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Evaluation of influencing factors on tea production based on random forest regression and mean impact value

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  • Yihui Chen

    (School of Economics and Management, Fuzhou University, Fuzhou, China
    Cooperative Innovation Centre of Modern Agricultural Industrial Park, Quanzhou, China
    Anxi College of Tea Science, Fujian Agriculture and Forestry University, Fuzhou, China)

  • Minjie Li

    (School of Economics and Management, Fuzhou University, Fuzhou, China)

Abstract

Overproduction of tea in the major producing countries is an important factor which restricts the development of tea. Therefore, the factors from the economic, social and environmental system affecting tea production have become the focus of both academia and practice. Random forest regression (RFR) and mean impact value (MIV) were applied to evaluate the weights of variables. Firstly, RFR was preliminarily used to build a well-trained model, and then the weights of variables combining with MIV were calculated. Then, a well-trained model was constructed after variable selection to evaluate the importance of tea production from 2007 to 2016. The results revealed that the economic system and the social system are the main factors that affect tea production. The net production value and total population have little negative effects on tea production, while the area harvested has a little positive effect. Based on the research findings, governments and enterprises should develop and upgrade tea production technology, promote the exchange and cooperation in the international tea trade, then ultimately achieve sustainable development of the tea industry.

Suggested Citation

  • Yihui Chen & Minjie Li, 2019. "Evaluation of influencing factors on tea production based on random forest regression and mean impact value," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 65(7), pages 340-347.
    • Handle: RePEc:caa:jnlage:v:65:y:2019:i:7:id:399-2018-agricecon
    DOI: 10.17221/399/2018-AGRICECON
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    References listed on IDEAS

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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. 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.
    3. R. P. Dayani Gunathilaka & James C. R. Smart & Christopher M. Fleming, 2017. "The impact of changing climate on perennial crops: the case of tea production in Sri Lanka," Climatic Change, Springer, vol. 140(3), pages 577-592, February.
    4. Zhu, Bangzhu & Han, Dong & Wang, Ping & Wu, Zhanchi & Zhang, Tao & Wei, Yi-Ming, 2017. "Forecasting carbon price using empirical mode decomposition and evolutionary least squares support vector regression," Applied Energy, Elsevier, vol. 191(C), pages 521-530.
    5. Mendez, Guillermo & Lohr, Sharon, 2011. "Estimating residual variance in random forest regression," Computational Statistics & Data Analysis, Elsevier, vol. 55(11), pages 2937-2950, November.
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    Cited by:

    1. Yihui Chen & Minjie Li & Assem Abu Hatab, 2020. "A spatiotemporal analysis of comparative advantage in tea production in China," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 66(12), pages 550-561.

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