If biomass needs to be compressed into pellets or briquettes, they need to be further comminuted into particles of a few millimetre lengths. This happens with refining mills that grind the biomass between serrated disks. These particles are then compressed in a pellet or briquette press to particles of uniform size and density. The heat produced during pressing degrades the lignin present in the biomass and allows it to flow freely between the particles. Lignin is a natural binder and as the particle leaves the press and cool it hardens resulting in a stable biomass particle (Fig. 8.2).

Large-scale changes in land-use, such as those proposed for intensive biomass production, constitute a change in the structure, functioning, species composition and management of the vegetation growing on the land. This, in turn, signifies a change in how water is intercepted, infiltrated, transpired and evaporated from the land surface. The resultant impacts on the availability and quality of water in rivers is of great importance to the downstream users of that water. Consequently a good understanding and quantification of land-use driven water resources impacts is required when land-use changes are proposed. Stream-flow changes associated with vegetative land-use changes may be described using a simplified water balance equation, namely:

Q = P — Et ± AS

where Q = streamflow, P = precipitation, Et = evapotranspiration and AS = changes in soil water storage.

This equation is best applied over a suitably long time period (e. g. several years), where changes in soil water storage are assumed to balance out, and rainfall is representative of the long-term mean for the area. In this case, changes in Et caused by vegetative land-use change equate to changes in streamflow at the landscape level, if the water use of the replacement vegetation is significantly different to that of the existing land-use. In South Africa, plantation forestry with introduced (exotic) species is an extensive and profitable land use in many of the high-rainfall regions of the country (Chamberlain et al. 2005) with an area of approximately 1.25 million hectares currently under commercial plantations (FSA 2010). Growth in the industry

Fig. 10.1 Hypothetical changes in the water-use of individual trees (L-tree-1) compared to a commercial plantation (mm), relative to increases in stand density. Current and future scenarios under intensive biomass production are illustrated

is restricted by legislation (National Water Act of 1998, Section 36), which, amongst other aspects, declares commercial plantation forestry to be a Stream — flow Reduction Activity (SFRA) due to the high water-use of forest plantations and their impact on catchment water yields (Dye and Versfeld 2007). However, with the resultant efforts to maximise biomass production in intensively managed commercial forests, it is important to consider the associated hydrological impacts. This section considers the potential hydrological impacts, particularly streamflow changes, likely to be associated with intensive woody biomass production at a landscape scale. The primary land-use changes predicted to occur under intensified biomass production, and for which hydrological impacts need to be considered, include increased stand densities, shorter rotation lengths, earlier canopy closure and changes in site/species preferences.

Thomas Seifert, Pierre Ackerman, Paxie W. Chirwa, Clemens von Doderer, Ben du Toit, Johann Gorgens, Cori Ham, Anton Kunneke, and Martina Meincken

1.1 Woody Biomass — An Antiquated or a Modern Source of Energy?

Bioenergy production from wood is one of the oldest forms of energy and it was for a long time considered a primitive energy source in many industrialised countries. However, it is currently experiencing an increase in attention worldwide. Consid­ering its importance and history, it is astonishing that the widespread cognizance of wood as an important energy source in modern times is a recent phenomenon. It has been mainly driven by the pressure of diminishing fossil fuel resources in industrialised countries, as well as the wish to become more independent from nuclear power and its risks in some developed countries. In addition, amongst other renewable energy sources, bioenergy was identified as an alternative to

T. Seifert (H)

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

P. Ackerman • B. du Toit • C. Ham • A. Kunneke • M. Meincken Department of Forest and Wood Science, Stellenbosch University, Private Bag X1, 7602 Matieland, South Africa

P. W. Chirwa

Forest Science Postgraduate Programme, University of Pretoria,

5-15 Plant Sciences Complex, Pretoria 0028, South Africa e-mail: paxie. chirwa@up. ac. za

