Farmers’ perceptions of a ‘push–pull’ technology for control of cereal stemborers and Striga weed in western Kenya

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Crop Protection 27 (2008) 976–987

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Farmers’ perceptions of a ‘push–pull’ technology for control of cerealstemborers and Striga weed in western Kenya

Zeyaur R. Khana,�, David M. Amudavia,b, Charles A.O. Midegaa,Japhether M. Wanyamaa,c, John A. Pickettd

aInternational Centre of Insect Physiology and Ecology (ICIPE), P.O. Box 30772, Nairobi 00100, KenyabEgerton University, P.O. Box 536, Njoro, Kenya

cKenya Agricultural Research Institute, Kitale, P.O. Box 450, Kitale 30200, KenyadBiological Chemistry Division, Rothamsted Research, Harpenden, Hertfordshire AL5 2JQ, UK

Received 14 August 2007; received in revised form 3 December 2007; accepted 4 December 2007

Abstract

Striga and cereal stemborers are major constraints to cereal production in sub-Saharan Africa causing serious food security concerns.

The International Centre of Insect Physiology and Ecology (ICIPE) and partners have developed a novel integrated management system

called the ‘push–pull’ technology (PPT) in mitigation. This involves inter-cropping maize with a stemborer moth-repellent forage legume,

silverleaf desmodium (push), and planting an attractive trap crop, Napier grass (pull), around the intercrop. Additionally, chemicals

produced from desmodium roots inhibit Striga. We evaluated farmers’ perceptions of the pests, PPT attributes and factors influencing

the likelihood of its adoption in 15 districts in western Kenya. A random sample of 923 farmers, with 478 having adopted the technology

(practicing) and 445 not yet adopted but attending PPT field days (visiting) were interviewed. The practicing farmers cited both Striga

and stemborers as major maize production constraints, alongside other constraints, as the main motivations for adoption of PPT.

Reduced infestation by the pests, improvement in soil fertility, increase in maize grain yields, improved fodder and milk productivity

were cited as main benefits of PPT. Similarly, the field day visiting farmers rated PPT as a more superior technology compared to their

own maize production practices. Farmer’s age, household headship by female farmers, technology attributes and exposure to a variety of

extension methods significantly influenced likelihood of PPT adoption. Effective dissemination pathways are needed to provide farmers

with appropriate information for evaluating potential benefits and tradeoffs of such a management-intensive technology. Further

research is needed to understand how PPT contributes to farmers’ livelihood improvement and how the efficacy of different

dissemination pathways in PPT technology transfer influences its adoption.

r 2007 Elsevier Ltd. All rights reserved.

Keywords: Farmer perceptions; Striga; Stemborers; ‘Push–pull’ technology; Kenya

1. Introduction

The agriculture sector in Kenya contributes about 26%to the Gross Domestic Product and accounts for over 75%of employment and about 60% of total export earnings(GoK, 2002). Within the sector, maize (Zea mays L.) is amajor staple and cash crop for majority of the small-holders. Maize production is severely constrained by cerealstemborers (Kfir et al., 2002), parasitic weed, Striga

e front matter r 2007 Elsevier Ltd. All rights reserved.

opro.2007.12.001

ing author. Tel.: +254 59 22216/7/8; fax: +25459 22190.

ess: [email protected] (Z.R. Khan).

hermonthica and low soil fertility (Oswald, 2005). While arange of technologies have been developed, and othersproposed, to address some of these constraints, maizeyields achieved by farmers are still low, generally o1.0 t/ha(Jagtap and Abamu, 2003). This has been partly due toweaknesses inherent in the technologies themselvesprompting low adoption rates by the farmers for bothbiological and socio-economic reasons, and overall weak-nesses in the extension delivery methods (Kamau et al.,2000; Anderson and Feder, 2004).Finding ways to reverse the trend of low and declining

agricultural productivity is imperative in Kenya and other

ARTICLE IN PRESSZ.R. Khan et al. / Crop Protection 27 (2008) 976–987 977

sub-Saharan countries. The International Centre of InsectPhysiology and Ecology (ICIPE) in Kenya, in collabora-tion with Rothamsted Research in UK and KenyaAgricultural Research Institute (KARI), has developed anovel integrated cropping system, dubbed ‘push–pull’technology (PPT), for management of stemborers andStriga. The PPT is based on a stimulo-deterrent diver-sionary strategy (Miller and Cowles, 1990), where insectpests are repelled from a harvestable crop and aresimultaneously attracted to a ‘discard’ or ‘trap’ crop (Cooket al., 2007). PPT involves inter-cropping maize with afodder legume, silverleaf desmodium (Desmodium uncina-

