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

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1 Crop Protection 27 (28) Farmers perceptions of a push pull technology for control of cereal stemborers and Striga weed in western Kenya Zeyaur R. Khan a,, David M. Amudavi a,b, Charles A.O. Midega a, Japhether M. Wanyama a,c, John A. Pickett d a International Centre of Insect Physiology and Ecology (ICIPE), P.O. Box 3772, Nairobi 1, Kenya b Egerton University, P.O. Box 536, Njoro, Kenya c Kenya Agricultural Research Institute, Kitale, P.O. Box 45, Kitale 32, Kenya d Biological Chemistry Division, Rothamsted Research, Harpenden, Hertfordshire AL5 2JQ, UK Received 14 August 27; received in revised form 3 December 27; accepted 4 December 27 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 27 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 6% of total export earnings (GoK, 22). Within the sector, maize (Zea mays L.) is a major staple and cash crop for majority of the smallholders. Maize production is severely constrained by cereal stemborers (Kfir et al., 22), parasitic weed, Striga Corresponding author. Tel.: /7/8; fax: address: zkhan@mbita.mimcom.net (Z.R. Khan). hermonthica and low soil fertility (Oswald, 25). While a range of technologies have been developed, and others proposed, to address some of these constraints, maize yields achieved by farmers are still low, generally o1. t/ha (Jagtap and Abamu, 23). This has been partly due to weaknesses inherent in the technologies themselves prompting low adoption rates by the farmers for both biological and socio-economic reasons, and overall weaknesses in the extension delivery methods (Kamau et al., 2; Anderson and Feder, 24). Finding ways to reverse the trend of low and declining agricultural productivity is imperative in Kenya and other /$ - see front matter r 27 Elsevier Ltd. All rights reserved. doi:1.116/j.cropro

2 Z.R. Khan et al. / Crop Protection 27 (28) sub-saharan countries. The International Centre of Insect Physiology and Ecology (ICIPE) in Kenya, in collaboration with Rothamsted Research in UK and Kenya Agricultural Research Institute (KARI), has developed a novel integrated cropping system, dubbed push pull technology (PPT), for management of stemborers and Striga. The PPT is based on a stimulo-deterrent diversionary strategy (Miller and Cowles, 199), where insect pests are repelled from a harvestable crop and are simultaneously attracted to a discard or trap crop (Cook et al., 27). PPT involves inter-cropping maize with a fodder legume, silverleaf desmodium (Desmodium uncinatum), and planting Napier grass (Pennisetum purpureum) as a trap crop around the crop field. Green leaf volatiles emitted by the desmodium repel the stemborer moths away from the maize field (push component), while those released by the Napier grass attract them (pull component) (Khan et al., 2, 21). Because stemborer moths prefer Napier grass to maize for oviposition (Khan et al., 26b, 27; Van den Berg, 26), majority of the eggs are laid on the trap crop, leaving the maize protected. Most of the resultant stemborer larvae, however, do not survive on the trap crop due to a range of factors including poor nutrient composition of Napier grass, production of sticky sap that entangles and kills the larvae and abundant natural enemies associated with the grass (Khan and Pickett, 24; Khan et al., 26b, 27; Midega et al., 26; Van den Berg, 26). In addition, desmodium inhibits and eliminates Striga through a range of mechanisms including nitrogen fixation and addition of organic matter into the soil, smothering due to dense ground cover, and allelopathy, which is by far the most important (Khan et al., 22). A field trial at ICIPE-Mbita in western Kenya showed a significant increase in total nitrogen in field plots under maize intercropped with various species of desmodium as compared to maize monocrop or maize cowpea intercrop (Khan et al., 26a). Desmodium roots produce chemical compounds some of which stimulate Striga seed germination while others inhibit attachment of Striga roots to those of the maize (suicidal germination) (Tsanuo et al., 23). Since desmodium is a perennial crop, this ensures continual depletion of Striga seed bank in the soil even during periods when there is no cereal in the field. The PPT is targeted at smallholder farmers in eastern Africa and at the time of the study was being promoted in the 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 involved in assessing and adopting