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Determining smallholder farmers' preferences for technologydissemination pathways: the case of 'push-pull' technology in the controlof stemborer and Striga weeds in KenyaA. W. Murageabc; D. M. Amudavid; G. Obarebe; J. Chianuf; C. A. O. Midegaa; J. A. Pickettg; Z. R. Khana

a International Centre of Insect Physiology and Ecology (icipe), Mbita Point, Kenya b Department ofAgricultural Economics and Business Management, Egerton University, Egerton, Kenya c KenyaAgricultural Research Institute (KARI), National Animal Husbandry Research Centre (NAHRC),Naivasha, Kenya d Department of Agricultural Education and Extension, Egerton University, Egerton,Kenya e Food Security Center, University of Hohenheim, Stuttgart, Germany f Tropical Soil Biologyand Fertility Institute of CIAT (CIAT-TSBF), c/o World Agroforestry Centre (ICRAF), Nairobi, Kenya g

Rothamsted Research, Harpenden, Herts, UK

Online publication date: 06 February 2011

To cite this Article Murage, A. W. , Amudavi, D. M. , Obare, G. , Chianu, J. , Midega, C. A. O. , Pickett, J. A. and Khan, Z.R.(2011) 'Determining smallholder farmers' preferences for technology dissemination pathways: the case of 'push-pull'technology in the control of stemborer and Striga weeds in Kenya', International Journal of Pest Management, 57: 2, 133— 145To link to this Article: DOI: 10.1080/09670874.2010.539715URL: http://dx.doi.org/10.1080/09670874.2010.539715

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Determining smallholder farmers’ preferences for technology dissemination pathways:

the case of ‘push–pull’ technology in the control of stemborer and Striga weeds in Kenya

A.W. Muragea,b,c*, D.M. Amudavid, G. Obareb,e, J. Chianuf, C.A.O. Midegaa, J.A. Pickettg and Z.R. Khana

aInternational Centre of Insect Physiology and Ecology (icipe), Habitat Management Project PO Box 30-40305, Mbita Point,Kenya; bDepartment of Agricultural Economics and Business Management, Egerton University, PO Box 536-20115, Egerton,Kenya; cKenya Agricultural Research Institute (KARI), National Animal Husbandry Research Centre (NAHRC)PO Box 25-20117, Naivasha, Kenya; dDepartment of Agricultural Education and Extension, Egerton University,PO Box 536-20115, Egerton, Kenya; eFood Security Center, University of Hohenheim, 70599 Stuttgart, Germany; fTropical SoilBiology and Fertility Institute of CIAT (CIAT-TSBF), c/o World Agroforestry Centre (ICRAF), PO Box 30677-00100 Nairobi,Kenya; gRothamsted Research, Harpenden, Herts AL5 2JQ, UK

(Received 27 April 2010; final version received 9 November 2010)

The push–pull technology (PPT) has widely been disseminated to control insect stemborers [Chilo partellus andBusseola fusca] and Striga weeds [Striga hermonthica and Striga asiatica] in maize fields in Kenya. This studyexamined farmers’ preferences for various dissemination pathways in order to proffer better targeting of resources inan optimal dissemination strategy. The pathways considered were public meetings (barazas), radio, farmer fieldschools (FFS), field days (FD), farmer teachers (FT), fellow farmers (FF) and printed materials. Using a weightedscore index and ordered probit regression, the different pathways were sequentially ranked as FD, FT, FFS, FF,print materials, Radio, and barazas. Marginal effects from the ordered probit regression model showed that farmershad the least preference for baraza and radio pathways. The farmer categories with the highest preference forparticular pathways were: less educated farmers for FD, farmers with small land sizes for FT, farmers belonging togroups for FFS, and young educated farmers for the printed materials. This information would help disseminatorsand extension agents to target different groups of farmers with different pathways of knowledge transfer based ontheir preferences.

Keywords: push–pull technology; Stemborer; Striga weed; dissemination pathways; preferences

1. Introduction

Cereal stemborers (Chilo partellus [Swinhoe] (Lepidop-tera: Crambidae) and Busseola fusca Fuller (Lepidop-tera: Noctuidae)) and parasitic Striga weeds (Strigahermonthica and Striga asiatica [Scrophulariaceae]) area major challenge to sustainable maize production insome parts of Kenya. It is estimated that thestemborers cause between 20% and 80% of maizeyield losses (Kfir et al. 2002) while Striga causes 30–50% and sometimes up to 100% maize yield lossesunder severe infestation (Hassan et al. 1994; Khanet al. 2001; Vissoh et al. 2004). These losses translate toan annual cash income loss of up to $40.8 million(Kanampiu et al. 2002; Khan et al. 2008a) and presenta high risk of food insecurity and poverty to theaffected families. In response to these challenges, theInternational Centre of Insect Physiology and Ecology(ICIPE), in collaboration with other research organisa-tions, developed a habitat management strategy forcontrolling stemborers and Striga weeds simulta-neously (Khan et al. 2001; Midega et al. 2010). Thismeans of control termed ‘push-pull’ technology (PPT)

is based on a stimulo-deterrent diversionary strategy(Miller and Cowles 1990; Cook et al. 2007).

In this study, PPT involved intercropping maizeand Desmodium spp. in maize fields, with Napier grassplanted as a border around the intercrop (Khan et al.2001; Midega et al. 2010). The desmodium repelsstemborer moths (‘push’), while the surroundingNapier grass attracts them (‘pull’) (Khan et al. 2001).In addition, desmodium suppresses Strigaweeds througha number of mechanisms, with allelopathy (root to rootinterference) being the most important in this case (Khanet al. 2008b; Midega et al. 2010). With effective controlof these biotic stresses, a significant cost–benefit return of2.2 was reported in the PPT regime relative to 0.8 for themaize monocrop regime (Khan et al. 2001) whilesustainable increases in maize grain yields and higherreturns to labour were also reported by Khan et al.(2008a). This technology is currently practiced byaround 25,000 smallholder farmers in East Africa andis being promoted through various dissemination path-ways to improve output in cereal production whileminimising negative environmental effects (Cook et al.2007; Amudavi et al. 2008, 2009; Khan et al. 2008c).

