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Accelerating technical change through ICT-
enabled agricultural extension
Evidence on technology adoption, gender, and pro...
Introduction
Sources: Nakasone &
Torero (2016); Aker (2011)
ICTs are a powerful medium for agricultural
development and rural economic ...
Video is an especially powerful medium
for improving farm management
Appealing Customizable Consistent Low cost
But small design attributes can have a big
effect on outcomes
Context matters in video-based interventions
• What works in...
And it requires more than just a flashy video!
Credible,
localized
content
Capacity to
communicate
and learn
Back-end
anal...
Our questions
1. Can video-mediated extension accelerate technical change among
smallholder farmers?
2. Can video-mediated...
A simple impact pathway
Information Exposure,
awareness
Knowledge,
understanding
Uptake,
adoption
Productivity,
welfare
Ch...
Experimental designs and the search for
causal relationships
Ethiopia
• Impact evaluation of a video-mediated community-ba...
Randomization
Random
assignment
Treatment
1
Treatment
2
Control
The distribution of
observable and
unobservable
characteri...
Ethiopia: Focus crops and practices
Teff
Wheat
Maize
Row planting
Precise seeding rate
Precise urea
dressing
Uganda: Focus crops and practices
Maize
Timing of planting
Seed spacing
Striga control
Timing of weeding
Saving and invest...
Findings Results
Can video increase farmers’
exposure to extension?
Information Exposure,
awareness
Knowledge,
understanding
Uptake,
adopti...
Ethiopia: Household head access to extension
Crop ITT
↑ over
control
Control
mean
Teff 0.11*** 24% 0.45
Wheat 0.16*** 37% ...
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%
Te...
Can video increase farmers’
content knowledge?
Information Exposure,
awareness
Knowledge,
understanding
Uptake,
adoption
P...
Ethiopia: Household head knowledge scores
Crop ITT
↑ over
control
Control
mean
Teff 1.81*** 5% 37.5
Wheat 1.14 - 38.3
Maiz...
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 Whea...
0%
20%
40%
60%
80%
100%
Seeding rates Integrated
practice
Optimal
weeding
Uganda: Female knowledge scores
Technology ATE
↑...
Can video increase farmers’
adoption of technologies,
practices, and inputs?
Information Exposure,
awareness
Knowledge,
un...
Ethiopia: Adoption of row planting
Crop ITT
↑ over
control
Control
mean
Teff 0.06*** 36% 0.16
Wheat 0.03 -- 0.23
Maize 0.0...
Ethiopia: Adoption of precise seeding rates
Crop ITT
↑ over
control
Control
mean
Teff 0.07*** 22% 0.31
Wheat 0.09*** 34% 0...
Ethiopia: Adoption of urea top/side dressing
Crop ITT
↑ over
control
Control
mean
Teff 0.08*** 22% 0.37
Wheat 0.09*** 23% ...
Uganda: Female co-head adoption
Technology ATE
↑ over
control
Control
mean
Timing of planting 0.021*** 49% 0.043
Seeding r...
Uganda: Female co-head input use
Technology ATE
↑ over
control
Control
mean
DAP 0.017*** 106% 0.016
Urea 0.01*** 500% 0.00...
Can video increase farmers’
productivity?
Information Exposure,
awareness
Knowledge,
understanding
Uptake,
adoption
Produc...
0
10
20
30
40
50
Teff Wheat Maize
Quintals/ha
Ethiopia: Crop yields
Crop ITT
↑ over
control
Control
mean
Teff 1.54*** 15% ...
0
2
4
6
8
10
12
14
Yield
Quintals/ha
Control
Video
Video + IVR
Video + IVR + SMS
Uganda: Maize yields
Indicator ATE
↑ over...
Conclusion
Conclusion
Conclusions
Video-based extension approaches can have measurable effects
Outcomes may vary by context
But even small, gend...
And a look into the future
Farm data
Weather
data
Farm inputs and
technology
Remote
sensing
Soil data
Commerce
and trade
F...
Developing Local Extension Capacity
Powered by Digital Green and Partners
Join the DLEC Extension and Advisory Services Co...
Additional materials
Ethiopia evaluation design
Medium
Video No video
Recipient
Male
T1=798
C=812
Couple T2=812
Uganda experimental design
Messenger
Male Female Couple
Recipient
Male
T1=385 T2=385 T3=369
Female T4=385 T5=385 T6=369Cou...
Sampling frame
Ethiopia
• Smallholder cereal-farming households in Ethiopia’s 4 main regions (N=2,422)
• Randomly drawn fr...
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David Spielman (IFPRI) • 2019 IFPRI Egypt - WB “Innovations for Agricultural Development in Egypt”

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

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David Spielman (IFPRI) • 2019 IFPRI Egypt - WB “Innovations for Agricultural Development in Egypt”

  1. 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. 2. Introduction
  3. 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. 4. Video is an especially powerful medium for improving farm management Appealing Customizable Consistent Low cost
  5. 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. 6. And it requires more than just a flashy video! Credible, localized content Capacity to communicate and learn Back-end analytics Appropriate channels
  7. 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. 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. 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. 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. 11. Ethiopia: Focus crops and practices Teff Wheat Maize Row planting Precise seeding rate Precise urea dressing
  12. 12. Uganda: Focus crops and practices Maize Timing of planting Seed spacing Striga control Timing of weeding Saving and investing Fertilizer use
  13. 13. Findings Results
  14. 14. Can video increase farmers’ exposure to extension? Information Exposure, awareness Knowledge, understanding Uptake, adoption Productivity, welfare
  15. 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. 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. 17. Can video increase farmers’ content knowledge? Information Exposure, awareness Knowledge, understanding Uptake, adoption Productivity, welfare
  18. 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. 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. 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. 21. Can video increase farmers’ adoption of technologies, practices, and inputs? Information Exposure, awareness Knowledge, understanding Uptake, adoption Productivity, welfare
  22. 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. 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. 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. 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. 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. 27. Can video increase farmers’ productivity? Information Exposure, awareness Knowledge, understanding Uptake, adoption Productivity, welfare
  28. 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. 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. 30. Conclusion
  31. 31. Conclusion
  32. 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. 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. 34. Developing Local Extension Capacity Powered by Digital Green and Partners Join the DLEC Extension and Advisory Services Community of Practice at https://dlec.hivebrite.com
  35. 35. Additional materials
  36. 36. Ethiopia evaluation design Medium Video No video Recipient Male T1=798 C=812 Couple T2=812
  37. 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. 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

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