Assessing farmer behaviour as affected by policy and technological innovations: bio-economic farm models

Farm Management,

Methods to Support the Project Selection Problem With Non-Linear Portfolio Objectives, Time Sensitive Objectives, Time Sensitive Resource Constraints, and Modeling Inadequacies

The United States Air Force relies upon information production activities to gain insight regarding uncertainties affecting important system configuration and in-mission task execution decisions. Constrained resources that prevent the fulfillment of every information production request, multiple information requestors holding different temporal-sensitive objectives, non-constant marginal value preferences, and information-product aging factors that affect the value-of-information complicate the management of these activities. This dissertation reviews project selection research related to these issues and presents novel methods to address these complications. Quantitative experimentation results demonstrate these methods’ significance

Development and Application of an Optimum Machinery Complement Selection System

Agricultural Economic

On the design of a European bioeconomy that optimally contributes to sustainable development

The inevitability for a change in humankind's resource and fossil energy consumption is demonstrated by global crises such as the climate change, disturbances of natural cycles, and the loss of biodiversity. The sun provides sufficient energy to generate electricity and by photosynthesis, solar radiation is converted into energy chemically bound in biomolecules, which provide building blocks for the production of various materials, chemicals, or fuels. The bioeconomy puts biomass at the center of an economy that attempts to cover resource and energy demand by renewable materials to address the global challenges. However, the finiteness of the terrestrial surface limits renewables, requiring a prioritization of use. The Sustainable Development Goals (SDGs) provide a common ground for global peace, prosperity, improved health and education, reduced inequality, and spur economic growth while tackling climate change and biodiversity loss, making it the most comprehensive framework for defining objectives in the design of the bioeconomy. Against this background, this dissertation is particularly dedicated to the design of bioeconomic value chains based on agroforestry residues in the European Union, considering economic, environmental, and social objectives to optimally exploit the potential to contribute to a sustainable development. All objectives are matched to SDGs to unveil congruencies, conflicts and trade-offs between different goals, and to provide aggregated insights and courses of action in the agroforestry residue-based bioeconomy to politics, the scientific community, and corporate decision-makers. The availability of agroforestry residue volumes and their current uses is the first major concern of a bioeconomy aligned with the SDGs to be assessed in this work. Key findings are that the most promising agricultural residue in the EU is wheat straw, followed by maize stover, barley straw, and rapeseed straw, which together account for about 80% of EU’s cereals and oil crops residues. In forestry, waste bark from the two coniferous species, spruce and pine, are most promising with the highest supplies in Scandinavia and central EU. The time-series-based forecast model predicts a total increase of the bioeconomic potential of the prioritized agricultural feedstocks from 113 Mt in 2017 to 127 Mt in 2030. The forecast indicates the largest increase of all investigated crops for corn stover at up to 20% until 2030, while rapeseed straw production is forecasted to decrease in many regions. To take environmental and social aspects into account on a regional level, along with international competitiveness, this dissertation develops a multi-criteria strategic network design model for the planning of bioeconomic value chains. The environmental and social objectives are derived by means of Life Cycle Assessment and Social Life Cycle Assessment, respectively. The developed set of 35 economic, environmental, and social objective functions allows for the consideration of 16 of the 17 SDGs. The model is applied for the planning of a second-generation bioethanol production network based on agricultural residues in the EU. Single-criteria optimization shows that sustainably available agroforestry residues could substitute up to 22% of the petrol demand in the EU in 2018 under optimal production networks for certain objectives (i.a., global warming). For environmental objectives, the decision to substitute petrol or edible crops-based ethanol has the highest impact. The greenhouse gas benefits could amount to up to 59 Mt CO2 eq., conforming to about 1.35% of the EU’s 2018 total emissions. However, global warming optimization leads to opportunity costs for other objectives. While for ecosystem quality, for example, the achieved value reaches 50% of its optimum, other categories like land use and water consumption could even be net deteriorated by optimizing global warming. For objectives such as land use, only 19% of the total agroforestry residues is used to substitute 100% of the edible crops-based ethanol, which would free up 11.7 billion m2 crop land. Social objectives lead to large and labor-intensive production networks distributed all over the EU. Depending on the social objective, the value creation slightly shifts regionally. To optimize local employment, the network relocates to regions with high unemployment rates, such as Spain, Italy, and parts of France. Economically strong metropolitan regions are at a disadvantage in favor of weaker regions of Central and Eastern EU when optimizing economic development. At best, up to 140,000 new jobs could be created in the EU while 12,000 jobs could be lost due to substitution of reference products. In terms of network extend, most socially and environmentally optimal production networks are similar, although the substitution decision has little impact for social objectives. This means that interesting trade-offs between social and environmental objectives can be found with only minor sacrifices. Economically optimal networks are much smaller and more centralized than environmental ones, and lead to costs of about 0.75 €/l second-generation ethanol. Environmental optimization results in cost between 0.88 €/l to 2.00 €/l, which implies that large-scale bioethanol production is not economically feasible with today’s oil prices and taxes. While the single-criteria optimization reveals conflicts within and between the environment, social, and economic dimensions, Pareto optimization is conducted to unveil trade-offs between conflicting goals. Significant environmental and social benefits can often be realized with only small economic detriments, and vice versa, economic profitability can substantially be improved at low environmental opportunity cost. Furthermore, the applied Pareto optimization shows that the endpoints human health and ecosystem quality are suitable aggregators of environmental impact categories, wherefore they could serve as representative of the environmental dimension in decision-making. Nonetheless, a transparent consideration of a broad range of impacts and knowledge about the categories’ contributions remains indispensable to reveal possible negative consequences of a decision. In a final step, the objective functions are matched to SDGs, and opportunity cost between the objective functions are calculated to unveil congruencies and conflicts between different goals. The assessment of relationships between the different SDGs supports the perception that different aspects of sustainability are not equally directed. Sustainability, expressed by the SDGs, is rather case-specific and varies between a multitude of interdependent social, environmental, and economic criteria. Decision-makers, whether at the corporate level pursuing one or more business objectives or at the policy level, using the SDGs as a framework, should be aware of the reciprocities between the different criteria. The dissertation shows that the European bioeconomy has a great potential to contribute to sustainable development. Multi-criteria optimization models enable sound trade-off decisions that are aligned to the SDGs

