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sustainability
Review
Patterns of Inequalities in Digital Agriculture: A Systematic
Literature Review
Sarah Hackfort
Citation: Hackfort, S. Patterns of
Inequalities in Digital Agriculture:
A Systematic Literature Review.
Sustainability 2021,13, 12345.
https://doi.org/10.3390/su132212345
Academic Editor: Dalia Štreimikien ˙
e
Received: 29 September 2021
Accepted: 5 November 2021
Published: 9 November 2021
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Agricultural and Food Policy Group, Humboldt-Universität zu Berlin, Unter den Linden 6,
10999 Berlin, Germany; sarah.hackfort@hu-berlin.de
Abstract:
Digitalization of agriculture is often hailed as the next agricultural revolution. However,
little is yet known about its social impacts and power effects. This review addresses this research gap
by analyzing patterns of inequality linked to the development and adoption of digital technologies
in agriculture and reviewing the strategies developed to reduce these inequalities and challenge the
power relations in which they are embedded. Analysis of 84 publications found through a systematic
literature review identified five patterns of inequality: (1) in digital technology development; (2) in
the distribution of benefits from the use of digital technologies; (3) in sovereignty over data, hardware
and digital infrastructure; (4) in skills and knowledge (‘digital literacy’); and (5) in problem definition
and problem-solving capacities. This review also highlights the existence of emancipatory initiatives
that are applying digital technologies to challenge existing inequalities and to advance alternative
visions of agriculture. These initiatives underscore the political nature of digital agriculture; however,
their reach is still quite limited. This is partly due to the fact that existing inequalities are structural
and represent expressions of corporate power. From such a perspective, digitalization in agriculture
is not a ‘revolution’ per se; rather, digital technologies mirror and reproduce existing power relations.
Keywords:
digital agriculture; inequalities; power; data sovereignty; political economy; systematic
literature review
1. Introduction and Rationale for the Review
‘Smart farming’, ‘agriculture 4.0’ and ‘digital agriculture’ are largely interchangeable
terms used to describe the phenomenon of increased use of data-related technologies in the
farming and food production process. These technologies make use of big data, artificial
intelligence, and automation along the entire commodity chain, from input production
to crop production and harvesting, packaging, transportation and consumption. At the
input level, they include digitally enabled genome editing and biofortification, as well
as microfinance programs and insurance systems. On the farm, smart machinery is used
for crop production and harvesting. Sensors are used to monitor soil moisture and plant
nutrition requirements, and to detect the presence of pests and diseases. Decision support
apps help farmers to apply fertilizers and pesticides when and where they are needed.
Remote satellite imagery is utilized to monitor biomass growth, complemented by pictures
and data collected from drones and robots scanning the fields. Farm management software
is used by farmers to prepare the documentation required to comply with regulations,
obtain subsidies, and market their products. Much of the agricultural data generated by
these digital technologies is stored on data platforms and in clouds hosted by technology
and service providers [
1
–
4
]. ‘Digital agriculture’ encompasses both digitization, which
refers to the technical process of converting analogue information into digital data, and
digitalization, understood as the social process of adoption of computer technologies [
5
].
The ever-greater penetration of these technologies into social and economic life brings
about a digital transformation, including in agriculture, where digital technologies are
contributing to a reshaping of production processes across the globe. The digitalization
of agriculture is widely hailed as the next agricultural revolution [
6
]. However, while
Sustainability 2021,13, 12345. https://doi.org/10.3390/su132212345 https://www.mdpi.com/journal/sustainability
Sustainability 2021,13, 12345 2 of 18
numerous case studies analyze specific instances of digitalization, aggregated data is
scarce and little progress has been made towards a systematic overview of the adoption
of different types of digital agriculture on a global scale [
1
]. Even less is known about
the social impacts and power effects of the digitalization of agriculture. As portrayed
by policies and industry, digital agriculture benefits the environment and farmers by
increasing productivity. Corporate leaders and policy institutions argue that digitalization
offers the solution to feeding a growing world population, while at the same time mitigating
the negative environmental and climate consequences of industrial agriculture (see [
7
,
8
]).
This picture is an affirmative and positive one, generally conflict-free and with few if any
downsides. In contrast, some civil society organizations adopt a more critical attitude,
drawing attention to problematic impacts on, for example, labor relations and social justice.
These organizations see digitalization as a threat to food sovereignty and the livelihoods
of smallholders [
9
]. The academic literature presents a more nuanced picture. Studies
highlight the social and economic opportunities, while acknowledging that digitalization
also entails challenges and risks [10–12].
For example, Regan et al. [
13
] report that farmers perceive both the benefits of digital-
ization and the risks it entails, including loss of knowledge, devaluation of their profession,
and potential misuse of data. In this context, and given the pace of the development of
digitalization in agriculture, the question of who gains and who loses from its adoption
should receive particular attention. However, only few studies have examined the so-
cial impacts and distributive dimensions linked to the adoption of digital technologies
in agriculture (see for example [
14
–
17
]). Klerx et al. [
16
] review the recent social science
literature on digital agriculture, focusing on power, ownership, and ethical issues, and
make suggestions for a future research agenda. Rotz et al. [
17
] diagnose key challenges
and tensions in the field from a social justice perspective, highlighting issues relating to
the ownership and control of data, production technology, and data security. They also
point to the need for more research on the impacts of digitalization on agricultural labor,
and how these impacts affect vulnerable social groups in the agrifood sector. Some of
these contributions highlight the unequal distribution of the benefits of digitalization in
agriculture. Several studies of digital agriculture within the field of responsible research
and innovation emphasize the importance of equity and inclusiveness in digital innova-
tion, and have developed frameworks to analyze and facilitate the inclusion of farmers in
innovation processes [14,15,18–24].
These insights are acknowledged in this review; however, the aim is to go a step fur-
ther. Simply calling for more inclusion and developing tools to this end fails to address the
underlying structural reasons why farmers are often excluded from innovation processes.
Elucidation of these structural constraints on inclusion requires an analysis of the social
inequalities affecting the individual and collective access to goods, resources, and employ-
ment across multiple and sometimes overlapping axes, including race, ethnicity, class,
gender and age. Against this background, this review addresses the following research
questions:
•
What patterns of inequality are linked to the development and adoption of digital
technologies in agriculture?
•What strategies to reduce inequality are being developed in the field?
