Emerging Blockchain & Distributed Ledger Technologies in Digital Agriculture – Perspectives in the AI era and Challenges

XXX-X-XXXX-XXXX-X/XX/$XX.00 ©20XX IEEE Emerging Blockchain & Distributed Ledger Technologies in Digital Agriculture - Perspectives in the AI Era and Challenges Effrosyni Bitakou Agricultural University of Athens Athens, Greece [email protected] Constantina Costopoulou Agricultural University of Athens Athens, Greece [email protected] Eugenia Karamouzi Rezos Brands S.A. Patras, Greece [email protected] Maria Ntaliani Agricultural University of Athens Athens, Greece [email protected] Caterina Constantinou Rezos Brands S.A. Patras, Greece [email protected] Kopilović Nikola Terra Consulting Novi Sad, Serbia [email protected] Konstantinos Demestichas Agricultural University of Athens Athens, Greece [email protected] Panagiotis Panopoulos Rezos Brands S.A. Patras, Greece [email protected] Dimitrios Tsolis Rezos Brands S.A. Patras, Greece [email protected] Abstract—This article studies the benefits, challenges and future potential of combining blockchain, Distributed Ledger Technologies and Artificial Intelligence in digital agriculture. Their convergence presents significant opportunities in critical areas of agriculture, such as supply chain traceability, financial inclusion, environmental monitoring, and decentralized data governance. These technologies can shape the future of digital agriculture by developing inclusive, intelligent, and decentralized systems that are both technologically advanced and aligned with social equity and environmental sustainability. Keywords—Blockchain, Distributed Ledger Technologies, Digital Agriculture, Security, Traceability, Smart Contracts. I. INTRODUCTION The convergence of digital technologies is redefining the landscape of modern agriculture. As global food systems face increasing pressure from climate challenges, population growth, and sustainability demands, the integration of innovative digital solutions has become imperative. Among these, blockchain and Distributed Ledger Technologies (DLTs) have emerged as transformative tools with the potential to enhance transparency, traceability, and trust across agricultural value chains [1], [2]. Concurrently, the rapid evolution of Artificial Intelligence (AI) is unlocking new dimensions in data-driven decision-making and automation, further amplifying the capabilities of blockchain-based systems. This paper explores the synergies between blockchain, DLTs, and AI in the context of digital agriculture (DA), offering a forward-looking perspective on how these technologies can jointly address long-standing challenges, such as supply chain inefficiencies, data silos, and provenance verification. It also critically examines the barriers to adoption that should be overcome to realize the full potential of these technologies [3]. By situating blockchain and DLTs within the broader AI-driven transformation of agriculture, this study aims to contribute to the ongoing discourse on sustainable innovation in agri-tech ecosystems. The structure of the paper is as follows: the background section includes basic notions of blockchain and DA, as well as the main barriers that hinder blockchain integration into agriculture. Then, two case studies demonstrate good practices of blockchain application in specific agricultural domains. In the next two sections, the convergence of blockchain and AI in DA is presented, elaborating on the benefits and the adoption perspectives correspondingly. Next, the challenges and future research opportunities are discussed. Finally, the conclusions of the paper are given. II. BACKGROUND Blockchain is a form of DLT that facilitates the secure, transparent, and immutable recording of data across a decentralized network [4]. Transactions or data entries are organized into blocks, cryptographically connected to preceding ones, and confirmed through consensus mechanisms, like Proof of Work or Proof of Stake. DLTs encompass more than just traditional blockchains, supporting diverse structures, such as directed acyclic graphs and permissioned ledgers, each with unique consensus protocols and levels of decentralization [5]. These systems enable reliable collaboration among multiple parties, eliminating the need for central intermediaries, making them particularly effective in complex, multi-stakeholder environments, like agriculture. Once primarily linked to cryptocurrencies, such as Bitcoin, blockchain has transformed into a general-purpose technology with wide-ranging applications across various industries. In the financial sector, it supports decentralized finance (DeFi) and enables seamless cross-border payments. In supply chains, it enhances transparency and traceability from source to end user. In healthcare, it safeguards patient data and consent processes and in the energy industry, it enables decentralized energy trading. These diverse applications underscore its flexibility in asset tracking, permission management, and automation through smart contracts, capabilities that are increasingly being applied in DA [6]. DA involves leveraging information and communication technologies to improve and optimize agricultural processes. This digital shift