




































In ternationa l
Scholars
Journa ls

 

African Journal of Agricultural Marketing ISSN 2375-1061 Vol. 5 (7), pp. 001-009, July, 2017. Available online at 
www.internationalscholarsjournals.org © International Scholars Journals 

 

Author(s)                                           Author(s) retain the copyright of this article. 
 
 
 

Full Length Research Paper 

 

Determinants of adopting imazapyr-resistant maize for 
Striga control in Western Kenya: A double-hurdle 

approach 

 
D. B. Mignouna1,2*, V. M. Manyong1, K. D. S. Mutabazi2 and E. M. Senkondo2

 
 

1
International Institute of Tropical Agriculture (IITA), P. O. Box 34441, Dar es Salaam, Tanzania. 

2
Department of Agricultural Economics and Agribusiness, Sokoine University of Agriculture (SUA), P. O. 

Box 3007 Morogoro, Tanzania. 
 

Accepted 10 May, 2017 
 
Discussions and debates have been on going about Striga, a major constraint to increasing maize production 
and food security in western Kenya. To inform these debates this paper applies econometrics to farm survey 
data in order to estimate and determine the factors which drive farmers’ decision to adopt imazapyr-resistant 
maize (IRM), a novel technology for Striga control. The study uses data from a multistage, random sample of 
600 households of which, 169 were IRM adopters and 431 were non-adopters. This paper tests the hypothesis 
that the factors affecting farmers’ decision to adopt IRM are not necessarily the same as those affecting their 
extent of adoption. Results from the double-hurdle model indicates that age of the household head, household 
size, membership to social group, access to extension services and perception towards IRM for Striga control 
were found to influence the decision to adopt IRM. And, household size, gap between maize production and 
consumption per capita, access to extension services and perception towards IRM for Striga control influenced 
the extent the farmer is willing to adopt. The paper concludes with policy implications aimed at renewing the 
focus on IRM transfer in western Kenya and other areas with similar conditions. 
 
Key words: Adoption, double-hurdle model, imazapyr-resistant maize (IRM) technology, Striga, Kenya. 
 
 
INTRODUCTION 

 
Since mid-seventies Striga has entered Africa (Klingman, 
1961), there is increasing evidence now that Striga is a 
main constraint in increased food production, food 
security and poverty reduction in western Kenya (De 
Groote et al., 2007; Kanampiu et al., 2006; Manyong et 
al., 2008; Woomer and Savala, 2008). After several 
years, discussions and debates are still going on about 
many issues regarding Striga such as: the severity and 
incidence of the disease, the magnitude of its economic 
losses, clear mechanisms of spreading and the new 
approaches for its control to overcome this nightmare.  

There is no doubt that a strategic decision should be 
made to fight Striga in order to attain self-sufficiency in  
 
 
 
*Corresponding author. E-mail: mignon_001@yahoo.fr. Tel:  
+255-78-6960907/+255-22-2700092. Fax: +255-22-2775021. 

 
 
 
 

 
maize grains and increase food security. Despite the 
introduction of various technologies, traditional and novel 
ones such as push-pull to farmers, Striga continues to 
expand and the appropriate techniques and strategies to 
contain Striga epidemic deemed as failed. The parasite 
competes with its host for resources; changes host plant 
architecture and reduce the photosynthetic rate and the 
water use efficiency of the host (Van Ast et al., 2000; 
Watling and Press, 2001). This has led to the emergence 
of a new technology known as imazapyr-resistant maize 
(IRM) which has proven to be efficient for Striga control 
(Kanampiu et al., 2006; De Groote et al., 2006). The 
International Maize and Wheat Improvement Center 
(CIMMYT), Badische Anlin and Soda Fabrik (BASF), 
African Agricultural Technology Foundation (AATF) and 
other stakeholders have made efforts in bringing IRM 
technology to farmers as assistance for Striga control. 
IRM technology utilizes herbicide resistant maize seed 

file:///C:\Users\user\Documents\REPUBLICATION\AGRICULTURAL%20SCIENCES\AppData\Local\Temp\www.internationalscholarsjournals.org


 
 
 

 

coated with the herbicide imazapyr. The herbicide used is 
derived from a naturally occurring gene in maize originally 
identified by BASF and made available to CIMMYT. The 
technology has been introduced to a large scale since 
2004 after field trials and tests organized for farmers in 
2002 and 2004. However and surprisingly, only 28% of 
the sampled households in western Kenya adopted it 
(Mignouna et al., 2011) and the reasons for this low 
adoption are unclear. Therefore, there is a need to better 
understand why before the introduction of any further new 
agricultural technology.  

