FARMERS’ WILLINGNESS TO PAY FOR COCOA GRAFTING 

IN THE EASTERN REGION OF GHANA 

 

 

 

 

 

 

 

DOMPREH ERIC BRAKO 
 

 

 

 

 

 

THIS THESIS IS SUBMITTED TO THE UNIVERSITY OF GHANA, LEGON IN PARTIAL 

FULFILMENT OF THE REQUIREMENTS FOR THE AWARD OF MASTER OF 

PHILOSOPHY DEGREE IN AGRIBUSINESS 

 

 

DEPARTMENT OF AGRICULTURAL ECONOMICS AND AGRIBUSINESS, SCHOOL OF 

AGRICULTURE, COLLEGE OF BASIC AND APPLIED SCIENCES, UNIVERSITY OF 

GHANA, LEGON 

 

 

 

 

 

 

 

 

 

 

                                                                                                                     JULY, 2015 

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DECLARATION 

I, Dompreh Eric Brako, do hereby declare that except for the references cited, which have been 

duly acknowledged, this thesis titled “Farmers’ Willingness to Pay for Cocoa Grafting in the 

Eastern Region of Ghana” is the product of my own research work in the Department of 

Agricultural Economics and Agribusiness, University of Ghana from July 2014 to July, 2015. I 

also declare that this thesis has not been presented either in whole or in part for another degree in 

this University or elsewhere.  

…………….………………….. …………………………….  

Dompreh Eric Brako  

(Student)  

 

 

 

 

 

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

…………………………………                                                ……………………………  

Dr. Henry Anim-Somuah                                                           Prof. Daniel Bruce Sarpong       

(Major Supervisor)         (Co-supervisor) 

Date………………………………                                             Date……………………….........  

 

 

 

 

 

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DEDICATION 

I dedicate this work to my inspiring aunt, Miss Augustina Afum and to my parents Mr. Maxwell 

Dompreh and Esther Ampomaah. 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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ACKNOWLEDGEMENT 

I wish to express my heartfelt appreciation to the Holy Spirit for his sufficient and unfailing grace 

throughout my life especially during the period of writing this thesis.  

I register my sincere gratitude to my main supervisor, Dr. Henry Anim-Somuah for working 

relentlessly to give shape to this work through his mentoring, guidance, necessary and timely 

comments and suggestions. I am also very grateful to my co- supervisor Prof. Daniel Bruce 

Sarpong for conceiving the idea for this research area and setting me up for the study. Thanks for 

the many hours you both spent guiding me to come out with the best. I am also indebted to all 

lecturers of the Department of Agricultural Economics and Agribusiness, University of Ghana for 

the support and advice given me towards this work not forgetting Dr. John Kuwornu and Dr. Yaw 

Osei-Asare.  

 

I am indeed grateful to the Leventis Foundation for awarding me with a thesis scholarship to 

successfully carry out this work. I wish also to acknowledge Dr. G. Anim-Kwapong (Director of 

CRIG), Mr. Richard B. Armah of CRIG, Mr. Charles Gyamfi of CHED (Tafo District) and Mr. 

Peter Agyekum of SPU for the support and contribution given me during the data collection and 

writing of this thesis. I wish to thank specially Miss Eunice Dadzie, Mr. Alfred Narh, Mr. Derrick 

Taylor, Sergyiman Kwao, Douglas Attoh, Matthew Eweh and Sarah Koduah for their contribution 

to this piece. I extend my appreciation to my wife, Mrs. Joana Okailey Dompreh and my bosom 

friend, Mr. Peter Koomson for their support throughout this period of my academic life. 

 

I am also indebted to all the respondents from whom the data was gathered for their sacrifice of 

time and effort. Finally, to all those who have contributed in diverse ways to the success of the 

work I say thank you. 

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ABSTRACT 

Cocoa production is very critical to the livelihood and survival of many smallholder farmers in 

Ghana. However, one of the challenges of the sector is low yields which is partly attributed to the 

widespread presence of old cocoa trees. It is therefore imperative to provide solutions to improve 

yield, of which the side grafting technology is a proven method. The study assesses Farmers’ 

Willingness to Pay for Cocoa Grafting in the Eastern region of Ghana. The research sought to find 

the level of awareness of side grafting in cocoa among farmers, the amount farmers are willing to 

pay for cocoa side grafting as well as the factors influencing willingness to pay amount. The multi-

stage sampling technique was used to sample 217 cocoa farmers in the Eastern region. A well-

structured questionnaire was used to solicit data from farmers. The contingent valuation method 

was used to estimate the willingness to pay amount of cocoa farmers. The double hurdle model 

was then used to estimate the factors influencing willingness to pay for side grafting. From the 

analysis of the data, it was realized that majority (87%) of the cocoa farmers are unaware of side 

grafting as a canopy substitution method, with majority (89%) willing to adopt the technology 

should it be introduced. The contingent valuation analysis also revealed that the maximum amount 

farmers are willing to pay for a side grafted cocoa tree is GH₵10 and the minimum amount is 

GH₵.10. The average WTP amount is GH₵2.84.  On adoption of side grafting, farmers’ yield of 

0.98 tons is higher as compared to the national average of 0.4 tons. The percentile distribution of 

WTP amounts show that at the 25th percentile, farmers are willing to pay GH₵0.50 or less. The 

result also showed that education, frequency of extension visit, age of cocoa farm, income from 

cocoa and household head status positively influence farmers’ WTP for side grafting whiles farm 

size negatively influences WTP. Furthermore, education, frequency of extension visit, age of farm, 

awareness, income from cocoa, and household head status positively influence farmers’ WTP 

amount for side grafting whiles farm size and yield negatively influences farmers’ WTP amount. 

The study recommends that demonstration centers should be set up in various areas of the region 

to aid acceptance of cocoa side grafting among farmers. Moreover, FBOs should be supported to 

enhance their capacity so they can effectively educate farmers on the technology. For a wider 

coverage of the side grafting technology, implementing institutions could peg the price for side 

grafting a cocoa tree at GH₵0.50 or less. Produce buying companies and NGOs could also 

intervene by providing the technology for farmers on deferred payment.  

 

 

 

 

 

 

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TABLE OF CONTENTS 

 

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

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

ACKNOWLEDGEMENT ............................................................................................................. iii 

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

TABLE OF CONTENTS ................................................................................................................ v 

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

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

LIST OF ABBREVIATION ........................................................................................................... x 

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

INTRODUCTION .......................................................................................................................... 1 

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

1.2 Problem statement ..................................................................................................................... 5 

1.3 Objectives of the study.............................................................................................................. 8 

1.4 Relevance of the study .............................................................................................................. 8 

1.5 Organization of the study .......................................................................................................... 8 

CHAPTER TWO ............................................................................................................................ 9 

LITERATURE REVIEW ............................................................................................................... 9 

2.1 Cocoa production: Global Perspectives .................................................................................... 9 

2.2 Overview of Cocoa Industry in Ghana ................................................................................... 11 

2.3 Cocoa Yield Improvement Intervention Strategies ................................................................. 14 

2.3.1 Cocoa Rehabilitation Programme .................................................................................... 16 

2.4 Measuring Willingness-To-Pay .............................................................................................. 19 

2.5 Contingent Valuation Method and Elicitation methods ...................................................... 21 

2.6 Factors influencing willingness to pay for technology ........................................................... 24 

2.6.1 Socioeconomic characteristics ......................................................................................... 25 

2.6.1.1 Age............................................................................................................................. 25 

2.6.1.2 Gender ....................................................................................................................... 26 

2.6.1.3 Educational attainment .............................................................................................. 26 

2.6.1.4 Income ....................................................................................................................... 26 

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2.6.1.5 Household Head ........................................................................................................ 27 

2.6.2 Farm characteristics.......................................................................................................... 27 

2.6.2.1 Age of the farm .......................................................................................................... 27 

2.6.2.2 Farm size.................................................................................................................... 28 

2.6.2.3 Yield .......................................................................................................................... 29 

2.6.3 Institutional characteristics ............................................................................................... 29 

2.6.3.1 Membership in FBOs................................................................................................. 29 

2.6.3.2 Access to Credit ......................................................................................................... 29 

2.6.3.3 Frequency of Extension Visits ................................................................................... 30 

2.7 Models used in farmers’ willingness to pay decisions estimations ........................................ 31 

2.7.1 Tobit model ...................................................................................................................... 32 

2.7.2 Heckman’s sample selection model ................................................................................. 33 

2.7.3 Double hurdle model ........................................................................................................ 34 

2.8 Conclusion .............................................................................................................................. 35 

CHAPTER THREE ...................................................................................................................... 36 

METHODOLOGY ....................................................................................................................... 36 

3.1 Introduction ............................................................................................................................. 36 

3.2 Study Area .............................................................................................................................. 36 

3.3 Data Collection ....................................................................................................................... 37 

3.3.1 Types and Sources of Data ............................................................................................... 37 

3.4 Conceptual Framework ........................................................................................................... 39 

3.5 Theoretical Framework ........................................................................................................... 41 

3.6 Measuring Willingness to Pay ................................................................................................ 44 

3.7 Analytical framework ............................................................................................................. 45 

3.7.1 Farmers’ Awareness of Cocoa Side Grafting ................................................................... 45 

3.7.2 Factors Influencing Farmers’ Willingness to Pay for Side Grafting ................................ 45 

3.7.3 Definition of variables ...................................................................................................... 47 

CHAPTER FOUR ......................................................................................................................... 51 

RESULTS AND DISCUSSION ................................................................................................... 51 

4.1 Introduction ............................................................................................................................. 51 

