Bibliometric Analysis of MCDM in Agricultural Supply Chains

Turkish Journal of Agriculture - Food Science and Technology, 14(4): 1023-1037, 2026
DOI: https://doi.org/10.24925/turjaf.v14i4.1023-1037.8461
Turkish Journal of Agriculture - Food Science and Technology
Available online, ISSN: 2148-127X │www.agrifoodscience.com │ Turkish Science and Technology Publishing (TURSTEP)
A Bibliometric Analysis of Applications of Multi-Criteria Decision Making in
Agricultural Supply Chains: Trends and Insights
İlker İbrahim Avşar1,a,*, Emre Kadir Özekenci2,b
1
Osmaniye Korkut Ata University, Vocational School of Bahce, Department of Logistics, 80500, Osmaniye, Türkiye
Çağ University, Faculty of Economics and Administrative Sciences, Department of International Trade and Logistics, 33800, Yenice,
Mersin, Türkiye
*
Corresponding author
2
ARTICLE INFO
Research Article
Received : 17.10.2025
Accepted : 10.12.2025
Keywords:
Multi-Criteria Decision Making
Agricultural Supply Chain
Decision
Trends and Insights
Models into agricultural policy
a
[email protected]
ABSTRACT
The efficiency of agricultural supply chains is critical not only for the success of individual
companies but also for the overall economic development of nations. This study aims to analyze
general trends in publications that apply Multi-Criteria Decision Making (MCDM) methods within
the context of agricultural supply chains. The bibliometric analysis reveals a noticeable increase in
academic publications on this topic over the past five years. India, China, and the United Kingdom
stand out as the leading countries in terms of publication volume. Furthermore, frequently used
keywords such as “sustainability” and “technology” highlight the growing importance of
environmental and technological considerations in this field. The identified trends provide valuable
insights for policymakers and stakeholders involved in future-oriented decision-making processes.
This research is significant as it introduces a systematic approach to decision-making in agricultural
supply chains, which are vital for global food security and economic stability. By mapping the
current state of academic interest and highlighting key focus areas, the study contributes to the
growing body of literature on the application of MCDM methods in agriculture. It also serves as a
foundation for future studies aiming to integrate complex decision-making models into agricultural
policy and practice.
https://orcid.org/0000-0003-2991-380X
b
[email protected]
https://orcid.org/0000-0001-6669-0006
This work is licensed under Creative Commons Attribution 4.0 International License
Introduction
Agriculture is a broad term that encompasses the
methods by which crop plants and domesticated animals
contribute to feeding the global population. It provides
essential food, feed, bioenergy, and industrial materials.
This sector contains a diverse range of activities, including
crop cultivation, domestication of species, horticulture,
arboriculture, and vegeculture, as well as various livestock
management practices such as mixed crop-livestock
farming, pastoralism, and transhumance. Currently, the
agricultural sector is under increasing pressure on two
primary fronts: (i) the urgent need to operate sustainably,
ensuring that current demands are met without
compromising the capacity of future generations to meet
their own needs, and (ii) the necessity to produce the food,
energy, and industrial resources required to satisfy the
needs of a growing global population (Borodin et al.,
2016). Since the beginning of the new millennium, the
concept of the agricultural value chain, also known as the
farm supply chain, has garnered significant attention from
professionals in the field. While there is no universally
accepted definition, the term typically encompasses the full
range of products and services required for the
transportation and distribution of agricultural products
from the farm to the end consumer (Khandelwal et al.,
2021).
The COVID-19 pandemic has had a profound impact
worldwide, disrupting global supply chains. Many experts
consider this pandemic a declared disaster and forecast
severe consequences for the worldwide economy.
Developing and third-world nations, which heavily depend
on agriculture and agricultural imports, are especially
vulnerable and face unprecedented challenges to their
agricultural supply chains due to COVID-19. According to
the Food and Agriculture Organization, the pandemic
affects these supply chains in two primary ways: it
influences both food demand and supply (Sharma et al.,
2024). Throughout the global pandemic and its aftermath,
agriculture plays a critical role in sustaining all human
activities. Ensuring agricultural sustainability is crucial for
achieving food security and eradicating hunger in a rapidly
growing population. It is estimated that global food
production will need to increase by 60–110% to feed 9–10
billion people adequately by 2050. However, significant
challenges such as overpopulation and resource
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Avşar and Özek / Turkish Journal of Agriculture - Food Science and Technology, 14(4): 1023-1037, 2026
competition threaten the planet’s food security. To address
the increasingly complex issues in agricultural production
systems, innovations in smart farming and precision
agriculture offer essential tools to address sustainability
concerns (Sharma et al., 2020).
In addition, the primary challenges facing the agri-food
supply chain include insufficient industrialization, poor
management, inaccurate information, and ineffective
supply chain processes. Since the World Summit on
Sustainable Development, multiple initiatives have
emerged across diverse sectors to promote sustainable
development. Notably, the agriculture sector has received
considerable attention, underscoring the need to adopt best
management practices in farming and to improve both
social and ecological conditions to achieve sustainable
growth (Kamble et al., 2020). Consequently, the adoption
of quantitative methods, such as Multi-Criteria Decision
Making (MCDM), data analytics, and operational research,
has become increasingly prevalent in ensuring future food
security, improving supply chain performance, and
promoting ecological sustainability (Wang et al., 2022;
Zhai et al., 2023; Nguyen et al., 2025). In parallel with this
methodological expansion, there has been a notable
increase in the use of bibliometric analyses by researchers
to identify research trends, evaluate knowledge structures,
and highlight future directions in the areas of agriculture
supply chain (Wang et al., 2023; Lwesya & Achanta, 2024;
Ikasari et al., 2025; Kumar & Sahoo, 2025; DieguezSantana et al., 2025). Despite the widespread application
of MCDM methods in agriculture, there is a notable lack
of systematic analyses of trends and research patterns in
this field (Francik et al., 2017; Priyambada et al., 2023). To
address this gap, the current study conducts a
comprehensive bibliometric analysis of MCDM techniques
utilized in agricultural research, with a specific emphasis
on supply chains, logistics, and transportation contexts.
To the best of the author’s knowledge, this study
represents the first comprehensive analysis of research
trends in MCDM techniques in agricultural research, with
a specific focus on supply chains, logistics, and
transportation. The current study uses the Web of Science
(WoS) and SCOPUS databases and covers the period from
2015 to 2024. This research aims to address a significant
gap in the literature by employing two advanced
visualization tools, VOSviewer and Bibliometrix, to
systematically analyse relevant scholarly publications from
these databases during the specified timeframe.
