Abstract:
This study investigates the application of Artificial Neural Networks (ANN) in forecasting
agricultural yields in Kazakhstan, highlighting its implications for economic management and
policy-making. Utilizing data from the Bureau of National Statistics of the Republic of
Kazakhstan (2000-2023), the research develops two ANN models using the Neural Net Fitting
library in MATLAB. The first model predicts the total gross yield of main agricultural crops,
while the second forecasts the share of individual crops, including cereals, oilseeds, potatoes,
vegetables, melons, and sugar beets. The models demonstrate high accuracy, with the total gross
yield model achieving an R-squared value of 0.98 and the individual crop model showing an R
value of 0.99375. These results indicate a strong predictive capability, essential for practical
agricultural and economic planning. The study extends previous research by incorporating a
comprehensive range of climatic and agrochemical data, enhancing the precision of yield
predictions. The findings have significant implications for Kazakhstan's economy. Accurate yield
predictions can optimize agricultural planning, contribute to food security, and inform policy
decisions. The successful application of ANN models showcases the potential of AI and machine
learning in agriculture, suggesting a pathway towards more efficient, sustainable farming
practices and improved quality management systems.
