




































In ternationa l
Scholars
Journa ls

 

African Journal of Agricultural Marketing ISSN 2375-1061 Vol. 5 (2), pp. 001-008, February, 2017. Available online at 
www.internationalscholarsjournals.org © International Scholars Journals 

 

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

 

 

Full Length Research Paper 

 

Credit constraints and adoption of modern cassava 
production technologies in rural farming communities 

of Anambra State, Nigeria 

 
Benjamin Okpukpara 

 
Centre for Rural Development and Cooperatives, University of Nigeria, Nsukka, Nigeria. 

E-mail: benedozie@yahoo.com. 
 

Accepted 18 November, 2016 
 
This study was motivated because of low adoption of cassava production technologies in rural areas of Nigeria. The 
supply of cassava product, which is major staple food in Nigeria has not match up with the demand especially now 
the government is trying to increase her non-oil export through cassava export. It expected that increase in 
adoption of modern production technologies will scale-up cassava production and reduce poverty and increase 
food safety net in Nigeria. The study was carried out with random selected rural farmers. Data were collected and 
analyzed with combination of descriptive and univariate probit regression model. The major findings are that though 
credit is important in adoption of modern cassava technologies, credit from informal institutions appears to be the 
major influencing factor in adoption of modern cassava production technologies. Education and availability of 
modern input in the rural areas are also important variables that affect adoption. In view of this, increasing financial 
base of informal institutions and vigorous rural education campaign and increasing availability of the technologies 
through rural community based organizations are among the recommendations made. 

 

Key words: Cassava, adoption, technologies, credit, poverty, Nigeria. 
 
 
INTRODUCTION 

 
Cassava (manihot esculenta) is a staple food of average 
household (especially for rural poor household) in 
Nigeria. Cassava or its derivatives form part of daily food 
for both poor and non-poor households. Therefore, 
cassava is an important factor in food security, poverty 
alleviation and reducing unemployment among others. 
Cassava can grow in poor marginal soil where most 
crops cannot grow (Ali, 2005). Cassava matures in 12 to 
18 months after planting. Cassava can be processed into 
variety of products such as garri, fufu, tapioca, cassava 
chips and flour among others.  

In recognition of the benefit of cassava, the present 
President of Federal Republic of Nigeria – Chief Olusegu 
Obasanjo has taken steps to ensure that cassava export 
forms one the major non-oil export (Asadu, 2004). 
Recently, cassava has occupied a prominent place in 
national non-oil export commodity especially exports in 

 
 
 
 

 
sub-Saharan Africa that international demand is far 
above the supply (Frecso, 1993; Nweke et al, 2002). This 
increase in cassava export has also negatively affected 
local supply. It is also important to note that cassava 
production is mostly done by rural smallholder farmers 
using low-level production techniques (Omonona, 2009; 
Oyegbami et al., 2010; Nweke et al., 2002). Though 
government at various levels has been trying in various 
ways to encourage rural farmers to adopt the modern 
cassava production technologies in order to increase the 
rural farmer’s productivity (Fresco, 1993; Otoo, 1994; Ali, 
2005), there are constraints to adoption in rural farming 
communities (Nweke et al., 2002; Teklewold et al., 2006). 
In some instances, farmers reject some of modern 
technology due to their cultural background and 
inhibitions due to perhaps illiteracy and religious beliefs. 
Nevertheless, credit constraint has been singled out as a 

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


 
 
 

 

major factor militating against adoption of modern 
cassava production techniques (Nweke et al., 2002).  

The technologies are herbicides application, use of 
hybrid cassava stake, use of insecticides, use of 
herbicides, use of inorganic fertilizer, use of tractor, 
appropriate spacing, planting date and tillage practices. 
The adoption of modern cassava production technologies 
is an important route out of poverty for many in the 
developing world including Nigeria because of the major 
role cassava play in food security. Many studies have 
noted poor technology adoption in cassava production 
(Barham and Boucher, 1994; Ogboso, 2005).  

