## 4.3 Improving integration within the agricultural regional or economic ecosystem

Aside from the potential benefits for agricultural production, digital technology could reshape the way in which farmers – in the context of the agroecological transition – interact with the agricultural ecosystem, both in terms of the economic sector (upstream with agricultural services or downstream through value chains) and land management.

### Agricultural services reshaped by digital technology

**Advice** – Advice is very much central to innovation systems in agriculture (Labarthe, 2009). It encourages interaction between stakeholders within these systems: agricultural organizations (including cooperatives), research institutions, NGOs, public bodies, industries both upstream and downstream, intermediaries, etc.

The question of the impact of digitalisation on farming advice services has been the subject of recent research (Fielke et al., 2020), and there are projects aimed at supplying farming advisors with the digital tools they need, drawing on participatory design.<fnref n="51" /> Digitalisation has had a significant impact on the activity of advisors, both at front office level (new interfaces and applications linking advisors to farmers) and back office level (developing new services via the widespread use of data or agronomic models). But alongside digitalisation, we have also seen the emergence of new players (start-ups, firms from the IT sector) capable of completely overhauling technical advice services and the dynamics of agricultural innovation systems (Fielke et al., 2019).

At the same time, a number of public policies have been introduced at EU, national and regional level in relation to farming advice, the aim being to contribute towards the sustainable development of agriculture (Dhiab et al., 2020). Here there are two issues at stake. On one hand, digitalisation is set to transform the very nature of farming advice; on the other, advice must support the digitalisation of agriculture in the interests of sustainable development, overcoming social, economic or environmental contradictions linked to digital technology: possibilities of inequality of access to information, of unsuitability of digital solutions, of loss of autonomy, of risk of power imbalances or locking (see Section 5: risks).

<footnote n="51">51. See, for example, the EU projects https://www.h2020fairshare.eu/ or https://www.agrilink2020.eu/</footnote>
**Insurance** – Financial protection is essential when it comes to improving living standards within agriculture, owing to its sensitivity to adverse weather. There are various different systems, either in the form of funds (e.g. 'agricultural disaster funds') or in the form of insurance, irrespective of whether or not this is private. These different systems provide compensation for damages, which digital technology can help to identify. Insurance is either "traditional" – based on claims for losses (harvests, yield, etc.) – or, more recently, "index-based", whereby clients are compensated based on indexes linked to these losses (De Leeuw et al., 2014): regional performance indexes, climate indexes, indexes based on satellite imaging (Vroege et al., 2019), composite indexes (De Leeuw et al., 2014). Digital technology could help to improve index-based insurance through observation systems and modelling. When it comes to building indexes, information – traditionally taken from public authorities (weather forecasts, spatialised estimated yield) (De Leeuw et al., 2014; Rao, 2010) and remote sensing (De Leeuw et al., 2014; Vroege et al., 2019) – must verify four principles, which is not trivial: it must be (i) worthy of trust and verifiable, (ii) closely correlated with the damage, (iii) continuously accessible and (iv) collected over a sufficiently long period of time (Vrieling et al., 2014). In traditional insurance models are used to estimate contingencies, whereas in index-based insurance the data is linked to the damage using the index. The imperfect correlation between the index and the damage is the "baseline risk", which is sought to be reduced by creating composite indexes, e.g. by combining satellite data, climate data and even land use data (De Leeuw et al., 2014; Rao, 2010; Vroege et al., 2019). The dangers lie in (i) creating complex indexes which farmers are unable to interpret (Vroege et al., 2019), (ii) incorrectly incorporating weather patterns caused by climate change, further complicating the relationship between meteorological data and output, and (iii) the incorrect use of big data – multisource, multiresolution, non-stationary – in the parametric statistical analysis of traditional actuarial models (Ghahari et al., 2019).

## Reshaping value chains with greater market connectivity

Digital technology opens up possibilities for remodelling both the food system and value chains. In global chains it can reduce commercial costs, ensure compliance with standards and facilitate international trade, while in shorter chains it can increase the visibility of and ensure transparency. In this way it gives power back to those at either ends of the value chain: small farmers and consumers (Jouanjean, 2019).

**Platformisation** – Platforms are central to new economic channels for sales of agricultural products, food or services (e.g. in agriculture cofarming.info, hellotractor.com) (ANRT, 2018). These open interfaces intermediate between suppliers and
clients, delivering technical and economic synergy (Tirole, 2016). The fact that they are free and easy to use helps to get as many users as possible to engage with them, which is the main value proposition for encouraging suppliers to use a given platform (Leibovici, 2015). E-commerce in the agribusiness sector concerns giants such as Walmart and Amazon, but it can also be found at a local level, with a new model for rural and agricultural development borrowing from both modern approaches (based on globalisation) and postmodern approaches (centred around regional integration) (Rieutort, 2009). Many regional authorities are seeking to build platforms aimed at matching supply to demand and thus enabling isolated rural areas to access high-value market segments and to create stable relations with consumers in urban areas –, supplying school cafeterias and satisfying citizens expectations,, in both the Global North and Global South (IPES-Food, 2016). Through digital technology and platforms, the global market for “collaborative” consumption is expected to grow from 15 to 335 billion dollars between 2017 and 2030 (Claquin et al., 2017). This level of development will require tailored logistics, which could also draw upon digital technology (Messmer, 2013).

There are two bottlenecks for these new channels: visibility of the offer and logistics. The offer is currently scattered across multiple platforms, limiting the network effect (Metcalfe's law) and, therefore, the appeal of platforms, which find it difficult to identify an economic model. Furthermore, a lack of digital and logistical flexibility are significant obstacles to farmers joining these platforms. Collective catering requires food to be sourced locally (EGAlim law): how can this be ensured and kept secure with a fragmented offer? Research – particularly operational research – could be called upon for the purposes of planning this fragmented supply, for the management of distributed databases (across various platforms) and to devise logistics systems compatible with these fragile but low added value products.

**Traceability and trust** – The traceability of both human and animal food is mandatory between companies (the EU's General Food Law from 2002) and optional within companies. Intra-company monitoring has become widespread in factories through automation and information systems (Fountas et al., 2015), but take-up has been less common in agriculture (Galliano and Orozco, 2011): in France, technical and economic monitoring software is used by an average of 7% of farmers within collective organisations, with a significant amount of variability (between 2 and 35%)<fnref n="52" /> This is a growing market (in the USA it is expected to double between 2016 and 2023, growing by more than 14% year on year)<fnref n="53" />

<footnote n="52">52. http://agrotic.org/observatoire/2017/11/06/usage-du-numerique-pour-la-gestion-technico-economique-des-exploitations-agricoles/</footnote>

<footnote n="53">53. https://www.marketsandmarkets.com/Market-Reports/farm-management-software-market-217016636.html</footnote>
given the desire to automate data capture in order to prevent input errors and to
reduce people's workloads: optical codes (barcodes, QR codes), electronic codes
such as RFID (Luvisi, 2016), voice recognition (Bellon-Maurel et al., 2014), etc.
The recording of practices will result in the massification of private data, which
could be valued by providing consumers with better information on production
conditions, meeting their expectations (Jouanjean, 2019). The emergence of
"hyper-transparency" (Kos and Kloppenburg, 2019) has transformed the way in
which the value chain is governed, with new roles for consumers – who influence
distributors and processors – and for small farmers, who are better paid by buyers
willing to pay a higher price for desired "properties", including a fair price for
farmers (Jouanjean, 2019). This has a double effect in that it helps consumers to
make an informed choice while also helping producers to show that they have
adopted improved practices and standards through labelling (Gardner et al.,
2019; Kos and Kloppenburg, 2019) justifying the willingness to pay (Caquet et al.,
2020). Similarly, this is a key aspect of (largely voluntary) sustainability certifica-
tion initiatives (Mol et Oosterveer, 2015), which could contribute to participatory
guarantee systems – eliminating the need to pay third parties for checks – or
assist with carrying out "automatic" LCAs (Life-Cycle Assessments) (Bellon-Maurel
et al., 2014, 2015; Miah et al., 2018).

In this push for transparency, technology which helps to build trust – a key
issue – can be drawn upon (Jouanjean, 2019). Blockchain technology is a good
example of this. The blockchain is a transparent and secure means of storing
and sharing information which operates without any central control body<fnref n="54" />: it is
a distributed system with no central authority. It creates a database recording all
previous exchanges that is shared by different users, allowing the validity of the
data to be verified. However, in supply chains, implementing blockchains is far
from straightforward. The problem is that while the blockchain guarantees the
validity of the information shared (its origin, its integrity and its temporality), it
cannot guarantee its truthfulness, i.e. consistency between data flows and product
flows. This issue is currently dealt with using data consolidation (building confi-
dence indexes in relation to the data) or technology (RFID,<fnref n="55" /> combining RFID/3D
videogrammetry/digital fingerprints (Gopalakrishnan and Behdad, 2019). Lastly,
given that food products are perishable, it is worth tracking them across the
logistics chain, particularly if they are long, by recording data during transport:
quick identification of who is responsible in the event of a defect, anticipated
reassignment of products in the event of a breakdown, preventing food waste,
detecting the falsification of products during transit (Jouanjean, 2019).

<footnote n="54">54. https://blockchainfrance.net/</footnote>

<footnote n="55">55. https://www.wwf.org.nz/what_we_do/marine/blockchain_tuna_project/</footnote>
<figure><img src="image_52.png" /></figure>

Find out more

Was hypertransparency in 2017 driven by the launch of the brand “C’est qui le patron?” (CQLP - which translates as “Who’s the boss?”)? Using the internet, CQLP worked with consumers to design a range of ethical products (in terms of the price paid to producers), questioning them on products’ technical and social specifications and willingness to pay accordingly. Another example is Yuka, an application which provides “information on health impact” based on the open database *Open Food Facts*<fnref n="56" /> (670,000 products referenced by consumers in April 2020), helping to change consumption patterns and influencing manufacturers, who will change the formulation of products which receive a low score.<fnref n="57" />

However, some authors have expressed concerns regarding the risks of this hyper-transparency: it is only partial and will guide our priorities (Gardner et al., 2019), it could exclude small farmers (Jouanjean, 2019; Kos and Kloppenburg, 2019) and it could require assistance from private intermediaries, increasing the asymmetric nature of information. Lastly, there is no guarantee that it will benefit farmers. It also assumes that consumers will be willing to pay for these attributes (quality, origin, social/environmental footprint).

Managing resources at a regional level

Regional governance can be defined (Rey-Valette et al., 2011) as “*a dynamic process of coordination on the subject of regional issues held between public and private stakeholders with multiple identities and asymmetric resources, working together to set objectives and initiatives by implementing multiple schemes centred around collective learning and which contribute towards both institutional and organisational innovation at a regional level.*”

Agriculture is taking on an increasingly prominent role in regional projects, not only because of its impact on land planning, but also because of the reterritorialisation of the food production, which is now seen as a way of promoting regional resilience (IPES-Food, 2016). The agroecological transition is strengthening the position of agriculture within this regional dialogue owing to the fact that

<footnote n="56">56. https://fr.openfoodfacts.org/</footnote>

<footnote n="57">57. https://www.franceinter.fr/yuka-l-application-qui-force-intermarche-a-revoir-ses-recettes</footnote>
landscape ecology – which is crucial to the success of the agroecological project – requires a collective approach at a regional level. Furthermore, the closing of the cycles (nitrogen and carbon), – an essential lever of agroecology, can take place beyond the farm at a regional level, employing a “circular biomass economy”. Digital technology is opening up various types of opportunities in this field.

Within regions, new biobased and circular economies are being established: agricultural waste is becoming a resource (Klerkx et al., 2019), with the recent emergence of specialist platforms, marketplaces for organic materials (e.g. *Organix* from Suez) or for trading food products with short shelf lives (the app *toogoodtogo*). Knowledge of the material flows involved at each stage of the process (production, processing, exchange, consumption, waste) is gaining interest when it comes to (i) questioning the use of natural resources and identifying any problems with competing uses (e.g. first generation biofuels vs. food use, feed for livestock animals vs. food for human consumption), (ii) understanding vulnerabilities both upstream and downstream (e.g. dependence on imports), and lastly (iii) estimating environmental footprints (e.g. carbon, energy, water, chemical pollution, soil use, etc.) (Bioteau et al., 2013). Over and above these purely quantitative aspects, involving both environmental science and digital science, the social sciences will have a vital role to play in understanding how the networks controlling flows or affected by them operate. There are two issues at stake here: the re-integration of agricultural production at a regional level (material and social integration) while staying within planetary limits. What is more, the deployment of a biobased economy at a European, national and local level will require consistency between levels and between regions when it comes to implementing plans of action; but, presently, there is little evidence of such a multi-level vision.

Digital technology will also expand the “tool kits”, helping regional bodies to promote dialogue both within agriculture and with other regional stakeholders. This should help with the coordination, participation and education of stakeholders and with the adoption of new digital-based practices. More generally, it should serve the development and management of regional projects, ensuring their development models allocate an inclusive, explicit place to agriculture.

Further research will be needed in digital science and technology in order to (i) compensate for the lack of data at a regional level and on systems about which there is little knowledge, (ii) improve the temporal and spatial modelling and representation of these systems and the visualisation of models’ outputs, (iii) promote mediation between stakeholders, and (iv) secure systems and information channels.
## 4.4 Supporting the transition: sharing data, information and knowledge

Farmers and sectors must be supported in the agroecological transition, as it brings with it a significant amount of risk. This support must be compatible with the agroecological approach, which promotes "individual and collective learning [as] a source of innovation" (Meynard, 2017), drawing upon: (i) modelling, combined with indications regarding uncertainty in order to identify bottlenecks, risks and capacity for resilience; (ii) collective learning; (iii) risk identification and socio-economic and the relevant support (Caquet et al., 2020). This chapter outlines the response from digital technologies when it comes to sharing and learning.