Furthermore, the research seeks to create visual knowledge
maps that illustrate key trends and patterns in the field. To
achieve this, the study will explore the following research
questions:
•
•
•
RQ1: What is the distribution of publications on the
use of MCDM techniques in agricultural supply
chains, logistics, and transportation research in the
WoS and SCOPUS databases from 2015 to 2024?
RQ2: Which keywords are most frequently used in
publications related to the use of MCDM techniques
in agricultural supply chains, logistics, and
transportation research in the WoS and SCOPUS
databases?
RQ3: Which countries have the highest number of
publications on the use of MCDM techniques in
agricultural
supply
chains,
logistics,
and
transportation research in the WoS and SCOPUS
databases?
• RQ4: How is the cooperation between countries on the
use of MCDM techniques in agricultural supply
chains, logistics, and transportation research in the
WoS and SCOPUS databases?
• RQ5: Who are the researchers with the most coauthored works on the use of MCDM techniques in
agricultural
supply
chains,
logistics,
and
transportation research in the WoS and SCOPUS
databases?
• RQ6: Which researchers have the highest publication
counts on the use of MCDM techniques in agricultural
supply chains, logistics, and transportation research
in the WoS and SCOPUS databases?
• RQ7: Which publications are most cited in the WoS
and SCOPUS databases on the use of MCDM
techniques in agricultural supply chains, logistics, and
transportation research?
• RQ8: How is the use of MCDM techniques in
agricultural
supply
chains,
logistics,
and
transportation research in the WoS and SCOPUS
database publications distributed among various
journals?
• RQ9: What are the relationships in the co-occurrence
network of keywords in publications on the use of
MCDM techniques in agricultural supply chains,
logistics, and transportation research in the WoS and
SCOPUS databases?
• RQ10: What is the trend topic of keywords in
publications on the use of MCDM techniques in
agricultural
supply
chains,
logistics,
and
transportation research in the WoS and SCOPUS
databases?
The rest of the paper is structured as follows: Section two
provides a comprehensive review of previous research within
the relevant field. Section three describes the materials and
methods used in the research, including a comprehensive
description of the research design, data sources, and
methodologies applied. Section four presents the significant
findings from the analysis, which are critically evaluated and
interpreted in light of existing literature and theoretical
models. The concluding section summarizes the main
findings and their broader significance, highlights the study’s
contributions to the field, discusses potential policy and
practical applications, and provides suggestions for future
research.
In conclusion, this study investigates the
implementation of MCDM analysis models within the
agricultural supply chain framework. A thorough literature
review was conducted using unique keywords not used in
prior research. The original contributions of this study arise
from the strategic selection of these keywords, the specific
time frame of the research data, and the analytical methods
employed. Although there are bibliometric studies on
supply chains, none have specifically addressed the
intersection of MCDM methods with agricultural logistics
in the context of the model used in this research. Therefore,
this study provides a novel interdisciplinary mapping that
highlights its significant original contribution.
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Literature Review
In recent decades, MCDM methods have been widely
applied in the agricultural sector, primarily due to the
inherent complexity of agricultural practices and the need
to balance economic, environmental, and social objectives.
Decision-making processes within agriculture often
involve interconnected criteria aimed at optimizing results.
Accordingly, researchers are increasingly utilizing MCDM
techniques across a diverse range of agricultural contexts,
including agricultural production, warehouse selection,
water management, climate change adaptation, fruit and
vegetable selection, risk assessment in agricultural supply
chains, supplier selection, and the digitalization of
agriculture.
Banihabib and Shabestari (2017) introduced a fuzzy
MCDM model to handle uncertainty in agricultural water
demand management. They proposed a hybrid model that
combines the Modified TOPSIS with AHP, applied in both
non-fuzzy (MTAHP) and fuzzy (FMTAHP) contexts.
Their findings indicated that MTAHP achieved superior
ranking resolution among non-fuzzy MCDM models,
while FMTAHP notably improved the coefficient of
variation of final scores and overall resolution. Raut et al.
(2018) investigated the key causal factors contributing to
post-harvest losses (PHL) in the fruits and vegetables
supply chain, specifically within the Indian context.
Through a comprehensive literature review and expert
insights, they identified 16 causal factors. Utilizing the
AHP method, they assessed the relative significance of
these factors within a comparative framework. The
resulting model highlighted the critical factors to prioritize
for effective PHL reduction. The three most significant
causal factors identified were: insufficient linkages among
institutions, industry, and government; the effects of
climate and weather conditions; and inadequate
connections within the marketing channel from the farm
gate to the market, particularly due to the prevalence of
small-scale farmers. Qureshi et al. (2018) analyse the crop
selection patterns in India, with a particular focus on
sustainable farming practices. They identify 12 criteria
encompassing socioeconomic, soil, water, environmental,
and climatic factors, based on an extensive literature
review and expert input. Their study centres explicitly on
eight common Ravi season crops. The results highlight
strategies to address the resource scarcity challenges faced
by Indian farmers in their efforts towards sustainability.
Zamani et al. (2020) developed a fuzzy-based decisionsupport system to assess and rank proposed adaptation
scenarios for climate change within the Jarreh agricultural
water resources system in southwest Iran. The findings
indicated an increase in annual mean temperature, a
reduction in runoff entering the reservoir, and an increase
in agricultural water requirements. Furthermore, the results
indicated a decline in the system’s reliability. Ultimately,
this study highlighted the effectiveness of improving
irrigation efficiency and reducing the cultivated area
compared to the other proposed scenarios. Yazdani et al.
(2021) explored factors influencing flood risk and their
implications for the sustainability of agricultural supply
chains within the framework of a circular economy
strategy. They propose an MCDM model for assessing
flood risks in agricultural regions and validate it through a
case study in Spain. The findings confirm the effectiveness
of the methodology, enabling agricultural organizations to
identify the primary risk drivers and select projects that
best mitigate flooding impacts. Kieu et al. (2021)
introduced a hybrid MCDM model that combines the
Spherical Fuzzy Analytic Hierarchy Process (SF-AHP)
with the CoCoSo Algorithm to address the distribution
location selection problem for perishable agricultural
products. This model was subsequently applied to a
numerical case study focusing on sweet potatoes from the
Mekong Delta region of Vietnam. The key contribution of
this research is the provision of an MCDM framework to
enhance the efficiency of the agricultural supply chain by
optimizing the selection of distribution center locations.
