Mobile plus pp.ficarelli (final)

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An overview about issues of content management related to communication theory and information systems principles, applied to the context of agriculture for development. Two developed conceptual frameworks for the analysis of content management process for ICT based platforms in general are applied to selected concrete Indian cases, such as RML, IKSL and Lifelines. The presentation emphasises the need for mainstreaming m-agriculture initiatives, proven to be a useful as agro-information service to smallholder farmers, into major public poverty alleviation programmes with an agricultural component to broaden social impact. Public-private partnerships seem the most promising funding mechanism to ensure non-exclusivity of services by mobile VAS or MNOs, foster convergence of most successful applications on multipurpose mobile platforms to ensure continuity of services and affordability for users.

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Mobile plus pp.ficarelli (final)

  1. 1. Content Management Process in Selected m-Agriculture Initiatives in India<br />Pier Paolo Ficarelli<br />
  2. 2. What some say about m-Agriculture <br />2<br /><ul><li>What is the use of delivering market information to a farmer with a phone, if there is no road to market the produce (IFAD, report 2003)
  3. 3. M-agriculture is about tools, not about access and dissemination of agricultural information (FAO, Rome 2010)
  4. 4. We have farmer field schools for extension. Mobiles add no value to it. (CGIAR Researcher, Delhi, 2011)
  5. 5. Use of mobiles in rural areas is the last tactic for squeezing money at the bottom of the pyramid (GIZ staff, Delhi 2010) </li></li></ul><li>Defining Content Management for ICT <br />3<br />Source: Glendenning & Ficarelli, 2011<br /><ul><li>Combine information system and information chain concepts (Heeks 2005,2007)
  6. 6. The Info system describes contextual factors influencing information conveyance to users (design-reality check!)
  7. 7. The info chain defines relevance as the functional linkages from content all the way through to users
  8. 8. Feedback from users as a key feature </li></li></ul><li>Managing Relevant Content Means:<br />4<br />Source: Glendenning & Ficarelli, 2011<br /><ul><li>Highly processed and refined information relevant to local need and context
  9. 9. Verifiable, clear and accurate content, delivered timely in an appropriate format and language
  10. 10. A time and resource intensive process, normally underestimated
  11. 11. A learning PROCESS that requires continuous improvement
  12. 12. Co-evolving content & technology apps in the social space of users </li></li></ul><li>m-Agriculture Case Studies <br />5<br />Common features of cases<br /><ul><li>Sizable scale
  13. 13. Targeted to farmers
  14. 14. Low-cost devices
  15. 15. Exemplary content generation/delivery mechanisms
  16. 16. Innovative business models </li></ul>Main research questions<br /><ul><li>How relevant content is generated and managed by the different initiatives?
  17. 17. To what extent the initiatives analysed strengthen key extension functions?
  18. 18. Is human intermediation a key success factor?
  19. 19. Is digital agro-information made more accessible through ICTs?
  20. 20. What kind of mobile app and/or media are most suitable for m-agriculture?
  21. 21. Where does lay the mainstreaming potential for m-Agriculture? </li></li></ul><li>Case 1: Reuters Market Light (RML)<br />6<br /><ul><li>Agency: Thomson Reuters (2007)
  22. 22. Business model: spin off mobile VAS of Thomson Reuters
  23. 23. Scale: + 15 000 villages 13 states 8 languages (2010)
  24. 24. Info Type: push model (SMS; voice messages on testing)
  25. 25. Content sources: various </li></ul>Source: Glendenning & Ficarelli, 2011<br />
  26. 26. Case 2: IFFCO Kisan Sanchar Limited (IKSL)<br />7<br /><ul><li>Agency: IFFCO & BhartiAirtel (2007)
  27. 27. Business model: Partnership with major mobile operator
  28. 28. Scale: 1 million active users over 18 states
  29. 29. Info Type: push & pull model (Voice messages+ Helpline)
  30. 30. Content sources: various; content managers, experts </li></li></ul><li>Case 3: Lifelines<br />8<br /><ul><li>Agency: OneWorld.net British Telecom & Cisco (2006)
  31. 31. Business model: funded project
  32. 32. Scale: 150,000 farmers 2,000 villages in 3 Indian states (2010)
  33. 33. Info Type: pull model (IVR audio Q&A platform)
  34. 34. Content sources: Farmers & FAQ Database </li></li></ul><li>Content Management in the Case Studies<br />9<br />Need identification <br />All initiatives have carried out systematic farmer surveys to identify information gaps<br />Sourcing<br />Academic/Researcher knowledge and young graduates : Lifelines, IKSL; Hired professionals Databases: RML; International Organizations: IKSL <br />Localisation<br />Lifelines = Q&A; RML= subscriber details/local data collectors; IKSL= agro-ecological zones/crop calendars<br />Format & Adaptation<br />IKSL: Fact sheets/voice messages RML: SMS (voice); Lifelines: audio files /transcripts <br />Storage and retrieval <br />All initiatives rely on the development of their own digital repositories<br />Delivery <br />Automated daily: IKSL, RML; Q&A: Lifelines, IKSL with human intermediation <br />Learning & Feedback<br />Lifelines IKSL: Internal quality audits; RML: ?<br />
  35. 35. Content Management: Key Challenges <br />10<br />Format<br />Limits of SMS and voice messages on mobile for agro-advisory: Videos? Audio + Pictures?<br />Sourcing<br />Content is dispersed and scattered: Agri-Google? Experts: 24H Access? Motivation? <br />Access<br />Identified trend is towards close access of digital repositories: Regulations? <br />Localisation<br />Mainly based on outsiders’ knowledge and area criteria: Farmer knowledge? <br />Quality<br />It is delegated to experts and relies on implicit knowledge: External validation? <br />Feedback<br />Client satisfaction mainly anecdotal/based on sample surveys: Sufficient?<br />“Infomediaries”<br />Info-need articulation and advice translation require human facilitation: Viable?<br />
  36. 36. m-Agriculture: Mainstreaming Potential <br />11<br />Agro-information dissemination <br /><ul><li>All initiatives seem very successful in the delivery of micro-information, otherwise unavailable to farmers (weather, local market prices, pest & disease alerts, agro-news, standard agricultural practices, government programmes etc.)</li></ul>Scale of farmer outreach <br /><ul><li>All initiatives have shown the capacity of reaching and delivering some agro-advisory services to a large number of farmers in their field and in potentially viable way</li></ul>Farmer-Expert links <br /><ul><li>Some initiatives show the possibility of facilitating direct expert – famer linkages and customisation of agro-information to farmer demands through Q&A at a scale</li></ul>Customisation of Information<br /><ul><li>All initiative are showing the potential of “personalising” information based on farmer profiles in digital form</li></ul>Use of different media <br /><ul><li>All initiatives indicates the potential of mobile as a multipurpose communication platform to increase effectiveness of content delivery and interactivity for farmers and field staff</li></li></ul><li>m- Agriculture: Success Factors? <br />12<br />Bottom-up integration into existing service models<br />Viability and affordability <br />Mobile operator or mobile VAS involvement <br />PPP arrangements <br />Content validation mechanisms <br />Youth entrepreneurship development<br />Innovation and R&D <br />
  37. 37. Why keep engaging with m-Agriculture <br />The problem with the world is not that people know too little, but that they know many things that ain't so <br />Mark Twain<br />13<br />

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