=Paper=
{{Paper
|id=Vol-3791/paper10
|storemode=property
|title=Innovative Distributed Ledger Technologies for Local Product Traceability in Agro-Touristic Economies
|pdfUrl=https://ceur-ws.org/Vol-3791/paper10.pdf
|volume=Vol-3791
|authors=Carmelo Felicetti,Antonella Guzzo,Domenico Saccà,Francesco Pasqua
|dblpUrl=https://dblp.org/rec/conf/dlt2/FelicettiGSP24
}}
==Innovative Distributed Ledger Technologies for Local Product Traceability in Agro-Touristic Economies==
https://ceur-ws.org/Vol-3791/paper10.pdf
Innovative Distributed Ledger Technologies for Local
Product Traceability in Agro-Touristic Economies
Carmelo Felicetti1,* , Antonella Guzzo1 , Domenico Saccà1,2 and Francesco Pasqua2
1
DIMES Dept, Università della Calabria, 87036 Rende, Italy
2
Relatech SpA, 87036 Rende, Italy
Abstract
The integration of innovative technologies, particularly Distributed Ledger Technologies (DLT), holds significant
promise for enhancing traceability in local product supply chains, particularly within the context of territorial
agro-touristic economies. This paper explores the potential of DLT-based solutions in bolstering traceability for
local products, focusing on their role in fostering transparency, trust, and economic development in agro-touristic
regions. The paper exploits a recently proposed requirements methodology, called GOReM (Goal Oriented
Requirements Methodology), to support the conceptualization and subsequent implementation of DLT-based
framework for Local Product Traceability in Agro-Touristic Economies.
Keywords
DLT, blockchain, traceability, agrifood, supply chain
1. Introduction
Traceability in local agrofood product supply chains is a pivotal element that ensures not only the
safety and quality of food but also the preservation and appreciation of agricultural heritage and
biodiversity. This comprehensive system documents and links various stages in the food’s journey,
from production to consumption, providing a transparent path back to its origins. Its significance is
multifaceted, encompassing food safety, consumer confidence, market access, and compliance, while
also delving into the evolutionary and cultural dimensions of agriculture.
Firstly, traceability is crucial for food safety and public health. It allows for swift identification
and action in the event of food safety issues, reducing health risks and safeguarding public trust. For
consumers, traceability offers a transparent window into the origins and journey of their food, enhancing
trust and enabling informed choices. Moreover, it is essential for market access and compliance with
regulatory standards, ensuring that local producers can compete and thrive in broader markets.
Beyond these practical benefits, traceability plays a vital role in the evolutionary reconstruction of
local crop varieties. By tracking the history and development of these varieties, traceability systems
help preserve unique genetic resources and the cultural heritage tied to traditional foods. This aspect is
crucial for maintaining biodiversity and the authenticity of regional specialties.
Additionally, traceability enriches the cultural and traditional dimensions of agrofood supply chains.
It highlights the historical and cultural significance of plant genetic resources, connecting products
with their ancestral roots and the communities that have nurtured them. This connection not only
adds value to the products but also supports the conservation of invaluable agricultural traditions and
knowledge.
In the development of traceability systems, Requirements Engineering (RE) [1, 2] plays a crucial role.
RE is a discipline within systems and software engineering that focuses on determining the needs or
conditions to meet for a new or altered product. This process involves the identification, documentation,
and continuous maintenance of system requirements, ensuring they align with the possibly conflicting
needs of various stakeholders, such as end-users, regulators, and technology providers.
DLT2024: 6th Distributed Ledger Technology Workshop
*
Corresponding author.
$ carmelo.felicetti@unical.it (C. Felicetti); antonella.guzzo@unical.it (A. Guzzo); domenico.sacca@unical.it (D. Saccà);
francesco.pasqua@relatech.com (F. Pasqua)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
To effectively model and communicate the complexities of traceability systems, graphical notations
such as the Unified Modeling Language (UML) [3] and Business Process Model and Notation (BPMN) [4]
are employed. UML is primarily used to visualize the design of a system, particularly software systems,
through diagrams that depict elements like interactions, processes, and lifecycle of system components.
BPMN complements UML by providing a graphical notation focused on the logical steps of a business
process, thus enabling a clear depiction of process flows and decision points that are essential in
managing the traceability of products from farm to consumer.
The integration of Distributed Ledger Technologies (DLT) in our approach, combined with these
methodologies, aims to fortify traceability and enhance transparency, trust, and economic development
in agro-touristic regions. The adoption of innovative technologies, particularly DLT, holds significant
promise for enhancing the efficiency and transparency of local product supply chains.
