ORIGINAL RESEARCH
published: 15 April 2022
doi: 10.3389/fnut.2022.769626
Assessing the Aftermath of
COVID-19 Outbreak in the Agro-Food
System: An Exploratory Study of
Experts’ Perspectives
Elena Raptou 1*, Konstadinos Mattas 2 , Efthimia Tsakiridou 2 and George Baourakis 3
1
Department of Agricultural Development, Democritus University of Thrace, Orestiada, Greece, 2 Department of Agricultural
Economics, Aristotle University of Thessaloniki, Thessaloniki, Greece, 3 Mediterranean Agronomic Institute of Chania, Chania,
Greece
Edited by:
Daniel Rodriguez,
La Salle University, United States
Reviewed by:
Asterios Tsioumanis,
International Institute for Sustainable
Development, Canada
Siti Musa,
Universiti Brunei Darussalam, Brunei
*Correspondence:
Elena Raptou
elenra@agro.duth.gr
Specialty section:
This article was submitted to
Eating Behavior,
a section of the journal
Frontiers in Nutrition
Received: 02 September 2021
Accepted: 04 March 2022
Published: 15 April 2022
Citation:
Raptou E, Mattas K, Tsakiridou E and
Baourakis G (2022) Assessing the
Aftermath of COVID-19 Outbreak in
the Agro-Food System: An
Exploratory Study of Experts’
Perspectives. Front. Nutr. 9:769626.
doi: 10.3389/fnut.2022.769626
Frontiers in Nutrition | www.frontiersin.org
The present study explored COVID-19 outbreak impacts on the food system in
terms of agro-food production, distribution networks efficiency, and emerging food
consumption patterns according to food experts’ perspectives. Individual level data were
selected from a sample of 59 executive managers of different domains representing
agro-food businesses, agro-food cooperatives, and agro-food consulting firms and
public institutions. The empirical analysis addressed the effects of the COVID-19 crisis
to all the stages in the food chain and attempted to indicate the factors that could
influence the trajectory from “farm to fork” under uncertain circumstances. Factor analysis
elicited the underlying dimensions of experts’ viewpoints toward the operation of the food
system during COVID-19 pandemic. Data were also elaborated through hierarchical and
k-means cluster analysis and the cluster structure was further validated by discriminant
analysis. A two-cluster solution emerged, revealing differences in experts’ perceptions
toward the aftermath of the pandemic on agriculture (socioeconomic impacts on rural
areas, impacts on agricultural production), food processing businesses (decline in the
economic viability of food businesses, sharp economic downturn in the food industry,
economic recession, incentives for innovation), food distribution networks (distribution
channels fallout, food supply disruption), and consumers’ food habits and preferences
(increasing interest in health protection, adoption of unhealthy eating habits, demand for
innovative and sustainable foods). These segments were identified as “skeptical food
experts about COVID-19 impacts” (33.9%) and “alarmed food experts about COVID-19
impacts” (66.1%). Our findings highlighted the main disruptions that the food sector
should overcome to meet consumer demand for safe and healthy food products and
also ensure food availability and food system resiliency.
Keywords: COVID-19, factor analysis, food expert clusters, agro-food production, food supply networks, food
consumption emerging trends
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COVID-19 and the Agro-Food System
INTRODUCTION
and emerging individuals’ food preferences and consumption
patterns. Individual level data were selected from a sample of
experts that were the executive managers of Greek agro-food
businesses. The empirical analysis addressed the effects of the
COVID-19 crisis to all the stages in the food chain and attempted
to indicate the factors that could influence the trajectory
from “farm to fork” under uncertain circumstances. Although
vaccination to protect against COVID-19 virus is in progress, it
is impossible to ignore potential repeated population infections
and lockdown waves in the near future due to the ongoing
pandemic breakouts till the end of 2021, putting further pressure
on the food industry (20, 21).
The novel coronavirus disease, widely known as COVID-19
and caused by the SARS-CoV-2 beta coronavirus virus, started
as a localized zoonotic outbreak in China in December 2019
and was announced as a pandemic by the World Health
Organization in March 2020 (1). In order to support the health
care systems and “flatten the curve,” governments enforced
strict measures to ensure social distancing, including mandatory
quarantine, border shutdowns and travel restrictions, largescale electronic surveillance and closure of schools, workplaces,
and transit systems (2). Declared as a “black swan” event (3,
4), the COVID-19 pandemic has spread quickly across 185
countries in six continents resulting in an unprecedented health
and socioeconomic crisis. As of 18 April 2021, there have
been 140,322,903 globally confirmed cases of COVID-19 and
3,003,794 deaths reported to World Health organization (5). In
addition, the strict lockdown measures imposed to offset the
health impacts have caused tremendous disparities in all aspects
of the economy (6–8).
The new pandemic has created instability in the food sector,
which has to confront new challenges from supply chain
disruptions and their effects on the food system (9) to health
protection of the workforce and maintenance of food availability,
accessibility and usage (8, 10). COVID-19 pandemic threatened
the production systems and governments were enforced to
critically evaluate and regulate agro-food policies to ensure
food supply availability and affordability to the public (11, 12).
Recent literature underlined the special nature of the food
industry and noted that food organizations differ from other
organizations because of their “daily life-essential” products,
making it imperative to avoid disruptions in the food chain
(13, 14).
The food sector in Greece has a vital role since it accounts
for one-third of total revenue of the manufacturing sector and
its workforce represents 36% of total employment (15). During
the last decade, the food processing industry has experienced
the impacts of a severe economic recession and had to make
adjustments for adapting to the new economic reality (16).
Today, the food sector has to overcome the pandemic obstacles
while upholding the safety standards, dealing with the constant
lockdowns in food business and maintaining sustainability
practices to the fore (17). Food businesses have to respond to
consumers’ awareness for increasing food safety, their turn to
online delivery food services and their preference for long-shelflife foods (18). Since consumers’ time for in-store food shopping
is limited, food stockpiling behaviors have arisen to mitigate
consumers’ fear for potential food shortage (19).
The present study sought to investigate the COVID-19
pandemic crisis impacts on the food industry in terms of
agro-food production, distribution networks efficiency and
consumers’ purchase behavior and attitudes toward the
new turbulent food environment. To shed light into the
pandemic aftermath on food sector, food experts were asked
to express their perceptions toward COVID-19 consequences
on agricultural and food production, rural welfare, agro-food
business operation, food distribution and delivery networks,
Frontiers in Nutrition | www.frontiersin.org
COVID-19 PANDEMIC CRISIS AND THE
FOOD INDUSTRY
To diminish COVID-19 infections within the food supply
environment, appropriate response plans were designed to
secure and direct the operations of the food supply chain at
the time of the outbreak. Response plans included various
control requirements for cleaning, sanitization, disinfection of
facilities, and monitoring and screening of the food workers
and supervisors to prevent COVID-19 transmission (22). It was
of major importance for food businesses to protect employees’
health during this pandemic crisis in order to retain the sufficient
workforce for their operation and manage employees’ unplanned
absenteeism due to COVID-19 infections (13, 23).
Since food security depends on both agro-food production
and trade, a robust supply chain is necessary to transport and
deliver food products where consumers are. During the last
year, COVID-19 pandemic restrictions have disordered the agrofood system by disrupting both production and distribution.
The workforce health preventive measures have resulted in labor
shortages in the food industry, especially within downstream
food processing and distribution systems due to worker illness,
self-isolation, or travel restrictions (24, 25). Furthermore, the
majority of food and beverage processing business are small
and medium-sized operations in terms of their staff size and
constitute over 95% of firms in the food sector (26, 27). To
the extent that small and medium sized-enterprises are more
labor intensive than large-sized enterprises, they may be more
vulnerable to labor disruptions (24). Small and medium sized
enterprises are usually deficient in financial or managerial
resources and experience difficulties in adjusting to disruptions,
especially in case they keep on longer than expected (28–
30). The fact that the small and medium-sized food businesses
usually make decisions based on routine transactions and
depend on a small number of customers increase the risks for
raw material and stock shortages, production slowdown, and
economic shrinkage (30, 31). The reduced productivity, or even
closures, in food processing and distribution plants have in turn
resulted in backlogs in farms with negative consequences for
the harvest management, agricultural production, and animal
welfare (24).
