Available online at www.jpsscientificpublications.com
Life Science Archives (LSA)
ISSN: 2454-1354
Volume – 4; Issue - 3; Year – 2018; Page: 1352 – 1364
DOI: 10.22192/lsa.2018.4.3.2
©2018 Published by JPS Scientific Publications Ltd. All Rights Reserved
Research Article
SOCIO-ECONOMIC DETERMINANTS TO ADOPTION OF IMPROVED
CASSAVA VARIETIES BY AGRICULTURAL DEVELOPMENT
PROGRAMME (ADP) CONTACT FARMERS IN ANAMBRA STATE,
NIGERIA
C. I. Ezeano*1, S. I. Ume
2 and B. O. Gbughemobi
1,
1Department of Agricultural Economics and Extension, Nnamdi Azikiwe University Awka, Anambra State,
Nigeria. 2Department of Agricultural Extension and Management, Federal College of Agriculture, Ishiagu Ivo Local
Government Area of Ebonyi State, Nigeria.
Abstract
The socio-economic determinants adoption of improved cassava varieties by ADP farmers in
Anambra State, Nigeria was studied. Multistage random sampling technique was used to select 120 farmers
for detailed studies. A structured questionnaire was used to collect information from the respondents.
Percentage response, likert scale, profit model and gross margin analysis were used to analyze the objectives
of the study. The results show that most farmers were aged, educated and with moderate household size. The
following technologies were adopted by the farmers, as they had adoption score of above 3.0, include
planting geometry, fertilizer, tillage, quality of planting materials, fertilizer and ridging. The determinant
factors to the adoption of the improved cassava technologies were educational level, farm size and credit
access. Furthermore, improved cassava production is profitable in the study area with net farm income of
N433, 464. The major constraints to the adoption of the improved cassava production technologies were land
problems, extension services, labour cost and poor access to credit. The need to enhance farmers‟ access to
education, credit and labour saving devices were recommended.
Article History Received : 10.02.2018
Revised : 13.03.2018
Accepted: 18.04.2018
Key words: Socio - economic, Determinants,
Adoption, Improved Cassava varieties,
Agricultural Development Programme, Contact
Farmers, Anambra State and Nigeria.
1. Introduction
Agriculture plays an important role in
economic growth, enhancing food security,
poverty reduction and rural development (FAO,
2003; Onyenweaku et al., 2010; Ume, 2017).
Studies show that among the arrays of crops
grown in the tropics by small holder farmers that
*Corresponding author: Dr. C.I. Ezeano
constitute the bulk of farming population, cassava
stands prominent (Ofor, 1997; Amadi, 2003;
National Root Crop Research Institute [NRCRI],
2016). Cassava is a major source of calories for
more than 600 million people worldwide and
ranked the fourth most important staple in the
world after rice, wheat and maize (FAO, 2003).
Nigeria is the largest producer of cassava in the
C. I. Ezeano/Life Science Archives (LSA), Volume – 4, Issue – 2, 2018, Page – 1352 to 1364 1353
©2018 Published by JPS Scientific Publications Ltd. All Rights Reserved
world and over 85 % of Nigeria farming
populations cultivate it (Anyanwu, 2015).
In Nigeria, Tropical Manihot Series (TMS)
(TMS 30555, 30572 and 419) are the most popular
improved varieties cultivated by the farmers in
South east Agro ecological zone of the country, as
replacement to most of their local best varieties
that are fast deteriorating in terms of yield
(NRCRI, 2016). However, in recent times,
literatures show that these TMS varieties have
broken down in terms of yield and disease and
pest tolerance, thus resulting in farmers reaping
meager output after season of toiling (Bassey et
al., 2002). This scenario prompted development of
improved NR cassava varieties by National Root
Crop Research Institute (NRCRI) Umudike and
prominent among the new cassava varieties were
National Root (NR) 8081, 8082 (NRCRI, 2016).
These improved NR varieties have rareness of
being high yielding, tolerance to disease and pest,
acceptable food quality, high dry matter content
and high stem multiplication ratio (Bassey et al.,
2003). These varieties had been long disseminated
to the farmers through „contact farmers‟ of
Agricultural Development Programme (ADP) for
onward adoption in order to enhance their
production and productivity (Nkematu et al.,
2005; Ume et al., 2016). The „contact farmers‟ are
selected farmers from group of farmers by the
extension agent, since the change agent cannot
work effectively with all the farmers in the group.
The contact farmers will help in teaching other
farmers in the group (Nkematu et al., 2005). The
selection of the farmers are often based on among
others ability to represent proportionately the main
socioeconomic and farming conditions of their
groups, much farmer should be regarded as able
and worthy of imitation, must be hard working
and genuine farmers (Bassey et al., 2003; Chinaka
et al., 2007; Ichaobuo, 2015).
Adoption as reported by Akinloye (2014)
is the degree to which a new technology is used on
long run equilibrium when farmers have complete
information about the technology and its gains.
The important of technology adoption to
agricultural productivity cannot be
overemphasized. For instance, adoption of
technologies is capable of increasing higher
earnings and increase productivity, improved
nutritional status, lower staple food prices,
increase employment opportunities as well as
earning for land less labour (Adams, 1990; FAO,
2003). Furthermore, Gabriel (2013) reported that
adoption studies are important as it tends to access
impacts of agricultural research, aid to priority
setting for research and provide information for
policy reforms. Nevertheless, the adoption of any
innovation is a function of access to extension and
institutional information as related to physical
market, credit availability, improved farming
practices and crop varieties, climate change and
potential adaptive strategies (Hulugalle and Opara
- Nadi; 2001; Ume, et al; 2017).