Tork et al. (2021) evaluated the effectiveness of various
approaches for modernizing surface water distribution
systems to reduce aquifer water withdrawal. Their study
explored structural innovations, enhancements to
traditional distribution methods, and the integration of
automation. By utilizing technical, social, economic, and
environmental criteria, they employed the AHP to assign
weightings and prioritized the scenarios using both the
AHP and the COPRAS methods. The results indicated that
automating surface water distribution systems offers a
practicable solution for balancing aquifer levels.
Rouyendegh and Savalan (2022) developed a framework
to aid in selecting agricultural production techniques in
Türkiye. Their methodology is based on the B-FAHP and
F-TOPSIS. It incorporates three primary criteria:
Satisfaction, Economic, and Environmental impact. The
findings indicate that Organic farming is preferred over
Conventional farming, which in turn is favored over
genetically modified crops. Overall, Organic farming
emerged as the most suitable option among the
alternatives. Wang and Van Thanh (2022) present a robust
solution that integrates the triple bottom line encompassing
social, environmental, and economic factors with the SFAHP and CODAS approaches to help agricultural
companies select the most suitable fertilizer supplier. The
findings suggest that the second supplier represents the
most suitable option for establishing a sustainable supply
chain in the agricultural sector. This study’s primary
contribution lies in offering an effective and high-quality
method that enables businesses to make informed decisions
about supplier selection for a sustainable supply chain.
Puška et al. (2022) determined green suppliers that can
effectively aid agricultural producers in adopting
sustainable practices. To resolve potential ambiguities in
expert evaluations, they employed Z-numbers in
conjunction with the fuzzy LMAW and fuzzy CRADIS
methods. By integrating Z-numbers into the fuzzy LMAW
framework, the researchers established weighting
coefficients for the criteria, enabling experts to evaluate
them and convey their confidence in their assessments. The
findings revealed that price and quality were the most
significant factors in the selection process. Cicciù et al.
(2022) conducted an extensive literature review focused on
MCDM methods for evaluating agricultural sustainability.
Their analysis revealed publication trends, identified
leading authors, and examined prevalent MCDM
techniques. The study concluded that the available range of
MCDM methods is limited, indicating that the field
remains underexplored and presents significant
opportunities for further development. Mishra and
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Satapathy (2023) assessed farmers’ understanding of
maintenance activities for agricultural machinery in India,
highlighting a significant need for improved guidance,
particularly regarding the effective maintenance of
tractors. Their research involved a comprehensive
literature review followed by consultations with experts to
identify maintenance plans for tractors. They utilized the
SWARA method to prioritize these MPs. The findings
revealed that checking the overall condition of the
machinery ranked highest, followed by inspections of the
batteries, fluid levels, and tires and wheels. Khan et al.
(2023) conducted a comprehensive assessment of risks in
the livestock supply chain, drawing on insights from an
extensive literature review and input from experts.
Employing the AHP to prioritize these risks, they identified
“input supply risk” as the highest priority, followed closely
by “production risk,” “post-harvest risk,” and “marketing
and price risk.” Among the 17 risks identified, notable
concerns include “poor quality and inadequate supply of
feed and fodder,” “insufficient waste disposal practices,”
and the “absence of certification for animal quality.”
Nguyen et al. (2025) developed a Fuzzy decision-making
tool to assess supplier sustainability based on the Triple
Bottom
Line,
which
encompasses
economic,
environmental, and social dimensions. Their novel model
integrates Fuzzy Set Theory, the Delphi method, AHP, and
GRA methods. In its application to the Vietnamese
agricultural sector, the Fuzzy DELPHI analysis revealed
that factors such as environmental costs, competencies,
information disclosure, and agility were considered less
significant. Conversely, the Fuzzy AHP analysis
highlighted that product price and robustness were the
primary criteria for selecting sustainable suppliers in
Vietnam’s agricultural supply chain. Saqlain et al. (2025)
investigated the use of Fuzzy Hypersoft Sets to optimize
agricultural decision-making, focusing on a case study in
crop economics. The researchers employed methodologies
such as SPOTIS, RF, and MULTIMOORA to identify the
most suitable crop for Jane’s farm. Their analysis
considered various factors, including weather conditions,
agricultural production costs (such as water and land
usage), pesticide resistance to pests, and market demand.
The results indicated a consensus among all three methods,
with Maize emerging as the top alternative, followed by
Tomatoes and Rice as the next most favorable options
based on the calculated scores.
In recent years, academic research has increasingly
centered on the intersection of agricultural supply chains
and MCDM methods. This study advances the existing
literature by delving more deeply into this intersection. For
instance, Singh and Dwivedi (2025) explored the resilience
of the agricultural food supply chain by applying MCDM
methods to risk assessment and strategy formulation
processes. Concurrently, Gholian-Jouybari et al. (2024)
employed MCDM to evaluate the performance of
sustainable agricultural supply chains. Similarly, Krstić et
al. (2024) used MCDM to assess various risks, while
another study by the same authors in 2023 ranked etraceability factors using MCDM methods. Additionally,
Gupta et al. (2025) addressed the challenges associated
with blockchain applications using MCDM. Finally,
Tirkolaee et al. (2021) demonstrated the effectiveness of
MCDM methods in supplier selection.
These examples highlight the crucial role that MCDM
methods play in agricultural supply chains. The importance
of this research is evident in the bibliometric analysis that
merges these two fields. This analysis is particularly
valuable as it provides a comprehensive overview of the
existing literature from an interdisciplinary viewpoint. The
established general framework identifies trends in the joint
application of both disciplines and serves as a reference
point for future research endeavours.
Research Methodology
As demonstrated in studies conducted by Gherțescu et
al. (2025), Maria et al. (2025), Hammad et al. (2025),
Pietrzak et al. (2025), Mohd Aridi et al. (2025),
bibliometric analysis encompasses various techniques such
as country, keyword, and author co-occurrence analyses,
the identification of influential authors in the field, the most
highly cited articles, and co-author network mapping.
These analyses provide quantitative insights into the
research landscape of a given topic. Moreover, clustering
methods can also be applied within bibliometric analysis to
identify thematic structures and research trends. Overall,
bibliometric analysis plays a critical role in understanding
the intellectual structure of a field, guiding future research
directions, and supporting evidence-based decisionmaking in academia.