C. von Doderer

Department for Agricultural Economics, Stellenbosch University, Private Bag X1, 7602 Matieland, South Africa

J. Gorgens

Department of Process Engineering, Stellenbosch University,

Private Bag X1, 7602 Matieland, 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__1,

© Springer Science+Business Media Dordrecht 2014

fossil fuels, which could also help to prevent furthering an anthropogenic climate change by attempting to reduce greenhouse gas emissions. Currently, two different development routes are recognizable, which appear to go in opposite directions and result in competition for land resources in tropical countries. One route is driven by developing countries and the other by developed countries. Globally, wood is the most important source of renewable energy and is used to produce more energy than all other renewable energy sources combined (ren21 2013; FAO 2011). According to (FAO 2011), the global annual woodfuel consumption, which comprises fuelwood, charcoal and other wood based energy sources, sums up to 1.87 billion m3. Of this amount, 13 % are consumed in the tropical and sub-tropical regions of America and the Caribbean region, 30 % in Africa and 30 % in Asia and the Pacific region. In total, almost three quarters of the global woodfuel consumption occur in tropical countries.

An apparent challenge in the modelling of biomass is heteroskedasticity. This effect of growing variance with growing dimension of trees is characteristic of tree data. The problem is that heteroskedasticity (in-homogeneity of variances) violates a basic assumption of ordinary least squares regression (van Laar and Akga 2007).

To solve this problem, transformations are often applied to the data, which have the positive side effect that the well-established framework of linear regression can be used instead of nonlinear statistics. However, this transformation process comes with the flaw that the estimation is biased after back-transformation and has to be corrected. Different methods have been suggested for bias correction (Finney 1941; Baskerville 1972; Yandle and Wiant 1981; van Laar and Akqa 2007). Alternative estimation methods to ordinary least squares regression have been proposed to deal with the problem of heteroskedasticity. Linear weighted least squares and iterative non-linear least squares regression (for nonlinear relationships) methods showed good results (Verwust 1991; Parresol 2001). With growing computing capacity weighted nonlinear least squares estimation might be the most elegant choice to avoid transformation bias and heteroskedasticity (Schabenberger and Pierce 2002).

Pierre Ackerman, Bruce Talbot, and Bo Dahlin

6.1 Introduction

As with conventional timber harvesting and transport, the selection of machine systems for biomass production is often based on local availability, traditional harvesting methods and systems and the innovative spirit of entrepreneurs. However, piecing together an optimal biomass harvesting and transport systems to fulfil sustainable biomass supply requires substantial knowledge and insight into and of the whole biomass supply chain. When considering the number of potential options available at any decision point in the chain, it becomes apparent that biomass supply chains are in fact unidirectional supply networks and not single or unique chains. The best employment of production factors represents the minimum cost flow through the network, from standing tree to boiler grate. Knowledge of the options available and the consequence of employing each of these is therefore important in plotting the best way forward through the network.

Biomass production networks are characterized by a number of state and form combinations. The required state or form of the biomass, e. g., Full-tree (FT — felled trees with branches and top intact), Tree-length (TL — trees felled, debranched and top removed), Cut-to-length (CTL — log assortments), and comminuted material,

P. Ackerman (H)

Department of Forest and Wood Science, Faculty of AgriSciences, Stellenbosch University, Stellenbosch, South Africa e-mail: packer@sun. ac. za

B. Talbot

Forest Technology and Economics, Norwegian Forest and Landscape Institute, As, Norway e-mail: bta@skogoglandskap. no

B. Dahlin

Department of Forest Sciences, Faculty of Agriculture and Forestry, University of Helsinki,

Helsinki, Finland

e-mail: bo. dahlin@helsinki. fi

T. Seifert (ed.), Bioenergy from Wood: Sustainable Production in the Tropics, Managing Forest Ecosystems 26, DOI 10.1007/978-94-007-7448-3__6,

© Springer Science+Business Media Dordrecht 2014 at each stage in the network (e. g., at stump, roadside landing, terminal, plant) determines, or is determined by, the production methods. Some of these can be directly linked in function and time, while others can be totally detached. A full year may pass between extraction and processing of stumps while in some cases hardwood trees can be felled, chipped and combusted on the same day.

