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Ecological Economics 196 (2022) 107422
Available online 1 April 2022
0921-8009/© 2022 The Author. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-
nc-nd/4.0/).
Linking the diversity of ecologisation models to farmers’ digital use proles
´
El´
eonore Schnebelin
*
UMR Innovation, DigitAg, INRAE Montpellier, Institut Agro, France
ARTICLE INFO
Keywords:
Agriculture
Ecology
Digital technology
Trajectory
Farmer heterogeneity
Organic farming
ABSTRACT
Digitalisation is promoted by both private and public actors as a way of contributing to the ecologisation of
agriculture. However, this idea remains controversial. The debate is all the more crucial, as different ecolog-
isation models exist, and as agriculture is experiencing new levels of industrialisation. In the literature, use of
digital technology in agriculture has mainly been approached from a linear perspective of adoption but is rarely
linked to ecologisation. In this paper, we aim to dene digital use proles of farmers and explain how they relate
to ecologisation models. We distinguish production and information technologies. Based on 98 interviews with
crop farmers in Occitanie (France), we show that there is a diversity of digital proles. Through a mixed-method,
we relate these proles to a set of variables representing ecological and economic transformation in agriculture.
It highlights links between some digital proles and the further industrialisation of agriculture intertwined with
weak or symbolic ecologisation. However, some digital uses associate with new forms of ecologisation that are
based on input substitution. Digital use does not appear to support stronger ecologisation of farming. This study
highlights the risk of a single model of digitalisation that only promotes one type of ecologisation pathway.
1. Introduction
The objective of this paper is to investigate how the diversity of
farmers’ use proles of digital technology can be tied or not to different
models of ecologisation in agriculture. Ecologisation is dened as “the
growing importance of environmental issues within agricultural policies
and practices” (Lamine, 2011; Lucas, 2021). A diversity of farming
models exists, each claiming to ecologise agriculture. Digital technology
encompasses a wide range of technologies, including precision farming
equipment, digital platforms, decision support systems for farmers and
advisors, etc. (Prause et al., 2020). Different actors promote these
technologies, including some representatives from the elds of science,
farming and public policy. A series of high-level documents mention
digitalisation as a way of improving farm productivity and reducing the
adverse effects of agriculture on the environment (Lajoie-O’Malley
et al., 2020). Public policies have participated in the rapid development
of digital technology for farming, with increasing public and private
investments, and a rising number of start-ups (Barrett and Rose, 2020).
However, the actual effects of digital technology on the ecologisation
of farming practices is a matter of both scientic controversy and po-
litical debate (HLPE/FAO, 2019; Lioutas and Charatsari, 2020; Walter
et al., 2017). One side of the debate concerns the compatibility of these
technologies with the different models of agricultural ecologisation
(Fleming et al., 2018; Klerkx and Rose, 2020; Knierim et al., 2019).
Among the diversity of ecologisation models, some seek to reduce the
impact of conventional or industrial farming by pursuing an optimisa-
tion of input use (fertilizers, pesticides). Others aim for a more radical
reconception of farming systems. This later trend includes for instance
organic or soil conservation farming models (Duru et al., 2015). Critical
analyses suggest that digital technologies might reinforce the industrial
model of agriculture, to the detriment of more alternative and ecolo-
gised models (Carolan, 2020; Lioutas and Charatsari, 2020; Rotz et al.,
2019a; Wolf and Buttel, 1996). Other analyses are more positive on the
impact of digital technology on ecologisation (Walter et al., 2017).
A paradox is that there is still a lack of social science research that
links knowledge about farmers’ actual uses of digital technologies on the
one hand with knowledge about their practices and technological tra-
jectories towards ecologisation on the other (Klerkx et al., 2019). The
majority of research into the subject focuses on better understanding the
adoption of precision farming technologies. They most often posit that
these technologies have positive effects on the environment (Barnes
et al., 2019) and go on to identify a series of obstacles and factors
impacting the adoption of technologies, including variables of farm
structure (farm size, specialisation), equipment, and farmer proles
(age, education, etc.). However, such research has not examined how
digital technologies interact with the diversity of practices and
* Corresponding author.
E-mail address: eleonore.schnebelin@inrae.fr.
Contents lists available at ScienceDirect
Ecological Economics
journal homepage: www.elsevier.com/locate/ecolecon
https://doi.org/10.1016/j.ecolecon.2022.107422
Received 16 July 2021; Received in revised form 14 March 2022; Accepted 20 March 2022
Ecological Economics 196 (2022) 107422
2
organisations of farms. Other social science research has started to
investigate this issue, particularly in sociology and the management
sciences. However, most of these studies do not make the connection to
broader debates about changes in farm economic models and trajec-
tories (Eastwood et al., 2017; Lioutas et al., 2019; Moreiro, 2017). In
total, even though digital technologies are promoted by public policy,
very little is actually known about their use and effects on different
categories of farms related to different agricultural models.
This leads us to the following question: does the development of
digital technology benet all models of ecologisation, or does it favour
some models over others? To answer this question, it is rstly necessary
to study the uses of digital technologies in all their diversity, and sec-
ondly, to associate these uses with different production and ecologisa-
tion models.
We have used a mixed methods research protocol (Fakis et al., 2014),
based on 98 interviews with farmers from a region in South-West of
France, which is engaged both with digital technologies and ecologisa-
tion. Our contribution is twofold: First, a quantitative analysis has
allowed us to construct use proles using a hierarchical cluster analysis
(HCA) for two types of technologies - production digital technologies on
the one hand, and information and communication technologies on the
other. Moreover, it has allowed us to highlight the statistical differences
between farmers with different use proles of digital technologies.
Second, the qualitative analysis has allowed us to understand the causal
relationships and elements of the trajectory that are behind these dif-
ferences. Combining the quantitative and qualitative analysis has
contributed to an understanding of how these use proles integrate into
the different paths towards the ecologisation of farming, and more
generally, of how they are tied to economic models.
