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Current situation (issues and challenges) on data exchange in agriculture in the EU

Current situation (issues and challenges) on data exchange in agriculture in the EU Henri Holster (NL) Gianfranco Giannerini (IT) Jerzy Weres (PL). Main objectives and deliverables. establish a platform on data exchange in agriculture in the EU, consisting of

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Current situation (issues and challenges) on data exchange in agriculture in the EU

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  1. Current situation (issues and challenges) on data exchange in agriculture in the EU Henri Holster (NL) Gianfranco Giannerini (IT) Jerzy Weres (PL)

  2. Main objectives and deliverables • establish a platform on data exchange in agriculture in the EU, consisting of • technical infrastructure • community of practice • develop a reference framework for interoperability of data exchange in agriculture • identify the main challenges for harmonizing data exchange in agriculture in the EU => Strategic Research Agenda

  3. Work packages WP6 Stakeholder integration and dissemination WP2 Analysis of current situation in EU-27 WP1 Project coordination and management WP3 Set-up of agriXchange platform WP4 Development of a reference framework for interoperability (tested against use cases) WP5 Synthesis, recommendations and defining SRA

  4. State of the art on data exchange in agriculture in the EU27+Switzerland

  5. Us..

  6. WP2 State of the art • in depth analysis literature review • methodology for inquiry in EU countries • description of current situation in EU

  7. Investigating EU27+Sw (1) EU Report Aim:overview of state of the art of current data exchange in general and per EU region, with a focus on farmers in connection with internal and external processes. External processes like business/chain and national and EU legislations. Making clear the main gaps/problems as well. Level of abstraction:mostly qualitative describing data integration levels on processes, data and physical infrastructure. Country Reports

  8. Investigating EU27+Sw (2) • With special attention to the sectors: • Arable • Animal (cattle mostly) • Using the framework of data integration • Done by 6 focusgroup leaders& expertteams

  9. Template ENTERPRISE 1 • Agricultural characteristics • Automation level • Data integration levels Process co-ordination Application INTRA Enter prise Inte gration interoperability Data data sharing Physical Infrastructure connectivity (adapted from Giachetti 2004)

  10. Data Exchange levels Process relevant processes Application kind of software, databases Data (sharing) syntaxes, semantics, organization, maintenance, availability, ownership Physical broadband infrastructure, network protocols, database structures, information hubs/brokers

  11. Results - Agricultural characteristics • Trends • Decrease of number of farms • Decrease of labour / sometimes scarity due to moving of people (Romania) • Fast growing size of farms • Decrease of dairy cows but steady production of milk • Increasing yield in crop production per ha • Automation will rapidly continue but mainly on/by • big farms • young farmers

  12. Arable - average size - Dairy 0 55 110 Dairy cows per holding 0 55 110 UAA per arable holding (ha)

  13. Arable - big farms - Dairy 0 15 30 % of arable holdings > 100 ha 0 25 50 % of dairy holdings > 100 cows

  14. Arable - small farms - Dairy 0 45 90 % of arable holdings < 2 ha 0 50 100 % of dairy holdings < 10 cows

  15. Results – PC & Internet 30 60 90 % of households with internet access 20 50 80 % of households with broadband connections

  16. 0 50 100 % of farm holdings with PC

  17. Data integration processgeneral remarks • CAP/(national) governments are boosting dataintegration in countries - portals, shared databases • Public systems are relatively open compared to private systems (except in well standardised countries) • Many systems and databases rather closed

  18. Processes, each domain its standardization (?) Trade Crop/Dairy Industry Delivery/ suplyiing industry Public CAP Transport Advisory services Farmers production Veterenary/ medicin/ chemical Financial/accountancy Animal Crop Operations Administrations Breeding

