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Crop Protection 28 (2009) 987–996

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Crop Protection

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Assessment of technical efficiency of farmer teachers in the uptake anddissemination of push–pull technology in Western Kenya

D.M. Amudavi a,b, Z.R. Khan a,*, J.M. Wanyama c, C.A.O. Midega a, J. Pittchar a, I.M. Nyangau a,A. Hassanali a, J.A. Pickett d

a International Centre of Insect Physiology and Ecology, Plant Health Division, P.O. Box 30772-00100, Nairobi, Kenyab Egerton University, P.O. Box 536-20155, Egerton, Kenyac Kenya Agricultural Research Institute, Kitale, P.O. Box 450-30200, Kitale, Kenyad Rothamsted Research, Harpenden, Hertfordshire AL5 2JQ, UK

a r t i c l e i n f o

Article history:Received 24 February 2009Received in revised form22 April 2009Accepted 23 April 2009

Keywords:Push–pull technologyFarmer teachersFollower farmersFarmer-to-farmer extensionTechnical efficiency

* Corresponding author. Tel.: þ254 59 22216/7/8; fE-mail address: zkhan@mbita.mimcom.net (Z.R. K

0261-2194/$ – see front matter � 2009 Elsevier Ltd.doi:10.1016/j.cropro.2009.04.010

a b s t r a c t

Use of farmers as extension agents to disseminate new technologies to others is increasingly beingadapted in smallholder farming systems. This paper examined technical efficiency (TE) of farmerteachers in the uptake and dissemination of a ‘push–pull’ technology (PPT) for control of Striga weed andstemborers in Western Kenya. A total sample of 112 farmer teachers (FTs) and 560 follower farmers (FFs)who had adopted the PPT were randomly selected and interviewed between July and August 2007. Thefarm production constraints significantly reduced with an overall 53% margin following PPT uptake.Overall, there were considerable benefits from training resulting in significant differences in under-standing and applying of PPT. The farmers’ extension strategy had a significant multiplier effect inincreasing PPT uptake. The average TE by FTs was 78% while FFs had 71% suggesting room forimprovement. The TE was influenced by farmers’ interactions with neighbouring farmers, membershipsin local groups, type of farmer, farmer’s age, marital status and farmer’s level of education. The efficiencycan be improved by providing farmers with incentives and training, increasing field demonstrations,providing Desmodium seed and credit for other needed inputs to accelerate PPT transfer.

� 2009 Elsevier Ltd. All rights reserved.

1. Introduction

Recently, there has been a growing interest to use farmers inupscaling and outscaling new technologies to many farmers (Grisly,1994; Noordin et al., 2001; Chikozho, 2005; Erbaugh et al., 2007). Thisstrategy is relevant where public extension is either insufficient orineffective as in Kenya and other sub-Saharan African countries. Thefarmer-to-farmer extension (FFE) strategy serves a shared informa-tion and learning function of achieving economies of scale in tech-nology diffusion and system financial sustainability; issues thatperpetually constrain public extension in providing services (Quizonet al., 2001; Feder et al., 2003). In this strategy farmers are expected toinfluence fellow farmers to adopt new technologies and practices.

Several studies have assessed the efficacy of using FFE model intechnology transfer and have produced varying results andconclusions, partly because of differences in study locations,sample sizes, production practices and model specifications. Effi-ciency measurement is very important because it is a factor for

ax: þ254 59 22190.han).

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productivity growth. Whereas Hasnah et al. (2004) found that useof ‘progressive farmers’ as agents of promoting palm production inWest Sumatra did not appear successful, Alene and Manyong(2006) on the other hand found in their study that the ‘leadfarmers’, were more technically competent than the followerfarmers in improved cowpea technology uptake in NorthernNigeria. Other studies have found that although farmers may gainskills and knowledge through farmer advising, they are oftenreluctant to share information (Tripp et al., 2005; Davis, 2007). Suchefficiency studies help to determine the extent to which produc-tivity can be raised by improving a neglected source, i.e. efficiency,with the existing resource base and the available technology.However, the conflicting results in examining use of farmers asextension agents to disseminate technologies to many others raisean important question about the relevance and efficacy of thisextension education approach. The process of information sharingamong farmers is considered to be interactive and facilitatesmultidirectional information exchange. Use of farmers as extensionagents contributes to strategies for overcoming barriers to utiliza-tion of information, understanding client information needs, anddesigning effective information delivery systems.

D.M. Amudavi et al. / Crop Protection 28 (2009) 987–996988

In this study, conducted in Western Kenya, we evaluated therelevance and technical efficiency (TE) of farmer teachers (FTs) in theuptake and dissemination of a ‘push–pull’ technology (PPT). Thisinnovation involves intercropping maize with a stemborer mothrepellent fodder legume, Desmodium uncinatum (Jacq.), and plantingaround the intercrop an attractant trap plant, Napier grass, Pennise-tum purpureum (Schumach). In this strategy a selective control agentis used to reduce the pest population by combining the ‘‘push’’ and‘‘pull’’ effects multiplicatively (Miller and Cowles, 1990; Cook et al.,2007). Specifically, the Desmodium plant produces volatiles whichrepel the moths while volatiles produced by the trap plant, Napiergrass, attract them (Khan and Pickett, 2004; Chamberlain et al., 2006;Pickett et al., 2007). The emergent larvae from oviposition are trappedby a sticky substance produced by the Napier grass which inhibits thelarvae’s full maturation to adulthood (Khan et al., 2006, 2007; Van denBerg et al., 2006). Further, Desmodium roots produce chemicalcompounds, some of which stimulate Striga germination and othersinhibit expected lateral root growth, thereby hindering its parasiticattachment to maize roots (Khan et al., 2000; Tsanuo et al., 2003).Consequently, the ensuing suicidal germination process suppressesStriga and effectively reduces in situ seed bank accumulation (Khanet al., 2002).

