Parameters of the strategy for managing the economic growth of agricultural production in Russia

https://doi.org/10.17221/255/2019-AGRICECONCitation:Anokhina M. (2020): Parameters of the strategy for managing the economic growth of agricultural production in Russia. Agric. Econ. – Czech, 66: 140-148.
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Agricultural economic growth requires management due to poor structurization. The study aimed to determine the parameters of the management strategy for the economic growth of agriculture in Russia. The research methodology relies on cognitive technologies of modelling the strategic alternatives of the economic development of the industrial complex using fuzzy cognitive logic. Static and dynamic analysis of the fuzzy cognitive maps on structural and dynamic indicators of agricultural economic growth in Russia allowed the forecast of the industry trends, influenced by various management factors. The option of an integrated management strategy for the economic growth of agriculture in Russia is proposed together with strategic maps, justified as a tool for its implementation. The created strategic alternative will allow the Russian agricultural and industrial complex to use the existing agricultural potential to achieve the target growth indicators and ensure sustainability.

References:
Aguilar J. (2005): A survey about fuzzy cognitive maps papers. International Journal of Computational Cognition, 3: 27–33.
 
Anokhina M.Y. (2017): Strategy of managing growth of agricultural production in Russia. Journal of Experimental Biology and Agricultural Sciences, 5: 793–805. https://doi.org/10.18006/2017.5(6).793.805
 
Axelrod R. (1976): The Structure of Decision: Cognitive Maps of Political Elites. Princeton N.J., Prinсeton University Press: 422.
 
Christen B., Kjeldsen C., Dalgaard T., Martin-Ortega J. (2015): Can fuzzy cognitive mapping help in agricultural policy design and communication? Land Use Policy, 45: 64–75. https://doi.org/10.1016/j.landusepol.2015.01.001
 
Dodouras S., James P. (2007): Fuzzy cognitive mapping to appraise complex situations. Journal of Environmental Planning and Management, 50: 823–852. https://doi.org/10.1080/09640560701610578
 
Gray S.A., Gray S., De Kok J.L., Helfgott A., O`Dwyer B., Jordan R., Nyaki A. (2015): Using fuzzy cognitive mapping as a participatory approach to analyze change, preferred states, and perceived resilience of social-ecological systems. Ecology and Society, 20: 11. https://doi.org/10.5751/ES-07396-200211
 
Huff A.S. (ed.) (1990): Mapping Strategic Thought. Chichester, John Wiley & Sons: 11–49.
 
Jetter A.J., Kok K. (2014): Fuzzy cognitive maps for futures studies – a methodological assessment of concepts and methods. Futures, 61: 45–57. https://doi.org/10.1016/j.futures.2014.05.002
 
Khan M.S., Quaddus M. (2004): Group decision support using fuzzy cognitive maps for causal reasoning. Group Decision and Negotiation, 13: 463–480. https://doi.org/10.1023/B:GRUP.0000045748.89201.f3
 
Kokkinos K., Lakioti E., Papageorgiou E., Moustakas K., Karayannis V. (2018): Fuzzy cognitive map-based modelling of social acceptance to overcome uncertainties in establishing waste biorefinery facilities. Frontiers in Energy Research, 6: 112.  https://doi.org/10.3389/fenrg.2018.00112
 
Kosko B. (1986): Fuzzy cognitive maps. International Journal of Man-Mashine Studies, 24: 65–75.  https://doi.org/10.1016/S0020-7373(86)80040-2
 
Kulinich A.A. (2014): Software systems for situation analysis and decision support on the basis of cognitive maps: Approaches and methods. Automation and Remote Control, 75: 1337–1355. https://doi.org/10.1134/S0005117914070157
 
Lopez C., Salmeron J.L. (2014): Dynamic risks modelling in ERP maintenance projects with FCM. Information Sciences, 256: 25–45. https://doi.org/10.1016/j.ins.2012.05.026
 
