Category Archives: Bioenergy from Wood

Management Practices for Improved Productivity in Extensively Managed Woodlands in Southern African

The management systems are designed to increase the woody biomass of different woodland systems. The response of the woodlands in terms of biomass accumu­lation varies from place to place as it is a function of a number of factors which include rainfall, soil type, temperature and management practices. Because of the low returns from the dry forest and woodlands in terms of physical products, some of the management practices are deemed uneconomic. The management practices are designed to meet specific tangible products (see Chidumayo et al. 1996; Chirwa et al. 2008a). Wood production, for example, in miombo woodland is also affected by the way miombo trees respond to harvesting. Responses depend on the phenological state, degree of resistance to fire, ability to resprout, seeding patterns, seed germination characteristics and seedling development (Chidumayo et al. 1996). Miombo woodland usually responds to wood harvesting by coppice regeneration, but the rate of regeneration is affected by human activities (Chirwa et al. 2008b). The best harvesting techniques and management practices in the post­harvest era are those that promote regeneration.

Storage of Chips at Roadside Landing

Irrespective of whether the material was chipped in the stand and extracted to the landing, or chipped at landing, the storage of chips at roadside landing is normally a short term process but with numerous implications. As a result of the chipping and/or extracting process, the material can either be stored on the ground or in some form of bin container.

• Chips stored on the ground

The benefits of storing chips temporarily on the ground are that there is no direct coupling with transport and that there is a large space/volume capacity. This option is good for high performing chipping production systems with transport constraints. The immediate disadvantages of chipping onto the ground are that the loading of chips requires specialized equipment or additional machinery and that some of the volume must be forfeited in ensuring that chips contaminated with soil and stones are left in situ. It is therefore not a suitable method when harvesting biomass from small, dispersed stands.

• Chips stored in containers

The chips that have either been extracted from the stand and transloaded into a container or they have been chipped directly into a container at the landing. In the first instance, the availability of containers has to match the performance of the production system or a very high ‘interference’ penalty will be paid (Talbot and Suadicani 2005). Irrespective of production system, the assumption underlying this storage method is that transport is imminent. Chipping into containers with buffer capacity requires a lower performance (i. e., cheaper) chipper. Full containers left for a weekend for example, should be covered or fitted with sufficient drainage if there is a possibility of rain.

Economies of Scale

The potential for small-scale, rural application of bioenergy production processes depends to a large extent on the economic costs associated with small-scale application, which will be considered here on the basis of several examples. The economic aspects presented here should be integrated with the biomass harvesting and transport aspects considered in Chap. 6.

Impact on Environmental and Social Sustainability

While biofuel production offers socio-economic opportunities it also presents environmental and social dilemmas. It has been claimed that if biofuel production leads to land degradation and deforestation any potential carbon savings benefits will be compromised (Gamborg et al. 2011). This compromise is illustrated by the clearing of forests to make way for oil palm plantations where it will take up to 150 years for the carbon savings from palm oil harvesting to replace the carbon lost from forests (Friends of the Earth 2010).

Biofuel production has the potential to lead to soil degradation and nutrient depletion (Chap. 10). Chemical inputs including fertilisers and pesticides can contaminate soils and lead to soil erosion. The removal of crop residues for co-firing may cause further declines in soil fertility (Cushion et al. 2010). Land clearing for biofuel plantations can cause considerable topsoil run-off and increase sediment loads in rivers. Soil erosion is five to seven times greater during clearing while sediment loads in rivers increase with a factor of four. Soil erosion is especially problematic when oil palms are planted on steep slopes and at high altitude (Friends of the Earth 2005).

Biofuel has also been described as one of the thirstiest products on the planet because of the amount of water needed to produce fuel. One litre of biodiesel from soya is estimated to require 9,100 L of water. A litre of bioethanol from maize requires 4,000 L of water and a litre of bioethanol from sugarcane can also use as much as 4,000 L of water (Friends of the Earth 2010).

