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

2026-06-27

AI Summary

研究通过结合斯里兰卡特定地区的天气数据和茶拍卖信息，利用格兰杰因果分析及多种机器学习模型，揭示了天气条件对不同种类茶叶价格的影响，特别是降水、阳光时长和温度的作用。

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<0.05) across 1-3-week lags. Off-Grade and Dust catalogues also exhibit significant responses to temperature variations. Catalogue-specific modelling outperformed unified approaches, with LightGBM emerging as the superior model for three out of four catalogues. Overall, this study highlights the importance of considering both localized weather patterns and catalogue-level differences when forecasting tea prices, offering a more precise and practical framework for the tea industry.

Problem

• Weather Conditions
• Tea Prices
• Colombo Tea Auction
• Granger Causality
• Machine Learning

Innovation

• Granger causality analysis
• tree-based machine learning models
• weather conditions impact
• catalogue-specific modeling
• LightGBM