Mezei J., Sarlin P. (2016): Aggregating expert knowledge for the measurement of systemic risk. Decision Support Systems, 88: 38–50. https://doi.org/10.1016/j.dss.2016.05.007
 
Olazabal M., Neumann M.B., Foudi S., Chiabai A. (2018): Transparency and reproducibility in participatory systems modelling: The case of fuzzy cognitive mapping. Systems Research and Behavioural Science, 35: 791–810. https://doi.org/10.1002/sres.2519
 
Özesmi U., Özesmi S. (2004): Ecological models based on people’s knowledge: a multi-step fuzzy cognitive mapping approach. Ecological Modelling, 176: 43–64. https://doi.org/10.1016/j.ecolmodel.2003.10.027
 
Papageorgiou E.I., Markinos A., Gemtos T. (2009): Application of fuzzy cognitive maps for cotton yield management in precision farming. Expert Systems with Applications, 36: 12399–12413. https://doi.org/10.1016/j.eswa.2009.04.046
 
Roberts F. (1978): Graph Theory and its Applications to Problems of Society, Society for Industrial and Applied Mathematics. Philadelphia, Society for Industrial and Applied Mathematics: 122.
 
Silov V.B. (1995): Strategic Decision-Making in a Fuzzy Environment. Moscow, INPRO-RES: 228.
 
Solana-Gutiérrez J., Rincón G., Alonso C., García-de-Jalón D. (2017): Using fuzzy cognitive maps for predicting river management responses: A case study of the Esla River basin, Spain. Ecological Modelling, 360: 260–269. https://doi.org/10.1016/j.ecolmodel.2017.07.010
 
SSDS "IGLA" (2019): The software system of decision support “Intelligent Generator of the Best Alternatives”. Developed in Bryansk State Technical University under the supervision of Podvesovskii A.G. Available at http://iipo.tu-bryansk.ru/quill/download.html
 
Stach W., Kurgan L., Pedrycz W., Reformat M. (2004): Parallel fuzzy cognitive maps as a tool for modelling software development project. In: Proceedings IEEE Annual Meeting of the Fuzzy Information, 2004. Banff, Alberta, Canada, North American Fuzzy Information Processing Society Conference (NAFIPS ‚04), June 27–30, 2004: 28–33.
 
Stach W., Kurgan L., Pedrycz W. (2010): Expert-based and computational methods for developing fuzzy cognitive maps. In: Glykas M. (eds): Fuzzy Cognitive Maps. Studies in Fuzziness and Soft Computing, 247. Berlin, Heidelberg, Springer: 23–41.
 
Aguilar J. (2005): A survey about fuzzy cognitive maps papers. International Journal of Computational Cognition, 3: 27–33.
 
Anokhina M.Y. (2017): Strategy of managing growth of agricultural production in Russia. Journal of Experimental Biology and Agricultural Sciences, 5: 793–805. https://doi.org/10.18006/2017.5(6).793.805
 
Axelrod R. (1976): The Structure of Decision: Cognitive Maps of Political Elites. Princeton N.J., Prinсeton University Press: 422.
 
Christen B., Kjeldsen C., Dalgaard T., Martin-Ortega J. (2015): Can fuzzy cognitive mapping help in agricultural policy design and communication? Land Use Policy, 45: 64–75. https://doi.org/10.1016/j.landusepol.2015.01.001
 
Dodouras S., James P. (2007): Fuzzy cognitive mapping to appraise complex situations. Journal of Environmental Planning and Management, 50: 823–852. https://doi.org/10.1080/09640560701610578
 
Gray S.A., Gray S., De Kok J.L., Helfgott A., O`Dwyer B., Jordan R., Nyaki A. (2015): Using fuzzy cognitive mapping as a participatory approach to analyze change, preferred states, and perceived resilience of social-ecological systems. Ecology and Society, 20: 11. https://doi.org/10.5751/ES-07396-200211
 
Huff A.S. (ed.) (1990): Mapping Strategic Thought. Chichester, John Wiley & Sons: 11–49.
 