Inequalities influence the scope of action of social actors through unequal distribution
of risks and benefits, of access to resources and control over their allocation and use, and
through knowledge asymmetries. This conceptualization of inequality and the interest
in strategies of contestation draws on the work of political ecologists addressing unequal
power relations in the context of society–nature relations and, in particular, on studies
focusing on strategies of resistance that challenge existing power relations and inequal-
ities [
25
,
26
]. Empirical studies in this field have identified patterns of social–ecological
inequality in relation to different environmental issues (e.g., resource extraction), while
theoretical studies have mapped them out conceptually. Socio-ecological inequalities identi-
fied relate to: (a) the (re)distribution of environmental risks, costs and benefits: (b) access to
Sustainability 2021,13, 12345 3 of 18
natural resources; (c) the capacity to cope with changing environmental conditions; (d) re-
sponsibility for socio-ecological crises. and (e) asymmetries in power relations which shape
the production of knowledge, problem definitions, and the search for solutions (for an
overview, see [
27
–
29
]). Studies of food justice, food sovereignty, and food regimes analyze
and conceptualize food-related inequalities, while many of these focus on gender-related
and intersectional inequalities and the efforts of social movements to challenge existing
power relations [
30
–
32
]. Food regime theory, as developed chiefly by [
33
,
34
], pays special
attention to power effects and class inequalities arising from the “corporate–environmental
food regime” [
34
]. Critical agrarian studies address technology-related asymmetries in
agriculture and study the unequal effects of technological innovation [
35
,
36
]. Many of these
studies focus on the emergence of inequalities among countries and farmers as a result of
the adoption of agricultural biotechnology during the green revolution, considered as the
dawn of a neoliberal restructuring of agriculture [
37
] which led to a dramatic increase in
corporate power over food production [38].
However, only a few studies to date in the fields of political economy and ecology
and critical agrarian studies have specifically addressed the role of digitalization in agri-
culture. The analysis of inequalities linked to digital technologies in agriculture and the
development of strategies to challenge the power relations in which they are embedded is
a significant research gap. This review starts from the premise that bringing together these
two research traditions can help to unpack these issues and lead to a better understanding
of how digital technologies impact social inequalities in the agricultural and food systems.
This, in turn, is the starting point for identifying feasible pathways to change through
political action, both by social movements and policy makers.
This paper presents the results of a systematic literature review of the academic
literature relating to patterns of inequalities in digital agriculture. The next section describes
the methodology adopted for data sampling and analysis. It is followed by the presentation
of results which describe the principal patterns of inequalities and resistance strategies
identified in the literature review. The discussion of results, which includes suggestions for
further research, is followed by brief conclusion.
2. Methodology
The methodology adopted herein is based on standard systematic review procedures
(such as the PRISMA statement) incorporating a search strategy, extraction of records, and
reporting of results [
39
,
40
]. Such a review is appropriate as a research method when the
goal is to provide an overview of research in different fields on a specific topic and to
discuss research developments or compare research on the topic across disciplines. It can
also be used to explore themes, theoretical perspectives, or specific issues within a field of
research or discipline in order to identify elements of a concept or of a novel approach [
39
].
Deductive content analysis was employed to analyze the retrieved material in order to
identify patterns of inequality revealed in the literature.
Scopus was used as the database. This is one of the largest databases of abstracts and
citations, and is widely recognized as a trustworthy database for academic research [
41
]. It
was selected because of its comprehensive coverage of international and regional academic
journals, as well as the availability of metadata and the accessibility and ease of extraction
of data compared to other databases (e.g., Google Scholar). Scopus was searched using the
following key search terms:
(TITLE-ABS-KEY (“digital agriculture”) OR TITLE-ABS-KEY (“precision technolo-
gies”) OR TITLE-ABS-KEY (“digital farming”) OR TITLE-ABS-KEY (“agriculture 4.0”)
OR TITLE-ABS-KEY (“precision agriculture”)) AND “conflict” OR “social relations” OR
“power” OR “inequality” OR “governance” AND (LIMIT-TO (SRCTYPE, “j”)) AND (LIMIT-
TO (SUBJAREA, “AGRI”) OR LIMIT-TO (SUBJAREA, “SOCI”)) AND (LIMIT-TO (DOC-
TYPE, “ar”)) AND (LIMIT-TO (LANGUAGE, “English”)).
In addition to the key term ‘inequality’, other search terms were chosen in order
to identify articles related to the specific focus of the review (e.g., power, conflict, social
Sustainability 2021,13, 12345 4 of 18
relations). The search was not geographically restricted, and covered all relevant journal
articles published in English (“English”) at any time until the date of retrieval. These
included research papers and review papers (strings “j” and “ar”) in the fields of social
and agricultural sciences (strings “agri” and “soci”), while excluding conference reports
and papers and purely technical contributions from data or engineering sciences. A total of
340 results were retrieved on 28 January 2021. In a second step, the sample was narrowed
down to include only documents dealing with social relations and governance issues in
relation to the development and use of digital technology in agriculture. To this end, all
abstracts were checked for eligibility, and articles that did not address these issues were
excluded. Excluded articles included those focusing solely on the development or adoption
of a specific agricultural technology or model. This refinement reduced the number of
articles for consideration in the review to 51. To update this sample, a second retrieval
was performed on 11 August 2021. This resulted in 400 hits, from which 21 further articles
were added to the sample after the eligibility check. Finally, another 12 items were added
to the sample (including one conference paper and one technical report by the European
Commission), identified through a snowball search (i.e., recommendations from colleagues
and reviewers, see Figure 1). Excel was used to compile the data and perform the eligibility
assessment, using the inclusion and exclusion criteria to narrow the selection down to
relevant publications. The final sample of 84 was subjected to in-depth qualitative analysis
using deductive coding, with categories derived from the research focus on inequalities.
The analysis considered: (a) the regional context of the case studies reported in the articles;
(b) the types of technology and their application; (c) dimensions of inequality, including
asymmetries, conflicts, and other distributive issues relating to access to, control over,
and ownership of digital technology and/or data; and (d) impacts on skills, knowledge,
and problem definition and problem-solving capacities. The analysis also identified (e)
initiatives to address these inequalities and their impacts, and the actors involved.
Sustainability 2021, 13, x FOR PEER REVIEW 5 of 19
Figure 1. PRISMA-based methodology used for data collection.
