Fuzzy AHP and Fuzzy TOPSIS Methods for Fruit Tree Selection: The Case of İzmir

Alkan Durmuş; Abdurrahman İskender

doi:10.29133/yyutbd.1606278

Fuzzy AHP and Fuzzy TOPSIS Methods for Fruit Tree Selection: The Case of İzmir

Abstract

The application of fuzzy multi-criteria decision-making (MCDM) methods has become increasingly relevant in addressing the complexities of agricultural decision-making, particularly when multiple conflicting criteria are involved. This study investigates the selection of the most suitable fruit tree species for orchards in the Tire District of İzmir Province, Türkiye, by utilizing Fuzzy AHP and Fuzzy TOPSIS methodologies. The aim is to provide a systematic and robust framework to support long-term investment decisions in fruit cultivation. Five main criteria—economic viability, production and operational factors, environmental and regional suitability, social and support mechanisms, and market demand—were evaluated, along with 21 sub-criteria. The Fuzzy AHP results indicated that economic factors were the most critical, reflecting their importance in farmers' decision-making processes, while market and demand factors had the least impact. Among sub-criteria, "Government Incentives and Grants" emerged as the most influential, underscoring the importance of supportive policies in sustainable agriculture. Using Fuzzy TOPSIS, 12 fruit tree alternatives were ranked based on their proximity to an ideal solution. Olive (A2) was identified as the most suitable option, followed by fig (A1) and grape (A4). Sensitivity analysis confirmed the robustness of the framework, as the rankings of the top three alternatives remained stable across varying criteria Weights.This study contributes to the Turkish agricultural literature by demonstrating the practical application of fuzzy MCDM methods. It provides valuable insights for farmers, policymakers, and agricultural planners, underscoring the need for economic and policy support to promote sustainable agricultural practices. Future studies should explore dynamic frameworks to enhance decision-making adaptability.

Keywords

References

Afriliansyah, T., Nababan, E. B., & Situmorang, Z. (2019, Eylül). Performance analysis of fuzzy AHP in the rankings. Proceedings of the 1st Workshop on Multidisciplinary and Its Applications Part 1, WMA-01 2018, Aceh, Indonesia.
Aires, E., Aragão, C., Gomes, I., Souza, G., & Andrade, I. (2020). Alternative substrates for production of yellow passion fruit seedlings. Revista De Agricultura Neotropical, 7(1), 43-48.
Aktaş, A., Kaplan, F., & Koluman, A. (2023). Determinants of dried fruit products sector export: A gravity model for Türkiye. Mediterranean Agricultural Sciences, 36(2), 77-81.
Akyol, Ö., Özgül, A., Şen, H., Düzbastılar, F., & Ceyhan, T. (2019). Determining potential conflicts between small-scale fisheries and sea-cage fish farms in the Aegean Sea. Acta Ichthyologica Et Piscatoria, 49(4), 365-373.
Alphonce, C. B. (1997). Application of the analytic hierarchy process in agriculture in developing countries. Agricultural Systems, 53(1), 97-112.
Attar, Ş., Gündeşli, M., Urün, I., Kafkas, S., Kafkas, N., Ercişli, S., … Adámková, A. (2022). Nutritional analysis of red-purple and white-fleshed pitaya (Hylocereus) species. Molecules, 27(3), 808.
Burbano-Erazo, E., Pastrana-Vargas, I., Mejía-Salazar, J., & Cabrera, F. (2022). Selection criteria for tomato lines with defined growth habit. Trends in Horticulture, 5(1), 55.
Chang, D.-Y. (1996). Theory and methodology: Applications of the extent analysis method on fuzzy AHP. European Journal of Operational Research, 95, 649-655.