*Corresponding author. Email: alice_murage@yahoo.com

International Journal of Pest ManagementVol. 57, No. 2, April-June 2011, 133–145

ISSN 0967-0874 print/ISSN 1366-5863 online

� 2011 Taylor & Francis

DOI: 10.1080/09670874.2010.539715

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Implementation of scientific research findings intopractical solutions is faced with several challenges. Oneof these challenges is access to information and uptake ofnew technologies through effective dissemination path-ways that are crucial in optimising the adoption processespecially of ‘knowledge-based’ innovations (Padel2001). It has also been established that informationsources rather than subsidies are more effective inencouraging adoption through enhancing farmers’ allo-cative ability and revising their perceptions on profit-ability of the new technology (Lohr and Salomonsson2000; Genius et al. 2006). Since PPT is knowledge-intensive, the potential for uptake would be limited,especially among the smallholder farmers if appropriatedissemination pathways are not used to ensure itseffective transfer. The use of multiple disseminationpathways is likely to result in differences in farmers’preferences for the pathways, singly or simultaneously.

While knowledge of farmers’ preferences for variousinformation sources is imperative in evaluating theeffectiveness and efficiency of dissemination pathways,it has been shown that sources and methods ofinformation communication should not only be basedon their effectiveness and capacity to reach largernumber of farmers, but also according to their perceivedcredibility, relevance and preference among targetaudience (Gloy et al. 2000; Roderick et al. 2008). Unlikein developed countries where studies have analysedfarmers’ preferences for various information sources(e.g. Gloy et al. 2000; Ngathou et al. 2005; Roderick et al.2008), we are not aware of any study that has beenconducted in a developing country context such asKenya where requisite circumstances and informationsources for knowledge intensive technology and thesocio-economic circumstances of intended beneficiariesdiffer significantly. This study aimed at evaluatingfarmers’ preferences for the different pathways used inthe dissemination of the PPT technology in order torecommend a targeted dissemination strategy that wouldallow farmers to receive adequate information to enablethem to learn and make informed adoption decisions.

2. Materials and methods

2.1. Study sites

The study was carried out in four districts in Westernand Nyanza provinces of Kenya namely Homabay,Kisii, Busia and Bungoma, districts (see Figure 1) inFebruary andMarch 2009. Administratively, Homabayand Kisii districts are located in Nyanza province, whileBungoma and Busia districts are in Western province.These districts are mainly agriculturally-based produ-cing both cereal crops and livestock products. However,stemborer and Striga weeds are a serious setback tosustainable cereal production which renders the areafood insecure. The choice of the study districts wasmotivated by the predominant use of PPT as the controlstrategy for stemborers and Striga weeds.

2.2. Sampling and data collection

Primary data were collected through a cross-sectionalsurvey, using structured questionnaires. Since the dataselection criterion was to increase validity in additionto obtaining a representative sample as required instudies that aim to make inferences to the studypopulations, purposive sampling was used to select thedistricts with predominant use of PPT as a controlmeasure for the stemborers and Striga weed. This isbecause purposive sampling ensures that certainimportant segments of the target population arerepresented and also allows selection of rich informa-tion that provides a great deal of insight into the issuesof central importance to the research (Patton 1990). Atotal of 491 farmers were selected through simplerandom sampling technique from a sample frame ofboth adopters and non-adopters. The questionnairewas designed to collect information on general house-hold and socio-economic characteristics such as headof household’s age, education, gender, farm ownership,land size, total household income, group memberships,access to credit and distances from the farm to tarmacroads (used as a proxy for access to information, inputsand output markets) and also farmers’ preferences forgiven dissemination pathways. Seven commonly useddissemination pathways considered were: Barazas(public gatherings), radio, farmer field schools (FFS),field days (FD), farmer teachers (FT), fellow farmers(FF) and print materials (Brochure, leaflets and book-lets). The respondents were presented with a list of theseven pathways and asked to rank each one of themaccording to their preference using a 3-point likertscale indicator with 1 ¼ not preferred, 2 ¼ somewhatpreferred, 3 ¼ most preferred based on how theyperceived their effectiveness in information delivery.For this purpose of choice and limited dependentvariable analysis, these ranking were recoded as0 ¼ not preferred, 1 ¼ somewhat preferred, 2 ¼ mostpreferred.

2.3. Analytical strategy

A weighted rank index was used to assess farmers’preference ranking for the seven PPT disseminationpathways by farmer category as shown in Equation(1). The farmers were grouped into either adopters,non-adopters or both of these groups combined. Theoverall rank index for each pathway was computedas:

Ii ¼321

0@

1A X3

j�1 xj

h ij=Xn

k�1

321

0@

1AX3

j�1 xj

24

35k

ð1Þ

where Ii is the ranking index calculated as sum of scores[3 for most preferred þ2 for somewhat preferred þ1 fornot preferred] for each dissemination pathway divided

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by sum of scores [3 for most preferredþ2 for somewhatpreferred þ 1 for not preferred] for all preferences of allthe dissemination pathways. xj is the number ofrespondents ranking pathway i in the jth rank, and kis the sum of ranks for n number of pathways.

An ordered probit model was used to assess thefactors influencing preference for the seven dissemina-tion pathways. This model has been adopted in the

analysis of human attitudes, behaviours and choicesand has lately been applied in estimating coefficients topredict factors influencing farmer’s preference rankingof a certain dissemination pathway based on maximumlikelihood estimate (e.g. Ngathou et al. 2005). In theformulation of the model, the observed responses wererepresented by a variable Yi which denotes thepreference rank given to each dissemination pathway

Figure 1. Map of the study districts.