4.2 Socioeconomic Characteristics of Cocoa Farmers ................................................................. 51 

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4.3 Current use of cocoa technology and practices ....................................................................... 55 

4.4 Analysis of the Level of Awareness of Side Grafting Among Cocoa Farmers ...................... 57 

4.5 Analysis of Willingness to Pay Amount for Side Grafting a Cocoa Farm ......................... 59 

4.6 Factors Influencing Farmers’ WTP and WTP Amount for Cocoa Side Grafting................... 64 

4.6.1 Education (Educ) .............................................................................................................. 65 

4.6.2 Frequency of extension visits (freqofext)......................................................................... 66 

4.6.3 The size of the cocoa farm (toha) ..................................................................................... 66 

4.6.4 The age of the cocoa trees (agefarmdum) ........................................................................ 67 

4.6.5 Awareness of side grafting (awareness) ........................................................................... 67 

4.6.6 Total yield of the farm (Yield) ......................................................................................... 68 

4.6.7 Household head (Hhead) .................................................................................................. 68 

4.6.8 Income from cocoa (cocinc) ............................................................................................. 68 

4.6.9 Gender (Gender) ............................................................................................................... 69 

4.6.10 Membership in Farmer Based organizations (memFBO) .............................................. 69 

4.6.11 Access to credit (Actocre) .............................................................................................. 70 

4.6.12 Age of farmer (Age) ....................................................................................................... 70 

CHAPTER FIVE .......................................................................................................................... 71 

SUMMARY, CONCLUSION AND RECOMMENDATION ..................................................... 71 

5.1 Introduction ............................................................................................................................. 71 

5.2 Summary ................................................................................................................................. 71 

5.3 Conclusions and Implication of Findings ............................................................................... 72 

5.4 Policy Recommendation ......................................................................................................... 73 

REFERENCE ................................................................................................................................ 75 

APPENDICES .............................................................................................................................. 87 

 

 

 

 

 

 

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LIST OF TABLES 

Table 2. 1: Global cocoa production ............................................................................................. 11 

Table 3. 1: The distribution of farmer respondents....................................................................... 39 

Table 3. 2: Variable description .................................................................................................... 50 

Table 4. 1: Household heads by gender ........................................................................................ 52 

Table 4. 2: Descriptive statistics of socio-economic characteristics of cocoa farmers ................. 53 

Table 4. 3: Adoption of common cocoa cultural practices and technologies ............................... 56 

Table 4. 4: Cross tabulation of awareness of side grafting and willingness to adopt ................... 59 

Table 4. 5: Cocoa Farmers’ Willingness to Pay Amount for Cocoa Side Grafting ...................... 59 

Table 4. 6: Percentile distribution of willingness to pay amounts ................................................ 62 

Table 4. 7: Projected increase in yield due to result of side grafting ............................................ 63 

Table 4. 8: Double Hurdle Model of Factors Influencing Farmers’ Willingness to Pay for Side 

Grafting ......................................................................................................................................... 65 

 

 

 

 

 

 

 

 

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LIST OF FIGURES 

Figure 3. 1: Conceptual Framework for Factors Influencing Willingness to Pay for Side Grafting

....................................................................................................................................................... 40 

Figure 4. 1: Gender of farmers ...................................................................................................... 52 

Figure 4. 2: Awareness of cocoa side grafting .............................................................................. 58 

Figure 4. 3: Willingness to adopt side grafting ............................................................................. 58 

Figure 4. 4: Distribution of Willingness to Pay Amount for Cocoa Farmers ............................... 60 

Figure 4. 5: Quintile distribution of Farmers’ Willingness to Pay Amount ................................. 61 

Figure 4. 6: Willingness to pay in-kind (cocoa beans) ................................................................. 63 

 

 

 

 

 

 

 

 

 

 

 

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LIST OF ABBREVIATION 

COCOBOD    Ghana Cocoa Board 

CODAPEC    Cocoa Disease and Pest Control Programme 

CORIP     Cocoa Rehabilitation and Intensification Programme 

CRIG     Cocoa Research Institute of Ghana 

CRP     Cocoa Rehabilitation Programme 

CSSV     Cocoa Swollen Shoot Virus 

CSSVDU    Cocoa Swollen Shoot Virus Disease Unit  

ECOWAS    Economic Community of West African States 

FAO     Food and Agriculture Organization 

GAIN     Global Agricultural Information Network 

GDP     Gross Domestic Product 

GSS     Ghana Statistical Service 

Hi-TECH    High Technology 

ICCO     International Cocoa Organization 

ISSER     Institute of Statistical Social and Economic Research 

MoFA     Ministry of Food and Agriculture  

NGO     Non-Governmental Organization 

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OECD     Organization of Economic Co-operation and Development 

RSC     Rural Service Centre 

SWAC     Sahel and West Africa Club 

UNCTAD    United Nation Conference on Trade and Development 

UNDP     United Nation Development Programme 

WCF     World Cocoa Foundation 

WTP     Willingness to Pay 

 

 

 

 

 

 

 

 

 

 

 

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CHAPTER ONE 

INTRODUCTION 

1.1 Background 

Tree crops, particularly cocoa, coffee, oil palm, and rubber, have being the main agricultural 

exports in Ghana. Among the tree crops, cocoa is key to Ghana’s exports (Danso-Abbeam, Addai, 

& Ehiakpor, 2012; ISSER, 2012). Ghana is a second producer of cocoa in the world after Cote 

d’Ivoire (Dormon et al., 2004). Cocoa production in Ghana is concentrated in six regions namely 

Ashanti, Brong-Ahafo, Central, Eastern, Western, and Volta with the three northern regions and 

Greater Accra not conducive for production. The main cocoa-producing region is presently the 

Western Region, which has been divided into two zones as a result of the high production levels, 

to aid better administration (Anim-Kwapong & Frimpong, 2004). The Ashanti region follows as 

second and then Brong Ahafo region and the fourth leading producer being the Central region. 

Eastern region is the fifth leading producing of cocoa in Ghana.  

 

It is worthy to note that the Eastern region had been the leading producer of cocoa until farms were 

plagued with pests and diseases in the 1930s which eventually led to production declines in the 

region and in the nation at large (Omane-Adjepong, 2012). The region is very important in the 

cocoa sector because it was the first point of introduction of the crop. Since the Eastern region was 

the first area of introduction of the crop means majority of the cocoa trees are overgrown and 

passed their productive life. This has contributed to the relatively low production quantities 

recorded annually. To address this production shortfall, there is the need to rehabilitate old farms 

in the region to improve both regional and local production quantities.  

 

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In 2011, growth in the cocoa sector overtook the crop and the livestock sectors, and grew by about 

14% leading to the realization of government’s target of achieving one million metric tons of cocoa 

production per year. By this, foreign exchange revenue realized from cocoa exports stood at 23% 

of the total export earnings (ISSER, 2012). Overall, the land area allocated to cocoa production is 

1.6 million hectares with an average yield of about 400kg per Ha (MoFA, 2011). This production 

occurs in rural areas. According to Vos, Krauss, Petithuguenin, Perreira, and Nanga (2002), cocoa 

is an essential component of the rural livelihood system, with farmers highly committed to the 

cultivation of the crop. Cocoa cultivation is a way of life and farmers are very much attached to 

the crop socio-culturally, hence the economic and social importance of cocoa can scarcely be 

exaggerated. Mensah (2006) stated that there are few Ghanaians whose welfare is entirely 

independent on cocoa. According to Wegner (2012), around 6.3 million Ghanaians derive their 

source of livelihood from cocoa production, representing nearly 30% of the population. Estimates 

of per capita income show that households’ mean per capita daily income from cocoa was US$0.42 

out of a total income of US$ 0.63 in 2008 (Mensah, 2006). This indicates that household’s heavily 

depend on cocoa for their livelihood.  

 

As a result of the importance attached to the crop by farmers, Ghana is highly competitive in its 

production of bulk cocoa. This has been made possible by the specialized skills of farmers in 

producing high quality beans under hygienic and environmentally friendly conditions. Thus, 

Ghana's cocoa is highly appreciated and has almost unlimited demand on the global market 

(Mensah, 2006). From the above discourse, it is clear that cocoa is an important and the premier 

cash crop both in terms of quality and bulk value.  

 

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To increase the contribution of the cocoa sector, Ghana introduced various interventions that were 

geared towards enhancing production, productivity and quality. Owusu and Frimpong (2014) in 

their research reiterated that the cocoa sub-sector in Ghana has benefited immensely from the 

introduction of several programmes over the years, to increase production and productivity. These 

programmes include the Cocoa Disease and Pest Control (CODAPEC) programme and the Cocoa 

Hi-TECH programme. These were technology-based programmes to address some production 

challenges of the cocoa sector and also have both social and economic objectives that seek to 

improve the income and living standards of farm families, maximize foreign exchange contribution 

to the economy of Ghana, reduce poverty among cocoa farmers and to encourage the youth to go 

into cocoa farming. These technologies consist of attributes which could eliminate the two major 

cocoa pests; capsids and black pod disease, and increase productivity for that matter. As a result 

of these programmes, COCOBOD reported an unparalleled cocoa production level of 1,004,194 

MT in 2011 (Baffoe-Asare, Danquah, & Annor-Frimpong, 2013). 