Many bioenergy crops must be produced in large monocultures to be profitable. One of the risks of large scale bioenergy development is that land will be concentrated in the hands of a few wealthy companies or individuals and that small scale farmers will lose their land. Small scale farmers might have limited or no accesses to the capital required for large biofuel operations and as such miss out on investment opportunities and associated profits. The only benefit for many small scale farmers in biofuel production is to lease their land to producers. In Indonesia where 44 % of productive oil palm plantations are managed by small scale farmers there have been persistent reports that such farmers receive minimal compensation for their produce and remain in debt to the palm oil companies (Cushion et al. 2010).

In South Africa a survey amongst communities who could become involved in a potential bioenergy project in the Eastern Cape Province revealed that community members were concerned about the potential environmental, social and ecological risks associated with such a project. Some of the specific concerns included:

• fear of losing control over land seen as their inheritance;

• breakdown or distortion of the social fabric/character of the community;

• enslaving by big business and falling into poverty;

• water pollution and environmental damage;

• migration of other people to the communities; and

• unequal share of benefits from the project (Amigun et al. 2011).

The involvement of rural communities in the production of biofuels cannot be evaluated fully through simplistic proxies such as the number of jobs created and average wages paid to plantation workers. Long established and indigenous rural communities are experienced in living in a highly variable and seasonal environment and their traditional methods of survival are based on managing risk rather than optimising income. A switch from subsistence farming to farming of a cash crop brings with it an increased dependency on outside agents. This may be fine as long as there is a steady stream of income but when the prices of cash crops drop it might lead to food insecurity and increased poverty. It is therefore necessary to consider in much more detail how the livelihood strategies and outcomes of rural communities will change with a change in land ownership, land management and land use associated with a switch from the production of subsistence food crops to biofuel cash crops (Van der Horst and Vermeylen 2011). When there are uncertainties regarding the impacts of biofuels on environmental and social sustainability it is important to consider that changes in land use could significantly outweigh any carbon benefits that may result from planting biofuels (Cushion et al. 2009). The following section will consider ways of improving the benefits that biofuel production can play in the livelihoods of the rural poor.

Assessing Lignocellulosic Bioenergy Systems Using LCA — A Case Study

The following section provides a recent example for applying LCA in a bioenergy context: Following scheduled power cuts in 2007 due to outdated electricity infrastructure and low capacities of electricity generated, decision makers of the Cape Winelands District Municipality (CWDM), South Africa, seeked to investigate implementing local renewable bioenergy systems. By introducing alternative energy systems, the aim was to improve energy security and to reduce the dependency on the national energy supplier, ESKOM, while minimising the environmental impacts. A previous study (Von Doderer and Kleynhans 2010) identified bioenergy systems using lignocellulosic biomass grown in a short-rotation system as one of the most promising options for the CWDM. Thus, the LCA approach was used to determine the environmental performance of viable lignocellulosic bioenergy systems.

Modelling and Simulation of Tree Biomass

Thomas Seifert and Stefan Seifert

2.1 Introduction

A primary objective of sustainable bioenergy production is to quantify the available resource supply because all further planning of the value chain hinges on the available biomass that can be converted. Since biomass is costly to transport, the spatial quantification of the resource is also important. Thus, modern approaches to biomass supply chain management must embrace the resource quantity and location as a key element of the supply chain. Data on resource availability are usually obtained from different sources such as remote sensing and terrestrial inventories, as discussed in Chap. 2, which provide information on the spatial distribution of forests and trees and their dimensions but are, as such, not capable of estimating biomass directly with the necessary accuracy. Thus the main purpose of the application of modelling and simulation techniques in this context is the estimation of the biomass resource from broadly available tree and stand variables. This auxiliary information could be sourced from inventories and remote sensing or could be provided by model projections from growth models to estimate the biomass availability.

Biomass modelling is a typical upscaling process based on statistical modelling. Depending on the modelling and sampling method of choice, different upscaling steps are involved. An upscaling process normally involves two steps: upscaling from the biomass samples to the individual tree and from the tree to the stand (Fig. 3.1).

