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Blockchain Technology for Transparency in Agri-food Supply Chain:
Use Cases, Limitations and Future Directions
Sheetal Menon
Great Lakes Institute of Management
Gurgaon, India
sheetal.m@greatlakes.edu.in
Karuna Jain
Indian Institute of Technology Bombay
Mumbai, India
kjain@iitb.ac.in
Abstract : Modern agri-food supply chains have transitioned from autonomous, independent and local actors, to
globally inter-linked system of multi-actors connected by complex relationships, affecting ways in which food is
produced, procured, processed, transported, and delivered to end consumer. High incidences of fraudulent practices
exposes lack of transparency and vulnerability in agri-food supply chains, causing concerns related to economic
losses, eroding consumer trust and enterprise brand value. Traditionally associated with cryptocurrencies, banking
and finance, blockchains are now being applied in the agri-food sector to address supply chain-related challenges.
This research examines the question: How blockchain platforms facilitate transparency in agri-food supply chains?
Primary attributes of blockchain, namely traceability, immutability, auditability, and provenance, promote
transparency in supply chains. Using thematic analysis, literature review was conducted by combining academic
and practitioner-oriented studies, based on which three thematic areas were identified for implementing blockchain
in agri-food supply chains: (i) agri-food distribution; (ii) agri-food origin and sourcing; (iii) agri-food safety and
quality. By cross-mapping thematic areas with primary attributes, our analysis resulted in classification of twenty-
five uses cases. Preliminary findings of this study highlight uses of blockchain to enhance transparency in agri-food
supply chains via uses cases either in conceptual, proof-of-concept and commercial stages. It is imperative to sift
through the hype, and scrutinize inherent challenges which could interfere with its adoption, deployment, and
scalability in agri-food supply chains. Although promising, blockchains are far from a panacea to resolve deep-
rooted problems inflicting the agri-food sector.
Keywords: Blockchain, Industry 4.0, Use Cases, Thematic Analysis, Agri-food Supply Chain, Transparency,
Traceability, Regulatory
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Introduction
In recent years, we have come to witness unprecedented growth in the economy, resulting in continuous
improvement in peoples’ standard of living. With changing lifestyles and higher disposable incomes in
many parts of the world, consumers today place a higher premium on food quality and safety standards.
However, rapidly and globally expanding agri-food supply chains frequently encounter challenges in
production, processing, storage, distribution, and logistics, often raising food safety, quality, and
mismanagement concerns. According to World Health Organization (WHO) estimates, over 420,000
people worldwide annually die from food contamination. Children below five years are the most vulnerable
risk group, with over 125,000 deaths per year due to food borne illnesses. In a 2016 PwC report, it is
estimated that food fraud is costing the global food industry over $40 billion a year. For instance, a
multistate Salmonella outbreak in 2017 infected over 220 people across the US. The CDC and FDA officials
took over three weeks to trace the source of the outbreak to imported Maradol papayas from a single farm
in Mexico. This news received considerable media attention from a food safety standpoint; impacting
papaya consumption that resulted in economic losses for papaya farmers from unaffected areas. In yet
another high profile case, a multinational food and beverage company had to recall over 38,000 tons of
noodles from the Indian market due to higher than permissible levels of lead and MSG in their product. The
brand value took a hit with a loss of over 80% of the market share, with a spend of $70 million in recalls
and lost sales (in 2015) of over $277 million. The 2013 horse meat scandal exposed food labelling fraud in
the UK, where burgers and ready-to-eat meals at retail and fast food outlets contained traces of undeclared
horse meat instead of processed lamb and beef. This fraud affected more than 4.5 million processed
products, representing at least 1,000 tons of food [1]. The Chinese milk scandal in 2008 sparked widespread
food safety concerns globally. Over 53,000 children had been sickened and at least three children died after
consuming dairy products adulterated with melamine, a toxic industrial chemical, to artificially inflated
protein levels. The scandal shook consumer confidence in Chinese dairy ingredients, and resulted in a
number of countries banning food imports from China. More recently, the Centre for Food Safety in Hong
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Kong identified over 2000 food-related incidences in 2020 pertaining to excessive use of preservatives,
drug residues, undeclared allergens, microbial, viral and chemical hazards, resulting in stopped sales or
issued recalls of implicated products. In the light of such untoward incidences, there is growing pressure
on businesses and governments to meet consumers’ demands for stringent food safety and quality
regulations for fresh and processed agri-food products [2].
