ADOPTION OF IMPROVED MAIZE TECHNOLOGIES AND MAIZE …

ADOPTION OF IMPROVED MAIZE TECHNOLOGIES AND MAIZE YIELD

IN THE KWAHU AFRAM PLAINS NORTH DISTRICT

BY

WILLIAM OWUSU

(10507163)

THIS THESIS IS SUBMITTED TO THE UNIVERSITY OF GHANA, LEGON

IN PARTIAL FULFILLMENT OF THE REQUIREMENT FOR THE AWARD

OF MASTER OF PHILOSOPHY DEGREE IN AGRIBUSINESS

DEPARTMENT OF AGRICULTURAL ECONOMICS AND AGRIBUSINESS

COLLEGE OF BASIC AND APPLIED SCIENCES

UNIVERSITY OF GHANA, LEGON

JUNE, 2016

University of Ghana http://ugspace.ug.edu.gh

i

DECLARATION

I, William Owusu, do hereby declare that except for references to other people‟s work which

have been duly cited and a plagiarism report presented in Appendix B, the entire work

presented in this thesis: ‘‘Adoption of Improved Maize Technologies and Maize Yield in

the Kwahu Afram Plains North District”, is the result of my original work. This thesis has

not been presented either in whole or in part for another degree in this university or

elsewhere.

............................... Date: ...............................

William Owusu

(Student)

This thesis has been submitted for examination with our approval as supervisors:

................................................. .........................................

Prof. Ramatu Mahama Al-Hassan Dr. Henry Anim-Somuah

(Major Supervisor) (Co – Supervisor)

Date..................................... Date...................................


ii

DEDICATION

This work is dedicated to the glory of Almighty God, my wife and my mother for their

support and guidance.


iii

ACKNOWLEDGMENT

My first gratitude goes to Almighty God for sustaining me through to the completion of this

work. Again, my sincere thanks go to my supervisor, Professor Ramatu M. Al-Hassan, for

her patience and time and for supervising me throughout my work. I also thank my co-

supervisor Dr. Henry Anim Somuah and the lecturers of the Department of Agricultural

Economics and Agribusiness, University of Ghana, especially Prof. D. Bruce Sarpong, for

their advice and help. I dearly appreciate their constructive criticisms, inputs and guidance

which led to the successful completion of this work. I also thank all the non-teaching staff in

the department for their support and contribution to my learning process.

I am grateful to all the staff of Ministry of Food and Agriculture in the Kwahu Afram Plains

North District especially those who provided me with additional information on farmers and

farmer base organizations at their various operational areas.

Finally, I wish to thank all my colleagues, friends and all those who have assisted me in

diverse ways to the completion of this work.


iv

ABSTRACT

The study focuses on improved maize technology adoption and maize yield in the Kwahu

Afram Plains North District. Specifically, the study seeks to assess the maize farmers‟

awareness level on improved maize technologies and this was analyzed with simple

descriptive statistics. The two-mean comparison test was used to analyze maize yield

difference between non- adopters and adopters of improved maize technologies and

constraints to improved maize technology adoption were identified and ranked with the

Kendall‟s coefficient of concordance. The factors which influence improved maize

technology adoption were determined by the use of logit model. In collecting primary data,

structured questionnaires were administered to two hundred (200) maize farmers in the study

area using a two-stage sampling approach. In the first stage, ten (10) maize producing

communities were purposively selected based on their importance in terms of maize

production. In the second stage, a list of fifty (50) member maize farmer groups in each of the

ten (10) communities was made and twenty (20) farmers randomly selected using random

numbers generated with Microsoft Excel. In addition, literature was reviewed and research

scientists from Crop Research Institute (CRI) were interviewed to identify some of the

improved maize technologies that have been developed and released to maize farmers in

Ghana. In this study, four (4) different improved maize technologies were identified based on

the type of improved maize variety and the associated agronomic practices used by the

farmers. Three out these technologies; Obatanpa, Mamaba and Golden Crystal were released

by the Government of Ghana through Ministry of Food and Agriculture and Panaar was

released by a private agency. Only 0.05% of farmers were not aware of improved maize

technologies and 79.5% of maize farmers were adopters. With a rate of adoption of 52%,

Obatanpa variety and its agronomic practices was identified as the most widely adopted

technology. Adoption rates for other varieties were 14% for Mamaba, 9% for Golden crystal

and 5.5% for Panaar. The number of visits by Agricultural Extension Agents (AEAs) or

extension contacts, educational level, maize farming experience, age of farmer, farm size, and

farmers belonging to a farmers‟ group (FBO) were the factors found to have a positive and

significant effect on improved maize technology adoption. The age of the farmer was the

only factor that had a negative influence on improved maize technology adoption. However,

the gender of the farmer, access to credit, family labour and other sources of income had no

significant effect on improved maize technology adoption. The study concludes that

statistically, there is a significant difference in maize yield of maize farmers who adopt and

those who do not adopt improved maize technologies with adopters securing greater yields. It

is recommended that maize farmers should be educated on the need to use improved maize

technologies by intensifying campaign through regular farmer field days and visits by

researchers and extension workers. Government should design strategic and sustainable input

subsidy mechanisms to augment the constraint of high cost of production. Government and

donor agencies should increase funding for technology dissemination and adoption projects.

Maize farmers should encourage the formation of FBOs and be motivated to welcome ideas

of extension agents to acquire more knowledge about improved maize technologies. Maize

farmers should see farming as a business, commercialize their farms and adopt improved

maize technologies for greater returns through higher crop yields.


v

TABLE OF CONTENTS

DECLARATION .............................................................................................................. i

DEDICATION ................................................................................................................. ii

ACKNOWLEDGMENT................................................................................................. iii

ABSTRACT .................................................................................................................... iv

LIST OF TABLES ........................................................................................................ viii

LIST OF FIGURES ........................................................................................................ ix

LIST OF ABBREVIATIONS AND ACRONYMS ....................................................... ix

CHAPTER ONE ...............................................................................................................1

INTRODUTION ...............................................................................................................1

1.1 Background .................................................................................................................1

1.2 Problem Statement ......................................................................................................3

1.3 Objectives ...................................................................................................................6

1.4 Relevance of the Study ...............................................................................................6

1.5 Organization of the Thesis ..........................................................................................7

CHAPTER TWO ..............................................................................................................8

LITERATURE REVIEW .................................................................................................8

2.1 Introduction .................................................................................................................8

2.3 Adoption (Theory, Definition, Process and Influencing Factors) ...............................8

2.3.1 Definition of Adoption .............................................................................................8

2.3.2 Adoption Process .....................................................................................................9

2.3.3 Stages of Adoption .................................................................................................10

2.4 Socio-economic Factors that Affects the Adoption of Improved Technologies.......11

2.5 Adoption Theories ....................................................................................................17

2.6 Risk and Uncertainty in Adoption ............................................................................18


vi

2.7 Information Perspective on Adoption of New Technologies ...................................19

2.8 Theory of Diffusion ..................................................................................................20

2.8.1 Innovation-Decision Process Theory .....................................................................21

2.8.2 Individual Innovativeness Theory ..........................................................................21

2.8.3 Theory of Rate of Adoption ...................................................................................22

2.8.4 Theory of Perceived Attributes ..............................................................................22

2.8.5 Elements of Diffusion ............................................................................................22

2.9 Characteristics of Innovation or New Idea ...............................................................23

2.10 Empirical Studies on Technology Adoption and Statistical Models ......................24

2.10.1 The Logit Model ..................................................................................................25

2.10.2 Probit Model ........................................................................................................28

2.10.3 The Count Data Model.........................................................................................29

2.11 Ranking Techniques................................................................................................30

2.12 Constraints Facing Maize Farmers .........................................................................32

CHAPTER THREE ........................................................................................................34

METHODOLOGY .........................................................................................................34

3.1 Introduction ...............................................................................................................34

3.2 Conceptual Framework .............................................................................................34

3.3 Theoretical Framework .............................................................................................35

3.4 Assessing the Level of Awareness ............................................................................36

3.5 Estimating the Level of Adoption .............................................................................36

3.6 Analysing the Constraints Faced by Maize Farmers in Improved Maize Technology

Adoption .........................................................................................................................37

3.7 Description of Constraints ........................................................................................39


vii

3.8 Comparing the Means of Maize Yield of both Adopters and Non-Adopters of Improve

Maize Technologies. .......................................................................................................41

3.9 Identifying Factors that Influence adoption of Improved Maize Technology ..........41

3.9.1 Adoption level ........................................................................................................43

3.9.2 Description of Explanatory Variables in the Model: .............................................43

3.9 Data Collection and Sampling Procedures ...............................................................47

3.10 Software Applications used for Data Analysis .......................................................48

3.11 The Study Area .......................................................................................................49

CHAPTER FOUR ...........................................................................................................52

RESULTS AND DISCUSSION .....................................................................................52

4.1 Introduction ...............................................................................................................52

4.2 Demographic and Socio - Economic Characteristics of Maize Farmers ..................52

4.3 Level of Awareness...................................................................................................55

4.5 Constraints Faced by Maize Farmers in Improved Maize Technology Adoption ....56

4.5 Means of maize yield comparison between adopters and non-adopters. ..................58

4.6 Factors that Influence Improved Maize Technology Adoption ................................59

CHAPTER FIVE ............................................................................................................64

SUMMARY, CONCLUSIONS AND RECOMMENDATIONS...................................64

5.1 Introduction ...............................................................................................................64

5.2 Summary ...................................................................................................................64

5.3 Conclusions ...............................................................................................................66

5.4 Recommendations .....................................................................................................67

REFERENCES ...............................................................................................................68

APPENDICES ................................................................................................................79


viii

LIST OF TABLES

Table1.1 Types of Improved Maize Varieties Introduced In Ghana from 1984- 2015………..3

Table 3.1: Constraints to Improve Technology Adoption presented to farmers to Rank…...39

Table 3.2 Variable Definitions, Units of Measurements and Hypothesize Relationships…...46

Table 3.3 Distribution of Sample by Community……………………………….…………...48

Table 4.1 Socio-demographic Characteristics of Respondents………………….…………...54

Table 4.2: Socio-Demographic Characteristics (Continued)…………………….…………..54

Table 4.3 Distribution of Extent of Adoption…………………………………….…………55

Table 4.4 Farmers‟ Ranking of Maize Production Constraints……………………………...58

Table 4.5: T-Test Results from Yields of Maize of Adopters and Non-Adopters…………..59

Table 4.6 A Logistic regression model showing results of the factors influencing the adoption

of improved maize technology..............................................................…........60


ix

LIST OF FIGURES

Figure 3.1: Conceptual Model………………………………………………………….35

Figure 3.2 District Map………………………………………………………………...51

Figure 4.1: Level of Adoption of improved maize Technologies……………………...56


x

LIST OF ABBREVIATIONS AND ACRONYMS

AC ACRE

ADB Agriculture Development Bank

ADF African Development Fund

CAADP Comprehensive African Agricultural Development Programme

CIMMYT International Maize and Wheat Centre

CRI Crops Research Institute

FAO Food and Agriculture Organization

FASDEP Food and Agriculture Sector Development Policy

FBO Farmer Based Organization

FCDP Food crops development project

GGDP Ghana grains development programme

GSS Ghana Statistical Service

Ha Hectare

IFPRI International Food Policy Research Institute

ISSER Institute of Statistical, Social and Economic Research

KAPND Kwahu Afram Plains North District

KAPNDP Kwahu Afram Plains North District Profile

MCA Millennium Challenge Account

MoFA Ministry of Food and Agriculture

MTDS Medium Term Development Strategy

OLS Ordinary Least Squares

SRID Statistics research and information directorate


1

CHAPTER ONE

INTRODUTION

1.1 Background

Agriculture is the leading source of employment in most developing countries in the world

(Doss, 2004) and 57 percent of Ghana‟s population of 25 million engages in various forms

of agricultural activities (GSS, 2014). Maize production accounts for close to 55 percent of

the total cereal and grain production in Ghana (Bennett-Lartey & Oteng-Yeboah, 2008).

Although agricultural productivity is very critical for economic growth and development, it

is very low in most of these countries (Ehui & Pender, 2005; Jones, 2007; Meijerink &

Rosa, 2007).