Yet the influence of credit in adoption of modern 
agricultural innovations in cassava production, remain 
poorly understood in cassava production (Omonona, 
2009; Adesina and Forson, 1995; Ersado et al., 2004). 
Access to credit affects household welfare outcomes 
through at least two channels. First, it alleviates the 
capital constraints on agricultural households. Access to 
credit also reduces the opportunity costs of capital-
intensive assets relative to family labor, thus encouraging 
labor-saving technologies and raising labor productivity, a 
crucial factor for agricultural development, especially in 
many African countries (Delgado 1995; Zeller et al., 
1997). The second channel through which access to 
credit affects household welfare is by increasing its risk-
bearing ability and altering its risk-coping strategy. The 
household may therefore be willing to adopt new, more 
risky technologies (Baidu - Forson, 1999; Aliou et al., 
2000; Polson and Spencer, 1990).  

This paper presents evidence on the complementarities 
between the adoption decisions of farmers concerning 
modern cassava production technologies and credit 
constraints in cassava production in rural farming 
communities of Anambra State. This will help in 
formulating a well targeted policy in cassava production in 
Nigeria. Though this work is on credit and adoption of 
modern cassava technologies, effort was made to x-ray 
other factors that affect the adoption of modern 
technologies in cassava production. 
 

 
METHODOLOGY 

 
This study was conducted in rural farming communities of Anambra 
State in 2005. A random sampling technique was used to select two 
rural local government areas in Anambra State. These local 
government areas are Aguata and Orumba South Local 
Government Areas. Cassava farmers in each of the selected local 
government areas were identified with the help of Extension 
Agents. List of these cassava farmers were compiled. From the list, 
a random sampling technique was also used to select a sample of 
180 farmers.  

Primary and secondary data were used. However, primary data 
was the main source of data used for this analysis. Primary data 
was collected with questionnaire. The questionnaire collected 
information on socioeconomic characteristics of the farmers, credit 
obtained, awareness of modern cassava technologies available to 

 
 
 
 

 
farmers, used and extent of use of such technologies as well as 
cost of using such technologies. Secondary data was sourced from 
published and unpublished materials.  

Data collected were analyzed with descriptive statistics and 
univatiate probit regression model. It should also be noted that 
when a farmer adopts five out of eight modern cassava production 
technologies, the farmer is regarded as an adopter; otherwise the 
farmer is regarded as non-adopter. Specifically, two univariate 
probit regressions were run. The first was for general determinants 
of adoption. The second is specific on examining the effect of credit 
on adoption. 

 

Econometric methodology 
 
The basic theory underlying this study is household utility model. A 
complete model of household choice decisions posit that the 
household choose whether to adopt modern cassava technologies 
or not to adopt because of utility derived from it. The probability that 
household “h” chooses to adopt is equals to the probability that the 
utility derived from adopting modern technologies in cassava 
production is greater than not adopting. In this case, I have 
assumed that adopting and not adopting are independent. This 
therefore necessitates the use of univariate probit regression.  

However, in the bivariate probit, let the latent variable y1* 
represent the underlying continuous index affecting the decision of 
adopting cassava technologies and y2* represent the decision of 
household not to adopt the technologies. Therefore the general 
specification for a two-equation model would be 
 
y1* = β1 + ε1, y1 = 1 if y1*>0, 0 otherwise --------------- (1) 

y1* = β2 + ε2, y2 = 1 if y2*>0, 0 otherwise ---------------- (2) 
 
E[ε1] = E[ε2] = 0  
Var[ε1] = var[ε2] = 1, 
Cov[ε1, ε2] = ρ. 
 
and the likelihood function to maximize is  
 

1 1 2 2 

L   2( z1, z 2;   )dz 2 dz1  

 

where Φ2(z1,z2; ρ) = [2π(1- ρ
2
)
½

]
-1

 exp[-1/2(1- ρ
2
)
-1

(z1 
2
 + z2

2
 - 

2ρz1z2)] and ρ = Coefficient of correlation between the two 
equations. X1 and X2 = row vectors of exogenous variables which 
determine respectively, adopting and not adopting propensities. β1 
and β2 = associated parameter column vectors.  