Push–pull technology (PPT) is being disseminated to farmers ineastern Africa through various methods including the use offarmers as extension agents to advise other farmers (Khan et al.,2008a; Amudavi et al., 2009). The farmer teachers (FTs) wereselected by other farmers during group village meetings based ontheir experience with the technology, trust, interest, and commit-ment to reach out to their neighbours. They were trained by ICIPEtechnical field staff and let to promote the technology at the farmand village levels, conveying knowledge and facilitating discussionson principles and practices of PPT. As part of our continued effort todevelop effective and economical dissemination strategies, weundertook a detailed assessment of farmer teachers to examine the(i) influence of extension training on farmers’ competencies ofunderstanding and applying PPT on their farms, (ii) farmers’knowledge and skills of using PPT, (iii) influence of PPT on selectedfarm production constraints, (iv) extent of farmers’ PPT dissemi-nation to fellow farmers and their technical efficiency (TE) inpromoting PPT uptake, and (v) factors influencing farmer teachers’technical efficiency of PPT uptake. The results obtained would helpin improving competence and efficiency of farmer teachers asextension agents in PPT uptake and dissemination. Improvingtechnical efficiency of farmers in maize farming systems under PPTwill contribute immensely to improving the overall agriculturalproductivity of cereal crops in Western Kenya.

2. Materials and methods

2.1. Study area

The survey was conducted in 10 districts in Western Kenya,namely Vihiga, Siaya, Busia, Trans Nzoia, Bungoma, Kisii, Rachuonyo,Migori, Homabay and Suba, where on-farm trials and demonstra-tions of PPT had been conducted. This region generally experiencesfood insecurity and high poverty levels ranging between 59% and63%, exceeding the national average of 46% (Mariara and Ngeng’e,2004). Agriculture is mainly rain-fed and affected by two majorpests, stemborers and Striga weed, which are serious in all the studydistricts, except in Trans Nzoia with only maize stemborers. Thesedistricts produce most of cereal crops and livestock products inKenya (Nyaribo et al., 1992; Kristjanson et al., 2004).

2.2. Sampling procedure and data

The field work was conducted from July to August 2007 wherea total of 672 respondents including 112 FTs were interviewed usinga semi-structured questionnaire. A sampling frame of followerfarmers (FFs) was constructed from all the lists provided by the FTsof those who had adopted the PPT. Five FFs per one FT, totaling 560,were randomly selected and interviewed.

Data were collected on individual and household characteristics(age, gender, education level, marital status and family size); farmcharacteristics (land size and livelihood sources). Data were alsocollected on knowledge, adoption and benefits of PPT. Regardingmaize production constraints, self- and peer-assessments weredone based on a 3-point Likert type scale with 1¼ no problem;2¼moderate problem; 3¼ serious problem rating in order toassess actual effectiveness of PPT. Farmers’ perceptions towards PPTwere operationalized as the extent of their agreement with thestatements related to the seven selected indicators of the tech-nology’s effects: Striga weed infestation, stemborer infestation, soilfertility decline, low soil moisture, limited fodder and inability forfarming system to support multiple cropping before and after PPTadoption; knowledge and skills gained on PPT and its disseminationfollowing training. Farmers’ perceptions towards PPT were oper-ationalized as the extent of their agreement with the statementsrelated to the seven selected indicators of the technology’s effects.A single difference model on ‘before’ and ‘after’ situations wasapplied in assessing the net effect of the indicator variable on thetarget groups (FT and FF).

2.3. Analytical framework

Farmers’ perception and technology uptake provide an indica-tion of effectiveness and efficiency of the pathway in the tech-nology’s dissemination (Garforth, 1998). Technical efficiency (TE)implies ability to produce maximum output (frontier production),given a set of inputs and available technology (Coelli et al., 2005). Inthis study, we utilized the stochastic frontier model to estimate theTE of both FTs and FFs in the uptake and dissemination of PPT. TheTE of an individual farm is defined as the ratio of the observedoutput to the corresponding frontier output, conditional on thelevels of inputs used on the farm, calculated as:

TEi ¼ Yi=½f ðXi; bÞðexpðuiÞÞ�0expð�uiÞ ¼ f ðzi; aiÞ (1)

where TEi is the technical efficiency of ith farmer; Yi is the possibleproduction level of the ith farm (acreage under PPT); Xi is a vector ofefficiency factors; b- and a-unknown parameters to be estimated,and zi is a vector of inefficiency factors. We estimated the modelusing a Transdental logarithmic (Translog) functional form ofequation:

ln Yi ¼ b0 þX3

i¼1

biln xi þ 0:5X3

i¼1

X2

j¼1

bijln xiln xj þ v� u (2)

where ln is the natural logarithm; Yi denotes the acreage under PPTfor ith farmer; j represents the j-th input (j¼ 1, 2,.,k) of the i-thfarmer (1,2,.,N); Xi1(endogenous variable) is number of yearsfarmer ‘i’ has been practicing and disseminating PPT (endogenousvariable); Xi2 is number of active visits farmer ‘i’ has had in dis-cussing with other farmers about PPT leading to adoption (endog-enous variable); (ln Xi1)2 is the square product of the firstendogenous variable; (ln Xi2)2 is the square product of the secondendogenous variable; ([ln Xi1]� ln Xi2]) is crossproduct of the firstand second endogenous variables; Xi3 is the dummy variable, 1 forbeing a FT otherwise 0. It is assumed that the TE effects are

D.M. Amudavi et al. / Crop Protection 28 (2009) 987–996 989

independently distributed and mij arises by truncation (at zero) ofthe normal distribution with mean uij and variance, s2, where uij

represents the TE of the ith farmer and is defined as:

mij ¼ a0 þ a1ln GENDER þ a2ln AGEþ a3ln FARMþ a4ln MARITALþa5ln EDUCSECþ a6ln HHOLDþ a7ln CONSTþ a8ln SKILLþa9ln KNOWþ a10ln TLUþ a11ln GRPLOCþ a12ln GRPSUP

(3)

Based on various technology adoption and TE reviews (e.g.,Parikh and Shah, 1995; Adesina and Chianu, 2002; Rogers, 2003;Ogundele and Okoruwa, 2004) exogenous variables accounting forinefficiencies included: GENDER (dummy, male¼ 1 otherwise 0)hypothesized to positively or negatively influence efficiency; AGE(respondent’s age in years) hypothesized to either positively ornegatively influence efficiency; FARM size (owned in acres) wasassumed to positively influence efficiency due to possibility ofincreased production; MARITAL status (if respondent is married¼ 1,otherwise 0) was hypothesized to positively influence efficiency;EDUCSEC (respondent’s formal education at least secondary¼ 1,otherwise 0) was assumed to positively influence efficiency becauseof the need for mental processing of technology coded information;HHOLD (number of household members) was hypothesized topositively influence efficiency; CONST (a proxy index for ability ofPPT to solve farmer production constraints) was hypothesized topositively influence efficiency; SKILL (index on skills acquired aftertraining) and KNOW (index on knowledge acquired after training)were hypothesized to positively influence efficiency due to capacitydeveloped by the farmer to comprehend and practice PPT; TLU(tropical livestock units derived from the type, kind and number oflivestock owned) was hypothesized to positively influence effi-ciency due to the need for fodder for livestock; GRPLOC (number oflocal groups to which respondent belongs) was assumed to posi-tively influence efficiency due to social learning; GRPSUP (numberof higher level [supra] groups to which respondent belongs) wasassumed to positively influence efficiency due to opportunities forresources. The maximum-likelihood estimates of the b and ai

coefficients in Eqs. (2) and (3), respectively, were estimated simul-taneously using the program FRONTIER 4.1c (Coelli, 2007), specifi-cally designed to estimate stochastic production frontiers.