tum), and planting Napier grass (Pennisetum purpureum) asa trap crop around the crop field. Green leaf volatilesemitted by the desmodium repel the stemborer moths awayfrom the maize field (push component), while thosereleased by the Napier grass attract them (pull component)(Khan et al., 2000, 2001). Because stemborer moths preferNapier grass to maize for oviposition (Khan et al., 2006b,2007; Van den Berg, 2006), majority of the eggs are laid onthe trap crop, leaving the maize protected. Most of theresultant stemborer larvae, however, do not survive on thetrap crop due to a range of factors including poor nutrientcomposition of Napier grass, production of sticky sap thatentangles and kills the larvae and abundant naturalenemies associated with the grass (Khan and Pickett,2004; Khan et al., 2006b, 2007; Midega et al., 2006; Vanden Berg, 2006). In addition, desmodium inhibits andeliminates Striga through a range of mechanisms includingnitrogen fixation and addition of organic matter into thesoil, smothering due to dense ground cover, and allelo-pathy, which is by far the most important (Khan et al.,2002). A field trial at ICIPE-Mbita in western Kenyashowed a significant increase in total nitrogen in field plotsunder maize intercropped with various species of desmo-dium as compared to maize monocrop or maize–cowpeaintercrop (Khan et al., 2006a). Desmodium roots producechemical compounds some of which stimulate Striga seedgermination while others inhibit attachment of Striga rootsto those of the maize (suicidal germination) (Tsanuo et al.,2003). Since desmodium is a perennial crop, this ensurescontinual depletion of Striga seed bank in the soil evenduring periods when there is no cereal in the field.

The PPT is targeted at smallholder farmers in easternAfrica and at the time of the study was being promoted inthe 15 districts in western Kenya, three in central Kenya,five in eastern Uganda and one in northern Tanzania.However, the extent to which farmers are actively involvedin assessing and adopting this technology has not beenexamined in detail. Moreover, strong empirical evidence totest the common view about extension methods’ efficacyand their impact has been scanty.

Farmers’ perception of an agricultural technology isimportant in influencing adoption decisions (Adesina andBaidu-Forson, 1995; Rogers, 1995). Technology adoption,a multidimensional process, is influenced by factors suchas perceived profitability and costs of the technology, its

compatibility with production systems, and the clarity withwhich the new knowledge and information is commu-nicated in a recipient population (Boahene et al., 1999).Besides the efficacy of a technology, the severity of theexisting constraints also conditions the decision to invest innew technologies (Mbaga-Semgalawe and Folmer, 2000;Kalule et al., 2006).As part of our continued research on PPT, we evaluated

farmers’ perceptions of the attributes of the PPT and theirinfluence on adoption of the technology. Specifically, weassessed (1) perceptions of PPT-practicing farmers onseverity of Striga and stemborer constraints; (2) primarysources of information about PPT and the reasons for itsadoption among the practicing farmers; (3) perceptions ofPPT-practicing farmers on any benefits realized from PPTand any labour changes experienced following its adoptionon their farms; and (4) perceptions of non-participatingfarmers attending field days about PPT attributes andmotivational aspects for its adoption. This was importantin generating information on the motivational factorsinfluencing decisions and farmers’ knowledge of the PPTand consequently its adoption.

2. Materials and methods

2.1. Study sites

The study utilized farm-level cross-sectional data col-lected through surveys conducted between July and August2005 from 15 districts in western Kenya (Table 1). Thestudy sites covered a wide range of agro-ecological zones(AEZ) (Jaetzold and Schmidt, 1982, 1983), where PPT hadbeen introduced. In all districts except Teso, Nyando andBondo where PPT was introduced in 2005, farmers hadbeen practicing PPT for two or more seasons. Both Striga

and cereal stemborers are found in all the districts with theexception of Trans Nzoia which has no Striga. Given theKenya national population growth rate estimate of 3% perannum (McPherson and Rakovski, 1998), the pressure onland resources has led to agricultural intensification butwith limited investment in productivity-enhancing techno-logies in these districts. With threats from such pests andweeds, the food security of majority of the people in thesedistricts is threatened.

2.2. Data and data collection procedures

We conducted field days between July and August 2005in 15 districts in western Kenya (Table 2) in collaborationwith Kenyan government’s Ministry of Agriculture todemonstrate PPT to farmers and to collect informationon farmers’ perceptions about the technology. Both PPTpractising and visiting (non-practicing) farmers attendedthe field days. The visiting farmers were asked to compareand evaluate ‘push–pull’ and control plots as soon as theyarrived at the field days, after which they were asked tocomplete the questionnaires with the help of enumerators.

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Table 1

Location, agroecological zones and population density of the study districts

District Elevation (meters

above sea level)

Agroecological

zone

Mean annual

rainfall (mm)

District coordinates Population density

(persons/km2)