In Ghana, maize is a staple food and in the brewery industry the grain is used to prepare

malt. Maize is adapted to all the ecological zones in Ghana. In the forest and coastal

savannah zones, maize is grown twice a year (minor and major seasons), while one

cropping season is possible in the Guinea Savannah Zone, which occupies the three

Northern regions of Ghana. The total acreage of maize in Ghana (major and minor seasons)

is over 400,000 hectares (Frank et al., 2006). However, whereas in America and Australia

productivity is 10.3 and 5.8 metric tons per hectare respectively, 1.7 Mt/hectare/year was

the national average yield (MoFA, 2011), whereas 1.2 mt/hectare was the average yield in

2012 major season revealed by CRI/SARI/IFPRI survey. There is the need to increase the

yield of maize to appreciable levels considering the opportunity that exists. It has been

demonstrated from trials on farms and on-station that there is 4 to 6 Mt/ha achievable

levels. Maize is an important food and feed crop in Ghana and remains an important crop

for rural food security. The production must be increased in order to ensure food and


2

income security through the use of improved maize technologies. Maize is a staple food of

great socio-economic importance in developing countries and it has a wide range of uses.

In Ghana maize is used to prepare a variety of diets. For instance, in the three Northern

regions, it is used to prepare tuo zaafi (TZ,) porridge, maasa, and pito. While the dry stalks

are used as fuel for cooking. In the Southern sector, maize is used to prepare kenkey, banku

and akple. In the poultry industry, it is used to formulate feed for poultry consumption.

Fresh maize is taken boiled, parched or roasted. It is an important source of carbohydrates,

iron, vitamin B and minerals. A national survey conducted in 1990 revealed that about

almost every household during an arbitrary selected fourteen-day period had consumed

maize (Alderman and Higgins 1992). Maize and food made from maize accounted for over

10 percent expenditures on food by poor households, and 10.3 percent by all income

groups based on an analysis of 1987 data (Boateng et al., 1990).

Furthermore, many households have their source of income from revenues obtained from

the sale of maize, even subsistence farming households. In Ghana, over 16 percent of the

revenues received from the sale of crops earned by households that are poor is from maize

while that of “hard-core‟‟ poor households is over 18 percent (Boateng et al., 1990).

The common form of production system in Ghana is the traditional subsistence type, which

basically involves crop rotation and shifting cultivation among others (MoFA, 2010). The

use of chemical fertilizers, farm machinery and improved varieties by farmers who

cultivate maize is low and it is widely spread throughout the agro-ecological zones in

Ghana (Dankyi et al., 2005). Maize requires a good distribution of rainfall. In the early

stages, sufficient water in the soil allows the plant to develop a healthy root system. This

protects it against temporary periods of drought. The current improved varieties of maize


3

are fairly drought tolerant as compared to the local varieties which are fewer droughts

tolerant, but may wilt under prolonged water deficiency.

Table1.1 Types of Improved Maize Varieties Introduced In Ghana from 1984- 2015

Number Variety Maturity period

(days)

Planting space

(cm)

Date of release

1 Okomasa 120 90x40 1988

2 Golden crystal 110 90x40 1984

3 Obaatampa 110 80x40 1992

4 Mamaba 110 80x40 1997

5 Dadaba 110 80x40 9997

6 Dodzi 95 75x40 1995

7 DorkeSR 95 75x40 1990

8 Aburotia 110 80x40 1984

9 Cida-ba 105-110 80x40 1997

10 Golden Jubilee 105-110 80x40 2007

11 Etubi 105-110 80x40 2010

12 Aburohema 75-80 80x40 2010

13 Abontem 75-80 75x40 2010

14 Tigli 120 80x40 2012

15 CSIR-Sika aburo 105-115 80x40 2015

16 Kumjor-wari 80-90 80x40 2015

17 Suhudoo 110 80x40 2015

18 Kpari-faako 90 80x40 2015

Source: MoFA (2016)

1.2 Problem Statement

The Ghanaian population is estimated to be growing at a rate of 2.5% (GSS, 2014). As a

result, food production for the growing population has become an issue of great national


4

interest because of the low agricultural productivity levels in Ghana. Per capita

consumption has increased from 38.4 kg/head/year to 43.8 kg/head/year in 1980 and 2010 -

2011 respectively, representing an increase by 14 percentage points (MoFA, 2011). Cereal

production, especially maize in Ghana has been characterized by low yields, which in the

long run results in low income for farmers. This is as a result of the kind of inputs used by

farmers as well as inadequate knowledge of improved farming technologies that can

increase yields. As such, past and present governments have initiated and implemented

several programs and projects to curb the situation of low productivity in cereal crops.

From 1970 to 2013, the programmes and projects that were initiated and implemented were

the operation feed yourself program, the Ghana Grains Development Project (GGDP),

Sasakawa Global 2000 program, the Food Crops Development Project (FCDP), the

Agricultural Sub-sector Investment Program (AgSSIP), and the Youth in Agriculture

programme. All these programs and projects were geared towards increasing the

production of cereal crops to make Ghana self-sufficient in cereal production. Among the

reasons why agriculture productivity is low is that the level of adoption of agricultural

technology is very low (World Bank, 2008). According to Dankyi et al. (2005), just about

45 percent of farmers in Ghana adopted the use of improved seeds, fertilizer, and row

planting during production. Farmers continuously use local varieties and traditional

methods for maize cultivation. The Ministry of Food and Agriculture (2005) reported that

the lower productivity levels have been of a major concern in the country‟s agricultural

development for several decades.

In spite of the large area cultivated and the release of several improved technologies, maize

farmers in Ghana obtain an average yield of 2.00 Mt/ha instead of an expected yield of 6.00


5

Mt/ha (MoFA, 2011). The total number of maize farmers in the district stands at 13,686

with a total annual average production of 81,441.1 Mt from a total cropping area of 26,702

hectares from an average yield of 3.05 Mt/ha per annum (District Profile, KAPND, 2014).

Impact assessment by Morris, Tripp, and Dankyi (1998), indicates that GGDP which ended

in the year 1997 was very successful. Several improved maize technologies were developed

and disseminated under the project. Obatanpa, quality protein maize developed through the

project, has become widely popular in Ghana and in other countries in Africa south of the

Sahara. It was released to maize farmers in the Kwahu Afram Plains North District in the

year 2002 together with other improved maize technologies.

More importantly, the extent to which maize farmers in the Kwahu Afram Plains North

District have adopted the use of this improved maize variety and the agronomic practices

associated with it are unknown. The factors influencing the adoption or non-adoption are

also unknown as well as the constraints to adoption.

Given the foregoing, the following research questions arise;

1. Are farmers in the Kwahu Afram Plains North District aware of improved maize

technologies and what is the extent of adoption?

2. What are the constraints faced by maize farmers regarding use of improved maize

technologies in the Kwahu Afram Plains North District?

3. Is there a difference between maize yield of adopters and non-adopters of improved

maize technologies in the Kwahu Afram Plains North District?


6

4. What are the factors that influence adoption of improved maize technologies in the

Kwahu Afram Plains North District?

1.3 Objectives

The main objective of the study is to assess the adoption of improved maize technologies

and maize yield in the Kwahu Afram Plains North District.

The specific objectives are:

1. To assess the level of awareness and the extent of adoption of maize farmers on

improved maize technologies in the Kwahu Afram Plains North District.

2. To analyse and rank the constraints faced by maize farmers in adopting improved

maize technologies in the Kwahu Afram Plains North District.

3. To compare the maize yield of both adopters and non-adopters of improved maize

technologies in the Kwahu Afram Plains North District.

4. To identify factors that influence adoption of improve maize technologies in the

Kwahu Afram Plains North District.

1.4 Relevance of the Study

This thesis aims to provide additional knowledge on the awareness of improved maize

technologies and their adoption by farmers in the Kwahu Afram Plains North District. The

study also set out to identify factors that are likely to influence adoption of improved maize

technologies in the Kwahu Afram Plains North District.


7

Furthermore, constraints faced by farmers in adopting improved maize technologies would

also be revealed. This could assist research institutions and the Ministry of Food and

Agriculture to make informed decisions about the technology packages to promote among

farmers to suit the their local environment.

It will add to the existing literature on adoption of improved agricultural technologies and

help in developing further research.

1.5 Organization of the Thesis

The thesis is organized into five chapters. Chapter one introduces the subject matter of the

study whereas chapter two presents a review of literature on technology adoption. It

discusses among other things, the factors influencing adoption. Chapter three presents the

theoretical framework and the methods of analysis of the specific objectives. The chapter

also presents descriptions of data collection procedures, the sampling procedure and the

sample size used, and the study area. The results of the analysis are presented and discussed

in chapter four, while chapter five presents the summary, conclusions and policy

implications and recommendations made from the study.


8

CHAPTER TWO

LITERATURE REVIEW

2.1 Introduction

This chapter begins with a review on adoption (theory, definition, process and influencing

factors) and technology dissemination. The chapter seeks to identify the factors that

influence farmers‟ adoption behaviour as well as the constraints faced by maize farmers. It

explores the empirical studies on technology adoption and statistical models. The review of

literature also explains the methodological issues and techniques used in the analysis of

technology adoption to provide direction in terms of the choice of explanatory variables to

include in the models for this study and the most appropriate methods of analysis to

employ.

2.3 Adoption (Theory, Definition, Process and Influencing Factors)

2.3.1 Definition of Adoption

The decision to implement a new idea and its continual usage is termed adoption (Rogers

and Shoemaker, 1971). Studies of the adoption of agricultural innovations at the farmer

level are concerned with analysing the determinants of the extent of application of an

innovation. This is usually when the farmer has the needed information on a new

technology and its expected impacts (Feder et al., 1985). In Ghana, improved maize

technologies, mainly in the form of improved maize varieties and related agronomic

practices, have been developed and extended to farmers. Some of these varieties are:

Abeleehe, Aburotia, Dobidi, Dodzi, Dorke, Laposta, Mamaba, Okomasa and Obatanpa.

These varieties are superior over the traditional ones in terms of yield. On-farm tests are


9

usually used to confirm this before extending them to farmers together with the

complementary crop and soil management practices. Therefore, an improved crop

technology is often a bundle of innovations rather than a single technical or managerial

intervention (Mwabul et al., 2006). In order to improve farm yields, an entire package of

the components in a technology must be adopted if an agricultural technology consists of

perfectly complementary components. A lot of adoption technologies do not require any

unique skills for implementation although most of them have clear advantages (Mensah,

2006).

Research on adopting technological innovations as packages was done years ago (Byerlee

& Hesse de Polanco, 1986). According to Dankwa (2001), adoption is the acceptance and

use of technology for one season or more. But Ghana has seen just a little work in the area

of its agriculture.

2.3.2 Adoption Process

Adoption is a mental process through which an individual passes from first hearing of an

innovation to final adoption of same. It is about a decision to continue full use of an

innovation. Rogers and Shoemaker (1971) observe adoption process is affected by the

following major factors:

i. Socioeconomic status;

ii. Personality variables;

iii. Communication behaviour, among others.

Rogers (1962) maintains that the individual‟s identity and how he perceives the situation

affect his adoption behaviour. This is made up of his sense of judgement which includes;


10

security, dominant values, mental capacity and conceptual skill, status in society and as

well as the social system‟s norms on innovativeness, its economy and the unit‟s

characteristics.

Consequently, efforts have been directed towards farmers‟ awareness of certain agricultural

inputs and agronomic practices recommended to these farmers and making them readily

available to farmers and persuading them to adopt the new inputs and practices.

2.3.3 Stages of Adoption

Rogers (1962) outlines the following five stages of adoption and their main functions in the

adoption process:

i. Awareness Stage: The individual becomes aware of the new idea but has only little

knowledge about it. This stage is primarily to start the sequence involved in later

stages which may eventually end in adopting or otherwise of the new idea.

ii. Interest Stage: At this stage the individual seeks additional information about the

new idea and favours the innovation in a general way, but he has not yet decided on

it. Its main function seeks to enhance the individual‟s information about the new

idea, even as he becomes more psychologically involved in the innovation.

iii. Evaluation Stage: At this stage the individual mentally applies the new idea to his

or her current and future situations, and then makes a decision whether to

experiment it or not. If the individual feels that the merits of the new idea outweigh

the demerits he or she will try it.

iv. Trial Stage: Here the individual employs the new idea on a small scale in order to

ascertain its usefulness in his situation. It is to help demonstrate the innovation in


11

the individual‟s own environment so as to judge its importance for possible outright

adoption. Information is sought about the method of using the innovation at the trial

stage.

v. Adoption Stage: Here the individual decides full use of the new idea continuously.

It is to look at the experimented results and the decision to ratify sustained use of

the technology in the future.

Based on the time taken for an innovation to be adopted, researchers have classified

adopters into various groups as follows:

(i) Innovators; (ii) Early Adopters; (iii) Early Majority; (iv) Late Majority; (v)

Laggards (Rogers and Shoemaker, 1971).

2.4 Socio-economic Factors that Affect the Adoption of Improved Technologies

This section will review research on previous works already done on some socio-economic

factors that affect adoption of innovations by farmers. Such factors include level of formal

education, income, membership of farmer associations, household size, age and social

status.