in agriculture is powered by cloud computing, edge technologies, and integrated platforms that gather, process, and visualize real-time data from the farm. In addition, smart farming expands on this concept by incorporating sensor networks, Internet of Things (IoT) devices, drones, geographic information systems, satellite imagery, and machine learning to enable precise, data-driven decisions directly at the field level. Its main goals include improving resource efficiency, forecasting yields, managing pests, and promoting sustainable practices [7]. Blockchain remains a promising technology for DA. Nonetheless, there are still many challenges that have to be addressed. In the context of traceability within DA, the reliable acquisition of data collection, holistic data integrity and the complexity of integration into the existing farming systems are included amongst others. Future research activities may focus on existing and new DLT techniques and systems for achieving reliable traceability of food products in supply chains [8]. One of the biggest challenges with blockchain and DLT is scalability and performance, particularly when dealing with large-scale agricultural supply chains. Research should be conducted so as to investigate and develop methods and use of tools for optimizing the performance and scalability of blockchain and DLT, while also ensuring that they remain secure and decentralized. Moreover, there are many different and heterogenous blockchain and DLT platforms, and ensuring interoperability between them is critical for achieving widespread adoption in the agricultural sector. Research should be developed so as to analyze existing standards and protocols for interoperability, as well as to implement methods for integrating blockchain and DLT with other emerging technologies, such as IoT and AI [9]. The adoption of smart contracts in DA may face several challenges, including complexity, security and reliability. Smart contracts operate based on predefined rules and conditions. These can be complex to develop and execute, especially for farmers and stakeholders without technical expertise. Also, ensuring the security of smart contracts is paramount, as vulnerabilities in the code can lead to risks, such as hacking, manipulation of data [2], and financial losses [10]. Research efforts could focus on methodologies and good practices to achieve reliability for smart contracts, which depend on underlying blockchain technology for execution. In order to overcome the above-mentioned obstacles, it is proposed to study the convergence of blockchain and AI in DA. The combined integration of these technologies in DA can create powerful synergies that contribute to enhancing efficiency and trust and driving innovation in the sector [11]. III. CASE STUDIES In this section, two case studies, namely strawberries and walnuts, are presented to demonstrate good practices of blockchain application in specific agricultural domains. A. Strawberries Strawberries, one of the most widely traded fruits worldwide, have experienced consistent growth in their planting and harvest area, and yield in recent years [12]. They are in high demand across both the European fresh market and the food industry [13]. Technologies like blockchain and AI can greatly benefit this sector. Searching current bibliography, it becomes evident that the potential of combining blockchain and AI for strawberries is not yet explored. However, there are some good practices for blockchain implementation. Walmart uses blockchain to trace the origin of over twenty-five products, including strawberries, allowing in this way consumers to authenticate the products’ journey [14]. Another case regards the collaboration of berry producers with the IBM Food Trust platform that enables them to track their produce from farm to table, ensuring real-time visibility into product origin, handling conditions, and distribution pathways [15]. Moreover, another example regards integrating blockchain technology with IoT devices to improve the efficiency and security of strawberry shipments. In this approach, IoT sensors monitor critical parameters, such as temperature, humidity, and location, throughout the transportation process, ensuring optimal conditions for the perishable goods. The collected data is then recorded onto a blockchain, providing an immutable and transparent ledger accessible to all stakeholders, including farmers, transporters, retailers, and consumers [16]. Analyzing these case studies for strawberry cultivation, it is evinced that blockchain technology can be beneficial for this cultivation in various interdependent aspects: Transparency and Traceability: Blockchain technology enhances inventory traceability and transparency within supply chains. It allows for real-time tracking and ensures a secure and unalterable record for tracking the origin and journey of strawberries, namely from farm to fork; Food Safety: Blockchain helps mitigate food safety risks, particularly for strawberries that are perishable items, and improve food quality through monitoring. This blockchain implementation helps avoid errors, and inefficiencies and provides swift response to any anomalies detected to strawberries