Suba 1200–1600 UM 1 700–1150 01200–01520S, 341E 148

Bondo 1135–1350 LM1–2, LM3–4 750–1200 01250–0120S, 34100–341330E 246

Nyando 1135–1300 LM4 800–1200 01–01150S–341800–351750E 270

Teso 1135–1300 LM1–2, LM3–4 700–1500 0110–01460S, 331540–341260E 325

Busia 1200–1500 LM1–2, LM3–4 1200–1700 338

Bungoma 1500–2500 UM1, UM2–3,

UM4, LM2–3

630–1600 01250–01530S, 341210–351040E 424

Siaya 1135–1500 LM1–2, LM3–4 1200–1800 01260–01180S, 331580–341330E 316

Rachuonyo 1280–2100 LM1–4, UM2–4 740–1200 01170–01360S, 341260E 325

Homabay 1163–1219 UM1–2, LM1–4 740–1200 01400–01S, 01–341500E 249

Kuria 1140–1600 UM1–2, LM1–4 800–1400 01400–01S, 34–341500E 261

Migori 1135–1700 LM1–5, UM1–2 700–1800 257

Kisii 1600–2000 UM1, LH1–2 1350–2100 01300–01580S, 341380–341E 758

Vihiga 1300–1500 LM1–UM1 1800–2000 341300–35100E, 01–01150N 886

Butere-Mumias 1240–1641 LH–LM1–2 1600–2800 01090–01200S, 341290–341330E 508

Trans Nzoia 1600–3800 LH3 850–1000 01520–11180S, 341380–351230E 231

UM—upper midland; LM–lower midland; LH—lower highland; UH—upper highland.

Table 2

Sample size distribution of farmers interviewed during the field days

District Farm type Total

Practicing ‘push–pull’ Visiting

Bondo (1) 19 35 54

Bungoma (5) 46 15 61

Busia (5) 36 36 72

Butere–Mumias (5) 48 35 83

Homabay (5) 28 32 60

Kisii (5) 30 27 57

Kuria 33 33 66

Migori (5) 33 29 62

Nyando (1) 18 32 50

Rachuonyo (5) 35 20 55

Siaya (5) 27 35 62

Suba (15) 33 6 39

Teso (1) 19 35 54

Trans Nzoia (9) 40 40 80

Vihiga (5) 33 35 68

Total 478 445 923

Numbers in brackets indicate the number of years PPT had been practiced

in the various districts at the time of the study.

Z.R. Khan et al. / Crop Protection 27 (2008) 976–987978

Using two semi-structured survey questionnaires (one forPPT participating farmers and one for visiting farmers),trained enumerators interviewed a total of all 923 farmerswho attended field days in the 15 districts. A total of 478PPT practicing and 445 non-practicing farmers wereinterviewed. The non-practicing visiting farmers wereinterviewed during the field days after they visited PPTand control plots, while the enumerators interviewed thepracticing farmers on their farms during household visits.

The datasets for both groups of farmers includedpersonal (age and sex) and farm characteristics (farm sizeand grain yield outputs). Farmers’ perceptions of the PPT

were sought on five attributes: Striga control (takingcounts of Striga plants), stemborer control (checkingdamage by stemborers), health of the crop (rating physicalappearance of maize), size of cob (whether big, medium orsmall), and ability to improve soil fertility (comparingqualitative soil fertility indicators, e.g. colour, texture ofsoil samples) from PPT plots and farmer’s conventionalpractices. Using these attributes, the non-practicing farm-ers were asked to compare the plots with and rate PPTagainst farmers’ production practices, on a scale of 1–4(1 ¼ poorest; 4 ¼ best). Our inclusion of this indicator insubsequent analysis, alongside other indicators of adoption(Table 3), was based on the premise that the producers’agro-ecological, socio-economic and institutional contextsplay a central role in the adoption decision process(Scoones and Thompson, 1994). The PPT-practicingfarmers, on the other hand, were asked about theirperceptions of severity of Striga and stemborers on theirfarms, primary sources of information on PPT, whichhelped them to learn about and adopt the technology, themain reasons for adopting PPT, any benefits realisedfollowing adoption of the technology, and any changes inhousehold labour requirements as a result of adoption ofthe technology. Field days are considered important infacilitating experiential and collective learning that oftenleads to adoption of new agricultural technologies amongfarmers (Doss, 2003; Florencia, 2006; Knowler andBradshaw, 2007).

2.3. Analytical model specification

Farmers’ indicators used to evaluate technologies areperceived to motivate adoption or dis-adoption of agricul-tural innovations. Modelling technology adoption usuallyinvolves a binomial or multinomial variable approach, using

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Table 3

Variable descriptions in the logit regression model

Variable Description of independent variables Nature of variable Expected sign

Age Farmer’s age in years Continuous

Sex Sex of the household head (1 ¼ male; 0 ¼ female) Dummy Positive

Farm Farm size (in acres) Continuous Negative

Yield Yield of maize in 90 kg bags Continuous Positive

Technology

attribute

Summation of farmers’ ratings of PPT on plant health, cob size, Striga, stemborer

control and soil fertility rates scored on 1–4 scale

Continuous Positive

Extension Dissemination through extension programme 1 ¼ extension; 0 ¼ otherwise Dummy Positive

Farmer meetings Dissemination through farmer meetings 1 ¼ baraza; 0 ¼ otherwise Dummy Positive

Farmer teachers Dissemination through farmer-to-farmer extension 1 ¼ farmer teacher;

0 ¼ otherwise

Dummy Positive

ICIPE staff Dissemination through ICIPE staff awareness meetings 1 ¼ ICIPE awareness

meetings; 0 ¼ otherwise

Dummy Positive

Radio Dissemination through radio programme 1 ¼ radio; 0 ¼ otherwise Dummy Positive

Constant

Table 4

General socioeconomic characteristics of farmers interviewed

Variable Practicing (n ¼ 478) Visiting (n ¼ 445) t-Value w2

Mean SE Mean SE

Age of farmer 45.3 0.598 43.8 0.692 0.100NS

Farm size 4.8 0.527 3.2 0.0192 2.147�

Gender

% Male 48.0 51.8 0.523NS

% Female 52.0 48.2 0.699NS

�The mean difference significant at 0.5% level.

