Application of sustainable hybrid fuzzy MCDM approach for product quality analysis from different agricultural production methods

Öz

In recent years, with the increasing world population, the importance of agricultural production has been increasing rapidly. There are various approaches to agricultural production. Products obtained using genetic engineering methods are more cost-effective. However, this unnatural process has negative effects on nature in the long run. Industrial farming methods use a high level of resources while reducing the damage to nature and the environment. Organic agriculture, on the other hand, aims to protect both the environment and consumer health by eliminating the negative effects of industrial production methods and genetic engineering. In this study, a hybrid model was created by using fuzzy Analytical Hierarchy Process (B-AHS) and VIKOR methods together, and a decision support system was developed to propose a sustainable solution to the agricultural method selection problem. In the first step of the method, Satisfaction (C1), Economy (C2), and Environment (C3) criteria were evaluated with B-AHS and the weights were determined. The second step evaluated the proposed agricultural methods with the VIKOR method. This research aims to establish a complete framework for assessing and rating the suitability of agricultural technologies. To demonstrate the validity of the proposed method, an application was made on a real problem with current data.

Anahtar Kelimeler

• Agricultural Production Technique
• Fuzzy AHP
• VIKOR
• MCDM

Farklı tarımsal üretim yöntemleri sonucu elde edilen ürün kalitesi analizi için sürdürülebilir melez bulanık ÇKKV yaklaşımı uygulaması

Öz

Son yıllarda, bilinçli nüfusun artışı tarımsal üretim yöntemlerine dayanarak elde edilen ürün kalitesinin irdelenme gerekliliği artırmaktadır. Tarımsal üretim sonucu elde edilen ürün kalitesiyle ilgili çeşitli yaklaşımlar bulunmaktadır. Genetik mühendislik yöntemleri sonucu edilen ürünler (GDO) verimlidir. Ancak doğal olmayan bu sürecin uzun vadede doğaya ve insan sağlığına olumsuz etkilerinin ortaya çıkacağı varsayılmaktadır. Endüstriyel tarım yöntemleriyle elde dilen ürünlere bakıldığında ürün miktarı bakımından yüksek olsa da kullanılan yüksek miktar gübre ve insektisit insan sağlığını olumsuz yönde etkilemektedir. Son olarak Organik tarım ile elde edilen organik ürünler ise, endüstriyel üretim yöntemlerinin ve genetik mühendisliğinin olumsuz etkilerini ortadan kaldırarak hem çevreyi hem de tüketici sağlığını korumayı amaçlamaktadır ancak bu üretim yöntemiyle elde edilen ürün miktarı düşükken maliyeti yüksektir ve toplumun her kesimine hitap etmemektedir. Bu çalışmada Çok Kriterli Karar Verme (ÇKKV) yöntemlerinden bulanık Analitik Hiyerarşi Süreci (B-AHS) ve VIKOR metotları bir arada kullanılarak melez bir model oluşturulmuş ve tarımsal yöntemler ile elde edilen ürün seçim problemine sürdürülebilir bir çözüm önermek için karar destek sistemi geliştirilmiştir. Yöntemin ilk adımında Memnuniyet (C1), Ekonomi (C2) ve Sağlık (C3) kriterleri B-AHS ile değerlendirilmiş ve ağrılıklar belirlenmiştir. İkinci adımda ise VIKOR yöntemi ile önerilen tarım ürünün değerlendirilmesi yapılmıştır. Bu araştırmanın amacı, tarımsal teknolojilerini kullanarak elde edilen ürün uygunluğunu değerlendirmek ve derecelendirmek için eksiksiz bir çerçeve oluşturmaktır. Önerilen ürün geçerliliğinin gösterilmesi için, güncel veriler ile gerçek bir problem üzerinde uygulama yapılmıştır.

Anahtar Kelimeler

• Tarımsal üretim yöntemleri sonucu elde edilen ürün
• Bulanık AHS
• VIKOR
• ÇKKV

