Category Archives: Bioenergy from Wood

Storage of Trees, Tree Parts or Bundles

Trees, tree parts and bundles should be stored at roadside landings in stacks that are stacked as high as possible while maintaining stability. High stacking minimizes the surface area exposed to rain (i. e., only the top is exposed) and promotes a more uniform material in terms of bulk density and moisture content. Ground contact should be broken by stacking on a simple rack of logs. Stacking butt-ends facing the landing not only promotes the run-off of rain water away from the landing, but makes for easier crane operation when chipping or transporting. These resources are stable and can be stored for long periods of time. The options from this point are roadside chipping and transport of loose chips, or transport of the material “as is” to conversion sites.

Integration of Different Conversion Technologies

The cost disadvantages of bioenergy conversion from lignocellulose relative to fossil-based energy sources can be addressed through innovative methods of process integration, the goal of which is to minimize capital investment, maximize energy efficiency and thus improve overall economics. Such optimisation of overall process performance and energy efficiency will also increase the environmental benefits that can be derived from bioenergy production. Heat integration within biochem­ical and thermochemical routes of lignocellulose conversion has the potential to increase overall energy efficiency by as much as 15 % and can reduce capital and operational costs substantially (Van Zyl et al. 2011; van der Drift et al. 2004). Similarly, the integration of energy cycles for biomass conversion processes with adjacent/associated industrial processes can address both energy efficiency and production costs of the lignocellulose conversion process. Process integration with adjacent industrial processes can be broadly classified as (i) integration with electricity production from biomass or fossil fuels, (ii) integration with biomass processing for pulp or sugar production, (iii) integration of first and second — generation biofuel production by biochemical processing, (iv) integration of second — generation biofuel production by thermochemical processing with petrochemical processing and (v) integration of biochemical and thermochemical processing of lignocellulose to second-generation biofuels. The economics of process integration carries scale-dependent economic benefits, whereby more expensive, high efficiency equipment becomes affordable at larger production scales. Several of the conversion technologies presented in this chapter may be combined to form a value chain, in particular through the production of bioenergy intermediates such as wood chips, pellets and briquettes and liquid products such as bio-oil. These intermediates have higher bulk density than harvested lignocellulose which significantly reduces biomass feedstock transportation costs (Stephen et al. 2010; see Chap. 6). Further examples of integration of more than one conversion technology are presented below.

Combustion and gasification: Gasification combined with pre-combustion carbon capture can be used to produce either biofuels or electricity and improve the efficiency of Integrated Gasification Combined Cycle processes (IGCC) (Prins et al. 2012). Pre-treating biomass with hydrothermal carbonization (HTC) produces a coal-like substance (biocoal) which is potentially better suited for entrained flow gasification than raw biomass (Erlach et al. 2012).

Gasification and combustion: Biomass downdraft reactors coupled with recipro­cating internal combustion engines (RICEs) are a viable technology for small scale heat and power generation (Martinez et al. 2012). Dry gasification oxy-combustion (DGOC) is a process best described as a hybrid between gasification and oxy — combustion systems (Walker et al. 2011).

Torrefaction and gasification: The main idea behind combining biomass tor — refaction and gasification is that the heat produced during gasification in the form of steam is recovered for application to torrefaction (Van der Stelt et al. 2011;

Prins et al. 2006). Gasification using torrefied biomass allows for improved flow properties of the feedstock, increases levels of H2 and CO in the resulting syngas and improves overall process efficiencies.

Torrefaction and combustion: Combustion reactivity of torrefied biomass has been evaluated and shows promise for biomass co-firing in existing coal-fired power stations (Bergman et al. 2005; Bridgeman et al. 2008).

Torrefaction and fast-pyrolysis: Recent development of torrefaction as a pretreat­ment technology for fast pyrolysis results in enhancement of bio-oil properties by reducing oxygen-to-carbon ratios and water content (Meng et al. 2012).

Fast-pyrolysis and gasification: The adaptation of distributed fast pyrolysis biomass processing systems to central gasification systems in order to facilitate the production of hydrocarbon transport fuels is currently being developed by KIT (Dahmen et al. 2012). Fast pyrolysis bio-oil can also be gasified through a catalytic steam reformer (Czernik et al. 2002). Bio-oil gasification in entrained flow, oxygen blown pressurised gasifier systems is also feasible with applications currently in use by Texaco and Shell (Bridgwater 2011).