Firstly, we will dene and describe digital technology in farming for
the purpose of studying those uses in more detail. We will then outline
our methodology. Subsequently, we will present the use proles and
show how these proles are tied to i) structural and economic charac-
teristics, and ii) the ecologisation practices of farms. Finally, a qualita-
tive analysis and discussion will return to these ties, with the aim of
understanding how digitalisation and ecologisation are inter-connected.
2. Background to digital technology innovation and the
ecologisation of farming
Our analytical framework is based upon innovation and institutional
economics, with the aim to provide a systemic understanding of the
relations between digitalisation and ecologisation. This perspective has
led us to consider three steps: construction of farmers’ use proles for
digital technologies and characterisation of agricultural models (2.1);
grasping of digitalisation and ecologisation practices at farm level (2.2);
interconnection between digitalisation and ecologisation (2.3).
2.1. Linking use proles to agricultural models
The concept of use allows us to consider technologies in the light of
their effective utilisation, and in interaction with farming contexts. This
concept takes the technology beyond its purchase and prescribed use. It
also considers its recurring integration into farming systems and prac-
tices (Badillo and P´
elissier, 2015; DiMaggio and Hargittai, 2001).
Effective use is dened as “the capacity and opportunity to integrate IT
successfully into the achievement of objectives dened by the interested
actors themselves, or in collaboration with others” (Gurstein, 2007, p.
9). The use of a technology is therefore associated with the construction
of knowledge, and with adjustments and interactions between the
different components of a farm (Higgins et al., 2017).
This concept of use makes it possible to consider the in-
terdependencies between various digital technologies, but also between
these technologies and farm production systems. So far, most economic
research has studied digital technologies one by one, showing how in-
dividual, economic and technological variables affect their adoption
(Barnes et al., 2019; Lowenberg-DeBoer and Erickson, 2019; Michels
et al., 2020). However, digital technologies integrate and combine with
each other, and with other technologies (Clapp and Ruder, 2020). These
combinations of practices can be institutionalised over time. To facili-
tate accounting for these combinations, we decided to characterise
farmers’ use proles of digital technologies. This prole-based approach
has been applied in building typologies of digital uses in industrial
sectors (Frank et al., 2019) or more general typologies about Internet
uses (Brandtzaeg et al., 2011).
Farming models are used to characterise farming heterogeneity.
Institutional economics differentiates between productive models by
three means: productive organisation (methods and techniques, spatial
organisation, resource mobilisation etc.), employment relationships,
and product policy (target markets, volume, quality, etc.) (Boyer and
Freyssenet, 2000). In agriculture, farming models are distinguished by
their biotechnical types, their socio-economic contexts, or by both
(Therond et al., 2017). They also differentiate by their values (Plume-
cocq et al., 2018), by the organisations and institutions that support
them, and by their knowledge, their links to market, the state, the ter-
ritory and the associated farming practices (Gasselin, 2019). They are
also studied through the lens of “farming styles”, which proles farms
depending on their mobilisation of various resources (Van der Ploeg,
1994). This diversity of models is explained by a diversity of sub pro-
cesses that lead to complex and non-linear processes of differentiation
(Van der Ploeg, 2018). In France, farming models are still largely based
on familial structures. However, the number of familial structures is
decreasing (39% reduction between 2000 and 2016) (Forget et al.,
2019). Dominance of familial structure is questioned, with the existence
of diverse farming models and differentiation mechanisms including the
development of rm agriculture, the growing importance of outsourcing
as well as the defence of more traditional (peasant) agricultural models
(Gasselin et al., 2021; Nguyen et al., 2020).
Linking digital uses proles to variables that characterise farming
models allows us to go beyond adoption mechanisms and understand
how digitalisation is tied or otherwise to different farming models,
through reinforcement, lock-in and exclusion processes. Those processes
can inform technological trajectories and possible path-dependency
(Dosi, 1982).
2.2. Ecologisation and digitalisation trajectories
2.2.1. Digitalisation studied through two categories of technology
Digitalisation can be dened as the growing utilisation of Informa-
tion and communication technologies in the economy, and in society
(Lange et al., 2020). Several categories of digital technology can be
dened, depending on the techniques employed, the functions per-
formed and the impacts envisioned. For this study we differentiated two
major technological areas with respect to their potential impact on the
ecologisation of farming (Rotz et al., 2019b).
First, Digital Technology for Production (DTP) brings together
technologies designed to modify the process of production directly.
These include technologies for precision farming, which are categorised
in the literature as recording, guidance and implementation or response
technologies (Balafoutis et al., 2017; Schimmelpfennig, 2016). DTP is
based on the use of satellite guidance technologies, parcel mapping and
sensors. It can have a variety of impacts: on the management of inputs
such as fertilizers, pesticides, and seeds; on outputs such as yield and
production quality; on the implementation of certain practices such as
tillage or crop rotation; on the nature, organisation and arduousness of
work, and on productivity. DTP corresponds in part to the category of
“embedded technologies” described by Birner et al. (2021).
Second, Digital Technology for information and Communication
(DTC) brings together technologies used to access information and
communicate with peers, advisers and customers, in order to exchange
or co-create knowledge. It includes the use of specialised farming web-
sites, social media networks such as YouTube or Facebook and other
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E. Schnebelin
Ecological Economics 196 (2022) 107422
3
digital platforms or media. It can have an impact on the process of
gaining knowledge and training (Burton and Riley, 2018; Leveau et al.,
2019), or of getting information on markets, social movements or pol-
icies, which in turn, have an impact on the process of production.
Distinguishing between these two types of technology allowed us to
capture the diversity of digitalisation. We also studied the ties between
the two types of technology, which are often considered separately in
academic literature.
2.2.2. Ecologisation: a new factor in the differentiation of farms
Ecologisation means the integration of environmental components
into policies, knowledge and practices. The environmental challenge has
brought new dimensions of differentiation between farming models,
depending on their ecologisation strategy (Duru et al., 2015). We use the
concept of ecologisation here, to integrate a diversity of objectives,
means and temporality, towards a more environmentally friendly agri-
culture, as well as their political dimension. There are controversies on
the effectiveness and environmental performance of those models,
which create political confrontations between ecologisation models.
