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Decision analysis techniques1Inanagriculturalsystem,apartfromfarmers,policy,research,extension,andindustry

decisionmakers,playkeyrolesinimprovingpestmanagement.Eachgrouprequires

differenttypesofinformation,framedindifferentways,onwhichrespectivedecisions

arebased(Heong1989).Ausefulframeworktoconsideristhebasicdecisionmodel

developedbyMumfordandNorton(1984)(Fig5.1). Onthebasisofperceptionsofthe

problemandtheoptionsavailable,thedecisionmakerassessestheexpectedoutcomes

andthechoiceofactionisdependentonevaluationsintermsofpersonalobjectives.

AsillustratedinFigure5.1,thedecisionmakingprocessisanamalgamofbothrational

andsociopsychologicalfactors. Agriculturaldecisionanalysis(Raiffa1970,Halterand

Dean1971,Andersonetal1977)ormoderndecisionanalysis(KaufmannandThomas

1975)studiestherationalfactorinordertoclarifywaysinwhichdecisionsshouldbe

made. Itisanapproachthatallowsadecisionmakertocarryoutathoroughandlogical

evaluationof

alternative

strategies

in

order

to

determine

systematically

the

best

availablestrategyintermsofanobjectivecriterion.Thisapproachissometimescalled

prescriptiveanalysis,whereastheanalysisofthesociopsychologicalfactorsiscalled

descriptiveorbehavioralanalysis(Kleindorferetal1993).

Simons(1959)conceptofboundedrationalityisausefulmeanstothinkaboutdecision

makingand,recentlyGigerenzeretal(1999)addedanextensiontoboundedrationality

(Fig5.2). Thisframeworkviewsrationalityasunboundedandbounded.Unbounded

rationalityinvolvesmodelsthatoptimizebasedonBayesianapproaches,whilebounded

rationalityinvolvesmodelsthatreflectreallifedecisions.Recentstudiesonhuman

judgmentand

choices

have

shown

that

the

prescriptive

models

are

unable

to

account

for

howpeopleactuallymakedecisions(Slovicetal1977,Simon1978,RabiaandThaker

2001). Mostpeopleviolatetheprescriptiveprinciplesbecausedecisionmakingis

behavioralinnature(EinhornandHogarth1981). Inthischapter,weshalldiscuss

conceptsandtechniquesusedtounderstandboththeprescriptiveanddescriptive

aspectsofdecisionmaking.

2.1 Decision treesParticipantscontemplatingaproblemoftenseearangeofpossibledecisionsspreadover

aperiodoftime. Atreediagramisusefulinstructuringthesequenceofdecisionsand

allows

ones

to

break

down

a

big

decision

problem

into

a

series

of

smaller

problems

that

maybesolvedseparately. Thisdeviceenablesparticipantstoseeanarrayofpossible

optionsaswellasthesequentialnatureofdecisions.Figure5.3illustratesaseriesofpest

managementdecisionsaricefarmermayneedtomakeateachstageinacropcycle.

1 M.M. Escalada and K.L. Heong. 2009. Training Manual for the Workshop/Training on Decision Making,

Sociological Tools and Impact Assessment in Pest Management. IRRI-ADB RETA 6849. Reducing

Vulnerability of Crops to Pre-Harvest Losses Caused by Planthopper Pest Outbreaks.

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Ideally,thefarmerexamineshiscropanddecidesonwhethercontrolactionisneeded,

basedonhisrecognitionofthepestsandknowledge. Ifcontrolisneeded,hewilldecide

onwhichtypeofcontroltouse.Forconsideringpreplantingoptionssuchasthechoice

ofresistantvarieties,astrategicdecisiontreemaybeuseful(Fig5.4). Furtherdetailson

decisiontreesandtheirapplicationsmaybefoundinMooreandThomas(1976)and

Kaufmanand

Thomas

(1977).

Rule-based advisory matrixNorton(1987)developedthistooltostructureknowledgerequiredtomake

recommendationsforastoredgrainpest. Ithasbeenappliedtostructureinformation

forexpertisesystembuildinginbrownplanthoppermanagementinChina(Holtetal

1990)(Fig5.5). Thematrixwasappliedinaworkshoptoconsiderrecommendationsfor

aseriesofsituationswherevariousfactorswerepresent.InFig5.6adotinthematrix

cellmeansthatthefactorwaspresentandoneofninerecommendationscouldbe

chosen. Forinstance,insituation6,ifthericevarietywassusceptibletoriceblast,

weather

was

favorable,

nitrogenous

fertilizer

was

overused,

and

the

crop

is

at

heading

stage,thentherecommendationwastospraywiththefungicide,isoprothiolane.

