Drivers of Improved Cassava Variety Adoption among Farmers in Oyo State, Nigeria

Drivers of Improved Cassava Variety Adoption among Farmers in Oyo State, Nigeria
AJAERD
Drivers of Improved Cassava Variety Adoption among Farmers
in Oyo State, Nigeria
*1Obi-Egbedi Ogheneruemu and 2Olabamire Oluseun
Department of Agricultural Economics, University of Ibadan, Nigeria
Low cassava productivity in Nigeria has been linked to low adoption of modern technologies
amongst farmers, creating a large gap between the current and the potential yield of cassava.
Therefore, this study examined the level of adoption of improved cassava variety (TME 419) and
its drivers among cassava farmers in Oyo state, Nigeria. Data collected from 236 cassava farmers
with the aid of structured questionnaires were analyzed using descriptive statistics, adoption
index and logit regression model. Results showed that cassava farmers in Oyo state were 49 years
of age with farming experience of 21 years and farm size of 4 ha. About 69% of surveyed farmers
adopted the improved cassava variety while the adoption coefficient was 0.66. The likelihood of
adopting improved cassava varieties was significantly influenced by education, household size,
primary occupation, farming experience, farm size, land ownership and age. Therefore, increasing
the years of farmers’ education, farm size, ownership of land, entry of younger farmers, household
size and non-farm occupation will increase the likelihood of adopting improved cassava variety
among farmers.
Keywords: Improved varieties, technology adoption, adopters and non-adopters, TME 419, cassava hybrids.
INTRODUCTION
Africa produces over 54% of the world’s cassava and
Nigeria is the largest producer, leading with a production of
59.4 million metric tons in 2017 (Food and Agriculture
Organization - FAO, 2018). The Democratic Republic of
Congo (DRC), Thailand, Indonesia and Brazil follow
respectively, as the second to fifth largest cassava
producing nations globally with 31.6, 31.0, 19.0 and 18.9
million tons, respectively (see Figure 1). Among Africa’s
largest producers, after Nigeria and the DRC; Ghana,
Cameroon and Sierra Leone follow respectively, as the
third to fifth largest cassava producing nations in the
continent (FAO, 2018). In terms of area harvested of
cassava, Nigeria, the DRC and Thailand maintain the
position of the top three nations of the world with 6.8, 3.9
and 1.34 million ha respectively, while Brazil and Uganda
take the fourth and fifth positions with 1.31 and 1.19 million
ha, respectively (FAO, 2018). In Nigeria, cassava
production occurs mainly in 24 out of the 36 states of the
country with the North Central zone being the largest
producing zone followed by South-South and South West
zones. The South East, North West and North East follow
as the fourth to sixth producing zones in the country
(International Institute for Tropical Agriculture – IITA, 2012).
Average yield per hectare over the past 40 years stands at
about 10.2 tons (FAO, 2018). Moreover, cassava is a staple
of choice across cultures and social divides in Nigerian
households. Almost half of the quantity of tuber produced
is consumed locally as traditional meals (IITA, 2017). Apart
from its importance to the Nigerian diet, cassava is also an
important contributor to household income for producing
households as about 40% of total household income comes
from cassava (IITA, 2017). On the whole, it is the most
important crop by production and the second most
important crop by consumption, after rice (IITA, 2012).
*Corresponding Author: Obi-Egbedi Ogheneruemu,
Department of Agricultural Economics, University of
Ibadan, Nigeria.
E-mail: gheneobi@gmail.com
Research Article
Vol. 6(1), pp. 726-733, March, 2020. © www.premierpublishers.org, ISSN: 2167-0477
Journal of Agricultural Economics and Rural Development

Obi-Egbedi and Olabamire 727
Figure 1: Cassava production and area harvested of major
producing countries in 2017. Source: FAO (2018).