The aforementioned improved cassava
technologies when adopted by farmers in
conjunction with associated production
technologies such as growing 20 x 25 cm of
cassava cutting, 1 m x 1 m spacing, chemical
fertilizer and pesticides are capable of improving
farmers‟ production and productivity. Therefore, it
is of paramount important to state that most
technologies adoption studies in the study area
have considered fertilizer (Onyenweaku et al.,
2010) and improved Tropical Manihot spp (TMS)
technologies (Ezeano et al., 2017) and with little
done on NR improved varieties which are key
“drivers” of cassava varieties in the study area. It
is based on this premise that this research was
conducted so as to enhance the farmers‟ farm
productivity and incomes for improved livelihood.
Although, the existent of adoption of these NR
varieties and associated production technologies in
the study area are not yet known, hence there is
need to fill this research gap. This is paramount
since the improved varieties constitute the very
basic of the farming systems and an integral part
of the farmers‟ livelihoods and food security. In
the course of the study, the following research
questions were addressed.
Were the personal and socioeconomic factors limited farmers‟ technology
adoption?
What were the levels of improved NR cassava technology adoption?
C. I. Ezeano/Life Science Archives (LSA), Volume – 4, Issue – 2, 2018, Page – 1352 to 1364 1354
©2018 Published by JPS Scientific Publications Ltd. All Rights Reserved
What were the determinant factors to adoption of the improved NR Cassava
production technologies?
What were the costs and returns of
improved cassava production?
What were the limitations to cassava farmers‟adoption of the technology?
Specifically, the objectives of the study are
to describe the socio-economic characteristics of
the cassava farmers, ascertain the level of adoption
of the improved cassava technologies by the
farmers, analyze the effect of the farmers socio-
economic characteristics on their adoption rate,
estimate the costs and returns of cassava
production the farmers and identify the constraints
to the adoption of improved cassava technology.
2. Materials and Methods
The study was carried out in Anambra
State. The Anambra State is one of the five states
of South East Agro ecological zone of Nigeria and
located between latitude 5038 'N and 6047 'E of
Equator and longitude 6036 'N and 7021 'E of
Greenwich Meridian. The state is bounded in the
east by Enugu State, in the West by Delta State, in
the South by Imo State and inthe North by Kogi
State. Anambra State has Awka as capital with
population figure of 4.184 million people (NPC,
2006). The state has annual rainfall range of 1600
mm – 1700 mm, which is distributed from
February to December. The state has mean
temperature of 27 o
C all through the year, but
highest from February to April (NRCRI, 2006).
Anambra State comprised of three agricultural
zones; Onitsha, Aguata, Otuocha and Ihiala. The
major food crops grown include cassava, yam,
cocoyam, maize, melon, rice, sweet potato,
vegetables and fruits. The animals reared their
including goat, sheep, pig, rabbit and poultry. The
non agricultural activities involved by Anambra
people are trading, vulcanizing, barbing, tailoring
and others.
Sampling Procedure and Sample Size; In
the first stage, three (3) agricultural zones of Abia
State were purposively selected because of large
number of extension agents in the zones that could
help in the dissemination of the technologies to the
farmers. The selected zones were Ohafia,
Umuahia and Aba zones. In the second stage, a
multistage random sampling technique was
employed in selecting four (4) blocks out of 6
from each zone. This brought to a total of twelve
(12) blocks. In the third stage, one (1) circle each
was selected from each block, making a total of 12
circles. Finally, ten (10) respondents were
randomly selected from each circle and this
brought to a total of 120 respondents for detailed
study. A structured questionnaire was used to
elicit information needed for the study.
Descriptive statistics such as percentage responses
was used to analyze the data and draw conclusion
on objectives 1 and 5. The extent of adoption by
the respondent was measured using the seven
likert scale (Unaware (0), aware (1), interest (2),
evaluation (3), trial (4), adoption (5), rejection (6).
To determine the mean of adoption level x= £x the
mean core x, of each item was computed by
multiplying the frequency of each response patten
with its appropriate normal value and dividing the
sum with the number of respondents to the items.
This can be summarized with the equation become
Χ= Ʃfn
……………………………………………………
……………… (1)
n
where X = mean score
Ʃ = summation
N = frequency
n = likert norminal value
X = 0+1+2+3+4+5+6 = 21 = 3
7
Probit model analysis used to achieve objectives 3.
Probit analysis is expressed as ;
Yi = Bxi +
Ui,…………………………………………………
…….(2)
where N (o,i), I – 1……n
y = 1 { y>0} =1 if y>0
0 otherwise
where
Y1 = farmers participating in off farm income (if
participate = 1, otherwise =0)
Xn = Independent variable
C. I. Ezeano/Life Science Archives (LSA), Volume – 4, Issue – 2, 2018, Page – 1352 to 1364 1355
©2018 Published by JPS Scientific Publications Ltd. All Rights Reserved
It can be implicitly stated as follows.