Fast-pyrolysis and combustion: The integration of fast pyrolysis and combustion technologies have been extensively studied (Czernik and Bridgwater 2004) and commercially applied (VTT, Dynamotive, Ensyn, btg-btl), while also being under further development (Khodier et al. 2009).

Direct liquefaction and gasification: Pre-treating biomass with hydrothermal car­bonization (HTC) produces biocoal which is potentially better suited for entrained flow gasification than raw biomass (Erlach et al. 2012).

Job Creation

The main measurable economic impact of biomass production is likely to be income and employment generation. Modern fuels provide for formal employment opportunities where traditional fuels provide informal employment for the poorest members of communities (Cushion et al. 2010).

With respect to informal employment it is recognised that fuelwood production is labour intensive and require between 100 and 170 person-days per terrajoule (TJ) of energy and between 200 and 350 person days per TJ for charcoal production (Fig. 9.2). The benefit of this type of employment generation depends, however, on the value of the labour used for production and could be considered positive if unemployment is high but low or negative when there are alternative uses for this labour (FAO 2005). Fuelwood gathering and trading helps to bridge seasonal income gaps when there is no other employment available and serves as a “safety net” in time of hardship such as during droughts and associated crop failures (Arnold et al.

2003) .

The ease of entry into fuelwood trading means that it is usually characterised by strong competition and low returns. Reliance on an income from fuelwood selling is often seen as a livelihood of last resort and many fuelwood collectors best be assisted by helping them to move into more rewarding, alternative employment activities as these become available (Arnold et al. 2003).

Formal employment in biomass production to supply biofuels could serve as an alternative to informal fuelwood collection activities. Current trends indicate that biomass production for liquid biofuels could substantially increase employment


Fig. 9.2 Charcoal transported from rural producers to urban consumers in Lusaka, Zambia

opportunities in developing countries. In Brazil for instance, formal employment in the sugar sector rose by 53 % between 2000 and 2005 from about 643,000 to 983,000 people as a result of ethanol production. It is projected that the minimum number of people employed by liquid biofuel production in the world by 2030 would be 2 million with the majority in sugarcane and ethanol production. This projection assumes that most production will occur in large scale mechanised operations (Cushion et al. 2010).

The promise of jobs is, however, not always fulfilled. When jobs are created, levels of pay are sometimes so low that employees are not actually better off. In Mozambique employees of a biofuels company, with rights to plant 60,000 ha of Jatropha on previous communal farming and grazing land, were paid the national minimum wage but there was little improvement in their standard of living. Some employees earned less than what they could during a good farming year (Friends of the Earth 2010).

Labour requirements for liquid biofuels are also mostly restricted to short-term work for land clearing and planting and some work at harvest time. Some studies estimate that one permanent job is created for every 100 ha of biofuel planted, with greater potential for job creation in the processing and production industry. When mechanised farming methods are used, employment levels are even lower (Friends of the Earth 2010).

It is possible to deduct potential employment rates for bioenergy plantations from existing timber plantation operations. Silvicultural activities in bioenergy plantations are ideally suited to the creation of low skilled job opportunities and the formation of small scale forestry enterprises in rural areas. Most silvicultural activities require basic education and low levels of basic training (2 weeks to 2 months), enabling people from marginalised rural areas to participate in economic activities (Forestry Solutions 2007).

Silvicultural activities are spread over the life cycle of a hectare of trees that could range from as short as 6 years in the case of short rotation eucalypt plantations to 25 years in the case of pine saw timber in South Africa. Table 9.3 illustrates the typical life cycle of a pine sawtimber hectare, that could yield biomass residue for bioenergy production, with the timing of activities and labour (person days/per hectare) required to perform these activities (Established at 1,372 stems per hectare on fairly level land).

It is, important to observe that 50 % of the labour activities happen during the first 2 years after plantation establishment. Unthinned pulpwood and biomass working circles share the first operations until first thinning and would thus not differ substantially in person working days. As a result of this any job creation programme based on silvicultural activities will have to rely on a sustained programme of afforestation and/or reforestation to have a substantial economic effect in rural areas.

De Beer (2012) translates these man day values into labour per hectare and estimates that 0.15 labourers are directly employed per hectare of plantation forestry in the Western Cape of South Africa. Shackleton (2004) estimates that activities along the forestry value chain can create up to three jobs per primary forestry job.

