Wood from coniferous trees is an important source of renewable biomass. It can contribute to provision of carbon neutral energy, biomaterials and housing for a growing population. Softwood is mainly composed of cellulose, galactoglucomannan, xylan and lignin. This thesis focuses on the biosynthesis and function of Glucuronic acid (GlcA) decorations on softwood xylan. Results demonstrate that this GUX (GlucUronic acid substitution of Xylan)-dependent xylan branching is critical for the maintenance of biomass recalcitrance in a model vascular plant Arabidopsis thaliana. Experiments employing in vitro and in planta activity assays show that conifer transcriptomes encode at least two distinct GUX enzymes which are active glucuronosyltransferases. Interestingly, these enzymes have different specific activities, with one adding evenly spaced GlcA branches and the other one being able to add consecutive decorations. It is possible that these different patterns of xylan branching may have an impact on ability of xylan to interact with cellulose fibrils. To investigate the role for xylan binding to cellulose, Arabidopsis mutant plants in which this interaction is lost were evaluated alongside transgenic mutant lines in which the interaction may be restored. Results of this analysis indicate that the presence of cellulose-bound xylan might have an influence on plant vasculature integrity and thus it may have an effect on plant growth and biomass properties. Moreover, further results indicate that some xylan cellulose interaction is likely to occur in cell wall macrofibrils which can be detected in softwood. Taken together, this thesis provides insights into the process of conifer xylan glucuronidation and the possible role these branches may be playing in the maintenance of softwood recalcitrance and mechanical properties. In addition to identifying potential mutagenesis targets for improving softwood processing, this work is a proof of concept for the use of GUX enzymes for in vivo and in vitro biosynthesis of novel xylan structures with potential industrial application.

Cost effective production of biofuel from plant biomass (second generation biofuels) is currently a key challenge. To achieve this, accessibility of plant cell wall polysaccharides to chemical, enzymatic and microbial digestion could be improved by altering lignin structure and composition or by reducing lignin content, as lignin is one cell wall component that has already been shown to contribute to biomass recalcitrance. Therefore, this thesis reports the genetic manipulation of lignin biosynthesis through down-regulation of cinnamyl alcohol dehydrogenase (CAD) genes in barley (Hordeum vulgare L.). Barley has been chosen as the target plant for lignin manipulation for a few reasons: it is a major cereal crop that produces large amounts of lignocellulosic plant biomass that can potentially be used as animal feed or to produce second generation biofuels and also because it is a model grass for other bioenergy crops. CAD, as the final enzyme in the lignin pathway, is a perfect target for lignin manipulation. Characterised CAD mutants and transgenics have shown that down-regulation of CAD improves digestibility and does not influence plant growth and fertility. Due to the difficulty and complexity of transformation of monocot species, there are only a few reports describing down-regulation of CAD in monocots, and none in barley. Here, in this thesis, lignin was altered by down-regulating CAD genes using an RNAi construct with part of the HvCAD2 gene, the gene which has the highest expression level of all CAD genes. Transgenic barley plants showed reduced enzyme activity in the T0 generation (31% compared to EV plants) and enzyme activity was reduced even more in the T1 (to 3%) and T2 (to 2%) generations. The HvCAD2 RNAi barley lines had similar or slightly reduced Klason total lignin contents relative to control plants, but lignin structure and composition were altered. The RNAi plants had lower thioacidolysis yields, S/G ratio was reduced (1.59 in the empty vector controls versus 0.96–1.21 in the transgenic barley plants), the relative frequency of S units was reduced by 11–20%, the proportion of G units was increased by 17–32%, there was increased sinapaldehyde accumulation in lignin and ferulic acid abundance was reduced relative to control plants. Analysed transgenic barley plants had an orange stem phenotype. Growth season and conditions hugely affected the intensity of the phenotype. Because lignin plays a major role in culm strength and pathogen resistance, the influence of down-regulation of CAD on these features was characterised. The changed physicochemical nature of cell walls in HvCAD2 RNAi lines does not decrease the strength of the straw and does not decrease the resistance to the biotrophic Blumeria graminis and to the hemibiotrophic Rhynchosporium commune pathogens. The modified cell walls in the HvCAD2 RNAi lines had moderately improved sugar release for biofuel production. This study proves that it is possible to down-regulate CAD in cereal crops in order to change lignin structure and composition in plants without a negative impact on plant growth, fertility or pathogen resistance.