Distributed Ledger Technologies (DLTs), such as blockchain, are revolutionizing traceability in local
agrifood product supply chains by providing a secure, transparent, and immutable record-keeping
system. This technology’s relevance to the agrifood sector stems from its ability to enhance the integrity,
efficiency, and transparency of the supply chain, fostering trust among consumers and stakeholders
while promoting sustainability and ethical practices.
The main adavantages of using DLTs for traceability are:
• Enhanced Transparency and Trust: DLTs create a tamper-proof record of each transaction or
product movement within the supply chain, from farm to fork. This level of transparency enables
consumers to access detailed information about the origin, processing, and distribution of food
products, building trust in local brands and encouraging informed purchasing decisions.
• Improved Food Safety and Quality Assurance: In the event of a food safety issue, DLTs allow for
rapid, precise trace-back to the source, enabling quicker responses to contamination, disease
outbreaks, or quality concerns. This capability not only protects consumer health but also
minimizes economic losses by reducing the scope of product recalls.
• Efficiency and Reduction of Fraud: By automating and recording transactions on a distributed
ledger, the technology reduces the chances of human error and fraud. It eliminates the need for
intermediaries, streamlining the supply chain, reducing costs, and increasing efficiency. This
aspect is particularly crucial for local agrifood chains where resources are often limited.
• Support for Local Producers and Authenticity Verification: DLTs can empower local producers by
providing a platform to showcase the authenticity and quality of their products. Consumers
are increasingly willing to pay a premium for products that are locally sourced, organic, or
produced using sustainable methods. Through DLTs, producers can provide verifiable proof of
their product’s provenance and production methods.
• Facilitation of Compliance and Certification: The immutable and transparent nature of DLTs
simplifies the process of complying with regulatory requirements and obtaining certifications.
Producers can efficiently demonstrate adherence to safety standards, organic certifications, or
geographical indications, facilitating market access and consumer trust.
• Encouragement of Sustainable Practices: DLTs can support the promotion of sustainable agricultural
practices by providing a transparent record of environmental impacts, resource use, and ethical
considerations along the supply chain. This visibility can encourage producers to adopt more
sustainable practices and enable consumers to make choices that align with their values regarding
environmental stewardship and social responsibility.
In the face of the changing dynamics within the agritourism industry, we propose a comprehensive
framework that capitalizes on the benefits of distributed ledger technologies (DLT) while also enhancing
local resources for sustainable growth. The goal is to develop and test a traceability platform that
not only enhances traceability and quality certification but also supports economic empowerment.
By merging the capabilities of distributed ledger technologies with the strategic valorization of local
resources, the activity aspires to cultivate a sustainable and robust agritourism economy and to realize
the untapped potential of a region’s agritourism sector.
Adopting a DLT-based framework for traceability in local agrifood product supply chains introduces
new challenges that must be addressed during the management of its requirements. The process of
eliciting these requirements often demands significant human engagement and pinpointing the elements
crucial for success is a complex endeavor.
To navigate this complexity effectively, it is essential to accurately determine the key application
scenarios and their corresponding requirements. For this purpose, we utilize the Goal Oriented Require-
ments Methodology (GOReM), a novel approach developed by the University of Calabria [5], designed
to tackle requirements engineering in intricate software development endeavors.
GOReM, being a goal-oriented approach [6, 7], is adept at extracting requirements through the
examination of the stakeholders’ objectives, the business contexts, and the anticipated models of the ap-
plications. This methodology employs UML (Unified Modeling Language) graphical notation, facilitating
a smoother communication and knowledge sharing process among stakeholders. GOReM’s effectiveness
has been demonstrated in previous industrial research projects [8, 9, 10], where its application with
industrial partners shed light on several noteworthy aspects of requirements engineering.
In this work, GOReM is applied to assist in the conceptualization and the subsequent deployment of a
DLT-based framework for enhancing traceability in local agrifood product supply chains. This applica-
tion is intended to address the inherent challenges and ensure a structured and efficient implementation
of this advanced technological solution.
The remainder of this paper is organized as follows. In Section 2, we explore the related works,
comparing our approach that integrates the GOReM methodology with other significant research in the
field. In Section 3 we describe the GOReM methodology. In Section 4 we describe the conceptualisation
of DLT-based framework in GOReM. In Section 5 we provide some details on the adopted blockchain
technology. Finally, in Section 6 we draw the the conclusion and discuss future work.
2. Related Works
The application of Distributed Ledger Technologies (DLT) in agricultural and agro-touristic supply
chains has been explored by several researchers, but our approach integrates the GOReM methodology,
which uniquely supports the system’s conceptualization and implementation phases, ensuring that the
requirements are meticulously addressed.