### Digital technology: an asset for sharing knowledge

With regard to the deployment of the principles of agroecology, traditional knowledge – often specific to regions (Altieri et al., 2012) – must be protected: this will involve strengthening human capital through training and participatory initiatives which take into consideration the needs, expectations and circumstances of small farmers (Calvet-Mir et al., 2018). Knowledge-sharing platforms, featuring different levels of mediation, facilitate the gathering, exchange and distribution of knowledge: videos on agroecological practices produced by mediators linked to farmers (AccesAgriculture, DigitalGreen,<fnref n="58" /> Osea, etc.) (Bentley et al., 2019), knowledge gathered from farmers (like with CONECT-e, which created digital commons on traditional varieties for preventing the erosion of knowledge and hoarding by commercial companies) (Calvet-Mir et al., 2018), social media platforms without mediation (YouTube), etc. Wyckhuys et al. (2018) identified two points which are important for the success of digital technology in the adoption of new practices: (i) guaranteed access to digital technology, overcoming technical, psychological and organisational obstacles, and (ii) using the knowledge and practices employed by farmers as a basis for devising digital-based training courses. Digital technology also makes it easier for parties to work together to create knowledge, an appropriate strategy for agroecology given the way in which it “combines different types of knowledge: traditional knowledge, indigenous knowledge and scientific knowledge, in addition to knowledge from farmers” (Milgroom et al., 2016). According to Wyckhuys, et al. (2018), this social learning is well-suited to dealing with agricultural problems in that it opens up a space for different points of view, recognising diversity and local knowledge. For this reason, these authors recommended drawing on participatory experiments employing the use of digital devices (tablets): the Digital Farmer Field Schools.

<footnote n="58">58. More than 5,000 videos, in 50 languages, produced over 10 years with support from DigitalGreen (www.digitalgreen.org)</footnote>
However, there remain obstacles to exchanges between peers and individual learning, whether technological (identifying technology for capitalising on and promoting exchanges) or sociological (identifying which modes of learning to promote).

## A participatory approach and open innovation

The participatory approach is the cornerstone of open innovation and living labs, open innovation initiatives in which citizens, residents and users are given a key role in research and innovation processes. In agriculture, living labs can be supported by research initiatives around experiments involving agroecological systems or implemented within regional innovation projects. Open innovation is vital to agroecology: devising "pathways" (plausible future scenarios) and transition scenarios (instantiation of the model in accordance with the pathways identified) (Antle et al., 2017), the best way of representing phenomena occurring at different levels (biological processes, farm management, optimisation) (Groot et al., 2012). Digital tools are extremely useful for these participatory processes, in that they can be used to (i) store information from participatory workshops; (ii) show and visualise data (current, future, dynamic views... of the region); (iii) equip participatory processes (modelling and scenario-building tools, serious games, etc.); (iv) share and disseminate knowledge; (v) create new knowledge, drawing on the diversity of knowledge, discussions and interactions; (vi) create links between farmers, between farmers and researchers, between farmers and wider society, etc. (Bergez et al., 2016; Enkel et al., 2020; Leveau et al., 2019). Some tools, such as boundary objects, make it easier to analyse compromises and multi-criteria representations during participatory workshops (Duru et al., 2015). This includes companion models (Barreteau, 2003). To deal with any issues stakeholders may have in understanding models and in order to stimulate interactivity (Bécu et al., 2008), these are implemented in the form of serious games, what is known as gamification (Seaborn and Fels, 2015); the past five or six years have seen the emergence of games on digital platforms, making it easier for people to express their points of view or preferences, facilitating co-construction (Speelman et al., 2014), helping to raise awareness among stakeholders (Prada et al., 2014), stimulating learning (the GATES<fnref n="59" /> project, Speelman et al., 2014), etc. To this we can now add augmented reality, which could assist stakeholders in visualising future diversified landscapes at the time of crop systems being designed.<fnref n="60" />

<footnote n="59">59. https://www.gates-game.eu/en/project/overview
The H2020 project "Applying GAming TEchnologies for training professionals in Smart Farming — GATES" (Grant Agreement number: 732358 — GATES — H2020-ICT-2016-1)</footnote>

<footnote n="60">60. See #DigitAg PhD thesis "The use of digital technology in agroecology: Designing agroforestry systems using augmented reality" on www.hdigitag.fr</footnote>
At a sociological level, there are a number of obstacles to implementing a participatory approach: willingness on the part of farmers to head into uncharted territory, the capacity for collectively bringing about change, the capacity to gather and represent tacit knowledge, and the capacity to open up sources of information that will support change.

## Farmers as data producers

Although "multifunctional agriculture" has always existed (Renting et al., 2008, 2009), a new function has emerged thanks to digital data collection tools: data production.

Farmers can be committed to contribute to the digital capital at a regional level. Information – related e.g. to biodiversity, soil fertility, etc. – will be crucial when it comes to documenting, evaluating and paying for ecosystem services. Such information would be useful for the PES but the cost of gathering it is now such that payments for ecosystem services (PES) are distributed in a uniform way depending on the resources implemented (OCDE, 2011). In order to move from a resource-driven approach to a results-driven approach, it will be necessary to better characterise the environment, and to identify and quantify simple, measurable parameters representing how it works (Caquet et al., 2020). Looking beyond PES, farmers will contribute towards the creation of information commons, which Antle et al. (2017) view as a public good when it comes to public investment and political decision-making. Initiatives are already in place relating to data on soil quality (Della Chiesa et al., 2019) and biodiversity.<fnref n="61" /> Van der Burg et al. (2019) identified the capacity of digital agriculture to generate other services as a result of the data produced; research must be prioritised in order to clarify the social role played by farms, to stimulate imagination among stakeholders with regard to the other possible objectives which smart agriculture could serve, and to improve the way in which their relative values are understood.

Farmers are also producing data alongside – and for – research, the goal being to analyse and understand the biological processes underlying the provision of ecosystem services within new agroecological systems. Caquet et al. (2020) advocate new strategies "combining experiments carried out by researchers and the deployment of other data sources [...]", including experiments on farms (Cook et al., 2013). A number of authors see this field as a new avenue for research in agronomics (Reckling et al., 2020) for re-designing crop systems by understanding processes (Falconier et al., 2016), carrying out variety testing in real-life

<footnote n="61">61. http://observatoire-agricole-biodiversite.fr/</footnote>
conditions (Schmidt et al., 2018) and demonstrating new production systems (Leclère et al., 2018). Conducting experiments on farms has been made easier by automatic monitoring and measurement systems (Piepho et al., 2011) and precision agriculture (Adams and Cook, 1997; Panten et al., 2010), both of which reduce uncertainties linked to sampling and manual measurement.

These strategies, employed by farmers for collecting data for use in research or environmental documentation, have encountered a number of scientific and technical obstacles (e.g. what variables to measure? Where? At what frequency? What data- and knowledge-sharing infrastructure should be employed?) and socio-economic obstacles (motivation to share data, the value of data, changes to the profession, data governance, etc.).

## 4.5 Specific challenges facing the Global South

The majority of international organisations (FAO, 2020) and development funds (World Bank, 2019) see digital agriculture as something that will significantly transform and improve the agriculture sector, food systems and trade for countries in the Global South (Lixi and Dahan, 2014). In Africa, the reasons for developing digital agriculture are as follows:

* digital technology will help to diversify the service economy, with the right conditions for creating jobs: a good level of IT training, applied research in data science and geomatics, and a population familiar with mobile phones (72% of the population in 2014)<fnref n="62" />;

* this could impact many categories of agriculture and agricultural households; by promoting the inclusion of women and young people (El Hassane Abdellaoui et al., 2015) digital agriculture will counteract the rural exodus;

* Africa is a land of opportunity for agriculture, with vast tracts of land and the potential for the agribusiness sector to provide jobs within a range of agricultural sectors (Pesche et al., 2016).

The specific context of agriculture in Africa must be covered by digitalization:

* production systems are far more diverse than they are in temperate countries: inter- and intra-country diversity; diversity between agroclimatic zones, resulting in significant contrasts between agroecosystems (tropical and Mediterranean, arid and wet regions); the wide variety of contexts in rural regions, structures and land tenure systems; the coexistence of varied socioeconomic structures,

<footnote n="62">62. https://donnees.banquemondiale.org/indicator/IT.NET.USER.ZS?end=2016&start=1960&view=chart</footnote>
with a high prevalence of families engaged in subsistence farming –, either commercially or in conjunction with other forms of work (75% of arable land in the world; Lowder et al., 2016), linked to a range of methods and practices and specialist structures for monoculture, often for export purposes;

* production systems are also more complex: the high prevalence of integrated and multifunctional multi-species systems, such as agropastoral systems (in dry regions) or agroforestry systems (cocoa and coffee in wet regions) generates complex landscapes and organisational frameworks, with multiple rules and governing bodies for shared regional resources (pastureland for mobile pastoral systems, tropical forests), in circumstances in which regional information is sorely lacking and, when available, is rarely shared;

* distribution channels are highly varied (short, local distribution chains primarily for food production; regional and national sectors for supplying towns and cities; and international sectors, which take in products from small producers) and can be fragile (lack of infrastructure, fragmentation of the offer, difficulties adapting to standards, etc.);

* as has been the case in the North, there has been a significant change to food systems, accelerated by the emergence of new stakeholders and investors in agricultural supplies, production and agricultural marketing – generating tension as a result of the co-existence of different agricultural models – and by digital tools, with e-commerce platforms and the revolution in decision support and regional information systems (the use of drones for proxy detection, information systems on markets, enterprise resource planning, etc); it is also worth noting that digital technology is boosting the participation of women and young people;

* there is a distinct lack of organisation with regard to agricultural data: no metrics (measurement data), no pooling and archiving of data, weaknesses on the part of certain public information systems – in terms of property (property deeds, land registers), resources (soil quality, water availability), the quantities of inputs used, the quantities produced and origin (traceability);

* intermediation, communication and modes or levels of interaction (information exchange) between stakeholders within the agricultural sector are made more complex by low levels of training among users, illiteracy and the number of different dialects, paving the way for the development of ad-hoc digital solutions (farming advice using voice assistants speaking regional dialects).

When we are targeting the needs of "intermediary" and multifunctional farms engaged in multiple activities, as well as their production ecosystems, i.e. logistics channels and regional information, we have to thrown up a number of obstacles. Therefore, the objective will be to develop digital technology capable of tackling the following priorities:
* promoting the development of "local and regional food systems" centred around alternative production models (agroecology, biomass recycling, etc.)

* contributing towards the structuring of information capital in regions in which data is sorely lacking, benefiting everyone (individual holdings, intermediary organisations, institutions, etc.);

* facilitating communication with farmers, overcoming issues such as poor network coverage, inequality in terms of access to energy, illiteracy, multiple languages and dialects, etc.;

* improving supplies in distribution channels.

Both on farms and in the supply chain, the scientific and technical obstacles are broadly the same as those encountered in the Global North: a need for technology capable of anticipating risks (early detection of errors, customised decision making support, etc.), collective management of rare resources such as water or organic matter, access to markets (information, logistics). However, these are exacerbated by the specific challenges facing the Global South: diversity in terms of systems, solvency, the technical aptitude of farmers, illiteracy, lack of communication infrastructure (networks, data centres, etc.) and energy distribution infrastructure. Over and above these technical aspects, political, social and economic considerations must also be explored in order to anticipate the impact digital technology will have on businesses, agricultural households engaged in multiple activities, markets, local sectors and global value chains, companies and regions (Tsan et al., 2019), given the number of unanswered questions regarding the use of digital technology in agriculture in the Global South (Bonnet et al., 2019; Deichmann et al., 2016; Pingali, 2012). The conditions for innovation and the transition towards digital agriculture will need to be studied at both an institutional level (exploring the political, socio-technical and socio-economic context required in order to develop digital agriculture and, more generally, the digital economy) and a process level (identifying innovation processes that will lead to applications with a proven impact on family agriculture), with questions regarding the research methods employed in digital agriculture and the innovation systems that will need to be put in place.
Conclusion

This chapter presented an overview of the fields in which digital technology
could contribute towards the scaling up and development of agriculture, to meet
the principles of agroecology with regard to production and integration into
its social and economic environment (value chains, regions, etc.). This overview
revealed technological and methodological needs in terms of observation, data
science, modelling, knowledge extraction, data storage and exchange and specialist
agricultural equipment for assisting humans, highly sought after in agroecology.
But although there could be many opportunities, there are also risks to deve-
loping digital technology in agriculture. These must be identified and analysed
(Chapter 5) in the interests of guiding future research (Chapter 6), the goal being
to develop responsible digital technology for sustainable food systems that are
compatible with planetary limits.
5

Risks

**Authors** – Frédérick Garcia, Pierre Labarthe, Emmanuel Prados, Véronique Bellon-Maurel, Grégoire Chambaz.

**Acknowledgements (contributions, proofreading, editing)** – Ludovic Brossard, Nathalie Gandon, Aurélie Javelle, Nathalie Mitton, Alexandre Termier.

<figure><img src="image_53.png" /></figure>
The ongoing digital revolution has had a significant impact on the world we live in. Chapter 4 explored the opportunities opened up by this new technology, which could help to make agriculture more agroecological, more sustainable and more productive. More broadly, the disruptive potential of digital technology seems boundless, and the digital revolution could be seen as a revolution of empowerment, bringing about a considerable increase in the capacity of stakeholders to efficiently transform all areas of society – not just agriculture, but also health, transport, culture, the environment, etc. Observing, predicting, anticipating and controlling the natural and social processes at play on Earth could soon be possible through digital technology.

The growing development of digital technology in the agricultural sector has raised a number of questions with regard to delivering on promises made and the social acceptability of the accompanying transformations. This awareness of the risks inherent to the digital revolution is not specific to agriculture. Analysis conducted on issues linked to democracy, the economy, the environment, work, education, information, etc. have confirmed the extent of the changes that are taking place, stressing how important it is for society to tackle challenges linked to digital, incorporating the fact that technology is not neutral (Stiegler, 2015; Boullier, 2019).

Despite the wide range of opportunities it opens up, we feel that the development of digital technology in agriculture brings with it a number of risks: not living up to expectations in terms of agriculture and food systems being made more environmentally-friendly; exacerbating the negative impact of digital technology on society in terms of a loss of autonomy and widening inequality; sliding towards a loss of digital and food sovereignty; and aggravating vulnerability and weakening the governance of an overly complex food system, aggravating vulnerability, weakening the governance and reducing the yields of an overly complex food system.

