Vegetables market and its infrastructure, imitation model, scenario forecast, production and consumption of vegetable production in Russia.


The article is devoted to studying the alternatives of development of the Russian vegetables market from the point of view of change of the level and structure of production and consumption of vegetables. The main objectives of the research are to collect and analyze data of the Russian market of vegetable production, modeling and scenario forecasting vegetables market, a substantiation of directions of development of the market under study. The methodological basis of the research is developing the combined economic & mathematical imitation model that is based on creation of the differential equations system. As any qualitative and quantitative changes of market factors lead to shifts in consumer behavior and the structure of consumed products, the scenario variants of development of the situation at the Russian vegetables market was analyzed depending on foreign trade limitations, level of development of infrastructure, and pricing factors of the market. As a result of the research, the volume of consumption of various types of vegetables is predicted for the variants of the forecasts, as well as consequences of the change of the situation for the Russian vegetable sphere on the whole.

Full Text : PDF

  1. Analytical Center of the Government of the Russian Federation "Food embargo: results of 2015. Analytical report", available at: (accessed 21 October 2017). 
  2. Akopov A.S. (2014), Simulation Modelling. Handbook (in Russian), Urait, Moscow. 
  3. Christopher A. (2003), Simulation Modeling Handbook A Practical Approach, Chung CRC Press, Inc. Boca Raton, FL, USA.
  4. Jarkko K. Niemi H. L. (2011), "Modelling pig sector dynamic adjustment to livestock epidemics with stochastic-duration trade disruptions", European Review of Agricultural Economics, Vol. 38, Issue 4, October, pp. 529–551. 
  5. Kosorukov O. (2012). Forecast of Separate Indicators for Socio-Economic Development of the Russian Federation up to 2020. World Applied Sciences Journal, 18, 5-10.
  6. Lagi M., Bar-Yam Y., Bertrand K.Z., Bar-Yam Y. (2015), "Accurate market price formation model with both supply-demand and trend-following for global food prices providing policy recommendations" Proceedings of the National Academy of Sciences 112 (45), E6119-E6128. 
  7. Luo C., Wei Q., Zhou L., Zhang J., Sun S. (2010), "Prediction of Vegetable Price Based on Neural Network and Genetic Algorithm", Computer and Computing Technologies in Agriculture IV, Vol. 9, pp. 672-681.
  8. Mutuc, M. E., Pan, S., and Rejesus, R. M. (2007), "Household Vegetable Demand in the Philippines: Is there an Urban-Rural Divide?" Agribusiness, Vol. 16, No 23(4), pp. 511-527. 
  9. My, N.H.D., Rutsaert, P., Van Loo, E.J., Verbeke, W. (2017), "Consumers’ familiarity with and attitudes towards food quality certifications for rice and vegetables in Vietnam", Food Control, 82, с. 74-82
  10. Nzaku K., Houston J.E. (2009), "Dynamic Estimation of U.S. Demand for Fresh Vegetable Imports" paper presented at the Agricultural & Applied Economics Association & ACCI Joint Annual Meeting, July 2009, Milwaukee, Wisconsin, , available at: (accessed 20 October 2017).
  11. Põldaru,  R.,  Roots,  J.,  Viira,  A-H.,  Värnik,  R. (2008), "A  Macroeconomic  (simultaneous equation) Model of the Estonian Livestock Sector"  in Proceedings of the 2015 International Conference “ECONOMIC SCIENCE FOR RURAL DEVELOPMENT No37” Jelgava, LLU ESAF, 23-24 April 2015, pp. 54-63.
  12. Popkova, E.G., Morkovina, S.S., Patsyuk, E.V., Panyavina, E.A., Popov, E.V.  (2013), "Marketing strategy of overcoming of lag in development of economic systems", World Applied Sciences Journal, Vol. 26, Issue 5, pp. 591-595.
  13. Popkova, E.G., Sukhodolov, Y.A. (2017), "Foreign trade as a vector of economic growth in the globalizing world". Contributions to Economics, Vol. 20,  pp. 25-45.
  14. Rajkumar P. (2014), "System Dynamics Simulation Model for Decision Making in Retailer Selection", Iberoamerican Journal of Industrial Engineering, Vol. 15, No 6 (11), pp. 367-382.
  15. Rachmina, D. Daryanto, A. Tambunan, M. Hakim, D.B. (2014), "Impact of infrastructure on profit efficiency of vegetable farming in West Java, Indonesia: stochastic frontier approach" available at: 
  16. (accessed 15 October 2017).  
  17. Robledo C.W. (2002), "Dynamic econometric modeling of the U.S. wheat grain market": PhD Dissertation. Louisiana State Univ., Vol. 184.
  18. Sakurai G., Yamaji, N., Mitani-Ueno, N., Yokozawa, M., Ono, K., and Ma, J.F. (2017), "A Model of Silicon Dynamics in Rice: An Analysis of the Investment Efficiency of Si Transporters", available at:
  19. (accessed 21 October 2017). 
  20. Shukla M., Jharkharia S. (2011), "Applicability of ARIMA Models in Wholesale Vegetable Market: An Investigation" in Proceedings of the 2011 International Conference on Industrial Engineering and Operations Management, Malaysia, January 22 – 24 2011, Kuala Lumpur, pp. 1125-1130.
  21. Singh R.K.P., Jha A. K., Singh K. M., (2011) "Strategies for Efficient Marketing and Distribution of Fruits and Vegetables in Bihar" Development of Horticulture of Bolivar. January 2011, Vol.3,  pp. 106-110.
  22. Soboleva Y. P., & Parshutina, I. G. (2015). Marketing approach to forecasting of regional market consumption potential. Indian Journal of Science and Technology, 8.
  23. Subić, J., Jeločnik, M. (2013).  Economic and environmental aspects of controlled vegetable production within the Region of Danube Basin (Book Chapter). Sustainable Technologies, Policies, and Constraints in the Green Economy. June 30, 2013, pp. 39-61.
  24. Subić J., Jeločnik M., Ivanović L. (2011), "Dinamic evaluation of investment projects - practical approach to sustainable development of agriculture in Serbia".  Quality - Access to Success. Vol. 12, Issue SUPPL. No 2, pp. 136-143.
  25. Xu Q., Liu M. (2013), "Simulation and Forecast About Vegetable Prices Based on PSO-RBFNN Model, in: Sun Z., Deng Z. (eds) Proceedings of 2013 Chinese Intelligent Automation Conference. Springer, Berlin, Heidelberg, pp. 255-260.
  26. Yang H., Hu. J, (2013) "Forecasting of Fresh Agricultural Products Demand Based on the ARIMA Model" Advance Journal of Food Science and Technology, No 5(7) July, Vol.3,  pp. 855-858.
  27. Yoo D. (2015), "Developing Forecasting Model of Vegetable Price based on Climate Big Data" in Agricultural and Applied Economics Association, Western Agricultural Economics Association (AAEA & WAEA) Joint Annual Meeting, July 26-28, San Francisco, California, available at:
  28. (accessed 21 October 2017). 

