UDC 338.27
DOI 10.36461/NP.2023.68.4.021

ECONOMIC AND MATHEMATICAL METHODS AND MODELS IN FORECASTING THE DEVELOPMENT OF AGRICULTURE
N.F. Zaruk¹, Doctor of Economic Sciences, Professor; G.A. Volkova², Candidate of Economic Sciences, Associate Professor; O.N. Sukhanova², Associate Professor; O.V. Mentyukova², senior lecturer; V.D. Badov², assistant
¹Federal State Budgetary Educational Institution of Higher Education “Russian State Agrarian University - Moscow Timiryazev Agricultural Academy”, Moscow, Russia;
²Federal State Budgetary Educational Institution of Higher Education “Penza State Agrarian University”, Penza, Russia, tel. (937) 4119433, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

The article describes the stages of development of economic forecasting in Russia from the beginning of the XX century to the present. The problem of forecasting socio-economic processes is very important due to the fact that in present situation, the effectiveness of enterprises and organizations depends on the ability to anticipate market situation and development trends in the future. In the practice of forecasting a significant number of different processes, time series models are used. One of the priorities of the agrarian state policy is to improve the efficiency of the dairy industry. Although the last decade in Russia has been characterized by a decrease in the number of cows, the dynamics of milk production has not been strong, yet steady. This is due to the increased productivity of the dairy herd. If we analyze the dynamics of milk production in Russia for the period from 1990 to 2022, it tends to decrease [1]. This was confirmed by a trend model based on the equation of a third-order polynomial. In the article, mathematical dependencies are obtained and analyzed, that describe with a high degree of reliability the dynamics of changes in some indicators of the dairy industry. For this pur-pose, the selection of factors that affect milk production was carried out, and a linear regression model was built, which, despite the relatively simple mathematical apparatus, provides a lower risk of significant forecast errors compared with nonlinear models. The regression equation included variables: raw milk production, cow density per 100 hectares of agricultural land, feed consumption per 1 average annual head, and the share of agriculture in the Russian economy. The estimation of multiple regression parameters was performed using the STADIA statistical package. The forecast of milk production was made using the multiple regression equation. The values of the factors included in it were previously predicted using trend models.
Keywords: forecast, mathematical methods, models, agriculture, milk production, trend, regression.

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