BLACK SOLDIER FLY LARVAE-BASED FISH FEED PRODUCTION: 
FINANCIAL FEASIBILITY AND ACCEPTABILITY ANALYSIS 
 
 
 
 
 
BY 
MIRIAM OPPONG 
(10551617) 
 
 
 
 
THIS THESIS IS SUBMITTED TO THE UNIVERSITY OF GHANA, LEGON IN 
PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE AWARD OF 
MASTER OF PHILOSOPHY DEGREE IN AGRIBUSINESS  
 
 
 
 
 
DEPARTMENT OF AGRICULTURAL ECONOMICS AND AGRIBUSINESS, 
COLLEGE OF BASIC AND APPLIED SCIENCES, UNIVERSITY OF GHANA, 
LEGON 
 
 
 
 
 
 
JULY, 2017 
 
i 
 
DECLARATION 
I, MIRIAM OPPONG, author of this thesis do hereby declare that except for references 
which have been duly cited, the work presented in this thesis, “BLACK SOLDIER FLY 
LARVAE BASED FISH FEED PRODUCTION: FINANCIAL FEASIBILITY AND 
ACCEPTABILITY ANALYSIS” was entirely done by me in the Department of 
Agricultural Economics and Agribusiness, College of Basic and Applied Sciences, University 
of Ghana, Legon from August 2016- July, 2017. This work has never been presented either in 
whole or in part for any other degree in this University or elsewhere. 
 
 
 
         ………………………….. 
         Miriam Oppong 
         (Student) 
         Date: 
……………………. 
 
 
 
 
This thesis has been submitted for examination with our approval as supervisors: 
 
……………………..      ……………...……..       …………………… 
Dr. (Mrs.) Irene S. Egyir     Dr. Edward E. Onumah      Dr. Comfort Freeman 
  (Major Supervisor)      (Co- Supervisor)       (Co- Supervisor) 
Date: …………………     Date: ………………      Date: …………… 
 
 
ii 
 
DEDICATION 
This thesis is dedicated to the Almighty God 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
 
 
iii 
 
ACKNOWLEDGEMENTS 
I wish to express my sincere gratitude to my supervisors Dr. (Mrs) Irene Egyir, Dr. Edward 
E. Onumah and Dr. Comfort Freeman for their supervision and immense contributions to my 
thesis work. I thank all lecturers and students in the department for their contributions to my 
work during seminar presentations.  
 
I would also like to acknowledge and thank the Swiss National Science Foundation (SNSF) 
and the Swiss Agency for Development Corporation (SDC), Switzerland for suggesting this 
area of study and providing the grant for the research, which was done within the frame work 
of an ongoing project “Insect-based feed and fertilizer production via waste transformation 
for small holders in Ghana. The research was done by the following partners: Research 
Institute of Organic Agriculture (FiBL), University of Ghana (UG), Biotechnology and 
Nuclear Agriculture Research Institute (BNARI), the Water Research Institute of Council for 
Scientific and Industrial Research (WRI-CSIR), and University of Cape Coast (UCC).   
 
I would like to thank Dr. Seth K. Agyakwah of the Water Research Institute (WRI), 
Christopher Teye Gaga (MPhil. Student) and Mr E. A. Ewusi (Research Scientist and Ph.D 
candidate) of the Biotechnology and Nuclear Agriculture Research Institute (BNARI), Ghana 
Atomic Energy Commission, for providing data needed for the financial feasibility studies.  
 
I would also like to recognise Mr. Godfred Alimo, the head of Azofa farmer-based 
organisation in Asougyaman district for paving a way for me to interview members of the 
association. I would like to acknowledge Mr. Famous Gbetey for acting as a guide during my 
visits to the various farms in the district. My appreciation also goes to James Kofi Aggrey 
Junior and Salma Suhuyini Fuseini, my fellow M.Phil students, for their assistance in various 
ways to complete my research. 
 
Finally, I am especially grateful to my family members; my husband, Thomas Amoako 
Kutin, my parents, Mr. Ernest Oppong and Ms. Doreen Oduro and my siblings John 
Abbebrese Boateng (Ph.D), Adwoa Serwaa Oppong, Esther Oppong, Akua Agyeiwaa 
Oppong and Raymond Amponsah for their support and prayers during the project period. 
Miriam Oppong 
 
 
 
iv 
 
ABSTRACT 
Conventional feed, which uses fishmeal is expensive and expected to be limited in the future 
due to potential decline in capture fisheries. Researchers have suggested the use of insects 
such as the Black Soldier Fly larvae (BSFL) as an alternative to fishmeal in fish feed. The 
major objective of the study was to ascertain the financial viability of BSFL meal and BSFL-
based fish feed production and fish farmer’s acceptance of BSFL-based fish feed. Indicators 
such as Net Present Value (NPV), Benefit Cost Ratio (BCR) and Internal Rate of Return 
(IRR) were used for the financial viability analysis. A sample of 150 farmers were 
interviewed and a three point Likert scale used to solicit farmer’s responses to perception 
statements. The bidding game and the Tobit regression model were used to determine the 
maximum amount farmers were willing to pay and factors influencing the willingness to pay 
(WTP) amount respectively. Finally, a partial budget was used to estimate the gains made by 
fish farmer from substituting conventional feed with BSFL-based feed. The results of the 
study showed that the production of BSFL meal is viable with NPV of GH¢5.7m, BCR of 
2.4, IRR of 32%. The production of BSFL-based fish feed is viable with NPV of GH¢8.1m, 
BCR of 1.95 and IRR of 30%. There was a high degree of acceptance amongst fish farmers 
as farmers perceived BSFL feed to be beneficial and were willing to pay an average of GH¢ 
186.00/20Kg for the BSFL-based feed. The factors affecting WTP were marital status, annual 
income and awareness of insects as feed. A gain of GH¢6.53 can be made by fish farmers in 
replacing 14.6kg of conventional feed with 14.6 kg of BSFL-based fish feed. The study 
recommended the sensitization of entrepreneurs on the profitability of BSFL meal and BSFL-
based fish feed production in order to encourage investment. Also, potential entrepreneurs 
should recognise the mean WTP amount when making pricing decisions in order to penetrate 
the market and allow fish farmers to make savings from substituting conventional fish feed 
with BSFL-based fish feed.  
 