Y = f(x1 x2 x3 x4 x5 x6 x7 ---------xn +u)
………………………………………….(3)
Y= adoption index (adopted = 1, non adopted = 0)
Yd = Adoption rate in percentage
X1 = Age of farmers in years
X2 = Credit in Naira
X3 = Access to extension services (Access; 1;
otherwise,0)
X4 = farm size in Hectares
X5 = Number of years of farming experience
X6 = Level of formal education of farmers in years
X7 = House hold size and number of dependents
The objective 4, estimation of cost and
returns was determined using gross margin
analysis, which is the difference between the total
revenue (TR) and the total variable cost (TVC)
G.M. = TR –
TVC……………………………….………………
…… (4)
i.e. G.M =
m
ij
ii
n
xrQP11
11…………………………………
……….. (5)
The net farm income can be calculated by
gross margin less fixed input. The net farm
income can be expressed as thus:
NFI =
kxrQPm
ij
ii
n
11
11 …………………………
……………. (6)
Where: GM = Gross margin (N), NFI =
Net farm income (N), P1 = Market (unit) price of
output (N)
Q = Quantity of output (kg), ri = Unit price of the
variable input (kg), xi = quantity of the variable
input (kg),K = Annual fixed cost (depreciation)
(N)
i = 1 2 3 …….. n ; j = 1 2 3 …….. m
3. Results and Discussion
The Table - 1 indicated that youths
(farmers with age range of 15-45) dominated the
farming population and constituted about 45.8
percent of the total respondents.
The aged farmers, 45 - 60 years accounted
for 37.5 percent of the sampled farmers. Age is
vital in agricultural production especially
agricultural labour which is very intensive and
could be better accomplished by able-bodied and
energetic individuals (Gabriel, 2013).
Furthermore, Ume et al. (2017) reported that age
affects individuals flexible to decision making.
However, the relatively low participation of young
farmers as indicated in Table - 1 can be attested to
little regard for farming as a vocation in
preference to commercial motor cycle popularly
known as Okada in Nigeria and other menial jobs
that command high wage rate (Akinloye, 2014;
Ume et al., 2016).
In addition, Table - 1 shows that 52.0
percent of the total farmers studied were females,
while 48.0 percent were males. The domination of
female farmers in the study area could be attested
to the fact that cassava is regarded as female crop
in the study area and many cassava producing
areas in the country (Amadi, 2003). However, in
recent times, men are closing up the gap because
of important of cassava as household food security
and income generation source to cushion the effect
of recent ongoing economic depression in the
country (Howler, 2001; Ume et al., 2016).
Majority of the farmers interviewed (66.7
percent) had access to extension services, while
33.3 percent did not have access. Extension
according to literatures is the major medium
through which farmers could get access to recent
research findings (Hulugalle and Opara – Nadi,
2001; NCRCI, 2007). The poor access to
extension services could be linked to high
extension personnel – farmers‟ ratio in the state.
This scenario is common in most of the
developing countries of the world, thus, resulting
in wide spread of poverty and food insecurity
(Norman, 1999)
C. I. Ezeano/Life Science Archives (LSA), Volume – 4, Issue – 2, 2018, Page – 1352 to 1364 1356
©2018 Published by JPS Scientific Publications Ltd. All Rights Reserved
Table – 1: Distribution of Respondents According to Socio-economic characteristics
Variable Frequency Percentage
Gender
Male 58 48.0
Female 62 52.0
Age
15-30 20 16.6
31-45 55 45.6
46-60 45 37.5
Marital Status
0.5-1.5 60 50
1.5-2.5 40 33.3
2.5-3.5 20 16.6
No access 40 33.3
Level of education
No formal 30 25
FSLC 40 33.3
Primary
WAEC 25 20.8
Tertiary 25 20.8
Size of Household
1-3 26 21.6
4-6 60 50
7-9 30 25
10-12 41 3.3
Farming
Experience > 5
6 – 10 6 5
11 – 15 46 38.3
16-21 20 16.6
16 and above 48 40
Access to
Extension
Access 26 43.3
No access 34 56.7
Acess to climatic
inform.
Access 38 63
No access 22 36.6
Member. Of
Organ.
Members 42 70
Non members 18 30
Access to Credit
Access 38 63
Non Access 22 37
Source; Field Survey, 2017
C. I. Ezeano/Life Science Archives (LSA), Volume – 4, Issue – 2, 2018, Page – 1352 to 1364 1357
©2018 Published by JPS Scientific Publications Ltd. All Rights Reserved
The Table moreover indicated that most
sampled farmers (75 percent) were educated with
first school leaving certificate (FSLC) (33.3
percent) being the highest, while only 25 percent
had no formal education. According to
Ugwungwu (2008) and Ume et al. (2016)
education helps one to comprehend extension
guides and understand written messages on
innovation, hence facilitating technology adoption
for high yield to ensue.
The Table - 1 showed that the household
size between 4 - 6 member were reported by 50
percent of the total respondents and 25 percent had
7-9 household size, 21.6 percent had 1 - 3 persons
and 3.3 percent had 10 - 12 persons. Household
components are husband, wives, children,
grandchildren and extended families. Household
members, if of labour age, could help to overcome
labour limitations in adoption of technology that
are labour intensive (Ochiaka et al., 2015;
Ichaobuo, 2015). Besides, most (78.3 percent)
farmers had farming experience of above 10 years,
while only 21.7 percent had farming experience of
less than 10 years. Studies show that farmers with
many years of farming experiences could aid in
boosting crops production as they can easily
overcome the intricacies involved in cultivation of
such crop (Onyenweaku et al., 2010; Mbavai et
al., 2015). In addition, long years of farming
experience could help farmers in setting their
goals and efficient use of resources Mercer, 2014).