Exploring these risks will give citizens, farmers and researchers the opportunity to reflect on their practices, their choices and their priorities, guiding them and helping digital technology to become more responsible in order to minimise these risks. This chapter will present an overview of these risks.
## 5.1 Compromising the ecological transition in agriculture

There are a number of factors which could impact the capacity of digital technology to contribute towards the ecological transition. Some see digital technology as an “obstacle” in itself, in that it is deployed in an attempt to treat symptoms as opposed to correcting the causes of problems and hazards facing us, arguing that its widespread deployment is an evasion tactic preventing any possibility of more systemic, radical change. The use of digital technology in agriculture is also seen as stretching thin the ties still linking man and nature. Lastly, although little is known about it and it is rarely taken into consideration, digital has its own environmental footprint, which could call into question any environmental benefits.

### The agroecological transition and technological lock-in

Technological or socio-technical lock-in, a concept taken from theories of innovation (David, 1985; Arthur, 1994), refers to such situations where an innovation is blocked as a result of the economic and technological strategies implemented by different stakeholders – known as the sociotechnical system – coming together in such a way as to prevent any destabilisation or change, even if the innovation could be widely beneficial.

This concept is often raised in relation to the agroecological transition (Meynard, 2018). It aims to guide production systems towards practices which use fewer chemical inputs. This is done with reference to two specific features of agriculture in developed countries: i) crop protection based primarily on the use of pesticides and ii) the increasing specialisation of production alongside the increasing scarcity of holdings which combine crops with livestock breeding. The systemic, integrated way in which the food supply chain is structured around these aspects is an obstacle for the agroecological transition in that all economic, technological and regional stakeholders must act in concert with each other.

This raises the question: could the development of digital technology in agriculture also carry the risk of further technological lock-in, thereby limiting the chances of success for the agroecological transition, in all its diversity? Digital would appear to be an excellent driver of integration between the different stakeholders within the agricultural supply chain, at all levels. It is also, broadly speaking, compatible with the current agricultural model's sociotechnical system, particularly in terms of its associations with agricultural machinery (tractor-GPS-modulated
application software) or satellite technology, highlighting the objective of greater control over the consumption of inputs (Labarthe, 2010). In this way digital technology could reinforce the technological lock-in of the current situation, further restricting the possible emergence of alternative innovations promoting agricultural practices which are radically more environmentally-friendly and less production-driven, and which could eventually help the current system to evolve. Digitalisation can thus be characterised by a sort of path dependence<fnref n="63" />, excluding alternative forms of agriculture (Clap and Ruder, 2020). The concern is not, therefore, linked to digital agriculture "not being environmentally-friendly", but rather to the digitalisation of agriculture reinforcing the dominant, production-driven model, when in fact the goal is to make agriculture more agroecological.

## Taking humans further away from nature

The digital revolution and the new technology it has brought with it have transformed our perception of the world, through interfaces designed to expand and enrich our physical and cognitive capacities.

In agriculture this has resulted in "augmented farming" through the use of smart robots and sensors, forming a new interface between farmers and the living world of their farms, animals or plants. Research has been carried out in the social sciences on the consequences of these new interfaces, particularly in livestock breeding, exploring whether or not "machines separate humans from matter" through data or if robots are "a liberating or restricting force for animals and humans" (Lagneaux and Servais, 2014). Although little consideration has been given to the world of plants, we have started to see some research into the way in which digital technology is transforming our relationship with plants (Javelle et al., 2021).

Over and above the risk of losing our material connection to nature through an increase in digital interfaces, a number of authors have also tackled the issue of the reification of the living world brought about through precision agriculture, and the ethical questions this raises (Bos et al., 2018). This is particularly true in livestock farming, some seeing the growing engineering and artificialisation of agricultural production as evidence of possible transanimalism<fnref n="64" />, geared towards developing "augmented" animals in order to not only improve their well-being but also to boost productivity. What impact will this reductionist approach – which reinforces the perception of the animal machine (Meuret et al., 2013) – have

<footnote n="63">63. Having first emerged in political science in the nineties, path dependence is a term used to describe how influential decisions made in the past and decisions taken by political bodies are on present decision-making.</footnote>

<footnote n="64">64. Droit Animal Ethique & Sciences (2017). Trans-animalism, augmented animals and cyborg animals: towards the status of "sub-machine"? 93, https://www.fondation-droit-animal.org/documents/revue93.pdf</footnote>
on human-animal relations (Larrère and Larrère, 1997) or the efficiency of the production system as a whole? How do citizens view this type of agriculture? These are all issues which have been explored in the humanities in conjunction with agronomy.

## Contributing to digital's growing environmental imprint

Digital technology has an environmental impact which has been given little or no consideration in agriculture. As is the case with other areas of society, the development of digital technology in agriculture will involve an increase in the use of equipment for data capture, transfer (deploying wireless sensor networks, or even employing the use of 5G), storage and processing (see platforms offered by tractor manufacturers), requiring increasingly powerful and energy-intensive electronic components and systems, with all of the environmental consequences<fnref n="65" /> that this entails (resource depletion, climate change, etc. (Marquet et al., 2019)).

<figure><img src="image_54.png" /></figure>

### Find out more

The agricultural supply chain is currently responsible for 13% of overall energy consumption in France (particularly for maintaining the cold chain); across all sectors digital is responsible for 12% of electricity consumption and 3% of total energy consumption, the biggest contribution coming from video streaming. Although there does not seem to be anything particularly alarming about current statistics for agriculture, attention will need to be paid to the rising contribution made by digital agriculture.

The increase in the number of various different types of connected sensors will result in greater reliance on resources such as the precious metals (silver, gold, palladium) and rare-earth elements (neodymium, praseodymium, gallium, germanium, etc.) found within these electronic components, the extraction and separation of which uses up vast quantities of energy and water. The geographical distribution of production sites is also highly uneven, the majority currently found in China (Pitron, 2018). This will also pose challenges when it comes to end-of-life management for materials, with the not properly controlled risk of the spreading of technological waste, similar to what we have seen with the reprocessing of mobile phones in France: only 15% of handsets are collected out of more than 25 million phones sold every year, despite the existence of a specialist stream for the recycling of electronic waste (Blandin, 2016).

<footnote n="65">65. https://www.lemonde.fr/blog/binaire/2019/01/29/impacts-environnementaux-du-numerique-de-quoi-parle-t-on/</footnote>
## 5.2 Widening inequality and power imbalance

One of the risks associated with the digitalisation of agriculture, first flagged up in the nineties (*Wolf and Buttel*, 1996), relates to the increasing industrialisation of agriculture, the social and environmental consequences of which are a source of controversy. It is argued that there will be a sort of co-evolution between the roll-out of digital technology and production becoming concentrated in ever greater production units, driven by a desire for greater efficiency and productivity at the expense of other types of agriculture or groups of agricultural workers. These risks have been discussed using strategies from different disciplines in the humanities, primarily sociology, political science and institutional economics (*Klerkx et al.*, 2019).

### Risks of exclusion

There are a number of risks of exclusion associated with digital agriculture, linked to various different debates on the subject of the diversity and coexistence of agricultural production models.

The first risk relates to agricultural holdings with a small economic impact. The issue of small farms being excluded is not specific to digital technology: it has been shown how the modernisation of agriculture in France has excluded small agricultural holdings, chiefly through the economies of scale which are typical of technological development (*Deléage*, 2013). Digitalisation is part of this technological trajectory for agriculture, which is centred around increasing the size of agricultural holdings. It could even accelerate it given that, by its very nature, some digital technology (such as that based on satellite imaging) requires holdings of a minimum size in order for it to be profitable. This risk of exclusion can be compared to the incompatibility outlined in 5.1 between digital technology and certain ways of making agriculture more ecological, which require a more extensive overhaul of production systems.

The second risk is linked to aggravating the precarious nature of agricultural work, at a time when there is a growth in the percentage of salaried workers in agriculture and a desire to reduce labour costs in the interests of increased productivity. The development of robotics – which could either replace human labour or limit the human workforce to certain, more qualified positions – could further exacerbate precarity among certain groups, particularly the poor in society or immigrant workers.
The third risk is linked to the difficulty of accessing digital technology and/or the skills needed to use it, which could also drive exclusion in the agricultural sector. This debate is partly linked to the digital divide and the risk of excluding certain rural areas lacking in digital equipment at an infrastructure level.

## A loss of autonomy for farmers

Research has been carried out in the field of rural sociology to assess the potential implications of digitalisation on the decision-making autonomy of farmers and the meaning they place in their work. This could have significant repercussions for the cultural fabric of rural areas and farmers in that it will alter what it means to be a farmer (Burton and Riley, 2018). Digitalisation may bring about a shift in agriculture from "practical", experience-based management towards a data-driven approach. It could "discipline" working routines for farmers, conditioning them through a new form of "algorithmic rationality" (Miles, 2019). As a result, digitalisation which is not controlled by farmers is a topic for debate.<fnref n="66" /> Questions have also been raised regarding the effect of digitalisation on the autonomy of farmers, including a fear that farmers could become "data workers" (Rotz et al., 2019).

## Upstream and downstream control

Another issue relates to the imbalance between agriculture and its upstream and downstream sectors, which digitalisation could exacerbate. Agriculture has often been described as a sector that is dominated by upstream and downstream, and particularly by its upstream (mechanisation, the chemical industry, seeds/grains, etc.) when it comes to innovation dynamics. A number of authors have questioned the role played by digitalisation in transforming (or exacerbating) the balance of power between agriculture and other sectors.

Upstream, digital technology could increase farmers' dependency on certain inputs (pesticides, mineral fertiliser, etc.) while optimising and limiting their use. This paradox can chiefly be explained by the fact that digital technology takes the form of specialist equipment which embeds models, standardises decision-making and leads to asymmetry of knowledge. This is changing the way in which knowledge is controlled (Bronson and Knezevic, 2016).

<footnote n="66">66. https://www.amisdelaterre.org/communique-presse/nouveau-rapport-agriculture-et-numerique-vers-une-fuite-en-avant/</footnote>
Downstream, new tools for sharing and controlling information (particularly blockchain and big data technology) have the capacity to change or strengthen the positions of different stakeholders in innovation ecosystems and value chains. Questions relate to the consequences on value sharing, how sectors are governed, the risk of forms of subsidiarisation within agriculture and control by agribusiness companies situated downstream from agriculture.

One new issue relates to the role which new stakeholders – firms in the digital technology sector, from startups to multinationals – could play; digital giants have, for example, made significant investments in agriculture, sometimes in conjunction with equipment manufacturers. Alongside this investment, a number of questions have been raised regarding data governance in agriculture, and the capacity of stakeholders in the agricultural sector to control knowledge integrated into digital technology and to grasp the value which it produces (Carbonell, 2016).

Digital technology is therefore associated with cross-sectoral dynamics, calling for multidisciplinary research to be carried out on the resulting institutional changes and the risk of potential lock-in (Carolan, 2020; Labarthe, 2010).

## Accessing information and training - what role can advice play?

A fundamental feature of digital technology is that it is not neutral for innovation systems and agricultural knowledge: it has the capacity to completely overhaul the way in which knowledge and information within the sector is constructed and disseminated (Busse et al., 2015).

Research carried out recently has revealed both the potential of digital technology and the threat it poses to certain stakeholders or roles within innovation systems. This is particularly true for farming advice – its participants, methods, content and even legitimacy are all called into question by digital technology (Fielke et al., 2020).

However, there are a number of issues linked to the role advisors or other-intermediaries in innovation systems can play with the advent of digital technology: how can more and more information be integrated without generating excess stress or mental strain for farmers? Who will be in a position to evaluate the efficiency, durability and suitability of digital tools? Who will have the capacity to monitor the content of knowledge (agronomic models, validity testing, etc.) contained within these tools and applications?
The question of digital's impact on the dissemination of information and knowledge within the agricultural sector also takes us back to the issue of inequality, as discussed in the previous subsection. This inequality is linked to issues surrounding accessibility (financial, cognitive, connectivity) to digital technology. The issue of unequal access to advice and information is not a new one (Mundler et al., 2006); the goal will be to determine whether digital helps to reduce or exacerbates this inequality. This refers us back to issues such as cost and the digital infrastructure of rural regions, but goes further than that; it will be necessary to train rural and agricultural communities, in all of their social diversity, giving them the opportunity to acquire the skills needed to use this technology in an effective and appropriate manner.

## 5.3 Loss of sovereignty

The final report from the 2019 French Senate inquiry "Digital Sovereignty" (Longuet, 2019) proved that the issue of digital sovereignty has never been more topical. The report was keen to stress that this is a threefold problem for France, relating to "ethics, security and economic liberty", at a time when our societies are finding their values are being questioned and humans are "increasingly collections of data to be exploited". Although agriculture is an area in which the question of national sovereignty – at both an individual and a collective level – may be thought of as a given (production is by nature rooted in regions, there are strong cultural ties to the land, public authorities have a track record of supporting and guiding agricultural production, the importance of public research into agronomy is recognised in France, the CAP is a cornerstone of the European project), the development of digital technology is bringing forth new challenges linked to digital sovereignty (Klerkx et al., 2019).

### A loss of autonomy over food supplies

The increasing digitalisation of the supply chain – from producers to processors to distributors to consumers –, the primary aim of which is to bring production into line with needs, to minimise logistics and processing costs and maximise customer satisfaction, could potentially lead towards ever greater integration of agriculture. Sovereignty becomes an issue when monopolies develop, as can be seen with the current offensive being led by digital giant Amazon in the food distribution industry<fnref n="67" /> Also worthy of note is the rapid development and use of connected tools on smartphones for evaluating food and other consumer products (in terms of environmental impact, nutritional value, etc.), which could have a significant

<footnote n="67">67. https://siecledigital.fr/2020/08/31/amazon-ouverture-de-son-premier-supermarche-connecte-amazon-fresh/</footnote>
long-term impact on modes of consumption<fnref n="68" /> There is no guarantee of democratic control over these new tools, resulting in the risk of a monopoly developing. Lastly, the economic model for the digital transition is partly based on start-ups, some of which have designs on being bought over by major groups. This inevitably raises questions linked to national sovereignty with regard to the digital technology and services developed for agriculture and the data it produces (Schneider, 2020).

