What will be the contributions of digital agriculture to the transition to bioeconomy?

Véronique BELLON-MAUREL Director of Evironmental Technology Department, IRSTEA, France Director of #DigitAg, Digital Agriculture Convergence Laboratory 28th of June, 2017 Introducon

§ Would bioeconomy be a buzzword, refreshing the painng of agricultural and forestry biomass producon ?

§ Or would it be something really new, ie a new way to consider those producons, within other sources of bio-components ? What is new in bioeconomy

ECONOMY ! From bioproducon to bioeconomy

§ Beer producon : « On- issues » Opmizing the producon with regard to constraints, market and costs § Reducing expenses, ie reducing wasted inputs and useless operaons § Beer use of natural funconalies § Recycling § Improving product quality with regard to the market & industrial requirements § Long-term thinking in farm management

§ Beer inclusiveness of farmers in the bioeconomy : « Off-farm issues » Acknowledging the role of farmers in territories and value chains § Farmers are paramount actors of the territories and related ecosystem services § Farmers are paramount actors of biomass producon § More interconnecons between farmers and other actors (F, C, B, A) are possible Once this has been said…

§ How to help farmers to embrace this transion towards bioeconomy?

§ Would digital technologies / digital agriculture be a lever to boost this transion? Digital Agriculture

One of the 4 levers idenfied in 2015, in the « Agriculture innovaon 2025 » report to French Ministries of Research and of Agriculture, eg: Robocs, Biocontrol , Biotechnologies, Digital Agriculture Digital Agriculture ó use of ICT in agriculture

= « On-farm » issues => a beer producon : Data-driven agriculture: , high throughput phenotyping

= « Off-Farm » issues => a beer inclusiveness Data-sharing and digital network for improving interrelaons in value chains and in territories Outline

§ Introducon

§ On-farm issues : Beer producon § Precision agriculture § High throughput phenotyping

§ Off-farm issues : Inclusiveness § Related to territories § Related to value chains

§ Conclusion On-farm issues : Beer producon Data driven Agriculture

Precision agriculture

Precision agriculture Cultural operations are adapted to present needs of plants (animals)

= sowing (variety, density) = fertilization (dose) = irrigation (dose) = crop protection (product type, dose) = weed management

DATA NEEDED !!! The data chain

Sensors ! The data chain

Data processing (big data, deep learning) The data chain

Data acquisition Data processing Decision aid systems

2.55

1.80

1.40

1.00

0.60

0 17 M 1:850

Recommendation / Control for - irrigation - fertilization

Multispectral zoning NDVI Oenoview – Montpellier Supagro Ex Farmstar

Now 0,8 M ha

Precision farming

- Saves - Saves times - Saves money - Avoids over fertilizing and subsequent pollution due to nitrate High throughput Phenotyping

Crops are selected with regard to their traits expressed in a specific environment and their expected properties, with fast High throughput phenotyping

Allows companies to deliver

- Advices about which varieties are most appropriate to given soil/climate conditions;

- Advices about seeding with regard to intra-plot hetero- geneity (precision farming) = abiotic stress: water / nutriment stress - New varieties adapted to = biotic stresses Climate change = production quality (yield, composition)

Phenotyping – The TERRA*-Ref project

Using remote sensing to quantify plant traits eg plant architecture, carbon uptake, tissue chemistry, water use, etc, to predict the yield potential and stress resistance of 400+ diverse sorghum lines. *Transportation Energy Resources from Renewable Agriculture Phenotyping Reference Platform

13 sensors inside (mainly optical)

Lemnatec Field Sensors Off-farm issues : Beer inclusiveness for Agriculture

Better inclusiveness in value chains

B2C Early 20th century

B2B Late 20th century

B2B

B2C

Future ? Better inclusiveness in value chains

Are today’s prices OK for selling? Information

Disintermediation B2C Direct product design 18 Beer inclusiveness in territories Sharing data through digital applications Data / agricultural & ecological facts

Massive data / agriculture & ecology -> Links to ecosystem services (models) -> Payment of ecosystemic services 19 Beer inclusiveness in territories Sharing resources through digital applications

Pays de Fougères (Irstea Rennes- L. Aissani)

Sharing biomass ! Local biomass cycling 20 Beer inclusiveness in territories Sharing resources through digital applications

Hub for ag machinery sharing

Precision agriculture… and more?

Sharing equipment: better use of resources / economical and social benefits Conclusion

Conclusion Digital agriculture is the 3rd revolution in agriculture

= Opportunity to improve agricultural production, to reduce costs and to better link farmers to others : farmers, consumers, land managers

With regard to bioeconomy

= Keywords are long-term production optimisation, traceability, transparency, better information to downstream (either consumers or food compagnies)

= The data chain, from farming operations to food products. Massive agricultural information will help food industry to better understand relationships between farm practices and food quality

= Opportunities: access to biomass, tailored/ qualified biomass better incomes (incl. ecosys services / global economy), agility, « visible » farmers, new solidarities

= Risks: sensors & 4G network coverage! Social turmoil Research highly needed linked to agriculture is expected and must be anticipated Contribuon to a posion paper

Priority research needs for the next 10 years = Sensors for monitoring agricultural production = Interoperability for streamlining the data chain = Deep-learning for turning data into valuable knowledge = Understanding legal, social and economic issues and anticipating the social changes due to the spreading of digital technologies in agriculture Interdisciplinarity is a must!

Which tools for research and development are lacking today ? = a platform with agricultural and auxilliary data (food chain, territory) = living labs

Which types of partnerships? Which new stakeholders ? = Better linking upstream and downstream, building trust = Data sciences, computer sciences Merci pour votre attention

Thank you for your attention