Z.R. Khan et al. / Crop Protection 27 (2008) 976–987 979

either a latent variable or random utility factor (Rogers,1995). We used the former in which there is an unobservedlatent variable (yn), such as expected gain from thetechnology, which underlies the observed binary variableof adopting or not adopting the technology (Table 3). Weassumed a linear relationship between the latent variable yn

i

and the observed explanatory variables Xi through astructural model of the equation:

yn

i ¼ X i þ ei ði ¼ 1; . . . ;NÞ. (1)

This further linked the latent variable to the observedbinary variables Yi, which represent farmer’s adoptiondecisions of adoption or non-adoption, thus

Y i ¼ 1 if yn

i 40; Y i ¼ 0 if yn

i p0. (2)

Finally, a logistic regression (Eq. (3)) examined whetherfarmers’ perceptions of technological attributes, farmcharacteristics and selected dissemination methods esti-mated the farmers’ decision-choice model of adopting ornot adopting the technology:

Y i ¼ b0 þ bi1xi1 þ bi2xi2; . . . ;bikxik þ ui

ði ¼ 1; . . . ; k; i ¼ 1; . . . ;NÞ, ð3Þ

where Yi ¼ 1, if a farmer had adopted PPT, otherwise 0, k isthe number of independent variables. The conditional meanof y given the independent variables xi (i ¼ 1,y,k) is

EfY ig ¼ myijxi¼ pi,

where pi ¼ Pr(Yi ¼ 1jxi ¼ pi (xi) ¼ Pr(xiX1). b0 and bi arethe coefficients estimated using the suite of independentvariables proposed in Table 3 and ui is an independentlydistributed error term assumed to be normal with mean zeroand constant variance one. The model helps to understandwhich factors are most important in explaining the decisionto likely adopt PPT, and therefore inform on the aspects ofthe technology an extension programme can concentrate onto increase its spread among many farmers.

3. Results and discussion

The average farm size of PPT-practicing farmers was4.74 acres, whereas that of farmers visiting field days was3.24 acres (Table 4). The sex of the respondents comprised48% males and 52% females for the participating andabout 52% male and 48% females for the visiting farmers,indicating a gender-balanced participation in the study.Moreover, the average age of the farmers did notsignificantly differ between practicing and visiting farmers(Table 4).Majority of the PPT-practicing farmers in the study cited

both stemborers and Striga as severe maize productionconstraints in their districts (Table 5). Given suchcircumstances, availing an appropriate technology that isaffordable and fits well into farmers’ farming system islikely to stimulate its uptake. Farmers’ perceptions on theseverity of production constraints, such as Striga andstemborers, and suitability and effectiveness of anymanagement strategies are a key determining factor onwhether farmers adopt or do not adopt such technologies(Emechebe et al., 2004).After making observations during the field days, the

visiting farmers rated the PPT significantly superior(po0.05) to the farmers’ own practices on all attributes,indicating that they perceived it as an effective technology

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Table 5

Participating farmers’ indications of severity of Striga and stemborer problems

District Resp % Response District Response % Response

Stemborer Striga Stemborer Striga

Bondo Yes 100 100 Migori Yes 100 97

No 0 0 No 0 3

Bungoma Yes 73 100 Nyando Yes 94 100

No 27 0 No 6 0

Busia Yes 100 100 Rachuonyo Yes 100 89

No 0 0 No 0 11

Butere–Mumias Yes 100 97 Siaya Yes 100 100

No 0 3 No 0 0

Homabay Yes 94 100 Suba Yes 100 100

No 6 0 No 0 0

Kisii Yes 100 100 Teso Yes 94 97

No 0 0 No 6 3

Kuria Yes 88 97 Vihiga Yes 97 100

No 12 3 No 3 0

Trans Nzoia Yes 100 0

No 0 0

Z.R. Khan et al. / Crop Protection 27 (2008) 976–987980

for the control of stemborers and Striga, improved soilfertility and increased maize production (Table 6). Major-ity of them (about 90%) observed that the technologycontrolled Striga and increased soil fertility (83%),controlled stemborers (52%), and provided quality fodder(33%) (Fig. 1). Others indicated that the technologystabilized the farming system (14%), provided an alter-native strategy of improving soil fertility (14%), conservedsoil moisture (10%) and can reduce farm workload (7%).Such attributes of the technology suggest the motivationsand propensity for its adoption.