In comparison to El Mane et al. [11], who also deploy blockchain for agricultural supply chain
management, our framework provides a more detailed integration with goal-oriented requirements
engineering, enhancing the adaptability and effectiveness of the system in real-world scenarios.
Wang et al. [12] introduced a smart contract-based system for agricultural food supply chain
traceability. While their approach focuses on smart contract automation, our work further supports
local economic development by enhancing the traceability and authenticity verification which is critical
in agro-touristic markets.
Furthermore, Hu et al. [13] discuss the enabling of blockchain and edge computing technologies
for organic agricultural supply chains. Our framework differentiates itself by not only addressing
technological deployment but also focusingon sustainability and the socio-economic benefits within
agro-touristic economies, ensuring that technology aligns with local cultural and economic objectives.
Moreover, our implementation using Hyperledger Fabric offers a contrast to the typical use of
Ethereum-based solutions in similar frameworks, such as that proposed by Pranto et al. [14]. This
choice is motivated by the need for a permissioned blockchain environment that is better suited for
regulated and sensitive agricultural data, providing a higher level of security and control over the
transactions.
In addition, Bistarelli et al. [15] explored the *-chain platform for livestock product traceability,
utilizing a different blockchain framework. Our solution, while similarly focusing on traceability,
extends its functionality to encompass a wider range of products and integrates with local tourism
sectors, promoting a broader economic impact.
Finally, the concept of using secure tag-based identification in supply chains has been extensively
explored and validated in [16], demonstrating the efficacy of tag-based solutions in enhancing the
traceability and security of supply chains, which aligns with our use of DLT to enhance product
traceability in agro-touristic economies. Additionally, the use of deep learning for identification
purposes further reinforces the robustness of our proposed framework [17], which integrates advanced
AI techniques to improve authentication and traceability within the supply chain.
This comparison highlights our framework’s unique integration of DLT with a goal-oriented require-
ments engineering approach, its focus on local economic and cultural development, and the utilization
of a permissioned blockchain, which collectively enhance the practical deployment and strategic value
of the technology in agro-touristic economies.
3. Overview of GOReM
GOReM is a goal-oriented methodology [18] for Requirements Engineering that has been devised
and experimented at the University of Calabria, with the initial aim to help establish requirements
inside large industrial research projects where many different stakeholders cooperate regarding a
specific context. GOReM is centered on the UML notation [3]; as a consequence, it is easy to employ
and it simplifies the sharing of knowledge among a wide variety of stakeholders [19]. The resulting
requirements modeling activity has been recognized by the users to be easy and effective. Typical
activities of Requirements Engineering (RE) [1, 2], i.e. elicitation of requirements, analysis, validation,
verification and management, are expressed in GOReM mainly in terms of: (i) stakeholders and their
goals, (ii) use cases and involved processes and (iii) work-product documentations. The methodology
consists of three main phases, each of which is devoted to modeling specific aspects of a RE process:
Context Modeling, Scenario Modeling and Application Modeling (see Fig. 1).
These phases are repeated iteratively and feedback among them is allowed in order to support an
incremental refinement process. Furthermore, scenarios and applications are specified concurrently. A
BPM (Business Process Modeling) model [4] of the reference process for GOReM, along with its main
work-products, is reported in Fig. 1.
Context Modeling aims at clearly representing the reference domain [20]. The work-products of this
phase are: a Stakeholder Diagram, which shows a, often hierarchical, specification of all the stake-
holders involved in the specific context, each of which is in turn characterized by a set of Softgoals [21]
they intend to pursue; a Softgoal Dependency Diagram, which shows the relationships between the
stakeholders and the softgoals, as well as the relationships among softgoals (i.e., contribute, hinder,
include, specialize); moreover, the rules and regulations that govern the context are individuated and
analyzed in a work-product.
Scenario Modeling specifies different business scenarios in terms of roles that are played by the
involved stakeholders, their specific goals, and the specific rules and regulations that govern the
business scenario.
The scenario modeling phase includes also “rules and regulations selection” activity that considers
which rules and regulations, among those identified in the Context Modeling phase, must be considered
in the modeled scenario, by specifying if they are laws, policies and best practices, coding them,
identifying and highlighting possible links and timeline.
Application Modeling defines application scenarios in order to specify the functionalities which should
be provided by a single business scenario resulting from the previous phase. Each application scenario
is characterized by functionalities that are modeled by UML-based Use Cases, Actors and Processes.