There are two main approaches, sometimes referred to as “weak” and
“strong” forms of ecologisation (Duru et al., 2015). One seeks to opti-
mise the existing conventional model. The other, agroecology, seeks to
reconceive production systems more systematically by relying on
ecosystem services. This involves agronomic practices as well as meth-
odological and socio-economic principles (Stassart et al., 2012). Organic
farming in France is recognised as a form or prototype of agroecology
which has been gradually institutionalised (Bellon and Penvern, 2014).
However, the reality of these practices is more complex, with, on one
hand, the process of a “silent agroecology”, a “farmer-led movement for
agricultural change […] that is largely unrecognized and poorly un-
derstood” (Lucas, 2021, p. 18); and on the other, a ‘conventionalisation’
of organic farming (Darnhofer et al., 2010). Introducing variables such
as soil management, inputs and biodiversity management into agro-
ecological practices helps to ne-tune the distinction between conven-
tional and organic farming.
These approaches to ecologisation are intertwined with the struc-
tural and socio-economic characteristics of production systems. Certain
forms of ecologisation might require specic organisations and knowl-
edge systems (Stassart and Jamar, 2009), as well as adapted economic
models, for instance market segmentation for organic farming (Van der
Ploeg, 2018).
2.3. How digitalisation can link to ecologisation trajectories
We have examined the relationship between digitalisation and eco-
logisation in farming through a consideration of two types of digital
technology, which have potentially different impacts on ecologisation
pathways.
DTP aims to enhance the efciency of agricultural production pro-
cesses, and was designed for the optimisation of the industrial farming
production system. Some authors argue it only has sense within such
systems (Bronson, 2019). As a result, economic variables such as Utilised
Agricultural Area (UAA), economic size and workforce are expected to
have positive and signicant effects on the adoption of DTP (Barnes
et al., 2019). Further, the subcontracting of certain tasks to an agricul-
tural outsourcing company, as well as membership of a cooperative, is
playing a growing role in farming businesses and with their equipment
(Nguyen et al., 2020; Wolf and Nowak, 1995). Moreover, DTP have been
shown to foster industrial agriculture and to increase dependence on
chemical inputs (Wolf and Buttel, 1996). As mentioned in 2.2, ecolog-
isation trajectories are connected to economic models, and this leads us
to our rst proposition on the ties between the use of DTP and
ecologisation:
•Proposition 1: the use of DTP is associated with weak forms of eco-
logisation that are intertwined with industrial economic models of
farming.
DTC modies the ow of information and knowledge exchanges and
opens new possibilities for the co-creation and sharing of them. Through
its technical, social and political dimensions, ecologisation requires
changes in knowledge creation and circulation, especially concerning
agroecological models (Duru et al., 2015). DTC can facilitate those
knowledge transformations and promote a strong ecologisation (Bonny,
2017). This brings us to our second proposition:
•Proposition 2: The use of DTC is associated with stronger forms of
ecologisation, which are based on a reconception of production
systems.
However, this proposition is worth challenging empirically. In fact,
digital technologies can lead to information and knowledge commodi-
cation, concentration and industrialisation (Rikap and Lundvall, 2020;
Wolf and Wood, 1997). It could lead to de-skilling and path-
dependencies that encourage the continuity of conventional agricul-
tural systems. Furthermore, strong ecologisation requires learning that
remains based on local and on-farm knowledge exchanges and experi-
mentation (Van der Ploeg et al., 2019).
We could add social and institutional factors here, which may differ
according to the farming model, and could play a role in the adoption of
technology use. Regulations, funding, specications and social norms
have an impact on the use of these technologies (Fielke et al., 2020). In
France in particular, environmental regulations, or the CAP (Common
Agricultural Policy) are examples of institutions that can have an effect
on farming equipment. Other research has shown that these environ-
ments are not neutral in the ecologisation trajectory that they support
(Vanloqueren and Baret, 2009). At farm level, the micro socio-economic
environment, such as relations with suppliers of advisory services or
cooperatives, also inuences the use of these technologies (Barnes et al.,
2019; Wolf and Nowak, 1995).
3. Research method
In order to understand the diversity and complexity of farmers’ uses
of digital technology, we conducted 98 face-to-face interviews, using a
mixed method that combined quantitative and qualitative approaches. It
allowed us to integrate two types of data into the construction of our
interpretation (Watkins and Gioia, 2015).
3.1. A mixed method for a better understanding of interrelations between
digitalisation and ecologisation
In order to study farms with a systemic approach the questionnaire
included closed-ended questions on structural, individual, socio-
economic and agronomic dimensions of farms (as detailed in the Ap-
pendix A). The answers were then entered into a data base. Subse-
quently, each technology used was studied in more detail. More
open–ended questions and moments of discussion gave substance to the
collected material. They allowed us to i) adopt a systemic perspective on
farming, ii) adjust the questions depending on the technology used, and
iii) gain clarity about the uses and their impacts.
We used a hierarchical cluster analysis to categorize these uses while
taking into account the connections between different digital technol-
ogies. Most of our variables being qualitative, we rst completed a
multiple-component factor analysis (MCFA). More specically, we used
the HCPC (Hierarchical Clustering on Principle Components) of the
FactoMineR R software package (Lˆ
e et al., 2008). We selected technol-
ogies that are used by no less than 5% of the population for the two types
of technology chosen, DTP and DTC.
For DTP, we selected 12 binary variables concerning the use of the
´
E. Schnebelin
Ecological Economics 196 (2022) 107422
4
following technologies: guidance, automatic guidance, section control,
connected weather station, eld mapping, variable-rate technology
(VRT) for fertilizers, variable-rate technology (VRT) for seeding, con-
nected tensiometer, yield map, decision support tool for crop protection,
connected irrigation technology and farm management software. The
percentage of inertia explained by the classication is 41%.
For DTC, we took the following seven variables for nding technical
information: the frequency with which the internet is used, the use or
otherwise of social networks, Facebook, YouTube, specialised agro-
nomic websites, technical institute websites, and farming press websites.