2.4. Data analysis

The analysis of variance (ANOVA) using F-tests was used for theratio/interval variables to examine whether significant differencesfrom zero existed among the respondents across the study districts.Chi-square test was used to investigate any differences betweenparticipants’ district of residence and categorical variables of age,marital status, education, and livelihood strategies pursued by therespondent. The regression models were used to estimate thetechnical efficiencies of the FTs and FFs.

3. Results

3.1. Household demographic, farm and institutional characteristics

Table 1 indicates that on average the respondents were about 47.1years old, with FTs being 48.7 years and the FFs 46.7 years; 56% of therespondents were male. About 52% of the FTs and 38% of the FFS hadat least secondary education. A majority (84%) of the respondentswere married. The farming experience ranged from 19 years for theFFs to 23 years for the FTs. On average, the FTs had eight and FFsseven household members. The FTs and FFs owned about 6.1 and 3.7acres of land, respectively. With regards to livestock size, the averageowned was 2.42 TLUs with FTs having 3.4 and FFs 2.2 TLUs. The FTsand FFs had practiced PPT for about six and two years respectively.The mean membership in local groups was 1.2 for FTs and 0.9 for FFs

while memberships in supra (higher level) groups were much lowerwith 0.06 and 0.2 for FTs and FFs, respectively. The majority (93%) ofthe respondents depended on food crops and about 23% kept live-stock for their livelihood. Other livelihood sources indicated by lessthan 10% of the respondents included cash cropping, off-farm labour,petty trade, fishing and pension.

3.2. Farmers’ knowledge of and skills about PPT

Farmers’ PPT knowledge was measured using a set of questionsdesigned to first gauge their familiarity with and understanding of thetechnology and then their level of skills in practicing the technology.Table 2 presents results of assessment of the two aspects of thetechnology. Overall, there were significant differences among FTs andFFs in their PPT knowledge (KNOW) rating after training (Table 2). TheFTs differed significantly in their responses to four of the seven ratedPPT aspects compared to FFs who differed on all the seven items.Specifically, the farmer teachers had statistically significant differ-ences on responses to name of plant (Desmodium) used to controlStriga weed (p< 0.01), name of plant (Desmodium) used to controlstemborers (p< 0.01), name of plant (Napier grass) used as trap or‘pull’ plant (p< 0.05) and plant (Desmodium) commonly used asa repellent or ‘push’ plant (p< 0.1). Whereas mean scores by FFsdiffered on these four questions they also differed on knowledge ofhow the ‘pull’ plant is planted in relation to maize (leaving 1 m pathround a maize field and planting 3–4 rows of Napier grass with threenode canes at 75 cm apart and 75 cm between the plants) (p< 0.05),identification of the ‘push’ plant (Desmodium) (p< 0.01), planting of‘push’ or trap plant in relation to maize (intercrop planting in a furrow1–2 cm deep made between rows of maize), and identification ofplant (Desmodium) used in the PPT to improve soil fertility (p< 0.01).The least score among the two groups of farmers was the name of theplant (Desmodium) used to control Striga. Overall, the FTs rated 94%(19.7 scores out of a total of 21) their understanding of the compo-nents and application of PPT whereas FFs rated 90% (18.9 scores out ofa total of 21) on the same. This suggests that both groups of farmershad a good know-how of the PPT but have room for improvement.

3.3. Effectiveness of training in understanding andcommunication of PPT

There were significant differences in the overall scores ofassessment of effectiveness of training in understanding 11 aspectsof PPT. Results in Table 3 showed that overall, the FTs rated 95%(31.33/33) effectiveness of training in understanding and manage-ment of PPT whereas the FFs rated the FTs as being 92% (30.24/33)effective in teaching the 11 aspects of PPT. Among the FTs, therewere only three aspects with significant differences namely: layoutof PPT field, harvesting and processing of Desmodium andmanagement of PPT field during off-peak season. All the meanscores were above 2.7 out of 3.0 except in harvesting and pro-cessing of Desmodium (2.47). On the other hand, the FFs signifi-cantly differed in their scores of assessing effectiveness of FTs inenabling them to understand the 11 PPT aspects. Similarly, all themean scores were over 2.6 out of 3.0 except in harvesting andprocessing of Desmodium (2.17). Thus, whereas FFs differed on thethree significant mean scores of FTs, there were significant differ-ences with respect to understanding all the 11 aspects of PPT.

The above results were corroborated by the FFS’ assessment oftheir satisfaction with the quality of services provided by the farmerteachers. Fig. 1 shows that the majority (95%) of the FFs were eithersatisfied or very satisfied with the quality of extension servicesprovided by their FTs. From a gender perspective, more women (54%)than men (42%) indicated that they were satisfied overall with qualityof extension services provided on PPT application. On the other hand,

Table 1General socioeconomic characteristics of farmers interviewed (n¼ 672).

