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Assessing climate change effects on Turkish tea farming through a dual approach using MMQR and machine learning

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Abstract

Climate change increasingly threatens the productivity of region-specific strategic agricultural products such as tea cultivation in Türkiye, posing a serious risk to both food security and rural economies. However, existing literature is notably limited in terms of studies that draw attention to this risk and examine the effects of climate change on tea productivity at a regional scale through rigorous quantitative methods. To this end, this study investigates the influence of climate change on tea productivity in Türkiye’s tea–growing provinces (Artvin, Giresun, Ordu, Rize, and Trabzon) between 2004 and 2022. Distinct from previous studies, we integrate advanced machine learning techniques with the method of moments quantile regression (MMQR) approach to provide comprehensive, reliable, and methodologically robust results for the first time in this context. The results of the MMQR demonstrate that although humidity reduces tea productivity, temperature and precipitation significantly increase it. Furthermore, the results of machine learning research indicate that the tea farming area is the variable with the highest importance, whereas humidity emerges as the least influential factor. These findings indicate that policymakers need to implement integrated agricultural policies in the five tea–growing provinces of the Eastern Black Sea region, including effective moisture management, soil fertility, erosion control, and irrigation infrastructure tailored to the climate and land conditions.

Data availability

This study used publicly available data and needed no informed consent. Tea production and agricultural area data were obtained from the Turkish Statistical Institute (https://biruni.tuik.gov.tr/medas), and climate change data (precipitation, temperature, and humidity) were retrieved from the Turkish State Meteorological Service (https://www.mgm.gov.tr/). All datasets used in the analysis are cited and described in detail within the manuscript.

Code availability

All custom R scripts used for the machine learning analyses, as well as all Stata scripts used for the coefficient estimation procedures, are available at: https://doi.org/10.5281/zenodo.17610824.

References

  1. Michael Alurame Eruaga. Policy strategies for managing food safety risks associated with climate change and agriculture. Int. J. Sch. Res. Reviews. 4, 021–032 (2024).

    Google Scholar 

  2. Malhi, G. S., Kaur, M. & Kaushik, P. Impact of climate change on agriculture and its mitigation strategies: A review. Sustainability 13, 1318 (2021).

    Google Scholar 

  3. Oriekhoe, O. I. Olawale adisa & Bamidele Segun Ilugbusi. Climate change and food supply chain economics: a comprehensive analysis of impacts, adaptations, and sustainability. Int. J. Appl. Res. Social Sci. 6, 267–278 (2024).

    Google Scholar 

  4. Kløve, B. et al. Climate change impacts on groundwater and dependent ecosystems. J. Hydrol. (Amst). 518, 250–266 (2014).

    Google Scholar 

  5. Gernaat, D. E. H. J. et al. Climate change impacts on renewable energy supply. Nat. Clim. Chang. 11, 119–125 (2021).

    Google Scholar 

  6. Bibi, F. & Rahman, A. An overview of climate change impacts on agriculture and their mitigation strategies. Agriculture 13, 1508 (2023).

    Google Scholar 

  7. Chen, S., Chen, X. & Xu, J. Impacts of climate change on agriculture: evidence from China. J. Environ. Econ. Manage. 76, 105–124 (2016).

    Google Scholar 

  8. Corwin, D. L. Climate change impacts on soil salinity in agricultural areas. Eur. J. Soil. Sci. 72, 842–862 (2021).

    Google Scholar 

  9. Karimi, V., Karami, E. & Keshavarz, M. Climate change and agriculture: impacts and adaptive responses in Iran. J. Integr. Agric. 17, 1–15 (2018).

    Google Scholar 

  10. Steiger, R., Knowles, N., Pöll, K. & Rutty, M. Impacts of climate change on mountain tourism: a review. J. Sustainable Tourism. 32, 1984–2017 (2024).

    Google Scholar 

  11. Sesana, E., Gagnon, A. S., Ciantelli, C., Cassar, J. & Hughes, J. J. Climate change impacts on cultural heritage: A literature review. WIREs Clim. Change 12, e710 (2021).