Several adoption studies concentrate on cross-
sectional analysis of the determinants of agricultural 
adoption at the farm level. For instance, the CIMMYT 
studies in Kenya and other East African countries 
(Mwangi et al., 1998; Doss, 2003) examined adoption 
decision processes for maize seed and fertilizer 
technologies and showed that farmer characteristics such 
as age, gender, and wealth are keys to adoption 
decisions. For Ouma et al. (2002), they found agro-
ecological differences, gender, manure use, hiring of 
labour and extension are important to adoption of fertilizer 
and hybrid seed on maize production in Embu district. 
Suri (2011) showed that technology profitability, farmers’ 
training as well as observed and unobserved differences 
among farmers and across farming systems are the 
major determinants affecting maize technology adoption 
in Kenya. According to the study done by Jayne et al. 
(2006), the national-level, region-specific, and household 
specific factors are associated with smallholders’ use of 
improved maize technologies in Kenya. Mwabu et al. 
(2006) in their research on the adoption of improved 
maize varieties and their impact on poverty in Laikipia 
and Suba districts found that the price of maize, 
education level, and distance to the roads are the main 
determinants of hybrid maize adoption by farmers. 
 

To inform these debates, this paper aims to support the 
decision of adopting IRM as a practical answer to the 
Striga crisis and applies econometrics to farm survey 
data in order to estimate and determine the factors 
affecting farmers’ decision to adopt IRM technology in 
western Kenya. This paper tests the hypothesis that 
factors affecting farmers’ decision of adopting IRM are 
not necessarily the same factors affecting their extent of 
adoption. 

 

METHODS 
 
Study area 

 
Nyanza and Western provinces in the Lake Victoria zone in western 
Kenya were chosen for this study based on the importance of maize 
as a major food and cash crop for small-scale farmers in the region 
and of Striga, which constitutes the most important biological 
constraint to the maize production (Manyong et al., 2008). Soil 
fertility decline is a major problem in the area as a result of 
continuous cropping with little use of inputs. Furthermore, the mean 
annual rainfall which ranges from less than 1000 mm near the 

  
  

 
 

 
shores of Lake Victoria to 2000 mm away from the lakeshore is 
suitable to Striga as it grows well in areas receiving less than 1500  
mm rainfall per annum (Oswald, 2005). This explains partially the 
severe infestation of Striga (Striga hermonthica), a parasitic weed 
that substantially reduces maize yield in the region. 

 

Study design and data 
 
The household survey was carried out between September and 
December, 2008. A structured survey questionnaire was prepared 
and trained enumerators collected the information from households 
via personal interviews. A multistage, random sampling procedure 
was adopted to get the total sample size of 600 households 
comprising 169 adopters and 431 non-adopters of IRM from 
Nyanza, and Western provinces of western Kenya. First, two 
provinces and six districts were selected regarding the importance 
of maize production and level of Striga infestation. Second, 100 
farmers were randomly selected from each district and stratified into 
two, namely adopters of IRM and non-adopters. Adopters were 
identified by using the list made available by the front-line extension 
workers and farmers assisted in confirming it. The data collected 
valuable information on several variables including socio-economic, 
farm-related, institutional and technological factors. The technology-
adoption decision of a household is assumed to be motivated by 
utility maximization. 

 

Theoretical model and empirical specifications 

 
Adoption is conventionally conceptualized to be the mental process 
through which an individual passes from first learning about an 
agricultural innovation to final adoption (Mutandwa et al., 2007). 
Adoption is seen as the decision by an individual to become a 
regular user of the new idea (Kotler and Armstrong, 1994). Authors 
argue that adoption of a new technological innovation is 
underpinned by its underlying attributes or consists of inter-related 
stages (Batz et al., 1999; Fernandez-Cornejo et al., 2002; Rogers, 
2003).  

