Forecasting of prices in the field of crops-growing in Ukraine and regions

Economic Annals-ХХI: Volume 136, Issue 11-12(2), Pages: 26-29

Citation information:
Vasylieva, N. (2013). Forecasting of prices in the field of crops-growing in Ukraine and regions. Economic Annals-XXI, 11-12(2), 26-29. https://ea21journal.world/index.php/ea-v136-07/

Natalya Vasylieva
D.Sc. (Economics),
Professor,
Dnipropetrovsk State Agrarian University
25 Voroshilova St., Dnipropetrovsk, 49600, Ukraine
VasylievaN@i.ua

Forecasting of prices in the field of crops-growing in Ukraine and regions

Abstract. Introduction. The Ukrainian agriculture is one of the leading sectors of the native economy. More than 8% of all enterprises in our country are the agrarian ones, while their part is over 10% in 8 regions. The Ukrainian crops-growing is well known in the world and gives about 65% of the national agrarian production. But the external and internal crisis factors resulted in unstable gross harvests and yields of grain and oil crops, vegetables, fruit and berries. The changes of the profitability level of crops-growing are about 34%. It is a real threat to the stable increasing in the Ukrainian economy and gives an actual task for scientists – to ground the prospects of development of the national agriculture. The methods of economic and mathematical modeling give an objective support in solving this problem. Purpose of this research is to analyze the progress of plant-growing trends in the basic national directions of production and to build the economic and mathematical models of the prices forecasting in the field of crops-growing, which will help to determine the prospects of the Ukrainian agricultural entrepreneurs competitiveness increasing. Methods. This research is conducted by means of system analysis method. The statistical data is considered by means of correlation and regression method, while proposed economic and mathematical models are created under the neural nets theory. Results. It is set that the yields of grain and oil crops, vegetables, fruit and berries in Ukraine are 1,5-2 times less, than those for the European and world leaders. An internal market is saturated with grain, seed of sunflower, vegetables. But only 50% from the norm of fruit and berries is produced. To expose export potential of the Ukrainian plant-grower till 2020, it is necessary to increase the production of grain and oil crops on 70%, and 20% due to agricultural enterprises. It is obligatory to increase production of vegetables, fruit and berries on 40%, and 120% till 2020 due to the households. It is shown that in Dnipropetrovsk region the realization price does not correlate with harvests of grain. The natural and climatic risks of agriculture in steppe explain this fact. For their decline, it is necessary to bring in new agrarian technologies and high-performance sorts of grain-crops. Close direct dependences of price increasing and harvests of oil cultures and vegetables are present in Ukraine, and in Dnipropetrovsk region particularly. The increase of realization prices on fruit and berries does not result in their gross production stabilizing. The built economic and mathematical models in the form of neural networks at the sample data of 1996-2013 set price dependences better, than econometric regressions, at least on 10%.

Conclusions. Trends of progress in the Ukrainian crops-growing specify the positive approaching to 25% level of profitability, which will provide extended recreation and justify the strategy of the national agriculture stable increase support. On the basis of the model prices forecasts, the producers of grain and oil crops, vegetables, fruit and berries will be able to calculate future profitability of own products, change production plans, correct charges and, as a result, strengthen their competitiveness. Further scientific researches will be connected with the economic and mathematical models creation to determine the regional capacities of the Ukrainian agrarian producers at the internal and external markets.

Keywords: Agriculture; Crops-Growing; Economic and Mathematical Modeling; Neural Networks; Prices Forecasting

JEL Classification: C45; E37; Q11

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Received 12.09.2013