Traditional agri-food supply chains (SCs) encompass all activities involved in production, manufacturing,
and distribution of food until final consumption [3]. Over time, modern agri-food SCs transitioned from
autonomous, independent and local actors, to globally inter-linked system of multi-actors connected by
complex relationships, affecting ways in which food is produced, procured, processed, transported, and
delivered to end consumer [4]. Complexities arise by virtue of information sharing, reciprocal scheduling,
product quality assurances, transaction volume commitments, timely performance of production and
delivery commitments [5]. Current practices in the SC involve exchange of goods between actors based on
complex, paper-based settlement processes. These transactions suffer from lack of transparency,
vulnerability to fraud among intermediaries, low responsiveness, and increased costs [6, 7]. The inability
to track products in the SC stems from arcane record-keeping practices – such as the ‘one up one down’
(OUOD) approach, wherein SC actors track only the immediate supplier (one link up the chain) and the
immediate customer (one link down the chain) for their products (Figure 1). Such systems are largely
insufficient, especially for multi-ingredient foods which include elements from various sources across
countries, making traceability more complicated [8]. In cases of suspected contaminations, as a
precautionary step, entire shipments are discarded as per the OUOD parameters. Regulatory endorsements
such as Food Safety Modernization Act (FSMA 2011) and HACCP (Hazard Analysis and Critical Control
Points) would inevitably impact global agri-food SCs, by enforcing hazard analysis, good record keeping
practices, and transparency. In addition to regulatory compliance, consumers are demanding information
on where and how their food is sourced, produced and delivered in a transparent, safe, and sustainable
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manner [9]. Untoward incidences of food safety or health hazards could significantly plummet enterprise
brand value and erode consumer trust, resulting in litigations and product recalls. To address these
problems, academicians and practitioners envision the application of blockchain technology to
revolutionize agri-food SCs, potentially altering current practices.
Figure 1: Challenges of transparency in typical agri-food SCs
The contribution of this study is two-fold. Firstly, we review current literature in the areas of blockchain
technology, industry 4.0, and supply chain management (SCM) in the agri-food sector. We employ thematic
analysis to investigate enablers of transparency by blockchain adoption in agri-food SCs. Secondly, we
explore the implementation of blockchain technology by analysing use cases in agri-food distribution,
origin and sourcing, and safety and quality. In the following sections of this paper, we discuss review of
contemporary literature, research methodology, use case analysis, challenges in technology adoption and
implementation, as well as future directions for research in this area.
Literature review
Touted as the next disruptive technology under the umbrella of industry 4.0, blockchain has been likened
to have similar impact to that of the internet [10, 11]. Blockchain applications also entail interoperability
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with other industry 4.0 technologies like cloud computing and big data analytics, radio frequency
identification (RFID), internet of things (IoT) and sensors, near field communication (NFC), artificial
intelligence (AI), machine learning (ML), and global positioning systems (GPS). Early applications of
blockchain were predominantly associated with cryptocurrencies, banking and financial services. However,
transformative features of this technology has resulted in applications extending across non-financial
domains as well, including healthcare [12], retail and ecommerce [13, 14], energy [15, 16], e-governance
[17], and sustainability [18]. More recent studies have also examined the technology’s potential to address
challenges inflicting the agri-food SC [19, 20, 21, 22]. Blockchain is a distributed append-only timestamped
data structure [23], meaning it is a digital record of transactions (referred to as a ledger) that are
decentralized (i.e. no single entity is controlling the network) and distributed (every record is shared
simultaneously with every participant in the network). Within this distributed ledger system, data is shared
with all participants, verified and validated by anyone with appropriate permissions to do so [24, 25, 26].
Once validated, transactions are stored in ‘blocks’, which are then ‘chained’ to each other in a chronological
order - making it time-stamped. Because blockchains are managed by a global network of computers or
servers (“nodes”) on a peer-to-peer (P2P) basis, transactions can be authenticated without the need for any
intermediaries [27]. Blockchain networks can be configured as public, private or hybrid in terms of
governance rules; permissions attributed to participants to control actions and co-determine transactions on
the blockchain are used to categorise it further as permissioned or permissionless. Governance rules on
public blockchains allow everyone to participate, read, submit, use existing services, whereas private and
hybrid forms are exclusive e.g. single organization or a consortium of participating organizations [28, 29,
30]. This type of configuration affects the consensus process, i.e. the premise that all nodes (participants)
must reach to an agreement (or consensus) regarding transactions that should be stored on the blockchain.
Public blockchain consensus protocols include Bitcoin’s Nakamoto Protocol, while Proof-of-work (PoW),
Proof-of-Stake (PoS), Byzantine Fault Tolerance (BFT) are more suited to permissioned blockchain. The
choice of consensus protocol would determine incentive mechanisms, authentication and verifiability, fairer
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distribution among participants in the network, as well as to decrease power consumption and improve
scalability [31]. The most transformative aspect of blockchain technology are smart contracts, which are
computerised protocols programmed to assess the status of any transaction, and automatically execute
actions (e.g. disbursing payments once shipments arrive) once contractual obligations are met [32]. Smart
contracts reduce costs and delays associated with traditional arm’s length contracts. Depending on the
requirement, smart contracts can be made partially or fully self-executing and self-enforcing [33],
eliminates payment delay issues [34], improves efficiency and reduces operational costs in supply chain
automation by reducing manual interventions and intermediaries [35].