## Seizure of agricultural data

Digital sovereignty entails control over data. Whether it is down to major manufacturers of agricultural equipment or digital giants, there is a risk of agricultural data being seized, either by access to data simply being restricted or by data being opened in formats which are not practical to use. Agricultural machinery could act as a Trojan horse for the collection of data in agriculture. This includes milking robots in livestock breeding, but also tractors and harvesting machines for field crops. These agricultural machines feature an increasing number of sensors, gathering data on tasks performed which is then shared with manufacturers. Purchase agreements govern their use in a way which benefits manufacturers (it is often stipulated that farmers must share all agricultural data). This helps to maintain a lack of transparency along the data chain (what does the data contain, where is it going, and for what purpose?), resulting in a near lock-in situation (it is sometimes very difficult for farmers to gain access to their data, and even harder to put it to any purpose) (Carbonnel, 2016). There is an awareness within the profession of this risk, farmers in France having come together via the Data Agri<fnref n="69" /> charter put forward by two trades unions, the FNSEA and the JA. This is aimed at improving the handling, transparency and security of agricultural data in contracts. France would appear to be somewhat ahead of the game at a European level when it comes to reflecting on the use of agricultural data, building independently on the GDPR regulation on personal data.

The sharing of agricultural data is a priority both for the agricultural profession and for research in agronomy, the goal being to support the development of agronomic knowledge and digital technology and services in agriculture. This is a key issue in relation to digital sovereignty. Agdatahub<fnref n="70" />, a data exchange platform for the agricultural sector developed by a number of agricultural organisations (chambers of agriculture, technical institutes, etc.) and businesses, is a good illustration of how a trusted system can be built around data (French companies DAWEX and 3DS OUTSCALE were selected for the Agdatahub platform).

<footnote n="68">68. https://www.capital.fr/conso/peut-on-faire-confiance-a-yuka-pour-ses-courses-1319721</footnote>

<footnote n="69">69. https://www.data-agri.fr/</footnote>

<footnote n="70">70. https://agdatahub.eu/</footnote>
## A loss of control over production equipment

Sovereignty is also an issue when it comes to control over production equipment in agriculture. Digitalisation is resulting in this equipment becoming increasingly complex (Bournigal, 2014) and maintenance becoming more and more difficult, for both farmers and distributors, who are experiencing a loss of technical autonomy. The same is true when it comes to training: teachers at agricultural high schools have encountered difficulties training future professional users of agricultural equipment in what is a high-tech and constantly changing field (Isaac and Pouyat, 2015).

Another issue linked to sovereignty is the lack of French companies among the world leaders in agricultural machinery (AGCO, John Deere, New Holland, Lely, De Laval), although France does boast a number of pioneering companies when it comes to mobile agricultural robots (e.g. Naïo Technologies).

## A challenge for cybersecurity

In the field of cybersecurity, the first challenge concerns the risk of attacks via connected objects and sensors (Dhar, 2021). This either involves the connected object itself becoming a source of a denial-of-service type attack, or it is hacked for malicious purposes. The latter example is the most troubling, particularly in the case of highly-integrated agricultural systems where farmers have granted significant autonomy to automatic control systems (automated greenhouses, milking robots, etc.). The fact that these devices are often manufactured outside of Europe and that we have no say over design (to ensure security by design), means we must be even more vigilant as to the risk of backdoors.

A second challenge relates more broadly to protection against the recovery and hacking (theft, modification, destruction) of agricultural data. The choices made in designing the platforms used to share this data clearly have a significant impact on the possible level of protection. Although the most notable examples of cyberattacks have targeted institutions of key strategic importance to society (hospitals, airports, banks, etc.), the crucial importance of our food production and consumption systems could see them becoming potential targets in the future (Gupta et al., 2020).
## 5.4 Accentuating vulnerabilities and negative yields

### The vulnerabilities of the agrifood system

As described in 2.3, modern agriculture interacts with a range of sectors and stakeholders of various types and sizes. This results in very "long" supply chains and decision-making systems. Agricultural activity has also expanded into increasingly specialist areas (Bowler, 1986). This system is centred around a large number of asymmetrical relationships of dependency between these stakeholders. It also centres around the intensive use of technology, which users are gradually becoming dependent on. The digitalization of this system could increase dependencies<fnref n="71" /> between several of its elements and create new ones. These developments increase the risk that the partial or interrupted functioning of one element could paralyze the entire system. These changes are increasing the risk of an error affecting one element paralysing the entire system. These issues (blockages, interdependency) were highlighted during the Covid-19 crisis, with the emergence of a number of areas of tension in different parts of the agrifood system<fnref n="72" />. By disrupting supply chains, the Covid-19 crisis also resulted in a shortage of goods – including copper and microchips – in a number of sectors, highlighting the risks linked to dependency on such goods (Bouissou and Albert, 2021). This warning is all the more striking given that a number of crises expected over the next two decades (regional and systemic),<fnref n="73" /> most notably peak oil (Delannoy et al., 2021), are likely to have a far greater impact on society and the agrifood system in particular (Servigne, 2014). At a time when increasing the resilience of the agrifood system has become critical, its digitalisation runs the risk of making it more vulnerable.

### Increasing complexity, diminishing returns and associated risks

As discussed earlier, the agrifood system is centred around a number of increasingly specialist regions, sectors and stakeholders, of various types and sizes. It is also centred around a number of regulatory mechanisms and various relationships of dependency. Agriculture and its upstream and downstream sectors can now be said to form a complex sociotechnical system in the sense understood by Taintier (Allen et al., 1999).

<footnote n="71">71. See section 5.1 ("Technological lock-in and the agroecological transition") and section 5.2.</footnote>

<footnote n="72">72. This includes risks linked to logistics and halting migratory flows, in addition to the instability generated by the introduction of non-collaborative, "every man for himself" national policies.</footnote>

<footnote n="73">73. The probability and intensity of which are set to increase in the decades to come. See Chapter 2.</footnote>
Taintier demonstrated that human societies have a tendency to become more complex as they solve the problems facing them – this is because the solutions deployed require the addition of new elements to the system and the introduction of new regulations (Taintier, 1990; Chambaz, 2019). Ultimately, this complexity is “paid for” through energy costs: the more complex a society becomes, the more energy is required for its basic functions (Taintier, 2016). This problem is exacerbated by the fact that this increasing complexity follows the law of diminishing returns: above a certain threshold, the benefits of a society increasing in complexity grow more slowly than the costs, until a critical situation is reached at which point costs may be higher than the benefits<sup>74</sup> (known as negative returns), as was the case prior to the collapse of a number of civilisations (Taintier, 2009). The challenge of complexity is keeping overall energy costs lower than the profits it brings in; otherwise there is a risk that the evolutionary trajectory of the system will get out of control and that any attempt to correct the system will only result in rendering it more volatile, vulnerable and uncontrollable.

The food and agricultural system is already a particularly complex sociotechnical system, the overall costs of which include indirect costs (sometimes very far removed) linked to negative externalities such as environmental, health and sociopolitical issues, which are either invisible or ignored by the vast majority of stakeholders (see 2.1). Our inability to evaluate these consolidated overall costs (energy, materials, pollution) and to fully grasp the aforementioned dynamic makes us liable to take major risks each time the system develops further complexity.

For this reason, it will be necessary to explore the impact of the development of digital technology in relation to this risk, particularly in agriculture. Indeed, as was discussed previously, the increasing digitalisation of the agrifood supply chain risks making this system more complex and strengthening or expanding ties and dependency. Uncontrolled use of AI and big data<sup>75</sup> could trap us further in a spiral of increasing complexity.

<footnote n="74">74. The phenomenon of diminishing returns followed by negative returns has been widely studied and documented, including in agriculture (Brue, 1993), in security (Elhefnawy, 2004), hydrocarbon extraction (Taintier and Patzke, 2012) and, more generally, in global macroeconomics (Elhefnawy, 2008).</footnote>

<footnote n="75">75. With this technology the quantity and the complexity of the services and materials required will significantly increase (data generation, circulation, storage and processing – sensors, platforms, networks, etc.).</footnote>
Conclusion

This chapter has covered a number of risks which the development of digital
technology in agriculture has brought with it. These risks vary in nature and relate
to economic, political, societal, psychological and environmental dimensions,
among others. These risks could potentially be inherent to the deployment of
this technology, and could be unavoidable or event uncontrollable. How this
technology is deployed and used will obviously depend on upstream research, but
also – and most importantly – on how stakeholders (citizens, farmers, stakeholders
in agribusiness and the food industry, politicians) engage with it, as well as the
general functioning of society (economic models, political regimes, standards
frameworks, ideologies, etc.). It has long been understood how difficult it is for
societies to control the development of technology (Ellul, 1977). It will be essential
to take all of these factors into consideration when guiding future research in the
field, as we have sought to do by identifying the challenges outlined in chapter 6
for making digital responsible, relevant and shared.
# 6

## Challenges for the future

Providing digital tools to help address
the challenges and gaps facing agriculture

**Authors** – Ludovic Brossard, Véronique Bellon-Maurel, Pierre Bisquert, Tassadit Bouadi, Frédérick Garcia, Roland Lenain, Pierre Labarthe, Evelyne Lutton, Pierre Maurel, Nathalie Mitton, Alexandre Termier.

**Acknowledgements (contributions, proofreading, editing):** – Lluís Miquel Pla Aragones, Isabelle Piot-Lepetit, Emmanuel Prados, Xavier Reboud.

<figure><img src="image_55.png" /></figure>
This chapter highlights the needs and challenges linked to the development of responsible digital agriculture in the interests of promoting agroecology, agricultural diversity (including family agriculture) and sustainable food systems. The aim is to look beyond the state-of-the-art (Chapter 3) and to respond to the opportunities presented by digital technology for the agroecological transition and for balancing out value chains (Chapter 4), while avoiding the pitfalls that have been identified (Chapter 5). The focus will be on the challenges facing technological research, while acknowledging the associated economic and organisational challenges, particularly relevant to agriculture.

Based on our needs analysis for promoting implementation of agroecology and balancing out value chains, the chapter is divided into four sections:

* improving collective management, including at the regional level;

* improving farm management;

* balancing out the value chain, both upstream and downstream;

* creating and sharing data and knowledge.

## 6.1 Providing digital tools for collective management at a regional level

Three key areas have been identified for overcoming the obstacles linked to the use of digital technology for land management (chapter 4.3):

* measurement and monitoring on a large scale;

* data visualisation;

* digital devices for participation, mediation and governance.

### Monitoring and measurement at a regional level

The ambition to make agriculture less artificial, getting the most out of local assets and reusing natural resources, will be determined by the capacity to take advantage of material flows, the potential of biological regulation and functions beyond the farm (ecosystem services, land ecology, etc.). Many of the various different interactions can only be understood through a systemic point of view. This extends the scope of our consideration, both in space and in time: some characteristics can only be appreciated at a regional level, such as the extent to which a piece of land can be crossed (which will depend on the intensity of green
and blue belts<fnref n="76" /> on it); while others must be considered over time, such as the capacity for resilience and speed of recovery when faced with climatic hazards. Therefore, if we want to employ the principles of agroecology, we must quantify parameters which are difficult to detect using traditional methods. This calls on the need for measurement and monitoring, evaluation (modelling) and the management of data on a large scale.

In terms of measurement and monitoring, the aim will be to identify data which is relevant, useful and currently missing for collective agricultural management at a regional level and to develop the tools for obtaining it, with the following challenges:

* Measuring new, difficult-to-grasp parameters (such as biodiversity, soil/water quality, etc.) as non-intrusively and as frugally as possible.

* Adjusting sampling frequency (temporal and spatial), a crucial component of information theory. Systems either collect data regularly – at different levels of granularity in time and space (sensor networks) – or sporadically (through crowdsourcing, mobile applications, mobile collection vehicles, robots, drones, etc.). Networks must adapt to these types of data, which feature different traffic patterns, in order to be able to convey it within the time limit with minimum data loss. This is applicable at all levels and is dealt with in greater detail in section 6.2.

* Managing heterogeneous data. This results from diversity in terms of the objects observed, sensing and collection techniques (including crowdsourcing), stakeholders, parameters measured, formats (value, images, localisation, etc.), metrological properties (precision, frequency, etc.). In order to deal with this heterogeneity, appropriate filtering and fusion methods will need to be developed. It will sometimes be possible to perform fusion at different levels and more or less iteratively, factoring in the uncertainty linked to each piece of data, the variability of this uncertainty and any consequences it may have on the rest of the information chain. These questions are applicable to all types of data – physical, biological, economic, social, etc. In order to produce coherent reports (e.g. on material flows) and the corresponding uncertainties, mathematical and computing tools for data reconciliation will be employed (these tend to be based on constrained optimisation) (Courtonne et al., 2015).

<footnote n="76">76. The term “green belt” is used to refer to natural and semi-natural land environments, while “blue belt” refers to wetlands and aquatic environments (rivers, tributaries, ponds, peatlands, etc.). The term “green and blue belt” refers to a set of ecological networks allowing populations of species to move around. These are comprised of wildlife corridors which connect reserves where biodiversity is richest and best represented. These corridors can be linear (hedges, along footpaths, grass strips...) or various different types of landscape structures (https://dicoagroecologie.fr/encyclopedie/trame-verte-et-bleue/).</footnote>
* Data governance, an issue which is exacerbated in multi-source data systems. This is a general question, which will be explored in 6.4.

In the Global South these needs are becoming ever more pronounced: as outlined in section 4.5, information capital is sorely lacking in these countries at a regional level. This capital is essential for national administrations (for agriculture) and local authorities, producer organisations, research bodies, etc. when it comes to open innovation, anticipating risks (climate risks, health risks) and improving the organisation of regions and sectors. In this context our research will need to take into consideration additional difficulties resulting from the digital divide, illiteracy, multiple dialects, etc. But these difficulties also provide us with avenues to explore with a view towards rethinking our systems and methods and adapting them to this context.

## Visualisation

Our visualisation methods will need to be revolutionised for data management at a large regional level. Given its particularities, the agricultural sector has raised research questions with no current equivalents in the field of visualisation, such as:

* visualising multi-scale, heterogeneous data, sometimes in large quantities and sometimes rare: spatial data, symbolic data, temporal data, variable data, incomplete data, uncertain data, erroneous data, semi-quantitative data and even qualitative data depending on variations in structure such as mapping (GIS), images (from satellites, drones), time series, graphs and networks;

* visualising extreme scales, connecting them in a fluid and clear manner – short-range and long-range (time, geographical, etc.) –, and developing suitable and appropriate tools for aggregation and statistics;

* revealing new information semi-automatically by comparing maps or time series, highlighting symmetries, regularities, trends, correlations, etc.;

* meeting contradictory needs such as, for example, the visualisation of massive data, but with mobile applications (mobile phones, tablets, etc.), or guiding users while respecting their autonomy;

* finding innovative ways of representing complex objects, dependencies or models, capable of being used by individuals from a diverse range of backgrounds.

