Wu and Strahler (1994) discusses a method of estimating total standing biomass of coniferous stands, using an inverted canopy reflectance (geometric-optic) model approach with 30 m Landsat TM data. The use of MODIS data is discussed in Cartus et al. (2011), where a MODIS product, vegetation continuous field (VCF), is used in fusion with SAR data. The VCF product provides global estimates of tree canopy cover at 500 m x 500 m pixel size and where tree canopy cover refers to the fraction of skylight obstructed by canopies of trees that are at least 5 m high. Although the VCF product was validated only partially, it was suitable for the fusion since it is only used to identify areas with low and high canopy cover and the accuracy of the canopy cover estimates is not crucial. The study provided an approach for fully automated stem volume estimation with ERS-1/2 tandem coherence although topographic effects still constraints the use of coherence in general. This presents just one of many studies that show the usefulness of multispectral imagery for biomass assessment, with these kinds of sensors often used in tandem with fine — scale assessment to scale estimates to larger areas, e. g., Duncanson et al. (2010) and Roberts et al. (2011). The reader is referred to this latter study for a comprehensive overview of such approaches.

The impacts of selective and clear cutting on miombo woodland regeneration and indeed in many extensively managed types of woodland depends on the characteristics of the trees. Most inventories in miombo woodland have tended to exclude plants in the pre-sapling phase which probably form the largest reservoir for the future tree crop. Chidumayo et al. (1996) reported that about 95 % of the plants at the sites they sampled were suppressed saplings of which canopy species constituted 44 and 84 % of total species and plants, respectively. Shoot growth among suppressed miombo saplings is slow; but the plants tend to accumulate a relatively large belowground biomass which constitutes the perennating organ that regenerates new shoots following repeated shoot die-back during the dry season (Chidumayo 1993). It has therefore been suggested that shading by canopy trees contributes to slow shoot growth of suppressed saplings in miombo woodland (Lees 1962; Werren et al. 1995); although competition for nutrients and water stress during the long dry season, fires and herbivory probably also contribute to this retardation. Notwithstanding, removal of canopy trees accelerates the rate of recruitment from suppressed saplings to sapling and tree phases (Chidumayo 1993; Werren et al. 1995; Chidumayo et al. 1996). While there may be additional tree recruitment from suppressed saplings after tree cutting, resulting in tree density in young regrowth being usually very high, this is a temporary phenomenon because density eventually returns to the pre-felling level at maturity (Chidumayo et al. 1996). Studies (Trapnell 1959; Chidumayo 1997) carried out at different ages of the regrowth stands demonstrated that recruitment is a temporal phenomenon in the Miombo woodland. Chidumayo (1997) demonstrated that although high tree densities in the early stages of woodland recovery like the ones observed by Trapnell (1959) in 11 year-old regrowth may be observed, the density tend to reduce drastically up to 95 % as the regrowth stand attains maturity. Thinning of stems in regrowth woodland is therefore a desirable silvicultural practice that replaces the slow natural thinning process during woodland maturation from regrowth.

Road transport vehicles are the predominant mode of transport. Permissible loads are governed by the legal gross vehicle (or gross combination) mass and the allowable axle or axle unit mass/es. In South Africa for example, the maximum allowable mass on a single axle (non-steering) is 9,000 kg (7,700 kg on a steering axle), 18,000 kg for a two axle unit and 24,000 kg for a three-axle unit. The least of either the sum of the axle unit masses or the maximum legal gross combination mass (GCM) represents the gross legal allowable mass. The maximum permissible gross vehicle mass for South Africa is 56,000 kg. The maximum width for a vehicle exceeding a gross vehicle mass of 16,000 kg is 2.6 m. Below this gross vehicle mass the width is limited to 2.5 m. The maximum height for all vehicles is 4.3 m (FleetWatch 2012). Container trucks are popular due their versatility while truck — tractor and semitrailer configurations allow larger payloads.