It is proposed that blockchain has the ability to impact cost, speed, quality, dependability, sustainability,
risk management, and flexibility, which are key considerations of SCM [36]. It is forecasted that by 2025
over 20% of the top global grocers will use blockchain to achieve food safety and quality [37]. Blockchain
in SCM is expected to grow 87% annually, and increase from $45 million in 2018 to $3314.6 million by
2023 [38]. To shed light on how blockchains facilitate transparency in agri-food SCs, we review academic
and practitioner literature to identify use cases that exemplify the technology’s applications. Supply chain
transparency requires that actors have access to information on what happens upstream and downstream in
the SC, and communicate this knowledge to both internal and external participants [39]. Within this broad
context, our study specifically focuses on three following research questions:
1. What are the blockchain enablers to promote transparency in agri-food supply chains?
2. How are blockchains implemented in agri-food supply chains to promote transparency?
3. What are the challenges faced by firms in the adoption of blockchain in agri-food supply chains?
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Figure 2 depicts blockchain-mediated interventions in a typical agri-food SC. Every actor (suppliers,
producers, processors, retailers, consumers, and regulatory bodies) would have access to key information
on the distributed ledger. Data capture and transmission across the SC would be stored on the blockchain
[40, 41]. The blockchain ledgers also interface with a plurality of sensors for collecting, recording and
tracking data at various stages, while data privacy and security is maintained. Once in-flow of data from
SC actors (suppliers, producers, processors, wholesalers and retailers) is captured onto the blockchain, this
information is made available to consumers and regulators in addition to other actors. Each actor uses
blockchain technology as common communication and collaboration channel, thereby allowing each
participant to view and validate information. Validation is achieved by a consensus algorithm and security
of transactions is achieved via a series of digital cryptographic public and private keys.
Figure 2: Blockchain-based interventions in agri-food supply chain
A central feature of blockchain technology is the notion of smart contracts, which pertains to computer
protocols that permit a contractual agreement to be automatically executed or enforced triggered by
predefined set of terms and conditions being met. Once this happens, payment terms are accepted and
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processed for concerned parties. The smart contract takes into account information provided by upstream
and downstream SC members registered in the blockchain [23]. With such flow of information, businesses
can improve traceability, gather auditable history of any product; retailers can track shipments’ location
and condition; producers and processors can better monitor storage conditions; consumers can have greater
visibility over farm-to-fork movement of various food products; and regulators can avail access to validated
data useful for compliance monitoring [24].
The preceding discussion brings us to the central premise of this study on addressing the overarching
question of how do blockchains facilitate transparency in agri-food supply chains? We define transparency
as the extent to which all actors in the agri-food SC have a shared understanding of, and access to, product-
related information that is requested, without delay, distortion, or noise [43, 44]. Blockchains eliminate
trust-related concerns, reduce lead times to bring in efficiencies, build responsiveness, and prevent
fraudulent practices and other SC disruptions [45, 46, 47]. Highly cited studies in literature identify four
primary attributes of blockchain (Figure 3), which act as significant enablers for transparency [48, 49],
namely traceability [50, 51], immutability [52], auditability [53], and provenance [54]. These attributes
were considered relevant within the domain of agri-food SCs.
(a) Traceability: It is the ability to access any or all information of a product under consideration across
its entire life cycle by means of recorded identification [55]. Blockchain offers real-time traceability by
capturing, sharing, and transferring trustworthy information (stored in the form of blocks) across
upstream and downstream actors of the SCs [56, 57]. Transaction data can be traced back to each block
on the blockchain via time-stamp [58]. The system becomes transparent as every actor in the system
has a digital profile containing information on their location, certification, and association with the
product [59].
(b) Immutability: It is a property of being unchangeable or unable to be changed over time. By the virtue
of its design, blockchain is a distributed ledger technology managed by a collective of nodes
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(computers) in a decentralized manner. All entries on the system are visible to SC actors, and any entry
in the ledger must be approved by consensus among all actors. Once committed, it is not possible to
alter historical operations on the ledger, thereby reducing susceptibility to data manipulation and
forgery [33]. Visibility, consensus-building, inspection and verification of data brings immutability to
the system, reducing the need for intermediaries, and promotes transparency [19, 36].
(c) Auditability: It is the virtue of tracking historical operations on the blockchain chain, including all
actors involved in those operations. Ensuring auditability is a challenging task given the prevalence of
OUOD approach currently practiced in agri-food SCs. As a distributed ledger, blockchains have robust
time-stamped and tamper-proof mechanisms of record-keeping and archiving all forward and backward
linkages along the SC [60, 61]. The ability to audit resource and product flows in the system equip
customers with better knowledge and transparency.
(d) Provenance: It depicts the chronology and record of ownership or geographic origin of a product. Most
agri-food products derive their value through provenance (tracing origins) of the product throughout
the value chain [62, 59, 63]. By deploying blockchain, a unique fingerprint can be created for products
in the way of digital tokens. At each stage of value addition, the actor assigns a digital token to the
underlying asset. As the asset moves from one actor to the next, digital token corresponding to that
asset is also reassigned on the ledger [64, 21]. Ability to inspect and track the uninterrupted chain of
custody from product origins till end consumer contributes to transparency along the SC.
Research Methodology
We employed thematic analysis as the methodology to organize and identify specific patterns currently
being researched in the state-of-the art literature, and subsequently categorizing these into coherent themes.
In the context of this study, we explore literature articles on blockchain in agri-food SCM, and identify
specialized categories (or themes) in the agri-food SCs whereby blockchain intervention brought about
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transparency. The process of thematic analysis [65] involved following sequence of steps: (a) conduct in-
depth review of literature articles, (b) generate first order descriptive or in-vivo codes during initial phase
of coding, (c) search for patterns to generate second order codes, (d) generate categories based on identified
patterns for grouping into emergent themes [66], (e) assessments of codes and themes by independent
reviewers (researcher triangulation) for reliability and validation.