In modeling the utility or satisfaction derived from the use and 
integration of IRM into the smallholder farming system, the 
economic values or benefits associated with traditional maize 
varieties such as local maize and systems with the novel variety 
IRM need to be considered. A typical smallholder-farming 
household seeks to maximize a multi-dimensional objective 
function, including increasing incomes and food security, and 
reduction of all forms of risk (Strauss et al., 1989). When there is a 
change in economic parameters associated to IRM technology, the 
central question is related to how much compensation, whether 
paid or received, would make the decision maker indifferent about 
the change. Thus the change in welfare associated with this 
development was used as the basis for economic valuation 
process. When an individual farmer faces a change in a 
measurable attribute, for example higher control for Striga from new 

IRM variety (q), then q changes from q0 to q1 (with q1 > q0). The 

indirect utility function u after the change becomes higher than the 
status quo. Now the status quo can be represented econometrically 
as follows: 
 

u1j  = ui (yi, zj , q°, ε0j  ) 

 
On the other hand, the changed or final state due to the introduction 
of IRM is shown by: 
 

u2j = ui (yi, zj , q
1
, εij) 

 
Where, yi, refers to the farmer’s income, Zj is a vector of the 

farmer’s socio-economic variables and attributes of choice, and εj is 



 
 
 

 
the stochastic error term representing other unobserved utility 
components,  

The farmer would opt, pay and adopt IRM if the following 
condition holds: 
 

ui  (yi  – Pi, zj , εij  ) > u0 (yi , zj , ε0j  ) 
 

Where: Pi is the monetary investment associated with the new 
variety.  

Since the random components of the preferences are not known 
with certainty; it is only possible to make probabilistic statements 
about expected outcomes. Thus, the decision by the farmer to 
adopt IRM is the probability that he/she will be better off if this 
variety is used. This is represented as follows: 
 

Prob (Yesi) = Prob [ui  (yi  – Pi, zj, εij  ) > u0  (yi, zj, εij  )] 

 
Since the afore-mentioned utility functions are expressed generally, 
it becomes critical to specify the utility function as additively 
separable in deterministic and stochastic preferences. Using, this 
argument, the function becomes: 
 

ui  (yi  , zj , εij  ) = ui  (yi, zj  ) + εij 

 
Where: The first part of the right hand side is the deterministic part 

and the second part is the stochastic part. The assumptions that εij 
are independently and identically distributed with mean zero 
describes most widely used distributions.  

Two widely used distributions are the normal (probit) and logistic 
regression models. In this study, the statistical dichotomous choice 
data is modeled by superimposing a probability function. The 
dependent variable takes the value 1 if the smallholder-farming 
households are willing to adopt IRM or 0 if they are not willing to 
adopt. And if the farming households adopt, how much could they 
adopt? The observed adoption of IRM is hypothesized to be the end 
result of combined effects of a number of factors related to the 
farmer's goals and means of achieving them.  

Several hypotheses can be derived from these two sets of 
decision; factors that affect adoption and factors that affect intensity 
of IRM. The following variables in the models were hypothesized to 
influence the adoption of IRM in different directions. External 
influences include institutional support systems such as marketing 
facilities, credit and extension services which are important in 
affecting adoption (Feder, 1980). Credit was not included as factor 
influencing the IRM adoption because very few households in the 
study area used credit to purchase farm inputs. Also access of the 
introduced IRM was not included as determinant explaining 
adoption because access of the novel technology in the study area 
was mainly done through extension services and farmer’s social 
groups which were already hypothesized to influence IRM adoption. 
Farmer’s age may influence both the decision to adopt and extent 
of adoption of IRM. It may be that older farmers are more risk 
averse and less likely to be flexible than younger farmers and thus 
could have a lesser likelihood of adopting new technologies. 
However, it could also be that older farmers have gained over time 
farming knowledge and experience and could be better able in 
evaluating technology information than younger farmers, and hence 
could have a higher probability of adopting the practice (Feder et 
al., 1985; Belknap and Saupe, 1988). For the gender of the 
household head the assumption made was that the head of the 
household is the primary decision maker and gender difference 
could be found to be one of the factors influencing the adoption of 
new technologies. Due to many socio-cultural values and norms, 
males have more access and control over vital production 
resources such as land, information, credit and labour than women 
(Njeri, 2007). Therefore, it was hypothesized that gender could be 
related to the adoption of IRM package. Education level of the 

 
 