Variables Follower farmers(n¼ 560)

Farmer teachers(n¼ 112)

Overall (n¼ 672) Chi-square significance t-test significance

Mean SE Mean SE Mean SE

Age of farmer (yrs) 46.72 0.53 48.72 1.05 47.05 0.48 NSHousehold size (#) 6.73 0.11 8.88 0.31 6.96 0.11 ***Farming Experience (yrs) 18.54 0.47 23.16 0.99 19.32 0.43 ***Farm size owned (acres) 3.25 0.17 5.37 0.60 3.61 0.18 ***Farm size rented (acres) 0.41 0.04 0.67 0.17 0.45 0.05 **Total farm size (acres) 3.66 0.18 6.10 0.60 4.07 0.18 ***Longevity of PPT practice (# of years) 2.47 0.04 6.36 0.19 3.12 0.73 ***Size of PPT (m2) 699.54 20.06 1090.44 78.75 764.70 21.95 ***Memberships in local development groups (#) 0.94 0.04 1.24 0.09 0.99 0.04 ***Memberships in supra development groups (#) 0.20 0.02 0.44 0.08 0.24 0.02 ***Tropical livestock units (unitless index) 2.23 0.09 3.40 0.27 2.42 0.12 ***

Gender (%)Male 53.6 66.1 55.7 **Female 46.3 33.9 44.2

Education level (%)None 3.1 0 2.6 ***Non-formal 6.9 1.8 6.9Primary 52.2 40.0 52.2Secondary 32.3 50.9 32.3Tertiary 6.0 7.3 6.0

Marital status (%)Married 84.0 92.6 85.4 *Widowed 13.7 7.4 12.7Single 1.4 0 1.2Divorced 0.9 0 0.8

Livelihood sources (%)Food crop farming (yes¼ 1) 92.9 87.5 92.0 **Livestock rearing (yes¼ 1) 26.6 4.5 22.9 ***Cash cropping (yes¼ 1) 5.4 7.1 5.7 nsOff-farm casual labour (yes¼ 1) 6.8 0 5.7 ***Small-scale trade (yes¼ 1) 6.1 0 5.1 ***Off-farm permanent job (yes¼ 1) 2.0 0.9 1.8 nsFish farming (yes¼ 1) 1.4 0 1.2 nsPension (yes¼ 1) 0.7 0 0.7Remittance (yes¼ 1) 5.2 0 4.3 ***Food aid (yes¼ 1) 0.2 0 0.7 ns

Notes: statistically significant levels at *p< 0.10, **p< 0.05, ***p< 0; and ns – not significant.

D.M. Amudavi et al. / Crop Protection 28 (2009) 987–996990

more men (53%) than women (43%) indicated being very satisfiedwith FTs’ quality of service provision on PPT. Only a small proportion(less than 5%) was significantly either dissatisfied or very dissatisfied(chi-square¼ 9.818, df¼ 3, p< 0.05).

3.4. Changes in selected constraints following PPT uptake

One of the objectives of the PP project was to alleviate farmers’field production constraints. When the FTs were asked what

Table 2Farmer teachers’ and follower farmers’ understanding of components and application of

Aspect of knowledge rated Farmer teachers’ rating

n Mean SE F-valu

1. Name of plant used to control Striga weed 92 2.74 0.058 3.2952. Name of plant used to control stemborer 112 2.90 0.031 3.2653. Plant used as a trap or ‘pull’ plant 111 2.84 0.042 2.1804. Planting trap/‘pull’ plant in relation to maize crop 112 2.93 0.028 1.5575. Plant commonly used as a repellent or ‘push’ plant 112 2.96 0.022 1.6836. Planting ‘push’/repellent plant in relation to maize 112 2.93 0.028 1.427. Plant used in PPT for improving soil fertility 111 2.93 0.031 0.548

Overall aggregate mean score 112 19.69 0.150 4.929

***Significant at 0.01 level; **significant at 0.05 level; and *significant at 0.10 level.a Rating of aspects of PPT learnt on Likert type scale with 3¼ answered correctly, 2¼

a good understanding the components and application of PPT and vice-versa.

motivated them to start practicing push–pull, they gave eightreasons: the majority (91.1%) of them said to control stemborer(chi-square¼ 47.7, p< 0.01), 54.5% mentioned to control Striga (chi-square¼ 92.9, p< 0.01), 87.5% cited to improve soil fertility (chi-square¼ 61.3, p< 0.01), 90.2% indicated to increase maize yield(chi-square¼ 90.2, p< 0.01), 83% mentioned to increase fodder(chi-square¼ 49.8, p< 0.01), 73.2% said to increase income fromfarm products (chi-square¼ 52.3, p< 0.01), 19.6% felt convincedafter seeing other farmers in their neighborhood were practicing

PPTa.

Follower farmers’ rating Mean differences between FTs andtheir respective followers

e n Mean SE F-value n Mean SE t-value

*** 395 2.79 0.026 33.782*** 78 �0.16 0.063 �2.497***** 559 2.89 0.015 10.503*** 112 �0.02 0.034 �0.533 ns** 560 2.77 0.021 15.103*** 111 0.07 0.044 1.681*ns 560 2.84 0.017 11.322*** 112 0.01 0.033 2.873***

* 560 2.78 0.020 10.277*** 112 0.19 0.030 6.349***ns 560 2.84 0.017 9.522*** 112 0.09 0.036 2.438**ns 560 2.87 0.016 8.339*** 111 0.06 0.039 1.550 ns

*** 560 18.95 0.088 20.246*** 112 0.404 0.197 2.502**

somehow struggled and 1¼ did not know. A higher aggregate mean score implies

Table 3Farmer teachers’ self-rating of effectiveness of training and Follower farmers’ rating of effectiveness of FTs in communicating of components and application of PPT.

Aspect of training received/communicated Farmer teachers’ ratinga Follower farmers’ ratingb Mean differences between FTs andtheir respective followers

n Mean SE F-value n Mean SE F-value n Mean SE F-value

1. Introducing the concept of PPT strategy 112 2.97 0.015 1.573ns 560 2.96 0.009 3.293*** 112 0.018 0.019 0.917ns2. Land preparation for implementing PPT 111 2.96 0.018 1.458ns 560 2.94 0.012 2.114** 111 0.024 0.027 0.909ns3. Layout of PPT field 110 2.88 0.034 3.500*** 560 2.93 0.012 2.167** 110 �0.046 0.036 �1.279ns4. Planting of PPT field 112 2.99 0.009 0.648ns 560 2.94 0.011 6.361*** 112 0.047 0.018 2.627**5. Weeding of Desmodium 112 2.92 0.029 0.418ns 558 2.86 0.017 3.439*** 112 0.050 0.033 1.490ns6. Managing a PPT field 107 2.92 0.030 0.581ns 558 2.88 0.017 5.382*** 107 0.026 0.038 0.696ns7. Harvesting and utilization of Napier and Desmodium 112 2.93 0.028 1.177ns 556 2.68 0.026 3.272*** 112 0.236 0.039 6.108***8. Harvesting and processing of Desmodium 111 2.47 0.077 6.804*** 559 2.17 0.037 37.202*** 111 0.304 0.076 4.015***9. Management of PPT field during off season 112 2.75 0.055 1.1660* 560 2.60 0.028 10.234*** 112 0.256 0.056 4.549***10. Planting PPT field during the next season 112 2.87 0.039 1.146ns 560 2.67 0.027 24.324*** 112 0.186 0.058 3.208***11. Utilization of PPT products 112 2.90 0.036 1.370ns 560 2.66 0.026 5.329*** 112 0.238 0.049 4.909***