A study examining 58 trucks showed a mean payload of 23,500 kg for container trucks and one of 29,164 kg for articulated trucks. The tare mass of the container trucks was 24,246 kg and for the semitrailer 16,180 kg meaning that the semitrailer is 8,000 kg lighter and has a larger load space (Fig. 6.8). Unloading times were 20.95 min on average for the former and 45.36 min for the latter (Talbot and Suadicani 2006).

Even though the bulk density of forest fuels is low, an increasing moisture content implies that less energy is transported per truck cycle (calculated as the lower heating value), i. e., at the same transport cost. However, at some stage, the total mass limit of the truck is exceeded and the load volume has to be reduced as well, drastically reducing the amount of energy being transported (Fig. 6.9). At 50 % moisture content, the semitrailer (upper line) carries 16 % more energy than the container truck (lower line) while at 55 % MC, it carries 30 % more. This is an important relationship to understand as chipped residues have a higher density than chipped stemwood (shown here) due to the heterogeneity of the material and the higher density of the branches.

In recognizing that increasing volumes of biomass will be transported over increasing distances in the future, efforts are being made to develop vehicles with increased volumetric and mass carrying capacities. EU directive 96/53/EC allows member states to test and adopt the European Modular System (EMS) which allows for vehicles up to 25.25 m long with a gross mass of 60 tonnes. Countries like Sweden and Finland have benefitted greatly from applying these trucks in transporting forest fuels. In South African Performance Based Standard (PBS) type rigid truck and drawbar trailer combinations are now allowed to operate, with special permits, at lengths of 27 m and 70,000 kg GCM and a payload of up to 49 tonnes (Fig. 6.10). Solely used as roundwood pulpwood vehicles currently (and normally mass limited), these PBS trucks will fulfil a specific role in biomass transport for the same reasons mentioned above.

Martina Meincken and Luvuyo Tyhoda

8.1 Introduction

Biomass used for energy conversion ranges from wood, especially planted for energy purposes, over harvesting residues, other woody biomass, such as shrubs or bamboo to waste materials, such as sawdust or pulp residues. These types of biomass differ widely in their properties and furthermore there is a variation within each species or type of biomass, due to biological variation. An assessment of the biomass quality is therefore vital to decide on its feasibility for conversion, the most suitable type of conversion and the need for further processing.

Wood, for example, differs in density, moisture content (MC) and chemical composition within one tree in horizontal and vertical direction. The variation between different trees is even larger, as these properties depend on the quality of the site where the tree is grown (water availability, temperature etc.) and of course there is a difference between wood species as well, although the difference due to site may be larger than the differences due to species. Therefore, in order to characterise biomass, care has to be taken, to work with statistically meaningful values. This means that samples should be obtained at the very least from different positions of a tree, but rather also from different trees and that sufficient samples have to be measured in order to obtain a representative average value. Practically, biomass is collected on a much larger scale than just a few trees, so sampling for quality assessment is commonly done by mixing e. g. the wood chips of many trees thoroughly and taking a few randomly chosen chips for sampling.

M. Meincken (H) • L. Tyhoda

Department of Forest and Wood Science, Stellenbosch University, Private Bag X1, Matiland 7602, South Africa

T. Seifert (ed.), Bioenergy from Wood: Sustainable Production in the Tropics, Managing Forest Ecosystems 26, DOI 10.1007/978-94-007-7448-3__8,

© Springer Science+Business Media Dordrecht 2014

The obvious advantage of biomass compared to fossil fuels is its renewable character. Its disadvantages are low density, which makes it bulky and difficult to store, the inhomogeneous form, which could be a problem for industrial equipment, a fairly large content of inorganic substances, which remain as ash and generally a high MC, which leads to an energy loss, because energy is needed to evaporate the water. Due to these characteristics, biomass usually requires additional processing before it can be used as a biofuel. Preparations, such as chipping, dehydration, densification and removal of incombustible material might be necessary.