Data Collection
To investigate the research questions, we conducted in-depth analysis of diverse use cases selected by
combining academic research articles and practitioner-oriented studies. Relevant publications were selected
from high impact-factor and peer-reviewed journals indexed in ABI/Inform, Scopus, Science Direct, IEEE
Xplore, Web of Science, Elsevier, EBSCO and JSTOR, as well as articles from free databases such as
Google Scholar and ResearchGate. To keep abreast with rapid pace of technological developments in this
technical field, sole reliance on peer-reviewed journal publications would provide a limited view of
technical progress taking place in this field. To track state-of-the-art industry trends, our searches also
included articles developed by practitioners (grey literature) to explore latest developments in the context
of our study. We referred to reports from leading consulting agencies (Deloitte, McKinsey, EY, and KPMG,
among others), trade magazines, technical blog-posts and news and opinion articles from prominent ag-
tech websites (AgFunder, Ledger Insights, TechRepublic, Venturebeat, Coindesk, etc.), as well as white
papers from international funding agencies such as FAO, OECD and World Economic Forum. Proprietary
reports were also retrieved using proprietary databases. Literature search was conducted using a
combination of keywords: ‘blockchain’, ‘supply chain’, ‘agri’, and ‘food’. We identified over 177 studies
relevant to our research, by selecting only those studies containing use cases with specific focus on
blockchain applications in the management of agri-food supply chains, while screening out applications in
non-agri-food sectors and generic supply chain management issues (Figure 4 & 5).
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Figure 4: Search strategy for literature review
Figure 5: Inclusion and exclusion criteria
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Thematic Analysis
Thematic analysis was conducted in a phased manner, which involved immersion into the dataset
comprising of academic and grey literature articles. Team members individually generated initial codes
after multiple rounds of review of shortlisted articles using the NVivo software. Each round led to
researchers independently identifying important sections of text and associated codes (labels), while
providing brief description in the coding manual on how each code captures the phenomenon under
consideration (blockchain-enabled transparency). We followed the Miles and Huberman (1994) approach
for generating first and second-order coding themes [67], which subsequently led to identifying over-
arching patterns that represent emergent themes (Figure 6).
Figure 6: Representation of thematic analysis
Using NVivo’s built-in feature of coding comparison query, we were able to determine inter-rater reliability
testing using Kappa coefficient and percentage agreement for individual codes as well as average levels of
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Kappa’s agreement for a set of codes. Average Kappa’s coefficient was calculated at 0.83, while percentage
agreement was recorded at 92.3% which reflected moderate-to-strong levels of agreement among coders
[68, 69]. Assessing reliability is a critical exercise in thematic analysis, in order to identify codes that require
refinement, discard poorly performing codes, and treat instances of inter-coder disagreement by adopting a
consensus approach [70]. Thematic analysis led to clustering of use cases into three narratives or emergent
themes wherein blockchains were predominantly implemented in agri-food SCs: (a) agri-food distribution,
(b) agri-food origin and sourcing, and (c) agri-food safety and quality. Furthermore, use cases were
identified and selected from literature analysis based on applications which include both – specific roles in
agri-food SCs (e.g. for a particular product/service offering) as well as general roles in agri-food SCs (e.g.
for wide range of offerings across channel partners).The next section discusses details of each use case to
be analysed by cross-mapping emergent themes from literature (food distribution, origin and sourcing, and
safety and quality) with primary attributes of the technology (traceability, immutability, auditability, and
provenance) to ascertain blockchain’s role in transparency of agri-food SCs.
Use Case Analysis
This discussion brings us to the main research question: how blockchains facilitate transparency in the agri-
food SCs. As depicted in Table 2, we analyzed twenty-five use cases of blockchain by cross-mapping
thematic areas with primary attributes: seven use cases with applications in agri-food distribution, seven in
agri-food origin and sourcing, and eleven in agri-food safety and quality. Each use case refers to business
scenarios where blockchain technology is either at a conceptual stage, or proof-of-concept, or commercially
implemented. Based on publicly available data, use cases were analyzed using content analysis [71] to
categorize the relevance of blockchain attributes for each use case in terms of critical, desirable or optional.
It is proposed that the ability to achieve transparency in agri-food SC is higher if 3-4 attributes are identified
as critical per use case. In other instances, ability to achieve transparency is proposed to be weaker wherein
emphasis is only on 1-2 critical or desirable attributes per use case. Findings presented in this paper are
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preliminary part of an ongoing study. Future course of action will entail testing and validation of our
hypothesis using survey based questionnaire among supply chain practitioners. An empirical study would
strengthen our propositions on blockchain’s role in agri-food SC transparency.
(a) Thematic area 1: Blockchain-enabled transparency in agri-food distribution: Many interesting use
cases of blockchain are now being developed, with prominence of applications in food distribution.