Almost all final consumption plants, whether for combustion or as a raw material in further processing to; e. g., briquettes or pellets, requires biomass in a chipped or crushed form. This process of conversion is called comminution. A challenge for the operations manager is determining at what stage in the network comminution should happen. Every alternative has consequences for the choice of harvesting, extracting, processing and transport equipment. In the following overview, examples of a supply network in which comminution takes place at each cardinal point; in­field, at roadside, at a terminal, and at a conversion plant are provided. This chapter provides the reader with broad insight into making these comminution decisions through discussion of the positive and negative aspects at each of these cardinal points.

The proliferation of publications, trade fairs, seminars and internet sites (e. g., www. forestenergy. org) providing information on biomass production equipment and machinery, and the rapid technical developments that are being undertaken limit the relevance of a detailed technical description. In the following section, the working principles and intentions behind the main categories of equipment and machinery are presented and the reader is urged to keep abreast of developments through other media.

Thermal conversion of biomass to liquids can proceed via non-pyrolytic processes, in which the feedstock is directly heated in a liquid medium that may or may not interact with the biomass (Cheng et al. 2010; Klass 1998; Titirici et al. 2007). Low temperature and atmospheric pressure in the presence of a solvent with acidic or basic catalysts can be used, but these solvents have to be recycled, rendering the process energetically and financially costly. The use of higher temperatures and pressures in water have shown promising results and recently an increase of inter­est regarding hot-compressed or sub-/supercritical water technologies (hydrother­mal technologies) for biomass conversion has appeared (Peterson et al. 2008). Hydrothermal processing can be divided into three main regions, namely: lique­faction, catalytic gasification and high-temperature gasification, depending on the

temperature and pressure conditions (Klingler and Vogel 2010). The production of biocrude, aqueous organics, combustible gases (H2, CO, CO2, CH4) and light hydrocarbons is expected.

Ash is the inorganic residue such as silica, potassium, calcium, sulphur and chlorine that remains after combustion at high temperatures and constitutes between 0.2 and 20 % of the biomass weight. In general most biomass contains similar levels of C, H and O, but the amounts of Si, Ca, Mg, K, P, S, Cl, Al, Fe and Mn, or heavy metals such as Cu, Zn, Co, Mo, As, Ni, Cr, Pb, Cd, V and Hg can differ widely (Obernberger et al. 1997).

The ash content of different types of biomass used in South Africa for energy conversion is given in Table 8.5.

A simple index of nutritional sustainability has been proposed by du Toit and Scholes (2002) to gauge the nutritional sustainability of a variety of management regimes across different site types. While this is a fairly coarse indicator (it does not take transformations within the system into account) it is comparatively easy to use because it requires estimates of only (a) the larger input-output fluxes and (b) the major system nutrient pools sizes of the macronutrients. These can be estimated to an acceptable degree of accuracy in many regions of the world. Minor nutrient fluxes (such as weathering rates in very old soils) does not have to be gauged to high degrees of accuracy as they will not materially influence the system. Du Toit and Scholes (2002) proposed to express the net nutrient output from a system as a fraction of either (1) readily available or (2) potentially available nutrient pools in the system, to judge potential short — and long term effects. The index of nutritional sustainability thus developed carries the acronym pINS, where:

Net annual nutrient loss

p(INS) = — log!0 ‘

In its original form, du Toit and Scholes (2002) made provision for the nutrient pool to be either can be calculated as the readily plant available fraction or the long term (potentially) plant available fraction. We have used the fraction of the nutrient pool that is likely to be available to trees on a time scale of months to several years, because estimation of the long term potentially available pool sizes requires more developmental work.

