Cluster model for big data analysis in livestock production

Intelligent methods of analysis, which include the problem of clustering, are widely used in the field of economics of the agro-industrial complex. The clustering problem belongs to the class of unsupervised learning problems. The essence of the problem is the grouping of research objects according to the use of similarity. If the regions of the Russian Federation are selected in terms of livestock production, they can also be automatically grouped according to the similarity recipe. The k-means method is currently a successful method for solving clustering problems. The main stage of solving the problem is the collection of data, which includes all the main characteristics of the object. For example, if you set up production in the region in terms of animal husbandry, then it would be more logical to x1 - meat production per capita and x2 – milk production per capita. The criterion for choosing the number of clusters is the root mean square error. In total, 79 regions of the Russian Federation participated in the analysis. It turned out that the potential to break all regions of the Russian Federation into 7 clusters of similarity. Regions with high milk and meat production (clusters 4 and 6), regions with high milk and meat production (clusters 2, 3, 5) and regions with low milk and meat production (clusters 0, 1) were identified.

1. Pogonysheva D. A., Savin A.V., Seraya G. V., Tasoeva E. V. Digital technologies in personnel management in agriculture. Bulletin of the Bryansk State Agricultural Academy. 2021;3(85):60–66. DOI 10.52691/2500-2651-2021-85-3-60-66. EDN ZYGAOT. (In Russ.)

2. Smelik N. L. The essence of transformation of the economic system and its mechanism. Izvestiya of the St. Petersburg State Agrarian University. 2014;(37):97–103. EDN UXWKXL. (In Russ.)

3. Smagin B. I. The use of cluster analysis in the analysis of economic processes of agricultural production. Science and Education. 2021;4(2). EDN FPYMAK. (In Russ.)

4. Menshikova M. A., Khodyrevskaya V. N., Simakhina O. N., Shakirova D. F. Territorial analysis of the level of development of construction in Russia by the method of cluster analysis. Economics and entrepreneurship. 2021;2(127):456-460. DOI 10.34925/EIP.2021.127.2.086. EDN KSZPEO. (In Russ.)

5. Zambrzhitskaya E. S. Cluster analysis as a preliminary stage of break-even analysis. Application of mathematics in economic and technical research. 2020;1(10):109–115. EDN WXKTIH. (In Russ.)

6. Shamsutdinova T. M. Technologies of intellectual analysis of statistical data (on the example of cluster analysis of agricultural production indicators of the subjects of the Russian Federation). In: Modern scientific and practical solutions in agriculture : Materials of the international scientific and practical conference, Voronezh, June 06-07, 2017. Voronezh: Voronezh State Agrarian University named after Emperor Peter I; 2017:467–473. EDN ZWGBHD. (In Russ.)

7. Kosyakova L. N., Popova A. L. Innovative policy in the agricultural sphere. British Journal for Social and Economic Research. 2016;1(2):29–38. EDN XCPWLV.

8. Lavrova A. P. The role of personal subsidiary farms of rural population in food supply. Izvestiya of St. Petersburg State Agrarian University. 2015;(40):186–191. EDN UXWNVZ. (In Russ.)

9. Ulyanova N. D. Artificial intelligence in electronic document management systems. In: Problems of energy supply, automation, informatization and environmental management in agriculture : Collection of materials of the international scientific and technical conference, Bryansk, April 30, 2022. Bryansk: Bryansk State Agrarian University; 2022:214–220. EDN DWLLIB. (In Russ.)

10. Grin M. G., Grin A. M. Internal reporting of agricultural organizations and its use in the management system. Bulletin of the Bryansk State Agricultural Academy. 2011;(4):52–56. EDN THKTWH. (In Russ.)

11. Zolotareva Yu. P., Galanina O. V. Strategic planning and programming of regional and municipal development of rural territories. Proceedings of the International Academy of Agrarian Education. 2021;(56):69–72. EDN FBYYFL. (In Russ.)

12. Galanina O. V. Big Data in the planning of restoration of dairy cattle herd. Proceedings of the International Academy of Agrarian Education. 2022;(59):92–95. EDN NAMWLU. (In Russ.)