This paper reports on findings of a study to examine the sources used by farmers in search of agricultural information on striga and/or stemborers control technologies and factors that influence acquisition of such information in Western Kenya region. A random sample of 476 households in Suba and Vihiga districts were interviewed and 15 information pathways were identified. Using principle component analysis (PCA) to derive few latent variables that encapsulate maximum variance in the pathways, two components (latent variables) proxying for ‘agricultural knowledge’ were extracted. Type I-knowledge (first component) loaded heavily with sources that had ‘group’ information searching. Type II-knowledge (second component) loaded heavily with sources requiring individual farmer search. Both types of knowledge positively and significantly influenced the likelihood of households using improved technology to control stemborer, while only Type-II knowledge and social economic factors were important in influencing the farmers’ likelihood of using an improved technology to control striga. This study shows that information is an important factor in the households’ likelihood of using improved technologies in the control of striga and stem borer in Vihiga and Suba, Kenya. Methods of individual interaction are important to striga control.Agricultural information, improved technology, striga, stemborers, control technologies, Kenya, Agricultural and Food Policy, Community/Rural/Urban Development, Demand and Price Analysis, Farm Management, Food Consumption/Nutrition/Food Safety, Food Security and Poverty, Institutional and Behavioral Economics, International Relations/Trade, Marketing, Productivity Analysis, Research and Development/Tech Change/Emerging Technologies, Resource /Energy Economics and Policy,

Amudavi, David M.

Karugia, Joseph Thuo

Khan, Zeyaur R.

Mbogoh, Stephen G.

Njuguna, Esther M.

Nyikal, Rose Adhiambo

English

Research Papers in Economics

AAAE Conference Proceedings (2007) 183-187 
 
Classification and influence of agricultural information on striga and stemborer control 
in Suba and Vihiga Districts, Kenya 
Esther M. Njuguna
1, 2.*, David M. Amudavi
2, Zeyaur R. Khan
2, Stephen G. Mbogoh
1, Rose A. Nyikal
1, Joseph 
Karugia
1 
1University of Nairobi, Department of Agricultural Economics, P.O. Box 25093 – 00625 Nairobi; 
2International Centre for Insect *Enjuguna@mbita.mimcom.net 
Abstract 
This paper reports on findings of a study to examine the sources used by farmers in search of agricultural 
information on striga and/or stemborers control technologies and factors that influence acquisition of such 
information in Western Kenya region. A random sample of 476 households in Suba and Vihiga districts were 
interviewed and 15 information pathways were identified.  Using principle component analysis (PCA) to derive 
few latent variables that encapsulate maximum variance in the pathways, two components (latent variables) 
proxying for ‘agricultural knowledge’ were extracted.  Type I-knowledge (first component) loaded heavily with 
sources that had ‘group’ information searching. Type II-knowledge (second component) loaded heavily with 
sources requiring individual farmer search.  Both types of knowledge positively and significantly influenced the 
likelihood of households using improved technology to control stemborer, while only Type-II knowledge and 
social economic factors were important in influencing the farmers’ likelihood of using an improved technology to 
control striga.  This study shows that information is an important factor in the households’ likelihood of using 
improved technologies in the control of striga and stem borer in Vihiga and Suba, Kenya. Methods of individual 
interaction are important to striga control. 
Key words: Agricultural information, improved technology, striga, stemborers, control technologies, Kenya 
 
