Method of statistical tests in solving problems of food production management

Abstract

The article discusses some of the problems of forecasting and planning the production of agricultural products and harvesting of wild plants in conditions of uncertainty. Variants of their solution using the method of statistical tests are proposed. The factorial regression equations for modeling the yield of grain crops depending on precipitation in the initial growing season and time are given. Two dependencies are considered for different agrolandscape areas. In one case, the relationship between the yield of grain crops and precipitation and time was determined, and in the second, a dependence on precipitation was found. The direct and inverse problems are considered. The conditions for obtaining high and low yields were determined. A linear model for optimizing the placement of grain crops is proposed using the Monte Carlo method for assessing random indicators. The extreme problem is applied to a municipal district with developed agriculture. The results of modeling are presented, which make it possible to estimate the volume of production of grain crops for different levels of productivity, which correspond to the reference probabilities. A multi-criteria parametric model of the functioning of clusters for obtaining food wild-growing products with probabilistic estimates in constraints and a parameter included in the objective function is described and implemented. The yield of wild plants was used as a parameter, on which the sales price depends.