Defuzzification in the process of managerial estimating the value of agricultural lands
Purpose. The main purpose of the research is to substantiate the methodological approach of defuzzification and to define its peculiarities in the process of estimating the value of agricultural lands.
Methodology / approach. The research purpose included the use of a set of appropriate methods. In particular, the fuzzy logic techniques formed the basis of the research. The system approach was used in order to determine the role of land resources in the enterprise potential and the corresponding spheres of their management. The analysis and synthesis methods were used in the process of definition of impact factors of land resources value. The cartographic method was used for the needs of graphical display of humus content in the land plots of the analyzed enterprise. The generalization method was used in the process of forming conclusions.
Results. The article defines the peculiarities of defuzzification in the process of estimating the value of agricultural lands. The results provided the methodological basis for considering the qualitative metrics in the process of estimation as well as for granting the numerical interpretation for linguistic variables. The corresponding methodology was overviewed at the example of land plot size. The quantitative reference limits for “small”, “medium” and “large” land plots were defined. Research results made it possible to form the sequence of stages, which are to be undertaken, in order to provide numerical values for qualitative characteristics of agricultural lands. A decision tree was built for the needs of formation of management decisions. According to the data of researched enterprise, the dependence of the value of agricultural lands (for the needs of management accounting) on the size of the land plot and the humus content was determined.
Originality / scientific novelty. The article improves the methodological approach to determining the value of agricultural lands as of an element of enterprise potential based on the use of fuzzy logic techniques, which, in contrast to existing approaches, allows taking into account both quantitative and qualitative factors in the process of estimating the value of land resources for the needs of their management. Applying the respective approach increases the level of accuracy, relevance, and adequacy to market realities of the results of estimating the value of agricultural lands for the needs of their management.
Practical value / implications. The results of the research provided an opportunity to improve the quality and efficiency of the process of estimating the value of agricultural lands. The corresponding process is characterized by a high level of complexity and uncertainty due to the presence of a significant number of qualitative factors influencing the value of the land resources. The approach considered in the article makes it possible to take into account the influence of relevant qualitative factors by giving them numerical certainty through the use of fuzzy logic techniques. The proposed approach will provide an opportunity to increase the accuracy and relevance of estimating the value of land resources as of an element of enterprise potential for the making of corresponding managerial decisions. The proposed methodological approach was implemented with the use of data of agricultural enterprise, which made it possible to take into account linguistic variables (land plot size and chemical properties of the soil) when forming the managerial decisions about land plots. The decision tree was also formed, which serves as a means of supporting management decisions in the process of forming the value of agricultural lands.
2. Kurylo, V., Pantaliienko, P., Bogdanets, V. and Ovcharuk, S. (2017), Land fragmentation in Ukraine: agricultural land-use management and jurisprudence issues. Problems and Perspectives in Management, vol. 15, is. 2, pp. 102–109. https://doi.org/10.21511/ppm.15(2).2017.10.
3. Brukhanskyi, R. F., Yazlyuk, B. and Bincharovska, T. A. (2018), Effective land management in Ukraine using accounting and analytical support. Problems and Perspectives in Management, vol. 16, is. 2, pp. 241–251. https://doi.org/10.21511/ppm.16(2).2018.22.
4. Kozhukhіvska, R., Kulbitsky, V., Kyryliuk, I., Maliuga, L. and Podzigun, S. (2018), Managing the efficiency of enterprises based on assessment of the land resource potential. Problems and Perspectives in Management, vol. 16, is. 2, pp. 164–178. https://doi.org/10.21511/ppm.16(2).2018.15.
5. Yerseitova, A., Issakova, S., Jakisheva, L., Nauryzbekova, A. and Moldasheva, A. (2018), Efficiency of using agricultural land in Kazakhstan. Entrepreneurship and Sustainability Issues, vol. 6, no. 2, pp. 558–576. https://doi.org/10.9770/jesi.2018.6.2(7).
6. Fan, W., Chen, N., Li, X., Wei, H. and Wang, X. (2020), Empirical research on the process of land resource-asset-capitalization – a case study of Yanba, Jiangjin district, Chongqing. Sustainability, vol. 12(3), 1236. https://doi.org/10.3390/su12031236.
7. Adamopoulos, T. and Restuccia, D. (2020), Land reform and productivity: a quantitative analysis with micro data. American Economic Journal: Macroeconomics, vol. 12, no. 3, pp. 1–39. https://doi.org/10.1257/mac.20150222.
8. Zaiets, O., Vlasenko, Yu., Busuyok, D. and Pozniak, E. (2021), Ecological aspect of legal provision of modern land reform as a factor of sustainable development. European Journal of Sustainable Development, vol. 10, no. 1, pp. 168–184. https://doi.org/10.14207/ejsd.2021.v10n1p168.
9. Skliar, Yu., Bohinska, L., Kapinos, N. and Prokopenko, N. (2021), Improvement of land management in Ukraine. Journal of Optimization in Industrial Engineering, spec. is., pp. 175–183. https://doi.org/10.22094/JOIE.2020.677866.