Notes:

• In most intensively managed forestry and agricultural systems, there is a net loss of nutrients over time until such time as ameliorative action is taken.

• If the net input-output budget does not constitute a loss, it is simply reported as a gain and the pINS index is not calculated.

• A value of 1 (log scale) has been tentatively chosen as a value that should raise a red flag (i. e. if the net nutrient loss is more than 1/10th of the readily available nutrient pool) as defined by du Toit and Scholes 2002, the site may be at risk of nutrient depletion if the current management regime continues to be implemented.

• A feature of the pINS index is that different scenario’s can be developed, for example where the portion of biomass harvested is increased or the rotation length is shortened (as is likely to happen in bio-energy crops), and the new scenario’s can be compared with conventional systems.

This approach was developed further by Dovey and du Toit (2006) and by du Toit and Dovey as part of a more comprehensive study reported by Ackerman et al. (2013) dealing with nutrient fluxes and nutrient pools, respectively, in South African short-rotation plantation systems. Ackerman et al. (2013) chose three scenario’s each for short-rotation pine and eucalypt systems as shown in Table 10.2. These scenario’s were applied to 28 short-rotation pine sites and 21 short-rotation eucalypt sites in Southern Africa for which adequate data was available.

The pine sites in the study of Ackerman et al. (2013) virtually all showed net gains in N and P with relatively higher pINS values for K, Ca and Mg than the eucalypt sites. The main reason for this is twofold: Firstly, the longer average rotations of pine pulpwood (18.0 years as opposed to 7.1 years for eucalypt sites tested) has the effect that harvesting outputs are offset by a larger number of years’ worth of atmospheric deposition. The regions where most of the pine test sites are located receives higher loads of atmospheric deposition than other remote rural locations, due to its proximity to a large number of coal-fired power plants (Olbrich 1993; Lowman 2004). Secondly, the pine sites are mostly located on more fertile sites in the region (clays and loams with high organic matter contents in the topsoils) whereas many of the eucalypt test sites in the test battery are located on sandy soils with low levels of organic matter. Nonetheless, the case study does illustrate the relative resilience from the most vulnerable to the more resilient sites in the region.

A summary of the results for the eucalypt sites is presented in Fig. 10.9, where it can be seen that the pINS value frequency curves all shift to the left (lower pINS values) when moving from Scenario A via B to C. This means that under scenario C (whole tree harvesting with 75 % efficiency), a large number of stands are coming close to a situation where nutrients may be depleted over the scope of several rotations unless corrective action is taken. Many tropical soils under short-rotation plantations have undergone more intensive leaching and are substantially poorer in nutrient pools and organic carbon, than Southern Africa’s eucalypt sites presented in the case study in Fig. 10.9 (Gonsalves et al. 1997; Deleporte et al. 2008; Tiarks and Ranger 2008). It follows that intensively harvested bio-energy plantations on infertile sites are at much higher risk of nutrient depletion than is the case for the Southern African eucalypt data set.

It is important to keep in mind that increasingly intensive harvesting regimes and shortened rotations may result in a net loss of nutrients in many plantations, yet this may not have any immediate effect of decreasing the subsequent rotation’s productivity. This may happen because the new rotation may still have access to sizable pools of readily available nutrient reserves on the site. Furthermore, there may not always be a positive growth response when the net loss of nutrients are replaced (e. g. by fertilization). Most intensively managed, short-rotation plantation forests respond mainly to macronutrient additions of N and P (Gonqalves et al. 1997). Indeed, it is only after several rotations of intensive biomass harvesting in plantations and/or plantations grown on poor soils that widespread responses to the addition of base cations started to become common (Gonqalves et al. 2008a). In short rotation tree stands where fertilization regimes are very basic or non-existent, there may be a net loss of several nutrient elements, and although there may not be an immediate growth response to (say) replacing Ca lost during harvesting, there will still be a constraint on the ability of the site to supply Ca in successive rotations. Furthermore, Laclau et al. (2010a) have presented evidence to show that short-rotation plantations of eucalypts may be capable of extremely efficient nutrient conservation and cycling, but that many such plantation systems in the tropics apparently depend on soils having been pre-enriched with nutrients by the natural vegetation before the plantations were established. For these reasons, and because bio-energy plantations are usually grown on very short rotations with large percentages of the biomass harvested, it would be wise to monitor nutrient exports in bio-energy plantations very intensively, and to upgrade the fertilization regime where necessary.