Introduction 
Maize crop is an important part of the food security 
equation in East Africa where per capita consumption 
is about 81 kg/year (FAO, 2002). However, production 
levels per hectare remain below the optimum potential 
for most available varieties. The low yields are 
attributable to biotic and abiotic production 
constraints, among others. Striga weeds and stemborer 
in particular are biotic constraints that farmers struggle 
to control. Maize yield losses due to stemborers range 
from 20-80% depending on the severity of the 
infestation and the growth stage of the crop (Khan et 
al, 1997). On the other hand, striga related maize yield 
losses are estimated at 5% loss per every striga plant 
per m
2 (Parker and Riches, 1993), implying an almost 
complete loss. Striga and stemborer have been a 
menace to maize production in western Kenya. Several 
research efforts by different institutions have targeted 
western Kenya for intervention in the management of 
striga and/or stemborer. In the process, technologies 
such as imidazolinone-resistant (IR) maize (Kanampiu 
et al., 2003), improved fallows, soil fertility 
improvement (use of manure and fertilizers) and use of 
trap crops have been developed and disseminated to 
 the farmers for the control of striga (see a review by 
Oswald, 2005). Early planting, applications of bull-
dock (betacyfluthrin) and bio-pesticides have targeted 
the management of stemborers. Push pull technology 
(PPT) has also been recently developed for the 
management of both striga and stemborer in 
smallholder cereal production systems (Khan et al., 
2002, Cook et al., 2007). In spite of the advances 
made, adoption of striga and stemborer control 
technologies is still low among most pest-constrained 
farmers. 
Studies documenting factors that influence choice of 
technology, adoption decision and intensity of use of 
the chosen technologies in the control of striga and 
stemborer are very few. This lacuna motivated this 
study to evaluate factors that influence farmers’ choice 
and adoption of striga/stemborer control technologies 
in Vihiga and Suba districts of western Kenya in order 
to inform research programmes and policy makers on 
how to enhance effective diffusion and adoption of the 
technologies. This paper reports on sources of 
information used by farmers by classifying type of 
knowledge gained through them and the factors that Agricultural Information on Striga And Stemborer Control in Kenya 
184  AAAE Ghana Conference 2007 
influence the acquisition of the information in the two 
districts. 
Methodology 
A random sample of 476 households in Suba and 
Vihiga districts were interviewed in March-April 2007 
by trained enumerators using a semi-structured 
questionnaire. Vihiga is a high potential with high 
population density (over 1100 persons per km
2) while 
Suba is of low potential with low population (about 
150 persons per km
2). Data collected included farm 
and farmers’ socioeconomic characteristics and 
information sources accessed to learn about stemborer 
and striga control. 
Farmers sources of information on striga and 
stemborer control methods 
Fifteen sources of information considered to provide 
agricultural knowledge on striga and stemborer control 
were identified. These were: 1. Farmer groups, 2. On-
farm demonstrations. 3. Contact farmer. 4. Community 
leaders. 5. Visits to agricultural organization. 6. Radio 
agricultural programs. 7. Agricultural extension. 8. 
Pamphlets and brochures 9. Agricultural shows. 10. 
Attending local meetings (Baraza’s). 11. Agricultural 
field days. 12. Neighbours and friends. 13. Posters on 
agricultural information, 14. Farmer teachers. 15. Self 
(farming experience). 
Data were obtained on the households’ access to these 
15 information sources (coded 1 if accessed the 
pathway, 0 otherwise); whether the source provided 
information on striga and/or stemborer control for the 
household (coded 1, 0 otherwise). Further, the 
household members’ judgement of effectiveness of 
such a pathway in disseminating striga and/or 
stemborer control information coded as 0 if not 
effective, 1 if effective, and 2 if very effective. The 
effectiveness variable was rescaled to binary variable 
with 0 if the pathway was considered ineffective and 1 
if effective or very effective. Farming experience was 
recorded as the number of years a household had been 
farming independently. A mean of the farming 
experience was obtained and the variable was re-
scaled to 1 if the farming experience was equal or 
greater than the mean and 0 otherwise. This was done 
  to obtain binary variables to be analysed by the 
principle component analysis (PCA) technique. Using 
the ‘effectiveness’ of a variable for the fourteen 
sources of information and binary variable for farming 
experience, a ‘knowledge index’ was generated for 
each household. 
Generating knowledge index 
It was assumed that interaction of farm households 
with various sources of information on striga and 
stemborer control leads to ‘agricultural knowledge’ on 
practices of management and control of striga and 
stemborer control. The more a household learned from 
many sources of information and judged them as 
effective, the higher the ‘agricultural knowledge’ such 
a household accumulated on the control of striga 
and/or stemborer. However, it is not easy to measure 
‘agricultural knowledge’ accumulated by the 
households directly from the many sources of 
information. By using the principle component 
analysis (PCA) (Amudavi, 2005) approach, 
components extracted capturing maximum variance in 
the 15 sources of information identified by the sample 
farmers were assumed to measure the underlying 
bundle of agricultural knowledge for each farmer. The 
resultant two principle components were viewed as 
‘knowledge indices’ for each farmer. Data on the 
knowledge indices were fitted to a linear regression 
model (Dallal, 2000) to identify socio-economic 
factors that influence the farmers’ ability to 
accumulate the two types of knowledge. To find out if 
the knowledge indices influenced farmers’ likelihood 
of using improved striga and stemborer control 
technologies, the data were fitted onto logistic 
regression model (Agresti, 1996). 
Results 
Types of agricultural knowledge 
Four principle components, each with eigenvalue 
greater than one, were considered. The first component 
explained 24.3% of the variance in the farmers’ 
perception of effectiveness of the sources of 
information. The second component explained 10.3% 
of the variance in the data. The third and the fourth 
components accounted for 7.4% and 7.2% of the  Njuguna. E. et al. 
Advancing Technical Change in Agriculture  185 
 
