Agricultural Production Connectedness and Networks in Türkiye

Year 2023, Volume: 20 Issue: 4, 799 - 810, 25.12.2023

Türker Açıkgöz

https://doi.org/10.33462/jotaf.1166050

Abstract

The world’s population has been growing rapidly and since the 2006–2008 global food crisis, it has been questioned many times that how the world’s growing population will be fed properly. According to reputable international institutions, the world may be insufficient to supply enough food in the near future, and this fact may cause many economic, social, and government problems. In Türkiye, these problems will be realized more harshly than in peer countries for some reasons. Türkiye has one of the highest population growth rates in the world, while it hosts the highest number of refugees in the world. In addition, Türkiye’s agriculture sector has been experiencing a harsh downfall recently and the country has been dependent on importing food and agricultural commodities. Therefore, in this paper, I investigate the connectedness and networks of agricultural production in Türkiye by using the connectedness approach of Diebold and Yilmaz (2012, 2014), which is based on the forecast error variance decomposition methodology of generalized vector autoregressive models. I use Türkiye’s most produced agricultural commodity data, which are barley, wheat, rye, paddy, lentil, chickpea, and oat. The material consists of annual production data from 1938 to 2019. According to the analysis results, Türkiye’s agricultural production has been highly connected. Our findings show that production shocks arising from wheat and barley have spilled over to other commodities. Agricultural production networks and pairwise spillovers also exhibit a similar result that most of the commodities are highly interconnected to wheat and barley production. Besides, pairwise connectedness results show that there are some strong and weak connectivity relations, and these can be used for the decision-making process, risk aversion, and risk-seeking purposes. Our findings have important implications for policymaking for institutions, diversification, and risk management for producers, suppliers, and traders.

Keywords

Agricultural production, Agricultural policymaking, Network analysis, Risk management, Spillover effects

Türkiye’de Tarımsal Üretim Bağlantılılığı ve Ağları

Abstract

Dünya nüfusu hızla artmakta ve 2006-2008 küresel gıda krizinden bu yana dünya nüfusunun nasıl doyurulacağı sorusu defalarca kez sorulmaktadır. Saygın uluslararası kuruluşlara göre, dünya yakın bir gelecekte yeterli gıdayı tedarik etmekte yetersiz kalabilir ve bu durum birçok ekonomik, sosyal ve devlet sorununa neden olabilir. Türkiye'de bu sorunların bazı nedenden dolayı benzer ülkelere göre daha sert bir şekilde gerçekleşeceğine inanılmaktadır. Türkiye, dünyadaki en yüksek nüfus artış oranlarından birine sahipken, dünyanın en fazla mülteciye ev sahipliği yapan ülkesidir. Buna ek olarak, Türkiye'nin tarım sektörü son yıllarda sert bir düşüş yaşamakta ve ülke gıda ve tarımsal emtia ithalatına bağımlı hale gelmektedir. Bu bağlamda, bu çalışmada, Vektör Otoregresif Modellerinin tahmin hata varyans ayrıştırma metodolojisine dayanan Diebold ve Yılmaz'ın (2012, 2014) bağlantılılık yaklaşımı kullanılarak Türkiye'deki tarımsal üretimin bağlantılılık ve ağları araştırılmaktadır. Çalışmada Türkiye’de en çok üretilen yedi tarımsal emtianın verisi kullanılmıştır. Bu ürünler; arpa, buğday, çavdar, pirinç, mercimek, nohut ve yulaf şeklindedir. Araştırmada kullanılan veri seti 1938 yılından 2019 yılına kadarki süreyi kapsayan tarımsal üretim verisidir. Analiz sonuçlarına göre, Türkiye'nin tarımsal üretimi yüksek oranda bağlantılıdır. Araştırmanın bulguları, buğday ve arpa kaynaklı üretim şoklarının diğer tarım ürünlerine de önemli ölçüde sıçradığını göstermektedir. Tarımsal üretim ağları ve ikili yayılmalar, emtiaların çoğunun buğday ve arpa üretimiyle yüksek oranda bağlantılı olduğu konusunda da benzer bir sonuç sergiler. Ayrıca, ikili bağlantılılık sonuçları, bazı güçlü ve zayıf bağlantı ilişkilerinin olduğunu ve bunların karar verme süreci, riskten kaçınma ve risk arama için kullanılabileceğini göstermektedir. Bulgularımızın kurumlar için politika oluşturma, çeşitlendirme ve üreticiler, tedarikçiler ve tüccarlar için risk yönetimi için önemli etkileri vardır.

