Despite some of the problems associated with large scale bioenergy production such as security of tenure, food security and environmental concerns, bioenergy has the potential to generate more income and employment than any other type of fuel. The higher levels of employment generated by bioenergy are a result of the relatively small size of production facilities and the large volumes of material used to produce each unit of energy output (Cushion et al. 2010). Human well-being depends, however, on more than income but includes availability of food, access to energy for cooking, shelter and heating, health care and cultural components such as political rights, education communication, transport and material comfort (Buchholz et al. 2007).

Maintaining and improving human well-being is the moral foundation of most societies. The creation of wealth in terms of products and services has a nearly one to one relationship with the use of energy per capita but when energy generation impacts negatively on human well-being the role of energy supply for wealth creation and sustainable development can be questioned (Buchholz et al. 2007). The risks of negatively affecting human well-being in bioenergy production systems can be addressed through innovative business ventures between rural producers and biofuel companies that empower rural producers of biomass and include them as equal business partners in bioenergy ventures (Ham and Thomas 2008). Such a strategy will depend on the risks associated with the production of biofuel crops, role of rural producers in relation to bioenergy companies and institutional support.

For the case study the technical system boundaries were structured in five production phases:

1 Primary production of biomass in SRC plantations;

2 Harvesting and subsequent primary transport of the biomass from in-field to the roadside;

3 Pretreatment of the biomass, including comminution, drying and mobile fast pyrolysis;

4 Secondary transport of the bioenergy feedstock from the roadside to a central conversion plant; and

5 Further processing of the feedstock and its conversion into electricity.

This resulted in a set of 37 lignocellulosic bioenergy systems, which are characterised by different combinations of the production phases (Table 11.2). A general description of each module of the five production phases is given in the life-cycle inventory below (Von Doderer 2012). Best operating practices (BOP) are assumed for all activities/processes throughout the life-cycle.

Table 11.2 Overview of CWDM bioenergy pathways leading to a set of 37 lignocellulosic bioenergy systems Central conversion Plantation Roadside Road site LBS Primary Pro- Motor-Manual Extraction with Mobile Chipping Transport of Comminuted Biomass Direct Combustion of Biomass 01 duction of Harvesting forestry of Biomass Direct Gasification of Biomass 02 Biomass (whole tree) machinery Central Pyrolysis + Conversion 03 (whole tree) Mobile Pyrolysis Transport of Combustion of Pyrolysis Products 04 Mobile Bio-Oil in Gas Turbine/Bio-Char sold 05 Pyrolysis to Industry Products Transport of Uncomminuted Biomass Stationary Direct Combustion 06 Chipping at of Biomass Landing Direct Gasification 07 of Biomass Central Pyroly- 08 sis + Conversion Motor-Manual Extraction with Mobile Chipping Transport of Comminuted Biomass Direct Combustion of Biomass 09 Harvesting agricultural of Biomass Direct Gasification of Biomass 10 (log) machinery Central Pyrolysis + Conversion 11 (manual Mobile Pyrolysis Transport of Combustion of Pyrolysis Products 12 loading of Mobile Bio-Oil in Gas Turbine/Bio-Char sold 13 logs) Pyrolysis to Industry Products Transport of Uncomminuted Biomass Stationary Direct Combustion 14 Chipping at of Biomass Landing Direct Gasification 15 of Biomass Central Pyroly- 16 sis + Conversion

Harvesting with Forestry Machinery (whole tree)	Extraction with forestry machinery (whole tree)
Extraction with agricultural machinery (loading of whole trees with three — wheeler)
Harvesting with Agricultural Machinery (whole tree)	Extraction with agricultural machinery (whole tree)
Source: Von Doderer (2012)

