The paper presents an algorithm for modeling production and economic characteristics for obtaining crop products, with the help of which planning problems are solved using multi-level parametric programming methods under average and unfavorable operating conditions for commodity producers. Since time series of characteristics associated with the production of products can be described by trends to assess adverse events, an algorithm for their identification was used based on the construction of multi-level trends according to the idea of ​​​​the hierarchical structure of the time series. When using this algorithm, a sequence of local minima is formed from the original series, a trend is built, and levels located below this trend, called unfavorable events, are identified. The assessment of the probabilities of these events is determined by the distribution law, which describes a number of differences in actual data and trend values ​​of a sequence of local minima. In the absence of trends and considering series of characteristics in the form of random variables, statistical and physical criteria are applicable to identify unfavorable events. As such, it is proposed to use the average value of local minima. Of the adverse events received, the smallest of them are distinguished, which represent rare events. Based on the identified events and other characteristics, the problem of optimizing the production of agricultural products is formed, the solution of which allows us to obtain optimal production volumes in accordance with maximum incomes corresponding to the calculated probability of events. A comparative analysis of planning results under average conditions and taking into account unfavorable events shows the likely losses of commodity producers at the enterprise and municipal district level.

Keywords: parametric programming, trend, adverse event, production and economic characteristics, crop production, losses, risks