Therefore, a bibliometric analysis was conducted in this
study to review the existing literature systematically. The
primary objective of the research is to identify and analyse the
prevailing trends in studies that apply MCDM methods. By
examining the evolution of research themes, influential
publications, authorship patterns, and citation networks, the
study aims to offer a comprehensive overview of the field.
Additionally, it seeks to provide valuable insights to inform
and guide future academic work, highlight emerging areas of
interest, and support the strategic development of MCDMrelated research across disciplines.
Data and Query
On 27 August 2025, WoS and Scopus databases were
searched using the query shown in Figure 1. This yielded
174 publications in WoS and 273 in Scopus. These
publications were then filtered by language (English),
document types (article), and year (2015–2024). Although
the first publication was in 2001, the analysis covers only
the last 10 years, as only 1 or 2 publications were found in
the early years. After filtering, the number of publications
decreased to 114 in both databases. The publications in
both databases were then compared, with duplicate records
removed. Ultimately, 146 publications were obtained, and
the analyses in this study were performed based on these.
The filtering process for the WoS and Scopus databases
was conducted manually. Subsequently, duplicate records
were identified using R and the “mergeDbSources”
function.
Main Statistics
According to Table 1, the use of the MCDM method in
agriculture is increasing and has a significant scientific
impact. Several researchers typically conduct these studies
and engage in international collaborations, which
highlights the subject’s interdisciplinary and global
importance. The presence of various keywords indicates
that the studies have a broad scope and cover different
MCDM applications. The main themes of publications on
the subject are given below.
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Figure 1. Data model
Table 1. Main information.
Description
Results
2015:2024
98
146
11.61
3.1
39.74
587
579
454
9
3.75
36.3
145
1
Timespan
Sources (Journals, Books, etc.)
Documents
Annual Growth Rate %
Document Average Age
Average citations per doc
Keywords Plus (ID)
Author’s Keywords (DE)
Authors
Authors of single-authored docs
Co-Authors per Doc
International co-authorships %
Article
Article; book chapter
General publication information: Between 2015 and
2024, a total of 146 documents using the MCDM method
in agriculture were published. These publications came
from 98 different sources (journals, books, etc.). The
average annual growth rate of these publications was
11.61%, suggesting that the body of literature in this field
is growing. The average age of documents is 3.1 years,
indicating that the literature in this field is relatively recent.
On average, each document received 39.74 citations,
suggesting significant scientific impact.
•
Content analysis: Many keywords are used in the
documents. “Keywords Plus” uses 587 keywords, while
the author’s own keywords use 579. This suggests that the
research covers a wide range of topics in detail.
•
Authors and collaboration: A total of 454 different
authors contributed to the studies. Only 9 of these were
single-author publications, suggesting that MCDM studies
in agriculture are primarily conducted collaboratively. On
average, there are 3.75 authors per document. The
international collaboration rate is 36.3%, meaning that
approximately one in three publications involves
international collaboration.
Document types: Most documents are articles (145),
with only one classified as a book chapter. This suggests
that research in this field is predominantly published in
journals.
Software
In this study, the R Bibliometrix package (Aria &
Cuccurullo, 2017) and the Vosviewer (2025) software
were used.
Data and Results
Annual Scientific Production (RQ1)
MCDM methods have become increasingly prevalent in
agriculture over the past few years. An analysis of annual
publication numbers reveals a significant increase,
particularly over the last five years. This may be due to the
growing recognition of the importance of MCDM methods in
decision-making processes related to agricultural
sustainability, resource management, crop productivity, and
risk analysis. Furthermore, the emergence of complex,
multidimensional agricultural production issues, such as
climate change, water management, and soil quality, has
increased the applicability of these methods. The usage rates
of different MCDM techniques (such as AHP, TOPSIS, and
VIKOR) in various subfields also influence the annual
publication trend. The increase in annual publications
demonstrates growing interest in methodological and applied
research, underscoring the importance of basing decisionsupport systems in the agricultural sector on scientific
principles. This suggests that MCDM-based studies will
become increasingly diverse in the future.
Most Frequent Keywords (RQ2)
According to Table 2, the term “agri-food supply
chain” appears 38 times, highlighting the area’s primary
focus. Studies focusing on this supply chain primarily aim
to apply MCDM methods to decision-making processes.
The frequent use of terms such as “sustainability” and “the
circular economy” underscores the growing importance of
environmental and economic sustainability in agricultural
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supply chains. Research considers not only efficiency, but
also ecological and circular approaches.
The more general terms “agri-food” and “agricultural
supply chain” emphasise the sectoral scope and supply
chain management perspective. The nine uses of “Covid19” suggest that the pandemic’s effects are being examined
through risk management and decision-support systems
within the agricultural supply chain. The eighth use of
“MCDM” highlights studies with a methodological focus.
The seven mentions of blockchain demonstrate the
presence of digitalisation and data transparency trends in
the supply chain.
Overall, this distribution of terms indicates that MCDM
is being applied to key areas of agricultural supply chains,
including sustainability, digital transformation, and crisis
management. This suggests that MCDM is an emerging
trend in literature.
Countries’ Scientific Production (RQ3)
Table 3 shows how publications using MCDM methods
are distributed within the agricultural supply chain,
categorised by country. Some countries are clearly
working intensively in this area. India tops the list with 73
publications, a figure attributed to its large agricultural
sector, high population, and concerns about food security.
Similarly, China’s second place with 41 publications
reflects its intensive agricultural production capacity.
Among European countries, the United Kingdom (30
publications), Italy (28 publications), and Spain (10
publications) stand out. These countries are seen as
prioritising this issue due to their diverse agricultural
products and the European Union’s sustainable agriculture
and supply chain policies. Despite its high technological
capacity, the United States has fewer publications than
European and Asian countries.
Iran’s inclusion on the list, with 14 publications,
reflects the region’s efforts to address agricultural
productivity and logistical challenges. Countries with
agriculture-export-based economies, such as Australia (12
publications) and Chile (seven publications), have also
focused their efforts in this area.
Overall, the table shows that MCDM methods are
attracting global interest in agricultural supply chains,
particularly from Asian and European countries.