 

Besides these and other programmes implemented, government has long established institutions 

such as the Cocoa Research Institute of Ghana (CRIG), The Seed Production Unit (SPU) as well 

as the Cocoa Swollen Shoot Virus and Diseases Unit (CSSVDU) under the Ghana Cocoa Board 

to coordinate programmes to improve production. The primary focus of these projects and the 

mandates of these institutions is the rehabilitation of cocoa farms, developing cocoa hybrids 

resistant to field challenges like diseases and pests, raising clones and seedlings for improved 

production (COCOBOD & UNDP, 2013) 

 

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Despite some major achievements, the level of cocoa productivity is still below the achievable 

yield of 1 metric tonne per hectare (MoFA, 2011). One technology that has proven useful in 

addressing the challenge of low productivity is the use of side grafting technique to rehabilitate 

farms (Effendy, Hanani, Setiawan, & Muhaimin, 2013). Pina and Errea (2005) define grafting as 

the natural or deliberate fusion of plant parts so that there is vascular continuity and the resulting 

composite organism functions as a single plant. With this process, two adjacent intact plant parts 

or different branches of the same plant can become naturally or intentionally/artificially grafted 

together. Artificial grafting of which side grafting is an example, involves inserting a previously 

cut shoot or scion into an opening in another plant growing (known as the root stock). To provide 

suitable environment for the grafting process the rootstock which is also referred to as the 

understock must be in its active growth phase (Mudge, Janick, Scoffield, & Goldschmidt, 2009). 

This method has various uses such as vegetative propagation, cultivar change, avoidance of 

juvenility, repair, size control as well as biotic and abiotic resistance. Grafting has been introduced 

in the cocoa sector in many countries to improve yield and help plants to develop resistant varieties 

(Vos, Ritchie, & Flood, 2003). 

 

The Seed Production Unit in Ghana produces grafted seedlings from improved varieties obtained 

from CRIG and markets them to farmers for onward cultivation. This process involves top grafting 

improved varieties on resistant rootstock. Farmers can then obtain these top grafted plants or 

purchase pods at a low price from seed gardens scattered throughout the six cocoa growing regions, 

and nurse them for later transplant. These grafted plants are however good for starting new farms 

and not suitable for rehabilitating old farms. However, cutting down cocoa trees entirely and 

planting grafted seedlings has been the primary way by which farmers rehabilitate their farms. 

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Using this method for rehabilitating farms takes quite a long time to mature because the plants still 

have to go through the juvenile stage until they mature, which takes about three years. This means 

that farmers’ income will be reduced as a result of cutting down of old cocoa trees in order to start 

new ones. Despite this challenge of farmers possibly having a sharp decline in income, cocoa 

farmers have only that option of cutting entirely their cocoa trees to rehabilitate their farms. The 

side grafting on the other hand overcomes this challenge by grafting a bud wood to the side of an 

old tree. As such, farmers’ incomes are stabilized in addition to yield increases in later years. 

However, this all important technology has not been officially introduced to farmers. 

1.2 Problem statement 

More than 90 percent of cocoa beans produced globally is grown on 5.5 million smallholder farms 

(Wegner, 2012). Ghana is no exception with cocoa widely characterized by small-scale production. 

Cultivated area per household is 2 hectares or less (Barrientos et al., 2008). According to MoFA 

(2011), the average yield per hectare of cocoa is 0.4 ton which is relatively low compared to on-

station research trials that have an achievable yield of about one ton.  This level of productivity is 

on the low side compared to per hectare yields of 1.8tons/ha, 0.8tons/ha and 0.8tons/ha in 

Malaysia, Indonesia and Cote d’Ivoire respectively (Danso-Abbeam et al., 2012). Though the 

country has been able to realize total output above the 1 million tons target, increase in production 

is largely due to the increase in the area cultivated rather than by improving yield or productivity 

levels (MoFA, 2006; COCOBOD, 2007). Cocoa yields in Ghana is therefore described as been 

below global production averages (FAO, 2005; ICCO, 2007).  

 

Out of the six cocoa growing regions, the major production region which is currently the Western 

Region is the only region for the expansion of cocoa acreage given the presence of unexploited 

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forest areas (Asare, 2005; Gockowski & Sonwa, 2008). In spite of the growth possibilities of the 

Western region, the Eastern region which initially was the leading producer of cocoa does not have 

the benefit of such growth. 

 

One challenge that must be addressed to improve productivity in Ghana is that about 25% of cocoa-

trees are over 30 years old and hence have passed their productive life, leading to yield and 

productivity falls (Anim-Kwapong & Frimpong, 2004).  Dormon et al. (2004), also identified the 

issue of low productivity of cocoa farms in Ghana and opined that these production shortfalls could 

be attributed to non-adoption of improved technologies. Cocoa farms are major income sources 

for farmers implying that any decline in yield has worrying effects on farmers’ survival and living 

standards.  

Though the practice of cutting down old cocoa trees and replanting has good intentions of 

increasing yield, it exposes farmers to several survival shocks because their income source is 

drastically reduced. As a result, many farmers have not subscribed to the cutting down of their old 

cocoa trees. It is therefore imperative to implement technologies in a way that sustains the income 

of farmers. The technology of cocoa side grafting proves a very important technology that 

surmounts these challenges. Side grafting is done by inserting a bud wood on an opening on the 

bark of old cocoa trees. Whilst the bud fuses with the established tree, the old branches continue 

to bear. The old branches are then cut down after the scion matures and is ready to bear fruits. This 

ensures continuous flow of income whilst improving the yield of the farm.   

Effendy, Hanani, Setiawan, and Muhaimin (2013) in their research on the adoption of side grafting 

in the Sigi Regency of Indonesia stated that side-grafting can increase productivity of cocoa farms. 

They further explained that normally cocoa produce fruits after 2-3 years of cultivation, but when 

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side-grafted reduces the fruiting to a year or less with production ranging from 1.8 to 2.75 tons/ha, 

about 4-7 times yields in Ghana. Consequently, Indonesian farmers doubled or tripled their yields 

by replacing old trees with high-yielding varieties or grafting bud wood from superior varieties 

onto old trees (Mars, 2015). This method having been introduced in Southern Bahia, Brazil by 

Cargill also helped farmers increase their production by 180% during the first two years of 

introducing the technology to them (Cargill, 2015).  

Ghana can take advantage of this rehabilitation technique given that the yield of cocoa farms in 

Ghana are low compared to other cocoa growing countries. The only hurdle to this technique is 

that varieties that better withstand Ghana’s major soil borne diseases should be planted and side 

grafted in later years to ensure smooth implementation of the technology.  

 

One drawback is that this technology entails significant cost given the level of expert knowledge 

and experience required to execute the procedure. This will require awareness creation among 

farmers to encourage easy adoption. Asrat, Belay, and Hamito, (2004) found that farmers are 

willing to pay for technology if they are aware of the benefits associated with its adoption. 

However, given the way cocoa farmers have been treated over the years in terms of distribution of 

free cocoa pods for rehabilitation and expansion of their farms, free spraying of farms and other 

incentives, their willingness to pay for such a technology is doubtful and needs researching into.    

This study addresses the following research questions 

i. Are farmers aware of the technology of cocoa side grafting? 

ii. How much are farmers willing to pay for grafting cocoa farms? 

iii. What are the factors influencing farmers’ willingness to pay for cocoa side grafting? 

 

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1.3 Objectives of the study 

The main objective of this study is to analyze the willingness of cocoa farmers in the Eastern 

Region of Ghana to pay for cocoa grafting. The main objective will be pursued as follows; 

i. Describe farmers’ awareness of the technology of side grafting 

ii. Determine the amount farmers are willing to pay for grafting trees on a cocoa farm 

iii. Identify and estimate the factors influencing farmers willingness to pay amount for side 

grafting 

 

1.4 Relevance of the study 

Cocoa is a major contributor to the GDP of Ghana also to farmers’ incomes. Efforts at improving 

the agricultural sector must target the cocoa sector. This research will provide valuable information 

to promote the adoption of technologies that will improve cocoa production in Ghana. In addition, 

their yields are low relative to achievable yields and therefore very essential to be included in yield 

improvement schemes. This research will make recommendations for efficient policy making.  

This study will also make valuable methodological contribution to understanding issues facing the 

cocoa sector. 

1.5 Organization of the study 

The study is organized into five chapters. Chapter two reviews relevant literature on global 

perspectives on cocoa production, cocoa production in Ghana, cocoa rehabilitation, and 

methodology used in willingness to pay assessments. Chapter three discusses the methods used in 

achieving the study objectives. Empirical results of the survey conducted is presented in chapter 

four. Finally, Chapter five presents the findings, conclusion and policy recommendations. 

 

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CHAPTER TWO 

LITERATURE REVIEW 

2.1 Cocoa production: Global Perspectives 

According to Amos (2007), cocoa originated from the upper Amazon region of South America 

and subsequently gained widespread attention with the discovery of varied uses of the beans.  The 

primary cocoa growing regions are Africa, Asia, and Latin America (World Cocoa Foundation, 

2014). Africa continues to lead global cocoa production with the supply of 70% of cocoa beans to 

the market, followed by Asia and Oceania occupying 17% and the Americas supplying 15% of 

global cocoa beans (World Cocoa Foundation, 2014). Countries other than the south Americas 

from where cocoa began became lead producers due to favourable weather conditions. In West 

Africa, cocoa is a major agricultural export as well as a livelihood supporting crop contributing 

about 70% of the world market of cocoa. The increased efforts in cocoa production in this region 

helped to increase global production levels. 

 

In terms of annual production, the eight largest cocoa-producing countries are Côte D’Ivoire, 

Ghana, Indonesia, Nigeria, Cameroon, Brazil, Ecuador and Malaysia. These countries represent 

90 percent of world production with Côte D’Ivoire alone representing about 40 percent of global 

cocoa supply. This market share is estimated to account for about 1.2 million metric tonnes per 

annum of overall cocoa supplied (United Nations Conference on Trade and Development, 2009). 