These questions open up new prospects for certain basic subjects, including the visualisation of uncertainties (Boukhelifa and Duke, 2009; Potter et al., 2012) and progressive visualisation (Fekete et al., 2019), at the interface between visualisation and AI. It is worth noting that, with regard to visualisation, there has
not been much discussion on issues surrounding privacy and usage rights for
data when it comes to building trust (Charvat et al., 2018). The questions listed
above could eventually lead to research being carried out on visualisation and
HCI specific to the agricultural sector.

Digital devices for participation, mediation and governance

The multi-actor approach is essential at a regional level and requires support tools: the knowledge production mode is changing, with transdisciplinary research requiring significant contributions from external stakeholders, something which may be easier in the digital era (Bergez et al., 2019). In sectors operating at a regional level, it is increasingly common for individual and collective interests to come into conflict with each other (Ryschawy et al., 2019). New digital devices from regional engineering are anticipated, to facilitate dialogue within the world of agriculture and with other regional stakeholders (figure 2)..

<figure><img src="image_56.png" /><figcaption>Figure 2: Tools and devices for regional governance engineering (Rey-Valette et al, 2011)..</figcaption></figure>
These digital tools and devices could fulfil a range of functions: analytical, creative, cognitive, interpersonal, decision-based, operational, etc. (Rey-Valette et al., 2011). They could also help to develop collective action by facilitating participation and open innovation, collective decision-making and mediation.

## PARTICIPATION AND OPEN INNOVATION

Digital can provide the means to implement open innovation and participation. Confronted with complex problems, analytical approaches (in the lab) and participatory approaches (with stakeholders from a wide range of backgrounds) must be devised jointly – digital technology having the capacity to bridge the gap between the two (facilitating negotiation through modelling and visualisation).

To encourage farmers to engage in the agroecological transition, a gradual approach is the preferred option: the capacity to bring about change collectively will be necessary. It is anticipated that there will be new digital tools available for participatory strategies: support models, digital gamification, digital tools for analysing participatory sessions (video and audio processing for identifying and labelling participants and points of view, etc.).

Open innovation also generates additional research needs, involving management sciences, social sciences and law: on types of collaborations and sources of information in open innovation assisted by digital technology, on economic models, on managing tacit knowledge, etc. (Enkel et al., 2020). Evaluating individual creative contributions in open innovation processes where intellectual protection will come into play, also known as “the paradox of openness” (Arora et al., 2016), raises questions from the perspective of law and economics. Finally, there is the issue of the way in which learning networks are organised so as to facilitate innovation in digital agriculture (Klerkx et al., 2019).

## COLLECTIVE DECISION-MAKING

This type of decision-making is based on three different processes: deliberation, negotiation and voting. When it comes to deliberation (Besnard and Hunter, 2008), by allowing arguments to be studied in a logical and automatic way, digital technology could help to ensure deliberations are rational, while correcting any erroneous conclusions. With regard to negotiation (Kilgour and Eden, 2010), it is argued that a more standardised approach aimed at reaching a fair compromise would lead to engagement and satisfaction on the part of stakeholders and, thus, to sustainable decisions. Lastly, on the subject of voting itself, digital technology could be used to characterise these principles in order to arrive at relevant, desirable decisions, by taking different expressed preferences into account, for example (Brandt et al., 2016).
Tools must be easy to use, complementing other modes of collective decision-making and deliberation, and be capable of being seamlessly integrated into individuals' daily routines (particularly when strategic decisions are being taken) in order to collect their arguments and preferences, for instance. As a consequence, the visualisation of data and decisions is vitally important.

## MEDIATION

Digital is reshaping boundary objects (Trompette and Vinck, 2009) – through which social groups of diverse interests, practices and codes are able to enter into dialogue and reach a mutual understanding – and intermediary objects (Vinck, 1999), which retain a trace of the different stages involved in the collective design of a project or system, helping to boost acceptance and reuse. In Africa and the Global South, the use of commons such as land (agropastoralism, forestry) or water (irrigation) remains widespread. In this context, digital technology could also be employed to reshape management methods. Ongoing experiments with collective learning, living labs and joint, participatory management in places such as West and North Africa could be analysed and replicated.

## 6.2 Helping farmers to manage their technical journey

Three levers could be applied in tackling the obstacles identified in 4.2 with a view towards the scaling up of agroecology:

* systems for monitoring animals, plants and their environment;

* decision-support tools;

* robotics.

### Acquisition and diagnostic systems

The challenge here lies having access in farm to accurate, reliable data at a low cost and with a low environmental impact, providing farmers with rapid, easy-to-understand information about the status of their systems (animals, plants, harvests, etc.), enabling the early detection of malfunctions and assisting them with the decision-making process. Mass, comprehensive capture of data could also help to promote large-scale phenotyping on farms, the goal being to develop new knowledge in the field of agroecology. For livestock breeding, we can add constraints linked to measurement and transmission, ethical questions and a recognised need for unconventional forms of livestock breeding. In the context
of agroecology, one key issue is detecting malfunctions, with the compromise of "coverage" (the area covered by the detection system) versus specificity. Specific measurements (e.g. detecting a virus or bacteria) are complex due to the need to establish contact, the cost, energy supply and the issue of false alarms in livestock breeding (Dominiak and Kristensen, 2017). Research must be inclusive, geared towards "moderate levels of instrumentation" and devices accessible to all farmers (Bergez et al., 2019; Dumont et al., 2018).

Research on acquisition systems, sensors and IoT, data management systems and associated digital models linked to farmers' core business and consistent with their strategies could focus on:

* **creating new sensors while respecting constraints typical of agriculture (frugality, cost, energy use, etc.).** It will be necessary to seek compromises between the autonomy of the sensor, its environmental impact, its spatial and temporal resolution weighed against specificity, measurement quality, durability, suitability with regard to the object under study and to the measured environment simplicity of use and maintenance, the last two factors being essential for solution acceptance. With the same goal of simplifying human-computer interfaces, research could be devoted to the development of audio devices enabling farmers to input information (e.g. electronic crop registers): voice recognition, ontology alignment, etc. Finally, in order to improve understanding of these agroecological systems, it is becoming increasingly clear that we must take into account not only the physical parameters of an environment, but also its biological parameters (animal/soil microbiota), which would generate needs in terms of omics methods.

* **Optimising the mode of data transfer so that data is transferred automatically to processing centres, practically eliminating co (Wolfert et al., 2017), a major factor for large-scale phenotyping on farms; this raises research questions linked to power supplies for sensors, sensor networks (e.g. swarm intelligence), etc.**

* The desire to limit the number of sensors (in line with frugality) and to make it easier to measure certain parameters in a non-invasive way also calls for research into smart sensors, i.e. combinations of data from "simple" sensors for estimating these complex parameters through appropriate data processing (e.g. machine learning). The impact of these developments on the quality and uncertainty of information has still to be assessed.
Once collected, information can be used for diagnostic purposes, for characterising the state of agricultural systems and detecting any malfunctions that might require a response. Research could explore the building of diagnostic models. Although this issue is not specific to agriculture and affects other sectors as well, research must incorporate knowledge of the domain (agriculture) in order to address the following priorities:

* selecting which indicators to integrate, factoring in the natural variability of indicators, the propagation of uncertainty from these indicators, sensitivity and specificity adapted to use, adapting to local conditions (farm type and location, risk acceptance, agricultural practices, etc.);

* fusion of big data with point data from varied sources, specific processing (SVM, deep learning), data sharing (individual or collective);

* the hybridisation of data-driven approaches developed in artificial intelligence, based on big data, with modelling approaches which are more concept-driven but less suited to real-time data (Ellis et al., 2020). This will require research into the explainability of data-driven approaches, in addition to research on knowledge-based systems (ontologies).

In a general sense, it is to be hoped that these developments linked to acquisition, communication and processing tools can be made in an integrated and scalable way in order for the system as a whole to be capable of adapting dynamically to each crop or livestock profile, size of farm or agricultural strategy, which all throw up a real scientific and methodological challenge.

## The challenges posed by robotisation and the digital transformation of agricultural labour

Digital tools are transforming agricultural labour. How can these be directed in a positive way so that the labour of farmers and agricultural employees is made less arduous and is better respected? Robotics could provide a way of shifting human labour to tasks with higher added value, but there are still a range of scientific and technological obstacles to overcome in the following areas:

### SCENE PERCEPTION AND INTERPRETATION IN DYNAMIC ENVIRONMENTS

Improvements will need to be made in scene perception and interpretation in order to boost detection capacity (fruit, leaves, diseases, etc.). Deep learning and, in particular, machine learning will open up avenues, especially given that robots will be equipped with sensors and will therefore generate data. One alternative is to utilise human expertise in perception, which raises questions in relation to human-robot cooperation. Finally, it must be possible to explain and interpret
decisions taken by robots, and robots must be able to refer to experts for difficult
detection or decision. This will involve determining confidence criteria for decisions
and clarifying decision-making rules taken from learning, an open theme.

ADVANCED APPROACHES TO DECISION-MAKING

Robots are currently limited to one single operating mode. For complex tasks, shifts between command modes are sequential and planned in advance. It is anticipated that significant breakthroughs will be made in the realm of situation and scene recognition (the robot's dynamic, its operating environment, agroenvironmental constraints, etc.), borrowing from artificial intelligence (Hill et al., 2019) for the purposes of adapting features. These problems go beyond the boundaries of autonomous navigation, and are also applicable to active tools so that they work with precision.

DESIGNING NEW ACTIVE TOOLS

Innovation in the field of agricultural robotics is currently focused on auto-
nomous navigation; tools worn are either passive or controlled independently
(Wu et al., 2019). For greater repeatability and enhanced execution speed, active
tools capable of being synchronised with mobile carriers are expected. In order
to achieve this, research will need to be carried out on mobile handling and
coordination with a moving carrier.

HUMAN-MACHINE INTERACTION AND SHARED AUTONOMY

In addition to issues surrounding perception or communication interfaces,
human-machine interaction also raises questions on autonomy and collaboration:
when and how should control be given back to a remote operator? How can
robots cooperate with humans? In agriculture cobotics is starting to emerge
commercially, with assistance robots (Laneurit et al., 2016), people-carrying robots
and, to a lesser extent, exoskeletons – particularly passive exoskeletons – aimed at
facilitating the lifting of heavy loads. With more complex levels of collaboration,
it will be necessary to interpret human behaviour in order to adapt the actions
of robots. Such an approach will help to popularise robots, which will still not
replace humans, just as we must ensure that these devices operate safely.

OPERATIONAL SAFETY

This is a crucial aspect for autonomous machinery operating in open
environments. Scientific, technological and legislative progress will need to be
made, drawing on driverless vehicles but factoring in difficulties linked to natural
environments: (i) maintaining precision in terms of positioning (avoiding obstacles
or not crushing crops), (ii) navigating within a pre-determined space, (iii) guarding
against the risk of collision, or loss of stability or controllability. Infrastructure
and protocols will be needed to validate operational safety and other types of
performance (technical, environmental, etc.).

## ADAPTING TO NEW PRODUCTION SYSTEMS

Robots must be designed with frugality and inclusion in mind: the choice of materials and components (minimising the use of rare-earth elements), limited energy requirements, reduced maintenance, repairability, the scalability of robots and their capacity to be updated. Similarly, robotics must provide solutions for all types of agriculture, with levels of sophistication and autonomy adapted to production systems. New crop systems, with a mix of species and the possible introduction of trees (agroforestry), will present problems for navigation.

There are also issues relating to the humanities in terms of how digital technology and robotics are transforming labour, on the loss of autonomy (deskilling) as a result of the use of machinery to replace humans and the rationale of practitioners. In order to avoid these risks, one of the challenges will be to incorporate – from the design phase onwards – the conditions for use, impact on the work and satisfaction of farmers (Hansen and Straete, 2020; Vik et al., 2019) and other categories of workers (employees, associates, sub-contractors, etc.).

### Modelling to incorporate systemic effects and build practical, usable decision-support tools

Challenges for research involve a number of aspects linked to the building of models and, in particular: representing and understanding interactions; including expert knowledge; building practical, integrated models for farmers; and dealing with uncertainty. Details of these can be found below.

## REPRESENTING THESE NEW SOCIO-AGROECOLOGICAL SYSTEMS

This is a first challenge in that agroecological systems are much more far-reaching (incorporating value chains) and much more complex (based on interactions) than is the case with conventional agriculture. Difficulties with modelling are linked to selecting which characteristics and parameters to include (determined by measurement capacity), natural variability in terms of how these characteristics are expressed to the other factors inherent to plants or animals, environments, production or breeding systems (factoring in other individuals from their group), the equipment used and the agricultural strategy employed.

Data-driven approaches (based on statistics, artificial intelligence, etc.) could be combined with concept-driven approaches (biological, economic or social models based on known mechanisms). Consideration may even be given to
creating “digital twins”, integrating models developed for sub-systems, in order to test scenarios at a system level (e.g. climate change, local supply on a mass scale, etc.). However, this integration will bring with it alignment difficulties<fnref n="77" /> when there is no guarantee of concept correspondence between sub-models or between digital twins and the system being studied.

## THE LEVEL OF INTEGRATION FOR EXPERT KNOWLEDGE

This second challenge to overcome in the building of decision-support tools (description, prediction, prescription, see 4.2) is of interest to the humanities: will it be necessary to go as far as prescription, or is observation (or possibly a diagnostic) sufficient, leaving the decision (prescription) up to farmers for precision livestock farming, as suggested by *Ingrand* (2018)? With regard to risk management, other formalisms could be actionable, such as the theory of viability (*Aubin*, 1991), which in principle is compatible, but which poses problems for researchers owing to the fact that models must provide a framework for small (<10) dynamic systems which are both controlled and constrained (*Brias*, 2016). This opens up research questions: what should be done if the model is not dynamic and constrained (compartmentalised models, multi-agent systems), or is even unknown? How can weak signals be utilised in time series (tipping points)? How can a compromise between complexity and control be reached (*Anderies et al.*, 2019)?, etc.

