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

Auth

ors

Affili

atio

ns

CSA 2015 ≠ CSA 2050P

roble

m

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

Solu

tion

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

Solu

tion

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

Solu

tion

Big task

Solu

tion

Big task

Solu

tion

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

Solu

tion

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

Solu

tion

So

lutio

n

Imple

menta

tion

Imple

menta

tion

Imple

menta

tion

Imple

menta

tion

1

2

3

Imple

menta

tion

Imple

menta

tion

Imple

menta

tion

Imple

menta

tion

Imple

menta

tion

Imple

menta

tion

Results

Victoria

Macuzalito

Chepe

Cedrón

Campechano

Amadeus 77

Don Kike

Overall performance

Bradley-Terry model results

Worse Better

Results

Results

“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 farmersResults

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

esults

Conclu

sio

ns

1. Local learning fostered, accelerated

2. Reliable, generalizable results

1. Reduced costs comparing to otherparticipatory approaches

2. Simple field experiments makeupscaling possible

Next 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