Sorghum (Sorghum bicolor L. Moench spp.) is a bioenergy crop with several appealing biological features to be explored in plant breeding for increasing efficiency in bioenergy production. The possibility to connect the influence of quantitative trait loci over time and between traits highlight the Bayesian networks as a powerful probabilistic framework to design novel genomic prediction models. In this study, we phenotyped a diverse panel of 869 sorghum lines in four different environments (2 locations in 2 years) with biweekly measurements from 30 days after planting (DAP) to 120 DAP for plant height and dry biomass at the end of the season. Genotyping-by-sequencing was performed, resulting in the scoring of 100,435 biallelic SNP markers. We developed and evaluated several genomic pre- diction models: Bayesian Network (BN), Pleiotropic Bayesian Network (PBN), and Dynamic Bayesian Network (DBN). Assumptions for BN, PBN, and DBN were independence, dependence between traits, and dependence between time points, respectively. For benchmarking, we used multivariate GBLUP models that considered only time points for plant height (MTi- GBLUP), and both time points for plant height and dry biomass (MTr-GBLUP) modeling unstructured variance-covariance matrix for genetic effects and residuals. Coincidence indices (CI) were computed for understanding the success in selecting for dry biomass using plant height measurements, as well as a coincidence index based on lines (CIL) using the posterior draws from the Bayesian networks to understand genetic plasticity over time. In the 5-fold cross-validation scheme, prediction accuracies ranged from 0.48 (PBN) to 0.51 (MTr- GBLUP) for dry biomass and from 0.47 (DBN-DAP120) to 0.74 (MTi-GBLUP-DAP60) for plant height. The forward-chaining cross-validation showed a substantial increment in prediction accuracies when using the DBN model, with r = 0.6 (train on slice 30:45 to predict 120 DAP) to 0.94 (train on slice 30:90 to predict 105 DAP) compared to the BN and PBN, and similar to multivariate GBLUP models. Both the CI and CIL indices showed that the ranking of promising inbred lines changed minimally after 45 DAP for plant height. These results suggest that 45 DAP is an optimal developmental stage for imposing the two-level indirect selection framework, where indirect selection for plant height at the end of the season (first-level target trait) can be done based on its ranking with 45 DAP (secondary trait) as well as for dry biomass (second-level target trait). With the advance of robotic technologies for field-based phenotyping, the development of novel approaches such as the two-level indirect selection framework will be imperative to boost genetic gain per unit of time. / O sorgo (Sorghum bicolor L. Moench spp.) é uma cultura bioenergética com várias características atrativas para serem exploradas no melhoramento de plantas para aumentar a eficiência de produção de bioenergia. A possibilidade de conectar informações genômicas em caracteres quantitativos ao longo do tempo, e entre caracteres, destacam as Redes Bayesianas como uma ferramenta probabilística poderosa para delinear novos modelos de predição genômica. Neste estudo, um painel diverso de 869 linhagens de sorgo foi fenotipado em quatro ambientes diferentes (2 locais em 2 anos) com medidas a cada duas semanas de 30 a 120 dias após o plantio (DAP), para altura de plantas e biomassa seca no fim da safra. Um procedimento de Genotipagem por sequenciamento foi executado, resultando na chamada de 100.435 marcadores baseados em