Jetter A.J., Kok K. (2014): Fuzzy cognitive maps for futures studies – a methodological assessment of concepts and methods. Futures, 61: 45–57. https://doi.org/10.1016/j.futures.2014.05.002
 
Khan M.S., Quaddus M. (2004): Group decision support using fuzzy cognitive maps for causal reasoning. Group Decision and Negotiation, 13: 463–480. https://doi.org/10.1023/B:GRUP.0000045748.89201.f3
 
Kokkinos K., Lakioti E., Papageorgiou E., Moustakas K., Karayannis V. (2018): Fuzzy cognitive map-based modelling of social acceptance to overcome uncertainties in establishing waste biorefinery facilities. Frontiers in Energy Research, 6: 112.  https://doi.org/10.3389/fenrg.2018.00112
 
Kosko B. (1986): Fuzzy cognitive maps. International Journal of Man-Mashine Studies, 24: 65–75.  https://doi.org/10.1016/S0020-7373(86)80040-2
 
Kulinich A.A. (2014): Software systems for situation analysis and decision support on the basis of cognitive maps: Approaches and methods. Automation and Remote Control, 75: 1337–1355. https://doi.org/10.1134/S0005117914070157
 
Lopez C., Salmeron J.L. (2014): Dynamic risks modelling in ERP maintenance projects with FCM. Information Sciences, 256: 25–45. https://doi.org/10.1016/j.ins.2012.05.026
 
Mezei J., Sarlin P. (2016): Aggregating expert knowledge for the measurement of systemic risk. Decision Support Systems, 88: 38–50. https://doi.org/10.1016/j.dss.2016.05.007
 
Olazabal M., Neumann M.B., Foudi S., Chiabai A. (2018): Transparency and reproducibility in participatory systems modelling: The case of fuzzy cognitive mapping. Systems Research and Behavioural Science, 35: 791–810. https://doi.org/10.1002/sres.2519
 
Özesmi U., Özesmi S. (2004): Ecological models based on people’s knowledge: a multi-step fuzzy cognitive mapping approach. Ecological Modelling, 176: 43–64. https://doi.org/10.1016/j.ecolmodel.2003.10.027
 
Papageorgiou E.I., Markinos A., Gemtos T. (2009): Application of fuzzy cognitive maps for cotton yield management in precision farming. Expert Systems with Applications, 36: 12399–12413. https://doi.org/10.1016/j.eswa.2009.04.046
 
Roberts F. (1978): Graph Theory and its Applications to Problems of Society, Society for Industrial and Applied Mathematics. Philadelphia, Society for Industrial and Applied Mathematics: 122.
 
Silov V.B. (1995): Strategic Decision-Making in a Fuzzy Environment. Moscow, INPRO-RES: 228.
 
Solana-Gutiérrez J., Rincón G., Alonso C., García-de-Jalón D. (2017): Using fuzzy cognitive maps for predicting river management responses: A case study of the Esla River basin, Spain. Ecological Modelling, 360: 260–269. https://doi.org/10.1016/j.ecolmodel.2017.07.010
 
SSDS "IGLA" (2019): The software system of decision support “Intelligent Generator of the Best Alternatives”. Developed in Bryansk State Technical University under the supervision of Podvesovskii A.G. Available at http://iipo.tu-bryansk.ru/quill/download.html
 
Stach W., Kurgan L., Pedrycz W., Reformat M. (2004): Parallel fuzzy cognitive maps as a tool for modelling software development project. In: Proceedings IEEE Annual Meeting of the Fuzzy Information, 2004. Banff, Alberta, Canada, North American Fuzzy Information Processing Society Conference (NAFIPS ‚04), June 27–30, 2004: 28–33.
 
Stach W., Kurgan L., Pedrycz W. (2010): Expert-based and computational methods for developing fuzzy cognitive maps. In: Glykas M. (eds): Fuzzy Cognitive Maps. Studies in Fuzziness and Soft Computing, 247. Berlin, Heidelberg, Springer: 23–41.
 
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