 
 

 
household head increases farmer's ability to obtain, process, and 
use information relevant to the adoption of IRM. Thus educated 
farmers have been found to be more likely to adopt innovations 
(Nkamleu and Adesina, 2000; Asfaw and Admassie, 2004). Thus it 
is hypothesized that farmers with more education could be more 
likely to adopt IRM than farmers with less education depending also 
on the education level (that is, primary, junior and secondary levels 
of education). Household size, a proxy to labour availability can be 
an incentive to produce more to meet the needs hence looks for 
high-yielding varieties in the Striga environment. Therefore a 
positive relationship was hypothesized between IRM package and 
household size is the major source of labour for farm activities 
(Adesina et al., 2000). Large households have the capacity to relax 
the labour constraints required during the introduction of new 
technologies (Ayuya et al., 2011). It is expected that a larger 
household size could affect the decision of adopting IRM. Farm size 
is hypothesized in a way that those producers with more land could 
be more likely to increase the intensity of adoption. Having a large 
land contributes to perceived security and increased willingness to 
invest in new technology (Caveness and Kurtz, 1993). As a result, 
positive relationship was hypothesized between farm size and IRM 
adoption. Gap between maize production and consumption per 
capita is hypothesized to be a stimulant of IRM adoption. The 
difference between maize production and consumption per capita 
which can result into deficit can stimulate the demand for high-
yielding varieties. Belonging to a rural social group enhances social 
capital allowing trust, idea and information exchange. Better social 
relations and communication among farmers are crucial for 
technology adoption and diffusion. Thus membership to a group 
could increase the technology adoption. Access to extension 
services is hypothesized to relate to adoption by exposing farmers 
to new information and technical skills about Striga control. 
Perception of the farmer towards IRM for Striga control is critical in 
the adoption decision (Dolisca et al., 2006) in motivating farmers. 
Farmers who perceived IRM as being consistent with their needs 
and compatible to their environment were expected to adopt it since 
they find it as a positive investment. Perception towards IRM is 
hypothesized to be positively related to the adoption decision. 
 
 
Econometric specification: The double-hurdle model 
 
While other studies have approached a similar problem using the 
logistic analysis (Kavia et al., 2007), Heckman procedure (Nkonya 
et al., 1998; Adeoti, 2009); this paper compares the results from a 
joint Tobit and a Double-Hurdle (DH) models because we believe 
that factors that affect farmers’ choice of an option should not 
necessarily be the same as those that affect the intensity of use. 
This is because the decision to choose a particular maize option is 
obviously associated with some threshold effects. In terms of policy 
relevance, our analysis clearly shows that adoption and intensity 
may be different decisions and that estimation of intensity on the 
basis of factors affecting adoption, as implied by other approaches, 
may be liable to error.  

The DH model, originally proposed by Cragg (1971) has been 
extensively applied in several studies (Martínez-Espiñeira, 2006; 
Moffatt, 2003; Newman et al., 2001; Burton et al., 1996). However, 
it has not been much used in the area of adoption of agricultural 
technologies; an exception would be Berhanu and Swinton (2003). 
Double-hurdle model was used in this case to determine the factors 
that influence the decision to adopt and the extent of adoption of 
IRM in order to identify areas of intervention. The underlying 
assumption in the DH approach is that farmers make two decisions 
with regard to their decision to grow IRM. The first decision is 
whether they will grow IRM. The second decision is about the 
amount of land that they will allocate, conditional on the first 
decision. The two decisions are, therefore, whether to grow IRM 
and how much to grow. The importance of treating the two 



 
 
 

 
decisions independently lies in the fact that factors that affect one’s 
decision to adopt may be different from those that affect the 
decision on how much to adopt. This implies that households must 
cross two hurdles in order to adopt. The first hurdle needs to be 
crossed in order to be a potential adopter. Given that the 
households is a potential adopter, their current circumstances then 
dictate whether or not they do in fact adopt: this is the second 
hurdle (Moffatt, 2003). The DH model allows for the possibility that 
these two decisions are affected by a different set of variables. The 
advantage with this approach is that it allows us to understand 
characteristics of a class of households that would never adopt 
IRM. Thus the probability of a household to belong to a particular 
class depends on a set of household characteristics. The DH model 
is a parametric generalization of the Tobit model, in which two 
separate stochastic processes determine the decision to adopt and 
the level of adoption of technology. The first equation in the DH 
model relates to the decision to adopt (y) can be expressed as 
follows: 