Polimorfismos de Nucleotídeos Únicos (SNPs) bialélicos. Neste estudo foram desenvolvidos e avaliados os modelos de predição genômica: Rede Bayesiana (BN), Rede Bayesiana Pleiotrópica (PBN), e Rede Bayesiana Dinâmica (DBN). Os pressupostos para BN, PBN, e DBN foram independência, dependência entre caracteres, e dependência entre pontos no tempo, respectivamente. Para fins comparativos, formulações de modelos multivariados GBLUP foram utilizados considerando dependência entre pontos de tempo para altura de plantas (MTi-GBLUP), e ambos os pontos de tempo para a altura de plantas e biomassa seca (MTr-GBLUP), modelando matriz de variância-covariância não estruturada para efeitos genéticos e residuais. Índices de coincidência (IC) foram calculados para entender o sucesso na seleção indireta de biomassa seca usando medidas de altura de plantas, bem como um índice de coincidência baseado em linhagens (CIL), usando as amostras das posteriores das redes Bayesianas para entender a plasticidade genética ao longo do tempo. No esquema de validação cruzada 5-fold, as acurácias das predições variaram de 0,48 (PBN) a 0,51 (MTr-GBLUP) para biomassa seca e de 0,47 (DBN-DAP120) a 0,74 (MTi-GBLUP-DAP60) para altura de plantas. A validação cruzada forward-chaining mostrou um incremento substancial nas acurácias das predições ao usar o modelo DBN, com r = 0,6 (treinando no intervalo 30:45 para prever 120 DAP) até 0,94 (treinando no intervalo 30:90 para prever 105 DAP) em comparação com o BN e PBN, e semelhante aos modelos multivariados GBLUP. Os índices CI e CIL mostraram que o ranking de linhagens promissoras mudou minimamente após 45 DAP para altura de plantas. Estes resultados sugerem que 45 DAP é um estágio de desenvolvimento ideal para impor a estrutura de seleção indireta em dois níveis, onde a seleção indireta para a altura da planta no final da estação (caractere alvo de primeiro nível) pode ser feita com base na sua classificação com 45 DAP (caractere secundário), bem como para a biomassa seca (caractere alvo de segundo nível). Com o avanço das tecnologias robóticas para a fenotipagem baseada em campo, o desenvolvimento de novas abordagens, como a estrutura de seleção indireta em dois níveis, serão imperativas para aumentar o ganho genético por unidade de tempo.

Plants are being engineered for enhanced ethanol production; however, challenges remain in meeting the demand for bioenergy that is expected to double by 2030. Therefore, targeting carbon accumulation in the form of energy-dense oils in nonseed biomass is considered a superior alternative for bioenergy production. Although oils in the form of triacylglycerols (TAGs) are typically stored in seed tissues, various nonseed tissues such as mesocarp, tubers, stems and leaves also serve as storage tissues for TAG accumulation in plants. Moreover, the biomass of these tissues is generally far greater than seed biomass. In order to increase oil content in nonseed biomass for bioenergy and nutritional purposes, it is important to understand how such plants naturally accumulate TAG in nonseed tissues. Several molecular approaches, including transcriptomics, have been undertaken to elucidate the metabolic and regulatory mechanisms of carbon partitioning in oil-rich nonseed tissues. Such studies are expected to generate important transgenic tools that can be used to alter fatty acid metabolism and engineer plants to produce oil-rich biomass successfully. This review focuses on the potential of different oil-rich nonseed tissues and the strategies developed for enhancing oil biomass.