The Table - 1 also indicated that 50 percent
of the total respondents cultivated between 0.5 -
15 hectares, 33 percent cultivated between 1.5 -
2.5 hectares and the least, 16 6 percent cultivated
2.5 - 3.5 hectares. According to Ume et al. (2016)
and Hussien et al. (2015) farm size plays an
important role in farm success, as it reflects the
availability of capital, access to credit and even
good management ability. However, there is need
for urgent land reforms, policies and programmes
that would give farmers access to more land
holdings for increasing agricultural production
should be enacted.
The Table - 2 showed that those technologies
whose scores were above mean score of 3.0 were
accepted, while those less than 3.0, were rejected.
Table - 2: Level of Adoption of the Improved
Cassava Varieties Production Technologies
Improved Cassava Varieties
Technology
Mean
score
Decision
Pesticide application 2.0 Rejected
Timely weeding 2.87 Rejected
Pest and Disease control 2.08 Rejected
Plant geometry 3.52 Accepted
Quality of planting material 3.67 Accepted
Fertilizer application 3.0 Accepted
Timely harvesting 1.39 Rejected
Ridging 3.0 Accepted
Tillage 3.0 Accepted
Planting depth 2.0 Rejected
Source, Field Survey, 2016 The Table - 2 shows that plant geometry had
mean of 3.52 and hence accepted. Plant density
has effects on higher yields of smaller roots, a
greater labour requirement for weed control
(during the establishment phase of the crop) and
intense use of available planting material
(Anyanwu, 2015). Thus a plant density of 10 000
stands per hectare is often recommended for
cassava production planted at 1metre x 1 metre
(IITA, 2009). Cassava‟s flexible planting
schedule, wide interspacing and slow rate of
growth make it suitable for intercropping (Okigbo
et al., 1976, FAO, 2003). Nevertheless, planting
densities and spatial arrangements currently used
by farmers are determined by several factors,
included the availability of planting and land
preparation implements, the practice of
intercropping, weed incidence, water holding
capacity of the soil, and market considerations are
among the most important of these factors
(Lozano, 1986).
Furthermore, the quality of planting
material (mean of 3.67) was accepted. A good
cassava quality planting material is capable of
enhancing the root yield of cassava through
vigorous plant growth. In Nigeria, cassava
production has been characterized by dominant
use of poor-quality planting materials of disease-
prone local varieties with long maturation period
and low yield potentials of 9 - 12 tons/ha (Jirigi et
C. I. Ezeano/Life Science Archives (LSA), Volume – 4, Issue – 2, 2018, Page – 1352 to 1364 1358
©2018 Published by JPS Scientific Publications Ltd. All Rights Reserved
al., 2009). In addition, ridging technology (mean =
3.0) and therefore accepted. Ridging is energy
demanding activities and has received
comparatively little research attention, hence only
a few practical recommendations are available.
Ridging is beneficial in soils that are at risk of
flooding (IITA, 2009). Tillage was accepted since
it has mean value 3.0. Studies showed that the
performance of cassava under different tillage
systems is rather site specific. Although, literature
revealed that different tillage types such as no-
tillage, reduced tillage and conventional tillage
have been tested in different ecosystems with
variable results shown in terms of yield (NRCRI,
2016). Generally, good tillage necessary for
effective weed control than as a means to improve
the microenvironment for root bulking. This is
because studies revealed that acceptable yields are
obtained by small farmers using zero-tillage,
whilst no significant differences in yields have
been obtained between “conventional” tillage
(plough and harrow) and different forms of
reduced tillage (Trousthr, 2008). Deep soil
preparation does not result in better yields, except
under very specific circumstances. The most
common form of reduced tillage is the local
removal of soil around the area where the cassava
stake is to be planted. This practice, although
rather primitive is still widely used today in
different parts of the world (Okigbo et al., 1976).
Moreover, the fertilizer application had
mean of 3.0, hence accepted. Cassava thrives well
with little or no fertilization, but responds well to
fertilizer application in infertile soils (Bassey et
al., 2002). Generally, cassava responds to P
application in infertile oxisols, except in those
with high mycorrhizal populations, while N
response is found only in sandy soils that are low
in organic matter content (NRCRI, 2007). There is
also a marked positive response in root production
to Furthermore, applications of K helps to boost
cassava yield especially where cassava is grown
continuously in the same field for more than 2 – 3
years (NRCRI, 2006).
The coefficient of age of the farmer was
negative and significant at 5 % and 10 % levels of
probability for NR 8081 and NR 8082 respectively
as shown in Table - 3.