To evaluate the magnitude of the effect of a change in the 
regressors on the outcome variables, the coefficients need to be 
adjusted to be marginal effects, unlike standard linear regression 
models. In this probit model E[y] = Φ(β’x), then the marginal effects 
are   

( ' x) 
(  ' x)   .  

∂   =  

   

  xi  
   

These marginal effects would obviously vary with the values of x 
because of the nonlinearity of the model. It worth noting that all the  
coefficients β would have the same scale factor ( ' x) applied. 
 
Except for dichotomous variables, these marginal effects would be 
valid for small changes in explanatory variables. In case of 
dichotomous variables, it is better to calculate the difference in 



 

 

 

 

Table 1. Distribution of respondents according to awareness of 
modern cassava production technologies.  

 

 Technologies Frequency Percentage 

 Hybrid cassava stakes 150 83% 

 Appropriate planting date 70 39 

 Tillage practices 89 49 

 Use of herbicides 60 33 

 Use of insecticides 55 31 

 Use of inorganic fertilizer 180 100 

 Use of tractor 100 55 

 Appropriate spacing 58 32 
 

Note multiple responses were recorded. Source: Field survey, 
2005. 

 

 

probabilities when the variable is 1 and when it is zero. Hence the 
marginal effect for the dummy variable I,(δi), would be defined as  

 

i   ( i x i   i   ( 1 x i ) 
 

where the –i subscript represent all the variables but the it
h,

 and x i 

are their sample means. 
 
However, because am only interested in whether farmer adopt or 
not. I therefore use univariate probit model. The reduced form of the 
econometric model is explained below.  

The decision of farmer to adopt is described by the following 
latent variable model.  
 

Wi* X 1i 1 1i ----------------------------------  
 

Where 
W * 

is the net benefit attained by the farmer by adopting  i 
 

modern cassava technologies,  X1i are the vector that expected to 
 

influence the farmer’s  decision to adopt or not to adopt modern 
 

cassava technologies, 
W * 

, and 1iis the random  error, with   i 
   

zero  mean  and  unit  variance.  However, Wi * is  not  observed. 
What is observed is the following binary variable: 
 

(1, if the farmer adopts modern cassava production technologies  
 

 

0, otherwise (2) 
 

We can put these two equations (Equations 1 and 2) into 
observable form (Yi), which is written as thus: 
 

Yi = W*I > 0, otherwise, 0 (if a farmer adopt modern cassava 
technologies. 
 

The general univariate probit regression model is implicitly stated 
as:  

Yi = f(X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11) + U --------(1) 

Where   Yi  is  dichotomous choice  model,  which is  “1” if  farmer 

 
 

 

 

 

adopted at least 5 of modern cassava production technologies, 0 
otherwise 
 

X1 = age of the farmer (years), 
X2 = age of the farmer squared (years) 
X3 = level of education of farmer (years), 
X4 = gender  of  the  farmer  (dummy  1  if  male,  0 
otherwise)  
X5 = type of institution where credit is sourced (dummy 1 if informal, 
0 otherwise) 
X6 = number   of   extension   contacts   (continuous 
variable)   

X7 = family size (continuous variable), 
X8 = distance to informal financial institution (km), 
X9 = distance to formal financial institution (km), 
X10 = total income apart from credit available to the 
farmer   

X11 = amount of credit obtained from formal financial 
institutions  

X12 = amount of credit obtained from informal financial 
institutions,  

X13 = farming experience (years) 
X14 = farm size (hectares) 
X15= Accessibility (dummy 1 if 5 out of the eight technologies 
are available, 0 otherwise) 
U = error term 
 

For the effect of credit on adoption of modern cassava production 
technologies, the model is implicitly specified as follows 
 

Yi = f(X1, X2, X3, X4, X5) + U ------------------ (2) 
 

Where Yi is dichotomous choice model, which is “1” if farmer 
adopted modern cassava production technologies, 0 otherwise  
X1 = type  of  institution  where credit  is  sourced (dummy 1  if 
informal, 0 otherwise), 
X2 = distance to informal financial institution (km), 

 = distance to formal financial institution (km) 
X4 = amount of credit obtained from formal financial 
institutions  

X5 = amount of credit obtained from informal financial 
institutions  

U = error term 

 

RESULTS 

 

First, the level of knowledge of modern cassava 
production technologies is analyzed. This is presented in 
Table 1. The result shows that all the respondents are 
aware of fertilizer as one of cassava production 
technologies. This is closely followed by those that are 
aware that modern cassava cultivars is one of cassava 
production technologies. However, the knowledge of 
tractor and appropriate tillage practices as cassava 
production technologies were fairly known by 
respondents. The use of insecticides and herbicides are 
the least known cassava production technologies. These 
results show that more sophisticated production 
technologies are less known to farmers. This has 
implication in the terms of usage of modern cassava 
production technologies.  

