1CASOS (July 2000)

A decision process model mixing rational A decision process model mixing rational anticipations and social influences : the example of anticipations and social influences : the example of

agri-environmental measure adoption in Breadalbane agri-environmental measure adoption in Breadalbane ESA (Scotland)ESA (Scotland)

Guillaume Deffuant, Frédéric Amblard, Nils Ferrand (Cemagref, France)

Nigel Gilbert (university of Surrey, UK)

Gérard Weisbuch (Ecole Normal Supérieure, France)

CASOS (July 2000)

Agri-environmental measuresAgri-environmental measures

+ $$

Farmer

• Pilot zones from 1986Pilot zones from 1986• Generalised to all Europe in 1992Generalised to all Europe in 1992

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European projectEuropean project

• First goal : evaluate the model First goal : evaluate the model on the paston the past

+ $$

• Then evaluate prescriptive useThen evaluate prescriptive use

• Breadalbane ESA ScotlandBreadalbane ESA Scotland

CASOS (July 2000)

Breadalbane ESABreadalbane ESA

• 150 farms, mainly 150 farms, mainly sheep, average size : sheep, average size : 650 ha650 ha

• 39 farmers 39 farmers interviewedinterviewed•10 re-interviewed10 re-interviewed•10 interviews with 10 interviews with institutional actorsinstitutional actors

• First ESA : From 1986 to 1992First ESA : From 1986 to 1992• Second ESA : From 1992 to 1999Second ESA : From 1992 to 1999

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Breadalbane ESA : implementationBreadalbane ESA : implementation

• End 1986 :End 1986 :•Press : first rough Press : first rough description of the description of the measuremeasure•The measure is about The measure is about landscape and landscape and biodiversitybiodiversity

CASOS (July 2000)

Breadalbane ESA : implementationBreadalbane ESA : implementation

• End 1986 :End 1986 :• SAC : contact 10 SAC : contact 10 "leader" farmers and "leader" farmers and promote the measurepromote the measure• More financial More financial information information

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• March 1987 :March 1987 :• Organisation of the Organisation of the official presentation official presentation meetingmeeting•All farmers invitedAll farmers invited•Participation 50%Participation 50%

Breadalbane ESA : implementationBreadalbane ESA : implementation

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Breadalbane ESA : implementationBreadalbane ESA : implementation

• The interested farmer calls The interested farmer calls SAC advisorSAC advisor

• SAC advisor comes to the SAC advisor comes to the farm and makes to ecological farm and makes to ecological diagnosisdiagnosis

• The still interested farmer The still interested farmer calls SAC advisorcalls SAC advisor

• The advisor comes to the The advisor comes to the farm and the farm plan is farm and the farm plan is negotiatednegotiated

0

20

40

60

80

100

1987 1988 1989 1990 1991 1992

adoption %

• From April 87 to End 92:From April 87 to End 92:

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The modelThe model

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The decision model : variablesThe decision model : variables

d

a

-1 +1

• Uncertain anticipations of Uncertain anticipations of impacts (or impressions)impacts (or impressions)

• IncomeIncome

• MotivationsMotivations

• IndependenceIndependence a

d

-1 +1

a

d

-1 +1

• NatureNature

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The decision model : InteractionsThe decision model : Interactions

Before meetingBefore meeting

F’F

a a’

F F’

a a’a

d

d’

d

After meetingAfter meeting

dd’

CASOS (July 2000)

The decision model : rational The decision model : rational evaluationevaluation

• The impact on income is evaluated "rationally" with the advisor when the farm plan is established

• The calculation of this impact takes into account :– the size of the farm– the level payments– the level of the associated costs

CASOS (July 2000)

The decision model : decision stagesThe decision model : decision stages

• mm..aa < < TT11• Farmer not interested by the measure.

• TT11 < < mm..aa < < TT22• Interested,• Discussions with colleagues

• mm..aa > > TT22• Interested,• willing to begin the procedure

• mm..aa - - mmrr.d.dr r >T>T33 • Ready to adopt•can if procedure completed

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Initialisation of anticipated impactsInitialisation of anticipated impacts

d

a-1 +1

• Uncertain anticipations Uncertain anticipations (or impressions)(or impressions)

• IncomeIncome

• MotivationsMotivations

• IndependenceIndependence

• NatureNature

d

a-1 +1

d

a-1 +1

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Initialisation of the motivationsInitialisation of the motivationsNature

0 5 10 15

0

1

2

3

4

Revenu

0 5 10 15

0

1

2

3

4

Indépendence

0 10 20 30

0

1

2

3

4• Motivation Motivation distribution for distribution for interviewed farmsinterviewed farms

• Randomly Randomly affected to non affected to non interviewed interviewed farmsfarms

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Social networkSocial network

• link probability = exp (- link probability = exp (- DD((FF,,F'F'))))• Average link number : 4.8Average link number : 4.8

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Institutional messagesInstitutional messages

d

a-1 +1

d

a-1 +1

d

a-1 +1

• PressPress

• IncomeIncome • IndependenceIndependence • NatureNature

d

a-1 +1

d

a-1 +1

• SACSAC

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First resultsFirst results

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test 4 40-50

0

20

40

60

80

100

120

140

1 253 505 757 1009 1261 1513 1765 2017 2269

phase 1

phase 2a

phase 2b

phase 2bc

phase 3

Messages from the pressFirst messages from SAC

Meeting

Beginning of advisor visits no interest

discussing

visit ready

non adopteradopter

Example of resultsExample of results

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Average fitting the first adoption pointsAverage fitting the first adoption points

0

10

20

30

40

50

60

70

80

90

1000

234

468

702

936

1170

1404

1638

1872

2106

2340

une simulation

moyenne

données

number of adopters

average

data of adoption

CASOS (July 2000)

Fitting first point and last point Fitting first point and last point

0

20

40

60

80

100

120

1 265 529 793 1057 1321 1585 1849 2113 2377

une simulation

Moyenne

Données

number of adopters

average

data of adoption

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ConclusionConclusion

• Progressive refinement of the model :– institutional scenarios– dynamic model– more impacts and motivations

• Study for the use in a prospective purpose