PERSONAL SUBJECTIVITY IMPACT REDUCTION IN CHOICE OF SOUR CHERRY VARIETIES FOR ORCHARD ESTABLISHMENT USING FUZZY SYSTEM
DOI:
https://doi.org/10.5937/ekoPolj1802545PKeywords:
sour cherry, choice of varieties, multi-criteria modelling, fuzzy systemAbstract
This paper analyzes the problem of multi-criteria decision making methods (MCDM) when selecting the optimal type of sour cherry for planting. Choice of varieties in agriculture is a very complex problem and is usually characterized by the interaction of a large number of factors, including often limited resources and uncertain information (price, time). In this context, mathematical models can represent valuable support for farmers when deciding on the choice of crops and plants for planting. An integrated MCDM method is presented, along with expert knowledge and Fuzzy Interference System (FIS) for sour cherry varieties choosing. The proposed approach assists decision makers in complex calculations and diminishes the impact of personal subjectivity and perception in order to defne the overall evaluation. Data is incorporated in proposed fuzzy system and validated by a numerical example.
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References
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26. Schwab, W., & Schreier, P. (1990). Studies on bound aroma compounds of sour cherry fruit (Prunus cerasus L.), Z. Lebensm. Unters. Forsch, 190, 228–231.
27. Wang, H., Nair, M.G., Strasburg, G.M., Chang, Y.C., Booren, A.M., Gray, J.I., & DeWitt, D.L. (1999). Antioxidant and antiinflammatory activities of anthocyanins and their aglycon, cyanidin, from tart cherries, J Nat Prod. 62(2), 294-6.
28. Weintraub, A., & Romero, C. (2006). Operations research models and the management of agricultural and forestry resources: a review and comparison. Interfaces, 36(5), 446–457.
29. Zenga, Xieting, Shaozhong Kanga, Fusheng Li , Lu Zhangc, & Ping Guoa. (2010). Fuzzy multi-objective linear programming applying to crop area planning. Agricultural Water Management, 98 134–142. doi: 10.1016/j. agwat.2010.08.010
2. Bernalte, M.J., Sabio, E., Hernandez, M.T., & Gervasini, C. (2003). Influence of storage delay on quality of Van sweet cherry. Postharvest Biol Tec, 28, 303–312
3. Brzozowski, P. (2005). Perspectives of sour cherry growing in Poland. XLIV Congress of fruit growers. Skierniewice, 27.10.2005, 68–75. [in Polish: Brzozowski, P. (2005). Skierniewice Perspektywy uprawy wiśni w Polsce. XLIV Zjazd Sadowników].
4. Crisosto, C.H., Crisosto, G.M., & Metheney, P. (2003). Consumer acceptance of Brooksand Bing cherries is mainly dependent on fruit SSC and visual skin colour. Postharvest Biol. Technol. 28, 159–167.doi:10.10161/S0925- 5214(02)00173-4.
5. Detlefsen, N.K., & Jensen, A.L. (2004). A stochastic model for crop variety selection. Agric. Syst. 81(1), 55–72.
6. Evans, K., Brutcher, L., Konishi, B., Barritt, B. (2010): Correlation of sensory analysis with physical textural data from a computerized penetrometer in the Washing-ton State University apple breeding program, HortTechnology, 20(6), 1026–1029.
7. Food and Agriculture Organization of the United Nations (FAO). FAOSTAT Database. Available at: http://www.fao.org/statistics/en/
8. Filippi, C., Mansini, R., & Stevanatoa, E. (2017). Mixed integer linear programming models for optimal crop selection. Computers and Operations Research, 81(C), 26–39. doi:10.1016/j.cor.2016.12.004
9. Fillion, L., & Kilcast, D. (2002). Consumer perception of crispness and crunchiness infruits and vegetables. Food Qual. Prefer. 13, 23–29.
10. Francisco, S.R., & Ali, M. (2006). Resource allocation tradeoffs in Manilas peri-urban vegetable production systems: an application of multiple objective programming, Agric. Syst. 87, 147–168.
11. Gajovic, V., Kerkez, M., & Kocovic, J. (2018). Modeling and simulation of logistic processes: risk assessment with a fuzzy logic technique. Simulation. Transactions of the Society for Modeling and Simulation International, 94(6), 507–518. doi: 10.1177/0037549717738351
12. Harker, F.R., Maindonald, J., Murray, S.H., Gunson, F.A., Hallett, I.C., & Walker, S.B. (2002). Sensory interpretation of instrumental measurements. 1: Texture of apple fruit. Postharvest Biol. Technol. 24(3), 225–239. doi:10.1016/S0925-5214(01)00158-2
13. Joubert, J.W., Luhandjula, M.K., Ncube, O., le Roux, G., & deWet, F. (2007). An optimization model for the management of a South African game ranch, Agric. Syst. 92, 223–239.