this technology has not been examined in detail. Moreover, strong empirical evidence to test the common view about extension methods efficacy and their impact has been scanty. Farmers perception of an agricultural technology is important in influencing adoption decisions (Adesina and Baidu-Forson, 1995; Rogers, 1995). Technology adoption, a multidimensional process, is influenced by factors such as perceived profitability and costs of the technology, its compatibility with production systems, and the clarity with which the new knowledge and information is communicated in a recipient population (Boahene et al., 1999). Besides the efficacy of a technology, the severity of the existing constraints also conditions the decision to invest in new technologies (Mbaga-Semgalawe and Folmer, 2; Kalule et al., 26). As part of our continued research on PPT, we evaluated farmers perceptions of the attributes of the PPT and their influence on adoption of the technology. Specifically, we assessed (1) perceptions of PPT-practicing farmers on severity of Striga and stemborer constraints; (2) primary sources of information about PPT and the reasons for its adoption among the practicing farmers; (3) perceptions of PPT-practicing farmers on any benefits realized from PPT and any labour changes experienced following its adoption on their farms; and (4) perceptions of non-participating farmers attending field days about PPT attributes and motivational aspects for its adoption. This was important in generating information on the motivational factors influencing decisions and farmers knowledge of the PPT and consequently its adoption. 2. Materials and methods 2.1. Study sites The study utilized farm-level cross-sectional data collected through surveys conducted between July and August 25 from 15 districts in western Kenya (Table 1). The study sites covered a wide range of agro-ecological zones (AEZ) (Jaetzold and Schmidt, 1982, 1983), where PPT had been introduced. In all districts except Teso, Nyando and Bondo where PPT was introduced in 25, farmers had been practicing PPT for two or more seasons. Both Striga and cereal stemborers are found in all the districts with the exception of Trans Nzoia which has no Striga. Given the Kenya national population growth rate estimate of 3% per annum (McPherson and Rakovski, 1998), the pressure on land resources has led to agricultural intensification but with limited investment in productivity-enhancing technologies in these districts. With threats from such pests and weeds, the food security of majority of the people in these districts is threatened Data and data collection procedures We conducted field days between July and August 25 in 15 districts in western Kenya (Table 2) in collaboration with Kenyan government s Ministry of Agriculture to demonstrate PPT to farmers and to collect information on farmers perceptions about the technology. Both PPT practising and visiting (non-practicing) farmers attended the field days. The visiting farmers were asked to compare and evaluate push pull and control plots as soon as they arrived at the field days, after which they were asked to complete the questionnaires with the help of enumerators.

3 978 ARTICLE IN PRESS Z.R. Khan et al. / Crop Protection 27 (28) 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/km 2 ) Suba UM S, 341E 148 Bondo LM1 2, LM S, E 246 Nyando LM S E 27 Teso LM1 2, LM S, E 325 Busia LM1 2, LM Bungoma UM1, UM2 3, S, E 424 UM4, LM2 3 Siaya LM1 2, LM S, E 316 Rachuonyo LM1 4, UM S, E 325 Homabay UM1 2, LM S, E 249 Kuria UM1 2, LM S, E 261 Migori LM1 5, UM Kisii 16 2 UM1, LH S, E 758 Vihiga LM1 UM E, N 886 Butere-Mumias LH LM S, E 58 Trans Nzoia LH S, E 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) Bungoma (5) Busia (5) Butere Mumias (5) Homabay (5) Kisii (5) Kuria Migori (5) Nyando (1) Rachuonyo (5) Siaya (5) Suba (15) Teso (1) Trans Nzoia (9) Vihiga (5) Total Numbers in brackets indicate the number of years PPT had been practiced in the various districts at the time of the study. Using two semi-structured survey questionnaires (one for PPT participating farmers and one for visiting farmers), trained enumerators interviewed a total of all 923 farmers who attended field days in the 15 districts. A total of 478 PPT practicing and 445 non-practicing farmers were interviewed. The non-practicing visiting farmers were interviewed during the field days after they visited PPT and control plots, while the enumerators interviewed the practicing farmers on their