  12. Keenan, R. J. Climate change impacts and adaptation in forest management: a review. Ann. Sci. 72, 145–167 (2015).

    Google Scholar 

  13. Caminade, C., McIntyre, K. M. & Jones, A. E. Impact of recent and future climate change on vector-borne diseases. Ann. N Y Acad. Sci. 1436, 157–173 (2019).

    Google Scholar 

  14. Onyeaka, H. et al. The ripple effects of climate change on agricultural sustainability and food security in Africa. Food Energy Secur 13, e567 (2024).

  15. Rezaei, E. E. et al. Climate change impacts on crop yields. Nat. Rev. Earth Environ. 4, 831–846 (2023).

    Google Scholar 

  16. Kiley, M. T. Growth at risk from climate change. Econ. Inq. 62, 1134–1151 (2024).

    Google Scholar 

  17. Verma, K. K. et al. Climate change adaptation: challenges for agricultural sustainability. Plant. Cell. Environ. 48, 2522–2533 (2025).

    Google Scholar 

  18. Ma, Y., Feng, G. & Chang, C. From traditional innovation to green innovation: how an occurrence of natural disasters influences sustainable development? Sustain. Dev. 32, 2779–2796 (2024).

    Google Scholar 

  19. Ahmed, S. et al. Springer Singapore, Singapore,. Global Climate Change, Ecological Stress, and Tea Production. in Stress Physiology of Tea in the Face of Climate Change 1–23 (2018). https://doi.org/10.1007/978-981-13-2140-5_1

  20. Krishnakumar, V., Kumar, T. R., Murugesan, P. & Tea (Camellia sinensis (L.) O. Kuntze). in Soil Health Management for Plantation Crops 391–486 (Springer Nature Singapore, Singapore, doi:https://doi.org/10.1007/978-981-97-0092-9_10. (2024).

    Google Scholar 

  21. Prakash, S. Impact and assessment of climate change on agricultural production in india: A geographical perspective. Int. J. Multidisciplinary Res. 6, 64–77 (2024).

    Google Scholar 

  22. Food and Agriculture Organization of the United Nations. FAOSTAT: Crops and livestock products. (2025).

  23. Agricultural Economics and Policy Development Institute. Agricultural Products Market Reports. (2024). https://arastirma.tarimorman.gov.tr/tepge/Belgeler/PDF%20%C3%9Cr%C3%BCn%20Raporlar%C4%B1/2023%20%C3%9Cr%C3%BCn%20Raporu%202023-383%20TEPGE.pdf

  24. FAO. Assessing the carbon footprint of tea production: case studies and challenges. Preprint at (2024). https://openknowledge.fao.org/handle/20.500.14283/cd0658en

  25. Turkish Statistical Institute. https://data.tuik.gov.tr/Bulten/Index?p=Bitkisel-Uretim-Istatistikleri-2023-49535

  26. Turkey’s Tea Imports. Market Overview, Production, and Consumption Trends| Tendata. https://www.tendata.com/blogs/import/6198.html

  27. FAO. Current global market situation and medium term outlook. Preprint at (2024). https://openknowledge.fao.org/handle/20.500.14283/cd0688en

  28. Global Tea Market Expected. to Reach USD 38.1 Billion by 2033 – IMARC Group. https://www.imarcgroup.com/tea-market-statistics

  29. Trade Map -. Trade statistics for international business development. https://www.trademap.org/Index.aspx

  30. Kannadhasan, S. & Nagarajan, R. Green House Gases: Challenges, Effect, and Climate Change. in Biomass and Bioenergy Solutions for Climate Change Mitigation and Sustainability (eds. Rathoure, A. & Khade, S.) 65–74IGI Global, (2022). https://doi.org/10.4018/978-1-6684-5269-1.ch005

  31. Burke, M. & Emerick, K. Adaptation to climate change: evidence from US agriculture. Am. Econ. J. Econ. Policy. 8, 106–140 (2016).

    Google Scholar 

  32. Altieri, M. A. & Nicholls, C. I. The adaptation and mitigation potential of traditional agriculture in a changing climate. Clim. Change. 140, 33–45 (2017).

    Google Scholar 

  33. Aryal, J. P. et al. Climate change and agriculture in South asia: adaptation options in smallholder production systems. Environ. Dev. Sustain. 22, 5045–5075 (2020).

