Determining Alternative Crops with Multi Criteria Decision Making Methods within the Framework of Land Risk Criteria

Abstract

Natural, societal, and economic hazards have a negative impact on agricultural production. In the field of agriculture, productivity studies are common, but election studies are rare. The goal of the study was to figure out which product to plant based on the region’s characteristics by anticipating risk factors in advance. The most appropriate crop kind to grow based on the risk variables faced in agricultural production was explored in this study. The nine risk factors in the Çukurova region, as well as three alternative crops, were determined for this study. Input costs, changes in climatic conditions, changes in yield loss due to pests, agricultural tools and machinery failure, theft, fire, crop damage due to excessive water, crop loss due to drought and lack of technical information were chosen as criteria. Citrus, cereal, and legume were chosen as alternative crops. First, using the Analytical Hierarchy Process method, the weights of the score were determined. As a consequence of the weighing, the input costs criterion had the greatest weight value of 0.29. The criterion with the lowest score was a lack of technical information (0.01). Then, using the steps of the Elimination and Choice Translating Reality English method, which is one of the Multi Criteria Decision Making methods, the best relevant alternative ranking was determined. The comparison was also done using the Technique for Order Preference by Similarity to Ideal Solutions method. The cereal alternative was the best in both methodologies as a result of their application. In the first method, legumes and citrus were chosen, however in the second method, the opposite outcome was obtained.

Keywords

• Risk criteria
• Alternative crops
• AHP
• ELECTRE
• TOPSIS

References

1. Akçaöz H V & Özkan B. 2002. Tarımsal üretimde karşılaşılan riskler ve uygulanabilecek stratejiler. Türkiye V. Tarım Ekonomisi Kongresi, Erzurum.
2. Alper D & Başdar C. 2017. A comparison of TOPSIS and ELECTRE methods: an application on the factoring industry. Business and Economics Research Journal 8(3): 627.
3. Benayoun R, Roy B & Sussman B. 1966. ELECTRE: Une méthode pour guider le choix en présence de points de vue multiples. Note de travail 49: 2-120.
4. Hwang C L & Yoon K. 1981. Methods for multiple attribute decision making. Multiple attribute decision making, Springer: 58-191.
5. Karaca E. 2013. ELECTRE I ve TOPSIS yöntemlerini kullanarak bir otomotiv firması için bayi seçimi, Kocaeli Universitesi, Fen Bilimleri Enstitusu.
6. Myers J H & Alpert M I. 1968. Determinant buying attitudes: meaning and measurement. Journal of Marketing 32(4_part_1): 13-20.
7. Ömürbek N, Karaatli M & Cömert H G. 2016. AHP-SAW ve AHP-ELECTRE yöntemleri ile yapı denetim firmalarının değerlendirmesi. Yönetim Bilimleri Dergisi 14(27): 171-199.
8. Rani A, Bandyopadhyay K, Krishnan P, Sarangi A & Datta S. 2020. Simulation of tillage, crop residue mulch and nitrogen interactions on yield and water use efficiency of wheat (Triticum aestivum) using DSSAT model. Indian Journal of Agricultural Sciences 90(10): 20-28.

9. Saaty T L. 1977. A scaling method for priorities in hierarchical structures. Journal of mathematical psychology 15(3): 234-281.
10. Sánchez-Lozano J, García-Cascales M S & Lamata M T. 2016. Comparative TOPSIS-ELECTRE TRI methods for optimal sites for photovoltaic solar farms. Case study in Spain. Journal of cleaner production 127: 387-398.
11. Seyedmohammadi J, Sarmadian F, Jafarzadeh A A, Ghorbani M A & Shahbazi F. 2018. Application of SAW, TOPSIS and fuzzy TOPSIS models in cultivation priority planning for maize, rapeseed and soybean crops. Geoderma 310: 178-190.
12. Triantaphyllou E. 2000. Multi-criteria decision making methods. Multi-criteria decision making methods: A comparative study, Springer: 5-21.
13. Tunca M, Aksoy E, Bülbül H & Ömürbek N. 2015. Ahp Temelli Topsıs Ve Electre Yöntemiyle Muhasebe Paket Use Of Ahp-Based TOPSIS and ELECTRE Methods on Accounting Software Selection. Niğde Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi 8(1): 53-71.
14. Tümer E İ, Birinci A & Aksoy A. 2010. Tarımsal üretimi etkileyen risk faktörleri ve stratejilerinin belirlenmesi: Erzurum İli Örneği. Türkiye IX. Tarım Ekonomisi Kongresi: 22-24.
15. Yalçiner D & Karaatli M. 2018. Mevduat bankasi seçimi sürecinde TOPSIS ve ELECTRE yöntemlerinin kullanılması. Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi 23(2): 401-423.