Apart from  the knowledge, the data was also analyzed 

(1) X3 



 
 
 

 
Table 2. Distribution of respondents according to use of modern cassava production technologies.  

 
Technologies Frequency Percentage 

Hybrid cassava stakes 80 44 

Appropriate planting date 60 33 

Tillage practices 63 35 

Use of herbicides 3 2 

Use of insecticides 3 2 

Use of inorganic fertilizer 120 67 

Use of tractor 1 0 

Appropriate spacing 43 24 
 

Note multiple responses were recorded. Source: Field survey, 2005. 
 

 
Table 3. Distribution of respondents according to reasons for difficulty in adoption of modern 
cassava production technologies.  

 
Reasons Frequency Percentage 

Not available 90 50 

High cost 150 83 

Farm size 87 48 

Harmful 30 17 

Lack of knowledge 67 37 
 

Note multiple responses were recorded. Source: Field survey, 2005. 
 
 

 

based on the level of usage of these production 
technologies. This result is presented in Table 2.  

Table 2 shows generally, that 55% of respondents use 
modern cassava production technologies. Specifically, 
most of the respondents use fertilizer and modern 
cassava cultivars. However, almost no respondent use 
tractor and insecticides in cassava production probably 
because of land tenure arrangement inherent in study 
area where a farmer owns small and scattered pieces of 
land usually less than 0.5 ha. In addition, about 2% of the 
respondents use herbicides in their cassava production 
because of the tenure and cost of input, which is usually 
high per unit output. The overall analysis of this table 
shows that farmers use less sophisticated and cheap 
cassava production technologies because of land tenure 
system in the area, which do not usually encourage 
mechanization.  

The reasons for difficulty in adoption of cassava 
production technologies were also analyzed. The result is 
presented in Table 3. The table shows that 83% of the 
respondent attributed their low adoption to high cost of 
technologies. This is closely followed by those that 
attributed the low usage of technologies to non-
availability of the technologies. The level of use of these 
two factors shows that production credit and accessibility 
are important factors in adopting particular technologies. 
However, fairly less than 50% (48%) of respondent 

 
 
 

 

attributed their low adoption to farm size.  
Table 4 shows the relationship between credit and 

adoption of modern cassava production technologies in 
descriptive terms. The table shows that there is a positive 
relationship between credit obtained and adoption of 
cassava production technologies. For instance, rural 
farmers who are able to secure more than N50000 credit 
were able to adopt 74% of modern cassava production 
technologies. In addition, farmers who secure less than 
N10000 credit from financial institutions are able to adopt 
15% of the recommended technologies.  

Table 5 presents the univariate probit regression 
estimates of probability of farmer adopting modern 
cassava production technologies or not. The dependent 
and independent variables are already defined in 
methodology. The general result shows that a number of 
variables determine adoption of modern cassava pro-
duction technologies in the study area. These variables 
vary in magnitude as revealed by marginal effect results. 
Generally, the model is significant as explained by Chi-
square and loglikelihood ratio. The result shows that age, 
education, access to informal credit institution, extension 
contact, amount of credit from informal credit institutions 
and access to technology were positively associated with 
adoption in cassava production. The marginal effect 
shows a fairly strong effect of this variable on adoption of 
modern cassava production technologies. 



 
 
 

 
Table 4. Distribution of technology adopted and credit obtained.  

 
Credit obtained Frequency Percentage 

<10000 14 15% 

10001 – 20000 26 27 

20001 – 30000 38 40 

30001 – 40000 55 58 

40001 – 50000 58 62 

>50000 70 74 
 

Note multiple responses were recorded. Source: Field Survey, 2005. 
 