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by farmer i and takes on j different values which arenaturally ordered, in this case 3 values (j ¼ 0, 1, 2).However, these observed values are assumed to derivefrom some unobservable latent variable Yi*:

Y�i ¼ Xibþ ei ð2Þ

where Xi represents the observable individual specificfactors on which data were collected, b is a vector ofparameters to be estimated and ei is the stochasticdisturbance term whose distribution is estimated to benormal (Greene 2003). The values for observed choiceoutcome Yi are assumed to be related to the latentvariable Yi* as follows:

Y ¼ 0) not preferred if Y� < mo where m ¼ 0

Y ¼ 1) somewhat preferred if 0 � Y� < m1Y ¼ 2) most preferred if m1 � Y� < m2

ð3Þ

where mi is unknown threshold parameter for outcomei that separates the adjacent boundary values and isestimated together with the bs. The estimated m followsthe order m05 m15 m2. The probability of each obser-ved outcome falling in a given category is given as:

PrðY ¼ 0Þ ¼ fð�b0xÞ

PrðY ¼ 1Þ ¼ fðm1 � b0xÞ � fð�b

0xÞ

PrðY ¼ 2Þ ¼ 1� fðm1 � b0xÞ

ð4Þ

where f is the cumulative density function of mi. Usingmaximum likelihood estimates technique, the valuesfor the parameters b can be estimated. However,interpretation of coefficients from an ordered probit isnot straight forward. To elicit causal relationshipbetween the explanatory variables and qualitativedependent choice outcome variable computation ofpartial changes or marginal effects in the probabilitiesof an outcome for a given change in each dependentvariable is conducted. Marginal effects measure theexpected change in predicted probability associatedwith changes in the explanatory variables. The margin-al effect of an independent variable is the derivative(that is, the slope) of the prediction function, which, bydefault, is the probability of success following probit.The marginal effect measurement is required tointerpret the effect of the regressors on the dependentvariable (Greene 2003). These marginal effects areestimated by taking the first derivative of the loglikelihood functions as:

@ PrðY ¼ 0Þ@x

¼ �fð�b0xÞb

@ PrðY ¼ 1Þ@x

¼ ½fð�b0xÞ � fðm1 � b

0x�b

@ PrðY ¼ 2Þ@x

¼ ½fðm1 � b0xÞ�b

ð5Þ

For this study the estimated marginal effects willshow the change in the likelihood that a respondentwould ‘somewhat prefer’ or ‘most prefer’ (as opposedto ‘not prefer’) as a result of the unit change in thatparticular variable. In other way, the marginal effectsshow the probabilities that a farmer would rank adissemination pathway in any of the two preferencecategories, given a set of farmer characteristics andfarm attributes. The signs in the parameter estimatesand their statistical inferences indicate the direction ofthe relationship (Verbeek 2004).

An ordered probit regression was fitted for each ofthe seven dissemination pathways to obtain estimatesof the coefficients and marginal effects. The followingempirical model was specified and used to estimate therelation between preference rankings and other attri-butes (farmer, institutional and spatial).

Prefrank¼b0þb1Genderþb2Ageþb3Prieducþb4Seceducþb5Pseceducþb6Tenureþb7Lansizþb8Pptadoptþb9Inclev2þb10Inclev3þb11Grpmemberþb12Distarmac

þb13Kisiiþb14Busiaþb15Bungomaþe ð6Þ

Table 1. Description of dependent and explanatory vari-ables and their expected signs as used in the ordered probitmodel.

Variable Description

Dependent VariablePrefrank 0 ¼ not preferred, 1 ¼ somewhat preferred,

2 ¼ most preferred

Explanatory variablesGender Gender of the main farmer (1 ¼ Male,

0 ¼ Female)Age Age of the farmer in years (continuous)Noeduc 1 if farmer has no formal education,

0 if otherwise (reference category)Prieduc 1 if farmer has primary education,

0 if otherwiseSeceduc 1 if farmer has secondary education,

0 if otherwisePseceduc 1 if farmer has post secondary education,

0 if otherwiseTenure Land owner ship (1 ¼ Owned, 0 ¼ otherwise)Landsiz Total land size in acres (continuous)Pptadopt If the farmer has adopted PPT (1 ¼ Yes,

0 ¼ No)Inclev1 1 if farm income is 5 Ksh 20,000, 0 if

otherwise (reference category)Inclev2 1 if farm income is Ksh 20,000 to Ksh

40,000, 0 if otherwiseInclev3 1 if farm income is 4Ksh 40,000, 0 if otherwiseGrpmember 1 if a farmer is in an organised farmers’

group, 0 if otherwiseDistarmac Distance from the farm to the nearest

tarmac road (km)Hbay Dummy for Homabay district (1 ¼ Yes,

0 ¼ No) (reference category)Busia Dummy for Busia district (1 ¼ Yes, 0 ¼ No)Bungoma Dummy for Bungoma district (1 ¼ Yes,

0 ¼ No)Kisii Dummy for Kisii district (1 ¼ Yes, 0 ¼ No)

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The description, measurement and the a priori de-pendent variable effect expectations of the variables usedin the model are presented in Table 1. Prefrank is thedependent variable and is measured as the observedordered response for a particular pathway which takeson any of the three possible outcomes (0, 1 and 2). Thechoice of the explanatory variables was based on thegeneral working hypothesis. Depending on the pathwayunder consideration, the explanatory variables wereexpected to have a positive or negative effect on prefer-ences. Socio-economic factors such as age, gender andeducation represent the ability of the farmer to processinformation received from dissemination pathways.Some farmers tend to prefer pathways that providegeneral information, while others tend to prefer path-ways which provide customised and simplified informa-tion to support the specific needs of the informationconsumers. Farm characteristics such as size and tenurerepresent the independence over the decision-makingprocess, which is likely to influence the choice ofpathway. Tropical Livestock Units (TLU) and incomerepresent the wealth status, while a variable of PPTadoption was used to distinguish the preferences ofdissemination pathways among adopter and non-adop-ters. Group membership represents the effect of attitude

a farmer develops from group contacts, while distance tothe tarmac road represents the effects of accessibility toinformation sources on preferences. Regional variableswere considered to control for factors outside thefarmers’ production decision context possibly affectingpathway use decisions. These variables include dummyvariables for the districts where the study was conducted.

