IFPRI and USAID are discussing the potential of digital tools to support farmers in Egypt, launching a new digital app repository that aims to provide a list of agriculture digital tools meant to support smallholder farmers in Egypt.

1. Digital tools and agricultural
market transformation in Africa
Kibrom Abay,a Gashaw T Abate,b Jordan Chamberlin,c Yumna Kassim,a &
David J Spielmand
a International Food Policy Research Institute, Cairo, Egypt
b International Food Policy Research Institute, Washington, DC, USA
c Food and Agriculture Organization of the United Nations, Nairobi, Kenya
d International Food Policy Research Institute, Kigali, Rwanda
Why aren’t they at scale yet, and what will it
take to get there?

2. The number of digital agriculture innovations is…
mindboggling!
Farm data
Weather
data
Farm inputs and
technology
Remote
sensing
Soil data
Commerce
and trade
Feedback
loops
Multichannel
delivery
Productivity
growth
Welfare
improvement
Analytics
Networks
People and
community

3. 0
50
100
150
200
250
300
350
0
10
20
30
40
50
60
70
80
90
100
1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Per
million
people
Percentage
Secure Internet servers (per 1 million people) Mobile cellular subscriptions (per 100 people)
Individuals using the internet (% of population) Access to electricity (% of population)
Access to electricity, rural (% of rural population)
Connectivity in lower- and middle-
income countries, 1999-2019
Source: World Bank (2020); Spielman et al. (2021)
Connectivity, server capacity & mobile telephony are
growing rapidly

4. Digital innovations
have the potential
to transform
agricultural
markets
Wide geographic reach at low
per-transaction costs
Real-time feedback loops to
adjust farm supply to consumer
demand
Solutions to common and
persistent failures in
agricultural markets

5. But why haven’t
we seen digital
innovations lead
to major
transformations in
agricultural
markets?
Are the challenges just too difficult to
overcome?
Do we not understand the problem?
Are our innovations too piecemeal?
Is it just too soon?
??????

6. Hypothesis 1: Known suspects
 Small IT sectors, few innovators and investors  functionality/quality of digital solutions
 Many resource-poor, digitally illiterate farmers
 Small landholdings, low productivity  small quantities of marketable surpluses
 Long, fragmented, and poorly coordinated value chains
 High levels of crop, country, and context specificity  no generic solutions
 Etc.
But do these well-established constraints fully explain the minimal impact of
digital innovations on agricultural market transformation?

7. Hypothesis 2: Tech concentration
Category Description Example (country)
Market advisory
and information
services
Tools to deliver market information and advisories to farmers
80-28 Farmer Hotline (Ethiopia)
iCow (Kenya)
Ethiopia Commodity Exchange (Ethiopia)
Farmerline's 399 Service (Ghana)
Verdant Agritech (Nigeria)
Market
transaction
services
Platforms that link farmers to suppliers of inputs and services
and/or wholesalers or retailers
Hello Tractor (Nigeria); SunCulture (Kenya)
Kobiri (Guinea); Lima Links (Zambia)
Agro Market Day (Uganda)
Financial and
market
transaction
services
Services that facilitate financial transactions and market
exchanges among value-chain actors
Akellobanker (Uganda); M-Pesa (Kenya)
Bayseddo (Senegal); SmartMoney (Tanzania)
AgroPay (Ghana)
Market data
collection and
crowdsourcing
services
Tools that link farmers to farmers, value-chain actors to each
other, or both to analysis platforms to collect, exchange, and
analyze data
KAZNET (Kenya); DigitalGreen (Multiple)
Farm.ink (Multiple); N-frnds (Multiple)
Nuru (Kenya)

8. Hypothesis 3: Crowding out effects
 Digital innovation continues to be bankrolled by public and donor funding
o Many donor-funded pilot projects with few paths to profitability
► Perverse incentives: innovators chase grant funding rather than profitability
 Relatively little venture capital or other forms of private investment
o Too much risk, too few risk-takers willing to bet on Africa

9. Hypothesis 4: Dis-enabling environments
 State micro-management of agricultural markets and digital technologies
o Agricultural price and margin caps
o Ad hoc interventions, e.g., temporary price controls, internet closures
o Costly agriculture and business regulations, tolls, taxes
o Absence of regulation for ICT ventures
o Taxes on internet, social media usage
o Intensive state monitoring of market activity and internet traffic
o Tendencies toward centralized state-run platforms rather than distributed networks
o Insufficient protection of commercial and digital rights of farmers, workers, traders,
consumers

10. Hypothesis 5: Market power
 Agricultural markets are susceptible to anti-competitive behavior
o Monopolies and oligopolistic cartels: One seller controls supply/price
o Monopsonies and oligopsonic cartels: One buyer controls demand/price
 Anti-competitive behavior beats digital solutions every time
o Strategic corporate behavior and political economy factors can readily crush the next
disruptive innovation in agricultural markets (e.g., ridesharing apps like Uber and Lyft)

11. Conclusion: Seven thoughts on how to get to scale
1. Invest more in talent: human capital in the IT sector and agriculture sector
2. Invest in the essential infrastructure: both tangible and intangible
3. Focus on digital solutions with credible revenue-generation models
4. Develop “smart” regulations to incentivize, not control, digital and market innovation
5. Break anti-competitive behavior in the market: promote pro-market policies
6. Facilitate strong alliances among digital actors
7. Continuously evaluate the performance and impact of digital innovations to demonstrate
both progress and pitfalls

12. Thank you