A review of a switch grass bioenergy project in the United States (Rossi and Hin — richs 2011) highlighted participating farmers’ generally strong scepticism regarding the role of large scale agribusiness and energy companies. Most project participants felt that for small scale farmers and local communities to receive substantial economic benefits from bioenergy, corporate dominance in the bioenergy industry should be avoided or restricted (Rossi and Hinrichs 2011).

These views were mirrored by communities in the Eastern Cape Province of South Africa where 54 % of participants in a household survey regarding bioenergy development were not willing to produce crops for biodiesel production. One of the main objections was that local people were excluded from the project development and were asked to accept industrial scale development that could lead to further poverty (Amigun et al. 2011).

Disagreement between communities and bioenergy companies increase when the development is involuntarily imposed on the community’s locality, when technology is unfamiliar, the community has no decision making power and the development is for corporate profit rather than local benefit. The lack of community support for bioenergy projects in developing countries has completely prevented development of some bioenergy projects and caused significant delays in others (Amigun et al. 2011).

Distrust and scepticism on behalf of small scale producers can be overcome when bioenergy companies co-develop solutions to problems through two-way information flow. The focus should be on working with local partners to co­design every aspect of the bioenergy value chain instead of imposing pre-existing solutions from above (Hart 2005). In managing diverse linkages between small scale producers and bioenergy companies, techniques such as participatory rural appraisal and rapid assessment processes open up valuable ways of communicating with grassroots partners and helping with mutual learning and the creation of responsive strategies (Ham and Thomas 2008).

Tenure arrangements and assets of the small scale producers are not always clearly defined nor can they be easily turned into capital or used for collateral for loans or investments (De Soto 2000). This increases the vulnerability of the arrangements between the producer and the bioenergy company since conventional legal arrangements would be impossible. In this regard trust and social capital, rather than legal contracts, could form the basis for sustainable and fair busi­ness arrangements between small scale producers and bioenergy companies (Hart 2005).

The purpose of the third phase of an LCA, the life-cycle impact assessment (LCIA), is to assess a product system’s life-cycle inventory results, to better understand their environmental significance (ISO 14042 2000). The impact assessment is achieved by translating the environmental loads from the inventory results into environmental impacts.

For this study the so-called CML 2001 method (normalisation factors from November 2009) was applied to translate the environmental loads of the 37 lignocellulosic bioenergy systems into environmental impacts. CML 2001 is a collection of impact assessment methods that restricts quantitative modelling to the relatively early stages in the cause-effect chain, to limit uncertainties and to group LCI results into midpoint categories, according to themes (Guinee et al. 2002).

Only one of the global impact categories, the global warming potential (GWP100 years), calculated as t CO2-equivalent, will be further discussed in this chapter. The results in terms of other environmental impacts of the assessed lignocellulosic bioenergy systems, such as the abiotic depletion potential (ADP, measured in gigajoules), acidification potential (AP, t SO2-equivalent), eutrophication potential (EP, t phosphate-equivalent) and photochemical ozone creation potential (POCP, t ethene-equivalent), can be found in Von Doderer (2012).

Climate change may lead to a broad range of impacts on ecosystems and our societies, but greenhouse gases (GHG) have one property in common, which is useful for characterisation in an LCA. Characterisation of GHGs is based on the extent to which they enhance the radiative forcing in the atmosphere, i. e. their capacity to absorb infrared radiation and thereby heat in the atmosphere (Baumann and Tillman 2004: 149).

The mechanism of the greenhouse effect can be observed on a small scale, as the name suggests, in a greenhouse. These effects also occur on a global scale. Short­wave radiation from the sun reaches the earth’s surface and is partially absorbed and partially reflected as infrared radiation. The reflected fraction is absorbed by greenhouse gasses (GHGs) in the troposphere and is re-radiated in all directions, including back to earth. This results in a warming effect on the earth’s surface (PE International 2010).

