Neural Network System of Grain (Wheat) Yield Forecasting in Risky Agricultural Conditions on the Example of the North Kazakhstan Region

Abstract

The presented paper is relevant as forecasting of crop yields is one of the main tasks of agricultural planning in any state. The purpose of the study is to assess the practical prospects of using a neural network system for forecasting crop yields in risky agricultural conditions at agricultural enterprises of the Republic of Kazakhstan. The basis of the methodological approach is a combination of quantitative and qualitative methods of investigating the prospects for the development and practical implementation of a neural network system for forecasting grain yield in the activities of agricultural enterprises of the North Kazakhstan region, using the MATLAB software suite that considers a number of key factors from the standpoint of the effectiveness of the described processes. The findings logically reflect the practical value of using a neural network system for forecasting grain yields in risky agricultural conditions and identifying the main factors influencing the accuracy of forecasting grain yields.