The percentage of inertia explained by the classication is 36%. During
the interview, we asked farmers about their use of digital technologies to
create or share knowledge. However, the majority of farmers were only
using these technologies for consultation, so we did not include more
participatory use in this prole construction.
The clusters obtained in this way can be considered as proles of
types of use. We linked them to the ecological practices and the socio-
economic status of the farms.
The HCPC R function enabled the identication of specic in-
dividuals- i.e. the ideal type for each of the proles. The transcriptions of
these individuals were coded with MaxQDA and analysed. This quali-
tative analysis allowed us to 1) better understand the connections be-
tween variables to explore causality, 2) reveal other aspects which had
not surfaced before, and 3) illustrate the analysis with quotes from the
transcript.
3.2. The Occitanie region as a eld of study
We chose to concentrate on eld crops. This sector is emblematic of
the history of modernisation of French agriculture. It is characterised by
a substantial capacity for investment, and has been the focus of devel-
oping digital technologies for many years (Lowenberg-DeBoer and
Erickson, 2019). The eld interviews were conducted in the Occitanie
region of France. This region is pioneer in both digital technologies for
farming, and in ecologisation, while being in the average range of
French farms in terms of size and production. Occitanie is the leading
region in France for organic eld crops, with 24.5% of France’s organic
farmland (Interbio Occitanie, 2018).
Our sampling was not representative but purposive to explore the
diversity of proles. To that end, we made contact with farmers through
farming organisations (cooperatives) and via organic farming di-
rectories, and afterwards by snowball sampling (Atkinson and Flint,
2001). We aimed to have users and non-users of digital technology, as
well as a range of agronomic practices (conventional and organic
farming, conservation farming or other ecological practices). We could
assess the selection bias of our farm sample through a comparison with
data from the ofcial census of the Ministry of Agriculture (using vari-
ables such as Utilised Agricultural Area, standard gross production and
workforce).
4. Results of cluster analysis: relation between digital uses and
Farms’ characteristics
4.1. Survey description
Our sample brought together a diverse range of farms (n =98), as can
be seen on Table 1. The utilised agricultural area of farms ranged from
9.5 Ha to 570 Ha, with an average of 162 Ha, which is greater than the
regional average of 59 Ha. This difference can partly be explained by the
inclusion of farms run by multi-active farmers or retirees in the ofcial
census. We chose not to integrate these categories in our sample as they
do not carry much economic weight. The standard gross production
(SGP) and the workforce were consequently greater in our sample than
the regional average, and with a high diversity. The interviewees were
between 24 and 67 years old.
Table 2 shows the population distribution dened by some
qualitative variables. ‘Mixed’ farms are those where only a proportion of
the crops are farmed organically, with the other crops being not certied
organic.
1
Interviewees had a range of levels of education.
The clustering method resulted in three groups for DTP and three
groups for DTC, as shown in Fig. 1.
Two proles were constructed to characterise the two types of
technologies: for production (DTP), and for information (DTC). By cross-
referencing these proles, (Table 3), we demonstrated that there is no
signicant overlap. The Pearson test allowed us to reject a dependence
hypothesis between the types of digital use on farms.
4.2. Use proles for digital technologies for production (DTP)
The three DTP use proles are described below. We characterise the
farms of those proles. Table 4 recaps the variables that show the
greatest differences between the three groups. A detailed description of
the proles and all their characterisations can be found in the Appendix
B.
4.2.1. No-DTP prole
The rst prole, that we called ‘No-DTP’, makes up 39% of the
sample. In this group, most of the farmers do not have digital technology
for production. A little less than one third of them, however, do have
farm management software (land and administrative management).
In this prole, farms that provide outsourcing services are under-
represented, as are those which cultivate seeds, or which have con-
tract farming. Organic farms, farms that sell directly to the customer, or
that farm livestock, are, on the other hand, over-represented. The farms
Table 1
Descriptive statistics – quantitative variables.
Var Obs mean sd min max Regional
mean
a
Utilised
Agricultural
Area
98 162.1 113.0 9.5 570 59
Standard
Gross
Production
98 243,156 193,841 5559 1,053,895 58,123
Total
workforce
98 1.9 1.1 1 6 0.9
Age 98 44.1 10.8 24 67 49.1 in
France
b
a
Eurostat for farms specialising in grain or oil and protein crops - Midi-
Pyr´
en´
ees −2016 data – Eurostat.
b
French statistics from MSA, 2018 - available at : https://statistiques.msa.fr
Table 2
Descriptive statistics – qualitative variables.
Modality Number %
Crop certication Organic 28 28.6%
Conventional 58 59.2%
Mixed 12 12.2%
Education level
a
<Bac2 31 31.6%
Bac +2 42 42.9%
>Bac2 25 25.5%
Gender
Male 89 90.8%
Female 9 9.2%
a
Bac +2 is equivalent to a bachelor degree.
1
European Organic certication allows to have clearly separated units which
are not all managed under organic production, only if units grow different
varieties that can be easily differentiated and do not store organic and non-
organic production (Council Regulation, 2007. No 834/2007 of 28 June 2007
on organic production and labelling of organic products and repealing
Regulation).
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E. Schnebelin
Ecological Economics 196 (2022) 107422
5
in this group are smaller in terms of economic size
2
and agricultural
area, have fewer annuities and a smaller workforce than the average.
However, they do not have a lower Gross Operating Surplus (GOS). The
costs for inputs per hectare and yields are signicantly lower.
4.2.2. Average DTP prole
The second prole, named ‘average DTP’, consists of 32% of the
sample. It includes farmers who use some digital technology for pro-
duction, but who do not have the full precision farming software
package: nearly all use guidance technologies (97%) or automatic
guidance (63%), and the great majority (87%) have land management
software, but none of them use variable-rate technology for inputs.
Furthermore, between 20 and 40% of them use section control tech-
nologies, decision making tools for their treatments, weather stations,
and connected tensiometers and irrigation technologies.
Farms tting this prole generally represent the sample average, but
they also use more irrigation, have a higher salaried workforce, a greater
expenditure on fertiliser per hectare, and do less direct marketing. The
organic farms in this group (N =8), have greater GOS, SGP, annuities,
more irrigation, and do more trading within cooperatives.
