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?”

0

5

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0

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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

30,00

40,00

50,00

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100,00

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