Voh (1979) and Atala (1980) identify socio-economic factors like age, household size,

formal education, income, cosmo-politeness and community status as influencing related to

adoption. They equally found that non-adopters were older than adopters. Akanya (1989)

also conclude that certain socio-economic variables were severally related to adoption of

agricultural innovations.


12

Furthermore, Ekenta (2004) citing Nweke and Akerhe (1993) states that certain factors

influences improved maize technology adoption and is a function of farmers‟

characteristics, extension agency and the new technologies themselves as material

components. Similarly Awogbade (1981) posits that due to family/village structural

arrangement in which the head of household acts as both the legal and political

spokesperson on all issues, his decisions often influence others to either adopt agricultural

innovations or not.

Onazi (1973) in his inquiry of Northern Nigeria found inter-alia certain reasons for

farmers‟ non-adoption of agricultural practices as reluctance to give up their old ways and

unfavourable producers‟ prices. By and large, farmers‟ response to agricultural innovations

is attributable to a number of institutional and national economic and environmental factors

such as credit, extension agent, input delivery, land tenure and sources of information.

Patel and Anthonio (1971), Akinola (1986a), Okwoche et a., (1998) find a positive relation

between farmers‟ adoption behaviour and access to credit, while Akinola (1986b) attests

same for product prices, quantity of inputs available for sale, number of active selling

points and advertisement.

Similarly, for such communication factors as period of awareness and cosmopolitanism,

Onu (1985), Amotsuka (1988), Agbamu (1993), Umeh (1998) and Ladebo (1994) equally

concur. Iyere (1985), Ngwu (1989), Adebayo (1994), Akinola (1986a), Vabi et al (1993),

Adekoya and Ajayi (2000), Agbamu (1993), Musa (1998), Umeh (1998), Akinola (1986b),

Asifat (1986) and Chukwu (1995) find negative relations between household/family size,


13

membership of cooperatives, social position/cultural positioning, extension agent contact,

cost of innovation and difficulty in use of innovation and adoption behaviour.

Mijindadi and Njoku (1985) in a study to assess the extent to which tomato innovations

were adopted in cooperative and individual farms in Ikara area of Zaria found significant

associations between extension contact, membership of associations, credit availability and

input delivery and farmers‟ awareness and adoption of horticultural innovations. In

traditional African societies, elders are widely considered to be more experienced than the

younger ones in the society, because exposure and judgement‟s relating to adoption of

innovations are positively affected by the age factor Pannin (1988). However, some

researchers have argued that adventurous as younger farmers are and with a long term

planning ability, are more likely to adopt new ideas than older farmers (Polson and

Spencer, 1992).

In most farming households, decision – making is paramount and is vested in the family

head which in most cases are males. According to Adesina and Chinu (2000), gender of the

farmer influences adoption differently depending on the innovation. In this study a positive

or negative relationship between gender of a farmer and improved maize technology

adoption is expected. Buyinda and Wumbede (2008) found a positive effect of education

and adoption.

However, Chagunda et al (2006) found education not to have any effect on farmers

„willingness to adopt exotic cattle rearing in Malawi. But Oyekale and Idjesa (2009)

reported a relationship between education and adoption to be negative. Gockowski and

Ndoumbe (2004) found a negative relationship between a farmer‟s farm size and adoption


14

but Daverkow and Mcbride (1998) and Payne et al., (2003) found a positive correlation

between the likelihood to adopt improved maize technologies and farm size. In this study

positive or negative relationship is expected between the likelihood to adopt improved

maize technologies and farm size.

Shield et al., (1993) found a positive relationship between family labour and technology

adoption. Lee (1972) sums up his findings to show that household size and labour

availability influences adoption of soil conservation investments in Philippines and

Ethiopia. On the other hand, Polson and Spencer, looking at high yielding variety (HYV)

cassava among smallholder farmers in Nigeria, found that availability of family labour has

no effect on adoption.

The availability and accessibility of extension agents to farmers as well as frequent visits

will help farmers to obtain and update their knowledge on current technologies. There

exists a positive relationship between extension contact and adoption of improved maize

seeds (Adeogun et al., 2008).

The presence of income from non-farm could work in various ways to influence adoption.

Off-farm income could positively affect adoption by minimizing the financial problems

that the farmer would encounter in making his or her adoption decision. Furthermore,

households with low levels of off-farm income or poor access to credit facilities are less

likely to be able to afford newer and cost-intensive technologies.

Farmers who have access to loan facilities are considered to have permanent source of

income to purchase expensive inputs for their farming activities, while those without access

often find it difficult to purchase expensive inputs to expand their farms. Ahmad (2011)


15

found availability of credit facility to have positive influence on output as it is used to

purchase inputs to enhance increase in production. In this study a positive relationship is

expected.

Ntege Nanyeenya et al., (1997) found a positive relationship between farmer-based

organization membership and adoption of improved maize production technologies and

also reports that information flows easily in such associations. Based on this the farmer can

make a decision on adoption relative to what he or she comes in contact with.

Land tenure is the system of owning a land either temporary or permanently. Garming et

al., (2007) found that permanent landowners were more likely to adopt alternative pests

control than those who only occupied it temporarily.

From empirical and theoretical studies on innovation adoption in agriculture, the individual

decision making process that leads a farmer to adopt an innovation is determined by five

factors: geographical, institutional and social environment, farm structure and technological

constraints, Farmer‟s socio-demographic characteristics and personal attitudes, attributes of

innovation and policy and market attributes.

The interactions between various factors located at different scales and involving different

stakeholders leads to innovation adoption (Joly and Lemarie, 2000). There are a number of

factors relating to farmer‟s personal attitudes. That of risk, change and uncertainty can

significantly affect adoption through its ability to minimize the perceived utility of

innovations. And be associated with risk aversion of change (Feder, Just and Zilberman

2001; Abadi et al., 2005).

Other researchers have separated the population of farmers into different subgroups


16

according to their attitude towards adoption of innovation: early adopters and late adopters

(Feder, 1984). This allows capturing attitudes related to the search of better social status to

be innovative (Diederen et al., 2003, Abadi and Pannell, 1999). Finally, how farmers‟ want

the future of their farms to be can influence their perceived adoption of innovations

(Amodor et al., 1990).

Production inputs and constraints such as access to credit, financial capacities and cash

flows limitations are related to the second type of determinants of adoption (Boahene et al.,

1999, Feder & Umail, 1993). Geographical indicators such as regional dynamics, distance

to other innovators, distance to market, demographic pressure, and the pressure of

extension services forms a third category of determinants of adoption (Adesina & Mbila,

2002; Lapar & Ehui, 2004). These are also related to farmers‟ number of contacts with

extension agents in the cropping seasons, the involvement of farmers in research

programmes, or the affiliation to a farmer base organization. Foster and Rosenweig (1995)

farmers may not adopt a new technology at the initial stages because of knowledge gap

about management of the technology; however, it eventually occurs due to own experience

and neighbours‟ experiences.

In a similar manner, Conley and Udry (2002), in their study to ascertain whether usage of

fertilizer by a farmer and its associated yield increase influences his/her neighbour‟s

decision to adopt same, concluded that as a farmer increases or decreases the quantity of

his fertilizer application, changes in yield between the two treatments will be seen. If a

farmer gains higher yield than expected, with the use of plenty of fertilizer than he did the

previous year, it will stimulate the interest of non-adopters to practice the new technology.


17

On the other hand, three paradigms are commonly employed in explaining adoption

behaviour and determinants of technology adoption. These are: the innovation diffusion

model, the adoption perception model and the economic constraints model. Feder and

Slade (1984), Shampine (1998) and (Smale et al., 1994) suggested that even though there is

an assumption by the innovation diffusion model that technically and culturally the

technology is suitable, the problem associated with its adoption is one of information

asymmetry and very high cost in terms of information search. The second paradigm on the

other hand suggests that the perceived attributes of the technology and attitude of the

farmers directly influence farmers‟ decision to adopt a new technology. This means that,

farmers may have full information on their farm household and may still by themselves

evaluate the technology differently by themselves compared to scientist (Kivlin and

Fliegel, 1967; Ashby et al., 1989; Ashby and Sperling, 1992). Thus, how farmers perceive

of a given technology must be understood in the generation and diffusion of new

technology as well as farm household information dissemination.

The economic constraint model, such as access to credit, land, labour or other critical

inputs limits production flexibility and conditions of the technology and adoption decisions

relative to input fixity in the short run (Aikens et al., 1975; Smale et al., 1994Shampine,

1998).The use of these paradigms in modelling technology adoption improves the

explanatory power of the model in relation to individual paradigm (Adesina and Zinnah,

1993; Morris et al, 1999, Gemeda et al., 2001).

2.5 Adoption Theories

The decision to adopt or reject is essentially seen by adoption theories in agriculture as a


18

„risky choice‟ problem. This is risky because the farmer is not sure if by adopting the

innovation he will be worse or better off. The likelihood of making a correct or incorrect

decision is determined by the knowledge of the relevant parameters on the part of the

decision maker. The more that is known the more likely it is that a correct decision will be

made. Adoption is essentially a dynamic learning process. Conventional research into

farmer adoption of new technologies describes adoption-decision and its timing, (early or

late) primarily in terms of the decision maker‟s perception and inherent characteristics,

with innovator at one extreme and laggard at the other (Rogers,1995).

However, Pannel (1999) acknowledges the notion that the decision to adopt or reject an

innovation depends on what is in the producer‟s best interest is deceptively simple by

stating that “we can identify the conditions necessary to achieve adoption of an innovation

but it remains difficult to meet those conditions”. He further listed these conditions as:

a) Awareness of the new idea or innovation

b) Perception that it is both feasible and worthwhile trialling the innovation; and

c) Perception that the innovation promotes the achievement of the farmer‟s objectives.

2.6 Risk and Uncertainty in Adoption

Just and Zilberman (1983) used the expected utility framework to propose a technology

adoption theory under uncertainty. Their model is seen as an extension of the original

expected utility approach to producer behaviour under uncertainty by Baron (1970) and

Sandmo (1971). A theoretical basis for study of the role played by size of firm, risk

attributes and the combine sharing of income, lack of credit, and fixed costs of adoption in

choosing between two technologies that are risky was provided by this approach. One


19

theoretical result of their model is that there may be a limit to the proportion of a farmer‟s

area cropped dedicated to a new profitable innovation, when there is a reduction in absolute

risk aversion as wealth becomes sufficiently large, and there is also a high covariance of

returns between the obsolete and modern technologies.

The review of the relationship between the riskiness of an innovation and the utility of a

risk-averse decision maker, Marra et al. (1987) reports that the role of risk and uncertainty

in adoption has not been adequately addressed by empirical studies in general. Feder et al.

(1985), in reviewing literature on adoption, attributed this inadequacy to the fact that it is

very difficult to observe and measure risk and uncertainty, as noted by Lindner et al. (1982)

and Akinola (1986).

2.7 Information Perspective on Adoption of New Technologies

When modelling the micro-level process of adoption from a vibrant perspective,

consideration should be given to the process of adoption as involving gaining of

information and continuous practicing. From literature, there are two different methods use

to model this process. First, the individual decision on adoption per time period should be

modelled by the intermediate changes in some independent variables. For “divisible”

innovations (for example a new crop variety) which is possible to be adopted in stages, to

adopt also involves a decision regarding the adoption intensity at a point in time along the

path of adoption, (Marra et al., 2002).

Warner (1974) suggested that the story of adoption involves learning and imitation. He

implied that there is a cautious approach initially by potential adopters toward adopting the

new idea. In most cases, they experiment at the initial stage with the technology on a trial


20

basis. Before considering the decision to adopt or not, information is sought on the value

and cost of the new idea from their own and other users‟ trials. By gathering enough

information, the individuals‟ knowledge with regards to the overall attributes of the

technology is increased and the uncertainty about its potential benefits is reduced, thus

increasing the rate of adoption. He asserts that the learning and imitation methods is been

included mathematically in the diffusion model by assumption and derivation. He argued

that there was strong empirical evidence that efficiency in the use of a new innovation

increases with experience. Linder and Pardey‟s (1979) work supported this proposition.

