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Gıda Güvencesi Düzeyi Sınıflandırılmasında Kullanılan Önemli Göstergelerin Random Forest Yöntemine Göre Belirlenmesi

Year 2022, Volume: 6 Issue: 1, 68 - 77, 24.03.2022

Özlem Eştürk

https://doi.org/10.31200/makuubd.1038467

Abstract

Mevsimlik tarım işçileri, Türkiye'de tarım ekonomisine önemli katkılar sağlasalar da, yoksulluk açısından en dezavantajlı işgücü grubudur. Bu çalışmada mevsimlik tarım işçilerinin hane halkı gıda güvencesi durumlarının belirlenmesi ve gıda güvencesi sınıflandırmasına etki eden en önemli değişkenlerin belirlenmesi hedeflenmiştir. Malatya'da mevsimlik kayısı işçilerinin 18 sorudan oluşan Hanehalkı Gıda Güvencesi Anket Modülü (HFSSM) sorularına verdikleri yanıtlar Random Forest (RF) algoritması ile analiz edilmiştir (n = 65). Sonuçlar, hanehalklarının %55.4'ünün gıda güvencesiz olduğunu, bunlardan %7.7'sinin orta düzeyde, %13.8'inin ise ciddi düzeyde açlığın olduğu gıda güvencesizliği durumu yaşadıklarını göstermiştir. RF modelinin sınıflandırma doğruluğunu gösteren eğri altındaki alan değeri, 0.846 olarak tahmin edilmiştir. Gıda güvencesi gruplarının sınıflandırılmasında en önemli değişken “Daha fazla gıda alacak paraya sahip olmadan gıdanın bitmesi” sorusu olmuştur. Mevsimlik tarım işçileri, Türkiye'de ve dünyada düşük gelir ve iş güvencesizliği nedeniyle gıda güvencesizliği ve yoksulluk tehdidi altındadır. Bu nedenle mevsimlik tarım işçileri gibi risk gruplarında gıda güvencesizliği probleminin çözümüne yönelik sosyal yardım programlarının uygulanması önemlidir.