during transit and rapid recalls; Fair Trade and Authenticity: Blockchain enables direct payments to strawberries producers, reducing reliance on intermediaries. Also, it verifies certifications and prevents fraud; Smart Contracts for Payments: Blockchain ensures that payment terms are executed exactly as agreed upon; Sustainability: Blockchain contributes to sustainability by improving inventory management and reducing waste; and Consumer Τrust: Blockchain ensures compliance with regulatory standards thus meeting growing consumer demands for food safety and quality. It can offer interactive experiences with farmers’ farm-to-table stories with consumers, given evidence on ethical practices, ultimately enhancing the market opportunities of strawberries. B. Walnuts The Walnut Fund serves as a practical case study of blockchain application in agriculture, demonstrating the feasibility and benefits of integrating digital ledger technologies into farming investments. Its approach aligns with the broader movement towards DA, where technology enhances sustainability, transparency, and economic viability [17]. The Walnut Fund represents an innovative model at the intersection of sustainable agriculture and blockchain technology. Established as the world's first online agriculture investment platform focused on walnut plantations, the Walnut Fund allows individuals to invest in fully managed walnut trees. Investors can participate with a minimum investment, making it accessible to a broader audience. The fund emphasizes long-term, sustainable returns, with walnut trees offering yields over several decades. In 2024, the Walnut Fund launched its first initial token offering, marking a significant step in tokenizing agricultural assets. Each token corresponds to a share in the yield of a walnut tree, providing a transparent and decentralized method for investors to track and claim their returns. This case exemplifies how blockchain can be applied in agriculture to enhance transparency, traceability, and investor engagement. By tokenizing real-world agricultural assets, it bridges the gap between traditional farming and modern digital finance. This model showcases the potential for blockchain to revolutionize investment in agriculture, making it more inclusive, efficient, and democratic. IV. THE CONVERGENCE OF BLOCKCHAIN AND AI IN DA The convergence of blockchain and AI in DA represents a significant leap forward in the quest for more transparent, autonomous, and data-intelligent food systems. While each technology independently offers substantial benefits, their combined application unlocks novel functionalities that address persistent challenges in agricultural ecosystemsranging from data fragmentation and fraud to inefficiencies in decision-making and resource management. In the following, significant benefits of this convergence for DA are analytically presented. A. Data Integrity Meets Intelligent Analytics Blockchain provides a tamper-resistant, verifiable data layer, ensuring that inputs into AI models are trustworthy. In agricultural contexts, where data often comes from diverse, unverified sources [18]- such as IoT sensors, mobile apps, and field agents - this synergy is crucial. For instance, AI algorithms trained on authenticated blockchain-stored data (e.g., crop yields, weather patterns, pesticide usage) can deliver more accurate predictions and diagnostics, reducing the risks of model bias or manipulation. B. Automated and Trusted Decision-Making Combining predictive capabilities of AI with blockchainenabled smart contracts makes feasible real-time, automated decision-making that is both auditable and self-enforcing. In a precision farming scenario, AI can determine optimal irrigation schedule based on weather forecasts and soil data, while blockchain-based contracts automatically activate irrigation systems or notify stakeholders without manual intervention, ensuring transparency and accountability at each step. C. Decentralized AI Marketplaces and Federated Learning The emergence of decentralized AI and federated learning models further strengthens this convergence. Farmers and agri-tech companies can train AI models locally on their devices or edge nodes without transferring sensitive data, then aggregate insights on a blockchain for wider use. This promotes data privacy, model robustness, and collaborative innovation, especially in smallholder farming communities wary of centralized data harvesting. D. Tokenization and Incentive Mechanisms Blockchain tokenization capabilities introduce new incentive structures to encourage data sharing and sustainable practices. AI can assess the quality or relevance of shared data (e.g., pest outbreak reports, crop monitoring), while blockchain enables fair and transparent distribution through digital tokens. This model not only drives participatory data ecosystems, but also democratizes access to agri-digital economies. E. Enhanced Trust in Human-AI Collaboration In agricultural domains, where trust in digital systems remains low, the auditable nature of blockchain builds confidence in AI-driven decisions. Stakeholders can trace the data lineage, model logic, and decision rationaleenabling explainable AI (XAI) within