 
 
v 
 
TABLE OF CONTENTS 
Content                               Page 
DECLARATION i 
DEDICATION  ii 
ACKNOWLEDGEMENTS iii 
ABSTRACT  iv 
TABLE OF CONTENTS v 
LIST OF TABLES viii 
LIST OF FIGURES ix 
LIST OF ACCRONYMS x 
CHAPTER ONE 1 
INTRODUCTION 1 
1.1 Background of the Study 1 
1.2 Problem Statement of the Study 3 
1.3 Objectives of the Study 5 
1.4 Justification of the Study 6 
1.5 Organisation of Thesis Report 7 
CHAPTER TWO 8 
LITERATURE REVIEW 8 
2.1 State of Aquaculture in Ghana 8 
2.1.1 Overview 8 
2.1.2 Fish feed production 9 
2.2 Rearing of Insects as Fish Feed 11 
2.2.1 The Black Soldier Fly (BSF) 11 
2.2.2  Socio economic, health and environmental impact (risk or benefit) of using BSFL in fish 
feed (production via waste conversion) 12 
 
 
vi 
 
2.3 Empirical Results on the Technology for Production and Harvest of BSFL for Fish 
Feed   14 
2.4 Financial Analysis 15 
2.4.1 Measures of project worth 15 
2.4.2 Empirical studies on profitability analysis 17 
    2.4.3        Partial Budget Analysis 19 
2.4.4 Overview of partial budgeting 19 
2.4.5 Empirical studies on partial budget analysis 19 
2.5 Perception Analysis 20 
2.5.1 Likert scale 20 
2.5.2 Empirical studies on perception analysis 21 
2.6 Willingness to Pay Analysis 22 
2.6.1 Overview 22 
2.6.2 Empirical studies on willingness to pay (WTP) analysis 24 
CHAPTER THREE 27 
METHODOLOGY 27 
3.1  Conceptual Framework 27 
3.2 Theories Underlying the Study 29 
3.3  Method of Data Analysis 30 
3.3.1 Determining the level of profitability of BSFL-based feed production 30 
3.3.2 Determining the perception of farmers toward BSFL-based feed 39 
3.3.3 Determining farmers’ willingness to pay (WTP) for BSFL-Based feed 41 
3.3.4 Estimating the gains in substituting the conventional feed for BSFL-based feed 46 
3.4  Method of Data Collection 47 
3.4.1 Questionnaire 48 
3.4.2 Sampling procedure 49 
 
 
vii 
 
3.5 Profile of Study Area 49 
3.5.1   Asuogyaman district 49 
3.5.2 Lower Manya Krobo district 51 
3.6  Scope and Limitations of the Study 53 
CHAPTER FOUR 55 
RESULTS AND DISCUSSIONS 55 
4.1 Socio Economic Characteristics of Respondents 55 
4.2 The Profitability of BSFL-based Feed Production 64 
4.2.1 Scenario A: Production of BSFL meal 64 
4.2.2 Scenario B: Production of BSFL-based fish feed 68 
4.3 The Perception of Farmers towards the use of BSFL in Fish Feed 70 
4.4 The Mean Amount Farmers are Willing to Pay for BSFL and the Factors Influencing 
the Decision  73 
4.4.1 The mean amount farmers are willing to pay for BSFL-based fish feed 73 
4.4.2 Factors influencing the amount farmers are willing to pay for BSFL-based fish feed 74 
4.5 The Gains Made from Substituting Conventional Feed with BSFL-Based Feed 76 
CHAPTER FIVE 78 
SUMMARY, CONCLULSION AND RECOMMENDATIONS 78 
5.1 Summary 78 
5.2 Conclusions 79 
5.3 Recommendations 80 
REFERENCES 81 
APPENDICES 88 
 
 
 
 
 
 
viii 
 
LIST OF TABLES 
Table                    Page 
 Table 2.1: Summary of financial feasibility results for conventional feed production in 
Ghana (Amount in USD)         18 
Table 2.2: Summary of financial feasibility results for BSFL meal production in Costa 
Rica (Amount in USD)         18 
Table 2.3: Summary of financial feasibility results for BSFL meal production in USA 
(Amount in USD)          18 
Table 2.4: Summary results of perception analysis     21 
Table 3.1: Summary of profitability analysis performed on price scenarios  33 
Table 3.2: Summary of analysis performed      36 
Table 3.3: Perception statements of perceived risk and benefit    40 
Table 3.4: Description of dependent and explanatory variables    45 
Table 3.5: Standard layout for partial budget      46 
Table 4.1: Summary results for discounted measures     65 
Table 4.2: Summary of results for the sensitivity analysis    68 
Table 4.3: Summary of results for discount measures     69 
Table 4.4 Results from sensitivity analysis      70 
Table 4.5: Perception statements and various means     70 
Table 4.6: Tobit regression results on factors of willingness to pay   75 
Table 4.7: Partial Budget analysis       77 
 
 
 
 
ix 
 
 
LIST OF FIGURES 
Figure                     Page 
Figure 2.1: Growth trend of aquaculture production (2010-2016)   9 
Figure 2.2: Steps in Black Soldier Fly Larvae production    15 
Figure 3.1: Conceptual framework illustrating investment decisions   28 
Figure 3.2: Map of Asuogyaman district       50 
Figure 3.3: Map of Lower Manya Krobo District      52 
Figure 4.1:      Gender profile of respondents       55 
Figure 4.2: Educational profile of respondents      57 
Figure 4.3:      Marital profile of respondents       57 
Figure 4.4: Residential profile of respondents      58 
Figure 4.5:      Distribution of respondents by religion     59 
Figure 4.6: Profile of type of business operated by respondents    60 
Figure 4.7: Frequencies and bidding amounts for willingness to pay survey  74 
 
 
 
  
 
 
x 
 
LIST OF ACCRONYMS 
BCR    Benefit Cost Ratio 
BSFL     Black Soldier Fly Larvae 
BSF     Black Soldier Fly 
BNARI   Biotechnology and Nuclear Agriculture Research Institute 
FASDEP    Fisheries and Aquaculture Sector Development Plan 
FAO     Food and Agricultural Organisation 
FIBL    Research Institute of Organic Agriculture 
GBN     Ghana Business News 
GSS     Ghana Statistical Service 
IRR    Internal Rate of Return 
MoFAD   Ministry of Fisheries and Aquaculture Development 
MWTP   Mean Willingness to Pay 
NPV    Net Present Value 
WRI    Water Research Institute 
WTP    Willingness to Pay 
 
 
 
1 
 
CHAPTER ONE 
INTRODUCTION 
1.1 Background of the Study 
Globally, fish production has increased steadily in the last five years from 145.9 million 
tonnes in 2009 to 167.2 million tonnes in 2014 (FAO, 2016). Capture fisheries remained 
fairly stable with a minimal increase from 90.2 million tonnes to 93.4 million tonnes within 
the five years. This is because the world’s oceans have been exploited to its maximum 
potential and beyond and currently 30% of fish stocks are fished at unsustainable levels 
(FAO, 2014). The increase in fish production has been mainly due to increase in 
aquaculture’s contribution to fish production from 55.7 million tonnes to 73.8 million tonnes 
(FAO, 2014). Fish production has increased in response to increased demand for fish with an 
increase in consumption from an average of 9.9 kg in the 1960’s to 19.2kg in 2012. This is as 
a result of population growth, urbanisation and increased income (FAO, 2014). To reduce the 
pressure on the world’s oceans and seas, aquaculture is expected to expand further to meet 
growing demand.  
 