    Google Scholar 

  34. Sinore, T. & Wang, F. Impact of climate change on agriculture and adaptation strategies in ethiopia: A meta-analysis. Heliyon 10, e26103 (2024).

    Google Scholar 

  35. Valizadeh, N. & Hayati, D. Development and validation of an index to measure agricultural sustainability. J. Clean. Prod. 280, 123797 (2021).

    Google Scholar 

  36. Thompson, P. B. Agricultural sustainability: what it is and what it is not. Int. J. Agric. Sustain. 5, 5–16 (2007).

    Google Scholar 

  37. Arora, N. K. Agricultural sustainability and food security. Environ. Sustain. 1, 217–219 (2018).

    Google Scholar 

  38. Muhie, S. H. Novel approaches and practices to sustainable agriculture. J. Agric. Food Res. 10, 100446 (2022).

    Google Scholar 

  39. Bilal, A. & Känzig, D. The Macroeconomic Impact of Climate Change: Global vs. Local Temperature. (2024). https://doi.org/10.3386/w32450

  40. Kahn, M. E. et al. Long-term macroeconomic effects of climate change: A cross-country analysis. Energy Econ. 104, 105624 (2021).

    Google Scholar 

  41. Lemaire, T. Essays on the Macroeconomic Effects of Climate Change in Developing Countries. (2023).

  42. Byrne, J. P. & Vitenu-Sackey, P. A. The macroeconomic impact of global and Country-Specific climate risk. Environ. Resour. Econ. (Dordr). 87, 655–682 (2024).

    Google Scholar 

  43. Trinh, T. A. The impact of climate change on agriculture: findings from households in Vietnam. Environ. Resour. Econ. (Dordr). 71, 897–921 (2018).

    Google Scholar 

  44. Adams, R., Hurd, B., Lenhart, S. & Leary, N. Effects of global climate change on world agriculture: an interpretive review. Clim. Res. 11, 19–30 (1998).

    Google Scholar 

  45. Lu, S., Bai, X., Li, W. & Wang, N. Impacts of climate change on water resources and grain production. Technol. Forecast. Soc. Change. 143, 76–84 (2019).

    Google Scholar 

  46. Dubovitski, A. A., Konovalova, M. E., Strelnikova, T. D. & Pilipchuk, N. V. Shvetsova, I. N. Assessment of the impact of climate risks on agriculture in the context of global warming. IOP Conf. Ser. Earth Environ. Sci. 845, 012145 (2021).

    Google Scholar 

  47. Fankhauser, S. & Tol, S. J. On climate change and economic growth. Resour. Energy Econ. 27, 1–17 (2005).

    Google Scholar 

  48. Dell, M., Jones, B. & Olken, B. Climate Change and Economic Growth: Evidence from the Last Half Century. (2008). https://doi.org/10.3386/w14132

  49. Raddatz, C. The Wrath of God: Macroeconomic Costs of Natural Disasters (World Bank, 2009). https://doi.org/10.1596/1813-9450-5039

  50. Tol, R. S. J. The economic impacts of climate change. Rev. Environ. Econ. Policy. 12, 4–25 (2018).

    Google Scholar 

  51. Wijeratne, M. A. Vulnerability of Sri Lanka tea production to global climate change. Water Air Soil. Pollut. 92, 87–94 (1996).

    Google Scholar 

  52. Biggs, E. M., Gupta, N., Saikia, S. D. & Duncan, J. M. A. The tea landscape of assam: Multi-stakeholder insights into sustainable livelihoods under a changing climate. Environ. Sci. Policy. 82, 9–18 (2018).

    Google Scholar 

  53. Mallik, P. & Ghosh, T. Impact of climate on tea production: a study of the Dooars region in India. Theor. Appl. Climatol. 147, 559–573 (2022).

    Google Scholar 

  54. Lou, W., Sun, K., Zhao, Y., Deng, S. & Zhou, Z. Impact of climate change on inter-annual variation in tea plant output in Zhejiang, China. International J. Climatology 41, E479–E490 (2021).

  55. Cheserek, B. C., Elbehri, A. & Bore, J. Analysis of links between climate variables and tea production in the recent past in Kenya. Donnish J. Res. Environ. Studies 2, 5–17 (2015).