Despite continuous agricultural growth in Nigeria overtime,
land area expansion rather than increase in land
productivity accounts for much of the growth. Nigeria’s yield
levels have continued to lag behind other leading cassava
producers in the world. For instance, as revealed in Figure
2, Nigeria’s yield in 2017 amounted to only 8.8 MT/ha
(metric tons per hectare) which is very low compared to that
of Indonesia and Thailand which were 24.4 and 23.1
MT/ha, respectively in the same year (FAO, 2018). In terms
of area harvested, Indonesia and Thailand only cultivate
about 0.8 and 1.3 million ha, respectively compared to
Nigeria’s area harvested of 6.8 million ha (see Figure 1).
The high yields of Indonesia and Thailand are mostly due
to technology adoption which the green revolution of Asian
countries helped to achieve (Hossain and Narciso, 2005).
Improved varieties in Nigeria could yield as much as 67
tons/ha but the low yielding traditional varieties are still
preferred by many farmers (IITA, 2012). Therefore, there
exists a large gap between the current yield of cassava and
the potential yield per hectare. The country’s low cassava
yield has implications for food productivity, food security
and poverty considering the importance of cassava in the
nation’s diet. Food productivity is conditioned on raising
land productivity and not merely expanding land area.
Further, population has continued to increase coupled with
other environmental challenges including urbanization and
climate change which result in the decline of new cultivable
areas. Hence, the need for greater emphasis on
productivity growth.
Figure 2: Cassava yields of major producing countries in
2017. Source: FAO (2018).
One major pathway to achieving greater productivity growth
in cassava production lies in the adoption of improved
varieties by the farmers (IITA, 2012). Adoption of
technological innovation is however, considered low among
many small scale farmers in Nigeria as well as in other
developing countries (Feder et al. 1985; Ogunlana, 2004;
Asuming-Brempong et al. 2016). Farmers’ awareness of
the economic incentives accruable from a new technology
is crucial to the adoption process (Aromolaran et al, 2017).
The decision of farmers to either replace old/traditional
varieties or to supplement their stock of planting materials
with new improved varieties, usually follows awareness of
benefits. The decision to adopt precedes actual adoption
while the level of adoption can be inferred in several ways
one of which is from the actual hectare cultivation of
improved cassava varieties versus the local/ traditional
varieties (Asfaw et al, 2010). Improved cassava varieties,
also known as cassava hybrids, have a number of
economic benefits above the traditional varieties. For
instance, they have better sequestration power for soil
nutrients than the local/traditional varieties. Although, they
need fertilizer and irrigation in case of drought for optimum
yield, improved cassava varieties can survive, perform and
give higher yields than the traditional varieties when grown
under the same conditions or in the absence of
accompanying inputs (Bentley et al, 2018). Improved
varieties of cassava have potential yields as high as 67
tons/ha whereas, local varieties usually do not yield more
than 11 tons/ha (IITA, 2017). Given the large gap between
the current yield and the potential yield of cassava in
Nigeria, and the increasing demand for industrial purposes
and trade, it is apparent that adoption of improved
technology is required to increase yields beyond what the
commonly cultivated traditional varieties can give.
Consequently, the determinants of farmers’ adoption are
important for policy efforts at encouraging improved variety
adoption among farmers. Several studies have assessed
the determinants of adoption of improved varieties for
cereals, oil crops and a number of other crops and they
include: farmers’ sex, age, education, experience,
membership of association and access to credit and
extension services (Bayissa, 2014; Asfaw et al, 2011;
Solomon et al., 2014; Baruwa et al, 2015). Moreover,
Bamire, et al, (2002) found out that extension service is a
positive factor in promoting the uptake of new technologies
whereas, Donatha (2014) and and Kunzekweguta et al,
(2017) found that family labour, family dependency ratio,
number of livestock units owned by the farmer, distance to
the nearest market and ownership of an ox-drawn plough
influence adoption of improved technologies. There are
only few studies on adoption of improved cassava varieties.
For instance, Amao and Awoyemi (2008) and Abdoulaye et
al., (2012) found that household size, education, total
livestock unit owned, access to extension services,
participation in demonstration trials and crop yield were the
major factors responsible for the adoption of improved
cassava varieties. This study thus aimed to: assess the
level of adoption of improved cassava varieties in the study
area and determine the factors affecting the adoption of

J. Agric. Econ. Rural Devel. 728
improved cassava varieties in Nigeria, using Oyo state as
a case study. The specific improved variety assessed in this
study was TME 419.
