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Agricultural emissions represent greenhouse gas emissions from crop and livestock production. There are various estimates on agricultural emissions, however on average about 14 to 25 percent of total global emissions comes from agriculture. The main goal of this paper was to present distribution of agricultural emissions among OECD countries with the help of clustering analysis. Clustering analysis is one of the tools used in the field of exploratory data mining. Two methods were used in the analysis: K-means and HDBSCAN algorithms. Both techniques are part of unsupervised learning tasks, which group data into multiple clusters. Finally, an appraisal of obtained classifications was performed.

zeszyty naukowe szkoły głównej gospodarstwa wiejskiego w warszawie. problemy rolnictwa światowego/scientific journal warsaw university of life sciences-sggw. problems of world agriculture

Classification of Agricultural Emissions Among OECD Countries with Unsupervised Techniques