Therefore, the inconsistency in the food system due to
problems in food production, distribution and delivery during
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COVID-19 and the Agro-Food System
The findings of the qualitative research were used for the
construction of the formal online questionnaire employed in
the subsequent quantitative research design. The quantitative
research involved three district stages, namely questionnaire
design, sampling procedure, and data elaboration. The
questionnaire design was mainly of a closed response format
and considered experts’ perceptions on the impact of COVID-19
crisis on agricultural and food production, food supply and
distribution channels, consumer preferences, and respondent’s
agro-food business/organization operation and activity. It
also included scale-questions for rating experts’ views on food
industry’s initiatives for public health protection and contextual
information on respondent’s area of expertise and business
personnel. The average time for the questionnaire completion
was 15 min. The final questionnaire was pre-tested on a limited
sample of food experts, who agreed to complete it and comment
on its comprehensibility and clarity of questions, technical
performance, and usefulness of instructions (42).
In the context of the present study, food experts were
defined as stakeholders or agents with professional interest on
varied backgrounds in the agro-food sector. To identify the
sample, a generic list was adopted (43, 44) in order to consider
potential survey participants. The key element was to obtain a
target population that could represent the food industry and
would minimize biased conclusions. We invited to participate
in this survey a total of 102 executive managers from 102
different domains (i.e., different businesses, organizations, etc.),
representing the following groups: (a) agro-food businesses (i.e.,
farm producers and food processing businesses), (b) agro-food
cooperatives, and (c) agro-food consulting firms and public
institutions. All business/organizations had been established for
more than 15 years, whereas the great majority was small-sized
and medium-sized enterprises of up to 249 employees (91.4%).
Furthermore, all participants held an MSc certification and had
a professional experience of at least 10 years in the agro-food
sector (45). Recruitment was purposive and participants engaged
in the survey voluntarily, with no specific reward after personal
invitation. All the list members were invited to participate via
email and were informed on the main purposes and stages of
the survey (46, 47). The link to access the online survey and
response confidentiality were provided within the email context.
To achieve the maximum response rate, two reminding emails
were sent with a 10-days frequency in those cases where no reply
had been received, insisting on the importance of participation.
Finally 59 completed questionnaires were selected between June
and July 2020, corresponding to a response rate of ∼58% for
this online survey. The response rate is quite satisfactory and
significantly higher to the average response rate of 11% for the
online surveys (48).
the pandemic has eventually impacted the availability and access
of food products to consumers (9, 12). Further obstacles in the
availability of agro-food products were imposed from the closure
of hotels and restaurants resulting in considerable reductions in
food donations to food banks. This put additional pressure to
the food industry, which had to meet the increasing demand as
people suffered loss of incomes (24). In addition, the pandemic
crisis has shaken consumers’ confidence in the resilience of the
food system. It remains a vital question for the food industry
how consumers’ purchase behavior has been affected after having
seen empty store shelves in food stores at the initial stages of the
pandemic (25). Food hoarding was a common practice during the
lockdowns since consumers felt insecure about food availability
and subsequent food price hikes. Reasons of food hoarding
might stem from both rational and irrational motives. From
the rational side, consumers might wish to stockpile essential
food products for the 2-week-home-quarantine period in case
of infection or tried to avoid transportation to food stores.
To minimize personal contacts and maintain social distancing,
consumers also increased purchases of food items with long
shelf-life or takeaway meals (32). From the irrational side, peer
influence played the key role since consumers seemed to imitate
and follow others’ consumption patterns, the so called herd effect
(33). The irrational hoarding was the result of panic buying
behavior and further complicated food shortages by putting extra
burden in the food chain (4). Feelings of instability, uncertainty,
and precariousness also incited panic purchase behaviors in an
attempt to decrease anxiety and fear (34–36).
METHODOLOGY
Data and Sample Selection
To shed light into experts’ perceptions toward the impact of
COVID-19 pandemic on the food industry, this study employed
a hybrid approach and incorporated both qualitative and
quantitative research designs. The novelty of the specific topic
of interest complicated the proper definition of the important
elements for establishing the survey variables (37). Therefore,
a qualitative approach could offer a deep understanding of
experts’ views on the impact of COVID-19 on food industry.
The descriptive nature of the qualitative research could provide
a primary insight into the research objectives through the unique
viewpoints of those who have directly experienced the impact of
the pandemic on the food chain (38, 39). Owing to the paucity of
literature in this specific area, we also sought to build a holistic
picture of COVID-19 economic and infrastructure impacts on
the food industry by establishing a consensus of experience
from food experts and stakeholders. Therefore, the qualitative
research design included semi-structured interviews (40) that
were held with eight food industry experts in May 2020 in order
to provide a better understanding of COVID-19 consequences
on food systems. An informal questionnaire with 15 open-ended
questions was developed for the qualitative research interviews,
whereas the sample selection was based on purposive sampling
and snowballing techniques (41). The average interview duration
was 50 min ranging from 34 to 93 min.
Frontiers in Nutrition | www.frontiersin.org
Measures
The formal questionnaire comprised the main data selection tool
and was administered online to a specific sample of executive
managers in the agro-food sector. Due to the dearth of the
relevant literature and the novelty of the study, the questionnaire
development and the variable construction were based on the
outcomes of the precedent qualitative research (49, 50).
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COVID-19 and the Agro-Food System
on the consequences of the pandemic on agricultural produce,
rural areas welfare, and employment in the agricultural sector,
participants were asked to score an 11-item Likert type scale
rating from “totally disagree” to “totally agree” (totally disagreedisagree-neither disagree nor agree-agree-totally agree). This
scale is presented in Table 2. Furthermore, a 14-item variable
rated on a five-point Likert scale (totally disagree-disagreeneither disagree nor agree-agree-totally agree) was employed to
investigate experts’ views on the impact of COVID-19 on the
food manufacturing industry, including food production, food
processing businesses efficacy, and food innovation (Table 4).
The effects of the pandemic on food supply and the efficiency
of the distribution networks were expressed through a 9-item 5point Likert type scale ranging from “totally disagree” to “totally
agree” (Table 6).
The third part of the questionnaire focused on consumer
food habits and preferences and how they were influenced
by COVID-19 crisis. To assess experts’ opinions on the
impact of the pandemic on consumer demand, a 13-item 5point scale was adopted ranging from “extremely unlikely”
to “totally likely” (extremely unlikely-unlikely-neutral-likelyextremely likely”) (Table 8).
Finally, contextual information was included in the last
part of the questionnaire. Respondents were asked to declare
their employment in the agro-food business/organization on a
categorical variable taking the value of 1 for “farmers,” the value
of 2 for “food processing enterprises,” the value of 3 for “food
distributors and sellers,” and the value of 4 for “consultants
and policy makers.” The personnel size was expressed through
an ordered indicator taking the value 1 for micro enterprises
(up to 10 employees), the value 2 for small-sized enterprises
(11–49 employees), the value 3 for medium-sized enterprises
(50–250 employees), and the value 4 for large enterprises (over
250 employees).
TABLE 1 | Experts’ profile (N = 59).
Variable
Frequency
Occupation
Farmers
27.1%
Food processing enterprises
33.9%
Food distributors—sellers
25.4%
Consultants—policy makers
13.6%
COVID-19 pandemic has affected the
business/organization I work for
93.2%
To what extent do you think the
activity of the
business/organization in which
you are employed will be affected
by COVID-19 pandemic?