In Ghana, the fish production industry is a steadily growing sector that contributes 
significantly towards GDP and employment. This steady increase is as a result of increase in 
the production of aquaculture from 19,091 metric tonnes in 2011 to 44,515 metric tonnes in 
2016 (MoFAD, 2016). On the other hand, capture fisheries production has declined from 
about 402,000 metric tonnes in 2010 to 370,000 metric tonnes in 2014 (MoFAD, 2016). The 
growing demand for fish protein in Ghana has motivated active development of aquaculture 
both at the commercial and peasant levels. The contribution of aquaculture production to the 
Ghanaian economy has grown over the past decade, with an annual average growth rate of 
12.4% (FAO, 2012).  Aquaculture is seen as an important foreign exchange earner, 
 
 
2 
 
contributing to food security as well as providing much needed employment to many people 
in Ghana.  
 
Most aquaculture operators (fish farmers) in Ghana use earthen ponds and rely on mother 
nature while others supplement with agricultural by-products. Other systems of culture 
include the cages, pens and raceway systems, which are not commonly practised nationwide 
(Awity, 2005). In terms of numbers, cages come after ponds. For instance, in 2012, a total of 
2, 278 cages whilst 4, 749 ponds were recorded in the country (FD, 2013). However, fish 
production during the same 2012 year was higher in cages than in ponds. Production from 
ponds was 1, 771.50 and that of cages was 24, 248.50 metric tonnes.     
 
Traditionally, aquaculture depends capture fisheries that is fish that has been captured from 
the sea such as pout, capelin, sand eel, and mackerel amongst others for feed supplement 
namely fishmeal (Olsen & Hasan, 2012). Fishmeal is a ground solid product obtained from 
removing the water and oil from fish or fish waste. Fishmeal has been an ideal protein 
supplement for fish feed due to its high protein content, balanced amino acids, vitamins and 
minerals, fatty acid content and cheap price (Gatlin et al., 2007). The use of fishmeal as the 
main protein supplement to fishmeal has been touted as unsustainable and expensive. Around 
10% of global fish produced is reused as fishmeal and most of this is used in aquaculture 
(FAO, 2012). This is unsustainable since capture fisheries, which is the main source of 
fishmeal is declining in the face of increasing demand. As a result, Researchers have tried 
various feed supplements to replace fishmeal such as bean meal, soybean meal, sunflower 
meal and other agricultural products. However, these are resources with a lot of competing 
uses both by humans and animals. As another alternative, researchers are currently revisiting 
the use of insects, specifically the use of black soldier fly Larvae (BSFL) in aquaculture feed. 
 
 
3 
 
This is known to be able to replace fishmeal successfully in aquaculture diet and is ideal for 
industrial scale production (Burtle, 2008; Diener et al., 2011).  
 
The black soldier fly is a non-pest fly usually found in the tropics. It is originally a native of 
temperate zones in America but has moved to other temperate regions around the world 
including Africa (Leclercq, 1997). Its larvae feeds on organic waste reducing the waste by 
50% and converting it into biomass rich in protein and fat. Various research has suggested 
that the black soldier fly larvae meal, that is the larvae of the black soldier fly when grounded 
into meal, can successfully replace fishmeal in the diet of fish. Research has shown 
successful feeding trials of fishmeal replacement by BSFL meal in rainbow trout, blue tilapia 
and channel catfish (Bondari & Sheppard, 1987; Stamer et al., 2014; St-Hilaire et al., 2007). 
 
1.2 Problem Statement of the Study 
Feed is pertinent to the success of aquaculture development. It accounts for 60 to 70% of total 
cost of production (FAO, 2012). With increasing demand for fish and decline of capture 
fisheries, fish production in Ghana is shifting to commercial high intensive fish farming. This 
means that the demand for commercial formulated feed is rising. However, the sector has not 
reached its full potential since it is constrained by high costs of feed (Hiheglo, 2008). In 
2016, Ghana’s aquaculture production of 44, 919 tonnes fell short of the 100,000 metric 
tonnes projected in Ghana’s FASDEP report (MoFAD, 2011) 
 
Fishmeal is the main source of protein in formulated fish feed and it makes up to 20- 60% of 
fish diets (Glencross et al., 2007; Watanabe, 2002). The continuous use of fishmeal for 
aquaculture production is however not sustainable, currently since water bodies are heavily 
exploited in order to feed the fishmeal industry (FAO, 2012). In recent times due to 
 
 
4 
 
increasing pressure on water bodies, tightened controls and increased quotas on world 
fisheries, the supply of fishmeal has declined whilst the demand for fish has increased, 
resulting in higher prices (FAO, 2012). This has caused fish feed to be expensive on the 
market since dietary protein is the major and most expensive component of formulated fish 
feeds (Wilson, 2002). For instance, the cost of fish feed on the international market increased 
by 73 % from 2005 to 2008 due to increase in fishmeal prices (FAO, 2012). 
 
The implication of the high cost of feed for fish farmers is that fish feed is less accessible and 
aquaculture production unsustainable. A locally manufactured feed that uses a more cost 
effective supplement, with sustainable supply and socially acceptable method of production 
than fish meal is therefore a much welcome intervention to curtail this problem. The mass 
rearing of insect larvae as an alternative to fishmeal has been suggested by researchers 
(Sheppard et al., 1994). The black soldier fly is the preferred insect because it is easy to 
handle and it does not spread diseases.  The larvae of the BSF feeds on waste reducing it by 
up to 50% and the larvae is rich in protein and fat of 43% and 35% respectively (Sheppard et 
al., 1994; Stamer et al., 2007). The production of BSFL meal on an industrial scale has 
therefore been suggested by researchers as the sustainable alternative to fishmeal. According 
to Stamer et al. (2007), the production of BSFL meal can be realised below the cost of 
production of existing fishmeal.  Research on feeding trials has shown that BSFL meal can 
successfully replace fishmeal in fish diet (Diener et al., 2011; Stamer et al., 2014).  
 
Against this background, the Research Institute of Organic Agriculture (FiBL), a Swiss 
organisation, in 2014, funded a project in Ghana at BNARI and WRI to establish a colony of 
BSFL and convert it into BSFL meal and BSFL-based fish feed. The aim of the project was 
to produce fish feed that is sustainable, cost effective and socially acceptable. The beginning 
 
 
5 
 
stages of the project entailed scientist on the field establishing a colony of the BSFL, turning 
it into BSFL meal and BSFL-based fish feed and performing feeding trials on fish growth 
performance (Yield). The final stage of the research, which is the basis of this study, was to 
perform: 1) A financial analysis of the BSFL meal and BSFL-based fish feed production and 
2) An analysis of fish farmers’ acceptance of BSFL-based fish feed pertaining to their 
perception of the feed and their willingness to pay for the feed. 
This study seeks to answer the following questions: 
1. Is it profitable for entrepreneurs to invest in BSFL meal and BSFL-based fish feed 
production?  
2. What is the perception of fish farmers towards the use of BSFL in fish feed?  
3. What is the mean amount fish farmers are willing to pay for the BSFL-based fish feed 
and the factors influencing the amount they are willing to pay? 
4. Will fish farmers gains or lose by substituting the conventional feed with BSFL-based 
fish feed? 
 
