Input Credit Scheme Effects on the Adoption of Cocoa Production Technologies and Productivity Of

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INPUT CREDIT SCHEME EFFECTS ON THE ADOPTION OF

COCOA PRODUCTION TECHNOLOGIES AND

PRODUCTIVITY OF SMALLHOLDER COCOA FARMERS IN

GHANA

AGYEKUM, AMPOMA PETER

THIS THESIS IS SUBMITTED TO THE UNIVERSITY OF GHANA,

LEGON IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR

THE AWARD OF MASTER OF PHILOSOPHY (M.PHIL) DEGREE IN

AGRIBUSINESS

DEPARTMENT OF AGRICULTURAL ECONOMICS AND

AGRIBUSINESS

COLLEGE OF BASIC AND APPLIED SCIENCES

UNIVERSITY OF GHANA

LEGON

JULY, 2015

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DECLARATION

I, Peter Agyekum Ampoma, the writer of this thesis. “Input Credit Scheme

Effects on the Adoption of Cocoa Production Technologies and Productivity of

Smallholder Cocoa Farmers in Ghana”, do hereby declare that with the exception of the various forms of assistance and references made to literature, which are duly cited the entire research study was done by me at the Department of

Agricultural Economics and Agribusiness, University of Ghana Legon, from

August 2013 to July 2015. I further declare that this thesis has never been presented in whole or in part for any degree in this University or elsewhere.

…………………………………………………….

Peter Agyekum Ampoma

(Student)

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

…………………………………… ……………………………

Prof. Daniel Bruce Sarpong Dr. Y. B. Osei-Asare

(Major Supervisor) (Co-Supervisor)

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DEDICATION

My wife, Mrs. Georgina Agyekum and Children: Sharon, Christine, Benedicta,

Emmanuella and Edna. This is in recognition and appreciation for the sacrifices

you have made towards my education.

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ACKNOWLEDGEMENT

To God be the glory for His guidance and protection granted me throughout my course of study. I wish to particularly extend my profound gratitude to Professor

Daniel B. Sarpong and Dr. Y. B. Osei-Asare, Supervisor and Co-Supervisor respectively, for their painstaking critiquing, encouragement, support and assistance during the course of this work. The other senior members of the

Department also deserve commendation for the tremendous contributions made towards the final work especially during the seminars. I also wish to express my gratitude to Mr. Opoku-Boamah, Executive Secretary of Cocoa Abrabopa Input

Credit Scheme for the assistance given me. Special thanks also goes to Mr.

Appiah-Kubi, Western-North zonal coordinator of Cocoa Abrabopa, Oliver and

Ofori-Atta who are both promoters (Extension Agents) of the scheme in the

Western region, and the entire Cocoa Abrabopa farmers in the four districts I visited in the Western Region for their patience and tolerance during the interview. In the Eastern Region, my sincere gratitude goes to Mr. Mawutor, the regional coordinator of Cocoa Abrabopa, and Augustine Ofosu a Promoter in the Region. I wish to also acknowledge the sacrifices made by Peter Koomson,

Alfred Appiah, Michael Asante and Wisdom Dogbe for helping in the data collection at the field and other useful contributions towards the final work. Last but not the least, I wish to express my sincerest gratitude to my classmates especially to Felix Larry Essilfie, Ellen Acquaye, Eunice Dadzie, and Abiba

Yayah, for their assistance during my studies at the University of Ghana, Legon.

Finally, I wish to express my gratitude to my sister Mary Agyekum and Richard

Gyedu who assisted me in various ways when I was in School.

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ABSTRACT

The study sought to assess the effects of input credit scheme on the adoption of cocoa production technologies and productivity of smallholder cocoa farmers in the Eastern and Western Regions of Ghana. The thesis specifically studied the nature and value of the input provided under the input credit scheme, by Cocoa Abrabopa. Also, the extent of adoption of cocoa technologies by Cocoa Abrabopa (CAA) farmers, the effects of adoption of these cocoa technologies on productivity of cocoa farmers in the two regions and the constraints faced by cocoa farmers in accessing the inputs credit scheme were analyzed. The nature and value of input technologies offered under the scheme was achieved with descriptive statistics. The Z-test was used to determine the differences in proportions of cocoa farmers’ that adopted the recommended cocoa production technologies within the two regions. The augmented Cobb Douglas production function was used to analyze the factors influencing productivity of Cocoa Abrabopa farmers in the two regions. Farmer constraints were analyzed using the Garrett ranking model. Analyses of the data indicate that there are slight variations in the value of input technologies used by farmers under the scheme in the two regions. Input cost were GHS 1030.80/year and GHS 884.4/ year for farmers in Western and Eastern region, respectively, in that farmers in the Western Region receive all the inputs given out to members of the scheme, whilst the Cocoa members in the Eastern Region do not collect the contact fungicides for controlling Black pod disease of cocoa. The augmented Cobb-Douglas regression shows that productivity of scheme farmers is influenced by fungicide usage, household size, experience as well as regional location of the farm. Productivity per unit area was found to be higher in Western region than the Eastern region for all the Cocoa Abrabopa farmers. Finance and access to inputs were identified as the most pressing and least constraints respectively to production in both regions. The study recommends, among others that prices of inputs could be subsidized to enable more farmers join the Cocoa Abrabopa scheme, and that the current inputs given for two acres farm size should be extended to cover a bigger land size.

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TABLE OF CONTENT

DECLARATION...... i

DEDICATION...... ii

ACKNOWLEDGEMENT ...... iii

ABSTRACT ...... iv

TABLE OF CONTENT ...... v

LIST OF TABLES ...... ivii

LIST OF FIGURES ...... ix

LIST OF ACRONYMS ...... x

CHAPTER ONE: INTRODUCTION ...... 1

1.1 Background ...... 1

1.2 Problem Statement ...... 5

1.4 Objectives of the study...... 9

1.5 Justification of the Study ...... 9

1.6 Organization of the Thesis ...... 10

CHAPTER TWO: LITERATURE REVIEW ...... 11

2.0 Introduction ...... 11

2.1 Definition and Concepts of Technology Adoption ...... 11

2.2 Determinants of Technology Adoption ...... 14

2.3 Productivity ...... 17

2.3.1 Partial Factor Productivity ...... 17

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2.3.2 Total Factor Productivity (TFP) ...... 18

2.3.3 Use of Productivity Measures ...... 18

2.4 Agricultural Input Credit Schemes ...... 19

2.5 Effects of Input Credit Schemes on Crop Productivity, Income and Poverty ...... 21

2.6 Farmer Groups and Technology Adoption ...... 21

2.7 Cocoa Productivity...... 24

2.8 Cocoa Disease and Pest Control Programme and Cocoa High Technology Adoption on

Productivity ...... 25

2.9 Socio – Economic Factors Affecting Adoption of Technology ...... 29

2.10 Institutional Factors Affecting Adoption ...... 32

2.11 Empirical Studies of Input Credit Schemes on adoption of technology, productivity and poverty reduction ...... 34

CHAPTER THREE: METHODOLOGY ...... 35

3.1 Introduction ...... 35

3.2 The Conceptual Framework of the study ...... 35

3.4 Methods of Analyses...... 38

3.4.1 The nature and value of input credit scheme packages for Cocoa Abrabopa ...... 38

3.4.2 Determination of level of adoption of Cocoa Technologies in the Western and Eastern Regions ...... 39

3.4.3 Effect of Cocoa Abrabopa Input Credit Scheme on Cocoa Crop productivity...... 40

3.5 The Study Area ...... 43

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3.5.1 Western Region ...... 43

3.5.2 Eastern Region ...... 43

3.6 Sources and Method of Data Collection ...... 44

3.6.1 Sources of Data ...... 44

3.6.2 Method of Data Collection and Sampling Technique...... 45

CHAPTER FOUR: RESULTS AND DISCUSSIONS ...... 47

4.0 Introduction ...... 47

4.1 Socio-economic characteristics of Cocoa Abrabopa farmers ...... 47

4.2 The Nature and Value of Inputs provided by Cocoa Abrabopa Scheme ...... 48

4.3 Adoption of Cocoa Production Technologies ...... 51

4.4 Effects of Input Credit Scheme on Productivity of Farmers in the two Locations ...... 52

4.5 Constraint Analyses ...... 60

CHAPTER FIVE : SUMMARY, CONCLUSION AND RECOMMENDATIONS...... 62

5.0 Introduction ...... 62

5.1 Summary ...... 62

5.2 Conclusion...... 64

5.3 Recommendations ...... 65

REFERENCES ...... 66

APPENDIX ...... 90

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LIST OF TABLES

Table 3. 1: Description of Variables used in Productivity Analysis in the two locations ...... 41

Table 3. 2: Communities Selected from Western Region ...... 46

Table 3. 3: Communities Selected from Eastern Region ...... 46

Table 4. 1: Socio-economic Characteristics of Eastern and Western Region ...... 47

Table 4. 2: Socio-economic Characteristics of Eastern and Western Region continued ...... 48

Table 4. 3: Nature and Value of Inputs for Western and Eastern Region in the year

2012/2013 ...... 49

Table 4. 4: Proportion of Cocoa Farms Adopting Cocoa Technologies ...... 51

Table 4. 5: Regression Results of Input credit Scheme on Productivity of Cocoa

Abrabopa Cocoa farms in the Western and Eastern Regions ...... 53

Table 4. 6: Results on mean scores of Garret ranking of constrains for Eastern and

Western region ...... 60

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LIST OF FIGURES

Fgure 3. 1: Linkages in the Adoption of input scheme, Cocoa Technologies and

Productivity ...... 35

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LIST OF ACRONYMS

ACC Africa Cocoa Coalition

AGRA Alliance for Green Revolution in Africa

CEPS Customs, Excise and Preventive Service

CFC Common Fund for Commodities

CHES Centre for Human and Environmental Security

CIMMYT International Maize and Wheat Improvement Centre

CNFA Citizen Network for Foreign Affairs

COCOBOD Ghana Cocoa Board

CODAPEC Cocoa Diseases and Pest Control Programme

CRIG Cocoa Research Institute of Ghana

FAO Food and Agriculture Organization

GDP Gross Domestic Product

GSS Ghana Statistical Service

ICCO International Cocoa Organization

IFPRI International Food Policy Research Institute

IITA International Institute of Tropical Agriculture

KO Potassium Oxide

KPMG Klynveld Main Goerdeler and Peat Marwick

LBC Licensed Buying Company

N Nitrogen

NPK Nitrogen Potassium and Phosphorus

NRSCE National Roundtable Sustainable Cocoa Economy

OH Hydroxide

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PBC Produce Buying Company

PQP Productivity and Quality Programme

SPSS Statistical Package for Social Sciences

TFP Total Factor Productivity

TOT Transfer of Technology

USDA United States Department of Agriculture

UNDP United Nations Development Program

WP Wettable Powder

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CHAPTER ONE

INTRODUCTION

1.1 Background

In West Africa, agriculture has played a major role in the provision of food, raw materials for industries, employment for the majority of people and foreign exchange earnings, of which cocoa contributes significantly (Danso-Abbeam, et al., 2012). The countries producing cocoa are Côte d’Ivoire, Ghana, Nigeria, Togo and Cameroun. They contribute more than 70% of the total world production

(ICCO, 2011). Côte d’Ivoire is the world leading producer of cocoa, producing annual tonnage of over 1,200,000 followed by Ghana and Indonesia producing

1,024, 000 and 830,633 respectively (ICCO, 2011).

Cocoa in Ghana is grown by small-scale farmers who grow about 2 hectares or less of the cocoa (Amos, 2007). Asante-Mensah (1999) indicates that cocoa cultivation is carried out in six regions of Ghana’s ten regions namely Ashanti,

Brong Ahafo, Central, Eastern, Volta and Western regions. Cocoa produced by

Brong Ahafo and Western regions accounts for 60% of the country’s tonnage of cocoa due to the large land size, coupled with the relocation of settler farmers from the old cocoa growing regions of the Eastern, Ashanti, Central, and Volta regions in the 1960s, as a result of the upsurge of swollen shoot virus disease in the four regions.

Cocoa has played a pivotal role in the socio-economic development of the country, providing foreign exchange, offering employment for millions of

Ghanaians and thus providing livelihood, since commercial cocoa production

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started 130years ago in Ghana (COCOBOD, 2012). The product is a source of income for nearly 1.5 million people in the country including 800,000 to

1,000,000 small holder farmers (COCOBOD, 2012). Agricultural exports are dominated by industrial tree crops like cocoa, coffee, oil palm and rubber (Danso-

Abbeam et al., 2012). The Bank of Ghana (2011) report indicates that export of cocoa accounted for an average earnings of 28.88% of merchandise exports between 2006 and 2010 (COCOBOD, 2012). The value of processed cocoa-based exports in Ghana has gone up from $18million in 1998 to US$152.9 million as at

2006 (Danso-Abbeam et al., 2012). Ghana levies an export tax on cocoa that contributes directly to government revenue even though there has been a decline in revenue, due to falling world prices of cocoa.

After emerging as the world’s leading producer of cocoa, Ghana experienced a major fall in production in the 1960s and 1970s and the sector nearly collapsed in the early 1980s (Obeng-Agyina and Opoku, 2010). However, production increased slowly in the mid-1980s after the introduction of nation-wide reforms. The 1990s marked the beginning of revitalization of the cocoa sector, with production nearly doubling between 2001 and 2003 and at the end of 2010/2011 crop year; Ghana attained a cocoa production of over one million tonnes which is the highest so far in the history of cocoa production in Ghana. However, cocoa production has increased annually on the average in recent times of about 65,000 metric tonnes mainly due to land expansion and partly to gains in productivity (Opoku-Ameyaw et al., 2010). The interventions that helped in achieving these sustained increase in cocoa production included regular farmer’s education on good agronomic practices; distribution of subsidized fertilizers to farmers, Cocoa Diseases and Pest

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Control Programme (Mass Spraying) and free cocoa seedlings to farmers, liberalization of the internal market system that has brought many licensed buying companies (LBC) in the system, planting of moribund farms and the introduction of national cocoa rehabilitation programme (Asante-Mensah, 1999). However,

Grinsven (2010) asserts that cocoa production in Ghana is more of extensive than intensive production. This was corroborated by Gockowski et al. (2000) and

Nkamleu et al. (2003) to imply that the cocoa sector thrives on increase in area cultivated (extensive) rather than improving yield and improving technical efficiency (intensive).

The limited productivity gains in Cocoa production in Ghana have been attributed to lack of inputs and knowledge in production practices (Aneani & Ofori-

Frimpong, 2013). Lack of appropriate agriculture financing is the main reason why farmers cannot access the necessary inputs or participate in input credit schemes. (CNFA, 2009). Financing is vital for the agricultural production sector.

Apart from buying inputs, credit is used by farmers to support household during off season. Lack of adequate credit facilities and rural banking infrastructure has become a major problem for farmers. In effect, a very limited proportion of farmers actually have access to formal credits. Small holders frequently lack collaterals to obtain investment capital (Pfitzer, 2009). As a result of the lack of creditworthiness on the part of the farmer, neither the input suppliers nor the financial institutions are willing to directly conduct business with poor smallholder farmers (CNFA, 2009).

In many cases, farmers do not know where to access services or where to go for support. But, producer associations have the potential to facilitate the access to

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farm inputs, to monitor cocoa quality, to obtain credits as an investment capital, and to access knowledge (market information and farming practices) and increase farmers negotiating position in the value chain (Wiredu et al., 2011).

This failure of the rural financial organisations to respond to the financial demands of the credit constraint of cocoa farmers have contributed to low fertilizer usage and has hampered the adoption of recommended farming practices that has stifled yield and income (Pfitzer, 2009). Current average yields are only 400 kg/ha compared to well managed intensified practices that achieve

1000kg/ha to as high as 2500 kg/ha in some places (Baah et al., 2011). Producers are forced to depend on older cocoa tree stocks and the continued use of farmer- selected planting material of low yield potential. In addition, the depletion and loss of ecosystem services once offered by cocoa landscapes has left many cocoa farms more susceptible to a range of plant health problems. Low productivity in the cocoa sub-sector is aggravated by continued production of cocoa on nutrient- depleted forest soils. At a national roundtable discussion on sustainable cocoa production, participants underscored four thematic areas which were insufficiently addressed in the Ghanaian cocoa sector and therefore needed more urgent attention. These were: remuneration for quality cocoa, productivity and improved farmers’ income, access to credit and rural development services and diversification support for farmers’ and workers organizations; and conservation and wise use of biodiversity (NRSCE, 2010).

To reverse this trend of low productivity, stakeholders in cocoa production sector, have resorted to introducing policy reforms to improve upon efficiency in the cocoa production process of which the input credit schemes are key to enable the

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adoption of cocoa production technologies, thereby raising cocoa farm productivity to increase the farmers cocoa income.

1.2 Problem Statement

Cocoa farmers in Ghana are the least efficient in the world for their low yields

(Binam et al., 2008; Dormon et al., 2004). Farmers have attributed this to poor access to cash/credit, labour, spraying machines among others (Aneani et al.,

2013).

Low productivity is identified as resulting from two main factors: biological and socioeconomic factors. The biological factors include the incidence of pests and diseases, most of which have received extensive research attention in Ghana, and that of epiphytes, which have been neglected. The socio economic causes are indirect and include the low producer price and the lack of social amenities like electricity, which leads to migration, resulting in labour shortages and high labour costs (Dormon et al., 2004).

The need to increase cocoa yields has been a policy focus aimed at increasing farmer productivity. There are several interventions since the 1930s in the Cocoa sector aimed at increasing the yield levels of the resource-poor farmer (GCB,

1997; Asamoah, 2006). Currently, the national production stands at 830,840 metric tonnes.

The Government has liberalized marketing of cocoa internally since 1993. Initially

Produce Buying Company was the sole buyer of cocoa and enjoyed monopoly.