4.2.3. Intensive DTP prole
The third prole, ‘intensive DTP’, includes 29% of the sample, and is
characterised by the intensive use of digital technologies for production.
We found wide use of precision farming technologies: guidance (96%),
variable rate fertiliser (92%), connected weather stations (65%), and
yield monitoring (46%). 23% of the group use variable rate for seeding.
In this prole, farms practicing outsourcing, mixed farms, farms with
seed cultivation and integrated pest management are over-represented.
This group is higher than average with the variables Utilised Agricul-
tural Area (UAA), SGP, total employed workforce and salaried work-
force, GOS, costs of pesticides and fertiliser, and soft wheat yields. There
are fewer certied organic or mixed crops.
4.3. Use proles of digital technology for information and communication
(DTC)
We will describe below the three DTC proles,
3
and characterise
farms belonging to those proles. Table 5 recaps the variables that show
the greatest differences between the three groups. A detailed description
of the proles and all their characterisations can be found in the Ap-
pendix B.
4.3.1. No-DTC prole
The rst prole, ‘No-DTC’, is dened by a limited use of the Internet
to nd information. Two thirds only use the internet to search for in-
formation from time to time, or use it rarely or never. Three quarters do
not use social networks and do not consult specialist sites. Nearly none of
them use YouTube, or the technical institute websites. However, 45%
consult the online farming press.
This prole is characterised by a lower percentage of farmers with
the French equivalent of a bachelor’s degree or who belong to farmers’
knowledge exchange groups (CETA). In this group, there is a higher
percentage of farmers who get advice within cooperatives and have the
lowest level of digital literacy. These farms have a higher expenditure on
pesticides per hectare than average, have more ploughed land, and less
no-tillage.
4.3.2. Website DTC prole
The second prole, ‘Website DTC’, is dened by the use of the spe-
cialised sites to look for information. Although they do not use Face-
book, YouTube, or the farming press online, all use specialised websites,
in particular from technical institutes (36%).
The farmers in this prole cultivate less seeds, with more non-tillage
or minimum tillage. On average, they grow a higher proportion of
Fig. 1. R classication results.
Table 3
Cross-referencing digital use proles.
No-DTC Website DTC Network DTC DTC N/A Total
No-DTP 8 19 10 0 37
Average DTP 6 11 13 1 31
Intensive DTP 8 8 11 1 28
DTP N/A 0 1 1 0 2
Total 22 39 35 2 98
2
Economic size indicates a standard output criterion but is not a performance
indicator.
3
The constitution of DTC clusters was less obvious. Also, it seems that the use
of DTC is more characterised by a continuation of practices, rather than distinct
groups of uses.
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Ecological Economics 196 (2022) 107422
6
pulses, have lower costs of pesticides and higher yields of irrigated
maize and hard wheat. The farmers are older on average, a higher
proportion of them have an intermediate grasp of IT tools, and they are
less likely to belong to a trade union.
4.3.3. Network DTC prole
The third group, ‘Network DTC’, is made up of farmers who use social
networks in a professional context (100%), in particular Facebook and
YouTube. They either use the internet often or very often (91%), and
they consult specialised websites (89%).
Farms in this prole have more irrigation. Farmers have started
farming more recently than the average, and are younger. In this group,
members generally have the best grasp of digital tools, as well as a more
negative opinion of the economic health of their farm.
Table 5 summarises the effects of the variables that show the greatest
differences between the three groups. Compared to production tech-
nology clusters, fewer variables are signicantly different between one
group and another.
5. Qualitative insights and discussion of results
The proling of digital uses has allowed us to highlight how these
technologies are interconnected within productive models (Clapp and
Ruder, 2020). By these means, we have been able to identify some use
proles that correspond with reoccurring combinations of these
technologies. We complement those proles description with a quali-
tative analysis. This allows us to discuss the relationship between digi-
talisation and ecologisation trajectories.
5.1. Digital technologies for production, industrialisation and
ecologisation
Our rst proposition was that DTP use is tied to weak forms of eco-
logisation intertwined with the industrialisation of farming models. Our
analysis conrms the link between the use of DTP and industrialisation,
and ne-tunes its link towards ecologisation.
5.1.1. DTP uses support enlargement and industrialisation of farms
The importance of economic size stands out as one of the main fac-
tors driving the use of DTP, as demonstrated in previous research
(Barnes et al., 2019; Konrad et al., 2019). Firstly, economic size in-
uences the capacity to invest in this type of tool. This capacity may be
attained by increasing farm size, by collective investment (relatively
little-used in our sample) or by expansion through outsourcing. Nguyen
et al. (2020) have demonstrated that outsourcing is a way of being able
to access use of digital device without investing in it. We have completed
this analysis by showing how having an outsourcing activity could be
seen as a means of investing, as a way of expanding agricultural land and
revenue, and consequently, expanding investment capacity.
Secondly, the results show that DTP are also a response to the specic
Table 4
Synthesis of the ties between digital technology for production use proles.
Variables No-DTP Average DTP Intensive DTP
Structural
Utilised Agricultural Area (UAA) - *** +***
Has outsourcing business - *** +***
Standard Gross Production (SGP) - *** +***
Total workforce - * +**
Salaried workforce - ** +*** +**
Annuities - *** +*
Gross Operating Surplus (GOS) +**
Farms livestock +*** - *
Individual
Digital skills 3: +** 3: - *
Socio-economic environment
Gets advice from cooperative - ** +*
Sells wholesale - ** +**
Sells seeds - *** +**
Does contract farming - ***
Part of a CETA =Farmer’s knowledge exchange group - **
Part of an Organic Farmers’ Group +** - *
Part of a CUMA =Farmers’ machinery exchange group +*
Attends agricultural shows - *** +***
Does direct marketing +*** - **
Agricultural practices
Type of input management (1 conventional; 2 integrated; 3 organic) 1: - **
2: - *
3: +***
1
2: +**
3: - ***
Cultivated biodiversity (1 meadow, 2 cereals 3 diverse 4 pulses) 1: +***
2: - *
1: - **
Main type of soil management (1 ploughed, 2 deep, 3 shallow, 4 no-till) 2: - *
3: +**
Crop type Organic+***
Conv - **
Organic
Mixed - *
Conv +*
Organic - ***
Mixed +***
Conv
Has associated crops +** - ***
Cost of fertiliser per ha - *** +*** +***
Cost of pesticides per ha - *** +* +***
Soft wheat yield - *** +***
Hard wheat yield - *** +*** +*
Irrigated land - *** +***
+: positive effect on the probability for being in the group; −: negative effect; * p <0.1; ** p <0.05; *** p <0.01.