This effect is amplified by human activities, in addition to the natural mechanism. Carbon dioxide is not the only gas that causes climate change. Methane, chlorofluo — rocarbons (CFCs), nitrous oxide and other trace gases also absorb infrared radiation. Compared with CO2, they absorb much more effectively. The potential contribution of a substance to climate change is expressed as its global warming potential (GWP) (Baumann and Tillman 2004: 149).

The LBSs’ overall performance in terms of global warming potential is presented in Fig. 11.8, above. Significantly, different results can be seen for LBSs 5, 13, 21, 29 and 37. These alternatives have bioenergy system V in common, where only

■ Unstablebio-charin soil

Fig. 11.9 Global warming potential of selected lignocellulosic bioenergy systems subdivided into production phases bio-oil produced in mobile fast-pyrolysis units is used for electricity generation. The other product from the fast-pyrolysis process, bio-char, is assumed to be sold to the fertilising industry for application to soil. Eighty percent of the bio-char is assumed to be stable in the soil, resulting in negative GWP levels of more than 32,000 t CO2-equivalent. For the other LBSs, a similar observation can be made as for the acidification and eutrophication potential impact categories: the greater the overall-conversion efficiency of the bioenergy conversion system applied, the fewer up-stream activities are required and the lower the GWP. Other LBSs also show negative GWPs, which can be explained by the positive effects of carbon stock changes when introducing SRC plantations. In these cases the increase in carbon stock compensates for the GHG emissions caused during harvesting, forwarding, pre-processing and secondary transportation. In comparison, the South African power-grid mix shows a GWP of more than 44,000 t/CO2-equivalent, assuming the same functional unit.

Figure 11.9, above, shows the GWP performance of five selected LBSs, sub­divided in production phases. LBS 14 uses bioenergy conversion system I, while LBS 2, 20, 27 and 37 deploy BCS II, IV, III and V respectively. The relatively large fraction of GWP for the harvesting phase for LBS 14 can be explained by the 30 % of unutilised biomass remaining infield. Although there is no direct relation between the harvesting and decomposition of the unutilised biomass, it is during the harvesting phase that the trees are felled, de-branched and cross-cut, leaving the tops and branches behind. LBS 37 entails an unstable carbon fraction. When using biochar as additive to soil around 20 % are assumed to be unstable, resulting in the decomposition thereof.

Stem biomass is reconstructed from samples based on the fact that oven-dry biomass is a function of stem volume and basic density (Eq. 3.1)

BM = V ■ R (3.1)

Here BM is the oven-dry biomass (kg), V is the stem volume (m3), and R is the basic density defined as dry weight divided by green volume (kg/m3).

Volume calculation, although a long time established part of forest mensuration, is not always trivial in biomass studies because unlike in stem volume determination the non-merchantable branch volume matters in biomass studies. The predicament is that there is no standardised definition for a twig as opposed to a branch. Nor is it always clear in broad-leaved species that tend to grow multi-stemmed or with forked stems what is to be labelled as stem and what as branch. As indicated in Fig. 3.3 a solution is, for example, to select the largest branch as a stem and define all other parts as branches. It must, however, be noted that this practice is not compatible with most taper and volume models that are based on volume calculated from the sum of all stems of a merchantable size and thus are based on a virtual, single stemmed tree. With redefining all other forks of a stem as big branches the merchantable stem volume and the derived stem biomass will be underestimated compared to estimations based on available volume functions and basic density. Thus a method compatible to stem volume determination, where the basal area of all merchantable stems and branches is added up to a defined cut-off diameter might be more useful.