 

yi = 1 if  yi
*
  > 0 and 0 if  yi

*
    ≤ 0 (1) 

yi
*
    = xi

'
α  ε i  

 

Where: y 
*
   is latent adoption variable that takes the value of 1 if a 

 
household grew IRM and 0 otherwise, x is a vector of household 
characteristics and α is a vector of parameters.  
The second hurdle, which closely resembles the Tobit model, is 

expressed as: 

 

ti = ti
*
 > 0 and  yi

*
 > 0 

 

ti = 0 otherwise   
 

t 
*
   = z 

'
 β  u 

i 
(2) 

 

i i   
 

 

Where: ti is the observed response on how much land one allocated 
to IRM, z is a vector of the household characteristics and β is a 
vector of parameters.  

The decision of whether or not to adopt IRM and about how much 
land to allocate to IRM can be jointly modelled, if they are made 
simultaneously by the household; independently, if they are made 
separately; or sequentially, if one is made first and affects the other 
one as in the dominance model (Martínez-Espiñeira, 2006). If the 
independence model applies, the error terms are distributed as 
follows: 
 

ε i  ~ N (0, 1) and ui  ~ N(0,δ
2
). 

 
If both decisions are made jointly (the Dependent DH) the error 
term can be defined as: 
 

( ε i   ui) ~ BV N(0,Υ) 

 

1 ρδ 
 

Where Y   

ρδ δ 
2  

 

   
 

 
The model is said to be a dependent model if there is a relationship 
between the decision to adopt and the intensity of adoption. This 
relationship can be expressed as follows: 

  
  

 
 

 

ρ  
 COV(ε i ui )  

 

 

   

VAR(ε i ) VAR(ui ) 
 

 

   
  

 

If  ρ = 0 and there is dominance (the zeros are only associated to 
 
non-participation, not standard corner solutions) then the model 
decomposes into a Probit for participation and standard OLS for Y.  

Following Smith (2003) we assume that the error terms and ε i  
and ui are independently and normally distributed and thus we have 
the following expression: 
 

ε   01 0  
 

 
i
     N  ,      

  
 

 
 2   

 

δ 

 
 

u
i  00   

 

And finally, the observed variable in a DH model is ti  yi ti
*
 and 

the log-likelihood function for the DH model is:  

  z 
' 
β  1  y  − x 

' 
β 

 

LogL  ∑LN  1 − Φxi
'
α Φ  i 

 ∑LN  Φzi
'
α   φ i i 

 

 

δ δ δ 
 

 

0        
 

            
  

Where: Φ (·) refer to the standard normal probability and 

φ (·) refer to density functions. 

 
Thus in this study we estimate the decision to adopt and the extent 
of adoption using a DH model. 
 

 

RESULTS AND DISCUSSION 

 

Descriptive statistics 

 

Table 1 presents the t-test and chi-square comparison of 
means of selected variables by adoption status for the 
surveyed households. Some of these characteristics are 
the explanatory variables of the estimated models we 
present further on. The dataset contains 600 farm 
households and of these, about 28% were adopters, that 
is, planted at least a unit of square meter of IRM during 
2007/08 cropping season. The area planted of IRM is 
about 0.23 ha for adopters. The analysis of the data 
shows that there is a significant (P < 0.01) mean 
difference between age of adopters and non-adopters. 
Average age of sample household head is about 49 years 
with non-adopters. No significant difference is observable 
in the gender of the household head although the groups 
vary significantly in terms of their education level. Primary 
and junior levels of education (1 to 8 years) are lower for 
adopters however adopters have higher proportion of 
household heads with secondary education. This 
suggests that education might be correlated with decision 
to adopt. The household size is 6.22 persons for adopters 
and 5.28 for non-adopters and the difference is 
statistically significant suggesting the importance of family 
size for adoption of new technologies. No significant 
difference is observable in total farm size and gap 
between maize production and consumption per 



 
 
 

 
Table 1. Descriptive summary of variables used in estimations (N = 600).  