2.1.1 Selection of the General Method for Destructive Tree Sampling

A first step to create a biomass model typically involves destructive sampling of trees. There is a multitude of biomass sampling methods which would warrant a book on their own. In the following section a pragmatic approach is followed since biomass sampling does not form the major focus of this chapter. The different approaches in assessing the biomass of trees can be roughly classified in bulk sampling approaches where more than one tree is sampled and individual tree sampling (Fig. 3.2). The first method is usually based on in-field chipping and is a

more industrial than scientific practice. Bulk sampling is frequently applied in short rotation plantations when e. g. the biomass of coppiced trees is to be measured and entire rows or stands are harvested and chipped for fresh weight calculation (e. g. Hytonen et al. 1987). Another typical application of bulk sampling is harvesting studies of invasive woody vegetation with a high proportion of multi-stemmed trees and bushes, where only a biomass value per area is required (Kitenge 2011). However, in most cases dry weight is usually not determined from all chips but from a randomised sub-sample, in order to minimise transport and handling losses and to increase the efficiency of the method. The advantage of the technique is that bulk sampling of many trees is highly efficient and mimics real harvesting conditions rather closely. But the results obtained by the bulk method are sometimes difficult to compare with other studies because the chipping usually involves as loss which is specific to the machine equipment used (see Chap. 6). A more scientific sampling approach is based on individual trees. In this instance a bulk sampling of the entire tree or the aboveground part can be done with all the positive and negative aspects mentioned above. To avoid chipping losses a full tree fresh weight can be determined for example by a harvester mounted scale (Pettersson and Njordfjell 2007). However, in this instance representative sub-sampling for dry weight determination might prove to be a challenge.

In many biomass studies an estimation of the different biomass components such as bark, wood, leaves and branches is a main objective because different proportions of biomass components will influence ash contents and calorific values of the tree (see Chap. 8). The quantification of biomass components is also a prerequisite to assess the impact on nutrient balances as a result of biomass export from the stand (see Chap. 10).

The practical choice of the individual tree sampling method is mainly determined by the number of components that should be differentiated between, but also by the

tree size and the available time and work force. A full measurement of the fresh weight of the stem is always preferable since it eliminates an upscaling step (part of the upscaling step 1 in Fig. 3.1) and thus a possible source of error. The method has been successfully applied in several biomass studies for example in short rotation Eucalypt plantations (du Toit 2008; Dovey 2009). While harvester mounted hanging scales can support the measurement of stem fresh weight, the separation of twigs and foliage on site is only feasible for smaller trees under the normal time and money constraints. A regression based sampling approach, which is introduced in detail in the following section, is thus necessary for bigger trees to maximise efficiency. In the end the choice of the method always depends on the scope and specific objectives of the biomass study (Table 3.1). In addition to the listed method archetypes a multitude of mixed approaches can also be applied.

To understand the fundamental processes and mechanisms driving stand productiv­ity, we need to introduce the so-called production ecology equation (after Landsberg and Waring 1997), which states that

NPP = iPAR * ac * R,

where

NPP = net primary production

iPAR = intercepted photosynthetically absorbed radiation

ac = canopy quantum efficiency (mol of C sequestered per mol radiation absorbed) R = Respiration (a fairly constant value in young tree crops).

Figure 5.2 shows the theoretical development of leaf area index over time in short rotation eucalypt pulpwood crops with relatively high stand densities (after data from du Toit and Dovey 2005; du Toit et al. 2008; White et al. 2009). The three scenario’s in Fig. 5.2 are: (i) low-level silvicultural inputs, (ii) intensive silvicultural inputs that temporarily improve resource availability, and (iii) Intensive site or silvicultural treatments that ensured a prolonged improvement in resource availability. The latter two responses have also been labelled as Type A (Type II) and Type B (Type I) responses (Snowdon and Waring 1984; Snowdon 2002; Rubilar et al. 2008). The areas under the curves represent the cumulative leaf area that is deployed over the rotation, which is responsible for radiation interception and hence, photosynthesis.

