JUSTIFICATION OF OPTIMAL PARAMETERS OF FRUIT PRODUCTION EFFICIENCY USING DIGITAL TECHNOLOGIES

DOI 10.12737/2073-0462-2025-20-3-91-98 УДК 338.43:634.1

Authors

  • Evgeniy Alekseevich Egorov North Caucasus Federal Scientific Center for Horticulture, Viticulture, and Winemaking, Krasnodar, Russia.
  • Zhanna Aleksandorvna Shadrina North Caucasus Federal Scientific Center for Horticulture, Viticulture, and Winemaking, Krasnodar, Russia.
  • Gayane Agopovna Kochyan North Caucasus Federal Scientific Center for Horticulture, Viticulture, and Winemaking, Krasnodar, Russia.

Keywords:

fruit growing, production, efficiency, factors, parameters, optimization, digital technologies, regulators.

Abstract

The decline in fruit production efficiency (for 2022-2024, the decrease in profitability by approximately 6.3 percentage points annually) due to the negative impact of macroeconomic, technological and economic factors necessitates determining optimal parameters using digital technologies to develop sustainability management tools to ensure the reliability of management decisions and competitiveness of industry entities. The scientific novelty of this study lies in the development of a model for determining optimal parameters of fruit production efficiency using software and development of regulators system for managing the sustainability of reproduction processes in specialized fruit-growing organizations. It has been established that the main macroeconomic factors influencing industry development include government (subsidiary) support measures for agricultural producers and rising input costs due to high inflation. Internal factors influencing fruit production efficiency in Krasnodar Krai include structural imbalances, high resource intensity of production and technological processes, failure to maintain optimal comparability of production and economic indicators. According to the developed digital model, it was established that in order to achieve the standard level of product profitability, the wholesale selling price of apples should be 23% higher than the 2024 level, the income from the production of pome crops (apple trees) should be 2.2 times higher than the actual value in 2024, the cost should be no more than 32 rubles/kg, which is 9% lower than the 2024 level. Bringing the efficiency indicators of fruit production to optimal values determine the need to improve the system of regulators to ensure sustainability in industry entities: increase in subsidy support for the creation of new plantings; establishment of a mechanism for setting quotas for the import of fruit crop seedlings; introduction of a regulator in the form of a subsidy per kilogram of produced and sold products; use of digital technologies in the development of agro-technological regulations and monitoring their compliance; improving the pricing system for products sold.

Information about the authors

Evgeniy Alekseevich Egorov, North Caucasus Federal Scientific Center for Horticulture, Viticulture, and Winemaking, Krasnodar, Russia.

Academician of the Russian Academy of Sciences, Doctor of Economics, Professor,
advisor

Zhanna Aleksandorvna Shadrina, North Caucasus Federal Scientific Center for Horticulture, Viticulture, and Winemaking, Krasnodar, Russia.

Professor of the Russian Academy of Sciences, Doctor of Economics, Associate
Professor, Head of the Laboratory of Economics

Gayane Agopovna Kochyan, North Caucasus Federal Scientific Center for Horticulture, Viticulture, and Winemaking, Krasnodar, Russia.

Ph.D. of Economic Sciences, Associate Professor, senior researcher of the Laboratory of Economics

References

 1. Рахимова Е.А. Развитие отечественного садоводства на основе цифровизации // АПК: Экономика, управление. 2023. № 11. С. 23-32. doi: 10.33305/2311-23.

 2. Системный подход к цифровизации деятельности садоводческих предприятий / Придорогин М.В.и др. // Вопросы современной науки и практики. Университет им. В.И. Вернадского. 2020. № 1(75). С. 115-124. doi: 10.17277/voprosy.2020.01.pp.115-124.

 3. Терновых К.С., Леонова Н.В., Кузнецова Е.В. Оптимизация параметров эффективного развития отрасли садоводства // Вестник Воронежского государственного аграрного университета. 2018. №4. С. 182-189. doi: 10.17238/issn2071-2243.2018.4.182.

 4. Цифровые решения для растениеводства [Электронный ресурс]. URL: https://www.softlab.ru/blog/resheniya-dlya-rastenievodstva/?ysclid=m9scd92rxt371742629 (дата обращения: 18.03.2025).

 5. Измайлов А.Ю., Смирнов И.Г., Хорт Д.О. Цифровые агротехнологии в системе «Умный сад» // Садоводство и виноградарство. 2018. № 6. С. 33-39. doi: 10.31676/0235-2591-2018-6-33-39.

 6. Фактор государственной поддержки в тенденциях развития отрасли садоводства / Егоров Е.А., Шадрина Ж.А., Кочьян Г.А., Парамонов П.Ф. // Вестник российской сельскохозяйственной науки. 2020. № 5. С. 4-8. doi: 10.30850/vrsn/2020/5/4-8.

 7. Егоров Е.А., Шадрина Ж.А., Кочьян Г.А. Инструменты нормативного управления устойчивостью и эффективностью воспроизводственных процессов в промышленном плодоводстве // Вестник российской сельскохозяйственной науки. 2022. № 6. С. 7-12. doi: 10.31857/2500-2082/2022/6/7-12.