trusted frameworks. This is especially critical in policy-sensitive areas like food safety, land use, and climate adaptation. From the above-mentioned examples, it is shown that together, blockchain and AI, are not merely additive in agriculturethey are mutually enabling a new class of decentralized, intelligent agricultural systems. Their convergence supports a shift from siloed digital tools toward interoperable, farmer-centric ecosystems that can scale sustainably across regions and value chains. V. ADOPTION PERSPECTIVES OF BLOCKCHAIN AND AI IN DA The adoption of blockchain and AI in DA can be viewed through different perspectives. In this work, four major perspectives are examined, namely technological, economic, regulatory and policy, and social and ethical. Initially, from the technological perspective, the convergence of blockchain and AI with IoT and edge computing is a critical driver for precision agriculture. IoT devices collect high-frequency data, which AI processes to guide farm operations. Blockchain can then record and verify this data for traceability and automation through smart contracts [19]. However, issues of scalability (especially in public blockchains, like Ethereum) and interoperability between different platforms and legacy systems hinder widespread adoption [20]. Current solutions, such as sidechains, sharding, and cross-chain protocols, are promising, but still in early deployment stages in agricultural contexts. From an economic standpoint, blockchain and AI technologies can reduce transaction costs, eliminate intermediaries, and minimize fraudespecially in supply chains and agri-finance. Smart contracts automate payments, reducing time and administrative overhead. Yet, for smallholder farmers, the cost of implementationhardware, internet access, technical supportcan outweigh perceived short-term benefits. Cost-benefit analyses in Sub-Saharan Africa and South Asia suggest that adoption is more feasible when bundled with digital advisory or cooperative-based models [21]. From the regulatory and policy perspective, a major barrier to blockchain-AI adoption in agriculture is the lack of clear regulatory frameworks. Issues, like smart contract legality, data sovereignty, and digital asset classification are unresolved in many jurisdictions. Governments have shown interest in blockchain for land registries and subsidy disbursement, but policy remains fragmented. International organizations, such as FAO, ITU, and ISO are beginning to define interoperability and data governance standards, which will be crucial for cross-border agri-data flows and blockchain-based trade. Regulatory sandboxes may offer a path to controlled experimentation and policy co-design. Finally, from the social and ethical perspectives, the human dimension of adoption is often overlooked. Digital literacy, particularly in rural farming communities, remains low. Without inclusive training and support, farmers may be excluded or become passive data providers in systems they do not control. Ethical concerns raise questions, such as: “Who owns the sensor and farm-level data?”; “Are farmers adequately compensated?”; “Can farmers understand and contest decisions made by AI models influencing their livelihoods?”. Moreover, blockchain offers auditable transparency, but its immutability raises privacy challenges, particularly in sharing sensitive land or genetic data. Efforts to build farmer-centric data governance frameworks and XAI models are essential for building trust and ethical integrity [19]. TABLE I. ADOPTION PERSPECTIVES AT A GLANCE Perspective Key Opportunities Main Barriers Technological IoT/AI integration, edge processing Scalability, interoperability Economic Lower transaction costs, DeFi High initial costs for smallholders Regulatory Land registry, compliance tracking Policy gaps, lack of standards Social & Ethical Farmer empowerment, XAI Digital literacy, data ethics VI. CHALLENGES AND FUTURE DIRECTIONS Despite the promising potential of blockchain and AI in transforming DA, several technical, economic, institutional, and ethical challenges continue to hinder widespread adoption and scalability [22]. Understanding these limitations is crucial for developing realistic deployment strategies and designing inclusive, resilient agri-tech ecosystems. Blockchain systems, particularly public ledgers, often suffer from limited scalability due to consensus mechanisms that require significant computational resources. This constraint is particularly relevant in agriculture, where realtime data from IoT devices and sensors should be processed continuously. Similarly, interoperability between various blockchain platforms and with existing agricultural information systems remains limited, creating data silos and hindering integration with AI-driven analytics tools. Also, the deployment of these technologies can be costprohibitive, especially for smallholder farmers and stakeholders in low-income regions. Initial investments in digital infrastructure, sensor technologies, and secure networks, along with the ongoing costs of data storage and model training, can exceed local budgets [23]. This raises concerns about digital equity and the risk of excluding vulnerable