Abstract
Agriculture is one of the key sectors of the country's economy. However, due to the uncertainty and specifics of this industry, traditional forecasting methods do not provide the desired results. Therefore, this study is aimed at creating models for forecasting the development of the agricultural sector until 2030 using multifactorial regression and trend modeling. The methods of correlation regression made it possible to take into account the influence of a number of key factors on the total volume of agricultural production in the forecasting process. The use of linear, exponential, power and parabolic trend models helped to determine the general trends in gross agricultural output and the main indicators of productivity of both crop production and livestock, based on data for the period from 2013 to 2023
Keywords
References
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