IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2608.24894.html

Forecasting Weather-Driven Price Dynamics Across Sri Lankan Tea Market Catalogues

Author

Listed:
  • Hesandi Mallawarachchi
  • Senilka Madurapperumage
  • Nadil Kulathunge
  • Thilokya Angeesa
  • Nethsith Gunaweera
  • Sandeepa Weerasekara
  • Patalee Narasinghe
  • Nisansa de Silva
  • Sandareka Wickramanayake

Abstract

The Colombo Tea Auction (CTA) plays a vital role in determining global tea prices, yet the relationship between local weather conditions and price behavior across different tea catalogues has not been thoroughly explored. In this study, we develop a novel, structured dataset by extracting information from 105 weekly broker reports spanning late 2023 to 2026, and combined with region-specific weather data. Our analysis focuses on four main tea catalogues of Sri Lankan tea: High Grown, Low Grown, Off-Grade, and Dust. To better understand the factors influencing tea prices, we apply Granger causality analysis alongside tree-based machine learning models: Random Forest, XGBoost, LightGBM, and Gradient Boosting. Our results show that while market dynamics are primary drivers, weather conditions also have significant effects. Notably, Low Grown tea shows strong sensitivity to precipitation and sunshine duration (p

Suggested Citation

  • Hesandi Mallawarachchi & Senilka Madurapperumage & Nadil Kulathunge & Thilokya Angeesa & Nethsith Gunaweera & Sandeepa Weerasekara & Patalee Narasinghe & Nisansa de Silva & Sandareka Wickramanayake, 2026. "Forecasting Weather-Driven Price Dynamics Across Sri Lankan Tea Market Catalogues," Papers 2608.24894, arXiv.org.
    • Handle: RePEc:arx:papers:2608.24894
    as

    Download full text from publisher

    File URL: https://arxiv.org/pdf/2608.24894
    File Function: Latest version
    Download Restriction: no
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2608.24894. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: https://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.