ARIMA-Based Projections for Tea Agriculture in Türkiye (2025–2030)

Year 2025, Volume: 12 Issue: 4, 1129 - 1138, 17.10.2025

Ahmet Semih Uzundumlu , Avni Birinci , Ceyda Akdemir

https://doi.org/10.30910/turkjans.1732498

Abstract

This study employs time series analysis to examine the dynamics of tea production in Türkiye. Data on tea production, cultivated area, and yield from 1988 to 2024 were analyzed separately to forecast trends in production volume, productivity, and cultivated area for the 2025–2030 period. The ARIMA (AutoRegressive Integrated Moving Average) model, widely used in time series forecasting, was applied. Tea cultivation in Türkiye is predominantly concentrated in the Eastern Black Sea Region, particularly in the provinces of Rize, Trabzon, Artvin, and Giresun. Based on the national data, the findings suggest that the cultivated area is likely to decline from 810,000 hectares in 2024 to approximately 793,000 hectares by 2030. In contrast, production volume is projected to increase from 1.41 million tons to 1.55 million tons, while yield is expected to rise from 1,745 kg/ha to 1,935 kg/ha over the same period. These projections indicate a need to enhance land productivity through the adoption of modern agricultural practices. Promoting agricultural technologies and strengthening extension services and farmer education are crucial for sustaining productivity. Moreover, Türkiye could have a greater say in world production if precautionary policies are implemented against the effects of climate change, which has been frequently mentioned recently, and if the forecasts are near to accurate.

Keywords

Tea , Eastern Black Sea , Production Volume

Thanks

This article is based on data collected as part of the author’s M.Sc. thesis at the Department of Agricultural Economics, Atatürk University.