## BUILDING PRACTICAL DECISION-SUPPORT SYSTEMS FOR FARMERS

The issue of practicality is central to the design brief, and there are a number of key points that any future research must take into consideration:

* **the user interface:** both for visualising inferred outputs, which are essential to effective decision-making, particularly in the context of multi-criteria optimisation (*Lepenioti et al.*, 2020) or in the context of collective approaches (cf. part 6.2), but also for gathering data and identifying strategic objectives or preferences among farmers and incorporating these into decision models: visualisation of compromises, gamification (e.g. the serious digital game *C-Real Game*). It could be worthwhile to explore human-machine interfaces based on oral communication in order to make it easier to input and reproduce data and information in situations where farmers must handle...

* **the "personalisation" of inferred information** i.e. adapting models to individual farms or farmers in order to avoid one-size-fits-all prescriptions, to be in alignment with farmers' strategies and factor in their objectives (turnover, revenue, operating modes, etc.). Current prescription models are taken from "broad spectrum" knowledge models from experts in the field; how can it be

<footnote n="77">77. Alignment involves indicating that a concept outlined in one ontology is semantically identical to another concept outlined in a different ontology, even if the two concepts have different names.</footnote>
made so that only – or primarily – data collected on individual farms is used, the goal being to infer prescriptions which are more compatible with farms and farmers? This obstacle has raised questions relating to the integration of knowledge from farmers in order to "personalise" inferred information and to increase its relevance in relation to their farm, similar to personalised medicine.

* the capacity to create scalable model capable of being adapted to environments which are liable to change as a result of both internal factors (strategy) and external factors (environmental, regulation, economic, etc.). This also raises questions linked to updating models (what is known as 'concept drift');

* the security of the recommendations made, i.e. the guarantee that a recommendation will not lead to a worsening of the situation, particularly in relation to automatic control. This problem is down to the stacking of models and the propagation of uncertainty (Trnka et al., 2007), as well as the characteristics of the actuator. The latter must be made part of the model in order for relevant decisions to be taken (see Tisseyre and McBratney's opportunity index, 2008).

## UNCERTAINTY AND ITS PROPAGATION

Uncertainty is mentioned in 76% of articles on modelling (Lepenioti et al., 2020): how can it be reduced, how can it be characterised (epistemic, ontological, random) and how can it be represented (Caquet et al., 2020; Crespo et al., 2010; Groot et al., 2012)? How can the issue of incomplete and noisy data and the subjective nature of human knowledge be addressed, particularly in the context of prescription (Lepenioti et al., 2020)? How can a compromise be reached between overly complex, unmanageable modelling and modelling which is simplistic and not sufficiently relevant (Caquet et al., 2020)? Exploring different ways of simplifying models would certainly be useful (stochastic models, mechanistic-stochastic models, metamodels, etc.).

Looking at the Global South in particular, decision-support systems must be designed in such a way as to incorporate the characteristics of agriculture in these countries; these must be multifunctional, with a prevalence for spatial-temporal reasoning and high levels of uncertainty. Decision-support systems and associated information systems must prioritise: (i) introducing or continuing agroecological practices and collective learning (collecting and exchanging data using digital technology); (ii) improving the management of resources (water, organic materials, etc.), from individual plots to whole regions, and harvests (dates, quantities), (iii) building new knowledge based on data and expertise in the context of rare data, but also emerging big data (see 6.5).
## 6.3 Transforming relationships between stakeholders within sectors

Balancing out sectors to better integrate farmers and consumers will be vital in order to keep family farming attractive and to meet the expectations of consumers with regard to food. In response to these challenges, three key points have been especially identified, both upstream and downstream:

* service: advice, insurance;

* traceability;

* platformisation<fnref n="78" /> and the reconfiguration of distribution networks.

### Service: advice and insurance

Regarding advice, each of the obstacles identified in chapter 4 (access to digital, the individualisation of decision-making and maintaining decision-making autonomy, the imbalance between upstream and downstream) open up avenues for research on advice and digitalisation in both digital science and the humanities, with three primary areas of focus:

* **developing decision-support tools capable of integrating the specific features of individual farms (pedoclimate, the agronomic techniques employed, agricultural equipment) and the preferences of farmers.** In addition to the points discussed in 6.2, the development of these tools could also draw on a deeper understanding – based on agronomic, sociological, managerial and ergonomic analysis – of the role of advisors and the bonds of trust they form with farmers in the usage profiles of digital solutions;

* **continuing the economic analysis of the modes of decision-making employed by farmers and the dynamics for the adoption of digital innovations** in a context impacted as much by the diversification of how farmers seek out information as by the fragmentation of services as a result of the privatisation of advice. Research could also be conducted to identify sustainable economic models for digital advice;

* **institutional analysis of the governance of the digitalisation of agriculture,** taking us back to the issue of transparency regarding the use of data, the regulation of power relationships and advice as a key factor in the digitalisation process.

<footnote n="78">78. Platformisation is a business model in which organisations employ the use of a web platform in order to act as an intermediary between consumers, as opposed to a supplier of goods and services. To find out more: https://www.decideo.fr/Entreprise-3-0%C2%A0-vers-une-ineluctable-%C2%A0plateformisation%C2%A0-du-Business-de-l-IT_a9280.html</footnote>
On the subject of insurance (section 4.2), technological breakthroughs will still need to be made in order to reduce the baseline risk for index-based insurance; this could draw on new data sources (satellites, connected stations, etc.) or suitable types of processing (De Leeuw et al., 2014; Ghahari et al., 2019). Finally, usage-based insurance – which has emerged in the transport sector (Husnjak et al., 2015) – is still unknown in agriculture, but it could be a useful avenue for exploration in the context of connected agriculture. Could this usage-based insurance assist with the adoption of agroecological practices – more complex to implement given the need for greater monitoring, but more resilient in the event of a health or weather disaster – by guaranteeing revenue, provided that crops or herds have been correctly monitored and recommendations from decision-support tools applied? A multidisciplinary approach must be taken when addressing these questions.

## Traceability, full supply chain transparency, data life-cycle

As shown in section 4.3, in the current context, the traceability of flows and products in agriculture is crucial in the interests of establishing trust between farmers and consumers. There is a growing interest in the blockchain, for example, for sharing and distributing details on a product's entire life while also limiting fraud. But there remain a number of challenges to overcome in relation to data management at a technical and institutional level, particularly with regard to the overall traceability of practices and products.

## THE TECHNICAL CHALLENGES OF THE BLOCKCHAIN

How can current blockchains, which were designed for banking information, be adapted to this new type of data, linked to flows of products which are often perishable, in order to monitor it and archive it efficiently without violating the basic principles of storing data in a blockchain? How can the flow of information which characterises traceability in blockchains be unquestionably linked to the flow of products? How can the costs of identification systems be lowered and who should cover these costs, which benefit everyone along the chain? How can data be protected within an ecosystem with a growing number of data sources?<fnref n="79" /> Similarly, as explored in chapter 3, public blockchains use up a lot of energy – in order to be unquestionable, the validation of information is open to a huge number of participants “in virtual competition with each other”, known as ‘miners’, resulting in a huge number of simultaneous calculations. Preference may be given to a private, less energy-intensive blockchain (based on a restricted number of

<footnote n="79">79. White paper published by the US National Institute of Standards and Technology, outlining security problems affecting the Internet of Things (IoT), in October 2018 (https://csrc.nist.gov/CSRC/media/Publications/white-paper/2018/10/17/iot-trust-concerns/draft/documents/iot-trust-concerns-draft.pdf)</footnote>
authorised participants) that is also better suited to use in agriculture. However,
this raises the issue of the governance of blockchains.

THE STORAGE OF DATA FROM THE AGRICULTURE AND FOOD CHAIN

This data can be said to be industrial in that it relates to agricultural production,
but also to upstream and downstream industries. Should it be stored in specific
locations or in a distributed manner? How can data sovereignty be ensured?
Should certain operators be avoided in light of the Cloud Act?<fnref n="80" />

DATA INTEGRATION

This is an important aspect when it comes to facilitating subsequent analy-
sis. Owing to the significant increase in the volume of data, the need to verify
its quality and the value of the information, systems capable of enabling access
by relevant and reliable information will become value sources. This will involve
verifying company information systems – such as ERP (Enterprise Resource
Planning) and CRM (Customer Relationship Management) – and engaging them
in dialogue with data generated by connected objects managed through the
Internet of Things (IoT). It will also involve evaluating and recognising the value
created by each individual component of the data production and processing
chain, exploring the following questions: what ways are there of getting more
out of data in value chains, particularly vis-a-vis consumers? How can these raise
awareness of virtuous transitions within agriculture among consumers?

BLOCKCHAIN GOVERNANCE

The challenge here lies in designing a fair and secure system involving all
stakeholders in an equitable way, without any individual stakeholder imposing its
vision on others or taking control of data usage. This raises a number of questions:
how should the data that is generated be shared? What must be put in place in
terms of data governance? To what extent will accessibility to information impact
the improvement of supply chain governance (Gardner et al., 2019) and power
shifts in value chains? What impact will digital technology have on trustworthy
relationships and the ways in which value is shared within the sector (Jakku et
al., 2019)? How can we prevent the value that is created from being collected
exclusively by digital giants (ANRT, 2018)? Is there a risk that digital technology
will exacerbate existing power imbalances (Bronson and Knezevic, 2016; Carolan,
2017, 2018; Wolf and Buttel, 1996).

<footnote n="80">80. The "Clarifying Lawful Overseas Use of Data Act" ("CLOUD Act" 162") was passed by the US Congress in March 2018: Its primary aim is to reaffirm the right of US authorities to demand that technical intermediaries subject to their jurisdiction share all data stored, even data stored overseas. Independently, it also provides for specific, reciprocal bilateral agreements with the United States. (https://www.senat.fr/rap/r19-007-1/r19-007-13.html)</footnote>
Platformisation and reconfiguration of channels

Platforms, new virtual meeting places, are helping to change the economic model within agriculture, facilitating dialogue and collective dynamics. Automatic data input in agriculture, hyperconnectivity, the Internet of Things and automation produce real-time information for optimising the running of the value chain, either individually or as a whole. This growing computerisation has led to increased agility capacities on the part of distribution and processing channels. It will be necessary to introduce agile planning for agricultural and food production in order to meet the growing need for local supply in towns and cities and contract catering. In the interests of promoting family agriculture for towns and cities and contract catering, major issues linked to production planning, coordination between different levels of the supply chain and logistics will need to be overcome in order to ensure everyone's expectations are met and to be resilient to crises (as illustrated by the Covid-19 pandemic). These three points are explored below.

In agriculture production planning is a reality for farmers contracted to agribusiness industries, particularly for frozen, tinned or ready-to-eat vegetables (Ahumada et al., 2012; Li et al., 2015). The challenge now lies in production planning for fresh products in order to guarantee supplies for contract catering, incorporating uncertainties (weather, health, social, etc.) and to factor in demand (Balaji Prabhu and Dakshayini, 2020).

One solution to the issue of coordination is the creation of "food hubs", innovative commercial models which bring together small producers in order to meet wholesale demand (Berti and Mulligan, 2016). The most integrated "food hubs" are intermediary organisations which use the internet for commercial transactions and which pool together, distribute and market food products from the source (small local and regional producers) to customers (individual consumers or wholesalers). These hubs must have access to production and distribution chain models featuring realistic characteristics, including soft information, logistical integration, risk modelling, the regulatory environment, and the quality and safety of products. Stochastic modelling could be useful in this context (Ahumada and Villalobos, 2009).

In order to build urban logistics distribution networks for suburban production with shorter commercial channels, it will be necessary to improve inventory management and distribution planning (particularly for cold products) in order to reduce food waste and the resulting carbon footprint. Little research has gone into planning applied to food supply chains compared to the industry. In particular, there is a clear lack of adequate models for planning operational decisions for the
production, harvesting and distribution of fresh agricultural products (Ahumada and Villalobos, 2009). The environmental dimension will need to be taken into consideration (Melkonyan et al., 2020)

In the Global South, challenges linked to improving supply in local distribution channels are even more difficult: this will involve reducing post-harvest losses through organisational support and improved logistics management (channel modelling and optimisation, cold chain logistics, etc.). For longer channels, it is anticipated that frugal, secure traceability systems will be developed for national and international chains.

Finally, there is also the possibility of a move towards personalised food production. Svetlin et al. (2016) have proposed "online" and individualised co-design of products through linguistic analysis of consumer preferences and translation into formulation parameters (applied to an orange drink). This type of approach could be employed with more complex food items produced in accordance with demand, restrictions, budgets and individual needs, and delivered ready-made to people's homes (Académie des technologies, 2021). This would also make it easier to connect to personalised health monitoring applications.

## 6.4 Creating and sharing data and knowledge

Data and knowledge are central to digital technology helping to promote agroecology: data feeds into knowledge and knowledge feeds into agroecology. This information capital has brought about new technological, regulatory, organisational and institutional challenges. These challenges relate to the origin, the quality (crowdsourcing) and the governance of data, but also to the formalisation and sharing of knowledge, challenges which will require a response in order to build an ethical digital agriculture.

### PARTICIPATORY DATA (CROWDSOURCING)

With the development of connectivity and acquisition systems (smartphones, precision agriculture, connected objects, etc.), the collection of data by operators (farmers, advisors, etc.) or laypersons (citizens) has developed, adding to more conventional methods for the gathering of experimental data by scientists. There are technical challenges relating to participatory data collection for environmental documentation or research purposes (4.4): what infrastructure is needed for managing and exchanging this participatory data? How can the quality of data collected through crowdsourcing be ensured? How can the data that is produced be traced in the interests of the fair sharing of intellectual property? There are also questions of interest to the humanities and economics: what has to be done
to encourage farmers to share their data and information and to build trust-based relationships with their advisory and training environments (Sutherland et al., 2013; Wiseman et al., 2019)? How can value be attributed to the data that is produced? What impact will this new role of data collector have on the evolution of the job of being a farmer?

<figure><img src="image_57.png" /></figure>

Subcellular and weekly mapping of soil moisture by remote sensing based on Sentinel 1 and 2 images. © Theia et INRAE

## GOVERNANCE AND THE SHARING OF DATA AND KNOWLEDGE

When talking about regions or sectors, we have shown that data is increasingly coming from various actors (multisource data). Given that data is generated by separate parties through different systems and is potentially hosted across multiple sites, it will be necessary to determine what the data usage rights are; needs can be contradictory from the point of view of data sharing and data protection (collaborating while remaining competitive). What modes of governance should be employed, in a context in which digital firms and upstream stakeholders are investing heavily in the management of data on agricultural risks and in which agricultural innovation systems are being digitalised (Fielke et al., 2019)? Within chains, an understanding must be reached as to the role played by information in the emergence of cooperation and compliance between stakeholders from different sectors and at different levels of global supply chains (Gardner et al., 2019). How can “ethical and secure” data circulation and state sovereignty be promoted<fnref n="81" />?