Policy option matrixThepolicyoptionmatrixprovidesameansforparticipantstoconsidervariousoptions

withrespecttodifferentobjectives(NortonandHeong1988).TheexampleinFig5.7

concernsoptionsavailabletotheDepartmentofAgricultureinMalaysiatorespondto

anoutbreakofthebrownplanthopper. Thisframeworkprovidesameansfordiscussion

amongofficialsintheDepartmenttocollectivelyarriveatanoptionthatbestfitsthe

situationinthetechnical,economic,andpoliticalcontexts.Asidefromhelpingthe

analystsidentifypoliticallyunfeasibleoptions,thematrixcanalsohelpdeterminethe

mostsensitive

criteria

on

which

further

analysis

should

concentrate.

Pest belief modelThepestbeliefmodel(Fig5.8)providesaframeworkforunderstandingandquantifying

relationshipsbetweenbeliefsandpestmanagementdecisions(HeongandEscalada

1999).Fourbasicbeliefsdeterminefarmersdecisions:

(1) Perceivedbenefits:thedegreetowhichacertainactionwillbeseenasreducing

theperceivedsusceptibilityorseverityofthepestattack.

(2) Perceivedbarriers:theperceivednegativeaspectsofaparticularaction.

(3) Perceivedsusceptibility:

the

subjective

risk

of

getting

pest

attacks

if

no

countermeasuresaretaken;and

(4) Perceivedseverity:theseverityofthepestattack.

Byusingfarmersurveytechniquesdiscussedinthenextchapter,wecanobtain

estimatesofthesebeliefs,quantifythem,andestablishrelationshipswithdependent

variablessuchassprayfrequenciesandpesticideexpenditures.

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2.2 Understanding social contextsInanalyzingdecisionmaking,socialcontextsareimportantconsiderations.Aspointed

out by Gigerenzer (1996), traditional axioms and rules are insufficient to explain

behavioralchoicesas theydependon socialobjectives,valuesandmotivations.Social

emotionsalsoplayasignificantroleindecisionmaking(Elster1999).

2.2.1 Theory of Reasoned Action/Theory of Planned BehaviorFishbeinandAzjen (1975) first formulated theTheoryofReasonedAction (TRA)and

Heong and Escalada (1999) used it to analyze rice farmers stemborer management

decisions.Thismodelprovidesaframeworkforunderstandingmotivationalinfluences

on decision behaviors and to help identify how and where to target strategies for

changingbehaviors. The TRA suggested that a personsbehavior is determinedby

his/herintentiontoperformthebehaviorandthatthisintentionisinturnafunctionof

his/herattitudes towards thebehaviorandhis/her subjectivenormattitudes. Thus in

addition topestbeliefsasoutlined in thepestbeliefmodeldiscussedearlier, theTRA

suggeststhat

equally

important

are

attitudes

related

to

social

pressures

which

make

up

the subjective norm components. In some cases, subjective norm attitudesmay have

stronger influence thanbeliefattitudes in farmersdecisionsas shown inLao farmers

(Heongetal2002).

TheTRAassumes thatbehavior isvoluntaryanddecisionmakershave fullcontrolof

takingthepreferredaction. However,oftenthismaynotbethecaseandAjzen(1991)

revised theTRAmodelby adding the component, perceivedbehavioral control, and

formulated the Theory of Planned Behavior (TpB) (Fig 5.9). This model provides

additional power to analyze farmers pest management decisions, especially in

understanding the constraint to adopting certain management options. Numerous

applications of the TpB are reported in health sciences (Godin and Kok 1996,

http://hsc.usf.edu/~kmbrown/TRA_TPB.htm) and further details are available in

http://www.people.umass.edu/aizen/

2.2.2 Ethnoscience techniquesEthnoscience isthestudyofperceptions,knowledge,andclassificationoftheworldas

reflected in their use of language. Ethnoscience has been used by many different

disciplines; thus there are studies in ethnobotany, ethnopedology, ethnoforestry,

ethnoveterinarymedicine,andethnoecology. Mostethnoscienceresearchhasdealtwith

specificdomains,suchasfolkmedicine;classificationsofplants,fish,andbirds;andpest

management(Bentley

and

Rodriguez

2001).