There are currently over 50 improved cassava varieties
released by the National Root Crop Research Institute
(NRCRI), International Institute of Tropical Agriculture
(IITA) and other Agricultural Research institutions in
Nigeria. Some of these include NR8083, NR 208, CR41-10,
CR36-5, TME 419, TMS 1980581, TMS 1011412 and TMS
1070593. These improved varieties have a number of
desirable attributes over the traditional varieties planted by
many smallholder farmers. Some local cassava varieties in
the southern part of Nigeria include Oko iyawo, Onikoko,
Tomude, Nwugo, Nwaiwa, Ekpe and Okotorowa. The
improved cassava varieties are higher yielding, disease
resistant, often more effective in weed control and have
desirable starch content compared with the local varieties.
Moreover, Muhammed-Lawal et al (2012) in a comparative
study, found higher profitability level for improved cassava
varieties than local varieties.
The TME 419 is one of the many existing improved cassava
varieties which was introduced to Nigerian farmers by IITA
in 2005. It is an early maturing variety of nine months. It
suppresses weeds with its tall stem and branches that form
an umbrella shape. It has a high resistance to cassava
mosaic disease (CMD) and high dry matter of about 25%.
Compared to the local varieties which give between 2-10
tons/ha (Anikwe and Ikenganyia, 2018), it gives a yield of
over 25 tons/ha with 6 to 10 roots per stand which store well
in the soil. The produce can be pounded and has a high
starch content more than other varieties. It is good for food
and its low sugar content makes it a recommended meal
for diabetics. Howbeit, its height makes it susceptible to
falling during heavy breeze and this affects its growth
(Bentley et al., 2017). In addition, its high starch content
may not make it a favorite for garri processors, however;
TME 419 is the preferred variety in all the cassava-using
factories for other end products other than garri. This
includes products such as high quality cassava flour, edible
starch and odorless fufu which are in high demand on the
export market. Hence, the variety is becoming popular
among many farmers who are either at different stages in
the adoption process or have actually adopted the variety.
METHODS AND MATERIALS
This study was carried out in Oyo State, Southwestern
Nigeria. The state comprises of thirty-three Local
Government Areas (LGAs) with total land area of about
28,454 square kilometers and population of 5,591,589
(National Population Commission – NPC, 2006). Ibadan is
the capital of Oyo state and is the largest indigenous city in
West Africa. Farming is the main occupation of the people
and commonly cultivated crops include: cassava, maize
and vegetables, among others.
A multi-stage sampling technique of four stages was used
to select the cassava farmers. The first stage was the
random selection of three out of the five agro-ecological
zones namely: Ibadan, Okeogun and Oyo zones since
cassava is cultivated in all the zones. The second stage
involved the purposive selection of six LGAs from the agro-
ecological zones that are known for cassava production.
Three LGAs were selected out of eleven in Ibadan zone
(Lagelu, Akinyele and Ido), two LGAs out of ten in Okeogun
(Saki West and Saki East) and one local government out of
four local governments in Oyo zone (Afiijo), proportionate
to size. In the third stage, two wards were randomly
selected from each local government making a total of 12
wards. Finally, a total of 20 cassava farmers were randomly
selected from each ward in the fourth stage, making a total
of 240 respondents. However, only 236 were used for the
analysis due to incomplete responses from the surveyed
cassava farmers.
The analytical techniques used include; adoption index to
assess the adoption status of cassava farmers and logit
regression model to estimate the determinants of adoption
of the improved cassava variety in the study area.
Adoption was inferred using the actual hectare cultivation
to improved cassava varieties as against the local or
traditional varieties. Following Saka et al., (2009); Owusu
and Donkor (2012), the adoption index is given by:


=
=
= n
i
T
n
i
vi
v
C
C
0
0

Equation (1)
Where 𝛽𝑣 = the adoption level for cassava variety v,
𝐶𝑣𝑖= land area grown to cassava variety v by farmer i (i=1,
2………...n), and
𝐶 𝑇= total land area grown to cassava by farmer i
Logit regression model was employed to determine the
factors influencing the adoption of improved cassava
variety. The logit model is a probabilistic statistical
classification model which measures the relationship
between a categorical dependent variable and one or more
independent variables, which are usually (but not
necessarily) continuous, by using probability scores as the
predicted values of the dependent variable.