Table - 3: Determinant Factors to Technology
Adoption using Probit Model
Variable Parameter
NR 8081 NR 8082
Intercept 15.453(6.054)*** 7.564(4.098)***
Age f the Farmer 0.005(2.411)** 2.007(1.890)*
Access to credit 1.057(2.003)** 0.909(1.080)*
Access to
extension
services
0.607(2.019)** 2.004(2.033)**
Farming
experience
0.498(3.007)*** 1.2000)**
Level of formal
education
3.003(4.650)*** 0.9002(3.007)***
Farm size 0.632(2.009)** 1.213(2.860)**
Household size 0.042(2.008)** 1.442(2.802)**
*, ** and *** implies significance at 10%, 5% and 1%
respectively
Source; Field Survey, 2017
Nevertheless, the variable had inversely
relationship to the rate of adoption of NR 8081
cassava technologies, while positive to the NR
8082. The negative relationship could imply that
youthful farmers can adopt technologies easily
than older ones, as they (youthful farmers) are
more adventurous, motivated and adaptive
(Anyanwu, 2015). Furthermore, coefficient of
credit in contrary to apriori knowledge had inverse
relationship with the adoption of NR 8081 and NR
8082 cassava technologies and significant at 5 %
and 10 % risk levels respectively. This is not in
harmony with a priori expectation that the more
the volume of credit farmer has, the more
likelihood that technologies that involving extra
costs would be readily adopted. The negative sign identity of the coefficient could be attributed to
diversion of credit by some farmers to non-farm
activities (Obeta and Nwagbo, 1990). The work of
Onyenweaku et al. (2010) reported a positive
relationship between credit and technology
adoption. They opined that credit access aids in
the promotion of the adoption of risky
technologies, solving of liquidity constraints as
well as boosting the household risk bearing ability.
The coefficient of extension contact
positively influenced the extent of adoption of NR
C. I. Ezeano/Life Science Archives (LSA), Volume – 4, Issue – 2, 2018, Page – 1352 to 1364 1359
©2018 Published by JPS Scientific Publications Ltd. All Rights Reserved
8081 and 8082 technologies at 5 % significant
levels respectively in the study area. The
implication is that frequency of extension visits
for dissemination of information and advisory
services could encourage farmers to have
confidence to sustain the use of production
technology package (Iheke, 2010; Mercer, 2014).
The influence of extension contacts can counter
balance the negative effect of poor access to
formal education in the overall decision to adopt
certain technologies, hence creating better
awareness about the potential gains of improved
agricultural innovations (Chinaka et al., 2007).
This is in tune with Bassey et al. (2002) who
reported that increase in the number of extension
visits and services offered to farmers can
significantly enhance their decision making ability
for technology adoption.
Nevertheless, coefficient of the farming
experience was positively related to the dependent
variable for both NR 8081 and NR 8082 and
significant at 1 % and 5 % levels respectively.
Similar findings were reported by Jirigi (2010)
and Ichaobi (2015) that experience advances
farmers‟ skill in production which entails that a
more experienced farmer may have a lower level
of uncertainty about innovations performance and
able to evaluate the gains of technology being
considered. Also as expected, the coefficient of
levels of education had positive relationship to the
rate of adoption of improved NR 8082 and 8281
cassava technologies and significant at 1 % alpha
level respectively. Education according to Onunka
et al. (2017) influences the farmer‟s managerial
ability, skill and receptivity to technology
adoption. In the same vein, Iheke (2010) reported
that the level of educational attainment by farmer
could not only increase his farm productivity but
also enhance his ability to understand new
production technologies.
Moreover, the coefficient of farm size was
positively related to the adoption of 8081 and
8082 cassava technologies and significant at 5 %
alpha levels respectively in line to a priori
expectation as contain in Table - 3. Farmers with
large farm size can afford to devote part of their
farms for improved cassava production without
significantly affecting the total land left for the
production of the other staple food crops
compared to small land holders (Obinna, 2012).
This finding concurred with Gabrriel (2013), who
reported that lumpy technologies such as
mechanized equipment requires economic of size
of land to ensue profitability. Conversely, Howler
et al. (2000) opined that small size farm may
provide an incentive to adopt technology
especially in the case of input intensive
innovations such as a labour-intensive or lad-
solving technology (Green house technology and
Zero grazing).
Furthermore, the coefficient of the
household size was positively related to rate of
adoption of improved NR 8081 and NR 8082
cassava technologies and significant at 5 % alpha
levels respectively. The impact of household size
to agricultural production depends on the
magnitude, age structure and availability of farm
labour among members. For instance where
household‟ members are of productive age, they
will be proxy to family labour especially at the
peak of harvest (Okoye et al., 2009). As well,
family labour could help to generate income as
hired labour by many poor resource households
(Kainga et al., 2003). Conversely, large household
members could be a burden especially, where the
members are not of labour age and more of
dependent population (Onyenweaku et al., 2010).
The costs and return of cassava production is
shown in Table – 3.