3. Results
Descriptive analysis of the data showed that digital agriculture is a new research
topic that has received considerably increased attention over the past 10 years. Of the 84
articles, one was published in 2004, four in 2017, 12 in 2018, 17 in 2019, 21 in 2020, and 21
in the eight months leading up to August 2021 (see Figure 2). This growth in the number
of published articles indicates the increasing importance of the topic and its relevance in
academic discourse.
Figure 1. PRISMA-based methodology used for data collection.
Sustainability 2021,13, 12345 5 of 18
3. Results
Descriptive analysis of the data showed that digital agriculture is a new research topic
that has received considerably increased attention over the past 10 years. Of the 84 articles,
one was published in 2004, four in 2017, 12 in 2018, 17 in 2019, 21 in 2020, and 21 in the
eight months leading up to August 2021 (see Figure 2). This growth in the number of
published articles indicates the increasing importance of the topic and its relevance in
academic discourse.
Sustainability 2021, 13, x FOR PEER REVIEW 6 of 19
Figure 2. Year of publication of articles analyzed in the review, based on a literature search that
identified articles published up to August 2021.
There is a significant regional bias in the analyzed literature towards the more indus-
trialized countries (Figure 3). Among country studies, fifteen focus on the USA, eight on
Australia, and seven on Canada. In addition, there are ten regional studies of Europe and
five of North America. In contrast, only six articles explicitly cover developments on the
African continent, including three regional studies and three country studies (two of
Ghana and one of Nigeria). Given this bias towards North America and Europe in the
literature, it is inevitable that the findings presented in this review mainly reflect devel-
opments and inequalities in these industrialized countries. However, this bias in the liter-
ature does not necessarily mean that digitalization in agriculture is not affecting other
regions. It might reflect the fact that the development of inequality in digital agriculture
in the Global South is not yet a focus of academic research and needs to be further ex-
plored. The preponderance of studies of the Global North, and North America in particu-
lar, could also be the result of biases in the compilation of the SCOPUS database, leading
to underrepresentation of journals and authors from other parts of the world. This is also
suggested by the composition of the sample initially retrieved from Scopus (before limit-
ing the search to English articles), which contained only two articles in Chinese, and one
in each of German, Portuguese, and Spanish.
0
5
10
15
20
25
Number of articles
Year of publication
Growth of interest in digital agriculture
Figure 2.
Year of publication of articles analyzed in the review, based on a literature search that
identified articles published up to August 2021.
There is a significant regional bias in the analyzed literature towards the more indus-
trialized countries (Figure 3). Among country studies, fifteen focus on the USA, eight on
Australia, and seven on Canada. In addition, there are ten regional studies of Europe and
five of North America. In contrast, only six articles explicitly cover developments on the
African continent, including three regional studies and three country studies (two of Ghana
and one of Nigeria). Given this bias towards North America and Europe in the literature, it
is inevitable that the findings presented in this review mainly reflect developments and
inequalities in these industrialized countries. However, this bias in the literature does not
necessarily mean that digitalization in agriculture is not affecting other regions. It might
reflect the fact that the development of inequality in digital agriculture in the Global South
is not yet a focus of academic research and needs to be further explored. The preponderance
of studies of the Global North, and North America in particular, could also be the result of
biases in the compilation of the SCOPUS database, leading to underrepresentation of jour-
nals and authors from other parts of the world. This is also suggested by the composition
of the sample initially retrieved from Scopus (before limiting the search to English articles),
which contained only two articles in Chinese, and one in each of German, Portuguese, and
Spanish.
The qualitative analysis identified five main patterns of inequalities (see Table 1be-
low), relating to: (1) control of technology development; (2) distribution of the benefits
of technologies; (3) sovereignty over data and hardware; (4) knowledge and skills; and
(5) capacity to define and solve problems. These are described in the following sections,
with examples from the analyzed literature. Each section includes a review of the differ-
ent strategies that are being developed by actors in the field to challenge the pattern of
inequality in question.
Sustainability 2021,13, 12345 6 of 18
Table 1. Patterns of Inequality in Digital Agriculture.
Patterns of Inequality in Digital Agriculture
Inequalities in Control over
Technology Development
Unequal Distribution of Benefits
from Technologies
Uneven Sovereignty over Data
and Hardware
Inequalities in Knowledge
and Skills
Unequal Problem Definition and
Problem-Solving Capacities
Multinationals
produce agricultural inputs (Syngenta,
Bayer AG) and machinery (John
Deere, BOSCH), and technology
companies (IBM, SAP) control
technology development
Innovation is mainly directed
towards capital-intensive solutions
benefiting large-scale farms
Data storage is controlled by
technology providers (e.g., Bayer
AG’s Climate Corporation)
Unequal digital literacy: even if
farmers are given access to data,
they lack the software and skills
used by corporate actors to extract
value from the data
Dominance and prioritization of
capital-intensive technology and
productivist solutions to structural
problems
Lack of interoperability of systems
and machines and incompatibility of
software solutions lead to dependence
of farmers and consumers on
providers
Uneven access to digital
infrastructure deepens the
urban–rural divide among farmers
Farmers are disadvantaged by
disparities in bargaining power,
since single-farm data is less
valuable than aggregated big data
Farmers are increasingly
dependent on providers of big
data-generated knowledge and
maintenance services
A productivist approach based on
digitalization is the dominant vision
of the future of agriculture in policy
discourses
Technical lock-ins limit farmers’
options and constrain their decision
making
While farmers take huge financial
risks when investing, technology
providers benefit from the use of
data supplied free of charge by
farmers
Private license agreements,
including legal lock-ins, deny
farmers access to their farm data;
sometimes even self-repair of
hardware is prohibited
Opportunities to hire skilled
employees differ among farmers,
especially across the urban–rural
divide, disadvantaging farmers in
rural areas and those with fewer
financial resources
The fixation on technological
solutions diverts attention and
material investments away from
other approaches, such as
agroecology and food sovereignty,
that define problems differently and
propose alternative solutions
Sustainability 2021,13, 12345 7 of 18
Sustainability 2021, 13, x FOR PEER REVIEW 7 of 19
Figure 3. Frequency of countries and regions covered in the sample.
