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<p>A sustainable development of entrepreneurship will not be possible in such a complex andchallenging environment as today’s society, without the attainment of effective learning andbusiness support capabilities (Williams, 1998). One such support is obtained through havingexperienced entrepreneurs mentor less experienced entrepreneurs, transferring knowledge(Clutterbuck, 2004) and facilitating learning (Sullivan, 2000). As Leonard Bisk (2002)and Sullivan (2000) among other researchers (Deakins et al. 1997) stress, there is a need tolook beyond the start-up process of a firm and the use of mentorship in this early phase,and focus more on how entrepreneurs who have been in business for a while can benefitfrom a mentor program, an area referred to as “the nature of timing and support” (Sullivan,2000, p. 163).</p><p>The purpose of this thesis is to explore how an experienced entrepreneur, a mentor, canhelp a less experienced entrepreneur, an adept, achieve entrepreneurial development duringand throughout different phases of the entrepreneurial life cycle, in the most efficient manner.</p><p>An entrepreneurial life cycle can be divided into several phases, which can be used in orderto examine the entrepreneur’s development process within different time periods of runninga firm. Start-up support generally involves providing entrepreneurs with the crucial“tools” for survival, such as basic financial support, bookkeeping and marketing (Sullivan,2000). Mature entrepreneurs generally request psychological benefits, such as reassuranceand improved confidence as they wonder whether or not their experiences are normal andhow they should be interpreted (Megginson et al. 2006). There are two types of directivementoring styles, coaching and counseling, and two types of non directive mentoring,counseling and networking.</p><p>The main objective with our research in this thesis was to explore how entrepreneurs’ developmentthroughout and during different phases was affected by active participation in amentor program. To gather information we used a qualitative method, in which we interviewedten entrepreneurs who were currently active in a mentor program, or had been activewithin the last 12 months. The empirical findings were later analyzed in the light of theframe of references and the authors own viewpoint, by conducting a within case/cross casecomparisons.</p><p>The results indicate that a mentor can best help an entrepreneur achieve entrepreneurialdevelopment by providing non directive support, enabling the entrepreneur to draw his orher own conclusions and stimulate self reliance. This support is best delivered after thestart-up and conception phase, the first phase of the life-cycle.</p>

Dairy production systems are an important global contributor to anthropogenic greenhouse gas (GHG) emissions including methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2). Due to the role GHG play in climate change, it is important to investigate ways to minimise their global warming potential (GWP) and to maximise the efficiency of dairy production systems. Finding a balance between improving productivity and suppressing the range and quantity of GHG produced in dairy production is crucial in order to maintain sustainability in the future. The Langhill herd is part of a long term genetic x feeding systems study, representative of a range of dairy production systems which may be found in the UK. Two feeding regimes (low forage (LF) and high forage (HF)) were applied to each of two genetic lines (control (C) and select (S) genetic merit for milk fat plus protein) giving four contrasting dairy production systems (LFC, LFS, HFC, HFS). Biological efficiency (production and energetic) and environmental efficiency (GWP) were assessed by way of life cycle analysis (LCA), accounting for dairy system inputs and outputs from off-farm production of imported feeds and fertilisers to raw milk leaving the farm gate over a period of seven years. Calculations were conducted using the Intergovernmental Panel on Climate Change (IPCC) methods, with system specific data implemented where possible. Select genetic line under low forage regime (LFS) had the highest gross production and energetic efficiencies (p<0.001). In LFS, milk yields were 56% higher per cow than the lowest ranked HFC system, representing a difference of around 3500kg per cow. Milk solids yield per kg dry matter intake was 18% higher in LFS compared to HFC. High forage with control genetic line required 17% more net energy intake than LFS to produce each kg of milk solids. LFS allocated the highest proportion of net energy to lactating after accounting for body maintenance (p<0.001). Rate of change in efficiency throughout lactation varied significantly (p<0.001) amongst systems, with loss of efficiency minimised in LFS and greatest in HFC. However, LFS involuntary culling rate was significantly higher than other systems (p<0.001). LFS was the most environmentally efficient system and HFC the least (p<0.001), both per unit productivity and per unit total land use. Implementing low forage regime with select genetic line lowered GWP per kg energy corrected milk (ECM) by 24% compared to HFC (p<0.001). GWP of LFC was around 8% lower per kg ECM than HFS (p<0.001). Methane from enteric fermentation contributed the greatest proportion of overall GWP (46-49%) in all systems. However, key factors in the differences amongst systems were higher off-farm CO2 equivalent emissions under low forage, and higher on-farm N2O emissions under high forage regime. HFC produced 91% more nitrous oxide per kg ECM from animal manures compared to LFS, and 65% more N2O from applied manufactured fertilisers (p<0.001). Conversely GWP associated with off-farm production of imported feeds in LFS was 11% higher than in HFC (p<0.001). In low forage systems high gross emissions were offset by high productivity but this was not the case for the high forage systems. Cows of high genetic merit managed under a Low Forage feeding regime had improved production, energetic and environmental efficiencies. However, issues with animal health and fertility raise questions about long term sustainability of the LFS dairy production system, emphasising the importance of examining trade offs between systems.