Keywords

Gıda güvencesi Random forest HFSSM Mevsimlik tarım işçisi

References

  • Anonymous. (2019). Global Food Security Index New York, USA: https://foodsecurityindex.eiu.com/.
  • Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123-140. doi:10.1023/a:1018054314350
  • Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5-32. doi:10.1023/a:1010933404324
  • Breiman, L. (2004). Consistency for a simple model of random forests (Technical Report 670). California: University of California at Berkeley, https://www.stat.berkeley.edu/~breiman/RandomForests/consistencyRFA.pdf.
  • Davison, K., & Kaplan, B. (2015). Food insecurity in adults with mood disorders: Prevalence estimates and associations with nutritional and psychological health. Annals of General Psychiatry, 14. doi:10.1186/s12991-015-0059-x
  • Evans, J. S., Murphy, M. A., Holden, Z. A., & Cushman, S. A. (2011). Modeling Species Distribution and Change Using Random Forest. In Predictive Species and Habitat Modeling in Landscape Ecology (ss. 139-159). New York: Springer.
  • FAO. (2002). The State of Food Insecurity in the World 2001. Erişim tarihi: March 14 2021, http://www.fao.org/3/y1500e/y1500e00.htm
  • FAOSTAT. (2017). FAO Statistics. Erişim tarihi: March 15 2021, http://www.fao.org/faostat
  • Fereli, S., Aktaç, Ş., & Güneş, F. E. (2016). Working conditions, nutritional status and problems seen on seasonal agricultural workers. Gazi Üniverstesi Sağlık Bilimleri Dergisi, 1(3), 36-47.
  • Freund, Y., & Schapire, R. E. (1996). Experiments with a new boosting algorithm. Paper presented at the Proceedings of the Thirteenth International Conference on International Conference on Machine Learning, Bari, Italy.
  • Godrich, S., K. Loewen, O., Blanchet, R., Willows, N., & Veugelers, P. (2019). Canadian Children from Food Insecure Households Experience Low Self-Esteem and Self-Efficacy for Healthy Lifestyle Choices. Nutrients, 11, 675. doi:10.3390/nu11030675
  • Grossmann, E., Ohmann, J., Kagan, J., May, H., & Gregory, M. (2010). Mapping ecological systems with a random forest model: Tradeoffs between errors and bias. Gap Analysis Bulletin, 17(1), 16-22.
  • Gucciardi, E., Vogt, J. A., DeMelo, M., & Stewart, D. E. (2009). Exploration of the relationship between household food insecurity and diabetes in Canada. Diabetes care, 32(12), 2218-2224. doi:10.2337/dc09-0823
  • Heflin, C. M., Siefert, K., & Williams, D. R. (2005). Food insufficiency and women's mental health: Findings from a 3-year panel of welfare recipients. Social Science & Medicine, 61(9), 1971-1982. doi:https://doi.org/10.1016/j.socscimed.2005.04.014
  • Martin, M. S., Maddocks, E., Chen, Y., Gilman, S. E., & Colman, I. (2016). Food insecurity and mental illness: disproportionate impacts in the context of perceived stress and social isolation. Public Health, 132, 86-91. doi:https://doi.org/10.1016/j.puhe.2015.11.014
  • Nord, M., Andrews, M., & Carlson, S. (2008). Household Food Security in the United States, 2008 (Economic Research Report No. 83). Washington, DC, US: https://www.hsdl.org/?view&did=31871.
  • Öz, C. S., & Bulut, E. (2013). The status of seasonal agricultural workers in Turkish legislation. Labour World, 1(1), 94-111.
  • Özdemir, S. (2018). Potential Distribution Modelling and mapping using Random Forest method: An example of Yukarıgökdere Distric. Turkish Journal of Forestry, 19(1), 51-56.
  • Payne-Sturges, D., Tjaden, A., Caldeira, K., Vincent, K., & Arria, A. (2017). Student Hunger on Campus: Food Insecurity Among College Students and Implications for Academic Institutions. American Journal of Health Promotion, 32(2), 349-354. doi:10.1177/0890117117719620
  • Süel, H. (2014). Mapping habitat suitability of game animals in Sütçüler district, Isparta. (PhD). Suleyman Demirel University, Graduate School of Natural and Applied Sciences, Isparta.
  • TURKSTAT. (2019). Crop Production Statistics. Erişim tarihi: April 2 2020, http://www.turkstat.gov.tr/
  • Vozoris, N., & Tarasuk, V. (2003). Household food insufficiency is associated with poorer health. The Journal of Nutrition, 133, 120-126. doi:10.1093/jn/133.1.120
  • Wirth, C., Strochlic, R., & Getz, C. (2007). Hunger in the fields: Food insecurity among farmworkers in Fresno county: California Institute for Rural Studies.

Determination of Important Variables in Food Security Classification Using Random Forest

Year 2022, Volume: 6 Issue: 1, 68 - 77, 24.03.2022

Özlem Eştürk

https://doi.org/10.31200/makuubd.1038467

Abstract

Seasonal agricultural workers are the most disadvantaged group of work forces in terms of poverty even though they are significant contributors to the agricultural economy in Turkey. The objectives of this study were to determine the food security status of seasonal agricultural workers and to determine the most important variables in the classification of household food security status for the seasonal agriculture workers. Responses of seasonal apricot workers in Malatya to 18 questions of the Household Food Security Survey Module (HFSSM) were analyzed using the Random Forests (RF) algorithm (n = 65). Results indicated that 55.4% of households suffered from food insecurity, where 7.7% of them with moderate hunger and 13.8% of them with severe hunger. The area under curve value of the RF model was estimated at 0.846 as the classification accuracy. The question “running out of food before having money to buy more” was the most important variable in the classification of the food security groups. Seasonal agricultural workers are prone to food insecurity and poverty due to low income and job insecurity in Turkey and in the world. Therefore, it is important to implement social aid programs to solve food insecurity issue in risk groups like seasonal agricultural workers.