Chen, C. T. (2000). Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets and Systems, 114, 1-9.
Chen, C.-T., Lin, C.-T., & Huang, S.-F. (2006). A fuzzy approach for supplier evaluation and selection in supply chain management. International Journal of Production Economics, 102, 289–301.
Deliboran, A. (2024). Evaluation of ecological and health risks of trace elements in soils of olive orchards and apportionment of their sources using the APCS-MLR receptor model. Environmental Geochemistry and Health, 46(9), 1-17.
Demir, Z. (2020). The evaluation of basal respiration and some chemical properties of soils under cover crop treatments in a cherry orchard. Eurasian Journal of Soil Science, 9(2), 151-164.
Derse, O., & Göçmen, E. (2023). An optimization approach for a biogas supply chain using goal programming and mixed integer linear programming. Bitlis Eren University Journal of Science and Technology, 12(2), 402-411.
Efe, B. (2022). An integrated fuzzy approach based failure mode and effects analysis for a risk assessment. Bitlis Eren University Journal of Science and Technology, 11(2), 678-693.
Eroğul, D. (2018). An overview of sweet cherry fruit cultivation in Turkey. Trends in Horticulture, 1(2), 1-4.
Gao, X. (2023). Detection of fruit using YOLOv8-based single stage detectors. International Journal of Advanced Computer Science and Applications, 14(12), 83-91.
Gardašević-Filipović, M., & Saletic, D. (2010). Multicriteria optimization in a fuzzy environment: The fuzzy analytic hierarchy process. Yugoslav Journal of Operations Research, 20(1), 71-85.
Getahun, S., Kefale, H., & Gelaye, Y. (2024). Application of precision agriculture technologies for sustainable crop production and environmental sustainability: A systematic review. The Scientific World Journal, 2024(1), 2126734.
Göksu, A., & Güngör, İ. (2008). Fuzzy analytic hierarchy process and its application in university ranking. Süleyman Demirel University Faculty of Economics and Administrative Sciences Journal, 13(3), 1-26.
Grošelj, P., & Stirn, L. (2017). Soft consensus model for the group fuzzy AHP decision making. Croatian Operational Research Review, 8(1), 207-220.
Guillén, S., Casas, A., Terrazas, T., Vega, E., & Martínez-Palacios, A. (2013). Differential survival and growth of wild and cultivated seedlings of columnar cacti: Consequences of domestication. American Journal of Botany, 100(12), 2364-2379.
Hayashi, K. (2000). Multicriteria analysis for agricultural resource management: A critical survey and future perspectives. European Journal of Operational Research, 122(2), 486-500.
Horák, M., Šnurkovič, P., Ondrášek, I., Balík, J., & Srilaong, V. (2019). Comparison of some physico-chemical parameters of kiwiberry (Actinidia arguta) cultivars from a cold climate. Folia Horticulturae, 31(2), 375-383.
Javanbarg, M., Scawthorn, C., Kiyono, J., & Shahbodagh, B. (2012). Fuzzy AHP-based multicriteria decision making systems using particle swarm optimization. Expert Systems with Applications, 39(1), 960-966.
Kadanalı, E., & Kaya, E. (2020). Agricultural loan and agricultural production value in Turkey. Alınteri Zirai Bilimler Dergisi, 35(1), 93-98.
Kahraman, Ç., Ruan, D., & Doğan, I. (2003). Fuzzy group decision-making for facility location selection. Information Sciences, 157, 135–153.
Karahan, M. (2023). Determining factors of retaining young farmers in agriculture: A case study in Turkey and Japan. Agris on-Line Papers in Economics and Informatics, 15(2), 41-54.
Karakaşoğlu, N. (2008). Fuzzy multi-criteria decision making methods and application [Yayımlanmamış yüksek lisans tezi]. Pamukkale University, Denizli.
Karaşan, A., Erdoğan, M., & İlbahar, E. (2018). Prioritization of production strategies of a manufacturing plant by using an integrated intuitionistic fuzzy AHP & TOPSIS approach. Journal of Enterprise Information Management, 31(4), 510-528.