Table 9.3 Typical life cycle of a pine plantation, excluding harvesting and transport of logs




Person daysa /ha


Preparation of planting pits



Planting of seedlings



Fertiliser application



Manual & chemical weed control



Chemical weed control x 2



Chemical weed control x 2



1st Pruning to 1.5 m & chemical weed control



2nd Pruning to 3 m



3rd Pruning to 5 m



Marking for thinning



Marking for thinning



Marking for thinning


Total man days


aMan day values derived from Forestry Solutions (2007) Best Operating Practice estimates

Over and above direct employment benefits, Ofoegbu (2010) found in a study related to the social benefits of plantations in South Africa that rural communities adjacent to plantations also have access to harvest residues as fuelwood and source of building material, could utilize non-timber resources from plantations and had access to free accommodation, free farmland and free grazing land.


The objectives of the life-cycle interpretation are to analyse results, reach conclu­sions, explain limitations, and provide recommendations based on the findings of the preceding phases of the LCA or LCI study, and to report the results of the life cycle interpretation in a transparent manner. Furthermore, the interpretation phase is intended to provide a readily understandable, complete and consistent presentation of the results of an LCA or an LCI study, in accordance with the goal and scope definition of the study (ISO 14043 2000). Figure 11.5 shows the relationship of the elements within the interpretation phase with other phases of the LCA.

Case Study: Parametric, Non-parametric, and Non-linear Statistical Modelling

The development of a statistical method with which to relate LiDAR-derived tree height to IKONOS spectral reflectance involved the testing of three statistical paradigms, namely parametric, non-parametric, and non-linear artificial neural net­works. Parametric statistical procedures, e. g., regression, make certain assumptions regarding the underlying distribution of the data. It is primarily assumed that the data is normally distributed and that the input variables have similar variances (Cohen et al. 2003). However, as with most environmental data, this is not always the case and certain input variables had to be linearized using statistical transformations (Hudak et al. 2006). Two of the five input variables (IKONOS green and red bands) displayed non-normal distributions and were transformed using standard logarithmic methods. The five input variables were then used as independent variable inputs to a multiple linear regression, where the four IKONOS bands plus the age of the sample compartment were regressed against maximum LiDAR height. Results from this analysis were then interrogated for outliers. Outliers were removed using Cook’s distance measure (Cook 1977) and the regression was re­run using the resulting cases. The statistical approach is similar to that employed by Wulder and Seemann (2003); however, we employed only the per-band mean spectral reflectance values as independent variable. The reason for this is that the resultant regression model was applied to the imagery at the pixel level; hence distributional measures would have been unsuitable in this instance (Wulder and Seemann 2003).

The second statistical paradigm employed in this research makes use of non­parametric statistical methods. These approaches are known as “distribution-free” methods and do not rely on the assumption that data are drawn from a given probability distribution. The ^-nearest neighbour approach is such a non-parametric method that imputes forest inventory variables using reference samples and target mapping units (Reese et al. 2002). Reference samples typically are derived from remote sensing spectral reflectance and co-located forest variables of interest, which in our study was maximum LiDAR height as variable of interest within the reference sample plot. The goal of this approach is consistent with our primary objective, namely to evaluate canopy height estimation at locations not sampled by the LiDAR sensor using spectral reflectance and compartment age as predictor variables. Each target location is assigned a reference value based on the weighted Euclidean distance from its k nearest reference plot(s) according to this approach. The k nearest reference plots are typically defined using weighted Euclidean distance calculated in spectral feature space, while the target variable is estimated by the weighted average of the distances to the k nearest neighbours.

The weighted average distance (in spectral feature space) was calculated in our study using the random-Forest algorithm (Breiman 2001). This algorithm differs from the standard Euclidean distance measure in that it does not make use of a weight matrix, but instead classification and regression trees are used to classify reference and target observations: If a target and reference observation ends up in the same node, they are regarded as being similar. The distance measure is computed as one minus the proportion of trees which contain the same variable, and where a target observation is in the same terminal node as a reference observation. Crookston and Finley (2008) identify two advantages of using random-Forest as opposed to other distance metrics, namely that variables can be a mixture of continuous and categorical types and that the method is non-parametric. Hudak et al. (2008) compared a range of methods using several different error metrics (e. g., root mean square difference) and concluded that the random-Forest method was more robust and flexible than standard distance measures, e. g., Euclidean and Mahalanobis distance (Mahalanobis 1936). Preliminary tests conducted by the authors confirmed this finding, which resulted in the random-Forest approach being chosen as the appropriate distance measure.

