Forecasting Thai rubber Prices: A system dynamics approach to economic and policy impacts

This study develops a system dynamics (SD) approach, which is rarely used in Thailand's rubber sector to forecast long-term price dynamics and policy outcomes. Previous studies mainly employed econometric and machine learning models which emphasized short-term fluctuations but paid limited attention to the combined and structural impacts of policy, environmental, and economic drivers. To address this gap, we develop an SD model (2018–2027) that integrates global demand, domestic consumption, plantation areas, climate variability, and macroeconomic conditions to predict farm-gate, cup lump, and raw rubber sheet prices. Simulation results demonstrate that a 10 % reduction in plantation area could increase farm-gate prices by 40.1 %, whereas the combined strategy of plantation area reduction and demand expansion yields the most pronounced impact, with smallholder net revenue projected to rise from USD 179 per hectare in 2025 to USD 412 per hectare in 2027. Sensitivity analysis identifies plantation area as the primary driver of price fluctuations, with rainfall, humidity, and temperature strongly affecting yields. These findings indicate that coordinated supply reduction and demand expansion offer the most effective policy for stabilizing markets, improving farmer incomes, and supporting sustainable growth under climate uncertainty. • System dynamics model forecasts Thailand's rubber prices and assesses policy effects. • The model integrates climate, economic, and policy factors to predict price trends. • Combining plantation reduction, global demand growth, and domestic expansion boosts markets. • Sensitivity analysis identifies plantation area control as the key driver of price fluctuations. • Climate factors, particularly temperature and humidity, significantly impact production and price.