Pattern Formation Of Business Conditions In Domestic Market Of Crop Production

The purpose of the article is to detect crop production on agricultural enterprises clusters at a price by defining their amplitude of price fluctuations. Methodology. The study is based on grouping of statistical data from agricultural enterprises using cluster analysis, followed by reliability evaluation of pre-selected clusters by t-test and charting the scope by the selected index. Cluster analysis of agricultural enterprises in Ukraine has been conducted using "STATISTICA" program. Distance between clusters was calculated as the Euclidean distance. The object of the study was data on the prices for agricultural enterprises by regions of Ukraine in 2013. As a result, an appropriate number of groups according to the produce types in the regions of Ukraine, plane in market prices, have been determined. The process of consistent combination of objects in clusters is shown in the graphs as agglomerative clustering dendrogram of the regions of Ukraine for such products as wheat, grain corn and sunflower seeds. In general, there have been examined: 7311 businesses growing wheat, 5034 – growing corn and 6124 companies growing sunflower. Results. During a year-long study of price fluctuations in agricultural enterprises within regions of Ukraine similarities in nature of absolute and relative changes in the formed clusters were established. Four clusters on wheat, five clusters on corn, three clusters on sunflower seeds have been allocated during the study. The study of the selected groups confirms significant differences between them and allows the sectors and enterprises of the cluster with high variability of prices to build their own marketing strategy based on the position of expectations and search for sale options according to the most favorable price. Practical value. The established differences on the selected clusters make it possible to forecast the price situation in various regions of Ukraine in terms of its differences from average by clusters for each product. Accordingly, it will enable specific producers to define the marketing strategy for pricing in the region for each product. Value/originality. The data on groups of growing crops permit to select forecast marketing strategies or rapid sale according to the prevailing prices.