farms during household visits. The datasets for both groups of farmers included personal (age and sex) and farm characteristics (farm size and grain yield outputs). Farmers perceptions of the PPT were sought on five attributes: Striga control (taking counts of Striga plants), stemborer control (checking damage by stemborers), health of the crop (rating physical appearance of maize), size of cob (whether big, medium or small), and ability to improve soil fertility (comparing qualitative soil fertility indicators, e.g. colour, texture of soil samples) from PPT plots and farmer s conventional practices. Using these attributes, the non-practicing farmers were asked to compare the plots with and rate PPT against farmers production practices, on a scale of 1 4 (1 ¼ poorest; 4 ¼ best). Our inclusion of this indicator in subsequent analysis, alongside other indicators of adoption (Table 3), was based on the premise that the producers agro-ecological, socio-economic and institutional contexts play a central role in the adoption decision process (Scoones and Thompson, 1994). The PPT-practicing farmers, on the other hand, were asked about their perceptions of severity of Striga and stemborers on their farms, primary sources of information on PPT, which helped them to learn about and adopt the technology, the main reasons for adopting PPT, any benefits realised following adoption of the technology, and any changes in household labour requirements as a result of adoption of the technology. Field days are considered important in facilitating experiential and collective learning that often leads to adoption of new agricultural technologies among farmers (Doss, 23; Florencia, 26; Knowler and Bradshaw, 27) Analytical model specification Farmers indicators used to evaluate technologies are perceived to motivate adoption or dis-adoption of agricultural innovations. Modelling technology adoption usually involves a binomial or multinomial variable approach, using

4 Z.R. Khan et al. / Crop Protection 27 (28) 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; ¼ female) Dummy Positive Farm Farm size (in acres) Continuous Negative Yield Yield of maize in 9 kg bags Continuous Positive Technology Summation of farmers ratings of PPT on plant health, cob size, Striga, stemborer Continuous Positive attribute control and soil fertility rates scored on 1 4 scale Extension Dissemination through extension programme 1 ¼ extension; ¼ otherwise Dummy Positive Farmer meetings Dissemination through farmer meetings 1 ¼ baraza; ¼ otherwise Dummy Positive Farmer teachers Dissemination through farmer-to-farmer extension 1 ¼ farmer teacher; Dummy Positive ¼ otherwise ICIPE staff Dissemination through ICIPE staff awareness meetings 1 ¼ ICIPE awareness Dummy Positive meetings; ¼ otherwise Radio Dissemination through radio programme 1 ¼ radio; ¼ otherwise Dummy Positive Constant either a latent variable or random utility factor (Rogers, 1995). We used the former in which there is an unobserved latent variable (y n ), such as expected gain from the technology, which underlies the observed binary variable of adopting or not adopting the technology (Table 3). We assumed a linear relationship between the latent variable y n i and the observed explanatory variables X i through a structural model of the equation: y n i ¼ X i þ e i ði ¼ 1;...; NÞ. (1) This further linked the latent variable to the observed binary variables Y i, which represent farmer s adoption decisions of adoption or non-adoption, thus Y i ¼ 1 if y n i 4; Y i ¼ if y n i p. (2) Finally, a logistic regression (Eq. (3)) examined whether farmers perceptions of technological attributes, farm characteristics and selected dissemination methods estimated the farmers decision-choice model of adopting or not adopting the technology: Y i ¼ b þ b i1 x i1 þ b i2 x i2 ;...; b ik x ik þ u i ði ¼ 1;...; k; i ¼ 1;...; NÞ, where Y i ¼ 1, if a farmer had adopted PPT, otherwise, k is the number of independent variables. The conditional mean of y given the independent variables x i (i ¼ 1,y,k) is EfY i g¼m yi jx i ¼ p i, where p i ¼ Pr(Y i ¼ 1jx i ¼ p i (x i ) ¼ Pr(x i X1). b and b i are the coefficients estimated using the suite of independent variables proposed in Table 3 and u i is an independently distributed error term assumed to be normal with mean zero and constant variance one. The model helps to understand which factors are most important in explaining the decision to likely adopt PPT, and therefore inform on the aspects of the technology an extension programme can concentrate on to increase its spread among many farmers. ð3þ Table 4 General socioeconomic characteristics of farmers interviewed Variable Practicing (n ¼ 478) Visiting (n ¼ 445) t-value w 2 Mean SE Mean SE Age of farmer NS Farm size Gender % Male NS % Female NS The mean difference significant at.5% level. 