Overall scores 112 31.33 0.213 3.707*** 560 30.24 0.141 11.542*** 112 1.126 0.259 4.352***

***Significant at 0.01 level; **significant at 0.05 level; and *significant at 0.10 level.a FT rated effectiveness of training received in terms of understanding components and practice of PPT on Likert type scale with 3¼ effective 2¼ somewhat effective and

1¼ not effective.b FFs rated effectiveness of FTs in explaining and demonstrating components and practice of PPT on the same scale. A higher aggregate mean score implies a FTs that FTs

were effective in teaching about the PPT to other farmers.

D.M. Amudavi et al. / Crop Protection 28 (2009) 987–996 991

push–pull (chi-square¼ 20.2, p< 0.05), and 15.2% of them indi-cated that ICIPE field staff convinced them (chi-square¼ 27.7,p< 0.01). Table 4 showed that the computed differences betweenvalues of the before and after PPT adoption indices on a 3-pointLikert type scale were significant. The majority (>50%) rated theproblem as serious. The results showed that on PPT adoption, FTsexperienced significant reduction in constraint index; soil erosion(1.38), stemborer infestation (1.53), low soil fertility (1.39), low soilmoisture (1.23) and Striga infestation (1.24). This is likely to haveled to substantial reduction in perception of low yield by 1.43 unitsand consequently increased yield output.

FFs also experienced significant (p< 0.01) reduction of about1.53 units in stemborer infestation, low soil erosion (1.55), soilfertility (1.51), and Striga reduction (1.21) enabling them to realizesignificant (p< 0.01) yield improvement (farmers’ indicateda severity reduction of 1.6 units). For both farmer types, the dataindicated that adoption of PPT improved fodder availability therebysignificantly reducing the perceived severity of low fodder

Rating Quality of Extension Services

Very satisfiedSatisfiedDisatisfiedVery disatisfied

Fol

low

er F

arm

ers

60

50

40

30

20

10

0

Gender

Female

Male

Fig. 1. Follower farmers’ rating of overall satisfaction with quality of extension servicesprovided by farmer teachers.

availability by over 1.5 units. The overall constraint index for FTssignificantly reduced from 19.9 units to 9.4 units and for FFs from20.4 units to 9.4 units after adoption respectively, representing anoverall 53% mitigation.

3.5. Diffusion of PPT and farmers’ TE estimates in uptake

There were significant differences in the number of yearsfarmers had been practicing PPT (longevity means: FT¼ 4.2,FF¼ 3.5, t¼ 8.5, p< 0.01) and the number of farmers influenced toadopt PPT (means of farmer outreach: FT¼ 16.8, FF¼ 2.4, t¼ 25.1,p< 0.01). On average, the FTs influenced about 17 other farmers toadopt PPT while FFs influenced about two within the 3–4 yearperiod. This suggests that FTs played an important role in PPTdissemination to fellow farmers and beyond. The majority (97.3%)of the FTs were selected by fellow push–pull technology practicingfarmers, 8.9% by the project promoting dissemination of the tech-nology, 6.3% by some key people in the community and 5.4% by self-selection. Acceptance of being FT was based on the understandingthat one would be willing to be trained and to train and motivateothers to adopt PPT. When asked why they were motivated tobecome FTs, 95.5% of the farmer teachers valued training otherfarmers, 71.4% of them encouraged group learning (chi-square¼ 33.8, p< 0.01), 67% indicated the desire to link farmers toexternal sources of information (chi-square¼ 63.6, p< 0.01), 51.8%wanted to link farmers to markets (chi-square¼ 57.1, p< 0.01),76.8% wanted to recommend new technologies to other farmers(chi-square¼ 59.4, p< 0.01), 30.4% wanted to earn some incomethrough obtaining field allowance (bicycle maintenance allowanceof $10 per month) (chi-square¼ 47.7, p< 0.01), 67% mentioned thatthey wanted to increase social status in the community (chi-square¼ 56.1, p< 0.01), 67.9% wanted to continue being active inthe community (chi-square¼ 59.4, p< 0.01), 78.6% expressed thedesire to learn more about new ideas and information in agricul-ture (chi-square¼ 40.1, p< 0.01) and 3.6% wanted to be kept busy.All the reasons indicated differed significantly across the 10districts except the reason for training other farmers and being keptbusy.

The frequency distributions in Fig. 2 showed significant differ-ences in TE between FTs and FFs. The overall mean TE of PPT uptakewas 72%, indicating that PPT uptake involved an average ineffi-ciency of 28%. The TE scores between the trainers and those traineddiffered significantly (p< 0.05), from 15% to 92%. The TE indices by

Table 4Farmers’ self-rating of their farm constraints before and after uptake of PPTa.

Constraint FFs [mean(SE)] FTs [mean(SE)]

Before After Mean difference t-value Before After Mean difference t-value

1. Striga infestation 2.57 (0.036) 1.36 (0.027) 1.211 (0.037) 32.847*** 2.32 (0.094) 1.24 (0.044) 1.083 (0.091) 11.9120***2. Stemborer infestation 2.692 (0.022) 1.186 (0.017) 1.527 (0.064) 23.789*** 2.682 (0.053) 1.155 (0.037) 1.527 (0.064) 23.789***3. Low soil fertility 2.770 (0.019) 1.264 (0.020) 1.506 (0.026) 58.245*** 2.598 (0.054) 1.205 (0.042) 1.393 (0.061) 22.722***4. Soil erosion 2.698 (0.024) 1.149 (0.016) 1.548 (0.029) 53.499*** 2.482 (0.061) 1.098 (0.031) 1.384 (0.066) 20.901***5. Low soil moisture 2.608 (0.023) 1.191 (0.018) 1.417 (0.030) 47.003*** 2.482 (0.052) 1.250 (0.043) 1.231 (0.071) 17.453***6. Lack of fodder 2.698 (0.023) 1.166 (0.017) 1.532 (0.029) 53.068*** 2.696 (0.055) 1.161 (0.039) 1.536 (0.066) 23.332***7. Support of other crops 2.511 (0.029) 1.308 (0.022) 1.203 (0.039) 30.995*** 2.451 (0.055) 1.353 (0.051) 1.098 (0.074) 14.759***8. Low maize yield 2.841 (0.016) 1.289 (0.021) 1.552 (0.026) 59.204*** 2.643 (0.049) 1.214 (0.049) 1.429 (0.067) 21.395***

Overall mean index 20.434 (0.019) 9.423 (0.109) 11.011 (0.149) 74.024*** 19.893 (0.272) 9.429 (0.200) 10.464 (0.348) 30.086***

Figures in parentheses represent standard errors.***Significant at 0.01 level; **significant at 0.05 level; and *significant at 0.10 level.

a Rating on a 3-point Likert type scale 1¼ no problem; 2¼moderate problem; 3¼ serious problem.