But with liberalized marketing of cocoa internally, the company had a stiff competition from other Licensed Buying Companies (COCOBOD, 2012).

Currently, Ghana has over twenty (20) buying firms that oversee to the internal

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purchases of cocoa from farmers. COCOBOD’s initial programme (COCOBOD

Hi-tech) of supplying fertilizer on credit and other inputs to farmers run into problems due to poor repayment. A private fertilizer company, Wienco (GH)

Limited, convinced about the good agronomic package under the programme,

(COCOBOD Hi-Tech) has established for cocoa farmers, a scheme called Cocoa

Abrabopa (COCOBOD, 2007). Cocoa Abrabopa involves farmers who desire to increase the productivity of cocoa farms. Farmers who join the association sign to receive a package of Hi - tech Cocoa inputs sufficient to cover two acres of mature cocoa farm on credit with the view to repay the total amount of the credit facility after harvest.

The Cocoa Abrabopa Input Credit Scheme (Cocoa Scheme)

The Cocoa Association known as Cocoa Abrabopa Association was founded in

2007 and its headquarters is located in Dunkwa-on-Offin in the middle of Ghana’s

Western cocoa region. The association is a brainchild of Wienco, and currently operates in all seven cocoa growing regions (Ashanti, Brong Ahafo, Central,

Eastern, Western North, Western South, and Volta). In the 2012 season, 16,000 cocoa farmers cultivating 1 – 2 ha each signed up for Cocoa Abrabopa scheme.

More than 50 % of the farmers were double-certified by UTZ and Rainforest

Alliance. In 2013, the number of farmers registering for the Cocoa Abrabopa scheme decreased to 10,000 farmers, 98 % of whom were double-certified. The total volume of cocoa produced under the umbrella of the association however, did not drop as farmers decreased. The reduced number of farmers according to

Wienco was mainly as a result of political intervention in the market, such as free supply of inputs through the association. The cocoa farmers receive regular

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agronomic training as well as quality agricultural inputs, while the off-take is predominantly organized through private buying companies, licensed by the state and run by COCOBOD.

Additional services provided by the association to its farmers include GPS mapping of land with the intention of improving traceability of cocoa and allowing farmers to apply for loan from financial institutions using land as collateral (more than 8,200 members are mapped) and also make farmers professional farmers. The utilization of land as collateral however has little support by local financial institutions so far. The scheme members are supplied with six(6) bags of Asasewura fertilizer and two bags of Nitrabor fertilizer for the two acre farm, as well as forty eight(48) sachets each of Ridomil gold and Nordox, the rest are Matabi Pneumatic Knapsack and Safe farming inputs.

However, Cocoa Abrabopa farmers’ in the Eastern Region collect all the inputs except the Nordox fungicide which is a contact fungicide in that with the judicious usage of the Ridomil fungicide coupled with good agronomic practices there is no need of using Nordox fungicide in addition.

Cocoa Abrabopa provides farmers in groups with credit schemes to help them increase cocoa farm productivity, and thus increase their cocoa incomes to improve their standard of living. The input credit scheme has the ability to sell their products on credit. In cocoa production, the bulk of cocoa services, such as credit schemes, are provided by Cocoa Abrabopa in the Western and Eastern

Cocoa Regions, which represents the “new” and “old” cocoa regions respectively

(COCOBOD, 2007)

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Due to the upsurge of the Swollen Shoot disease in the Eastern Region in the

1960s and the decline in Land suitable for cocoa farmers moved away from the

Eastern Region towards other cocoa regions. The Eastern Region is therefore referred to as “old” cocoa region, in that it is the first region in which cocoa plantation was established, by Tetteh Quarshie in 1897 (Agyina & Opoku, 2010).

Currently, most farmers have moved to the Western Region that has several uncultivated tracks of forest lands (“new” cocoa growing region). Given that

Cocoa Abrabopa members are provided with inputs in the two regions, the main question is: are there any productivity differences between Cocoa Abrabopa members in the two regions.

1.3 Research Questions

Given that Cocoa Abrabopa cocoa farmers in the “new” and “old” cocoa growing regions receives Cocoa Abrabopa facilities

The additional specific research questions are:

1. What is the nature of the credit scheme packages provided by Cocoa Abrabopa to farmers in the scheme for the two regions?

2. How has the input credit scheme influenced the level of intensification

(adoption of cocoa technologies) of cocoa farmers?

3. Has these input credit schemes affected cocoa crop productivity differently in the two regions?

4. What are the major constraints faced by cocoa farmers in the Western and

Eastern Cocoa Regions under the Cocoa Abrabopa credit scheme? These are the issues that are being addressed by the study.

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1.4 Objectives of the study

The main objective of this study is to assess the effects of Cocoa Abrabopa input credit scheme on the adoption of cocoa production technologies and productivity of smallholder cocoa farmers in the Western and Eastern Cocoa Regions of

Ghana. The specific objectives are:

1. To describe the nature and value of the input credit scheme packages for

Cocoa Abrabopa farmers at current market prices in the two regions.

2. To determine the level of adoption of cocoa intensification technologies of

Cocoa Abrabopa scheme participants in both Western and Eastern Cocoa Regions.

3. To estimate the effect of Cocoa Abrabopa Input Credit Scheme on cocoa crop

Productivity in the two regions.

4. To Identify and discuss the major constraints cocoa farmers face under the input credit schemes.

1.5 Justification of the Study

The study is undertaken in the Western and Eastern Cocoa Regions, in that the regions are the largest producer of cocoa and the second lowest producer of cocoa respectively, in Ghana. The objective is to contribute to increased cocoa productivity. The Western Cocoa region (“new” cocoa region) produces over 50% of the national tonnage whilst the Eastern Region (“old” cocoa region) makes the second lowest contribution of about 9%. In the 2013/2014 cocoa production season, for instance the Eastern Region produced 80,691 metric tonnes of cocoa as against Western region produce of 483,279 metric tonnes (Cocoa Statistics, 2014).

The Eastern Region has the potential to produce more tonnage of cocoa.

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Achieving the objective of ensuring that higher levels of productivity are obtained for cocoa farmers who are members of schemes is a major justification for this study. Furthermore, productivity gains, if any from the input credit schemes in the two regions will guide other similar organizations, in understanding the lapses and improve on their performance to increase the productivity level of farmers. It will also guide policy formulation at the national level especially in the cocoa sector

The effects of inputs credit schemes on the level of adoption of technology and other farm innovations will also guide other scheme operators in the field as to how input schemes are impacting on farmers’ technology adoption.

Farmers’ constraints will also help scheme operators address pressing problems in accessing inputs under the scheme. The study will add to existing knowledge and serve as a point of reference for further study in the Western and Eastern cocoa regions.

1.6 Organization of the Thesis

The study comprises five chapters. Chapter two is on literature review. Chapter three focuses on the study area and the research methodology which are used to address the specific objectives. The fourth chapter is on the results and discussions from the study. Chapter five covers summary, conclusions and recommendations for policy makers in the cocoa industry.

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CHAPTER TWO

LITERATURE REVIEW

2.0 Introduction

This chapter reviews literature on the definition and concepts of technology adoption, determinants of technology adoption, the concept of productivity, agricultural input schemes and their effects on technology adoption and crop productivity and socio-economic factors affecting adoption of cocoa production technologies and productivity.

2.1 Definition and Concepts of Technology Adoption

Farmer adoption usually differentiates between individual (farm level) and aggregate adoption (Boateng, 2003). Feder et al. (1984) defined final adoption of the individual farmer as the degree of use of new technology in long run equilibrium when the farmer has adequate information about the new technology and its potentials. This definition is similar to Schultz’s (1964) argument that the introduction of new technologies results in a period of disequilibrium behaviour where resources are not optimally utilized by individual farmers and learning and experimenting lead the farmer to new equilibrium levels. This implies that adoption has two different components: a time component indicating length of time the technology has been used, and an intensity of use component indicating effectiveness of its use. Such long run information is seldom obtained, however, and the adoption of a technology is generally reduced to a binary variable indicating use of technology or not (Kabila et al., 2000).

Adesina and Zinnah (1993) indicated that farmers generally are assumed to respond to innovation adoption individually. There is a group that respond

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partially, others are quick adopters while yet another group may not respond at all.

For instance, there are two types of technology adoption and diffusion, which are the adoption-diffusion paradigm and the economic constraint paradigm. Though both assume that the technologies characteristics determine the adoption and diffusion, these are included only in few empirical models (Adesina & Zinnah,

1993; Adesina & Baidu Forson, 1995; Phegel & Kiulin, 1996; Boateng, 2003).

The nature of diffusion is to begin at a point in time when an innovation is ready for use, and the main aim of diffusion is to explain how the innovation or technology is made available to the interested users (Jabbar et al., 1998). The earliest users of the technology may be called innovators and the diffusion process involves the spread of the innovation to the rest of the population (Boateng, 2003).

However, adoption studies consider the behavior of individuals in relation to the use of technology, particularly the reasons for adoption at a point in time, or the reasons for time adoption for individual users, are of major interest. Diffusion may be viewed as a dynamic process over time, relative to adoption (Stoneman, 1983;

Thirtle & Ruthann, 1987).

The technology characteristics- user context model integrates approaches which assume that characteristics of a technology underlying users ecological, socio – economic and institutional context play the central role in the adoption decision and diffusion process (Biggs, 1990; Scooner & Thompson, 1994).

There are more steps and complexities involved in the process of learning and adoption. A period of awareness and learning precede any adoption process

(Boateng, 2003). Initially, only limited information may be available or only a limited amount of information may be digested.

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The process involves information that include knowledge about how the innovation functions and where and how to get access to it. The optimal level of information is reached when information acquired over a period of time reaches a threshold level at which a decision can be made.

In recent times, technologies come in a form of packages. For example, the recommended technologies under the highest level are considered as packages which the cocoa farmer must adopt together (Boateng, 2003). The practices under the highest and at the recommended intensity are considered adopters, while non- adopters are those that consider part but not all.

The adoption of a new technology is vital for evaluating the importance of agricultural research investments (CIMMYT, 1993; Collinson & Tollens, 1994;

Boateng, 2003) and for guiding technology development to satisfy the needs of the clients. Sanginga (1998) emphasized that technology adoption brings potential impact at the farm household level.

In a crude sense, adoption studies are intended to analyze the process of farmer decision-making in adopting new technologies. According to Kalyebara (1999), such studies usually involve identification of factors which constrain or enhance adoption (determinants), spatial and temporal patterns of adoption (adoption pattern), when various types of farmers adopt (rate of adoption), the extent to which a technology is applied by farmers (extent of use) and which farmers do not adopt and why (Boateng, 2003).

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2.2 Determinants of Technology Adoption

The adoption of innovations in agriculture has seen wide studies since the earlier works of Griliches (1957) on adoption of hybrid corn in the USA. The majority of adoption research has been concerned with answering the questions: What determines whether a particular producer adopts or rejects a technology, and

What determines the pattern of diffusion of the innovation through the population of potential adopters? (Tsur et al., 1990; Leathers & Smale, 1995; Feder & Umali,

1993; Saha et al., 1994; Marsh et al., 1995; Rogers, 1995; Boateng, 2003).

Lindner (1987) states that overall, despite numerous studies, the results of research in this field have been disappointing as most of the statistical models developed have low levels of explanatory power, despite long lists of explanatory variables.

Moreover, the results from different studies are often contradictory regarding the importance and influence of a given variable (Amir et al., 1999). Factors that influence farmers’ adoption are mostly the conventional (traditional) ones; resource endowments, socio-economic status, demographic characteristics and access to institutional services such as extension, input supply, markets and credit

(Negatu & Parikh, 1999).

Farmer’s adoption behaviour especially in low income countries is influenced by a complex set of socio-economic, demographic, technical, institutional and bio- physical factors (Feder et al., 1985). Smallholder farmers’ decisions to adopt or not to adopt agricultural technologies depend on their objectives and constraints, as well as cost and benefits associated with the technology.

According to Ntege-Nanyeonya et al. (1997) and Boateng (2003), studies on the effects of conventional factors on adoption are extensive and numerous. Feder et

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al. (1985), Feder and Umali (1993), and Getahun et al. (2000) identify the various socio-economic, institutional and technical factors influencing adoption of improved maize varieties in Ethiopia. Their outcome showed that membership of organization, livestock ownership, educational level and access to credit have significant influences on the adoption decisions of small-scale farmers in the country.

Livestock ownership was used as a proxy for off-term income for procurement of inputs. In a study of determinants of fertilizer use on maize and tef by Million

(2001) in Ethiopia, the researcher also states that farmers’ adoption decisions are significantly influenced by the age of the farmers, availability of credit, frequency of contact with extension agents, livestock ownership and off-term income.

Mussei et al. (2001) in a study on adoption of improved wheat technologies in

Tanzania indicated that adopters were more slightly younger, educated, had larger family labour, had smaller farm sizes, had more off-term income, but have few years of farming experience. In Kenya, Ngatia and Kabaara (1976) observed that institutional factors (input constraints and extension influence) and farmer characteristic (including off-term employment) were major determinants of adoption of coffee production recommendations. In Kenya, Njagi (1980) concludes that availability of cash (credit), access to inputs such as manure, which are all institutional factors affected soil fertility management recommendations

Kamau (1980) indicate that adoption of weed control recommendation is influenced by institutional factors such as availability and cost of labour and cash flow constraints. Other more general studies such as by Green and Ng’ong’ola

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(1993) carried out in Malawi reported institutional factors as the main factor affecting adoption of fertilizer recommendation. Kebede et al. (1990) on the other hand, observed farmer characteristics (farm size, family size, farm income and educational level) and institutional factors such as access to information as having effects on fertilizer adoption.

According to Adejobi and Kormawa (2002), institutional factors (inaccessibility to inorganic fertilizer, extension sources, etc.) and farmer characteristics such as membership of cooperative society and ownership of livestock are determinants of manure use in Nigeria. Degu et al. (2000) found off-term income, availability of labour, use of credit and being a contract farmer (extension) to be significant in fertilizer adoption; while credit extension and membership of an organization were found to be determinants of improved maize adoption in Ethiopia. Other studies have also identified farmer characteristics and institutional factors associated with adoption (Daberkow & Mcbride, 1998; Khanna, 2001; Croppenstedt & Demeke,

1996; Boateng, 2003).

In summary, empirical studies on agricultural technology adoption generally divide a population into adopters and non-adopters (potential adopters) and analyses the reasons for adoption or non-adoption at a point in time principally in terms of socio-economic characteristics of adopters and non-adopters, as well as technical and institutional factors (Boateng, 2003; Thirstle & Ruttan, 1987; Feder

& Umali, 1993). The underlying characteristics of these factors are that they are assumed to affect the demand for the technology.

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Overall, the factors that determine a household’s decision to use a new technology fall into socio-economic, or household-level, market or institutional factors and technical factors.

2.3 Productivity

Productivity is defined as the output per unit input. Given that a farmer uses a set of u different inputs to produce a quantity of cocoa, two productivity indices can be derived. The ratio of the value of cocoa produced to the total value of inputs used represents the total factor productivity of the production programme. Partial factor productivity on the other hand, is the ratio of the quantity of the cocoa produced to the quantity of a particular input applied on the farm (Wiredu et al.,

2011). On the contrary, the more comprehensive measure of total factor productivity is a ratio that relates the aggregation of all outputs to the aggregation of all inputs. This concept is often used in a dynamic framework, where change in

Total Factor Productivity, that is to say productivity improvement is investigated

(Latruffe, 2010)

2.3.1 Partial Factor Productivity

According to Vora (1992), the standard definition of productivity is actually what is known as a partial factor measure of productivity, in the sense that it only considers a simple input in the ratio. The formulae for partial-factor productivity would be the ratio of total output to a single input. Partial factor productivity is generally used in situations when data are readily available. For the analysis, it is also easier to relate to specific processes. Labour-based hours (generally, readily available information) is a frequently used input variable in the equation. When

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this is the case, it would seem that productivity could be increased by substituting machinery for labour. However, that may not necessarily be a wise decision.

2.3.2 Total Factor Productivity (TFP)

Total factor productivity is measured by combining the effects of all the resources used in the production of goods and services (labour, capital, raw materials, energy etc.). Total productivity ratios reflect simultaneous changes in outputs and inputs. As such, total productivity ratios provide the most inclusive type of index for measuring productivity and may be preferred in making comparisons of productivity (Latruffe, 2010; Vora, 1992).

2.3.3 Use of Productivity Measures

Productivity measures can be used to assess the performance of an entire industry

(smallholder farmer) or the productivity of a country as a whole. It also calls for aggregate measures determined by combining productivity measures, or various companies, industries or segments of the economy, in our case, small-holder cocoa farmers (Lattrufe, 2010; Vora, 1992).

In order to improve the productivity of cocoa production, intensification of cocoa production should be emphasized. Intensification is a term used to describe a new trend of cocoa production in which the crop is grown with the aim of increasing productivity and at the same time ensuring sustainability by protecting the environment. To intensify cocoa production farmers, governments and the private sector (cocoa buying companies, input dealers, banks, credit institutions) will need to make some changes at the individual level the farmer must see cocoa production as a business venture, plant improved materials from designated Seed

Gardens in Ghana, use recommended inputs, strictly adhere to good agricultural

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practices related to cocoa production, and be a master of their cocoa farms and also assure the buyer and consumer of continuous supply (Manu & Tetteh, 1987;

Asare & Sonii, 2011; Asare, 2006). These characteristics can be realized through training as done by Cocoa Abrabopa. At the institutional level, there should be reliable supply of improved cocoa seed pods coupled with a good distribution network to reach the cocoa farmer and, prices of inputs must be highly subsidized if not free. There should be more support for cocoa research and extension programmes to ensure a higher productivity, and lastly, amendment of laws on land acquisition and tenancy agreement to encourage the youth to go into commercial cocoa production, as the aging cocoa farmers cannot adopt some technologies that is more labour intensive like mistletoe removal (Asare & Sonii,

2011; Asare, 2006)

2.4 Agricultural Input Credit Schemes

Meeting the challenge of raising rural incomes in Africa will require some form of transformation out of the semi-subsistence, low-input, low productivity farming systems that currently characterizes much of rural Africa. High-valued cash crops represent one potential avenue of crop identification (Jayne et al., 2007). Cash crops can be produced by coordinated input credit and output marketing systems which can raise incomes of smallholder farmers, Cocoa has guarantee minimum price, in effect paying the loan facility will not be much of a problem (Little &

Watts, 1994; Kelly et al., 1996; Dorward, Kydd & Poulton, 1998; Boateng, 2003).

