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Predicting Possible Distribution of Tea ( Camellia sinensis L.) under Climate Change Scenarios Using MaxEnt Model in China

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  • Yuncheng Zhao

    (Institute of Environment and Sustainable Development in Agriculture, Chinese Academy of Agricultural Sciences (CAAS), 12 Zhongguancun South Str., Beijing 100081, China)

  • Mingyue Zhao

    (Institute of Environment and Sustainable Development in Agriculture, Chinese Academy of Agricultural Sciences (CAAS), 12 Zhongguancun South Str., Beijing 100081, China)

  • Lei Zhang

    (Institute of Environment and Sustainable Development in Agriculture, Chinese Academy of Agricultural Sciences (CAAS), 12 Zhongguancun South Str., Beijing 100081, China)

  • Chunyi Wang

    (Institute of Environment and Sustainable Development in Agriculture, Chinese Academy of Agricultural Sciences (CAAS), 12 Zhongguancun South Str., Beijing 100081, China)

  • Yinlong Xu

    (Institute of Environment and Sustainable Development in Agriculture, Chinese Academy of Agricultural Sciences (CAAS), 12 Zhongguancun South Str., Beijing 100081, China)

Abstract

Climate change has dramatic impacts on the growth and the geographical distribution of tea ( Camellia sinensis L.). Assessing the potential distribution of tea will help decision makers to formulate appropriate adaptation measures to use the altered climatic resources and avoid the damage from climate hazards. The objective in this study is to model the current and future distribution of tea species based on the four SSPs scenarios using the MaxEnt model in China. For the modeling procedure, tea growth records in 410 sites and 9 climate variables were used in this paper. The area under the receiver operating characteristic (ROC) curve (AUC) was used to evaluate the performance of the model. The AUC value was over 0.9 in this study, showing the excellent simulation result of the model. In relation to the current distribution, areas of 82.01 × 10 4 km 2 (8.51% of total land area in China), 115.97 × 10 4 km 2 (12.03% of total land area in China), and 67.14 × 10 4 km 2 (6.97% of total land area in China) were recognized as Marginal, Medium, and Optimal climate suitable habitats for tea over China. Compared to the current distribution, most of the Optimal suitability areas in southeast China would be lost in four scenarios. The area of Marginal and Medium suitable habitats would expand in SSP370 and SSP585, especially in 2041–2061 and 2081–2100. The suitable area of tea would expand northwards and westwards, suggesting that additional new suitable habitats could be created for tea production with the future climate change, especially in Shandong, Henan, Guizhou, and Yunnan Provinces. This research would provide vital scientific understanding for policy making on tea production, tea garden site chosen and adopyion of adaptation methods in the future.