  56. Bayraç, H. N. & Dogan, E. Impacts of climate change on agriculture sector in Turkey. Eskişehir Osmangazi Univ. J. Econ. Administrative Sci. 11, 23–48 (2016).

    Google Scholar 

  57. Akcan, A. T., Kurt, U. & Kilic, C. Effects of climate change on agricultural sector in turkey: ARDL bounds test approach. Trends Bus. Econ. 36, 125–132 (2022).

    Google Scholar 

  58. İrdem, C. Effects of temperature and precipitation on tea yield in Turkey. Black Sea J. Agric. 5, 126–136 (2022).

    Google Scholar 

  59. Tutal, O. Impact of climate change on tea yield: an analysis for the black sea region. J. Finance Politics Economic Commentary. 59, 25–52 (2022).

    Google Scholar 

  60. Yazıcı, K. Possible effects of climate change on Turkish tea and future prospects. in Current Studies on Fruit Science (ed Pakyürek, M.) 301–323 (Iksad, Ankara, (2021).

    Google Scholar 

  61. Yıldız, S. & Özcan, M. Effects of global warming on tea agriculture. Int. J. Agric. Wildl. Sci. 10, 47–68 (2024).

    Google Scholar 

  62. Bania, J. et al. Highly suitable areas for tea (Camellia sinensis) production will decline under future climate change scenarios. ElsevierJK Bania, JR Deka, A Paul, AJ Nath, GW Sileshi, AK DasEnvironmental and Sustainability Indicators, 2025•Elsevier https://www.sciencedirect.com/science/article/pii/S2665972725001412

  63. Shrestha, S., Miles, W. S. U. M. & Vernon C. WSDA nursery tea project report, (2023).

  64. Jayasinghe, S. L. & Kumar, L. 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, 619 (2021).

    Google Scholar 

  65. Yan, Y. et al. Effects of extreme temperature on china’s tea production. Environ. Res. Lett. 16, 044040 (2021).

    Google Scholar 

  66. Turkish Statistical Institute. E-Services. (2025). https://biruni.tuik.gov.tr/medas/?locale=tr

  67. Turkish State Meteorological Service. Official climate statistics. (2025). https://www.mgm.gov.tr/eng/forecast-cities.aspx

  68. Leng, C. et al. An empirical assessment of the effect of natural resources and financial technologies on sustainable development in resource abundant developing countries: evidence using MMQR Estimation. Resour. Policy. 89, 104555 (2024).

    Google Scholar 

  69. Shan, H. et al. Fintech innovation for sustainable environment: Understanding the role of natural resources and human capital in BRICS using MMQR. Resour. Policy. 88, 104468 (2024).

    Google Scholar 

  70. Almulhim, A. A., Inuwa, N., Chaouachi, M. & Samour, A. Testing the impact of renewable energy and institutional quality on Consumption-Based CO2 emissions: fresh insights from MMQR approach. Sustain. 2025. 17, 704 (2025).

    Google Scholar 

  71. Chen, J., Zhao, C., Liu, S. & Li, Y. How do digitalizing ICT and supply chain globalization affect renewable energy in ASEAN nations? The mediating role of sustainable environmental practices using the MMQR and PCSEs model. Energy Econ. 142, 108166 (2025).

    Google Scholar 

  72. Pesaran, M. H. General diagnostic tests for cross-sectional dependence in panels. Empir. Econ. 60, 13–50 (2021).

    Google Scholar 

  73. Juodis, A. & Reese, S. The incidental parameters problem in testing for remaining Cross-Section correlation. J. Bus. Economic Stat. 40, 1191–1203 (2022).

    Google Scholar 

  74. Fan, J., Liao, Y. & Yao, J. Power enhancement in High-Dimensional Cross-Sectional tests. Econometrica 83, 1497–1541 (2015).

    Google Scholar 

  75. Xie, Y. & Pesaran, M. H. A Bias-Corrected cd test for error Cross-Sectional dependence in panel data models with latent factors. SSRN Electron. J. https://doi.org/10.2139/ssrn.4198155 (2022).