D.M. Amudavi et al. / Crop Protection 28 (2009) 987–996992

FTs ranged from 61% to 90% with an average of 77%. This means thatif the average FT in the sample was to achieve the TE level of itsmost efficient counterpart, then the average farmer could realizea 14% increase in PPT uptake (i.e, 1� [77/90]).The majority (90%) ofthe FTs had TEs between 71% and 80%. On the other hand, thecorresponding TEs of FFs ranged from 15% to 92% with a mean of71%. This means that if the average FF in the sample was to achievethe TE level of its most efficient counterpart, then the averagefarmer could realize a 23% competence in PPT uptake (i.e, 1� [71/92]).The majority (about 60%) had TEs in the upper two groups of71% to 80% and 81% to 90%. There were significant differencesacross male and female FTs and FFs (F¼ 54.1, p< 0.01). All (100%)the male FTs and about 95% of the female FTs achieved on averageTE of 71–80%. Similarly, about equivocal (33.3% male and 32%female) of the FFs attained the same modal category of TE. About48% of the female FFs compared to 3% of the female FTs achieveda TE of 81–90%. There were more female than male farmers acrossthe two farmer types who scored TE above 81%.

When they were asked if they would be interested in taking onnew roles in advising other farmers, 99 out of the 104 FTs (95%)responded in affirmative. It is only in three districts (Vihiga, Siayaand Homa Bay) that a few (1–3) FTs were not willing to take onfurther PPT advisory work. Consistent with this question 75 out of

0.0 20.0 40.0

0-30

31-40

41-50

51-60

61-70

71-80

81-90

>91

0-30

31-40

41-50

51-60

61-70

71-80

81-90

>91

Mal

eF

emal

e

Tec

hnic

al E

ffic

ienc

y by

Gen

der

().

Fig. 2. Farmers’ technical efficiency distribution

93 FTs (80%) were willing to become facilitators of farmer fieldschools (FFSs) if the schools would be introduced in their villages.Majority (45%) of the FTs interested in being FFS facilitators werefrom Trans Nzoia district. Additionally, all FTs from Bungoma, Busia,and Rachuonyo were willing to facilitate FFS. All the districts,except Siaya and Migori, had some FTs expressing this interest(c2¼ 34.962, df¼ 9, p< 0.05).

The few FTs (5%) who indicated that they were not willing tocontinue with the role of advising other farmers cited reasons of:discontinuation of field allowance (14.3%), farmers in their villagesare already linked up to external information sources (8%), lack ofpush–pull planting materials (7.1%), discouragement due to discon-tinuation in the use of push–pull by some (3.6%) farmers, personalcommitments (3.6%) and lack of follow-up by technical staff (1.8%).

3.6. Factors influencing farmers’ TE of PPT uptake

After the frontier was estimated and reasonable estimates oftechnical efficiency or inefficiency obtained, we examined thedeterminants of technical efficiency in PPT uptake. Table 5 presentsthe maximum-likelihood estimates of the parameters of thetranslog stochastic frontier and inefficiency model. The estimatedcoefficient, g¼ 0.98, associated with the variances [ratio of farm-

60.0 80.0 100.0 120.0 Farmers

Farmer Teachers

Follower Farmers

by gender in PPT uptake in Western Kenya.

Table 5Maximum-likelihood estimates of the parameters of the translog stochastic frontierand inefficiency model for FFEA of PPT in western Kenya.

Frontier model Parameter Coefficient SE t-ratio

Constant b0 �2.5729 0.3066 �8.3919ln PER b1 0.0405 0.1701 0.2381ln PER^2 b2 0.0098 0.0675 0.1448ln FREG b3 0.2402*** 0.0813 2.9553ln FREQ^2 b4 0.0256*** 0.0084 3.0542ln PER_ln FREQ b5 0.0134 0.0476 0.2824FARMTYPE b6 0.3791*** 0.0901 4.2061

Inefficiency modelConstant a0 �27.4883 18.4350 �1.4911GENDER a1 0.0132 0.0465 0.2851AGE a2 0.0028* 0.0023 1.2276FARMSIZ a3 �0.0003 0.0053 �0.0561MARITAL a4 0.1838*** 0.0663 2.7744EDUCSEC a5 0.0585** 0.0444 1.3163HHSIZE a6 �0.0071 0.0082 �0.8678SDCONST a7 0.0007 0.0059 0.1167SKILL a8 �0.0028 0.0076 �0.3689KNOW a9 �0.0039 0.0058 �0.6600TLU a10 0.0105 0.0098 1.0773GRPLOC a11 �0.0573** 0.0230 �2.4939GRPSUP a12 0.1402*** 0.0442 3.1687

Variance parametersSigma-squared s2¼ (sm

2þ sn2) 11.3501*** 7.3044 1.5539

Gamma g¼ (sm2/[sm

2þ sn2]) 0.9854*** 0.0100 99.02

Likelihood function �555.0478LR test of the one-sided error 32.921379Number of observations 672Mean technical efficiency 0.72

***Significant at 0.01 level; **significant at 0.05 level; and *significant at 0.10 level.

D.M. Amudavi et al. / Crop Protection 28 (2009) 987–996 993

specific performance of TE (su2) to the total variance of output (s2)]

in the stochastic frontier is significantly different from zero. Thismeans that 98% of the variation in PPT uptake among the farmerswas due to the difference in efficiencies. The significance is alsoillustrated by the high asymptotic t-ratio of 99.02. This indicatesthat the random component in the inefficiency effects is highlysignificant in the analysis of the level and variability of PPT uptakeby farmers using the FFE approach in the study area. The resultsshowed that the longevity signs of PPT use (PER) by the sampledfarmers and the square of the longevity were positive, but insig-nificant. However, the natural log of the number of active contactsmade with neighbouring farmers (ln FREG) leading to PPT adoptionand its square were positive and significant. The coefficient of theFARMTYPE, included to indicate type of farmer, whether FT or FF,was positive and significant.