 

 
Table 5. Parameter estimation of univariate probit regression for the determinants of adoption of 
modern cassava production technologies.  

 
Variables Coefficient t-cal Marginal effects 

X1 *  .318 19.38 .098 

X2 *  -0.201 -16.38 .041 

X3 *  0.010 3.99 0.071 

X4   -0.067 -1.03 .008 

X5 *  0.169 4.92 .183 

X6 *  0.104 2.00 .036 

X7   0.096 1.82 .005 

X8   0.016 1.62 .008 

X9   0.072 1.72 .029 

X10 * 0.308 6.57 .113 

X11  0.053 1.80 .010 

X12 * 0.154 10.24 .261 

X13  0.051 1.54 .005 

X14  0.024 1.09 0.014 

X15 * 0.189 5.10 0.093 

Chi-square
*
 245.08   

Log-likelihood ratio 1721.35   

Pseduo R
2
 0.06   

 
Values asterisk are significant at 5% probability level. Computed from Field survey, 2005. 

 
 

 

The econometric model used to specifically analyze the 
effect of credit variables on adoption of modern cassava 
production technologies shows that type of institution 
dictates the probability of adopting cassava production 
technologies or not (Table 6).  

The result shows that there is a more likelihood of 
adopting modern cassava production technologies when 
a farmer obtain loan from the informal financial 
institutions than formal institution. Further analysis of the 
result shows that access to informal financial institution is 
negatively related to adoption of cassava modern 
production technologies. It should also be noted that 
credit from informal financial institutions has strong effect 
on probability of adopting modern cassava technologies 
or not. The result of marginal effect analysis of this 

 
 
 

 

variable shows that a one naira increase in the amount of 
credit from the informal financial institutions increases the 
probability of adopting modern cassava technologies by 
38%. 
 

 

DISCUSSION 

 

The discussion of the result is presented in this section. 
Fairly less than 50% (48%) of respondent attributed their 
low adoption to farm size. This is not surprise because in 
most rural communities, fragmented land tenure system 
is practiced. This system does not encourage mechani-
zation or use of most modern cassava production 
technologies because of higher production cost per unit 



 
 
 

 
Table 6. Parameter estimation of univariate probit regression for relationship between 
credit and adoption of modern cassava production technologies.  

 
 Variables Coefficient t-cal Marginal effects 

 X1 * .041 14.75 .296 

 X2 * -.058 -3.41 .093 

 X3  -.020 -0.44 .024 

 X4  0.213 1.78 .073 

 X5 * 0.134 29.76 .381 

 Chi-square
*
 376.23   

 Loglikelihood ratio 1938.99   

 Pseduo R
2
 0.15   

 
Values asterisk are significant at 5% probability level. Computed from Field Survey, 2005. 

 
 

 

output. This also shows that rural farmers are always 
willing to adopt technologies when they are sure that 
benefit outweighs the cost, which is a rational behaviour 
in economic sense. The result also shows that in addition, 
farmers who secure less than N10000 credit from 
financial institutions are able to adopt 15% of the 
recommended technologies. This is not surprising 
because rural farmers are not able to adopt most modern 
production technologies because they are relatively poor. 
Therefore, for them to adopt most technologies they must 
require external funding, which could come in form of 
subsidy and credit. This finding is supported by various 
researchers on credit and adoption technologies (Barham 
and Boucher, 1994; Feder et al., 1990; Meloria, 2003).  