Each upscaling step is normally characterised by sampling and a regression modelling components. The biomass models are first established on a subset of data of a bigger population with independent variables that are easier to measure than

T. Seifert (H) • S. Seifert

Department of Forest and Wood Science, Stellenbosch University, Private Bag X1, 7602 Matieland, South Africa e-mail: seifert@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__3,

© Springer Science+Business Media Dordrecht 2014

sampling

Подпись:the biomass itself. In a next step this auxiliary data from the bigger population of interest is entered into the model to estimate the biomass for the entire population. This applies equally to upscaling from samples to the individual tree and from trees to the stand.

Each upscaling step is subject to a specific error. To assess the uncertainty of the biomass estimation the error of the biomass has to be estimated as well. Since upscaling can be complex as a result of the combined involvement of various regression models and sampling procedures that all contribute to a specific error budget, the calculation of the error-propagation forms an essential part of biomass estimation.

This chapter follows the structure of the upscaling concept, describing the sampling and modelling for upscaling from samples to trees in Sect. 3.2 and details the modelling process for upscaling from a tree to a stand in Sect. 3.3. This is followed by Sect. 3.4 on error-propagation where the error budgets of both upscaling steps are integrated. Finally, some implications of using biomass models in growth simulators are discussed in Sect. 3.5.

Optimising Growth Conditions at Time of Establishment Through Harvest Residue (Slash) Management and Soil Tillage

Site preparation techniques (soil tillage and/or slash management) as well as harvesting impacts management aim to improve conditions for early tree and root growth by improving some or all of the following properties: soil aeration in waterlogged soils (Zwolinski et al. 2002), water infiltration, microclimatic conditions near the transplant (Carlson et al. 2004), soil nutrient mineralisation rates (du Toit and Dovey 2005; du Toit et al. 2008), or by ameliorating root growth impediments such as hard-setting soils (Gonsalves et al. 2008), semi-impenetrable or compacted layers (Smith et al. 2000, 2001) or competing vegetation (Little et al. 2001). Experimental evidence show that, where site-appropriate slash management or site preparation techniques have been applied, it had a significant impact on transplant survival and eventually on stand productivity at time of harvesting (du Toit and Dovey 2005; Gonqalves et al. 2007; du Toit et al. 2010). Conversely, the application of intensive tillage operations to situations where it did not alleviate growth-limiting situations have been shown to result in poor long-term growth responses, which, considering the high input costs, may be uneconomical (Smith et al. 2001; Lacey et al. 2001; Carlson et al. 2006; Lincoln et al. 2007). The key is thus to recognise opportunities where site preparation activities can successfully be applied and to rather implement minimum cultivation and tillage and slash conservation measures on site where the risks are high or where responses are likely to be small (Smith et al. 2000, 2001; Gonqalves et al. 2008). Some of the most important findings emanating from intensive experimentation on these issues in South Africa, Brazil, South-eastern USA and Australia are highlighted below.

When afforesting for the first time into dense native vegetation, such as grassland, it is advisable to implement intensive surface cultivation techniques, provided that the slope is not too steep and that so-called duplex soils are avoided (i. e. light textured topsoils such as sands/loamy sands with an abrupt transition to a heavy texture such as clays or silty clays). The surface cultivation eliminates the competing vegetation and stimulates an increase in nutrient mineralization after tillage, which will boost early tree growth. Basal area improvements at maturity of 11-52 % have been recorded in South Africa for a range of eucalypt stands with this treatment (Smith et al. 2000; du Toit et al. 2010).

In re-establishment situations, significant responses to surface cultivation are far less likely due to the beneficial effect of old root channels from previous crops, especially if soil structure or consistency does not limit root growth (Smith et al. 2001; Nambiar and Sands 1990), and the (generally) lower levels of competing vegetation that can form a dense root mat. In all these cases, minimum cultivation is recommended (Gonsalves et al. 2008). In exceptional cases, where soils have a hard — setting consistency (cohesive soils) or have suffered compaction, surface cultivation techniques such as shallow ripping could improve tree growth significantly. This approach is especially attractive likely on short rotations where drought risk in the mature phase of the crop is less likely (Gonqalves et al. 2008).

