To facilitate the creation of a data ecosystem in the agricultural sector that allows the realisation of its full potential, existing barriers that complicate data collection, integration and exploitation should be lowered. Codes of conduct, such as that of the European Union, are aimed at these difficulties. The experience gained during the development of the Global Forest Biodiversity Initiative data portal shows that following the recommendations of the code of conduct facilitates the emergence of a community of data providers and an ecosystem for its exploitation.

1. Facilitating an agricultural data ecosystem
The EU Code of conduct on agricultural data sharing
by contractual agreement
Roberto García González
Deputy Vice-Rector for Research & Transfer
Universitat de Lleida, Spain

2. Contents
• Motivation
• Codes of conduct
• EU Code of conduct
• Originator
• Contracts
• Trust
• Practical assessment
• GFBI UdL Hub
• Conclusions

3. Motivation
• Agricultural value chain data 
• Potential for...
• ...efficiency improvements, productivity and sustainability...
• ...threatened by the lack of enough data
• Reluctances:
• Insecurity, lack of transparency, power unbalances, lack perceived benefits...
• Alternatives:
• Mandatory sharing?
• Autoregulation (codes of conduct)
Data
Ecosystem
Collect
Store
Share

4. Agricultural Data Codes of Conduct
• Not binding regulations, only conduct recommendations
• Main agricultural data codes of conduct worldwide:
• US American Farm Bureau Federations’
Privacy and Security Principles for Farm Data, 2016
• New Zealand’s
Farm Data Code of Practice, 2016
• EU Code of Conduct on Agricultural Data Sharing by Contractual Agreement,
2018
• Commonalities:
• Terminology
• Criteria about data property
• Data rights
• Trust mechanisms

5. UE Code of Conduct
• Supported by main European associations of farmers, breeders,
manufacturers, etc.

6. Data Originator
• Originator: agriculture value chain participant that generates data as
a result of its activity
• Even if they have commissioned its collection
Example: the farmer is the originator of farm or farming operations data
• Originator Rights:
• To control access and use of originator’s data
• To benefit from data sharing and reuse
• Sharing: explicit, express and informed permission via contractual arrangement

7. Agricultural Data Sharing Contractual Agreements
• Simple and understandable contracts: clearly specify:
1. Most relevant terms and definitions
2. The purpose of collecting, sharing and processing the data
3. Parties’ rights and obligations
4. Information about data storage and use
5. Verification mechanisms for the originator
6. Transparent mechanisms for adding new uses

8. Trust Mechanisms
• Privacy: recommended data pseudonymisation
• Originator identification requires consent
• Apply GDPR if data used for decision making about originator
(e.g. commercial decision)
• Contract changes or additions require consent from all parts
• Originator informed about new uses or transfers to third parties

9. Practical Assessment: GFBI UdL Hub
• Forestry data from the
Global Forest Biodiversity Initiative:
• 1,2 million plots
• 30 million trees
• 30,000 species
• 100 countries
• 82 data sets with 150 owners
• Data hub developed by
Universitat de Lleida and
supported by project INNO4AGRO
https://gfbi.udl.cat

10. Practical Assessment: GFBI UdL Hub
• GFBI UdL Hub
• Facilitate data integration assisted
by an automated wizard
• Track data ownership

11. Practical Assessment: GFBI UdL Hub
• GFBI UdL Hub (cont.)
• Manage data requests, require
specifying intended uses
• Notify owners when their data
requested

12. Practical Assessment
• GFBI UdL Hub (cont.)
• Required owner consent prior to
data reuse
• Generate agreement
• Contract as text document (PDF)
• Tricky signing procedure due to
worldwide parties
• Blockchain contracts
• Digital signature from
mobile phone
(Self-Sovereign Identities)
• Auditable evidence

13. Conclusions
• GFBI UdL Hub lessons learned:
• Trust mechanisms facilitate data sharing and the development of a data
ecosystem
• Streamline user experience, automate or assisted consent procedure for
each reuse request
• Developed solution naturally aligned with the code de conduct
• Future work: Extrapolate results to other data ecosystems,
specially agricultural data

14. Thank you for your attention
Contact:
Roberto García González (roberto.garcia@udl.cat)
Deputy Vice-Rector for Research & Transfer
Universitat de Lleida, Spain