C. I. Ezeano/Life Science Archives (LSA), Volume – 4, Issue – 2, 2018, Page – 1352 to 1364 1360
©2018 Published by JPS Scientific Publications Ltd. All Rights Reserved
Table - 4: Cost and Return of Cassava Production
Item Unit Quantity Price/unit Cost/value
Revenue
Tubers Kg 7000 100 700000
Sales of cassava stem Kg 100 750 75,000
Total Revenue 775,000
Total Physical input
stem cutting Bundle 50 800 40,000
Fertilizer Kg 8 8000 64000
Miscellaneous 20,000
Total 124,000
Clearing Md 12 1500 18,000
Mounding / ridging Md 20 3000 60000
Cutting of stem Md 1 1000 1000
Planting Md 6 1200 7200
Fertilizer application Md 8 1000 8000
Weeding Md 18 2500 45000
Harvesting Md 10 2000 20000
Bagging/Transportation 3,600
Total labour costs 162,800
Total variable costs - 286800
Gross margin (TR - TVC)
Depreciation of fixed assets
excluding land
Total cost (TVC+TFC)
Farm income (TR-TC)
Benefit cost ratio
488200
2,548
289348
484652
2.7
Source; Field Survey; 2017
The cost elements in cassava production
are cassava stem cuttings fertilizer and tools. No
attempt was made to value land of which minimal
or no rent is paid. The farm tools such as cutlasses
and hoes were depreciated. On cost of inputs, the
average quantity of cassava stem cutting per
hectare used was 50 bundles (at 50 sticks per
bundles costing N800 per bundle), totally N
40,000. In addition, eight (8) bags of fertilizer
(NPK) costing N 64,000 at N8, 000/bag was
applied to a hectare of cassava. The total cost of
physical inputs was N104,000. On labour cost,
hours worked by men women and children were
converted into a common frame following [27]. A
total number of 72 man-day was used to produce
one hectare of cassava. Wage rate varied with the
nature of the farm operations. Clearing attracted
N1,500 per man day, cutting of stems; N1000,
fertilizer application; N1,000 and weeding;
N2500. The total cost of labour was N162,800,
which constituted about 46 % of the total cost of
production. The high cost of total cost of cassava
production could be linked to non mechanization
of most cassava production activities in most
developing countries (Ofor, 1997). The Net Farm
Income (NFI) for cassava production in the study
area was N484, 652 with Benefit Cost Ratio
(BCR) is 1:1; 2.7 and Gross Margin was N488200
Table 4 indicated that 88.3 percent of total
sampled farmers complained about land problem
as a limitation to the improved cassava technology
adoption.
C. I. Ezeano/Life Science Archives (LSA), Volume – 4, Issue – 2, 2018, Page – 1352 to 1364 1361
©2018 Published by JPS Scientific Publications Ltd. All Rights Reserved
Table - 5: Distribution of Respondents
According To Constraints to Technology
Adoption
Constraint Frequency Percentage
Land problem 100 88.3
Cost of labour 80 66.7
High cost of fertilizer 69 57.5
Theft 25 20
Rodent attack 30 25
Pest attack 52 45
Pesticides 40 33.3
High cost of
herbicides
50 41.7
Poor access to
extension service
68 56.7
Sources: Field Survey, 2017.
*Multiple Responses
This could be due to land fragmentation
caused by land tenure system and government
taking over land particularly farming land for
industrial development. This is followed by high
cost of labour, which was encountered by 66.7 %
of the total respondents. The high cost of labour as
reported by Trousthr (2008) could be ascribed to
urban drift of able - bodied youths for white collar
job and the few among them that stays behind
charges high in order to keep afloat with the urban
counterparts. Furthermore, unavailability and
high cost of fertilizer was reported by 57.3 % of
the sampled farmers. This could be as result of
removal of fertilizer subsidy by Federal
Government of Nigeria as well as high exchange
rate of naira to dollar as result of the present
economic recession in the country (Ugwungwu,
2008; Ume et al., 2017). In addition, poor access
to extension service was reported by 56.7 % of the
sampled farmers. The findings of (FAO, 2008;
Ezeano et al., 2017) attributed the poor access to
extension services to wide ratio of extension
agents - farmers in many developing countries and
poor motivation of the change agents while
discharging their duties.
4. Conclusion and Recommendations
The following major conclusions were
drawn from the study.
The farmers studied were young, educated,
large household size, had access to extension
services and small farm size. Furthermore,
planting geometry, fertilizer, tillage, quality of
planting material, fertilizer and ridging were used
in determining the farmers‟ level of adoption of
the technologies. More so, level of education,
access to extension agent, farming experience and
household size were the determinant factors to the
adoption of the improved cassava varieties. In
addition, cassava production was profitable in the
study area apart from being constrained by factors
such as land, labour and poor extension services
problems.
Based on the findings, the following
recommendations were made.
Farm size was found to have positive
influence on technology adoption. It becomes
imperative that the Nigeria land use Act of 1990
be reviewed to eliminate difficulties associated
with land acquisition for agricultural purposes for
genuine farmers. This would facilitate agricultural
growth and development. There is need to enhance
farmers‟ adoptability through enhancing their
educational status through adult education,
conferences and workshops. Agricultural inputs
(improved cutting, fertilizer, chemicals, etc)
should be subsidized and made available to
farmers at affordable prices and at appropriate
time.
The coefficient of the years of farming
experience was found to be positive, therefore,
policies that will encourage experienced farmer to
remain in the cassava production should be
intensified and such policies, included provision
of improved input at subsidized rate to the
farmers.
High frequency of contact can be achieved
by either reducing the extension-farmer‟s ratio or
providing the extension agents with mobility and
other incentives. There is need to enhance high
frequency of farmers „contact with extension
services in order to enhance the former production
and productivity. This can be achieved by either
reducing the extension-farmers ratio or providing
the extension agents with mobility and other
C. I. Ezeano/Life Science Archives (LSA), Volume – 4, Issue – 2, 2018, Page – 1352 to 1364 1362
©2018 Published by JPS Scientific Publications Ltd. All Rights Reserved
incentives. On high cost of labour, there is need to
develop labour saving devices such as hand driven
plough and distributed to the farmers at affordable
prices.