Transparency in agri-food distribution is contingent upon the extent and flow of information related to
collection, warehousing, transportation, and supply of agri-food products, which is readily available to
all actors within the system, as well as to consumers and observers (regulatory agencies). Blockchain
applications in agri-food distribution gained momentum when leading enterprises such as IBM
partnered with major retailers in the industry. The IBM Food Trust platform started in 2017 with co-
partners Dole, Kroger, Nestle, Tyson Foods, Unilever, and Walmart. On this platform IBM offers
Blockchain-as-a-Service (BaaS) to build and participate in one of the largest private blockchain
networks for agri-food distribution on a global scale. Walmart is investing $25 million over the coming
five years to build a food traceability system using blockchain technology, with an ongoing pilot with
IBM for tracking mango SCs. Using IoT enabled smart sensors and blockchain-connected devices; data
is collected at various stages of the SC. For instance, temperature and moisture levels during storage
and transportation process, fruit quality during harvest, packing, wholesale and retail markets, data
from shipping and logistics providers regarding bills and invoices, cargo provenance, and dispute
resolution [72]. It is estimated that by conventional methods Walmart would take 6 days, 18 hours, and
26 minutes to trace the exact farm location of mangoes. Using blockchain pilot, the same task can be
completed in 2.2 seconds. Another major player leveraging blockchain in agri-food distribution is
Cargill’s Honeysuckle White®, partnering with small and medium turkey farmers to ensure traceability
in turkey distribution. Technology start-ups such as Ambrosus, FreshSurety, TE-FOOD, AgriChain,
and HarvestMark are integrating inputs from digital technologies like IoT, sensors, and smart-contracts,
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onto blockchain for data capture at every stage from farm-to-fork. As depicted in Table 1, use case
analysis highlights attributes like traceability, immutability, and auditability as critical and/or
important, while limited emphasis is given to provenance. Since distribution activities are primarily
concerned with tracing and record-keeping of movement (end-to-end and not merely OUOD), as well
as storage and transport conditions of agri-food products, use case applications under this theme
typically focus on the described attributes.
(b) Thematic area 2: Blockchain-enabled transparency in food origins and sourcing: Recent scandals
have caused consumers to question where and when did their food originate, how was it sourced, and
how truthful are the label claims. The demand is higher for information of products’ origin and
sourcing, authenticity, and integrity across the SC. Several enterprising companies have started to
leverage blockchain to address these concerns. For instance, growing demand for beef and struggles to
address domestic demands has resulting in China largely importing beef from Australia. To leverage
this opportunity, Australian blockchain firm BeefLedger developed a food provenance and monitoring
platform. BeefLedger is a token-driven platform where SC actors participate in the network by
purchasing BEEF-tokens. Importers, retailers, and wholesalers could use these tokens as payments for
beef shipments. The blockchain stores all the information related to the cattle - to track rearing and
health history of cattle, transport and storage conditions, meat processing, and accurately predicts shelf-
life. Another example is MasterCard, having over 100 blockchain patents on their proprietary
technology. MasterCard collaborated with Envisible, an enterprise that develops systems for tracking
food sources. Envisible’s Wholechain traceability systems are powered by MasterCard’s blockchain-
based Provenance Solution for developing a pilot to help supermarkets trace origins of seafood, better
insights into ethical sourcing, and environmental compliance of seafood selection sold at supermarkets.
Other ventures such as Bext360, Greenpeace, Provenance, ConsenSys and WWF have implemented
blockchain applications to improve their responsiveness. Use cases under this theme tend to focus on
provenance and auditability to provide value (i.e. transparency) to the consumers.
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(c) Thematic area 3: Blockchain-enabled transparency in food safety and quality: Several offerings
are combining big data technologies with blockchains to tackle fraudulent practices resulting in food
safety and quality concerns. While IoT solutions provide services such as location tracking and
temperature and/or humidity monitoring, blockchain provides a digital platform where all the incoming
data can be stored securely, and accessed in real-time by farmers, retailers, consumers, and regulators.
For instance, the implementation of traceability system for rice value chain management [46, 73]. This
system records data from sowing and harvesting to distribution stage, and monitor security and quality
of rice during transportation process. All members register their digital profiles and unique identifiers
on the blockchain. It is proposed that this traceability system will greatly improve the efficiency of the
rice value chain. Downstream Beer is an early mover in the beer industry to use blockchains to provide
complete transparency about beer ingredients and brewing techniques. Using sensors, every aspect of
craft beer-making process is recording and securely stored on the blockchain as a guarantee of
transparency and authenticity. Consumers can use their smartphones to scan QR codes labeled on their
bottles, where they are provided with relevant information related to raw ingredients, processing
method, bottling process, storage and temperature conditions, etc. An e-commerce platform,
Gogochicken Company, uses ankle monitors to track chickens’ movements (currently 100,000 birds
with plans to expand to 23 million birds over next 3 years) and behaviour via GPS tracking. Using
blockchain, the company guarantees its consumers that its free-range chickens are disease-free by
storing data on nutrition, slaughtering, and meat packaging dates, and results of food safety tests [74].
Danone, Verified Organic, TATTOO, and start-ups like Ripe.io, Zego, FoodLogiQ, and Bytable, focus
on preventing fraudulent practices and improve responsiveness by leveraging an ecosystem of AI, IoT,
and sensors on the blockchain ecosystem to bring transparency into agri-food SC. Organizations are
undertaking practices to prevent food fraudulences, ingredient provenance, and food safety
compliances. Use case applications in food safety and quality reflect criticality or importance of all key
blockchain attributes to enable transparency.