Not at all
6.8%
Slightly
5.1%
Moderately
28.8%
Very much
44.1%
Extremely
15.5%
How the economic performance
of the business/organization, in
which you work, will be affected
in the near future?
It will deteriorate
78.0%
It will not be affected
10.2%
It will improve
11.9%
Personnel (number of employees)
Micro enterprises (up to 10
employees)
43.1%
Small-sized enterprises (11–49
employees)
27.6%
Medium sized enterprises (50–250
employees)
17.2%
Large enterprises (over 250
employees)
12.1%
Methods of Analysis
Factor Analysis
Exploratory factor analysis (EFA) is widely used in social sciences
to (i) assess theories of learning, cognition, and personality,
(ii) explore scale validity, and (iii) decrease dimensionality in
a set of variables so that they can be more easily adapted in
further statistical analysis (51–53). In the present study, EFA
was employed to test the potential interdependencies among
the observed variables and the underlying theoretical constructs
referred as latent variables (54). In particular, EFA was used
to uncover the underlying structure of the multi-item variables
expressing experts’ opinions on the impact of COVID-19 crisis
on agro-food sector and reduce it into smaller sets to test
construct validity and enhance the interpretability of the multiitem scales (55–57).
Prior to EFA implementation, several prerequisites were
worked out before proceeding to the analysis. First, suitability
of the data was highlighted since the sample size might be
considered as arguably small (N = 59). Recent literature
has suggested that in case over-determination is strong and
communalities are high (over 0.60), even relatively small sample
sizes can assure accurate EFA solutions (58–61). According to
In the first part of the questionnaire, participants were asked
to assess the COVID-19 consequences on the activities and
practices of the agro-food businesses/organizations. In particular,
participants had to state whether COVID-19 pandemic had
affected the activity of the business/organization in which
they were employed. A dichotomous indicator was constructed
taking the value 1 in case of a positive response and zero
otherwise. Second, a five-point scale ranging from “not at
all” to “extremely” (not at all-slightly-moderately-very muchextremely) was used to express the extent to which participants
believe that the operation of the agro-food business/organization
will be affected in the future. Then, respondents had to specify the
aftermath of COVID-19 pandemic by responding to an ordered
variable taking the value 1 if the economic performance of the
business/organization was expected to deteriorate, the value 2 for
a neutral impact (no effect), and the value 3 in case the economic
performance improved in the near future.
In the second part of the questionnaire, multi-item scales were
employed to describe experts’ perceptions toward the impact of
COVID-19 on the food sector. To explore experts’ viewpoints
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TABLE 2 | Experts’ perceptions toward COVID-19 impacts on agriculture (%).
Variables
Strongly disagree
Disagree
Neither disagree nor agree
Agree
Strongly agree
COVID-19 pandemic will affect agricultural production
5.08
10.17
40.68
28.81
15.25
COVID-19 pandemic will cause significant shortages of raw
materials used in agricultural production
8.47
33.90
30.51
15.25
11.86
COVID-19 crisis will cause a considerable reduction in agricultural
production
8.47
30.51
35.59
20.34
5.08
COVID-19 pandemic will exacerbate economic inequalities
between small and large agricultural producers
6.78
10.17
30.51
35.59
16.95
COVID-19 pandemic will decrease agricultural incomes
5.08
11.86
37.29
28.81
16.95
The consequences of COVID-19 crisis will force many producers
to abandon agricultural profession
20.34
27.12
35.59
13.56
3.39
COVID-19 pandemic will cause job losses, especially in rural areas
16.95
23.73
27.12
15.25
16.95
COVID-19 pandemic will have more severe consequences in
Greek farm productivity performance compared to other countries
20.34
27.12
30.51
16.95
5.08
COVID-19 economic impacts will be more deeply felt in rural areas
18.64
27.12
27.12
18.64
8.47
Unemployment rates will increase in rural areas
20.34
33.90
30.51
8.47
6.78
Social welfare will mostly decrease in rural areas
13.56
25.42
38.98
13.56
8.47
maximum differentiation between clusters and the maximum
homogeneity within each cluster (70). Since this study focused
on participants’ classification rather than building a predictive
model, cluster analysis was suggested as the most appropriate
tool to assign experts into different homogenous segments and
ascertain differences in perceptions toward the aftermath of
COVID-19 in the agro-food sector (71–73).
The clusters were defined by both hierarchical and nonhierarchical (k-means) clustering techniques. First, hierarchical
cluster analysis was employed to define the adequate number
of clusters after classifying cases into homogenous clusters by
combining them together one at a time in a series of sequential
steps (74, 75). The Ward’s method was used as the agglomeration
method to determine the optimal number of clusters. In Ward’s
method, cases are combined so as to ensure the lowest increase
of the variance in the cluster, and hence its highest homogeneity
(76). The squared Euclidean distance was adopted as a measure
of similarity between cases. Second, the validity of hierarchical
clustering was enhanced by k-means algorithm, in which it was
set a priori the number of clusters resulted from the Ward’s
hierarchical clustering method. Finally, the cluster structure was
further validated by discriminant analysis, which estimated a
discriminant function for examining the accuracy by which the
study participants were allocated in the clusters (77, 78).
Sapnas and Zeller (62), even sample sizes of 50 individuals can
be appropriate for performing EFA. Furthermore, MacCallum
et al. (63, 64) noted that all items in a factor model should
have communalities of over 0.60 or an average communality of
0.7 to justify EFA application to small sample sizes. To evaluate
the quality of the present study, the average communality was
assumed to be at least 0.7. In addition, the Kaiser–Meyer–
Olkin (KMO) criterion of sampling adequacy and the Bartlett’s
test of sphericity were adopted to assess the suitability of
sample sizes for factor analysis and the fitness of the data
(60, 65). KMO estimations over the value of 0.50 depicted data
adequacy (66) and the Bartlett’s test of sphericity was statistically
significant for ensuring the suitability of EFA (60). Extracting
factors with eigenvalues over 1 were retained according to
K1 rule, whereas extracting factors with eigenvalues <1 were
discarded without losing much of the original variability (67).
To achieve the optimal factor solution, all variables with
estimated factor loadings over 0.40 on more than one factor
were removed from the analysis (68). Finally, reliability analysis
(Cronbach’s α coefficient >0.60) was conducted to measure the
unidimensionality of the set of the variables representing each
factor (69).
For the present study, five EFA applications were considered
to determine the underlying components that explain experts’
perceptions toward the impact of COVID-19 pandemic on
agricultural production, food processing businesses, food supply
and distribution networks, purchase behavior, and food industry
initiatives for public health safety. Principal component analysis
(PCA) was employed with the Varimax rotation method for
aggregating variables that load highly on a specific factor.
RESULTS
Table 1 depicts the profile of food industry experts engaged in
the present study. Of the 59 participants, 27.1% were farmers
(and also the managers of the farms), 33.9% were executive
managers in food processing enterprises, 25.4% were food
distributors and sellers, and the rest 13.6% corresponded to
consultants and policy makers. In total, the great majority of
experts (43.1%) were engaged in micro enterprises of up to 10
employees, whereas the 86.1% were the managers of small and
medium-sized enterprises (SME). According to recent evidence,
SMEs constitute the overwhelming majority of Greek enterprises,
Cluster Analysis
In order to group experts according to their perceptions
toward COVID-19 pandemic and its impacts on the food
industry, cluster analysis was conducted for providing similar
expert segments based on internal homogeneity and intragroup
heterogeneity. Each cluster was mutually exclusive with the
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COVID-19 and the Agro-Food System
overwhelming majority of respondents underlined the pandemic
consequences on Greek economy and seemed to believe that the
subsequent economic decadence will be more intense compared
to the crisis on the health system (77.96%). Furthermore,
respondents were convinced that the current pandemic induced
crisis will mostly affect Greek economy (57.63%) but they
perceived its impact on food industry as minor compared to
other economic sectors (57.62%). Food experts stated that the
competition between food businesses will become more intense
(55.93%) with small businesses being more difficult to adjust
to the turbulent economic environment. They also expressed
their concerns about the viability of the small food businesses
(49.15%) and the subsequent failure of many food processing
businesses (55.93%). However, participants were optimistic that
new opportunities could emerge in food industry since COVID19 crisis may reveal incentives for food innovation (59.32%).