Country Collaboration Network (RQ4)
The explanation of the clusters presented in Figure 3
and Table 4 is as follows:
• Cluster 1 (Malaysia, Norway, Brunei): This group
includes relatively small research producers with low
PageRank values, but with occasional bridging roles
(e.g., Norway with Betweenness = 136). Their
collaboration patterns likely depend on bilateral or
limited international projects, rather than extensive
global networks. They cluster together because of their
peripheral yet similar levels of collaboration intensity.
• Cluster 2 (Iran, Lithuania, Switzerland): Iran has very
high betweenness, while Switzerland shows moderate
bridging capacity. Lithuania, however, is peripheral.
The grouping suggests a hub-and-spoke model, in
which Iran and Switzerland connect smaller partners
(such as Lithuania) to the broader network. They are
clustered by regional or methodological alignment,
with Iran serving as a central connector.
•
Cluster 3 (India, China, UK, Australia, France,
Morocco, Brazil, Bangladesh, Guinea, South Africa,
Colombia, Ecuador, New Zealand, Pakistan): This is
the largest and most influential cluster, containing
global leaders (India, China, UK, France, Australia)
alongside emerging or peripheral countries (Brazil,
Bangladesh, South Africa). The reason for their comembership is that these countries form the core of
international collaboration networks, where large
research hubs (India, China, and the UK) attract and
integrate smaller countries into joint projects.
Thematic overlaps such as food security,
sustainability, and global supply chain resilience bind
them together.
Figure 2. Annual scientific production
Table 2. Most frequent words (more than 4 used)
Words
Occurrences
Agri-Food Supply Chain
38
Sustainability
20
Agri-Food
12
Circular Economy
12
Supply Chain
12
Covid-19
9
MCDM
8
Agriculture Supply Chain
7
Blockchain
7
Agriculture
6
DEMATEL
6
Resilience
6
Agri-Food Supply Chain (AFSC)
5
Agri-Food Supply Chains
5
Blockchain Technology
5
Internet of Things (IoT)
5
IoT
5
Table 3. Countries’ scientific production
SN
Country
1
India
2
China
3
UK
4
Italy
5
USA
6
Iran
7
Australia
8
Spain
9
France
10
Chile
Freq
73
41
30
28
18
14
12
10
8
7
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Figure 3. Country collaboration network
Table 4. Country collaboration network
Node
Cluster
Malaysia
1
Norway
1
Brunei
1
Iran
2
Lithuania
2
Switzerland
2
India
3
China
3
United Kingdom
3
Australia
3
France
3
Morocco
3
Brazil
3
Bangladesh
3
Guinea
3
South Africa
3
Colombia
3
Ecuador
3
New Zealand
3
Pakistan
3
U Arab Emirates
4
Croatia
4
USA
5
Canada
5
Mexico
5
Peru
5
Finland
5
Ghana
5
Greece
6
Cyprus
6
Turkiye
7
Czech Republic
7
Poland
7
Italy
8
Serbia
8
Spain
9
Chile
9
Kenya
10
Nigeria
10
Denmark
11
Betweenness
0
136
0
219.564
0
55.723
202.186
59.227
237.113
49.285
46.839
2.778
0
0
0
0
0
0
0
0
36
0
91.534
0
0
0
0.438
0
36
0
0
70
0
0
0
0
1.314
0
0
70
Closeness
0.008
0.011
0.008
0.013
0.009
0.012
0.014
0.012
0.015
0.013
0.012
0.011
0.01
0.011
0.01
0.01
0.008
0.01
0.011
0.011
0.007
0.006
0.012
0.008
0.008
0.01
0.009
0.009
0.007
0.006
0.009
0.009
0.009
0.008
0.008
0.009
0.011
1
1
0.009
PageRank
0.013
0.03
0.013
0.042
0.008
0.021
0.106
0.068
0.095
0.047
0.051
0.026
0.011
0.018
0.009
0.008
0.006
0.008
0.016
0.016
0.02
0.012
0.057
0.006
0.011
0.009
0.019
0.011
0.02
0.012
0.02
0.028
0.02
0.023
0.023
0.013
0.016
0.025
0.025
0.02
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•
Cluster 4 (UAE, Croatia): A small but distinct
cluster. The UAE has significant betweenness, while
Croatia remains peripheral. Likely grouped due to specific
regional collaborations in logistics and trade, rather than
broad academic integration.
•
Cluster 5 (USA, Canada, Mexico, Peru, Finland,
Ghana): This cluster reflects North and South American
integration, with Finland as an outlier connected through
methodological links. The USA serves as the central hub,
while Canada and Mexico are natural collaborators given
their regional proximity (in the NAFTA/USMCA context).
Ghana and Peru may be included as case-study partners
due to agricultural trade relations.
•
Cluster 6 (Greece, South Cyprus): A small
regional grouping, reflecting Mediterranean agricultural
collaboration.
•
Their clustering indicates tight but localized
partnerships, possibly around olive oil, viticulture, or
Mediterranean supply chain case studies.
•
Cluster 7 (Türkiye, Czech Republic, Poland): This
cluster shows Eastern European and regional integration,
with the Czech Republic having notable betweenness.
They likely collaborate around EU-funded projects in
agriculture, sustainability, and logistics.
•
Cluster 8 (Italy, Serbia): Another regional cluster,
reflecting Balkan and Southern European cooperation.
•
Italy’s strong agricultural research capacity may
be drawing Serbia into joint studies.
•
Cluster 9 (Spain, Chile): Both are strong
agricultural exporters (fruits, olives, etc.). Their clustering
likely arises from comparative studies on Agri-export
supply chains.
•
Cluster 10 (Kenya, Nigeria): Both are African
countries with high closeness values (1.0), suggesting
potential centrality if collaborations expand. Their
grouping highlights shared regional challenges, such as
food security, post-harvest losses, and export chains for
coffee, cocoa, and horticultural products.
Cluster 11 (Denmark): Denmark forms a standalone
cluster due to its unique collaboration profile, possibly tied
to specialized projects in sustainability, organic farming,
and advanced logistics systems.
An analysis of the country cooperation network indicates
that nations with similar political alignments frequently
collaborate on academic research. For instance, Greece and
Southern Cyprus are part of the sixth cluster, exemplifying
this trend. Additionally, significant partnerships exist among
countries in the third cluster, including India, China, Australia,
and the UK. Over time, these collaborations could foster
further economic and political cooperation among these
nations. Another noteworthy aspect is the USA’s absence
from these groups; instead, it operates within its own network
in the fifth cluster, which notably excludes developing East
Asian countries. While the USA is not represented in East
Asia, the UK’s presence aligns with the region’s historical
context.