The statistical significance of individual parameterswas interpreted using the P-value which is thealternative way to assess individual significance esti-mates in maximum likelihood estimates. The P-value isthe lowest significance level at which a null hypothesiscan be rejected (Greene 2003). Potential multicolli-nearity among the explanatory variables, based on thevariance inflation factor (Maddala 1993), was tested ina preliminary analysis and was found to have insignif-icant influence on estimates from the model.

3. Results and discussion

3.1. Sample summary statistics

Table 2 presents the descriptive statistics of the keyvariables describing farmers’ and farm characteristicsin the four districts where the study was conducted andthe overall responses. Chi-square (w2) or F-tests were

Table 2. Descriptive statistics and frequencies for selected farmers’ and farm characteristics per study district and for the overallsample.

Demographic variablea

Respondents districtsb

SampleN ¼ 491

HomabayN ¼ 122

KisiiN ¼ 121

BusiaN ¼ 120

BungomaN ¼ 128 F-statistic w2

Gender of the farmer (%) 7.32**Female 57 62 59 60 47Male 43 38 41 40 53

PPT adoption (%) 10.52***Adopted 84 80 80 93 83Not adopted 16 20 20 7 17

Education level of the farmer (%) 34.62***No formal education 6 8 10 7 1Primary education 52 57 59 51 40Secondary education 36 27 26 33 54Post-secondary education 7 7 6 9 5

Household income category (%) 42.18***Level 1 (5Ksh 20,000) 31 48 31 27 20Level 2 (Ksh 20,000 to Ksh 40,000) 35 38 37 29 36Level 3 (4Ksh 40,000) 33 13 32 44 44

OthersOwnership of land (%) 97 96 98 94 100 8.99**Group membership (%) 87 89 79 96 82 17.27***Age of the farmer (years) 44 (11.5) 43.7 (10.9) 41.5 (10.2) 46.0 (12.4) 45.4(11.8) 3.88***Household size (persons) 7 (3.2) 6.5 (2.9) 6.5 (2.4) 7.4 (3.1) 8.0 (4.0) 7.32***Total land size (acres) 3.9 (3.7) 3.5 (3.7) 3.2 (2.3) 4.2 (2.8) 4.7 (5.2) 4.03***Tropical Livestock Units (TLU) 2.6 (2) 3.3 (2.4) 1.7 (1.2) 2.5 (1.9) 3.0 (2.3) 16.31***Distance to tarmac (Km) 4.9 (5.4) 2.3 (3.1) 4.7 (4.8) 5.2 (4.9) 7.5 (6.9) 21.1***Number of pathways used 3.7 (1.6) 3.2 (1.4) 3.5 (1.9) 4.1 (1.4) 4.1 (1.4) 8.56***

aSocioeconomic characteristics of the farmer and farm characteristics; bFigures in the parenthesis are the standard errors associated with themeans for the continuous variables; and 3Pathways for information dissemination; FD, Field days; FT, Farmer Teachers; FFS, Farmer FieldSchools; FF, Fellow farmers.

**P 5 0.05, ***P 5 0.01.

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used where appropriate for statistical significance orotherwise, for the differences between the responses(overall sample and study districts). For most of thevariables, the differences were statistically significantbetween the districts. Although the majority (84%) ofthe respondents had taken up the technology, a few(16%) had not adopted PPT. A majority of the non-adopters (73%, n ¼ 89) stated lack of plantingmaterials and insufficient information as the mainreasons for non-adoption. This may be an indicationthat some of the dissemination pathways used to trainthe respondents may not have been effective. Alter-natively, it implies that there is need to understand theclients’ information needs and preferences prior tousing particular dissemination pathways. Other rea-sons for non-adoption were; farmers still in the processof starting to use the technology (13%), farmers onrented land (7%), PPT considered labour intensive(5%) and lack of interest (2%).

There were slightly more female respondents thanmales, a scenario which is common in many developingcountries, whereby women are more involved infarming activities while their male counterparts oftengo for off-farm activities. Furthermore, the resultsshow a middle aged farming community (mean age ¼44 years) with an education level that reflects relativelyhigh literacy levels. This is quite relevant in technologyup-scaling since the two (age and education) reflectfarmers’ ability to obtain and evaluate informationabout an innovation and therefore an indication of themode of technology dissemination to be used.

The average household size was seven members. Onaverage, land sizes were 1.58 hectares an indicationthat farmers in this region are smallholders. Theaverage TLU was estimated to be 2.6. On averagethe respondents had received information about PPTfrom four pathways out of the seven that wereassessed. This reflects the information-seeking beha-viour, whereby farmers tend to seek informationfrom different sources before making a technology

adoption decision. Such a scenario is likely tohappen if the pathways used do not provide sufficientinformation, such that farmers have to look for extrainformation from other sources. This would suggestthat using tested and effective pathways is likely tominimise farmer information search costs. Land wasmainly owned by the majority (97.1%) of the farmersbut without title deeds and about 86.5% of thefarmers belonged to organised groups mainly womenand men social welfare groups, production andmarketing groups, all of which represent the socialcapital among the respondents. Household incomewas fairly distributed along the three levels (31.4% inincome level 1, 35% in income level 2 and 33.4% inlevel 3) (1 $ ¼ Ksh 75 at the time of survey).

3.2. Weighted scores based dissemination pathwaypreferences

Table 3 shows farmers’ preferences for the variousdissemination pathways by farmer category and basedon weighted rank index. The result shows that FDswere the most preferred dissemination pathways, withthe highest index of 0.171 for adopters, 0.167 for non-adopters, and 0.170 for the overall sample. Farmerteachers and FFS were ranked second and thirdrespectively. The ranking for all the pathways wassimilar both in position and the score index (Table 3).The overall farmers’ preferences for the FD comparedto other pathways is probably due to their ability tocatalyse interactive learning among participants andthe tendency to elicit interest from farmers comparedto other forms of dissemination (Doss 2003). Inaddition, FDs have been a predominant extensiontechnique used by various agents and non-govern-mental organisations to disseminate information.During these open days, farmers are able to interactwith the facilitators as well as with other farmers andexchange ideas and experiences (Madukwe 2006). Insome cases, hands-on training and physical

Table 3. Farmers’ perception for the various dissemination pathways by farmer category based on a weighted rank index.