Kaynakça

1. Akhmetshina, L., Sergeev, A., & Mottaeva, A. (2019). Influence of organic agriculture on the development of green economy. In E3S Web of Conferences (Vol. 91). https://doi.org/10.1051/e3sconf/20199106008
2. Alem, S. (2021). Special Agriculture Production Selection Using Interval Type-2 Fuzzy AHP. In Advances in Intelligent Systems and Computing (Vol. 1197 AISC, pp. 557–566). https://doi.org/10.1007/978-3-030-51156-2_64
3. Arvas, Y. E., & Kaya, Y. (2019). Potential impact of genetically modified plants on biodiversity. Yuzuncu Yil University Journal of Agricultural Sciences, 29(1), 168–177. https://doi.org/10.29133/yyutbd.468218
4. Atsan, T., & Erem Kaya, T. (2008). Genetiği Değiştirilmiş Organizmaların (GDO) Tarım ve İnsan Sağlığı Üzerine Etkileri. Cilt (Vol. 22). Retrieved from https://dergipark.org.tr/tr/pub/ziraatuludag/issue/16752/174160
5. Buckley, J. J. (1985). Fuzzy hierarchical analysis. Fuzzy Sets and Systems, 17(3), 233–247. https://doi.org/10.1016/0165-0114(85)90090-9
6. Demirel, N. Ç., Yücenur, G. N., Demirel, T., & Muşdal, H. (2012). Risk-Based Evaluation of Turkish Agricultural Strategies using Fuzzy AHP and Fuzzy ANP. Human and Ecological Risk Assessment, 18(3), 685–702. https://doi.org/10.1080/10807039.2012.672902
7. Eraslan, E., & Iç, Y. T. (2011). A multi-criteria approach for determination of investment regions: Turkish case. Industrial Management and Data Systems. https://doi.org/10.1108/02635571111144964
8. Fabris, M., Abbriano, R. M., Pernice, M., Sutherland, D. L., Commault, A. S., Hall, C. C., … Ralph, P. J. (2020). Emerging Technologies in Algal Biotechnology: Toward the Establishment of a Sustainable, Algae-Based Bioeconomy. Frontiers in Plant Science. https://doi.org/10.3389/fpls.2020.00279