The qualitative analysis identified five main patterns of inequalities (see Table 1 be-
low), relating to: (1) control of technology development; (2) distribution of the benefits of
technologies; (3) sovereignty over data and hardware; (4) knowledge and skills; and (5)
capacity to define and solve problems. These are described in the following sections, with
examples from the analyzed literature. Each section includes a review of the different
strategies that are being developed by actors in the field to challenge the pattern of ine-
quality in question.
Table 1. Patterns of Inequality in Digital Agriculture.
Patterns of Inequality in Digital Agriculture
Inequalities in Control over
Technology Development
Unequal Distribution of
Benefits from Technologies
Uneven Sovereignty over
Data and Hardware
Inequalities in Knowledge
and Skills
Unequal Problem Definition
and Problem-Solving Capac-
ities
Multinationals
produce agricultural inputs
(Syngenta, Bayer AG) and ma-
chinery (John Deere, BOSCH),
and technology companies
(IBM, SAP) control technology
development
Innovation is mainly directed
towards capital-intensive so-
lutions benefiting large-scale
farms
Data storage is controlled
by technology providers
(e.g., Bayer AG’s Climate
Corporation)
Unequal digital literacy:
even if farmers are given
access to data, they lack the
software and skills used by
corporate actors to extract
value from the data
Dominance and prioritization
of capital-intensive technol-
ogy and productivist solu-
tions to structural problems
Lack of interoperability of sys-
tems and machines and incom-
patibility of software solutions
lead to dependence of farmers
and consumers on providers
Uneven access to digital in-
frastructure deepens the ur-
ban–rural divide among
farmers
Farmers are disadvantaged
by disparities in bargaining
power, since single-farm
data is less valuable than
aggregated big data
Farmers are increasingly
dependent on providers of
big data-generated
knowledge and mainte-
nance services
A productivist approach
based on digitalization is the
dominant vision of the future
of agriculture in policy dis-
courses
Technical lock-ins limit farmers’
options and constrain their de-
cision making
While farmers take huge fi-
nancial risks when investing,
technology providers benefit
from the use of data supplied
free of charge by farmers
Private license agreements,
including legal lock-ins,
deny farmers access to their
farm data; sometimes even
self-repair of hardware is
prohibited
Opportunities to hire
skilled employees differ
among farmers, especially
across the urban–rural di-
vide, disadvantaging farm-
ers in rural areas and those
The fixation on technological
solutions diverts attention
and material investments
away from other approaches,
such as agroecology and food
0
2
4
6
8
10
12
14
16
USA
Europe
Australia
Canada
North America
New Zealand
Global
India
Africa
UK
Ghana
Ireland
China
Scotland
Czech Republic
Germany
Netherlands
Iran
Nigeria
Switzerland
Developing World
Asia
Hungary
France
Brazil
Japan
Latin America
Netherlands
Countries and regions covered in sample
Figure 3. Frequency of countries and regions covered in the sample.
3.1. Inequalities in Control over Technology Development
The digital transformation of the food system is largely driven by private multinational
firms. In [
1
], the authors identify four main groups of actors: (a) giant corporations
producing agricultural inputs, such as Bayer Ag and SYNGENTA; (b) new agri-tech players
from the software and big data sector, such as Alphabet, IBM, SAP, and Alibaba; (c)
machinery and hardware companies, such as BOSCH and John Deere; and (d) private sector
start-ups. The first two groups dominate technological innovation along the entire food
commodity chain, driving the development of GMO seeds, farm management platforms,
and automated warehouses [
1
,
2
]. Their economic power enables them to control the
direction of technological development, including how it is used by farmers [
12
,
19
]. The
leading role of agribusiness and tech firms in the development and commercialization
of digital agriculture technologies consolidates economic concentration, increasing the
dependency of farmers and consumers on these powerful actors as well as their control
over agricultural production [
19
,
42
]. This could exacerbate the existing divide between
large-scale and small-scale farms, leading to a situation where a small number of digitally
equipped large-scale farms produce an ever-greater share of agricultural output [
42
].
Similar observations have been made with regard to the impact of Green Revolution
technologies [24] in both developed [11,17,43,44] and developing countries [6].
Innovation generally occurs when investment capital is available and responds to
the needs of the investors. Because of the scale of capital investment in agriculture and
the pressure this creates to produce a return on investment, most agricultural innovation
is undertaken by high-tech and capital-intensive companies and directed towards their
needs. This effect is self-reinforcing and favors the concentration of digital technology in
the hands of ever-fewer powerful actors [2,24].
New digitally-driven biotechnologies such as genome editing as well as the large
number of digital farming start-ups that are proliferating across the globe raise some
hope for the democratization of access and control, in particular through significantly
decreased costs [
12
,
45
]. Novel technologies might create some potential for a more level
Sustainability 2021,13, 12345 8 of 18
playing field; however, large corporate actors still predominate through the concentration
of technological know-how and infrastructure, ownership of data, ownership of patents
on new genome editing technologies [
2
], and the imposition of legal or technical lock-
ins that prevent independent use of new technology [
46
]. Moreover, many successful
start-ups are quickly bought up by one of the big players, as in the widely-publicized
case of the purchase of Climate Corporation’s farming platform by Bayer AG (formerly
Monsanto) for USD 930 million in 2013 [
2
,
10
,
12
]. One expression of this inequality in
control over technological development is the lack of interoperability, which limits the
ability of information technology systems to exchange data with each other and to make
use of information held by other systems [
17
,
47
]. This imposes technical lock-ins, a barrier
that prevents farmers from using technology systems from different service providers in
accordance with their needs. Agricultural input and machinery companies impose similar
lock-ins. They extract value from the data they collect on the use of digital technologies
to lock farmers into their own product ecosystems (e.g., by targeted advertising of their
own farm inputs or machinery). Some farm management platforms offer multi-tiered
service packages, sometimes with a free basic version designed to attract a critical number
of users and thus enhance market share. Through these mechanisms, farm management
platforms, like other digital platforms, create legal and technical data lock-in mechanisms
that restrict the freedom of their users [
46
]. This means that while the costs of using
the platform may be low, switching to a different provider is often expensive or even
impossible (e.g., due to the incompatibility of data formats) [
2
,
12
]. This lack of control
over technology considerably limits autonomous decision making by farmers regarding
their choice of software or hardware. Interoperable data production technologies and data
management systems with transparent terms of use are essential if they are to become
effective knowledge-enhancing and decision-making tools for farmers under their own
sovereignty and ownership [48].