populations from the benefits of DA. In addition, the need for large datasets for AI and blockchain immutability affects data privacy and ownership. While blockchain can secure data provenance, it also makes it difficult to delete or alter sensitive information [24]. Moreover, questions on who controls agricultural data, how it is monetized, and whether farmers truly benefit from datadriven systems or are merely data providers for external entities, are raised. Also, there is a significant regulatory gap. Legal uncertainties around smart contracts, tokenization, and crossborder data flows limit innovation and investment. However, the absence of standardized protocols for data quality, and AI explainability hampers trust and adoption among public institutions and international stakeholders. Another major barrier is the limited digital literacy among end-users, including farmers, cooperatives, and even extension workers. The complexity of these technologies can lead to discouragement or misuse. User-centered design, capacity building, and localized solutions are essential to ensure meaningful adoption and long-term impact. Furthermore, blockchain technologies, especially those relying on proof-of-work, can have substantial energy demands, raising sustainability concerns in agricultural settings. Efforts to shift toward greener consensus mechanisms (e.g., proof-of-stake, DAGs) are ongoing but not yet widespread. Future research and innovation should prioritize the creation of interoperable DA ecosystems that integrate blockchain, AI, IoT, and geospatial technologies. Open standards and APIs will be crucial for enabling seamless data exchange across platforms, reducing redundancy, and facilitating collaboration among diverse agricultural stakeholders. There is an emerging opportunity to redefine data ownership through farmer-centric governance frameworks, such as data cooperatives or token-based data exchange systems. By giving farmers control over how their data is accessed, shared, and monetized-potentially through blockchain-enabled smart contracts-future systems can ensure ethical, transparent, and equitable participation in the digital economy. In addition, future blockchain architectures should focus on lightweight, energy-efficient consensus mechanisms and edge AI computing to support scalable, low-cost applications suitable for rural and resource-constrained environments. Advancements in satellite internet and 5G will further enhance connectivity and real-time data capabilities. As AI continues to guide decision-making in agriculture, there will be a growing need for XAI solutions that can be understood and trusted by non-expert users. Furthermore, the rise of blockchain-based DeFi can improve access to credit and investment in agriculture. Tokenized assets, microloans, and blockchain crowdfunding platforms could help de-risk smallholder agriculture and open up new funding models, particularly in underserved markets. In parallel, governments and international bodies have an opportunity to foster innovation by creating regulatory sandboxes, where emerging blockchain-AI solutions can be tested in real-world agricultural contexts with supportive oversight. Finally, addressing the above-mentioned challenges requires a multi-stakeholder approach that combines technological innovation with inclusive policies, local capacity-building, and international cooperation. A balanced view of both the potential and the limitations is essential to ensure that blockchain and AI serve the needs of all actors in the agricultural value chain-especially those at the margins. VII. CONCLUSIONS As the agricultural sector continues to undergo digital transformation, the convergence of blockchain, DLTs, and AI presents unprecedented opportunities to reshape food systems. As this paper has demonstrated, the convergence of these technologies holds significant promise across key agricultural sectorsfrom supply chain traceability and financial inclusion to environmental monitoring and decentralized data governance. By leveraging the strengths of blockchain in data integrity, decentralization, and auditability, alongside the capabilities of AI in pattern recognition, prediction, and automation, stakeholders in agriculture can address longstanding challenges, such as information asymmetries, inefficiencies, and trust deficits. However, the realization of this potential is not without barriers. Issues related to scalability, digital inclusion, regulatory uncertainty, and ethical data use must be addressed through targeted innovation, supportive policy, and inclusive design. Looking forward, the path to sustainable and equitable agri-digital transformation will depend on collaborative efforts among farmers, IT professionals, policymakers, and researchers. It will require not only technological development, but also a deep commitment to aligning these tools with the real-world needs and values of agricultural communities. In summary, while blockchain and AI in DA are still emerging, their convergence represents a critical frontier for shaping the next generation of agri-food systems-ones that are not only smarter and more efficient, but also more transparent, trustworthy, and sustainable. ACKNOWLEDGMENT The paper has been supported by - TALLHEDA Project, “TRANSFORMING ACCESS TO EXCELLENCE WITH SUCCESSFUL ALLIANCES OF HIGHER EDUCATION IN DIGITAL AGRICULTURE”, GA No 101136578. This project is funded by the European Union under Horizon Europe research and innovation programme. REFERENCES [1] Chen, H.