In particular, each main use case should be a functionality of the overall application scenario, to be
developed in some way which should be specified at design and development time. In addition, some
process can be specified using UML or BPMN notations [4], if considered useful to better understand
requirements by specifying flow of activities. Every UML based diagram can be enriched with the UML
comment symbol which allow to add a description to all the GOReM diagrams. However, a textual
description and complete information is located in the corresponding work-product.
SoftGoal
Stakeholder Dependency
Diagram Diagram Role Diagram Goal Diagram
Use Cases Actors Processes
SoftGoals Rules and Rules and SWOT
Regulations Regulations Analysis
Context
«L ane »
Modeler
Context
Modeling
«P ool» Req u ir eme nt Mod e ling
«L ane » Sc ena r i o
Scenario
Moldelers
Modeling
«L ane »
Application
Modelers
Scenarios
Application
for
Modeling
Figure 1: Reference Process Model of GOReM and Work Products
4. GOReM Specification of the DLT-based framework for enhancing
traceability
4.1. Context Analysis
In the context of the DLT-based framework for enhancing traceability in local agrifood product supply
chains, several key stakeholders have been identified, representing the primary actors involved, with
the possibility of additional stakeholders. These stakeholders, depicted in Figure 2, are listed below:
• Raw Material Supplier: This entity is responsible for providing and delivering the essential inputs
required for agricultural production, such as seeds, seedlings, water, fertilizers, soil, electricity,
and packaging materials for product preservation and distribution, as well as fuels and lubricants
for agricultural machinery and vehicles.
• Agricultural Producer: This individual is primarily focused on ensuring the quality and sale of
agrifood products.
• Retailer: This company is interested in purchasing agrifood products from the agricultural
producer to resell them.
• Agricultural Credit Institution (CIA): This institution provides financial support to agricultural
producers and businesses.
• Shipping Company: Operating in the logistics sector, this company is responsible for the trans-
portation of products, aiming to guarantee safe transit.
Figure 2: Context Model: Softgoal Stakeholder Diagram
• Final Consumer: This person purchases and consumes the products and is keen on verifying the
quality of the agrifood items.
• Digital Intermediary: This stakeholder is involved in managing the platform implementing the
DLT-based framework.
• Ministry of Agriculture, Food Sovereignty, and Forests (MASAF): This governmental body addresses
food safety legislation and product traceability.
• Central Inspectorate for Quality Protection and Fraud Repression of Agrifood Products (ICQRF): As
an Italian public entity under MASAF, ICQRF oversees the quality and authenticity of agrifood
products and combats fraud and unfair trade practices.
• Certification Body: This independent organization verifies and certifies that a company or prod-
uct meets certain quality, safety, or sustainability standards. It often adheres to nationally or
internationally recognized standards, providing a certification mark that the company can use to
demonstrate compliance. Specifically, two secondary stakeholders are identified:
– Final Product Certifier: This entity inspects the final agrifood product to ensure it meets the
set standards for quality, hygiene, safety, and regulatory compliance.
– Production Process Certifier: This body certifies the entire production process, from soil
quality control (conducted by agronomists) to process reviews and equipment inspections,
assessing if the production meets established standards and quality requirements.
We observe that the approach proposed in [12] for tracking and executing transactions in the
agricultural food supply chain identifies the following stakeholders involved in the entire process
of the agricultural food supply: Agricultural Bureau, Farmer, Processor, Quality Supervision Bureau,
Distributor, Retailer, Consumer.
4.2. Scenario Definition
The specific scenario to be analyzed concerns the production and transportation phase of products
during which quality certification takes place. Below are the roles of stakeholders directly involved in
the specific scenario of the Project Idea:
• Agricultural Producer plays the role of:
– Producer: ensures the production of high-quality agrifood products.
– Product Promoter: promotes products to maximize sales.
• Third-Party Certification Entity plays the role of:
– Product Certifier: guarantees that products meet established quality and safety standards,
issuing valid certifications in accordance with the certification system.
– Production Process Certifier: checks that the production process follows the guidelines.
• Shipping Company: manages the transportation of agrifood products from the producer to the
retailers.
• Intermediary plays the role of:
– Platform Manager: is responsible for the management and maintenance of the platform.
– Vertical Integrator: focuses on a specific segment of an industry by adopting a targeted
methodology and offering specialized solutions.
– Personnel Trainer: trains staff who will interact with the system (operators who need to
interface in a specific area).
• Final Consumer plays the role of:
– Quality Tester: verifies the quality of the products.
– Purchaser: buys the selected products.
– Reviewer: reviews the manufacturing company.