The use of biomass as a potential feedstock for the production of liquid hydrocarbon fuels has been under investigation in the last few decades. This paper discusses a preliminary design and a feasibility study of producing liquid hydrocarbon fuels from biomass through a combined biochemical and thermochemical route. The process involves anaerobic digestion (AD) of the biodegradable portion of the biomass to produce methane rich gas. The methane rich biogas stream is purified by removing contaminants and upgraded to liquid hydrocarbon fuel in a gas to liquid facility (GTL) via thermochemical conversion route. The biogas conversion involves two major steps: tri-reforming step to produce syngas (a mixture of CO and H2), and Fischer-Tropsch Synthesis (FTS) step to convert the syngas to a spectrum of hydrocarbons. Separation and upgrading of the produced hydrocarbon mixture allows production of synthetic transportation fuels. AD is ranked as one of the best waste management options as it allows for: energy recovery, nutrient recovery, and reduction in greenhouse gases emission. A detailed process modeling of the process was carried out using ASPEN Plus process design software package. Data for the process was based on literature on AD combined with laboratory results on the biogas to liquid conversion process. The composition of the final liquid hydrocarbon from the ASPEN model has been compared to the composition of commercial diesel fuel, and results have shown good agreement. As a result, the most current commercial diesel prices were used to evaluate the potential revenue from selling the product in the open market. The total capital investment to construct the plant with a capacity of handling 100,000 ton per year of wet biomass is $16.2 million with a potential of producing 2.60 million gallons of diesel. The base case feedstock is corn stover. The annual operating cost to run the plant is estimated to be $8.81 million. An annual revenue from selling the diesel product is estimated to be $14.6 million taking into account a green energy incentive of $3.00/gallon of diesel sold. The net present worth at the end of the plant life is $8.76 million with a discounted cash flow of return of 26.2%. The breakeven cost of diesel is determined to be $4.34/gallon assuming no tipping fees are charged for handling the waste. Sensitivity analyses results concluded that the profitability of the process is most sensitive to variation in diesel selling price. Based on these results, it can be concluded that the process is profitable only if incentives are provided for renewable fuels due to the current low prices of fossil fuels.

Growing energy use and greenhouse gas emissions have implied an increased attention to the development of renewable energy sources. Bioenergy from forest biomass is expected to be one of the cornerstones in reaching renewable energy targets, especially in forest-rich countries such as Sweden. However, forest biomass is a limited resource, and an intensified use of bioenergy could affect roundwood and forest products’ markets in several ways. The overall purpose of this thesis is to analyze price formation and resource allocation of forest raw materials in the presence of increased bioenergy demand. The empirical focus is on the competition for wood fibres between bioenergy use and the traditional forest industries, as well as synergy effects between the various sectors using forest raw materials. The methodologic approach is partial equilibrium modeling (forest sector model), and the geographical focus is on Sweden. The thesis comprises three self-contained articles, which all address the above issues. The first paper presents an economic assessment of two different policies – both implying an increased demand for forest ecosystem services – and how these could affect the competition for forest raw materials. A forest sector trade model is updated to a new base year (2016), and used to analyze the consequences of increased bioenergy use in the heat and power (HP) sector as well as increased forest conservation in Sweden. These overall scenarios are assessed individually and in combination. The results show how various forest raw material-using sectors are affected in terms of price changes and responses in production. A particularly interesting market impact is that bioenergy promotion and forest conservation tend to have opposite effects on forest industry by-product prices. Moreover, combining the two policies mitigates the forest industry by-product price increase compared to the case where only the bioenergy-promoting policy is implemented. In other words, the HP sector is less negatively affected in terms of increased feedstock prices if bioenergy demand target are accompanied by increased forest conservation. This effect is due to increasing pulpwood prices, which reduces pulp, paper and board production, and in turn mitiges the competition for the associated by-products. Overall, the paper illustrates the great complexity of the forest raw material market, and the importance of considering demand and supply responses within and between sectors in energy and forest policy designs. The second article investigates the forest raw material market effects from introducing second-generation transport biofuel (exemplified by Bio-SNG) production in Sweden. Increases in Bio-SNG demand between 5 and 30 TWh are investigated. The simulation results illustrate increasing forest industry by-product (i.e., sawdust, wood chips and bark) prices, not least in the high-production scenarios (i.e. 20-30 TWh). This suggests that increases in second-generation biofuel productions lead to increased competition for the forest raw materials. The higher feedstock prices make the HP sector less profitable, but very meagre evidence of substitution of fossil fuels for by-products can be found. In this sector, there is instead an increased use of harvesting residues. Fiberboard and particleboard production ceases entirely due to increased input prices. There is also evidence of synergy (“by-product”) effects between the sawmill sector and the use of forest raw materials in the HP sector. Higher by-product prices spur sawmills to produce more sawnwood, something that in turn induces forest owners to increase harvest levels. Already in the 5 TWh Bio-SNG scenario, there is an increase in the harvest level, thus suggesting that the by-product effect kicks in from start. Biofuels and green chemicals are likely to play significant roles in achieving the transition towards a zero-carbon society. However, large-scale biorefineries are not yet cost-competitive with their fossil-fuel counterparts, and it is therefore important