A large variety of sampling methods were tested for biomass sampling such as systematic sampling in absolute or relative heights or more recently randomised branch sampling techniques (Jessen 1955; Valentine and Hilton 1977; Valentine

et al. 1984; Gregoire et al. 1995; Gaffrey and Saborowski 1999; Saborowski and Gaffrey 1999; de Gier 2003), which have been proven to improve accuracy for a set sampling effort. Experience shows that the choice of the sampling method is not in the first place driven by aspects of the sampling theory, but rather by the level of sophistication and experience of the sampling team. In developing countries computers for field work are often unavailable. Field teams for biomass sampling are frequently recruited from an inexperienced body of persons rather than from scientific personnel or experienced field technicians. Therefore highly sophisticated sampling designs that rely on computer software in the field are often not feasible. Thus, often the principle “the simpler the better” pays off. A gain in efficiency as a result of the use of e. g. randomised sampling techniques can be easily negated by the incorrect application of the sampling method by inexperienced field crews, while straightforward systematic sampling methods (e. g. samples taken every 3 m) have a better chance to be implemented correctly. However, a decrease in sampling efficiency is to be taken into account.

The volume calculation of stem sections between measuring points is typically based on Smalians’s formula (van Laar and Akqa 2007) or alternatively on the geometric formula of a frustum of a cone (Eq. 3.2).

V = (R2 + Rr + r2) (3.2)

Here V is the section volume (m3), R is the bigger end radius of the stem section and r the smaller top end radius. In case no cut-off diameter was defined, the volume of the stem tip can be determined as a cone by setting the top end radius r to zero.

The initially introduced Eq. 3.1 implies that stem volume and basic density calculation are equally important for the successful determination of stem biomass. Trees are known to vary considerably in wood density within the stem in radial and longitudinal direction and also between trees and sites. Thus, pre-information on longitudinal density gradients in the species of interest should be used wherever possible to adjust the sampling design of discs beforehand accordingly. To base biomass upscaling merely on mean literature values of basic density is a very crude approach that might, due to density variations within and between trees lead to seriously biased estimates.

The upscaling from sample discs typically contains a measurement component where basic density is determined at disc level and a modelling component based on the estimation of fresh weight/dry weight ratios or a regression approach to obtain information for the entire stem. In general, biomass should be provided as dry mass. Fresh mass is site and species specific (Marden et al. 1975; Kokkola 1993) and also subject to a substantial intra-annual and inter-tree tree variation (Kokkola 1993). In addition, the harvesting technique (debarking) and weather conditions after felling can modify the fresh weight (Adams 1971; Kokkola 1993) rendering it a suboptimal variable for tree biomass characterisation. A comparative quantification of biomass is only possible when an equilibrium in moisture content is reached that can be determined in accordance with scientific lab standards. The rich body of publications on biomass prove that there is a considerable variation in the definition of dry mass between studies that varies from about 40 to 105 °C. In nutrient studies with nitrogen content in phytomass as a response variable the drying temperature is usually limited to a maximum of 60-65 °C to avoid nitrogen loss as a result of volatility of some chemical components. Cones are often dried below 40 °C, for germination to remain possible. Oven-dry weight of wood refers per convention in wood science to drying to a state of constant weight at about 103 ± 2 °C (see e. g. DIN EN13183-1). Using drying temperatures of 70 °C and below, for example to maintain the volatile nitrogen parts for further nutrient analysis, leads to higher biomass values of a magnitude of 2-3 % in wood (Forrest 1969; Barney et al. 1978). Seifert and Muller-Starck (2009) reported similar weight changes for Norway spruce cones. The cone weight was 84 % (dried at 38 °C), 80 % (at 60 °C) and 78 % (at 105 °C) in proportion to the fresh weight. Evidence shows that it is paramount that differences in drying temperature are taken into account when pooling data and also when comparing established functions. Furthermore the drying regime

should be provided in publications for a reliable comparison of biomass studies. However, the establishment of further drying series at different temperatures seems to be warranted to obtain transfer functions for the different tree components, which would facilitate a conversion of biomass at different drying temperatures and would thus facilitate a comparison of results obtained in different biomass studies.