The indirectly involved stakeholders are as follows:
• MASAF: responsible for the regulation and supervision of the Italian agrifood sector.
• ICQRF: oversees the monitoring and control to prevent fraud and ensure the quality of agrifood
products. It is not directly involved in the specific scenario as it is not practical to interact directly
with a public body.
• Raw Material Supplier: not directly included because the specific scenario focuses on product
certification, which primarily concerns production, and therefore the producers. Furthermore,
initially, the raw material suppliers may not be ready to use this technology, but later it could be
useful to integrate them as direct stakeholders.
4.3. Use Cases
The main Use Cases are the following:
1. Vertical integration, installation, and startup of the platform.
2. Quality control of the production process and the final product with data recording on the
blockchain and QR Code generation.
3. Transportation of products with information recording by the Shipping Company.
4. Scanning the QR Code to obtain product information and product reviews.
After the producer has provided detailed specific requirements related to the particular production
sector, the Intermediary vertically integrates the platform to include the functionalities described by
the producer. Moreover, training of the personnel who will interface with the platform is conducted,
and the customized platform is installed, integrating it with the existing systems.
The Producer begins the production of agrifood products and logs onto the platform. They collect
data related to production and record it on the blockchain. The Production Process and Final Product
Certification Bodies log onto the platform and verify compliance with production standards to ensure
product quality. In particular, the Production Process Certifier records a video to attest to the production,
which is uploaded to an offchain platform. The video’s metadata and its hash value, however, are
uploaded to the blockchain. Once the product certification phase is completed, a QR Code is generated
that refers to all the information entered on the blockchain. Finally, the Producer affixes the QR Code
on the finished product.
The Producer transfers his products to the Shipping Company, providing details regarding the
destination of such items. Once the shipping company has taken charge of the shipment, it logs onto the
platform and records this operation on the blockchain. Subsequently, the Shipping Company proceeds
with recording the information regarding the traceability of the products, accurately noting the date
and time of arrival at the final destination.
The Final Consumer in the role of Quality Tester selects the product and scans the QR Code to
access the information that has been uploaded to the platform. After purchasing the product, the Final
Consumer has the opportunity to review it based on their opinions.
4.4. Processes
In this section, the main processes involved in tracking agrifood products are described in detail.
The first process covers the entire journey of the products, beginning with their production, moving
through quality control, and concluding with their delivery via a logistics company. This sequence is
repeated for each product or package, depending on the type.
Next, the data registration process by the producer is outlined. The producer logs into the platform
using their credentials (username and password). Once authenticated, they can register information for
each product or package on the blockchain. This involves activating a smart contract, which validates
the transaction and allows data entry into the blockchain. Following this, the producer hands the
product to the Certification Body for the next phase.
The third process involves product certification. The Certification Body records a video to verify
product quality and uploads it to an offchain storage. Then, it accesses the platform to upload the
video’s metadata and hash value to the blockchain via a smart contract. After certifying the product
quality based on the blockchain data, an intermediary generates a QR Code for the product or package,
which the producer attaches. The products are then ready for delivery to the shipping company.
Finally, the shipping process is described. A shipping company operator logs into the platform to
notify the blockchain of the delivery takeover. The operator records traceability data and the date/time
of delivery, with data entry performed through a smart contract activation as previously described.
5. Blockchain Implementation Issues
The selection of an appropriate DLT framework is crucial for the success of any blockchain-based
application. In this project, we considered both public and private blockchain architectures due to their
distinctive characteristics and suitability for different types of applications.
Public blockchains, such as Ethereum, offer decentralization, immutability, and transparency. They
allow anyone to participate and view transactions, which is ideal for applications that benefit from public
scrutiny and full transparency. However, the main drawbacks include lower transaction throughput,
higher latency, and less privacy, which may not be suitable for supply chain applications where business
sensitivity and scalability are concerns.
Private blockchains, such as Hyperledger Fabric, provide controlled access and more privacy while
maintaining many of blockchain’s core benefits. They are optimized for high transaction speed and
scalability, which are essential for managing the complex and large-scale transactions typical in supply
chain management. The cons include less decentralization, which could potentially compromise the
trust model that blockchain aims to enhance.
After evaluating both types of blockchains, Hyperledger Fabric was selected for the following reasons:
• Performance and Scalability: Hyperledger Fabric offers higher transaction throughput and
lower latency compared to public blockchains. It is well-suited for the high demands of supply
chain operations.
• Privacy and Security: Fabric’s channel architecture and private data collections enable a more
granular control over privacy and confidentiality, allowing businesses to share data selectively.