 

Variable Unit 
Adopters Non-adopters t-stat (chi- 

 

(N=169) (N=431) square)  

  
 

Dependent variable     
 

Area planted of IRM Ha 0.23 0.00 0.23*** 
 

Adoption 1/0 1.00 0.00 - 
 

Independent variable     
 

Age of the HHH Years 48.92 45.19 3.73*** 
 

Gender of HHH (male=1) 1/0 0.71 0.75 -0.04 
 

HHH education 1-4 years (yes=1) 1/0 0.09 0.27 -0.17*** 
 

HHH education 5-8 years (yes=1) 1/0 0.36 0.48 -0.12*** 
 

HHH education greater than 8 years (yes=1) 1/0 0.54 0.25 0.29*** 
 

HH size Count 6.22 5.28 0.94*** 
 

Farm size Ha 0.85 0.41 0.44 
 

Gap between maize production and consumption per capita Kg -116.66 8.21 -124.87 
 

Membership to social group (yes-1) 1/0 0.75 0.58 0.17*** 
 

Access to extension services (yes=1) 1/0 0.70 0.39 0.32*** 
 

Perception towards IRM for Striga control 1/0 0.96 0.72 0.25*** 
 

 
Statistical significance at the 99% (***), 95% (**) and 90% (*) confidence levels; HHH= household head. 

 
 

 

capita. There is significant difference in terms of house-
hold membership in different rural institutions. The result 
also depicts that the adopter categories are distin-
guishable in terms of their access to extension services 
and perception towards IRM for Striga control. This 
simple comparison of the two groups of smallholders 
suggests that adopters and non-adopters differ signify-
cantly in some proxies of socio-economic characteristics. 
 

 

Econometric results 

 

All parameter estimates of the models were generated 
using Stata version 11 (StataCorp, 2009) and the results 
from the study showed that the coefficients of most of the 
variables hypothesized to influence the decision and 
extent of adoption of IRM have the expected signs. The 
Probit results on the decision to adopt IRM and truncated 
regression analysis results on the extent of adoption are 
presented in Table 2. 
 

 

Determinants of IRM adoption 

 

To identify the factors influencing the decision to adopt 
IRM, the Probit model was estimated (first hurdle). The 
results shown in Table 2 reveal that five factors are 
significant in influencing farmers’ decision to adopt IRM of 
whose four at 1% namely: age of household head, 
household size, access to extension services and 
perception towards IRM for Striga control. Membership to 
any rural association is significant at 5%. The log 

 
 
 

 

likelihood for the fitted model was -231.29402 and the χ2 
value of 232.48 indicates that all parameters are jointly 
significant at 5%. Age has been found to have a positive 
relationship with the decision to adopt IRM technology 
implying that old farmers are more willing to adopt IRM 
than young farmers as a result of age based knowledge 
gained and probably experiences accumulated over 
years’ differences. However these results were 
inconsistent with those of Langyintuo and Mulugetta 
(2005), Rahelizatovo and Gillespie (2004), Barham et al.  
(2004), and Baidu-Forson (1999), who argued before that 
younger farmers are more receptive towards newly 
introduced technologies than older farmers. The effect of 
household size was found to be positive and significant 
suggesting that the larger in number of persons in the 
household the more likely the farmer is willing to accept 
high-yielding variety in Striga infestation environment. 
The interpretation for this is that the bigger the household 
size the more the farmer flexibility in their decision 
making certainly due to availability of more labour to use 
on the new practices. Similar results were found by 
Amsalu and Jan de (2007) who stated that the household 
size had a significant and positive effect on determinants 
of adoption and continued use of stone terraces for soil 
and water conservation in an Ethiopian highland water-
shed. Membership to a social group which assessed 
whether the farmer or household is part of a community 
organization or cooperative was found to be positively 
and significantly associated with a higher probability of 
adopting IRM. This agrees with Walton et al. (1969) that 
the most important issue in adopting a new technology is 
group unity. Such unity is attributed to a spirit of teamwork 



  
 
 

 
Table 2. Maximum likelihood estimates for the joint Tobit and Hurdle models.  