The most common principle in density determination of sample discs is the Archimedean principle of water displacement (American Society for Testing and Materials 1999). A basic physical principle is used that relates the buoyant force of a body directly to its volume. The method is simple and implies establishing of weight of a sample in air and in water with a scale. The density of water is 1 g/cm3 and that of air is negligible, which results in Eq. 3.3 for the final density (Gerthsen 1997, p. 97).

Wwater p sample Pwater) * V ^ (3 3)

Wair (psample ~ pair) * V psample

Here W is weight (g), p is density (g/cm3) and V is volume (cm3).

The weight in water is determined by full immersion of the wood sample in a water basin, which is placed on top of a scale. The scale reading in gram after immersion equals the volume of the sample in cm3. It is important that the sample is fully saturated with water (to constant weight), so that no additional water is taken up into the sample during the displacement measurement.

Based on individual disc density a longitudinal and partially a radial density variation can be taken into account, if a regression is applied that models basic density as a function of height in the stem. This procedure also averages out measurement errors to a certain degree (Fig. 3.4).

Based on the assumption that tree stems are rotationally symmetric along their longitudinal axis, basic density of a stem can be obtained by mathematical integration of the density regression function multiplied with the cross-sectional area along the tree height from the lower end to the upper end of the stem section (Eq. 3.4).

hu

j A(x)p(x)dx (3.4)

hi

Here hu and hl represent the height of the top and bottom end of the section, A(x) is the cross-sectional area of the stem at height x between hu and hl and p(x) is the basic density at the same height determined by a regression function.

A mathematically simpler, but still sufficiently accurate solution can be achieved by determining the height of the centroid (centre of gravity) of each section if enough sections are taken. The centroid of the frustum of a cone is calculated according to Weisstein (2013), based on Eshbach (1975), Harris and Stocker (1998) and Kern and Bland (1948) as indicated in Eq. 3.5.

i R2 C 2Rr C 3r2 4 (R2 C Rr C r2/

where z is the height of the center of gravity, R is the radius of the bottom end of the stem section (m), r is the radius at the top end (m), and l is the length of the stem section.

This height of the centroid z is then recalculated into the absolute height at the stem of that tree and then used to obtain a basic density value from the regression of density over height, as a representative value for the section. The obtained basic density is finally multiplied with the section volume. Using the regression example illustrated in Fig. 3.4 and a log of 3 m length and R = 0.40 m, r = 0.36 m, one would come to a height of the centroid (z) within the section of 1.44 m. If we assume that this log was the second 3 m section of the stem we have to add the stump height (0.3 m) and the first log (3.0 m) to z, resulting in an absolute height of the centroid that is 4.74 m. For this stem section an average basic density of 417 kg m“3 would be determined according to the tree-specific regression obtained in Fig. 3.4. Using this calculation template section by section will result in a reliable approximation of stem biomass.

Silvicultural treatments that ensure the prolonged improvement of growth resources to tree stands usually have the greatest improvement on stand productivity, and these have been dubbed Type A effects. This happens because not only the LAI development process is accelerated, but also because of changes to the canopy quantum efficiency and carbohydrate partitioning to above-ground parts (Stape 2002; Giardina et al. 2003; du Toit 2008). Examples are: Fertilization of a nutrient deficient stand where water availability is not limiting (Giardina et al. 2003); Irrigation of a stand where water is limiting growth (Stape 2002); P fertilization that is efficiently re-cycled and remains in the system for subsequent rotations (Snowdon 2002; Crous et al. 2007, 2008) or site preparation options improving the rooting volume accessible to trees (Zwolinski et al. 2002). The mechanism for responsible for the (usually large) Type A responses appears to revolve around an increase in canopy quantum efficiency (often accompanied by improvements in partitioning of carbohydrates to above-ground tissues), rather than a primary reliance on an accelerated LAI development, as is common in Type B responses (du Toit 2008). Where it is thus possible to implement operations that would elicit a Type A response (e. g. operations that can fundamentally change resource availability) consideration should be given to the fact that it may greatly improve stand productivity and resource use efficiency, and that these improvements are likely to recur over several future rotations. This may offset the costs of fertilizer or even the (often higher) costs of operations such as trenching, land surface modifications or subsoiling. However, as described in Sect. 5.4, conditions where intensive land preparation options are effective are limited to specific site and soil types.