5. References
1) Adams, M.E (1990) Agricultural Extension
in Developing Countries. Intermediate
tropical Agricultural series, Longman
Singapore, publishers Limited pp 14-28.
2) Anyanwu, K.C (2015) Factors affecting
the adoption of new technologies of
cassava in Imo State of Nigeria. Bulletin of
Agronomic Research of Benin., 36 - 39.
3) Amadi, T.K (2003) Constraints to small
holders cassava production and Processing.
47th
Annual Conference of Agricultural
society of Nigeria. Abuja 2015‟. Pp: 123 –
128.
4) Akinloye, C B.(2014) Household-level
determinants of adoption of improved
cassava production practices among
smallholder farmers in Western Nigeria.
Food Policy. 32: 515 - 536.
5) Bassey A.E.U, Akpani E.E and Asuquo
P.E (2002) on farm Evaluation of the
economics and acceptability of Newly
release cassava cultivars (NR. 8081 and
NR. 8082) in cassava, Cocoyam and
Telferia and Maize moisture in Akwa Ibom
State. Processing of the 16th
Annual Zonal
Research Extension, farmers input,
Linkage systems (REFILS) work shop
South East Zones of Nigeria 19-23rd
November, 2001.
6) Chinaka, C.C, Ogbokiri, L.C. and Chinaka
E.C. (2007), Adoption of Improved
Agricultural Technologies by Farmers in
Aba Agricultural Zone of Abia State.
Proceedings of the 41st conference of the
Agricultural Society of Nigeria, IAR/ABU
Zaria, Nigeria. pp. 531-534.
7) Ezeano C. I. Okeke, C C, Obiekwe, N J
and A. I. Onwusika, A I (2017); Adoption
and Profitability of Cassava in Enugu
South LocaL Government Area of Enugu
State, Nigeria. Indo – Asian Journal of
Multidisciplinary Research, 3(3): 1125 –
1134.
8) F.A.O (2003) Food agricultural
Organization of the United Nation. An
Assessment on the productivity of cassava
in Africa vol. 11, pp 17.9.
9) Gibreel, T.M., (2013). Crop
commercialization and adoption of gum-
arabic agroforestry and their effect on
farming system in western Sudan.
Agroforestry systems, 87 (2), 311–318.
10) Howeler, R.H. and Cadavid, L.F. (2000).
Sho rt and long term fertility trials
Colombia to determine the nutrient
requirements of cassava. Fertilizers
Research 24; 2; 345 – 365.
11) Howeler, R.H. (2001). Phosphorus
requirements and management of tropical
roots and tuber crops. In Proceedings of
the Symposium on P Requirements for
Sustainable Agriculture in Asia and
Oceania. March 6–8 1989. IRRI, Los
Banos, Philippines (In press).
12) Hulugalle, R.L., R and Opara-Nadi, O.A.
(2001). Management of plant residues for
cassava (Manihot esculenta) production in
an acid ultisol in southeastern Nigeria.
Field Crops Research 16: 1–18.
13) IITA. (2009). Biological control: a
sustainable solution to crop pest problems
in Africa. Yaninek, J.S. and Herren, H.R.
Eds. IITA, 210 pp.
14) NPC (National Population Commission),
(2006): Population census of Federal
Republic of Nigeria: Analytical report at
the national level. National Population
Commission, Abuja.
15) Norman, N.J.T. (1999). Annual Crop ping
Systems in the Tropics. An introduction,
Univ. of Florida Press. 276 p.
16) National Root Crop Research Institute
(NRCRI), (2012): Annual report of
National Root Crops Research Institute,
Umudike, Umuahia.
17) National Root Crop Research Institute
(NRCRI). (2006). Annual report of
National Root Crops Research Institute,
Umudike, Umuahia.
18) National Root Crop Research Institute
(NRCRI). (2007). Annual report of
C. I. Ezeano/Life Science Archives (LSA), Volume – 4, Issue – 2, 2018, Page – 1352 to 1364 1363
©2018 Published by JPS Scientific Publications Ltd. All Rights Reserved
National Root Crops Research Institute,
Umudike, Umuahia.
19) Hussein S, Abukari A, Katara S. (2015).
Determinants of farmers adoption of
improved maize varieties in the Wa
municipality. American International
Journal of Contemporary Research, 5(4):
13 – 15.
20) Ichaou M (2015) Perception and adoption
of agricultural technical innovations in the
cotton basin of Banikoara in Benin.
African Journal of Agricultural Economic,
10(2): 87 - 102.
21) Iheke, R. O. (2010). Market access,
income diversification and welfare status
of rural farm households in Abia State.
Nigeria. Nigeria Agricultural Journal,
4(2): 13-18.
22) Jirgi, A. J., Abdulrahman, M. and Ibrahim,
F.D. (2009). Adoption of Improved
cassava Varieties among Small-Scale
Farmers in Katcha Local Government Area
of Niger State, Nigeria. Journal of
Agricultural Extension, 13(1): 95 - 101.
23) Lozano, C. (1986). Cassava bacterial
blight: a manageable disease. Plant
Disease 70: 1089 - 1093.
24) Mbavai J J, Shitu M B, Abdoulaye T,
Kamara AY and Kamara SM (2015).