With respect to the application of EFA in the 14-item scale,
the KMO measure of sampling adequacy was 0.815, implying
data suitability for the PCA (77, 80). Furthermore, the Bartlett’s
test of Sphericity was highly significant (Chi-square = 418.057,
p < 0.01). Four components arose from the EFA explaining
the 70.685% of the total variance. The calculated Cronbach’s α
reliability coefficients ranged from 0.612 to 0.892 indicating an
adequate internal consistency of each factor (81).
Table 5 shows the EFA and reliability analysis output on
experts’ views toward the food processing businesses. The first
factor labeled as “decline in the economic viability of food
businesses” included five variables explaining 41.911% of the total
variance and had a reliability coefficient of 0.892. This factor
loaded attributes related to the competition in food industry, the
severe consequences on small food businesses and the potential
decrease in employment rates. The second factor labeled as
“sharp economic downturn in the food industry” comprised four
variables, which explained 12.247% of the total variance and had
a reliability coefficient of 0.725. This factor involved attributes
about the ability of food business owners to adapt themselves to
the meta-COVID era and cope with individuals’ food needs. The
third factor included three items and was labeled as “economic
recession” to describe experts’ attitudes toward the impending
global economic crisis and its effects on Greek economy and food
production. This factor had a Cronbach’s α reliability coefficient
equal to 0.664 and explained 9.298% of the total variance. Finally,
the fourth factor entitled as “incentives for innovation in the
food sector” loaded two items regarding potential incentives
for the production of innovative agricultural and food products
explaining the 7.229% of the total variance and presenting a
reliability coefficient of 0.612 (Table 5).
corresponding to 85% of private employment and have suffered
the most by the prolonged economic recession in the last decade
(79). Approximately 93% of respondents stated that COVID19 pandemic has already affected the economic activity of
the enterprise in which they are employed with almost 78%
predicting that the economic performance of food businesses will
deteriorate in the near future.
The Impact of COVID-19 Crisis on
Agricultural Production
Participants’ viewpoints on the consequences of COVID-19
on agricultural production and Greek farms are described on
Table 2. Agro-food experts seemed to agree that COVID-19
crisis will affect agricultural production (44.06%) and may
exacerbate inequalities between farmers (52.54%), although the
productivity of Greek farms is not expected to be influenced
to a greater extent compared to other countries (47.46%). The
stability of agricultural incomes was also questioned since a great
proportion of respondents stated that agricultural income decline
is inevitable (45.76%) and rural areas will experience significant
job losses as an aftereffect of the pandemic (32.2%).
Explorative factor analysis (EFA) through principal
component analysis (PCA) with varimax rotation was employed
to evaluate the dimensionality of the most important factors
describing experts’ perceptions toward COVID-19 aftermath
on agriculture and productivity. The tests that examined the
quality of EFA met the common requirements. In particular,
the Kaiser–Meyer–Olkin (KMO) measure was 0.880, indicating
that the data employed were adequate for the PCA (77, 80). The
Bartlett’s test of Sphericity was highly significant (Chi-square =
427.980, p < 0.01) showing that the variables were correlated
and suitable for structure detection. The EFA of the 11 variables
provided a two-component solution and the total variance
explained was 69.851%. The Cronbach’s α coefficients were
satisfactory since both measures were over 0.60 (81).
The results of the EFA and reliability analysis are
analytically presented on Table 3. The first factor entitled
as “socioeconomic impacts on rural areas” included seven
variables that explained 57.170% of the total variance and
had a reliability coefficient of 0.931. This factor loaded
attributes related to respondents’ perceptions on COVID-19
impacts on the welfare of rural regions, on farm productivity
performance in isolated areas and on agricultural incomes.
The second factor, “impacts on agricultural production,”
incorporated four variables explaining 12.682% of the total
variance with a reliability coefficient of 0.824. This factor
involved attributes regarding stakeholders’ concerns about
the availability and accessibility to raw materials used in
agriculture, the potential reduction in agro-food production
and the economic inequalities between small and large farmers
(Table 3).
The Impact of COVID-19 Crisis on Food
Distribution Networks
Respondents’ perceptions toward the consequences of the
pandemic on food distribution channels are described on
Table 6. The vast majority of agro-food experts were convinced
that COVID-19 crisis will substantially result in higher food
production costs, leading in turn to higher retail prices of
food products (52.54%). Furthermore, ∼56% of participants
The Impact of COVID-19 Crisis on Food
Production and Food Processing Industry
Table 4 presents experts’ perceptions toward COVID-19 effects
on food processing business and food production. The
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COVID-19 and the Agro-Food System
TABLE 3 | Factor analysis (PCA) and reliability analysis output on experts’ perceptions toward the impact of COVID-19 crisis on agriculture.
Eigenvalue
6.289
1.395
Explained variance
%
57.170
12.682
Factors
Factor loading
m
S.D.
Factor 1: Socioeconomic impacts on
rural areas
COVID-19 economic impacts will be more
deeply felt in rural areas
0.870
2.712
1.218
COVID-19 pandemic will result in job
losses, especially in rural areas.
0.861
2.915
1,320
Unemployment rates will increase in rural
areas
0.828
2.475
1.120
Social welfare will mostly decrease in rural
areas
0.813
2.780
1.152
The consequences of COVID-19 crisis will
force many producers to abandon
agricultural profession
0.765
2.525
1.073
COVID-19 pandemic will have more
severe consequences in Greek farm
productivity performance compared to
other EU countries
0.720
2.593
1.147
COVID-19 pandemic will decrease
agricultural incomes
0.705
3.407
1.069
Factor 2: Impacts on agricultural
production
COVID-19 pandemic will cause significant
shortages of raw materials used in
agricultural production
0.864
2.881
1.146
COVID-19 crisis will cause a considerable
reduction in agricultural production
COVID-19 pandemic will affect agro-food
production
0.864
2.831
1.020
0.684
3.390
1.034
COVID-19 pandemic will exacerbate
economic inequalities between small and
large agricultural producers
0.630
3.458
1.069
Cronbach’s α
0.931
0.824
m, mean; S.D., standard deviation.
ability to deliver products to consumers in a timely manner,”
“Overloaded transport networks may lead to market shortages of
perishable agro-food products (e.g., grocery),” “Food production
costs will dramatically increase”). Reliability analysis provided
the Cronbach’s α calculation equal to 0.890. The second factor
accounted for 15.158% of the total variance and was characterized
by five of the nine variables (“COVID-19 pandemic will lead
to a reduction in imports of agricultural/livestock products,” “I
believe that the prices of agricultural products will considerably
increase in the near future,” “I believe that there will be significant
food shortages in regions far from urban areas,” “COVID19 pandemic will lead to significant shortages of agricultural
products,” “COVID-19 pandemic will lead to a reduction in the
exports of Greek agricultural/livestock products”). This factor
was labeled as “food supply disruption” and the Cronbach’s α
coefficient had a value of 0.842 (Table 7).
believed that overloaded transport networks may lead to market
shortages of perishable agro-food products (55.93%), whereas
nearly 53% agreed that foods may be delivered to consumers
after the scheduled time. A significant proportion of respondents
expressed their concerns about the export and import volume of
agricultural/livestock products, considering that there will be a
noticeable decrease of both in the meta-COVID era (Table 6).