Kılıç, N., Burgut, A., Gündeşli, M., Nogay, G., Ercişli, S., Kafkas, N., … Szopa, A. (2021). The effect of organic, inorganic fertilizers and their combinations on fruit quality parameters in strawberry. Horticulturae, 7(10), 354.
Latinopoulos, D. (2009). Multicriteria decision-making for efficient water and land resources allocation in irrigated agriculture. Environment, Development and Sustainability, 11, 329-343.
León, L., Velasco, L., & Rosa, R. (2014). Initial selection steps in olive breeding programs. Euphytica, 201(3), 453-462.
Li, T., Jin, J., & Li, C. (2012). Refractured well selection for multicriteria group decision making by integrating fuzzy AHP with fuzzy TOPSIS based on interval‐typed fuzzy numbers. Journal of Applied Mathematics, 2012(1), 1-21.
Liu, C. (2024). Automated fruit sorting in smart agriculture system: Analysis of deep learning-based algorithms. International Journal of Advanced Computer Science and Applications, 15(1), 828-837.
Maksimović, A., Grgić, Z., & Ćejvanović, F. (2017). Multi-attribute analysis of orchard according to the integrated production concept. Economics of Agriculture, 64(1), 69–79.
Markovic, Z. (2010). Modification of TOPSIS method for solving of multicriteria tasks. Yugoslav Journal of Operations Research, 20(1), 117-143.
El Moussaoui, E. H., Moumni, A., Khabba, S., Er-Raki, S., Ait Hssaine, B., Chehbouni, A., & Lahrouni, A. (2025). Bibliometric and review analysis of argan trees studies: global research trends and challenges. Agroforestry Systems, 99(5), 132.
Mugiyo, H., Chimonyo, V. G., Sibanda, M., Kunz, R., Masemola, C. R., Modi, A. T., & Mabhaudhi, T. (2021). Evaluation of land suitability methods with reference to neglected and underutilised crop species: A scoping review. Land, 10(2), 125.
Nabizadeh, R., Yousefzadeh, S., Yaghmaeian, K., Alimohammadi, M., & Mokhtari, Z. (2021). Bottled water quality ranking via the multiple-criteria decision-making process: A case study of two-stage fuzzy AHP and TOPSIS. Environmental Science and Pollution Research, 29(14), 20437-20448.
Nabloussi, A., Bahri, H., Lakbir, M., Moukane, H., Kajji, A., & Fechtali, M. (2019). Assessment of a set of rapeseed (Brassica napus L.) varieties under waterlogging stress at different plant growth stages. OCL, 26, 36.
Nedeljković, M., Puška, A., Doljanica, S., Virijević Jovanović, S., Brzaković, P., Stević, Ž., & Marinkovic, D. (2021). Evaluation of rapeseed varieties using novel integrated fuzzy PIPRECIA—fuzzy MABAC model. PLOS ONE, 16(2), e0246857.
Oğuz, İ., Oğuz, H., Vural, A., & Kafkas, N. (2022). Goji berry (Lycium spp.) cultivation in Turkey. Proceedings of the Latvian Academy of Sciences Section B Natural Exact and Applied Sciences, 76(4), 409-416.
Özdağoğlu, A. (2008). Sensitivity analyzes in fuzzy AHP approach: Adding a new raw material supplier to the solution. Istanbul Commerce University Journal of Science, 7(13), 51-72.
Özkan, B., Dengiz, O., & Turan, İ. D. (2019). Site suitability assessment and mapping for rice cultivation using multi-criteria decision analysis based on fuzzy-AHP and TOPSIS approaches under semi-humid ecological condition in delta plain. Paddy and Water Environment, 17, 665-676.
Pala, O. (2015). Fuzzy analytical hierarchy process and its application in career selection. Dokuz Eylül University Social Sciences Institute Journal, 18(3), 427-445.
Qureshi, M. N., Pradeep, K., & Dinesh, K. (2009). Framework for benchmarking logistics performance using fuzzy AHP. International Journal of Business Performance and Supply Chain Modelling, 1, 82–98.
Ribas, J., Ribas, J., Suárez‐García, A., Arce, E., González-Peña, D., & Rodríguez, A. (2021). A multicriteria evaluation of sustainable riparian revegetation with local fruit trees around a reservoir of a hydroelectric power plant in Central Brazil. Sustainability, 13(14), 7849.
