The only way in which a comprehensive understanding of nutritional sustainability of a particular forest system can be gained, is by studying the majority of the more important nutrient fluxes in the system as brought about by specific management regimes or operations, (e. g. Ranger and Turpault 1999; du Toit and Scholes 2002; Laclau et al. 2005, 2010a; Dovey 2012). However, it has been known for a long time that nutrient dynamics may change significantly with the stage of stand development (Miller 1995). It follows that such studies has to be repeated in time, (or perhaps be done in a chronosequence approach) to paint the full picture. This fact, coupled to the reality that many different site types (and potentially even more than one management regime per site type) will have to be studied, makes it a very daunting task.

Nutrient fluxes consist of some processes in the biogeochemical cycle that are comparatively easy to monitor, e. g. uptake from the soil to the plant or return to the slash/soil layer following clear felling, thinning or through litterfall and fine root turnover. However, it also includes more complex processes that are difficult to quantify, such as transformations in the soil that are specific to each nutrient and the prevailing microclimatic and edaphic conditions in a specific soil, which are largely responsible for plant nutrient availability (e. g. nitrogen mineralisation, sorption and desorption processes on soil phosphate, oxidation/reduction processes on soil sulphur, etc.) This chapter deals with these fluxes in a simplistic way (see Fig. 10.8), and the reader is referred to soil chemistry texts for more detailed descriptions of these processes.

The impact assessment phase (LCIA) follows the LCI and involves an assessment of all relevant environmental impacts associated with the input and emissions mapped in the LCI. The LCIA also covers other chemically related impacts like global warming and tropospheric ozone formation, as well as the physical impacts on the land and input-related impacts or the availability of resources (Birkved and Hauschild 2006; Wenzel et al. 1997; Hauschild and Wenzel 1998). The level of detail, choice of impacts evaluated and methodologies used depend on the goal and the scope of the study (ISO 14040 1997).

The purpose of the LCIA is to provide additional information to help assess the results from the LCI so as to better understand their environmental significance (ISO 14040 1997). Thus the LCIA should translate the inventory results into their potential impacts in what are referred to as the "areas of protection” of the LCIA (Consoli et al. 1993), i. e. the entities that are to be protected by using the LCA. Today there is acceptance in the LCA community that the areas of protection offered by the LCA are human health, the natural environment, natural resources, and to some extent, the man-made environment (Udo de Haes et al. 2002; Finnveden et al. 2009).

LCA software packages such as the PE International’s GaBi 4.4 or PRe’s SimaPro 7.3 (PRe 2012) offer a variety of LCIA methods, including CML 2001, Eco-Indicator 95, EDIP 2003, Impact 2002+ or TRACI. One of the most commonly used, the CML 2001 method, is a collection of impact assessment methods that restricts quantitative modelling to the relatively early stages in the cause — effect chain, to limit uncertainties and group LCI results in mid-points categories, according to themes.

An important consideration for the LCIA is the spatial differentiation concerned. As the impacts caused by an emission depend on the quantity of substance emitted, the properties of the substance, the characteristics of the emitting source, and the receiving environment (Finnveden et al. 2009). The site-generic approach (or global default) followed in current characterisation modelling includes only the first two aspects, inherently assuming a global set of average/standard conditions concerning the properties of the source and the receiving environment. For truly global impact categories like climate change and stratospheric ozone depletion, this is not a problem, since the impact is independent of where the emission occurs. For the other impacts modelled in the LCIA, however, the situation can be different. They are often regional or local in nature, and a global set of standard conditions can disregard large and unknown variations in the actual exposure to stimuli of the sensitive parts of the environment (Potting and Hauschild 1997; Finnveden et al. 2009). At the same time, spatial differences can be reduced in the case of sources from multiple locations, particularly when these result in uniform emission distributions.

The selection process of representative trees for a stand has to consider the number of trees that should be sampled and how they should be selected from the population.

