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The integration of information and communication technologies into agriculture lead to the development of precision agriculture. Nowadays it relies on Internet of Things devices, geospatial data, historical and real-time information, which has the potential to transform farming into smart farming. However the use of Big Data requires significantly different skills and knowledge, compared to what many farmers and agronomists possess, which is an obstacle for their effective use. This study aims to summarize and provide insight into the common methods used for data analysis in a wide variety of agricultural applications. Initially, the basic characteristics and sources of agricultural data are explained. Next, a review of the common data analysis methods (classification, clustering and regressions) is provided, containing information about the data sources used as well as the desired goal of the analysis. At the end of the paper, a summary is given on the applicability of data analysis methods depending on the desired goals.

This paper presents a new approach for reducing the CO 2 emissions in the transport sector based on emission compensation. A cost-benefit analysis method of investments in photovoltaic parks at petrol stations is used, which accounts for all the initial investments and maintenance costs in terms of expenses. The benefits are divided into financial and ecological. The method has been implemented in the specialised cost-benefit analysis tool, and an analysis has been performed for the city of Ruse, Bulgaria. Risk analysis on the influence of the main risk factors -the price of conventional energy and the buying price of energy from renewable energy sources is also performed. The results showed that investments in photovoltaic parks at petrol stations could pay off within 6 to 9 years in the more likely scenarios. The investment could reduce the CO 2 emissions, related to the petrol station, by 10% to 37% depending on the scenario and the criterion used.

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