Farmer category

Adoptersa Non-adoptersa Combineda

Pathwayc 1 2 3 n Rankingb 1 2 3 n Rankingb 1 2 3 n Rankingb

FD 12 106 295 413 0.171 2 21 52 75 0.167 14 127 347 488 0.170FT 32 89 291 412 0.167 6 17 53 76 0.167 38 106 344 488 0.167FFS 45 86 281 412 0.163 7 23 46 76 0.160 52 109 327 488 0.163FF 35 192 186 413 0.151 4 31 39 74 0.153 39 223 225 487 0.151Print 112 198 102 412 0.125 18 36 22 76 0.131 130 234 124 488 0.126Radio 87 256 70 413 0.125 16 45 14 75 0.124 103 301 84 488 0.125Baraza 213 163 33 409 0.098 37 33 5 75 0.099 250 196 38 484 0.098

a1, not preferred; 2, somewhat preferred; 3, Most preferred; bRanking index ¼ Sum of [3 for most preferred þ 2 for somewhat preferred þ 1 fornot preferred] divided by [3 for most preferred þ 2 for somewhat preferred þ 1 for not preferred] for all preferences of all the disseminationpathways; cPathways: FD, Field days; FT, Farmer Teachers; FFS, Farmer Field Schools; FF, Fellow farmers.

n, number of farmers ranking the pathway.

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participation of the farmers is encouraged. Theseresults corroborate the findings of Amudavi et al.(2008) who found that the farmers’ propensity toseek new agricultural knowledge motivated them toattend the FDs and overall, it was favourably ratedin terms of its effectiveness in information dissemina-tion. However, Amudavi et al. (2009) acknowledgethat in this era of tight budgets for public extensionand the challenges faced in extension delivery tocover large geographical areas, well trained FTs canbe alternative effective pathways of knowledgedissemination to other farmers. The results fromthe current study complement this observation sinceFT ranked second in preference.

3.3. Determinants of preference ordering and marginaleffects

Table 4 presents ordered probit coefficients, for thevarious factors influencing farmers’ preferences for theseven PPT dissemination pathways. All the modelswere significant at 1% (P 5 0.01). The estimatedthresholds or cut-off points (m) shows the range ofnormal distribution associated with the specific valuesof the response variable and satisfy the conditionsm0 5 m1 5 m2 implying that the categories are ordered(Knight et al. 2005). The first cut-off point (Y ¼ 0 for‘not preferred’) was used as reference for comparisonpurposes. The cut-off points show the likelihood orprobability of ranking a particular pathway high orlow. The coefficients presented in Table 4 show varyinglevels of significance both in magnitude and directionfor some explanatory variables. Gender of the farmer(Gender) was significant with respect to preference forFF, print media and Baraza; farmer’s age (Age) withrespect to print alone; primary education (Prieduc)with respect to FD and radio; secondary education(Seceduc) with respect to print and radio; postsecondary education (Pseceduc) with respect to print,radio and Baraza; land size (Landsiz) with respect toFT; PPT adoption (Pptadopt) with respect to FF;group membership (Grpmember) with respect to FFSand FF; and distance to the tarmac road (Distarmac)was significant with respect to preference for FT, FFSand radio. The significance of dummies representingthe regional effects varied across all the pathways.However, since the interpretation of coefficients inordered probit is not straightforward, marginal effectswere estimated.

The estimated model marginal effects are reportedin Tables 5 and 6. The marginal effects are partialderivatives which represent the probabilities of farmersranking the pathways as ‘somewhat preferred’ and‘most preferred’ as opposed to ‘not preferred’. Theymeasure the change in one of the regressors whileholding the other regressors constant. The predictedmarginal effects assist in understanding the relationshipbetween the dependent and independent variables. The

signs of the parameter estimates and their significanceindicate the direction of the relationship.

In this study, gender was significant with respect tofarmers’ preference for FF, print and Baraza pathways.For print material, the marginal effect was positive(ME ¼ 0.064) implying that male farmers preferredinformation received from print materials compared totheir female counterparts. This scenario would beexpected especially in a developing country like Kenya,where men are likely to be found reading printedmaterials such as newspaper articles and magazines.This implies that any effort to use printed materials fortechnology dissemination should target male respon-dents as opposed to female respondents. The marginaleffects for gender was however negative for FF andBaraza, implying that male farmers put less emphasison FF (ME ¼ 0.061 for somewhat preferred andME ¼ –0.091 for most preferred) and Baraza (ME ¼0.063 for somewhat preferred and ME ¼ 0.022 mostpreferred).

Farmer’s age had a negative influence on his/herpreference for print materials (ME ¼ –0.004 for mostpreferred rank), while it had no significant influence onthe other dissemination pathways. This can be possiblybe attributed to low literacy levels among olderfarmers, which would make them prefer other sourcesof information other than print. On the other hand,older farmers are considered to have enough expertisethrough own experience compared to the young onesand therefore more likely to adopt new farmingmethods without consulting external informationsources. These findings are contrary to previous studieswhich found age to strongly influence farmers’attitudes towards preferences for various informationsources (e.g. Ford and Babb 1989; Schtinkey et al.1992). However, the result agrees well with findings byRoderick et al. (2008) who reported a decreasingpreference for information source with advance in age.Similarly, Ngathou et al. (2005) observed that olderfarmers placed less emphasis on risk informationcompared to young farmers, while Gloy et al. (2000)reported a significant preference for technical specia-lists as an information source by young farmerscompared to the old ones. Other adoption studieshave found age to be irrelevant in the preferenceranking for farmer information sources (e.g. Pompelliet al. 1997). These results indicate that use of printedmaterials as a dissemination strategy would bear fruitsif the young farmers are targeted.