The actual number of sample trees is determined by the level of precision required and the variability of the resource, with the principle that model bias should be entirely avoided as stated by van Laar and Theron (2004). There is always a trade­off between accuracy and efficiency of the sampling procedure. Higher accuracy results in a higher cost and time investment. Improving the standard error of the mean by 50 % requires a four times higher sampling number, since the standard error of the mean is inversely proportional to the square root of the sample size (Eq. 2.2). This well-known fact and the effort involved in biomass studies are a strong motivation for choosing a sampling number close to the theoretical minimum number, which is still representative of the population.

Sy = P (2.2)

where

Sy is the standard error of the estimate of the arithmetic mean of a population s is the estimate of the standard deviation of the population n is the number of samples

The standard error of the mean, Sy, is then multiplied by the appropriate z-score, obtained from the Normal-(z)-Distribution, e. g., 1.96 for a 95 % confidence level, to obtain the margin of error (ME). The margin of error defines the confidence interval, when subtracted from or added to the estimate, E.

The minimum sampling percentage for simple random sampling from large populations depends on the variance in the population and the accepted error probability of the outcome, defined by the chosen confidence limits (Eq. 2.3).

z ■ s [1]

E

where

N is the minimum number of samples to be representative of the population E is the margin of error, the maximum accepted difference between estimated (x)

and true mean (д) of the population. z is the z-score obtained from a Normal-(z)-Distribution s is the estimate of the standard deviation of the population

Usually the true variance is unknown, thus the sample variance has to replace the population variance when calculating the t-statistic or the z-score, as indicated in Eq. 2.4 (van Laar an Akga 2007).

As stated by van Laar and Theron (2004) “There is no universally accepted criterion for the size of the acceptable margin of error, which is a function of the population variance, sample size and the statistical risk of exceeding the margin of error.” The margin of error for volume calculations in forest inventory data should preferably not exceed 3-6 % of the mean (Smith et al. 2004; Bundeswaldinventur 2008 cited by Waggoner 2009). For biomass and carbon inventories sometimes higher error margins, ranging from 10-15 %, are accepted for the estimation (van Laar and Theron 2004; Hollinger 2008).

However, the minimum sampling number of trees has to be determined itera­tively. Stauffer (1982) suggested an iterative procedure and expressed the margin of error as a percentage of the mean, which requires the substitution of the standard deviation by the coefficient of variation, according to Eq. 2.4. An iterative determination of the sample size with increasing n, converges after a few iterations (for an example see van Laar and Akga 2007, p. 258).

where

n is the number of samples ta is the t-statistic at n-1 degrees of freedom s% is the coefficient of variance and E% the relative margin of error.

If variance information on biomass is not readily available, which might be typical, other methods can be applied like regression-based sampling. This method

is based on the use of an auxiliary variable, which is easier and cheaper to measure and which is ideally linearly correlated to biomass. For example, tree basal area (BA) lends itself as an auxiliary variable for biomass sampling, since it is highly correlated to tree biomass and easy to determine from stem diameter measurements, which can be conducted in high numbers. The process is then a two-phase sampling procedure, where first the population mean of the BA is estimated and then a sub­sample is drawn for biomass sampling. However, Cunia (1990) raised doubts about the unbiased nature of the sample if anything other than diameter at breast height (DBH) or height were used as auxiliary variables.

Once the minimum tree number for sampling has been determined, the selection of trees which represent the stand is a substantial task. It requires prior knowledge about the representation of the independent variables that will be used for modelling. Tree height and DBH were chosen as independent variables in this example. A DBH-height-plot produced from a preceding terrestrial inventory of the stand or the region where the function should be applied, is a good help to choose the sample trees. A way to obtain robust regression estimates, which can be applied in similar stands, is to divide the classes of DBH and height and sample in each class in a stratified sampling procedure, so that the selected sample trees cover the full range of DBH-height relations. This can be done visually, based on equidistant classes as indicated in Fig. 2.2, or alternatively, in a bivariate binning procedure via a
classification based on quantiles for the DBH and the height. If too few DBH-height pairs are available, a selection based on DBH-classes presents a feasible alternative.

