As part of the IFPRI Egypt Seminar in partnership with the World Bank, IFPRI Egyprt seminar “Innovations for Agricultural Development in Egypt”.

1. Accelerating technical change through ICT-
enabled agricultural extension
Evidence on technology adoption, gender, and productivity
Gashaw T. Abate, Tanguy Bernard, Bjorn van Campenhout,
Els Lecoutere, Simrin Makhija, and David J. Spielman
International Food Policy Research Institute, University of Bordeaux, KU Leuven, University of Antwerp

2. Introduction

3. Sources: Nakasone &
Torero (2016); Aker (2011)
ICTs are a powerful medium for agricultural
development and rural economic growth
Farm and natural
resource
management
Market and price
information
Rural enterprise
and finance
Data and analytics
Subject to the constraints of
connectivity, content, and capacity

4. Video is an especially powerful medium
for improving farm management
Appealing Customizable Consistent Low cost

5. But small design attributes can have a big
effect on outcomes
Context matters in video-based interventions
• What works in one setting may not work in another
• Small changes in design can make big differences
• This creates opportunities for replication, learning
Context

6. And it requires more than just a flashy video!
Credible,
localized
content
Capacity to
communicate
and learn
Back-end
analytics
Appropriate channels

7. Our questions
1. Can video-mediated extension accelerate technical change among
smallholder farmers?
2. Can video-mediated extension be gender-sensitive or gender-
transformative?

8. A simple impact pathway
Information Exposure,
awareness
Knowledge,
understanding
Uptake,
adoption
Productivity,
welfare
Changes in preferences,
behaviors, expectations
Changes in rules, norms,
cultures, policies

9. Experimental designs and the search for
causal relationships
Ethiopia
• Impact evaluation of a video-mediated community-based extension
approach to promote recommendations for cereal cultivation
• Pioneered by Digital Green (2014-16) and scaled-up by government (2017-18)
Uganda
• Field experiment with video-based advisory services to individual
farmers to promote recommendations for maize cultivation
• Implemented by IFPRI based on government maize package (2017)

10. Randomization
Random
assignment
Treatment
1
Treatment
2
Control
The distribution of
observable and
unobservable
characteristics in these
3 groups are exactly
the same except for
their exposure to the
video-based approach

11. Ethiopia: Focus crops and practices
Teff
Wheat
Maize
Row planting
Precise seeding rate
Precise urea
dressing

12. Uganda: Focus crops and practices
Maize
Timing of planting
Seed spacing
Striga control
Timing of weeding
Saving and investing
Fertilizer use

13. Findings Results

14. Can video increase farmers’
exposure to extension?
Information Exposure,
awareness
Knowledge,
understanding
Uptake,
adoption
Productivity,
welfare

15. Ethiopia: Household head access to extension
Crop ITT
↑ over
control
Control
mean
Teff 0.11*** 24% 0.45
Wheat 0.16*** 37% 0.43
Maize 0.12*** 25% 0.50
0%
20%
40%
60%
80%
100%
Teff Wheat Maize
Control
Video
Denotes effect on household heads’ access to extension
from residing in a kebele where the video-mediated
approach was used vs. the conventional extension
approach

16. Crop ITT
↑ over
control
Control
mean
Teff 0.03 - 0.24
Wheat 0.05* 25% 0.19
Maize 0.05* 20% 0.26
0%
20%
40%
60%
80%
100%
Teff Wheat Maize
Control
Video
Video+Spouse
Ethiopia: Female access to extension
Denotes effect on (female) spouses’ access to extension
from residing in a kebele where the video-mediated
approach was used vs. the conventional extension
approach

17. Can video increase farmers’
content knowledge?
Information Exposure,
awareness
Knowledge,
understanding
Uptake,
adoption
Productivity,
welfare

18. Ethiopia: Household head knowledge scores
Crop ITT
↑ over
control
Control
mean
Teff 1.81*** 5% 37.5
Wheat 1.14 - 38.3
Maize 0.94 - 43.8
0%
20%
40%
60%
Teff Wheat Maize
Control
Video
Denotes effect on knowledge test scores for respondents
residing in a kebele where the video-mediated approach
was used vs. the conventional extension approach

19. Crop ITT
↑ over
control
Control
mean
Teff 1.398* 4% 32.2
Wheat 1.609* 5% 33.8
Maize 0.506 -- 40.2
0%
20%
40%
60%
Teff Wheat Maize
Control
Video
Video+Spouse
Ethiopia: Female knowledge scores
Denotes effect on knowledge test scores for respondents
residing in a kebele where the video-mediated approach
was used vs. the conventional extension approach

20. 0%
20%
40%
60%
80%
100%
Seeding rates Integrated
practice
Optimal
weeding
Uganda: Female knowledge scores
Technology ATE
↑ over
control
Control
mean
Seeding rates 0.066*** 52% 12.7
Integrated practice 0.051*** 6% 81.7
Optimal weeding 0.005 -- 88.0
Denotes effect on woman’s answers to knowledge questions about
individual technologies when a video was screened with a woman
co-head (alone or with male co-head) vs. only screening the video
with the male co-head
Control
Treatment