<footnote n="81">81. The French Academy of Technology has recommended “the introduction of a label for circulation solutions at a European level to prove that they are ethical and secure” and “emphasise the importance of developing solutions for bringing European clouds together”. See https://www.academie-technologies.fr/blog/categories/publications-de-l-academie/posts/pour-une-circulation-vertueuse-des-donnees-numeriques</footnote>
Clearly, these issues relating to data governance and the risk of power being
seized by certain stakeholders within sectors (agricultural supplies, downstream)
or by digital companies specialising in artificial intelligence and networks are even
more acute in the Global South, where there are fewer regulations.

FORMALISATION AND SHARING OF KNOWLEDGE

Digital technology can help to promote the co-construction (participation) and exchange of knowledge, but there remain a number of challenges: how can knowledge be constructed in such a way as to incorporate the uses and knowledge of farmers (expertise gathering, contextualisation, etc.) in order to increase the likelihood of it being adopted? How can satisfactory governance be developed, not only in terms of data but also the knowledge generated through this data? How can the construction of digital commons be accelerated in order to establish knowledge and, in particular, to compare it and gather it together? This raises non-trivial questions regarding the gathering of expertise and ontologies. In particular, how to make ontologies built on different principles compatible (stackable, associable): different uses, different authors, different fundamental ontologies, etc.? Lastly, in the interests of frugality and efficiency, moving away from multiplying tools and getting the most out of existing resources, might it be beneficial to mobilise non-specialist social media sites and platforms for exchanging knowledge? If so, how can this be achieved?

In the Global South, a first challenge is to use digital technology to rethink participatory approaches for collective learning and co-innovation – through “enhanced” interdisciplinarity (cognitive psychology, ergonomics, immersive serious games, design thinking and management science) – and evaluating their impact (Tesfaye et al, 2019). The goal will also be to facilitate communication with farmers and between farmers, in a context of low network coverage, plurality of languages and dialects, etc. (see 4.5).

Conclusion

In light of the risks and issues discussed above, the challenges identified in
this chapter should be considered within a general overall context, enabling a
multi-faceted framework to be built:

* The need for a systemic vision for agriculture and digital technology.
  Systems and sectors in agriculture are complex systems, comprising multiple elements and stakeholders interacting with each other at different levels (farms, regions, sectors, etc.). Anticipated digital developments will need to be designed and evaluated in light of their direct impact at the level
at which they are applied, but also their indirect impact throughout the
system and society as a whole, from a biotechnical, economic, social (e.g.
labour), environmental (biodiversity, resources, etc.) and ethical perspective.
Considering these indirect impacts and developing methods through
which they can be evaluated will be essential in order to have the capacity
to ensure that overall energy costs fall some way below the benefits of
the development of a given type of technology, therefore without the
risk of increasing complexity (see 5.4). Furthermore, developing systemic
approaches will be essential in order to anticipate retroaction, such as the
rebound effect<fnref n="82" /> that often occurs with digital technology. Research is faced
with major methodological and conceptual difficulties, primarily relating
to the systemic analysis of problems. Approaches must be fundamentally
transdisciplinary; certain frameworks, such as the concept of "complex
thought" introduced by Edgar Morin, could be useful here (Morin, 2014).

* **Searching for frugality.** This involves reducing energy expenses, consumption of other resources (both renewable and non-renewable) and pollution caused by the use of technology. It must incorporate all stages of the data chain, from collection and gathering to reproducing and decision-making. The development of digital solutions must take costs into consideration, whether this is the cost of materials (e.g. components used, size, number, particularly for sensors, robotics, etc.), of the data produced (type, quantity, storage, etc.) or of the processing power required in order to be economic with regard to natural resources (water, minerals, etc.) and energy. This analysis must factor in the entire life cycle of the materials used (resource extraction, manufacturing, transport, use, end-of-life). Although digital calls for a reduction in the use of agricultural inputs and resources (such as water, for example), its own environmental footprint must be taken into consideration when calculating the overall environmental footprint of any new agricultural practice. This will also mean taking a sober, cautious approach when developing and scaling up technological solutions, beforehand exploring organisational and sociopolitical solutions and alternatives which do not use up directly resources or emit pollution.

* **Searching for resilience.** Optimising production and sectors from a cost point of view has guided technological innovations for decades, resulting in specialisation, reductions in stock levels, less room for manœuvre and less autonomy on the part of different stakeholders. This has resulted in a

<footnote n="82">82. More efficient technology often leads to increased consumption of the resource that is sought to be preserved as a result of changes to consumer behaviour; see https://ecoinfo.cnrs.fr/2015/12/23/les-effets-rebond-du-numerique/</footnote>
reduction in the resilience of agricultural systems and sectors, i.e. their
capacity to resist and to adapt to – at different levels – external crises
such as weather events, scarcity of resources and supply chain breakdowns,
and economic or health crises (Biggs et al., 2015). Digital solutions must
endeavour to promote this resilience, by being the component within a
complex system which is based on the seven principles of resilience outlined
by Biggs et al. (2015),<fnref n="83" /> avoiding the trap of complexity, which results in
technical or social dependency or security risks (data, operational, etc.),
contributing factors to fragility.

* **Cybersecurity.** Although not specific to agriculture, this remains a crucial topic in agriculture given the impact it has on food sovereignty. This is as much about maintaining continuity of food and agricultural production and distribution as it is about the security of information relating to agricultural production. Cybersecurity was covered in detail in a previous white paper (Inria, 2019). The European Union is behind the project GAIA-X (www.data-infrastructure.eu), the aim of which is to develop autonomous, sovereign data infrastructure which respects European standards, chiefly through a cloud computing network. Agriculture is one of the themes identified in GAIA-X.

Factoring in these aspects will help to promote responsibility, relevance and sharing in relation to digital technology, helping to make food systems sustainable, particularly in the context of the agroecological transition.<fnref n="84" /> Plotting this course will guide research, not just in terms of the choice of research topics – identified in this chapter – but also in terms of research positioning. We would recommend as a minimum drawing on approaches such as Responsible Research and Innovation or RRI (Stilgoe et al., 2013). Still rarely employed in digital agriculture, RRI is based on the following principles: anticipation (what will happen if…positive/negative impact), reflexivity (what does digital responsibility mean, what limits are there to our hypotheses/choices/knowledge, etc.), inclusion (with who and for who, what values) and responsiveness (how to adjust development trajectories in response to changing circumstances). It draws upon transdisciplinary research. Developing technology for digital agriculture within an RRI framework would help to meet

<footnote n="83">83. https://whatisresilience.org/wp-content/uploads/2016/04/Applying_resilience_thinking_FR_aktiv.pdf</footnote>

<footnote n="84">84. **Responsibility:** fairness, inclusivity, frugality (environmental impact), moving towards a much-needed diversification of cultures, practices and products in a context of reduced inputs, ensuring take-up by a wide-range of stakeholders. **Relevance:** meeting actual needs, delivering effective, acceptable solutions which preserve diversity and freedom. **Sharing:** where users are able to make use of their expertise and local data, give their opinion on outputs (assuming these are clear and well-communicated, with uncertainty estimated), and act on the parameters of tools while remaining within a plausible framework.</footnote>
the challenges identified while factoring in the global context and considerations
on the need to integrate a systemic vision and issues such as frugality, security
and resilience.

<figure><img src="image_58.png" /></figure>

Definition

The concept of RRI (Responsible Research and Innovation), introduced
in the 2010s (Owen et al., 2012; Pellé et Reber, 2015; Stilgoe et al., 2013)
is characterised by four key aspects, "anticipation, reflexivity, inclusion
and responsiveness", all of which must be implemented throughout the
research and innovation process (Stilgoe et al., 2013). Research into RRI
in agriculture remains limited and does not deal specifically with digital
agriculture. However, since the end of the 2010s, Klerkx and Rose (2020)
have noted a growing interest in RRI within agriculture 4.0 (Bronson, 2019;
Eastwood et al., 2019; Rose and Chilvers, 2018)
Conclusion

<figure><img src="image_59.png" /></figure>
The evolution in agriculture and food systems will be one of the most important challenges over the coming years. As has been explained in this white paper, agriculture is already facing and will continue to face major challenges some of them are: food security and how to feed the world in difficult ecological and climate conditions (climate change, the erosion of biodiversity, loss of soil fertility, etc.); changes to agricultural models via the agroecological transition in order to limit environmental impact; diversification of production systems; a move towards greater regional integration; food sovereignty, etc.

## Challenges for research

The digital revolution is in progress in a number of fields and digital agriculture is both a component and a consequence of it. This white paper has outlined current research into digital technology aimed at tackling challenges facing agriculture. The topics covered were data at large (from acquisition to storage), modelling, learning and knowledge extraction, knowledge engineering for decision-support purposes or automation and robotics. We have also covered the range of opportunities presented by the use of digital technology in agriculture for improving production, promoting agroecology and adapting to external change. This range of opportunities also includes better integration within value chains and improving sharing, learning and understanding. But current research only deals with some of the opportunities that have been identified. Furthermore, although this technology has the potential to be empowering, the risks are also great, including missing out on the agroecological transition, widening inequality and power imbalances, loss of sovereignty and excessive complexity. With regard to these opportunities and risks and the current state of research, we have sought to show the main obstacles that will need to be overcome in order to scale up digital agriculture in a sustainable and responsible way, promoting agroecology, adapting to climate change and balancing out and reterritorialising value chains.

Among the many challenges outlined in this paper, there are a few which we believe to be particularly important and suitable for being tackled jointly by INRAE and Inria:

* In terms of data, what is the most relevant data to acquire at different levels (plants, animals, plots, herds, farms, regions, etc.)? How can this data be acquired and exchanged while minimising energy costs and guaranteeing access and privacy? Data heterogeneity is inherent to an increase in the number of different systems. It will be necessary to take advantage of this heterogeneity, which provides redundancy and complementarity both being, factors for improving measurement results.
* When it comes to supporting farmers: what knowledge will need to be created? What is the best way of using and analysing data in order to discover this knowledge? And how can new knowledge be combined with existing knowledge? How can information be shared with farmers (visualisation)? How can they be assisted with decision-making through the use of diagnostic or decision-support tools? What robots will need to be developed in order for agroecology to be scaled up? Stakeholders from the digital and agricultural spheres will need to work together to come up with these new solutions.

* Uncertainty is inherent to processes in biology, the climate, meteorology, etc., particularly in agricultural production systems applying an agroecological approach. How can complexity be modelled by linking models together (e.g. digital twins) while on the propagation of uncertainty under control? How can complexity be incorporated into models and relayed in order to enable informed decision-making? Handling this uncertainty will require combined expertise from different observational disciplines (depending on the scale, this may be biology, physiology, agronomy, regional engineering, etc.), as well as from mathematics and computer science.

* Participation has been shown to be an effective lever in the context of the agroecological transition and the transformation of food systems: data collection, mediation, governance, experience-sharing, etc. The aim will be to utilise digital technology for participatory processes: crowdsourcing; support models; mediation or boundary objects; sharing, managing and integrating (with other systems) data and knowledge.

* Traceability, transparency and, in a broader sense, product documentation, all along the production chain, are becoming key considerations for agri-food systems. Consideration must now be given as to what data should be “traced”, documented and shared (particularly in relation to perishable goods), and how to choose the right tools for storage and transmission (e.g. the blockchain).

* Cybersecurity is crucial to food sovereignty in terms of protecting agricultural production and the information produced as part of it or which is linked to it.

Lastly, it should be borne in mind that there are a number of different agricultural models and that there will be even greater diversity in the future (see the forecast from the JRC<fnref n="85" /> or from the CSIRO)<fnref n="86" />, which will result in the development of types of digital technology adapted to each model.

<footnote n="85">85. https://publications.jrc.ec.europa.eu/repository/bitstream/JRC122308/farmers of the future final online.pdf</footnote>

<footnote n="86">86. https://research.csiro.au/digiscape/digiscapes-social-dimensions-project-what-did-we-learn/</footnote>
Recommendations

In conclusion to this paper, we have a number of recommendations to make for research which we feel to be important for the future of digital technology in agriculture.

In light of the major challenges cited above which agriculture is currently
facing and will face in the future, digital technology can be a major lever in
enabling agriculture to meet these challenges by promoting the agroecological
transition and the transition in food systems. However, as we have seen, digital
technology also brings with it risks, both in environmental and human terms (the
cost of resources, the dehumanisation of agriculture, widening inequality, etc.),
which must be taken into consideration in future research. We recommend the
following main principles: i) adopt a systemic vision by considering the system
as a whole and the complexity of food and agriculture systems, ii) promote
frugality so that the benefits of digital technology do not come at the expense of
rising energy consumption, damaging the overall impact, iii) prioritise resilience,
i.e. the capacity of digitally-aided agricultural systems to withstand crises and
adapt to different types of changes (linked to the climate, the economy, health,
working methods, etc.), iv) draw on the diversity of agricultural systems, using
digital technology to support agriculture in all its forms. In this regard, we would
recommend taking the approach of responsible research and innovation or RRI
(see chapter 6). Finally, in the interests of taking a systemic vision and in light
of the complexity of the possible topics for study, steps must be taken to avoid
research becoming overly sector-specific in order to really get to grips with
the issues raised (see Chapter 5). As a result, it will be important to really take
an interdisciplinary and participatory approach to our research into the use of
digital technology in agriculture and to emphasise the importance of this type
of approach and research within our institutes.

