Advertisement

Fuzzy Method of Assessing the Intensity of Agricultural Production on a Set of Criteria of the Level of Intensification and the Level of Economic Efficiency of Intensification

  • Tamara V. Alekseychik
  • Taras V. Bogachev
  • Denis N. Karasev
  • Lyudmila V. Sakharova
  • Michael B. Stryukov
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 896)

Abstract

The aim is a fuzzy-multiple work to develop a methodology to assess the dynamics of agricultural development in the region on the basis of a set of diverse indicators, as well as to compare (Rangers) agricultural facilities or regions. The technique is shown on the application of multi-level standard net [0, 1]–classification. It allows you to calculate complex numerical score for the level of intensification of agriculture on the criteria of the two groups for any number of years studied: level of intensification of production in the economy of the silk and the level of economic efficiency of intensification of production on the farm the silk, and also give practical recommendations for the further development of agriculture in the region. The proposed method has the following advantages: (1) a simple calculation scheme; (2) taking into account the large number of Razor estimates of significant indicators; (3) the use of only indicators that objectively reflect the effectiveness of the use of material and financial resources of agriculture; (4) the possibility of deviations vest dressed in the studied indicators in a complex assessment of the intensity of agricultural production in the region; (5) universality, which allows to apply it to the assessment of the intensity of not only agricultural but also industrial production.

Keywords

Methodology Complex estimation Intensity of agricultural production Indicators Theory of fuzzy sets 

References

  1. 1.
    Bondarenko, L.: Ecological and economic efficiency and stability of grain production (based on the materials of the Krasnodar region). KubSU Publishing Center, Krasnodar (2000)Google Scholar
  2. 2.
    Minakov, I., Kulikov, I., Sokolov, V.: The Economics of Agriculture. Koloss, Moscow (2008)Google Scholar
  3. 3.
    Vartanyan, E.: Comparative analysis of methods for rating the effectiveness of agricultural enterprises. In: Interaction of business, state, science: a three-sided view on economic development, vol. 2, pp. 75–95 (2012)Google Scholar
  4. 4.
    Karminsky, A., Peresetsky, A., Petrov A.: Ratings in economy: methodology and practice. Finance and statistics, Moscow (2005)Google Scholar
  5. 5.
    Nedosekin, A.: Fuzzy Sets and Financial Management. AFA Library, Moscow (2003)Google Scholar
  6. 6.
    Stryukov, M., Sakharova, L., Alekseychik, T., Bogachev, T.: Methods of estimation of intensity of agricultural production on the basis of the theory of fuzzy sets. Int. Res. J. 7(61), 123–129 (2017)Google Scholar
  7. 7.
    Sakharova, L., Stryukov, M., Akperov, I., Alekseychik, T., Chuvenkov A.: Application of fuzzy set theory in agro-meteorological models for yield estimation based on statistics. In: 9th International Conference on Theory and Application of Soft Computing, Computing with Words and Perception, Budapest, Hungary, pp. 820–829 (2017). Procedia Comput. Sc. 120Google Scholar
  8. 8.
    Kramarov, S., Sakharova, L.: Management of complex economic systems by fuzzy classifiers method. Sci. Bull. South. Univ. Manag. 2(18), 42–50 (2017)Google Scholar
  9. 9.
    Kramarov, S., Sakharova, L., Khramov, V.: Soft computing in management: management of complex multivariate systems based on fuzzy analog controllers. Sci. Bull. South. Univ. Manag. 3(19), 42–51 (2017)Google Scholar
  10. 10.
    Albekov, A., Arapova, E., Karasev, D., Stryukov, M., Sakharova, L.: Program for evaluation of agricultural production intensity by means of fuzzy 5-point classifier. Certificate of registration of computer program № 2018613875. Federal service for intellectual property, Moscow (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Tamara V. Alekseychik
    • 1
  • Taras V. Bogachev
    • 1
  • Denis N. Karasev
    • 1
  • Lyudmila V. Sakharova
    • 1
  • Michael B. Stryukov
    • 1
  1. 1.Rostov State University of EconomicsRostov-on-DonRussia

Personalised recommendations