Whether planning the location or capacity of a new plant, or supplying an existing plant, the procurement manager needs to have a good idea of the cost profile for feedstock supply. The location of the resource in relation to the conversion plant is more or less fixed, with annual supply deviations depending on the harvesting or thinning plans. A relatively simple and visually cognitive way of representing the economic availability of the resource is to develop a marginal supply cost curve. This implies deriving and plotting the cost of the last most expensive resource against the cumulative volume acquired up until that point. The cost is a composite figure, which includes harvesting, storage, handling and transport of each biomass type, and from each geographic source point, to a given destination, commonly the plant gate. This means that each resource point (stand) is handled individually in terms of harvesting method, yields, sequencing etc. Such an overview can easily

be constructed and maintained in a spreadsheet, although the accuracy is fully dependent on the cost estimations made underway. These can be based on rough estimates e. g., Fig. 6.12 (where each circle represents the volume available in a 1 km2 grid cell and the lines represent distance to the plant) or modelled in detail in GIS (Moller and Nielsen 2007). The following section provides a step by step guide on how to develop such a curve.

Developing a supply cost curve for managing biomass feedstock

• Step 1. Using a spreadsheet, list all the sources, including either the net energy content or the tonnes of dry matter available as the primary unit.

• Step 2. Estimate a harvesting and transport cost for each source. This can vary considerably depending on the kind of operation (early thinning vs. clearfelling), the terrain, the anticipated transport method and distance.

• Step 3. Sum these costs in a new column and rank the spreadsheet according to increasing delivered cost.

• Step 4. Generate a new column showing the cumulative quantity of energy (sum of all preceding energy quantities). You now have the necessary data for plotting the marginal supply cost curve. However, it is important to know the mean cost as well as the marginal cost.

• Step 5. The mean cost is more complex to calculate as it requires the sum-product of all preceding energy quantities and their respective prices to be divided by the cumulative volume. We use a simpler method to get there: calculate a total cost column (GJ * unit cost) and then sum this up in a cumulative cost column. The mean cost is then the cumulative cost divided by the cumulative volume. Now the supply cost curve can be plotted as in Fig. 6.13).

Fig. 6.13 An example of marginal and average value supply cost curves

—0— Marginal

■Mean

The resulting plot from step 4 provides a marginal cost of supply curve. The pro­curement manager can read what volume is available at what price directly from the curve. However, what is more interesting for procurement management is the mean cost of supply, as managers will normally be working from an operational budget. The manager might have settled on a maximum marginal cost that he/she is willing to pay a supplier, but this might not be the best course of action as is explained below.

The shape of the cost curve function (whether convex or concave) in arriving at the same marginal cost for the same quantity of energy, could imply two vastly different mean costs (Fig. 6.14). The shaded area represents the total cost of supply,

i. e. incremental cost multiplied with the incremental quantity. In this example it is easy to see that two very different mean costs are arrived at when the total cost is divided by the total volume. Even if the marginal cost is the same, the mean cost can
be quite different as the shapes of these two cost curves illustrate. The mean cost is represented by the shaded area (Fig. 6.14).

There are a number of ways of extending the utility of these curves. Including a code for the present state of the biomass (e. g., loose residues, bundled residues, chipped residues) and/or the stage of where the biomass is in the supply chain (planned harvest, harvested, at landing, at terminal) gives the procurement manager insight into the dynamics of the supply pipeline within a given time horizon, whether it be a week or a year. This means that the manager is able to negotiate prices, or incur heavy costs, in procuring biomass from specific suppliers or sources without compromising the budget.

6.7 Conclusion

This chapter described the possible sources of bioenergy from forests, early thinning, harvesting residues, salvage operations and stumps. Furthermore, the options of collection, extraction, haulage and comminution have been discussed. It has been shown that the place of comminution within the supply chain is decisive for the design and cost-efficiency of this bulky, low value commodity with limited potential for transport efficiency gains. One rule of thumb is that the shorter the transport distance, the later comminution should be performed in the supply chain. On the other hand, for long transport distances and a bulky assortment, comminution could be carried out in an earlier stage of the supply chain in order to decrease transport costs. In most cases the supply is not made up of one chain, but consists of a network of supply chains, where the challenge is to utilize machinery where it is best suited and to minimize costs. Utilising supply cost curves can provide insight into the most suitable supply chain for a particular situation.

