ON SOME METHODS OF MATHEMATICAL MODELING IN SOLVING THE PROBLEMS OF FORECASTING AND PLANNING AGRICULTURAL PRODUCTION
The paper deals with the issues of mathematical modeling related to solving applied problems for forecasting production and economic indicators with different anticipation and optimization of the receipt of food products. Comparison of simulation results using linear and nonlinear trends is performed. Attention is paid to the peculiarities of using growth models with saturation for solving predictive problems, taking into account the fact of the high uncertainty of indicators of agricultural production. The factorial models are analyzed in the conditions of growth of commodity producers' products. The possibilities of using various forecasting models to optimize the production of food products are considered. The problems of mathematical programming that allow obtaining optimal solutions in conditions of decreasing uncertainty by identifying statistical patterns of variability of time series and the main factors affecting production and economic indicators are highlighted. In this direction, deterministic models of parametric programming and models of parametric programming with random estimates are of great scientific and practical importance. In addition, the uncertainty of many parameters of mathematical models can be reduced through data granularity. In particular, this concerns the consideration of agricultural land as heterogeneous areas. The development of land reclamation involves solving the problems of optimizing the production of food products not only on non-irrigated lands, but also on irrigated areas. Moreover, sharp changes in climatic parameters strongly affect the yield of agricultural crops and sales of products. Therefore, to manage complex production processes, it is necessary to assess various situations associated with long-term and seasonal price fluctuations. Optimal options for selling products in accordance with cost contribute to higher profits. Examples of the use of mathematical models are given.