Keywords

Food security Random forest HFSSM Seasonal agricultural worker

References

  • Anonymous. (2019). Global Food Security Index New York, USA: https://foodsecurityindex.eiu.com/.
  • Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123-140. doi:10.1023/a:1018054314350
  • Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5-32. doi:10.1023/a:1010933404324
  • Breiman, L. (2004). Consistency for a simple model of random forests (Technical Report 670). California: University of California at Berkeley, https://www.stat.berkeley.edu/~breiman/RandomForests/consistencyRFA.pdf.
  • Davison, K., & Kaplan, B. (2015). Food insecurity in adults with mood disorders: Prevalence estimates and associations with nutritional and psychological health. Annals of General Psychiatry, 14. doi:10.1186/s12991-015-0059-x
  • Evans, J. S., Murphy, M. A., Holden, Z. A., & Cushman, S. A. (2011). Modeling Species Distribution and Change Using Random Forest. In Predictive Species and Habitat Modeling in Landscape Ecology (ss. 139-159). New York: Springer.
  • FAO. (2002). The State of Food Insecurity in the World 2001. Erişim tarihi: March 14 2021, http://www.fao.org/3/y1500e/y1500e00.htm
  • FAOSTAT. (2017). FAO Statistics. Erişim tarihi: March 15 2021, http://www.fao.org/faostat
  • Fereli, S., Aktaç, Ş., & Güneş, F. E. (2016). Working conditions, nutritional status and problems seen on seasonal agricultural workers. Gazi Üniverstesi Sağlık Bilimleri Dergisi, 1(3), 36-47.
  • Freund, Y., & Schapire, R. E. (1996). Experiments with a new boosting algorithm. Paper presented at the Proceedings of the Thirteenth International Conference on International Conference on Machine Learning, Bari, Italy.
  • Godrich, S., K. Loewen, O., Blanchet, R., Willows, N., & Veugelers, P. (2019). Canadian Children from Food Insecure Households Experience Low Self-Esteem and Self-Efficacy for Healthy Lifestyle Choices. Nutrients, 11, 675. doi:10.3390/nu11030675
  • Grossmann, E., Ohmann, J., Kagan, J., May, H., & Gregory, M. (2010). Mapping ecological systems with a random forest model: Tradeoffs between errors and bias. Gap Analysis Bulletin, 17(1), 16-22.
  • Gucciardi, E., Vogt, J. A., DeMelo, M., & Stewart, D. E. (2009). Exploration of the relationship between household food insecurity and diabetes in Canada. Diabetes care, 32(12), 2218-2224. doi:10.2337/dc09-0823
  • Heflin, C. M., Siefert, K., & Williams, D. R. (2005). Food insufficiency and women's mental health: Findings from a 3-year panel of welfare recipients. Social Science & Medicine, 61(9), 1971-1982. doi:https://doi.org/10.1016/j.socscimed.2005.04.014
  • Martin, M. S., Maddocks, E., Chen, Y., Gilman, S. E., & Colman, I. (2016). Food insecurity and mental illness: disproportionate impacts in the context of perceived stress and social isolation. Public Health, 132, 86-91. doi:https://doi.org/10.1016/j.puhe.2015.11.014
  • Nord, M., Andrews, M., & Carlson, S. (2008). Household Food Security in the United States, 2008 (Economic Research Report No. 83). Washington, DC, US: https://www.hsdl.org/?view&did=31871.
  • Öz, C. S., & Bulut, E. (2013). The status of seasonal agricultural workers in Turkish legislation. Labour World, 1(1), 94-111.
  • Özdemir, S. (2018). Potential Distribution Modelling and mapping using Random Forest method: An example of Yukarıgökdere Distric. Turkish Journal of Forestry, 19(1), 51-56.
  • Payne-Sturges, D., Tjaden, A., Caldeira, K., Vincent, K., & Arria, A. (2017). Student Hunger on Campus: Food Insecurity Among College Students and Implications for Academic Institutions. American Journal of Health Promotion, 32(2), 349-354. doi:10.1177/0890117117719620
  • Süel, H. (2014). Mapping habitat suitability of game animals in Sütçüler district, Isparta. (PhD). Suleyman Demirel University, Graduate School of Natural and Applied Sciences, Isparta.
  • TURKSTAT. (2019). Crop Production Statistics. Erişim tarihi: April 2 2020, http://www.turkstat.gov.tr/
  • Vozoris, N., & Tarasuk, V. (2003). Household food insufficiency is associated with poorer health. The Journal of Nutrition, 133, 120-126. doi:10.1093/jn/133.1.120
  • Wirth, C., Strochlic, R., & Getz, C. (2007). Hunger in the fields: Food insecurity among farmworkers in Fresno county: California Institute for Rural Studies.
There are 23 citations in total.

Details

Primary Language English
Subjects Economics
Journal Section Articles
Authors

Özlem Eştürk 0000-0003-4324-0912

Publication Date March 24, 2022
Acceptance Date February 23, 2022
Published in Issue Year 2022 Volume: 6 Issue: 1

Cite

APA Eştürk, Ö. (2022). Determination of Important Variables in Food Security Classification Using Random Forest. Mehmet Akif Ersoy Üniversitesi Uygulamalı Bilimler Dergisi, 6(1), 68-77. https://doi.org/10.31200/makuubd.1038467
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