Abstract

The systems thinking approach has allowed for a detailed understanding of complex interactions within the supply chain, including the roles of various stakeholders such as farmers, traders, and regulatory bodies. This holistic perspective has been crucial in identifying potential constraints and opportunities for improvement. The primary factors affecting the grain supply chain, such as certification, quality compliance, and information transparency, are further analysed using the design science methodology. This methodology involves a rigorous, iterative problem identification, solution design, and evaluation process. It helps in developing practical solutions that are grounded in real-world contexts. Based on the analysis, a proposed conceptual framework introduces a novel digital solution named HARVEST™(Holistic Agricultural Records and Verification for Exchange, Supply, and Trading). HARVEST™ will act as an intermediary, leveraging data from existing digital solutions in Australia and India to provide valuable insights and knowledge to multiple stakeholders. The proposed solution seeks to improve transparency, efficiency, and trust throughout the grain supply chain. The system will have the feature to verify the authenticity of certificates obtained from issuing bodies. Furthermore, the solution will facilitate the prediction of grain production and availability and the creation of training materials designed to support key stakeholders, such as farmers and traders. By integrating these functionalities, HARVEST aims to benefit all stakeholders involved in the grain supply chain.

Conceptual Model: HARVEST™

HARVEST (Holistic Agricultural Records and Verification for Exchange, Supply, and Trading) is a proposed conceptual digital tool designed as part of the comprehensive analysis performed to address the challenges in the grain supply chain. The key features of HARVEST include:

• Integrated Digital Certification: Centralised platform for managing and verification of certificates from issuing bodies.

• Traceability and Transparency: End-to-end traceability of grain shipments from farm to point-of-sale. Real-time data sharing on grain quality, origin, and handling.

• Regulatory Compliance: Automated updates on regulatory changes and compliance requirements. Tools for managing compliance with SPS and TBT standards.

• Stakeholder Collaboration: Platforms for communication and collaboration among stakeholders. Training modules and resources to educate stakeholders on regulatory differences and best practices.

HARVEST System Architecture

The HARVEST system architecture consists of several layers, each with specific functions and responsibilities.

1. Interface Layer: The front-end layer responsible for user interactions, visualisation, querying, and now, verification of certifications and documents.

• Visualisation Module: Provides graphical representations of supply chains, market trends, crop yield data, etc.

• Crop Yield Analytics: Offers yield predictions based on historical data and environmental factors.

• Supply Chain Mapping: Visualises the entire agricultural supply chain, tracking products from farm to market.

Market Demand Insights: Analyses market trends and forecasts demand.

• Query Interface: Allows users to interact with the system and retrieve data.

• Query Reformulation Engine: Modifies queries based on input constraints and context.

• Query Optimiser: Refines and optimises user queries for faster and more relevant results.

• Query in Local Schema: Adapts queries to local databases and schemas for context-specific responses.

Certification Document Verification:

• Document Upload Validation: Users can upload trade certifications, export documentation, and other regulatory records. The system will verify their authenticity and compliance against global standards.

• Phytosanitary Certificate Verification: Verifies the authenticity of phytosanitary certificates ensuring compliance with pest and disease control regulations.

• Trade Certification Validation: Checks the validity of export permits, certificates of origin, and compliance with trade rules.

• Cross-Referencing with Regulatory Databases: The system cross-references uploaded documents with the relevant regulatory systems (e.g., PEMS, EXDOC, PQIS) to ensure the documents’ legitimacy.

• Compliance Alerts: Alerts users if the document fails validation, indicating potential issues or non-compliance.

• Document Status Tracking: Tracks the status of documents in real-time, showing whether they are pending verification, approved, or flagged for issues.

2. Analysis Layer: This layer performs data analysis, predictions, and decision-making.

• Data Enrichment AI Models: Enhance raw data with machine learning algorithms for improved decision-making. • Climate Impact Analysis: Evaluates how climate conditions (e.g., temperature, rainfall) affect crops and the supply chain.

• Market Price Prediction: Forecasts the prices of crops based on supply and demand.

• Supply Chain Risk Analysis: Identifies potential risks in the supply chain (e.g., disruptions, fraud).

• Trade and Compliance Analysis: Monitors trade practices and regulatory compliance.

• Phytosanitary Compliance Checks: Verifies pest and disease control measures to ensure compliance with international standards.

• Trade Certification Validation: Confirms authenticity of trade certifications (e.g., phytosanitary certificates, export permits).

Traceability Fraud Detection: Tracks agricultural products through the supply chain and detects any fraudulent activity.

3. Knowledge Layer: This layer maintains the core knowledge base, standards, and resolves conflicts.

• Global Agricultural Ontology: A structured model of agricultural knowledge, ensuring standardised terminology.

• Standardised Farming Trading Terms: Ensures consistent terminology across the system for farming practices and trade regulations.

• Mapping with Regional Crop Data: Maps global agricultural data to regional contexts, aligning local and international data.

• Knowledge Extraction: Extracts relevant agricultural knowledge from various sources to improve decision-making.

• Entity Resolution (Farmers, Traders, Exporters): Identifies and consolidates records for farmers, traders, exporters, etc. • Duplicate Removal (Supply Chain Records): Eliminates duplicate entries in the supply chain data to maintain integrity.

• Conflict Resolution (Trade Disputes, Certifications): Resolves conflicts in trade disputes or discrepancies in certification data.

4. Acquisition and Abstraction Layer: This layer collects and integrates data from multiple sources into a unified system.

• Data Collection from All Connected Underlying Systems: Integrates data from external systems like certification, regulatory, trade, and logistics systems.

• Automated Data Pipelines: Data is gathered through automated processes, reducing human errors and delays in compliance verification.

• Parsing Wrapping Modules: Prepares and structures data for integration into the system.

• Integration of Certification Data: Incorporates certification data such as phytosanitary certifications and trade documents.

• Transaction Export Documentation: Manages trade documentation (e.g., export permits, invoices).

• Phytosanitary Compliance Records: Incorporates records of pest and disease control measures.

• Export Documentation: Processes export-related records, including customs clearance, permits, and trade certificates.

5. External Entities (Data Sources): These are the systems from which the HARVEST system extracts data.

Primary Systems (Certification Export Documentation):

Australia:

– Plant Export Management System (PEMS): Manages plant export certifications.

– Export Documentation System (EXDOC): Facilitates export documentation.

– Next Export Documentation System (NEXDOC): Successor to EXDOC, streamlining digital processes.

– eCert System: Electronic certification for trade compliance.

• India:

– Plant Quarantine Information System (PQIS): Regulates phytosanitary certification.

– Customs Electronic Data Interchange (CEDI): Facilitates trade documentation and customs clearance.

– Lab-One Assessment Portal: Manages laboratory test results for export quality assurance.

– Export Alert Monitoring Portal: Tracks alerts for non-compliance in agricultural exports.

Secondary Systems (Regulatory Trade References):

• Australia:

– Micor: A database for export conditions of agricultural commodities.

– Grain Trade Australia (GTA) Publications: Standardised trade rules and market data.

• India:

– Trade Regulatory Portals: Compliance databases for export regulations.

– Market Surveillance Systems: Monitors domestic and international trade risks.