1. The Role of Mobile Applications in Data Use for Agriculture Benjamin K Addom, PhD ICT4D Programme Coordinator, CTA Brussels, 16 September 2015

2. Roadmap 2 I.ICT4Ag Strategy at CTA a.CTA’s approach to ICTs for Agricultural activities II.Mobile Apps for Big & Open Data a.How Mobile Apps aid Big/Open Data use III.Big & Open Data for Mobile Apps a.How Big/Open Data aids Mobile App Development

3. 3 I: ICT4Ag Strategy at CTA

4. 4 ICT4Ag @CTA Three steps to make a difference Enhancing institutional and grassroots ICT capacity Supporting entrepreneurship & Youth Promoting the enabling environments & Uptake 1 2 3

5. 1: Enhancing institutional and grassroots ICT capacity 5

6. 2. Supporting entrepreneurship & youth 6

7. 3: Promoting the enabling environments & uptake 7 CTA as an honest knowledge broker e-Agriculture Strategy An e-Agriculture Strategy Toolkit (CTA, ITU, FAO)

8. 8 II: Mobile Applications for Big & Open Data

9. 9 Mobile Apps and Agricultural Data  Mobile Apps are facilitating access to data and dissemination of information:  Big & Open data revolution for Ag. needs to be exploited  We need to ensure that our stakeholders rip the benefits  One step is the development of an Apps4Ag Database  Also a Usability and Functionality Framework for the apps

11. 11 E.g. of Data Apps – Data Collection

12. 12 E.g. of Data App - Market Intelligence

13. 13 E.g. of Data App - Extension Services

14. 14 E.g. of Data App - Farm Level Crowdsourcing

15. 15 Big & Open Data Improving Access Mobile applications are facilitating access to and sharing of big & open data for agriculture

16. 16 III: Big & Open Data for Mobile Applications

17. 17 Big/Open Data Aiding Mobile App Revolution  Making sense of the big data  Data Analytics - examines large data sets containing a variety of data types to uncover  Hidden patterns  Unknown correlations  Market trends  Customer preferences

18. 18 Big & Open Data Analytics Linking data analytics through Application Programming Interface (API) for data intelligence  Agronomic tips on amount of inputs use  Daily weather on timing, length, etc. of season  Preventive practices/early warnings  Rehabilitation in case of pests or plant disease attacks  Financial services  Alert users on where & when to buy

19. 19 Example of Big & Open Data for Agriculture

20. III: Information Exchange a) Pre-Production Information: Planning, decision making, sourcing of inputs b) Production Information: Land preparation, planting, weather, efficient use of inputs such as water, seed, fertiliser, and soil, pest and disease management, and pre-harvesting c) Post-harvest Information: Postharvest handling, marketing, transport, traceability, tracking, storage and processing d) Cross-cutting Information: Digital financial services such as payment, credit, saving, insurance e) Cross-cutting Information: Research, monitoring, and evaluation Data Acquisition Satellite imagery acquisition – manage and execute imagery orders; rapid pre- processing; development of derivatives with best practices Data Processing & Storage Archiving of imagery through automated protocols, execute protocols for imagery storage and access. Execute protocols for imagery processing, manage imagery procurement database and generate regular reports, ensure efficient quality control and assurance metrics Data Analysis & Modelling Compare, test, and evaluate varieties of satellite data assimilation models and approaches II: Knowledge Brokering 1. Demand Articulation Context analysis 2. Network Formation Support formation of alliances/networks Gate-keeping of new innovations Match-making of new demands from users 3. Training & Capacity Building Add value & repackage knowledge products Mobilize extra resources for project mgt. Mediate among partners for Signal the presence of new info. products Communicate the know-how 4. Monitoring and Evaluation Assess & evaluate information products Decision Support Services Develop and maintain DSS with networks of deliverables, data streaming platforms, geospatial data acquisition, integration, visualization, display, plans to optimize the system I: Satellite Data Example of Big & Open Data for Agriculture

21. 21 Conclusions  We are experiencing two revolutions:  Mobile  Data  It is simply impossible to separate them  Successful mobile application development depends on big/open data  Effective utilization of big/open data relies on the mobile applications

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