Stakes and Challenges


#DigitAg research is conducted within a matrix framework. On the one hand , Research communities are identified with 6 distinct research axes. On the other hand, we have identified 2 major societal issues related to agriculture, which are divided into 8 associated challenges, and then used as operational supports to address these issues.

Stake 1 –  Agricultural production improvement using ICT-enabled agriculture

Challenge 1: ICT and the agroecology challenge

Digital technology is a powerful lever for agroecological transition

Agroecology uses biodiversity and ecological processes to design efficient, sustainable and still productive agricultural systems. Managing more complex systems with solutions adapted to local conditions requires collecting and processing a lot of information about the state of the physical and biological components (plant, animals, soil, weather…). Digital technology would therefore appear to be a powerful, but still under-estimated, lever for agroecological transition. Addressing this challenge requires the development of: new sensors (Axis 3), new information systems and data processing techniques to connect local and global data (Axis 4 and Axis 5), and new models to predict and support decision making (Axis 5 and Axis 6). This will also rely on cooperation with social sciences (Axis 1 and Axis 2) to connect processes, techniques and people, for instance in the framework of a living lab project.

An example of research would be to study, based on case studies, how ICT can be mobilized to continuously monitor and assess ecosystem services to help farmers to manage their transition to agroecology, while maintaining their farming system within the expected balance between provisioning and other ecosystem services.

Mediterranean systems (© UMR System)

Leader: Christian Gary (INRA)

Research axis involved in Challenge 1: 1 - 2 - 3 - 4 - 5 - 6
Challenge 2: Digital solutions to optimize the genotype in changing production systems and markets

ICT-based integration of phenotyping tools and methods helps to tailor plants ideotypes to new production systems and markets

Fast developing high-throughput plant and animal phenotyping is a useful tool to set up new production systems optimized to local conditions. However, until now, sensors, data storage, data processing and modeling solutions for phenotyping have been designed rather independently making the whole phenotyping chain not fully efficient and limiting its use outside research laboratories.

Tools and methods should be standardized and improved: e.g. more physiological understanding must be inputted to develop new sensors (Axis 3) and models (Axis 6) in parallel. In addition, an integrative approach should be followed for data and knowledge mining and reuse (Axis 2, Axis 4 & Axis 5). This ICT-based integration of phenotyping tools and methods, coupled with the understanding of farmer’ and market’ needs (Axis 1), will streamline operational research to tailor ideotypes, in silico, to new production systems and markets.

Examples of research to study:

  • How genotype-to-phenotype models calibrated using information from highthroughput phenotyping could be used to inform varietal choices or to develop varieties that are better adapted to future cropping systems;
  • How ICT could help linking data collected in farmer networks with genotypic information to optimize varietal choice and crop management.


PhenoArch, Phenotyping plateform (© UMR LEPSE)

Leader: Pierre Martre (INRA)

Research axis involved in Challenge 2: 1 - 2 - 3 - 4 - 5 - 6
Challenge 3: ICT and crop protection

Could pesticide usage be re-thought through ICT-supported services combining economical and technical insurances?

Reducing pesticide usage is a major issue, with simultaneous requirements of transparency, safety, environment preservation and securing farmer income. Technical, sociological, economical and organisational barriers hinder widespread change in crop protection practices and a massive reduction in pesticide use.

New perpectives are required. We propose to work with a new hypothesis for a scientific interdisciplinary co-construction, based on the fact that “with crop protection, the farmer looks for a revenue and a market”, and therefore “crop protection must be re-thought through tailored services supported by ICT that combine economical and technical insurances”. The design, organization and implementation of such services rely on understanding individual and collective risk aversion, setting up customized services (Axis 2) and developing appropriate sensors (Axis 3), traceability devices (Axis 1 and Axis 4), Information systems (Axis 4) and data-driven and expert-based models (Axis 5 and Axis 6).

 

A first research objective would be to study, design and experiment new means of risk assessment based on the combination of human-based and new sensor-based information, both at the field scale and bioclimatic region scale.

 


Picore System, spraying optimization (© UMR ITAP)

Leader: Olivier Naud (Irstea)

Research axis involved in Challenge 3: 1 - 2 - 3 -4 - 5 - 6
Challenge 4: ICT and sustainable animal production

Connected animals ensure more sustainable animal production

Animal production is one of the most advanced agricultural sectors with regards to ICT, with new livestock technologies, e.g. RFID identification, sensors and robots (milking and feeding robots) and could further benefit from human connected health technologies. Not only does this influx of technology improves livestock productive efficiency but it also changes the farming profession, while creating a new relationship with the animal, and induces the need for a massive transition of the whole animal production sector to integrate these new approaches.

Whereas farmers use these devices for monitoring, the different actors of advisory, selection, processing and health offices could benefit from this information for broader objectives. This raises questions on the following generic issues: quality of data, interoperability (Axis 4), evolution of current models to better integrate individual and longitudinal dimension of data in livestock management (Axis 5 and  Axis 6), renewed multicriteria objectives of livestock performance (Axis 1 and Axis 2), and finally on social and economic issues about farmer comfort, economic viability, attractiveness and the acceptability of these tools regarding the model of livestock systems to be promoted (industrial vs agroecological) (Axis 2).

