Agricultural administration in the context of the digital transformation of the economy is becoming more important when ensuring the competitive advantages of our country, especially taking into account the challenges of the modern geopolitical situation. The introduction of various kinds of innovations requires prompt actions in order to facilitate the development of domestic technical, technological and information products. The article deals with the issue of automation of decision support in the management of agriculture subsystems by means of a developed software product with adaptive characteristics that does not require additional digital and qualification resources. Methods of system analysis, logical approach and synthesis, optimization, algorithmization, etc., were used. Official statistical data were used, which made it possible to present the dynamics of a number of indicators (acreage and yield of fodder crops, number of cows and milk production, etc.) of agricultural production in Russia for the period 2017–2021. The conclusion is made about the intensification of production activities in the field of dairy cattle breeding. Special attention is paid to the development of algorithms and their software implementation with a view to adjusting the diet of dairy cows with consideration to scientifically based requirements, breed restrictions, norms, etc. Flowcharts for designing a user interface and functions for calculating the required amount of minerals, algorithms for calculating energy and protein requirements are given. Developed in Python, the program takes into account the selected parameters for calculating the productive feeding ration of cows and is an integral part of the intellectual system being developed. By means of simulation, it helps to choose the most suitable values of output parameters for their further use in the form of numerical restrictions when solving the problem of minimizing the cost of the feeding ration by linear programming methods. The program has a universal character regarding the introduction and use by agricultural producers, provides automation of the decision support system and does not require additional time-consuming training of decision makers who aim to achieve the efficiency of domestic agriculture performance.
1. Bryanskikh S.P. Economics of agriculture. M.: Agropromizdat; 2017. 326 p. (In Russ.).
2. Nagoev Z.V., Shuganov V.M., Brzhikhatlov K.Ch., Zammoyev A.U., Ivanov Z.Z. Prospects for increasing productivity and efficiency of agricultural production using an intelligent integrated environment. Izvestija Kabardino-Balkarskogo nauchnogo centra RAN = Proceedings of the Kabardino-Balkarian Scientific Center of the Russian Academy of Sciences. 2021;104(6):155–165. DOI: 10.35330/1991-6639-2021-6-104-155-165 (In Russ.).
3. Speshilova N.V., Shepel V.N. Intellectualization of the technology of preparation of managerial decisions in the conditions of digitalization of the economy (on the example of regional agricultural production). OUP VO "ATISO"; FGBOU VO "OSU". Orenburg, Printing house "Express-print"; 2022. 152 p. (In Russ.).
4. Kosnikov S.N., Kornienkov I.M., Zhikhareva I.A. The problem of introducing artificial intelligence into agricultural production. Auditorskie vedomosti = Audit statements. 2022;2:124–128. DOI: 10.24412/1727-8058-2022-2-124-128 (In Russ.).
5. Fedotova G.V., Slozhenkina M.I., Mitrofanova I.V., Lamzin R.M. Artificial intelligence as an innovative vector of regional agro-industrial complex management. Regional'naja jekonomika. Jug Rossii = Regional economy. South of Russia. 2021;9(1):152–162. DOI: 10.15688/re.volsu.2021.1.13 (In Russ.).
6. Golubeva O.L. The use of mathematical modeling and DSS in the management of the agro-industrial complex. Nauka i sovremennost' = Science and modernity. 2013;24:271–275 (In Russ.).
7. V.N. Shepel, N.V. Speshilova, M.V. Kitaeva. The Stimulation Model for the Criterial Decision-Making at the Agricultural Enterprise. 17th International Scientific Conference “Problems of Enterprise Development: Theory and Practice” (Samara, Russia, November 26-27, 2018). SHS Web of Conferences, Volume 62, 2019. URL: https://www.shs-conferences.org/articles/shsconf/pdf/2019/03/shsconf_pedtp2018_08004.pdf. DOI: 10.1051/shsconf/20196208004.
8. Fedorov V.I. Growth, development and productivity of animals. Moscow, Kolos; 2017. 345 p. (In Russ.).
9. Waldman E.K., Karelson M.K. Highly productive dairy cattle breeding. Moscow, Kolos; 1982. 235 p. (In Russ.).
10. Golushko V.M. [et al.]. Physiology of digestion and feeding of cattle. Grodno, 2005. 433 p. (In Russ.).
11. Zavodchikov N.D., Speshilova N.V., Zabrodina L.A. Efficiency and cost management in dairy production. CJSC "Publishing House "Economics"; 2009. 131 p. (In Russ.).
12. Lapshin S.A. New in the mineral nutrition of farm animals. Moscow, Rosagropromizdat; 2018. 45 p. (In Russ.).
13. Shupik M.V., Raichman A.Ya. Feeding of farm animals. Feeding cattle, sheep, goats and horses. Slides: BGSHA; 2014. 237 p. (In Russ.).
14. Kurilov N.V. The use of animal feed protein. Moscow, Kolos; 2017. 345 p. (In Russ.).
15. Priporov I. E., Gavrilov E.V. Development of technology for the preparation of compound feed using modern computer technology. Izvestija OGAU. 2021;90(4):142–145 (In Russ.).
Rakhmatullin Rustam Ravilevich
Candidate of Technical Sciences, Associate Professor
eLibrary |
Orenburg State University
Orenburg, the Russian Federation
Speshilov Evgeny Alekseyevich
eLibrary |
Orenburg State University
The Orenburg Branch of the Institute of Economics of the Ural Branch of the Russian Academy of Sciences
Orenburg, the Russian Federation
Chumakov Aleksandr Alekseyevich
Orenburg State University
Orenburg, the Russian Federation
