2020 COVID-19 Dönemi Verileriyle Küresel Gıda Güvencesi Endeksi Göstergelerinin Değerlendirilmesi ve Ülke Karşılaştırmaları

Abstract

Dalgalı küresel ekonomik büyüme, artan eşitsizlik, siyasi istikrarsızlık ve zorunlu göç, nüfusun iyi beslenip beslenmemesi üzerinde önemli bir etkiye sahip. İklim değişikliği ve doğal kaynakların tükenmesi bu olumsuzlukların artmasına neden olurken, 2030 yılına kadar Birleşmiş Milletler'in Sürdürülebilir Kalkınma Hedeflerine (BM SKH'ler) ulaşılmasını zorlaştırıyor. BM Gıda ve Tarım Örgütü (FAO) tarafından yapılan araştırmaya göre, 2030 yılına kadar 35 ila 122 milyon insanın yoksulluğa düşeceği ve iklim kaynaklı sorunlar nedeniyle daha az gıda güvencesi olacağı belirtiliyor. En savunmasız toplulukların gıda güvencesi ve beslenme durumunun, COVID-19 salgınının sağlık ve sosyo-ekonomik etkileri nedeniyle daha da kötüleşmesi bekleniyor. Ayrıca Dünya Gıda Programı'nın (WFP) yayınladığı rapora göre, gıda güvensizliğindeki her yüzde 1'lik artış, insanların fazladan yüzde 1,9’unun yiyecek bulmak için göç etmesine neden olmaktadır. Yiyecek bulmak ya da satın almak mümkün değilse bu göç hareketliliği devam etmektedir. Orta Doğu ve Kuzey Afrika ülkeleri başta olmak üzere birçok ülke bu riskleri diğer ülkelere göre daha fazla hissetmektedir. Çalışmada, Türkiye’nin de yer alacağı ülkelerin Birleşmiş Milletler 2030 Kalkınma Hedefleri temel başlıkları arasında yer alan gıda güvencesi açısından birbirlerine göre karşılaştırmalı durumları Küresel Gıda Güvencesi Endeksi (Global Food Security Index-GFSI) göstergelerinin 2020 COVID-19 dönemi verilerinin kullanılmasıyla Çok Kriterli Karar Verme (ÇKKV) yöntemleri ile analiz edilmesi planlanmaktadır. Çalışmanın amacı, küresel düzeyde yaşanan COVID-19 salgınıyla artan gıda güvencesi sorununa dikkat çekmek ve ayrıca bu konuda ÇKKV yöntemlerinin ve veri madenciliği yöntemlerinden kümeleme analizinin kullanılmasıyla literatüre bir yenilik sunmaktır. Bu araştırmanın sonuçlarının ve metodolojisinin hem ülkemizde hem de dünyada yapılacak çalışmalara ve politika yapıcılara yardımcı bir kaynak olunması hedeflenmektedir.

Keywords

• Sürdürülebilir Kalkınma Hedefleri
• Gıda Güvencesi
• İklim Değişikliği
• COVID-19
• Kümeleme Analizi
• Çok Kriterli Karar Verme Yöntemleri

Evaluation of Global Food Security Index Indicators with 2020 COVID19 Period Data and Country Comparisons

Abstract

Increased inequality in the world as well as political instability and forced migration have a substantial influence on the population's ability to feed themselves. While climate change and natural resource depletion worsen these negatives, they make meeting the United Nations' Sustainable Development Goals (UN SDGs) by 2030 more challenging. According to UN Food and Agriculture Organization (FAO) study, 35 to 122 million people would fall into poverty by 2030, and food security will be reduced owing to climate-related issues. The health and socio-economic effects of the COVID-19 pandemic are likely to impair the food security and nutritional condition of the most vulnerable communities. Furthermore, according to World Food Program (WFP) research, every 1% rise in food insecurity drives an extra 1.9 percent of individuals to migrate in search of food. This migratory movement continues if food cannot be found or purchased. Many nations, particularly those in the Middle East and North Africa, are more vulnerable to these threats than others. To determine whether nations are in a better position than others in terms of food security - one of the United Nations 2030 Development Goals - data from 2020 COVID-19 period of the Global Food Security Index (GFSI) indicators will be used in the study. There are two main goals of the study: first, call attention to the growing problem of food security in light of the COVID-19 pandemic on a worldwide scale, and second, introduce an innovative approach in the literature through the use of MCDM and cluster analysis. It is hoped that the findings and methods of this study will be a useful resource for researchers and policymakers in these nations and throughout the world.