 
 

Model specification 
Joint Double-hurdle  

 

 

Tobit Probit Truncated 
 

  
 

 Dependent variable Area planted of IRM Dummy = 1 if IRM adopted Area planted of IRM 
 

 Variable Coefficient Coefficient Coefficient 
 

 Age of the HHH 0.0129*** 0.0635*** 0.0005 
 

 Gender of HHH (male=1) -0.0741** -0.2380 -0.0120 
 

 HHH education 1-4 years (yes=1) -0.1118 -0.9284 0.2132 
 

 HHH education 5-8 years (yes=1) 0.1361 0.1802 0.2366 
 

 HHH education greater than 8 years (yes=1) 0.4140** 1.5767 0.2544 
 

 HH size 0.0377*** 0.0901*** 0.0380*** 
 

 Farm size -0.0010 -0.0052 0.0160 
 

 Gap between maize production and consumption per capita 0.0001*** 0.0000 0.0002*** 
 

 Membership to social group (yes-1) 0.0769** 0.3545** 0.0035 
 

 Access to extension services (yes=1) 0.1017*** 0.4486*** 0.0602*** 
 

 Perception towards IRM for Striga control 0.2715*** 1.3262*** -0.0908** 
 

 Constant -1.4336*** -6.0134*** -0.2328 
 

 Model summary    
 

 Number of observations 573 573 169 
 

 Log-likelihood -141.54089 -231.29402 196.83067 
 

 LR chi2(11),Wald chi2 (11) 233.21 232.48 364.27 
 

 Prob > chi2 0.0000 0.0000 0.0000 
 

 AIC (-LOG-L + k/N) 0.27  -1.09 
 

 LR test for Tobit vs. Truncated regression   214.16 (0.0000) 
 

 
*, **, *** Coefficients are significantly different from zero at the 99% (***), 95% (**) and 90% (*) confidence levels, respectively. 

 
 

 

and cooperation where there is communication. 
Membership to a group may enable farmers to 
learn about a technology via other farmers and 
from other development agencies (Nkamleu, 
2007). Information flow between members of 
farmer groups is usually very rapid and important. 
Farmer groups give their members a wider 
opportunity for educating each other. Higher 
interactions among members of a group increase 

 
 
 

 

chances to widen understanding of new 
technologies and their advantages.  

These results underscore the importance of 
social capital in accessing new technologies by 
the poor smallholder farmers. Access to extension 
services was found positively significant, which 
implies that the contact with an extension agent is 
necessary to enhance the rate of adoption. As 
extension services popularize the innovation by 

 
 
 

 

providing necessary information, appropriate 
knowledge and special skills, they enable farmers 
to apply innovation. Majority of the farmers in 
western Kenya have not been able to obtain 
technological information due to lack of know-how, 
access to communication media and technical 
training. This finding is in conformity with other 
studies (Abebaw and belay, 2001). Likewise 
Adesina et al. (2000) found that the probability 



 
 
 

 

of adoption is also higher for farmers organized in groups. 
The decision to adopt is also influenced by farmers 
subjective perceptions of the characteristics of new 
agricultural technologies as found Adesina and Baidu-
Forson (1995). Perception towards IRM for Striga control 
is positively and significantly associated with a great 
likelihood of adopting IRM having in mind the yield 
performance that will be generated from IRM. Positive 
perception increases the probability of adoption (Ostlund, 
1974). Farmers who perceived the technology as 
beneficial to them would adopt it more than those whose 
perception is negative or indifferent. 
 

 

Determinants of extent of adoption 

 
The estimated results for DH and Tobit models on 
adoption of IRM in western Kenya are presented in Table  
2. The Tobit model results have been presented for 
comparison. The results from the two models were 
comparable which show the robustness of our results to 
model specification. All the statistically significant 
variables except the perception towards the technology 
for Striga control had the same directional effects in all of 
the two models. The likelihood ratio test statistic specified 
in Table 2 favoured the DH model over the Tobit. The 
Akaike Information Criterion (AIC) estimates also 
confirmed the same model as a better fit for the data. 
Henceforth, we shall base our discussion on the results 
from the DH model. Four variables were found to have 
significant effects in explaining the level of adoption of 
IRM by households, measured in term of area planted 
under IRM. These included household size, gap between 
maize production and consumption per capita, access to 
extension services and perception towards IRM for Striga 
control.  