1.3 Objectives of the Study 
The major objective of the study is to determine the financial viability of BSFL meal and 
BSFL-based feed production and the acceptance of the BSFL-based fish feed by fish farmers.  
The specific objectives are as follows: 
1. To determine the level of profitability of BSFL meal and BSFL-based fish feed 
production,  
2. To determine the perception of farmers toward the use of BSFL in fish feed, 
3. To determine the mean amount farmers are willing to pay for BSFL-based feed and the 
factors influencing the amount they are willing to pay  
 
 
6 
 
4. To estimate the gains or losses made by fish farmers in substituting the conventional fish 
feed for BSFL- based fish feed. 
1.4 Justification of the Study 
The study was undertaken with the intention of providing empirical evidence on the financial 
viability of BSFL meal and BSFL-based fish feed production. The results from the 
profitability analysis will serve as a referral point for entrepreneurs hoping to invest in BSFL 
meal and BSFL feed production. If such a project is taken up, at any scale of production, the 
socio-economic opportunities in developing countries for such a project are immense. These 
include bridging the aforementioned supply deficit through local feed production, creation of 
jobs, enterprise development, increased global trade and waste management.  
 
The results from the perception of farmers towards the use of BSFL in fish feed, this study 
can serve as a manual and guideline for the sensitization of farmers towards the use of BSFL 
in fish feed. The results from the study on farmers perceived benefit and risk of the use of 
BSFL in fish feed will provide insight into the specific areas that more information is needed 
on the part of Ghanaian fish farmers so that sensitization can become more targeted. 
 
The results from the WTP analysis will inform entrepreneurs and other stakeholders on the 
amount that farmers are willing to pay for the BSFL-based fish feed and the factors that 
influence their decisions. Pricing is a major decision for any enterprise since that is one of the 
main drivers of revenue. However, pricing should not be made in isolation from the 
consumers since they may not patronise the product at a particular price. The study into the 
mean price that farmers are willing to pay is therefore relevant since it will enable 
entrepreneurs to fix prices that are acceptable to the market. The Tobit model used to analyse 
 
 
7 
 
the WTP revealed the factors that influence farmers’ decision to pay this price and this can be 
incorporated into promotional and marketing strategies.  
 
The results from the study on gains made from substitution will inform farmers as to which 
feed to use in order to reduce cost and maximise profit. Unfortunately, the profit from fish 
farming in Ghana is low due to increasing cost and decreasing revenue. Due to this, 
aquaculture in the country has not met expectations for expansion. The results of the study 
will allow farmers to switch to a feed which will increase the gains made from fish farming 
and increase their profit. These results will also boost investor confidence in the fact that 
there will be demand for the product since there is gain in substituting with existing feed.  
 
Finally, there is little available literature on the financial feasibility of BSFL meal and BSFL-
based fish feed production and farmers perception of the feed. The results of the study will 
contribute towards existing literature on BSFL meal and feed production and farmers 
perceived benefit and risks of the product. 
 
1.5 Organisation of Thesis Report 
The study is divided into five parts. The first Chapter is the introduction of the study, a 
review of existing literature relevant to the study forms the second chapter. The third chapter 
explains the methodology used to achieve the objectives of the study and the forth chapter 
shows the results and a discussion of the results. Finally, the fifth chapter summarises and 
provides conclusions and recommendations arrived at by the study.  
 
 
 
 
 
8 
 
CHAPTER TWO 
LITERATURE REVIEW 
Introduction 
This chapter presents a review of existing literature relevant to the study. The review covers 
the state of aquaculture in Ghana, current fish feed produced and the various ingredients of 
fish feed. It is followed by a review of BSF and the technique for its production and harvest 
into valuable feedstuff. The impact of BSFL production on the socioeconomic, health and 
environment is also reviewed. The review further looks at empirical results on the financial 
feasibility of BSFL production and empirical results on the perception and knowledge of 
consumers towards the use of insects in feed. Finally, the various analysis; feasibility, 
perception, WTP and partial budget analysis are reviewed.   
 
2.1 State of Aquaculture in Ghana 
2.1.1 Overview  
Aquaculture contribution to GDP in Ghana has not been separated from total fisheries; 
however, total fisheries contributes 1.1% towards total GDP (GSS, 2015). As at 2016, 
aquaculture contributed 11.3% towards total fisheries production (MoFAD, 2016). The main 
type of fish cultured is tilapia and catfish with the majority of culture fish either in ponds or 
in cages (Rurangwa et al., 2015). Cage farmers contribute more than 80% towards total 
production (Rurangwa et al., 2015). The commercial operators do not make their own feed 
but rather buy feed from fish feed companies. In recent years, as seen in Figure 2.1, 
aquaculture production has increased steadily. The sector is constrained by subsistence 
farming, lack of inputs and high cost of commercial feed production. 
 
 
 
9 
 
60000
50000
40000
30000
20000
10000
0
2010 2011 2012 2013 2014 2015 2016
Year
 
Figure 2.1: Growth trend of aquaculture production (2010-2016) 
Source: MoFAD (2016) 
 
2.1.2 Fish feed production 
Development and manufacture of fish feed play a vital role in aquaculture growth and 
expansion. According to EL-Sayed (2013), fish feed production is one of the least developed 
sectors in aquaculture in developing countries including Ghana. Ghana currently has locally 
produced and imported feed on the market. There is no readily available statistics on the fish 
feed produced in the country though currently. Some farmers in Ghana rely on imported feed 
from developed countries as the main source of feed (Rurangwa et al., 2015). This is because 
the country’s production of fish feed is not enough to meet demand. The main companies 
selling on the market are Raanan Feed (local manufacture), Agricare feed (local 
manufacturer), Multi Feed, Coppens Feed and Cargill Feed. Raanan feed is the only company 
that manufactures the feed in the country on a large scale using some local ingredients, the 
rest of the feed is currently imported (Rurangwa et al., 2015). 
 
 
 
 
 
 
Aquaculture Produciotn
10 
 
Fish feed ingredients 
The ingredients used to prepare fish feed for the aquaculture production can be categorised 
into three: animal nutrient source, plant nutrient source and microbial nutrient source (Tacon 
et al., 2012). 
Animal Nutrient sources 
 The main animal protein that is used in making of feed is fish meals and oils and 
zooplankton meals and oils (FAO, 2012). Fishmeal and oils have been preferred by 
commercial operators due to its high level of protein, fats and oils and omega three. Volumes 
of fishmeal has originated from fisheries specifically from capture fisheries and aquaculture. 
According to Tacon et al. (2012), although some zooplankton have the potential to be used as 
feed, there is only a small number of commercial operators producing feed with zooplankton. 
Plant nutrient sources 
The plants used in making compound fish feed is cereals, including by-products of cereals in 
the form of meals and oil, pulses and protein concentrate meals. When plants are used for 
feed it should meet a particular nutritional characteristics including low levels of fibre, starch 
and anti-nutritional compounds. It must be high in protein, be palatable, easy to digest and 
contain amino acid (Lim et al., 2008). 
Microbial Ingredient sources 
This type of feed is feed made from sources such as algae, fungi and yeast (Tacon et al., 
2011). However, these sources are not available in commercial quantities except the yeast 
derived feed (FAO, 2012). These cost less in production than the aforementioned nutritional 
sources. 
 