Keywords

• Sustainable Development Goals
• Food Security
• Climate Change
• COVID-19
• Cluster Analysis
• Multi-Criteria Decision Making Methods

References

1. [1] H. Thomas, 2013. Trade reforms and food security: Conceptualizing the Linkages, ed: Food and Agriculture Organization of the United Nations. Retrieved from http …, 2003.
2. [2] R. Patel, 1996. Food sovereignty” is next big idea,Financial Times.
3. [3] Food and A. Organization, Rome Declaration on World Food Security and World Food Summit Plan of Action: World Food Summit 13-17 November 1996, Rome, Italy: FAO.
4. [4] U. E. R. Service, 2012. Food security in the United States: measuring household food security, 2008.
5. [5] F. Agricultural, Development Economics Division (June 2006), Food Security, vol. 2.
6. [6] G. Bickel, M. Nord, C. Price, W. Hamilton, and J. Cook, 2000. "Guide to measuring household food security," ed: Revised.
7. [7] P. K. Pachapur, V. L. Pachapur, S. K. Brar, R. Galvez, Y. Le Bihan, and R. Y. Surampalli, 2020. Food Security and Sustainability, Sustainability: Fundamentals and Applications, pp. 357-374.
8. [8] F. Nouh, 2021. Prevalence of Food Insecurity in Eastern Part of Libya: A Study of Associated Factors, Sch Acad J Biosci, vol. 8, pp. 192-198.