Co-Authored Works (RQ5)
Studies conducted by Ashurbayli-Huseynova &
Garmidarova (2025), Rubiales-Núñez et al. (2025), and
Karlsson & Hammarsssfelt (2025) have demonstrated the
application of co-authorship analysis in bibliometric
research. This bibliometric method examines collaboration
networks among researchers based on co-authored
scientific publications. The analysis reveals and visualises
the partnerships formed between authors, institutions, and
countries. This method helps demonstrate scientific
collaborations, understand the structure of these networks,
identify leading researchers and institutions, and reveal
research trends and international collaborations. The
method is also used to inform the development of science
policy and the formulation of strategies for universities and
funders.
Figure 4 shows the results of the analysis performed
according to the options presented in Table 5. The coauthorship analysis reveals four distinct groups among the
authors. The first group consists of four authors, including
one named “Gunesekaran”. The second group comprises
three authors, including one named “Garg”, while the third
and fourth groups comprise two authors each.
Groups of authors identified through co-authorship
analysis are important because they reveal the structure of
collaborations and the extent of scientific interaction within a
given field of research. They shed light on collaboration
dynamics by showing which networks researchers belong to.
Groups containing names such as “Gunesekaran” and “Garg”,
for example, suggest that these authors occupy central
positions within the scientific network. Conversely, smaller
groups suggest limited collaboration or specialisation in a
specific area. Such analyses reveal existing collaboration
structures and play an important role in our understanding of
information flow within a research field. They also help to
identify potential collaboration opportunities and future trends
in scientific research.
Most Relevant Authors (RQ6)
Table 6 shows that Luthra S. is the author who has
published the most articles (nine) on MCDM methods in
the field of agricultural supply chain, with a fractionalised
value of 2.37. However, Yadav S (with a contribution
intensity ratio of 2.12) and authors such as Garg D and
Sharma R have made a greater contribution. This suggests
that authors such as Luthra and Raut have taken on the role
of network builders in more collaborative, large-team
studies, while authors such as Yadav have made more
intensive contributions to smaller teams. Therefore,
fractionalised value provides a more balanced assessment
of leadership and collaboration dynamics in the field,
reflecting not only productivity but also the level of
individual authors' contributions to publications.
Most Global Cited Documents (RQ7)
According to Table 7, the ten most highly cited
publications show that themes such as sustainability,
digitalisation, and traceability are at the forefront of
research into the agricultural supply chain. The two studies
with the highest number of citations focus on sustainability
and traceability: those by Sharma et al. (2020; 601
citations) and Kamble et al. (2020; 586 citations). This
suggests that these topics have become fundamental
reference sources in the literature. Despite being relatively
new, Y. Liu et al. (normalised TC 7.23; 421 citations) have
attracted attention due to their high annual citation and
normalised values, indicating that “Agriculture 4.0” and
digital transformation are current research trends.
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Figure 4. Co-Authored works
Table 5. Analysis explanations
Method
Analysis
Counting Method
Minimum number of documents for an author
Minimum number of citations of an author
Table 6. Most relevant authors
Authors
Luthra S
Raut R
Yadav S
Agnusdei G
Garg D
Kamble S
Miglietta P
Cheikhrouhou N
Krstic M
Sharma R
Tadic S
Explanation
Co-authorship
Full Counting
2
1
Articles
9
7
7
5
5
5
5
4
4
4
4
Articles Fractionalized
2.37
1.52
2.12
1.12
1.67
1.15
1.12
0.90
0.87
1.12
0.87
Table 7. Most globally cited documents
Paper
Theme
Sharma et al. (2020)
Sustainable agriculture supply chain performance
Kamble et al. (2020)
Traceability in the agricultural supply chain
Y. Liu et al. (2021)
Agriculture 4.0
Ahad et al. (2020)
Sustainable smart cities
Mangla et al. (2018)
Sustainable initiatives in agri-food supply chains
Banasik et al. (2017)
Closing loops in agricultural supply chains
Rueda et al. (2017)
Supply chain sustainability: Agri-food industry
P. Liu et al. (2020)
Green agri-food supply chain: Big data and blockchain
Allaoui et al. (2019)
Decision: sustainable supply chains
Trivellas et al. (2020)
Green logistics management: agri-food sector
Total
Citations
601
586
421
306
248
194
180
139
131
125
TC per
Year
100.17
97.67
84.20
51.00
31.00
21.56
20.00
23.17
18.71
20.83
Normalized
TC
3.62
3.53
7.23
1.84
2.18
2.10
1.95
0.84
3.42
0.75
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Early studies (Mangla, 2018; Banasik, 2017; Rueda,
2017) have laid the field's conceptual foundations, despite
receiving fewer total citations. Studies by P. Liu (2020) and
Trivellas (2020) on specific themes such as blockchain, big
data, and green logistics received fewer total citations.
However, these studies are important in representing future
trends. Overall, the most cited publications focus on
sustainability-oriented performance measurement in
agricultural supply chains and on the integration of
digitalisation and innovative technologies (IoT,
blockchain, and big data). This demonstrates that
environmental concerns and technological transformation
lie at the heart of the field.
Research and Mathematics focus on methodological
contributions that support the theoretical aspects of
MCDM methods. Publications such as the British Food
Journal and Sustainable Production and Consumption
specialise in agriculture, food, and sustainable production
issues. Furthermore, technology-focused journals such as
Expert Systems with Applications and IEEE Transactions
on Engineering Management contribute to the fields of
digitalisation, artificial intelligence, and decision support
systems. Overall, the table shows that publications in this
field are interdisciplinary, focusing on sustainability, the
environment, logistics, operations management, and digital
technologies.
Most Relevant Sources (RQ8)
According to Table 8, examining the journals with the
highest number of publications reveals that Sustainability
and the Journal of Cleaner Production stand out by a wide
margin, with nine and seven articles, respectively. This
suggests that research in the fields of the agricultural
supply chain and multi-criteria decision-making is
predominantly published on sustainability-focused
platforms. Thanks to their broad coverage and open access,
these two journals offer researchers high visibility. Other
journals, such as the International Journal of Logistics
Management and Operations Management Research,
emphasise supply chain management and operational
decision-making. Meanwhile, Annals of Operations
Keywords Co-occurrence Network (RQ9)
Figure 5 was created using the parameters set out in
Table 10. Examining the co-occurrence network of
keywords in the table reveals key trends and conceptual
clusters within agricultural supply chain literature (Figure
5 and Table 9). Four main themes emerge: food security
and
digitalisation;
methodological
approaches;
sustainability and resilience; and new technological trends.