Pattern of adoption and constraints to
adoption of improved cowpea varieties in
the Sudan Savanna zone of Northern
Nigeria. Journal of Agricultural Extension
and Rural Development, 7(12): 322 - 329.
25) Mercer, D. (2014). Adoption of
agroforestry innovations in the tropics: A
review. Agroforestry systems, 61(1): 311 -
328.
26) Nkematu, J. A., Obinabo, C. N and Uzoka,
I. G. (2003). Anambra State Agricultural
Development Programme extension report
for the 1st Annual South East Zonal
Research Extension farmers Input Linkage.
System (REFILS) Workshop held at
National Root Crops Research Institute
(NRCRI) Umudike 19-23 November,
2001.
27) Obeta, A,O and Nwagbo, E.C (1990).
Economics of Rice production in Imo
State, Nigeria: A paper presented on
appropriate technology by resources poor
farmers; proceeding of the Nigeria
National farming system research Network
held in Calabar Cross River State, Nigeria
March 14-16.
28) Obinna, C.P. (2012), “Communication
factors determining adoption of improved
cassava technologies in small holders
agriculture”, The Nigerian Journal of
Rural Extension and Development, 1 (2);
23 - 29. University of Ibadan, Ibadan.
Nigeria.
29) Ochiaka S, Ume S . I. and Ebe, FE (2015)
Determent to discontinue adoption of
catfish by farmer in Anambra State of
Nigeria. Journal of Agriculture, Food,
Technology and Environment 11 (2). 60-
65. Faculty of Agriculture Ebonyi State
University.
30) Ofori, C. S. (1997). The effect of
ploughing and fertilizer application on
yield of cassava (Manihot esculenta
Crantz). Ghana Journal of Agricultural
Sciences 6: 21–24.
31) Okigbo, B. N, Greenland, D. J and
Hardler, S G. (1976). Intercropping
Systems in Tropical Africa. In Multiple
Cropping. ASA Special Publication, 27: 63
- 101.
32) Okoye, B. C., Okoye, A. C., Dimelu, M.
U., Agboeze, C. C., Okorafor, O. N and
Amefunna, A. B. (2009). Adoption scale
analysis of improved cocoyam production,
processing and storage technologies in
Enugu North Agricultural Zone of Enugu
State, Nigeria. International Journal of
Agriculture Science, 7(11): 714 - 728.
33) Onyenweaku, C. E., Okoye, B. C. and
Okorie, K. C. (2010). Determinants of
fertilizer adoption by rice farmers in Bende
Local Government Area of Abia State,
Nigeria. Nigeria Agricultural Journal,
41(2): 1 - 6.
34) Truogthi, N.C (2008) Factors affecting
technology adoption among cassava
farmers In Mekong Delta through the lens
of the Local Authoral managers. An
C. I. Ezeano/Life Science Archives (LSA), Volume – 4, Issue – 2, 2018, Page – 1352 to 1364 1364
©2018 Published by JPS Scientific Publications Ltd. All Rights Reserved
Analysis of Qualitative Data. Omonrcie
Journal. China 2008. Pp109-112.
35) Onunka, B N, Ume, S I. Ekwe, K P and
Silo, B. J. (2017). Attitude of farmers
towards “pro-vitamin a” cassava
production technologies in Abia state,
Nigeria. Life Science Archives, 3(3): 1050
– 1059.
36) Ugwungwu, M. N. (2008). Advance in
Rice Research and in Nigeria. Training
Manual on Rice production and
processing. NCRI Badeggi, Niger State,
Nigeria.
37) Ume, S. I., Eluwa, A. N., Okoro, G. O and
Silo, B. J. (2017). Adoption of improved
crop production technology by
Agricultural Development Programme
(ADP) Contact Farmers in Anambra state,
Nigeria: a Training and Visit (T&V)
System Approach. International journal of
innovations in Agricultural Science, 1(2):
72 – 82.
38) Ume, S. I. Ezeano,C I, Onunka, B N and
Nwaneri, T C (2016) Socio-economic
determinant factors to the adoption of
cocoyam production technologies by small
holder farmers in South East Nigeria. Indo
- Asian Journal of Multidisciplinary
Research, 2(5): 760 – 769.
39) Ume, S. I., Onuh N. O., Jiwuba, F. O and
Onunka, B. N. (2016). Technical
Efficiency Among Women Cassava Small
Holder Farmers In Ivo Local Government
Area Of Ebonyi State. Asian Journal of
Agricultural Economics, Extension and
Rural Sociology, 6(1): 1 – 12.
40) Ume, S. I., Onunka B. N., Nwaneri, T. C
and Okoro, G. O. (2016). Socio-economic
Determinants of Sweet Potato Production
among Small Holder Women Farmers in
Ezza South Local Government Area of
Ebonyi State, Nigeria. Global Journal of
Advance Research, 3(9): 972 – 883.
Access this Article in Online
Quick Response Code
Website www.jpsscientificpublications.com
DOI Number DOI: 10.22192/lsa.2018.4.3.2
How to Cite this Article:
C. I. Ezeano, S. I. Ume and B. O. Gbughemobi. 2018. Socio-economic determinants to
Adoption of Improved Cassava varities by Agricultural Development Programme (ADP)
Contact Farmers in Anambra State, Nigeria. Life Science Archives, 4(3): 1352 – 1364.
DOI: 10.22192/lsa.2018.4.3.2