References

• Alabdulrazzaq, H., Alenezi, M. N., Rawajfih, Y., Alghannam, B. A., Al-Hassan, A. A., & Al-Anzi, F. S. (2021). On the accuracy of ARIMA based prediction of COVID-19 spread. Results in Physics, 27, 104509.
• Box, G. E., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time series analysis: forecasting and control. John Wiley & Sons.
• Castellana, F., De Nucci, S., De Pergola, G., Di Chito, M., Lisco, G., Triggiani, V., ... & Zupo, R. (2021). Trends in coffee and tea consumption during the COVID-19 pandemic. Foods, 10(10), https://doi.org/10.3390/foods10102458.
• ÇAYKUR, (2023). Faaliyet raporları. https://www.caykur.gov.tr/Pages/Yayinlar/YayinDetay.aspx?ItemType=2&ItemId=1001.
• Cengiz, S., & Okan, Y. T. (2021). Tüketicilerin ithal çay tüketim tercihlerinin belirlenmesi: Güneydoğu Anadolu Bölgesi örneği. Güncel Pazarlama Yaklaşımları Ve Araştırmaları Dergisi, 2(2), 84-97.
• Einöther, S. J., & Martens, V. E. (2013). Acute effects of tea consumption on attention and mood. The American Journal of Clinical Nutrition, 98, 1700S-1708S.
• FAO, (2025). Tea production by region, 1961 to 1990. Our World in Data. https://ourworldindata.org/grapher/tea-production-by-region.
• Heiss, M. L., & Heiss, R. J. (2007). The story of tea: A cultural history and drinking guide. Ten Speed Press.
• Hossain, M. M., & Abdulla, F. (2015). Forecasting the tea production of Bangladesh: Application of ARIMA model. Jordan Journal of Mathematics and Statistics, 8(3), 257-270.
• Hu, Z., Li, H., Ji, L., & Yang, Y. (2024). Effects of reducing chemical fertilisers application on tea production and soils quality: An in situ field experiment in Jiangsu, China. Agronomy, 14(8), 1864. https://doi.org/10.3390/agronomy14081864
• Huner Yigit, M., Atak, M., Yigit, E., Topal Suzan, Z., Kivrak, M., & Uydu, H. A. (2024). White Tea Reduces Dyslipidemia, Inflammation, and Oxidative Stress in the Aortic Arch in a Model of Atherosclerosis Induced by Atherogenic Diet in ApoE Knockout Mice. Pharmaceuticals, 17(12), https://doi.org/10.3390/ph17121699.
• Ihwah, A., & Putri, R. A. (2019, February). Forecasting of export demand of black tea in PT XYZ Central Java. In IOP Conference Series: Earth and Environmental Science (Vol. 230, No. 1, p. 012012). IOP Publishing.
• ITC, (2025). Trade statistics and market analysis on tea exports. International Trade Centre Statistics. https://www.trademap.org/
• Jayasinghe, S. L., & Kumar, L. (2021). Potential impact of the current and future climate on the yield, quality, and climate suitability for tea [Camellia sinensis (L.) O. Kuntze]: A systematic review. Agronomy, 11(4), https://doi.org/10.3390/agronomy11040619
• Kadılar, C. (2009). SPSS Uygulamalı Zaman Serileri Analizi. Bizim Büro Kitabevi, Ankara.
• Kaiser, R., & Maravall Herrero, A. (2000). Notes on time series analysis, ARIMA models and signal extraction. Banco de España. Servicio de Estudios.
• Kara, M. (2015). Osmanlı’dan Cumhuriyet’e çayın Türkiye’ye gelişi ve yaygınlaşması. Tarih Araştırmaları Dergisi, 37(1), 23-45.
• Karaçimen, E., & Değirmenci, E. (2019). Doğu Karadeniz’de çay tarımının çelişkili sürekliliği. Toplum ve Bilim, 150, 63-93.
• Mahanta, K. K. (2023). A time series analysis of tea production in South Bank of Assam from 1961 to 2013 using Arima model. IJASS, 725.
• Mammadov, T. (2024). Importance and role of tea plant (Camellia sinensis L.) In agriculture of Azerbaijan and Türkiye. Bursa Uludag University, Master’s thesis Graduate School of Natural and Applied Sciences Department of Soil Science and Plant Nutrition, Bursa, Türkiye.
• Muangkhoua, S. (2019). Time series forecasting by using Box-Jenkins method. Vajira Medical Journal: Journal of Urban Medicine, 63(Supplement), S185-S192.
• Niranjan, H. K., Kumari, B., Raghav, Y. S., Mishra, P., Al Khatib, A. M. G., & Abotaleb, M. (2022). Modeling and forecasting of tea production in India. JAPS: Journal of Animal & Plant Sciences, 32(6), 1598-1604. https://doi.org/10.36899/JAPS.2022.6.0569.
• Önçırak, M. (2019. Tea Industry and Turkish Economy. Bursa Uludağ University, Master’s Thesis. Graduate School of Social Sciences, Department of Economics, Bursa, Türkiye.
• Saklı, A. R. (2018). Cumhuriyet’le Yaşıt Bir Ürün: Rize Çayının Tarihçesi. Cumhuriyet Döneminde Rize-I 1923-1950).
• SAS (2025). SAS 13.2 user’s guide: The ARIMA procedure. https://support.sas.com/documentation/onlinedoc/ets/132/ARIMA.pdf (Erişim tarihi: 09.06.2025)
• Tortum, A., Gözcü, O., & Çodur, M. Y. (2014). Türkiye’de hava ulaşım talebinin ARIMA modelleri ile tahmin edilmesi. Journal of the Institute of Science and Technology, 4(2), 39-54.
• TURKSTAT (2025). Turkish Statistical Institute Agricultural Price and Economic Accounts. https://data.tuik.gov.tr/Kategori/GetKategori?p=tarim-111.
• Uzundumlu, A. S., & Demir, G. (2025). Türkiye koyun eti üretiminin 2023-2027 dönemi öngörüleri. Journal of Animal Science and Economics, 4(2), 75-80.
• Uzundumlu, A. S., Karayar, S., & Bilgiç, A. (2021). Efficiency and cost analysis of growing tea in Turkey. Custos E Agronegocio On Line, 17(4): 92-112.
• Uzundumlu, A. S., Zeynalova, A., & Engindeniz, S. (2023). Cotton production forecasts of Azerbaijan in the 2023–2027 periods. Ege Üniversitesi Ziraat Fakültesi Dergisi, 60(2), 235–245. https://doi.org/10.20289/zfdergi.1296642
• Uzundumlu, A.S., Pınar, V., Ertek Tosun, N. & Kumbasaroğlu, H. (2024). Global pistachio production forecasts for 2020–2025. KSU Journal of Agriculture and Nature, 27 (5), 1105-1115. https://doi.org/10.18016/ksutarimdoga.vi. 1397897.
• Yaffee, R. A., & McGee, M. (2000). An introduction to time series analysis and forecasting: With applications of SAS and SPSS. Academic Press.
• Yan, Z., Zhong, Y., Duan, Y., Chen, Q., & Li, F. (2020). Antioxidant mechanism of tea polyphenols and its impact on health benefits. Animal Nutrition, 6(2), 115-123.
• Yildirim, O., & Karaca, O. B. (2022). The consumption of tea and coffee in Turkey and emerging new trends. Journal of Ethnic Foods, 9(1), https://doi.org/10.1186/s42779-022-00124-9.
• Zhao, X., Yu, P., Zhong, N., Huang, H., & Zheng, H. (2024). Impact of storage temperature on Green tea quality: Insights from sensory analysis and chemical composition. Beverages, 10(2), https://doi.org/10.3390/beverages10020035
• Zuo, A. R., Dong, H. H., Yu, Y. Y., Shu, Q. L., Zheng, L. X., Yu, X. Y., & Cao, S. W. (2018). The antityrosinase and antioxidant activities of flavonoids dominated by the number and location of phenolic hydroxyl groups. Chinese Medicine, 13(1), https://doi.org/10.1186/s13020-018-0206-9.