The functional form of the Logit model is given by Friendly
(1995) as:
𝜋 (𝑋𝑖𝑗) =
𝑒
𝛼+𝛽𝑋 𝑖𝑗
1+𝑒
𝛼+𝛽𝑋 𝑖𝑗
Equation (2)
This is transformed into the logistic regression model by a
linear function of explanatory variables:

Logit (𝜋𝑖𝑗) =𝛼 + 𝛽𝑋𝑖𝑗 Equation (3)
Where
𝜋𝑖 = adoption decision of farmer i assuming binary form of
(1) for adoption and (0) for non-adoption,
𝑋𝑖𝑗 = 𝑗𝑡ℎ predetermined (covariates) household or
technology attributes,
𝛼 = constant term of the regression equation to be
estimated, and
𝛽 = parameters to be estimated.
𝑋𝑖 = explanatory variables
Hence, following Gujarati and Porter (2009) and Faleye
(2013) the explanatory variables used are described on
Table 1.
Table 1: Description of variables specified in the model
Variable
number
Description Measurement Expected
signs
Π Adoption Dummy (Adoption – farmers who cultivate some proportion
of their land to the improved cassava variety = 1, No adoption
- farmers who do not cultivate the improved variety= 0)
X1 Sex Dummy (male = 1, female = 0) +/-
X2 Age Age of cassava farmers in years +/-
X3 Years of education Years of formal education +
X4 Farming experience Years in farming business +
X5 Membership of a farmers’ group Dummy (member = 1, not a member = 0) +
X6 Land ownership Dummy (own land = 1, do not own land = 0) +
X7 Household size Number of household members -
X8 Primary occupation Dummy (farming = 1, non farming = 0) +
X9 Agricultural training Dummy (training = 1, no training = 0) +
X10 Cassava farm size Measured in hectares +/-
X11 Access to extension services Dummy (access = 1, no access = 0) +
RESULTS AND DISCUSSION
The description of the cassava farmers’ socioeconomic
characteristics in relation to their adoption status are shown
on Table 2. The results reveal that the age of adopters (48
years) of the improved cassava variety was significantly
lower than that of the non-adopters (52 years), suggesting
that younger farmers adopt improved varieties compared to
older farmers. This contradicts Shuaibu (2018); Okoruwa et
al. (2015) who found that adopters were older than non-
adopters. It is expected that younger farmers would
embrace innovations more easily than older farmers due to
better education, access to information and being open to
new ideas (Rogers, 2003). The years of farming experience
for adopters (19.83 years) was also significantly lower than
that of non-adopters (24.09 years). This agrees with the
results of. Ojeleye (2018) that TME 419 adopters have a
mean farming experience of about 20 years. Similarly,
significant differences were found between the mean years
of formal education for both groups. Adopters had about 9
years of formal education compared to non-adopters with 5
years. This result is also expected as farmers with more
years of education are more likely to adopt improved
cassava varieties than the less educated ones. Conversely,
there was no significant difference between the household
size of adopters and non-adopters, with both groups having
a mean household size of about 6 persons. Similarly, there
was no significant difference in the farm sizes of the two
groups with adopters and non-adopters having a mean
farm size of about 4 ha.