Level of education was significant for FD, print,radio and Baraza, but was insignificant in all the otherpathways. The results indicate that more educatedfarmers preferred print materials and radio, but less onFD and Baraza. The probability of farmer withprimary education ranking for FD as somewhatpreferred was higher (ME ¼ 0.17) than for ranking itas most preferred (ME ¼ –0.205). However, farmerswith secondary and post secondary education preferred

International Journal of Pest Management 139

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Table

4.

Ordered

probitcoeffi

cients

(coef),andstandard

errors

(SE)forthevariousfactors

influencingfarm

ers’preferencesfortheseven

pathways.

Pathwaysb

FD

FT

FFS

FF

Print

Radio

Baraza

Variablesa

Coef.

SE

Coef.

SE

Coef.

SE

Coef.

SE

Coef.

SE

Coef.

SE

Coef.

SE

Gender

0.052

0.126

–0.173

0.127

–0.062

0.125

–0.230**

0.114

0.208**

0.110

0.147

0.113

0.213**

0.118

Age

–0.001

0.006

0.006

0.006

0.001

0.006

–0.002

0.005

–0.014***

0.005

–0.001

0.005

0.000

0.006

Prieduc

–0.617**

0.290

0.073

0.261

–0.151

0.262

0.118

0.237

0.153

0.229

0.522**

0.233

–0.032

0.243

Seceduc

–0.332

0.307

0.137

0.280

–0.055

0.280

–0.058

0.252

0.464**

0.244

0.419*

0.249

–0.294

0.260

Pseceduc

–0.247

0.382

0.144

0.361

–0.228

0.352

–0.138

0.316

0.758***

0.311

0.675**

0.315

–0.554*

0.334

Tenure

–0.215

0.373

–0.105

0.391

–0.130

0.390

–0.175

0.356

0.047

0.320

–0.315

0.327

0.074

0.346

Landsiz

0.020

0.020

–0.035**

0.017

–0.020

0.019

0.001

0.017

0.009

0.017

–0.008

0.017

–0.019

0.018

Pptadopt

0.076

0.170

–0.005

0.167

–0.030

0.165

–0.314**

0.158

–0.013

0.147

0.118

0.153

–0.048

0.160

Inclev2

0.119

0.138

0.102

0.139

–0.172

0.142

0.029

0.122

–0.119

0.127

–0.017

0.124

0.105

0.132

Inclev3

0.144

0.198

0.225

0.193

–0.131

0.202

–0.127

0.171

–0.130

0.178

–0.227

0.175

0.574

0.179

Grpmem

ber

0.129

0.191

–0.211

0.191

0.319**

0.176

0.253**

0.169

0.099

0.167

–0.412**

0.171

–0.117

0.177

Distarm

ac

0.017

0.012

–0.024**

0.011

0.050***

0.014

–0.005

0.010

–0.003

0.010

0.046***

0.010

0.053***

0.011

Busia

–0.390**

0.174

0.510***

0.189

0.066

0.183

0.685***

0.166

–0.650

0.155

0.302**

0.158

0.821***

0.173

Bungoma

–0.230

0.190

–0.395**

0.180

–0.052

0.192

–0.182

0.166

0.499

0.165

–0.181

0.168

0.356**

0.183

Kisii

0.261

0.187

0.018

0.183

–1.281***

0.179

0.219

0.163

–0.046

0.156

0.645***

0.164

0.521***

0.179

m 1–2.503

0.584

–1.609

0.583

–1.436

0.574

–1.984

0.528

–0.928

0.492

–0.582

0.498

1.419

0.527

m 2–1.093

0.572

–0.663

0.579

–0.458

0.570

–0.366

0.521

0.511

0.490

1.334

0.501

3.034

0.540

Statisticsc

–LogL

323.45

355.36

350.90

414.55

463.68

415.01

383.16

Observations

484

484

484

483

484

484

481

LR

chib(15)

26.35

44.75

114.27

57.47

94.39

68.34

103.97

Prob4

chib

0.004

0.000

0.000

0.000

0.000

0.000

0.000

PseudoRb

0.039

0.059

0.140

0.065

0.092

0.076

0.120

AIC

1.411

1.543

1.524

1.791

1.990

1.789

1.668

BIC

–2233

–2170

–2179

–2044

–1953

–2050

–2093

aSee

Table1fordescriptionofexplanatory

variables;

bFD,Field

days;FT,Farm

erTeachers;FFS,Farm

erField

Schools;FF,Fellowfarm

ers;

cAIC

,AkaikeInform

ationCriteria;BIC

,BayesianInform

ation

Criteria.

*Significantat10%;**Significantat5%;***Significantat1%.

140 A.W. Murage et al.

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Table

5.

Marginaleff

ects

(ME)andtheircorrespondingstandard

errors

for‘somew

hatpreferred’preference

rankfortheseven

pathways.