EFA revealed the most important components that depict
experts’ perceptions toward the pandemic effects on food
distribution. Two factors were extracted explaining 71.066% of
the total variance, whereas the KMO measure with a value of
0.861 and the Bartlett’s test of Sphericity (Chi-square = 306.198,
p < 0.01) met the common requirements and justified the
implementation of EFA through PCA with varimax rotation
(77, 81). The Cronbach’s α coefficients were satisfactory with
values over 0.60 (81).
The first factor accounted for 55.908% of the total variance
and was characterized by four of the nine variables that
constructed the 5-point scale. This first factor reflected experts’
views on the consequences of COVID-19 crisis on distribution
systems efficiency and was labeled as “distribution channels
fallout” (“the pressure on distribution networks may reduce
food quality,” “COVID-19 crisis will reduce the supply chain’s
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The Impact of COVID-19 Crisis on
Consumers’ Food Preferences
Experts’ perceptions toward the impact of COVID-19 crisis on
consumers’ food habits are reported on Table 8. Most of the
participants believed that the pandemic will bring changes to
consumers’ food preferences and consumption patterns with
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COVID-19 and the Agro-Food System
TABLE 4 | Experts’ perceptions toward COVID-19 impact on food processing businesses (%).
Variables
Strongly disagree
Disagree
Neither disagree nor agree
Agree
Strongly agree
COVID-19 crisis will decrease food
production
16.95
25.42
32.20
20.34
5.08
COVID-19 pandemic will have a major
impact on the operation of small food
businesses
10.17
22.03
18.64
30.51
18.64
COVID-19 impacts will make small
local food businesses less
economically viable than large food
businesses
11.86
15.25
23.73
23.73
25.42
COVID-19 crisis will intensify
competition between businesses in
food industry
6.78
11.86
25.42
33.90
22.03
COVID-19 crisis will reveal incentives
for the production of innovative
agricultural products
1.69
13.56
25.42
30.51
28.81
COVID-19 crisis will result in the
closure of many processing
businesses
10.17
20.34
13.56
35.59
20.34
Food industry will have to face a
sharp decline in employment rates
13.56
16.95
25.42
18.64
25.42
COVID-19 crisis will cause significant
food shortages
13.56
27.12
32.20
18.64
8.47
Greek food processing businesses
lack organizational skills to cope with
the growing food needs
16.95
28.81
23.73
22.03
8.47
COVID-19 crisis will have long-run
impacts on food industry
6.78
22.03
28.81
33.90
8.47
COVID-19 crisis will have a minor
impact on food industry compared to
other industries
8.47
8.47
25.42
28.81
28.81
COVID-19 economic fallout will long
outlive the health crisis
0.00
5.08
16.95
32.20
45.76
The current pandemic-induced crisis
will mostly affect Greek economy
compared to other countries
3.39
10.17
28.81
33.90
23.73
Food processors’ interest will move
toward innovative food products (e.g.,
functional foods)
11.86
18.64
44.07
15.25
10.17
noticeable increases in the demand for long shelf-life products
(76.27%), packaged foods (84.74%), and locally-produced foods
(76.27%). Furthermore, consumers may become more oriented
toward ready-to-eat meals (44.07%), innovative (38.98%), and
sustainable food products (49.15%). Although a significant
proportion of participants were convinced that the pandemic
crisis may turn consumers’ interest to healthier eating choices
and enhance the prevalence of the Mediterranean diet, a
significant proportion of respondents considered that it is very
possible that consumers’ interest for cheaper and unhealthy food
options will increase.
EFA revealed three factors that explain ∼63.50% of the
total variance, whereas the KMO test for sampling adequacy
had a value of 0.778 that was above the acceptable level of
0.60. The Bartlett’s test of Sphericity (Chi-square = 321.540,
p < 0.01) also justified the implementation of EFA through
PCA with varimax rotation (77, 81). The Cronbach’s α
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coefficients were satisfactory with values ranging from 0.610 to
0.857 (81).
The first factor accounted for 35.944% of the total variance
and included eight of the 13 items describing experts’ viewpoints
on consumers’ awareness for food safety and consciousness
toward health issues. This first factor was characterized as
“consumers’ increasing interest in health protection” and had
a reliability coefficient of 0.857. The second factor accounted
for 18.623% of the total variance and was characterized by
three of the 13 variables presenting participants perceptions
toward consumers’ food choices and preferences in the metaCOVID era. Food experts seemed to believe that consumers
were more likely to shift to convenience and cheaper meals
and hence the second factor was entitled as “adoption of
unhealthy eating habits” and had a reliability coefficient
equal to 0.679. Finally, the third factor was found to
explain 8.922% of the total variance and was labeled as
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TABLE 5 | Factor analysis (PCA) and reliability analysis output on experts’ perceptions toward the impact of COVID-19 crisis on food production and food industry.
Eigenvalue
Explained variance
%
5.868
1.715
1.302
1.012
41.911
12.247
9.298
7.229
Factors
Factor loading
m
S.D.
Factor 1: Decline in the economic
viability of food businesses
COVID-19 crisis will intensify
competition between businesses in
food industry
0.779
3.525
1.165
COVID-19 impacts will make small
local food businesses less
economically viable than large food
businesses
0.753
3.356
1.336
COVID-19 pandemic will have a major
impact on the operation of small food
businesses
0.740
3.254
1.281
COVID-19 crisis will result in the
closure of many processing
businesses
0.666
3.356
1.297
Food industry will have to face a
sharp decline in employment rates
0.656
3.254
1.372
Factor 2: Sharp economic
downturn in the food industry
Greek food processing businesses
lack organizational skills to cope with
the growing food needs
0.740
2.763
1.223
COVID-19 crisis will have major
impacts on food industry compared
to other industries
0.705
2.398
1.232
COVID-19 pandemic will cause
significant food shortages
0.658
2.814
1.152
COVID-19 crisis will have long-run
impacts on food industry
0.638
3.153
1.080
Factor 3: Economic recession
The current pandemic-induced crisis
will mostly affect the Greek economy
compared to other countries
0.798
3.644
1.063
COVID-19 economic fallout will long
outlive the health crisis
0.775
4.186
0.900
COVID-19 crisis will decrease food
production
0.509
2.712
1.130
Factor 4: Incentives for innovation
in the food industry
COVID-19 crisis will reveal incentives
for the production of innovative
agricultural products
0.894
3.712
1.084
Food processors’ interest will move
toward innovative food products (e.g.,
functional foods)
0.663
2.932
1.112
Cronbach’s α
0.892
0.725
0.664
0.612
m, mean; S.D., standard deviation.
Cluster Analysis Results
“consumers’ demand for innovative and sustainable foods”
according to the content of the two variables included.
Reliability analysis provided a Cronbach’s α measure equal to
0.610 (Table 9).
Figure 1 summarizes the results derived from the EFA
application and provides a thorough presentation of the experts’
perceptions toward the impacts of COVID-19 pandemic on
the various facets of the food industry, from the production
stage to the final purchase and consumption of the agrofood products.
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Cluster analysis was conducted on two stages to classify
experts’ segments on the basis of their attitudes toward the
impact of COVID-19 on agriculture, food processing industry,
food distribution networks, and consumers’ food habits and
preferences. Cluster analysis resulted to the identification of two
expert segments. T-tests for the equality of means indicated
statistically significant differences between the two clusters in
terms of perceptions toward agro-food production and food
business viability, food distribution problems, and potential
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COVID-19 and the Agro-Food System
TABLE 6 | Experts’ perceptions toward COVID-19 impact on food distribution networks (%).