Cocoa is a major contributor to the Ivoirian economy accounting for 80% of the country’s 

commodity exports, over 50 percent of all exported goods and services, and  a 21 percent of GDP 

(Bogetic, Noer, & Espina, 2007). Ghana and Nigeria contribute about 20 percent and 7 percent 

respectively to the World Market (Lundstedt & Pärssinen, 2009). The Americas including Brazil 

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and Ecuador however supply relatively small quantities to the world market with a heavy 

concentration on fine flavor cocoa beans used in premium chocolates. Globally, the supply of 

cocoa has depended almost entirely on smallholder farms, who supply between 80 to 90 percent 

of cocoa beans. Despite the large quantities of cocoa beans supplied by Africa and Asia as 

compared to the Americas as seen in Table 2.1, the yield differences are very remarkable. It is 

estimated that a typical cocoa farm in Africa and Asia covers two to four hectares with each hectare 

producing about 400 kg of cocoa beans. Cocoa farms in the Americas as a result of the vast 

adoption of technologies are comparatively high yielding, producing about 550 kg of cocoa beans 

per hectare (World Cocoa Foundation, 2014).  

 

The realization of this high global cocoa production levels has over the years experienced major 

setbacks due to structural adjustment policies, crop infestations, diseases as well as market 

speculation issues (ECOWAS-SWAC/OECD, 2007). These problems facing the cocoa economies 

are exacerbated by changing weather patterns. The World Cocoa Foundation predicts that increase 

in global cocoa production may be reduced in the future since cocoa trees are sensitive to changing 

weather patterns (World Cocoa Foundation, 2014). 

 

To help formulate international and national policies geared at providing effective solutions to the 

problems facing this sector, the International Cocoa Organization (ICCO) was set up in 1973 and 

led to the enactment of the first International Cocoa Agreement under the auspices of the United 

Nations (Eskes & Efron, 2006).  This was after development partners realized that cocoa imports 

and prices are minor issues for cocoa consuming countries whilst changes in prices of the 

commodity adversely affect the producing countries. To help address this conflicting interest and 

to ensure sustainable cocoa economies, the ICCO consisting of both importing and exporting 

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countries meet intermittently to design new agreements and strategies that meets current and future 

challenges of the sector. Moreover, the World Cocoa Foundation also puts forward development 

projects that help develop the sector. One of the latest projects of the foundation is targeted at the 

four major producers in the West African sub-region including Ghana, Cote D’Ivoire, Nigeria and 

Cameroon. The project targets farmers to improve their capacity to increase production (World 

Cocoa Foundation, 2015). 

Table 2. 1: Global cocoa production 

 

Source: (World Cocoa Foundation, 2012) 

 

2.2 Overview of Cocoa Industry in Ghana 

Cocoa cultivation started in Ghana in 1871. During this early period, the major area of cocoa 

cultivation was in the Eastern Region and was encouraged by the missionaries.  In spite of 

advocacy to increase propagation, widespread adoption of the crop was low until Tetteh Quarshie 

in 1876 brought cocoa from Fernando Po and cultivated it in his garden in the Eastern region where 

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farmers appreciated it and took seeds to cultivate. Its cultivation was promoted by the botanical 

gardens, following the wake of the road and rail infrastructure gradually being developed 

(ECOWAS-SWAC/OECD, 2007). Exports of cocoa to Great Britain followed in 1881, a major 

boost to farmers to increase their source of income. In order to supplement the pods being supplied 

from the farms of Tetteh Quarshie, Sir William Brandford Griffith, the then Governor of the Gold 

Coast, also facilitated importation of cocoa pods from Sao Tome in 1886. From these pods, cocoa 

seedlings were produced at Aburi Botanical Garden for broader distribution to farmers (Asuming-

Brempong, Sarpong, Asenso-Okyere, & Amoo, 2007). Interest in cocoa growing grew and Ghana 

emerged as the leading producer of cocoa in the world. However, the infestation of crops with 

black pod and swollen shoot virus disease threatened the sector and has since the 1970s kept Ghana 

as the second producer of cocoa. 

 

Despite the disease challenge, Ghana has been able to increase its production and export from a 

low of 63 tons in 1937 to over 1 million tons in 2011 (ISSER, 2012; Grossman-Greene & Bayer, 

2009)). Once the government recognized the great contribution of the cocoa sector it established 

the Ghana Cocoa Marketing Board (CMB), now reconstituted into the Ghana Cocoa Board to 

develop the cocoa industry in Ghana. For proper management and administration, COCOBOD 

organized the cocoa growing areas into six major areas namely: Eastern, Central, Brong-Ahafo, 

Volta, Ashanti and Western regions. The Western region is further divided into two zones due to 

its high production levels (Omane-Adjepong, 2012). The Eastern region was the largest producer 

of cocoa until the infestation of the swollen shoot virus disease in the region by the cocoa swollen 

shoot virus disease. Ashanti region became the largest producer of cocoa from the late 1960s to 

the early part of 1980s after which the region suffered from the black pod disease and the cocoa 

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swollen shoot virus. The Western region remains the last frontier for cocoa expansion and is 

currently the largest cocoa producer in Ghana (Anim-Kwapong & Frimpong, 2004) and accounts 

for more than 50% of national production.  

 

Ghana’s success in cocoa production is dependent on about 700,000 households cultivating cocoa 

mostly on plots of about 2.5 ha (Asante-Poku & Angelucci, 2013). Ghana previously cultivated 

the “Amazons” and “Amelonado” cocoa varieties which are low-yielding. Government introduced 

hybrid varieties in 1984 which are better in terms of productivity than the Amelonado and the 

Amazons. The introduction of hybrid varieties helped the Western region to achieve the high yields 

(Asante-Poku & Angelucci, 2013). Vigneri (2005) in his study points out that as a result of the 

huge production potential, farmers adopted the hybrid variety to the extent that more than half of 

cocoa farmers in the three major growing areas took to the cultivation of the hybrid varieties. In 

addition to the high yielding ability, the gestation period of the hybrids is shorter compared to the 

older varieties (Kolavalli & Vigneri, 2011). This characteristic also adds to why the hybrid 

varieties are widely cultivated in Ghana.  

 

One would have expected that such adoption level should significantly impact the productivity 

levels. The reason for the lower than expected yields is that hybrid varieties need more care and 

have the highest output in the presence of optimal weather conditions, in addition to the application 

of additional farming practices such as fertilizer application, pruning and spraying of pesticides. 

These additional requirements have not been met by farmers who depend almost entirely on 

traditional methods of cultivation (Global Agricultural Information Network, 2012). Mohammed, 

Asamoah, and Asiedu- Appiah (2011) found that Ghana’s yield is about 25% below the yield of 

the ten major producers of the crop and about 40% below the yield of Cote d’Ivoire. One 

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explanation of this low yield is that more than 25% of cocoa trees are above 30 years old and have 

passed their productive life.  

 

In addition to Ghana governments efforts at improving the sector there are several initiatives by 

organizations to improve this sector. For example, the World Cocoa Foundation through the Cocoa 

Livelihoods Programme, is helping farmers by expanding access to credit, inputs and improved 

planting materials. Also, certification programmes such as undertaken by GTZ have provided 

support to the sector by encouraging farmers to adopt recommended technologies. Farmers are 

encouraged to continue recommended methods as they are offered premium price for their 

produce. This leads to increased adoption and improvement in yields (UNDP/COCOBOD, n.d.). 

 

2.3 Cocoa Yield Improvement Intervention Strategies 

Several intervention strategies have been established to improve the cocoa sector given the major 

contribution of the sector to Ghana’s economy. These strategies include the Cocoa Diseases and 

Pest Control Program (CODAPEC), the High Technology Programme (Hi-Tech programme) as 

well as National Cocoa Rehabilitation Programme (Owusu-Achaw, 2012; Mensah, 2006).  

 

Two major diseases have ravaged the country, since the beginning of cocoa farming in Ghana. Out 

of these two pests, the first to be discovered was Capsid which was identified in 1910. Its spread 

was so fast that after three years of its discovery it had become rife throughout the cocoa growing 

regions. This resulted in about 25 percent of cocoa trees being damaged within that period.  

 

Shortly after that the Cocoa Swollen Shoot Virus (CSSV) disease also surfaced in 1936. CSSV 

nearly destroyed Ghana’s entire cocoa industry and in response, the state took drastic control 

measures. These measures involved the cutting down of over 81, 000 trees on 300 farms 

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(Grossman-Greene & Bayer, 2009). These measures could not completely eliminate the virus and 

by 1938 the virus was prevalent in the Eastern region and spreading to the other cocoa growing 

areas. To strengthen efforts in eliminating the disease, the colonial government emphasized 

research by establishing the Central Cocoa Research Station now Cocoa Research Institute of 

Ghana (Grossman-Greene & Bayer, 2009).  

 

 

The CODAPEC programme is a national pest and disease control programme initiated in 2001 by 

the Government of Ghana to address the decline in cocoa production. The programme also aims 

at assisting farmers to maintain production levels and generate the needed foreign exchange for 

the development of Ghana. The “CODAPEC”programme is made up of two components: the 

maintenance of cocoa farms which includes weeding twice, or thrice in a year; general pruning 

and pruning of mistletoes and chupons, and spraying against diseases and pests, twice or thrice a 

year (Owusu-Achaw, 2012). Out of the CODAPEC programme the mass spraying exercise 

received massive acceptance by government and is consistently carried out to curb the spread of 

diseases and pests.  