Biomass Production in Intensively Managed Forests

Ben du Toit

5.1 Introduction

Intensively managed plantations of fast-growing trees, planted on a short rotations at high stand densities, is arguably one of the most productive and energy efficient ways to produce biomass. In this chapter we discuss silvicultural options to establish and manage highly productive bio-energy plantations, based on case studies from short-rotation plantation forests in warm-climate regions. Our focus is on the growing of biomass as a main product. We also explore the energy and green house gas balances from such intensively managed systems.

A topic that has been extensively researched in short-rotation pulpwood plan­tations in tropical and warm-climate countries, is that of intensive management to boost stand productivity (Schonau 1984; Nambiar 2008; Stape et al. 2008, 2010; Gonsalves et al. 2007; Fox et al. 2007b; du Toit et al. 2010). This management style has been dubbed “Intensive, Site-specific Silviculture” in Southern Africa (Schonau 1984; du Toit et al. 2010), and has been responsible for large improvements in productivity. It has also been categorised under the more general field of “Precision Forestry” by some authors because the management philosophy hinges on choosing and implementing a suite of management operations that are specifically suited to the ecological capability of a specific site type, or to alleviate constraints to productivity on a specific site type (Pallett 2005; du Toit et al. 2010). This production system, with some adaptations, is arguably the most suitable starting point to design silvicultural regimes for intensively managed, highly productive biomass plantations. In its current form, it is usually a man-made (afforested) monoculture tree crop managed under a clear felling system. However, it has some important

B. du Toit (H)

Department of Forest and Wood Science, Stellenbosch University, Private Bag X1, Matieland 7602, South Africa e-mail: ben@sun. ac. za

T. Seifert (ed.), Bioenergy from Wood: Sustainable Production in the Tropics, Managing Forest Ecosystems 26, DOI 10.1007/978-94-007-7448-3__5,

© Springer Science+Business Media Dordrecht 2014 differences to conventional agricultural monocultures, namely minimum soil cul­tivation where feasible, relatively low dependence on artificial chemical inputs for sustained productivity, prolonged periods of minimal cultural interventions, a net carbon footprint that is strongly positive, and the incorporation of significant biodiversity/conservation areas for the maintenance of ecosystem services (usually in the form of ecological networks or corridors) within the broader system in the landscape (cf. Chap. 10). In Sects. 5.2, 5.3, 5.4 and 5.5, we will discuss the most important elements of “intensive, site-specific silviculture” from the existing body of knowledge which essentially consist of a number of strategic choices (e. g. choice of genetic material and planting density/rotation length combination) as well as cultural practices (e. g. site preparation, vegetation management and fertilization). We will also focus on the most promising adaptations of conventional systems may be necessary to optimise this kind of silvicultural system for bio-energy plantations.

Owner Supplies Directly to Plant

For a small forest owner, this is the simplest form as the forest owner internalises all costs and delays in preparing the biomass for delivery. The basis for measurement is weighbridge mass, corrected for moisture content (see generic description of this below). If the forest owner has utilised contractors for harvesting or transport, the bill for this can be settled in conjunction with conventional harvesting on a volume basis (m3 — solid) costs using a suitable biomass expansion factor (BEF) which is multiplied against the roundwood harvest. The transport operator can be remunerated directly from the weighbridge bill. The forest owner incurs the costs at the time of each operation.

For a forestry company delivering to its own energy plant (e. g., CHP plant at saw-mill), the model would depend on the accounting processes between internal business units (e. g., harvesting business unit is separate from CHP business unit). From the energy conversion facility’s perspective, dealing with a large number of small forest owners (and other suppliers) is complex given that supply is almost impossible to schedule, there is little information on what is in the pipeline, variation in material and its properties can be large, and guarantee of delivery uncertain. These (considerable) disadvantages will be reflected in the price.