Early growth responses to deep subsoiling have been recorded on many soil types, only to diminish over time and becoming insignificant or uneconomical during drought periods or by rotation end (Smith et al. 2001; Gonqalves et al. 2008). Deep subsoiling is only economically justifiable in highly specialised situations, e. g. where inaccessible layers of soil or highly weathered saprolite can be made acces­sible to tree roots following subsoiling. Additional examples of inappropriate soil tillage operations are deep subsoiling operations in soils that have no macrostructure or hard-setting attributes, or excessive tillage and cultivation of duplex soils that are highly erodible. Soil quality can also be degraded by nutrient depletion (e. g. high — intensity slash burning on nutrient poor soils) or by excessive and frequent tillage of soils that will speed up mineralisation and subsequent leaching losses of soil carbon and nitrogen in young stands (Smith et al. 2001;du Toit et al. 2001,2010) This issue is discussed more fully in Chap. 10.

Land surface modifications such as ridging and trenching can be highly beneficial where permanent or prolonged waterlogging (e. g. on flat slopes) limits root aeration and nutrient availability (Zwolinski et al. 2002; Kyle et al. 2005), especially in short rotation crops. However, this practice is certainly not suitable for moderate to well drained soils (especially those in dry climates), as it will render stands more vulnerable to drought stress.

In the preceding paragraphs, we discussed the effects of slash management operations in the inter-rotation period on nutrient supply to newly established tree stands. In very intensive biomass production systems, where most of the above ground biomass is harvested in ultra-short rotations, minimal harvest residue will remain on site. Furthermore, ultra short rotations will mean that between roughly 20 and 50 % of the stand’s lifespan is spent in a pre-canopy closure state, where litterfall is nil or minimal. It follows that the forest floor will most probably be greatly reduced in size (compared to longer rotations) because litterfall inputs are lower and mineralisation rates in the semi-open canopy are usually faster due to increased temperature and water availability. An international trial series in tropical climates simulating such an intensive utilization scenario has recently been completed where harvest residue plus forest floor material was removed in certain treatments. Results showed that removal of residue plus forest floor will almost certainly result in depressed growth of subsequent rotation(s) of trees on nutrient poor soils (Deleporte et al. 2008; Gonsalves et al. 2007; Mendham et al. 2008), but interestingly, also on sites that are nutrient rich by forestry standards (du Toit et al. 2008; Mendham et al. 2008). The reason for this seems to be not just impact of removing a certain percentage of the total nutrient pool in the system, but rather the removal of a substantial percentage of the readily mineralizable nutrient pool (du Toit and Dovey 2005; du Toit et al. 2008). This finding has serious implications for the long-term nutritional sustainability of biomass harvesting systems that collect all (or most of) the above-ground biomass, and will be discussed further in Chap. 10. The removal of predominantly woody material from the harvesting residue (i. e. leaving the fine twigs and foliage plus the forest floor in situ) has shown much smaller impacts and can potentially be managed sustainably with much smaller external nutrient inputs (Dovey 2012).

At the landing or terminal

Solid content conversion rates for stacked harvesting residues, FT, or roughly debranched stems should be developed regionally and according to species. Apart from errors arising from edge effect, a good relationship can be obtained between running metre of stacked volume and solid volume. A simple way of developing such conversion rates is by chipping the stack into a container of known volume. The dry mass of the chips arising from this process is then compared with the dry mass of the tree species in question in determining solid volume equivalents. It should be remembered that high branch content will yield a dry mass higher than the mean for stem-wood for the same species.

6.8.3.2 On Site (pre — or postharvest)

On site estimation of biomass involves an enumeration of standing trees or residues/stumps after harvesting. Methods of ensuring reliable estimations are given in Chap. 3. Some adjustment needs to be made as varying percentages of the measured volumes are utilisable. For harvesting residues after the CTL method, about 70 % of volume is typically recovered.

. Density

Table 8.2 Typical density values of South African

Species

Density (kg/m3)

Eucalypts

700-800

biofuels as determined in

house for various samples

Acacias

600-900

Pines

400-550

Other woody biomass

350-500

(shrubs etc.)

Bark

450-500

When looking at biomass, however, care has to be taken as to which mass and volume are regarded, as both parameters depend on the moisture content, wood structure (earlywood/latewood, etc.) and chemical composition.