According to Goetz (1993) and Govereh and Jayne (2003), there is some evidence indicating that participation in cash crops schemes can improve household’s access to crop inputs and training that provide the needed benefits to their food

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crops. Thus, in addition to the direct effect of cash crops on household incomes, there are important indirect effects of cash cropping on the productivity of other household activities such as food cropping, and thus cash cropping on the productivity of other household. These potential synergies between food crops and cash crops have been generally not recognized in food crop research and extension programs, despite having important implications for programmes designed to promote smallholder food crop production growth.

According to Jayne, et al. (2002), it is suggested that participation in interlinked cash crop schemes has enabled small farmers in Kenya to acquire key inputs that allows them to substantially increase the level of fertilizer usage on crops other than the one featured in commercialized marketing schemes. The synergies can help in the design of policy strategies to intensify food crop production in Africa.

Matsumoto and Yamano (2010) assert that to feed the large and increasing population, agricultural production has to step up by improving the agricultural productivity per land area because most of accessible fertile lands have been cultivated. To improve the agricultural productivity, the Ethiopian government has been implementing policies under the Sustainable Development and Poverty

Reduction Program (SDPRP). Currently, it is said that about 90% of fertilizer is delivered on credit at below-market interest or even at zero interest. Subsequently, the total fertilizer use has increased from the 250,000 tons in 1995 to 400,000 metric tons in 2008 (Spielman et al., 2010).

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2.5 Effects of Input Credit Schemes on Crop Productivity, Income and

Poverty

Farmers’ access to credit facilities is supposed to be an accelerator of agricultural development through a widespread break away from traditional technology and by fostering the general adoption of developed and improved technology. Flores

(2004) corroborating this assertion found out that institutional credit if made available to farmers could lessen some of the farmers problems such as small farm size, low output, low income and low socio–economic status. It can also prevent farmers of the excessive interest rate imposed on them by the informal creditors who usually charge high interest rate of between 100-300 percent per annum (Bolarinawa & Fakoya, 2011). Based on the above consideration, Nigerian government came out with different policy measures for extending financial assistance to small-scale farmers through farm credit institutions (Alabi et al.,

2007). These farm credit schemes have been functioning for many years. This system has helped in improving productivity thus increasing farmers’ income and subsequently reducing poverty (Bolarinwa & Fakoya, 2011).

2.6 Farmer Groups and Technology Adoption

The absence of collateral securities and guarantor for the poor is the major impediment to accessing credit from the formal financial organizations. Most banks cannot ascertain applicant’s risk type due to inability of the poor farmers to prove their credit worthiness (Feroze et al., 2011). Self-Help Groups have come out as a channel to reach the poor (Bharamappanavara & Hanisch, 2009). In India, it is being practiced in different forms and are grouped and monitored in three different arrangements, which are categorized based on how the self help groups

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are linked with their supporting organizations. It can be Bank model, Government model, or NGO model. Cocoa Abrabopa scheme is more or less a company model because it is owned by Wienco that has come out with its own programmes to help the rural poor. Supporting rural producer groups is another field where the government can play an important role, including capacity building for leaders that is (trainer of trainees) to manage and participate in negotiations and for the weaker members of the groups to achieve a voice within the groups. Promoting modern information and communication systems helps enabling producer in high groups to access market information and acquire professional advice necessary for modern supply chain management and effective participation in the policy dialogue (World Bank, 2008).

According to Singh (1995), self-help groups are voluntary groups that come together to obtain loans from financial institutions in order to meet their financial needs to adopt improved technologies. Rogers (2003) identifies two characteristics of innovations that best explain different adoption rates. The perceived relative advantage of using technology vis-à-vis the technology it supersedes and its perceived compatibility with existing values needs experiment. Adoption of technology may be measured by both the timing and extent of new technology utilization by individuals (Sunding and Zilberman 2001). The extent of adoption can be measured by intensity of cultivation. Example, in terms of number of farmers, total area, area within farms or harvest (CIMMYT, 1993). It is important to note that the adoption process is a dynamic one, not only in terms of the diffusion of new technologies over time and space, but also from the perspective

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of the individual farmer. That is whether the farmer has the financial capital to access the technology in its entirety or not.

The interventions in the cocoa sector like Cocoa Abrabopa to provide input access to ensure the adoption of productivity enhancing inputs is therefore an opportunity for farmers to easily adopt technologies. This is in line with the studies of

Semglame (1998) who finds that, farmer’s decision to adopt improved soil conservation measures in Tanzania is influenced by institutional support to make credit available. In a related study by Njagi (1980), the availability of credit by related institutions positively influences the use of improved soil management practices. The study of Adejosi and Kowama (2002) found that other institutional factors like improved access to extension services positively influence farmer’s adoption decisions. The farmers in the Cocoa Abrabopa scheme have grouped themselves into formal groups to positively influence the acquisition and use of improved technologies. This is in line with the study by Degu et al. (2000). In addition to these institutional factors other factors like resource endowments, socioeconomic status and demographic factors have implications for technology adoption by farmers. According to the findings from International Maize and

Wheat Improvement Centre (1993), younger farmers are more likely to adopt a new technology than older persons, perhaps because they have been exposed to new ideas and can also learn faster, thus are able to adopt a technology faster and better. Household size has a role to play to cause farmers to adopt technology. The study by Doss and Morris (2001) reveals that the number of adult males in a household significantly affects the use of improved varieties of maize in Ghana.

As revealed by other relevant studies that institutional provisions of credit to

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farmers have positive impacts on technology adoption is not a surprise to the study of Opoku et al. (2009) who finds that Cocoa Abrabopa credit scheme is positively related to the application of best cocoa farm practices to consequently affect its productivity positively. But that study does not account for the differences in productivity between old and new frontiers of cocoa production in

Ghana.

Matsumoto and Yamano (2010) study finds that the fertilizer credit is found to increase input application for crop production and as a consequence, it has a substantial impact on the yield. However, the impact on net crop income per cultivated area and also on per capita income is marginal because of the low profitability due to the low output price and high input cost of agricultural production.

2.7 Cocoa Productivity

Cocoa production in West Africa is characterized by smallholder type production whose action involves whole families as working units. These working units are often characterized by low input and output, ageing farmers and farms, disease plagued farms, inefficient use of resources, highly de-motivated youth and poor farming strategy (Kyei et al., 2011). Binam et al. (2008) observed that Ghana is the least efficient cocoa producing country among other countries in the West

Africa sub-region, a situation which may be attributed to the above listed problems. In broad terms, Gockowski et al. (2000), and Nkamleu and Ndoye

(2003) established that in Africa, the cocoa sector thrives on increase in area cultivated rather than improving yield and improving technical efficiency. To reverse this situation, stakeholders of cocoa growing countries have resorted to

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introducing policy reforms to improve upon efficiency in the production of the crop.

According to Kyei et al. (2011) low production efficiency in cocoa production means that output can be increased without requiring additional conventional inputs for cocoa, which implies that empirical measures of efficiency and technical progress is the rational strategy to adopt in order to improve performance significantly with the given technology in every cocoa production process. The productivity of cocoa tree begins to decline after around 20years (KPMG, 2013).

Although cocoa productivity has recently been increasing in Ghana it is still low compared with that of other countries such as Cote D’Ivoire and Malaysia.

Ghana’s low productivity can be attributed to the low adoption of cocoa productivity technologies (Aneani & Ofori-Frimpong, 2013). According to Aneani and Ofori-Frimpong (2013), government should encourage cocoa farmers through proper measures, to adopt improved technologies for enhancing productivity instead of focusing on excessive land expansion which eventually leads to low productivity. Increasing agricultural productivity or yield is critical to economic growth and development, which can be achieved by using improved agricultural technologies and management systems aimed at increasing productivity.

2.8 Cocoa Disease and Pest Control Programme and Cocoa High Technology

Adoption on Productivity

In an attempt to revive the cocoa industry, the government of Ghana through

COCOBOD re-introduced a pilot project in 2001 code name CODAPEC (Cocoa

Disease and Pest Control Programme) which was first introduced in the mid

1960s. This contributed to Ghana been the leading cocoa producer in the World, in

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which 560,000 metric tonnes was obtained in 1964/65. This programme coupled with Cocoa High-Tech technology, consisting of packages of cocoa technologies and social intervention developed by Cocoa Research Institute of Ghana (CRIG), was aimed at enhancing productivity. Through this intervention positive gains was realized by Ghana recording the highest production level of cocoa to the tune of

1,024,000 metric tonnes in 2011 (COCOBOD, 2012) which has never been recorded in the history of cocoa production in the country. This programme initially though did not cover all the cocoa growing districts across the nation due to financial constraints. However, other cocoa growing districts realizing its benefits in terms of cocoa yields decided to adopt this good technology package

(Danquah et al., 2015). Moreover, due to the expensive nature of the technology package, it prompted some farmers to partially choose to adopt some components of the total package, in spite of the fact that the CODAPEC and Cocoa High-Tech technology package comprises 25 unique characteristics (Baffoe-Asare et al.,

2013). Though it has been replicated throughout all cocoa growing regions productivity is still low compared to other countries like Cote D’Ivoire, Malaysia and Indonesia.

In general, cocoa technology consists of all the body of traditional knowledge and skills acquired over generations that go into production, and postharvest activities of the cocoa beans, as well as marketing of the cocoa beans (Laryea, 1981;

Danquah et al., 2015). In the attempt to augment the productivity of old and new farms as result of rising cost of control of black pod, capsids and the spread of swollen shoot virus disease, that called for the government of Ghana reintroducing in 2001 ‘mass spraying exercise’ also known as (Improved Cocoa Disease and

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Pest Control Progamme) (Danquah et al., 2015). COCOBOD later introduced

Cocoa Rehabilitation Programme which was launched in April 2012, with the view of replanting moribund cocoa farms. This is in a bid to enhance the productivity of the cocoa farmer (Aneani et al., 2011). The CODAPEC recommended the use of three main insecticides; Actara 240SC, Confidor 200 SL and Akate Master for the control of capsids and six fungicides; Champion 80WP,

Funguran- OH50WP, Nordox Super 75WP, Ridomil Gold6 6Plus WP, Koicide

101WP, and Metalm 72WP for control of black pod and related fungal disease

(Duker & Sakpaku, 2011; Abankwah et al., 2010; Adjinah & Opoku, 2010).

CODAPEC amongst other things provided free inputs and labour for the control of capsids and black pod disease on pilot project. However, due to budgetary constraints on the part of the government the coverage was limited and the frequency of spraying under CODAPEC was inadequate in spite of its positive impact on cocoa production levels (Ofori-Frimpong, 2010; Danquah et al., 2015).

Thus, farmers were expected to compliment the effort of the government with additional spraying schedule (Aneani et al., 2011).

According to Ofori-Frimpong (2010), cocoa “High Technology” is defined as the 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 resources. Nevertheless, over the years most cocoa agronomic research objectives focus on pest control and management as well as improve yield breeding programmes to the detriment of cocoa production and soil interface (Ofori-

Frimpong, 2010). However, cocoa as tree crop actively mined the soils of essential

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nutrients and these nutrients are lost through harvesting under traditional cocoa agro forestry system without being compensated for by artificial application of fertilizers. This tremendously led to decline in cocoa productivity per unit area as compared to that of Cote D’ivoire and Malaysia. Cocoa High-Tech technology therefore addresses these deficiencies in the agronomic practices. Cocoa High-

Tech technology package involves frequent weeding; planting high yielding hybrid cocoa varieties and judicious application of inorganic fertilizers (Aneani et al., 2011). There are two main fertilizer formulations under High-Tech technology. These are granular fertilizers (trade name: Assasewura, Cocofeed, and

Cocoa Master) and liquid fertilizers (trade name: Sidalco Balanced and Sidalco

Potassium rich) (Ofori-Frimpong, 2010). In addition, cocoa high-tech technologies emphasized on improved harvesting and drying technologies (Bosompem et al.,

2011). The socioeconomic dimension of CODAPEC and Cocoa High-Tech technologies was to address cocoa production inefficiencies.

From the above discourse, it is clear that without supplementing government efforts there will be a huge gap in cocoa productivity. Thus the need for input credit scheme packages like that of Cocoa Abrabopa (Danquah et al., 2015). This will help improve yield and income. Consequently, it will lead to a reduction in chronic poverty amongst smallholder cocoa farmers and rural-urban migration of the productive youth and improve their living standards from receipts of foreign exchange contribution of cocoa earnings (Vigneri, 2005; Danquah et al., 2015).

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2.9 Socio – Economic Factors Affecting Adoption of Technology

There are several socio-economic factors that influence farmer technology adoption and productivity. Age is one of the farmers’ characteristics that are essential in examining adoption studies and may influence adoption in one of various ways. Age of head of household has been found to be a significant factor influencing the use of an innovation but may not be always be the case, on the contrary may be insignificant in some research; According to the findings from

International Maize and Wheat Improvement Centre (1993), younger farmers are more likely to adopt a new technology than older persons perhaps because they have been exposed to new ideas. Education is seen as a benchmark of the modernization process in agriculture, and research work shows that adoption is strictly related to the educational level of farmers (Lin & Jeffries, 1998;

Abdelmagid & Hassan, 1996). Highly educated farmers are more able to adopt innovations and may understand extension services better (IFPRI, 1995; Boateng,

2003). Educational level of household head is therefore, believed to have an important positive impact on the adoption and use of new technologies. Pinckney

(1995) discusses the example of World Bank Policy findings on Uganda, which stated that ‘raising educational levels of farmers enhances agricultural productivity through technology adoption’.

. For example Ma’zaki farmer groups in Northern Ghana serve as a medium in technological adoption in which farmers learn new technologies. Educational level is generally seen to have significant positive effects on adoption of technology as

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have been revealed by various adoption studies (Admassie & Asfaw, 1997;

Appleton & Balihuta, 1996; Rosenzweiig, 1995, Boateng, 2003).

Household labour size which serves as working units, may also affect a household’s ability and willingness to adopt and use a new technology (Feder et al., 1985). It is for this reason that labour size of household is typically seen to have a positive effect on a household’s decision to use a new technology

(Croppenoted & Demeke, 1996); Green & Ng’ong’ola, 1993). Doss and Morris

(2001) report the number of adult males in a household to significantly affect use of improved varieties of maize in Ghana as labour constraints in terms of cost and availability would have been solved. The most persistent finding in adoption according to Shields, et al. (1993) indicates a positive effect of labour availability from the household on the probability of increased technology adoption.

Members of farmer groups are put in an advantageous position with respect to other cocoa farmers, in terms of their access to information on an innovation as well as for loan facilities (Boateng, 2003). Cooperative societies most often provide credit for their members, especially for productive purposes and this tends to relief them of their financial constraints. The result is that members are able to acquire the needed inputs for the application of an innovation and thus positively influencing adoption. Saito et al. (1994) found membership of a cooperative society to be significantly and positively related to adoption of new bean varieties by both female and male farmers in Kenya.

According to Doss and Morris, (2001) heads of households are the major determinants of various rates of technological adoption. The gender of the head of household may influence the use of an innovation for several reasons. Male and

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female heads of households may have different extent of access to extension, credit or to transportation assets. Some cultural beliefs also distinguish the roles of males and females in farm activities, in effect the adoption of the recommended agronomic practices in cocoa production, is influenced by gender. Most cocoa farms are owned by males who are generally heads of households. Female owners of cocoa farms are mainly widows or by inheritance. According to Mehra (1994) in sub-Saharan Africa including Ghana, Nigeria, Coite D’ivoire, and Cameroun there is a gender division of labour. However, gender of household heads has been found to be insignificant in some studies (Croppenstedt & Memeke, 1996). This assertion was debunked by Doss and Morris (2001) to suggest that gender may play an important role through other institutional constraints such as access to credit, extension and other resources..

The level of farm income derived from the sales of farm produce is an important determinant of the farmer’s purchasing power and capabilities. According to

Million (2001) farmer’s ability to use an innovation depends on the proceeds derived from the farm. Higher returns from the farm will enable the farmer to acquire the necessary inputs needed for applying the technology, thus relating positively to adoption. For instance farmers with higher returns can purchase enough fertilizer for their entire cocoa farm to ensure productivity.

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2.10 Institutional Factors Affecting Adoption

The distance the farmer has to travel before acquiring inputs will have great influence on the farmer’s willingness to apply a new technology. The additional and extra costs to be incurred in procuring inputs from distantly located input stores will serve as a hindrance for adopting an innovation (Adesina, 1996).

Earlier studies in West Africa by Prudentia (1983) and Matlon (1994) came out that adoption is negatively related to distance to source of inputs. Accesses to farm inputs serve as a mechanism for adopting an innovation. Farmers are negatively affected if the Agro- inputs required for using an innovation are not easily available in terms of location. Findings from Inaizumi et al. (1999) came out that access to market for inputs is important in farmers’ adoption of new varieties, especially cocoa farmers who had access to good planting materials because of their proximity to designated Seed Gardens had higher yields than farmers who are not located in distant places. This was corroborated by findings by Adesina et al., (1997), Adesina and Baidu-Forson, (1995), Sanginga (1998), and Boateng

(2003).

The various adoption rates in many parts of the world are due to credit constraints.

