Problems of analysis and forecasting of the level of technical equipment of agricultural enterprises (on the example of the Ryazan region)

Abstract

Currently, the level of technical equipment of agricultural enterprises in the Russian Federation remains at a low level. On average, tractor availability in the country is 2–3 times less than in Kazakhstan and Belarus, and 20–30 times lower than in Western Europe and the United States. At the same time, agricultural production is faced with the task of achieving the level defined by the Food Security Doctrine in a number of industries, and increasing exports in a number of industries (production of cereals and industrial crops). In the Ryazan region, there is an appropriate resource potential for solving these problems. But the main factor constraining the increase in production is the insufficient development of the material and technical base. The dynamics of the number of major types of equipment over the past 10 years remains negative. At the same time, the region has a State program of the Ryazan region “Development of the agro-industrial complex until 2025”, which provides for a number of measures to maintain the level of technical equipment of agriculture. In this regard, the issue of scientific justification of the required amount of equipment and forecasting for the near future becomes relevant. On the regional scale, this is a difficult task, since traditional forecasting methods are not fully suitable for solving this problem due to the wide variety of production conditions in farms — soil fertility, climatic conditions, economic situation, specialization, etc. Therefore, we propose a method for determining the required amount of equipment and the amount of investment, based on the allocation of cluster groups and, then, the development of economic and mathematical models for the use of machine and tractor fleet for typical farms of each cluster group. This article analyzes the level of technical equipment of agriculture in the Ryazan region and identifies cluster groups of farms.