The econometric result shows that some variables have 
significant impact with varied strength on the technology 
adoption. Specifically, the marginal effect shows that a 
unit increase in age will result 10% increase in adoption. 
The negative age square coefficient for farmer suggests, 
however, that the inflow into non-adoption becomes 
weaker in the younger farmers. The result also suggests 
that educated farmers are more likely to adopt 
technologies than illiterate farmers. However, the 
marginal effect is not very strong (7%). This result shows 
that education is an important variable in adoption. 
Similar results have been found elsewhere (Adesina and 
Forson, 1995; Ersado et al., 2004). The result also shows 
that male farmers are more likely to adopt cassava 
production technologies than female farmers. This result 
is not surprise because studies have shown that male 
have more asset than female in rural areas, which has 
shown to have strong correlation with adoption of 
technologies (Okpukpara, 2006). The type of institution 
patronized also dictates the adoption of modern cassava 
production technologies. The result shows that farmers 
that obtain their credit from informal financial institution 
are more likely to adopt modern cassava production 
technologies than those that got theirs from formal 
financial institution. This is surprising 

 
 
 

 

because most studies have attributed adoption to 
educated farmers who always patronize formal financial 
institutions. However, it could mean that informal financial 
institutions are more accessible in rural areas, which 
make them more responsive to farmer’s demand for 
credit. Nevertheless, the effect of this variable on 
adoption is strong (18%), as explained by marginal effect 
analysis. The number of extension contact is also 
significant in dictating adoption of cassava production 
technologies. The result also shows that farmers are 
more likely to adopt a technology if the number of 
extension contacts is increased. Income of the farmer 
also is an important variable in explaining adoption in 
cassava production. The effect is positive and strong. 
The result is not surprising because as the farmer’s 
income increases, there is more opportunity for the 
farmer to afford the cost of modern production 
technologies. Similar result has been found elsewhere 
(Zeller et al., 1997).  

The result also shows that amount of credit obtained 
from informal financial institution are more likely to 
increase adopt modern cassava production technologies. 
This is not surprising because most studies have noted 
the importance of informal credit institutions in rural 
communities in developing countries (Okpukpara, 2005). 
In view of popularity of this type of institutions, which give 
farmers more access to their credit than other form of 
financial institutions, most farmers obtain credit from 
these institutions though at a very high rate of interest. In 
addition, credits that are mostly given by these 
institutions are small; yet, farmers prefer it. The effect of 
this variable on adoption is strong (26%). Accessibility in 
terms of availability also increases the adoption of 
cassava production technologies. This not surprising 
because it is possible that farmers will have the money to 
buy modern cassava input but when it is not available in 
the area, farmers will be handicapped in procuring the 
input. The marginal effect of this variable posit that if 
farmers modern cassava production input is available to 



 
 
 

 

farmer, the adoption of technologies is likely to be (9%). 
The overall analysis of this econometric result shows, 
farmer’s age, education, gender, type of institution, 
number of extension contacts, income of the farmer, 
amount of credit from informal financial institutions and 
accessibility are important variables that should be 
considered in promoting adoption of cassava production 
technologies in rural areas. However, emphasis should 
be on age of the farmer, type of institution patronized, 
income apart from credit, amount of credit from informal 
financial institutions and accessibility of the modern input. 
As these variable shows relatively large effect on 
adoption of modern cassava production technologies.  

The econometric result that dealt specifically on 
influence of credit variables on adoption shows that the 
number of variables were significant. The result shows 
that there is a more likelihood of adopting modern 
cassava production technologies when a farmer obtain 
loan from the informal financial institutions than formal 
institution. This indicates that informal financial 
institutions are the most accessible institution for 
financing rural farming.  

Further analysis of the result shows that access to 
informal financial institution is negatively related to 
adoption of cassava modern production technologies. 
This further strengthened the importance of informal 
financial institution in financing adoption of technologies 
in rural areas. It should also be noted that credit from 
informal financial institutions has strong effect on 
probability of adopting modern cassava technologies or 
not. The result of marginal effect analysis of this variable 
shows that a one naira increase in the amount of credit 
from the informal financial institutions increases the 
probability of adopting modern cassava technologies by 
38%. This is not surprise because farmers access to 
production inputs like improved varieties of cassava 
cultivars, herbicides and farming tools and implements 
depends on their access to credit. A number of countries 
in the region practice some sort of rural credit scheme to 
assist smallholder farmers improve their agriculture. In 
most cases the banks handling the credit fund are found 
in the city and larger towns, away from the farming 
community. Accessing such credit for agricultural 
purposes has proved difficult for small-holder farmers. 
Worse still, formal banking institutions also demand 
collateral as a prerequisite of giving loan to rural farmers. 
This collateral is often in form of land, house or title to 
some immovable assets.  