With respect to the binary variables used in the study, the
results reveal that most cassava farmers were male, both
among adopters (70.99 percent) and non-adopters (81.08
percent) of improved cassava varieties. This indicates that
cassava farming was a male dominated activity in the study
area and agrees with Aromolaran et al, (2017) that male
farmers dominate cassava production. In the same vein,
majority of the cassava farmers were members of farmer
groups both among adopters (97.53 percent) and non-
adopters (100 percent) of improved cassava varieties. This
may have positive implications for adoption of cassava
hybrids in the study area. Further, 87.65 percent of the
improved cassava variety adopters and 66.22 percent of
the non-adopters own their farms, indicating land
ownership among most of the cassava farmers. This
agrees with Floro et al, (2017) that most farmers who adopt
improved varieties own their farms. Similarly, 78.40 percent
of adopters and 90.54 percent of non-adopters engage in
farming as their primary occupation, suggesting that they
may be well disposed to adopting improved cassava
varieties. With respect to agricultural training, all the
adopters of improved variety had received formal
agricultural training while 90.54 percent among the non-
adopters had received training. Finally, only 14.20 percent
of the adopters and 8.11 percent of the non-adopters had
access to extension services. This also agrees with Floro
et al, (2017) that most farmers do not access extension
services.
The adoption level of improved cassava varieties among
cassava farmers in the study area is shown on Table 3. The
result showed that a substantial proportion of cassava farm
land was cultivated to the improved variety with about 64
percent of the cassava farmers having an adoption
coefficient greater than 0.6. The mean adoption coefficient
of 0.66 indicates that majority of the farmers have adopted
the improved cassava variety by cultivating same on about
two third of their total farm land. Only about 31 percent of
the farmers did not adopt the improved variety.

Table 2: Description of Socioeconomic Variables by Adoption Status
Variables Adopters Non-adopters P values
Continuous variables Mean S.E Mean S.E
Age 47.51 7.21 52.33 8.20 0.0000***
Years of farming experience
Years of formal education
19.83
8.87
8.31
1.33
24.09
4.66
8.72
0.48
0.0040***
0.0001***
Household size 6.10 1.71 5.64 1.94 0.0650
Farm Size 4.44 1.35 3.86 1.25 0.1749
Binary variables Frequency Percentage Frequency Percentage
Sex
Male 115 70.99 60 81.08
Female 47 29.01 14 18.92
Membership of farmer group
Member 158 97.53 74 100
Non-member 4 2.47 -
Land ownership
Own land 142 87.65 49 66.22
Do not own land 20 12.35 25 33.78
Primary occupation
Farming 127 78.40 67 90.54
Non-farming 35 21.60 7 9.64
Agricultural training
Trained 162 100 67 90.54
Not trained 0 0 7 9.64
Access to extension services
Access 23 14.20 6 8.11
Do not access 139 85.80 68 91.89
*** represent 1% significant level
Source: Field survey (2017)
Table 3: Adoption index of improved cassava varieties
among farmers
Adoption coefficients Frequency (%)
0 73 (30.93)
0.1 – 0.60 13 (5.50)
0.61 – 1.0 150 (63.56)
Total
Mean
Standard Deviation
236 (100)
0.6645
0.4547
Source: Authors’ computation, 2017
The estimates of the logistic regression model for the
determinants of the likelihood of adoption of improved
cassava variety in the study area are presented on Table 4.
The log likelihood of -92.7927 and Chi-square value of
96.83, which is statistically significant at 5 percent, suggest
that the estimated model is highly significant. The Pseudo
R2 shows that 34 percent of the variation in farmers’
decision to adopt the improved cassava variety in the study
area was collectively explained by the independent
variables.
The result revealed that age, education, farming
experience, membership of farmer’s association, land
ownership, household size, primary occupation and farm
size, were significant in influencing the adoption of
improved cassava varieties. Age was negatively associated
with the likelihood of adopting improved cassava varieties,
and significant at 1 percent level. Hence, an increase in the
age of the farmer by one year, decreased the likelihood of
adopting improved cassava variety by 0.006 percent. This
is expected since technology adoption is easier for younger
farmers than older farmers, who are more risk-averse
(Pierpaolia et al., 2013; Rogers, 2003). Education, on the
other hand, positively influenced the likelihood of adopting
the improved cassava variety and significant at 5 percent
level. Hence, increasing the farmer’s education by an
additional year of schooling increased the likelihood of
adopting the improved variety by 0.06 percent. This is
expected since a literate farmer would appreciate the
benefits of adopting improved cassava varieties than an
illiterate farmer (Obayelu et al., 2017). Similarly, increasing
farming experience by 1 year increased the likelihood of
is expected as experienced farmers would understand the
need for increased productivity through adopting improved
varieties. Further, the estimated coefficient for membership
of farmer group was negatively associated with the
likelihood of adopting improved cassava variety implying
that not belonging to a farmers’ association increased the
likelihood of adoption by 0.013 percent. This contradicts the
findings of Asfaw et al, (2011) and Solomon et al. (2014).