to identify biorefinery concepts with high economic performance in order to achieve widespread deployment in the future. For evaluations of early-stage biorefinery concepts, there is a need to consider not only the technical performance and the process costs, but also the performance of the full supply chain and the impact of its implementation in the feedstock and products markets. The third article presents – and argues for – a conceptual interdisciplinary framework that can form the basis for future evaluations of the full supply-chain performance of various novel biorefinery concepts. This framework considers the competition for biomass feedstocks across sectors, and assumes exogenous end-use product demand and various geographical and technical constraints. It can be used to evaluate the impacts of the introduction of various biorefinery concepts in the biomass markets in terms of feedstock allocations and prices. Policy evaluations, taking into account both engineering constraints and market mechanisms, should also be possible. Overall, the thesis illustrates the importance of considering the market effects when designing and evaluating forest policies and bioenergy policy targets. The forest industry sector and the bioenergy sector are closely interlinked and can both make or break one another depending on the policy design. The results indicate that for an increased demand of bioenergy, an industrial transformation is to be expected, as well as increased roundwood harvest.

Doctor of Philosophy (PhD) / The aim of this thesis is to understand what drives the evolution of industrial networks and how such understanding can be used to stimulate sustainable development. A complex adaptive systems perspective has been adopted to analyse the complex interaction between organisational behaviour and industrial network evolution. This analysis has formed the basis for the development of a modelling approach that allows for quantitative exploration of how different organisational perceptions about current and future uncertainty affect their behaviour and therefore the network evolution. This analysis results in a set of potential evolutionary pathways for an industrial network and their associated performance in terms of sustainable development. Subsequently, this modelling approach has been used to explore the consequences of interventions in the network evolution and to identify robust interventions for stimulating sustainable development of industrial networks. The analysis, modelling approach and development of interventions has been developed in the context of a bioenergy network in the region of KwaZulu-Natal in South Africa. Industrial networks are an important aspect of today’s life and provide many goods and services to households and individuals all over the world. They consist of a large number of autonomous organisations, where some organisations contribute by transforming or transacting natural resources, such as oil, agricultural products or water, while other organisations contribute to networks by providing information or setting regulation or subsidies (local or national governments) or by influencing decision making processes of other organisations in networks (advocacy groups). Throughout the process from natural resource to product or service, industrial networks have important economic, environmental and social impacts on the socio-economic and biophysical systems in which they operate. The sum of complex interactions between organisations affects the rate in which natural resources are used, environmental impacts associated with transformation and transaction of resources and social impacts on local communities, regions or countries as a whole. The aim of this thesis is to understand how industrial networks evolve and how they can be stimulated towards sustainable development. The first question that has been addressed in this thesis is how to understand the complex interaction between organisational behaviour and industrial network evolution. Organisational behaviour is affected by many functional and implicit characteristics within the environment in which the organisation operates, while simultaneously the environment is a function of non-linear relationships between individual organisational actions and their consequences for both the function and structure of the network. This thesis has identified four different characteristics of industrial networks that affect organisational behaviour: 1) Functional characteristics 2) Implicit behavioural characteristics 3) Implicit relational characteristics 4) Implicit network characteristics. Functional characteristics are those characteristics that are formally recognised by all organisations within an industrial network and which affect their position within the network. Examples of functional characteristics are the price and quantity of resources available, the location and distance of organisations within a network, infrastructure availability or regulation. Implicit characteristics, on the other hand, are those characteristics that impact the decision making process of organisations, but which are not formally part of the network. From an organisational perspective, implicit characteristics are the rules, heuristics, norms and values that an organisation uses to determine its objectives, position and potential actions. Implicit relational characteristics, most importantly trust and loyalty, affect an organisations choice between potential partners and implicit network characteristics are those social norms and values that emerge through social embeddedness. Collectively, these functional and implicit characteristics and their interactions determine the outcome of organisational decisions and therefore the direction of the industrial network evolution. The complex interaction between these large numbers of characteristics requires quantitative models to explore how different network characteristics and different interactions result in different network evolutions. This thesis has developed an agent-based simulation model to explore industrial network evolutions. To represent the multi-scale complexity of industrial networks, the model consists of four scales. Each scale represents different processes that connect the functional and implicit characteristics of an industrial network to each other. The two basic scales represent the strategic actions of the organisations on the one hand and the industrial network function and structure on the other. The third scale represents the processes that take place within the mental models of organisations describing how they make sense of their environment and inform their strategic decision