In response to this need, and despite the overarching dominance of large corporations,
some alternatives have emerged. One example from Canada is the farmer-owned Three
Rivers Farmers Alliance [
49
], whose members have developed their own smartphone
app to help organize such farming activities as harvesting and processing. The app also
connects them to local customers such as shops, schools or restaurants, who can use it to
place orders for delivery [
17
]. Another example in the United States is Ag Hub (formerly Ag
Xchange), an open and corporate-neutral farm data platform that enables farmers’ control
of data and promotes sharing among data users [
50
]. It is the result of cooperation between
two non-profit initiatives, the farmer-owned Grower Information Services Cooperative and
the Agricultural Data Coalition, an initiative by farmers, lawyers, business groups, and
researchers. It claims to be the “industry’s first cloud-based platform that will be controlled
by growers and open to all industry service partners and technology providers” [43].
3.2. Unequal Distribution of Benefits from Technologies
The way digital technology is designed contributes to the unequal distribution of
its benefits. Much of agricultural big data and much of the associated infrastructure is
primarily designed to service farmers who follow a productivist strategy, which aims
to maximize the output of commodity export crops; this is a model based around the
intensification, industrialization and externalization of agriculture [
2
,
11
]. The exclusion
of farmers who adopt a different approach contributes to increased inequality not only
between farmers and agribusinesses but also among farmers themselves (e.g., small scale
vs. large scale, conventional vs. organic) [
11
]. Adoption and use of digital farming tech-
nologies is already higher on large farms dedicated to the production of commodity crops,
particularly in industrialized countries with a highly concentrated farm
structure [12,51]
.
Here digitalization deepens the digital divide between larger, capital-intensive farms and
those unable or unwilling to purchase digital technologies [
11
,
24
,
44
]. Their adoption might
further speed up the growth of larger farm holdings at the expense of smaller ones, a
phenomenon already evident in the United States and increasingly also in Europe [43].
Sustainability 2021,13, 12345 9 of 18
Existing socio-economic and spatial asymmetries in digital infrastructure development
contribute to unequal access to the internet and impact the ability of farmers to use digital
technologies [
8
]. These include the growing urban–rural divide, whereby rural areas are at a
disadvantage in this respect compared to urban ones in many parts of the world, in addition
to other disparities among different groups of smallholder farmers, including gender
inequality [
52
]. Such inequalities are reinforced by the pressure on farmers to comply with
regulations (e.g., global food safety standards or requirements for receiving agricultural
subsidies). These compel farmers to adopt digital technologies in order to remain in the
supply chain and compete with other early adopters [
43
]. For individual farmers, the costs
of digitalization are not recoverable and, in contrast to conventional farm machinery (e.g.,
tractors), there is only a small resale market for digital infrastructure. This is partly because
items of digital farming equipment such as sensors or drones are often customized and
cannot be adapted for other uses. In addition, hardware prices are falling, so farmers have
little motivation to buy or sell second-hand. As a consultant study highlights, the price
for an automotive LiDAR sensor for self-driving tractors declined by 90% between 2010
and 2017, from USD 75,000 to USD 7000 [
53
]. Farmers assume all the financial risks of
investing in digital services and devices to compete on the market [
43
] while the technology
providers benefit from data freely provided by farmers, which has high use value as an
input for the further development of data-based services. While the farmers still own the
fields, they cede control over their data to the service providers. Farmers then have to
pay the same service providers to access digital information generated in part from their
own farm data, which they fed into the system without receiving any remuneration [
44
,
46
].
Rotz et al. [
44
] consider that this “unpaid work under digital capitalism” turns farmers into
“digital labourers” who, moreover, have to pay “rent” to access data they themselves have
produced. Thus, the financial returns on investments in implementing and using digital
platforms are unevenly distributed. They are received by input suppliers, or technology
providers, who benefit from reduced transaction and product optimization costs, and only
to a lesser extent by farmers themselves [47,54].
3.3. Uneven Sovereignty over Data and Hardware
Digital agriculture increasingly depends on the extraction and analysis of large
amounts of data. As stated in the previous section, agricultural data is often collected
and stored using infrastructure manufactured, owned, and controlled by large companies
providing digital technology, machinery, or other inputs to farmers [
2
]. Both access to this
data and control over its use are very unequally distributed. Technology providers, in
particular, enjoy “a privileged position with unique insights into what farmers are doing
around the clock, on a field-by-field, crop-by-crop basis,” over large areas of the world [
19
].
For example, by 2018, the Climate Corporation platform Climate Field View had more than
100,000 registered clients in the United States, Canada and Brazil, who together farmed
about 120 million acres [
2
,
10
]. Some machinery manufacturers (such as John Deere or
AGCO) place “legal and digital ‘locks’ on hardware and software packages” that they sell
to farmers [
55
]. This prevents farmers from accessing the data which these companies
collect from the fields and from fully using their products. Sometimes, the full extent of the
data collected and the uses it is put to are hidden from farmers in accordance with privacy
and access agreements that the companies require them to sign [11].
In most cases, by signing such user agreements, farmers making use of a digital
platform hand over control of their data to the provider company. These agreements drawn
up by the data aggregators not only authorize data collection, but also limit farmers’ access
to the data and place restrictions on its further use [
44
]. Theoretically, farmers own the
data they generate. However, the aggregated data is property of the company that collects,
processes, and stores it [
56
]. Thus, in reality, farmers usually do not have full control of the
data they generate. The end-user license agreements drawn up by the companies give rise
to an unequal relationship, authorizing a ‘data grab’ that has been described as a form of
dispossession [
43
]. Many platforms do not disclose their back-end processes to customers,
Sustainability 2021,13, 12345 10 of 18
and withhold information about how customer data is used and for what purposes. This
uneven sovereignty reflects and at the same time is partly a result of the differences in
bargaining power of the parties involved. It shows the weaker position of the farmers, due
to the fact that data about a single farm has less economic value than aggregated big data
compiled by the technology providers [46].