Keywords

Tarımsal üretim, Tarımsal politika, Ağ analizi, Risk yönetimi, Yayılma etkileri

References

• Alter, A. and Beyer A. (2014): The dynamics of spillover effects during the European sovereign debt turmoil. Journal of Banking & Finance, 42: 134-153.
• Antonakakis, N. and Kizys, R. (2015). Dynamic spillovers between commodity and currency markets. International Review of Financial Analysis, 41: 303-319.
• Asim H. and Akbar M. (2019): Sectoral growth linkages of agri¬cultural sector: Implications for food security in Pakistan. Agricultural Economics – Czech, 65: 278–288.
• Balkan, A., Özdüven, M., Nizam, İ., Teykin, E. and Tuna., M. (2011). The effect of grazing applied in the different phenological stages on yield and yield components of bread wheat and triticale. Journal of Tekirdag Agricultural Faculty, 8(1): 93-100.
• Balli F., Naeem, M. A., Shahzad, S. J. H. and de Bruin, A. (2019): Spillover network of commodity uncertainties. Energy Economics, 81: 914-927.
• Barunik, J., Kočenda, E. and Vácha L. (2016): Asymmetric connectedness on the US stock market: Bad and good volatility spillovers. Journal of Financial Markets, 27: 55-78.
• Bozkurt İ. and Kaya M. V. (2021): Agricultural production index: International comparison. Agricultural Economics– Czech, 67(6): 236-245.
• Bruinsma, J. (2009). The Resource Outlook to 2050: by How Much Do Land, Water, and Crop Yields Need to Increase by 2050?, Rome, Italy: Food and Agriculture Organization of the United Nations. Available at: https://www.cabdirect.org/cabdirect/abstract/20093293510 (Accessed April 2, 2022).
• Cristea, M. and Noja, G. G. (2019): European agriculture under immigration effects: New empirical evidence. Agricultural Economics – Czech, 65: 112–122.
• Crost, B., Duquennois, C., Felter, J. H. and Rees, D. I. (2018): Climate change, agricultural production and civil conflict: Evidence from the Philippines. Journal of Environmental Economics and Management, 88: 379–395.
• Dhahri, S. and Omri, A. (2020): Foreign capital towards SDGs 1 & 2 – Ending poverty and hunger: The role of ag¬ricultural production. Structural Change and Economic Dynamics, 53: 208–221.
• Diebold, F. X. and Yilmaz, K. (2009): Measuring financial asset return and volatility spillovers, with application to global equity markets. The Economic Journal, 119(534): 158-171.
• Diebold, F. X. and Yilmaz, K. (2012): Better to give than to receive: Predictive directional measurement of volatility spillovers. International Journal of Forecasting, 28(1): 57-66.
• Diebold, F. X. and Yilmaz, K. (2014): On the network topology of variance decompositions: Measuring the connectedness of financial firms. Journal of Econometrics, 182(1): 119-134.
• FAO (2009): Global Agriculture Towards (2050) How to Feed the World 2050, High-level Expert Forum. Available at: http://www.fao.org/fileadmin/templates/wsfs/docs/Issues_papers/HLEF2050_Global_Agriculture.pdf (Accessed April 2, 2022).
• Ferrer, R., Shahzad, S. J. H., López, R. and Jareño, F. (2018): Time and frequency dynamics of connectedness between renewable energy stocks and crude oil prices. Energy Economics, 76: 1-20.
• Johnston, B. F. (1970): Agriculture and structural transformation in developing countries: A survey of research. Journal of Economic Literature, 8: 369–404.
• Lovcha, Y. and Perez-Laborda, A. (2020): Dynamic frequency connectedness between oil and natural gas volatilities. Economic Modelling, 84: 181-189.