Farmer’s age, marital status and farmer’s level of education, atleast secondary, had a significant inefficiency-increasing effect onTE while membership in local groups had a significant inefficiency-reducing effect whereas membership in higher level groups (supragroups) had a significant inefficiency-increasing effect. Thissuggests that membership in local groups enhances farmers’ TE ofPPT uptake whereas membership in supra groups depresses it. Totalfarm size owned, household size proxying for labour, and skills andknowledge on communication of PPT received from training hadthe expected negative signs signaling inefficiency-reducing effects,but were insignificant. Coefficient for livestock ownership had aninefficiency-increasing effect, but was also insignificant.

The estimated coefficients of the explanatory variables in themodel for TE effects are of particular interest to this study. Thestudy run two tests of hypotheses involving the parameters of thestochastic frontier and inefficiency model obtained using a gener-alized likelihood-ratio statistic. The statistic had a chi-squaredistribution defined by l¼�2[ln L(H0)� ln L(H1)] , where L(H0) and

L(H1) are the values of the likelihood function under the specifi-cation of the null hypothesis, H0, and the alternative hypothesis, H1.The first null hypothesis that there was no technical inefficiencyamong the two groups of farmers across the 10 districts (for H0:gi¼ 0) was rejected at the 5% level, indicating that the coefficientsof the frontier production function were significantly different fromthe average production function estimated with the ordinary leastsquares (OLS) model. The second null hypothesis that the explan-atory variables in the model for the TE effects had zero coefficients,H0: a1¼ a2,.,a12¼ 0, was rejected at 5% level. This implied that,taken together, the explanatory variables had a significant impacton TE. The directions of influence of most of the explanatory vari-ables generally conformed to the expected signs.

4. Discussion and conclusions

The farmer-to-farmer extension (FFE) in PPT transfer is clearlyproducing a multiplier effect. Due to limited number of extensionworkers to serve the many farmers who need extension services,the FFE was considered a quicker way of reaching and encouragingmany farmers to learn from their trained fellow farmers. In thisstudy one FT on average influenced some 34 farmers within 3–4years. In this era where public extension faces tight budgets and thechallenge to cover large geographical areas to increase visibility andimpact (Alila and Atieno, 2006), the FFE in technology dissemina-tion seems prudent. The training received was effective inpreparing the FTs as farmers’ extension agents. This suggests thatwell trained FTs can be effective nodes of knowledge disseminationto other farmers. The 53% mitigation provided by PPT adoption,suggested that the technology had a statistically significant effecton reduction of farmers’ field constraints. The mean scores of theinfluence of PPT on reducing production constraints suggested thatthe respondents had positive perceptions about it. This concurswith the results of earlier work which showed that the PPT wasecologically effective and economically viable (Khan et al.,2008a,b). Hence, an effective extension delivery is necessary inorder to reduce the knowledge gap between farmers andresearchers on new technologies (Heemskerk, 2006; Sinja et al.,2004). As PPT demand increases, the low rating on harvesting andprocessing of Desmodium suggests the need for training farmers onthese skills in order to increase the supply of readily availableDesmodium planting material.

The main objective of this study was to assess the technicalefficiency of farmers’ uptake and dissemination of PPT. The resultsshowed that if the average farmer in the sample among the FTs orFFs was to achieve the TE level of its most efficient counterpart,then the average farmer could realize a 14% or 23% increase in PPTuptake, respectively. The farmer’s age, marital status and formaleducation had a significant inefficiency-increasing effect. The effectof age was consistent with our expectation that older farmers couldbe technically less efficient than younger farmers in PPT uptake. Assuggested by Speelman et al. (2008) why age did not contributesignificantly to higher technology use efficiency in small-scaleirrigation in South Africa, the ambivalence is that whereas olderand more experienced farmers may be knowledgeable on variouspractices, they are often less willing to adopt new ideas. Ike et al.(2006) and Tauer (1995) also found that labour productivity,necessary for farm-based technologies, decreased with age. On theother hand, younger farmers are more productive than their oldercounterparts, because of the arduous nature of farm operations.The two studies also indicated that younger farmers were techni-cally more progressive than older ones, since the younger onesshowed greater willingness to adopt new practices. In contrast,education level in this study had unexpected inefficiency-increasing effect. Our study results suggest that farmers who had

D.M. Amudavi et al. / Crop Protection 28 (2009) 987–996994

higher formal education, at least secondary level, were less efficientin PPT uptake than those with lower education. This could bebecause higher education opens up better opportunities for liveli-hoods such as off-farm employment and, hence creates lowerincentives to adopt PPT in maize production. Intuitively, farmerswith lower education are more likely to be limited in such oppor-tunities and hence depend more primary production for theirlivelihoods.

The frequency of contacts between FTs and non-PPT farmerssignificantly increased the likelihood of PPT adoption, suggestingthat the farmers were still operating in the inefficiency zone andthe marginal productivities were still increasing. This is the initialstage of the production frontier implying that FTs and FFs had thepotential of increasing area put under PPT and hence increased totalproduction during subsequent years. The results showed significantdifferences in TE between FTs and FFs in PPT uptake, with the FTsshowing superior application of the technology. This suggests thatfarmers who receive technical advice and logistical support aretechnically more efficient in PPT uptake than those who just receivetraining from fellow farmers. Given that FTs’ and FFs’ TE measuresaveraged over 70%, the small differences between them furtherindicated that many farmers operated at a technical inefficient leveland that addressing the limiting factors could improve the effi-ciency. Enhanced TE will not only enable farmers to increase the useof productive resources, but it will also give direction for theadjustments required in the long run to achieve food sustainability(Al-hassan, 2008).