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Table 2: Classification of blockchain use cases in agri-food supply chain
Thematic Areas
Use Case
Organization
s involved
Stage of implementation
Description
Relevance of Attributes # #
Blockchain
Features
Traceability
Immutability
Auditability
Provenance
(1) Agri-Food Distribution
IBM Food
Trust
IBM,
Walmart,
Nestle, Tyson
Foods, Dole,
Unilever,
Kroger
Proof-of-concept;
Implementation in actual
business scenario is not
disclosed
Blockchain and IoT based platform to track
mangoes through supply chain; Real-time
certifications, food storage and handling data
+++
+++
+
+++
Hyperledger Fabric; Open source;
private and permissioned blockchain;
13k-20k TPS; Raft consensus algorithm
Honeysuckle
White ®
Cargill
Implemented in business
scenario
Traceability program for turkey that represents
complete digitalization of the supply chain
+++
+
++
●
Splinter based on Hyperledger Transact
/ Hyperledger Sawtooth Sabre; OS,
private and permissioned blockchain;
13k-20k TPS; BFT consensus
Ambrosus
Proof-of-concept;
Implementation in actual
business scenario is not
disclosed
Use smart contracts and IoT to transfer data on
blockchain for supply chain management
+++
+
+
●
Ambrosus Network (AMB-NET) based
on permissioned Ethereum; Proof. of
Authority (PoA) consensus algorithm
FreshSurety
Driscoll,
Whole Foods,
Amazon
Fresh
Implemented in business
scenario
Data from IoT sensors into blockchain using
proprietary wireless communication
+++
+
●
●
Information unavailable in public
domain
TE-FOOD
Proof-of-concept
Food identification, data capture, storage,
processing, by monitoring journey of food
products via IoT sensors
+++
+
+
●
TE-FOOD Blockchain (FoodChain)
based on Hyperledger; Permissionless;
CHECO consensus protocol based on
BFT
AgriChain
Proof-of-concept
Blockchain platform to link SC actors to
support logistics and transact internationally
+++
+
●
●
BlockGrain blockchain platform based
on Ethereum; Practical Byzantine Fault
Tolerance (PBFT)
HarvestMark
Proof-of-concept;
Implementation in actual
business scenario is not
disclosed
Food item-level traceability from farm-to-fork
distribution (full chain traceability)
+++
+
+
+
Information unavailable in public
domain
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(2) Agri-Food Origin & Sourcing
BeefLedger
Queensland
University of
Technology,
Soar Labs,
TSW Global
Proof-of-concept;
Implementation in actual
business scenario is not
disclosed
Combination of blockchain, IoT, big data
analytics, smart contracts and digital tokens to
ensure provenance of ethically produced and
ecologically sourced beef
+
+
+
+++
Ethereum-based permissioned
blockchain platform to issue BEEF
digital crypto-tokens to purchase beef
credentialed by the BeefLedger
blockchain system
Bext360
Proof-of-concept
Use of Stellar blockchain to record timestamps
and value transactions on real time basis
+
+
+
+++
Information unavailable in public
domain
ConsenSys
WWF
Proof-of-concept
IoT, sensors, smart tagging, and blockchain for
curbing illegal fishing
+
●
+++
+++
Ethereum-based permissioned
blockchain platform
Greenfence
Proof-of-concept
Remote auditing and certification of food
supply chain; Authentication gateway using
IoT, big data, and blockchain
+
●
●
+++
Information unavailable in public
domain
OpenSC Food
Provenance
WWF &
Boston
Consulting
Group Digital
Venture joint
partnership
Implementation in actual
business scenario is not
disclosed
Blockchain platform for food supply chain; Use
of QR codes and RFID for tracing source of
any product - where and how it was produced,
storage temperature, etc.