  19. European regions division in areas, countries with • Mainly small farms, often poor countries. No ICT, no standardization • Focus on ICT highly related to basic local challengesIrrigating/water, erosion, cross border trade, lack of market transparency. • Aging, adapting ICT by farmers problematic, but less in N + W • Fast upcoming production areas = relative new countries in agri IT • Countries with an standardizations past (to deal with ‘old fashioned’ structures) • Countries with no or bad internet infrastructure • Private business involvement on ICT& standardization vs public • Business exporting the standards • Centralized or hub-based data integrated models

  20. Data exchange standardisationlevel Clear, no development • None or hardly (BGR, Rom) • no private action, public just starting (LPIS, I&R) • Poor (most Southern, Eastern, Baltic States) • Push of standardiby CAP/Governments • Some shared databases and portals • Hardly integrated private systems • Rather good (Northern, CZ, UK, IR) • Some involvement by private • Some datadictionaries developed and used • Fairly good (FR, DE, NL, DK, .. ) • Private standardization bodies • Own and global standards • Infrastructure based on hubstructures (communicating and transporting systems) • Towards open /shared community and integrated models Mess More Mess Mega Mess

  21. ‘Standardization level’ Communicating processesDatadictionariesOwn and global standards

  22. Data exchange standardisationlevel Fairly good(FR, DE, NL, DK, .. ) is the mega mess • Each nation its own .. • Solutions, providers, standards • Hardly cross border data integration • Like spagetti Next level • Integrated business process models • Private-public collaborations on shared datainfrastructure. Issues to come there • Data protection (privacy, e-authentication, authorisation) • Availability of internet • How to become an open EU information society?

  23. Min Agriculture Nature Food quality NL, EC, Global consumer Chambers agriculture Food Auth Inspection EU chain service NPPC TRACES distribution • Services • - transport • - finance • insurance • advice • communication • certification Eurostat Inspire Taxes NSIR distribution Customs I R B B E Statistics processing chain data UWV CWI processing • Resources • buildings • equipment • machines • fertilizer • medicine Waterboard • Basisregistration • Municiaplity - persons • Chamber of commerce • Adress • Cadastre • buildings • Topography • Cars Municipality production Province breeding Genetic breeding What a mess..

  24. Consumer Certyfying organization Breeding organization AnimalHealth Imspector FoodSafety Imspector Veterinarian Slaugtherhous Network PigNetwork CollectingPlace Network FarmNetwork Transport Dataflow NL/general..

  25. Data exchange Animal (FR) Small ruminants INTERBEV Sanitary Str. Reproducer database INTERBULL SIMOC Normabev INTERBULL State France Génétique Elevage PF1 SIG Central SIGAL INRA IE BDIR BDNI Slaughterhouse SIG Régional I&T HB BDNU LABOGENA OP COM EDE PRO SOL DNA AIC BDPORC CNIEL BDPORC Infolabos Breeder Milk analysis Labs

  26. Conclusions and outlook • Aging population of farmers lack of adaption and investments on new technology • Broadband availability in rural areas. In quantitative and in qualitative way. • Mobile internet infrastructure in most countries not capable • Potential for quick developing countries.to adapt new data exchange infrastructural models and skip the old complex and inefficient structures. • Differences across the EU on the level of data integration and standardization. 4 levels

  27. Discussion • Work as bias for further project work (no pure scientific work) • Identification of key factors and indicators not precisely or quantitatively elaborated. • Opportunities/discussions • Collaborative approach and common framework • Mobile network challenges. Who is financing & no copying • Standardisation should be done at the business service layers and not on processes • Focus on demonstrating how processes can work, but keep them flexible • Open network, with flexible relationships between network partners, which implies less hierarchical or linear chain structures

  28. Recommendations • Quantify the benefits arising from overcoming the barriers through future research. • Demonstration of the effect of adapting new technologies • Organizingdata integration through open networks

  29. Discussions & validating Do You recognize this picture? Where are You in your country? Your big issues and challenges?

  30. Debate on conclusions “Taking away barriers ..” What does it mean? For example mobile data communications.. “Organizing data integration by open networks?” How and what’ can be your role in this?

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