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National Repository of Grey Literature 2 records found  Search took 0.00 seconds. 
Strategies for reducing the negative effects of open aquaculture fish farming on the environment - a comparison of EU and ASEAN countries
 Kryčer, Jan
The aim of this thesis is to describe the differences in strategic plans for aquaculture in the Czech Republic, Sweden, Vietnam, and the Philippines. Another target that has been prioritized is the foreign and domestic investments in the development of aquaculture in individual countries. Major contributors to the sector in terms of investments are described in each country. The strategic plans were examined in terms of sustainable development and environmental pollution. The nations were chosen based on their availability of adequate indicators of freshwater aquaculture productivity and the presence of aquaculture strategy guidelines or equivalent documents. The findings indicated a substantial difference in terms of focusing on a variety of objectives. The European Union's member states have laid great emphasis on sustainability and quality, whereas ASEAN member states have placed a priority on production. However, it should be noted that Asian countries are gradually introducing more sustainable production methods. The conclusion of the thesis provides an outline of the major environmental risks associated with the development of open aquaculture fish farming. Additionally, it covers the overall differences in each state's aquaculture development strategy.
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Food Security and Machine Learning: Opportunities and Challenges
 Hruška, Adam ; Špelda, Petr (advisor) ; Plattner, Simon Antonin (referee)
The emergence of the effects of global warming, as well as the ongoing depletion of fossil fuels and fertile soil pose a serious threat for the future of the agricultural industry. Alternatively, the continuous population growth mainly in the less developed regions highlights the future need of approximately 70-110 percent increase in the overall output of contemporary food production. While the current conventional agriculture deploys a multitude of technologies including the precision agriculture framework, the future needs of the population exceed the projected capabilities of the industry. Machine learning as the current fastest growing technology represents the potential remedy for the emerging issues, yet the extent of successful implementation remains uncertain. The thesis aims to uncover the potential future implications of implementation of machine learning based technology in agriculture through the use of the new scenario building methodology. The analysis builds on a varying set of empirical data, current state of art projects in machine learning and multiple future trend projections. Albeit the scenario building technique allows for a potentially endless number of constructed scenarios, the thesis concentrates on three main plot lines. First scenario tackles the more probable...
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