+
+
+
+++
Use of OpenSC blockchain platform to
integrate data from QR codes, RFID
tags
Provenance
Provenance,
IPNLF,
Humanity
United
Proof-of-concept
Blockchain, mobile and smart tagging to track
and ensure responsibly caught tuna;
Certifications from catch-to-customer
+
●
+
+++
Ethereum-based blockchain to integrate
SMS from fisherman, RFID and QR
tags
Wholechain
Traceability
System
Envisible,
MasterCard
Proof-of-concept
Based on MasterCard’s blockchain platform
called Provenance Solution
+
+
+++
+++
MasterCard’s Provenance Solution;
Permissioned blockchain platform
(3) Agri-Food Quality & Safety
Downstream
IPL
Irish Craft
Beers
Proof-of-concept;
Implementation in actual
business scenario is not
disclosed
Blockchain for independent breweries to
uniquely identify authenticity of each bottle
from brewery using arc-net technology
+
+++
+
+++
Graphene-based Arc-net blockchain
platform; Proprietary permissioned;
Delegated Proof-of-stake (DPoS)
consensus protocol
Track &
Connect
Service
Danone
Proof-of-concept;
Implementation in actual
business scenario is not
disclosed
Farm-to-fork transparency solution on
blockchain for baby formula brands such as
Aptamil using ScanTrust - a special technology
to prevent counterfeiting
+
+
+++
+++
Track & Connect uses the SAP
Blockchain As A Service (BaaS)
platform
Bytable
Bytable,
Farmers Hen
House
Proof-of-concept
Public blockchain tracking organic, free range
pasture raised eggs from farm-to-consumer;
Scan QR code on product packaging to access
data collected throughout supply chain
+
+++
+
+++
Hyperledger Sawtooth; Permissioned
blockchain; Proof of Elapsed Time
(PoET) consensus protocol; 3-15 TPS
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19
(3) Agri-Food Quality & Safety
TATTOO
Blockchain
Wine, EY
Proof-of-concept;
Implementation in actual
business scenario is not
disclosed
TATTOO (Traceability, authenticity,
transparency, trade, origin, opinion); Purchase
premium wines on blockchain using digital
tokens to trace provenance, quality, authenticity
of new and vintage wines
+++
+
+++
+++
EY OpsChain permissioned blockchain
platform based on Ethereum Mainnet;
20K TPS
Verified
Organic
Proof-of-concept
Data capture for soil-to-table activities in
organic food supply chain; Verification in
production and distribution-related activities
+
+
+
+++
Ethereum-based permissioned
blockchain built by Treum
Wine
Traceability
Platform
VeChain,
Penfolds
Proof-of-concept
NFC Chip implanted in wine bottles containing
product's provenance information on the
blockchain
+
+
+
+++
VeChainThor public blockchain; Point
of Authority (PoA) consensus protocol
Gogochicken
ZhongAn
Insurance
Proof-of-concept;
Implementation in actual
business scenario is not
disclosed
Using tracking devices, facial recognition to
follow movement of free-range chickens from
hatching-to-packaging, with data logged onto
blockchain
+++
+
+++
+++
Based on Anlink network which is a
blockchain cloud network composed of
Ann-Router and Ann-Chain; BFT
consensus algorithm
Devoleum
Devoleum,
Loom
Network
Proof-of-concept;
Implementation in actual
business scenario is not
disclosed
Using AI models to inspect data correctness
and real-time prediction prior to loading on
blockchain
+
+
+
+
Ethereum blockchain; Adapted as
private permissioned blockchain; Proof-
of-Authority (PoA) consensus protocol;
10-30 TPS
FoodLogiQ
Proof-of-concept;
Implementation in actual
business scenario is not
disclosed
SaaS-based blockchain platform designed for
wholechain traceability
+
+
+
+++
Hyperledger Fabric; Open source;
private and permissioned blockchain;
13k-20k TPS
Ripe.io
Proof-of-concept;
Implementation in actual
business scenario is not
disclosed
Ecosystem of sensors, IoT, & blockchain for
data collection during agri-food SC workflow
+++
+
+
+++
R3 Corda Blockchain, private and
permissioned; BFT or Raft algorithms;
600 TPS
Zego
Proof-of-concept
Integrated blockchain for tracking over 400 ag-
based allergens, chemicals, heavy metals, and
gluten in food products
+++
+
+++
+++
Z-CODE; tailored blockchain (limited
information available in public domain)
# #Relevance of attributes based on a combination of research teams’ individual qualitative assessment, supported by independent review by external subject matter expert
(Notations: +++ denotes critical attribute; + denotes desirable attribute; ● denotes optional attribute)
Pre-proof version submitted to IEEE Transactions on Engineering Management
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Potential challenges in adoption and implementation
A large body of research supports the proposition that blockchain technology offers tremendous potential
in agri-good SCs. However, the technology is in exploratory and testing phase, with several enterprises still
developing proof-of-concepts, or experimenting with deployment and implementation. It is imperative to
sift through the hype surrounding blockchains, and scrutinize inherent challenges that may interfere with
its adoption, deployment, and scalability.
(a) Privacy and data security: One of major strengths of blockchain – traceability – also raises privacy
concerns. When each transaction on the platform is recorded, checked and audited, and every user can
be identified by their hash, questions are raised concerning users’ privacy. In a trade-off between
transparency and confidentiality, such systems may disincentivise companies and individuals from
publishing sensitive information onto the blockchain, given that some members of the platform may be
competitors [75, 76, 77]. There are also concerns regarding robustness of data capture systems that feed
data into blockchains. For instance, building robust digital infrastructures of IoT devices, smart sensors,
and other big data applications is critical to ensure correctness of feed data. Such systems require all
participants (producers, processors, suppliers, distributors) to partake on the blockchain platform.
Refusal to participate by any of the key actors would render traceability and auditability difficult to
achieve.
(b) Implementation costs: An economic concern of SC actors is the time and cost of embedding the
technology into ongoing practices [78]. With growing number of transactions, amount of data to be
stored and computed will increase, resulting in bulky blockchains. Additionally, every actor in the
system will need to constantly store all transaction data for validation. Complex blockchains will
inevitably result in higher expenditures to support more computing power by the platform. Higher costs
are expected to be incurred by various actors in the process of transitioning from traditional SC practices
to blockchain-powered agri-food SCs.
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(c) Regulatory lacunae: Another critical roadblock in adoption of the technology are concerns regarding
the legitimacy of blockchain. The absence of any centralized regulatory authority, censorship
guidelines, or international trade and arbitration laws raises questions on the legal value of blockchains
[32, 79]. The lack of standards and laws related to blockchain can create uncertainties for
manufacturers, service providers, and consumers alike.