The practicing farmers from various districts cited anumber of sources from which they first obtainedinformation about PPT that motivated their interests init, leading to its subsequent adoption. Over 60% of thefarmers in Bungoma and Rachuonyo, and over 40% inTrans Nzoia, Suba, Busia, Butere-Mumias and Vihigareceived information from early adopters (Fig. 2). In Teso,Nyando and Bondo, early adopters as a source ofinformation were not evident as the technology had beenrecently introduced by the mass media. In these districts,between 50% and 70% of the farmers received informationthrough a national radio programme (Tembea na majira)and some, less than 20%, through extension and non-governmental organisations’ (NGO) staff. In Homabaydistrict, over 60% of farmers received information throughfarmer-teachers while field days were a major source ofinformation for farmers in Kuria district (Fig. 2). This thusrevealed the technology transfer methods that could beeffectively employed in the different target areas and onwhich incremental resources could be placed to disseminatethe PPT. Access to information about an agricultural

technology with a demonstrated efficacy is one of the keyfactors determining a technology’s uptake (Mowo et al.,2004; Doss, 2003). Such access can be facilitated bydifferent information channels including mass media,information bulletins, field days, technical support (likefarmer-teachers) and farmer field schools (Panell, 1999).The different communication channels and learning toolsare effective at different stages of adoption decision making(Oladele and Adekoya, 2006; Garforth, 1998). Therefore, itis important to determine returns to investments in thedissemination methods used in PPT transfer.While majority of the PPT-practicing farmers in Trans

Nzoia district (470%) were motivated to adopt thetechnology for control of stemborers, more than 50% ofthose in other districts (except Bondo and Kisii) weremainly motivated by the need to control Striga (Fig. 3).Similarly, the need to improve soil health and increase farmproductivity were additional reasons why farmers acrossthe districts adopted the technology. Control of stemborerswas also cited by farmers in Bungoma, Kisii, Migori andVihiga (Fig. 3).Majority of the PPT-practicing farmers (over 80%) in all

the districts reported a reduction in Striga infestation ontheir farms following adoption of the PPT (Fig. 4). Morethan 80% of those in Trans-Nzoia, Homabay, Kisii andSuba districts, and more than 50% of those in Bungoma,Butere-Mumias, Migori and Teso districts reported re-duced stemborer infestation (Fig. 4). Over 80% of thefarmers in Busia, and over 50% in Bungoma, Migori, Subaand Teso reported an improvement in soil fertility. Over80% of farmers in all the districts (except Vihiga) and over70% of those in all the districts reported an increase in

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Table 6

Visiting farmers’ rating of ‘push–pull’ technology (PPT) compared to farmers’ practice (FP) during field days

District Technology Rating technology attributes

Reduced stemborer Reduced Striga Increased soil fertility Increased maize grain yield

Mean t-Value Mean t-Value Mean t-Value Mean t-Value

Bungoma PPT 3.7 (0.1) 4.2� 3.6 (0.2) 6.2� 3.4 (0.1) 7.3� 4.0 (0.0) 20.6�

FP 2.2 (0.3) 1.5 (0.3) 2.0 (0.1) 1.8 (0.1)

Busia PPT 3.3 (0.2) 3.6� 3.5 (0.1) 6.9� 3.2 (0.1) 4.2� 3.9 (0.1) 7.7�

FP 2.2 (0.2) 2.0 (0.2) 2.5 (0.2) 2.5 (0.2)

Butere–Mumias PPT 4.0 (0.0) 17.7� 3.9 (0.1) 15.3� 3.3 (0.1) 16.5� 3.9 (0.1) 39.3�

FP 1.4 (0.1) 1.6 (0.1) 1.4 (0.1) 1.1 (0.1)

Homabay PPT 3.7 (0.1) 8.7� 3.9 (0.1) 12.8� 3.6 (0.1) 9.8� 3.9 (0.1) 10.2�

FP 1.9 (0.1) 1.7 (0.1) 1.9 (0.1) 1.9 (0.1)

Kisii PPT 3.8 (0.1) 3.5� 3.9 (0.1) 8.7� 3.3 (0.1) 3.1� 4.0 (0.0) 3.6�

FP 3.1 (0.2) 2.6 (0.1) 2.8 (0.1) 3.3 (0.2)

Kuria PPT 3.7 (0.1) 9.5� 3.5 (0.2) 5.1� 3.5 (0.1) 9.1� 4.0 (0.0) 10.8�

FP 1.9 (0.2) 2.2 (0.2) 2.1 (0.1) 1.9 (0.2)

Migori PPT 3.9 (0.1) 8.4� 3.8 (0.1) 14.5� 3.4 (0.1) 8.3� 4.0 (0.0) 11.7�

FP 2.2 (0.2) 1.3 (0.1) 1.9 (0.1) 1.8 (0.2)