 

The ‘high technology’ of cocoa production (Cocoa HI TECH) is a holistic approach to sustainable 

cocoa production in which all the recommended technologies by CRIG are contained in a single 

package. It is defined as “sustainable cocoa production by which the farmer increases and 

maintains productivity, through soil fertility maintenance at levels that are economically viable, 

ecologically sound and culturally acceptable using efficient management of resources”. The aim 

of the project is to assist the farmer to increase yields by application of high technology package 

developed by CRIG (Anim-Kwapong & Frimpong, 2004; Appiah, 2004).  

 

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Various studies including the one by Aneani, Anchirinah, Owusu-Ansah, and Asamoah (2012) 

have indicated varied rates of adoption of cocoa high technology components. For example, 

adoption of fungicides had a rate of about 7.5% and adoption of hybrid had a rate of 44%. This 

varied rates of adoption could be because farmers easily adopt less expensive technologies as 

compared to technologies that are cost involving. Moreover, their experience with technologies in 

terms of its ability to yield the required result will also influence the adoption of the yet to be 

introduced side grafting technology. 

 

Cocoa Rehabilitation Programme 

According UNDP/COCOBOD (n.d.), due to unsustainable farming methods farmers have spread 

to other forest areas. These practices with little consideration on improving productivity per area 

have left cocoa farmers a few acres of land for further expansion. Since cocoa production occupies 

a sensitive portion of Ghana’s economic activities, many cocoa farms need to be rehabilitated to 

offset productivity declines (Danso-Abbeam et al., 2014). This option despite the many promises 

it holds pose a great threat to farmers’ livelihoods and hence they have little incentive and capital 

to undertake. In response to this, Cocoa Rehabilitation Programmes have been enacted by 

Ghanaian governments such that before 1988 about two rehabilitation programmes had been fully 

enacted with little success (Amoah, 1995). The Cocoa Rehabilitation Programme I and II failed to 

arrest the decline in Ghana’s cocoa production. This was attributed to the fact that farmers had no 

or little incentive to rehabilitate their farms as farmers were paid low prices for their commodity, 

which resulted in the neglect and abandonment of farms.  

 

For future rehabilitation programmes, such as the side grafting technology, farmers who adopt the 

technology could be given incentives that will encourage other farmers to adopt the technology. 

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This could be in the form of providing input subsidies and price premiums for a period after the 

side grafting tree begins to bear. By this, farmers will have enough reason to subscribe to the 

technology and ultimately lead to expected increased yields. 

 

CRP III, enacted between 1988–1996 was designed to address the challenge of low producer price 

after these rehabilitation programmes failed. The produce price of cocoa was increased by 

government which encouraged farmers to rehabilitate their farms. This resulted in an initial success 

by increasing production to 300,000 metric tonnes in 1988/89 and to about 400, 000 tons in 

1995/1996 cocoa season, further increasing productivity from 210kg to 400kg per hectare, due to 

the rehabilitation and maintenance of abandoned and neglected farms by farmers (Kolavalli, 

Vigneri, Maamah, & Poku, 2012). The programme had in itself a commitment by the government 

to compensate farmers that replanted trees infected with swollen shoot virus. This led to substantial 

rehabilitation, with a large number of farms replanting higher-yielding cocoa tree varieties 

developed by CRIG (Kolavalli et al., 2012). This programme was however abrogated due to 

financial reasons on the part of government and farmers. 

 

Currently, COCOBOD has commenced the National Cocoa Rehabilitation Programme to address 

the challenge of low productivity through rehabilitation and replanting of old and diseased cocoa 

trees. The programme which was launched in 2010 through the advice of CRIG has two main 

components of replacing old, moribund cocoa stock with hybrid variety, and also removal of 

mistletoes and application of fertilizer. 

 The objectives of the programme are: 

 “To embark on an aggressive nationwide control of mistletoes; 

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 To replace the old unproductive cocoa trees with hybrid cocoa variety, that is high 

yielding, disease tolerant and early maturing; 

 To encourage farmers to adopt Good Agronomic Practices, and use medium to high 

farming technologies, through back-up efficient Extension services; 

 To enhance food production in scheme areas, through the cultivation of grains, tubers and 

plantain to ensure food security; 

 To incorporate planting of economic shade trees as an alternate livelihood for farmers and 

improve the ecosystem in the long term; 

 To provide jobs for the rural communities particularly the youth to enhance their incomes 

and living standards, and motivate them to take up cocoa cultivation; 

 To assist farmers to achieve higher productivity in the rehabilitated and replanted farms 

and thereby increase income” (COCOBOD, 2013).  

 

Side Grafting as a Cocoa Rehabilitation Technique in Ghana 

Grafting is a proven way of joining living tissues from different plants to become a single plant. It 

is normally done on plants to induce individual plant superiority into a single plant. For instance, 

rootstock with resistance to soil borne diseases are used whilst high yielding scions are grafted on 

them (Mudge et al., 2009).  Some of the techniques include cleft grafting, veneer grafting, whip 

graft as well as side grafting. The side grafting technology employs the technique of slipping an 

improved rootstock to an already existing plant to induce superior qualities in the already existing 

tree after the graft has taken and grown to maturity. This method has proven to be an effective 

procedure for rehabilitating cocoa farms in cocoa producing countries in North America and Asia.  

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It has been widely accepted because it is a proven way to increase yield in the face of farmer 

concerns on income safety.  

 

In Indonesia for instance, the Indonesian Coffee and Cocoa Research Institute with the objective 

of expanding the cultivation of fine flavor cocoa undertook a lot of experiments, which sadly failed 

due to the fact that the fine flavor are susceptible to pests and diseases found in Indonesia.  

Convinced that the low cocoa yields in Indonesia are due to the age and variety of the existing tree 

stock (Saxbøl, 2014), the government of Indonesia launched the Gernas project in 2009 with the 

objective of side-grafting old cocoa trees throughout Sulawesi with superior planting material. 

Normally, replanting a farm with improved variety takes about two to three years. However, when 

side grafted, the plant matures in less than a year. This helped the Indonesia farmers to more than 

double their yield whilst ensuring a steady flow of income.  

 

It is based on the success of this technology that government in collaboration with the Embassy of 

the Kingdom of the Netherlands has initiated the Cocoa Rehabilitation and Intensification 

programme. It is aimed at providing support services to cocoa farmers through improvement in 

the cocoa production system. As part of the project, rehabilitation of farms will take place through 

Rural Service Centers, who will side-graft old cocoa trees for farmers at a fee. Currently, 

establishment of demonstration centers is on-going in all the cocoa regions to aid farmer 

appreciation of the technology.  

2.4 Measuring Willingness-To-Pay 

Mwaura, Muwanika, and Okoboi (2010) define Willingness To Pay (WTP) for a service as the 

maximum amount of money that the target person would be willing to pay for a product given that 

the person has enough information on the product. Estimations of willingness to pay for a particular 

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technology or service is very critical and of great importance in all aspects of research. This is 

because it is very useful in making policy recommendations through the expression of choices in 

a non-market situation (Telser & Zweifel, 2002). It involves targeted patrons for services in 

establishing the preferences of the services presented and the amount of money the respondents 

are ready to pay for the product or service. 

 

WTP studies are widely used in analyzing markets, goods, services, entrepreneurs and for 

environmental valuation. It has been used in the agricultural sector by several researchers in 

analyzing the amount of money farmers and other stakeholders are willing to pay for a particular 

service. Zakaria, Abujaja, Adam, and Nabila (2014) used this method in analyzing farmers’ 

willingness to pay for improved irrigation service in Northern Ghana. The use of this method 

enabled the researchers to recommend to management to repair broken canals in order to raise 

farmers’ willingness to pay. Taneja, et al. (2014) also employed this method in determining 

farmers’ willingness to pay for climate smart agriculture technologies. Several other researchers 

including Danso-Abbeam, Addai, and Ehiakpor (2014), Kouame and Komenan (2012) and  Falola, 

Ayinde, and Agboola (2013) have used this method in eliciting how much farmers are willing to 

pay for agricultural services such as insurance and water treatment. 

 

For determining the willingness to pay for a product or service, two main methods have been 

employed. These methods are the revealed preference and stated preference which includes the 

contingent valuation method and choice experiment. In the case of the revealed preference method, 

it is assumed that a relationship of substitutability exist between a good already on the market and 

a nonmarket good being researched (Taneja et al., 2014). This method works best in instances 

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where there is full market information about a product and the provision of information on already 

experienced good. In stated preference methods, however, such as the contingent valuation, 

respondents are asked about their preferences for a nonmarket good and how much they are willing 

to pay for the good. Stated preference methods have therefore proved useful in situations where 

market information about the product is very little or does not exist (Liu & Zhang, 2011). 

 

2.5 Contingent Valuation Method and Elicitation methods 

According to Shavell (1993), contingent valuation is a method of asking hypothetical questions to 

respondents to find out the value they place on the good. This helps to simulate the concept of 

choice in a market situation as respondents have the opportunity to accept or reject the product. 

  

As a result of the effectiveness of the contingent valuation method, it has been widely used in 

various agriculture related studies where it was used to elicit farmers’ willingness to pay for a 

service, product or technology. For instance, Ulimwengu and Sanyal (2011) adopted the method 

in analyzing farmers’ willingness to pay for agricultural services and the method was further used 

by Danso-Abbeam et al. (2014) and Kwadzo, Kuwornu, and Amadu (2013). According to Taneja 

et al. (2014), the contingent valuation method makes use of surveys that are particularly intended 

for measuring preferences and Willingness To Pay. In this case to avoid biases that will widely 

affect elicitation results, questions should be framed appropriately, noting that assessing the true 

Willingness To Pay for a good or service depends on how the questions are framed and how much 

the respondents are informed about the product or service. If not adequately handled, it could result 

in disparities between stated and actual Willingness To Pay (Cohen & Zilberman, 1997). 