2.8 Theory of Diffusion

The process by which an innovation spreads from the source of its invention to the targeted

users is termed diffusion. Whereas adoption process entails acceptance and use of an

innovation by an individual, diffusion process deals with the circulation of innovation in a

social system or between social systems or societies (Rogers, 1995). Thus, diffusion of an

innovation occurs within a social system which may embrace different situations. The

segments of the social system in a study area under reference can influence individual

farmers to display varying degrees of innovativeness, from innovators to laggards. Opinion

leaders who are generally early adopters enhance diffusion of innovations, while late

majorities and laggards hinder it. In the 1960s the theory of adoption – diffusion was the

prevailing method (Rogers, 1995). Diffusion theoreticians contended that psycho-social

attributes of adopters and non-adopters is the bases for understanding adoption. Between

early adopters and late adopters, the former were well endowed in education, less risk

averse, and have higher interest in investing in new innovations. As Rogers further hinted,


21

diffusion is not just a single comprehensive theory. But encompasses theoretical viewpoints

that relate to the total theory of diffusion; it is a meta-theory. Ryan and Gross (1943) in

their study on rural sociology mentioned that the diffusion of innovations is dealt with by

four major theories. These are the innovation – decision process theory, individual

innovativeness theory, rate of adoption theory, and theory of perceived attributes.

2.8.1 Innovation-Decision Process Theory

This theory is time centred with five separate steps. The first step involves knowledge

acquisition by possible adopters about the new idea. Second, the possible adopters need to

be influenced as to the advantages of the new idea. Third, the potential adopters need to

adopt the new idea. Fourth, once the potential adopters accept the innovation, it must be

implemented. Fifth, the potential adopters need to affirm that their choice to adopt was a

suitable one. The resultant effect is diffusion as soon as these steps are completed.

2.8.2 Individual Innovativeness Theory

This is grounded on who embraces the new idea and at what time. The percentage of

individuals that adopt an innovation is often illustrated by the use of a bell-shaped curve.

The first group of adopters are pacesetters (2.5%). They are considered as the adventurers

and innovators who lead the way. The second category is the early adopters (13.5%). They

join the pioneers quickly and help blowout the information about the new to others. The

subsequent two categories are the early majority and late majority respectively. Both form

34% of the likely adopting group. The pacesetters and primary adopters convince the early

majority whiles the late majority delays to ensure that adopting the innovation will serve


22

their interests. The last category is the foot-draggers (16%). They are those who are

extremely cynical and fail to adopt an innovation until it becomes completely essential.

They may certainly not adopt the new idea in most instances.

2.8.3 Theory of Rate of Adoption

The theory holds that embracing innovation is finest denoted by an s-curve on a graph. This

theory proposes that acceptance of a new idea moves through the following stages, slowly

and gradually at the start, then a rapid growth period that become stable and eventually

decline (Dearing,2009).

2.8.4 Theory of Perceived Attributes

The theory is centred on the notion that one will adopt an innovation by perceiving that the

new idea has the ensuing attributes - a relative advantage over an existing technology in

terms of superiority, it is harmonious with current morals and applications, not complex,

divisible and lastly the innovation must offer observable result with availability of the

components assured (Perkins, 2011).

2.8.5 Elements of Diffusion

Four key elements are involved in diffusion of innovations and these are:

1) The innovation

2) Its communication from one person to another

Communication, over appropriate passages, delivers information to a communal structure

about a new idea. Mass media use is one of the effective means of creating awareness on a


23

new idea, whereas individual interactions are more operative in establishing a judgment

about an innovation. Such relational communication is smoothened if individual conveying

information and the recipient are optimally alike in definite features (Grawe,2009).

3) Social system

The social system with its interconnected units has a common concern in discovering

answers to a common goal. Such a system has a common and communication arrangement

that accelerates or obstructs the dissemination of new ideas within it. Customs are part of

social system. Both opinion leaders as well as change agents are considered principal actors

in dissemination of new ideas and influencing system members (Grawe, 2009).

4) Time

Time is a central feature in the process which involves choice-making, ability to create new

idea and degree of adoption of an innovation (Rogers, 1995).

2.9 Characteristics of Innovation or New Idea

Level of adoption is explained based on the features of the new idea. Five of such

characteristics of importance as reported by Damampour and Schneider (2009) are detailed

below:

1) The relative advantage of the new idea; echoes how it is individually seen as loftier

to the preceding innovation;

2) Compatibility; echoes how the new idea is perceived “consistent with the existing

values, past experiences, and needs of potential adopters”;


24

3) Complexity; echoes the perceived required effort to comprehend and practice the

innovation;

4) Trialability is “the degree to which an innovation may be experimented with on a

limited basis”; and

5) Observability; echoes how the outcomes of a new idea or an invention are

noticeable to others. A new idea can further be modified by a user.

2.10 Empirical Studies on Technology Adoption and Statistical Models

Several different methods of analysis have been used in assessing the adoption of

innovations. The literature on this subject defines the course of adoption as captivating on a

logistic nature (Alston, et al., 1995). Most often, adoption studies have concentrated on the

individual as the component of observation. Most studies define adoption as dichotomous,

that is, an innovation is either adopted or not. The methods of analysis can generally be

categorized as qualitative or quantitative. According to Gujarati (2004), discrete choice

models such as the linear probability model (LPM), Logit, Probit, Tobit and Gombit

models are the common approaches used for estimating models involving qualitative

response or dummy dependent variables. The choice of one model over the other is also

subject to a number of factors. The LPM is inappropriate in identifying the determinants of

adoption because it is unable to restrict the predicted probability within a [0, 1] range as

guaranteed by probability theory and the heteroscedastic nature of the disturbance term

(Gujarati, 2004; Maddala, 2005; Wooldridge, 2009). The inconsistent nature of this

disturbance term makes the tests of significance of estimated coefficients impossible. To

overcome the problem of heteroscedasticity, the method of weighted least squares is used


25

where each parameter in the LPM is multiplied by the error variances. In spite of this, there

is no guarantee that the predicted probabilities would lie within the [0, 1] range. The

procedure is also sensitive to specification errors (Maddala, 2005). These boundaries of the

linear possibility model can be overcome by the Probit Model, Logit Model, Tobit model,

Gompit (Maddala, 1983), and the censored least absolute deviations (CLAD) regression

model (Chay & Powell, 2001).

2.10.1 The Logit Model

The usage of a binary variable signifying its up-to-date existence or not on a specific plot is

the simplest measure of technology adoption. The logit model is classified as a univariate

or a multivariate technique that helps predict the likelihood of an event occurring or not by

estimating a binary dependent outcome from a set of independent variables. Thangata et al.

(2003) conducted a study on factors impacting adoption of genetically modified cotton

using 2003 data from the agricultural resources management survey (ARMS) to estimate

two binary logit models for two definitions of genetically modified(GM) cotton seed

adoption. Results indicated that conservation tillage did not affect adoption of genetically

modified cotton with either of these definitions positively, while adoption of (GM) cotton

in the previous year affected adoption positively. Rahm &Huffman (1984) use a logit

model in their study on the adoption of reduced tillage: the role of the human capital and

other variables. Other authors (Shakya & Flinn, 1985, Hailu, 1990; Kebede & Coffin,

1990; Edwin, 1996, Hounkpe, 1999) also made use of logit and probit models for adoption

studies. Studies by Hassan et al. (1998), Salasya et al. (1998) and Kimenye (1997) to

institute the influencers of technology adoption made use of logit regression model with the


26

chance of accepting a technology being the dependent variable and the factors conjectured

to affect the likelihood of adoption as independent variables. Griliches (1957), Lionberger

(1960), Rogers (1983) and Alston et al. (1995) also describe the process of adoption as

taking on a logistic nature hence used logit models for analysing adoption. The logit model

is the most suitable model for adoption studies because, the probability models used are

preferred to conventional linear regression models conceptually. This is possible due to the

fact that the model provides parameter approximations which are asymptotically regular

and well-organized. In comparison to the probit model, the logit model is simpler to use

(Hosmer and Lemeshow, 2000; Agresti, 2007 and Long, 1997). The logit model mostly

employs a logistic cumulative distribution frequency (cdf). The logit model is abbreviated

in equation 2a as:

Logit[P(y=1)] =α+βx (2a)

The variants of the logit model include the ordinary/ binary, the ordinal /ordered nominal

and the multinomial logistic models. Many other researchers are of the view that to adopt

or not is a matter of binary choice. Binary choice models often employed by empirical

literature are mainly probit and logistic regression. Gillespie and Lewis (2008) performed

probit analysis in their quest to identify factors influencing processors „willingness to adopt

a crawfish peeling machine. Other empirical studies on the other hand, avoid the probit

model because of its computational complexity. Such studies posit that, the logistic

regression would be an appropriate method since it makes no assumption of the distribution

of the dependent variable. Padaria et al., (2009) for instance maintain that such a dependent

variable needs not be normally distributed, linearly related or has equal variance within

each group hence they grouped respondents as adopters or non-adopters based on whether


27

they cultivate a particular crop. The empirical data of Padaria et al. (2009) have therefore

been analysed with the aid of the logit model.

i. Binary Logistic Model

The binary logistic model helps describe the relationship between X and Y by taking the

likelihood of an occurrence, p=P(Y=1) instead of Y. The binary logistic model is the

general model which involves the prediction of the likelihood of adoption of a specified

practice (Y) as a function of a vector of independent variables (X) Thangata et al. (2003).

P(Y=1) = F (β‟X) (2b)

P(Y=0) = 1-F (β‟X) (2c)

Where: Yi is the observed response for the ith observation of the response variable Y.

Yi = 1 for a farmer willing to adopt, and Yi= 0 for a farmer unwilling to adopt, the X's are a

set of explanatory variables. The function F could assume the formula of a standard,

logistic, or other likelihood function and uses supreme likelihood approximation to assess

the chance of definite affiliation. The binary logit model has been employed in the work of

Harper et al., (1990) on factors influencing insect management technology adoption;

adoption of the use of fertilizer by Kebede, Gunjal and Coffin (1990), misuse of the

application of pesticides by Tjornhom, (1995) and hybrid Cocoa by Boahene, Snijders and

Folmer, (1999).

ii. Multinomial logistic regression

In a situation where the response variable Y is separate with more than two groupings then

the binary logistic regression model is not appropriate. The multinomial logistic regression


28

is used to estimate the likelihood of group affiliation on a dependent variable based on

several explanatory variables. The independent variables can either be binary or

continuous. Multinomial logistic regression is a simple upgrade of binary logistic

regression that allows for more than two groups of the dependent variable. One possible

way to handle such situations is to formulate one model for the categorical response by

means of generalized logits (Schwab, 2002).

2.10.2 Probit Model

The Probit model is a specialized regression model of binomial response variables. The

model was employed in a research conducted by Jatoe (2000) to identify the factors of

adoption and effect of improved sorghum varieties in the Upper West Region of Ghana.

The researcher established that adoption was positively influenced by age of the farmer,

family labour availability, non-farm income, perception about varieties, farm size and farm

type, whilst extension contacts, length of fallow periods and distance to the nearest

purchase point for improved seed variety negatively affected adoption. Uaiene et al. (2009)

and Zavale et al. (2005) also used the probit model to estimate households‟ technology

adoption. In this study the adoption of improved maize and bean varieties depended on

unobservable utility index that are determined by household definite characteristics (e.g.

gender of family head, age, and educational level; contact with extension officers and

credit; membership in a farmer base organization). The probit model has also been used to

estimate the probability of household adopting improved varieties of common beans and

maize at both the regional and national levels in a study conducted by Lopes (2010). The

results of this study indicated that level of education of family head; extension contacts and


29

credit access influences the decision by households to adopt. These findings suggest that

the probability of households adopting improved technologies is high if support services

are available to them. Aquilar and Kohliman (2006) studying the readiness to eat transgenic

bananas in Costa Rica used the probit model. Alike model was used to estimate the

outcome of dissimilar variables such as animal sales, area cultivated and pesticides use on

the farmer‟s preparedness to accept a new transgenic variety. Also, Owuor et al. (2004) in

the determination of who would gain from adopting biotechnology maize in Kenya used

the Heckman‟s two stage model which involves the application of a probit model in the

first stage. A major disadvantage of the probit model is that unlike the Logit model, it

lacks flexibility since it does not simply integrate two or more prediction variables. There is

therefore a restriction on the use of the model in limited dependent variable models as

indicate by Montgomery et al. (2001).

2.10.3 The Count Data Model

Available literature on the use of count data model to analyse technology adoption engages

parametric specifications such as the Negative Binomial or Poisson model. The total

number of technologies adopted is the dependent variable whiles a number of farm level

features are independent variables. In circumstances where a lot of technologies are

accessible to farmers, technology adoption is further accurately established as a numerous

technology choice problem (Mensa-Bonsu et al., 2010). In the study by Mensa-Bonsu et al.

(2010), thirteen main technologies employed for land and water management are assessed

among maize farmers. The Count Data model is appropriate to model technology

selection, where the dependent variable is the sum of the number of selected technologies.