Although TE estimates indicated that farmers fail to reach theiroptimal levels, the results confirm the importance of FFE indisseminating productivity enhancing technologies in smallholderfarming systems in Eastern Africa (Tizikara and Kwesiga, 2006). Thecurrently promoted platform1 technology allows achievement ofpotential yields in cereal production in these systems. As advised byTizikara and Kwesiga (2006) agricultural research is not merely todevelop and get new technologies to farmers but more importantlyto empower farmers to better understand and respond to changingemergent circumstances. This is underscored by Alene and Many-ong (2006), who report in their study that the failure to reachoverall efficiency can be explained by the limited capacity offarmers to catalyze the dissemination process. The findings concurwith those of Feder and Savastano (2006) in which farmers’capacity to utilize new technologies and influence other farmerswas increased by linking them to champions2 of new technologies.Our findings also imply that if farmer interaction with trainedfarmers is enhanced, then more farmers are likely to try andeventually adopt the PPT. This hypothesis was tested by Okoruwaand Ogundele (2004) and the conclusion made to the effect thatenhanced extension contacts with farmers increased the proba-bility of technology adoption. Elsewhere, Aw-Hassan et al. (2008)found that informal farmer-to-farmer seed dissemination in Syriawas an important vehicle for the diffusion of new barley varieties,which were grown on 27% of the barley area of monitored farmers,despite a complete lack of extension support. These insights implythat targeted capacity building and effective incentives cancontribute substantially to the efficiency of the FFE approach.

Membership in local groups had inefficiency-reducing effect onPPT uptake. For technology uptake, this is desirable and the effect is

1 Platform technology is capable of supporting multiple functions such asincreased diversified crop production, crop-livestock integration, and concomitantincreased income generation.

2 Technology champions are members of organizations or systems presentingnew technology to fellow members who are potential users (Lawless and Price,1992).

partly due to three reasons. First, farmers belonging to local groupsare more likely to be reached by extension agents with informationon the technology to make production decisions. Second, partici-pation in local associations and groups provides opportunities forinteractive learning about new innovations and technologies(Roling and Wagemakers, 1998). Third, membership in local groupshelps amplify the ‘‘neighbourhood effect’’ and reduce possible risks(Franzel et al., 2001). These suggestions are consistent withempirical findings about effects of group participation on house-hold welfare, a function of income and/or assets acquired bya household. Amudavi (2007) found that participation in localgroups may enhance social capital necessary for learning aboutnew agricultural technologies whereas participation in supragroups (higher level) generates outcomes which positively influ-ence a household’s well-being, thereby reducing dependence on-farm income sources. Narayan and Pritchett (2000) found thatmemberships local group networks provide low cost informationaccess and social capital that helps overcome imperfect informa-tion caused by market failures. This suggests that to accelerate PPTuptake then, local group approach would be desirable.

The positive and significant coefficient of FARMTYPE impliedthat there were significant differences in the efficiencies betweenFTs and FFs in PPT uptake, with the former being more efficient.Hence, an increased focus on FFE in technology transfer may bejustified. Its effectiveness requires building the farmers’ capacity inunderstanding and applying new technologies. Moreover, to useFFE to complement public extension provision, farmers need rele-vant knowledge, skills and abilities, and motivational incentives.The farmers must be nearly the same in wealth indicators as others.The study by Feder and Savastano (2006) on the role of opinionleaders in the diffusion of IPM knowledge found that if selectedopinion leaders driving technology transfer contrast excessively tothe rest of the community, their effectiveness diminish and theybecome irrelevant to knowledge diffusion thereby limiting thetrickle down effect to those with similar socioeconomic statuses.Hence, as suggested by the World Bank (Saito and Weidemann,1990), such farmers should be (1) representative of the diversity offarm size, cropping pattern, socioeconomic conditions; (2) beworthy of imitation by others; (3) active, practising farmers;(4) willing to adopt recommendations, encourage others to observethem, and explain to them; and (5) if possible, come from differentfamilies and geographical areas.

Where production constraints are important as in WesternKenya, an obvious implication is the need for investment in farmers’capacities that allow for recognition of constraints and their effects,individual uptake and extension of appropriate technologies.Improving the farmers’ efficiency in technology transfer may requiredeveloping their capacities (attributes they must possess) andcapabilities (what must they be able to do). Technical expertise,creative thinking skills, social skills, and organizational under-standing represent capacities that allow leaders to act effectively. Assuggested by Mumford et al. (2007) such preparations encouragefarmer innovation and uptake to: (1) identify and define the prob-lems worth pursuing given potential payoffs against possible risk,(2) establish a context that allows multiple parties to generate viableideas that have a chance of successful implementation, and(3) manage the context of idea development and fielding to ensuresuccessful development of new products and services. This is akin toknowledge and information management system that supportstechnology identification and development while ensuring sus-tained technology uptake (Mignouna et al., 2008).

Training offers opportunities for FTs to interact with otherfarmers and modalities for providing extension services toresource-poor farmers. Estimations of technical efficiencies of PPTuptake across different farmer types showed that frequency of

D.M. Amudavi et al. / Crop Protection 28 (2009) 987–996 995

farmer interactions and household-level factors (farmer’s age,having minimum secondary education, membership in localgroups, etc.) are critical factors. Currently, the farmers are operatingbelow their frontier output levels. PPT uptake can be increased byselecting willing farmers and training them to advise others,increasing PPT demonstrations, providing Desmodium seed andcredit for other needed critical inputs. By doing so, the FFE processwill enable farmers to take advantage of new opportunities offeredby adoption of PPT.

Whereas FFE is not a ‘‘silver bullet’’ to accelerate farmers’ access tonew technologies, it can catalyze the transfer of much needed tech-nologies to many smallholder farmers by linking them to research andextension in a more effective and sustainable way. This can be ach-ieved through targeted training, active involvement in setting priorityresearch goals and strategic linking of farmers to input and outputmarkets. To understand the impact of this approach, it is necessary toexamine impacts of PPT on production and consumption by differenthousehold social categories. Further studies are also needed on allo-cative and economic efficiencies to evaluate the scope of farmer-to-farmer outscaling and upscaling the multifunctional PPT. In additiona more in-depth qualitative analysis of the uptake process from thefarmer (and the farmer trainer) point of view would be relevant inguiding future action by policymakers and programme manages tostimulate PPT uptake by the smallholder farmers.

Acknowledgements

The authors are grateful to Gatsby Charitable Foundation (UK),Kilimo Trust (East Africa) and Biovision Foundation (Switzerland),for providing financial support. The study was conducted incollaboration with Rothamsted Research which receives grant-aided support from the Biotechnology and Biological SciencesResearch Council (BBSRC). The authors also acknowledge theassistance provided by ICIPE field staff, Ministry of Agricultureextension staff, and the farmers.

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