Where credit for smallholder farmers is severely limited, they may not be able to adopt innovations at the same rate as larger farmers (Boateng, 2003). Credit availability is an important facility in cocoa production technologies since communal methods of assistance (nnoboa) in the operation of cocoa farms in

Ghana is no more in existence and labour hiring, which demands ready cash payment, or some line of credit has taken its place in which most farmers cannot

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afford. This leads to low adoption of technology that involves huge expenditure.

(Ampofo, 1990). A study by Bhalla (1979) in India shows that small and large farmers differed in the reasons for not using fertilizer in 1970 – 71. Lack of credit was a constraint for 48 percent of small farmers. Lowdermilk (1972) indicated that many small farmers reported shortage of funds as a major setback in the fertilizer application technology. In Nepal and Malawi access to credit was found to be significantly associated with fertilizer use which involves financial payment

(Shakya and Flynn, 1985; Green & Ng’ong’ola, 1993).

Increased productivity through adoption can be realized through access to farm level extension (Trudy et al., 2001). According to Birkhaeuser et al. (1991) and

Evenson (1998), agricultural extension represents a medium by which information on new technology, better farm practices and better management can be disseminated to farmers. Extension contact is therefore found to be positively related to productivity through innovation adoption as was find out in Kenya by

Seyoum, et al. (1998).

According to Kheralla et al. (2001) and Adhikary (1994), input price has been found to have a negative effect on adoption in many studies. This is due to the fact that, as prices of goods, in our case inputs, increases demand for the inputs fall as farmers cannot afford expensive inputs thus based on economic theory of demand, which states that as price of a good increase, its demand falls. The higher the cost of inputs required for applying a technology, the lower the adoption is expected to be. A report from Kherallah et al. (2001) found that market price of fertilizers had a negative effect, as economic theory would suggest, on fertilizer use in Benin.

Farmers in Ghana do not adopt technologies which are very expensive. For

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instance, for fertilizer meant for cocoa production, only few farmers adopt such technology if not given to them free. The new initiative by COCOBOD in Ghana where Cocoa farmers are given free cocoa seedlings and fertilizers to increase productivity is a step in the right direction that will help augment productivity.

2.11 Empirical Studies of Input Credit Schemes on adoption of technology, productivity and poverty reduction

There are several empirical studies related to input credit scheme that has influenced the adoption of technology, increased farm productivity and thus increasing farmers’ income. Masumoto and Yamano (2010) evaluated the impact of fertilizer credit on crop choice, crop yield, and income using two-year panel data of 420 households in rural Ethiopia. The fertilizer credit was found to increase input application for crop production. As a result, it had a substantial impact on the yield of tef. It was also found that the impact on net crop income per cultivated area and also on per capita income was marginal because of the profitability due to the low output price and high input cost of agriculture. This implies that Cocoa Abrabopa members can increase cocoa productivity, by the adoption of recommended cocoa production technologies. As yield levels increases it will increase cocoa income thus reducing poverty levels of the farmer.

In summary, literature was reviewed in the following areas; definition and concepts of technology adoption, determinants of technology adoption, concept of productivity, agricultural input credit schemes and their effects on technology adoption and crop productivity as well as socio-economic factors affecting adoption of cocoa production technology and productivity.

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CHAPTER THREE

METHODOLOGY

3.1 Introduction

This chapter presents the conceptual and theoretical framework and analytical tools that were employed to achieve the study objectives. It also describes the data requirements, data collection, sampling techniques and estimation procedures for the study.

3.2 The Conceptual Framework of the study

Figure 3.1 presents the conceptual framework underlying the study. Figure 3. 1: Linkages in the Adoption of input scheme, Cocoa Technologies and Productivity

Socio-economic characteristics Training Age Entrepreneur training Sex Business skills Experience Agronomic training Educational Background Household Size Increased cocoa Input Adoption of productivity Schemes cocoa farm and cocoa technologies income

Government Policies Institutional Factors CODAPEC Access to Extension Free fertilizer Road network Cocoa Rehabilitation Access to Project agrochemicals Free Cocoa seedlings NGO’s in Extension Producer price of cocoa Input Credit Scheme

From Figure 3.1, the input credit scheme influences the adoption of cocoa farm technologies which could lead to increases in cocoa productivity and cocoa farm income.

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The adoption of cocoa farm technologies could also be influenced by government policy, institutional factors, cocoa training programmes, as well as socio-economic factors. Increased adoption of cocoa technologies and increase cocoa productivity may encourage other farmers to join the scheme and also encourage the members to stick to the group, to obtain the needed benefit.

3.3 Theoretical Framework of the Study

Agricultural financial institutions have faced difficulties’ extending credit (in-kind and cash) to poor and low-income households in developing countries.

However, several agricultural financial institutions are now focusing on new credit programmes and devices that help households manage their cash flows, save, and cope with risk to help the rural households especially farmers.(Rodrik et al.,

2009). The argument aligns itself with credit rationing that show that when lenders are deficient in good information on customers, and contracts are costly to enforce, outcomes are not all that efficient (Besley, 1994; Stiglitz and Weiss,

1981; Rodrik et al., 2009). Input supplier credit is a common form of in-kind financing to farmers at all stages, both in a fragmented and informal agricultural system and in strongly linked value chains in developing and developed countries.

(Miller and Jones, 2010)

Input supplier credit enables farmers to realize a cash flow benefit to access supplies or even equipment for production purposes in a timely manner. Suppliers provide this because credit is a critical marketing strategy to make their inputs and goods more attractive for sale. Yet, the financing results in a drain on the cash flow of their business (Miller and Jones, 2010). Consequently, suppliers often offer cash discounts to improve their cash flow and reduce the risks of non-

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payment in the future. The key agricultural inputs – seed, fertilizer, agrochemicals, equipment and fuel – are commonly financed in turn by their suppliers.

The supplier in turn may be financed by borrowing secured by the invoices based upon the strength of the sales and repayment records. Nevertheless, collection and account management can be difficult (Miller and Jones, 2010). Consequently, due to the difficulties in providing financing and ensuring repayment; more and more input supplier credit is done indirectly through a triangular relationship in which the input supplier facilitates finance through a financial organization so the buyers can pay the input suppliers. In our case the input credit scheme, Cocoa Abrabopa is financed by Wienco which is the brainchild of the organization. This has the advantage of letting financial entities handle the financing using their expertise and the systems they have in place to do so (Miller, 2007). It also frees up funds for increasing inventory Input supplier credit is relationship based, and suppliers or buyers prefer to extend inputs to local input supply retailers or to farmers whom they have known for a considerable time. For retailers, finance may be given directly in-kind by advancing products on consignment or commission. For proven inputs to farmers, it is much riskier since the products may be used in their fields making recovery difficult if crop or other failures occur (Miller, 2007). An advantage of the supplier providing finance to the farmer is that it can reduce the farmer’s transaction costs, since interest is embedded and paperwork is minimized

(Miller and Jones, 2010).

The experiences of the agricultural value chain finance model in Myanmar show that financing is an important issue for the development of agricultural value chains. The private sector providers sell the inputs to farmers on credit, yet this

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supplier credit rarely stands alone since these companies themselves lack sufficient funding. (Myint, 2007). They need financing which is hard to obtain. In order to recover sales revenue quickly, their preference is cash sales rather than selling inputs to farmers with deferred payment. Consequently, in Myanmar, the agro- input retailers offer deferred payment sales at a high interest cost which results in an inflated price for farmers. The farmers do benefit from at least having access to sales on credit, but it is expensive. Given that financing is a hindrance for both farmers and their agro-chemical suppliers. More financing is needed farther up the value chain but, currently, the very limited capacity of the banks in rural areas and the fragmented nature of the value chains makes this financing unavailable

(Myint, 2007). Thus calls for Abrabopa Model to finance cocoa production in

Ghana.

3.4 Methods of Analyses

3.4.1 The nature and value of input credit scheme packages for Cocoa

Abrabopa

The content of the input credit scheme packages is made up of Insecticides,

Fungicides, Fertilizer, Matabi Knapsack Sprayer, as well as Entrepreneurial

Training to enhance farmers’ production skills and thereby increasing productivity of the members of Cocoa Abrabopa. The facility is valued in monetary terms at current market prices. The content of the input credit schemes are solicited from the operators of the scheme. Individual farmers who are members of the input credit schemes Cocoa Abrabopa were asked also on the type and quantity of inputs supplied by the organization. Both the input suppliers and the scheme members’ were interviewed to ascertain the quantity of inputs given to them.

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3.4.2 Determination of level of adoption of Cocoa Technologies in the

Western and Eastern Regions

This study follows from Alao (1973) who defines the level of adoption of technical innovations as the proportion of farmers adopting a particular technology to enhance productivity. The cocoa technologies identified are: farmers using hybrid cocoa from designated Seed gardens, farmers using fertilizer to enhance productivity on their farms, farmers controlling pest and disease to improve yield, farmers integrating valuable shade trees into existing and new cocoa farms, farmers undertaking pruning and mistletoe removal, farmers brushing at regular intervals of three-four times, and lastly, proportions of farmers receiving training in any farm business skill (Sarpong & Asamoah, 2012). In the determination of the differences in the proportions in the levels of adoption of these technologies in the two regions, the z-test was used for the analysis.

The formula is given as:

Mathematically expressed as (Y Y )  0 z  2 1 s 2 s 2 1  2 n n 1 2

Y1-Population of farmers adopting the technology from the Western Region

Y2- Population of farmers adopting the technology from the Eastern Region

2 S2 –Sample Size of farmers from the Eastern Region

2 S1 -Sample size of farmers from the Western Region n1-Population of Farmers from Western Region n2-Population of Farmers from Eastern Region

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Ho: There is no difference in the mean proportion of technology adoption.

Ha: There is a difference in the mean proportion of technology adoption.

When the z-cal>z-crit we reject Ho

If otherwise fail to reject the Ho

3.4.3 Effect of Cocoa Abrabopa Input Credit Scheme on Cocoa Crop

Productivity

The effects of input credit scheme on productivity are represented by estimating partial factor productivity in the production process. By derivation, the partial factor productivity for land (land productivity) can be expressed as relationship between land productivity and the proportions of inputs per unit area (Wiredu et al., 2010).

(1)

The βs in the productivity model represents the marginal effects of the proportion of inputs used on productivity. Introducing variables to capture idiosyncratic management competence (h, i H) and adoption behaviour (w, i W) of farmers, land equation is re-expressed as (Wiredu et al., 2010):

(2)

Equation 2 suggests that all the variables except land have positive effect on land productivity, such that a marginal increase in a variable will results in a certain unit(s) increase in productivity. A positive relationship is also expected between the use of improved technology and productivity.

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Using an augmented Cobb-Douglas production function as employed by Wiredu

(2010) we specify as follows.

Age

Table 3. 1: Description of Variables used in Productivity Analysis in the two locations Dependent Variable Explanations Units Apriori Expectations

Ln Q/A Productivity Kg/ha

Independent variable

Ln Fert Quantity of fertilizer 3 Bags/acre +

Ln Fung Number of times sprayed 6-9 times +

Ln Insect Number of times sprayed 4 times +

Ln Lab Number of labour employed Man - days +

LnAge Age of the farmer years +

Gender 1=Male 0=Female +

LnExperience Cocoa farming experience Years +

LnHHS No. of People Number +

Loc(Region) Location Dummy (Western=1 +/-

Eastern=0)

Source: Survey Data (2013) Q=Total Quantity of Cocoa produced from the farmers’ farm

A=Total land area under cultivated cocoa. (The 2 acre land in which CAA provide inputs)

The Following Null Hypothesis (HO) will be tested against the alternative

Hypothesis (H1):

Ho: Fertilizer application has no effect on Productivity

HA: Fertilizer application has a positive effect on Productivity

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This hypothesis is repeated for the following: fungicide, insecticide, labour and education.

Validation of hypothesis

The z-test is used to validate the null hypothesis.

Z-calculated value of the Z value is the estimated parameter for the ith explanatory variable and SE ( represents the standard error of its ith term. If Z calculated is greater than the critical value of Z from the Z statistical distribution at a determined significant level, the null hypothesis (Ho) is rejected in favour of the alternate hypothesis (Ha). The opposite is true if the calculated value of Z is less than the critical Z-tabulated value.

3.4.4 Garret ranking technique was employed to rank the constraints for the two regions

To achieve this objective, the orders of importance given by the Cocoa Abrabopa farmers were converted into scores by using the formula:

100(R  0.5) %Position  ij (18) N j

Where; Rij is the constraint of the farmer and N j is the total constraints.

This technique is appropriate because of the merit it has over the Kendall’s coefficient of concordance as it controls for regional dummies. The respondents were asked to rank them in order of most demanding to less demanding, which affect their output and efficiency levels. For these constraints, the order of merit given by the respondents was converted into scores with the help of the ranking table given by Garrett and Woodworth (1969). The scores of individual constraints

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were added and the total was divided by the total number of respondents who ranked the particular constraint. The mean scores for the constraints were arranged in the order of their ranks and the constraint with the highest mean score is considered as the most pressing.

3.5 The Study Area

3.5.1 Western Region

The four cocoa districts selected in the Western region were Sefwi Bekwai,

Buako, Juaboso and Essam. They have a population of 490,553 with the male population been 247,551 and a female population of 243,002 (GSS, 2012). The

Western Region lies in the equatorial climatic zone that is characterized by moderate temperatures, ranging from 22°C at nightfall to 34°C during the day.

The Region is the wettest part of Ghana, with a double maxima rainfall pattern averaging 1,600 mm per annum. The soils are suitable for the cultivation of a variety of crops including Cocoa, Cola-nuts, Citrus, and Oil palm, Rubber and staple food crops such as Cassava, Yam, Cocoyam, Maize, Rice and Vegetables.

3.5.2 Eastern Region

The study area in the Eastern region were made up of four cocoa districts which are Osino, Kade, Asamankese, and Tafo they have a population of 666,429 out of which 326,045 are males and the female population stands at 340,384 (GSS,

2012). It is the sixth largest region in terms of land area. It lies between latitudes

6o and 7o North and between longitudes 1o30’ West and 0o30’ East. Temperatures in the region are high and range between 26oC in August and 30oC in March. The relative humidity which is high throughout the year varies between 70 per cent 80 per cent and annual rainfall ranges between 1500 to 2000mm. The forest and

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savannah type of soils are suitable for the cultivation of a variety of crops including Cocoa, Cola-nuts, Citrus, Oil palm and Staple food crops such as

Cassava, Yam, Cocoyam, Maize, Rice and Vegetables.

3.6 Sources and Method of Data Collection

3.6.1 Sources of Data

Eight Cocoa districts were purposively selected from the two regions due to the even spatial distribution of the districts to represent each region. In this case,

Abrabopa Farmers were the main target of the study and within the Communities the members were randomly selected based on members who have been with scheme for at least one year. In the Eastern region the districts purposively selected, were Tafo Cocoa district, Osino Cocoa district, Asamankese Cocoa district and Kade. Eighteen (18) communities within the districts were randomly selected for the interview out of which 150 respondents were also randomly selected. In the Western region the districts were Sefwi Bekwai Cocoa district,

Juaboso Cocoa district, Buako Cocoa district and Essam. Twenty five (25) communities were also randomly selected for the study to give an even spatial distribution of the communities. The farmers were randomly selected from

Cocoa Abrabopa farmers within each Community. The total farmers selected from the Communities were 205 from the Western Region and 150 from the Eastern

Region. The total farmers selected from the two regions were 355; Western region had the highest number of respondents in that they have the highest number of Cocoa Abrabopa members (3000) than that of Eastern region (1200). A summary of the Communities selected are presented in Table 3.2 and 3.3

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3.6.2 Method of Data Collection and Sampling Technique

The data sources were basically primary data. Semi-structured standard questionnaires were used to administer the questionnaires to the respondents selected. Multi stage sampling technique was used for the research work in which the study area was limited to two cocoa regions, the Western (new cocoa region) and Eastern Region (old cocoa region) which were purposively selected. This was also based on the increasing number of Cocoa Abrabopa members.

Following from Calderon (2003), the sample size is computed as:

{n=N/ (1+Ne2)} where n=Sample size, N=Population size of registered Cocoa

Abrabopa farmers in Ghana; e= margin of error (@5%)

The total number of CAA farmers at the time of the study for Western Region was

3000 and that of Eastern Region was 1200. Using Calderon (2003) formula, the computed total sample required for the study is 400 {4200/ {(1+4200)*(0.05)2}.

In terms of proportions the sample required from the Western and Eastern regions are: W/R=71%*400=286; E/R=29%*400=114

However, the study could not meet the targeted sample size of 286 for the Western

Region due to resource and logistics constraints. The study interviewed 205 respondents for the Western region and 150 for the Eastern region. (See Table 3.2 and Table 3.3).

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Table 3. 2: Communities Selected from Western Region Communities Selected from Western Region and number of sampled farmers (205)

NO. Sefwi-Bekwai District Juaboso District Buako District Essam District

1 Adobenwura 10 Akontombra 10 Mile 3 5 Essam 5

2 Ntom 10 Bonsan 10 Akurafu 5 Sayerano 5

3 Bethlem 10 Kama 10 Nsinsimu 5 Nkatieso 5

4 Anhwianso 10 Besease 10 Mile 5 5 Elluokrom 5

5 Humijbre 10 Datano 10 Abrabra 5 Kantankrobo 10

6 Adomebra 10 Bokaso 10

7 Anyimaase 10 Bodi 10

8 Abrokofe 10

Table 3. 3: Communities Selected from Eastern Region Communities Selected from Eastern Region and number of sampled farmers (150)

NO. Tafo District Osino District Asamankese District Kade District

1 Ettokrom 10 Banso 10 Asuofori 10 Apinaman 5

2 Asunafo 10 Osenase 10 Akanteng 5

3 Abomosu 10 Mataheko 10 Apampantia 5

4 Awenare 10 Sankua-Binase 10 Tweapease 10

5 Tumfa 10 Bomso 5

6 Pwamang 10 Mpeasem 5

7 Asuom 5

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CHAPTER FOUR

RESULTS AND DISCUSSIONS

4.0 Introduction

This chapter discusses the results of the study in accordance with the specific objectives set. It includes the determination and valuation of the input credit scheme, the level of adoption of cocoa production technologies, factors influencing the productivity of Cocoa Abrabopa members from both regions, and constraints Cocoa Abrabopa members face in accessing the inputs used for the production of cocoa.