Benefit of this model is that it permits one to dodge creating robust assumptions about


30

relationships between technologies under investigation, as no arbitrary aggregation of

techniques is assumed (Mensa-Bonsu et al., 2010). The problem with using logit or probit

(dichotomous choice) is that there is measurement error that is induced in the dependent

variable. Dichotomous choice models are only theoretically appropriate when adoption is

truly binomial, as is often the case in the more homogeneous, technology-driven and

resource abundant production systems characteristic of developed countries (Ramfrez &

Shultz, 2000). The adoption of improved agricultural technologies in most developing

countries by smallholder farmers entails a complex process. Most technologies consist of

several practices that are designed to work together but then they can also be used

individually. Farmers prefer to modify and adopt individual practices of a group of practice

according to their means and perceived needs (Ramfrez, & Shultz, 2000). Event Count

Duration Regression Models (ECDR) (King, 1989) has proven to be very useful in

analysing adoption data from most developing countries. These models assume that the

dependent variable results from a counting of events using positive integer numbers.

Predicting the expected level of adoption by a farmer, given the type of extension

programme in which the farmer participated and his/her socioeconomic profile, the ECDR

models in this case have the biggest advantage. It is also straightforward when quantifying

the impact of each independent variable on the level of adoption (Ramfrez & Shultz, 2000).

2.11 Ranking Techniques

The purpose of ranking techniques is to prioritize issues and actions, determine the relative

strengths and weaknesses of alternatives create schedules, and decide which functions are

more important for an alternative. The common ranking techniques used are the Henry


31

Garrett Ranking Technique and the Kendall‟s Coefficient of Concordance.

The Garrett‟s ranking technique has been used in several studies. For instance, the

technique was used in ranking the constraints confronted by farmers in Pondicherry Union

Territory in milk production (Manoharan et al., 2003). This technique was also recently

used in 2010 to determine the major purposes of getting a loan in a study conducted by

Dhavamani (2010) which sought to analyse the enabling of rural women through self-

support associations in Sattur Taluk of Virudhunagar district located in the South Indian

state of Tamil Nadu. In this technique, the respondents are asked to rank the given problem

according to the magnitude of the problem. The results of such rankings were converted

into score value by using equation 2d.

Percent position = 2d

Where: Nj = Number of items ranked by jth individual.

Kendall‟s coefficient of concordance (W);

The coefficient is a statistical tool that is used to rank a given set of variables in order of

most critical to the least critical. It measures the degree of agreement among the rankings

by the individuals. In studies in which three or more groups create rankings of items this

procedure is useful. The level of agreement among the groups in ranking the items is

represented by the resulting statistics. Mumma et al., in 2000 employed the Kendall

Coefficient of Concordance (W) to rank motives for looking for registration to ISO 9000

standards in a study to analyse the supposed effect of ISO 9000 standards on U.S.

Agribusiness Sites.

The Kendall‟s coefficient of concordance (W) is represented by equation 2e as:


32

W= (∑T - (∑T)2) / m

2(n

2– 1) 2e

12 n

T = sum of ranks for each factor being ranked

m =Number of respondents

n = number of factors being ranked

The Kendall‟s Coefficient of Concordance is applied in this study since it is more robust in

comparison to other ranking techniques and is able test whether there is agreement among

rankers after ranking.

2.12 Constraints Facing Maize Farmers

Maize farmers face various constraints which increase the risk and uncertainty they

encounter during maize production and act as disincentives for increased maize production.

Van Rooyen et al. (1987) assert that if the technical and economic constraints face by

subsistence farmers in traditional agriculture are removed, they will commonly be able to

make balanced cost-effective decisions. Generally, maize farmers in developing countries,

such as Ghana, are confronted with major encounters such as land inaccessibility, lack of

general infrastructure, financial constraints for production inputs, poor mechanization

facility, transport, and inadequate extension services. The common constraints maize

farmers encounter can be grouped into two classes, namely internal and external

challenges. Internal challenges affect the farmers‟ ability to operate effectively. These

include shortage of labour, lack of experience and education. External constraints on the

other hand originate from the bigger agricultural setting and are principally not in the

control of the single farmer. These include limited availability of inputs, credit,


33

mechanization, problems associated with land tenure and poor institutional and

infrastructural support. Farmers will allocate resources reasonably to increase productivity

if these constraints are removed.


34

CHAPTER THREE

METHODOLOGY

3.1 Introduction

This chapter discusses the study area, the scope of the study, the theoretical and analytical

frameworks, the empirical analysis and methods employed to meet the objectives, the

sources of data as well as the procedure for data collection. Section 3.2 details the

conceptual framework of the study. Section 3.3 presents the main theoretical framework of

the study. Sections 3.4 and 3.5 presents how to assess the level of awareness and the extent

of adoption among maize farmers on improved maize technologies in the study area

respectively while section 3.6 shows how to analyse the constraints faced by maize farmers

regarding the use of improved maize technologies in the study area. Section 3.7 provides a

description on the constraints to improve technology adoption. Section 3.8 compares the

means of maize yield of both adopters and non-adopters of improve maize technologies

and, finally, section 3.9 entails how to identify factors that influence improved maize

technology adoption in the Kwahu Afram Plains North District.

3.2 Conceptual Framework

This study seeks to assess the adoption of improved maize technologies and maize yield by

maize farmers. The idea for this study is centred on the point that greater agricultural

productivity comes from applying improved farming technologies. There is a bigger

challenge getting the farmer to adopt even in assuming that superior technology is

available, in view of the fact that adoption involves learning new technology, new risk

taking, and fostering new relations (Edillon, 2010). From the conceptual model below


35

(Figure 3.1), farmers in adopting an improved maize technology (dependent variable) are

influenced by farming experience, farm size, group membership, extension contact, credit,

other income sources, age, and educational level (independent variables) etc. The ability of

farmers to adopt the improved technology leads to high crop yields which represents the

outcome of the decision by the farmer. It is typically shaped by factors such as local

government, community, policies budget allocation for the program in particular as well as

with overall infrastructure and economic development (Benin et al., 2009).

Figure 3.1: Conceptual Model

Dependent Variables

Source: Adapted from Rogers (2003)

3.3 Theoretical Framework

The decision to welcome an innovation emerges from two main theoretical frameworks.

The first part is the theory of diffusion which explains the process of adoption. Literature

Adoption of improved

maize technologies

Improve maize variety

Fertilizer application

Row planting

Plant spacing

Planting population

Seeding rate

Weed control etc.

Farmer experience

Farm size

FBO membership

Extension contact

Credit access

Age

Educational level etc.

Increase in

productivity

Increase in farmer’s

income

Food security

enhancement

Outcome

Independent Variables


36

on diffusion theory maintains that adoption process emerges slowly at the initial stages,

reaches its maximum and begins to decline. This is often depicted by an S‟ shaped curve.

The diffusion process is often influenced by the manner in which innovators translate the

message to the adopters. The second part of the framework is the theory of utility

maximization. This has to do with the behaviour of the consumer in maximizing

satisfaction. The maize farmers will therefore adopt the innovation if it yields maximum

satisfaction at a lower expense. This is believed to be influenced by the characteristics of

the consumer, commodity or service in question.

3.4 Assessing the Level of Awareness

Descriptive statistics in the form of percentages were used to analyse the data, whiles pie

charts and frequency tables were used to show the number of farmers who are aware as

well as those who are unaware of improved maize technologies in the study area.

According to Rogers (2003), the first step in the adoption process is generally perceived as

awareness of a need. To assess the level of awareness among farmers many different

methods have been used. A previous research by Bhatta et al., (2009) used qualitative

measures by asking farmers whether they knew of new technologies and the responses

were categorized as aware and unaware. Percentages of respondents aware and unaware

were then estimated.

3.5 Estimating the Level of Adoption

The level of adoption for this study is measured as the percentage of maize farmers who

have adopted improved maize technologies, expressed as a percentage.


37

This was estimated as;

Where:

n = Number of sampled maize farmers who have adopted improved maize technologies

N = Total number of maize farmers sampled

In this case, farmers who have used improved maize varieties and related agronomic

practices were classified as adopters, while farmers who uses local varieties were classified

as non- adopters. According to Bonabana-Wabbi (2002), when the proportion of adopters is

less than 25% (<25%), it implies the level of adoption is low. However, if the proportion of

adopters is greater than 75% (>75%), then the level of adoption is high.

3.6 Analysing the Constraints Faced by Maize Farmers in Improved Maize

Technology Adoption

The maize farmers‟ rankings of their constraints were collated to get the total score for each

constraint; the major constraints were identified by ranking of the constraints using total

score. The maize farmers identified constraints were ranked according to the most

important to the least important using numerals 1, 2, 3, ...... n, in that order using the total

score for each constraint. The least score rank was the most important, whiles the highest

score ranked as the least important.

The Co-efficient (W) analysis was employed to test the agreement in the maize farmers

ranking of the constraints facing them. The formula can be written as:

W= (∑T - (∑T)2) / m

2(n

2– 1)

12 n

The test of significance can be done using the F distribution:


38

F ratio = [(m-1) W / (1 – W)] and with degrees of freedom for numerator and denominator

given as V1 and V2 respectively (Edwards, 1964). Where:

• V1= [(n – 1) – (2/m)]

• V2= m-1[(n – 1) – (2 /m)]

• W = Kendall‟s Co-efficient of Concordance

• Z = the tested significance of Kendall‟s Co-efficient of Concordance

• T = Sum of ranks for each factor being ranked

• M = number of farmers‟ rankings the constraints

• N = number of constraints ranked by maize farmers

The following hypothesis was tested,

Where:

Ho is null hypothesis and

H1 is the alternate hypothesis.

Computing the total ranked scores for each constraint, the constraint with the least ranked

as the least pressing among the sample maize crop farmers selected for the study. The total

ranked scores collated is then used to calculate for the coefficient of concordance (W). The

limit of W must be positive and not more than 1. It ranges from 0 – 1. It will be 1 when the

ranks assigned by each ranker are exactly the same as those assigned by others, and it will

be 0 when there is maximum disagreement among the rankers (Mattson, 1986).

Statement of hypothesis:

Ho: there is no agreement in the rankings of the constraints by the maize farmers

H1: there is agreement in the rankings of the constraints by the maize farmers


39

Decision rule: If F calculated is greater than the F critical, then the null hypothesis is

rejected in favour of alternate hypothesis or otherwise the null hypothesis is not rejected.

Table 3.1: Constraints to Improve Technology Adoption presented to Maize farmers

to Rank

Number Type of constraint

1 Lack of access to improve maize seeds

2 High cost of improve maize seeds

3 Difficulty in getting land

4 Marketing of the produce

5 Lack of access to credit facilities

6 Unavailability in getting fertilizer to use

7 High cost of fertilizer

8 Lack of storage facilities

9 Pest and diseases

10 Effect of climate change

11 Labour difficulties

Source: Author’s compilation (2016)

3.7 Description of Constraints

Lack of access to credit facilities: Credit facilities were not available to most of the farmers

because they are unable to come up with collaterals and most often the interest rates

charged on loans from financial service providers are too high. This makes it difficult for

them to purchase technologically improved inputs that they need for crop production.


40

Lack of storage facilities: the lack of storage systems makes it difficult for farmers to adopt

maize technologies. Since these technologies are expensive the farmers do not see why

they should spend so much money in production only to lose it during storage.

High Cost of Production (High cost of improve maize seeds and high cost of fertilizer):

Cost incurred in crop production is a contributing factor that affects the farmer in the

adoption of technology. The choice to adopt is regularly a venturous one. According to

Caswell et al., (2001), this choice creates a move in farmers‟ decision to invest.

Difficulty in getting land: The respondents identified difficulty in accessing land as a

constraint in the adoption of technology in maize production. For most farmers in the study

area their lands were rented because they are not indigenes of their various communities.

Effect of climate change: Change in climatic conditions for most farmers in the study area

is a factor that inhibits their adoption of most technology.

Availability of inputs (Unavailability of fertilizer to use and Lack of access to improve

maize seeds): technologically improved inputs are expensive in the markets making it

difficult for farmers to access and adopt them.

Availability of labour: Access to labour would enable farmers use maize technologies such

as application of fertilizer to their crops and planting maize in rows. However, because

wage rates are high for labour, it inhibits the level of adoption of technology.


41

3.8 Comparing the Means of Maize Yield of both Adopters and Non-Adopters of

Improve Maize Technologies.

Maize yields in metric tonnes per hectare were computed and then compared between

adopters and non-adopters of improved maize technology using t-test.