Smallholder farms in many countries in the region often 
cannot afford any of the above requirements. The 
situation is often more difficult when it comes to the 
women, who in most countries, have no rights to 
ownership of expensive property including housing and 
land. Therefore, these farmers seek credit from other 
financial institution that recognizes and able to give them 
credit considering their own peculiar circumstances. That 

 
 
 
 

 

is the reason for the high patronage of informal financial 
institutions. Therefore, failure to access soft loan and 
credit for agricultural activities and inputs has a severe 
bearing on technologies adoption. 
 

 

Conclusion 

 

It has been established that credit along with other socio-
economic variables are important in adoption of 
technologies. Therefore, a number of recommendations 
are made based on the findings. These 
recommendations are: 

 

1. Since education is important in adoption of modern 
cassava technologies, effort should be made by 
governmental and non-governmental agencies as well as 
other stakeholders in rural farming to organize and 
operationalize adult literacy campaign. This is necessary 
because most farmers in rural areas are illiterate which 
makes it difficult for them to adopt cassava production 
technologies. It also important to workout a suitable 
schedule for the adult education programme for the rural 
farmers, which will ensure non-interference with their 
normal farming activities timetable.  
2. It is also important to address the problem of lack of 
extension agent in the rural areas. Government should 
provide additional incentives for extension agents that 
agree to work with rural farmers. This will increase their 
activities in the rural areas. This also implies, for an 
effective information communication, the relationship 
between farmers and extension agents must be 
improved.  
3. Income of the farmer is also important variable. It will 
be the best technologies adoption strategies to give 
farmers more non-farm income opportunities to increase 
their level of technologies adoption. In addition, 
government should subsidize the prices of major cassava 
modern technologies input as strategy of increasing their 
value of income.  
4. Credit appears to have strong effect on probability of 
adopting modern cassava technologies especially credit 
from informal financial institutions. Therefore, government 
should strengthen the financial base of informal 
institutions by providing credit subsidies to them. This 
effort is aimed at encouraging them to continue to offer 
credit delivery to rural farmers. In addition, formal 
institutions should not only be encouraged to give credit 
to farmers but also “force” them to give soft loan to 
farmers. Effort should also be made by stakeholders in 
rural credit schemes to increase the establishment of 
informal institutions in the rural areas because it appears 
to be the major access to farm credit. In addition, the 
developments of rural financial institutions, such as the 
development of rural banking, are appropriate to 
encourage not only access to credit at reasonable terms 



 
 
 

 

but also savings, which will enhance access to credit by 
rural farmers.  
5. The issue of access to modern cassava technologies 
in terms of availability should also be tackled by the way 
of providing these inputs where it can be purchased. This 
can be done through providing these inputs in informal 
institutions such as cooperative societies, women and 
men organizations, age grades and others informal 
organizations in rural areas that are involved in not only 
extending credit but also other groups that are well known 
and has strong influence in rural communities.  
6. Government should encourage communities to 
liberalize ownership of land for those that are willing to 
farm. This will discourage traditional land tenure 
arrangement, which usually does not allow 
mechanization. 
 

 
REFERENCES 
 
Adesina AA, Forson JB (1995). Farmers' perceptions and adoption of 

new agricultural technology: Evidence from analysis in Burkina Faso 
and Guinea, West Africa. Agric. Econ., pp. 13-19.  

Ali NC (2005). Profitability analysis of cassava processing in Igbo-Eze 
South Local Government Area of Enugu State. An Unpublished 
B.Agric project, Dept of Agricultural Economics, University of Nigeria, 
Nsukka.  

Aliou D, Manfred Z, Manohar S (2000). “Empirical measurements of 
households' access to credit and credit constraints in developing 
countries: methodological issues and evidence” FCND DP No. 90.  

Asadu PO (2004). “Presidential Initiative on Cassava Production and 
Export-Genesis and Scope”. Being a Paper presented at conference 
organized by National Cereal Research Institute held at Moore 

Plantation Ibadan, Oyo, Nigeria from 24
th

 April – 27
th

 April, 2004.  
Baidu-Forson J (1999). “Factors influencing adoption of land-enhancing 

technology in the Sahel: Lessons from a case study in Niger”. Agric . 
Econ., 20: 231-239.  