A sustainable development of entrepreneurship will not be possible in such a complex andchallenging environment as today’s society, without the attainment of effective learning andbusiness support capabilities (Williams, 1998). One such support is obtained through havingexperienced entrepreneurs mentor less experienced entrepreneurs, transferring knowledge(Clutterbuck, 2004) and facilitating learning (Sullivan, 2000). As Leonard Bisk (2002)and Sullivan (2000) among other researchers (Deakins et al. 1997) stress, there is a need tolook beyond the start-up process of a firm and the use of mentorship in this early phase,and focus more on how entrepreneurs who have been in business for a while can benefitfrom a mentor program, an area referred to as “the nature of timing and support” (Sullivan,2000, p. 163). The purpose of this thesis is to explore how an experienced entrepreneur, a mentor, canhelp a less experienced entrepreneur, an adept, achieve entrepreneurial development duringand throughout different phases of the entrepreneurial life cycle, in the most efficient manner. An entrepreneurial life cycle can be divided into several phases, which can be used in orderto examine the entrepreneur’s development process within different time periods of runninga firm. Start-up support generally involves providing entrepreneurs with the crucial“tools” for survival, such as basic financial support, bookkeeping and marketing (Sullivan,2000). Mature entrepreneurs generally request psychological benefits, such as reassuranceand improved confidence as they wonder whether or not their experiences are normal andhow they should be interpreted (Megginson et al. 2006). There are two types of directivementoring styles, coaching and counseling, and two types of non directive mentoring,counseling and networking. The main objective with our research in this thesis was to explore how entrepreneurs’ developmentthroughout and during different phases was affected by active participation in amentor program. To gather information we used a qualitative method, in which we interviewedten entrepreneurs who were currently active in a mentor program, or had been activewithin the last 12 months. The empirical findings were later analyzed in the light of theframe of references and the authors own viewpoint, by conducting a within case/cross casecomparisons. The results indicate that a mentor can best help an entrepreneur achieve entrepreneurialdevelopment by providing non directive support, enabling the entrepreneur to draw his orher own conclusions and stimulate self reliance. This support is best delivered after thestart-up and conception phase, the first phase of the life-cycle.

Life cycle assessment is an important tool to evaluate the impact of 2nd generation lignocellulosic ethanol, and its potential greenhouse gas (GHG) emissions benefits relative to gasoline. The choice of feedstock, process technology, and co-products may affect GHG emissions and energy metrics. Co-products may improve both the financial and environmental performance of the biorefinery. 26 well-to-wheel models of future lignocellulose-to-ethanol pathways were constructed, considering corn stover, switchgrass, and poplar feedstocks, three pre-treatment technologies, four co-product options, and the use of ethanol in a light-duty vehicle. Model results showed that all pathways with lignin pellet co-production had significantly lower net GHG emissions relative to gasoline and corresponding pathways producing only electricity. Pathways co-producing xylitol had at least 66% greater GHG emission reductions relative to pathways co-producing only lignin pellets. All feedstock/pretreatment/co-product combinations led to GHG reductions of at least 60%, meeting the threshold stipulated under the Energy Independence and Security Act.

Life cycle assessment is an important tool to evaluate the impact of 2nd generation lignocellulosic ethanol, and its potential greenhouse gas (GHG) emissions benefits relative to gasoline. The choice of feedstock, process technology, and co-products may affect GHG emissions and energy metrics. Co-products may improve both the financial and environmental performance of the biorefinery. 26 well-to-wheel models of future lignocellulose-to-ethanol pathways were constructed, considering corn stover, switchgrass, and poplar feedstocks, three pre-treatment technologies, four co-product options, and the use of ethanol in a light-duty vehicle. Model results showed that all pathways with lignin pellet co-production had significantly lower net GHG emissions relative to gasoline and corresponding pathways producing only electricity. Pathways co-producing xylitol had at least 66% greater GHG emission reductions relative to pathways co-producing only lignin pellets. All feedstock/pretreatment/co-product combinations led to GHG reductions of at least 60%, meeting the threshold stipulated under the Energy Independence and Security Act.

