The rapid growth of organic agriculture has created both opportunities and challenges for young agripreneurs, who should navigate certification standards (e.g., Thai Organic and the European Union Organic), compliance requirements, and market-specific documentation for domestic and international trade.This study aims to design and implement an ontology-driven decision support system (DSS) that leverages Semantic Web Rule Language (SWRL) to provide transparent and context-specific recommendations for organic farming.Having adopted a design-and-development approach, the research collected data from 50 agripreneurs and integrated these insights into an ontology framework enriched with rule-based reasoning.Five structured sets of recommendation rules were developed to link organic products, target markets, certification standards, certifying agencies, certification services, and required supporting documents while their performance was evaluated using standard information retrieval metrics.Evaluation based on case-based rule validation indicated that the system returned no false positives across the tested scenarios (100% precision), with an average recall of 93.03% and an overall Fmeasure of 96.39%, thus demonstrating strong logical correctness and practical applicability within the defined evaluation scope.The study concluded that embedding SWRL-based "IF-THEN" recommendation rules within ontological structures could effectively bridge fragmented regulatory and market knowledge and actionable decision making, in order to offer agripreneurs a scalable and explainable tool to manage certification and market access.The significance of this work lies in its dual contributions: theoretically, it demonstrates how semantic technologies could advance knowledge-to-decision processes in agriculture; practically, it provides structured guidance to support certification compliance and market participation in organic farming.

This study investigated the impact of hi-tech innovation on environmental sustainability in the supply chains of organic agri-startups by using an Ordinary Least Squares (OLS) model.The results indicated that environmental sustainability in organic agri-startups was driven most strongly by eco-friendly production (0.440), followed by blockchain (0.269) and mobile platforms (0.250), while farm-to-table logistics (-0.093) and nanotechnology (-0.033) showed negative impacts.Using a regression-based prioritisation approach, the study revealed that organic agri-startups adopted hi-tech innovation pragmatically.They could then prioritise technologies to enhance production processes, ensure organic compliance, and stabilise operations under environmental uncertainty.Built upon these findings, the study strengthened the literature on sustainable and organic entrepreneurship by demonstrating how behavioural constructs shaped decision making.This divergence from previous studies contributes to behavioural decision theory in agri-startups, thus highlighting the importance of analyzing not only what entrepreneurs value but also what they choose, given constraints in resources, knowledge, and operational risk.

The organic rice supply chain in Indonesia, particularly in Banten Province, is characterized by high complexity and the involvement of multiple actors, which creates challenges related to transparency, traceability, and product authenticity.These issues reduce consumer trust and complicate regulatory supervision in organic farming systems.This study aims to design and evaluate a blockchain-based traceability model to enhance transparency, ensure product authenticity, and support food safety compliance in the organic rice supply chain.This research employs the Design Science Research Methodology (DSRM), encompassing problem identification, objective definition, artifact design and development, demonstration, and evaluation.Data were collected through interviews, field observations, and Focus Group Discussions (FGDs) involving organic rice supply chain actors, government regulators, and experts.The proposed model was empirically evaluated using Partial Least Squares-Structural Equation Modeling (PLS-SEM) based on responses from 220 participants.The resulting Organic Rice-Supply Chain Traceability (Organic Rice-SCT) model integrates farmers, farmer cooperatives, business actors, retailers, consumers, and government agencies within a blockchain-based system supported by quick response (QR) code technology.The findings indicate that operational excellence, cultural suitability, environmental conditions, quality assurance, and organizational resources significantly influence blockchain adoption.Conversely, data management, supply chain integration, technology maturity, and knowledge management show no significant effect.The model demonstrates its capability to improve supply chain visibility, reduce information asymmetry, strengthen regulatory oversight, and support compliance with Fresh Plant-Based Food (Pangan Segar Asal Tumbuhan, PSAT) certification.In conclusion, this study provides a validated blockchain-based traceability model that enhances transparency and trust in organic rice supply chains.Practically, the model supports stakeholders and regulators in ensuring food safety and product authenticity, while theoretically contributing to the literature on blockchain adoption in sustainable agricultural systems.