´
E. Schnebelin
Ecological Economics 196 (2022) 107422
7
needs of more industrial production models based on expansion,
outsourcing activities, salaried employees, specialisation and intensi-
cation. DTP sometimes become an indispensable managing tool for
organising man-power, knowing what has been done with what eld of
land, standardising, and managing logistics and traceability. Further-
more, these technologies facilitate expansion, as Baptiste explained
4
:
“I don’t know if I have saved time in fact. Because in the time that you
save, you have more other things to do. It’s a cycle […] my great
grandfather had 16 ha. My grandfather had 200. My father 400-450.
Now we work 8 or 900”. Baptiste (Intensive DTP)
Our results conrm a part of our rst proposition: the link between
DTP uses and industrialisation. Economic size inuences the adoption of
these technologies, which in turn facilitate the expansion trajectory of
these models. This reinforces the hypothesis put forward in many social
science research, that digital technologies, and more particularly those
for precision farming, tend to favour and lock in the dominant industrial
farming system (Bronson, 2019; Carbonell, 2016; Clapp and Ruder,
2020; Rotz et al., 2019a).
5.1.2. Value-chains organisations play a role in the use of DTP
Economic actors working for the industrialisation of farming can
favour the use of DTP. As stated in 2.3, technological trajectories are tied
to the socio-economic environment. We conrm the role of advisory
services provided by cooperatives or groups of farmers in the adoption of
DTP (Barnes et al., 2019). Our results show rst and foremost, that seed
cultivation and trading contracts, which are associated with a specic
trading and advice model, favours, or even imposes, the use of DTP.
Moreover, specic crops are high added value crops which also favour
investment (Lowenberg-DeBoer and Erickson, 2019). Downstream
businesses encourage or demand the use of these technologies through
contracts in order to standardise and better control their supply.
5.1.3. DTP to optimise or to justify conventional models of agriculture
These technologies aim for an optimisation of inputs (mostly fertil-
izers and pesticides). This can limit their interest for others, such as
organic farmers, who don’t use the same inputs and have other strategies
besides optimisation, as illustrated below:
“Other farmers who have GPS […] they save money on diesel, seeds,
fertiliser and weed killer. Because they only use what they need. So
perhaps they can pay for things with what they save. But me, I am only
going to save on diesel, and on time.” Thomas (No-DTP)
Moreover, a large percentage of organic farmers say that DTP is not
adapted to their production systems, or is even counterproductive. Some
farmers who have changed over to organic farming, have stopped using
land management software or decision support tools which were no
longer useful for them. In the No-DTP group, the Standard Gross Pro-
duction is weaker but the Gross Operating Surplus is not. This could
signal that farmers from the No-DTP prole implement different stra-
tegies to optimise and to increase prots, rather than those suggested by
precision farming. It can be through transformation or direct marketing,
or by optimising ecosystem services etc. DTP makes sense for farms that
have a strategy based on increasing productivity, efciency and
maximum yield (Bronson, 2019), but less so for those whose strategy is
more concerned with reducing external inputs by agronomic techniques,
or with diversication rather than specialisation. Then, DTP could be
linked with weak ecologisation trajectories focused on optimisation and
efciency.
However, it is evident from the interviews that this link between the
use of DTP and a better efciency is a matter of debate. The reasons
given for using DTP are essentially ergonomics, comfort, time-saving
and productivity. However, their effects are disputed by some users, in
particular in the case of variable rate technology. Some farmers state
that the variable rate does not save money, so they may stop using it.
Farmers question some effects on yield and input consumption. This is in
line with the critical assessment that nds DTP to be a tool for a “sym-
bolic ecologisation” based on quantication and justication, rather
than on real optimisation (Wolf and Wood, 1997).
5.1.4. DTP uses for input substitution strategies
The results show that the choice of agronomic practices and the
choice of tools have an effect on each other, and that the use of DTP
seems to encourage optimisation over redesign. This needs to be quali-
ed, however, as we have also observed links between more radical
changes of practice and the use of DTP. The implementation of
Table 5
Synthesis of the connections between DTC use proles and variables.
Factor No-DTC Website DTC Network DTC
Individual
Age +** - ***
Founding of the farm - * +***
Computer literacy 3: +** 1: - **
2: +***
1: +**
2: - *
Background >Bac +2 - *** >Bac +2 +*
Personal opinion on their farm economic performance Negative+**
Have children +**
Socio-economic environment
Advice from a cooperative +*
Trading with seed companies +* - ***
Contracts - *
Part of a CETA =Farmers’ knowledge exchange group - ** +*
Belongs to a professional union - ** +*
Part of a farmers’ knowledge exchange group on minimum/no tillage +*
Agricultural practices
Main type of soil management (1 ploughed, 2 deep, 3 shallow, 4 no-till) 1: +**
4: - **
No Till part - **
Pulse crops +**
Costs of pesticides/ha +** - **
Hard wheat yield - * +*
Irrigated land - ** +*
*p <0.1; **p <0.05; ***p <0.01.
4
Our indication in brackets signals that the interviewee is a specic indi-
vidual within this prole. Interviewees’ names have been changed.