• Modularity: The modular architecture of Fabric allows it to be tailored to a wide range of
industry use cases. It supports pluggable consensus protocols, which can be configured to balance
between decentralization and efficiency.
• Governance and Compliance: Being a permissioned blockchain, Fabric enables better gover-
nance and adherence to compliance requirements, critical factors for applications within regulated
industries such as agriculture and food safety.
These features make Hyperledger Fabric a compelling choice for our DLT-based framework intended
for enhancing traceability in local agrofood product supply chains. Its ability to handle complex
operations while ensuring data privacy and regulatory compliance aligns well with the goals of our
project.
Hyperledger is an open source collaborative project initiated by the Linux Foundation in 2015 to
promote blockchain digital technology and transaction verification [22]. It is the first distributed
ledger platform for enterprise application scenarios. Fabric is the most important application project
of Hyperledger technology, which is a general license blockchain with modular and extensible char-
acteristics, follows the execution-sequence-validation paradigm and fundamentally deviates from the
order-execution model.
Hyperledger Fabric has been used in [12] to establish consortium chains and smart contracts in their
tracking framework for agricultural food supply chain.
It is proposed to adopt Hyperledger Fabric technology, which supports the existence of a varied
and heterogeneous set of nodes, cooperating to support DLT services in business environments, by
readjusting or eliminating non-relevant cost factors, such as cryptocurrencies or the proof of work
paradigm. Indeed, Fabric operates on a voting-based paradigm to reach a consensus on the value of
the recorded data. In particular, Fabric automates and internally manages many needs that need to be
supported ad hoc in more public-oriented DLTs. Among these, the three most important are:
1. User Identity, managed autonomously via X.509 certificates within the network.
2. Lifecycle of Smart Contracts, including any code updates, so as not to report losses to data
managed by old versions of the same contract.
3. Division of the network into areas of competence and management by different organizations, to
better distribute governance skills, as well as the criteria for belonging and roles of users related
to the same organizations.
Furthermore, unlike other distributed ledger networks, Hyperledger Fabric has, structurally, different
types of nodes:
• Peer Nodes: tasked with a dual role
– Distributed storage of copies of the Ledger.
– Parallel execution of smart contracts, following the invocation of a signed transaction
towards a subset of peer nodes. The resulting value given by the invocation triggers cross-
checks between the invoked peers, to ensure their identity and the non-dissimilarity of the
result of the computation.
• Orderer Nodes: whose task is the temporal ordering of approved transactions into blocks, to be
distributed to peers not involved in the initial assessment, for updating.
• CA Nodes: X.509 Certificate Authorities, for the distributed management of the Public Key
Infrastructure (PKI), essential for the mutual authentication of the various messages circulating
in the network, between users and between the different nodes. They are managed by the various
present organizations, according to specifiable roles and hierarchical organization charts, in order
to reflect the real interdependence of the present users.
• MSP (Membership Service Providers): microservices belonging to the various nodes, with
the task of authenticating users through their certificates (issued by the CAs), with related roles,
present on the certificates themselves.
We envision the creation of a dedicated Smart Contract, devoted to the tracking of the production
process of certified agricultural goods. The process involves the interaction, at certain critical moments,
of actors corresponding to an identity on Hyperledger Fabric. The corresponding data describing a
precise critical moment will be incorporated into a Hyperledger Fabric transaction. While the actors
responsible for that critical moment will sign, under their responsibility, the correct execution of the
controls, at that particular critical moment, corresponding to a globally valid timestamp.
The responsibility for the blockchain infrastructure will be shared by the entire production chain, so
as not to give full control of the data to a single entity.
For this type of certified interaction, it is plausible to include in the information flow of documentary
files. They, in general, will always have a size of orders of magnitude greater than the data sent in the
context of a transaction.
In this situation, the best choice is to generate a link between a unique identifier of the file (for
example a hash), and store this last one in the context of a certified transaction on DLT, while the file
itself will be stored in an off-chain repository. Obviously, this repository will have to provide some
guarantee about its solidity and will have to be, as far as possible, protected from unwanted access.
How to add to the described flow, levels of encryption, so as to make the file itself, deposited in the
file storage, accessible only to one, or a group, of users. The unique identifier that will remain firmly
guarded on the ledger will serve as control, both for the existence of the file at that precise historical
moment, but also for validation at later times. In fact, it will be possible to verify if, in the meantime,
the file has been modified. The issues of handling the integrity of off chain agricultural data has been
recently investigated in [11].
In our approach, we add an additional level of trust by applying, together with the tracking via
Blockchain Hyperledger Fabric, also the certified time-stamping of the data pertaining to the critical
moment, files included. This last procedure will make use of a TSA.