variance, respectively. When the components were 
rotated using varimax rotation with Kaiser 
normalization (Wuensch, 2006), thirteen of the 
information sources loaded heavily (r coefficient ≥ 
|±0.4|) on the first and second principle components 
while the third and fourth components had only one 
information source loading heavily with each. (Table 
1). 
All the components are orthogonally independent. 
Hence, the first two components were observed to 
represent two distinct types of agricultural knowledge 
on striga and stemborer control that farmers had 
accumulated through their own experience and from 
interactions with the fourteen sources identified. In 
this study, principle component 1 was named Type I-
knowledge and principle component 2 named Type II-
knowledge. 
Factors influencing knowledge among farmers 
Type I-knowledge was significantly influenced by 
attainment of primary level of education, 
wealth(proxied by total livestock units), perception of 
minor and moderate stemborer infestation, moderate 
and major striga infestation levels, a household being 
located in villages hosting on-farm research activities 
or neighboring villages, market access and smaller 
farm sizes. On the other hand, Type II-knowledge was  
 
significantly influenced by the education level of the 
head of household (primary, secondary and post 
secondary), security of land tenure, wealth, and being 
located in villages hosting on-farm research activities 
and age of the head of household. 
Influence of the farmers’ knowledge on their likelihood 
to use improved technologies in the control of striga 
and stemborer 
A logistic model (with 70.2% correct prediction, R
2 = 
30.5%) shows that Type I-knowledge did not have 
significant influence on the farmers’ likelihood to 
control striga (Table 2). However, Type II knowledge 
improved the farmers’ likelihood to use an improved 
technology to control striga by about 7.5 times, 
holding all the other factors constant. Also, farmers’ 
probability to use an improved technology to control 
striga was about 14 times higher if a household was in  
Discussions and conclusion 
Information is an important factor of production 
(Antholt, 1995, Sumberg, 2004) like the classic land, 
labour and capital. This study demonstrates 
information is important in pest and weed control in 
maize farming in western Kenya. Type I-knowledge 
was closely related with group activities in sourcing 
information. This knowledge did not significantly  
Table 1. Principal component analysis of sources of information about striga and stemborer control used by farmers in 
Suba and Vihiga districts, western Kenya 
 Information Source  Principal component 
   1  2  3  4 
1. Community leaders  .691  -.174  .182  -.055 
2. Field days  .674  .391  .014  .006 
3. Agricultural Show  .670  .064  -.036  .017 
4. Chief’s Baraza  .644  -.052  -.177  .266 
5. Farmer groups  .585  .045  -.083  -.302 
6. Radio  .566  .248  .257  -.033 
7. On farm demonstrations  .479  .442  .189  -.097 
8. Neighbors and friends  .391  .262  -.214  .075 
9. Pamphlets and brochures  .133  .658  -.057  -.106 
10. Agricultural extension agent  .076  .645  -.094  .008 
11. Organizational visits  .051  .588  .309  .213 
12. Posters  -.042  .506  -.075  -.004 
13. Farmer teachers  .375  .421  .236  -.144 
14. Contact farmers  -.003  -.076  .862  .017 
15. Farming experience  -.007  -.014  .008  .914 
Eigenvalue (7.39)  3.64  1.55  1.11  1.09 
% of variance explained (49.25)  24.28  10.34  7.39  7.23 
Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. Agricultural Information on Striga And Stemborer Control in Kenya 
186  AAAE Ghana Conference 2007 
 