Barham B, Boucher S (1994). Credit constraints, credit unions, and 
small-scale producers in Guatemala. Mimeo.  

Delgado C (1995). Africa’s changing agricultural development 
strategies: Past and present paradigms as a guide to the future. 2020 
Vision Food, Agriculture, and the Environment Discussion Paper 3. 
Washington, D.C.: International Food Policy Research Institute.  

Ersado L, Amacher G, Alwang J (2004). “Productivity and land-
enhancing technologies in northern Ethiopia: Health, public 
investments, and sequential adoption”, Am. J. Agric. Econs., 86(2): 
321-331. 

 
 
 
 

 
Feder G, Lau LJ, Lin JY, Luo X (1990). The relationship between credit 

and productivity in Chinese agriculture: A microeconomic model of 
disequilibrium. Am. J. Agric Econ., 72(5): 1151–1157.  

Fresco P (1993). The dynamics of cassava in Africa: An outline of 
research issue COSCA working paper no. 9.  

Meloria M, Meschi (2003). “Technology Adoption in the Presence of 
Constraints: the Case of Fertilizer Demand in Ethiopia” Rev. Dev. 
Econ., 7: 58-70.  

Nweke FI, Spencer DSC, Lyman JK (2002). The Cassava 
Transformation Africa’s Best-Keep Secret, East Lansing: Michigan 
State University Press.  

Ogboso N (2005). Effect of Improved Technology on Cassava 
Production in Bende Local Government Area of Abia State. An 
Unpublished B.Agric project, Dept. Agric. Econ, Univer. Niger. 
Nsukka. 

Okpukpara BC (2005). Determinants of Choice of Financial Institutions 
Among Rural Savers: Implication for a Sustainable Rural Financial 

Savings Mobilization for Agricultural Development: Proceedings 19
th

 
Annual Conference of Farm Management Association of Nigeria 

(Faman) Held At Delta State University Asaba on 18
th

 – 20
th

 
October, pp. 309-315.  

Okpukpara BC (2006). Determinants and Strategies for Rural Financial 
Savings Mobilization by Female-Headed Households in Anambra 
State, Niger. J. Rural Dev., 25(3): 345-361.  

Omonona BT (2009). Efficiency of resource - use in cassava production 
in Kogi State, Nigeria: implications for food security and 
environmental degradation. Retrieved on 27th April, 2009 from 
http://www.cababstractsplus.org/abstracts/SearchResults.  

Otoo JA (1994). Rapid multiplication of Cassava, IITA Training Guide 
No. 51. IITA, Ibadan Nigeria.  

Oyegbami T, Oboh G, Omueti O (2010) Cassava processors 
awareness of occupation and environmental hazards associated with 
cassava processing in South Western Nigeria Afr. J. Food. Agric. 
Nutr. Dev., 10(1): 1982-2000.  

Polson RA, Spenser DSC (1990). “The technology adoption process in 
subsistence agriculture: The case of cassava in south-western 
Nigeria”, Agric. Syst., 36(1): 65-78.  

Teklewold H, Dadi L, Yami A, Dana N (2006). “Determinants of 
adoption of poultry technology: a double-hurdle approach” Livestock 
Research for Rural Development 18(3): 2006.  

Zeller M, Schrieder G, von Braun J, Heidhues F (1997). Rural finance 
for food security for the poor: Implications for research and policy. 
Food Policy Review No. 4. Washington, D.C.: International Food 
Policy Research Institute. 

http://ideas.repec.org/s/bla/rdevec.html
http://ideas.repec.org/s/bla/rdevec.html
http://www.cababstractsplus.org/abstracts/SearchResults
http://www.cipav.org.co/lrrd/lrrd18/3/cont1803.htm
http://www.cipav.org.co/lrrd/lrrd18/3/cont1803.htm
http://www.cipav.org.co/lrrd/lrrd18/3/cont1803.htm
http://www.cipav.org.co/lrrd/lrrd18/3/cont1803.htm