The t-test is expressed as:

Where: X1 is yield of adopters, X2 is the yield of non-adopters

Hypothesis:

H0: There is no substantial distinction between maize yield of adopters and non-adopters

HA: There is a substantial distinction between maize yield of adopters and non-adopters

3.9 Identifying Factors that Influence adoption of Improved Maize Technology

Here the logistic regression was employed. Gujarati (2008) indicates that the logistic

regression model is often used to analyse binary choice responses. The underlying

economic theory on factors influencing the choice to adopt a given technology is based on

the notion that the farmers are reasonable beings. They are able to find out the potential

cost and benefits of a technology through their own way either by trying with the

technology or through analysis of secondary information from early adopters in the

community before deciding to adopt or not to adopt. Following from previous studies (e.g.,

Payne et al 2003; Lwayo et al., 2008; Baker, 1992) this study will employ the logit model

to analyse the adoption of improved maize technologies by farmers in the Kwahu Afram


42

Plains North District. The logit model was use because of its simplicity. The general model

is the binary choice model involving prediction of the likelihood of adoption of a given

practice (Y) as a function of a vector of explanatory variables (X):

Probability (Y=1) =F (β'X) 3.1

Probability(Y=0) =1–F (β'X) 3.2

Where; Yi is the observed response for the ith observation of the response variable Y.

Yi = 1 for a farmer adopting improved maize technology, and Yi = 0 for non-adoption of

improved maize technology, and X is a set of explanatory variables such as age, gender,

farmer group membership, other sources income, education level, gender, family labour,

access to credit and farm size etc. which determines the likelihood of adoption of improved

maize technology.

The logit model uses a logistic cumulative distribution function to estimate the probability

as follows:

P(Y) =1/P (1+Y) =Pi/1+Pi = eβx

3.3

In order to make the right-hand side linear, the logit transformation is applied by taking

logarithm of both sides, this is giving as:

LogP(Y) =α+eβX

3.4

The empirical model is specified as:

Logpt/(1-pt) =β0+β1AGE+β2GEN+β3EDU+β4FEXP+β5FBO+β6FSIZE+β7INCOME+

β8ACREDIT+β9EXTCONT + β10FLAB+……….. ԑi 3.5


43

3.9.1 Adoption level

An adopter in this study represents a maize farmer who cultivates an improved maize

variety whereas a non-adopter represents a maize farmer who does not cultivate an

improved maize variety (1=Adopter, 0= non-adopter).

3.9.2 Description of Explanatory Variables in the Model:

Age: (AGE) this represents the actual years of the respondents. Literature on adoption

studies denotes a positive or negative relationship between adoption and age. Age is a

continuous variable that can have either a positive or negative influence on adoption of

improve maize technology. Younger farmers may have a risk loving attitude and are more

likely to adopt. However, more elderly people may have social networks that can have a

positive influence on adoption. Age is also squared to identify any quadratic relationship of

respondents‟ age and adoption of the technology.

Family labour (FLAB): This is a continuous variable that was measured as the number of

persons staying and working with farmer‟s family. It is expected that large family size will

have large labour force that will serve as a substitute to the labour force and hence, will

have a positive effect on the probability of adopting improved maize technology. However,

small households will have to rely on additional expenses to hire labour and hence reduces

the probability of adoption.

Gender (GEN): this is represented by gender of the respondents, measured by a dummy

variable. A value of 1 is assigned to male and 0 for a female.


44

Farming experience (FEXP): this refers to the number of years the respondent has been

cultivating maize. It is a continuous variable and expected to have a positive influence on

the probability of adopting improved maize technology. More experienced maize farmers

might have had knowledge of several farming technologies including improve maize

technology and are likely to be adopters (Bahadur and Siegfried, 2004).

Education (EDU): this variable was estimated by the number of years that the respondent

has attained formal education. It is expected that the literacy level of a farmer increases his

or her awareness of improved technologies and positively influence adoption.

Extension contact (EXTCONT): this was measured as the number of calls done by

extension officer to a maize farmer within a year. Farmers who have frequent contacts with

agricultural extension agents tend to have more information about improved maize varieties

and also its agronomic practices than farmers who do not have extension contacts. It is

therefore expected that this variable positively influence farmer‟s adoption decision.

Farm size (FSIZE): this measures the number of acres or hectares of land under maize

cultivation by the farmer.

Farmer-based organization member (FBO): Group membership is a dummy variable

taking a value of 1 if an individual belongs to an FBO and 0 if otherwise. Group

membership is used to find the outcome of information accessibility on adoption of a

technology. Group members are better placed to have access to information on improve

maize technology and this is expected to have a positive consequence on the probability of

adoption (Nzomoiet al., 2007).


45

Other sources income (INCOME): Additional source of income is a dummy variable. A

value of 1 is assigned to a farmer with additional source of income and 0 if otherwise.

Maize farmers with additional sources of income to finance the cost of adopting a new

technology are likely to become adopters of improved maize technology. Bahadur and

Siegfried (2004) indicate that people with additional source of income are adopters of an

improve technology.

Credit access (ACREDIT): Credit availability stands as proxy for access to investment

capital. It is a dummy variable, taking a value of 1 if an individual accessed credit and0 if

otherwise. Maize farmers with access to credit will have upper hand in meeting the cost of

adoption of a technology than those relying only on their personal savings (Bahadur &

Siegfried, 2004). It is therefore expected that credit will have positive effect on the

probability of adopting improve maize technology.


46

Table 3.2 Variable Definitions, Units of Measurements and Hypothesized

Relationships

Variable Definition Measurement Sign

Dependent (Y) Adoption level Dummy (adopter=1; non-

adopter then 0)

Independent

AGE Age of respondent Measured in years +/-

GEN Gender of

respondent

Dummy (if male then 1; else

0)

+/-

FEXP Farming experience Measured in years +

EDU Educational level Number of years spent in

school

+/-

EXTCONT Extension contact Dummy (Extension contact

then 1; else 0)

+

FBO Group member Dummy (Group member then

1; else 0

+

ACREDIT Availability of

credit facility

Dummy (Received loan the 1;

else 0)

+

FLAB Family labour No. of family members

assisting in farm work

+

INCOME Non-farm income Dummy (have non-farm

income then 1; else 0)

+

FSIZE Farm size Measured in acres +

Source: Author’s Computation (2016)


47

3.9 Data Collection and Sampling Procedures

The data used in this study is mainly primary data that was collected from both adopters

and non-adopters of improved maize technologies in the study area. Primary data was

collected through the use of structured questionnaires as shown in Appendix A. A two-

stage sampling approach was used. In the first stage, ten (10) maize producing communities

were purposively selected based on their importance in terms of maize production. In the

second stage, a list of fifty (50) member maize farmer groups in each of the ten (10)

communities was made and twenty (20) farmers randomly selected using random numbers

generated with Microsoft Excel. Collection of the data was achieved through a one on one

interview with the selected farmers using the structured questionnaire which covered issues

such socioeconomic and demographic features, land tenure, farm management practices,

crop yields, other sources of income, access to credit, technology adoption, input use,

constraints to maize production and other agronomic practices. The questionnaire was pre-

tested in a pilot survey with fifteen (15) maize farmers‟ in one of the study communities

(Donkokrom) with the aim to help address any fundamental problems in the questionnaire

design. Donkokrom was selected for the pilot survey based on its proximity. It was noted

after the pre-testing that the questionnaire was too loaded with repeated questions and a

few questions were not clearly stated. The questionnaire was re-designed to obtain a

reliable version which was used as the data collection instrument for this study. The final

survey instrument was administered between 20th

January, 2016 and 10th

February, 2016.


48

Table 3.3 Distribution of Sample by Community

Number Community Sample frequency Percentage%

1 Donkokrom 20 10

2 Amankwakrom 20 10

3 Abomesalfo 20 10

4 Asikasu 20 10

5 Gyakyekrom 20 10

6 Sabiakrom 20 10

7 Adiembra 20 10

8 Avatime 20 10

9 Krakyekrom 20 10

10 Bodua 20 10

Total 10 200 100

Source: Field Survey (2016)

3.10 Software Applications used for Data Analysis

SPSS software was used for data entry and cleaning. STATA software was used for the

estimation of the logit results. Excel software was used for drawing graphs/charts and for

the descriptive statistics. Tables, graphs and charts were used to present the analysed data

for easy discussion and interpretation of the results.


49

3.11 The Study Area

The study was carried out in the Kwahu Afram Plains North District in the Eastern Region

of Ghana. A total of ten (10) communities were visited for the collection of data. A map of

the district showing the locations of the sampled communities is in Figure 3.1. These

communities were purposively selected due to their high maize production levels in the

district. The district was considered for the research since it is one of the highest maize

producing districts in the Eastern Region. The Kwahu Afram Plains North District started

as Kwahu North in 1988 as a Sub-district of the erstwhile Kwahu District Council. Kwahu

Afram Plains North District is one of the twenty-six districts in the Eastern Region and it is

located at the Northern part. Kwahu Afram Plains North District is located between

Latitudes 6o

40I

N and 70 10

‟1 N; longitudes 0

O 40

I E and 0

o 10

I E. It covers an area of

2,341.3 km2 and is considered one of the largest districts in the Eastern Region with regards

to its land area. The District shares boundaries to the south with Kwahu Afram Plains

South District, with the Volta River to the east, to the west with two Districts precisely the

Sekyere-East and Asante-Akim District in the Ashanti Region and to the north also with

Sene and Atebubu districts in the Brong Ahafo Region.

Kwahu Afram Plains North District has a population of approximately 218,235 inhabitants.

The male population of the district constitute 116,633 and 101,702 were females. The

growth rate for the District is estimated at 3.6% higher than the regional average of 3.2%.

It is male dominant with the males constituting about 53% and the women making up 47%.

The higher male population is due to typical migration. The population is scattered in about

244 towns, villages and hamlets spread over the 2,341.3 km2. Hundreds of these villages

are on islands and can only be reached by boat or canoe. The district generally has low


50

lying lands. Most of the plains fall below the 185 meters contour. The Afram River takes

its source from the hills lying to the north of Abofour in the Ashanti region. (District

Profile, KAPND, 2013). The major soil group in the District is the Swedru-Nsaba-Offin

Compound. The soils are suitable for the cultivation of both food crops and cash crops. The

land is prone to wind erosion when the vegetation cover is removed. Land is owned for

agricultural purposes either by outright purchase, share cropping (Abunu, Abusa),

Leasehold, Renting or Freehold. Mixed cropping, sole cropping, inter-cropping, mixed

farming (crop animal farming) land rotation and crop rotation are the farming systems

practiced in the area. Farm sizes in the district ranges between 1 – 5 hectares. The

predominant occupation in the District is subsistence agriculture employing 66.8% of the

total labour force, Trade and Commerce employs 12.5%, transport sector 11.5%,

professional, technical and related works constitute 9.2% while 3.7 % fall within the

administrative and managerial sector and others being 0.6%.


51

Figure 3.2

Source: District Profile, KAPND, 2014.


52

CHAPTER FOUR

RESULTS AND DISCUSSION

4.1 Introduction

This chapter presents the results and discussion of the results. The chapter opens with a

brief description of the socio-demographic characteristics of the farmers based on the

information obtained from the maize farmers in section 4.2. Section 4.3 assesses the level

of awareness of farmers on improved maize technologies in the study area while section 4.4

provides analysis of the constraints faced by maize farmers on improved maize technology

adoption in the study area. Section 4.5 compares the means of maize yield of both adopters

and non-adopters of improved maize technologies and the section 4.6 identifies factors

influencing improved maize technology adoption decision.

4.2 Demographic and Socio - Economic Characteristics of Maize Farmers

The demographic and socioeconomic characteristics of farmers such as gender, age,

educational level, marital status is presented in this section of the chapter. The farmers‟

type of land tenure system practiced and farm size are also presented.

Table 4.1 shows that out of the 200 maize farmers interviewed, 63.5 percent of them were

males whilst only 36.5 percent were females. Similarly, 81.5 percent of the respondents

were literates and only 18.5percent were illiterates. The breakdown of those who have

acquired formal education is as follows; 60 percent of the respondents ended their

education at the basic school level, while 14.5 percent had acquired secondary education,

with only 7.0 percent having tertiary education. The remaining 18.5 percent had no formal

education. The mean level of education attained by the respondents was 6.46 years and a


53

standard deviation of 4.332. The number of years of education ranges from 0 to 16 years.

Sixty seven percent of the respondents were married, with the remaining 34 percent being

either single, divorced or separated.

For the type of land tenure system practised, 17.5 percent practised share cropping, 10

percent had outright purchase, 28.5 percent farm on family land and 44 percent leased their

farm lands.

