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

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

Fluctuating global economic growth, rising inequality, political instability and forced migration have a significant impact on whether the population is well-nourished. While climate change and depletion of natural resources increase these negativities, they make it difficult to reach the United Nations' Sustainable Development Goals (UN SDGs) by 2030. According to research by the UN Food and Agriculture Organization (FAO), 35 to 122 million people will fall into poverty by 2030 and there will be less food security due to climate-related problems. The food security and nutritional status of the most vulnerable communities are expected to worsen due to the health and socio-economic impacts of the COVID-19 pandemic. In the study, the comparative situations of the countries including Turkey were planned to be analyzed by Multi-Criteria Decision Making methods with the 2020 COVID-19 period data in terms of food security, which is among the main headings of the United Nations 2030 Development Goals. The study presents an novelty to the literature by drawing attention to the increasing food security problem with the global COVID-19 pandemic, and also by using Multi-Criteria Decision Making methods and cluster analysis from data mining methods.. According to the final ranking obtained by the Borda Count method in the study, Singapore ranks first, followed by Finland, Sweden, Switzerland, the United States and the Netherlands, respectively. In both the COPRAS and MAUT rankings, six of the top 10 nations are European Union members. Indonesia, India, South Africa, Thailand, Brazil, and Slovakia are at the bottom of the Borda ranking.

