## 3 The Critical Importance of Product Identity in Agricultural Supply Chains
### Economic Impact and ROI of Blockchain Traceability  
While blockchain implementation in agriculture incurs upfront investment, such as platform development, integration with legacy systems, and IoT sensor deployment, the long-term Return On Investment (ROI) can be significant. The economic benefits stem from reduced fraud, faster recalls, streamlined data sharing, improved consumer trust, and brand differentiation. Pilot projects from major retailers provide empirical support, as seen in Table 5:  
* Walmart, in partnership with IBM Food Trust [56], reduced the time required to trace the origin of sliced mangoes from almost seven days to 2.2 s, significantly improving operational efficiency and recall readiness. This speed not only reduces health risk exposure and potential legal costs but also protects brand reputation [57]. * Carrefour implemented blockchain for traceability on various food items (e.g., chicken, eggs, milk, citrus fruits). The result was a measurable increase in sales of tracked products due to enhanced consumer trust. Shoppers were willing to pay a premium for blockchain-verified transparency, particularly for organic or regionally certified goods [55]. * Provenance and Bumble Bee Foods demonstrated how traceability using blockchain and digital product IDs (QR codes) can increase export readiness, reduce disputes, and attract buyers in premium markets with traceability mandates (e.g., EU organic, fair trade) [58]. A comparation was performed between traditional traceability systems and blockchain-based approaches in agricultural supply chains (see Table 6). The comparison is structured across several key dimensions including data transparency, real-time tracking, verification effort, scalability, cost, and stakeholder trust. This matrix illustrates the enhanced capabilities and benefits of blockchain while also acknowledging the challenges in adoption relative to conventional methods. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Pilot Project/Organisation** & **Cost Factors** & **Observed Benefits** & **Estimated ROI Impact** \\ \hline Walmart (IBM Food Trust) & Development and integration costs; supplier onboarding & Recall time reduced from almost seven days to 2.2 s; lower food safety risks & Operational savings and legal risk reduction \\ \hline Carrefour & QR/NFC tagging, consumer engagement tools & Increased product trust and sales; brand loyalty growth & 5–10\% increase in sales for tracked products \\ \hline Provenance (Coffee/fish) & Deployment of DIDs; training for producers & Verified ethical sourcing; access to premium buyers in EU & Entry into regulated, higher-value markets \\ \hline Bumble Bee Foods & Blockchain tracking of tuna from catch to consumer & Verified seafood origin; improved consumer transparency & Enhanced brand equity and trust \\ \hline China JD.com & Full blockchain traceability for imported meat & Compliance with food safety mandates; restored trust in imported goods & Regulatory compliance + consumer loyalty \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Cost–benefit summary of blockchain traceability (pilot studies). Several companies have implemented blockchain solutions to enhance transparency and traceability in agricultural supply chains. However, these implementations typically focus on logistics traceability rather than addressing product identity in a holistic manner. This section compares the proposed framework's components with real-world initiatives to highlight its unique contributions and assess its practical feasibility. The analysis reveals several important observations. Elements such as immutable provenance and consumer transparency interfaces (e.g., QR codes) are already well established and validated in large-scale deployments by companies such as Walmart and Carrefour, confirming their practical feasibility. However, the use of digital twins at product-level granularity, capable of dynamic state updates such as condition monitoring, represents a partially novel contribution that extends beyond current batch-level implementations.