Pathwaysb

FD

FT

FFS

FF

Print

Radio

Baraza

Variablea

ME

SE

ME

SE

ME

SE

ME

SE

ME

SE

ME

SE

ME

SE

Gender

–0.015

0.036

0.037

0.027

0.014

0.027

0.061**

0.030

0.001

0.005

0.005

0.005

0.063**

0.034

Age

0.000

0.002

–0.001

0.001

0.000

0.001

0.001

0.001

0.000

0.000

0.000

0.000

0.000

0.002

Prieduc

0.170**

0.077

–0.016

0.055

0.033

0.057

–0.032

0.063

0.001

0.004

0.021

0.016

–0.009

0.072

Seceduc

0.094

0.087

–0.029

0.059

0.012

0.061

0.016

0.067

–0.010

0.016

0.003

0.012

–0.089

0.079

Pseceduc

0.071

0.111

–0.031

0.076

0.049

0.073

0.035

0.075

–0.094

0.074

–0.066

0.071

–0.172*

0.102

Tenure

0.058

0.096

0.022

0.082

0.028

0.085

0.050

0.107

0.001

0.009

0.011

0.035

0.022

0.106

Landsiz

–0.006

0.006

0.007**

0.004

0.004

0.004

0.000

0.004

0.000

0.000

0.000

0.001

–0.006

0.005

Pptadopt

–0.022

0.049

0.001

0.035

0.006

0.036

0.091**

0.049

0.000

0.001

0.007

0.012

–0.014

0.047

Inclev

b–0.033

0.039

–0.022

0.030

0.037

0.031

0.000

0.001

0.032

0.034

–0.001

0.005

0.030

0.038

Inclev

3–0.040

0.054

–0.047

0.040

0.028

0.043

–0.003

0.008

0.034

0.044

–0.016

0.019

0.140

0.036

Grpmem

ber

–0.037

0.055

0.044

0.040

–0.068**

0.036

–0.062*

0.037

0.002

0.007

0.014

0.021

–0.034

0.050

Distarm

ac

–0.005

0.003

0.005**

0.002

–0.011***

0.003

0.001

0.003

0.000

0.000

0.002

0.001

0.016***

0.003

Busia

0.111**

0.050

–0.105***

0.037

–0.014

0.040

–0.199***

0.050

–0.050**

0.026

0.000

0.010

0.196

0.034

Bungoma

0.066

0.055

0.082**

0.037

0.011

0.042

0.047

0.041

–0.021

0.019

–0.010

0.014

0.099

0.048

Kisii

–0.072

0.050

–0.004

0.039

0.206***

0.027

–0.061

0.047

–0.001

0.003

–0.026

0.024

0.139

0.042

aSee

Table

1fordescriptionofexplanatory

variables;

bFD,Field

days;FT,Farm

erTeachers;FFS,Farm

erField

Schools;FF,Fellow

farm

ers.

*Significantat10%;**Significantat5%;***Significantat1%.

International Journal of Pest Management 141

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Table

6.

Marginaleff

ects

(ME)andcorrespondingstandard

errors

(SE)for‘m

ost

preferred’preference

rankfortheseven

pathways.

Variablea

Pathwaysb

FD

FT

FFS

FF

Print

Radio

Baraza

ME

SE

ME

SE

ME

SE

ME

SE

ME

SE

ME

SE

ME

SE

Gender

0.018

0.042

–0.059

0.043

–0.022

0.044

–0.091**

0.045

0.064**

0.034

0.035

0.027

0.022*

0.013

Age

0.000

0.002

0.002

0.002

0.000

0.002

–0.001

0.002

–0.004***

0.002

0.000

0.001

0.000

0.001

Prieduc

–0.205**

0.093

0.025

0.088

–0.053

0.091

0.047

0.094

0.046

0.069

0.121**

0.054

–0.003

0.025

Seceduc

–0.115

0.108

0.046

0.092

–0.019

0.099

–0.023

0.100

0.147**

0.080

0.104

0.065

–0.028

0.023

Pseceduc

–0.088

0.142

0.047

0.113

–0.084

0.134

–0.054

0.123

0.272**

0.122

0.203*

0.113

–0.038**

0.015

Tenure

–0.068

0.110

–0.035

0.124

–0.044

0.128

–0.070

0.142

0.014

0.094

–0.085

0.099

0.007

0.031

Landsiz

0.007

0.007

–0.012**

0.006

–0.007

0.006

0.000

0.007

0.003

0.005

–0.002

0.004

–0.002

0.002

Pptadopt

0.026

0.059

–0.002

0.056

–0.010

0.057

–0.125**

0.062

–0.004

0.045

0.026

0.033

–0.005

0.017

Inclev

b0.040

0.047

0.034

0.047

–0.060

0.049

0.009

0.037

–0.047

0.050

–0.004

0.029

0.011

0.014

Inclev

30.047

0.063

0.073

0.059

–0.047

0.073

–0.037

0.049

–0.051

0.070

–0.049

0.035

0.083

0.034

Grpmem

ber

0.045

0.068

–0.068

0.058

0.117**

0.067

0.099

0.064

0.029

0.048

–0.111**

0.052

–0.013

0.021

Distarm

ac

0.006

0.004

–0.008**

0.004

0.018***

0.005

–0.002

0.004

–0.001

0.003

0.011***

0.003

0.005**

0.001

Busia

–0.138**

0.064

0.158***

0.052

0.023

0.063

0.267***

0.061

–0.172***

0.035

0.076*

0.043

0.118

0.035

Bungoma

–0.080

0.068

–0.139**

0.066

–0.018

0.068

–0.072

0.065

0.163***

0.058

–0.040

0.036

0.042

0.025

Kisii

0.084

0.058

0.006

0.061

–0.471***

0.061

0.087

0.065

–0.014

0.047

0.175***

0.050

0.066

0.029

aSee

Table

1fordescriptionofexplanatory

variables;

bFD,Field

days;FT,Farm

erTeachers;FFS,Farm

erField

Schools;FF,Fellow

farm

ers.

*Significantat10%;**Significantat5%;***Significantat1%.

142 A.W. Murage et al.

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print materials and radio to Baraza. This would be sopartly because print materials contain more technicalinformation that would require at least a farmer tohave some formal education in order to be able todiscern the contents (Gloy et al. 2000; Ngathou et al.2005). It has been argued that some farmers with highlevels of education tend to rely more on outside sourcesof information other than on their own experience(Ngathou et al. 2005). Such farmers would more oftenrely on print as an information source and thereforemay get more knowledge through reading than fromother sources. Furthermore, educated farmers are moreflexible in acquisition of information sources andwould often consult depending on the prevailingcircumstances to meet their information needs. Insuch cases they would be more associated with moresophisticated sources such as print than would be theirless educated counterparts. These results are consistentwith what has been reported in literature (e.g. Pompelliet al. 1997).