3. Results and discussion The average farm size of PPT-practicing farmers was 4.74 acres, whereas that of farmers visiting field days was 3.24 acres (Table 4). The sex of the respondents comprised 48% males and 52% females for the participating and about 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 not significantly 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 production constraints in their districts (Table 5). Given such circumstances, availing an appropriate technology that is affordable and fits well into farmers farming system is likely to stimulate its uptake. Farmers perceptions on the severity of production constraints, such as Striga and stemborers, and suitability and effectiveness of any management strategies are a key determining factor on whether farmers adopt or do not adopt such technologies (Emechebe et al., 24). After making observations during the field days, the visiting farmers rated the PPT significantly superior (po.5) to the farmers own practices on all attributes, indicating that they perceived it as an effective technology

5 98 ARTICLE IN PRESS Z.R. Khan et al. / Crop Protection 27 (28) Table 5 Participating farmers indications of severity of Striga and stemborer problems District Resp % Response District Response % Response Stemborer Striga Stemborer Striga Bondo Yes 1 1 Migori Yes 1 97 No No 3 Bungoma Yes 73 1 Nyando Yes 94 1 No 27 No 6 Busia Yes 1 1 Rachuonyo Yes 1 89 No No 11 Butere Mumias Yes 1 97 Siaya Yes 1 1 No 3 No Homabay Yes 94 1 Suba Yes 1 1 No 6 No Kisii Yes 1 1 Teso Yes No No 6 3 Kuria Yes Vihiga Yes 97 1 No 12 3 No 3 Trans Nzoia Yes 1 No for the control of stemborers and Striga, improved soil fertility and increased maize production (Table 6). Majority of them (about 9%) observed that the technology controlled Striga and increased soil fertility (83%), controlled stemborers (52%), and provided quality fodder (33%) (Fig. 1). Others indicated that the technology stabilized the farming system (14%), provided an alternative strategy of improving soil fertility (14%), conserved soil moisture (1%) and can reduce farm workload (7%). Such attributes of the technology suggest the motivations and propensity for its adoption. The practicing farmers from various districts cited a number of sources from which they first obtained information about PPT that motivated their interests in it, leading to its subsequent adoption. Over 6% of the farmers in Bungoma and Rachuonyo, and over 4% in Trans Nzoia, Suba, Busia, Butere-Mumias and Vihiga received information from early adopters (Fig. 2). In Teso, Nyando and Bondo, early adopters as a source of information were not evident as the technology had been recently introduced by the mass media. In these districts, between 5% and 7% of the farmers received information through a national radio programme (Tembea na majira) and some, less than 2%, through extension and nongovernmental organisations (NGO) staff. In Homabay district, over 6% of farmers received information through farmer-teachers while field days were a major source of information for farmers in Kuria district (Fig. 2). This thus revealed the technology transfer methods that could be effectively employed in the different target areas and on which incremental resources could be placed to disseminate the PPT. Access to information about an agricultural technology with a demonstrated efficacy is one of the key factors determining a technology s uptake (Mowo et al., 24; Doss, 23). Such access can be facilitated by different information channels including mass media, information bulletins, field days, technical support (like farmer-teachers) and farmer field schools (Panell, 1999). The different communication channels and learning tools are effective at different stages of adoption decision making (Oladele and Adekoya, 26; Garforth, 1998). Therefore, it is important to determine returns to investments in the dissemination methods used in PPT transfer. While majority of the PPT-practicing farmers in Trans Nzoia district (47%) were motivated to adopt the technology for control of stemborers, more than 5% of those in other districts (except Bondo and Kisii) were