Rachuonyo PPT 3.8 (0.1) 6.8� 3.7 (0.2) 5.9� 3.2 (0.1) 2.7� 4.0 (0.0) 11.1�

FP 2.1 (0.2) 1.6 (0.2) 2.5 (0.3) 1.7 (0.2)

Siaya PPT 3.5 (0.2) 3.9� 3.5 (0.1) 4.5� 3.5 (0.9) 8.7� 4.0 (0.0) 21.8�

FP 2.5 (0.2) 2.2 (0.2) 1.9 (0.2) 1.6 (0.1)

Vihiga PPT 3.7 (0.1) 9.5� 3.8 (0.1) 8.5� 3.6 (0.1) 9.5� 4.0 (0.0) 13.9�

FP 1.8 (0.1) 2.3 (0.2) 2.1 (0.1) 1.9 (0.1)

Trans-Nzoia PPT 3.7 (0.1) 22.1� – – 3.3 (0.1) 16.1� 4.0 (0.0) 33.6�

FP 1.6 (0.1) – 1.7 (0.1) 1.6 (0.1)

Bondo PPT 3.3 (0.1) 4.7� 3.4 (0.1) 5.7� 3.1 (0.1) 5.6� 3.9 (0.0) 13.6�

FP 2.3 (0.2) 1.9 (0.2) 2.2 (0.2) 1.5 (0.2)

Nyando PPT 3.5 (0.1) 9.9� 3.9 (0.1) 9.7� 3.4 (0.1) 7.6� 3.8 (0.1) 9.0�

FP 1.4 (0.2) 1.8 (0.2) 1.8 (0.2) 1.6 (0.2)

Teso PPT 3.8 (0.1) 10.4� 3.7 (0.1) 9.2� 3.5 (0.1) 11.7� 4.0 (0.0) 25.8�

FP 2.1 (0.14) 1.7 (0.2) 1.7 (0.1) 1.3 (0.1)

PPT, ‘push–pull’ technology; FP, farmers’ own practice; – no Striga in Trans Nzoia district.

Striga control: 1 ¼ very high infestation; 2 ¼ high infestation; 3 ¼ low infestation; 4 ¼ no infestation.

Stemborer control: 1 ¼ very high damage; 2 ¼ high damage; 3 ¼ low damage; 4 ¼ no damage.

Increased maize yield: 1 ¼ poor; 2 ¼ average; 3 ¼ good; 4 ¼ excellent.

Soil fertility improvement: 1 ¼ deteriorated; 2 ¼ not improved; 3 ¼ improved; 4 ¼ greatly improved.�The mean difference is significant at the 0.05 level. Figures in parentheses are standard errors.

Z.R. Khan et al. / Crop Protection 27 (2008) 976–987 981

maize and fodder production, respectively. Over 50% ofthose in Kisii, Suba and Trans-Nzoia reported an increasein milk production due to increased fodder production ontheir farms resulting from adoption of the PPT (Fig. 4).

The various benefits derived by the farmers fromadoption of the PPT suggest that PPT could contributeto the household well being of smallholder farmers whodepend on agricultural productivity of their land. Thispartly explains the growth of the technology uptake fromtwo pilot districts to the 15 districts at the time of this study(Fig. 5). In addition, there were about 500 farmers involvedin desmodium seed multiplication in Bungoma and TransNzoia collaborating with a commercial seed company to

produce, process and package desmodium seed to supplyto farmers in the districts where there is demand for thetechnology. There were also several, over 100, farmer-teachers and cluster leaders involved in dissemination ofthe technology (Gatsby Charitable Foundation, 2005).Labour as a factor of production is an important

constraint in the adoption of new technologies, particularlythose that are labour-intensive (Douglas et al., 2005). Inthis study, the PPT-practicing farmers reported an increasein labour requirement during the first season of theadoption of the technology, being the establishmentperiod, compared to their own practice of maize cultiva-tion. However, most of the farmers realised a decrease in

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Technology attributes observed

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s. m

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Fert

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str

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y

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ides

fod

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rers

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Con

trol

s st

riga

% R

espo

nse

100

80

60

40

20

0710

141414

33

52

83

89

Fig. 1. Lessons learnt on ‘push–pull’ technology attributes by visiting farmers attending field days.

0

20

40

60

80

100

120

Trans/

Nzoia

Suba Bungoma

'

% farm

ers

fro

m e

ach info

rmation s

ourc

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Busia Butere/

Mumias

Homabay Kisii Kuria Migori Rachuonyo Siaya Vihiga Teso Nyando Bondo

Districts

Early adopters Farmer teacher Field days Radio-'tembea na majira Extension staff and NGOs

Fig. 2. Primary sources of information about the ‘push–pull’ technology cited by participating farmers.