Land size was significant but with an inverserelationship to FT, but insignificant in all otherpathways. The negative marginal effects (–0.012) forthe most preferred rank indicates that personalinformation sources such as farmer teachers are lesspopular with large scale farmers. This suggests thatintervention strategies targeting the use of FT as adissemination pathway should actually aim at thefarmers with small pieces of land. In our study, the useof farmer teacher approach was an initiative of ICIPEwith an aim of taking advantage of them (FTs) beingpart of the social network and therefore able to closelyrelate with the fellow farmer (Amudavi et al. 2009).This finding corroborate that of Gloy et al. (2000) whoreported decreasing preference for personal informa-tion sources with increase in farm size. However, Fordand Babb (1989) and Schnitkey et al. (1992) reported apositive association between farm size and personalinformation sources arguing that large farms had thecapacity to mobilize resources to benefit from informa-tion provided by private extension providers andtherefore more likely to prefer personal information.This kind of arrangement is seldom applied in thedeveloping countries due to infrastructural and othereconomic factors that are prohibitive to use of privateextension agents and subsequently personal informa-tion (Muyanga and Jayne 2006).

The technology use variable (Pptadopt) had asignificant negative influence on FF but was notsignificant for the other pathways (Table 6) implyingthat adopters put less emphasis on fellow farmers as adissemination pathway as compared to the non-adopters. This would be expected because PPT is arelatively complex technology and farmers consultother farmers only for simple messages but as themessage becomes complex, they will most likely seekinformation on its implementation from more technicalsources. Similar observations were made by Gloy et al.

(2000) in a study on sources of information forcommercial farms.

Membership of group (Grpmember) had a signifi-cant positive influence on both FFS and FF pathways.Farmers who were members of organised farminggroups ranked FFS more favourably compared tothose who were not in any organised group (ME ¼0.117 for most preferred and ME –0.068 for somewhatpreferred, Tables 5 and 6). This is probably becausefarmers who belong to organised groups are likely tobenefit from the established social capital that is likelyto enhance information and knowledge sharing. Itfollows, therefore, that such farmers would prefer toreceive information from such pathways which engagesinteraction with colleagues. Furthermore, FFS areorganised farmer groups where farmers come togetherto learn about new technologies in groups. Moreover,while in a group, farmers are more likely to learn fromeach other, which would explain the positive preferencefor FF as a dissemination pathway. The results suggesta need to encourage formation of farmers groups sothat an intervention targeting the use of FFS as adissemination pathway can be effective. Similar resultsare also observed in the FF pathway whereby ‘some-what preferred’ rank decreased by 6.2% for farmerswho belonged to organised groups as opposed to thosewho were not in such groups (Table 5).

Distance to tarmac road (Distarmac) is oftenrelated to accessibility to information and otherservices. The coefficient for this variable was significantin FT, FFS, radio and Baraza pathways but notsignificant for the rest (see Tables 5 and 6). This impliesthat poor proximity to the main tarmac resulted infarmers preferring FFS and radio as compared to FTand Baraza. This is possible because, if an FFS isformed within a village, regardless of how far it is froma tarmac road, farmers are more likely to learn aboutthe technology without necessarily travelling for longdistances to acquire information. This also applies touse of radio where the farmer can receive informationat his/her convenience regardless of proximity to goodroad network. Use of FFS could be considered intechnology dissemination in areas where there is poorroad network as was in some cases in our study area.This is so because once a facilitator has been trained,farmers can conveniently learn about new technologieswithin their own village while at the same time accruingbenefits from the wealth of social capital. The lessemphasis placed upon FT in farms which are far fromthe tarmac road can possibly be as a result of pooraccessibility to these FTs.

Regional dummies representing study districtsindicate variations in preferences for the seven path-ways across the four districts. For example, farmers inBusia preferred FT (ME ¼ 0.158) and FF (ME ¼0.267) while Bungoma farmers place more emphasison print material (ME ¼ 0.163) all compared toHomabay which was the reference district. This

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variation reflects the heterogeneous nature of thefarmers in the four districts as well other differencesin the physical structure of the four districts.

4. Conclusions and implications

The role of information pathways in dissemination andup-scaling of promising intervention technologies andstrategies has been of interest in the recent past. Thisstudy examined farmers’ preferences for some of thesedissemination pathways and how various factorsinfluenced farmers’ choices for the informationsources. In general we have demonstrated that factorsaffecting farmers’ preferences for different dissemina-tion pathways vary among the different pathways andthat the significance of farm and farmer characteristicsin explaining preferences depends on the informationsource.

Although the majority of the farmers would preferthe FD as the pathway through which they wouldeffectively receive information about a new agriculturaltechnology, most of the other pathways evaluated hadniches within different farmers with selected character-istics. The results show that factors which positivelyfavour preference for a particular information sourcein a given region might not necessary translate tosimilar preference by farmers in other regions. Im-portant characteristics of local populations may bemasked by generalising from regional data. Thevariability that can exist in land characteristics, farm-ers’ perceptions, and socioeconomic conditions withinregions implies that broad-based use of disseminationapproaches for delivering agricultural information maynot be appropriate. This would be particularly criticalin order to avoid cases of dis-adoption (or non-retention) of promising intervention strategies on thebasis of applying a dissemination pathway that couldbe unpopular among farmers in certain regions.Therefore it is important to understand the socio-economic and other demographic factors within agiven region prior to using a particular informationtransfer mechanism. This implies that a ‘one-size fitsall’ approach is clearly not appropriate in technologydissemination. The results suggest that disseminationpathways would be more effective if the targetpopulation is segmented and right pathways utilisedfor the various farmer segments. This is likely toincrease the farmers’ likelihood of adopting the newtechnology (in this case the PPT). There is a need forfurther evaluation of the impact of these disseminationpathways on adoption, as well as their cost effective-ness in order to identify the most optimal in terms oftechnical and economic efficiencies.

Acknowledgements

The authors acknowledge the support of German AcademicExchange Service (DAAD) through African Region Post-graduate Program in Insect Sciences (ARPPIS) and Habitat

Management Project of ICIPE in collaboration withRothamsted research (United Kingdom) for facilitating thisresearch work. We greatly acknowledge the support of ICIPEfield staff, the enumerators and the smallholder farmers in thefour districts for logistical support and responses.

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