Infrastructure systems are critical to a country’s prosperity. It is extremely important to manage the infrastructure systems efficiently in order to avoid wastage and to maximize benefits. Deterioration of infrastructure systems is one of the primary issues in civil engineering today. This problem has been widely acknowledged by engineering community in numerous studies. We need to evolve efficient strategies to tackle the problem of infrastructure deterioration and to efficiently operate infrastructure. In this research, we propose stochastic models to predict the process of deterioration in engineering systems and to perform life-cycle analysis (LCA) of deteriorating engineering systems. LCA has been recognized, over the years, as a highly informative tool for helping the decision making process in infrastructure management. In this research, we propose a stochastic model, SSA, to accurately predict the effect of deterioration processes in engineering systems. The SSA model addresses some of the important and ignored areas in the existing models such as the effect of deterioration on both capacity and demands of systems and accounting for different types of failures in assessing the life-span of a deteriorating system. Furthermore, this research proposes RTLCA, a renewal theory based LCA model, to predict the life-cycle performance of deteriorating systems taking into account not only the life-time reliability but also the costs associated with operating a system. In addition, this research investigates the effect of seismic degradation on the reliability of reinforced concrete (RC) bridges. For this purpose, we model the seismic degradation process in the RC bridge columns which are the primary lateral load resisting system in a bridge. Thereafter, the RTLCA model along with SSA model is used to study the life-cycle of an example RC bridge located in seismic regions accounting for seismic degradation. It is expected that the models proposed in this research will be helpful in better managing our infrastructure systems.

Enhanced interest in biofuel production has renewed interest in bioenergy crop production within the United States. Agriculture’s role in biofuel production is critical because it has the potential to supply renewable energy while minimizing greenhouse gas (GHG) emissions. However, agronomic management practices influence direct and indirect GHG emissions, and both can have a significant impact on biofuel production efficiency. Our overall objective was to determine the carbon (C) footprint of bioenergy sorghum (Sorghum bicolor L.) production in central Texas. Specifically, we determined the impacts of crop rotation, nitrogen (N) fertilization, and residue return on direct and indirect GHG emissions, theoretical biofuel yield, C pools, and life cycle GHG emissions from bioenergy sorghum production in 2010 and 2011. An experiment established in 2008 near College Station, TX to quantify the impacts of crop management practices on bioenergy sorghum yield and soil properties was utilized, and included two crop rotations (sorghum-sorghum or corn-sorghum), two fertilization levels (0 or 280 kg N ha^(-1) annually), and two residue return rates (0 or 50% biomass residue returned) to assess management impacts on sorghum production, C cycling, and life cycle GHGs. Corn production was poor under moderate drought conditions, while bioenergy sorghum produced relatively large yields under both moderate and severe drought conditions. Nitrogen addition increased crop yields, and rotated sorghum had higher yield than monoculture sorghum. Fluxes of CO_(2) and N_(2)O were higher than those reported in literature and highest soil fluxes were frequently observed following precipitation events during the growing season. Residue return increased cumulative CO_(2) emissions and N fertilization increased N_(2)O emissions. Residue return also increased soil microbial biomass-C, an important indicator of soil quality. Continuous sorghum significantly increased soil organic C (SOC) concentrations near the soil surface and at two depths below 30 cm. Analysis of change in SOC across time to estimate net CO_(2) emissions to the atmosphere revealed bioenergy sorghum production accrued high amounts of SOC annually. Most treatments accrued more than 4 Mg C ha^(-1) yr^(-1) from 2008 to 2012, which indicated great potential for C sequestration and offsetting GHG emissions. Life cycle GHG emissions (as g CO_(2)-eq MJ^(-1)) were all negative due to high SOC increases each year and indicated all bioenergy sorghum production treatments sequestered atmospheric CO_(2) per unit of theoretical energy provided. Despite its relatively low production efficiency, rotated sorghum with N addition and residue return was selected as the ideal bioenergy sorghum production scenario due to a number of sustainability factors. Bioenergy sorghum may offer great benefit as a high-yielding biofuel feedstock with minimal impacts to net GHG emissions.