Suggested Citation

  • Yuncheng Zhao & Mingyue Zhao & Lei Zhang & Chunyi Wang & Yinlong Xu, 2021. "Predicting Possible Distribution of Tea ( Camellia sinensis L.) under Climate Change Scenarios Using MaxEnt Model in China," Agriculture, MDPI, vol. 11(11), pages 1-18, November.
  • Handle: RePEc:gam:jagris:v:11:y:2021:i:11:p:1122-:d:675951
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    1. Gunathilaka, Rajapaksha P. D. & Smart, James C. R. & Fleming, Christopher M. & Hasan, Syezlin, 2018. "The impact of climate change on labour demand in the plantation sector: the case of tea production in Sri Lanka," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 62(3), July.
    2. John Harte & Annette Ostling & Jessica L. Green & Ann Kinzig, 2004. "Climate change and extinction risk," Nature, Nature, vol. 430(6995), pages 34-34, July.
    3. ,, 1999. "Problems And Solutions," Econometric Theory, Cambridge University Press, vol. 15(5), pages 777-788, October.
    4. Rajapaksha P. D. Gunathilaka & James C. R. Smart & Christopher M. Fleming & Syezlin Hasan, 2018. "The impact of climate change on labour demand in the plantation sector: the case of tea production in Sri Lanka," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 62(3), pages 480-500, July.
    5. ,, 1999. "Problems And Solutions," Econometric Theory, Cambridge University Press, vol. 15(1), pages 151-160, February.
    6. Trevor H. Booth, 2017. "Assessing species climatic requirements beyond the realized niche: some lessons mainly from tree species distribution modelling," Climatic Change, Springer, vol. 145(3), pages 259-271, December.
    7. Detlef Vuuren & Jae Edmonds & Mikiko Kainuma & Keywan Riahi & Allison Thomson & Kathy Hibbard & George Hurtt & Tom Kram & Volker Krey & Jean-Francois Lamarque & Toshihiko Masui & Malte Meinshausen & N, 2011. "The representative concentration pathways: an overview," Climatic Change, Springer, vol. 109(1), pages 5-31, November.
    8. Chris D. Thomas & Alison Cameron & Rhys E. Green & Michel Bakkenes & Linda J. Beaumont & Yvonne C. Collingham & Barend F. N. Erasmus & Marinez Ferreira de Siqueira & Alan Grainger & Lee Hannah & Lesle, 2004. "Extinction risk from climate change," Nature, Nature, vol. 427(6970), pages 145-148, January.
    9. Uttam Babu Shrestha & Kamaljit S Bawa, 2014. "Impact of Climate Change on Potential Distribution of Chinese Caterpillar Fungus (Ophiocordyceps sinensis) in Nepal Himalaya," PLOS ONE, Public Library of Science, vol. 9(9), pages 1-11, September.
    10. Rulin Wang & Qing Li & Shisong He & Yuan Liu & Mingtian Wang & Gan Jiang, 2018. "Modeling and mapping the current and future distribution of Pseudomonas syringae pv. actinidiae under climate change in China," PLOS ONE, Public Library of Science, vol. 13(2), pages 1-21, February.
    11. ,, 1999. "Problems And Solutions," Econometric Theory, Cambridge University Press, vol. 15(3), pages 427-432, June.
    12. ,, 1999. "Problems And Solutions," Econometric Theory, Cambridge University Press, vol. 15(4), pages 629-637, August.
    13. Terry L. Root & Jeff T. Price & Kimberly R. Hall & Stephen H. Schneider & Cynthia Rosenzweig & J. Alan Pounds, 2003. "Fingerprints of global warming on wild animals and plants," Nature, Nature, vol. 421(6918), pages 57-60, January.
    14. Worthington, Thomas A. & Zhang, Tianjiao & Logue, Daniel R. & Mittelstet, Aaron R. & Brewer, Shannon K., 2016. "Landscape and flow metrics affecting the distribution of a federally-threatened fish: Improving management, model fit, and model transferability," Ecological Modelling, Elsevier, vol. 342(C), pages 1-18.
    15. Richard H. Moss & Jae A. Edmonds & Kathy A. Hibbard & Martin R. Manning & Steven K. Rose & Detlef P. van Vuuren & Timothy R. Carter & Seita Emori & Mikiko Kainuma & Tom Kram & Gerald A. Meehl & John F, 2010. "The next generation of scenarios for climate change research and assessment," Nature, Nature, vol. 463(7282), pages 747-756, February.
    16. Sillero, Neftalí, 2011. "What does ecological modelling model? A proposed classification of ecological niche models based on their underlying methods," Ecological Modelling, Elsevier, vol. 222(8), pages 1343-1346.
    17. Camille Parmesan & Gary Yohe, 2003. "A globally coherent fingerprint of climate change impacts across natural systems," Nature, Nature, vol. 421(6918), pages 37-42, January.
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    1. Yibo Xu & Xiaohuang Liu & Lianrong Zhao & Jiufen Liu & Xiaofeng Zhao & Hongyu Li & Chao Wang & Honghui Zhao & Ran Wang & Xinping Luo & Liyuan Xing, 2024. "Prediction of Potential Suitability Areas for Ephedra sinica in the Five Northwestern Provinces of China Under Climate Change," Agriculture, MDPI, vol. 14(10), pages 1-18, October.
    2. S. Abdul Rahaman & S. Aruchamy, 2022. "Land Suitability Evaluation of Tea ( Camellia sinensis L.) Plantation in Kallar Watershed of Nilgiri Bioreserve, India," Geographies, MDPI, vol. 2(4), pages 1-23, November.
    3. Ruijie Huang & Chenchen Wu & Hao Lu & Xuemei Wu & Baoyu Zhao, 2024. "Predicted Distribution of Locoweed Oxytropis glabra in China under Climate Change," Agriculture, MDPI, vol. 14(6), pages 1-13, May.

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