 8. Егоров Е.А., Шадрина Ж.А., Кочьян Г.А. Макроэкономические тенденции и параметры эффективного садоводства // Садоводство и виноградарство. 2015. № 6. С. 5-10.

 9. Fabian S.F. Hartmanna, Isuru A. Udugamab, Gerd M. Seibolda, Hirokazu Sugiyamab, and Krist V. Gernaey. Digital models in biotechnology: Towards multi-scale integration and implementation // Biotechnology Advances. Volu. 60, Nov. 2022, Р. 108015. doi: 10.1016/j.biotechadv.2022.108015.

 10. Hukare, V., Kumbhar, V., Shah, S.K. (2023). Machine Learning Methods for Crop Yield Prediction. In: Saini, M.K., Goel, N., Shekhawat, H.S., Mauri, J.L., Singh, D. (eds) Agriculture-Centric Computation. ICA 2023. Communications in Computer and Information Science, vol 1866. Springer, Cham. doi: 10.1007/978-3-031-43605-5_15.

 11. Gangwar D.S., Tyagi, S. Soni S.K. A techno-economic analysis of digital agriculture services: An ecological approach toward green growth // International Journal of Environmental Science and Technology, 2021. doi:10.1007/s13762-021-03300-7.

 1. Rakhimova EA. [Development of domestic horticulture based on digitalization]. APK: Ekonomika, upravlenie. 2023; 11. 23-32 p. doi: 10.33305/2311-23.

 2. Pridorogin MV. [A systematic approach to horticultural enterprises digitalization]. Voprosy sovremennoy nauki i praktiki. Universitet im. V.I. Vernadskogo. 2020; 1(75). 115-124 p. doi: 10.17277/voprosy.2020.01.pp.115-124.

 3. Ternovykh KS, Leonova NV, Kuznetsova EV. [Optimization of the parameters for the effective development of horticulture industry]. Vestnik Voronezhskogo gosudarstvennogo agrarnogo universiteta. 2018; 4. 182-189 p. doi: 10.17238/issn2071-2243.2018.4.182.

 4. Digital solutions for crop production. [Internet]. Softlab. [cited 2025, March 18]. Available from: https://www.softlab.ru/blog/resheniya-dlya-rastenievodstva/?ysclid=m9scd92rxt371742629.

 5. Izmaylov AYu, Smirnov IG, Khort DO. [Digital agricultural technologies in "Smart garden" system]. Sadovodstvo i vinogradarstvo. 2018; 6. 33-39 p. doi: 10.31676/0235-2591-2018-6-33-39.

 6. Egorov EA, Shadrina ZhA, Kochyan GA, Paramonov PF. [The state support factor in the development trends of horticultural industry]. Vestnik Rossiyskoy selskokhozyaystvennoy nauki. 2020; 5. 4-8 p. doi: 10.30850/vrsn/2020/5/4-8.

 7. Egorov EA, Shadrina ZhA, Kochyan GA. [Tools for regulatory management of the sustainability and efficiency of reproduction processes in industrial fruit growing]. Vestnik Rossiyskoy selskokhozyaystvennoy nauki. 2022; 6. 7-12 p. doi: 10.31857/2500-2082/2022/6/7-12.

 8. Egorov EA, Shadrina ZhA, Kochyan GA. [Macroeconomic trends and parameters of efficient horticulture]. Sadovodstvo i vinogradarstvo. 2015; 6. 5-10 p.

 9. Fabian SF. Hartmanna, Isuru A. Udugamab, Gerd M. Seibolda, Hirokazu Sugiyamab, Krist V. Gernaey. Digital models in biotechnology: towards multi-scale integration and implementation. Biotechnology Advances. Vol.60, Nov.2022. 108015 p. doi: 10.1016/j.biotechadv.2022.108015.

 10. Hukare V, Kumbhar V, Shah SK. (2023). Machine learning methods for crop yield prediction. In: Saini M.K., Goel N., Shekhawat H.S., Mauri J.L., Singh D. (eds). Agriculture-Centric Computation. ICA 2023. Communications in Computer and Information Science. Vol.1866. Springer, Cham. doi: 10.1007/978-3-031-43605-5_15.

 11. Gangwar DS, Tyagi S, Soni SK. A techno-economic analysis of digital agriculture services: An ecological approach toward green growth. International Journal of Environmental Science and Technology. 2021; doi:10.1007/s13762-021-03300-7.

Published

2026-07-21

How to Cite

Egorov Е. А., Shadrina Ж. А., & Kochyan Г. А. (2026). JUSTIFICATION OF OPTIMAL PARAMETERS OF FRUIT PRODUCTION EFFICIENCY USING DIGITAL TECHNOLOGIES: DOI 10.12737/2073-0462-2025-20-3-91-98 УДК 338.43:634.1. Vestnik of Kazan State Agrarian University, 20(3). Retrieved from http://vestnik-kazgau.ru/index.php/vestnik/article/view/68