Managing the Hydra in integration: developing an integrated assessment tool for agricultural systems

Keywords: modelling, bio-economic, farm, simulation, ontology, knowledge management, Europe, agricultural management, database, scenario Ex-ante assessment through science-based methods can provide insight into the impacts of potential policy measures or innovations to manage complex problems (e.g. environmental pollution, climate change, or farmers’ welfare). Integrated Assessment and Modelling (IAM) is a method that supports ex-ante assessment through modelling and modelling tools. One type of IAM links models focusing on particular processes on a specific scale into model chains covering multiple scales and disciplines. To achieve an operational model chain for IAM, methodological, semantic and technical integration is required of models, data sources, indicators and scenarios. In this thesis, methodological, semantic and technical integration focuses on two case studies. The first case study is on integration within bio-economic farm models covering two hierarchical systems levels involving a small team of scientists. The second case refers to modelling European agricultural systems. In this case, the integration covers five hierarchical systems levels and different types of models were linked by a large team of about hundred scientists. In the context of these two case studies, many different integration topics and challenges have been addressed: a review of the state-of-the-art in bio-economic farm models, a generic method to define alternative agricultural activities, development of a generic bio-economic farm model, development of an integrated database for agricultural systems, linking different agricultural models and a shared definition of scenarios across disciplines, models and scales. Ultimately, elaborating the methodological, semantic and technical integration greatly contributed to the development of an integrated assessment tool for European agricultural systems. This integrated assessment tool can be used across disciplines and for multi-scale analysis, and allows the assessment of many different policy and technology changes. </p

A Multiobjective Approach Applied to the Protein Structure Prediction Problem

Interest in discovering a methodology for solving the Protein Structure Prediction problem extends into many fields of study including biochemistry, medicine, biology, and numerous engineering and science disciplines. Experimental approaches, such as, x-ray crystallographic studies or solution Nuclear Magnetic Resonance Spectroscopy, to mathematical modeling, such as minimum energy models are used to solve this problem. Recently, Evolutionary Algorithm studies at the Air Force Institute of Technology include the following: Simple Genetic Algorithm (GA), messy GA, fast messy GA, and Linkage Learning GA, as approaches for potential protein energy minimization. Prepackaged software like GENOCOP, GENESIS, and mGA are in use to facilitate experimentation of these techniques. In addition to this software, a parallelized version of the fmGA, the so-called parallel fast messy GA, is found to be good at finding semi-optimal answers in reasonable wall clock time. The aim of this work is to apply a Multiobjective approach to solving this problem using a modified fast messy GA. By dividing the CHARMm energy model into separate objectives, it should be possible to find structural configurations of a protein that yield lower energy values and ultimately more correct conformations