´
E. Schnebelin
Ecological Economics 196 (2022) 107422
8
sustainable soil management has led farmers to use DTP, in particular,
precision auto guidance (RTK) as explained by Louis:
“We have come to RTK because we have changed agronomic practices. As
a lot of our work is with cover crop, […] when we sow maize among
beans, which are a bit taller, it is good to have guidance.” Louis (Average
DTP)
Some farmers have used DTP as a means of introducing organic
farming, as explained by Baptiste:
“If I had not had guidance for organic crops, perhaps I would not have
done it, plain and simple. Because we have the capacity, we have the size,
and so we have big tools. Where we are organic, we can use 8.2 m tools
with guidance, it’s great.” Baptiste (Intensive DTP)
In Louis’ case, a change in practice brought about the use of DTP.
Conversely, for Baptiste, the use of DTP led to the installation of new
agronomic practices.
The results show an over-representation of mixed farmers in the
Intensive DTP prole. We could propose the hypothesis that digital
technology allows some conventional farmers to move towards organic
farming, while retaining a somewhat similar way of working: organic
fertiliser rather than chemical, pesticides authorised in organic rather
than in conventional farming, mechanical weeding with precision hoe-
ing rather than chemical weed-killer. Thus DTP could be consistent not
only with efciency strategies, but also with substitution strategies (Hill
and MacRae, 1996). This over-representation of mixed farmers should
be contextualised by the fact that these farmers have a larger area of land
and often have outsourcing activities. They belong to a more ‘industrial’
model of organic farming.
Digital technology seems to favour another form of ecologisation: the
development of organic farming in big farms. This echoes a political and
academic debate about the ‘conventionalisation’ of the organic farming
(Darnhofer et al., 2010; Stassart and Jamar, 2009). Digital technology
could be an accelerant in this conventionalisation, to the detriment of
more radical organic farming, and smaller farms.
5.2. Digital technologies for information and communication, knowledge
and ecologisation
Our second proposition was that the use of DTC is tied to stronger
forms of ecologisation than DTP is. DTC proles characterisation high-
lights that DTC proles relate to completely different variables than
DTP. Consistently with Konrad et al. (2019), factors leading to adoption
and use depend on the digital technology studied. However, the links
between DTC and ecologisation models needs to be rened.
5.2.1. The use of DTC is linked to a socio-economic environment, but above
all, to individual characteristics
Individual characteristics of farmers: education, age and skills, as
well as individual preferences are key variables to distinguish between
DTC proles. However, economic factors are not signicant, contrary to
the results of Michels et al. (2020), although that research was only onto
the adoption of smartphones.
Socio-economic environment plays a role, for good or ill, in the use of
DTC. Both being a member of a cooperative and beneting from advice
by seed companies are tied negatively with the use of DTC. This could be
a result of the formalisation and standardisation of production processes
integrated within contracts with downstream industry, which limits
possible changes for farmers, making them less inclined to look for in-
formation and knowledge. It could also be explained by the fact that
production contracts and membership of a cooperative already include
knowledge exchanges (Cholez et al., 2020). Membership of a trade
union is tied positively with the use of social networks. Membership of a
Farmers’ knowledge exchange group or to other forms of farmers
groups, is also tied positively. Belonging to this kind of group could
signal an interest in agronomic and technical innovation information.
Moreover, the internet is a source of information that appears to be
complementary to local knowledge sources. It allows insight into what is
being done elsewhere as opposed to what is being done locally, or
having a lot of information as opposed to having precise information
that is adapted to local conditions.
5.2.2. The internet facilitates access to new information on new and more
radical practices, but with limitations
It came out during the interviews that the internet seemed to be
source of information for what to do in atypical situations, or for getting
knowledge that is not available in local networks. It equally acts as a
source of inspiration to try out new practices, and a means of monitoring
agronomic practices. These range from the adjustment of practices
(what to do when faced with the new conditions caused by climate
change, for example), to the search for information on more radical
changes in practice, in particular, because information is not available
on the usual information networks, as Baptiste explains:
“The cooperative was a bit behind in this, so I went to see what they are
doing there quite a lot. Also plant species mixes. I bought and made my
own little mixes.” Baptiste (Network DTC)
We should add to this the idea of exchange, participation, and being
themselves a source of information. However, interviewees often
mentioned problems with the reliability or the relevance of information,
in terms of local soil and weather conditions, as seen below:
“Well, people have different experiences, but something that works for
one person, does not necessarily work here”. Corentin
Our second proposition also needs some qualications. First, it seems
that the links between the use of DTC and agronomic practices could go
one of two ways: as both a cause and a consequence. Using the internet
means discovering new practices, and the desire to establish new prac-
tices encourages the use of the internet to search for information. Sec-
ond, the internet is not necessarily a privileged form of access to
knowledge, but it makes it possible to ll the gap in agro-ecological
information from traditional networks (advisors, neighbours, family)
(Lucas, 2021). For instance, the internet appears to be a major source of
information for cover crops and conservation agriculture. Knowledge
gained from the internet does not seem to be able to completely replace
knowledge acquired orally, in particular in the case of ecological,
localised and adaptive know-how (Burton and Riley, 2018). Moreover,
in our sample, there is no widespread use of DTC in order to co-create
knowledge and redesign knowledge systems.
DTC uses allow for a combination of information sources, rather than
replacing direct exchanges. It is therefore often the farmers who are
involved in local knowledge exchange groups that make the most use of
technical information on the internet.
5.3. Understanding use proles highlights trajectory mechanisms
Qualitative insights complement the quantitative description of the
proles. They made it possible to demonstrate trajectory mechanisms,
and in particular non-linearity processes. First, we have shown the non-
linearity of the process of digitalisation at the level of individual
farmers’ decisions. We note phenomena of farmers trying out the tech-
nologies, but then abandoning them. Second, farmers are not on a
unique ‘S curve’ of adoption (Rogers, 2010). Categorisation as “pio-
neers” or “laggards” does not seem relevant here. Not adopting a tech-
nology does not necessarily signal a resistance to change, or a slower
adoption process that “laggard” farmers would follow. It can be a
coherent choice that matches their practices and objectives, towards a
different technological trajectory (Eastwood et al., 2017; Rogers, 2010;
Van der Ploeg, 2018). The adoption and use of a technology may not be
so much the result of individual ‘pioneer’ behaviour, but the result of a
´
E. Schnebelin
Ecological Economics 196 (2022) 107422
9
production model interacting with a socio-economic system that en-
courages, or even imposes, these technologies.