 

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The ways in which willingness to pay questions are framed is referred at as elicitation methods. 

According to Damschroder, Ubel, Riis, and Smith (2007), early Willingness To Pay studies elicited 

values through the use of open-ended questions from a self-interest standpoint to obtain WTP 

values. As such, these questions elicited values without a starting point and without following a 

search procedure to aid respondents determine the value they place on a good. This procedure is 

susceptible to many problems including bias values that might be too low or high compared to the 

actual value of the good or service. Moreover, there is a high possibility of non-response or zero 

values. Also, there is a lot of brain activity involved with the respondents’ decision. According to 

Damschroder et al. (2007), the results of open-ended elicited WTPs are heavily biased. This led 

researchers to design new elicitation methods that are meant to address the challenges of the early 

methods, hence the close ended methods were introduced. According to Nakanyike (2014), close-

ended questions are simpler and more often reveal market-realistic WTPs as a result of the 

reduction in starting point bias. Some types of closed-ended methods are single-bounded 

dichotomous choice model and double bounded dichotomous model. 

 

The single bounded method is implemented with respondent offered only one bid on a product, 

technology or service, to accept or reject. In the end, respondents will only provide positive responses 

if their utility is greater than or equal to the bid presented hence compatible with the individual’s 

strategic interest (Mitchell & Carson, 1989). Moreover, this technique mimics real market situation 

faced by farmers in their adoption of technology, following a take it or leave it scenario and could be 

very reliable in eliciting WTPs. However, the main challenge with this method is that it results in 

limited information about the respondent’s economic value on a good or service. As a result in 

order to get estimates of good precision large sample size is required which could also increase 

cost (Hanemann, Loomis, & Kanninen, 1991). 

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The double bounded on the other hand is an approach that follows up with another question after 

the first question in the single bounded technique is asked. This method has been used by many 

researchers including Liu and Zhang (2011), who showed that that one could use a double sampling 

framework to ask a second binary discrete choice question conditional on the response to the first. 

So that in an instance of the first question eliciting a yes response, the question was repeated with 

a higher value for the product or service. If “no,” it was repeated with a lower value for the product. 

This could help gather more information on respondents’ welfare for better analysis. However, 

Damschroder et al. (2007) point out that this approach is also statistically inefficient as a result of 

starting point bias. Moreover, economic value placed on a product may vary as uncertainty in the 

market increases. 

 

The price card method was also introduced to bring out respondent welfare pertaining to a product 

or service. With this method, respondents are presented with a sequence of bids from which they 

are asked to choose an amount that represents their maximum willingness to pay for the product 

or service. The price card method has the advantage of not inducing a starting point bias. Moreover, 

it is more informative and yields little cognitive burden on respondents as compared to open-ended 

method. Stewart, O’Shea, Donaldson, and Shackley (2002) employed the use of the price card 

method in evaluating WTP for health care by presenting a set of monetary values to respondents 

to indicate how much they are willing to pay for healthcare services. Stewart et al. (2002) gave 

opportunity for respondents to indicate a higher amount they so considered to be their maximum 

WTP. Respondents however gave no response as a result of the additional cognitive burden. In 

addition, the price card is cheaper to implement as compared to close-ended methods. In spite of 

these advantages, it is prone to range bias which implies that the willingness to pay amount will 

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depend on how the range of monetary value is set (Damschroder et al., 2007). That 

notwithstanding, Zakaria et al. (2014) adopted the method to evaluate the WTP of farmers to pay 

for improved irrigation water. It was adopted because they realized that direct inquiry on a farmer’s 

willingness to pay for improved irrigation could be sensitive. The price range was therefore 

displayed on cards for the farmers to indicate their choice of the prices that represent their WTP 

amounts. 

The bidding game is an elicitation method that employs a series of iterative questions. This method 

starts with asking respondents about their decision on an initial economic value for a service. The 

amount is then changed until the respondent accepts to pay the final amount. This then is recorded 

as the Willingness To Pay amount (Namyenya, Sserunkuuma, & Bagamba, 2014). Despite being 

prone to starting point bias in the sense that the final WTP is related to the starting point WTP, the 

method is efficient in developing countries as compared to developed countries. This is because 

developing country respondents understand the concept of bargaining and is a common practice in 

everyday market transactions unlike developed countries where bargains are rarely done 

(Whittington, Briscoe, Mu, & Baron, 1990). This has therefore been employed by researchers 

including Namyenya et al. (2014) who employed the bidding game in assessing farmers 

willingness to pay for irrigation water. 

 

2.6 Factors influencing willingness to pay for technology 

Several authors have discussed the factors influencing farmers’ willingness to pay for technologies 

(Adesina & Baidu-Forson, 1995; Chiputwa, Langyintuo, & Wall, 2011; Falola, Banjoko, & 

Ukpebor, 2012; Tiamiyu, Akintola, & Rahji, 2009; Ulimwengu & Sanyal, 2011). These authors 

have suggested factors such as age of farmer, gender, age of farm, educational attainment, income 

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of farmer, access to credit, frequency of extension visits, membership in Farmer Based 

Organizations (FBOs), yield of the farm, farm size, being a household head as well as being aware 

of the benefits of side grafting as factors that are likely to influence farmers’ willingness to pay for 

a technology (Adesina & Baidu-Forson, 1995; Ulimwengu & Sanyal, 2011; Zakaria et al., 2014). 

These factors are characterized broadly into socioeconomic characteristics, farm characteristics, 

and institutional characteristics. 

 

2.6.1 Socioeconomic characteristics 

Socioeconomic characteristics, also referred to as personal characteristics are very relevant in 

determining the willingness to pay decisions for agricultural technology. Some of these factors 

include age, gender, educational attainment, income as well as household head.  

 

2.6.1.1 Age 

Age of a farmer has consistently been viewed by many researchers as an important factor that 

influences farmers’ willingness to adopt technology (Adesina & Baidu-Forson, 1995; Ntege-

Nanyeenya, Mugisa-Mutetikka, Mwangi, & Verkuijl, 1997; Tiamiyu et al., 2009). It is expected 

that younger farmers are more likely to adopt new agricultural technologies under the assumption 

of available cash resources. For older farmers, their tendency of adopting a technology is 

dependent on how much physical labour is required in executing the technology. In the cocoa 

sector specifically, older farmers are less likely to adopt side grafting since the technology requires 

parting away with money which might not benefit them in the immediate future hence risky 

(Baffoe-Asare et al., 2013). For this reason younger cocoa farmers are more likely to invest in side 

grafting than older (Wiredu, Mensah-Bonsu, Andah, & Fosu, 2011). 

 

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2.6.1.2 Gender 

Gender is also an important factor influencing the adoption of technologies. Doss and Morris, 

(2001) found that gender-related disparities in the adoption and willingness to pay for modern 

maize varieties and chemical fertilizer is not only an isolated case but also has a bearing on gender-

related disparities in access to complementary inputs such as land, labour and access to credit. Due 

to this women adopt technology at a lower rate as compared to men. Tanellari, Kostandini, and 

Bonabana (2013) found that male farmers are more likely to adopt new technology.  

2.6.1.3 Educational attainment 

Education exposes farmers to easily understand the concepts of technologies and the benefits 

associated with them. As a result several researchers have included an education level variable in 

studying farmers’ willingness to pay for agricultural technologies. Mwaura et al. (2010) in their 

research on the willingness to pay for extension services found educational level of respondents to 

have had a positive significant relationship with willingness to pay for extension service. Other 

authors have also emphasized the importance of education in determining willingness to pay for 

technologies though have been measured in various ways including educational level and number 

of years of education (Danso-Abbeam et al., 2014; Haba, 2004; Zakaria et al., 2014).  

 

2.6.1.4 Income 

Farm income as well as nonfarm income are often considered as a factor influencing willingness 

to pay for agricultural technologies. Ndetewio, Mwakaje, Mujwahuzi, and Ngana (2013) 

hypothesized income to have an impact on the willingness to invest in watershed irrigation. In their 

study, this hypothesis proved true as farmers’ income had a positive relationship with the WTP 

values. According to Pender and Kerr (1998) and Holden and Shiferaw (2002), nonfarm income 

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is likely to have a positive effect, under the hypothesis that broadening out of agriculture would 

allow farmers to expand their income, thereby making more money available for on-farm 

development investments. Whilst non-farm income helps farmers to easily adopt technology, 

Ulimwengu and Sanyal (2011) also found a statistically significant relationship between 

agricultural income and the willingness of farmers to pay for agricultural services. Other studies 

that have found a positive significant relationship between the level of income of farmers and 

willingness to pay for agricultural technologies are Agyekum, Ohene-Yankyera, Keraita, Fialor, 

and Abaidoo (2014), Taneja et al. (2014) and Zakaria et al. (2014). 

 

2.6.1.5 Household Head 

Household heads influence the adoption of technologies. This is because the head of the household 

is responsible for taking major decisions of the household such as decisions on farm investments. 

As a result of the role household heads play in the Ghanaian society, Baffoe-Asare et al. (2013) 

hypothesized a statistically significant positive relationship between the age of the farmer 

household head and adoption of technologies. Fadare, Akerele, and Toritseju (2014) modelled the 

factors that influence the adoption decisions of maize farmers in Nigeria using the educational 

level of household head and found a positive relationship. This implies a strong linkage between 

a farmer being a household head and adoption of agricultural technologies. 