This may be due to the fact that individual farmers in the
study area, usually make contacts with the research
institutions’ sales outlets to procure the hybrid stem
cuttings, not via the farmers groups. The estimated
coefficient of land ownership was positive and statistically
significant at 1 percent; implying that ownership of land

increased the likelihood of a farmer adopting improved
cassava variety by 0.124 percent. This also agrees with the
results of Floro et al. (2018) that ownership of land
increased the likelihood of a farmer adopting improved
cassava variety.
Household size positively influenced the likelihood of
adopting improved cassava variety and significant at 1
percent level. Hence, an additional member in the
household increased the likelihood of farmers’ adopting
improved cassava variety by 0.028 percent. This is
expected because a larger household needs more income
and may adopt improved varieties more readily due to its
potential of increased income arising from the increased
yield. The estimated coefficient for primary occupation
shows that having primary occupation other than farming,
was associated with the likelihood of adopting improved
cassava variety and significant at 10 percent level. This is
contrary to expectation and may be due to the fact that
people who are not primarily farmers but invest in cassava
farming, do so primarily for the profit incentive. Hence, they
may adopt improved cassava varieties more readily since it
has the potential of boosting their expected profits.
Similarly, farm size had a positive influence on the
likelihood of adoption and significant at 5 percent.
Increasing farm size by 1 ha will increase the likelihood of
is expected as farmers with larger farms will be more
disposed to cultivating a new variety on some parts of their
farmlands compared to farmers with very little farmland.
This agrees with the results of Floro et al. (2018) that
increasing farm size by will increase the likelihood of
adopting improved cassava variety.
Table 4: Determinants of improved cassava variety adoption
Variables Coefficient Standard Error Marginal Effect Standard Error
Constant 3.7539 2.0197
Sex 0.5148 0.4543 0.0028 0.2015
Age -0.1157*** 0.4632 -0.0064 0.4492
Education 1.1159** 0.5367 0.0617 4.3348
Farming experience 0.1514*** 0.0496 0.0084 0.5883
Membership of farmers group -0.2260*** 0.0533 -0.0125 0.8778
Land ownership 2.2797*** 1.4485 0.1237 7.5942
Household size 0.5085*** 0.1775- -0.0281 1.9751
Primary occupation -0.2088* 1.0215 -0.1156 8.9017
Trainings on improved practices 16.0839 1024.871 0.8904 5.7393
Farm size 0.4490** 0.1653 0.0248 1.7442
Access to extension agent -16.7721 1024.87 -0.9285 8.4114
Source: Author’s Computation 2017
*, ** and *** represent 10%, 5% and 1% significant level respectively
Number of observations = 236 Chi2 = 96.83
Log likelihood = -92.7927 Pseudo R2 = 0.3429
CONCLUSION
It was concluded that the level of adoption of improved
cassava variety in the study area was high. It was also
established in this study that years of formal education,
farm experience, land ownership, household size and farm
size positively influence the likelihood of adoption of
improved cassava varieties while age, membership of
farmer group and having farming as primary occupation
negatively influence the probability of adoption of improved
variety in Oyo state, Nigeria. Therefore, increasing the
years of farmers’ education, farm experience, ownership of
land, farm size and entry of younger farmers into cassava
production, will increase the likelihood of adopting improved
cassava variety.
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Accepted 28 February 2020
Citation: Obi-Egbedi O, Olabamire O (2020). Drivers of
Improved Cassava Variety Adoption among Farmers in
Oyo State, Nigeria. Journal of Agricultural Economics and
Rural Development, 6(1): 726-733.
Copyright: © 2020: Obi-Egbedi and Olabamire. This is an
open-access article distributed under the terms of the
Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any
medium, provided the original author and source are cited.

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