    Google Scholar 

  76. Bersvendsen, T. & Ditzen, J. Testing for slope heterogeneity in Stata. Stata Journal: Promoting Commun. Stat. Stata. 21, 51–80 (2021).

    Google Scholar 

  77. Taylor, M. P. & Sarno, L. The behavior of real exchange rates during the post-Bretton woods period. J. Int. Econ. 46, 281–312 (1998).

    Google Scholar 

  78. Westerlund, J. Panel cointegration tests of the fisher effect. J. Appl. Econom. 23, 193–233 (2008).

    Google Scholar 

  79. Westerlund, J. & Edgerton, D. L. A panel bootstrap cointegration test. Econ. Lett. 97, 185–190 (2007).

    Google Scholar 

  80. Machado, J. A. F. & Santos Silva, J. M. C. Quantiles via moments. J. Econom. 213, 145–173 (2019).

    Google Scholar 

  81. Beck, N. & Katz, J. N. What to do (and not to do) with Time-Series Cross-Section data. Am. Polit. Sci. Rev. 89, 634–647 (1995).

    Google Scholar 

  82. Zhou, Z. H. Ensemble learning. in Encyclopedia of Biometrics 270–273 (Springer US, Boston, MA, doi:https://doi.org/10.1007/978-0-387-73003-5_293. (2009).

  83. Chang, Y. C., Chang, K. H. & Wu, G. J. Application of eXtreme gradient boosting trees in the construction of credit risk assessment models for financial institutions. Appl. Soft Comput. 73, 914–920 (2018).

    Google Scholar 

  84. Chen, L. & Shapiro, S. S. An alernative test for normality based on normalized spacings. J. Stat. Comput. Simul. 53, 269–287 (1995).

    Google Scholar 

  85. Wu, Y., Li, K., Chen, Y., Zheng, W. & Chen, P. Loss Assessment on Agricultural Cultural Heritage Values Under Climate Change. Preprint at (2024). https://doi.org/10.2139/ssrn.4758762

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Funding

This study has been supported by the Recep Tayyip Erdoğan University Development Foundation under Grant Number: 02025005027504.

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Authors and Affiliations

  1. Department of Economics, Faculty of Economics and Administrative Sciences, Recep Tayyip Erdoğan University, Fener Street, Zihni Derin Campus, Rize, 53100, Türkiye

    Tunahan Haciimamoglu

  2. Master’s Student, Institute of Graduate Studies, Recep Tayyip Erdoğan University, Rize, 53100, Türkiye

    Gokan Bulbul

  3. Department of Political Science and Public Administration, Faculty of Economics and Administrative Sciences, Recep Tayyip Erdoğan University, Rize, 53100, Türkiye

    Korkmaz Yildirim

  4. Department of Business Administration, Faculty of Economics and Administrative Sciences, Recep Tayyip Erdoğan University, Rize, 53100, Türkiye

    Burcu Kartal

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  1. Tunahan Haciimamoglu
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  2. Gokan Bulbul
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  3. Korkmaz Yildirim
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Contributions

T.H., G.B., K.Y. and B.K. conceptualized the study and drafted the introduction. T.H., G.B. and K.Y. conducted the literature review and theoretical framework. T.H., G.B., and B.K. jointly developed the methodology. T.H. and G.B. compiled the dataset. T.H., G.B., and B.K performed analysis. All authors supervised the research process and revised the manuscript for intellectual content and structure. All authors reviewed the manuscript and approved the final version.

Corresponding author

Correspondence to
Tunahan Haciimamoglu.

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Competing interests

The authors declare no competing interests.

Ethics approval and consent to participate

This study does not involve human participants, personal data, or biological material. Therefore, ethical approval was not required. The research is based entirely on secondary data obtained from publicly available. All data sources comply with the relevant ethical guidelines and regulatory standards.

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Haciimamoglu, T., Bulbul, G., Yildirim, K. et al. Assessing climate change effects on Turkish tea farming through a dual approach using MMQR and machine learning.
Sci Rep (2025). https://doi.org/10.1038/s41598-025-29358-8

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  • DOI: https://doi.org/10.1038/s41598-025-29358-8

Keywords

  • Sustainable farming
  • Tea agronomy
  • Climate adaptation
  • Ensemble learning
  • Method of moments quantile regression

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