    Appendix 1: Trends of factor variables

    production of cucumbers tons

    x1 = 54.061t + 1,099

    production of tomatoes, tons

    x2 = 89.687t + 1,990.2

    production of beet, tons

    x3 = 27.693t + 785.4

    production of carrot, tons

    x4 = 43.206t + 1,258.1

    production of cabbage, tons

    x5 = 85.383t + 2,649.2

    production of bulb onion, tons

    x6 = 97.397t + 1,134

    production of garlic, tons

    x7 = 0.9848t + 232.01

    production of other vegetables, tons

    x8 =64.765t + 916.97

    import of cucumbers, tons

    x11 = 13,456t + 79,264

    import of tomatoes, tons

    x12 = 40.960t + 416.074

    import of beet, tons

    x13 = 1.613.6t + 48,203

    import of carrot, tons

    x14 = 10,288t + 131,520

    import of cabbage, tons

    x15 = 3,056.4t + 175,302

    import of bulb onion, tons

    x16 = -30,730t + 597,273

    import of garlic, tons

    x17 = 1,542.1t + 37,131

    import of other vegetables, tons

    x18 = 25,641t + 165,634

    export of cucumbers, tons

    x19 = 319.94t – 1,174.7

    export of tomatoes, tons

    x20 = 19.159t – 7.8678

    export of beet, tons

    x21 = 142.31t – 22.83

    export of carrot, tons

    x22 = 372.12t – 1,274.5

    export of cabbage, tons

    x23 = -131.53t + 1,508.8

    export of bulb onion, tons

    x24 = 689.47t + 4,863.6

    export of garlic, tons

    x25 = 21.269t – 33.317

    export of other vegetables, tons

    x26 = 83,770t – 104.152

    import price of cucumbers, RUB/kg

    x27 = 4.4987t + 5.3254

    import price of tomatoes, RUB/kg

    x28 = 3.7192t + 10.991

    import price of beet, RUB/kg

    x29 = 3.1472t + 0.9257

    import price of carrot, RUB/kg

    x30 = 1.5634t + 4.947

    import price of cabbage, RUB/kg

    x31 = 1.4777t + 4.2184

    import price of bulb onion

    x32 = 1.7731t + 2.1287

    import price of garlic, RUB/kg

    x33 = 5.9303t – 4.0923

    import price of other vegetables, RUB/kg

    x34 = 3.3392 t + 15.203

    export price of cucumbers, RUB/kg

    x35 = 2.4107t + 22.44

    export price of tomatoes, RUB/kg

    x36= 1.0761t + 39.785

    export price of beet, RUB/kg

    x37 = 2.0275t – 1.0969

    export price of carrot, RUB/kg

    x38 = 0.6588t + 20.977

    export price of cabbage, RUB/kg

    x39 = 1.4139t + 1.0507

    export price of bulb onion, RUB/kg

    x40 = 1.5213t – 0.6736

    export price of garlic, RUB/kg

    x41 = 3.9529t + 4.6282

    export price of other vegetables, RUB/kg

    x42 = 0.8361 t + 7.5652

    domestic price of cucumbers, RUB/kg

    x43 = 5.3267t + 44.999

    domestic price of tomatoes, RUB/kg

    x44 = 5.3913t + 57.028

    domestic price of beet, RUB/kg

    x45 = 1.4566t + 13.279

    domestic price of carrot, RUB/kg

    x46= 2.0437t + 14.404

    domestic price of cabbage, RUB/kg

    x47 = 1.3028t + 11.92

    domestic price of bulb onion, RUB/kg

    x48 = 1.3762t + 13.887

    domestic price of garlic, RUB/kg

    x49 = 10.195t + 34.401

    domestic price of other vegetables, RUB/kg

    x50=4.0643t + 35.334

    number of population, million people

    x51 = 0.1464t + 142.5

    income per capita, RUB

    x52 = 3,001.3t – 4,062.8

    expenditures per capita, RUB

    x53 = 1,124.7t + 3,242.4

    capacities of vegetables storage, thousand tons

    x54 = 18.885t + 2,552.6

    area of greenhouses, hectares

    x55 = 81.064t + 6,652.5