The Prediction of Tea Production Using Dynamic Rolling Update Grey Model: A Case Study of China

Suwen Xie, Wai Kuan Wong (), Hui Shan Lee () and Kee Seng Kuang
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Suwen Xie: Centre for Mathematical Sciences, Universiti Tunku Abdul Rahman, Bandar Sungai Long, Kajang 43000, Selangor, Malaysia
Wai Kuan Wong: Centre for Mathematical Sciences, Universiti Tunku Abdul Rahman, Bandar Sungai Long, Kajang 43000, Selangor, Malaysia
Hui Shan Lee: Faculty of Accountancy and Management, Universiti Tunku Abdul Rahman, Bandar Sungai Long, Kajang 43000, Selangor, Malaysia
Kee Seng Kuang: Centre for Mathematical Sciences, Universiti Tunku Abdul Rahman, Bandar Sungai Long, Kajang 43000, Selangor, Malaysia

Mathematics, 2025, vol. 13, issue 19, 1-23

Abstract: China is one of the world’s largest tea-producing countries, and its fluctuations in production affect the international market and domestic economic stability. Existing research often uses limited predictive models at the local scale and lacks systematic national analysis. This study evaluated five models—autoregressive integrated moving average model (ARIMA), grey model (GM (1,1)), Markov chain grey model (Markov-GM (1,1)), particle swarm optimization Markov chain grey model (PSO-Markov-GM), and dynamic rolling update grey model (DRUGM (1,1))—using three stages of annual tea production data from China (2004–2023). The results indicate that DRUGM (1,1) has the lowest prediction error, demonstrating superior ability to capture production trends. The dynamic update mechanism of this model enhances its adaptability, providing an efficient and scalable framework for predicting the production level of tea and other crops. Accurate predictions are crucial for improving agricultural planning, optimizing resource allocation, and providing information for trade policy design. This study provides practical tools for sustainable agricultural decision-making, helping to strengthen rural economic stability and resilient food systems.

Keywords: tea production prediction; grey model; dynamic rolling update; Markov chain model; particle swarm optimization (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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