Rouyendegh, B. D. (2014). Developing an integrated AHP and intuitionistic fuzzy TOPSIS methodology. Technical Gazette, 21, 1313–1319.
Rozman, Č., Maksimović, A., Puška, A., Grgić, Z., Pažek, K., Prevolšek, B., & Ćejvanović, F. (2017). The use of multi criteria models for decision support system in fruit production. Erwerbs-Obstbau, 59(3), 235–243.
Saaty, T. L. (1980). The analytic hierarchy process. McGraw-Hill.
Sansavini, S. (2014). A short history of scholarship in horticulture and pomology. Içinde Horticulture: Plants for People and Places, Volume 3: Social Horticulture (ss. 1257-1307). Springer.
Sari, F., Kandemir, İ., Ceylan, D. A., & Gül, A. (2020). Using AHP and PROMETHEE multi-criteria decision making methods to define suitable apiary locations. Journal of Apicultural Research, 59, 546–557.
Serdar, Ü., Mercan, L., Okumuş, A., & Soylu, A. (2013). Morphological and molecular comparison of selected chestnut (Castanea sativa Mill.) genotypes from Black Sea region of Turkey. Anadolu Journal of Agricultural Sciences, 29(1), 54.
Shih, H.-J. S., Shyur, H.-J., & Lee, S. L. (2007). An extension of TOPSIS for group decision making. Mathematical and Computer Modelling, 45(7–8), 801-813.
Silva, T., Melo, H., Campos, L., Tarazi, M., Cunha, L., & Nascimento, A. (2021). Growth and productivity of tomato under use of biostimulant and ages of seedling transplantation. Colloquium Agrariae, 17(4), 23-33.
Şener, S. (2023). Comparison of fruit quality criteria and heavy metal contents of strawberries grown in organic and conventional agriculture. Applied Sciences, 13(13), 7919.
Taşkın, E. (2024). Effect of the aquaculture on the seagrass Posidonia oceanica: A decade before and after in the Aegean Sea. International Journal of Environment and Geoinformatics, 11(1), 36-42.
Thọ, N., Dawal, S., Yusoff, N., Aoyama, H., & Case, K. (2015). An integrated approach of fuzzy linguistic preference based AHP and fuzzy COPRAS for machine tool evaluation. PLOS ONE, 10(9), e0133599.
Torfi, F., Farahani, R., & Rezapour, S. (2010). Fuzzy AHP to determine the relative weights of evaluation criteria and fuzzy TOPSIS to rank the alternatives. Applied Soft Computing, 10(2), 520-528.
Wang, S., Chen, C., Sciarappa, W., Wang, C., & Camp, M. (2008). Fruit quality, antioxidant capacity, and flavonoid content of organically and conventionally grown blueberries. Journal of Agricultural and Food Chemistry, 56(14), 5788-5794.
Yunandra, Y., Deviona, D., Zuhry, E., Syukur, M., Ardian, A., Effendi, A., … Auliyanda, F. (2023). Selection criteria for lowland tomatoes (Solanum lycopersicum L.). E3S Web of Conferences, 373, 03005.
Zeb, M., Ahmed, W., Moazzam, M., Warsi, S. S., & Habib, M. S. (2026). Optimizing sustainable agricultural production under uncertainty: a multi-objective model for crop selection and land allocation. Environment, Development and Sustainability, 1-40. https://doi.org/10.1007/s10668-025-07055-w
Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8, 338–353.
Zamani, R., Ali, A. M. A., & Roozbahani, A. (2020). Evaluation of adaptation scenarios for climate change impacts on agricultural water allocation using fuzzy MCDM methods. Water Resources Management, 34, 1093–1110.

Details

Primary Language

English

Subjects

Agricultural Land Planning

Journal Section

Research Article

Authors

Alkan Durmuş ^*
0000-0002-5806-9962
Türkiye

Abdurrahman İskender
0000-0001-8055-7869
Türkiye

Publication Date

March 16, 2026

Submission Date

December 23, 2024

Acceptance Date

February 12, 2026

Published in Issue

Year 2026 Number: 1

DOI

https://doi.org/10.29133/yyutbd.1606278

IZ

https://izlik.org/JA57NY85KW

Cite

RIS / Bibtex

APA

Durmuş, A., & İskender, A. (2026). Fuzzy AHP and Fuzzy TOPSIS Methods for Fruit Tree Selection: The Case of İzmir. Yuzuncu Yıl University Journal of Agricultural Sciences, 1, 1606278. https://doi.org/10.29133/yyutbd.1606278