Our aim is not to plot an exact course to follow, but given the broad panorama
which we have sketched over the course of this paper, we believe that a number
of key themes and principles have emerged which could inform decisions in the
future. As stated at the beginning of this paper, we firmly believe that agriculture
must evolve towards agroecology and sustainable food systems, and that digital
technology has the capacity to be a powerful tool in this transition provided we
are mindful of certain pitfalls. Our recommendations are based on this belief.
## Research topics

Within the research topics mentioned above, one point which we feel to be most important is that **agroecology will result in the need to produce large quantities of new knowledge**. This knowledge will most likely be produced using digital technology: gathering data on a large scale but also monitoring new indicators (e.g. intra- and interspecies biodiversity, characterised by genomic data), managing these large quantities of data and extracting knowledge based on entirely new data at all levels (from microbiota to satellite imaging). There are a number of ways in which this research can be distinguished from non-specialist data science:

1) the approaches employed must take local contexts into consideration, with a focus on factoring in existing knowledge and gathering and establishing the expert knowledge of farmers;

2) the production of this data (whether measured or simulated) is becoming a challenge, bringing with it questions linked to the quality of the data produced, pre-processing and sharing. The convergence of phenotype data – particularly in real environments – with genomic data should accelerate the creation of knowledge on processes within agroecology, selecting varieties or building new cultural routes in order to anticipate climate change. Another key point to consider will be **digital tools for assisting farmers, advisors and other stakeholders at a sector or regional level**. Firstly, this concerns decision-support tools, which will be the primary interface for implementing knowledge, particularly that discussed previously. Very much central to dialogue between humans and digital models, these tools will require specific research drawing on design science, visualisation and ergonomics in order to facilitate their adoption. Taking into consideration local specificities and technical choices made by operators / political decisions at a regional level will be essential in order to avoid a move towards homogeneity within agriculture. The approaches studied must be able to explain/justify any recommendations that are made in order to enable constructive dialogue with human participants. Meanwhile, new robotic assistance solutions will need to be invented in order to spare farmers having to carry out the most arduous or dangerous tasks. Aside from research questions linked to navigating in uncontrolled environments, safety and how tools and the robots carrying them interact with each other, we believe taking a participatory approach to designing these robot solutions to be a promising means of reducing tension between technology and ecology.
A third focus for research in computing is motivated by the fact that processes within agroecology – in a broad sense – extend beyond farms into value chains and regions. In these areas, priority should be given to **operational research on issues linked to planning** (in time and space) for the use of resources (water, soil) and agricultural production (large-scale supply based on fragmented sources).

Lastly, as is the case with all of the digital systems explored, **cybersecurity** is of vital importance. In the agricultural sphere this must take a number of forms. The aim will be to secure data itself, but also the material systems which produce it, the networks used to transport it and the tools which utilise it in order to ensure that none of these are hacked (data could be intercepted or false data introduced). One possible avenue to explore involves combining “traditional” security systems with new learning techniques specific to agricultural data in order to identify and isolate any malicious nodes and/or detect information leaks.

## Types of research

We recommend taking the approach of **responsible research and innovation** or RRI (see Chapter 6). In order to fully grasp the complexity of the subject matter, steps must be taken to ensure that research is not overly sector-specific (see Chapter 5), which will involve taking a systemic view. As a result, it will be important to really take an interdisciplinary and participatory approach to our research into the use of digital technology in agriculture and to emphasise the importance of this type of approach and research within our institutes.

This systemic view will also assist us in addressing the issue of the environmental impact of digital solutions. There is a range of different ways of tackling the avenues for research outlined above, some of which will require the consumption of a significant amount of energy or rare resources. In order to avoid this pitfall, it is our recommendation that a **frugal** approach be taken: the benefits of digital technology must not come at the expense of rising energy consumption, damaging the overall impact. The best way of assessing this impact is to employ a systemic view, considering the system as a whole and the complexity of agriculture and food systems (as opposed to optimising just one indicator).

We also feel it will be critical for any future research to place an emphasis on resilience, i.e. the capacity of digitally-aided agricultural systems to withstand crises and adapt to different types of changes (linked to the climate, the economy, health, working methods, etc.) Lastly, given that there are many different types of agriculture, it will be necessary to draw on the diversity of agricultural systems, **using digital technology to support agriculture in all its forms.**
## Social science

We hope this paper has demonstrated that the use of digital technology in agriculture is not simply a question of technology. At all levels, existing or planned solutions are in line with human stakeholders, and can even provide a way of bringing different stakeholders together. **Research in the humanities is therefore an integral part of research into the use of digital technology in agriculture.** One first issue, which may not be new but which will become increasingly significant, is the link between humans and increasingly complex technological solutions: how can a harmonious and fulfilling relationship be built between these solutions and the humans that use them? How can the risk of deskilling be avoided? One avenue that seems particularly promising is putting an emphasis on participatory approaches to design, identifying the best way of involving stakeholders within the sector at the earliest possible stage. These approaches go beyond designing tools: one key concern is to draw on digital technology in order to discover new ways for humans to work together. This will be particularly beneficial when it comes to improving the management of shared resources, where collective and individual interests can come into conflict with each other.

## Public policies

Finally, the "digital transition" in agriculture is bringing about changes that will need to be dealt with at a public policy level. The aspect which we feel to be most important is **data governance**. New technology has transformed farmers into data producers, either through the sensors which they use or manual entry on their part (participatory approaches). Urgent consideration must be given to the value of this data, as well as to copyright and privacy. This is a complicated issue: on one hand, sharing the vast quantities of data produced can benefit everyone, but on the other hand too much openness can make farms or regions less competitive. As things currently stand, part of this data is in the hands of private firms (e.g. providers of technology and services), which benefit from a global view without there necessarily being a great deal of reciprocity with regard to the agricultural sector. In order to avoid this pitfall, France's National Digital Council recommends granting agricultural data the status of common good. 87

Another key subject will be to work on better **quantifying and analysing material flows at different regional levels** so as to facilitate the introduction of alternative economic models falling within the bracket of bioeconomics or biophysical economics.

<footnote n="87">87. https://cnnumerique.fr/tribune-agriculture</footnote>
Inria-INRAE - an appropriate partnership for tackling this research

In line with their own missions, bringing together our two institutes – Inria and INRAE – will be beneficial in tackling the subjects outlined in relation to digital agriculture, thanks to the complementarity of the approaches and skills they can draw upon: INRAE, with its culture of experimental tools and data gathering; skills in modelling biological systems, with panels of experts and extensive models; and expertise in economics and social science, with a particular focus on agriculture and its stakeholders, regions and networks, enriched through contact with the profession and knowledge of needs; Inria, through its expertise in digital science and technology – mathematics and computer science –, particularly in modelling, simulation, artificial intelligence, data science, cybersecurity, networks and robotics, plus its culture, rooted simultaneously in research, technological development, transfer and innovation.

The two institutes have a long history of collaborating and interacting with each other (joint project-teams, jointly supervised PhDs, scientific days staged jointly, etc.), something which has intensified recently through flagship projects such as #DigitAg (see inset) and **PEPR (Priority Equipment Programme for Research)** "Agroecology and Digital Acceleration Strategy" SADEA, launched in 2021 and jointly led by the two institutes. These collaborations have proven highly rewarding for the researchers involved. Given the unprecedented challenges posed by the digital and agroecological transition in agriculture, this collaboration will surely only grow stronger. Together, and with their historic partners in agronomy (CIRAD, the grandes écoles of agronomy, IRD, CGIAR, etc.) and mathematics and computing (universities, CNRS, etc.), the two institutes clearly have the potential to address these challenges and to meet the high expectations of society with regard to agriculture.
<figure><img src="image_60.png" /></figure>

Find out more

**#DigitAg, the Digital Agriculture Convergence Lab**, led by INRAE, is one of ten French Convergence labs funded by the Investments for the Future Programme (€9.9m). Based in Montpellier and with branches in Toulouse, Rennes and Avignon, it brings together more than 550 individuals (2020 figures) from 8 public and parapublic bodies and is supported by 8 AgTech companies. Launched in 2017 for an initial duration of eight years, its aim is to prepare for the harmonious development of digital technology in agriculture, both in France and in the Global South. For this, its work involves developing multi- and interdisciplinary research, with 60 or so PhDs jointly funded, around 20 or so labelled PhDs, 12 postdoc researchers and 150 master's placements funded. A graduate school has been set up to create an inventory of educational resources for this sector and new courses have emerged (data science). Lastly, the institute is raising awareness of innovation among students through meetings with companies, courses on intellectual property, an IT development department for building web demos based on PhD results, and an observatory for the different uses of digital technology in agriculture. It is highly active internationally, with a particular focus on the Global South, a number of teams from INRAE and Inria working together.
# Contributions and acknowledgements

The following people contributed to the writing of this paper

<table><thead><tr><td>Name</td><td>Institute unit, site</td><td>Member</td><td>Chapters</td></tr></thead><tbody><tr><td>Bellon-Maurel, Véronique</td><td>INRAE, UMR ITAP, Montpellier</td><td>#DigitAg, Digigral</td><td>Co-editor</td></tr><tr><td>Bisquert, Pierre</td><td>INRAE, the mixed research unit IATE, Montpellier</td><td>#DigitAg, Digigral</td><td>6</td></tr><tr><td>Bonnet, Pascal</td><td>CIRAD, Dept E&S, Montpellier</td><td>#DigitAg</td><td>4, 6</td></tr><tr><td>Bouadi, Tassadit</td><td>Univ. of Rennes 1, Lacodam, Inria Rennes - Bretagne Atlantique</td><td>#DigitAg</td><td>3, 6</td></tr><tr><td>Brossard, Ludovic</td><td>INRAE, the mixed research unit PEGASE, Rennes</td><td>#DigitAg, Digigral</td><td>Co-editor</td></tr><tr><td>Chambaz, Grégoire</td><td>Unisanté, Lausanne, Suisse</td><td></td><td>5</td></tr><tr><td>Courtonne, Jean-Yves</td><td>Inria, STEEP, Grenoble</td><td></td><td>4</td></tr><tr><td>Dedieu, Benoit</td><td>INRAE, the research support unit ACT, Theix</td><td></td><td>2</td></tr><tr><td>Gandon, Nathalie</td><td>INRAE, 'IT and freedoms' officer</td><td></td><td>5</td></tr><tr><td>Garcia, Frédérick</td><td>INRAE, the research unit MIAT, Toulouse</td><td>#DigitAg</td><td>Co-editor</td></tr><tr><td>Gautron, Romain</td><td>CIRAD, AIDA</td><td>#DigitAg</td><td>3</td></tr><tr><td>Hilgert, Nadine</td><td>INRAE, the mixed research unit MISTEA, Montpellier</td><td>#DigitAg</td><td>3</td></tr><tr><td>Ienco, Dino</td><td>INRAE, the mixed research unit TETIS, Montpellier</td><td>#DigitAg</td><td>3</td></tr><tr><td>Javelle, Aurélie</td><td>L'Institut Agro-Montpellier SupAgro, the mixed research unit Innovation, Montpellier</td><td>#DigitAg</td><td>5</td></tr><tr><td>Labarthe, Pierre</td><td>INRAE, the mixed research unit AGIR, Toulouse</td><td>#DigitAg, Digigral</td><td>4, 5, 6</td></tr><tr><td>Lagacherie, Philippe</td><td>INRAE, the mixed research unit LISAH, Montpellier</td><td>#DigitAg, Digigral</td><td>Seminar</td></tr><tr><td>Largouët, Christine</td><td>L'Institut Agro- Rennes, Lacodam, Inria Rennes - Bretagne Atlantique</td><td>#DigitAg</td><td>3</td></tr><tr><td>Lenain, Roland</td><td>INRAE, the research unit TSCF, Clermont-Ferrand</td><td></td><td>3, 6</td></tr><tr><td>Lutton, Evelyne</td><td>INRAE, the mixed research unit MIA, Paris</td><td>Digigral</td><td>3, 6</td></tr></tbody></table>
<table><thead><tr><td>Name</td><td>Institute unit, site</td><td>Member</td><td>Chapters</td></tr></thead><tbody><tr><td>Martin-Clouaire, Roger</td><td>INRAE, the research unit MIAT, Toulouse</td><td>#DigitAg</td><td>3</td></tr><tr><td>Masson, Véronique</td><td>Univ. of Rennes 1, Lacodam, Inria Rennes - Bretagne Atlantique</td><td>#DigitAg</td><td>3</td></tr><tr><td>Maurel, Pierre</td><td>INRAE, the mixed research unit TETIS, Montpellier</td><td>#DigitAg</td><td>4, 6</td></tr><tr><td>Mitton, Nathalie</td><td>Inria, FUN, Lille</td><td></td><td>Co-editor</td></tr><tr><td>Molenat, Jérôme</td><td>INRAE, the mixed research unit LISAH, Montpellier</td><td>#DigitAg</td><td>Seminar</td></tr><tr><td>Mugnier, Marie-Laure</td><td>LIRMM, Inria, Montpellier</td><td>#DigitAg</td><td>3</td></tr><tr><td>Neveu, Pascal</td><td>INRAE, the mixed research unit MISTEA, Montpellier</td><td>#DigitAg</td><td>3</td></tr><tr><td>Piot-Lepetit, Isabelle</td><td>INRAE, the mixed research unit MOISA, Montpellier</td><td>#DigitAg, Digigral</td><td>6</td></tr><tr><td>Pla Aragones, Lluís Miquel</td><td>Université de Lleida, Espagne</td><td></td><td>6</td></tr><tr><td>Prados, Emmanuel</td><td>Inria, STEEP, Grenoble</td><td></td><td>2, 5</td></tr><tr><td>Preux, Philippe</td><td>Inria, Scool, Lille</td><td></td><td>3</td></tr><tr><td>Raynal, Hélène</td><td>The mixed research unit AGIR, Toulouse</td><td>#DigitAg</td><td>3</td></tr><tr><td>Reboud, Xavier</td><td>INRAE, Agroecology Mixed Research Unit, Dijon</td><td></td><td>6</td></tr><tr><td>Roussey, Catherine</td><td>INRAE, the research unit TSCF, Clermont-Ferrand</td><td></td><td>3</td></tr><tr><td>Termier, Alexandre</td><td>Univ. of Rennes 1, Lacodam, Inria Rennes Bretagne Atlantique</td><td>#DigitAg</td><td>Co-editor</td></tr></tbody></table>

**Financial support for programmes: #DigitAg, #DigitAg, the Digital Agriculture Convergence Lab – funding managed by the French National Research Agency (ANR-16-CONV-0004) as part of the Investments for the Future Programme – and Digigral, a multidisciplinary group commissioned by INRAE to build a programme on the digitalisation of agrifood systems.**

Our thanks go to Hervé Monod from INRAE, and Jacques Sainte-Marie and Pascal Guitton from Inria for keeping a close eye on proceedings, and to the management of both institutes for their trust and support.
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