4.1 Socio-economic characteristics of Cocoa Abrabopa farmers

The study was undertaken in two regions of Ghana, Western (new Cocoa region) and Eastern (old cocoa region) regions of Ghana. A sample size of 205 respondents from Western region and 150 respondents from Eastern region.

Table 4. 1: Socio-economic Characteristics of Eastern and Western Region Eastern Region Western Region

Characteristics Frequency Percentages Frequency Percentage

Gender

Male 140 93 170 83

Female 10 7 35 17

Major Occupations

Farmer 137 90 188 91

Trader 6 4 12 6

Other 10 7 6 3

Education

None 31 20 54 26

Basic school 100 65 133 65

SHS 12 8 10 5

University/college 10 7 9 4

Source: Survey Data (2013)

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Table 4.1 presents the socio-economic characteristics of the farmer respondents.

There are proportionally more male farmers in Western region than in the Eastern region. The major occupation of respondents in both regions was identified as farming (90% =Eastern region; 91% = Western region). About 65% of respondents in both regions had basic education.

Table 4. 2: Socio-economic Characteristics of Eastern and Western Region continued Eastern Region Western region

Characteristics Minimum Maximum Mean Minimum Maximum Mean

Total household size 1 14 8 1 44 8

Economically Active 0 9 3 0 21 3

Household members

Age of respondents 25 79 49 24 95 52

Source: Survey Data (2013)

From Table 4.2, the average ages for respondents, in the Eastern and Western region were 49 and 52 years respectively which is closer to the national average age of 55 years for cocoa farmers (Gockowski, 2012).

4.2 The Nature and Value of Inputs provided by Cocoa Abrabopa Scheme

All the farmer respondents were supplied with inputs in the 2012/2013 cocoa year which is the focus of the study. Field response from input suppliers indicate that fertilizers, fungicides, insecticides, and farming equipment were supplied to the farms, other inputs were training and entrepreneurial skills given to the farmers to enhance productivity. Although there are other input credit schemes in Ghana such as Sika Aba, Olam, Armajaro, and other private input credit scheme operators, the well-established is Cocoa Abrabopa. The members are also given training in business and entrepreneurial skills on topics like entrepreneurship and business

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management, group dynamics, record keeping, conflict resolution and the principles and objectives of the Cocoa Abrabopa members.

Table 4. 3: Nature and Value of Inputs for Western and Eastern Region in the year 2012/2013

Western Region Eastern Region Item Qty Unit Amt GHS Qty Unit Amt GHS Price Price GHS Asasewura fertilizer 6bags 52 312 6bags 52 312 Nitrabor Fertilizer 2 bags 27 54 2bags 27 54 Nordox 48 satchets 3.05 146.40 _ _ _ Ridomil Gold 72+ 48 satchets 4.05 194.40 48satches 4.05 194.40 Matabi Pneumatic Sprayer 1 79 79 1 79 79

Safe Farming Inputs 92 92 92 0 92 92

Confidor 16 of 30mls 5.8 93 16 of 30mls 93 93 Cost of Training received per production year 60 60 Total 1030.80/year 884.4/year

Members are also given a farm plan which protects their Cocoa farm from land litigation, can serve as collateral at the bank and evidence to prove that members are professional farmers. Apart from farms earmarked for the program, members are given the opportunity to allow their other cocoa farms which has not been earmarked for the project, to be surveyed and farm plans drawn for them. Table

4.3 presents the inputs provided by Cocoa Abrabopa scheme in the Eastern Region and Western region. Whilst the scheme provides farmers in the two cocoa regions with fertilizers, Insecticides, Matabi Pneumatic sprayer and Nordox. Nordox is supplied only to Western Region and not Cocoa Abrabopa members in the Eastern region because they prefer only the Ridomil gold which is a systemic fungicide, and very effective against all types of Phythophtera strains that causes Black pod

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disease. This confirms Gangopadhyay and Sengupta (1987) and Hayami and

Otuska (1993), assertation in Kenya, that potential synergies are measured between interlinked cash cropping schemes and intensification of fertilizer use on food crops, where the interlinked schemes are programmes where farmers receive inputs on loan from organizations and pay back the facility through sale of the crop at harvest. This is a similar model to that of Cocoa Abrabopa with the view of providing cocoa inputs for resource-poor farmers to increase cocoa production.

Agricultural productivity through diversification also exists with the view of raising rural and urban incomes and rapid urbanization, many agricultural products have moved from subsistence to higher technological level to enhance productivity of the individual farmer. In this regard, IDH’s cocoa productivity and quality programme (PQP) is geared towards increasing the income of 300,000 smallholder cocoa producers, together with major partners in the cocoa industry, including civil society and local government in producing countries. According to

Van Grinsen (2010) the only means of achieving sustainability is through increased productivity and quality top down credit schemes for farmers in developing countries. As is been implemented by Cocoa Abrabopa in the Western and Eastern Regions of Ghana, there is great evidence that shows the importance of self-help groups on the adoption of cocoa technologies and productivity.

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4.3 Adoption of Cocoa Production Technologies

Table 4.4 presents the analyses on the type of cocoa technologies adopted and differences in the proportion of farmers adopting these technologies in the two regions. Farmers undertake cocoa farm technologies such as pruning and mistletoe removal, brushing of the farm at regular intervals of 3 – 4 times per year, fungicide and insecticide application, chupon and shade control in their cocoa farms, to enhance productivity. The Table 4.4 also presents the proportion of farmers receiving training in their farmer field schools.

Table 4. 4: Proportion of Cocoa Farms Adopting Cocoa Technologies Observed Practices Western Region Eastern Region Z-test (proportion of farmers (proportion of farmers who adopt technology) who adopt technology) Fungicide Application 99.5 100.0 0.38978 Removal of Mistletoe 99.5 100.0 0.38978 Fertilizer Application 100.0 99.3 0.24604 Chupons removal 97.1 99.3 0.12602 Shade control 100.0 98.7 0.24604 Pruning 98.5 98.7 0.90448 Brushing 98.5 99.3 0.90448 Farmer field school 94.2 98.0 0.04338** FieldTrips/extension 92.7 96.7 0.18024 Contact ** @5% Source: Survey Data (2013)

There was no statistically significant difference in the level of adoption of all the cocoa technologies adopted. This implies that the level of proportions of farmers within the Cocoa Abrabopa members from both Western and Eastern regions who adopted these technologies were virtually the same. However, in the case of farmer field school meetings there were significant differences between Western and Eastern regions, as farmers in the Eastern region attend more farmer field schools than those in the Western region. However, this did not lead to any

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productivity gains of Eastern region Cocoa Abrabopa farmers. In recent times, technologies come in a form of packages. For example, the recommended technologies under the highest level are considered as packages which the cocoa farmer must adopt together (Boateng, 2003). This was a study carried out in the

Ashanti region to determine the adoption of the control of black pod disease on cocoa in 2003, which came out that without good agronomic practices adopted as package, the efficacy of the fungicides applied on a cocoa farm cannot lead to increase in yield. Base upon these recommendations Cocoa Abrabopa also introduces their technology to their scheme members as a package to increase in productivity. This conforms the fact that in the last few years, there have been signs that the recommended method of cocoa production which was difficult for cocoa farmers to apply, is been implemented by these otherwise resource-poor scheme members due to the coming into force of the cocoa input credit schemes, in Ghana, thus with reduction of forest lands, cocoa farmers have turned to more intensive methods of cocoa cultivation, using new seed technologies, fertilizer applications and technical innovations to increase production. (Ruf, 2007)

4.4 Effects of Input Credit Scheme on Productivity of Farmers in the two

Locations

This was to estimate the factors influencing productivity of farmers in two Cocoa growing locations, Western and Eastern Cocoa growing regions representing the

“new” and “old” regions respectively, on the assumption that the input credit scheme has influenced the adoption of cocoa technologies in the two regions. The augmented Cobb–Douglas production function was used to estimate factors influencing productivity with a location dummy to test for differences in

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productivity by location. Table 4.5 presents a regression result that explains factors influencing the productivity of Cocoa Abrabopa Cocoa farms in the

Eastern and Western Regions. The factors hypothesized to influence the productivity of Cocoa Abrabopa farmers include the use of fertilizer, fungicides, and insecticides. In addition, socio-economic characteristics of the farmers such as age, household size, experience, gender and regional differences status and more importantly the location of the farms (proxy for soil quantity, amount of credit inputs used; among others) are major explanatory factors of farm productivity.

Table 4.5: Regression Results of Input credit Scheme on Productivity of Cocoa Abrabopa Cocoa farms in the Western and Eastern Regions

LnQ/A Coef. Std. Err. t P>t

LnFert 0.052025 0.079318 0.66 0.512

LnFung 0.158929*** 0.053228 2.99 0.003

LnInsect 0.028537 0.047908 0.6 0.552

LnLab -0.06231 0.060349 -1.03 0.303

LnAge 0.087529 0.188437 0.46 0.643

LnHHS -0.63045*** 0.121874 -5.17 0.000

Lnexperience -0.24385** 0.113896 -2.14 0.033

Gender -0.23064 0.173929 -1.33 0.186

Region 0.646355*** 0.130354 4.96 0.000

_cons 3.736759 0.72581 5.15 0.000

Number of observation= 355 F(9, 343)= 12.65 Prob>F = 0.0000 R-squared= .2492 Adj. R-squared=

0.2295 Root MSE= 1.0867 ***, **=1%, 5%

Source: Survey Data 2013 From Table 4.5, use of fertilizers was not statistically significant in influencing productivity though most of the matured cocoa trees when applied with recommended fertilizer lead to increase in productivity (Teal et al., 2006). Thus debunking the assertion that when fertilizer application is practiced, it is possible

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for a farmer to obtain yields of over 2000 kg per hectare, with the fertilizer contributing as much as 20-40% of the increase in yield (Adomako et al., 1995).It could also be that due to the continuous application of the Asasewura fertilizer which contains Muriate of Potash and Ammonium Sulphate the soil will turn acidic and thus affecting the soils fertility level thereby inhibiting the better utilization of the soil nutrients for productivity of the cocoa farms. It may also be due to higher shade density on the cocoa farms that was not controlled by Cocoa

Abrabopa farmers, thus leading to less effect on productivity on the members farms. This confirms the assertation by Anim-Kwapong (2015) that trials done on

CRIG plots in Tafo with high shade density turns to inhibit the smooth utilization of fertilizer to obtain good results, thus farmers must ensure good shade management to achieve optimum yields from their cocoa farms.

The productivity of cocoa is enhanced by 15.9% of the application of Fungicide and also statistically significant at 1%. Fungicides contains broad-spectrum biocide) noted for the control of Black pod (Fleming and Trevors, 1989; Noyce et al., 2006; Mother et al., 2008). It stems from the fact that most of the farmers undertook the recommended spraying regimes and dosage of 6-9 times within the cocoa season, coupled with strict farm sanitation which enhanced the efficacy of the Fungicides, about 60% of black pod disease is controlled if there is proper farm sanitation and good farm practices as it was in the case of Cocoa Abrabopa farmers thus leading to high productivity compared to other cocoa farmers in the community.

Cocoa productivity in the two cocoa regions was improved by the use of insecticide by 2.8% but statistically insignificant under this study. The spraying of

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insecticides is done (3-4times) within a year. Cocoa Abrabopa farmers were given

480mls of Confidor for the two acre plot to be sprayed on their farms basically in

August, September, October and lastly in December. This is to avoid crop loss attributed to Capsid damage. It can also happen that the Capsid attack was so serious that in spite of the insecticides application it could not achieve its aim of killing the insects, because the insects may have built resistance to it, or the insect pest infestation may be so alarming. Thus affecting the expected effectiveness of the cocoa insecticides.

Farmers’ dependence on more than one insecticide is justified by Georghiou

(1980) who indicated that such practice helps to manage pesticide resistance.

Chemical control is not the only means to protect plants from insects’ attack.

Cultural practices such as cutting off chupons from the trees, maintaining a close canopy, among others, are practical ways to prevent mirids attack, (Leston, 1970).

These practices are carried out by farmers but most are not aware of the effect of such a practice on the management of insect pests especially mirids aside the agronomic benefits it gives to the plant.

According to Antwi-Agyakwa, et al. (2014) trimming chupons deprive mirids of their feeding and breeding sites and as such has the propensity of preventing

Mirids infestation; maintaining close canopy prevents the penetration of light into the farm and subsequently prevents the growth of offshoots (chupons), regular pruning, be it structural, architectural and sanitation pruning as well as regular brushing plays an important role in the control of mirids and capsids. Integrating biological control with selective insecticides can minimize the likelihood of pest resurgence and possibly reduce the number of insecticides application.

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However, in the case of Cocoa Abrabopa scheme, members were supplied with only Akatemaster (Bifenthrin) that is contact insecticides and only kills the insects when it comes in contact with the chemical so without proper cultural practices the efficacy of the chemical cannot be effective. Though farmers were taken through training it can be that not all farmers were able to undertake the cultural practices at the time of the chemical application, it may also be that at the time of spraying the chemicals was either delayed or the dosage was inadequate thus affecting the actual yields that the insecticides could have provided to Cocoa

Abrabopa farmers farm.

According to (Owusu-Manu & Somuah, 1984) good farm management without the use of insecticides can lead to an increase in yield by about 42% within

18months period, thus integrated pest management strategy is the best means of controlling these Mirids and Capsids on cocoa production. However, looking at the results of the study cocoa production increased by about 2.8% even with the application of the Akatemaster the insecticides given out to the scheme members.

In cocoa pest management, the best method to control Mirids and Capsids on cocoa is to alternate the recommended insecticides by COCOBOD every two years for example Confidor (Imidacloprid), Actara (Thiamethoxam) and lastly

Akatemaster (Bifenthrin).(Antwi-Agyakwa., et al., 2014)

Owusu-Manu (1996) asserted that capsids attack alone when not checked can lead to about 30% loss of cocoa beans annually through uncontrolled Capsid attack.

Moreover, the control of capsids can be effective when brushing of undergrowth and farm sanitation is strictly adhered to (Sonwa, 2008). Labour usage for the

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agronomic practices like brushing of undergrowth, pruning, mistletoe removal, fertilizer applications and insecticide application enhance productivity.

However, according to the study labour has negative and insignificant relationship with productivity. Productivity is influenced by age, older farmers are not able to work to achieve higher yields compared to younger farmers who are able to learn new technologies faster and apply it on their farms. Another study by Onu (1991), also indicates that the use of farm information from source decreases with increasing age of farmers, which implies that young farmers are more akin to obtaining information from sources that are aimed at enhancing their profession than older farmers. In another instance however, Kaliba et al. (2000) and Khanne

(2001) concluded that older heads of household were more likely to use fertilizer in Tanzania. Several similar studies on the usage of fertilizer in parts of Africa found age to be significant but of different effects on agriculture (Green &

Ng’ong’ola, 1993; Nkonya et al., 1997, Boateng, 2003).

However, under this study age was insignificant as members were in groups thus

Cocoa Abrabopa members learn together as a group, thus enhancing their productivity irrespective of age (Feder et al., 1984). Moreover, the average age of

Cocoa Abrabopa farmers in the Eastern and Western regions were 49years and

52years respectively, though it is above the youthful age, they were able to adopt the technologies because of the self – help group mechanism. This is because group cohesion influences the adoption of the technologies which enhance productivity (Gockowski, 2007).

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From the results, gender has a negative coefficient, implying that male productivity is relatively lower than female productivity. However, this relationship is not statistically significant in influencing cocoa productivity.

The coefficient of household size is negative and is statistically significant at 1%.

Household members serve as labour for the cocoa farms. Larger household size indicates larger working units to enhance productivity (Croppenstedt and Demeke

1996). However, according to the study, diminishing marginal returns sets in as labour increases by a unit, productivity decreases by 0.62. In that the cocoa farmer, may be using the same land size even when additional labour force is added to the household which eventually leads low productivity due to diminishing marginal returns. Generally, in Ghana most cocoa land size for individual farmers are smaller (0.2-2ha) in that most of the lands are family owned that calls for intensive system of cocoa production to increase yield per hectare.

Experience of the farmers was statistically significant at 5% but had negative relationship on productivity by reducing productivity to 0.24 this implies that cocoa farmers, who have experience, sometimes do adopt a particular technology with the aim of enhancing productivity, it is not always so, thus accounted for the negative coefficient (Kebede,1992). This could also be because the more cocoa farmers gain experience; they become complacent and apply the technology as they know best and not based on the recommended practices. This could potentially lead to low productivity. Experience is vital for any technological adoption. Highly experienced farmers are said to adopt innovations faster and implement them on their farms. Experience is commonly seen as a form of acquired wealth of knowledge, which has similar effects like education. With

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experience comes a buildup of confidence which tend to reduce risk and uncertainties and so increases the farmers’ decision-making skills and willingness to adopt a technology.

Saito and Weidman (1990) report that access to agricultural extension by households and their ability to understand and use technological innovation may be due to their level of education, which can influence or encourage them to adopt a technology faster and better, and vice versa.

The importance of education to agricultural production ought to be more apparent as technological innovation spreads more widely within a country (Sharada, 1999) and that there are likely benefits of schooling in agriculture in terms of increasing efficiency and the adoption of innovation. Hussein and Byerlee (1998) also noted that education highly influences productivity in the agricultural sector.