The only reproducible density values are the “ovendry” density, which is

r Ovendry Weight (8 3)

Ovendry Volume

and the basic density, which is defined as

R = Ovendry Weight 4)

Fully saturated Volume

Typical average density ranges of woody biomass regarded in this book are given in Table 8.2.

As with the moisture content a large variation of density can be found within one tree. Generally juvenile wood has a lower density than mature wood, heartwood is denser than softwood (this is more pronounced in hardwoods) and in softwoods the density decreases with height (Tsoumis 2009).

For bioenergy purposes the bulk density of chips is determined via shock impact (BS EN 15103). A cylindrical vessel with known volume is filled to the rim with chips and shock exposed (dropped from a certain height) to compact the chips. The vessel is then either refilled to maximum level or the surplus material is removed. The basic bulk density of the wet chips is then given by:

BDw = (m2 — mi) /V (8.5)

with m1: weight of vessel, m2: weight of vessel + biomass, V = inner volume of vessel

The bulk density of the dry chips can be calculated if the MC is known:

BDd = BDw * (100 — MC) /100 (8.6)

The Diana Smith method (Smith 1959) is often used to determine the exact basic density of oddly shaped samples, such as wood chips. This method is more time consuming, but disposes of the volume in the equation. The samples are submerged

Wood type

Cellulose (%)

Hemicelluloses (%)

Lignin (%)

Hardwood1

40-44

15-35

18-25

Softwood1

40-44

20-32

25-35

Pine2

26.4

44.7

18.6

Eucalyptus2

27.7-25.9

49.5-57.3

13.1-16.8

Black Wattle3

17.9-21.2

63.9

12.7

Table 8.3 Typical distribution of cellulose, hemicelluloses and lignin in wood

From: ^Walker (2006), 2Hamelick et al. (2005), 3Kumar and Gupta (1992)

Подпись: R = image105 Подпись: -cm3 Подпись: (8.7)

in water and cyclically exposed to pressure (to get water in) and under-pressure (to get air out). The basic density is then calculated according to:

msat = saturated weight; m0 = ovendry weight

The volume to weight ratio of most biomass is generally rather unfavourable, which decreases the possible energy output. The solid content of wood chips is only around 0.4, which is a major reason for densiflcation (e. g. pelletising). This decreases transport and storage costs and increases the energy density at the same time. The calorific value increases linear with density, because more material is available (Kataki and Konwer 2001; Munalula and Meincken 2009). For combustion and gasification the biomass should therefore have a density as high as possible, whereas for fermentation and digestion a low density is more desirable, because this is correlated to a looser wood structure, which can be degraded more easily (see Chap. 7).

The Biodiversity Threat of Commercial Timber Plantations

With the growth of the global human population, the demand for food, wood and fuel will increase, so more areas of the world will turn to intensive agriculture and timber cropping systems like plantation forestry (Cubbage et al. 2010). Plantation forestry is a serious risk to global biodiversity, as the plantations themselves are often non-native and contribute little to biodiversity (Samways and Moore 1991; Pryke and Samways 2009; Bremer and Farley 2010). Biodiversity can be directly impacted by the plantations themselves, especially as large amounts of natural habitat are often transformed into plantations. Even so there are often natural areas left between plantation blocks in planted areas. These are left aside as areas of high conservation value such as protected grasslands, wetlands or indigenous forests, or for management requirements such as firebreaks, power lines and vehicle tracks (Samways et al. 2010). While not directly affected by the plantations these natural areas are often indirectly impacted through landscape fragmentation. This fragmentation isolates populations and leads to ecological relaxation, which is the loss of species from these fragments due to stochastic events or loss of ecological interactions leading to further losses of overall biodiversity. Abiotic disturbances caused by timber plantations such as water loss and soil nutrient depletion also complicate the conservation of biodiversity in and around plantations. Ecological networks (ENs) are a way to mitigate many of the adverse effects of plantation forestry on the local biodiversity (Samways et al. 2010). ENs work by connecting as many of the fragments together, although they need to be designed properly and be well managed to optimise their conservation value.