 
 
11 
 
2.2 Rearing of Insects as Fish Feed 
Rearing of insects for various purposes has been with us for a while dating back 7000 years 
(Rumpold & Schulter, 2013). For example, the rearing of bees for honey and of silk worm for 
silk, and rearing of other insects for medicinal purposes has always been with us. Researchers 
are currently experimenting on insects as animal feed. A review of literature reveals that 
insects historically have been used to feed fish and farm animals. Research has revealed that 
silkworm pupae is important in the diet of carp in China (Hickling, 1962). Heidinger (1971), 
reported that aerial insects attracted to light traps increased bluegill production significantly. 
In recent times more research has been concentrated on BSFL as fish feed. Research showed 
successful feeding trial in rainbow trout (Stamer et al., 2014; St-Hilaire et al., 2007).  
 
2.2.1 The Black Soldier Fly (BSF) 
The black soldier fly is a non pest fly usually found in the tropics. It is originally a native of 
temperate zones in America but has currently moved to other temperate regions around the 
world including Africa (Leclercq, 1997). The black soldier fly is a wasp like sleek looking 
fly, however, unlike wasp they have two wings and do not have a stinger (Diclaro & 
Kaufman, 2009). Adult larvae vary in colour ranging from black, metallic blue, green or 
purple to brightly collared black and yellow patterns (Drees, 1998). It is 15-20 mm long 
(Sheppard et al., 2002). They have antennae that are elongated with three segments, and legs 
have a white coloration near the end of each leg (Diclaro& Kaufman, 2009). The adults are 
rather lethargic and poor flyers. They engage in mating and females oviposit in preferably 
cracks and crevices near feed source (Diclaro and Kaufman, 2009). When the egg is hatched, 
the larvae goes through various stages before it reaches maturity state. According to Hardouin 
and Mahoux (2003), the larvae may mature in two weeks under ideal conditions but may take 
up to 4 months depending on feed and temperature (Makkar et al., 2014). The larvae in their 
 
 
12 
 
last larval stage can reach up to 27mm in length and 6 mm in width and weigh up to an 
average of 220mm. The larvae contains up to 43% to 35% protein and fat respectively 
(Sheppard et al., 1994). This can serve as a nutritional replacement for fishmeal in the diet of 
fish. The larvae is dull whitish in colour, and possess a small head that contains its 
mouthpiece for feeding. At this state the larvae feeds rapidly and stores up the fat for use as 
an adult since they do not feed when they mature (Diclaro & Kaufman, 2009). At the 
prepupal stage, the larvae move away from the feed source in search of dry place (Sheppard 
et al., 1994).  Upon maturity, the adult fly can only live up to a few days or weeks. 
 
2.2.2  Socio economic, health and environmental impact (risk or benefit) of 
using BSFL in fish feed (production via waste conversion) 
The use of BSFL in fish feed gives rise to some social, economic, health and environmental 
impact (risk or benefit) such as:  
Diseases  
BSF is a non pest insect that is not attracted to human habitat. The BSF does not have mouths 
to feed and rely on the fat stored from the larval stage. As a result, unlike houseflies, the BSF 
cannot feed on unsanitary waste and transfer it to food therefore they are not a vector of 
diseases (Leclercq, 1997). On the contrary, the larvae of BSF is known to reduce the 
oviposition of housefly (a disease vector especially in developing countries where open 
defecation and inappropriate sanitation account for dangerous sources of pathogens) thereby 
serving as a disease control (Graczyk et al., 2001). According to Erickson et al. (2004), BSFL 
is able to inactivate E. coli 0157:H7 in chicken manure. BSFL is also proved to be able to 
inactivate the zoonotic bacteria such as Salmonella spp. (Lalander et al., 2013). This means 
that risk of transmission of diseases from animal to animal or animal to human is reduced.  
 
 
 
 
13 
 
Nutritional level 
An analysis of dried Black Soldier Fly larvae (ESR International, 2008) showed that it 
contains: 42.1% crude protein, 34.8% ether extract (lipids), 14.6% ash,  7.9% moisture, 7.0% 
crude fibre, 5.0% calcium, 1.5% phosphorus. In conjunction with these findings, various 
research has been done that essentially validate these statistics. For example according to St‐
Hilaire et al. (2007), the average prepupae is composed of 30% lipid and omega 3 acid. 
Waste management (BSF Bioconversion) 
BSFL are voracious feeders and are able to reduce waste by up to 50% and more and turning 
it into a rich biomass of 35% and 45% fats and protein respectively (Sheppard, 1994). This is 
known as the bioconversion technology of BSFL. Since the 1990s, BSFL has been suggested 
as a way to dispose organic waste by allowing BSFL to feed on it, converting it into biomass 
and the resultant waste used as fertilizer (Diener et al., 2011; van Huis et al., 2013). As a 
waste reduction scheme, a BSFL plant can serve as a collection place for disposal of organic 
waste thereby reducing the negative impact on the environment (UN-HABITAT, 2010). 
Noxious smell  
Organic waste naturally produces noxious smell and bad odour. As a result, the presence of 
vile smell is usually associated with any production which involves the use of organic waste. 
However, the black soldier fly restrains bacterial growth and therefore reduces odour of waste 
(van Huis et al., 2013). This means that BSFL can be successfully reared on waste without 
fear of noxious smell disrupting the environment.  
Consumer demand 
In terms of demand, people who feel that the use of fishmeal causes overexploitation of water 
bodies may shift to the purchase of BSFL-based fish feed thereby creating a whole new 
demand (Tiu, 2012). The exploitation of water bodies has been of major concern to various 
citizens, government organisations and NGO’s. Various stakeholders are expected to demand 
 
 
14 
 
more of BSFL-based fish feed based on the contributions it will make to sustainable 
aquaculture. This may however not be true of all regions since some countries are more 
particular about environmental sustainability than others 
 
2.3 Empirical Results on the Technology for Production and Harvest of BSFL for 
Fish Feed 
The conversion of organic waste into insect proteins for feed using larvae of the BSF is an 
emerging and promising technology producing an appealing alternative to other protein 
sources in animal feedstuff such as soybean meal and fishmeal. The prepupae contain 40% 
crude protein and 30% fat and can easily be used as a valuable feedstuff in fish (St-Hilaire et 
al., 2007).  
  