making process. The fourth scale represents the social embeddedness of organisations and how social processes create and destroy social institutions. The model has been developed such that it allows for exploring how changes in different network characteristics or processes affect the evolution of the network as a whole. The second question that has been addressed in this thesis is how to evaluate sustainable development of different evolutionary pathways of industrial networks. First of all, a systems approach has been adopted to explore the consequences of an industrial network to the larger socio-economic and biophysical system in which the network operates. Subsequently, a set of structural indicators has been proposed to evaluate the dynamic performance of industrial networks. These four structural indicators reflect the efficiency, effectiveness, resilience and adaptiveness of industrial networks. Efficiency and effectiveness relate to the operational features by which industrial networks provides a particular contribution to society. Resilience and adaptiveness relate to the system’s capacity to maintain or adapt its contribution to society while under stress of temporary shocks or permanent shifts, respectively. Finally, different multi-criteria decision analysis (MCDA) tools have been applied to provide a holistic evaluation of sustainable development of industrial networks. The third important question that is addressed in this thesis is how to systematically explore the potential evolutionary pathways of an industrial network, which has led to the development of agent-based scenario analysis. Agent-based scenario analysis systematically explores how industrial network evolutions might evolve depending on the perceptions of organisations towards the inherent uncertainty associated with strategic decision making in networks. The agent-based scenario analysis consists of two steps. Firstly, analysts develop a set of coherent context scenarios, which represents their view on the context in which an industrial network will operate within the future. For a bioenergy network, for example, this step results in a set of scenarios that each represent a coherent future of the socio-economic system in which the network might evolve. The second step is the development of a set of ‘agent scenarios’. Each agent-based scenario is based on a different ‘mental model’ employed by organisations within the network about how to deal with the inherent ambiguity of the future. The organisational perspective towards uncertainty is of major importance for the evolution of industrial networks, because it determines the innovative behaviour of organisations, the structure of the network and the direction in which the network evolves. One the one hand, organisations can ignore future ambiguity and base their actions on the environment that they can observe in their present state. On the other extreme, organisations can adopt a view that the future is inherently uncertain and in which they view social norms and values more important than functional characteristics to make sense of their environment. The mental models are differentiated according to two dimensions: 1) different mental representation of the world and 2) different cognitive processes that can be employed to inform strategic actions. Along these dimensions, different processes can be employed to make sense of the environment and to inform decision making. The thesis has shown that by systematically exploring the different perceptions possible, an adequate understanding of the different evolutionary pathways can be gained to inform the evaluation and development of interventions to stimulate sustainable development. The final part of this thesis has applied the analysis and methodology developed throughout this thesis to a bioenergy network in the province of Kwazulu-Natal in South Africa. The bioenergy network consists of a set of existing sugar mills with large quantities of bagasse, a biomass waste product, available. Bagasse is currently burned inefficiently to produce steam for the sugar mills, but can potentially be used for the production of green electricity, biodiesel, bioethanol or gelfuel. All of these products have important consequences for the region in terms of associated reductions in CO2 emissions, electrification of and/or energy provision for rural households and local economic development of the region. This thesis has modelled strategic decisions of the sugar mills, the existing electricity generator, potential independent energy producers, local and national governments and how their actions and interactions can lead to different evolutionary pathways of the bioenergy network. The agent-based scenario analysis has been used to explore how different perceptions of organisations can lead to different network evolutions. Finally, the model has been used to explore the consequences of two categories of interventions on stimulating sustainable development. The conclusions are that both categories of interventions, financial interventions by national government and the introduction of multi-criteria decision analysis (MCDA) tools to aid strategic decision making, can have both positive and negative effects on the network evolutions, depending on what ‘mental models’ are employed by organisations. Furthermore, there is no single intervention that outperforms the others in terms of stimulating both functional and structural features of sustainable development. The final conclusion is that instead of focusing on individual or collective targets, emphasis should be placed on the development of interventions that focus on evolutionary aspects of industrial networks rather than functional performance criteria. This thesis has also highlighted interesting research questions for future investigation. The methodology developed in this thesis is applied to a single case study, but there are still many questions concerning how different industrial networks might benefit from different organisational perceptions towards uncertainty. Furthermore, the role between the mental models and sustainable development requires further investigation, especially in the light of globalisation and the interconnectiveness of industrial networks in different countries and continents. Finally, this methodology has provided a platform for investigating how new technologies might be developed that anticipate needs of future generations. This thesis has provided a first and important step in developing a methodology that addresses the complex issues associated with sustainable development, benefiting both academics and practitioners that aim to stimulate sustainable development.