Wood contains a significant amount of carbohydrates and consists of about 50 % Carbon, 6 % Hydrogen and 44 % Oxygen and other elements, often grouped together under the name “extractives”. Wood is primarily composed of macromolec­ular substances, which are mainly polysaccharides (cellulose and hemicelluloses) and lignin (Table 8.3).

8.6.1 Cellulose

Cellulose is one of the most abundant, naturally-occurring organic compounds in the world. Approximately 40-45 % of dry substance in most wood species is
cellulose, located mainly in the secondary cell wall. It is a linear homopolysaccha­ride composed exclusively of "-D-anhydro-glucopyranose units, which are linked together by " (1! 4)-glycosidic bonds. It is the main structural component of plant cell walls. Because of its strong tendency for intra — and intermolecular hydrogen bonding, bundles of cellulose molecules aggregate into microflbrils, which form either highly ordered (crystalline) or less ordered (amorphous) regions. This highly ordered three-dimensional structure confers the mechanical strength of cellulose, and also results in its low susceptibility to chemical and enzymatic attack.

It is often assumed that wood of the same species is identical in all structural and physical characteristics. However, this is not true as different pieces of wood from the same tree are never identical but are similar only within broad limits. Therefore, structural components, such as cellulose, which determine the physical and chemical properties, are also never found in the same quantities throughout a tree or in different trees of the same species (Howard 1973). The cellulose content differs between the roots, stem, branches, normal wood versus reaction wood, juvenile wood versus mature wood, earlywood versus latewood and varies from the pith to the bark (Downes et al. 2000; Haygreen and Bowyer 2007).

Most studies on the variation of cellulose content have been carried out on softwoods, showing minimum values of cellulose of 40 % in earlywood and maximum values of 50 % in the latewood (Downes et al. 2000). The cellulose content in latewood is not only higher but the cellulose also has a higher degree of polymerisation, which is very important for most applications, which use cellulose as a raw material. The latewood cellulose also has a higher packing density and a higher degree of crystallinity than that in the earlywood.

The cellulose content increases from pith to bark correlated with the tracheid length, which increases from juvenile wood to mature wood. It has been found that the cellulose content decreases about 2 % vertically in Pinus densiflora S and in Pinus radiata (Panshin and De Zeeuw 1980).

Reaction wood also shows a considerable difference in cellulose content. The tracheids in compression wood are about 30 % shorter than in normal wood, leading to a decrease in cellulose content of about 10 %.

The different cellulose content in branches can be ascribed to a larger amount of bark, knots, as well as the presence of reaction wood. Branches also have narrower growth rings, resulting in overall lower cellulose content than in stem wood (Haygreen and Bowyer 2007).

ENs are systems of natural or semi-natural landscape elements that are configured to best conserve and maintain biodiversity and ecological function (Fig. 10.5) (Bennett and Witt 2001). ENs consist of core natural patches that comprise of either existing nature reserves, areas of high conservation value or even areas within plantations that for various reasons remain unplanted, and are then connected by natural linkages (Jongman 2004). These linkages usually are either stepping stone patches or corridors of natural vegetation (Jongman 1995). Corridors are often simply defined as movement corridors for focal species (Hilty et al. 2006), but they also can function as habitats per se, especially when connected among themselves to form ENs. As the aim of ENs is to conserve biodiversity, they also need to include the inherent abiotic and biological complexity of the whole ecosystem (Fig. 10.1) (Jongman 1995). Conceptually much work has gone into the biodiversity value of ENs, although only a few areas of the world have actually implemented them, most notably the Pan European Ecological Networks (Jongman et al. 2011), the greenways in China (Yu et al. 2006) and the South African timber industry (Samways et al. 2010).