One of the most important measures to ensure sustained productivity on infertile sites, is the conservation of organic matter in the system (Laclau et al. 2010a, b).

Fig. 10.9 pINS indices for five macronutrients (after Ackerman et al. 2013) calculated for 21 short-rotation eucalypt crops under scenarios A, B and C (Refer to Table 10.2 for scenario explanation)

Several experiments in a tropical network study (reviewed by Nambiar and Kallio 2008) tested the effects of slash management (and in particular, slash removal) on short rotation stand productivity. The slash removal treatments in this trial series constituted the removal of the slash plus the un-decomposed portion of the forest floor, which is a more intensive treatment than whole aboveground tree harvesting (which effectively only excludes the return of harvesting residue to the soil). However, it does give an indication of what can potentially be the result after successive rotations of either whole tree harvesting or some form of intensified biomass harvesting. The stand productivities (stem wood volume production at rotation end) of eucalypt case studies in this network of trials under slash retained and slash removed scenario’s are given in Fig. 10.10.

The case studies by (Deleporte et al. 2008 (Congo); du Toit et al. 2008 (South Africa); Gonsalves et al. 2008b (Brazil); Mendham et al. 2008 (Manjumup, Australia)) all showed decreases in forest productivity following removal of har­vesting residue and un-decomposed material in litter layers. The largest decline in stand productivity due to slash removal occurred in sandy soils (arenosols) and dystrophic loams (oxisols), both with low topsoil organic matter contents. This result underscores the point that a combination of poor soils, short rotations and intensified biomass harvesting means that many bio-energy plantation systems in the warm climate countries will not be nutritionally sustainable in the long run unless significant additional nutrient inputs are made. Inputs may be in the form of fertilizers, and/or ash replacement (from biomass burners), and/or incorporation of N fixation, either through mixed cropping (Binkley and Giardina 1997; Bouillet et al. 2012), or crop rotations with symbiotic N-fixers in the broader silvicultural management system.

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.

Remote sensing is based on the principle of measuring reflected electromagnetic energy from a target under investigation. The source of the energy can either be energy (photons) from the sun or an artificial source, like a laser pulse in the case of a LiDAR instrument. However, the energy measured by the sensor interacted with the target, as well as having travelled through layers of the atmosphere and sensor components. Pre-processing, or the process of transforming the measured energy to levels as it were just after being emitted or reflected from/off the target, therefore is required before data are used. This process ensures compatibility between different sensors and even different times, and includes efforts related to atmospheric compensation, e. g., conversion from digital numbers (DN) to radiance (W m“2) to reflectance (unitless). Feature extraction then measures the object under investigation (target), which could be a pixel, object, single tree crown, stand of trees, etc. The process is validated by comparing the extracted result with measured data from a field survey.

Inventory based on remote sensing data nearly always involves a multi-phase approach with a terrestrial component, used for calibration. In regional assessments, assumptions about the terrestrial component are made and the remote sensing

information is interpreted in accordance with measured point samples. Two phase inventories seem to be the most common option, in which detail terrestrial infor­mation is collected at a few sample locations, which is then used as training and verification of the image processing process (Fig. 2.4). A decision should be made during the planning phase on the appropriate sensor and time of acquisition, as well as the sampling strategy for field survey or ground truthing. In short, all remote sensing approaches or remote sensing inventory models should be calibrated and validated using a sample of field-measured values, especially when a model is used across different regions, site conditions, or species.