Variables
Strongly disagree
Disagree
Neither disagree nor agree
Agree
Strongly agree
COVID-19 pandemic will lead to
significant shortages of agro-food
products
15.25
20.34
37.29
20.34
6.78
I believe that the retail prices of
agro-food products will considerably
increase in the near future
6.78
16.95
23.73
37.29
15.25
I believe that there will be significant
food shortages in regions far from
urban areas
13.56
28.81
25.42
22.03
10.17
Food production costs will
dramatically increase
5.08
16.95
25.42
38.98
13.56
Overloaded transport networks may
lead to market shortages of
perishable agro-food products (e.g.,
grocery)
6.78
18.64
18.64
38.98
16.95
The pressure on distribution networks
may reduce food quality
15.25
16.95
32.20
23.73
11.86
COVID-19 pandemic will reduce the
ability of the supply chain systems to
deliver products to consumers in a
timely manner
8.47
15.25
23.73
42.37
10.17
COVID-19 pandemic will lead to a
reduction in the exports of Greek
agricultural-livestock products
10.17
13.56
25.42
30.51
20.34
COVID-19 pandemic will lead to a
reduction in imports of
agricultural/livestock products
6.78
11.86
23.73
47.46
10.17
scored highly on the “impacts on agricultural production” (3.560
vs. 2.325, t-test = −6.915, p < 0.01), “economic recession” (3.821
vs. 2.904, t-test = −4.831, p < 0.01) and “distribution channels
fallout” (3.673 vs. 2.500, t-test = −5.098, p < 0.01). Furthermore,
cluster 2 participants agreed that there is an increasing risk for
consumers’ to adopt less healthy eating habits (3.334 vs. 2.750,
t-test = −2.421, p < 0.05), although they noted the emerging
opportunities for food innovation in the food sector and the
rising demand for innovative and sustainable food choices. All
the food experts in this cluster declared that the pandemic has
clearly affected the activity of their enterprise, whereas 89.7%
agreed that its economic performance will deteriorate in the
short-run (Table 11). Finally, discriminant analysis confirmed
the classification accomplished through cluster analysis, showing
that the exactness of classification was 98.3%.
changes in food consumption patterns. The clusters were labeled
according to the factors that were considered to be of high
importance for each of them (Table 10). To provide a complete
profile of food expert segments, cross-tabulation and Pearson’s
χ 2 statistics were also estimated to define differences in business
characteristics between clusters (Table 11).
Cluster 1 was labeled as “skeptical to COVID-19 impacts”
and included 33.9% of respondents. This segment had the lowest
mean scores in “socioeconomic impacts on rural areas” (1.836
vs. 3.252, t-test = −7.321, p < 0.01), “decline in the economic
viability of small food enterprises” (2.290 vs. 3.892, t-test =
−7.576, p < 0.01) and “sharp economic downturn in the food
industry” (2.062 vs. 3.147, t-test = −5.608, p < 0.01) factors
compared to Cluster 2. Food experts that represented Cluster
1 had reservations on the pandemic effects on agricultural and
food production and were skeptical about potential changes in
consumers’ food patterns as a direct impact of the pandemic
on food preferences and choices (Table 10). The first cluster
grouped food experts who believed that the pandemic had
affected the activity of the enterprise/service in which they
were employed (80.0%), although they were convinced that its
economic performance will either remain stable or even improve
in the near future (45.0%) (Table 11).
Cluster 2 represented “alarmed food experts about COVID19 impacts” and included 66.1% of participants. The members
of this cluster were convinced that COVID-19 aftermath will be
severe for both the agro-food sector and the economy since they
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DISCUSSION
The present study sought to explore the aftermath of the COVID19 pandemic on the food sector. For this reason an explorative
research was designed to select information from a sample
of food experts regarding the impact of COVID-19 crisis on
all the dimensions of the food industry, including agro-food
production, agro-food products distribution and delivery, and
consumers’ food purchase patterns. Considering the catalytic role
of the new pandemic in the agro-food environment, food experts
were deemed the most appropriate to highlight the new reality in
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TABLE 7 | Factor analysis (PCA) and reliability analysis output on experts’ perceptions toward the impact of COVID-19 crisis on food distribution networks.
Eigenvalue
5.032
1.364
Explained variance
%
55.908
15.158
Factors
Factor loading
m
S.D.
Factor 1: Distribution channels
fallout
The pressure on distribution networks
may reduce food quality
0.873
3.000
1.232
COVID-19 crisis will reduce the supply
chain’s ability to deliver products to
consumers in a timely manner
0.849
3.305
1.119
Overloaded transport networks may lead
to market shortages of perishable
agro-food products (e.g., grocery)
0.843
3.407
1.176
Food production costs will dramatically
increase
0.746
3.390
1.083
Factor 2: Food supply disruption
COVID-19 pandemic will lead to a
reduction in imports of agricultural and
livestock products (e.g., milk)
0.86
3.424
1.053
I believe that the retail prices of
agro-food products will considerably
increase in the near future
0.702
3.373
1.143
I believe that there will be significant food
shortages in regions far from urban areas
0.687
2.864
1.210
COVID-19 pandemic will lead to
significant shortages of agro-food
products
0.668
2.831
1.132
COVID-19 pandemic will bring a
reduction in the exports of Greek
agricultural—livestock products
0.645
3.373
1.244
Cronbach’s α
0.890
0.842
m, mean; S.D., standard deviation.
During the pandemic, workers’ absenteeism because of COVID19 infection or transportation restrictions imposed by repeated
lockdowns made farm productivity more fragile, especially
in labor intensives sectors, such as planting and harvesting,
horticulture, and livestock productions (13, 14, 85). Reduced
capacity in agricultural supply resulted in volatility and increases
in agricultural prices, putting extra burdens on retail price for
agricultural commodities and tightening consumer purchase
power. For some agricultural products, such as meat, recent
evidence showed that although wholesale and retail meat prices
were surged to peak, livestock prices were decreasing (86, 87).
This boost in the farm-to-wholesale marketing margin (87)
could have major impacts on the welfare of both producers
and consumers.
Due to the nature of the agricultural activity, there is a specific
timetable to follow for maintaining stability in the production
process. Since the start of COVID-19 outbreak, many farmers
had no other choice left but destroy their products because
of the restrictive measures. According to the Dairy Farmers of
America, farmers were forced to dump ∼3.7 million gallons of
milk every day and chicken processors faced serious problems
with staff absenteeism and had to euthanize chickens because of
the reduced capacity in plants (88, 89). Under such conditions,
farmers have to come to terms with income uncertainty, whereas
the reduction of consumers’ purchasing power may further
push farmers’ incomes downwards in the long term (90, 91).
the food chain and assess evidence whether it will ever revert to
pre-COVID-19 “norms.”
COVID-19 Pandemic and Its Impact on
Agriculture and Agricultural Businesses
Our findings showed that the pandemic is expected to have major
impacts on the agricultural sector. A great proportion of experts
agreed that the COVID-19 breakout may exacerbate economic
inequalities between small and large producers and strike
farmers’ incomes to such an extent that may cause job losses and
increase unemployment rates, especially in rural areas. Although
experts seemed to be rather cautious to predict a subsequent
reduction in agricultural production, they were convinced that
the meta-COVID-19 era will signify crucial modifications in
the food sector. Besides, the majority (66.1%) were skeptical to
the pandemic repercussions affirming its socioeconomic impacts
and the welfare decrease in rural areas. Other studies have also
indicated the pandemic consequences in agricultural production
underlying their reflection to the root of the food system. For
instance in India, wheat and pulse harvesting were hindered due
to migrant workers’ shortage, whereas in Ethiopia, farmers had
to deal with income loss due to overstocked produce and input
shortage (82–84).
It is a common practice in many countriess that seasonal
farmworkers are usually employed for planting, sorting,s
harvesting, processing, and transporting cultivated crops (13).
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TABLE 8 | Experts’ perceptions toward COVID-19 impact on consumers’ food habits and preferences (%).