___

  • [1] P. K. Pachapur, V. L. Pachapur, S. K. Brar, R. Galvez, Y. Le Bihan, and R. Y. Surampalli, "Food Security and Sustainability," Sustainability: Fundamentals and Applications, pp. 357-374, 2020.
  • [2] F. Nouh, "Prevalence of Food Insecurity in Eastern Part of Libya: A Study of Associated Factors," Sch Acad J Biosci, vol. 8, pp. 192-198, 2021.
  • [3] F. Food Summit, "Declaration of the world summit on food security," World Food Summit, pp. 16-18, 2009.
  • [4] C. Rights, "General Comment No. 19," Geneva: United Nations, 1999.
  • [5] 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, 1996.
  • [6] Food and A. Organization, "The state of food and agriculture: Climate change, agriculture and food security," ed: FAO Rome, 2016.
  • [7] W. F. Program, "At the Root of Exodus: Food Security, Conflict and International Migration," 2017.
  • [8] P. Webb, J. Coates, E. A. Frongillo, B. L. Rogers, A. Swindale, and P. Bilinsky, "Measuring household food insecurity: why it's so important and yet so difficult to do," The Journal of nutrition, vol. 136, pp. 1404S-1408S, 2006.
  • [9] R. Pérez-Escamilla and A. M. Segall-Corrêa, "Food insecurity measurement and indicators," Revista de Nutrição, vol. 21, pp. 15s-26s, 2008.
  • [10] C. B. Barrett, "Measuring food insecurity," Science, vol. 327, pp. 825-828, 2010.
  • [11] A. Swindale and P. Bilinsky, "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, 2006.
  • [12] T. Ballard, J. Coates, A. Swindale, and M. Deitchler, "Household hunger scale: indicator definition and measurement guide," Washington, DC: Food and nutrition technical assistance II project, FHI, vol. 360, p. 23, 2011.
  • [13] D. G. Maxwell, "Measuring food insecurity: the frequency and severity of “coping strategies”," Food policy, vol. 21, pp. 291-303, 1996.
  • [14] W. H. Oldewage-Theron, E. G. Dicks, and C. E. Napier, "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, 2006.
  • [15] D. Maxwell, R. Caldwell, and M. Langworthy, "Measuring food insecurity: Can an indicator based on localized coping behaviors be used to compare across contexts?," Food Policy, vol. 33, pp. 533-540, 2008.
  • [16] K. Aboaba, D. M. Fadiji, and J. A. Hussayn, "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, 2020.
  • [17] I. FAO and UNICEF, "WFP, & WHO.(2020). The state of food security and nutrition in the world: Transforming food systems for affordable healthy diets," The state of the world, 2020.
  • [18] A. Saint Ville, J. Y. T. Po, A. Sen, A. Bui, and H. Melgar-Quiñonez, "Food security and the Food Insecurity Experience Scale (FIES): ensuring progress by 2030," ed: Springer, 2019.
  • [19] K. Chetia, "Food Security in India: A critical study on its Issus, Efforts and Challenges," 2021.
  • [20] G. I. Index, "Global Innovation Index," The Global Innovation Index Report. GII, 2019.
  • [21] Ç. Kahraman, E. Abdulhamit, and O. Özevin, "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, 2017.
  • [22] C. E. Shannon and W. Weaver, "A mathematical model of communication," Urbana, IL: University of Illinois Press, vol. 11, 1949.
  • [23] M. Zeleny, Multiple criteria decision making Kyoto 1975 vol. 123: Springer Science & Business Media, 2012.
  • [24] J. P. Burg, "Maximum entropy spectral analysis," Astronomy and Astrophysics Supplement, vol. 15, p. 383, 1974.
  • [25] R. Rosenfeld, "Adaptive statistical language modeling," PhD Thesis, Carnegie Mellon University, 1994.
  • [26] A. Golan, G. Judge, and D. Miller, "Maximum entropy econometrics: Robust estimation with limited data," 1997.
  • [27] M. Lihong, Z. Yanping, and Z. Zhiwei, "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, 2008, pp. 129-133.
  • [28] T.-C. Wang and H.-D. Lee, "Developing a fuzzy TOPSIS approach based on subjective weights and objective weights," Expert systems with applications, vol. 36, pp. 8980-8985, 2009.
  • [29] A. Shemshadi, H. Shirazi, M. Toreihi, and M. J. Tarokh, "A fuzzy VIKOR method for supplier selection based on entropy measure for objective weighting," Expert Systems with Applications, vol. 38, pp. 12160-12167, 2011.
  • [30] M. Apan, A. Öztel, and M. İslamoğlu, "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), 2015.
  • [31] P. C. Fishburn and R. L. Keeney, "Seven independence concepts and continuous multiattribute utility functions," Journal of Mathematical Psychology, vol. 11, pp. 294-327, 1974.
  • [32] E. Løken, "Use of multicriteria decision analysis methods for energy planning problems," Renewable and sustainable energy reviews, vol. 11, pp. 1584-1595, 2007.
  • [33] Ö. Konuşkan, A. Endüstri Mühendisliği, and Ö. UYGUN, "ÇOK NİTELİKLİ KARAR VERME (MAUT) YÖNTEMİ VE BİR UYGULAMASI," 2014.
  • [34] P. Chatterjee, V. M. Athawale, and S. Chakraborty, "Materials selection using complex proportional assessment and evaluation of mixed data methods," Materials & Design, vol. 32, pp. 851-860, 2011.
  • [35] M. C. Das, B. Sarkar, and S. Ray, "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, 2012.
  • [36] A. Kaklauskas, E. K. Zavadskas, J. Naimavicienė, M. Krutinis, V. Plakys, and D. Venskus, "Model for a complex analysis of intelligent built environment," Automation in construction, vol. 19, pp. 326- 340, 2010.
  • [37] H. TATLIDİL and U. Ç. D. İ. Analiz, "Akademi Matbaası," Ankara-2002, 1996.
  • [38] Ş. Kalaycı, SPSS uygulamalı çok değişkenli istatistik teknikleri vol. 5: Asil Yayın Dağıtım Ankara, Turkey, 2010.
  • [39] J. L. Leroy, M. Ruel, E. A. Frongillo, J. Harris, and T. J. Ballard, "Measuring the food access dimension of food security: a critical review and mapping of indicators," Food and nutrition bulletin, vol. 36, pp. 167-195, 2015.
  • [40] S. Desiere, M. D’Haese, and S. Niragira, "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, 2015.
  • [41] L. A. Garibaldi, B. Gemmill-Herren, R. D’Annolfo, B. E. Graeub, S. A. Cunningham, and T. D. Breeze, "Farming approaches for greater biodiversity, livelihoods, and food security," Trends in ecology & evolution, vol. 32, pp. 68-80, 2017.
  • [42] R. Pérez-Escamilla, M. B. Gubert, B. Rogers, and A. Hromi-Fiedler, "Food security measurement and governance: Assessment of the usefulness of diverse food insecurity indicators for policy makers," Global Food Security, vol. 14, pp. 96-104, 2017.
  • [43] C. Cafiero, S. Viviani, and M. Nord, "Food security measurement in a global context: The food insecurity experience scale," Measurement, vol. 116, pp. 146-152, 2018.
  • [44] M. D. Smith, W. Kassa, and P. Winters, "Assessing food insecurity in Latin America and the Caribbean using FAO’s food insecurity experience scale," Food policy, vol. 71, pp. 48-61, 2017.
  • [45] M. D. Smith, M. P. Rabbitt, and A. Coleman-Jensen, "Who are 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, 2017.
  • [46] M. N. Poulsen, P. R. McNab, M. L. Clayton, and R. A. Neff, "A systematic review of urban agriculture and food security impacts in low-income countries," Food Policy, vol. 55, pp. 131-146, 2015.
  • [47] M. K. Kansiime, J. A. Tambo, M. I. Mugambi, M. M. Bundi, A. Kara, and M. C. Owuor, "COVID-19 implications on household income and food security in Kenya and Uganda: Findings from a rapid assessment," World Development, p. 105199, 2020.
  • [48] N. Ömürbek and E. D. A. Urmak, "Forbes 2ooo Listesinde Yeralan Havacilik Sektöründeki Şirketlerin Entropi, MAUT, COPRAS ve SAW Yöntemleri İle Analizi," Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, vol. 23, pp. 257-278, 2018.