21. Can video increase farmers’
adoption of technologies,
practices, and inputs?
Information Exposure,
awareness
Knowledge,
understanding
Uptake,
adoption
Productivity,
welfare

22. Ethiopia: Adoption of row planting
Crop ITT
↑ over
control
Control
mean
Teff 0.06*** 36% 0.16
Wheat 0.03 -- 0.23
Maize 0.04* 5% 0.65
0%
20%
40%
60%
80%
100%
Teff Wheat Maize
Control
Video
Denotes effect on adoption from residing in a kebele
where the video-mediated approach was used vs. the
conventional extension approach

23. Ethiopia: Adoption of precise seeding rates
Crop ITT
↑ over
control
Control
mean
Teff 0.07*** 22% 0.31
Wheat 0.09*** 34% 0.26
Maize 0.03 -- 0.44
0%
20%
40%
60%
80%
100%
Teff Wheat Maize
Control
Video
Denotes effect on adoption from residing in a kebele
where the video-mediated approach was used vs. the
conventional extension approach

24. Ethiopia: Adoption of urea top/side dressing
Crop ITT
↑ over
control
Control
mean
Teff 0.08*** 22% 0.37
Wheat 0.09*** 23% 0.39
Maize 0.03 -- 0.51
0%
20%
40%
60%
80%
100%
Teff Wheat Maize
Control
Video
Denotes effect on adoption from residing in a kebele
where the video-mediated approach was used vs. the
conventional extension approach

25. Uganda: Female co-head adoption
Technology ATE
↑ over
control
Control
mean
Timing of planting 0.021*** 49% 0.043
Seeding rates 0.007*** 700% 0.001
Striga control 0.052 -- 0.080
Timing of 1st weeding 0.048 -- 0.157
Denotes effect on woman co-head’s decision to adopt when the
video was screened with a woman co-head (alone or with male co-
head) vs. only screening the video with the male co-head
0%
10%
20%
30%
Timing of
planting
Seeding
rates
Striga
control
Timing of
1st
weeding
Control
Treatment

26. Uganda: Female co-head input use
Technology ATE
↑ over
control
Control
mean
DAP 0.017*** 106% 0.016
Urea 0.01*** 500% 0.002
Organic fertilizer 0.011 -- 0.017
Hybrid seed 0.009 -- 0.013
Denotes effect on woman co-head’s decision to adopt when the
video was screened with a woman co-head (alone or with male
co-head) vs. only screening the video with the male co-head
0%
5%
10%
DAP Urea Organic
fertilizer
Hybrid
seed
Control
Treatment

27. Can video increase farmers’
productivity?
Information Exposure,
awareness
Knowledge,
understanding
Uptake,
adoption
Productivity,
welfare

28. 0
10
20
30
40
50
Teff Wheat Maize
Quintals/ha
Ethiopia: Crop yields
Crop ITT
↑ over
control
Control
mean
Teff 1.54*** 15% 7.95
Wheat -0.12 -- 20.26
Maize 3.14 -- 35.17
Control
Video
Denotes effect on yields from residing in a kebele
where the video-mediated approach was used vs.
the conventional extension approach. Based on
farmer-reported harvest quantities and GPS-
reported plot areas for a subsample of crop-specific
plots (n=757, 766, and 848, respectively)

29. 0
2
4
6
8
10
12
14
Yield
Quintals/ha
Control
Video
Video + IVR
Video + IVR + SMS
Uganda: Maize yields
Indicator ATE
↑ over
control
Control
mean
Video 0.99* 9% 10.57
Video + IVR 0.38 -- 10.57
Video + IVR + SMS 0.03 -- 10.57
Denotes effect on maize yields (quintals/ha) with incremental
treatments (video, +IVR, +SMS) vs. no treatment
Control
Treatment

30. Conclusion

31. Conclusion

32. Conclusions
Video-based extension approaches can have measurable effects
Outcomes may vary by context
But even small, gendered design attributes can influence the
effectiveness and inclusivity of agricultural extension

33. And a look into the future
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

34. Developing Local Extension Capacity
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Join the DLEC Extension and Advisory Services Community of Practice at
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35. Additional materials

36. Ethiopia evaluation design
Medium
Video No video
Recipient
Male
T1=798
C=812
Couple T2=812

37. Uganda experimental design
Messenger
Male Female Couple
Recipient
Male
T1=385 T2=385 T3=369
Female T4=385 T5=385 T6=369Couple
T7=342 T8=342 T9=369
Control
C=257

38. Sampling frame
Ethiopia
• Smallholder cereal-farming households in Ethiopia’s 4 main regions (N=2,422)
• Randomly drawn from 7 dev groups per kebele x 9-15 kebeles/district x 30 districts
• Broadly representative of the 68 districts under Digital Green’s scale-up, which is a
subset of the 157 districts under the Government’s “Agricultural Growth Program II”
Uganda
• Monogamous smallholder maize-farming households in eastern Uganda (N=3,588)
• Randomly drawn from 5 villages/parish x 5 parishes/district x 5 districts
• Broadly representative of the maize-farming population of eastern Uganda