Production cluster in the agro-industrial complex as a factor in ensuring food security
Keywords:
assessment methodology, growth rate, integral indicator, optimization model, trends, development, food security, strategy, socio-economic status, influence, efficiency, grain, trend, trigger, function, location, resultSynopsis
The research focuses on the functioning of grain product cluster enterprises. The study addressed the growth rate and operational characteristics of grain product cluster enterprises.
Sustainable development of Kazakhstan's economic sectors and sectors requires the exploration and implementation of new, more efficient forms of production and business activities. Given the current complex socio-economic conditions in agricultural production, the grain product cluster is becoming one of the most in-demand sectors in the agro-industrial sector. Therefore, a priority area of national policy is to increase production volumes both to fully meet domestic demand and to increase exports. This approach requires the unification of efforts by all agricultural sector entities, coordination of activities, and a focus on achieving high end results.
One of the triggers for solving this problem is the integration of commodity producers, which allows for the unification of all links in the production cycle in the technological chain "raw material production – finished product production" within a single complex. A study of domestic and international experience shows that integrated entities such as grain product clusters achieve high levels of efficiency and competitiveness. The development of grain product cluster models and mechanisms, the modernization of agricultural and processing industries, and the selection of methods and tools that enhance agribusiness's responsiveness to innovative development require appropriate theoretical and methodological support, taking into account the specifics of production in each industry. Practical experience shows that, despite the intensification of integration processes in grain product clusters, inefficiencies and the disintegration of a number of such formations are occurring. This is largely due to the fact that, under the new economic conditions, the traditional mechanism of the grain product cluster in Kazakhstan's agro-industrial complex does not allow for the systematic implementation of large-scale innovation processes, limiting itself to minor (local) changes.
Successful implementation of projects to form and develop grain product clusters requires in-depth study, generalization, and systematization of the experience of using such a mechanism by both national and foreign companies that have achieved high results in this area. Currently, the grain product cluster remains in its early stages of development, largely due to the specific high-risk characteristics of its industries.
References
Beisekova, P., Ilyas, A., Kaliyeva, Y., Kirbetova, Z., Baimoldayeva, M. (2023). Development of a method for assessing the functioning of a grain product sub-complex using mathematical modeling. Eastern-European Journal of Enterprise Technologies, 2 (13 (122)), 92–101. https://doi.org/10.15587/1729-4061.2023.276433
Mizanbekova, S. K. (2012). Cluster of Kazakhstan processing grain – the basis of the interstate agrofood cluster. Issledovaniia, rezultaty, 4, 119–123.
Hagerty, M. R. (1985). Improving the Predictive Power of Conjoint Analysis: The use of Factor Analysis and Cluster Analysis. Journal of Marketing Research, 22 (2), 168–184. https://doi.org/10.1177/002224378502200206
Kaufman, L., Rousseeuw, P. J. (2009). Finding groups in data: an introduction to cluster analysis. New York: John Wiley & Sons, 344.
Hastie, T., Tibshirani, R., Friedman, J. (20009). The elements of statistical learning: data mining, inference, and prediction. Vol. 2. New York: Springer, 764. https://doi.org/10.1007/978-0-387-84858-7
van Wieringen, W. N. (2019). The Generalized Ridge Estimator of the Inverse Covariance Matrix. Journal of Computational and Graphical Statistics, 28 (4), 932–942. https://doi.org/10.1080/10618600.2019.1604374
Zhang, Y., Lu, Z. P., Ma, S. G., Liaw, P. K., Tang, Z., Cheng, Y. Q., Gao, M. C. (2014). Guidelines in predicting phase formation of high-entropy alloys. MRS Communications, 4 (2), 57–62. https://doi.org/10.1557/mrc.2014.11
Patterson, K. D. (2003). Exploiting information in vintages of time-series data. International Journal of Forecasting, 19 (2), 177–197. https://doi.org/10.1016/s0169-2070(01)00145-5
Chen, C.-Y., Chou, Y.-L., Lee, C.-S. (2021). Social Innovation, Employee Value Cocreation, and Organizational Citizenship Behavior in a Sport-Related Social Enterprise: Mediating Effect of Corporate Social Responsibility. Sustainability, 13 (22), 12582. https://doi.org/10.3390/su132212582
Meyers, C. H. (1970). Handbook of basic graphs: A modern approach. Belmont, 214.
Hirsch, W. Z. (1970). The economics of state and local government. New York, 333.
Spiegel, S. (2015). Time series distance measures: segmentation, classification, and clustering of temporal data. Berlin, 212.
Box, G. E. P., Jenkins, G. M. (1976). Time series analysis: Forecasting and control. San Francisco, 575.
Nugus, S. (2009). Smoothing Techniques. Financial Planning Using Excel. Boston, 47–58. https://doi.org/10.1016/b978-1-85617-551-7.00004-5
Beisekova, P. D., Bolatkyzy, S., Abutalipova, Zh. A. (2022). Features of grain product cluster: market reorientation. Problems of AgriMarket, 1, 120–127. https://doi.org/10.46666/2022-1.2708-9991.14
Natcionalnoe biuro statistiki Agentstva Respubliki Kazakhstan po strategicheskomu planirovaniiu i reformam. Available at: https://www.stat.gov.kz/ Last accessed: 20.06.2025
Informatcionno-analiticheskaia sistema. Biuro Natcionalnoi statistiki Agentstva po strategicheskomu planirovaniiu i reformam Respubliki Kazakhstan (Taldau). Available at: https://taldau.stat.gov.kz/ru/Search/SearchByKeyWord Last accessed: 20.06.2025
Boer, F. R. de, Mattens, W. C. M., Boom, R, Miedema, A. R., Niessen, A. K. (1998). Cohesion in metals. Transition metal alloys. Netherlands, 774.
Narynbaeva, A. S. (2017). Rol agropromyshlennogo kompleksa kak strukturnoi sostavliaiushchei regionalnoi ekonomiki. Problems of AgriMarket, 1, 28–34.
Tireuov, K. M., Mizanbekova, S. K., Nurmanbekova, G. K. (2020). Feed grain market in Kazakhstan. Problems of AgriMarket, 1, 121–126.
Pavlina, E. J., Van Tyne, C. J. (2008). Correlation of Yield Strength and Tensile Strength with Hardness for Steels. Journal of Materials Engineering and Performance, 17 (6), 888–893. https://doi.org/10.1007/s11665-008-9225-5
Ivanova, T., Kadyshev, E., Ladykova, T., Brenchagova, S., Nemtsev, V., Ivanova, A. (2021). Forecasting Agricultural Production as a Tool for Effective Industry Management (On the Example of the Chuvash Republic). Robotics, Machinery and Engineering Technology for Precision Agriculture. Springer, 393–402. https://doi.org/10.1007/978-981-16-3844-2_36
Polezhaev, V. D., Polezhaeva, L. N. (2018). Nonlinear paired regression models in the econometrics course. Modern problems of science and education, 4. Available at: https://s.science-education.ru/pdf/2018/4/27855.pdf
Published
Categories
License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.