Moisture Content

The moisture content (MC) is the mass of moisture in biomass and can be either expressed as a percentage/fraction of the ovendry mass (mass with 0 % MC)

m — m0

MC = 0 x 100% (8.1)

m о

m = mass of wet wood; m0 = ovendry mass or as a percentage/fraction of the wet mass

m m0

MC = — x 100% (8.2)


m = mass of wet wood; m0 = ovendry mass

The MC on dry basis is commonly used in the wood processing industry, while the MC on a wet basis is used in the Forestry and Pulp & Paper industry. The MC on wet basis has a maximum value of 100 %, while the MC on dry basis can be larger than 100 %. The MC on a wet basis is mostly used for practical reasons, as all transport costs are based on the weight of the wet biomass. The problem with the MC on wet basis is that it is not well defined, because in contrast to the MC on dry basis the weight of wood is very variable.

Typically, considerable MC variation can be found in all biomass, depending on location, age, season etc. In wood, for example, heartwood generally has a lower MC than sapwood and in softwoods it tends to be larger close to the bark and higher up. Softwoods generally have a larger MC than hardwoods. Care has to be taken therefore to determine a statistically significant average value that describes the entire biomass. Typical values are displayed in Table 8.1.

The MC is the most important property for biomass utilisation as fuel, because it affects the entire supply chain and the related costs, i. e. transport, storage, energy content, conversion methods and end use.

Подпись: Biomass MC (%) Freshly harvested trees 80-180 Trees, 6 month stored 30-60 Freshly chopped wood 60-120 Air-dried chopped wood 15-30 Bark, fresh 60-120 Wood chips, sawmill waste 30-60 Chips, biomass 60-100 Wood pellets/briquettes 8-12 From Marutzky and Seeger (1999) Table 8.1 Typical MC values for different types of biomass

Stand Density

Increases in tree densities are usually associated with increases in water-use, due to greater competition for resources driving increases in leaf area per unit of land, higher root intensities and colonisation of greater soil volumes by roots. While it is true that the water-use of individual trees of a given age within a plantation will decrease as tree densities increase (due to competition), over-all water use for the plantation is likely to increase until a threshold is reached where water availability is the limiting factor and the water use levels off. This threshold tree density will vary depending on the site and species, but in general for intensively managed tree plantations of a given age, where effective understorey/weed control is practiced, moving from a typical pulpwood stand density of 3 m x 2 m (1,667 spha) to a spacing of 3 m x 1.5 m (2,222 spha), envisaged for intensive biomass production, will increase overall water-use of the stand (Fig. 10.1). A potential exception to this is when understorey vegetation with a particularly high water-use is suppressed by an increase in trees with relatively lower water-use rates (e. g. certain indigenous tree species).




Fig. 10.2 Accumulated streamflow reductions for Eucalypts simulated under a range of rotation lengths (Data from Gush et al. 2002)

Wood Based Energy in the Developing Countries

The strong contribution to woodfuel consumption by tropical and subtropical countries is not surprising since they account for a majority of the two billion people worldwide who are dependent primarily on firewood for cooking and heating (Mathews et al. 2000). The correlations of population growth, wood fuel demand and deforestation are well known (Allen and Barnes 1985; Barnes 1990). As an example for this process the Southern African Development Community (SADC) region in sub-Sahara Africa should be given, where the dependency on biofuel and charcoal is bigger than in most other regions of the world (Hall et al. 1994). Figure 1.1 illustrates the correlation between population growth and deforestation in the SADC countries. SADC has one of the fastest growing populations in the world and as such faces the challenge of increasing food and fuelwood demands (Barnes 1990; Hall et al. 1994). This has inevitably resulted in large forested areas being cleared or sequentially degraded. Non-sustainable fuelwood use is the second biggest cause of deforestation in that region (FANR 2011). The relatively limited access and high cost of electricity and fossil fuels in rural areas, where over 70 % of the population reside, and in urban areas worsens the situation (FANR 2011).

Due to a lack of methodological knowledge and financial means, biomass conversion to energy in the tropics comprises mainly of low-tech fuelwood use and charcoal production. With industrial growth in many tropical countries, there is a growing movement from traditional firewood use to coal and other fossil based energy sources. This change is furthered by the fact that the natural ecosystems are often not capable of sustaining the supply of fuelwood for the growing population, and thus demand. These challenges and trends are similar to those previously experienced in developed countries during the industrialisation process two centuries ago.


Fig. 1.1 Correlation between annual population growth rate (2010) and percentage forest loss in the SADC countries (excluding Zimbabwe) from 2005 to 2010, based on data from FAO (2010)