Sheppard et al. (1994) conducted a research inoculating BSFL on hen manure. The results 
show that the manure accumulation was reduced by at least 50%”. The chicken manure could 
be converted to a “42% protein and 35% fat feedstuff”.  
 
Newton et al. (2005) conducted an experiment using the black soldier fly larvae to treat hen 
and swine manure. Their developed swine and layer hen systems could convert the manure 
into larval mass which contained of “40+% protein and 30+% fat” and also reduced the 
manure mass by about 50%. 
 
Barry (2004) successfully converted food waste from university cafeteria into valuable feed 
stuff. This was done by introducing BSFL into containers filled with organic waste from the 
cafeteria of three universities. 
  
 
 
15 
 
 
 Step 1 
Organic waste is piled in containers 
 
       
Step 2 
 
BSFL is inoculated on the waste 
       
BSFL is introduced to the waste Step 3 
       
BSFL feeds on waste and self harvests into containers 
  
 Step 4 
 BSFL biomass rich in fat and protein  
 
Step 5 
 
BSFL meal for fish feed industry 
 
Figure 2.2: Steps in Black Soldier Fly Larvae production 
Source: Adapted from Sheppard et al. (1994.) 
 
2.4 Financial Analysis 
2.4.1 Measures of project worth 
One of the main ways of accessing the feasibility of any investment project is a profitability 
analysis. Profitability analysis is used to ascertain the viability of a project when cost is 
matched against cost in what is known as capital budgeting. Cash budgeting involves the 
detail mapping of net cash flow a project is expected to make over a period of time. 
According to Gittinger (1982) the usual method used under cost benefit analysis is NPV, 
BCR, IRR and payback period. However, NPV is the best indicator amongst the four since 
the IRR and payback period are subject to various constraints. 
 
 
 
16 
 
Net Present Value (NPV) 
According to Gittinger (1982), the NPV is a major indicator of the financial feasibility of a 
project. A project’s NPV is computed by discounting cash flow statement by taking time into 
consideration using an appropriate discount rate. This discount rate in general depends on the 
projects perceived risk as defined by financial institutions for their borrowing rate. The 
interpretation for NPV is that if it is positive then the project is viable however if it is 
negative then the project should be rejected. An advantage of using NPV is that it allows easy 
comparison of different projects. 
Internal Rate of Return (IRR) 
IRR is the discount rate at which NPV is zero. IRR analysis is used to ascertain whether 
investments returns will be enough to justify that investment (Gitman, 2006). It provides a 
clear indication of whether an investment will be greater than the cost of capital.  IRR can be 
computed using the trial and error method where different discount rates are tried until NPV 
is equal to zero (Jolly & Clonts, 1993). In evaluating investment decisions the IRR can be 
used concurrently with NPV, however, for long term project NPV is the preferable method 
(Mbugua, 2007). This is because the IRR has some inherent weaknesses that make NPV a 
better option (Brigham et la., 1999; Gitman, 2006). For example, the IRR assumes that future 
cash flows are invested at the IRR though it may be above current rates. 
Benefit Cost Ratio (BCR) 
The benefit cost ratio is also an indicator of the profitability of an investment. BCR is 
computed by dividing the discounted net benefits over the project’s projected lifespan over 
the discounted cost (Gittinger, 1982). A ratio above one indicates viability of the project and 
a positive NPV whereas a ratio below one shows negative NPV a venture which is not 
financially viable.  
  
 
 
17 
 
2.4.2 Empirical studies on profitability analysis  
A myriad of literature has used cost benefit analysis to ascertain the profitability of an 
investment. These include:  
Offei et al. (2014) used cost benefit analysis to ascertain the financial feasibility of producing 
urine based fertilizer for vegetable farming in Accra, Ghana. The results showed a positive 
NPV of 8,147.79, BCR of 1.03 and an IRR of 22.65%. 
 
Rurangwa et al. (2015) on the development of aquaculture in Ghana: Analysis of the fish 
value chain and potential business cases. In a research conducted in Ghana, Rurangwa et al. 
(2015) looked at the production cost and benefits of a conventional feed production in Ghana 
at different levels of production. Investment made in fixed assets amounted to 4, 2 million 
USD. Gross margin used in the calculation was approximately 60% of the estimated actual 
margins of Raanan feed. At this gross margin, the calculations still yielded a large amount of 
profit before marketing, depreciation and interest cost. The results of the study is presented in 
the Table 2.1. 
 
Diener et al. (2009) on the topic are larvae of the black soldier fly a financially viable option 
for organic waste management in Costa Rica conducted an experiment in Costa Rica on the 
financial viability of BSFL production as a waste management technique which showed 
promising results. In the results, it was shown that an investment of USD 85,000 in a 
treatment plant (annual operating cost of 35,700) processing three tonnes of waste a day can 
yield larvae (dry weight) of 150kg a day. This larvae, could be sold on the market (at 
prevailing fishmeal price of USD1000/ tonnes) for USD 55000. A summary of results is 
shown in Table 2.2. 
  
 
 
18 
 
Table 2.1: Summary of financial feasibility results for conventional feed production 
in Ghana (Amount in USD) 
 Production  Sales Total Gross Total Profit before 
tonnes margin operational marketing, 
cost depreciation& 
Interest 
1 Shift 8,000 5,600,250 2,000,000 984,000 1,016,000 
2 shift 16,000 11,200,000 4,000,000 1,566,000 2,434,000 
3 shift 25,000 17,500,250 6,250,000 2,166,600 4,084,000 
4 shifts 32,000 22,400,250 8,000,000 2,683,200 5,317,000 
Source: Rurangwa et al. (2015) 
 
Table 2.2: Summary of financial feasibility results for BSFL meal production in 
Costa Rica (Amount in USD) 
Project worth measure Value (USD) 
Investment cost 85,000 
Operating Cost 35,700 
Revenue from sale of larvae 55,000 
Source: Diener et al. (2009) 
 
According to Agrawal et al. (2011) however, the results for BSFL meal production was less 
than satisfactory. The research was conducted on latrines as substrate for BSFL production 
(900 pit latrines emptied per year).  The results of the research showed that, production of 
BSFL meal  will yield a loss of USD 37,888 and a negative return on initial capital (IRR) of -
227% . This was after an upfront investment cost of USD 16,680. A summary of results is 
shown in Table 2.3. 
 
Table 2.3: Summary of financial feasibility results for BSFL meal production in 
USA (Amount in USD) 
Project wealth measure Value (USD) 
Annual Profitability (37,888) 
Upfront Capital Investment 16,680 
Return on capital investment (227%) 
Total time to break even (no breakeven) 
Source:  Adopted from Agrawal et al. (2011) 
 
  
 
 
19 
 
2.4.3  Partial Budget Analysis 
2.4.4 Overview of partial budgeting 
Decisions on change in a farming system is very important since this may lead to increasing 
or declining profits. Partial budgeting is therefore needed to make these decisions (Alimi and 
Alofe, 1992). Partial budgeting is a technique that is employed to assess the costs and 
benefits resulting from changes on the farm system (Kay et al., 2008). This technique 
specifically focuses on the implications of the intended change in a business operation by 
comparing the benefits and costs resulting from implementing the alternative with respect to 
the current practice (Soha, 2014).  
 