Even in cases where farmers can access their own data, their data sovereignty often
remains limited because they lack the tools and capacity to analyze it. Thus, farmers’
growing reliance on farm data management platforms such as Climate Field View increases
their dependency on the firms providing these services. Farmers are sometimes not even
aware of the legal content of the terms and conditions they agree to [
48
]. Studies of the
legal regulation of farm management platforms in the European Union and United States,
and of the voluntary codes that service providers have signed up to, illustrate the legal
complexity of relationships among agricultural data users and providers. They also show
that ownership is almost exclusively governed by private license agreements, in some
cases based on existing voluntary codes of conduct [
46
,
48
,
56
]. It remains unclear who
actually produces the data and has the right to decide on its further use, “as farmers may
be data originators in one relationship with their advisor but then the advisor becomes
data originator when dealing with agribusinesses who provide digital services” [
48
]. This
might be one reason why there is currently no effective policy regulation in place in the
United States or in the European Union to protect and strengthen farmers’ sovereignty
over the data generated on their farms. In contrast, in the European Union, the existing
and rather “outdated” legal framework actually “enhances the position of the agricultural
technology providers and third-party aggregators” [
56
], thereby legitimizing the ‘data grab’
discussed in the previous section [43].
In addition to being dispossessed of “their” data, farmers are sometimes denied
sovereignty over the machines and equipment that they purchase. Legal locks in form of
license agreements prohibit farmers from repairing their smart tractors themselves, and
compel them to use only approved service providers. This is a consequence of legislation
originally intended to prevent digital piracy, but that now makes it illegal for farmers to fix
tractor engines or any other part of the equipment that is digitally controlled [17,52,57].
These inequalities have provoked some grassroots resistance. In the United States,
farmers organizations are demanding the right to own their data and to repair their own
farm equipment [
15
]. The US-based Right-to-Repair movement advocates for repair-
friendly legislation backed up by standards and regulations (e.g., to guarantee purchasers
the right to access information about products that they own, including the right to unlock
software) [
58
]. The movement supports farmers’ demands for the legal right fix their own
farm equipment [12,52].
Farm Hack [
59
] is another initiative that links up farmers across the globe via an online
platform where they can share experiences of assembling and repairing the hardware and
software used on their farms [
12
,
60
]. Farm Hack collectives directly challenge inequalities in
proprietary technology regimes in agriculture [
42
]. FarmOS is a tool developed by the Farm
Hack community that is intended to overcome “technological inequities by introducing
greater diversity into the digital agricultural socio-technical system” [
19
]. The free and open
platform was developed in close cooperation with farmers, and can be hosted, installed
and further developed by anyone who has the capacity (e.g., coding skills) to do so [
19
,
60
].
It enables farmers to stay at least partially independent of large corporations and to regain
or maintain sovereignty over how their data is shared [
2
,
10
,
19
,
43
]. FarmOS and similar
initiatives such as the OpenAg Data Alliance, Joindata, FarmLogs, and DJustConnect collect
and use data “for and by farm owners without ag-input company ownership” and under
their own sovereignty [
61
]. These initiatives provide alternative, low-cost open-source
software on shared platforms that acknowledge farmers’ ownership of their data and
operate outside of corporate control [10,17,43].
Sustainability 2021,13, 12345 11 of 18
3.4. Inequalities in Technology-Related Knowledge and Skills
Increasing digitalization requires skills that are not accessible to all [
62
]. Even if agri-
cultural data were completely ‘open-source’ and under farmers’ legal sovereignty, not all
farmers possess the knowledge and skills required to process and analyze the data, or to
correctly interpret the results [
47
]. The adoption of digital technologies requires farmers
to invest time and money in learning new skills in order to gain a basic understanding of
information technology systems and of how to interpret data outputs (e.g., for the identi-
fication of in-field management zones, which requires longitudinal data collection) [
63
].
As farmers become even more reliant on the use of digital technologies to guide their
farming practices, lock-ins become self-reinforcing. Without help to develop the requisite
knowledge and skills, they lose the ability to make decisions independently, or to repair
their own digital equipment and machinery [
12
,
44
]. This new knowledge is needed to
understand and analyze digital data. Few farmers have the financial resources to employ
or hire employees with digital skills, which in any case may be hard to find in rural areas.
Those that can hire such employees gain a competitive advantage over other farmers; thus,
digitalization is likely to exacerbate existing inequalities (e.g., between small and large
farms) [
63
]. Some studies portray this as a further manifestation of the urban–rural divide,
whereby farmers in remote rural areas find it more difficult to access the skills needed to
participate in digitalized agriculture than those based close to urban areas [8,64].
The increasing reliance on technical experts and technology may result in a loss of
tacit knowledge if the cognitive processing of information is delegated to machines or
algorithms [
65
]. Some fear that farmers will become even more dependent on the software
and platform providers as they lose the ability to “read” their plants and animals without
them [
66
]. On the other hand, delegation of some operational and basic activities to may
leave farmers more time for “higher level” learning processes [
65
]. Digitalization may
entail a readjustment of labor allocation on farms [
67
], possibly involving a reduction
in the human labor force [
52
]. It is still unclear to what extent farmers’ knowledge and
human labor will be replaced by algorithms and automatons, or complemented by them.
There is not much evidence of deskilling of farmers or farm laborers due to digitalization
thus far [
68
]; however, studies find that “digital tools are used to increase surveillance
and control” of the labor process on the farm, making it more transparent for employers.
Increased surveillance and control over the workforce could possibly limit the will and
ability of farm workers to collectively organize. This would further deepen the existing
class inequality between labor and capital, in particular in the case of poorly unionized
areas and for seasonal or migrant workers [68].
3.5. Unequal Definition and Problem-Solving Capacities
The capacity to define and shape the future of agriculture is unequally distributed
among actors. The productivist industrial agricultural model is often presented by its
proponents as key to achieving food security. However, it is criticized by movements for
food sovereignty and small-holder farmers’ alliances, among others, who call for a struc-
tural transformation toward agroecology or community-based organic farming. Several
scholars argue that the digitalization of agriculture is highly entrenched in productivist
farming systems and exacerbates their negative environmental effects, while increasing the
concentration of corporate power over the food system through multiple lock-in effects
and path dependencies [
2
,
11
,
12
,
57
,
69
,
70
]. The economic power of corporate agribusiness
actors allows them to shape public discourse, whereby the environmental benefits of digital
technologies are highlighted and the externalities often ignored [2,12].
These divergent visions of sustainable agriculture are linked to different assessments
of the power of technology alone to solve sustainability problems, and regarding the
social and ecological benefits and risks of digital precision technologies such as genome
editing. The emphasis in the policies of high-tech agribusiness solutions on structural
food system challenges demonstrates the power of corporate actors to define problems
and their solutions. This makes it hard for alternative ideas such as those of the food
Sustainability 2021,13, 12345 12 of 18
sovereignty movement, which identifies agro-industrial structures as the root cause of food
and environmental crises and associated local and global inequalities, to get a hearing, [
24
].