The term bioenergy refers to all types of energy derived from plant biomass such as the lignocellulose feedstocks under consideration here. The following bioenergy types can be obtained by application of different transformation technologies to the conversion of lignocellulose biomass:

1. Thermal energy is one of the most commonly used products of woody biomass transformation and provides heat required for cooking and heating through direct combustion. In addition, steam produced by combustion can be used for both domestic and industrial processes (e. g. drying, boiling, ceramic oven heating/baking, etc.). Heating needs differ and can be distinguished between urban and rural, domestic and industrial applications.

2. Electric energy can be obtained through several transformation technologies, the choice of which is mostly determined by the type and amount of biomass available. For instance, the steam generated in a combustion process can be used to produce electricity (cogeneration). Additionally, other conversion technologies such as gasification, pyrolysis and anaerobic digestion all produce gases (synthesis gas, bio-gas) suitable for electricity production.

3. Transportation fuel is the energy obtained in self-propulsion motors from biofu­els. These so-called ‘second generation biofuels’ originate from lignocellulosic biomass and can be obtained through several thermochemical and biochemical transformation technologies, as described in the sections below. Second gen­eration biofuels particularly refers to those that employ lignocellulose biomass resources as feedstock without competing with food production. Lignocellulose biomass can be collected as residues from various activities in different sectors, although it is also possible to specifically produce ethically suitable “energy crops” as feedstocks for bio-energy production (Sanchez and Cardona 2008).

Woody biomass is a renewable feedstock for the production of bioenergy that is available in relatively large amounts in many parts of the world. It can be collected as by-products from lumber, pulp and paper production or from dedicated energy crops such as short rotation woody crops (see Chap. 6). Lignocellulosic biomasses are characterized by their heterogeneous composition and structure, multiplying the possible approaches for conversion into bioenergy products. Among the dif­ferent feedstocks, this chapter will focus on the following genera: Pinus (pines), Eucalyptus (gums) and Acacia (wattles), given their wide and ready availability in the Southern hemisphere. Bioenergy transformation technologies are generally classified as being either thermochemical (combustion, pyrolysis, gasification, liquefaction) or biochemical (anaerobic digestion, microbial fermentation) in nature.

Lignin is the second most abundant organic compound after cellulose. It is an integral part of the wood cell wall. In wood, it carries the major part of the methoxyl content, is unhydrolysable by acids, readily oxidisable and is soluble in hot alkali and bisulphite (Schubert 1965). It has a network structure and lacks a defined primary structure (Herman 1987). According to Hatfield and Vermerris (2001), lignin can be described in two ways: either from a chemical point of view i. e. its functional groups and lignin-type sub-structure compositions, or from a functional point of view that stresses what lignin does within a plant. The lignin content and composition (syringyl/guaiacyl ratio) do not change significantly with tree height or diameter (Sykes et al. 2008).

Lignins contain up to 67 % carbon, depending on the method of isolation (Jakab et al. 1995). As such, lignin is a major energy-bearing compound in wood with a calorific value of about 23 MJ/kg (Blunk and Jenkins 2000). Softwoods in general have a higher energy content than hardwoods, due to the proportionally higher lignin content.

Lignin is a natural bonding agent for the cellulose fibres when combined with hemicelluloses and imparts rigidity to the cell wall, generating a composite structure that is outstandingly resistant towards impact, compression and bending. Lignin also protects the polysaccharides from microbial attack. This makes a high lignin content undesirable for anaerobic digestion or hydrolysis-fermentation.

Lignin, due to its structure and high molecular weight also serves as UV protection and flame retardant agent. The latter property is of benefit in forest fires where the lignin-carbohydrate complex is able to protect wood from the effects of fire (Wunning 2001).