9. [9] F. Food Summit, 2009. Declaration of the world summit on food security, World Food Summit, pp. 16-18.
10. [10] C. Rights, 1999. General Comment No. 19, Geneva: United Nations.
11. [11] Food and A. Organization, 2016. "The state of food and agriculture: Climate change, agriculture and food security," ed: FAO Rome.
12. [12] W. F. Program, At the Root of Exodus: Food Security, Conflict and International Migration.
13. [13] P. Webb, J. Coates, E. A. Frongillo, B. L. Rogers, A. Swindale, and P. Bilinsky, 2017. Measuring household food insecurity: why it's so important and yet so difficult to do, The Journal of nutrition, vol. 136, pp. 1404S-1408S.
14. [14] R. Pérez-Escamilla and A. M. Segall-Corrêa, 2008. Food insecurity measurement and indicators, Revista de Nutrição, vol. 21, pp. 15s-26s.
15. [15] C. B. Barrett, 2010. Measuring food insecurity, Science, vol. 327, pp. 825-828.
16. [16] A. Swindale and P. Bilinsky, 2006.Development of a universally applicable household food insecurity measurement tool: process, current status, and outstanding issues, The Journal of nutrition, vol. 136, pp. 1449S-1452S.
17. [17] T. Ballard, J. Coates, A. Swindale, and M. Deitchler, 2011. Household hunger scale: indicator definition and measurement guide, Washington, DC: Food and nutrition technical assistance II project, FHI, vol. 360, p. 23.
18. [18] D. G. Maxwell, 1996. Measuring food insecurity: the frequency and severity of coping strategies, Food policy, vol. 21, pp. 291-303.
19. [19] W. H. Oldewage-Theron, E. G. Dicks, and C. E. Napier, 2006. Poverty, household food insecurity and nutrition: coping strategies in an informal settlement in the Vaal Triangle, South Africa, Public health, vol. 120, pp. 795-804.
20. [20] D. Maxwell, R. Caldwell, and M. Langworthy, 2008. Measuring food insecurity: Can an indicator based on localized coping behaviors be used to compare across contexts?, Food Policy, vol. 33, pp. 533-540.
21. [21] K. Aboaba, D. M. Fadiji, and J. A. Hussayn, 2020. Determinants of food security among rural households in Nigeria: USDA food insecurity experience based measurement (forms) approach, Journal of Agribusiness and Rural Development, vol. 56, pp. 113-124.
22. [22] I. FAO and UNICEF, 2020. The state of food security and nutrition in the world: Transforming food systems for affordable healthy diets, The state of the world.
23. [23] A. Saint Ville, J. Y. T. Po, A. Sen, A. Bui, and H. Melgar-Quiñonez, 2019. Food security and the Food Insecurity Experience Scale (FIES): ensuring progress by 2030, ed: Springer.
24. [24] K. Chetia, 2021. Food Security in India: A critical study on its Issus, Efforts and Challenges.
25. [25] J. L. Leroy, M. Ruel, E. A. Frongillo, J. Harris, and T. J. Ballard, 2015. Measuring the food access dimension of food security: a critical review and mapping of indicators, Food and nutrition bulletin, vol. 36, pp. 167-195.
26. [26] S. Desiere, M. D’Haese, and S. Niragira, 2015. Assessing the cross-sectional and inter-temporal validity of the Household Food Insecurity Access Scale (HFIAS) in Burundi, Public Health Nutrition, vol. 18, pp. 2775-2785.
27. [27] L. A. Garibaldi, B. Gemmill-Herren, R. D’Annolfo, B. E. Graeub, S. A. Cunningham, and T. D. Breeze, 2017. Farming approaches for greater biodiversity, livelihoods, and food security, Trends in ecology & evolution, vol. 32, pp. 68-80.
28. [28] R. Pérez-Escamilla, M. B. Gubert, B. Rogers, and A. Hromi-Fiedler, 2017. Food security measurement and governance: Assessment of the usefulness of diverse food insecurity indicators for policymakers, Global Food Security, vol. 14, pp. 96-104.
29. [29] C. Cafiero, S. Viviani, and M. Nord, 2018. Food security measurement in a global context: The food insecurity experience scale, Measurement, vol. 116, pp. 146-152. [30] M. D. Smith, W. Kassa, and P. Winters, 2017. Assessing food insecurity in Latin America and the Caribbean using FAO’s food insecurity experience scale, Food policy, vol. 71, pp. 48-61.
30. [31] M. D. Smith, M. P. Rabbitt, and A. Coleman-Jensen, 2017. Who is the world’s food insecure? New evidence from the Food and Agriculture Organization’s food insecurity experience scale, World Development, vol. 93, pp. 402-412.
31. [32] M. N. Poulsen, P. R. McNab, M. L. Clayton, and R. A. Neff, 2015. A systematic review of urban agriculture and food security impacts in low-income countries, Food Policy, vol. 55, pp. 131-146.
32. [33] M. K. Kansiime, J. A. Tambo, M. I. Mugambi, M. M. Bundi, A. Kara, and M. C. Owuor, 2020. COVID-19 implications on household income and food security in Kenya and Uganda: Findings from a rapid assessment, World Development, p. 105199,.
33. [34] G. I. Index, 2019. Global Innovation Index, The Global Innovation Index Report. GII.
34. [35] Ç. Kahraman, E. Abdulhamit, and O. Özevin, 2017. Futbol Takımlarının Finansal Ve Sportif Etkinliklerinin Entropi ve TOPSIS Yöntemiyle Analiz Edilmesi: Avrupa’nın 5 Büyük Ligi ve Süper Lig Üzerine Bir Uygulama, Uluslararası Yönetim İktisat ve İşletme Dergisi, vol. 13, pp. 199-222,.
35. [36] C. E. Shannon and W. Weaver, 1949. A mathematical model of communication, Urbana, IL: University of Illinois Press, vol. 11.
36. [37] M. Zeleny, 2012. Multiple criteria decision making Kyoto 1975 vol. 123: Springer Science & Business Media.
37. [38] J. P. Burg, 1974. Maximum entropy spectral analysis, Astronomy and Astrophysics Supplement, vol. 15, p. 383.
38. [39] R. Rosenfeld, 1994. Adaptive statistical language modeling, PhD Thesis, Carnegie Mellon University.
39. [40] A. Golan, G. Judge, and D. Miller, 1997. Maximum entropy econometrics: Robust estimation with limited data.
40. [41] M. Lihong, Z. Yanping, and Z. Zhiwei, 2008. Improved VIKOR algorithm based on AHP and Shannon entropy in the selection of thermal power enterprise's coal suppliers, in 2008 International Conference on Information Management, Innovation Management and Industrial Engineering, pp. 129-133.
41. [42] T.-C. Wang and H.-D. Lee, 2009. Developing a fuzzy TOPSIS approach based on subjective weights and objective weights, Expert systems with applications, vol. 36, pp. 8980-8985.
42. [43] A. Shemshadi, H. Shirazi, M. Toreihi, and M. J. Tarokh, 2011. A fuzzy VIKOR method for supplier selection based on entropy measure for objective weighting, Expert Systems with Applications, vol. 38, pp. 12160-12167.
43. [44] M. Apan, A. Öztel, and M. İslamoğlu, 2015. Teknoloji Sektörünün Entropi Ağırlıklı Uzlaşık Programlama (CP) ile Finansal Performans Analizi: BİST’de Bir Uygulama, in 19th Finance Symposium, Hitit University Çorum, Turkey [online] https://www. researchgate. net/publication/283299704 (accessed 7 December 2017).
44. [45] P. C. Fishburn and R. L. Keeney, 1974. Seven independence concepts and continuous multiattribute utility functions, Journal of Mathematical Psychology, vol. 11, pp. 294-327.
45. [46] E. Løken, 2007. Use of multicriteria decision analysis methods for energy planning problems, Renewable and sustainable energy reviews, vol. 11, pp. 1584-1595.
46. [47] Ö. Konuşkan, A. Endüstri Mühendisliği, and Ö. UYGUN, 2014. Çok Nitelikli Karar Verme (Maut) Yöntemi Ve Bir Uygulamasi.
47. [48] P. Chatterjee, V. M. Athawale, and S. Chakraborty, 2011. Materials selection using complex proportional assessment and evaluation of mixed data methods, Materials & Design, vol. 32, pp. 851-860.
48. [49] M. C. Das, B. Sarkar, and S. Ray, 2012. A framework to measure relative performance of Indian technical institutions using integrated fuzzy AHP and COPRAS methodology, Socio-Economic Planning Sciences, vol. 46, pp. 230-241.
49. [50] A. Kaklauskas, E. K. Zavadskas, J. Naimavicienė, M. Krutinis, V. Plakys, and D. Venskus, 2010. Model for a complex analysis of intelligent built environment, Automation in construction, vol. 19, pp. 326-340.
50. [51] J. MacQueen, Some methods for classification and analysis of multivariate observations, 1967. Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, pp. 281-297.
51. [52] A. H. Azadnia, P. Ghadimi, and M. Molani-Aghdam, 2011. A hybrid model of data mining and MCDM methods for estimating customer lifetime value, in The 41st International Conference on Computers and Industrial Engineering (CIE41), Los Angeles, United States of America, pp. 44-49.