The mean number of years of cultivating maize was 15.5 years. The mean farm size was

1.3 hectares, whilst the mean age of the sampled maize farmers was 45 years


54

Table 4.1 Socio-demographic Characteristics of Respondents

Variable Category Respondents Percentage%

Gender Male 127 63.5

Female 73 36.5

Education (Formal) Primary 120 60

Secondary 29 14.5

Tertiary 14 7.0

None 37 18.5

Marital status Married 134 67.0

Others

66 33

Land tenure system Share cropping 35 17.5

Outright purchase 20 10.0

Family land 57 28.5

Lease 88 44.0

Table 4.2: Socio-Demographic Characteristics (Continued)

VARIABLE MEAN RANGE MIN MAX

Age 45 40 29 69

Farm size 1.3140 3.60 0.4 4.0

Farming experience 15.4750 46.0 1.0 47

Source: Survey Results, (2016)


55

4.3 Level of Awareness

Maize farmers who were aware of the existence of improved maize technologies in the area

were predominant. The total numbers of maize farmers who are aware of improved maize

technologies are 99.5% of the numbers interviewed and those who were unaware of

improved maize technologies were only 0.5% of the sample.

4.4 The Extent of Adoption

Figure 4.1 shows that 79.5% of farmers in the study area have adopted improved maize

technologies leaving only 20.5% as non- adopters. According to Bonabana-Wabbi (2002),

when the proportion of adopters is less than 25%, it implies the level of adoption is low and

when it is 75% it is considered as high. From Table 4.3 and Figure 4.1, the proportion of

adopters is higher than 25% but greater than 75% indicating that the extent of adoption is

very high.

Table 4.3 Distribution of Extent of Adoption

Adoption level Frequency Percent Valid percent Cumulative percent

Non-adopters 41 20.5 20.5 20.5

Adopters 159 79.5 79.5 100.0

Total 200 100.0 100.0

Source: Survey Results (2016)


56

Figure 4.1: Level of Adoption of improved maize Technologies

Source: Survey Data (2016)

4.5 Constraints Faced by Maize Farmers in Improved Maize Technology Adoption

The Kendall‟s Coefficient of Concordance (W) was employed to test the agreement in

ranking of the constraints facing the farmers. The F-test is used to test for the significance

of Kendall‟s Coefficient of Concordance. Table 4.4 shows that the Kendall‟s coefficient

(W) is 0.761 which gives an indication that there is a 76.1% agreement in the ranking of

constraints among the respondents and F-calculated was found to be 1521.90. The

Kendall‟s Coefficient of Concordance, W, was at the 1% level of significance with respect

to the asymptotic significance of 0.000 (Siegel et al., 1988). The Coefficient of

Concordance (W) was tested for significance in terms of the Chi-distribution. The null

hypothesis (Ho) is rejected in favour of the alternate.

Table 4.4 shows the ranking of the constraints that hinder maize technology adoption by

maize farmers in the Kwahu Afram Plains North District. The farmers ranked High cost of

Production, Credit access, High Cost of labour, Low produce Price, Difficulty in getting

Non- -adopters Adopters


57

land, Fertilizer unavailability, Effect of Climate change, Lack of storage Facilities, High

cost of improve seed, Pest and Diseases, Improved Seed Accessibility as the first, second,

third, fourth fifth, sixth, seventh, eighth, ninth, tenth and eleventh least pressing constraints

affecting maize technology adoption by maize farmers in the Kwahu Afram Plains North

District.

Among all the eleven constraints that were ranked among maize famers, high cost of

production, lack of credit access and high cost of labour are ranked as the three most severe

constraints facing maize famers in the study area. This implies that the cost incurred in crop

production is a contributing factor that affects the farmer in the adoption of improved

technology. The choice to adopt is regularly a venturous one. According to Caswell et al.

(2001), this choice creates a move in farmers‟ decision to invest.

Credit facilities are not available to most of the farmers because they are unable to come up

with collaterals and most often the interest rates charged on loans from financial service

providers are too high. This makes it difficult for them to purchase technologically

improved inputs that they need for crop production.

Access to labour would enable farmers use maize technologies such as application of

fertilizer to their crops and planting maize in rows. However, because wage rates are high

for labour, it inhibits the level of adoption of technology.


58

Table 4.4 Farmers’ Ranking of Maize Production Constraints


4.5 Means of maize yield comparison between adopters and non-adopters.

Table 4.5 presents the results of the difference in mean yields of maize between adopters

and non-adopters. P value of less done 5% shows the significance in the means of maize

Constraints Mean Rank Rankings

High cost of Production 1.68 1

Credit access 2.00 2

High Cost of labour 4.00 3

Low produce Price (marketing) 4.94 4

Difficulty in getting land 5.26 5

Fertilizer unavailability 5.65 6

Effect of Climate change 6.03 7

Lack of storage Facilities 8.13 8

High cost of improve seed 8.50 9

Pest and Diseases 9.47 10

Improved Seed Accessibility 10.35 11

No. of observations 200

Kendall's Wa .761

Chi-Square 1521.903

Degree of freedom 10

Asymptotic. Sig. .000


59

yields between adopters and non- adopters and therefore the null hypothesis is rejected in

favour of the alternative hypothesis. It indicates that adopters of improved maize

technologies have higher yields than non-adopters.

Table 4.5: T-Test Results from Yields of Maize of Adopters and Non-Adopters

Adopters Non-

adopters

Indicators

Observations Mean Mean T-test Degree of

freedom

Significance Decision

200 1.42 1.34 1.67 198 5% Reject

Source: Survey results (2016)

4.6 Factors that Influence Improved Maize Technology Adoption

The logistic regression model results in Table 4.6 presents a Likelihood Ratio Statistic of

55.94% and a chi square distribution at 10 degrees of freedom which is significant at 1%.

This indicates that a maize farmer‟s decision to adopt the improved maize technology or

not is jointly explained by the explanatory variables. The estimated model depicts a model

with good overall fit, being significant at 1%. Six out of the 10 covariates were observed to

have a statistically significant influence on the probability of adopting the improved maize

technology. They include EXTCONT, FBO, AGE, EDU, FSIZE and FEXP. All the

significant variables are in conformity with the apriori expectation.


60

Table 4.6 Logistic regression model showing results of the factors influencing the

adoption of improved maize technology

Variables Coefficients Standard Error Z P>│Z│

ACREDIT 0.2339027 0.6198549 0.38 0.706

EXTCONT 1.691295*** 0.660569 2.56 0.010

FBO 3.101732** 1.357873 2.28 0.022

INCOME -0.3111842 0.6910484 -0.45 0.65

GEN -0.4504508 0.613747 -0.73 0.463

FLAB 0.1104558 0.2411702 0.46 0.647

FEXP 0.344647*** 0.0791239 4.36 0.000

EDU 0.3488486*** 0.0787157 4.4 0.000

AGE -0.1082119*** 0.0418903 -2.58 0.010

FSIZE 0.9861639** 0.5016663 1.97 0.049

Constant -5.003969 2.135089 -2.34 0.019

Observations 200

Probability> chi2 0.0000

Pseudo R2 0.5594

LR chi2(10)

Log likelihood

113.50

-44.701669


From Table 4.6, extension contact variable was significant at 1% and had a positive effect

on improved maize technology adoption. This result is similar to that of Adeogun et al.

*=significant at 10%, ** = significant at 5% and *** = significant at 1%


61

(2008) who found a positive association between extension access and improved maize

technology adoption. The more extension agents visit farmers and introduce or educate

them on the benefits of adopting new improved technologies, the greater the likelihood for

farmers in adopting new technologies. Studies conducted by Hassan et al. (1998) and

Salasya et al. (1998) reveal that better access to information from extension agents

significantly affects adoption. Kafle and Shah (2012) also endorse this by concluding their

research work that it is important to enhance the activities of extension officers because

they have a positive influence on technology adoption. A study by Ajayi and Solomon

(2010) also found that extension officers perform an important task by sending information

on adoption of technologies through to farmers and for enhancement in crop cultivation.

On another similar vein, Yaron, et al., (1992) points out that the positive effect of extension

agent‟s promotional activities can counter the bad effect of lack of formal education in the

total choice by farmers to adopt certain new technologies.

Farmer based organization (FBO) member variable was found to be statistically significant

at 5% with a positive sign. This implies that maize farmers who belong to a farmer group

are more likely to adopt improved maize technologies than those who do not belong to any

farmer group. This observation is similar to the findings of Ntege Nanyeenya et al. (1997)

as they found a positive relationship between farmer-based organization membership and

adoption of improved maize production technologies. They also reported that information

flows easily in such associations. A farmer belonging to a group or a farmer based

organization is one factor that influences technology adoption positively. This reiterates the

observations of Kafle and Shah (2012) that farmers‟ adoption of technologies is positively

influenced by their membership in a farmer based group and cooperatives. Hassan et al.


62

(1998) and Salasya et al. (1998) in their studies also concluded that factor such as a

farmer‟s affiliation to a village group considerably affects adoption.

The model estimation results indicate that experience in maize farming was significant at

1% in the study area and exerted a positive effect on adoption of improved maize

technology in the area. This means that an increase in the experience of a maize farmer by

one year increases his or her likelihood of adopting improved maize technology by 0.34.

This reveals that the more the experience of a maize farmer, the greater the level of

adoption of improved maize technology. This finding is similar to the observation made by

Kebede (1992) who found experience to positively affect adoption of innovations. Also,

more experienced maize farmers might have had knowledge of several farming

technologies that may include improved maize technology and are likely to be adopters

(Bahadur & Siegfried, 2004).

The education variable positively influenced the adoption of improved maize technology

by maize farmers in the study area at a 1% significance level. It indicates that the higher the

level of education of a maize farmer, the greater the probability of adopting improved

maize technology compared to the less educated ones. This means that a year increase in

the educational level of a maize farmer will increase the likelihood of adopting an

improved maize technology by 0.35. This result is similar to the observation of Buyinza et

al. (2008) as they found out that a positive association exist between education and

adoption. However, the results of Oyakele et al. (2009) found a negative relationship

between education and adoption.

Age of the maize farmer variable has a negative relationship with adoption and was found


63

to be significant at 1%. This is theoretically consistent since young individuals are often

less risk averse compared to older ones. Such older maize farmers are most pessimistic

about the outcome of the innovation and hence become non-adopters of the technology.

Increasing the age of the farmer by 1 year decreases the likelihood of adopting the

improved maize technology by 0.11. This is contrary to the finding by Lapar et al. (2004)

who found that age positively affects the adoption of dual-purpose forages in the

Philippines Upland. Other researchers such as Feder et al. (1984) and Fernandez-Cornejo et

al. (2003) got similar results. Also, Pannin (1988) illustrated that leaders and grown-ups in

the traditional African cultures are generally accepted as superiors because experience and

decisions relating to adoption of innovations are positively influenced by the age factor.

Farm size was found to be significant at 5% and had a positive influence on improved

maize technology adoption. This suggests that farmers with smaller farm sizes are less

likely to adopt improved maize technologies. Thus, increasing the farm size of a maize

farmer by one hectare increases the likelihood of such farmer adopting improved maize

technology by 0.99%.


64

CHAPTER FIVE

SUMMARY, CONCLUSIONS AND RECOMMENDATIONS

5.1 Introduction

The summary of findings, conclusions and recommendations are presented in this chapter.

Section 5.2 reports the summary of the findings of the study. The conclusions and

recommendations of the study are reported in sections 5.3 and 5.4 respectively.

5.2 Summary

The agricultural sector is the leading source of employment and a major source of income

in Ghana. However, productivity levels are extremely low despite the critical role the sector

plays in the economy. The study assesses the adoption of improved maize technologies and

maize yield that were promoted in the Kwahu Afram Plains North District. To address the

objectives set for the study, structured questionnaires were administered to two hundred

(200) maize farmers in the study area using a two-stage sampling approach. In the first

stage, ten (10) maize producing communities were purposively selected based on their

importance in terms of maize production. In the second stage, a list of fifty (50) member

maize farmer groups in each of the ten (10) communities was made and twenty (20)

farmers randomly selected using random numbers generated with Microsoft Excel.

Collection of the data was achieved through a one on one interview with the selected

farmers using the structured questionnaires which covered issues such socioeconomic and

demographic features, land tenure, farm management practices, crop yields, other sources

of income, access to credit, technology adoption, input use, constraints to maize production

and other agronomic practices. In addition, literature was reviewed and research scientists


65

from CRI of CSIR were interviewed to identify some of the improved maize varieties and

their associated agronomic practices that have been developed and released to farmers in

Ghana.

A close ended question was used to assess the maize farmers‟ awareness level on improved

maize technologies and this was analysed with simple descriptive statistics. The two mean

comparison test was used to analyse maize yield difference between adopters and non-

adopters of improved maize technologies and constraints to improved maize technology

adoption were identified and ranked with the Kendall‟s coefficient of concordance. The

logit model was used to determine the factors influencing improved maize technology

adoption.

