Content Management Process in Selected m-Agriculture Initiatives in India

Pier Paolo Ficarelli

What some say about m-Agriculture

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• 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)

• M-agriculture is about tools, not about access and dissemination of agricultural information (FAO, Rome 2010)

• We have farmer field schools for extension. Mobiles add no value to it.

(CGIAR Researcher, Delhi, 2011) • Use of mobiles in rural areas is the last tactic for squeezing money at

the bottom of the pyramid (GIZ staff, Delhi 2010)

Defining Content Management for ICT

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• Combine information system and information chain concepts (Heeks 2005,2007) • The Info system describes contextual factors influencing information

conveyance to users (design-reality check!)• The info chain defines relevance as the functional linkages from content all the

way through to users • Feedback from users as a key feature

Source: Glendenning & Ficarelli, 2011

Managing Relevant Content Means:

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• Highly processed and refined information relevant to local need and context

• Verifiable, clear and accurate content, delivered timely in an appropriate format and language

• A time and resource intensive process, normally underestimated • A learning PROCESS that requires continuous improvement• Co-evolving content & technology apps in the social space of users

Source: Glendenning & Ficarelli, 2011

m-Agriculture Case Studies

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Common features of cases– Sizable scale – Targeted to farmers – Low-cost devices – Exemplary content generation/delivery mechanisms – Innovative business models

Main research questions– How relevant content is generated and managed by the different initiatives? – To what extent the initiatives analysed strengthen key extension functions?– Is human intermediation a key success factor? – Is digital agro-information made more accessible through ICTs? – What kind of mobile app and/or media are most suitable for m-agriculture?– Where does lay the mainstreaming potential for m-Agriculture?

Case 1: Reuters Market Light (RML)

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• Agency: Thomson Reuters (2007) • Business model: spin off mobile VAS of Thomson Reuters• Scale: + 15 000 villages 13 states 8 languages (2010)• Info Type: push model (SMS; voice messages on testing)• Content sources: various

Source: Glendenning & Ficarelli, 2011

Case 2: IFFCO Kisan Sanchar Limited (IKSL)

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• Agency: IFFCO & Bharti Airtel (2007) • Business model: Partnership with major mobile operator • Scale: 1 million active users over 18 states• Info Type: push & pull model (Voice messages + Helpline)• Content sources: various; content managers, experts

Case 3: Lifelines

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• Agency: OneWorld.net British Telecom & Cisco (2006) • Business model: funded project • Scale: 150,000 farmers 2,000 villages in 3 Indian states (2010)• Info Type: pull model (IVR audio Q&A platform)• Content sources: Farmers & FAQ Database

Content Management in the Case Studies

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Need identification All initiatives have carried out systematic farmer surveys to identify information gaps

SourcingAcademic/Researcher knowledge and young graduates : Lifelines, IKSL; Hired

professionals Databases: RML; International Organizations: IKSL

Localisation Lifelines = Q&A; RML= subscriber details/local data collectors; IKSL= agro-ecological zones/crop calendars

Format & AdaptationIKSL: Fact sheets/voice messages RML: SMS (voice); Lifelines: audio files /transcripts

Storage and retrieval All initiatives rely on the development of their own digital repositories

Delivery Automated daily: IKSL, RML; Q&A: Lifelines, IKSL with human intermediation

Learning & FeedbackLifelines IKSL: Internal quality audits; RML: ?

Content Management: Key Challenges

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FormatLimits of SMS and voice messages on mobile for agro-advisory: Videos? Audio + Pictures?

SourcingContent is dispersed and scattered: Agri-Google? Experts: 24H Access? Motivation?

AccessIdentified trend is towards close access of digital repositories: Regulations?

LocalisationMainly based on outsiders’ knowledge and area criteria: Farmer knowledge?

QualityIt is delegated to experts and relies on implicit knowledge: External validation?

FeedbackClient satisfaction mainly anecdotal/based on sample surveys: Sufficient?

“Infomediaries”Info-need articulation and advice translation require human facilitation: Viable?

m-Agriculture: Mainstreaming Potential

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Agro-information dissemination – 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.)

Scale of farmer outreach – 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

Farmer-Expert links – 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

Customisation of Information– All initiative are showing the potential of “personalising” information based on farmer profiles

in digital form

Use of different media – 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

m- Agriculture: Success Factors?

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Bottom-up integration into existing service models

Viability and affordability

Mobile operator or mobile VAS involvement

PPP arrangements

Content validation mechanisms

Youth entrepreneurship development Innovation and R&D

Why keep engaging with m-Agriculture

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The problem with the world is not that people know too little, but that they know many things that ain't so

Mark Twain