Variables
Extremely unlikely
Unlikely
Neutral
Likely
Extremely likely
The demand for foods with long
shelf-life will increase
1.69
5.08
16.95
47.46
28.81
Consumers will become more
demanding with the measures taken
by stakeholders to protect public
health
1.69
5.08
8.47
45.76
38.98
The pandemic will increase food
e-commerce
0.00
1.69
10.17
27.12
61.02
COVID-19 crisis will increase the
consumption of packaged agri-food
products
1.69
3.39
8.47
33.90
52.54
COVID-19 crisis will increase the
demand for ready-to-eat meals
8.47
25.42
22.03
30.51
13.56
Consumers will become more
skeptical about fresh foods
3.39
10.17
22.03
25.42
38.98
COVID-19 crisis may turn consumers
to unhealthy food choices
11.86
27.12
16.95
33.90
10.17
Consumers will turn to
locally-produced foods that are easier
to obtain
0.00
6.78
16.95
33.90
42.37
COVID-19 crisis will turn consumers
to adopt Mediterranean diet
1.69
10.17
35.59
25.42
27.12
COVID-19 crisis will increase
consumers’ attention to health issues
0.00
0.00
11.86
37.29
50.85
COVID-19 crisis will turn consumers
to cheaper food choices
5.08
18.64
38.98
23.73
13.56
COVID-19 crisis may increase
demand for innovative food products
(e.g., super-foods, functional foods,
etc.)
10.17
16.95
33.90
25.42
13.56
COVID-19 crisis may increase
consumer demand for sustainable
food products (e.g., organics)
6.78
8.47
35.59
33.90
15.25
economic failure and closure of a significant number of food
enterprises in the long-run. The burden on distribution networks
and the increase in production costs will affect food retail prices
that are expected to climb up in higher levels compared to the
pre-pandemic levels. This increase in food retail prices may cause
changes in consumer purchase behavior, turning potential buyers
to cheaper food options, even of lower quality. Food experts
also questioned the food industry capacity to meet consumers’
needs for perishable food products and highlighted the potential
of food quality reduction due to the burden on the distribution
networks and market shortages. Inadequate food availability,
especially in perishable commodities (such as fruits, vegetables,
dairy products), in conjunction with the subsequent increases in
food prices might oblige a significant proportion of consumers to
substitute fresh products with ultra- processed foods and adopt
less healthy eating patterns.
Within the food sector, micro-, small-, and medium-sized
enterprises (MSMEs) have a crucial role in food systems by
providing “almost half of total calories worldwide” (93, 94).
Since, most of the Greek food enterprises are SMEs and employ
∼85% of total workforce (79), any disruption in their operation
This shrinkage of farmers’s incomes could increase the risks for
lower quality of the production and undermines public health
protection since some farmers might move toward irregular
practices to decrease expenses associated with crop protection
(e.g., fertilizer and pesticide usage) and livestock health (e.g.,
disease control in farm animals).
On the other hand, COVID-19 crisis benefited
small producers over large agricultural companies from
consumers’ increasing interest on online purchases
of agricultural products (92). At the same time, the
engagement of small producers in online trading
platforms helped satisfy the urge of consumers for
variety seeking and their need for locally produced
agricultural commodities.
COVID-19 Pandemic and Its Impact on
Food Processing Businesses
Food experts in this study also noted the severe impacts of
COVID-19 on food production, underlining the increasing risks
for the economic viability of the small food businesses, the
escalated competition in the food industry, and after all the
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TABLE 9 | Factor analysis (PCA) and reliability analysis output on experts’ perceptions toward the impact of COVID-19 crisis on consumers’ food habits.
Eigenvalue
Explained variance
%
4.673
2.421
1.160
35.944
18.623
8.922
Factor
Factor loading
m
S.D.
Factor 1: Consumers’ increasing
interest in health protection
Consumers will become more
demanding with the measures taken
by stakeholders to protect public
health
0.834
4.153
0.906
COVID-19 crisis will increase
consumers’ attention to health issues
0.784
4.390
0.695
Consumers will turn to
locally-produced foods that are easier
to obtain
0.675
4.120
0.930
COVID-19 crisis will increase the
consumption of packaged agri-food
products
0.661
4.322
0.899
COVID-19 crisis will turn consumers
to adopt Mediterranean diet
0.654
3.661
1.044
The demand for foods with long
shelf-life will increase
0.650
3.966
0.909
Consumers will become more
skeptical about fresh foods
0.604
3.864
1.152
The pandemic will increase food
e-commerce
0.594
4.475
0.751
Factor 2: Adoption of unhealthy
eating habits
The COVID-19 crisis will turn
consumers to cheaper food choices
0.748
3.220
1.068
COVID-19 crisis will increase the
demand for ready-to-eat meals
0.740
3.153
1.201
COVID-19 crisis may turn consumers
to unhealthy food choices
0.726
3.034
1.231
Factor 3: Consumers’ demand for
innovative and sustainable foods
COVID-19 crisis may increase
demand for innovative food products
(e.g., super-foods, functional foods,
etc.)
0.888
3.153
1.172
COVID-19 crisis may increase
consumer demand for sustainable
food products (e.g., organics)
0.632
3.424
1.070
Cronbach’s α
0.857
0.679
0.61
m, mean; S.D., standard deviation.
to react to unexpected situations and mostly pay attention on
nothing more than their economic survival (95, 96). SullivanTaylor and Branicki (97) explained that the limited managerial
resources and financial constraints of the SMEs may hinder
their ability to adapt to risk management strategies, and hence
decrease their resilience to extreme situations, such as the
current pandemic.
Food experts also brought up the emerging opportunities for
innovation in the agro-food sector. COVID-19 crisis generated
challenges for the market of innovative functional foods designed
to boost consumers’ immune system through a combination of
target bioactive compounds, such as vitamins and antioxidants in
order to avoid infections and improve overall health (98). Recent
will have a critical impact on Greek economy. SMEs have
been reported as the major victims of the COVID-19 pandemic
because in comparison with the large enterprises, they have
fallen short of financial and managerial resources and have also
been unprepared to handle and overcome uncertainties created
by extreme events or natural hazards (30). In addition, food
processing enterprises seemed to comprise “hot spots” for the
pandemic expansion since social distancing measures were hard
to implement inside the food plants during the long shifts,
or even outside the food plants where employees traveled on
the same bus or train or used car-sharing systems to reduce
transportation costs (13). Recent evidence indicated that SMEs
usually organize short-term planning, have less strict protocols
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COVID-19 and the Agro-Food System
FIGURE 1 | Experts’ perceptions toward the impacts of COVID-19 pandemic on the various facets of the food industry.
other hand, there was a noticeable decrease in fresh food
consumption because consumers hesitated to visit food and
grocery stores and perceived packaged foods as more hygienic
(32, 101). The pandemic has also spread food e-commerce
since consumers were forced to turn to online food delivery
systems for avoiding personal contacts in food stores (102).
Furthermore, feelings of uncertainty about the availability of
specific food choices in food stores and lack of confidence for
shopping skills also motivated consumers to use online food
services (13, 103).
COVID-19 induced psychological changes might also
influence food purchase behaviors. Food experts in our study
highlighted the subsequent modifications in food choices and
the adoption of unhealthy eating habits. When individuals were
exposed to extensive communication about the pandemic health
risks, stress, and anxiety symptoms were triggered. In attempt to
regulate anxiety, many consumers increased the consumption of
processed “comfort foods,” such as chocolate and chips, whereas
others tried to make themselves think positive by eating or
drinking when under stress (32, 101, 104–106). Furthermore, the
unhealthy eating patterns during the quarantine period increased
health risks, especially for overweight and obese individuals.