Less capital-intensive solutions to food security challenges are marginalized from the
discourse on sustainable agriculture and starved of investment [
57
]. These inequalities are
apparent, for example, in food security discourse, which tends to focus on productivist
approaches and digital technology as the solution. The digitalization of agriculture might
further entrench this narrative and lead to the sidelining of alternative, low tech, or non-
technology-based responses to crises in the food system. This would make it more difficult
for proposals to combat unequal access to food and the means of food production to gain
a hearing, as pointed out by proponents of the food sovereignty concept. However, such
technological fixes suit powerful actors from agribusiness, because they contribute to
maintaining the status quo and to diverting attention away from the need to transform
agriculture and challenge long-standing inequalities [24,71].
The inequalities in the ability to define problems and solutions are revealed in a
recent analysis of high-level policy documents showing how international organizations
such as the Food and Agriculture Organization or the World Bank envision future food
systems. These organizations support the status quo, whereby industrial agriculture plays
a dominant role in the food system, and prioritize the maximization of food output through
the use of digital technology and “climate-smart” farming solutions. Organizational policy
papers see digital technologies in agriculture as ‘inevitable’, since they are driven by
technological innovation, and as ‘needed’ both to combat poverty and inequality and to
cope with an increasingly unpredictable climate [
8
]. High-tech visions also attract much
larger private and public investment than alternative agroecological approaches because
they are seen as the most effective way to increase food security [
8
,
57
]. This is why some
authors question whether the coexistence of the two systems, organic and agroecological
on the one hand and conventional “Agriculture 4.0” on the other, is even possible. These
authors argue that powerful actors will continue to dominate the trajectory of agricultural
development, leading to the marginalization of alternative approaches given in [
72
]. Others
argue that the two systems may be more complementary than is generally assumed by
scientists and politicians, because farmers themselves will find ways to make them work
together [
24
,
73
]. Discourse on “digital agroecology” explores avenues towards the use of
digital technologies in order to advance core principles of agroecology, including equity,
justice, participation, and co-construction of knowledge in agriculture [
74
]. As indicated by
the findings of this review, small-scale and agroecological farmers are indeed inclined to
adopt digital solutions that are open and affordable, such as those made available through
the FarmHack network and other grassroots initiatives. In addition to affordability, these
solutions are attractive to farmers because they are easy to apply and facilitate the sharing
of knowledge. The democratic ownership of knowledge is explicitly welcome [74,75].
In addition to these grassroots initiatives, a number of companies are developing
digital products tailored to the needs of agroecological, community-oriented or small-scale
farmers [
1
,
17
,
76
]. In East Africa, the digital start-up WeFarm claims to have set up the
largest farmer-to-farmer digital network, with more than a million users in Kenya and
Uganda. WeFarm allows farmers to share questions, information, and advice through
promotion of solutions that draw on local agricultural knowledge rather than marginalize
it. The German-based company Rukola Soft [
77
] offers a planning tool for vegetable
cultivation customized to the needs of community-supported agriculture [
2
]. Plantix is a
free mobile app that promotes knowledge sharing and mutual learning among farmers [
78
].
It is used by small-scale farmers to diagnose and solve problems such as pest damage, plant
disease, and nutrient deficiencies, and has links to La Via Campesina, the International
Peasants’ Movement, which campaigns for food sovereignty in the Global South [57].
4. Discussion and Conclusions
This review examined the social dimensions of digital agriculture based on published
research findings. The aim was to identify the main patterns of inequality linked to the use
Sustainability 2021,13, 12345 13 of 18
of digital technologies in agriculture. Five interlinked patterns of inequality were identified,
as summarized in Table 1.
First, there are inequalities in digital technology development, whereby corporate
actors largely control and shape the development of infrastructure, products and services.
Farmers are on the receiving end, struggling to cope with the lack of interoperability and
constrained in their options by technical and legal lock-in effects.
Second, there are inequalities in distribution of benefits from the use of the technolo-
gies. Most digital technology is developed by industrial agri-tech firms and is designed to
serve the needs of capital-intensive large-scale farms with high investment capacities. This
deepens the rift between farmers who use digital technologies and those who are unable or
unwilling to do so. It reinforces existing economic inequalities, since farmers who lack the
capital to invest in digital technologies find themselves further disadvantaged. In addition,
the uneven availability and accessibility of digital infrastructure (e.g., internet connectivity)
disadvantages rural areas, reinforcing existing spatial patterns of economic inequality.
Third, there is uneven sovereignty over data, hardware, and digital infrastructure.
Large technology providers benefit from using the data provided by farmers, while farmers
themselves have limited access to and control over the data they have generated. They
may even be prevented from repairing their own machines, under the terms of license
agreements designed to increase farmers’ dependency on licensed service providers.
Fourth, there are inequalities in ‘digital literacy’ among farmers, and between farmers
and corporate providers of digital services. Many farmers lack the knowledge and skills
required to process digital data, and find it difficult to find independent service providers
or employees who are able to use and manage digital tools. Here, again, a spatial divide
between farmers in urban and rural areas comes into play. Those farmers who lack the
resources or the opportunity to develop digital skills become increasingly dependent on
corporate providers not only for decision-making but also for the maintenance and repair
of farm machinery.
Fifth, there are inequalities of power and influence over problem definition and prob-
lem solving. Powerful corporate actors shape the discourse, ensuring that the productivist
vision of industrial agriculture remains predominant, while excluding alternative visions.
This allows them to shape policies, whereby digitalization in the service of industrial
agriculture is seen as the only way to achieve social and environmental goals. Alterna-
tive problem definitions, for example focusing on food sovereignty, and solutions, such
as agroecology, are excluded from the discourse, widely ignored by policy makers, and
starved of investment.
4.1. Power Dimensions in Digital Agriculture
The literature review also highlighted the existence of emancipatory initiatives dedi-
cated to developing the potential of digital technologies to challenge existing inequalities.
Coders and famers are collaborating to develop platforms for knowledge sharing and
mutual learning alternatives and using them to advance alternative visions of agriculture.
