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
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
Au
tho
rs
Affili
atio
ns
CSA 2015 ≠ CSA 2050P
rob
lem
CSA 2015 ≠ CSA 2050
CSA is location-specific
Pro
ble
m
CSA 2015 ≠ CSA 2050
CSA is location-specific
Constant, massive testing is required
Pro
ble
m
CSA 2015 ≠ CSA 2050
CSA is location-specific
Constant, massive testing is required
Can science be accelerated?
Pro
ble
m
So
lutio
n
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
So
lutio
n
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
So
lutio
n
Big task
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lutio
n
Big task
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lutio
n
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
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lutio
n
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
So
lutio
n
So
lutio
n
Imp
lem
en
tatio
n
Imp
lem
en
tatio
n
Imp
lem
en
tatio
n
Imp
lem
en
tatio
n
1
2
3
Imp
lem
en
tatio
n
Imp
lem
en
tatio
n
Imp
lem
en
tatio
n
Imp
lem
en
tatio
n
Imp
lem
en
tatio
n
Imp
lem
en
tatio
n
Re
su
lts
Victoria
Macuzalito
Chepe
Cedrón
Campechano
Amadeus 77
Don Kike
Overall performance
Bradley-Terry model results
Worse Better
Re
su
lts
Re
su
lts
“Why do you participate in crowdsourcing?”
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
Var
ieti
es
Farm
ers
Upscaling process in India
Number of varieties Number of farmersRe
su
lts
0.00
10.00
20.00
30.00
40.00
50.00
60.00
70.00
80.00
90.00
100.00
IGK
VR
-2
IGK
VR
-1
MA
LVIA
DH
AN
23
02
MA
HA
MA
YA
SWA
RN
A S
UB
-1
MTU
-70
29
MA
LVIA
SU
GA
ND
H-4
3
ND
R-9
7
DA
NTE
SHW
AR
I
IR-3
6
GO
TRA
VID
HA
N
IND
RA
BA
RA
NID
HA
N
RA
NJE
ND
RA
MA
NSU
RI
IMP
RO
VED
SH
AM
BA
MA
NSU
RI
RA
JSH
REE
PU
SA S
UG
AN
DH
-5
ND
R-3
59
SUG
AN
DH
-4
PU
SA-4
4
PU
SA B
ASM
ATI
-15
09
IR-6
4
MTU
-10
10
PU
RN
IMA
RA
JEN
DR
A S
WET
A
BP
T-5
20
4
PN
R-3
81
PR
AB
HA
T
SAR
YU-5
2
HU
R-1
05
AN
NA
DA
Farm
er 2
7p
31
Farm
er A
rize
64
44
Farm
er jk
Farm
er P
ion
eer
71
Farm
er D
haa
nya
see
ds
Yield of 31 varieties of rice (orange) and 5 locally grown varieties (blue)
(q/ha)R
esu
lts
Co
nclu
sio
ns
1. Local learning fostered, accelerated
2. Reliable, generalizable results
1. Reduced costs comparing to otherparticipatory approaches
2. Simple field experiments makeupscaling possible
Ne
xt
ste
ps
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