Also known as a Time Stamping Authority, it is a centralized service that applies a timestamp to an
electronic document to attest that an event has occurred at a specific time. The timestamp is a sequence
of characters that indicates a time, but the marking does not certify the moment when the document
was created; rather, it certifies the moment of its transmission and marking. The main goal of this
service is to provide unequivocal proof of the temporal location of a document, enforceable against
third parties. To achieve this goal, the service synchronizes its clock with an authoritative source to
ensure maximum temporal precision. Furthermore, since the timestamp is issued by a reliable entity,
it acquires legal value. The validity of a timestamp is limited in time, and the holder must request an
extension from the TSA responsible for the issue to continue to benefit from it.
The result of the marking consists of a Time-stamp token (TST), which represents proof of the
existence of the electronic document.
6. Conclusion
In this paper, we explored the potential of Distributed Ledger Technologies (DLTs) to enhance traceability
in local agrifood supply chains, particularly within the context of agro-touristic economies. DLTs provide
a robust framework for ensuring transparency, efficiency, and security in tracking local food products,
which in turn bolsters food safety, supports local economies, and strengthens consumer trust. The
adoption of DLTs is seen as a transformative step for the agrifood and agritourism sectors, enabling
sustainable practices and economic empowerment.
Our proposed framework leverages DLTs to improve product traceability and quality certification
while fostering the sustainable growth of local resources. This integration aims to unlock the potential
of agritourism, promoting a sustainable, economically empowered sector that aligns with modern
consumer expectations.
However, implementing a DLT-based traceability system presents its unique set of challenges, partic-
ularly in defining and managing the system requirements. To address these complexities, we utilized
the Goal Oriented Requirements Methodology (GOReM), which aided in developing a structured and
effective approach to integrating DLTs into the agrifood supply chain. This methodology facilitated the
identification of crucial application scenarios and their requirements, ensuring a targeted and successful
deployment of the DLT framework.
Current efforts are focused on deploying a DLT-based system for traceability and testing its application
in the local agrifood and agritourism supply chains. The objective is to transform these industries by
enhancing traceability, promoting sustainability, and fostering economic growth.
Acknowledgments
The research was conducted as part of the initiatives at the Agritech National Research Center and was
partly funded by the European Union’s Next-Generation EU under the National Recovery and Resilience
Plan (PNRR) - Mission 4 Component 2, Investment 1.4, as per decree D.D. 1032 of June 17, 2022, ref.
no. CN00000022. Specifically, this work is associated with Task 6.3.2 Implementation of agro-tourism
solutions in coastal and mountain areas of Spoke 6 Management Models to Promote Sustainability and
Resilience of Agricultural Production Systems. This manuscript reflects only the authors’ views and
opinions, neither the European Union nor the European Commission can be considered responsible for
them.
References
[1] I. Sommerville, Software Engineering, Addison-Wesley, 2011.
[2] B. Fabian, S. Gürses, M. Heisel, T. Santen, H. Schmidt, A comparison of security requirements
engineering methods, Requirements Eng. 15 (2010) 7–40. doi:10.1007/s00766-009-0092-x.
[3] Unified Modeling Language™ (UML® ) v. 2.5, Spec. formal/15-03-01, Object Management Group,
2015. URL: http://www.omg.org/spec/UML/2.5.
[4] A. Andrieux, Business Process Model And Notation™ (BPMN™) v. 2.0, Spec. formal/2011-01-03,
Object Management Group, 2011. URL: http://www.omg.org/spec/BPMN/2.0/.
[5] S. Citrigno, A. Furfaro, T. Gallo, A. Garro, S. Graziano, D. Saccà, Mastering concept exploration in
large industrial research projects, in: INCOSE Italian Conf. on Systems Eng. (CIISE 2014), Rome,
Italy, 2014, pp. 26–37.
[6] A. van Lamsweerde, E. Letier, Radical Innovations of Software and Systems Engineering in the
Future, volume 2941 of LNCS, Springer, 2004, pp. 325–340. doi:10.1007/978-3-540-24626-8_
23.
[7] W. Engelsman, R. Wieringa, Goal-oriented requirements engineering and enterprise architecture:
Two case studies and some lessons learned, in: Proc. of the 18th Int. Conf. on Requirements Eng.:
Foundation for Software Quality, REFSQ’12, Springer-Verlag, 2012, pp. 306–320. doi:10.1007/
978-3-642-28714-5_27.
[8] DICET-INMOTO - ORganization of Cultural HEritage for Smart Tourism and Real-time Acces-
sibility (OR.C.HE.S.T.R.A.) - Project funded by the Italian Ministry of Education, University and
Research (MIUR) - PON Project - Research and Competitiveness 2007-2013, 2015.