-Y., Sharma, K., & Sharma, C. (2023). Integrating Explainable Artificial Intelligence and Blockchain to Smart Agriculture: Research Prospects for Decision Making and Improved Security. Smart Agricultural Technology, 6(2), 100350. [2] Food and Agriculture Organization (FAO) and International Telecommunication Union (ITU). (2017). E-agriculture in Action: Big Data for Agriculture. This publication offers insights into the role of big data and digital technologies in modernizing agriculture. [3] Kumarathunga, M., & Ginige, A. (2023). Blockchain for Sustainable Agri-Food Ecosystems. Cutter Consortium. [4] Javadpour, A., AliPour, F. S., Sangaiah, A. K., Zhang, W., Ja'far, F., & Singh, A. (2023). An IoE blockchain-based network knowledge management model for resilient disaster frameworks. Journal of Innovation & Knowledge, 8(3), 100400. [5] Dinh, T. T. A., Liu, R., Zhang, M., Chen, G., Ooi, B. C., & Wang, J. (2018). Untangling blockchain: A data processing view of blockchain systems. IEEE transactions on knowledge and data engineering, 30(7), 1366-1385. [6] Liu, Y., He, J., Li, X., Chen, J., Liu, X., Peng, S., ... & Wang, Y. (2024). An overview of blockchain smart contract execution mechanism. Journal of Industrial Information Integration, 100674. [7] Fuentes-Peñailillo, F., Gutter, K., Vega, R., & Silva, G. C. (2024). Transformative technologies in digital agriculture: Leveraging Internet of Things, remote sensing, and artificial intelligence for smart crop management. Journal of Sensor and Actuator Networks, 13(4), 39. [8] Meer, M. (2024). Revolutionizing Food Supply Chains with AI and Blockchain. Meer.com. [9] Cambridge Centre for Alternative Finance. (2018). Distributed Ledger Technology Systems: A Conceptual Framework. This report offers a foundational understanding of DLT systems and their potential applications. [10] Etherisc. (2024). Using Blockchain Technology to Deliver Agricultural Insurance. Crypto Altruism. [11] Alzoubi, M. M. (2024). Investigating the synergy of Blockchain and AI: enhancing security, efficiency, and transparency. Journal of Cyber Security Technology, 1-29. [12] Guo, J., Yang, Z., Karkee, M., Jiang, Q., Feng, X., & He, Y. (2024). Technology progress in mechanical harvest of fresh market strawberries. Computers and Electronics in Agriculture, 226, 109468. [13] Ledger Insights (2020). U.S. berry producer joins IBM Food Trust blockchain - Ledger Insights - blockchain for enterprise. Available at: https://www.ledgerinsights.com/u-s-berry-producer-joins-ibm-foodtrust-blockchain/ [14] Bajwa, N., Prewett, K., & Shavers, C. L. (2020). Is your supply chain ready to embrace blockchain. Journal of Corporate Accounting & Finance, 31(2), 54-64. [15] Morillo, J. G., Martín, M., Camacho, E., Díaz, J. R., & Montesinos, P. (2015). Toward precision irrigation for intensive strawberry cultivation. Agricultural Water Management, 151, 43-51. [16] Gondal, M. U. A., Khan, M. A., Haseeb, A., Albarakati, H. M., & Shabaz, M. (2023). A secure food supply chain solution: blockchain and IoT-enabled container to enhance the efficiency of shipment for strawberry supply chain. Frontiers in Sustainable Food Systems, 7, 1294829. [17] The Walnut Fund. https://thewalnutfund.com/. [18] Wolfert, S., Ge, L., Verdouw, C., & Bogaardt, M.-J. (2017). Big Data in Smart Farming – A review. Agricultural Systems, 153, 69-80. This paper reviews the application of big data analytics in smart farming practices. [19] Chen, Hsin-Yuan & Sharma, Komal & Sharma, Chetan & Sharma, Shamneesh. (2023). Integrating Explainable Artificial Intelligence and Blockchain to Smart Agriculture: Research Prospects for Decision Making and Improved Security. Smart Agricultural Technology. 6. 100350. 10.1016/j.atech.2023.100350. [20] Ginige, Athula & Kumarathunga, Malni. (2023). Blockchain for Sustainable Agri-food Ecosystems. Cutter IT Journal. 36. 32-41. [21] Robinson, T. (2024). Blockchain and AI in Agriculture: Improving Food Traceability and Safety. LinkedIn. [22] Shepherd, M., Turner, J. A., Small, B., & Wheeler, D. (2018). Priorities for science to overcome hurdles thwarting the full promise of the 'digital agriculture' revolution. Journal of the Science of Food and Agriculture, 98(12), 5013-5024. This article discusses the scientific priorities needed to address challenges in digital agriculture. [23] Cambridge Centre for Alternative Finance. (2019). Cryptoasset Regulation: Global Cryptoasset Regulatory Landscape Study. This study examines the global regulatory landscape for cryptoassets, providing insights relevant to blockchain applications in agriculture. [24] Fosso Wamba, S., Kala Kamdjoug, J. R., Bawack, R. E., & Keogh, J. G. (2020). Bitcoin, Blockchain and Fintech: a systematic review and case studies in the supply chain. Production Planning & Control, 31(23), 115-142. This paper provides a systematic review of blockchain applications, including ethical considerations in supply chains.