Van Laar and Akqa (2007) refer to Attiwil and Ovington (1968), who compared different sampling methods for the estimation of biomass from an area and found that the regression-based method (N = 20), performed better than the estimation of the biomass from the mean DBH tree (N = 5) sampling of four trees in five DBH classes (N = 20) and regression-derived mean DBH tree multiplied by the tree num­ber. The other sampling methods produced biased results in the range of —8.5, —3.1, and —11.6 %. These results and the criticism of Cunia (1990) on importance sam­pling may serve as valid arguments to base tree selection on a regression approach that covers the spectrum of DBH-height relations in the population. This should also result in more generic models that are easier to adapt to other sites later on.

There are two different approaches to estimate biomass for forest areas. Both include a forest inventory component based on a sampling procedure. The first approach is based on dry mass/area relations, where upscaling to the stand level is done per sample plot first and then the area of sample plots is used as a ratio estimator to upscale biomass from the total sampled area to the total stand area. The second approach is based on single tree equations (Chap. 3) and relies on stem frequencies in diameter classes, as produced in the inventory. For each mean tree per diameter class, the biomass is upscaled with a ratio estimator relating the sampled tree numbers to the population tree numbers.

The following example illustrates the difference of the application of an estab­lished allometric biomass prediction model for Pinus radiata (van Laar and van Lill 1978) to estimate the total biomass based on different selections of trees from the population. The assumption that the model produces unbiased estimates for the population should fit the purpose here. The model is applied to estimate dry mass for a pine stand of one hectare in size, which was fully mapped for DBH and tree heights (Ackerman et al. 2013) to illustrate the influence of tree selection on the outcome of a model prediction. The application of an allometric equation to all individual trees serves as a reference. This is tested against the application of the biomass model, based on the diameter of the quadratic mean diameter tree (dq) and based on average dq trees in seven diameter classes. The diameter distribution of the stand is shown in Fig. 2.3.

The application of the functions for all 318 individual trees of that stand resulted in an estimated total stand dry mass of 424.99 Mg ha_1 (tons). The estimate based on the mean quadratic diameter was clearly negatively biased at 422.87 Mg ha_1 (—2.12 Mg ha_1), while the diameter class-based approach produced a nearly unbiased dry mass estimate of 424.88 Mg ha_1 (—0.11 Mg ha_1). It is obvious that the estimate of biomass for a full stand based on the dq does not take into account the nonlinear relation between diameter and biomass, and thus underestimates the contribution of the bigger diameter trees in the stand, while the estimate with diameter classes mitigated that effect to an acceptable degree. This effect can be expected to be much stronger for a more skewed diameter distribution.

A multitude of inventory techniques developed for volume estimation in forests can be easily adapted for biomass inventories. The same variables that are used for biomass estimation, namely DBH and height, are typically used to estimate volume. The interested reader might therefore consider some standard volumes on forest inventory, such as Kangas and Maltamo (2006), van Laar and Akga (2007), Mandallaz (2008), for detailed information.

More than 80 % of the rural population in sub-Saharan Africa is poor and traditionally relies on forests for their livelihoods (Schreckenberg et al. 2006); including slash and burn agriculture, charcoal and timber production. Fuel wood is one of the primary sources of energy for domestic use and processing (curing tobacco, drying fish, etc.) throughout the SADC region (Shackleton and Clarke 2007). It accounts for the highest percentage of the national energy budget in many Southern African countries namely; 85 % in Mozambique (Brigham et al. 1996); 76 % in Zambia (Chidumayo 1997); 95 % in Malawi (PROBEC 2009) and 52 % in Zimbabwe (Griffin 1999).