mainly motivated by the need to control Striga (Fig. 3). Similarly, the need to improve soil health and increase farm productivity were additional reasons why farmers across the districts adopted the technology. Control of stemborers was also cited by farmers in Bungoma, Kisii, Migori and Vihiga (Fig. 3). Majority of the PPT-practicing farmers (over 8%) in all the districts reported a reduction in Striga infestation on their farms following adoption of the PPT (Fig. 4). More than 8% of those in Trans-Nzoia, Homabay, Kisii and Suba districts, and more than 5% of those in Bungoma, Butere-Mumias, Migori and Teso districts reported reduced stemborer infestation (Fig. 4). Over 8% of the farmers in Busia, and over 5% in Bungoma, Migori, Suba and Teso reported an improvement in soil fertility. Over 8% of farmers in all the districts (except Vihiga) and over 7% of those in all the districts reported an increase in

6 Z.R. Khan et al. / Crop Protection 27 (28) 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 (.1) (.2) (.1) (.) 2.6 FP 2.2 (.3) 1.5 (.3) 2. (.1) 1.8 (.1) Busia PPT 3.3 (.2) (.1) (.1) (.1) 7.7 FP 2.2 (.2) 2. (.2) 2.5 (.2) 2.5 (.2) Butere Mumias PPT 4. (.) (.1) (.1) (.1) 39.3 FP 1.4 (.1) 1.6 (.1) 1.4 (.1) 1.1 (.1) Homabay PPT 3.7 (.1) (.1) (.1) (.1) 1.2 FP 1.9 (.1) 1.7 (.1) 1.9 (.1) 1.9 (.1) Kisii PPT 3.8 (.1) (.1) (.1) (.) 3.6 FP 3.1 (.2) 2.6 (.1) 2.8 (.1) 3.3 (.2) Kuria PPT 3.7 (.1) (.2) (.1) (.) 1.8 FP 1.9 (.2) 2.2 (.2) 2.1 (.1) 1.9 (.2) Migori PPT 3.9 (.1) (.1) (.1) (.) 11.7 FP 2.2 (.2) 1.3 (.1) 1.9 (.1) 1.8 (.2) Rachuonyo PPT 3.8 (.1) (.2) (.1) (.) 11.1 FP 2.1 (.2) 1.6 (.2) 2.5 (.3) 1.7 (.2) Siaya PPT 3.5 (.2) (.1) (.9) (.) 21.8 FP 2.5 (.2) 2.2 (.2) 1.9 (.2) 1.6 (.1) Vihiga PPT 3.7 (.1) (.1) (.1) (.) 13.9 FP 1.8 (.1) 2.3 (.2) 2.1 (.1) 1.9 (.1) Trans-Nzoia PPT 3.7 (.1) (.1) (.) 33.6 FP 1.6 (.1) 1.7 (.1) 1.6 (.1) Bondo PPT 3.3 (.1) (.1) (.1) (.) 13.6 FP 2.3 (.2) 1.9 (.2) 2.2 (.2) 1.5 (.2) Nyando PPT 3.5 (.1) (.1) (.1) (.1) 9. FP 1.4 (.2) 1.8 (.2) 1.8 (.2) 1.6 (.2) Teso PPT 3.8 (.1) (.1) (.1) (.) 25.8 FP 2.1 (.14) 1.7 (.2) 1.7 (.1) 1.3 (.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.5 level. Figures in parentheses are standard errors. maize and fodder production, respectively. Over 5% of those in Kisii, Suba and Trans-Nzoia reported an increase in milk production due to increased fodder production on their farms resulting from adoption of the PPT (Fig. 4). The various benefits derived by the farmers from adoption of the PPT suggest that PPT could contribute to the household well being of smallholder farmers who depend on agricultural productivity of their land. This partly explains the growth of the technology uptake from two pilot districts to the 15 districts at the time of this study (Fig. 5). In addition, there were about 5 farmers involved in desmodium seed multiplication in Bungoma and Trans Nzoia collaborating with a commercial seed company to produce, process and package desmodium seed to supply to farmers in the districts where there is demand for the technology. There were also several, over 1, farmerteachers and cluster leaders involved in dissemination of the technology (Gatsby Charitable Foundation, 25). Labour as a factor of production is an important constraint in the adoption of new technologies, particularly those that are labour-intensive (Douglas et al., 25). In this study, the PPT-practicing farmers reported an increase in labour requirement during the first season of the adoption of the technology, being the establishment period, compared to their own practice of maize cultivation. However, most of the farmers realised a decrease in

7 982 ARTICLE IN PRESS Z.R. Khan et al. / Crop Protection 27 (28) % Response Controls striga Increases fertility Controls stemborers Provides fodder System sustainable Controls s. erosion Fertility strategy Improves s. moisture Reduces workload Technology attributes observed Fig. 1. Lessons learnt on push pull technology attributes by visiting farmers attending field days. 12 Early adopters Farmer teacher Field days Radio-'tembea na majira ' Extension staff and NGOs % farmers from each information source Trans/ Nzoia Suba Bungoma Busia Butere/ Mumias Homabay Kisii Kuria Migori Rachuonyo Siaya Vihiga Teso Nyando Bondo Districts Fig. 2. Primary sources of information about the push pull technology cited by participating farmers. labour requirement in subsequent seasons of using the technology as compared to their own practices (Fig. 6). The reasons given for the increase in labour