Switchgrass (Panicum virgatum), a summer perennial grass native to North America, is currently being explored for its potential use in the production of biofuels. With these interests, genetic manipulation of switchgrass to produce plants that are easier to digest, have an increased resistance to diseases and stresses, and maintain viability longer in the field are required. Therefore, it was necessary to develop a reliable and efficient tissue culture system for the transformation of switchgrass. Current switchgrass tissue culture requires months for regeneration of transformants with relatively poor transformation efficiencies and are limited to derivatives of a single variety, Alamo. We have developed a tissue culture system, utilizing a novel media, LP9, which has demonstrated decreased time to the production of whole transgenic plants and with an increased efficiency. LP9 is not an MSO-based tissue culture system. It is comprised of both N6 macroelements and B5 microelements with the auxin, 2,4-D and does not include any cytokinin. After just 1 month on LP9 media, callus can be selected and used for Agrobacterium tumefaciens-mediated transformation or particle bombardment, and plants can be regenerated within 3 weeks of callus initiation. Our system is unique to previously explored MSO-based systems in that it is optimized for the production of type II callus, which has been shown to produce higher transformation efficiencies in other monocots. We have increased the transformation efficiency of switchgrass from to up to 4% to 34% efficiency by selecting for this type of callus.

Immobilization of photosynthetic cultures has been used to generate biofuels and high value compounds through direct conversion of CO2 and water using sunlight. Compared with suspended cultures, immobilized bacteria can achieve much higher densities resulting in greater areal productivity. Limitations exist however, on the density that can be reached without compromising access to light and other nutrients. In this thesis an optofluidic approach to overcoming the challenge of light delivery to high density cultures of cyanobacteria is described and proof of concept experiments presented. This approach uses optical waveguides to deliver light to cells through bacterial interaction with the evanescent field and is tailored to meet each cell's need for light and nutrients. Experiments presented here demonstrate biofilm proliferation in the presence of evanescent fields. Illumination of surfaces by surface plasmon enhanced evanescent fields is also shown to be an effective and potentially useful technique to grow biofilms within optofluidic architectures.

The limitations of renewable energy technologies require that pathways are carefully chosen such that renewable resources are used most effectively in addressing modern energy challenges. Optimized decision-making is particularly challenging for the forest bioenergy sector because of the multitude of potential pathways and because profit is highly sensitive to biomass procurement costs. I assessed energy wood recovery and procurement costs during semi-mechanized selection operations in the tolerant hardwood forests of Ontario. Logging contractors were able to recover unmerchantable sections of branches normally discarded during conventional operations, amounting to 1.3 to 2.7 dry tonnes of additional biomass per hectare. Supply chain scenarios are used to show that the biomass can be brought to market at a cost similar to mechanized operations. The need for prioritization of decentralization, efficiency, and integration with the value-added forest sector is discussed with reference to the relative scarcity and high cost of the forest resource.