The Role of Mobile Applications in Data Use for Agriculture

Benjamin K Addom, PhD ICT4D Programme Coordinator, CTA

Brussels, 16 September 2015

Roadmap

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I. ICT4Ag Strategy at CTAa. CTA’s approach to ICTs for Agricultural activities

II. Mobile Apps for Big & Open Dataa. How Mobile Apps aid Big/Open Data use

III. Big & Open Data for Mobile Appsa. How Big/Open Data aids Mobile App

Development

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I:ICT4Ag Strategy at CTA

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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

1: Enhancing institutional and grassroots ICT capacity

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2. Supporting entrepreneurship & youth

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3: Promoting the enabling environments & uptake

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CTA as an honest knowledge broker

e-Agriculture StrategyAn e-Agriculture Strategy Toolkit (CTA, ITU, FAO)

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II:Mobile Applications for

Big & Open Data

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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

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E.g. of Data Apps – Data Collection

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E.g. of Data App - Market Intelligence

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E.g. of Data App - Extension Services

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E.g. of Data App - Farm Level Crowdsourcing

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Big & Open Data Improving Access

Mobile applications are facilitating access to and sharing of big & open data for

agriculture

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III:Big & Open Data for Mobile Applications

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Big/Open Data Aiding Mobile App Revolution

Making sense of the big dataData Analytics - examines large data sets containing a variety of data types to uncover

Hidden patterns

Unknown correlations

Market trends

Customer preferences

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Big & Open Data Analytics

Linking data analytics through Application Programming Interface (API) for data intelligence

Agronomic tips on amount of inputs useDaily weather on timing, length, etc. of seasonPreventive practices/early warningsRehabilitation in case of pests or plant disease attacksFinancial servicesAlert users on where & when to buy

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Example of Big & Open Data for Agriculture

III: Information Exchangea) Pre-Production Information: Planning, decision making,

sourcing of inputsb) Production Information: Land preparation, planting,

weather, efficient use of inputs such as water, seed, fertiliser, and soil, pest and disease management, and

pre-harvestingc) Post-harvest Information: Postharvest handling, marketing, transport, traceability, tracking, storage and

processingd) Cross-cutting Information: Digital financial services such

as payment, credit, saving, insurancee) 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 Brokering1. Demand Articulation

Context analysis2. Network Formation

Support formation of alliances/networksGate-keeping of new innovations

Match-making of new demands from users3. 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-how4. 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 DataExample of Big & Open Data for Agriculture

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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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Thank you

https://dgroups.org/cta/ict4aghttps://dgroups.org/groups/web2fordev