Using panel data in six sites in Ethiopia, Dercon and Krishnan (1998) concluded that the educated were able to take advantage of opportunities of new innovation to augment output and consumption. Nkonya et al. (1997) indicated that education is an important factor in the households’ decision to adopt improved seeds. Sain and Martinez (1999) also found education to be significant but of different effects in a study of households in Guatemala; while level of education of household head was found to have a negative effect; participation in associations which is another form of education had a positive effect on adoption of improved maize seed.

During the data collection from the field most of Cocoa Abrabopa group heads were cocoa farmers whose educational level was above Senior High School thus significantly influence the adoption process and subsequently enhance productivity.

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The locational dummy coefficient is statistically significant at 1%. The implication is that the Cocoa Abrabopa input credit scheme results in higher productivity in the Western region than the Eastern Region. The higher productivity in the Western region than that of the Eastern stems from the fact that, though the technology adoption was similar, the farmers in the Western region have generally large farm size, coupled with new plantings and fertilizer applications on most of their plots that confirms the assertion by Gockowski

(2007) that about 83% of cocoa farmers in the Western region apply fertilizer on their farms.

4.5 Constraint Analyses

The mean score from the Garrett technique was used to rank constraints faced by

Cocoa Abrabopa farmers in the Eastern and Western regions (See Table 4.6). The higher the mean score, the more constraining the variable.

Table 4. 6: Results on mean scores of Garret ranking of constrains for Eastern and Western region

Garret Rankings of Constraints for the two Regions Eastern and Western-Pooled Ranks

Constraints Mean Score Mean Score Rank Capital/Finance (Average) High cost of input 77.21 High interest rate 75.93 74.26 1st Lack of Collateral 69.64 Access to inputs Non availability of Chemicals in the Market 73.38 70.22 4th Lack of Good Planting Materials 67.05 Access to Extension Lack of Personnel 74.88 Few days in extension contact 73.81 74.12 2nd Poor Extension Delivery 73.68 Weather High temperature 73.36 72.76 3rd Heavy Rainfall 72.51 Poor Rainfall pattern 72.42 Source: Survey Data (2013)

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The Garret ranking shows finance is the major constraints whilst access to inputs is the least constraint. The result is not surprising as Cocoa Abrabopa members are supplied with inputs for their cocoa farms, hence least constraint.

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CHAPTER FIVE

SUMMARY, CONCLUSION AND RECOMMENDATIONS

5.0 Introduction

The chapter presents the Summary and Conclusions, drawn from the study as well as Recommendations.

5.1 Summary

The study was about input credit scheme effects on adoption of cocoa production technologies and productivity of smallholder cocoa farmers in Ghana. In effect, the study looked at the composition of the input credit scheme and how it has influenced cocoa technological adoption and enhanced productivity. The study therefore described the nature and value of inputs at the prevailing market price, provided to the farmers in the Eastern and Western Regions, by Cocoa Abrabopa scheme. The extent of adoption of cocoa production technologies in the two

Regions, the factors influencing productivity of Cocoa Abrabopa farmers in the two locations and how different location influences productivity and lastly, the constraints faced by Cocoa Abrabopa members in accessing the input scheme. A sample size of 355 Cocoa Abrabopa farmers were interviewed from the two locations and the content and value of the scheme provided by both input suppliers and Cocoa Abrabopa members. Secondly, the extent of adoption of cocoa production technologies was analyzed using descriptive statistics and the Z-test was used to test difference in the level of adoption. The augmented Cobb-Douglas production function was used to analyze the factors influencing the productivity of

Cocoa Abrabopa farmers in the two locations. Lastly, the Garret Model was used to analyze the constraint faced by the Cocoa Abrabopa farmers.

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It was found that the cost of the inputs supplied to both regions was different as

Cocoa Abrabopa farmers paid GHS1030.80/year in the Western Region and whilst those of the Eastern Region paid GHS884.4/year. This is due to the fact that input component differed from both regions due to variations in the inputs supplied to the farmers. The proportion of famers adopting the cocoa production technologies in both regions were almost the same. The proportion of farmers in farmer field school was higher in the Eastern Region, and also members attend meetings twice a week, whilst Western Region farmers attend once within the week and with a lower proportion field school. In terms of productivity, the Cocoa Abrabopa farmers in the Western Region have higher productivity than those of Eastern region. The intensity of adoption of recommended practices in the two regions showed little variation. Among the eleven recommended practices only farmer field school was statistically different in the proportion of adoption in the two regions.

The productivity result indicates that fungicide, household size, experience, and locational dummy used in the production of cocoa in the two regions under the study have significant impact on productivity levels. It thus confirms the fact that, when the four technologies are judiciously implemented it can enhance productivity, for that matter such inputs meant to help the adoption of such technologies should be subsidized. In effect, much attention must be given to the above four variables as they are the bedrock of cocoa farm productivity. In contrast, fertilizer, insecticide, labour, and gender were found to influence productivity but the impact was inconsiderable in this study.

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5.2 Conclusion.

The variation in the proportion of farmers that have access to farmer field school did not reflect much the yield levels of Cocoa Abrabopa farmers in both regions.

The productivity result shows that fungicide, household size, experience and regional dummies are major determinant of productivity levels in the two regions among Cocoa Abrabopa farmers.

In addition, it was realized that there exist locational variation in the productivity levels in the two regions. Cocoa Abrabopa Farmers, in the Western region have higher yields than that of Cocoa Abrabopa members in the Eastern region in that, farmers in the Western region have large farm sizes, new plantings and also buy additional inputs such as fertilizer and fungicides to apply on other cocoa farms that leads to increase in productivity.

Lastly, finance is a major bottleneck in the production of cocoa for Cocoa

Abrabopa members as most respondents do not have enough money to purchase additional inputs for other farms when the need arises and the least constraint been access to inputs, in that Cocoa Abrabopa scheme operators give out inputs to its members and pay the facility after harvest. Most of the recommended inputs or similar ones are readily available in the open market to be used by the Cocoa

Abrabopa farmers in the two regions.

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5.3 Recommendations

1) The constraint, which was more pressing, was finance to enable members buy additional inputs for the other farms not earmarked for Cocoa Abrabopa farms. It implies that the scheme should increase the hectares from 0.8ha to the hectares that the farmer can pay to ensure a higher productivity for the entire farm of the

Cocoa Abrabopa member. This must be based on one’s ability to pay. The government should come out with more effective pro-poor programmes for cocoa farmers to acquire and access inputs to augment their cocoa production levels.

2) The technology that involves less expenditure, on the part of the farmer tends to be adopted faster and easier. In effect researchers must find ways of bringing out cost effective technologies to ensure easy adoption of cocoa production technologies for higher yields.

3) There can also be government intervention by subsidizing the prices of major cocoa inputs so that Cocoa Abrabopa farmers can purchase them at a relatively cheaper price, which will ensure higher productivity for members.

4) In spite of the enormous benefits that the members obtain, it should not debar them from collecting the free fertilizers and Cocoa seedlings given to every

Ghanaian Cocoa farmer, but can use it as supplementary inputs for other farms the scheme member might have.

5) For the purpose of advancing research in the cocoa sector, further research in productivity variation, among the two regions must be done.

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REFERENCES

Abankwah, V., Aidoo, R. & Osei, R. K. (2010). Socio-economic impact of

government spraying programme on cocoa farmers in Ghana. Journal of

Development in Africa 12(4), 116-126.

Abdelmagid, S. A. & Hassan, F.K. (1996). Factors affecting adoption of Wheat

production Technology in the Sudan. Quarterly Journal of International

Agriculture. Vol. 35, No.4.

Acs, Z., J., Morck, R., & Yenny, B. (1999). Productivity Growth and Firm Size

Distribution. In Acs, Z. J., Carlsson, B. and Karlsson, C. (eds.).

Entrepreneurship, Small and Medium-sized Enterprises and the Macro

economy. Cambridge: Cambridge University Press.

Adejobi, A. O. & Kormawa, P. (2002). Determinants of Manure use in Crop

Production Northern Nigeria. International Institute of Tropical

Agriculture (IITA), Oyo Road, 5320 Ibadan Nigeria

Adesina, A. A. (1996). “Factors affecting the Adoption of Fertilizers by rice

farmers in Cote D’Ivoire”. WARDA, Bouake, Cote D’Ivoire. Nutrient

Cycling in Agro-ecosystems, 46:29-39

Adesina, A.A. & Baidu-Forson, J. (1995). Farmers perceptions of New

Agricultural technology: Evidence from Analysis in Burkina Faso and

Guinea. West Africa Agricultural Economics v.13, 1-9

Adesina, A. A., Inaizumi, H. & Ingh, B. B.(1997). Determinants of farmers’ rapid

adoption of new improved Cowpea varieties for dry season cultivation in

Northern Nigeria. Mimeo, IITA, Ibadan, Nigeria.

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

Adesina, A. A. & Zinnah, M. (1993). Technology Characteristics, Farmers

Perceptions and Adoption Decisions: a Tobit model application in Sierra

Leone. Agricultural Economics. Vol.9. 297-311

Adhikary, M. (1994). Determinants of fodder tree adoption in the Mid-hills of

Nepal Ph.D. Thesis.

Adjinah, K. O. & Opoku, I. Y. (2010). The national cocoa disease and pests

control. Achievements and challenges. Modern Ghana News; 2010

Available:http://www.modernghana.com/news/273336/1/the-national-

cocoa diseases and-pests-control chi.html. Retrieved on the 30th June,

2015.

Admassie, A. & Asfaw, A. (1997). The importance of education on allocative and

technical efficiency of farmers: the case of Ethiopian smallholders. Paper

presented at the 7thAnnual Conference on the Ethiopian Economy in

Nazret. Addis Ababa: Addis Ababa University. Department of

Economics.

Adomako, D., Halm, B. J. & Amponsah, J. D. (1995). Summary of

Innovations/Recommended Technologies for Cocoa, Coffee, Shea nut and

Kola Production and Current Research Activities. Tafo: Cocoa Research

Institute of Ghana

Agrios, G. N. (2005). Plant Pathology. 5th Edition. San Diego, California:

Academic Press.

Alabi, R. A. A., Aigbokhan, B. & Ailem, M. J. (2007). Improving the Technical

efficiency of Nigeria Cocoa Farm Through institutional Farm Credit.

African Association of Agricultural Economics

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

Alao, J. A. (1973). Community Structure and Farmers Farm Practice Adoption.

Bulletin of Rural Economics and Sociology, Vol.8, No.2: 281-312

Amir, K., Abadi, G. & Pannell, D. J. (1999). A conceptual framework of adoption

of agricultural innovation. Department of Agricultural and Resource

Economics, University of Western Australia, Australia

Amos, T, T. (2007). An Analysis of Productivity and Technical Efficiency of

Smallholder Cocoa Farmers in Nigeria. Journal of Social Science 15(2):

127-133.

Ampofo, S. T. (1990). Farmers Adoption of Recommended Practices. Farmer-

Extension Linkages. The first Farming Systems Workshop, pp.14-16.

Aneani, F., Anchirinah, V. M., Asamoah, M. & Owusu-Ansah, F. (2011).

Analysis of economic efficiency in cocoa production in Ghana. African

Journal of Food Agriculture Nutrition and Development. 11(1):4507-4526

Aneani, F. F. & Ofori-Frimpong (2013). An Analysis of Yield Gap and Some

factors of Cocoa (Theobroma cacao) yields in Ghana. Sustainable

Agriculture Research; Vol. 2, No. 4.

Anim-Kwapong, E. (2015) Personal Communication 15th November 2015

Antwi-Agyakwa, A.K, Osekre, E.A., Adu-Acheampong, R. and Ninsin, K.D.

(2014) Insecticides use Practices in Cocoa Production in four Regions in

Ghana. Faculty of Agriculture Kwame Nkrumah University of Science

and Technology, Entomology Division Cocoa Research Institute of Ghana

Akim-Tafo, CSIR Animal Research Institute Accra: West African Journal

of Applied Ecology (vol 22 (1) page 46

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

Appiah, M. R., Ofori–Frimpong, K. & Afrifa, A. A. (2000). Evaluation of

Fertilizer application on some peasant cocoa farms in Ghana. Ghana

Journal of Agricultural Science. 33:183-190.

Appleton, S. & Balihatu, A. (1996). Education and Agricultural Productivity:

Evidence from Uganda. Journal of International Development. Vol.8,

No.3. 415-444.

Asamoah, M. (2006). Empowering Women through Self-help Microcredit

Schemes: A Model for Cocoa Farmers in Ghana. Tafo, Ghana: Cocoa

Research Institute of Ghana.

Asante-Mensah (1999). Lecture Notes. (January, 1998)

Asare, E. (2011) Modelling Cocoa Farmer Behaviour Concerning the Chemical

Control of Capsid in Sekyere area Ashanti Region (M.Phil Thesis)

Kwame Nkrumah University of Science and Technology Kumasi Ghana

Asare, R. (2006). Learning about neighbour trees in cocoa growing systems – a

manual for farmer trainers. Forest & Landscape Development and

Environment Series 4

Asare, R. & Sonii, D. (2011). Good Agricultural Practices for Sustainable Cocoa

Production: a guide for farmer training (IITA). Faculty of Life Sciences.

University of Copenhagen

Baah, F., Anchirinah, V. & Amon-Armah, F. (2011). Soil fertility management

practices among Cocoa farmers in the Eastern Region. Agriculture and

Biology Journal of North America http://www.scihub.org/ABJNA

Baffoe-Asare, R., Danquah, J. A. & Annor- Frempong, F. (2013). Socio-economic

factors influencing adoption of CODAPEC and Cocoa High-Tech

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

technologies among smallholder farmers in Ghana. American Journal of

Experimental Agriculture. 3(2):277-292

Bhalla, S. S. (1979). Farm size, Productivity and technical change in India

agriculture. Agrarian structure and productivity in developing countries.

Ed. Berry, R. & Cline, W. Baltimore: John Hopkins Univ. Press.

Bharamappanavara, S. C. & Hanisch, M. (2009). Measure of Social Performance

and Determinants Influencing the Repayment Status of Self-Help Groups

Microcredit Delivery Models in India- An Econometric Study

Department of Agricultural Economics, Humboldt University, Berlin.

Biggs, S. D. (1990). A multiple source of innovation model of agricultural

research and technology promotion”. World Development. Vol.18..1481-

1499.

Binam, J. N., Gockowski, J. & Nkamleu, G. B. (2008). Technical Efficiency and

Productivity Potential of Cocoa farmers in West African Countries. The

Developing Economics. XLVI-3, 242-263

Birkhaeuser, D., Evenson, R. & Feder G. (1991). The economic impact of

agricultural extension: A review. Economic Development and Cultural

Change. 39(3). 507-521

Boateng, O. P. (2003). Determinants of Adoption of Cocoa Black pod Disease

Control Technology in Ashanti Region of Ghana. Unpublished M.Phil

Thesis. Department of Agricultural Economics and Agribusiness.

University of Ghana

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

Bolarinwa, K. K. & Fakoya E. O. (2011). Impact of Farm Credit on Farmers

Socio-economic Status in Ogun State, Nigeria. Journal of Social

Science. 26(1):67-71

Bosompem, M., Kwarteng, J. A., & Ntifo-Siaw, E. (2011). Perceived impact of

cocoa innovations on the livelihood of cocoa farmers in Ghana. The

sustainable livelihood framework (SL approach). Journal of

Sustainable Development in Africa. 13(4)

Brambilla, I. & Porto, G. G. (2005). Farm Productivity and Market Structure

Evidence from Cotton Reforms in Zambia. Department of Economics,

Yale University New Haven.

Breisinger, C., Diao, X., Thurlow, J. & Alhassan, R. M. (2008). Agriculture for

Development in Ghana. New Opportunities and Challenges. IFPRI

Discussion Paper 00784. Development Strategy and Governance

Division

Breisinger, C., Xinshen, D., Shashidhara, K., & James, T. (2008). The Role of

Cocoa in Ghana’s Future Development; Ghana Strategy Support

Program (GSSP). Background Paper No. GSSP 11, International Food

Policy Research Institute (IFPRI), Washington D.C., U.S.A.

Calderon, M. M. (2003). Improved Management of Angat, Ipo, Umiray and La

Mesa Water sheds in Luzon, Philippines: A Contigent Valuation Study.

University of the Philippines.

CIMMYT (1993). The Adoption of Agricultural Technology: A Guide for survey

design. International Maize and Wheat Improvement Centre. Mexico.

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

CNFA Cultivating Entrepreneurship (2009). A report on Commercial

Strengthening of Smallholder Cocoa Production (CSSCPP) in Ghana.

www.cnfa.org

COCOBOD NEWS (2012). A Publication of Ghana Cocoa Board. Issue (2012)

COCOBOD (2007). Cocoa Abrabopa Formation in Ghana.

Collinson, M. P. & Tollens, E. (1994). The impact of international agricultural

research centres: Measurement, quantification, and interpretation.

Experimental Agriculture 30:395-419.

Croppendstedt, A. & Demeke, M. (1996). Determinants of Adoption and levels of

Demand for fertilizer for cereal growing farmers in Ethiopia. Centre for

the study of African Economics, Working Paper Series.

Cull, R., Demirguc-Kunt, A. & Morduch, J. (2009). Microfinance Tradeoffs

Regulation Competition and Financing. The World Bank Research

Group finance and Private Sector Team. Policy Research Working

Paper 5086.

Cullingwood, C. A. (1971). A Comparison of assessment methods in cocoa Mirid

controls trials. International Cocoa Research Conference, Accra, Ghana

161-168.

Daberkow, S. G. & McBride, W. D. (1998). Adoption of precision Agriculture

Technologies by U.S Corn Producers. Journal of Agribusiness. 16:151-

168.

Danquah, J. A., Kuwornu, J. K. M., Baffoe-Asare, R., Annor-Frimpong, F. &

Zhang, C. (2015). Smallholder Farmers Preferences for Improved cocoa

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

Technologies in Ghana. British Journal of Applied Science &

Technology 5(2): 150-165.

Danso-Abbeam, G. R., Aidoo, K., Agyemang, O. & Ohene-Yankyera, K. (2012).

