<![CDATA[Abstract. Ponanan K, Tangarommun T, Chansombat S, Patmanee J. 2026. A qualitative analysis of logistics constraints behind agricultural supply and demand gaps in Thailand. Asian J Agric 10 (2): g100205. https://doi.org/10.13057/asianjagric/g100205. Thailand's economy relies heavily on agriculture, but the sector is faced by complex imbalances in supply and demand, as well as logistics bottlenecks, leading to major inefficiencies in the market. This study aims to explore these systemic constraints and to propose a conceptual framework for an Ontology-based Semantic Database to improve the domestic supply-demand matching, focusing on the Lower Northern Provincial Cluster 1. After the knowledge acquisition phase of the methodology framework, a rigorous qualitative approach was applied. In-depth interviews with 15 key stakeholders from government agencies, agricultural entrepreneurs and local farmers were analyzed systematically by using data triangulation with thematic analysis and a cross-case synthesis matrix. The empirical results show a serious “Supply-Demand Gap” with a high degree of information asymmetry, and significant mismatches in volume, quality, price and time. The analysis shows that local farmers are challenged by fluctuations in yield, post-harvest storage constraints and technological limitations, whereas entrepreneurs face strict volume consistency, high standards of quality and rigorous logistics timing. Moreover, these local, ground-level frictions are often not in line with macro-level government policies. Traditional databases are not suitable for resolving these semantic conflicts. Therefore, this paper introduces a conceptual domain ontology model that consists of 5 domains, i.e., Farmers, Entrepreneur, Agricultural_products, Provinces, and Sorting_Packaging_Plant. The model is designed to facilitate the automated matching of raw production constraints with strict market logistics requirements through the application of rules. This semantic architecture provides a fundamental blueprint to develop intelligent matching platforms to reduce information asymmetry and build a more resilient data-driven agricultural ecosystem in Thailand.]]>
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