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
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Can citizen science accelerate climate adaptation by poor
farming households?
Jacob van Etten et al. (see next slide)Bioversity International
MontpellierMarch 16‐18, 2015
Jacob van Etten Jeffrey Alwang ElizabethArnaud Eskender Beza Luis CaldererRhiannon Crichton Anton Eitzinger Keesvan Duijvendijk Carlo Fadda BasazenFantahun Jeske van de Gevel ElisabettaGotor Dejene Kassahun Mengistu SSKaushik Yosef G. Kidane Prem MathurLeida Mercado Sarika Mittra AnneMarie Moeller Ashis Mondal M. EnricoPė Susan Richter Juan Carlos Rosas R.K.Singh Juan Carlos Rosas I.S SolankiJonathan Steinke Inge Van Den BerghKarl Zimmerer
CSA 2015 ≠ CSA 2050
CSA 2015 ≠ CSA 2050CSA is location‐specific
CSA 2015 ≠ CSA 2050CSA is location‐specific
Constant, massive testing is required
CSA 2015 ≠ CSA 2050CSA is location‐specific
Constant, massive testing is required
Can science be accelerated?
Citizen science (also known as crowd science, crowdsourcedscience, civic science or networked science) is scientific research conducted, in whole or in part, by amateur or nonprofessional scientists.
Wikipedia
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
Big task
Big task
Big task
Micro‐task1
Micro‐task5
Micro‐task2
Micro‐task3
Micro‐task4
Micro‐task6
Micro‐task7
Micro‐taskn
Big task
Micro‐task1
Micro‐task5
Micro‐task2
Micro‐task3
Micro‐task4
Micro‐task6
Micro‐task7
Micro‐taskn
Job done
1
2
3
Victoria
Macuzalito
Chepe
Cedrón
Campechano
Amadeus 77
Don Kike
Overall performance
Bradley-Terry model results
“Why do you participate in crowdsourcing?”
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2 000
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Pilot, 2011‐12Wheat, 2012‐13
Rice, 2013 Wheat, 2013‐14
Rice, 2014 Wheat, 2014‐15
Varie
ties
Farm
ers
Upscaling process in India
Number of varieties Number of farmers
0,00
10,00
20,00
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IGKV
R‐2
IGKV
R‐1
MALVIA DH
AN 2302
MAH
AMAY
ASW
ARNA SU
B‐1
MTU
‐7029
MALVIA SU
GAN
DH‐43
NDR
‐97
DANTESH
WAR
IIR‐36
GOTR
A VIDH
ANINDR
A BA
RANIDHA
NRA
NJENDR
A MAN
SURI
IMPR
OVE
D SH
AMBA
MAN
SURI
RAJSHR
EEPU
SA SUGAN
DH‐5
NDR
‐359
SUGAN
DH‐4
PUSA
‐44
PUSA
BAS
MAT
I‐1509
IR‐64
MTU
‐1010
PURN
IMA
RAJENDR
A SW
ETA
BPT‐5204
PNR‐381
PRAB
HAT
SARY
U‐52
HUR‐105
ANNAD
AFarm
er 27p
31Farm
er Arize 6444
Farm
er jk
Farm
er Pione
er71
Farm
er Dhaanya se
eds
Yield of 31 varieties of rice (orange) and 5 locally grown varieties (blue) (q/ha)
1. Local learning fostered, accelerated
2. Reliable, generalizable results
1. Reduced costs comparing to otherparticipatory approaches
2. Simple field experiments makeupscaling possible
1. User‐friendly information platform tobe launched mid‐2015 – GxE analysis
2. Input retail: suitable business models
3. Building capacity for implementation
4. Observe other variables and evaluatemore CSA technologies
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