9. Fan, Z. P., & Liu, Y. (2010). A method for group decision-making based on multi-granularity uncertain linguistic information. Expert Systems with Applications, 37(5), 4000–4008. https://doi.org/10.1016/j.eswa.2009.11.016
10. Firdaus, A., Adiprasetyo, T., & Suhartoyo, H. (2021). A Multicriteria Decision Making and Fuzzy-AHP Approach for Formulating Strategy to Develop Organic Agriculture in Bengkulu Province, Indonesia. In Proceedings of the International Seminar on Promoting Local Resources for Sustainable Agriculture and Development (ISPLRSAD 2020) (Vol. 13). https://doi.org/10.2991/absr.k.210609.034
11. Gelişmeler, T., Ataseven, Y., & Güneş, E. (2008). Türkiye’de İşlenmiş Organik Tarım Ürünleri Üretimi ve Ticaretindeki Gelişmeler. Uludağ Üniversitesi Ziraat Fakültesi Dergisi, 22(2), 25–33.
12. James, C. (2017). Global Status of Commercialized Biotech/GM Crops in 2017: Biotech Crop Adoption Surges as Economic Benefits Accumulate in 22 years. ISAAA Brief, (Brief 53), 1–143. Retrieved from https://www.isaaa.org/resources/publications/briefs/53/download/isaaa-brief-53-2017.pdf%0Ahttp://library1.nida.ac.th/termpaper6/sd/2554/19755.pdf
13. Khazaeni, G., Khanzadi, M., & Afshar, A. (2012). Fuzzy adaptive decision making model for selection balanced risk allocation. International Journal of Project Management, 30(4), 511–522. https://doi.org/10.1016/j.ijproman.2011.10.003
14. Mäder, P., Fließbach, A., Dubois, D., Gunst, L., Fried, P., & Niggli, U. (2002). Soil fertility and biodiversity in organic farming. Science, 296(5573), 1694–1697. https://doi.org/10.1126/science.1071148
15. Mokarram, M., Rangzan, K., Moezzi, A., & Baninemeh, J. (2010). Land suitability evaluation for wheat cultivation by fuzzy theory approache as compared with parametric method. In International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives (Vol. 38).
16. Muller, A., Schader, C., El-Hage Scialabba, N., Brüggemann, J., Isensee, A., Erb, K. H., … Niggli, U. (2017). Strategies for feeding the world more sustainably with organic agriculture. Nature Communications, 8(1). https://doi.org/10.1038/s41467-017-01410-w
17. Nasiri, H., Alavipanah, S. K., Matinfar, H. R., Azizi, A., & Hamzeh, M. (2012). Implementation of agricultural ecological capability model using integrated approach of PROMETHEE II and Fuzzy-AHP in GIS environment (case study: Marvdasht county). Journal of Environmental Studies, 38(63), 109–122.
18. Onder, E. (2014). VIKOR Method for Rankıng Logıstıc Vıllages in Turkey. Journal of Management and Economics Research, (23), 293–293. https://doi.org/10.11611/jmer236
19. Opricovic, S., & Tzeng, G. H. (2004). Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS. European Journal of Operational Research, 156(2), 445–455. https://doi.org/10.1016/S0377-2217(03)00020-1
20. Opricovic, S., & Tzeng, G. H. (2007). Extended VIKOR method in comparison with outranking methods. European Journal of Operational Research, 178(2), 514–529. https://doi.org/10.1016/j.ejor.2006.01.020
21. Özdemir, Y. S., & Üsküdar, A. (2020). Strategy selection by using interval type-2 fuzzy mcdm and an application. Journal of Engineering Research (Kuwait), 8(3). https://doi.org/10.36909/JER.V8I3.8176
22. Pilevar, A. R., Matinfar, H. R., Sohrabi, A., & Sarmadian, F. (2020). Integrated fuzzy, AHP and GIS techniques for land suitability assessment in semi-arid regions for wheat and maize farming. Ecological Indicators, 110. https://doi.org/10.1016/j.ecolind.2019.105887
23. Piya, S., Shamsuzzoha, A., Azizuddin, M., Al-Hinai, N., & Erdebilli, B. (2022). Integrated Fuzzy AHP-TOPSIS Method to Analyze Green Management Practice in Hospitality Industry in the Sultanate of Oman. Sustainability (Switzerland), 14(3). https://doi.org/10.3390/su14031118
24. Pourkhabbaz, H. R., Javanmardi, S., & Faraji Sabokbar, H. A. (2014). Suitability analysis for determining potential agricultural land use by the multi-criteria decision making models SAW and VIKOR-AHP (case study: Takestan-Qazvin plain). Journal of Agricultural Science and Technology, 16(5), 1005–1016.
25. Ren, C., Li, Z., & Zhang, H. (2019). Integrated multi-objective stochastic fuzzy programming and AHP method for agricultural water and land optimization allocation under multiple uncertainties. Journal of Cleaner Production, 210, 12–24. https://doi.org/10.1016/j.jclepro.2018.10.348
26. Saaty, T. L. (1980). The Analytic Hierarchy Process. Education, 1–11. https://doi.org/10.3414/ME10-01-0028
27. Saputri, V. H. L., Sutopo, W., Hisjam, M., & Ma’aram, A. (2019). Sustainable agri-food supply chain performance measurement model for GMO and Non-GMO using data envelopment analysis method. Applied Sciences (Switzerland), 9(6). https://doi.org/10.3390/app9061199
28. Tai, W. S., & Chen, C. T. (2009). A new evaluation model for intellectual capital based on computing with linguistic variable. Expert Systems with Applications, 36(2 PART 2), 3483–3488. https://doi.org/10.1016/j.eswa.2008.02.017
29. Tashayo, B., Honarbakhsh, A., Azma, A., & Akbari, M. (2020). Combined Fuzzy AHP–GIS for Agricultural Land Suitability Modeling for a Watershed in Southern Iran. Environmental Management, 66(3), 364–376. https://doi.org/10.1007/s00267-020-01310-8
30. Tilman, D., Cassman, K. G., Matson, P. A., Naylor, R., & Polasky, S. (2002). Agricultural sustainability and intensive production practices. Nature. https://doi.org/10.1038/nature01014
31. Vanbergen, A. J., Aizen, M. A., Cordeau, S., Garibaldi, L. A., Garratt, M. P. D., Kovács-Hostyánszki, A., … Young, J. C. (2020). Transformation of agricultural landscapes in the Anthropocene: Nature’s contributions to people, agriculture and food security. In Advances in Ecological Research (Vol. 63, pp. 193–253). https://doi.org/10.1016/bs.aecr.2020.08.002
32. Yang, Y. T., & Chen, B. (2016). Governing GMOs in the USA: Science, law and public health. Journal of the Science of Food and Agriculture, 96(6), 1851–1855. https://doi.org/10.1002/jsfa.7523
33. Zandi, P., Rahmani, M., Khanian, M., & Mosavi, A. (2020). Agricultural risk management using fuzzy topsis analytical hierarchy process (Ahp) and failure mode and effects analysis (fmea). Agriculture (Switzerland), 10(11), 1–28. https://doi.org/10.3390/agriculture10110504