10. Rossiter, G. D. (1995), Economic land evaluation: why and how. Soil Use & Management, vol. 11, is. 3, pp. 132–140. https://doi.org/10.1111/j.1475-2743.1995.tb00511.x.
11. Verheye, W. H. (2000), Use of land evaluation techniques to assess the market value of agricultural land. Agropedology, vol. 10, pp. 88–100.
12. De la Rosa, D., Van Diepen, C. (2002), Qualitative and quantitative land evaluation. Land Use and Land Cover and Soil Sciences, vol. II, available at: https://www.eolss.net/Sample-Chapters/C12/E1-05-02-02.pdf.
13. Yang, Y., Sun, Y., Li, S., Zhang, S., Wang, K., Hou, H. and Xu, S. (2015), A GIS-based web approach for serving land price information. ISPRS International Journal of Geo-Information, vol. 4 (4), pp. 2078–2093. https://doi.org/10.3390/ijgi4042078.
14. Dengiz, O. and Usul, M. (2018), Multi-criteria approach with linear combination technique and analytical hierarchy process in land evaluation studies. Eurasian Journal of Soil Science, vol. 7, is. 1, pp. 20–29. https://doi.org/10.18393/ejss.328531.
15. Berawi, M. A., Suwartha, N., Kurnia, K., Gunawan, Miraj, P. and Berawi, A. R. B., (2018), Forecasting the land value around commuter rail stations using hedonic price modeling. International Journal of Technology, vol. 9, no. 7, pp. 1329–1337. https://doi.org/10.14716/ijtech.v9i7.2589.
16. Berawi, M. A., Suwartha, N., Salsabila, F., Gunawan, Miraj, P. and Woodhead, R. (2019), Land value capture modeling in commercial and office areas using a big data approach. International Journal of Technology, vol. 10, no. 6, pp. 1150–1156. https://doi.org/10.14716/ijtech.v10i6.3640.
17. Kovalova, O., Yarova, I., Mishenina, H., Pizniak, T. and Dutchenko, O. (2021), Evolution of improving the normative monetary evaluation of agricultural lands. Agricultural and Resource Economics, vol. 7, no. 1, pp. 137–163. https://doi.org/10.51599/are.2021.07.01.08.
18. Sant’Anna, A. C. and Katchova, A. L. (2020), Determinants of land value volatility in the U.S. Corn Belt. Applied Economics, vol. 52, is. 37, pp. 4058–4072. https://doi.org/10.1080/00036846.2020.1730760.
19. Tu, F., Zou, S. and Ding, R. (2021), How do land use regulations influence industrial land prices? Evidence from China. International Journal of Strategic Property Management, vol. 25, no. 1, pp. 76–89. https://doi.org/10.3846/ijspm.2020.14051.
20. Badenko, V. and Kurtener, D. (2004), Fuzzy modelling in GIS environment to support sustainable land use planning. 7th AGILE Conference on Geographic Information Science, 29 April–1 May 2004, Heraklion, Greece, рр. 333–342.
21. Burrough, P. A. (2006), Fuzzy mathematical methods for soil survey and land evaluation. European Journal of Soil Science, vol. 40, is. 3, pp. 477–492. https://doi.org/10.1111/j.1365-2389.1989.tb01290.x.
22. Novaline, J. and Krishnan, R. (2008), Fuzzy logic approach for sustainable land use planning. COORDINATES, available at: https://mycoordinates.org/fuzzy-logic-approach-for-sustainable-land-use-planning/all/1.
23. Sharapov, O. D. and Kaidanovych, D. B. (2012), Estimation of probable bankruptcy on the basis of company’s financial state indicators with the use of counter-propagation neural networks. Neirono-nechitki tekhnolohii modeliuvannia v ekonomitsi, vol. 1, pp. 207–227.
24. Novak, V., Perfilieva, I. and Mochkorzh, I. (2006), Matematicheskiie pryntsypy nechetkoi lohiki [Mathematical principles of fuzzy logic], Fyzmatlyt, Moscow, Russian Federation.
25. Borisov, V. V., Fedulov, A. S. and Zernov, M. M. (2014), Osnovy teorii nechetkikh mnozhestv [Fundamentals of fuzzy set theory], Horyachaia liniia-Telekom, Moscow, Russian Federation.
26. Azhaman, I. A. and Zhydkov, O. I. (2018), The nature and structure of the economic potential of the enterprise. Ekonomika ta derzhava, vol. 4, pp. 22–25.
27. The Verhovna Rada of Ukraine (2001), Land Code of Ukraine, available at: http://zakon3.rada.gov.ua/laws/show/2768-14.
28. Kofman, A. (1982), Vvedenye v teoryiu nechetkykh mnozhestv [Introduction to the fuzzy set theory], Radyo i sviaz, Moscow.
29. Leonenkov, A. V. (2003), Nechetkoe modelyrovanye v srede MATLAB i fuzzyTECH [Fuzzy modelling in MATLAB and fuzzyTECH environment], BKhV-Peterburh, Saint-Petersburg, Russian Federation.
30. Shtovba, S. D. (2007), Proektirovanye nechetkikh system sredstvami MATLAB [Designing Fuzzy Systems Using MATLAB], Horyachaia liniia-Telekom, Moscow, Russian Federation.