This document summarizes a presentation on using citizen science to accelerate climate adaptation for poor farming households. It discusses how citizen science, which involves non-professional scientists, and crowdsourcing, which breaks large tasks into micro-tasks, can be used to conduct the massive and constant testing required to develop climate-smart agriculture practices tailored to local conditions. An implementation approach is described where farmers test new crop varieties using simple field experiments, generating reliable results at low cost. Initial results from India show local learning and yield improvements. Next steps include launching an information platform, exploring input retail models, capacity building, and further technology evaluation.

1. Can citizen science accelerate
climate adaptation by poor
farming households?
Jacob van Etten et al. (see next slide)
Bioversity International
Montpellier
March 16-18, 2015

2. Jacob van Etten Jeffrey Alwang Elizabeth
Arnaud Eskender Beza Luis Calderer
Rhiannon Crichton Anton Eitzinger Kees
van Duijvendijk Carlo Fadda Basazen
Fantahun Jeske van de Gevel Elisabetta
Gotor Dejene Kassahun Mengistu SS
Kaushik Yosef G. Kidane Prem Mathur
Leida Mercado Sarika Mittra Anne
Marie Moeller Ashis Mondal M. Enrico
Pė Susan Richter Juan Carlos Rosas R.K.
Singh Juan Carlos Rosas I.S Solanki
Jonathan Steinke Inge Van Den Bergh
Karl Zimmerer
Authors

3. Affiliations

4. CSA 2015 ≠ CSA 2050
Problem

5. CSA 2015 ≠ CSA 2050
CSA is location-specific
Problem

6. CSA 2015 ≠ CSA 2050
CSA is location-specific

Constant, massive testing is required
Problem

7. CSA 2015 ≠ CSA 2050
CSA is location-specific

Constant, massive testing is required

Can science be accelerated?
Problem

8. Solution

9. Citizen science (also known as
crowd science, crowdsourced
science, civic science or networked
science) is scientific research
conducted, in whole or in part, by
amateur or nonprofessional
scientists.
Wikipedia
Solution

10. Crowdsourcing is the process of
obtaining needed services, ideas, or
content by soliciting contributions
from a large group of people, and
especially from an online
community, rather than from
traditional employees or suppliers.
Wikipedia
Solution

11. Big
task
Solution

12. Big
task
Solution

13. Big
task
Micro-
task
1
Micro-
task
5
Micro-
task
2
Micro-
task
3
Micro-
task
4
Micro-
task
6
Micro-
task
7
Micro-
task
n
Solution

14. Big
task
Micro-
task
1
Micro-
task
5
Micro-
task
2
Micro-
task
3
Micro-
task
4
Micro-
task
6
Micro-
task
7
Micro-
task
n
Job
done
Solution

15. Solution

16. Implementation

17. Implementation

18. Implementation

19. Implementation
1
2
3

20. Implementation

21. Implementation

22. Implementation

23. Implementation

24. Implementation

25. Implementation

26. Results Victoria
Macuzalito
Chepe
Cedrón
Campechano
Amadeus 77
Don Kike
Overall performance
Bradley-Terry model results
Worse Better

27. Results

28. Results “Why do you participate in crowdsourcing?”

29. 0
5
10
15
20
25
30
35
40
45
50
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
16,000
Pilot, 2011-12 Wheat, 2012-
13
Rice, 2013 Wheat, 2013-
14
Rice, 2014 Wheat, 2014-
15
Varieties
Farmers
Upscaling process in India
Number of varieties Number of farmers
Results

30. 0.00
10.00
20.00
30.00
40.00
50.00
60.00
70.00
80.00
90.00
100.00
IGKVR-2
IGKVR-1
MALVIADHAN2302
MAHAMAYA
SWARNASUB-1
MTU-7029
MALVIASUGANDH-43
NDR-97
DANTESHWARI
IR-36
GOTRAVIDHAN
INDRABARANIDHAN
RANJENDRAMANSURI
IMPROVEDSHAMBAMANSURI
RAJSHREE
PUSASUGANDH-5
NDR-359
SUGANDH-4
PUSA-44
PUSABASMATI-1509
IR-64
MTU-1010
PURNIMA
RAJENDRASWETA
BPT-5204
PNR-381
PRABHAT
SARYU-52
HUR-105
ANNADA
Farmer27p31
FarmerArize6444
Farmerjk
FarmerPioneer71
FarmerDhaanyaseeds
Yield of 31 varieties of rice (orange) and 5 locally grown varieties (blue)
(q/ha)
Results

31. Conclusions 1. Local learning fostered, accelerated
2. Reliable, generalizable results
1. Reduced costs comparing to other
participatory approaches
2. Simple field experiments make
upscaling possible

32. Nextsteps
1. User-friendly information platform to
be launched mid-2015 – GxE analysis
2. Input retail: suitable business models
3. Building capacity for implementation
4. Observe other variables and evaluate
more CSA technologies