Inthefieldofeconomics,theuseoflocaltaxonomiccategorieshasbeenapplied

to analyze the effects of different types of soil on the adoption of new maize seed

varieties.BellonandTaylor (1993)asked farmersabout thevarioussoil typeson their

land,whatcharacteristicstheyattributedtoeachtype,andhowtheyrankedthosesoils

in terms of their suitability formaizeproduction.Theirhypothesiswas that farmers

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perceptions of the soil qualities on their farms significantly affect their decision on

whether to adopt new technology.Their results showed that the perceptions of land

qualitiesdidindeedaffecttheadoptionofnewseedvarieties.Itwassuggestedthatthis

typeofanalysiscanbetakenonestepfurtherbyexamininglocalclassificationsofsuch

economic terms asbenefits, costs, insurance, interest, security, and risk, in order to

determinewhether

these

are

locally

meaningful

concepts.

Since decisionmaking is defined as the intentional and reflective choice in

response toperceivedneeds,understanding farmersperceptionsandhow theyname

and classify nature is an important first step toward improving decisions. To obtain

some insights intofarmerscognitivestructures,wefoundtwoethnosciencetoolsvery

useful(Bentley1999).

Folk taxonomy

Folk taxonomy is consideredan important indicatorofdiversity relating tohow crop

populations

may

be

treated

differently.

Eyzaguirre

(2003)

noted

that

by

developing

manynames forcrop types, farmersareeffectively segregatingpopulationsandoften

treatingthemdifferently.Localknowledgeaboutacropvarietyhelpstotransmitplant

knowledgearoundthecommunitysuchasknowledgeofassociatedpestsanddiseases.

Folk taxonomieshavehierarchical levels similar to formalbiological classifications of

kingdom, phylum, class, order, family, genus, and species (Berlin 1992). In folk

taxonomy,thecommonlevelsare:

Life-form a high level of plants or animals that share some general shape or

characteristicinmorphology.Examples: tree,vine,bush,fish,snake,bird,mammal.

Genericthe

most

common

basic

level.

Examples

are

dog,

grass,

and

rice

ant.

Folk

generaoftendonotcorrespondtoscientificgenerabutsometimestoLinnaeanspeciesor

family. Forinstance,dogisafolkgenusandaLinnaeanspecies;antisafolkgenus

andbelongstoLinnaeanfamilyformicidae.

Specific usually separated from each other by a few characteristics. In some

languages, suchasSpanish,Bahasa Indonesia,andMalaysia, thegenericname comes

first,asinaLinnaeanname.InEnglish,Filipino,Chinese,VietnameseandThai,itisthe

otherwayaround.Specificnames tend tobeapneumonicdevice likecolor, shape,

and utility that makes the names easy to remember. Figure 5.10 shows farmers

classificationof

leaf

feeding

insects

in

Leyte,

Philippines

Besidesbeinghierarchical,folktaxonomymaybeappliedinnamingpartsofan

objectorstagesofthecrop(partonomy).Farmersmayhavenamesthatlumpgroupsof

partsthatbiologistsdifferentiateortheymayhavefinerdefinitionsofpartsthanwhat

biologistsdescribe. Forinstance,Figure5.11illustratesstagesofthericecropnamedby

Filipinofarmers.

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Emic-EticframeworkEticandemicaretermscoinedby linguisticanthropologistKennethPike(seeFranklin

1996),whichwerederivedfromananalogywiththetermsphonemicandphonetic.

Etic categories involve a classification according to some external system of analysis

considered as appropriate by science. This is the approach of biology where the

Linnaeanclassification

system

is

used

to

define

new

species.

It

assumes

that

ultimately,

thereisanobjectiverealitythatisseentobemoreimportantthanculturalperceptionsof

it. In contrast, emic categories involve a classification according to theway inwhich

membersofasocietyperceiveandclassifytheirownworld.

Thus emicetic roughly means local versus scientific knowledge and this framework

provides a convenient tool for researchers to obtain accuratedescriptions of farmers

knowledge or concepts and compare itwith scientific knowledge or concepts on the

sametopic(Fig.5.12).

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Figure 3.1

Emic EticFrameworkLocation :__________________________ Date: ____________________Topic :____________________________________________________________Variable/Character

DescriptionsEmic

(Local Knowledge)Etic

(Research Knowledge)