SOCIO-ECONOMIC DETERMINANTS TO ADOPTION …...The socio-economic determinants adoption of improved cassava varieties by ADP farmers in Anambra State, Nigeria was studied. Multistage

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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

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C. I. Ezeano/Life Science Archives (LSA), Volume – 4, Issue – 2, 2018, Page – 1352 to 1364 1353


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?



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



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)



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



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



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



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.



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.



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



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.

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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

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