 

A first example of research would be to develop a decision support tool to manage precision feeding and improve efficiency in livestock production.

 


Philippe Faverdin & Thermal camera, selection of cattle breed best adapted to climate change © UMR PEGASE

Japp van Milgen

Leaders: Japp van Milgen (INRA) and Philippe Faverdin (INRA)

Research axis involved in Challenge 4: 1 - 2 - 3 - 4 - 5 - 6

Stake 2 – A better society inclusiveness for ICT-enabled agriculture

Challenge 5: ICT and new farm advisory services

How may ICT-enabled tools make agricultural advisory services evolve

The agricultural advisory sector will be deeply altered by the spread of ICT-enabled advisory systems.

First, innovations are expected, with more individualized and targeted advices based on big data flows processed in real time.

Second, ICT may have an impact on the organisation of advisory services, which may evolve either towards local networks of collaborating agents, with simple ICT devices, or towards big consulting firms managing significant investments to collect and process data.

Finally, legal issues will be raised related to intellectual property and consequential value sharing.

ICT could generate discrepancies between farmers, some of them being empowered by embracing these new technologies, whereas others may become dependent of big companies to collect and process data.

To understand and anticipate these issues, there will be a need to call upon scientific communities such as legal, social and management sciences (Axis 1 and axis 2), computer and information sciences (Axis 4 and axis 5) and agronomists (Axis 6).)

 

One example of research work would be the analysis of new ICT tools providing advice at farm level and the change in decision-making and practices depending on farm characteristics.

 

Leader: Julie Labatut (INRA)

Research axis involved in Challenge 5: 1 - 2 - 3- 4 - 5 - 6
Challenge 6: ICT and agricultural territory management

Farmers can become the heart of territorial information systems

Data produced by farmers and by other actors operating on the same territory can help us to optimize the use of collective resources for agricultural purposes. For instance, data acquired thanks to a crowdsourcing campaign can be leveraged to support farming actions and/or define territorial monitoring systems.

How can data collection be boosted? (Axis 2 and Axis 3). How may we organize, manage (Axis 4), exploit and share (Axis 5) the hidden knowledge behind this data? These major challenges must be addressed in order to support the development of a territorial intelligence in which the farmer plays a central role and can benefit from.

 

One example of research work would be to study how farmers could collect data about their land and practices (e.g . technical itinerary, presence of species of ecological interest…) to be used by land managers to assess the ecological services provided by agricultural spaces. Another example would be to study how ICT can facilitate a better social integration of farmers in land governance structures.

Leader: Dino Ienco (Irstea)

Research axis involved in Challenge 6:  2 - 3 - 4 - 5
Challenge 7: ICT for a better acknowledgement of agriculture in the global value chain

Traceability will strengthen farmers position in the global value chain

The stake is to reach a healthy and sustainable food production whereas strengthening farmer position in the global value chain. An enhanced traceability, including the product whole story, will have several benefits, either directly - eg more transparency on prices and on environmental footprints, a restored relation of trust with consumers - or indirectly through “big data” it generates.

Knowledge extraction (Axis 5) from traceability-based big data (requiring efforts on information systems, Axis 4) will make it possible for agricultural products to better match stakeholders requirements (eg consumers, food industries, groceries).

Questions are raised about the impacts of such technologies on actors, the data property and transparency for all the actors in the value chain.

 

An example of research would be to design models able to extract from data potentially in-conflict stakeholders' preferences and constraints, to build consensual alternatives and to justify choices, helped by social sciences.

 

Leader: Patrice Buche (INRA)

Research axis involved in Challenge 7:  1 - 3 - 4 - 5
Challenge 8: ICT and agricultural development in Southern countries (Africa)

How to adapt ICT-supported tools and services to farmer' constraints in Southern countries?

Southern countries are very concerned by digital agriculture, and are bound by constraints that are different from European ones. Many ongoing experiments focus on agricultural advisory services, insurance, credit, and education, but there is still much room for innovation, in particular through a more holistic approach to understanding the factors leading to the success of digital agriculture in Southern countries. This calls for research that that would faciltate the development of services based on “frugal” ICT-based technologies, e.g. mobile and smartphones, internet (with audio blogs) and low-cost satellite images.

Specialists of innovation (Axis 2), data acquisition technologies (Axis 3), information system (Axis 4), and modelling (Axis 6) must work jointly to this purpose, in search of an ICT-enabled development model that meets both literate or illiterate farmers’ needs.

 

An example of a research subject would be the analysis of new services for farmers provided by advisory organizations and the design of a business model to support such services.

 

Leader: Guy Faure (Cirad)

Research axis involved in Challenge 8:  2 - 3 - 4 - 6

 

 

 

 

Christian Gary (INRA) leads the challenges of Stake 1, and Guy Faure (Cirad) the challenges of Stake 2