Charcoal production and the use of land for agriculture have resulted in defor­estation and forest cover loss (Brown 2001), and massive loss of fauna, flora and

high productive ecosystems (Syampungani 2008; Chirwa et al. 2008a; Syampungani et al. 2009). Large tracts of forestland are being converted to either agricultural fields or abandoned charcoal production sites. For example, the annual rate of deforestation per year between 1990 and 2000 ranged from 33,000 ha in Malawi to 445,000 ha in Zambia (FAO 2005). Mayaux et al. (2004) estimated forest area under agriculture to be 15.2 % in the Southern African region. Because of the nature of shifting cultivation that is widely practiced in the region, land under agriculture represents a number of cover types, including cropland, abandoned fields and fallow at various stages of recovery present; forming a typical mosaic landscape (Timberlake et al. 2010). Such mosaic landscapes have forest formations that are represented by tall, almost closed-canopy stands and many areas of cleared woodland for shifting cultivation and charcoal production (Syampungani 2008). This, to some extent, has affected the spatial integrity of most of the woodlands and forests in the region. When converting woodlands to agriculture not all trees are necessarily cleared and, as a result, cultivated land is often dotted with trees such as Adansonia digitata, Vitellaria paradoxa, Sclerocarya spp., Borassus spp., and Faidherbiaalbida (Timberlake et al. 2010).

Population pressure coupled with the drive to monocultures increased deforesta­tion in the region thereby contributing to loss of biodiversity (Geist 1999). The low rates of economic transformation to individual levels have led to widespread poverty in the region. This in turn has increased the demand for woodland resources, and therefore deforestation (Table 4.1). This is because the economies of the sub­Saharan Africa are still dependent on the premises of charcoal production, shifting cultivation and to some extent timber harvesting (see Syampungani 2008).

The previous section dealt with the harvesting and extraction of biomass in various forms to the roadside landing, which is almost always a discrete point between the primary supply phase (extraction) and the secondary phase of moving the material to the plant. However, the duration of time the material spends at the roadside landing varies from minutes if a “hot” container system is being used, to days, months or even years in the case of stumps. Most of the research looking at roadside storage and moisture management of biomass comes from boreal countries, where autumn and winter are characterized by large amounts of precipitation in the form of rain and snow, and where ice clumping is a problem. Few industrial plantations are located in these climate zones and local knowledge should be developed on good moisture management strategies.

Anaerobic digestion is a complex process involving various chemical reactions such as hydrolysis, acidogenesis and methanogenesis by means of several bacterial strains industrially applied for bio-gas production. This process can be applied to a wide range of biomass types, especially those with high moisture content. There is a growing interest in the application of this technology to transformation of lignocellulosic biomass. Pure lignocellulosic biomass represents an under-utilised source for biogas and — ethanol production (Hendriks and Zeeman 2008), primarily due to the recalcitrance of lignocellulose to biological degradation. Hydrolysis of lignocellulosic materials is the first step for either digestion to biogas or fermentation to ethanol, but it is considered to be the most rate-limiting step (Lissens et al. 2004; Sanders et al. 2000). Pretreatments developed for alcoholic fermentation (see Sect. 7.3.1) can improve the efficiency of lignocellulose hydrolysis during anaerobic digestion, which leads to increased yields and productivity while decreas­ing residence times for lignocellulose digestion (Taherzadeh and Karimi 2008). Among physicochemical processes, steam pretreatment, lime pretreatment, liquid hot water pretreatment and ammonia based pretreatments have high potential for application to biogas production (Hendriks and Zeeman 2008). Steam explosion and thermal pretreatments are widely investigated for improving biogas production from different materials such as forest residues (Hooper and Li 1996). Liu et al. (2002) investigated and developed steam pressure disruption as a treatment step to render lignocellulose-rich, solid municipal waste more digestible, which would result in increased biogas yields. During most steam pretreatment processes small amounts of sugar degradation products are formed, with varying amounts of furfural, acetic acid, HMF and soluble phenolic compounds present in the pretreated lignocellulose. These by-products may be inhibitory to fermentation by methanogenic bacteria and thus methane production. The consortium of microorganisms involved in anaerobic digestion is however capable of adapting to overcome the effects of such com­pounds, even though there are limits to the adaptive potential. Bacterial adaptation to fermentation in the presence of inhibitory compounds was demonstrated by Benjamin et al. (1984), Fox et al. (2003), over several bacterial generations.