Technical Efficiency of Cocoa Production. Evidence from Bibiani

Anhwianso Bekwai. Journal of Development and Agricultural

Economics. 4(10).287–294

Degu, G. (2001). Review of on-farm research and adoption studies on maize in

southern Ethiopia. Awassa Agricultural Research Centre, Ethiopia.

Degu, G., Mwangi, W., Verkuijl, H. & Wondimu, A. (2000). An assessment of

the Adoption of seed and fertilizer packages and the role of credit in

Smallholder Maize production in Sidama and North Omo Zones

Ethiopia

Dercon, S. & Krishnan, P. (1998). Changes in poverty in rural Ethiopia, 1989-

1995: a measurement, robustness tests and decomposition. Center for

the study of Africa Economics. Working Paper Series 98-97

Dormon E. N. A., Van Huis, A., Leeuwis, C., Obeng-Ofori, D. & Sakyi-Dawson

O. (2004). Causes of low productivity of cocoa in Ghana: Farmers

Perspectives and Insights from Research and Socio-Political

Establishment. Wageningen Journal of Life Sciences. 52-3/4

Dorward, A., Poulton, C., & Kydd, J. (1998). The Revival of Smallholder Cash

Crops in Africa: Public and Private Roles in the Provision of Finance.

Journal of International Development. 10(1)85-103.

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

Doss, C. R. & Morris, M. L. (2001). How does gender affect the adoption of

Agricultural Innovations? The case of improved maize technology in

Ghana. Agricultural Economics. 25:27-39.

Doss, C. R. (2006). Analyzing the Technology Adoption using Micro studies:

Limitations, Challenges and Opportunities for Improvements in

Agricultural Economies. 34:207–219

Duker, R. & Sakpaku, C. (2011). An assessment of the impact of the cocoa mass

spraying exercise on production and marketing of cocoa in the

Juaboso Cocoa District from 2001-2007. Master’s Thesis. Department

of Business Administration, Technology and Social Sciences. Lulea

University of Technology, Sweden.

Evenson, R. (1998). Economic impact studies of agricultural research and

extension. USA: Yale University Press

FAO (2008). Food information Guide for action: Practical Guide. EC–FAO Food

Security Programme. www.foodsec.org

Feder, G. & Slade, R. (1984).The Acquisition of Information and the Adoption of

New Technology. American Journal of Agricultural Economies. 66:312–

320.

Feder, G. & Umali, D. C. (1993). The Adoption of Agricultural Innovations: A

Review of Technological Forecasting and Social Change, 43 (3-4):218-

239.

Feroze, S. M., Chauhan, A. K., Malhotra, R. & Kadian, K. S. (2011). Factors

Influencing Group repayment Performance in Haryana: Application of

Tobit Model. Agricultural Economics Research Review. 24:57-65

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

http://www.fao.org/nr/water/aquastat/countries_regions/GHA/GHA-CP_eng.pdf

Fleming, C. A. & Trevors, J. T. (1989). Copper toxicity and chemistry in the

environment: A Review. Water Air and Soil pollution For Cocoa

Farmers.

Flores, I. M. (2004). Rural Development and Food Security in West Africa. FAO

Agriculture and Economic Development Analysis Division. Working

Paper No.04-02

Gahim, A. K. A. & Pannell, D. J. (1999). A Conceptual framework of Adoption of

an Agricultural Innovation. Agricultural Economics. 21:145–154

Gangopadhyay, S. & Sengupta, K. (1987). Small Farmers, Moneylenders and

Training Activity.

Garret, H. (1969). The Tragedy of the Commons. www.garretthardinsociety.org

Date Retrieved 5th February, 2015

Georghiou, G.P. (1980) Insects Resistance and Prospects for its Management.

Research Review 76: 131-145

Getahun, D., Mwangi, W., Verkuijl, H. & Abdishekum, W. (2000). An

Assessment of the adoption of Seed and Fertilizer Packages and the

Role of Credit in Smallholder production of Maize in Sidana Omo

Zones Ethiopia. International Maize and Wheat Improvement Centre

(CIMMYT), Mexico and Ethiopian Agricultural Research

Organization (EARO)

Ghana Cocoa Board (1997). Report of Advisory Committee on Credit Scheme.

Accra, Ghana: COCOBOD

Ghanadistricts.com. (2013).Retrieved on 8th May, 2014.

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

GSS (2012). Population and Housing Census 2010. Accra, Ghana: Ghana

Statistical Service

Gockowski, J. (2007). Cocoa Production Strategies and the Conservation of

Globally Significant Rainforest Remnants in Ghana. International

Institute of Tropical Agriculture

Gockowski, J. & Sonwa, D. (2007). Africa Farm Safety Interventions in the Cocoa

Sector. Impact Brief Issue Mo. 97. STCP, International Institute of

Tropical Agriculture.

Gockowski, J., Nkamleu, G. B. & Windt, J. (2000). Implications for Resource

Intensification for the Environment and Sustainable Technology

Systems in the Central African Rainforest” In: Lee D. R., Barrett, C. B.,

editors. Tradeoffs or synergies? Agricultural intensification, economic

development and the environment. UK: CABI, pp. 197–219.

Green, D. A. G & Ng’ongla, (1993). Factors affecting Fertilizer adoption in less

Developed Countries: An application of multivariate logistic analysis in

Malawi. Journal of Agricultural Economics. 44:99-109

Griliches, Z. (1957). Hybrid corn: An Exploration in the Economies of

Technological change. Econometrica. 25:501–522

Grinsven, P. (2010). Personal Communication October (2010)

Hayami, Y. & Ruttan, V. W. (1985). Agricultural Development: An International

Perspective. Revised Edition. Baltimore and London: John Hopkins

University Press.

Hayami, Y., & Otsuka, K. (1993). The Economics of Contract Choice. UK:

Oxford University Press,

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

Hussein, S. & Byerlee, D. (1995). Education and Farm productivity in post’ Green

Revolution’ agriculture in Asia. In: Peters, G. H. and Hedley, D.D. (eds),

Agricultural competitiveness, market forces and policy choice.

Proceedings of 22nd International Conference. Of Agricultural

Economists held in Harare, Zimbabwe, 554-569.

ICCO (2008). Overview of “Best Known Practices” in Cocoa production. ICCO

Consultative Board on the World Cocoa Economy.

IFPRI (2012). Production Markets and the future of Small holders: the role of

Cocoa in Ghana. Overseas Development Institute. International Food

Policy Research Institute.

Inaizumi, H., Singh, B.B., Sanginga, P. C., Manyong, V.M; Adesina, A.A. &

Tarawali, S. (1999). Adoption and impact of dry-season dual-purpose

cowpea in the semi-arid zone of Nigeria. IITA, Ibadan

International Cocoa Organization (ICCO) (2012). Retrieved from

http://www.icco.org/about-us/international-cocoa-agreements/doc

_download/272-icc-86-1-draft-council-agenda-en.html.

Jabber, M. A., Beyene, H., Mohammed-Saleem, M. A. & Gebreselassie, S. (1998).

Adoption pathways for new technologies: An approach and an application

to vertisol management technology in Ethiopia. Socioeconomic and Policy

Research Working Paper No. 23, Livestock Policy Analysis Project,

International Livestock Research Institute, Addis Ababa, Ethiopia.

Jayne, T., Strauss, J., Yamano, T. & Molla, J. (2002). Targeting of food aid in

rural Ethiopia: Chronic need or inertia? Journal of Development

Economics. 68:247-288.

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

Jayne, T. S., G. J. & Zu, X. (2007). Fertilizer Promotion in Zambia: Implications

for strategies to Raise Smallholder Productivity. Seminar at World Bank,

Washington DC: November 2007 In: Dorward, A. (2009) Rethinking

Agricultural Subsidy Programmes in a Changing World. FAO Paper

prepared for the Trade and Markets Division.

Jayne, T. S., Yamano, T. Weber, M., Tschirley, D., Benfica, R., Chapoto, A. &

Zulu, B. (2003). Smallholder Income and Land distribution in Africa:

Implications for Poverty Reduction Strategies. Food Policy 28:253-275

Kaliba, A. R. M., Verkuijl, H. & Mwangi, W. (2000). Factors affecting adoption

of improved Maize Seeds and Use of Inorganic Fertilizer for Maize

production in intermediate and lowland zones of Tanzania. Journal of

Agricultural and Applied Economics. 32 (1):35-47

Kalyebara, R. (1999). A Comparison of factors affecting adoption of Improved

Coffee Management recommendations between Small and Large farmers

in Uganda. Paper presented at the CIAT International workshop: Assessing

the Impact of Agricultural Research on Poverty Alleviation, San Jose,

Costa Rica, 1999.

Kamau, P. C. (1980). Economics of Herbicides use in Coffee” Kenya-Coffee, 445-

429.

Kebede, Y., Gunjal, K. & Coffin, G. (1990). Adoption of New technologies in

Ethiopian Agriculture: The Case of Teguelet-Bulga District, Shoa

Province. Journal of Agricultural Economics 4:27-43

Kelly, J. D., Afander, L. & Harley, S. D. (1995). Pyramiding Genes for Resistance

to Bean Common Mosaic Virus. Euphytica, 82:207-212

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

Khanna, M. (2001). Sequential adoption of site-specific technologies and its

implications for nitrogen productivity: A Selectivity Model. American

journal of Agricultural Economics. 83(1):35-51

Kherallah, M., Minot, N., Kachule, R., Soule, B. G. & Berry, P. (2001). Impact of

Agricultural Market Reforms on Smallholder farmers in Malawi and

Benin. International Food Policy Research Institute.

KPMG (2013). The Chocolate of tomorrow. What today’s market can tell us about

the future. Klynveld Main Goerdeler and Peat Marwick International.

Retrieved from kpmg.com

Kyei, L., Foli, G., & Ankoh, J. (2011). Analysis of factors affecting the technical

efficiency of cocoa farmers in the Offinso District –Ashanti Region, Ghana

American Journal of Social and Management Science

Laryea, A. A. (1981). Technology transfer to cocoa farmers in West Africa.

Proceedings of the 8th International Cacao Conference, October.

Cartagena, Colombia: Cocoa Producer Alliance (COPAL). 583-591.

Latruffe, L. (2010). Competitiveness, Productivity and Efficiency in the

Agricultural and Agri-Food Sectors. OECD Food, Agriculture and

Fisheries, Working Papers No.1-58.

Leathers, H. D. & Smale, M. (1995). A Bayesian approach to explaining

sequential adoption of Components of Technology Package. American

Journal of Agricultural Economics. 68:519-527

Lengyingtuo, A. & Mekuria, M. (2005). Modeling Agricultural Technology

Adoption using the software Data. CIMMYT–Alp Training Manual. No.

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

11. International maize and wheat improvement center (CIMMYT),

Harare, Zimbabwe.

Leston, D.(1970) Entomology of the Cocoa Farm Annual Review Entomology

15:273-294

Lin, C. A. & Jeffres, L.W. (1998). Factors influencing the adoption of multimedia

cable Technology. Journalism & Mass Communication Quarterly. 75(2)

Linder, R. K. (1987). Adoption and Diffusion of Technology: an overview. In

Champ, B. R., Highly, E, & Remenyi, J. V. (Eds). Technological

innovation in Postharvest handling and transportation of grains in the

humid tropics. ACIAR proceedings No.19, Australian Center for

International Agriculture Research, Canberra, 144-151

Lopez, C. P. and Requena, J.C. (2002). Adoption factors of the organic practices

in the Southern Spanish Olive Farming: A logit Model Specification.

Department of Agricultural Economy and Sociology, General

Directorate of Agricultural Research of the Andalusia Government,

Spain.

Lowdermilk, M. (1972). Diffusion of Dwarf Wheat Production Technology in

Pakistan’s Punjab. Ph.D Thesis, Cornell University.

Ludena, C. E., Hertel, T.W., Preckel, P. V., Foster, K. & Nin A. (2007).

Productivity Growth and Convergence in Crop, Ruminant and Non–

Ruminant production measurement and forecasts. Agricultural

Economics. 37:1-17.

Manu, M. & Tetteh, E. K. (eds) (1987). A guide for Cocoa Cultivation. New Tafo;

Cocoa Research Institute of Ghana.

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

Matlon, P. J. (1994). Indigenous Land use systems and investments in soil

fertilizer in Burkina Faso. In: Adesina, A. A. (ed.). Factors affecting the

adoption of fertilizer by rice farmers in Cote d’Ivoire, (1996).WARDA,

Bouake.

Matsumoto, T. & Yamono, T. (2010). The Impact of Fertilizer Credit on Crop

Production and Income in Ethiopia. Pages 2-10

Mehra, R. (1994). Raising of Agricultural Productivity through Women Farmers.

In: Peters, G.H. & Douglas, D. H. (Eds.). Agricultural, Competitiveness,

Market Forces and Policy Change. Proceedings of the 21st International

Conference on Agricultural Economics, University of Dartmouth

Miller, C. (2007) ‘Value chain financing models – building collateral and improving

credit worthiness’, paper and presentation at the Southeast Asian

Conference, paper in Digal, L. (ed.) (2009) Southeast Asia Regional

Conference on Agricultural Value Chain Financing Conference

Proceedings, Asian Productivity Organization, National Productivity

Council and FAO, Rome,

Miller, C. and Jones, L. (2010) Agricultural Value Chain Finance:

Published by Food and Agriculture Organization and Practical

Action (2010).

Million, T. (2001). Determinants of fertilizer use in Gununo Area, Wolaita Zone,

and Southern Ethiopia: an application of Logit Analysis. In: Million, T. &

Getuhun, D. (Eds). Review of on-farm research and adoption studies on

maize in Southern Ethiopia (2001). Awassa Agriculture Research Centre,

Ethiopia.

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

Min, A., Arndi, C., Hertel, T.W. & Preckel, P.V. (2003). Bridging the gap

between partial and Total factor productivity measures using Direct and

Distance functions. American Journal of Agricultural Economics. 89(4):

928-942 .

Morduch, J. & Johnston, D. Jr. (2008). The unbanked: Evidence from Indonesia.

World Economic Review. 22(3):517-537

Mussei, A., Mwanga, J., Mwangi, W., Verkuijl, H., Mungi, R. & Elang A. (2001).

Adoption of Improved Wheat Technologies by small-scale farmers in

Mbeya District, Southern Highlands, Tanzania. International Maize and

Wheat Improvement Centre (CIMMYT), Mexico and United Republic of

Tanzania.

Mull, L. D. & Kirk horn, S. R. (2005). Child labour in Ghana Cocoa production:

Focus upon Agricultural Tasks, Ergonomic Exposures and Associated

Injuries and Illness. Public Health Reports. 120:649-655.

Myint, K. (2007) “Value Chain Finance” at Asia International Conference”

Presentation

Negatu, W. & Parikh, A. (1999). The Impact of Perception and factors on the

adoption of agricultural technology in the Movet and Jiry Woreda

(Districts) of Ethiopia. Journal of Agricultural Economics. 21:205-221

Ngatia, S. C. E. & Kabaara, A, M. (1976). The state of Kenya Coffee Industry

With reference to research and extension. Kenya-Coffee. 480:94-99

Njagi, S. B. C. (1980). Economics of Fertilizer use in Coffee Production, Nitrogen

and Phosphates. Kenya-Coffee. 532:219-33

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

National Cocoa Production Statistics (2014). 2013/2014 Districts Cocoa

Production Statistics (Unpublished). Ghana Cocoa Board

Nkamleu, G. B. & Ndoye, O. (2003). Cocoa based farming systems in humid

forest zone of west and central Africa: constraints and opportunities.

Proceeding technology strategies. Dakar, Senegal. Inter Academy

Council. Amsterdam.

Nkonya, E., Schroeder, T. & Norman, D. (1997). Factors affecting adoption of

improved maize seed and fertilizer in Northern Tanzania. Journal of

Agricultural Economics. 48(1):1-12.

Noyce, J. O., Michels, H. & Keemil, C. W. (2006). Potential use of Copper

surfaces to reduce survival of Epidermic Methicillin–Resistant

Staphylococcus Enreus in the Healthcare Environment. Journal of

Hospital Infections. 63(3):289

NRSCE (2010). Report on the first National Roundtable on Sustainable cocoa

Economy. Africa Cocoa Coalition (ACC) Centre for Human &

Environmental Security (CHES)

Obeng-Agyina, & Opoku, I. Y. (2010). The National Cocoa and Pests and Disease

Control Challenges (CODAPEC). A Paper presented by (COCOBOD)

on Cocoa Disease and Pest Control.

Ofori-Frimpong, K. (2010). Application of high technology methods on cocoa

production in Ghana In: Copal Cocoa Info. A Weekly Newsletter of cocoa

producers’ alliance. 388:1-24

Onu, D. (1991). Communication and adoption of improved soil conservation

technologies by Small scale farmers in Imo State of Nigeria. In

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

Faye.(Ed.), Agricultural Systems in Africa. Journal of West Africa.

Farming System Research Network.

Opoku, I. Y., Assuen, M. K. & Aneani, F. (2007). Management of Black pod

disease of cocoa with reduced number of fungicide application and crop

sanitation. African Journal of Agricultural Research 2:601–604.

Opoku-Ameyaw, K., Baah, F., Gyedu-Akoto, E., Anchirinah, V., Dzahini-

Obiatey, K., H. Cudjoe, A. R., Aquaye, S., & Opoku, S. Y. (2010).

Cocoa Manual. Tafo: Cocoa Research Institute of Ghana.

Opoku-Boamah (2013). Personal Communication 22nd December, 2013.

Owusu-Manu, (1996). Frequency and Timing of Insecticide Application.

Quarterly Bulletin on Cocoa Statistics. Cocoa Research Institute of

Ghana. Oxford Economic Papers. 39:333–342.