According to Kay et al. (2008), in partial budgeting, the main factors looked at are: 
1. The new costs arising when insect based feed is used 
2. The new revenue arising when insect based feed is used 
3. The cost incurred from using conventional feed 
4. The revenue gained from using conventional feed 
The limitation of partial budget is that only two alternatives can be compared at a particular 
time that is the current situation and the new alternative.  
 
2.4.5 Empirical studies on partial budget analysis 
Various researchers have used partial budget analysis to estimate changes in enterprise. A 
few include: 
 
Offei et al. (2014) using partial budget analysis ascertained that a cabbage farmer in Accra 
would make savings of GHS 24.59 when he uses sanitized urine as a substitute for chemical 
fertilizer. 
 
 
20 
 
 
Soha (2014) used partial budget analysis for his study to estimate the effect on net benefit of 
changing from one level of Nitrogen-fertilizer application to another on sorghum production, 
The experiments consist of three treatments of 100kg N per fedden (T1), 200kg N per fedden 
(T2) and 300 kg N per fedden (T3). It was found out that substituting treatment 1 with 
treatment 2 has a higher net benefit of 9.61 as compared to substituting T2 with T3 with a net 
benefit of 0.72 where it was concluded that treatment 2 was most preferred.  
 
2.5 Perception Analysis 
2.5.1 Likert scale   
A consumer’s decision to buy a product depends on the perceived risks and benefits 
associated with the product. To ascertain this perception on various issues, marketing 
researchers have used rating scales (Likert, 1932). Respondents are asked to rate their 
opinions or perceptions on scales which are all positive ranching from lowest to highest or on 
scales that range from negative to positive. A Likert type scale questionnaire is used to assess 
the degree to which respondents agree or disagree with statements on benefits and risks of 
using BSFL-based fish feed. Likert scale is preferred because it can be built into an index and 
there is little loss in accuracy when interpreting ordinal data is treated as interval (Mcgiven, 
2006). To use the Likert scale, multiple statements are used in this type of questionnaire to 
solicit various answers from respondents. Multiple statements are used because it is easier to 
measure perception with multiple statements than with a single statement (Mcgiven, 2006).  
 
 
 
 
 
 
21 
 
2.5.2 Empirical studies on perception analysis 
Researchers have successfully used Likert scale type questionnaires to determine perceptions 
and opinions. Some of these include:  
 
Verbeke et al. (2015) used a five point Likert scale to ascertain the perceived benefits and 
risks of using BSFL in fish feed.  In the experiment, 14 benefit and risk statements were 
scored by farmers using a Likert scale ranging from (1) strongly agree to (5) strongly 
disagree. The research revealed composite overall mean scores of 3.71 and 3.04 for benefit 
and risk statements respectively. A paired sample and t- test (t=0.86; P<0.001) revealed a 
significant difference between the two scores. Researchers therefore concluded that farmers 
have a significantly higher perception of the benefit of insects in feed than they do of the risk. 
A summary of results is shown in Table 2.4. 
 
Table 2.4: Summary results of perception analysis 
 Benefit statement  Risk Statement  
Overall mean scores 3.71 3.04 
T-test  0.36  
P-value 0.001 
Source: Adapted from verbeke et al. (2015) 
 
In 3013, Ghosh and Hasan determined the attitude of Bangladesh farmers towards sustainable 
agriculture using 5-point Likert type scale for the analysis where 90 respondents were 
sampled in two villages. The result showed that majority of the respondents ‘agreed’ to the 
use of compost and green manure in their field as a substitute to chemical fertilizer.  
 
According to Assis and Mohd Ismail (2011), used a 5-point likert scale to determine the 
attitude of farmers towards organic farming. The likert scale comprised of six positive 
 
 
22 
 
statements and six negative statements. The study concluded that majority of the respondents 
have positive attitudes towards organic farming. 
 
2.6 Willingness to Pay Analysis 
2.6.1 Overview 
Estimation of a consumer’s willingness to pay for a product is a technique used to determine 
the amount consumers would pay for a product without an existing market (non-market 
goods) or a good without a well-established retail outlet (Lawi, 2014). The notion of 
willingness to pay is the largest amount an individual is willing to pay for an improvement in 
product or service. There are many approaches employed to ascertain a consumer’s 
willingness to pay for a product. This is classified into stated and revealed preferences.  The 
revealed preference is determined by watching the individuals purchasing price and 
expenditure habits to gain particular goods and service. This means that the approach to 
employ to ascertain WTP is on the basis of consumer behaviour (Samuelson, 1948). Unlike 
the revealed preferences, the stated preference requires respondents to give values to various 
goods and services rather than observing their purchasing habits (King et al., 2000). To 
determine this, methods such as contingent valuation, travel cost, hedonic pricing, 
experimental auctions and conjoint analysis can be used. The revealed preference uses 
information from experiments and market data whereas the stated preference uses direct and 
indirect market surveys. 
 
Contingent Valuation Method (CVM)  
This is a method usually used by researchers for goods that are not currently on the market. 
This is a survey approach where market simulation is done for a non-market good. Prices are 
obtained for the good based on hypothetical description given during the survey. Generally, 
 
 
23 
 
Cummings et al. (1986) purported that when contingent valuation method is used for goods 
that respondents are at least familiar with it can yield fairly accurate results. 
 
The CVM uses surveys to obtain what respondents will be willing to pay for a project. This is 
a way of determining consumer preference for the product. CVM circumvents the absence of 
a real market by providing respondents with a hypothetical one in which they can weigh the 
trade-offs of the product and make purchasing and pricing decisions. It involves asking 
individuals to value increments or decrements in a good or service by attaching WTP for the 
commodity. In the hypothetical market, these parameters are defined: the good under interest, 
the status quo, the level of provision and the increment or decrement there in, the institutional 
structure under which the good will be provided. To elicit highly accurate responses, CVM is 
structured in a well-defined way in order to solicit a choice of purchase contingent on the 
occurrence of the hypothised situation (Cummings et al., 1986) 
 
The Contingent valuations methods have been identified for soliciting responses from 
respondents. These include; open ended: In this method, respondents are not given a starting 
price to begin with. However, the disadvantage of this is that it leads to a lot of outliers (large 
amounts or very low amounts), bidding game: the bidding game offers respondents a series of 
bids to choose from. When acceptance is made or a respondent refuses to pay for a particular 
bid, the games closes and the WTP is elicited,  payment card: the payment card presents 
respondents with a visual aid containing large numbers of monetary amounts. Respondents 
are tasked with ticking sums they are willing to pay and crossing out sums they are unwilling 
to pay, Dichotomous or referendum format: this format can either be single bounded, doubled 
bounded or multiply bounded. 
 