Dilute acid

CO2 hydrothermal treatment Autohydrolysis at Severity = 4.67 Dilute acid (160 °С. 0.75 % acid concentration for 10 min)

Acid hydrolysis (1.2 % H2SO4, 121 °С for 45 min) Delignification (60 % ethanol) at H = 125,001 Autohydrolysis at So = 4.67 Autohydrolysis and organosolv So = 3.64,

TD = 198, C = 60 kg ethanol/100 kg liquor

Hot water (160 °С for 30 min) and ball milling (BM) (20 min)

Two step liquid hot water (180 °С for 20 min followed by 200 °С for 20 min)

Steam explosion (25 atm for 3 min) and anaerobic fermentation

Steam explosion (210 °С for 4 min)

Dilute acid 180 °С. 0.8 % H2S04 for 15 min Hydrothermal followed by alkaline treatment

(NaOH concentration = 4.5 %, 130 °С for 3 h). Autohydrolysis

CSF = 1-1.6 93 % xylose recovery CSF = 2.48 73 % glucan conversion and 18 g/1 ethanol

80 % glucose yield

15.1 g/1 ethanol

82 % of total sugars

28.7 g/1 ethanol 35 g/1 ethanol 67.4 g/1 ethanol

18.1 kg of saccharides (as monomers and sugars) from first step. 17.9 kg of soluble lignin and 41.9 kg glucose from second step

70 % yield of total sugar with a cellulase loading of 4 FPU/g substrate (10 times reduction) total sugar recovery of 96.63 %

80 % conversion of cellulose into biogas

17 g/1 ethanol (62.5 % yield)

10.3 g/1 ethanol

47.3 g glucose/100 g solids from autohydrolysis 77-99 % xylose solubilization

SHF overall ethanol yield of 72 %, SSF 63 % of theoretical total (22 g/1)

25.1 % of monomeric sugars

Total sugar recovery of 66.1 %

41 % recovery of total theoretical glucose 50 % of the theoretical glucose 270 1/t wood

55-60 % digestibility providing 70 % of total theoretical yield

The previous sections focussed on the use of biomass in simple energy production systems such as open fires made from gathered fuelwood. Such systems produce a large percentage of the cooking and heating energy required by the rural poor in developing countries. The “Climate Change Debate” has, however, brought with it an increasing demand for biofuels and the need for carbon sinks plus the system of carbon emissions trading, especially in developed countries (Tauli — Corpuz and Tamang 2007). “Modern bioenergy chains” encompass advanced concepts related to biomass feedstock production, supply chain logistics and conversion technologies such as combustion, gasification, fermentation, etc. The end uses of biomass derived energy can range from household heating with modern fuel pellets stoves to international distribution chains for liquid biofuels (Buchholz et al. 2007).

In developing countries the impact of these “modern bioenergy chains” on poverty alleviation will depend on the opportunities that are presented for agricultural development and the potential to increase poor peoples’ access to

Field data were collected during spring (October) 2006 in compartments of com­mercial plantations ranging in age from 4 to 9 years old. A total of 61 plots were sampled in 19 different stands. Plots were established no closer than 50 m to each other to ensure that the sample data set captured as much variability as possible in the chosen stand. The geographic location of the plot centre was recorded using DGPS. A circular plot was then established with an initial radius of 15 m, which was adjusted based on the slope of the compartment. The diameter at breast height (DBH) was recorded for each tree with a DBH larger than 5 cm, in every plot. Tree height was measured using a Vertex hypsometer with at least 15 DBH — height pairs recorded for each plot. The DBH-height pair data were employed to develop regression equations which were used to compute tree height for all trees based on the DBH (R2 = 0.98, p < 0.001). Several height metrics were subsequently calculated for each plot. The data collected were used for validation purposes only. Data were collected using a stratified sampling scheme applied to LiDAR height data and informed by an empirical semi-variogram analysis, as described below.