 

2.6.2 Farm characteristics 

2.6.2.1 Age of the farm 

In a study by Danso-Abbeam et al. (2014), age of cocoa farm was included as a factor that 

influenced cocoa farmers’ willingness to pay for crop insurance. The study found that the age of 

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the cocoa farm was positively statistically related to insurance but age squared was negatively 

statistically related to insurance decision. This is because at the beginning of the productive life of 

the cocoa tree, it has high yields but as the trees age and their productive capacity reduces, yields 

are reduced so that cocoa farmers are less willing to pay for insurance. Kazianga (2002) also found 

that cocoa output increases significantly at the beginning of the productive life of the crop and then 

declines in later years. The age of the cocoa trees is hypothesized to have a positive influence on 

farmers’ willingness to pay for the side grafting technology. This is because the higher the age the 

lower the yield influencing farmers to look for technologies to increase their yields and maintain 

a steady income. 

 

2.6.2.2 Farm size 

Farm size influences the decision by farmers to pay for a farm service such as side grafting (Abu, 

Taangahar, & Ekpebu, 2011; Kwadzo et al., 2013; Zakaria et al., 2014). A farmer who owns a 

large farm land, they are more likely to adopt yield enhancement technologies.  This is because 

farmers with larger farm lands are likely to commit larger sums of money into their farm in order 

to realize yield increases. On the other hand, farmers cultivating smaller farms will be less willing 

to pay for agricultural technology as a result of the low output. This outcome is explained by 

Adrian, Norwood, and Mask (2005) who found that farm size influences the willingness to adopt 

and also pay for agricultural technologies. In a study by Liu and Zhang (2011), farm size was 

found to have a statistically significant positive relationship with the willingness to adopt soil 

testing technology. 

 

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2.6.2.3 Yield 

The yield of a cocoa farm influences the adoption of technologies. The yield of farm affects the 

income that accrues to a farmer. If the yield decreases it directly affects the income of the farmer. 

Farmers experiencing low yields are willing to pay for side grafting. Aneani, Anchirinah, Owusu-

Ansah, and Asamoah (2012) examined the yield of cocoa farms as a function of the adoption of 

cocoa production technologies using a multinomial logistic regression. They found different levels 

of association between different technologies and yield of cocoa. For example, there was a positive 

statistically significant relationship between yield and weeding frequency, partial adopters of 

cocoa spraying and full adopters of cocoa spraying. The analysis varied levels of significance at 

5% for weeding frequency, 5% for partial adopters and 1% for full adopters. 

    

2.6.3 Institutional characteristics 

2.6.3.1 Membership in FBOs 

Farmer Based Organizations (FBOs) serve as platforms where information is disseminated among 

farmers. In effect, farmers are exposed to new technology as they engage with extension officers 

and industry players on an ongoing basis. Danso-Abbeam et al. (2014) modelled FBO membership 

as a factor in willingness to pay for crop insurance. A positive relationship was observed but the 

relationship was not statistically significant. However, Uaiene (2011) in his research on the 

determinants of agricultural adoption in Mozambique found FBO membership to be statistically 

significant using cross –sectional data and discrete choice methods; probit and logit models.  

2.6.3.2 Access to Credit 

Different authors have expressed different views of how access to credit influences the Willingness 

To Pay for a service or technology. Omondi, Mbogoh, and Munei (2014) found a statistically 

significantly relationship between access to credit and WTP for irrigation water. Similarly, studies 

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by Poulton, Dorward, and Kydd (2005) found that inadequate access to credit for enhancing farm 

activities also restricts agricultural productivity increase that rely on the use of inputs not directly 

available to the farmer. Hence, in the sense of cocoa grafting adoption, farmers’ access to credit 

might make them willing to pay for side grafting. Omondi et al. (2014) in their study measured 

the credit variable as a dummy with 1 representing access and 0 indicating no access. This way of 

measurement ignored the amount accessed through credit but lumped all those who accessed credit 

as having a higher probability of adopting. However, the amount of credit accessed could have an 

influence on adoption of agricultural technologies. As a result of the difficulty on the part of 

respondents in providing accurate figures on the amount of credit. Most researchers have measured 

access to credit as a dummy variable. 

 

2.6.3.3 Frequency of Extension Visits 

Extension agents are tasked to provide services to farmers on matters concerning their production. 

The agents serve as technology transfer agents and provide the needed information to farmers on 

a technology. Some researchers model effect of extension service on adoption using a dummy 

variable of access or no access. Others also model effect of extension using the number of 

extension visits, meaning that the frequency of visits to farmers will influence their level of 

adoption or not. Fadare et al. (2014) and Yu, Nin-Pratt, Funes and Gemessa (2011) who used a 

dummy variable for extension visits, found a positive relationship between access to extension 

visits and the adoption of agricultural technology. Tiamiyu et al. (2009) however modelled 

extension services using the frequency of extension visits and found a positive relationship with 

adoption of NERICA rice. One may argue that merely being visited by an extension agent is not 

adequate when measuring effect of extension on adoption. This is because the more farmers are 

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visited, the more they are exposed to information on available technology. Thus, lumping all 

farmers who have been visited could conceal actual effects on adoption. 

2.7 Models used in farmers’ willingness to pay decisions estimations 

Various models have been used to measure the effects of factors influencing farmers’ willingness 

to pay. For instance, Tiwari (1998) used the logistic regression model to estimate the factors 

influencing farmers’ willingness to pay for irrigation water. He however used this model in the 

case where the dependent variables were captured as close-ended responses. The author presented 

the actual amount farmers should pay for irrigation water and then asked whether they are willing 

to pay such amount or not. Thus, the WTP is captured as a binary dependent variable (Horna, 

Smale, & Oppen, 2005). Though such estimations may be useful, it does not help the researcher 

in bringing out strategic recommendations as to the actual amount farmers are willing to pay or 

what has been described in the literature as intensity. The challenges encountered with using the 

logit models are as follows; they cannot be used to denote random taste variation, they only give 

room for restrictive substitution patterns and they cannot be used with panel data when unobserved 

factors are correlated over time (Train, 2009).  

 

As a result of these challenges, other authors have modified the specifications of the logit model 

and have adopted the mixed logit model. Chung, Briggeman and Han (2008) found the mixed logit 

as a superior approach, in estimating WTP because it yields better estimates in the sense that it is 

able to account for heterogeneity in preferences of attributes. Whiles being effective in this sense, 

it fails to account for the sources of the heterogeneity in the preferences. To overcome these 

challenges, the Tobit model, the Heckman’s model as well the double hurdle model have been the 

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commonest methods of estimating factors influencing farmers’ willingness to pay decisions for 

agricultural technologies, depending on the assumptions one adopts in the analysis.  

 

2.7.1 Tobit model 

The Tobit model measures not only the probability that cocoa farmers will adopt the grafting 

technology but also the amount of money farmers are willing to pay, otherwise referred to as 

intensity in the use of the grafting technology (Adesina & Zinnah, 1993). The Tobit model is 

therefore a simultaneous and stochastic decision model. Furthermore, the Tobit regression model 

also interprets all the zero observations in the data set as corner solution. This means that the cocoa 

farmer will be assumed to be an adopter having a zero outcome. The Tobit model also assumes 

that both the willingness to pay for the cocoa side grafting technology and willingness to pay 

amount are explained by the same variables. It is therefore hypothesized that, the variables that 

increase the probability of cocoa grafting adoption and willingness to pay also increases the 

amount farmers are willing to pay. That is, the willingness to pay for cocoa grafting and the 

willingness to pay amount are jointly determined (Sindi, 2008). Basarir, Sayili and Muhammad 

(2009) used the Tobit model to analyze producers’ willingness to pay for high quality irrigation 

water. The authors found that large proportions of producers were not willing to pay any amount 

of money for increased water quality hence recorded zero in the survey. The Tobit model therefore 

proved an appropriate model for analyzing such a scenario as compared to ordinary least squares 

regression. Cho, Yen, Bowker and Newman (2008) applied the Tobit model in their analysis of 

the WTP for land conservation easement. The respondents’ decision is estimated as a joint 

procedure that involves the choice to reveal and the choice to value (willingness to pay amount). 

The choice to reveal part of the joint process was modeled as a binary response, and a Tobit was 

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used to model the WTP amount, taking into account zero Willingness To Pay amount. Despite the 

advantages of the Tobit model, it is too restrictive as it assumes all the zeros in the WTP amounts 

to be the result of the respondents’ deliberate choices which might not necessarily be the case.  

 

2.7.2 Heckman’s sample selection model 

Heckman sample selection model is one of the models that guard against sample selection bias. 

This model in contrast to the Tobit regression model is based on the assumption that the decision 

to pay for side grafting a hectare of cocoa farm and the decision on the amount the cocoa farmer 

is willing to pay may not necessarily be jointly determined by the same set of factors (Musah, 

2013). Cocoa is widely cultivated in six regions of Ghana. However, over 25% of cocoa farms are 

30 years old and therefore require an effective and efficient way of rehabilitating farms that will 

not erode completely farmers’ income within the period of the rehabilitation. Due to the 

complicated nature of the side grafting process, Rural Service Centres (RSC) will be established 

to provide side grafting services to farmers at a fee. With this, cocoa farmers are likely to decide 

first to adopt the technology and pay for it after which they will decide on the amount they are 

willing to pay. From this, it is clear that the decision to adopt and pay an amount of money for the 

service could precede that of the amount the farmer is Willing-To-Pay (Norris & Batie, 1987). 