These initiatives underscore the political nature of digital agriculture; however, their reach
is currently still quite limited. This is partly due to the fact that existing inequalities are
structural, and expressions of “complex dimensions of corporate power, and concentra-
tion” [
17
]. The patterns of inequality identified in this review mainly relate to economic
circumstances and the availabilities of financial and other resources. At the same time, they
entail power inequalities in the sense that the powerful actors who possess these resources
are able to shape not only the context but also the conduct of less powerful actors [
79
].
Actors from the agri-tech sector and technology providers promote proprietary products
and services that, in part through the widespread use of lock-ins, primarily serve their
own interests and preserve the status-quo of the agri-food system (context-shaping power).
They thereby also limit the options available to farmers with regard to both the choice of
tools to meet their needs and the use to which the data they generate is put. In addition,
the influence of powerful actors sidelines efforts by other actors to promote an alternative
Sustainability 2021,13, 12345 14 of 18
vision of agricultural systems, and deprives them of the opportunity to demonstrate the
viability of smaller-scale and less capital-intensive agriculture (conduct-shaping power).
Of course, these power relations in the digitalized agri-food system are not new;
they are a continuation of what food regime theorists have called the ‘corporate food
regime’ [
33
], and they are quite stable (for a discussion of continuities and shifts in the
global food system, see [
2
]). This highlights the fact that the inequalities identified in
this review are not caused by digital technologies but are new manifestations in the
socio-technical sphere of existing social inequalities and power disparities. From such a
perspective, digitalization in agriculture is not a ‘revolution’. Digital technologies do not
‘revolutionize’ structural power relations; they mirror and reproduce them. Because of its
tendency to leave underlying structures untouched, some authors even argue that the so
called ‘new’ digital precision agriculture is still conventional agriculture, and better un-
derstood as part of a long-established production systems characterized by rationalization
and control [
69
]. These authors identify continuities in the structural development and
dynamics of agriculture and technology development [
2
]. They note that some features of
the digital transformation of agriculture, for instance the use of the legal and digital lock-ins
as part of a “top-down, closed platform model for the digitization of agriculture,” were
also features of previous innovations such as genetically modified organisms, which are
widely known to exacerbate inequalities in the food system between smaller land-holders
and large agri-businesses [67]. As political economy scholarship argues, addressing these
patterns of inequality requires “structural shifts in food, environmental and trade policy at
the national and global scale.” [17].
Notwithstanding the structural character of these power relations and the need for
macro-level changes to reduce the structural inequalities in digital agriculture, the open-
source platforms and resistance movements identified in this review help to free farmers
from technological lock-ins and reduce their dependency on proprietary technology sys-
tems. Of course, they cannot achieve this all at once, not least because farmers lack the
capital to compete with proprietary systems. However, they go some way towards chal-
lenging existing power relations, and contribute to political change through their efforts
to democratize data as a means of production. Digital infrastructure such as Rukola Soft
facilitates trans-local food movements and the sharing of place-based knowledge for the
benefit not only of farmers but also food sovereignty groups promoting an alternative vi-
sion of agriculture. By facilitating the emergence of alternative technology and knowledge
regimes and bringing together the ideas of food sovereignty and data sovereignty [
43
],
they contribute to the democratization of technology while challenging the power relations
in which technology is embedded.
4.2. Policy Challenges in Digital Agriculture
The importance of national and supranational policies for the democratization of digi-
tal technology cannot be overstressed. Digital technology has the potential to contribute
to socially and ecologically sustainable agriculture. In order to realize this potential, the
social impacts and inequalities linked to digital agriculture need to become a key concern
of policy makers, especially against the background of the United Nation’s Sustainable
Development Goals, which includes the goals of both reducing inequality and making
agriculture more sustainable [
80
]. It is beyond the scope of this paper to analyze exist-
ing policy regulation and how it impacts the different patterns of inequality identified
here. However, it is clear based on this review that the fragmented regulatory and legal
framework governing the use of data and digital technologies in agriculture is in urgent
need of reform. In the European Union, the existing legal framework is fragmentary and
does not amount to a coherent policy framework. The existing assortment of instruments
does not cover the complexity involved in the regulation of property rights and the owner-
ship of digital assets. Among the principal existing policy instruments, the General Data
Protection Regulation [
81
] only covers personal data, leaving businesses, and thus much
of farm data, unaffected. However, it is not always clear whether farm data counts as
Sustainability 2021,13, 12345 15 of 18
personal or business data. This is the case, for example, when a farmer is sitting in a smart
tractor or combine harvester that is recording everything she does. The Regulation on a
Framework for the Free Flow of Non-Personal Data in the EU seeks to reduce the local
storage and processing of non-personal data [
82
]. It encourages private license agreements
and codes of conduct that allow the free flow of data and make it easy for customers to
switch to other solutions and services. It is thus an important step forward towards a legal
requirement for data portability and interoperability, and as such potentially contributes to
the data sovereignty of farmers. Yet, it does not address the question of where data comes
from and who ‘owns’ it. Finally, the European Union’s Database Directive [
83
] tends to
strengthen the position of data aggregators, providing neither ownership nor property
rights for farmers in their capacity as ‘raw data producers’. Thus, it is clear that the current
instruments in the European Common Agricultural Policy “are poorly linked to the needs
and challenges of digital economy” [
56
]. Of particular concern is that fact that there is no
effective policy instrument in place in the European Union or in the United States that
legally strengthens the sovereignty of farmers over their farm data [46,56].
This review has explored the patterns of inequality associated with digital agriculture
and identified some political initiatives from below that challenge these inequalities. The
conclusions highlight the need for both researchers and policymakers to explore options
and opportunities for the creation of a new digital agrarian law and to foster policy
integration in order to regulate digital agriculture at different political scales. Moreover, in
addition to adequate top-down policy regulation, attention should be given to the potential
of bottom-up political movements to contribute to the reform of digital agriculture policy.
Still very little is known about the actual and potential contribution of alliances
between open data initiatives and the food sovereignty movement to the democratization
of data, the reduction of inequality, and the transformation of power relations. This kind of
“emancipatory smart farming” [
76
], which was outside the scope of this review, is still in
its infancy and merits close attention in further research.
Funding:
This work was supported by the BMBF as part of the funding line ‘Bioeconomy as Societal
Change’ FKZ 031B0750.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement:
The sample used in this study is available on request from the author.
Conflicts of Interest: The author declares no conflict of interest.
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