2.5.1 Case Study: Semi-variogram Modelling and Sampling

The selection of an appropriate LiDAR sample size is critical, since the samples should in theory capture as much canopy variability as possible within each compartment. Richards et al. (2000) provide the following criteria when designing a sampling strategy: The scheme should be sensor independent, unbiased estimators and error variance should be computed, areas not of interest should be excluded, the sample should be selected from areas where change is expected to be high, and adjacent plots should not contain redundant information and should be systemati­cally spaced. The area sampled for this study consisted of 1,000 ha of plantation forests made up of Eucalyptus, Pinus, and Australian Acacia species. The species and age groups were defined prior to setting out the sampling strategy, thus the target population had already been defined as Eucalyptus compartments between the ages of 4 and 9 years old. Compartments satisfying these initial criteria were selected using a geographical information system based on information from the forest company. LiDAR canopy returns, normalised by a ground digital elevation model, were subsequently selected and added to the analysis data set for the defined compartments. Normalisation of LiDAR returns was necessary in order to account for varying ground height, i. e., the process involved subtraction of the associated DEM values from first return LiDAR point heights. The selection of a suitable sample with which to model tree height was then undertaken using geostatistical methods.

The criteria stipulated by Richards et al. (2000) indicate that the scheme should be sensor independent. In this case the LiDAR sensor was a standard two return system, which constitutes the lower limit in terms of LiDAR technology currently in use. Richards et al. (2000) furthermore state that the variance of the sample estimates should be calculated. We calculated sample and population statistics and evaluated these based on various descriptive statistical measures. However, the sampling scheme first needed to be designed and implemented. The geostatistical methods discussed above, which measured and quantified the spatial dependence of LiDAR canopy height returns, were employed. It was deemed imperative that the sample from the LiDAR height data set should capture as much of the vertical variability as possible present in the compartment of interest. Semi-variograms and their application to LiDAR canopy height returns represent an ideal tool to do just this (Butson and King 1999).

Semi-variograms were derived by first calculating an empirical semi-variogram. Empirical semi-variograms measure the spatial dependence of neighbouring obser­vations for any continuously varying phenomenon and have the advantage of relating key descriptors of the spatial statistics, namely range and sill, of the data (Treitz 2001). Range is especially important in this study as it contributed to determining the width of sample transects. The range value, as calculated by the empirical semi-variogram, quantifies the distance at which height values are no longer statistically related (Curran and Atkinson 1998). This implies that if a sample of points is selected based on the range distance (randomly selected training points), this sample should capture most of the canopy height variability present in the compartment (Woodcock et al. 1988a, b). We recorded the average range from semi-variograms for each of the 61 field plots. Spherical mathematical models were used, which were iteratively optimised using the residual sum of squares (Hiemstra et al. 2009).

The identified optimal LiDAR transect width subsequently was doubled to more accurately reflect an operational use of LiDAR transects and to ensure that the transect width captured the majority of height variation in the study area, with the semi-variogram range serving as an indicator of the minimum width. This resulted in pseudo flight lines of the determined width for the imagery and LiDAR data and a between-transect spacing of 150 m to ensure coverage of the entire study area and all compartments (Richards et al. 2000). The pseudo flight lines were further divided into 10 m2 blocks, which were sub-sampled to ensure that samples were systematically spaced in order to minimise the potential for redundancy within the sample data set. Training data for modelling purposes eventually were selected from transects located in 19 Eucalyptus compartments. Maximum LiDAR height values (dependent variable) were extracted, since these are recognised as being representative of actual tree height in closed canopy forests (Popescu et al. 2002). Co-located multispectral data were also extracted with the mean spectral reflectance (independent variable) used for modelling maximum LiDAR height.