requirements varied from district to district. Majority of the farmers in Kisii and Teso cited the need for land preparation to break soil into fine tilth for planting desmodium and marking of the field for establishing the technology. Majority of those in Butere-Mumias, Migori, Siaya, Nyando and Bondo reported the increased labour to be due to planting of the three crops (maize, Napier grass and desmodium). Farmers in Suba, Bungoma, Busia and Vihiga districts attributed the increased labour to weeding of young desmodium plants. Majority of those in Kuria reported the management of Napier grass as being labour-intensive. The decrease in labour requirement in the subsequent seasons, as reported by farmers, also varied from district to district. Whereas reduced weeding of PPT plots was cited as the main labour-saving feature of the technology by

8 Z.R. Khan et al. / Crop Protection 27 (28) Reasons given for push-pull adoption (%) 12 Striga control Soil improvement Increased farm productivity Stemborer control Bondo Bungoma Busia Butere/ Homabay Kisii Kuria Migori Nyando Rachuonyo Mumias Districts Suba Siaya Trans/ Nzoia Teso Vihiga Fig. 3. Reasons cited by push pull practicing farmers for adopting the technology Decrease in Striga infestation Decrease in stemborer infestation Increase in soil fertility 8 % of respondents realising push-pull benefits Increase in maize yield Increase in fodder production Increase in milk production xx 4 2 Bungoma Busia Butere/ Homabay Kisii Migori Suba Teso Mumias XX-No Striga in Trans Nzoia district Districts Trans/ Nzoia Vihiga Fig. 4. The benefits realized by push pull practicing farmers following adoption of the technology. 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 Siaya reported saving of their time for fodder fending because PPT produced enough fodder for their cattle. A reduced cost on land preparation during subsequent seasons was reported across the districts. The PPT begins to yield benefits in terms of increased production and decreased labour demand in the second and third years after introduction. Majority of the farmers find it relatively easy to simply cut back desmodium at the beginning of the subsequent seasons, being a perennial crop, and directly

9 984 ARTICLE IN PRESS Z.R. Khan et al. / Crop Protection 27 (28) plant maize between the rows (minimum tillage). In some instances, farmers either used hand hoes or ox-driven ploughs to prepare land between desmodium rows for planting maize with no need for harrowing. Weeding is No. of farmers Year and Number of Districts Fig. 5. Adoption patterns of the push pull technology across years and districts only done once in a season since by the time the maize attains knee height the desmodium would have grown and covered the soil thereby smothering the weeds in the plots. We sought to establish the impact of personal, farm characteristics and dissemination methods on adoption of PPT since its adaptation is an important innovation in the intensification of maize production in Kenya. The logistic regression of the technology adoption on a suite of explanatory variables correctly predicted more than 78% of the observed variation in comparing adoption and nonadoption (Table 7). The significant but negative effect of gender of the household head suggests that the femaleheaded households are more inclined than the male-headed households to adopt the technology. Age, as a proxy for farm experience, was significantly positive. This suggests that older farmers are more likely to adopt and invest in the PPT, perhaps partly due to the greater appreciation of the loss of farm productivity and partly due to skill improvement in the ability to implement the technology. On the other hand, the effect of land size on adoption of the technology was not significant. The aggregate attributes of the PPT are predicted to have a statistically significant Increased labour requirement -first season Planting 3 crops Land preparation Hand weeding desmodium Napier grass management First cropping season Reduced weeding Reduced fodder fending Reduced land preparation Reduced Striga uprooting Reduced labour requirement-subsequent seasons Trans/ Nzoia Suba Bungoma Busia xx- New districts where the push-pull technology was introduced in 25 Butere/ Homabay Kisii Kuria Migori Vihiga Siaya Teso Nyando Bondo Mumias xx xx xx Subsequent cropping seasons Fig. 6. Reasons for changes in labour requirements cited by push pull practicing farmers following adoption of the technology.