[9] District of cyber security, 2016. URL: https://www.distrettocybersecurity.it.
[10] A. Furfaro, T. Gallo, A. Garro, D. Saccà, A. Tundis, Cybersecurity compliance analysis as a service:
Requirements specification and application scenarios, Concurr. Comput. Pract. Exp. 30 (2018).
URL: https://doi.org/10.1002/cpe.4289. doi:10.1002/CPE.4289.
[11] A. El Mane, Y. Chihab, K. Tatane, R. Korchiyne, Agriculture supply chain management based
on blockchain architecture and smart contracts, Applied Computational Intelligence and Soft
Computing 2022 (2022) 8011525. URL: https://doi.org/10.1155/2022/8011525. doi:10.1155/2022/
8011525.
[12] L. Wang, L. Xu, Z. Zheng, S. Liu, X. Li, L. Cao, J. Li, C. Sun, Smart contract-based agricultural food
supply chain traceability, IEEE Access 9 (2021) 9296–9307.
[13] S. Hu, S. Huang, J. Huang, J. Su, Blockchain and edge computing technology enabling organic
agricultural supply chain: A framework solution to trust crisis, Computers & Industrial Engineering
153 (2021) 107079.
[14] T. H. Pranto, A. A. Noman, A. Mahmud, A. B. Haque, Blockchain and smart contract for iot enabled
smart agriculture, PeerJ Computer Science 7 (2021) e407.
[15] S. Bistarelli, F. Faloci, P. Mori, C. Taticchi, M. Miculan, Modeling carne PRI supply chain with the
*-chain platform, in: P. Mori, I. Visconti, S. Bistarelli (Eds.), Proceedings of the Fifth Distributed
Ledger Technology Workshop (DLT 2023), Bologna, Italy, May 25-26, 2023, volume 3460 of CEUR
Workshop Proceedings, CEUR-WS.org, 2023. URL: https://ceur-ws.org/Vol-3460/papers/DLT_2023_
paper_10.pdf.
[16] A. Falcone, C. Felicetti, A. Garro, A. Rullo, D. Saccà, PUF-based Smart Tags for Supply Chain
Management, in: The 16th International Conference on Availability, Reliability and Security, 2021,
pp. 1–7.
[17] C. Felicetti, A. Guzzo, G. Manco, F. Pasqua, E. Ritacco, A. Rullo, D. Saccà, Deep learning/puf-based
item identification for supply chain management in a distributed ledger framework, in: BCCA
2023, 2023.
[18] A. van Lamsweerde, Goal-oriented requirements enginering: a roundtrip from research to practice
[enginering read engineering], in: Proc. of 12th IEEE Int. Requirements Eng. Conf., 2004, pp. 4–7.
doi:10.1109/ICRE.2004.1335648.
[19] P. Caire, N. Genon, P. Heymans, D. L. Moody, Visual notation design 2.0: Towards user compre-
hensible requirements engineering notations, in: 21st IEEE Int. Requirements Eng. Conf. (RE),
2013, pp. 115–124. doi:10.1109/RE.2013.6636711.
[20] R. Ali, F. Dalpiaz, P. Giorgini, Reasoning with contextual requirements: Detecting inconsistency
and conflicts, Information and Software Technology 55 (2013) 35 – 57. doi:10.1016/j.infsof.
2012.06.013, special section: Best papers from the 2nd Int. Symposium on Search Based Software
Eng. 2010.
[21] P. Bresciani, A. Perini, P. Giorgini, F. Giunchiglia, J. Mylopoulos, Tropos: An agent-oriented
software development methodology, Autonomous Agents and Multi-Agent Systems 8 (2004)
203–236. doi:10.1023/B:AGNT.0000018806.20944.ef.
[22] E. Androulaki, A. Barger, V. Bortnikov, C. Cachin, K. Christidis, A. De Caro, D. Enyeart, C. Ferris,
G. Laventman, Y. Manevich, S. Muralidharan, C. Murthy, B. Nguyen, M. Sethi, G. Singh, K. Smith,
A. Sorniotti, C. Stathakopoulou, M. Vukolić, S. W. Cocco, J. Yellick, Hyperledger fabric: a distributed
operating system for permissioned blockchains, in: Proceedings of the Thirteenth EuroSys
Conference, EuroSys ’18, Association for Computing Machinery, New York, NY, USA, 2018. URL:
https://doi.org/10.1145/3190508.3190538. doi:10.1145/3190508.3190538.