Z.R. Khan et al. / Crop Protection 27 (2008) 976–987982

labour requirement in subsequent seasons of using thetechnology as compared to their own practices (Fig. 6). Thereasons given for the increase in labour requirementsvaried from district to district. Majority of the farmers inKisii and Teso cited the need for land preparation to breaksoil into fine tilth for planting desmodium and marking ofthe field for establishing the technology. Majority of thosein Butere-Mumias, Migori, Siaya, Nyando and Bondoreported the increased labour to be due to planting of the

three crops (maize, Napier grass and desmodium). Farmersin Suba, Bungoma, Busia and Vihiga districts attributedthe increased labour to weeding of young desmodiumplants. Majority of those in Kuria reported the manage-ment of Napier grass as being labour-intensive.The decrease in labour requirement in the subsequent

seasons, as reported by farmers, also varied from district todistrict. Whereas reduced weeding of PPT plots was citedas the main labour-saving feature of the technology by

ARTICLE IN PRESS

0

20

40

60

80

100

120

Bondo Suba

Striga control Soil improvement Increased farm productivity Stemborer control

Reasons g

iven for

push-p

ull

adoption

(%

)

Bungoma Busia Butere/

Mumias

Homabay Kisii Kuria Migori Nyando Rachu-

onyo

Siaya Trans/

Nzoia

Teso Vihiga

Districts

Fig. 3. Reasons cited by ‘push–pull’ practicing farmers for adopting the technology.

0

20

40

60

80

100

Increase in maize yield Increase in fodder production Increase in milk production

0

20

40

60

80

100

120Decrease in Striga infestation Decrease in stemborer infestation

xx

Bungoma Busia Homabay Kisii Migori Suba Teso

% o

f re

sp

on

de

nts

re

alis

ing

pu

sh

-pu

ll b

en

efits

Districts

Butere/

Mumias

Trans/

Nzoia

Vihiga

Increase in soil fertility

XX-No Striga in Trans Nzoia district

Fig. 4. The benefits realized by ‘push–pull’ practicing farmers following adoption of the technology.

Z.R. Khan et al. / Crop Protection 27 (2008) 976–987 983

Suba farmers, majority of the farmers in Kuria, Kisii,Busia and Bungoma reported reduced labour on Striga

uprooting. Majority of PPT farmers in Migori and Siayareported saving of their time for fodder fending becausePPT produced enough fodder for their cattle. A reducedcost on land preparation during subsequent seasons was

reported across the districts. The PPT begins to yieldbenefits in terms of increased production and decreasedlabour demand in the second and third years afterintroduction. Majority of the farmers find it relatively easyto simply cut back desmodium at the beginning of thesubsequent seasons, being a perennial crop, and directly

ARTICLE IN PRESSZ.R. Khan et al. / Crop Protection 27 (2008) 976–987984

plant maize between the rows (minimum tillage). In someinstances, farmers either used hand hoes or ox-drivenploughs to prepare land between desmodium rows forplanting maize with no need for harrowing. Weeding is

2 2 2

6

7

8

12

15

11

0

500

1000

1500

2000

2500

3000

3500

4000

1997 1998 1999 2000 2001 2002 2003 2004 2005

No

. o

f fa

rme

rs

Year and Number of Districts

Fig. 5. Adoption patterns of the ‘push–pull’ technology across years and

districts.

0

20

40

60

80

100

0

20

40

60

80

100

120

Planting 3 crops Hand weed

Reduced weeding Reduced fodder fending

Incre

ase

d la

bo

ur

req

uire

me

nt

-first

se

aso

nR

educed labour

requirem

ent-

subsequent seasons

xx- New districts where the push-pull technology was introduced in 200

Suba KisiiTrans/

Nzoia

Bungoma Busia Butere/

Mumias

Homabay

Land preparation

Fig. 6. Reasons for changes in labour requirements cited by ‘push–

only done once in a season since by the time the maizeattains knee height the desmodium would have grown andcovered the soil thereby smothering the weeds in the plots.We sought to establish the impact of personal, farm

characteristics and dissemination methods on adoption ofPPT since its adaptation is an important innovation in theintensification of maize production in Kenya. The logisticregression of the technology adoption on a suite ofexplanatory variables correctly predicted more than 78%of the observed variation in comparing adoption and non-adoption (Table 7). The significant but negative effect ofgender of the household head suggests that the female-headed households are more inclined than the male-headedhouseholds to adopt the technology. Age, as a proxy forfarm experience, was significantly positive. This suggeststhat older farmers are more likely to adopt and invest inthe PPT, perhaps partly due to the greater appreciation ofthe loss of farm productivity and partly due to skillimprovement in the ability to implement the technology.On the other hand, the effect of land size on adoption ofthe technology was not significant. The aggregate attributesof the PPT are predicted to have a statistically significant

xx xxxx

First

cro

pp

ing

se

aso

n

ing desmodium

5

Kuria Migori Vihiga Siaya Teso Nyando Bondo

Su

bse

qu

en

t cro

pp

ing

se

aso

ns

Napier grass management

Reduced land preparation Reduced Striga uprooting

pull’ practicing farmers following adoption of the technology.

