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

Hesandi Mallawarachchi, Senilka Madurapperumage, Nadil Kulathunge, Thilokya Angeesa, Nethsith Gunaweera, Sandeepa Weerasekara, Patalee Narasinghe, Nisansa de Silva and Sandareka Wickramanayake

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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

Date: 2026-06, Revised 2026-08
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