Several studies reported that significant proportions of the
population gained weight because of the higher consumption
evidence showed that research and innovative practices have
also recovered bioactive compounds from processed byproducts
to replace synthetic additives with natural health-stimulating
ingredients (99). Following consumers’ health priorities, food
companies will have to adjust to the new trends for the
commercialization and promotion of food products with
health benefits (21) and also meet the increasing demand for
nutraceuticals, healthier meal types, and home-prepared foods
(13). This shift in consumer purchase behavior signifies a major
change in prevalent health standard and a “move from a curative
to a preventive model” (100).
COVID-19 Pandemic and Its Impact on
Food Consumption
This study also showed that COVID-19 outbreak altered
consumers’ food habits and preferences. The demand for
long shelf-life and packaged foods was expected to increase
since consumers became more skeptical about fresh foods.
Our findings further support previous research noting that
during lockdown periods individuals shopped less frequently
and increased the consumption of longer shelf-life foods, such
as dried or canned foods, pasta, milk and frozen foods, due
to convenience, and daily cooking at home (13). On the
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COVID-19 and the Agro-Food System
TABLE 10 | Cluster analysis results for experts’ attitudes toward COVID-19 consequences for food industry.
Factors
Total sample
Cluster 1 (33.9%)
Cluster 2 (66.1%)
t-test for equality of means
S.D.
t-test
P-value
Mean
S.D.
Mean
S.D.
Mean
Socioeconomic impacts on rural areas
2.772
0.972
1.836
0.596
3.253
0.752
−7.321
0.000
Impacts on agricultural production
3.140
0.871
2.325
0.730
3.558
0.603
−6.915
0.000
Decline in the economic viability of small food enterprises
3.349
1.080
2.290
0.907
3.892
0.690
−7.576
0.000
Sharp economic downturn in the food industry
2.780
0.869
2.062
0.688
3.147
0.711
−5.608
0.000
Economic recession
3.514
0.801
2.916
0.844
3.821
0.582
−4.831
0.000
Incentives for innovation in the food industry
3.322
0.932
2.800
0.849
3.590
0.865
−3.340
0.001
Distribution channels fallout
3.275
1.001
2.500
1.020
3.673
0.728
−5.098
0.000
Food supply disruption
3.173
0.907
2.310
0.724
3.616
0.663
−7.140
0.000
Consumers’ increasing interest in health protection
4.119
0.651
3.775
0.809
4.300
0.474
−3.116
0.003
Adoption of unhealthy eating habits
3.136
0.912
2.750
0.930
3.334
0.848
−2.421
0.019
Consumers’ demand for innovative and sustainable foods
3.288
0.948
2.975
0.938
3.449
0.923
−1.855
0.069
S.D., standard deviation.
TABLE 11 | Cluster profile (N = 59).
Variables
Occupation
Cluster 1* (33.9%)
Cluster 2* (66.1%)
Farmers
20.0
30.8
Food processing enterprises
35.0
33.3
Food distributors—sellers
25.0
25.6
Consultants—policy makers
20.0
10.3
Micro enterprises (up to 10
employees)
26.3
51.3
Small-sized enterprises (11–49
employees)
42.1
20.5
Medium sized enterprises (50–250
employees)
21.1
15.4
Large enterprises (over 250
employees)
10.5
12.8
COVID-19 pandemic has affected the
No
20.0
0
business/organization I work for
Yes
80.0
100.0
To what extent do you think the
Not at all
20.0
0
activity of the business/organization in
Slightly
15.0
0
which you are employed has been
Moderately
35.0
25.6
affected by COVID-19 pandemic?
Very much
20.0
56.5
Enterprise size
Extremely
10.0
17.9
How the economic performance of
It will deteriorate
55.0
89.7
the business/organization, in which
It will not be affected
25.0
2.6
you work, will be affected in the near
future?
It will improve
20.0
7.7
Pearson chi-square
P-value
1.504
0.681
4.301
0.231
8.367
0.004
18.577
0.001
10.279
0.006
*Percentage.
of more processed and energy dense foods, snacking frequency
increase, and physical activity decrease (32, 106, 107). To
protect public health and maintain dietary balance during
the pandemic, nutrition, and health information programs
should provide incentives for cooking at home and enhance the
consumption of meals prepared with fresh and less processed
ingredients. The food market constitutes an important element
of the economy whose viability largely depends on consumer
viewpoints and trust in the food system. How consumers
receive food-purchasing decisions in times of uncertainty
Frontiers in Nutrition | www.frontiersin.org
has a critical impact for both the agro-food sector and the
whole economy.
Study Limitations
Although the present study sought to investigate disruptions in
all the aspects of the food system, there are some limitations
that should be reported. First, the data set selected for this study
included self-reported information, which might be affected by
reporting bias (108). Stakeholders who were more affected by
COVID-19 consequences, they may be more likely to express
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COVID-19 and the Agro-Food System
of communication technologies that can be implemented to
improve agro-food production and food supply systems and
also minimize food loss and waste. COVID-19 outbreak made
it clear that food systems have to adjust to the new turbulent
environment and increase food security. Effective responses to
the pandemic should first ensure that global food systems remain
open and operational, so that food can be delivered where it is
needed, even in deprived regions. Global cooperation is necessary
to speed up border procedures and ensure the stability of the
global supply chains. Food policy should also be oriented toward
making agricultural and food systems more sustainable and
resilient in order to help societies transition toward a climateneutral economy.
more pessimistic views of the food industry’s future. In addition,
the cross sectional design does not allow generating relationships
in the long run (109). Data selection started after the first
lockdown, when most food businesses had to adapt to the new
reality and overcome obstacles in the food chain. Longitudinal
follow-ups may provide a thorough picture of the COVID19 aftermath and illustrate the emerging opportunities for the
food industry in the meta-pandemic era. Furthermore, food
experts were recruited during the period of the first quarantine
when extra pressure was put on food managers to ensure food
availability and safety standards. It is possible that potential
respondents refused to participate in this survey, which might
result in data loss and significant differences between those
who responded and those who did not. Although our sample
size could be considered as small, the response rate is quite
satisfactory and significantly higher to the average response rate
of 11% for the online surveys (48). However, we believe that a
replication of the present study in the near future could attract
more participants and enhance the validity of our findings.
DATA AVAILABILITY STATEMENT
The datasets presented in this article are not readily available
because the present study analyzed data selected from a sample
of food experts. Participants engaged in the survey voluntarily
and confidentiality of their responses was stated in the invitation
letter. In particular, respondents were assured that data would
remain confidential and would not be shared. Therefore, data
(data set is in Greek) could be given only in exceptional
circumstances (and after reasonable request). Requests to access
the datasets should be directed to ER, elenra@agro.duth.gr.
Policy/Practical Implications
This pandemic has offered a unique opportunity to learn more
about the fragility of the food environment and increase readiness
to cope with future disruptions. COVID-19 crisis will likely
continue to interrupt the operation of the food industry in
the near future and food businesses will have to consider the
possibility of new food system disruptions in their strategic
plans, investment, and managerial efficacy. For instance, dairy
farmers in China transferred their milk production to processing
services in order to process it to milk powder for storage during
the pandemic (110). Training programs and seminars directed
to the food industry could help food managers and employees
adapt to potential food chain disruptions and develop strategic
plans for their most appropriate allocation of their resources
and also invest in processing facilities, which may increase
the storage facility, especially of perishable foods. Agricultural
cooperatives could also train their members to deal with price
fluctuations and instability in agricultural production. Beyond
its negative consequences in the food system, the pandemic
has created opportunities for innovation and the development
ETHICS STATEMENT
Ethical review and approval was not required for the study
on human participants in accordance with the institutional
requirements. The patients/participants provided their written
informed consent to participate in the study.
AUTHOR CONTRIBUTIONS
ER designed the formal questionnaire used for data selection,
conducted the statistical analysis, and wrote the manuscript draft.
All authors contributed to the study concept. All authors edited
and approved the submitted manuscript.
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