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Table 7

Logit coefficients of factors influencing probability of adoption of PPT

Variable Coefficient S.E Wald Exp (B)

Constant �6.49*** 0.945 47.21 0.002

Age (years) 0.02** 0.11 4.04 1.02

Sex (1 ¼ male, 0 ¼ female) �0.58** 0.279 4.34 0.56

Farm size (acres) 0.48 0.038 1.56 1.05

Technology attributes (aggregate index) 0.33*** 0.55 35.25 1.397

Interaction with extension (1 ¼ yes, 0 ¼ no) 4.08*** 0.753 29.4 59.27

Interaction with farmer teachers (1 ¼ yes, 0 ¼ no) 4.50*** 0.842 28.59 90.03

Interaction in farmer meetings (1 ¼ yes, 0 ¼ no) 3.53*** 0.517 46.60 34.03

Interaction with ICIPE technical field staff (1 ¼ yes, 0 ¼ no) 5.08*** 0.677 56.20 160.6

Listen to radio programme (1 ¼ yes, 0 ¼ no) 3.57*** 0.613 34.05 35.71

Overall predicted 78.6%

Adopters predicted corrected 88.2%

Non-adopters predicted corrected 64.5%

�2 log likelihood ratio (df ¼ 9) 345.6 po0.001

Cox and Snell R2 0.346

Negelkererke R2 0.467

Hosmer and Lemeshow (df ¼ 8) 7.926 p ¼ 0.419

***1% significant; **5% significant; and *10% significant.

Z.R. Khan et al. / Crop Protection 27 (2008) 976–987 985

influence on the likelihood of its adoption. This isconsistent with the expectation that farmers are likely toinvest in technologies that enable them to maximizeproduction and are compatible with their farming systems(Feder and Savastano, 2006).

Farmers’ interaction with extension methods, as ex-pected, positively influenced the likelihood of adoption ofthe PPT. All the methods used significantly increased theprobability of adopting PPT (Table 7). Notably importantwas interaction with ICIPE’s technical staff, whichsignificantly increased the likelihood of the technologyadoption. This is not surprising since ICIPE has been amajor player in the development of the technology.Similarly, the national extension system had a statisticallysignificant effect (Table 7). Extension builds the humancapital of farmers by exposing them to information thatincreases production, incomes and reduces uncertaintyabout the expected outcomes of the technology (Federet al., 1985).

On-farm studies have demonstrated that PPT controlsboth Striga and cereal stemborers, and significantlyimproves maize yields (Hassanali et al., 2008; Khanet al., 2008). These technological attributes were found topositively and significantly influence likelihood of PPT’sadoption (Table 7), suggesting that extension of such atechnology could receive positive response. Although someprevious technology adoption analyses have lacked astatistically significant extension variable (e.g., Adesinaand Baidu-Forson, 1995) while some others have lackedfarming systems ‘fit’ (e.g., Kroma, 2003), this study showsthat an extension programme increases the likelihood ofPPT adoption. Extension has its greatest impact on theearly stages of dissemination of a new technology whenthe information disequilibrium (and the productivitydifferential) is greatest (Anderson and Feder, 2004). Thepositive influence of extension on technology adoption is

consistent with findings from other studies that found asignificant influence of extension education on adoption ofland-improving technologies (e.g. Pender et al., 2004;Baidu-Forson, 1999). In a related study, Oloruntoba andAdegbite (2006) found that extension visits significantlyinfluenced the adoption of Across 97 maize variety, aStriga-resistant maize seed.The findings have implications for extension pro-

grammes seeking to disseminate new technologies. Theperceived efficacy of PPT to increase crop yields calls forefforts to out-scale and up-scale the technology. Policymakers and development practitioners might need to investin a range of extension programmes that promote widefarmer coverage. Whereas farmers can be reached with newtechnologies, researchers and extension agents need tolearn the farmers’ preferences and constraints in order toaddress effectively problems confronting them. Equallyimportant is analysis of how PPT could contribute topoverty reduction through food security and incomegeneration of farmers and rural families in different areas.Future work will also need to examine whether farmerstaking up the technology as a result of interaction withextension workers rather than with other farmers obtainbetter results or not. We are currently undertaking a studyto assess efficacy of the different diffusion pathways indiversified farming systems that will determine not only theaccess to extension methods but also the conditions and theextent to which scope and quality of extension influencesand sustains technology adoption.

Acknowledgements

The authors are grateful to Gatsby Charitable Founda-tion (UK), Kilimo Trust, East Africa and BiovisionFoundation (Switzerland), for providing financial support.These studies were conducted in collaboration with

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Rothamsted Research which receives grant-aided supportfrom the Biotechnology and Biological Sciences ResearchCouncil (BBSRC) and Kilimo Trust. The authors alsoacknowledge support provided by the ICIPE DirectorGeneral, KARI Director and KARI-Kitale Centre Direc-tor. Gratitude also go to ICIPE field staff, the lateN. Dibogo and the Government Ministry of Agricultureextension staff, farmers and other stakeholders whoactively lent assistance to this study either directly orindirectly. The authors also gratefully acknowledge com-ments from the two anonymous reviewers and the 23rdAssociation for International Agricultural and ExtensionEducation (AIAEE) Conference held in Polson, Montana,USA, in May 2007.

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