The first cluster emphasises the importance of the IoT,
food security, and the impact of the pandemic. This shows
that security and traceability are now central to postpandemic food supply chains, with digital technologies
playing a key role. The IoT plays a bridging role here,
linking crisis management and food security.
Table 8. Most relevant sources
Sources
Sustainability
Journal of Cleaner Production
International Journal of Logistics Management
Operations Management Research
Annals of Operations Research
British Food Journal
Environmental Science and Pollution Research
Expert Systems with Applications
IEEE Transactions on Engineering Management
International Journal of Production Economics
Journal of Enterprise Information Management
Mathematics
Sustainable Production and Consumption
Articles
9
7
4
4
3
3
3
3
3
3
3
3
3
Figure 5. Keywords co-occurrence network
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Table 9. Keywords co-occurrence network.
Node
Covid-19
Agri-food supply chain (AFSC)
Internet of Things (IoT)
Food security
Agriculture supply chain (ASC)
Agriculture supply chain
Agriculture
DEMATEL
Blockchain technology
Ism
Supply chains
Agri-food
Circular economy
Supply chain
MCDM
AHP
Best-Worst Method (BWM)
Agri-food supply chain
Sustainability
Blockchain
Resilience
India
Supply chain management
Big data
Best-worst method
IoT
Internet of things
Machine learning
Metaheuristics
Closed-loop supply chain
Cluster
1
1
1
1
1
2
2
2
2
2
2
3
3
3
3
3
3
4
4
4
4
4
4
4
4
5
5
5
6
6
Table 10. Method parameters
Method
Network Layout
Clustering Algorithm
Normalization
Node Color by Year
Number of Nodes
Repulsion Force
The second cluster focuses on methodological
intensity. DEMATEL has the highest bridge value,
indicating connections among various concepts. ISM and
blockchain technology are also in this group, emphasising
the importance of decision-making and traceability in
agricultural supply chains.
The third cluster comprises MCDM, the circular
economy, and agri-food concepts. This reveals that multicriteria decision-making methods are particularly linked to
sustainability and the circular economy. Methods such as
AHP and BWM emerge as supporting tools.
The fourth cluster is the most dominant. At the heart of
the entire network lies the agri-food supply chain, which
has the highest betweenness value. The concepts of
sustainability and resilience are also clustered around this
centre, reflecting the importance of resilient and
sustainable supply chains, especially in the wake of the
pandemic. Additionally, digital technologies such as big
data and blockchain have been evaluated alongside these
Betweenness
4
0
3
0
0
7.117
18
46.538
6.39
2.333
0
19.429
21.347
5.045
26.45
0
0
99.69
47.214
12.445
0
0
0
0
0
1
0
0
0
0
Closeness
0.2
0.143
0.167
0.143
0.111
0.02
0.017
0.023
0.024
0.018
0.013
0.02
0.026
0.021
0.023
0.016
0.014
0.03
0.027
0.023
0.02
0.02
0.02
0.021
0.02
0.5
0.333
0.333
1
1
PageRank
0.05
0.03
0.039
0.03
0.018
0.029
0.033
0.05
0.02
0.034
0.014
0.051
0.052
0.034
0.044
0.015
0.012
0.116
0.053
0.032
0.019
0.016
0.012
0.02
0.012
0.049
0.03
0.022
0.033
0.033
Parameter
Automatic
Walktrap
Association
No
50
0.1
concepts.
Finally, the smaller clusters reveal new trends,
including the IoT, Artificial Intelligence (AI), closed-loop
supply chains, and metaheuristics. This suggests that
optimisation, artificial intelligence, and circular-economy
applications will play an increasingly prominent role in
future agricultural supply-chain studies.
Trend Topic (RQ10)
The data in the table clearly illustrates key research
trends in agricultural supply chains and MCDM over the
past five years. The most prevalent trend concepts are
“agri-food supply chain” (38 occurrences) and
“sustainability” (20 occurrences), indicating a heightened
focus on sustainability in food supply chains in the
literature. While general terms such as “supply chain” and
“agri-food” remain prevalent, more specific sub-concepts
have emerged since 2022, including “agricultural supply
chain”, “resilience”, and “agriculture”.
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Table 6. Trend topic
Term
supply chain
covid-19
MCDM
agri-food supply chain
Sustainability
agri-food
agriculture supply chain
Agriculture
Resilience
Frequency
12
9
8
38
20
12
7
6
6
The increased use of the term “MCDM” since 2022
suggests that multi-criteria decision-making methods have
become a more popular decision-support tool in agriculture
and food supply chains. “Covid-19” (nine occurrences,
peaking in 2022–23) sparked a significant short-term wave
of research, particularly on crisis management and
resilience. The subsequent rise in the concept of
“resilience” indicates that the pandemic has drawn
attention to vulnerabilities in the supply chain, broadening
research to include resilience and adaptation alongside
sustainability. Since 2021, the field has focused on
sustainability, with crisis management coming to the fore
after 2022 due to MCDM methods and the pandemic’s
impact. From 2023 to 2024, two significant trends have
emerged in the literature: the digitisation of agriculture and
a focus on resilience.
Discussion and Conclusion
Agriculture is important (Kasgari et al., 2024). This
study conducted a bibliometric analysis of publications on
the application of MCDM methods to the agricultural
supply chain. The findings reveal a rapid increase in
interest in this field in recent years. MCDM methods have
become a fundamental component of decision support
mechanisms, particularly in areas such as sustainability,
food security, resource efficiency, and risk management.
The growing importance of complex issues such as climate
change, water management, and soil quality is likely to
pave the way for the use of these methods across broader
contexts.
Analyses have revealed that the agri-food supply chain
is a key concept in the literature on this topic. Within this
framework, the most frequently discussed themes are
sustainability, the circular economy, digitalisation,
traceability, and crisis management. During the COVID-19
pandemic, the concept of “resilience” emerged as a critical
factor in reducing supply chain fragility, thereby
demonstrating the importance of multi-criteria decisionmaking methods. Technologies such as blockchain, the
IoT, big data, and artificial intelligence are also being
incorporated into decision-making processes within the
agricultural supply chain.