As argued by Asfaw et al. (2010) awareness in 
technology transfer is very important. In most of the 
adoption cases in developing countries, adoption is 
hampered not only by the characteristics of the new 
varieties but by lack of awareness of the end users of the 
technologies. Farmers’ awareness about the available 
improved varieties is therefore critical in the adoption 
programme. Our results confirm with this preposition. In 
line with our expectation, access to extension services 
was statistically significant in explaining the level of 
adoption. The institutional setting of the farm system has 
a profound influence on the adoption of technologies and 
institutional factors like frequent extension contacts are 
positively related to the adoption decision of farmers 
(Tesfaye et al., 2001; Habtemariam, 2004). They may 
merely create social pressure for farmers to use inputs 
and the methods the agents advocate (Moser and 
Barrett, 2006). These contacts illustrate that the availa-
bility of reliable information sources will enhance the 
communication process and have significant associations 
with the adoption of improved technologies. Access to 

 
 
 
 

 

extension services enables farmers to get exposed and 
more familiar with a new variety. Extension service is one 
of the most prearranged conditions for creating 
awareness and building the necessary knowledge for 
using the innovation following the approach which is most 
convenient for farmers. Farmers’ perception towards IRM 
for Striga was negative and significant in explaining the 
extent of IRM adoption. The negative sign of the 
perception variable is unexpected and may be explained 
by the possibility that farmers’ positive perception about 
IRM has been distorted by other perceptions/attitudes or 
due to negative correlation between this variable and 
other varietal characteristics not included in the model. 
The significance of household size suggests that large 
households are more likely to invest in new technologies 
as they can guarantee an adequate supply of farm labour 
necessary for the expansion of farm enterprises. This 
may suggest that encouraging them to operate in large 
number could be regarded as a policy relatively likely to 
increase productivity. The gap between maize production 
and consumption per capita was statistically significantly 
(P < 0.05) and supports the hypothesized sign that the 
deficit of maize production per capita influences positively 
the adoption of technology. Any household failed to reach 
the expected level of maize production due to Striga, 
ends in a deficit which is consequently encourage to seek 
for high-yielding maize varieties to increase its production 
and therefore likely to adopt IRM. This result confirms the 
existence of substantial opportunities of increasing maize 
production via augmenting IRM adoption. 
 

 

CONCLUSION AND RECOMMENDATIONS 

 

This study provides an analysis of the determinants of 
adoption of IRM using a DH model due to a hypothesis 
that factors that affect the decision to adopt IRM may be 
different from those that influence the extent of adoption. 
The findings from this study indicate that although in 
general there is a positive correlation between probability 
of adoption and intensity of IRM use, we note some 
differences with regard to the factors that influence the 
two decisions. Results reveal that age had a positive 
effect on the decision to adopt while it had no effect on 
the extent of adoption. The similar effect has been 
observed with the gap between maize production and 
consumption per capita, which had a positive effect on 
the extent of adoption without effect on the decision of 
adoption. The effect of farmers’ perception towards IRM 
on adoption decision is another example of variable with 
an opposite effect between the two stages of adoption. 
Results indicated that while perception leads to increased 
probability of adoption, it has a negative effect on the 
extent of adoption. The results indicate also that although 
membership to any social group increases the likelihood 
of adoption, it does not influence the extent of IRM area 
cultivated. These results have a number of implications in 



 
 
 

 

terms of sustaining smallholder agriculture already in peril 
in western Kenya and which are critical for fighting Striga. 
An interesting lesson from this study is that it is important 
to consider the two stages of adoption in order to improve 
farmers’ ability to adopt, and increase intensity of IRM 
use because factors that affect the decision to adopt are 
not necessarily the same factors that affect the decision 
on the extent of adoption. Factors such as age, 
household size, extension services, membership to social 
group, gap between maize production and consumption 
per capita and perception may enhance or limit adoption 
and diffusion of IRM technology. To develop a successful 
Striga control programme in the area, these factors have 
to be taken into consideration focusing first on factors that 
affect households’ decision of adoption. Policy makers 
and stakeholders of the maize sector are hereby called 
upon to develop the sector thereby finding strategies 
regard to the key determinants in order to encourage 
households in western Kenya to be more decisive in their 
choice to adopt and intensify IRM technology. This is vital 
to reduce Striga in parallel with poverty. 
 
 
 
ACKNOWLEDGEMENTS 

 
This paper is an output from a PhD study funded by the 
International Institute of Tropical Agriculture (IITA), 
Ibadan, Nigeria through a research grant received from 
African Agricultural Technology Foundation (AATF), 
which is also acknowledged for their support. 

 
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