10 Z.R. Khan et al. / Crop Protection 27 (28) Table 7 Logit coefficients of factors influencing probability of adoption of PPT Variable Coefficient S.E Wald Exp (B) Constant 6.49*** Age (years).2** Sex (1 ¼ male, ¼ female).58** Farm size (acres) Technology attributes (aggregate index).33*** Interaction with extension (1 ¼ yes, ¼ no) 4.8*** Interaction with farmer teachers (1 ¼ yes, ¼ no) 4.5*** Interaction in farmer meetings (1 ¼ yes, ¼ no) 3.53*** Interaction with ICIPE technical field staff (1 ¼ yes, ¼ no) 5.8*** Listen to radio programme (1 ¼ yes, ¼ no) 3.57*** Overall predicted 78.6% Adopters predicted corrected 88.2% Non-adopters predicted corrected 64.5% 2 log likelihood ratio (df ¼ 9) po.1 Cox and Snell R Negelkererke R Hosmer and Lemeshow (df ¼ 8) p ¼.419 ***1% significant; **5% significant; and *1% significant. influence on the likelihood of its adoption. This is consistent with the expectation that farmers are likely to invest in technologies that enable them to maximize production and are compatible with their farming systems (Feder and Savastano, 26). Farmers interaction with extension methods, as expected, positively influenced the likelihood of adoption of the PPT. All the methods used significantly increased the probability of adopting PPT (Table 7). Notably important was interaction with ICIPE s technical staff, which significantly increased the likelihood of the technology adoption. This is not surprising since ICIPE has been a major player in the development of the technology. Similarly, the national extension system had a statistically significant effect (Table 7). Extension builds the human capital of farmers by exposing them to information that increases production, incomes and reduces uncertainty about the expected outcomes of the technology (Feder et al., 1985). On-farm studies have demonstrated that PPT controls both Striga and cereal stemborers, and significantly improves maize yields (Hassanali et al., 28; Khan et al., 28). These technological attributes were found to positively and significantly influence likelihood of PPT s adoption (Table 7), suggesting that extension of such a technology could receive positive response. Although some previous technology adoption analyses have lacked a statistically significant extension variable (e.g., Adesina and Baidu-Forson, 1995) while some others have lacked farming systems fit (e.g., Kroma, 23), this study shows that an extension programme increases the likelihood of PPT adoption. Extension has its greatest impact on the early stages of dissemination of a new technology when the information disequilibrium (and the productivity differential) is greatest (Anderson and Feder, 24). The positive influence of extension on technology adoption is consistent with findings from other studies that found a significant influence of extension education on adoption of land-improving technologies (e.g. Pender et al., 24; Baidu-Forson, 1999). In a related study, Oloruntoba and Adegbite (26) found that extension visits significantly influenced the adoption of Across 97 maize variety, a Striga-resistant maize seed. The findings have implications for extension programmes seeking to disseminate new technologies. The perceived efficacy of PPT to increase crop yields calls for efforts to out-scale and up-scale the technology. Policy makers and development practitioners might need to invest in a range of extension programmes that promote wide farmer coverage. Whereas farmers can be reached with new technologies, researchers and extension agents need to learn the farmers preferences and constraints in order to address effectively problems confronting them. Equally important is analysis of how PPT could contribute to poverty reduction through food security and income generation of farmers and rural families in different areas. Future work will also need to examine whether farmers taking up the technology as a result of interaction with extension workers rather than with other farmers obtain better results or not. We are currently undertaking a study to assess efficacy of the different diffusion pathways in diversified farming systems that will determine not only the access to extension methods but also the conditions and the extent to which scope and quality of extension influences and sustains technology adoption. Acknowledgements The authors are grateful to Gatsby Charitable Foundation (UK), Kilimo Trust, East Africa and Biovision Foundation (Switzerland), for providing financial support. These studies were conducted in collaboration with

11 986 ARTICLE IN PRESS Z.R. Khan et al. / Crop Protection 27 (28) Rothamsted Research which receives grant-aided support from the Biotechnology and Biological Sciences Research Council (BBSRC) and Kilimo Trust. The authors also acknowledge support provided by the ICIPE Director General, KARI Director and KARI-Kitale Centre Director. Gratitude also go to ICIPE field staff, the late N. Dibogo and the Government Ministry of Agriculture extension staff, farmers and other stakeholders who actively lent assistance to this study either directly or indirectly. The authors also gratefully acknowledge comments from the two anonymous reviewers and the 23rd Association for International Agricultural and Extension Education (AIAEE) Conference held in Polson, Montana, USA, in May 27. References Adesina, A.A., Baidu-Forson, J., Farmers perception and adoption of new a agricultural technology: evidence from analysis in Burkina Faso and Guinea, West Africa. Agric. Econ. 13, 1 9. 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