increase the farmers’ likelihood to use an improved 
technology in the control of striga, but rather increased 
the farmers’ likelihood to use an improved technology 
in stemborer. 
Type II-knowledge was closely related with individual 
activities in sourcing information. This knowledge 
type positively influenced a farmer’s likelihood of 
using improved technology in both striga and 
stemborer. Only the two agricultural knowledge 
variables were found to be significant in influencing 
the farmers’ likelihood to use improved technology in 
controlling stemborers. This suggests that farmers are 
mostly not aware that stem borer can be controlled, so 
that even those farmers that have the resources to do 
so do not. Among the factors that positively influence 
farmers’ accumulation of Type II-knowledge are 
higher education levels and participation in on-farm 
research. However, the farmers in rural areas are of 
low education levels. Among households used in this 
study, 17% had no education while 58% had only 
primary level education. It is hard to change education 
status of the farmers in the short run. The challenge 
then is to simplify agricultural messages so that the 
more than 70% of the population can also benefit from 
Type-II knowledge, which has significant impact on 
the control of striga and stemborer. Simplification can 
happen through more interactive methods, which 
contribute, to Type II knowledge. Due to logistical 
constraints only few farmers participate in on-farm 
research activities while this is identified as one 
important factor in influencing Type II-knowledge. 
Farmer field schools would be a recommended avenue 
where more and more farmers participate in on-farm 
research activities and they provide more interactive 
learning opportunities. Vihiga district compared to 
Suba; 8.4 times higher if the household was located in 
village hosting on-farm research activities; 3 times 
higher if the household was wealthier and 3.6 times  
 
higher if the household had more household labour. A 
logistic model (92.6% correct prediction, R
2 =29.8%) 
shows that only Type I and Type-II knowledge had 
positive partial effects on the farmer’s likelihood to 
use an improved technology to control stemborers. 
Marginal increases in the farmer’s Type I and Type II-
knowledge, holding all other factors constant, 
increases farmer’s likelihood to use an improved 
technology against stemborer by 5.4 and 7.1 times 
respectively. 
Acknowledgements 
The authors acknowledge the Gatsby foundation and 
DAAD for funding the research. Special thanks to 
ICIPE, University of Nairobi (UoN) - Kenya and 
Kenya Agricultural Research Institute (KARI) for 
providing logistical support for the study. C Midega 
and J Pittchar’s comments on earlier drafts are highly 
appreciated. We are also greatly indebted to the small-
scale farmers in Suba and Vihiga districts, who shared 
their farming experiences in this study 
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Household location in a research village  2.13**  0.68  ns    
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Advancing Technical Change in Agriculture  187 
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Help.htm 
 
 
 
 
 
 
 
 
 
 

An introduction to categorical data analysis. Wiley-Interscience,

Innovative approaches: back to the basics.

Linking Institutional Partnerships, Community Groups and Rural Livelihood Improvement in

Classification and influence of agricultural information on striga and stemborer control in Suba and Vihiga Districts, Kenya

Classification and influence of agricultural information on striga and stemborer control in Suba and Vihiga Districts, Kenya

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