In this study, four (4) different improved maize technologies were identified based on the

type of improved maize variety use by the farmers. Three out of these technologies;

Obatanpa, Mamaba and Golden crystal were released by the Government of Ghana through

Ministry of Food and Agriculture while Panaar was released by a private agency. Only

0.05% of farmers were not aware of improved maize technologies. There is therefore a

high level of awareness of improved maize technologies in the study area. It was revealed

in the review of literature that the individual decision making process that leads a farmer to

adopt an innovation entails five categories of determinants. Such as geographical,

institutional and social environment, farm structure and technological constraints, farmers‟

socio-demographic characteristics and personal attitudes, attributes of the innovation, as

well as policy and market attributes. Findings made from this study showed that there is a

difference in yield of adopters and non-adopters of improved maize technology adoption.

The number of visits by AEAs (Agricultural Extension Agents) or extension contacts,


66

educational level, maize farming experience, age of farmer, farm size, and farmers

belonging to a farmers‟ group (FBO) were the factors found to have a significant effect on

improved maize technology adoption. The age of the farmer was the only factor that had a

negative influence on improved maize technology adoption. However, the gender of the

farmer, access to credit, family labour and other sources of income were also not

significant in identifying the factors that influence adoption of improved maize technology

among maize farmers.

5.3 Conclusions

1. A high percentage of maize farmers in the Kwahu Afram Plains North District are

aware of improved maize technologies and the extent of adoption is very high. This

is as a result of farmer‟s acquired knowledge through regular extension contacts the

farmers receive and active group participation.

2. High cost of production, lack of credit access and high cost of labour are ranked as

the most severe constraints facing maize famers in the study area. This points to the

fact that the extent of adoption would have been higher if these constraints were not

in place.

3. Adoption of improved maize technology has a significant effect on the yield of

maize. This means that adopters of improved maize technologies have better maize

yields than non-adopters.

4. Age of a farmer limits technology adoption. This suggests that a younger farmer

has higher chance adopting of improved maize technology.


67

5. Large farm size, formal education, farmer based organization membership, and

extension contacts are important factors that facilitate adoption of improved maize

technologies

5.4 Recommendations

1. Programmes such as the Ghana Grain Development Project, launched in the early

1990s, in collaboration with Monsanto, Sasakawa Global 2000, the Food Crops

Development Project (FCDP) and the Ministry of Food and Agriculture (MOFA)

should be encouraged to develop more maize programmes which aim at developing

improved maize technologies to help boost maize productivity in Ghana.

2. Government should design strategic and sustainable input subsidy mechanisms to

augment the constraint of high cost of production.

3. Government and donor agencies should increase funding for technology

dissemination and adoption projects. There should be a budget not only for the

development of improved technologies but also for the promotion of these

technologies.

4. Maize farmers should encourage the formation of FBOs and be motivated to

welcome ideas of extension agents to acquire more knowledge about improved

maize technologies. Maize farmers should see farming as a business to keep

appropriate records and also commercialize their farms for greater returns.

5. Maize farmers should adopt improved maize technologies in their farm business to

obtain greater yields for higher returns.


68

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79

APPENDICES

APPENDIX A: Survey Questionnaire

DEPARTMENT OF AGRICULTURAL ECONOMICS AND AGRIBUSINESS

UNIVERSITY OF GHANA, LEGON

ADOPTION OF IMPROVED MAIZETECHNOLOGIES AND MAIZE YILD IN THE

KWAHU AFRAM PLAINS NORTH DISTRICT

QUESTIONNAIRE FOR FARMER SURVEY

Background: A study of the socio-economic, demographic characteristics, technical, and

institutional factors affecting the adoption of improved maize technologies and maize

yield in the Kwahu Afram Plains North District.

Name of community.................................................................................................................

Date...........................................................................................................................................

Section A: Demographic/ Socio-economic factors

1. Name of respondent............................................................................................................

2. Age of respondent................................................................................................................

3. Sex: a. Male b. Female

4. Marital status: a. Single b. Married c. Divorced d. Separated

5. Religion: a. Christianity b. Traditional c. Islam d. Others

6. Educational level: a. Primary b. Secondary c. tertiary d. No education

7. What is your major occupation? a. Farming b. Trading c. Other

(specify).................................................................................................................................

8. What is the size of your maize farm? .......................................................................acres

9. What type of land tenure system do you practice? a. Share cropping b. Outright

purchased c. Family land d. Lease


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10. Do you have other sources of income? a. Yes b. No

11. If yes, state them: a ………………………b …………………......................................

12. Do you employ labour to assist you in your farm work? a. Yes b. No

13. How many members make up your household? ...............................................................

14. Is any member of your household a salaried worker from another job? a. Yes b. No

15. How many family members often assist you in your farm work?

.................................................................................................................................................

Section B: Technical Factors

16. Do you have an idea of any maize technology? a. Yes b. No

17. Name any variety of maize that you know …………………………….........................

18. Which variety have been cultivating for the past ten years? a. Local variety b. mamaba

c. Golden crystal d. Obatanpa e. Panaar

19. Do you plant in rows? a. Yes b. No

20. If yes, provide a reason………………………………………………………...........

21. If no, provide a reason………………………………………………………...................

22. Do you apply fertilizer to your field? a. Yes b. No

23. If yes, what quantity of compound fertilizer (NPK) do you normally apply to an acre of

maize field? a.20kg b.25kg c. 50kg d. 100kg

24. At what period do you apply compound fertilizer to your maize field? a. Two weeks

after planting b. Three weeks after planting c. One week after planting d. A month after

planting

25. What quantity of sulphate of ammonia do you normally apply to an acre of maize field?

a. 20kg b. 25kg c. 40kg d. 50kg.

26. At what period do you apply sulphate of ammonia to your maize field? a. Two weeks

after planting b. Three weeks after planting c. One week after planting d. A month after

planting

27. How do you normally apply fertilizer on your maize farm?


81

a. Broadcasting. b. Placement method. C. Dig and burry.

28. How many maxi bags of maize do you normally harvest per acre of maize field?

............................. maxi bags.

29. Are plant fertilizers readily available to you in your area? a. Yes b. No

30. What is the cost of 50kg of compound fertilizer? a. GHc50.00 b.GHc100.00

c.GHc80.00 d.GHc150.00

31. What is the cost of 50kg of Sulphate of Ammonia fertilizer? a. GHc50.00 b.GHc100.00

c.GHc80.00 d.GHc150.00

32. How would you rate the cost fertilizer in your area? a. High b. Low c. Normal

33. Are improved maize seeds readily available to you? a. Yes b. No

34. What is the cost of 1kg improve maize seeds? a. GHc3.00 b.GHc6.00 c.GHc5.00

d.GHc4.00

35. How would you rate the cost of improve maize seeds in your area? a. High b. Low c.

Normal

36. What is the percentage of seed germination after planting? a.100% b.80% c.90% d.70%

Section C: Institutional Factors

37. Do extension agents visit your farm regularly? a. Yes b. No

38. If yes, how often? a. Weekly. b. Fortnightly. C. Monthly. D. Yearly

39. Do you know the extension agents advice on how to prepare your maize field? a. Yes b.

No

40. Do the extension agents advice you on how to plant your maize seeds? a. Yes b. No

41. Do the extension agents advice you on how to apply fertilizer on your maize field? a.

Yes b. No

42. Have you made any changes in your farming practices over the last ten years? a. Yes b.

No

43. If yes, what changes have you made? a. ………………………………...................

b. …………………………………….. c. …………………………………......................


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d. …………………………………………………………………………….......................

44. Do you have maize farmer‟s group in your area? a. Yes b. No

45. If yes, which one do you belong to? a. maize processors group b. maize marketing

group c. Maize producers group d. other

46. Have your group ever requested for a loan? a. yes b. no

47. Do you get readily access to loans or credit facility? a. Yes b. No

48. Please respond to the questions in the table below in relation to sources of information

on maize technologies:

Source of information Ease of access to source of

information

a. Good b. very good c. Excellent

d. poor

Quality of information

a. Good b. Very good c.

Excellent d. poor

Extension

agents/Researchers

Farmer groups

Other individual farmers

Radio/Television

Internet/newspapers

Community information

centres

49. Kindly tick in the appropriate box the constraints inhibiting the adoption of improved

maize technologies in your area:

Constraints Tick appropriate one

Lack of access to improved maize seed

High cost of improved maize seed

Access to land

Marketing of the produce

Lack of access to credit facilities

Unavailability of fertilizer for use

High cost of fertilizer

Lack of storage facilities

Effect of climate change


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Pest and diseases

High cost of production

50. Please rank from 1 for the most important constraint to 11for the least important

constraint

Constraints Rankings

Lack of access to improved maize seed

High cost of improved maize seed

Access to land

Marketing of the produce

Lack of access to credit facilities

Unavailability of fertilizer for use

High cost of fertilizer

Lack of storage facilities

Effect of climate change

Pest and diseases

High cost of production

Formal Institutional Questionnaire-Extension Officers survey

Introduction: This study is aimed at analysing the nature of adoption of improved maize

technologies and its agronomic practices by farmers in the Afram Plains North District.

Dear respondent your confidentiality is guaranteed.

A: General Background Information

Name of organization...............................................................................................................

Designation of respondent.......................................................................................................

District.......................................................................................................................................

Date of interview.......................................................................................................................

Employer: ………………………………………….................................................................

B: Socio-demographic characteristics

1. Age: ……………………………………………………………………..............................

2. Sex: a. male b. female


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3. Level of education: a. Secondary b. Agricultural College c. University

4. Marital status: a. single b. married c. divorced

5. Religion: a. Traditional b. Christianity. c. Islam

C: Extension services and approaches

6. What is the average farm size of your maize farmers? .....................

7. What other crops do your farmers cultivate? List them a.…………………..

………………………… b. ………………………………………………………

c. …………………………………………………………………………………...........

8. Do you have specific communities or a number of farmers that you visit on regular

basis? a. Yes b. No

9. If yes, how often do you visit a community?a. Weekly b. fortnightly c. monthly

10. What extension method do you often use to disseminate information to your farmers?

a. One- on- one method b. group method c. both methods

11. Which maize technology have you recommended to your farmers in the last ten years?

………………………………………………………………................................................

12. Why do you recommend this technology to them? a. High yields b. Early maturing c.

Drought resistance d. disease resistance

13. Are you satisfied with the way in which your farmers adopt new technologies with

regards to row planting, use of improved maize varieties, and recommended way of

fertilizer application? a. Yes b. No

14. If no, what reasons do farmers give for not following your recommendations?

(a).............................................................................................................................................

(b).............................................................................................................................................

(c)..............................................................................................................................................

(d)……………………………………………………………………………….....................

15. What are some of the challenges that you face in your extension service delivery?

(a) ………………………………………………….................................................................

(b)……………………………………………………………………………......................


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(c) …………………………………………………………………………..........................

Questionnaire for Seed System Actors Survey

Background: A study of the socio-economic, demographic characteristics, technical, and

institutional factors affecting the adoption of improved maize technologies in the Kwahu

Afram Plains North District:

Name of community.............................................................................................................

Date......................................................................................................................................

1. Name of respondent..........................................................................................................

2. Age of respondent...........................................................................................................

3. Sex: a. Male b. Female

4. Marital status: a. Single b. Married c. Divorced d. Separated

5. Religion: a. Christianity b. Traditional c. Islam d. Others

6. Educational level: a. Primary b. Secondary c. tertiary d. No education

7. What is your major occupation? a. Improve maize seed production b. retailing of

improve maize seeds c. Other (specify) ………………………………………..

8. Name the variety of maize seeds that you produce/sell a. Local variety b. mamaba

c. Golden crystal d. obatanpa e. others (specify)……………………………..

9. Why do you grow/sell this variety? a. Early maturing b. Preferred by most maize

farmers‟ c. Easy to produce

10. How would you rate the yields from the varieties you produce/sell? a. High b. Low c.

Normal


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APPENDIX B: PLAGIARISM CHECK ORIGINALITY REPORT

Turnitin Originality Report

Processed on: 28/7/2016

ID: 692371545

Word Count: 15708

PLAGIARISM CHECK ANALYSIS for ADOPTION OF IMPROVED MAIZE

TECHNOLOGIES AND MAIZE YIELD IN THE KWAHU AFRAM PLAINS NORTH

DISTRICT by WILLIAM OWUSU (10507163)

Similarity Index: 12%

Similarity by Source;

Internet: 5%

Publications: 7%

Student Papers: 6%


ADOPTION OF IMPROVED MAIZE TECHNOLOGIES AND MAIZE …

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