Moreover, we have observed reinforcement mechanisms: the use of
digital technologies for production (DTP) facilitates further industriali-
sation trajectory and expansion of farms, which in turn favours the use of
DTP. This leads to mechanisms of path dependency and reinforces the
dominant farming production systems as suggested by Bronson (2019)
or Vanloqueren and Baret (2009). However, our analysis is only one
snapshot in time, and it would be interesting to complete it with a long-
term longitudinal analysis. It would make it possible to go more deeply
into the processes tied to the trajectories. This would involve on-farm
and long-term research, but also underlines the importance of having
access to public databases on farms structures, practices and uses of
technology.
Our research also conrms the major role of intermediaries, such as
advisory services, but also training organisations and value-chain actors,
such as agricultural cooperatives, in digital uses and trajectories (East-
wood et al., 2017; Fielke et al., 2020). Moreover, regulation, agricultural
policies and private norms play a role in digital uses and participate in
farming trajectories and path dependency mechanisms. There is a need
to provide institutional analysis of the roles of intermediary actors on
rules and practices that impact the relation between digitalisation and
ecologisation trajectories. It also implies that digitalisation policies must
be considered alongside public policies on knowledge development and
extension services as well as with economic incentives and social and
environmental regulations.
Technologies and practices are interconnected within technological
systems (Clapp and Ruder, 2020). Only focusing on one form of digi-
talisation would mean supporting only one form of ecologisation of
agriculture. We can put forward a range of propositions to enable digi-
talisation to embrace more agroecological and diverse models, including
the following. The basis unit of digital technologies could move from one
crop to one complex system. Participatory conception could be pro-
moted (Jakku and Thorburn, 2010). Digitalisation objectives could
integrate a diversity of expectations, such as promoting on-farm exper-
imentation, systemic analysis and knowledge exchanges rather than
optimising inputs and increasing traceability (Lacoste et al., 2021;
Schnebelin et al., 2021). There is also a need to renew the economic and
political models of technologies, such as open or collective technology as
data commodication is tied with industrialisation (Carolan, 2017; Wolf
and Wood, 1997).
More broadly, to avoid a monolithic orientation of digitalisation,
there is a need to question who and what drive innovation trajectories.
Innovation trajectories depend on the complex interplay between eco-
nomic forces, institutional and political factors (Dosi, 1982). Authors
argue that digital agriculture is mostly driven by private industries,
notably agri-business and digital rms, supported by national policy
(Birner et al., 2021; Prause et al., 2020). There is a risk that this
conguration reinforces the lock-in of ecological innovation (Vanlo-
queren and Baret, 2009), especially as AgTech rms do not perceive the
ecologisation heterogeneity and its implication for digitalisation as
previously shown in Schnebelin et al. (2021). National policies such as
research funding and orientation, can play a role to escape such a lock-in
effect (Cowan and Hult´
en, 1996). These policies could acknowledge the
heterogeneity of innovation system to better include actors outside the
dominant paradigm (Klerkx and Rose, 2020). They could also re-balance
power and invest in alternative digitalisation pathways. There is also a
need to study digital technologies conditions of use and to provide ev-
idence on their real effects (Ingram et al., 2022). Digitalisation policies
are not neutral and there is a need to engage a reection on changes in
governance and orientation of innovation systems (Klerkx and Rose,
2020; Pigford et al., 2018).
6. Conclusion
Based on a large number of interviews with crop farmers in France
about their use of digital technology, our research identies a diversity
of digital use proles rather than a single digitalisation. These proles
relate to farming models. Intensive uses of Digital Technologies for
Production are tied to, and reinforce, the industrialisation of farming
that is characterised by expansion, outsourcing activities and a salaried
workforce. The use of DTP facilitates the industrialisation trajectory,
which favours the use of DTP in return. This leads to mechanisms of path
dependency, and reinforces the dominant farming production systems.
Uses of DTP can be linked to weak ecologisation or “symbolic ecolog-
isation” strategies. It can also support some substitution strategies such
as the development of industrial organic farming. The use of Digital
Technologies for Information and Communication appears to comple-
ment and to add new possibilities for knowledge exchanges while, thus
far, not challenging farmers’ knowledge and production systems. This
analysis invites a consideration of the adoption and use of technology as
the result of production models interacting with socio-economic sys-
tems, rather than the choice of an independent individual. This cross-
sectional analysis allows us a glimpse of the technological trajectory
that is being promoted by the current development of digital tools in
farms. At a time when French farm structures are being challenged and
are undergoing a more profound differentiation, current digital use is
mostly encouraging the development of industrialisation, rather than
the agroecological farming system. This work calls for other research, to
better understand technological trajectories. There is a need for multi-
disciplinary research, to evaluate changes and environmental perfor-
mances through a longitudinal analysis of digital use by farmers, and to
develop tools, digital or not, that support agroecological farming sys-
tems. The policies of digitalisation are not neutral, as the technologies
promoted are used more by certain models than by others. Limiting
focus to one technological model means promoting a specic farming
trajectory. To promote a diversity of ecologisation pathways, other
forms of digitalisation development should be considered alongside
knowledge, economic and social policies, that imply changes in policy
and in orientation of innovation systems.
Declaration of Competing Interest
The authors declare that they have no known competing nancial
interests or personal relationships that could have appeared to inuence
the work reported in this paper.
Acknowledgments
This work was supported by the French National Research Agency
under the Investments for the Future Program, referred as ANR-16-
CONV-0004. I am grateful for the high-quality comments made by the
anonymous reviewers, that enabled a great improvement of the article. I
would like to thank Jean-Marc Touzard and Pierre Labarthe for their
support and contributions throughout the research process. I extend my
thanks to the members of SIRA and Odycee teams that made feedbacks
on previous versions of this article.
Appendix A. Supplementary data
Supplementary data to this article can be found online at https://doi.
org/10.1016/j.ecolecon.2022.107422.
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