14. Lezzoni, A., Schmidt, H, & Albertain, A. (1990). Cherries (Prunus). In: Moore JN, Ballington JR (eds). Genetic resources of temperate fruit and nut crops. ISHS, Wageningen, pp. 109–174.
15. Mainuddin, M., Gupta, A.D., & Onta, P.R. (1997). Optimal crop planning model for an existing groundwater irrigation project in Thailand. Agric. Water Manage. 33(1), 43–62. doi: 10.1016/S0378-3774(96)01278-4
16. Milovanović, Ž., & Stojanović, M. (2016). Variety of cherries planting choice by the AHP methodology. Agroekonomika, 45(72), 11-19. [in Serbian: Milovanović, Ž., & Stojanović, M. (2016). Izbor sorti višnje za sadnju primenom AHP metodologije].
17. Mišić, D.P. (1989). New varieties of fruits, Nolit, Belgrade. [in Serbian: Mišić, D.P. (1989). Nove sorte voćaka].
18. Nafarieh, A., & Keller, J.M. (1991). A new approach to inference in approximate reasoning. Fuzzy Sets and Systems, 41(1), 18–36. doi:10.1016/0165-0114(91)90155-J
19. Poll, L., Petersen, M.B., & Nielsen, G.S. (2003). Influence of harvest year and harvest time on soluble solids, titrateable acid, anthocyanin content and aroma components in sour cherry (Prunus cerasus L. cv. Stevnsbaer), Eur. Food Res. Technol. 216, 212–216.
20. Raju, K.S., & Kumar, D.N. (1999). Multicriterion decision making in irrigation planning. Agric. Syst. 62, 117–129.
21. Sarker, R., & Quaddus, Q. (2002): Modelling a nationwide crop planning problem using a multiple criteria decision making tool. Computers Industrial Engineering, 42, 541–553.
22. Sarker, R., Talukder, S., & Haque, A.F.M.A. (1997). Determination of optimum crop-mix for crop cultivation in Bangladesh. Appl. Math. Model. 21(10), 621– 632. doi: 10.1016/S0307-904X(97)00083-8.
23. Serrano, M., Guillen, F., Martìnez-Romero, D., Castillo S., & Valero, D. (2005). Chemical constituents and antioxidant activity of sweet cherry at different ripening stages. J Agric Food Chem, 53:2741–2745.
24. South, D.B., Mexal J.G., & Buijtenen van, J.P. (1989). The relationship between seedling diameter at planting and long term volume growth of loblolly pine seedlings in east Texas, South. Forest Nursery Management Cooperative, Auburn Univ. Auburn, Ala. Rep. No32. p. 8.
25. Schmid, W., & Grosch, W. (1986). Quantitative-analysis of the volatile flavor compounds having high aroma values from sour (Prunus cerasus L.) and sweet (Prunus avium L.) cherry juices and jams, Z. Lebensm. Unters. Forsch. 183, 39–44.
26. Schwab, W., & Schreier, P. (1990). Studies on bound aroma compounds of sour cherry fruit (Prunus cerasus L.), Z. Lebensm. Unters. Forsch, 190, 228–231.
27. Wang, H., Nair, M.G., Strasburg, G.M., Chang, Y.C., Booren, A.M., Gray, J.I., & DeWitt, D.L. (1999). Antioxidant and antiinflammatory activities of anthocyanins and their aglycon, cyanidin, from tart cherries, J Nat Prod. 62(2), 294-6.
28. Weintraub, A., & Romero, C. (2006). Operations research models and the management of agricultural and forestry resources: a review and comparison. Interfaces, 36(5), 446–457.
29. Zenga, Xieting, Shaozhong Kanga, Fusheng Li , Lu Zhangc, & Ping Guoa. (2010). Fuzzy multi-objective linear programming applying to crop area planning. Agricultural Water Management, 98 134–142. doi: 10.1016/j. agwat.2010.08.010
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Published
2018-06-27
How to Cite
Paunović, M., Milutinović, O., & Puzić, G. (2018). PERSONAL SUBJECTIVITY IMPACT REDUCTION IN CHOICE OF SOUR CHERRY VARIETIES FOR ORCHARD ESTABLISHMENT USING FUZZY SYSTEM. Economics of Agriculture, 65(2), 545–554. https://doi.org/10.5937/ekoPolj1802545P
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