Management of forest resources and other natural resources in the miombo ecore­gion is governed by several acts and policies (Kayambazinthu et al. 2003). These regulatory systems provide access to forest resources through regulations, which involve the issuance of permits (Oduol et al. 2008). Reinforcement of conservation by-laws by traditional leaders has been known in some communities (Chirwa et al. 2008a; Syampungani et al. 2009). For example, in northern Zambia, by-laws regulating bush burning and the opening and closing dates of caterpillar collection are enforced by the chiefs or the paramount chief (Holden 1991; Chidumayo and Mbata 2002). In Tanzania, some chiefs declared some forests as traditional reserves (Kowero et al. 2003). Although the government acts and local regulatory systems could in principle help to protect natural resources, the drawback is that their implementation has been ineffective due to the independent nature of their oper­ations and subtle competition between institutions (Kowero 2003). Additionally, the management style emanating from the enforcement of the legal instrument has been restrictive in nature (Kowero 2003). For example, policies governing the management of protected areas have so far stressed the non-consumptive utilization of protected resources (Munthali and Mughogho 1992). This has hampered the opportunity for the rural communities to manage and live in harmony with their natural resources. Some government acts and policies also tend to take away some functions and responsibilities of the traditional leadership, central in an African setting (Virtanen 1999); yet traditional institutions provide for sustainable forest management. If policies have to be effective, they need to be inclusive by taking all stakeholders on board and provide for an opportunity for stakeholders to meet their needs as they contribute towards sustainable forest management. Policies that provide an opportunity for residents to have access to forest products tend to encourage or enhance participatory forest resource management.

Participatory forest management using communities in proximity to the resource, seems to be the most plausible way of ensuring sustainable management, provided issues of benefits and access are clearly defined from the onset (see Arnold 2001; Ham et al. 2008). Most policy and legal framework point to the need for devolution of powers (Dewee et al. 2011). Devolution where the rights of access, use, control and ownership are completely devolved to the local communities is perceived to be more effective than where there is artificial devolution of power (see Dewee et al. 2011). Furthermore, decentralized forest management can be enhanced if the subsistence and commercial use of the resources can be integrated through sustainable resource management systems for long-term socio-economic benefits of all stakeholders. Some local examples exists where participatory approaches have been successfully implemented including the Communal Areas Management Programme for Indigenous Resources (CAMPFIRE) in Zimbabwe and the Admin­istrative Management Design (ADMADE) for Game Management Areas (GMA), in Zambia (see Van Rijsoort 2000).

On bioenergy, policies conducive for promotion of small — scale, more sustainable technologies such as alternative (clean) energy development and agroforestry are lacking (see Ambali et al. 2011). Programs and policies which take into account the complex economic forces that influence energy production and consumption are urgently needed. It is therefore important that forestry and energy policies are complementary in order to foster the achievement of maximum benefits which wood can offer. Additionally, the promotion of more efficient energy use and the development of more modern production systems such as the use of wood for electricity production may contribute towards sustainable forest management.

1.3 Conclusions

The Zambezian phytoregion is a significant biome accounting for a large proportion of the African land masses. The Woodlands/forests of this region are widely exploited for charcoal production, slash and burn agriculture and timber. It is a source of livelihood for the majority of the rural poor of Southern Africa. Consequently, this has had implications on the vegetation cover in the region.

However, various studies have demonstrated that most of the woodland species of the region are capable of recovering once the disturbances arising from utilization for charcoal production, slash and burn agriculture ceases. This is because these species have both vertically and horizontally extensive root systems which facilitate recuperation after cutting. Additionally, a large number of seedlings and or saplings tend to be present at the time of clearing. The recovery rate and productive potential of the woodlands vary across the region. Limited data that indicates the productivity of the Southern African woodlands is influenced by several factors among which are (i) inherent climatic and edaphic factors; (ii) the resultant land use; (iii) the influences of fire; and (iv) standard assessment methods for both above and below ground biomass. The variation in productivity from one woodland system to another implies that management systems need to be developed specific to each woodland system if woody biomass is to be increased. This would also require that the influence of several factors on the productivity of the woodland systems is well understood. Furthermore efforts to develop capacity for the Forest Service or other government bodies responsible for forest management must be undertaken.

There is also need to formulate policies and legal framework that are inclusive. Currently, the local regulatory and the government regulatory systems do not work closely as the government system is considered to be superior over the local system (traditional system). There is need to understand how best the factors that make the two regulatory system operate independently. Formulation of policies conducive for promotion of small scale sustainable technologies such as alternative (clean) energy production and Agroforestry need to be encouraged.

The development of a good business process model for biomass supply is something that many enterprises see as a challenge. More specifically, the measurement of the biomass, the calculation of the heating value, and the varying lead times (from hours to years) are elements that many supply chains wrestle with. In the following section, some fundamental business process structures are presented.