At a national level, the data shows that major
agricultural economies such as India and China are leading
the way in this field. In contrast, European countries,
particularly those within the EU, are conducting intensive
research as part of their sustainable agriculture and food
policies. New players, such as Iran and some African
countries, are also contributing to the global research arena
Year (Q1)
2021
2022
2022
2021
2021
2022
2022
2023
2022
Year (Median)
2022
2022
2022
2023
2023
2023
2024
2024
2024
Year (Q3)
2023
2023
2024
2024
2024
2023
2024
2024
2024
by joining collaborative networks. Analyses of researchers'
roles reveal diverse leadership models in scientific
production, with some playing pivotal roles in establishing
networks or making significant contributions.
Notably, the most frequently cited publications focus
on sustainability, traceability, and digitalisation. This
indicates that environmental issues and technological
change lie at the heart of agricultural supply chain research.
While most of these publications appear in sustainabilityfocused journals such as Sustainability and the Journal of
Cleaner Production, many also feature in journals focusing
on logistics, operations management, food, and
technology. This distribution highlights the field's
interdisciplinary nature.
• Keyword network analysis revealed four main
research themes,
• Food safety and digitalisation,
• Methodological
approaches
(AHP,
TOPSIS,
DEMATEL, etc.),
• Sustainability and the circular economy.
• New technological trends (IoT, blockchain, artificial
intelligence, and closed-loop supply chains).
Overall, the results suggest that MCDM methods are
increasingly used in agricultural supply chains for strategic
purposes. Future research is expected to focus particularly
on digital transformation, sustainability, and resilience. In
this context, MCDM methods will continue to grow in
importance as powerful tools for solving multidimensional
problems in the agricultural and food industries.
The agricultural supply chains of developing countries
face significant challenges due to low technology usage,
inefficient resource management, and high vulnerability.
In this context, MCDM methods can help these countries
to increase the efficiency of their agricultural production
and supply chain processes. Using MCDM approaches at
key decision points, such as water and soil management,
crop selection, fertiliser use, post-harvest loss reduction,
and logistics planning, enables the most effective use of
limited resources. This increases production efficiency and
strengthens food security by reducing costs and stabilising
farmers' incomes. Integrating MCDM methods into
agricultural decision-making processes is also crucial for
building resilience against risks such as climate change,
drought, epidemics, and market fluctuations. However, as
small-scale enterprises primarily carry out agricultural
production in developing countries, these producers
require support in areas such as digitalisation and access to
information. When they are made affordable, accessible,
and tailored to local producers, blockchain, big data, and
IoT technologies will enhance traceability and facilitate
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Avşar and Özek / Turkish Journal of Agriculture - Food Science and Technology, 14(4): 1023-1037, 2026
access to international markets. Rather than viewing these
technologies purely as an academic field of interest,
developing countries should recognise MCDM methods as
a strategic policy tool that can boost agricultural
productivity, promote sustainability, and offer a
competitive edge in the global marketplace. To this end,
public institutions, the private sector, and universities must
collaborate to develop integrated strategies that focus on
productivity, sustainability, and digitalisation.
This study employs bibliometric analysis to explore
recent literature on agricultural supply chains and MCDM.
The reviewed literature emphasizes the crucial role of
technological transformation in this field. Additionally,
aligning with the findings of Kamble et al. (2020), it
highlights that sustainability is a central theme in current
research.
In light of these insights, the objective of this study is
to serve as a valuable resource for future research in this
area. By identifying key components in the literature on
decision-making in agricultural supply chains, this study
aims to enrich the existing academic discourse and inform
future scholarly investigations and commercial initiatives.
Policy Recommendation
The growing adoption of MCDM methods in
agriculture and food supply chains represents a significant
advancement
for
policymakers
and
industry
representatives alike. Developing MCDM-based decision
support systems for sustainability, resource management,
efficiency, and risk analysis in agriculture will promote
environmental and economic sustainability and efficiency.
In this context, policymakers should encourage
collaboration between universities and industry and
promote the sharing of methodological and applied
research. Meanwhile, industry representatives should
invest in digitalising agriculture and in blockchain-based
transparency and data management systems. They should
also integrate the outputs of MCDM methods into their
business processes. Furthermore, the pandemic has
emphasised the importance of resilience and crisis
management in reducing supply chain vulnerabilities.
Therefore, government policies and business strategies
must be shaped accordingly. In developing countries,
adopting MCDM-based decision support systems for
agricultural production would boost productivity, food
security, and export potential. Ultimately, a science-based
culture of decision-making in the agricultural sector is
essential for long-term sustainability and competitiveness.
Theoretical Contribution
This study addresses a significant gap in the existing
literature by performing a systematic bibliometric analysis
of the intersection between agricultural supply chains and
MCDM methods. The research presents a comprehensive
framework that illustrates the mutual development of these
two fields and highlights the evolving dynamics of current
studies. The findings elucidate the role of MCDM in
agricultural decision-making and identify themes likely to
shape future research. Consequently, this study organizes
theoretical discussions and enhances conceptual clarity.
Additionally, it proposes new avenues for interdisciplinary
research to integrate decision-making models into
agricultural supply chain management.
Limitations of the Study and Future Work
This study is limited by its reliance solely on WoS and
Scopus databases, which may narrow the scope of the
literature reviewed. Future research could broaden the
analysis by incorporating additional databases (e.g.,
Google Scholar, Dimensions) and applying a wider set of
keywords in database queries. Furthermore, integrating
new bibliometric techniques could provide deeper insights
into scientific collaboration, emerging trends, and
innovative approaches in the field. Given the critical role
of agricultural supply chains in ensuring sustainable living
and global food security, such extended analyses would
hold significant academic and practical value.
Declarations
Ethical Statement
This research used only open-source secondary data
obtained from bibliographic databases. No human
participants, animals, or primary field data were involved
in the study. Therefore, ethical approval and informed
consent were not required. The study fully complies with
research integrity and ethical standards, ensuring that all
data sources were cited correctly and used solely for
academic purposes.
Funding Statement
This research received no financial support from any
institution, organization, or funding agency.
Author Contributions
İlker İbrahim Avşar: Conceptualization, methodology,
data curation, formal analysis, writing – original draft.
• Emre
Kadir
Özekenci:
Conceptualization,
methodology, writing – original draft, writing –
review & editing, supervision.
•
Technology
For the final language editing of the article, Grammarly
Pro software was utilized
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