These ENs reduce the isolation of populations or even individuals, allowing better gene flow and reduce founder effects (loss of genetic variation that occurs when a new population is established by a very small number of individuals). ENs also allow species to recolonize areas after localized extinctions. This reduction of

isolation and fragmentation helps prevent ecological relaxation (the loss species and their interactions) and so prevent further biodiversity loss. This means that when ENs are designed and managed correctly, with large-scale interconnecting corridors and reserve areas, they can play an important role in ensuring connectivity between habitat patches for organism dispersal on evolutionary as well as on ecological time scales (Beier and Noss 1998; Samways et al. 2010) (Fig. 10.5).

This book is intended to close the short comings highlighted above. It is fully embracing the value-chain approach for sustainable production of bioenergy from wood. Furthermore it is meant to address all relevant aspects of biomass production and conversion along the production chain, with a particular focus on tropical and sub-tropical countries of the Southern Hemisphere.

The authors admit to bias towards Southern Africa, from where most of the case studies were taken but assume that the examples can be applicable to many other countries in the tropical and sub-tropical zone and in the Southern Hemisphere. The intention of this book is to present the state of the art in bioenergy production from a technical perspective, domestic and global consequences for the environment, economic feasibility and the socio-economic implications. All of these points address typical challenges of a bioenergy production system in tropical countries, considering both intensively managed plantation forestry and small growers and community forests.

This approach addresses all essential aspects of the value added chain of bioenergy production. The book is written for scientists that are involved or want to become involved with research on bioenergy as well as for forest practitioners and forest managers who are looking for an up to date compendium on the topic.

The book may also serve as a concise introduction into bioenergy production for stakeholders and decision makers that have to create the framework for sustainable production of bioenergy from forests and woodlands.

Chapter 2 introduces biomass inventory concepts for the localisation of woody biomass using terrestrial and remote sensing techniques. It is closely aligned to Chap. 3 which is dedicated to modelling and simulation of biomass. In Chaps. 4 and 5 silvicultural management aspects of biomass production for bioenergy in natural woodlands and commercial plantations are discussed. Chapter 6 introduces relevant topics of biomass harvesting, transport and logistics and provides the interface between biological production and biomass processing. The latter is dealt with in Chap. 7, and provides an overview on conversion techniques for woody biomass and their application range. Chapter 8 provides the reader with information on biomass quality testing, which is a prerequisite for establishing optimum conversion techniques. In Chap. 9 an analysis of socio-economic impacts of biomass production is presented, and provides the linkage to society as a major stakeholder when it comes to the implementation of bioenergy value chains. Chapter 10 introduces constraints to the implementation of biomass production systems resulting from potential impacts on water, soil fertility and biodiversity. Chapter 11 finally provides an overview on more global impacts of bioenergy production with a life cycle assessment based on a case study.

Paxie W. Chirwa, Stephen Syampungani, and Coert J. Geldenhuys

4.1 Introduction

The Southern African vegetation is generally referred to as the Zambezian Phytoregion. The region covers over ten countries in Central and Southern Africa lying between latitudes 3° and 26° south with a total area of 377 million ha (White 1983). The region falls within the tropical summer-rainfall zone with a single rainy season (November-April) and two dry seasons, a cool season from May to August and a hot season from September to November (Geldenhuys and Golding 2008). Annual rainfall ranges from 500 to 1,500 mm which decreases from north to south (Chidumayo 1997). The dominant soils of the region are rhodic and Haplic Nitosols and Chromic Xerosols with Calci-Chromic Cambisols and Pellic Vertisols in some places (Chidumayo 1997). The flora includes more than 8,500 species of which 54 % are endemic (Geldenhuys and Golding 2008). Based on variation in rainfall and soils, various distinct vegetation types are observed in Southern Africa.