Owusu-Manu, E & Somuah, J.M.(1984) Capsid Pocket Rehabilitation A Report

from Cocoa Research Institute of Ghana 1986/76/77-1978/79 page 58-60

Padi, B. & Owusu, G. K. (1998). Towards an Integrated Pest Management for

Sustainable Cocoa production in Ghana. Tafo, Ghana: Cocoa Research

Institute of Ghana

Parente, S. & Prescott, E. C. (2000). Barriers to Riches. USA: MIT Press. pp

12‐14.

Pfitzer, M., Krishnaswamy, R. & Genier, C. (2009). Market Development

Investments by Agricultural Input Companies and their Foundations:

Transforming Smallholder Agriculture. Prepared for The Syngenta

Foundation for Sustainable Agriculture. Pp. 3-4.

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

Pinckney, T. (1995). Does Education Increase Agricultural Productivity in Africa?

Department of Economics, Williams College. Williamstown.

Pisanelli, A., Franzel, S., Dewolf, J. R. & Pooli, J. (2001). The adoption of

improved tree fallows in Western Kenya: Farmer Practices Knowledge and

Perception. CNR Instituto per “Agroselvicoltura, Porano (TR), Italy;

ICRAF, Nairobi Kenya

Prudentia, Y. C. (1983). A village study of soil fertility management and food

crop production in Upper Volta: Technical and economic analysis. PhD

Dissertation, University of Tucson. USA.

Rodrik, D., Autor, D., Hines, J., Jones, J. & Taylor, T. (2009). Diagnostic before

Prescription. Journal of Economic Perspective Symposium on

Development Economics. Harvard Kennedy School

Rogers, E. M. (1995). Diffusion of Innovation. 4th Edition, New York: The Free

Press.

Rogers, E. M. (2003). Diffusion of Innovations, 5th Edition, New York: Free Press.

Rose, K. S., Golub, A. A. & Sohngen, B. (2013). Hybrid Cocoa and Land

productivity of cocoa farmers in Ashanti Region. Selected paper prepared

for presentation at the American Agricultural Economics Association

Annual meeting. 9 (2):297 –308

Rosenzweig, M. R. (1995). Why are there returns to schooling? AER papers,

Proceedings of the American Economic Association, 85:153-158.

Ruf, F. (2007) The Cocoa Sector Adoption of Technology: French Agricultural

Research Centre for International Development (Background Note)

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

Sanginga, C. P. (1998). Adoption and Social impact of agricultural technologies:

The case of soybean in Benue State, Nigeria. Ph.D. Thesis, Univ. of

Ibadan, Nigeria.

Saha, A., Love, H. A. & Schwartz, R. (1994). Adoption of emerging technologies

under output uncertainty. American Journal of Agricultural Economics.

76:836-846.

Sain, G. & Martinez, J. (1999). Adoption and use of improved maize by Small-

scale farmers in Southeast Guatemala”, CIMMYT Economics paper

V.99, n.4

Saito, K. A., Mekonnen, H. & Spurling, D. (1994). Raising the Productivity of

Women farmers in Sub-sahara Africa. In: World Bank Discussion

Papers, Africa Technical Department Series, 230. pp.39.

Saito, K. Mekonnen, A., & Wiedeman, J. (1994). Agricultural Extension for

Women farmers in Africa. World Bank Discussion Paper No. 103.

Washington D.C.

Sarpong, D. B. & Asamoah, C. A. (2012). Impact Assessment Study of Cocoa

Sector Support Programme Phase 2 (CSSPII) inn Cocoa-farming

Communities in Ghana, IITA.

Schultz, T. W. (1964). Transforming Traditional Agriculture. New Haven,

Connecticut, U.S.A: Yale University Press.

Scoones, I. & Thompson, J. (1994). Knowledge, power and agriculture towards a

theoretical understanding: In Scoones, I. and Thomsom, J. (Eds.), Beyond

Farmer First Rural Peoples’ Knowledge and Extension Practice, London:

Intermediate Technology Publication, pp.16-32

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

Semgalawe, Z. (1998). Household adoption behaviour and agricultural

sustainability in the north mountains of Tanzania; the case of the soil

conservation in the northern Pare and West Usambara Moutains. Wau

dissertations number 248

Shahabinejad, A., & Akbari, A. (2010). Measuring Agricultural Productivity

Growth in Developing Eight. MENA Countries. Journal of Development

and Agricultural Economics. 2(9):326-332

Shakya, P. B. & Flynn, J. C. (1985). Adoption of modern varieties and fertilizer

use on rice in the eastern terrain of Nepal. Journal of Agricultural

Economics, 36:409-419.

Sharada, W. (1999). The effects of education on farmer productivity in rural

Ethiopia. A working paper series 99. Centre for the study of African

Economics.

Shields, M. L., Rauniyar, C. P. & Goode, F. M. (1993). A longitudinal analysis of

factors influencing increased technology adoption in Swaziland, 1985-

1991. Journal of Developing Areas. 27 (4):469-484

Singh, A. (1995). A paper presented on International Seminar on Development of

Rural Poor through the Self Help Groups, May 29-30 Bangalore

NABARD APRACA

Sneock, D., Abekoe, M. K., Appiah, M. R. & Afrifa, A. A. (2006). Soil

diagnostics method for formulation fertilizer requirements on cocoa

plantations. In 15thInternational Conference on Cocoa Research. San

Jose, Costa Rica.

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

Spielman, D., Byerlee, D., Avid, J., Alemu, D. & Kelemework, D. (2010). Policies

to Promote Cereal Intensification in Ethiopia. The Search for appropriate

Public and Private Roles, Food Policy, 35:185-194

Stiglitz, J. E. & Weiss, A. (1981). Credit Rationing in Markets with Imperfect

Information. The American Economic Review. Vol. 11 Issue 3

Sunding, D. & Zilberman, D. (2001). The agricultural innovation Process:

Research and technology adoption in a changing agricultural sector. In

Gardnes, B.L & Rausser, G. C. (ed). Handbook of Agricultural

Economics. Oxford; Elsevier, pp. 207-261

Teal, F., Zeitlin, A. & Maanah, H. (2006). Ghana Cocoa Farmers Survey (2004):

A Report to Ghana Cocoa Board. Centre for the study of African

Economics, Accra. Cambridge: Cambridge University Press.

Thirtle, C. G. & Ruttan, V. W. (1987). The Role of Demand and Supply in the

Generation and Diffusion of Technical Change. London, UK: Hardwood

Academic Publishers,

Tijam; A. A. (2008). Profitability of Fungicide Use Decisions Among Cocoa

Farmers in South Western Nigeria. Journal of Social Science, 165–171.

Trudy, O., Hoddinott, J. & Bill, K. (2001). The impact of agricultural extension on

Farm Production in resettlement areas of Zimbabwe. Working Paper

series no.6. Centre for the study of Africa Economics

Tsur, Y., Sternberg, M. & Hachman, E. (1990). Dynamic modeling of innovation

process adoption with risk aversion and learning. Oxford Economic

Paper, 42:336-355.

Van Grinsven, P. (2010). Personal Communication (October, 2010)

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

Vora, J. (1992). Productivity and Performance Measures: Who Uses Them?

Production and Inventory Journal. 33(1): 46-49.

Vigneri, M. (2005). Trade liberalization and agricultural performance: Micro and

macro evidence on cash crop production in Sub Sahara Africa.

Unpublished D. Phil Thesis. Oxford University.

Wiredu, A. N., Mensah-Bonsu, A., Andah, E. K., & Fosu, K., Y. (2011). Hybrid

Cocoa and Land Productivity of Cocoa Farmers in Ashanti Region of

Ghana. World Journal of Agricultural Sciences 7(2): 172-178

Wiredu, A. N., Mensah-Bonsu, A., Andah, E. K. & Fosu, K. Y. (2010). The

Improved Technology and land Productivity among Smallholder cocoa

farmers in Ashanti Region. A paper presented at the joint 3rd African

Agricultural Economists Association

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

APPENDIX Garrett Ranking Conversion Table

0.09 99 22.32 65 83.31 31 0.2 98 23.88 64 84.56 30 0.32 97 25.48 63 85.75 29 0.45 96 27.15 62 86.89 28 0.61 95 28.86 61 87.96 27 0.78 94 30.61 60 88.97 26 0.98 93 32.42 59 89.94 25 1.18 92 34.25 58 90.83 24 1.42 91 36.15 57 91.67 23 1.68 90 38.06 56 92.45 22 1.96 89 40.01 55 93.19 21 2.28 88 41.97 54 93.86 20 2.69 87 43.97 53 94.49 19 3.01 86 45.97 52 95.08 18 3.43 85 47.98 51 95.62 17 3.89 84 50 50 96.11 16 4.38 83 52.02 49 96.57 15 4.92 82 54.03 48 96.99 14 5.51 81 56.03 47 97.37 13 6.14 80 58.03 46 97.72 12 6.81 79 59.99 45 98.04 11 7.55 78 61.94 44 98.32 10 8.33 77 63.35 43 98.58 9 9.17 76 65.75 42 98.82 8 10.06 75 67.48 41 99.03 7 11.03 74 69.39 40 99.22 6 12.04 73 71.14 39 99.39 5 13.11 72 72.85 38 99.55 4 14.25 71 74.52 37 99.68 3 15.44 70 76.12 36 99.8 2 16.69 69 77.69 35 99.91 1 18.01 98 79.17 34 100 0 19.39 67 80.61 33 20.93 66 81.99 32

University of Ghana http://ugspace.ug.edu.gh DEPARTMENT OF AGRICULTURAL ECONOMICS AND AGRIBUSINESS

UNIVERSITY OF GHANA, LEGON

QUESTIONNAIRE

Research Topic: Input Credit Scheme Effects on the Adoption of Cocoa Production Technologies and Productivity of

Smallholder Cocoa Farmers in Ghana

M.Phil Thesis Programme

Farmers’ Questionnaire

Questionnaire number…………………………………… Type of Scheme: 1=Cocoa Abrabopa ( )

Name of Enumerator ………………. Date of Interview…………………………………

Locality ……………………………………… Region…………………………………………..

Eastern Region- Osino Cocoa District () Asamankese District () Kade District () Tafo District ()

Western Region-Juaboso Cocoa District ( ) Sefwi-Bekwai Cocoa District ( ) Essam Cocoa District ( )Buako Cocoa District()

This questionnaire is meant for research purpose only. Kindly tick the appropriate response to each of the questions below or provide information where applicable

University of Ghana http://ugspace.ug.edu.gh SECTION A –PERSONAL DATA FARM-OWNER

Name of Respondent (Farm owner)………………………………………………………………………..

Telephone Number……………………………..

Age of respondent (Farm Owner) ………………………………………………………………………….

Gender (1) Male [ ] (2) Female [ ]

Marital status (1) Married [ ] (2) Single [ ] (3) Divorced [ ] (4) Widowed [ ]

Are you the head of the household (1) Yes [ ] (2) No [ ]

What is your major occupation? (1) Farmer [ ] (2) Trader [ ] (3) Commercial driver [ ] (4) other [ ] specify…………………

What is the total household size?......

Number of economically active household members (i.e. more than 18 years and less than 60 years)……………………………..

Highest educational level achieved?

(1) None [ ] (2) primary [ ](3) JHS/ MSLC [ ](4) SHS/commercial/vocational [ ] (5) University/college[ ] (6) Other

[ ]

SECTION B FARM CHARACTERISTICS

How long have you been farming cocoa?…………………………..years

What is the total land holdings?………………………….

University of Ghana http://ugspace.ug.edu.gh 13 How did you acquire your total land holdings?(multiple response allowed) i) Inheritance ii) Leasehold iii) Mortgage iv) Appropriation v) Other (Please specify………………………………..)

14 What is the total land holding under cocoa cultivation? ...... i) Conventional Cocoa…………………………… ii) Hybrid Cocoa…………………..

15 What proportion of the cultivated cocoa is matured bearing? …………………………………

How many cocoa farms do you have………………………..?

Farm 1 Farm 2 Farm 3 Farm 4

District

Acres

Annual Output(KG)

17 Which year did you join the scheme? ……………

18 Which year did you first receive inputs from the scheme? ……………..

19 What inputs do you receive from the scheme? (1) Insecticides [ ] (2) Herbicides [ ] (3) Fungicides [ ]

(4) Fertilizer [ ] (5) All packages from the scheme [ ]

20 Do you apply the technologies on all your farms? (1) Yes [ ] (2) No [ ]

If No why? Specify ………………………………………………………………………

21 Is labour for the application of technologies easily available to you? (1) yes [ ] (2) no [ ]

University of Ghana http://ugspace.ug.edu.gh 22 Do you use household labour for the application of the technologies? (1) yes [ ] (2) no [ ]

23 What is the cost of household labour per day? ………………………GH cedis

24 Do you employ non-household labour on your farm?......

25 If yes, how many non-household labourers do you employ? …………………………

26 Are they permanent ‘workers’? (1) yes [ ] (2) no [ ]

27 What is the cost per hired labour per day? ………………………GH cedis

28 Are you able to afford all the labour for spraying you require? (1) yes [ ] (2) no [ ]

29 Does the cost of labour use prevent you from spraying your farm? (1) yes [ ] (2) no [ ]

30 How many days do labourers work on your farm in a week? ……………

31 How many hectares do the input credit supplied cover? 1) 2ha 2)2.5 ha 3)

32 Were you given the input credits? (1) yes [ ] (2) no [ ]

33 Did you apply/request for input credit last year for your operations and did you receive it?

………………………………………………………………………………

University of Ghana http://ugspace.ug.edu.gh ADOPTION OF COCOA INTENSIFICATION TECHNOLOGIES

Item Source Number of Activities Yes=1 No=0 Quantity/Proportion Cost of Labour Remarks

times in the

applied application

Hybrid Planting Access to Hybrid planting

Materials Materials

Fungicide Fungicide application

Mistletoe Removal of Mistletoes

Fertilizer Fertilizer application

Chupons Chupons Removal

Shade Shade Control

Pruning Pruning of farms

Brushing Brushing of undergrowth

Herbicides Herbicides application

Entrepreneurial Farmers field School

Training

Extension Field trips/ Extension Contact

Education(FFS)

University of Ghana http://ugspace.ug.edu.gh QUANTITY OF INPUTS USED ON VARIOUS FARMS (PRODUCTIVITY)

Inputs Yes No Farm 1 Farm 2 Farm3 Farm 4 Area (Ha) Total Quantity

(Kg)

Fertilizer(KG)

Agronomy Training

Insecticide(Litres)

Fungicide(Sachets)

Non-tree crops (initial shade)

Shade Tree

Age of the tree

Desirable trees (permanent shade)

Planted shade trees

Reason for planting non-tree crops

Number planted per acre

Labour(Man-Days)

Herbicides(Litres)

Entrepreneurial Training

Hybrid Cocoa

Weeding

University of Ghana http://ugspace.ug.edu.gh CONSTRAINTS COCOA FARMERS FACE UNDER THE INPUT CREDIT SCHEME

PROBLEM RANK

High interest rate

Capital/Finance lack of Collateral

High input cost

Non-availability of

chemicals in the Access to inputs market

Lack of good planting

materials

Lack of Personnel

Poor Extension Access to Extension Delivery

Few days of Extension

Contact

Weather Poor rainfall Pattern

Heavy Rainfall

High Temperatures

1= Severe 2=Moderate 3= Never

University of Ghana http://ugspace.ug.edu.gh INPUT CREDIT SUPPLIER

Mention any of the inputs you supply to your farmers under the Scheme Cocoa Abrabopa { }

Inputs Yes No Quantity Price Value

2010 2011 2012 2013 2010 2011 2012 2013 2010 2011 2012 2013

Pesticide

Agronomic Training

Fungicide

Insecticide

Herbicides

Fertilizer

Knapsack

Mist Blower

Wellington Boot

Other specify…

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

2) When did you start the input credit scheme in the district?......

3) What is the Purpose of the input credit scheme? 1) To increase productivity 2) To get more clients for cocoa purchases 3)

Used as pension schemes for farmers. 4) All the above

4) How many times per year do you provide this input credit scheme? 1) Once 2) Twice 3) three times

5) What is the coverage of your scheme?

Buako District Bekwai District Juaboso Essam Others

Yes

6i) Does the input credit scheme cover the entire district of Buako a. Yes b. No

6 ii) Does the input credit schemes cover the entire district Bekwai a. Yes b.No

6iii) Does the input credit schemes cover the entire district of Juaboso a. Yes b. No

6iv) Does the input credit schemes cover the entire district of Essam a.Yes b. No

7) What has been some of the impact of your scheme on the cocoa farmers?

Buako Bekwai Juaboso Essam

Increase farm output

University of Ghana http://ugspace.ug.edu.gh Improve standard of living

Other

8) Do you supply other equipment apart from the inputs which are they specify? a. Yes b. No

If yes specify …………………………………………………………….

9) Do you organize training programmes for the farmers apart from inputs credit schemes you supply the farmers mentioned above a) Bookkeeping skills b)Business Training c) Other specify

4) How many times per year do you provide this input credit scheme? 1) Once 2) Twice 3) three times

5) What is the coverage of your scheme?

Osino District Asamankese District Kade District Tafo District Others

Yes

6i) Does the input credit scheme cover the entire district of Osino a. Yes b. No

6 ii) Does the input credit schemes cover the entire district Asamankese a. Yes b.No

6iii) Does the input credit schemes cover the entire district of Kade a. Yes b. No

100

University of Ghana http://ugspace.ug.edu.gh 6iv) Does the input credit schemes cover the entire district of Tafo a.Yes b. No

7) What has been some of the impact of your scheme on the cocoa farmers?

Osino District Asamankese District Kade District Tafo Districts

Increase farm output

Improve standard of living

Other

8) Do you supply other equipment apart from the inputs which are they specify? a. Yes b. No

If yes specify …………………………………………………………….

9) Do you organize training programmes for the farmers apart from inputs credit schemes you supply the farmers mentioned above a) Bookkeeping skills b)Business Training c) Other specify

101