When Prices Double in a Week: Forecasting of Agricultural Volatility in Import-Isolated Markets
arXiv:2606.29248v1 Announce Type: new Abstract: Vegetable prices in Sri Lanka are highly volatile because the market is largely import-isolated, so supply disruptions quickly drive prices up. This study develops a machine learning framework to forecast such volatility by incorporating supply-chain-aware features and explicitly modelling the country's two cultivation seasons, Maha (October-April) and Yala (May-September). An integrated dataset was constructed by combining retail and farmer-gate prices with origin-aligned weather variables, diesel costs, and exchange rates across 12 vegetable va
The increasing frequency of supply chain disruptions and climate-related events globally necessitates more robust forecasting models for essential goods, making this research particularly timely.
This development in AI-driven agricultural volatility forecasting can provide critical insights for food security and economic stability in import-isolated markets, impacting national planning and humanitarian aid.
The explicit modelling of cultivation seasons and integration of diverse data sources allow for more accurate and granular predictions of vegetable prices, improving the ability to anticipate and mitigate price shocks.
- · Sri Lankan farmers
- · Sri Lankan consumers
- · Agricultural technology firms
- · Governments in import-isolated nations
- · Speculators
- · Traditional agricultural forecasting methods
Improved early warning systems allow for proactive policy responses to mitigate food price volatility in affected regions.
Enhanced food security reduces social unrest and economic instability in vulnerable, import-isolated countries.
The methodology could be replicated and scaled to other essential commodities and regions, leading to a global improvement in supply chain resilience and price stability for basic goods.
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