(d) Human capital: The widespread lack of understanding on the technical aspects of blockchain would
inevitably result in a human capital challenge. Given the newness of the platform, limited number of
trained manpower have in-depth knowledge and skills to implement blockchain in SCs. Technical
training, skill-development, and general knowhow in blockchain would be central to adoption and
diffusion of technology.
(e) Digital infrastructure: Where many SC actors from developing and low-income economies have
limited access to basic internet and telecommunication technologies, or support services such as finance
and insurance, it currently seems impractical to propose implementation of blockchains with limited
adoption and dissemination of complex digital infrastructure and enabling technologies such as RFID,
IoT, NFC, cloud computing, and big data [46]. Although, efforts are underway to enable access via low
cost hardware (smartphones/tablets) and software to enable data capture by farmers, there is a long way
to go before blockchain and other digital technologies can create an impact on the industry.
(f) Interoperability: A prerequisite for effective blockchain implementation is its integration with legacy
systems to share information throughout the SC. Heterogeneous IT infrastructure and data management
(for instance, enterprise resource planning, warehouse management software used by SC actors) limits
the extend of end-to-end traceability [80].
(g) Complexity of smart contracts: Rules that govern smart contracts are contingent upon the
architecture, applications and structure of the blockchain network. This creates an inherent complexity
in determining the content and scope of smart contracts, as well as adherence to regulatory
requirements. A critical challenge is the built-in rigidity of smart contracts to changing preferences of
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SC actors and unique business scenarios, resulting in reliance on courts for interpretation and
precedence [81].
Bridging existing gaps can offer interesting opportunities for new business models and innovations in
digital infrastructure to emerge [47]. However, once challenges of mass participation are overcome, and
benefits of a more transparent SCs manifest themselves, early adopters will have a distinct competitive
advantage compared to late adopters and laggards.
Conclusions and future directions
Studying use cases of blockchain allows us better understanding of the future potential of the technology
to address existing challenges in agri-food SCs. In the following section, we discuss pertinent areas for
exploring future research endeavors as well as inherent limitations of the present study undertaken:
1. Majority of the recent studies in this area develop conceptual ideas and proof-of-concepts, with limited
empirical research on commercially implemented blockchain solutions. It is suggested that future
research be directed towards empirical studies for assessing performance of blockchain in real-time
agri-food SCs. Such data-driven evaluations could provide strong evidence supporting the benefits of
the technology for proposed attributes like traceability, auditability, provenance, and immutability.
2. Another critical research contribution, which is currently lacking in mainstream literature, is an
integrated assessment of blockchain’s performance in combination with newer complementary
technologies (such as IoT, AI, big data, and cloud computing) to capture realistic measures in terms of
cost, scalability, and efficiencies achieved in SCs by these interventions. While many of these emerging
technologies remain in their infancy, the current cost of implementing traceability systems is higher
than value of the food itself. It would be beneficial to undertake studies on how economies of scale and
scope can be achieved to make commercial sense of the technology, in turn incentivize more
participants to join such platforms.
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3. Blockchain are particularly important from an environmental sustainability perspective, by transparent
monitoring of raw material usage, energy consumption and emissions during production, product
lifecycle visibility in SCs, reduction in operational costs and waste generation due to costly product
recalls, thereby increasing efficiency. Exploring new use cases in this research area would yield insights
into how blockchains can contribute towards addressing sustainability-related challenges among SC
stakeholders.
4. Presently, blockchain technology is not governed by formal regulatory frameworks, creating valid
concerns regarding privacy, confidentiality, data security and governance. Such challenges slow down
technical advancements as well as technological adoption and diffusion in the light of regulatory
uncertainties and legal risks. Vast majority of national laws and regulations have been developed for
centralized governance and where control is the norm. The deviating nature of blockchain – by its
inherent decentralization – poses a challenge for regulators and raises enforcement issues. Deeply
examining such contradictions would bring out interesting research propositions from academics and
practitioners to deliberate on policy inputs concerning legal, ethical, and governance issues.
There is a general consensus around blockchain’s potential to increase transparency in agri-food SCs.
Consumers today demand safe, sustainable and fair practices in food production, with enterprises leveraging
blockchains to address these demands. Novel approaches have emerged that integrate blockchains with
other industry 4.0 technologies (e.g. big data, IoT, RFID, NFC, etc.) for improved responsiveness in agri-
food SCs. Despite proposed benefits, core challenges need to be addressed before blockchains can be widely
adopted. Wide disparity among nations in terms of digital infrastructure, awareness and human resource
skill-sets can skew technological development and control toward more developed economies, whereas
critical agri-food SC challenges predominantly occur in developing agrarian economies. Additionally,
technical issues pertaining to standardization of data capture in SCs, governance mechanisms, scalability
and economics of implementation need to be considered and duly addressed.
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Acknowledgments
Authors would like to thank all the anonymous reviewers for their valuable comments which helped
improve the quality of the paper. Authors also gratefully acknowledge the support rendered by Prof. Dr.
Daim Tugrul, Editor-in-Chief, IEEE Transactions on Engineering Management. This research did not
receive any grants from funding agencies in the public, commercial or not-for-profit sectors. The authors
declare no conflict of interest.
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