 For instance, there is high probability that a cocoa farmer will only pay a particular amount for 

the side grafting technology if he knows the returns over the years are greater than the amount he 

is paying.  In such instance, the decision to pay determinants and the factors that influence decision 

on the actual amount cocoa farmers are willing to pay could be different. Heckman therefore 

models these decisions as two separate processes. The first being whether to adopt and pay or not 

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for side grafting one hectare of cocoa farm, with the second being how much to pay as fee for 

grafting a cocoa farm (Sindi, 2008).  

 

2.7.3 Double hurdle model 

Zero responses are common in research. Respondents give zero WTPs but their marginal utility of 

the technology might not be zero, perhaps because they think other stakeholders like government, 

NGOs or other international organizations, rather than themselves, should pay for adoption of the 

grafting technology. In other situations, valid zeros are those who truly have a zero marginal utility 

of the side grafting technology. Moreover, zero responses may be generated as a result of the fact 

that cocoa farmer respondents refuse to answer due to a lack of knowledge or how complex the 

questions are perceived to be. Also, some cocoa farmers may only have partial information 

concerning their Willingness To Pay (Yu & Abler, 2010). For such a case, it is possible farmer 

respondents cannot give a number representing their WTP but may recognize the fact that they 

have a positive WTP. Such responses are classified as incomplete responses and are often dropped 

during analysis. Treating responses this way may result in sample selection bias because they are 

not missing at random.  

 

To deal with such scenarios, Cragg (1971) suggested a double-hurdle model in which adoption 

behavior consists of two decisions: an adoption decision, which is a binary choice modelled using 

a Probit; and a WTP amount decision, which is a truncated regression model. The Double hurdle, 

is used in a situation where an event may occur or not and when it does, it takes on continuous 

positive values (Gabre-Madhin, Barret, & Dorosh, 2003). It is assumed that, the cocoa farmer is 

faced with a two stage decision making process. In so doing, the decision to pay is made first 

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followed by the decision on how much to pay for side grafting a cocoa farm. The two equations 

are assumed to be independent.  

 

The Heckman sample selection model and the double-hurdle model are similar in terms of 

recognizing discrete (zero or positive) outcomes. The two models bring out the fact that WTP 

decision outcomes are expressed by the choice to adopt and willingness to pay amount otherwise 

referred to as intensity. As such, they allow for the estimation of both the first and second stage 

equations with different sets of explanatory factors. In spite of this similarities, the Heckman 

sample selection model assumes that no zero response will be present in the second hurdle of the 

analysis once the first hurdle is passed.  

 

The double-hurdle on the other hand recognizes the possibility of zero observations in the second 

stage (Wodjao, 2008). In analyzing the decision to sell staple crops in Mozambique and the 

quantity to sell, Salvucci (n.d.) first used the Heckman model of estimation and then used the 

double hurdle model to check the robustness of the estimation since the Heckman model might not 

recognize the possibility of a zero quantity of sale. The results obtained were very similar in signs 

and therefore robust. 

 

2.8 Conclusion 

It is evident from literature that as various studies have sought to adequately model WTP analysis 

in terms of factor measurements and choice of model, there are still gaps. To cater for zero 

responses, the study will adopt the Double hurdle model to adequately predict the effects. 

 

 

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CHAPTER THREE 

METHODOLOGY 

3.1 Introduction 

This chapter presents the methods used in achieving the objectives of the study. First, description 

of the study area, data collection methods, types and sources of data, and sampling procedures are 

presented. The conceptual and analytical frameworks underlining cocoa farmers’ willingness to 

pay for side grafting is discussed. Variables used in the study are also described. 

3.2 Study Area 

The Eastern Region located on 19,323 kilometers stretch of land. With this land mass, it is the 

sixth largest region in Ghana. The region is bounded by the Greater Accra, Central, Ashanti, Brong 

Ahafo and Volta Regions. It has 27 political administrative districts and municipalities 

(www.ghanadisricts.org). 

The region has a population of 1,227,612 people.  Out of this overall population, 75.5% are 

economically active and 24.5% are not economically active. The main occupations of the 

economically active population in the region is Agriculture and related work (GSS, 2012). This 

shows that agriculture is very important in the region. 

The region is located within the wet semi-equatorial zone. As such, the region experiences double 

maxima rainfall which occurs in June and October. The first experience of rain is from May to 

June with the second rainy season occurring between September and October. The favourable 

weather conditions in the Eastern region has made it very conducive for the cultivation of both 

cash and food crops such as cocoa, kola-nuts, citrus, oil palm and staple food crops such as cassava, 

yam, cocoyam, maize, rice and vegetables. The Eastern region is the first point of introduction of 

cocoa by Tetteh Quarhie before it spread to other areas and therefore has a high incidence of over 

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grown cocoa trees. Though the region lost its place as the largest producer of cocoa in Ghana, it 

contributes significantly to the total production of cocoa (ghanadistricts.com). The importance of 

the region in terms of cocoa production led government to establish the Cocoa Research Institute 

of Ghana in Tafo in the Eastern Region to conduct research into curbing the incidence of the 

swollen shoot virus disease and other diseases which has plagued the cocoa sector. 

3.3 Data Collection 

3.3.1 Types and Sources of Data 

The study used primary data for the analysis. Respondents were interviewed from 6 cocoa growing 

communities within the Tafo Cocoa district. The study utilized a structured questionnaire as survey 

instrument where cocoa farmers were asked questions in a face to face interview. 

The questionnaire was divided into five sections. The first section included questions on the 

demographic characteristics of farmers. Questions on age, gender, educational level, marital status 

and years of cocoa farming were asked. The second section asked questions on farm 

characteristics. This included the number of cocoa farms, acreage of the farms, the number of bags 

of cocoa harvested in the 2013/2014 cocoa production year as well as the price at which it was 

sold. The third section is related to the institutional characteristics of farmers. The questions 

included membership in a FBO, credit access and extension visits. The fourth section captured 

farmers’ knowledge and use of existing cocoa production technologies. Questions asked included 

knowledge of CODAPEC, hi-tech/mass spraying, use of fertilizer, spacing on the farm, number of 

trees per acre as well as disease control. The last section asked questions pertaining to farmer’s 

awareness of side grafting and the elicitation of the willingness to pay amounts. The questionnaire 

captures the lower, medium and higher bids.  

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In eliciting the maximum willingness to pay, grafting was explained to the respondent and then 

cocoa side grafting. The benefits and costs associated with cocoa side grafting were explained to 

the respondent. He/she was then asked if he is willing to adopt side grafting on his cocoa farm. If 

the farmer answers yes, the respondent is asked if he is willing to pay for side grafting. If the 

answer is yes, the respondent is asked to choose between the bids. The questions relating to the 

chosen bidding level are then asked. If the first answer is a yes, then the respondent is asked a 

higher amount. If the answer is no, the respondent is asked if he is willing to pay a lower amount. 

After this step in each case, the respondent is asked to give the maximum amount he is willing to 

pay. This is taken as the willingness to pay amount.   

3.3.2 Sampling procedure 

The study utilized the multistage sampling procedure where it started with a purposive selection 

of the Eastern region. It was selected because it is one of the oldest cocoa growing regions in 

Ghana and likely to have old cocoa trees. Moreover, it is home to the Cocoa Research Institute of 

Ghana where technologies are developed or tested. The Tafo Cocoa District was randomly selected 

using the lottery system of picking without replacement from the list of cocoa districts in the 

Eastern region. From the District level, six cocoa growing communities were randomly selected 

from the district. The communities included Anyinasin, Ettokrom, Tontro, Hemang, Dome and 

Bosuso. The sample size was calculated using sample size formula suggested by Miller and Brewer 

(2003) from a farmer population of 474 obtained from the Cocoa Health and Extension Division, 

Tafo Cocoa District. The systematic random sampling technique was then used to select farmers 

from the individual communities for interviewing.  The proportion of respondents from each 

community is calculated by finding the percentage of respondents. The interval at which the nth 

respondent is picked from the farmer list obtained from the Cocoa Health and Extension Division 

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of the Tafo Cocoa District was also calculated using the individual population and sample size 

from each community. The interval was calculated by dividing the overall number of cocoa 

farmers in the individual communities by the proportion of the sample size from the respective 

communities. Overall, 217 respondents were sampled and interviewed. The distribution of the 

respondents in each community is shown in Table 3.1. 

Table 3. 1: The distribution of farmer respondents 

Community Population Sampled farmers 

Tontro 103 47 

Anyinasin 92 42 

Ettokrom 37 17 

Dome 96 44 

Hemang 50 23 

Bosuso 96 44 

 

3.4 Conceptual Framework 

The conceptual framework for the analysis in this research is presented in Figure 3.1. Apart from 

the traditional rehabilitation method recommended by COCOBOD by cutting down cocoa trees 

entirely and starting with new seedlings, side grafting is also useful for rehabilitation. In the case of 

the side grafting technology, sharp decline in income is averted as well as reducing the number of 

years to rehabilitate a farm. Farmers’ decision to adopt the side grafting method of canopy 

substitution or rehabilitation is highly a matter of choice. It is based on whether they will be able to 

get the required utility in relation to the amount invested and in the face of other available choices. 

Considering the benefits and costs of the technology, farmers may decide to adopt, which will also 

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affect their willingness to pay for the technology. Farmers’ willingness to adopt the grafting 

technology is influenced by many factors such as socioeconomic characteristics of farmers (age, 

education, and being a household head); farm characteristics (farm size, age of farm, and yield); 

institutional factors (credit access and