberlake et al. (2010)


Regrowth (Kalahari)

Timberlake et al. (2010)

Basal area (m2)


Uneven aged mature woodland (Miombo)

Lowore et al. (1994), Freson et al. (1974)


Uneven aged mature woodland (South African Central lowveld)

Shackleton and Scholes (2011)

Mean biomass


Uneven aged mature

Chidumayo (1990, 1991),


woodland (Miombo & Mopane woodland)

Tietema (1993)


Uneven aged mature woodland

Guys (1981), Martin (1974)


Uneven aged mature woodland (South African Central lowveld)

Shackleton and Scholes (2011)

Growth rate (Mean


Regrowth (Miombo)

Syampungani et al. (2010)

annual ring


Uneven aged mature

Shackleton (2002), von Maltitz

width, mm)

woodland (Miombo & South African savannas)

and Rathogwa (1999), Chidumayo (1988a, b)

Miombo woodland to 7,264 to 9,700 stems ha_1 in Kalahari woodlands (Table 4.2). A variation in stocking per recovery stage/disturbance factor was reported by Strang (1974). Initially, a steady increase in stocking from 925 to 5,810 stems ha_1 was observed between 1.5 and 18 years since cutting in the Rhodesian (now Zimbabwean) Highveld which was protected against fires (Strang 1974). However, lower stocking levels were observed in the same locality which was constantly experiencing fires (Strang 1974).

The basal area ranged from 7 to 22 m2 ha_1 in old uneven aged stands of various woodland types (Table 4.2) with the lowest being recorded on lithosols in Southern Malawi at about 650 mm mean annual precipitation and the highest being recorded in wet miombo woodland deep soils of the Democratic Republic of Congo at 1,270 mm rainfall (Lowore et al. 1994; Freson et al. 1974). Lower basal area (e. g. 9.81 m2 ha_1) was mostly associated with young regrowth stands of up to 20 years old since cutting (Chidumayo 1987). However, higher values of stand basal areas of between 30 and 50 m2 ha_1 have been recorded in wet miombo and dry miombo of Zambia and Zimbabwe respectively, in small sized plots (Chidmayo 1985; Grundy 1995).

Storage of Harvesting Residues

Harvesting residues are stacked in the same way as FT or tree-parts, but given the nature of the material (no primary orientation); these stacks do not have the same natural ‘roofing’ tendency. In wetter climates it is common to cover the stack with

a 4 m wide heavy duty paper from a dispenser attached to the forwarder crane. The effect of doing so varies with the time of harvest in relation to the season with only limited differences seen if the material is harvested just prior to the rainy season, whereas there are significant differences in moisture content of up to 15 %, between covered and uncovered material that are relatively dry before going into the wetter period (Filbakk et al. 2011).

Technology Maturity and Economic Considerations for Biomass Conversion

The degree of technological maturity, economic cost considerations and conversion efficiency strongly influence technology selection decisions when conversion of woody biomass into bioenergy products is undertaken. Conversion efficiencies in particular have a key impact on the overall environmental impact of bioenergy value chains and therefore receive in-depth attention in this regard in Chap. 11, while not being discussed further here. Technology maturity and economic considerations are considered here for each of the thermochemical and biochemical conversion methods presented.

Land Availability

There is a common perception that developing countries have vast areas of available land. It is for instance estimated that there are about 800 million hectares of cultivable land across Africa of which less than a quarter appears to be used. Land prices in developing countries are in many places very low compared to developed countries and where the host country is supportive, land can be acquired on favourable terms. Such cheap access to land and cheap labour for biofuel development can be seen as a good business opportunity for foreign companies and has given rise to so called “land grabs” (Friends of the Earth 2010).

“Land grabs,” where land traditionally used by local communities is leased or sold to outside investors, are becoming increasingly common. The International Food Policy Research Institute (Headey et al. 2009, ex Friends of the Earth 2010) estimated that globally 20 million hectares of land have been sold since 2006 of which 9 million hectares were in Africa. Almost 5 million hectares are reportedly intended for biofuels, including Jatropha, oil palm and sweet sorghum.

International companies are keen to emphasise the benefits of these land deals to local communities in terms of job creation and economic development. A study by the Food and Agricultural Organisation (FAO) (Cotula et al. 2009, ex Friends of the Earth 2010) has, however, found that in Ethiopia, Ghana, Madagascar and Mali all biomass grown for biofuels will be exported with minimal direct benefit to the countries involved.

Rising demand for bioenergy has led to rapid expansion of large scale biofuel plantations (Cushion et al. 2010). Oil palm plantations have become the fastest growing monoculture in the tropics and have increased from 6.5 million hectares in 1997 to 14 million hectares in 2007 (Gerber 2011). The largest oil palm plantations are found in Malaysia and Indonesia with 4 and 6 million hectares, respectively, under oil palm cultivation (Tauli-Corpuz and Tamang 2007). The majority of oil palm plantations are located on land that was once tropical forest. The relationship between oil palm plantations and deforestation is debatable as it is unclear how much deforestation was caused by direct clearing for oil palm plantations or how much oil palm expansion occurred on land already deforested and degraded as a result of other factors (Gerber 2011).

The growth of biofuel from oil palm has resulted in economic benefits to national governments and companies involved but they come with serious social and environmental costs which adversely affect local people dependent on tropical forests for their livelihoods (Tauli-Corpuz and Tamang 2007). In Indonesia for instance, there is a lack of clarity of ownership over forested land, leading to widespread disagreements over land tenure. Land disputes with local communities were reported by more than 80 palm oil plantation companies in Sumatra in 2000. Large plantation areas have been cleared without adequate resettlement provisions for displaced communities (Cushion et al. 2010) and communities are deprived of common areas used for biomass collection and subsistence agricultural activities. These displaced community members become landless peasants, experience the decay of indigenous culture and are forced to engage in seasonal or long term migration to urban areas in search of employment (Gerber 2011).

Supporters of biofuel plantations often argue that bioenergy plantations are established on marginal, unused or degraded lands. Land that appears degraded or “idle” to outsiders often serves a vital function for communities as common grazing land or land to collect firewood (Friends of the Earth 2010). These areas are often managed under communal traditional laws for subsistence as well as for cultural and religious practices and local livelihoods evolved around the use of products from these areas (Friends of the Earth 2005). As a rule the more marginal their livelihoods are, the more likely rural people will depend on common, open areas for their day-to-day struggle for survival. The land will yield fuel, food, medicine and building materials to people who do not have the means to obtain similar products or services in the formal economy (Van der Horst and Vermeylen 2011). The question remains, why do local communities protest when this unused or degraded land is converted to what some perceive to be more productive land use? (Friends of the Earth 2005).

Given ambitious global targets for biofuel production it can be questioned if biofuel crops can be grown only on unused or degraded land or if it will take land out of agriculture and forestry (Cushion et al. 2009). Large changes in land use may occur as a result of biofuel production. Global estimates for the amount of land required for future biofuel production range from 118 to 508 million hectares or 36 % of the current arable land by 2030 (Ravindranath et al. 2009, ex UNEP 2012).