P. W. Chirwa (H)

Forest Science Postgraduate Programme, University of Pretoria, 5-15 Plant Sciences Complex, Pretoria 0028, South Africa e-mail: paxie. chirwa@up. ac. za

S. Syampungani

School of Natural Resources, Copperbelt University, P. O. Box 21692, Kitwe, Zambia e-mail: syampungani@cbu. ac. zm

C. J. Geldenhuys

Department of Forest and Wood Science, University of Stellenbosch, P/Bag X1,

7602 Matieland, South Africa e-mail: cgelden@mweb. co. za

T. Seifert (ed.), Bioenergy from Wood: Sustainable Production in the Tropics, Managing Forest Ecosystems 26, DOI 10.1007/978-94-007-7448-3__4,

© Springer Science+Business Media Dordrecht 2014

4.2 Vegetation Types of Southern Africa

The major vegetation types of Southern Africa include Miombo woodland, undif­ferentiated woodland, Mopane woodland and Semi-arid Shrubland.

Unlike forests managed under more natural conditions (e. g., natural forests and woodlands selective cutting and the retention of seed trees for regeneration), plantation forests are typically re-established through the planting of seedlings. The important connotations of this are that spacing is controlled and that trees are typically planted in a geometric pattern which promotes efficient harvesting.

Early thinning is a term often used in conjunction with pre-commercial or sub­economic thinning and this well justified term is continually verified in research. It refers to an operation in which re-spacing is required to be carried out for the benefit of stand development, but the economic results of doing so do not necessarily justify the operations in themselves. Ahtikoski et al. (2008) use a complex calculation showing that energy wood thinnings could be financially viable if the extracted volume at least 42 m3 ha_1 for an average stem volume larger than 0.015 m3 and the unit price delivered exceeded US$12.00 MW h_1. Plantation managers can partly avoid this cost by expanding initial planting espacement and accepting the consequences of later canopy closure, but the debilitating relationship between productivity and tree size cannot be totally avoided.

However, in cases where a market for smaller roundwood has fallen away (e. g., loss of contract or closure of plant) the demand for biomass could promote early thinnings. Also, the development of a bioenergy conversion facility should stimulate denser establishment (higher number of stems per unit area) on areas that have been managed more extensively (refer Chap. 5). To promote efficiency through mechanized operations, variable row spacing can be used (e. g., closer spaced double rows which will be removed in the first thinning and more widely spaced rows that will not be thinned).

Small trees can be felled in a number of ways, largely determined by the extraction system to be used. Motor-manual felling with a chainsaw is often the most cost effective way of felling trees when they do not need to be processed in whole-tree harvesting (i. e., tree parts and crown intact). This is especially true if the trees will be chipped as they lie in the stand or if they will be extracted with a cable system (e. g., high lead or monocable). If pre-bunching or processing is required, then mechanized felling is preferable. However, opening up of strip roads can be costly if the bunches have to be laid perpendicularly into the stand, as placing them in parallel requires wider striproads, and if driven on they can be contaminated with mineral soil and stones, resulting in increased ash content of the eventual product.

An agricultural tractor fitted with a boom and a multi-tree (or accumulating) felling head can provide a low investment alternative to a feller-buncher or harvester, but versatility is dependent on terrain conditions (Russell and Mortimer 2005). In a study in Scandinavian forests, productivity levels of 3-6 m3 h_1 were obtained for felling and loading small trees onto a trailer for extraction (Belbo 2010). Small tree size thinning harvesters fitted with accumulating felling or harvesting heads are popular because of their greater stability and terrain going capability. However, all harvesting systems are highly sensitive to tree volume (Fig. 6.3).

Volume per tree, m

Fig. 6.3 Influence of direct loading and the number of trees per crane cycle on the combined felling and loading time using a Nisula 280 felling head in small trees, by increasing tree size (Belbo 2010)

The extraction of small FT or energy roundwood to roadside can be done using forestry equipped agricultural tractor/trailer units, a grapple skidder or a forwarder (Fig. 6.4). FT have a low bulk density and the load can potentially be compacted with the crane and grab. If the trees are to be extracted individually (e. g., using a monocable or a chute) or if they are to be chipped with a terrain going chipper machine they can simply be left as they fall for transpiration drying and subsequent extraction.