• Machethe C. L. (2004): Agriculture and poverty in South Africa: Can agriculture reduce poverty? Conference on the Overcoming Underdevelopment in Pretoria, South Africa, Oct 28–29, 1–14. Pretoria, South Africa.
• Mellor, J. W. (1995): Agriculture on the Road to Industrialisation. London, UK, the Johns Hopkins University Press: 1–358.
• Mohammed, R. (2020): The causality between agriculture and economic growth in the Arab world. Eurasian Journal of Economics and Finance, 8: 54–67.
• Negiş, H. (2017). Effects of four different crops harvest processes on soils compaction. Journal of Tekirdag Agricultural Faculty, The Special Issue of 2nd International Balkan Agriculture Congress: 25-29.
• Pingali, P. L. (2012): Green revolution: impacts, limits, and the path ahead. Proceedings of the National Academy of Sciences, 109(31): 12302-12308.
• Polat, O. (2020): Frequency connectedness and network analysis in equity markets: evidence from G-7 countries. Akdeniz Journal of FEAS, 20(2): 221-226.
• Reboredo, J. C., Ugolini, A. and Aiube, F. A. L. (2020): Network connectedness of green bonds and asset classes. Energy Economics, 86: 104629.
• Rosa, L., Rulli, M. C., Davis, K. F., Chiarelli, D. D., Passera, C. and D’Odorico, P. (2018): Closing the yield gap while ensuring water sustainability. Environmental Research Letters, 13(10): 104002.
• Shahzad, S. J. H., Bouri, E., Arreola-Hernandez, J., Roubaud, D. and Bekiros, S. (2019): Spillover across Eurozone credit market sectors and determinants. Applied Economics, 51(59): 6333-6349.
• Singariya, M. R., Naval, S. C. (2016): An empirical study of inter¬sectoral linkages and economic growth in India. American Journal of Rural Development, 4: 78–84.
• Su, X. (2020): Dynamic behaviors and contributing factors of volatility spillovers across G7 stock markets. The North American Journal of Economics and Finance, 53: 101218.
• TGB (2022): Grain purchases tables. Turkish Grain Board. Available at: https://www.tmo.gov.tr/info-center/38/tables (Accessed April 2, 2022).
• Tian, X., Engel, B. A., Qian, H., Hua, E., Sun, S. and Wang Y. (2021): Will reaching the maximum achievable yield potential meet future global food demand?. Journal of Cleaner Production, 294: 126285.
• Toyoshima, Y. and Hamori S. (2018): Measuring the time-frequency dynamics of return and volatility connectedness in global crude oil markets. Energies, 11(11): 2893.
• TURKSTAT (2022): Foreign trade statistics of Turkey. Turkish Statistical Institute. Available at: https://data.tuik.gov.tr/Bulten/Index?p=Dis-Ticaret-Istatistikleri-Aralik-2021-45535 (Accessed April 2, 2022).
• UNCHR (2022): Refugee data finder. The United Nations Refugee Agency. Available at: https://www.unhcr.org/refugee-statistics/#:~:text=Between%202018%20and%202020%2C%20an,a%20refugee%20life%20per%20year.&text=Some%20126%2C700%20refugees%20returned%20to,with%20or%20without%20UNHCR's%20assistance (Accessed April 2, 2022).
• Van Wart, J., Kersebaum, K. C., Peng, S., Milner, M. and Cassman, K. G. (2013): Estimating crop yield potential at regional to national scales. Field Crops Research, 143: 34-43.
• World Bank (2022): Population growth (annual %) data. The World Bank. Available at: https://data.worldbank.org/indicator/SP.POP.GROW (ccessed April 2, 2022).
• Zhang, D. (2017): Oil shocks and stock markets revisited: Measuring connectedness from a global perspective. Energy Economics, 62: 323-333.