 
 
24 
 
Contingent valuation method is a method well used by researches because there is little 
information on actual consumer behaviour, its popularity is not due to the accuracy of the 
method in determining willingness to pay. One problem with the demand is that demand and 
price can be overstated since the survey is hypothetical. Respondents may be unwilling to pay 
in real life situations of the product. This can lead to distortions in demand projections made 
by various enterprises. This arises when the method is not administered well thereby giving 
consumers a false idea of the product. According to Whittington (1998), contingent valuation 
method when used should include adjustments to fit the conditions and cultural differences of 
various regions. 
 
2.6.2 Empirical studies on willingness to pay (WTP) analysis 
There is a myriad of literatures on assessing consumer willingness to pay using the contingent 
valuation method. 
 
Asenso-Okyere et al., (1999) discussed willingness to pay for health insurance in a 
developing economy. The pilot study of the informal sector of Ghana used contingent 
valuation (Asenso-Okyere et al., 1997). Using the bidding game, the research solicited the 
maximum amount farmers were willing to pay.\ The results showed that over 90% of the 
respondents agreed to participate in the scheme and up to 63.6% of the respondents were 
willing to pay a premium of ¢5000 or $3.03 a month for a household of five persons.  
 
Campiche et al. (2004) used CVM in their research to examine the impact of consumer 
characteristics on willingness to pay for natural beef in the southern plains of United States of 
America. They used the Dichotomous Choice Contingent Valuation Method (DC-CVM) to 
solicit survey responses to choice between amount paid for regular and natural beef.  
 
 
25 
 
Other recent applications of CVM to ascertain consumers’ willingness to pay include: 
Research to investigate the willingness to pay for Farm Insurance by Smallholder Cocoa 
Farmers in Ghana (Abbeam et al., 2014).Owusu and Anifori (2013) used CVM to assess 
consumers’ willingness to pay premium for organic fruits and vegetables in Ghana.  
 
Factors affecting willingness to pay  
Most of willingness to pay studies generally include socio-demographic characteristics such 
as gender, income level, age, household, marital status, access to extension, membership of 
FBO, awareness of the product, experience and education as factors influencing WTP. These 
socio-demographic characteristics were found to be significant in explaining the decision to 
buy organic foods, pay for health insurance amongst others.  
 
For example, Asenso-Okyere et al. (1997) using an ordered probit model, the level of 
insurance premiums households were willing to pay were found to be influenced by 
dependency ratio, income or whether a household has difficulty in paying for health care or 
not, sex, health care expenditures and education.  
 
Campiche et al. (2004) using the multinomial model revealed that household income and 
location were significant determinants of willingness to pay for natural beef, However, the 
research found age, education, gender, and educational level were not significant.  
 
Engel (2008), Carried out a survey of the determinants of consumer willingness to pay for 
organic foods in South Africa, empirical results indicates that age, marital status, and level of 
education are the socio-demographic factors that significantly explain consumers’ willingness 
to pay a premium for organic food.   
 
 
26 
 
 
A research conducted by Abbeam et al. (2014) on cocoa farmers’ willingness to pay for farm 
insurance using independent double-hurdle model determined that awareness of insurance, 
marital status, number of years in cocoa farming, educational level, age of farmer and income 
level significantly affected WTP. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
27 
 
CHAPTER THREE 
METHODOLOGY 
Introduction 
This chapter describes the various methods that were used to achieve the objectives of this 
study. It begins with the conceptual and theoretical framework underlying the study. The 
chapter further explains the various methods that were used to achieve the objectives of the 
study, the method of data collection, and provides a brief description of the study area. 
 
3.1  Conceptual Framework 
For every investment the main objective is profitability. The questions that the entrepreneur 
asks when introducing a novel product to the market is; if a product is manufactured will 
there be demand for it and at what price? If there is, will it be profitable to produce taking 
into account revenue and costs of inputs needed for production? This study as shown in 
Figure 3.1 seeks to review a complete business model for analysis to serve as a road map for 
entrepreneurs wanting to manufacture BSFL meal and BSFL feed. It looks at the production 
of BSFL meal and BSFL based fish feed as the main output, and the various factors that drive 
the production of these products. 
 
To invest in a new venture, as suggested by the neoclassical theory the investor must consider 
both profitability and demand. Profitability entails considering market interest rates and price 
of capital, whereas demand looks at the market for the product. As shown in Figure 3.1, 
profitability is assessed by comparing revenue and cost of production to determine whether 
the difference is negative of positive. This will determine whether to invest or not to invest in 
a venture. On the other hand, profit cannot be looked at in isolation, to drive production of 
BSFL based fish feed, an entrepreneur must look at the demand for the feed by farmers. In 
 
 
28 
 
this case a consumer’s decision to buy or willingness to pay for the BSFL based fish feed is 
assumed to be influenced by the price of the product, demographic, socio economic factors, 
perception and the gains and losses of its conventional substitute (Campiche et al., 2004). 
This is consistent with the factors affecting demand.Thus, an investment decision to produce 
BSFL is determined by profitability (taking into revenue and cost) and demand for the 
product.  Figure 3.1, shows how these factors work together to drive the industrial scale 
production of BSFL meal and BSFL based fish feed. 
 
 
 Industrial scale BSFL 
meal and BSFL feed 
 production 
 
 Perception of 
BSFL product 
 risks and Profitability 
benefits of Production 
       
 Demographic,  
Socio-Economic 
 Characteristics:  
age, household Farmer Revenue 
 size, education, willingness to and cost of 
Awareness, pay for BSFL production 
 
Access to credit, feed 
extension, FBO  
 membership 
 
 
Farmer gain or 
loss from 
 substitution 
 
Figure 3.1: Conceptual framework illustrating investment decisions of feed 
entrepreneurs and fish farmers 
Source:  Adapted from Neupane et al. (2002) 
 
 
29 
 
3.2 Theories Underlying the Study 
Investment theory 
Investment decisions are important for micro economic and macro-economic growth of a 
country. An investment that pays off is able to ripple off to the entire economy in terms of job 
creation and GDP contribution. As a result investment decisions are taking after meticulous 
consideration of all factors and different theories have been propounded to explain 
investment decisions and behavior. 
 
The neoclassical flexible accelerator model theory is the main theory underlying this study. It 
takes into consideration profitability, depreciation, and interest rate while also considering the 
changes in aggregate demand. This theory was propounded by Jorgenson (1967) and it 
stipulates that an investment behaviour is explained by profitability, interest rate on the 
market and the changes in aggregate demand for the product. This means that for an 
investment to be made in any venture all these factors put together must be considered.   
 
In terms of profitability as an investment decision, costs and benefits must be identified and 
compared. Gittinger, (1982), stipulates that for costs and benefits to be compared for projects 
with varying costs and benefit streams, these costs and benefits must be discounted. This 
study uses four main discounted measures such as Net Present Value (NPV), Benefit Cost 
Ratio (BCR), Internal Rate of Return (IRR) and Payback period to ascertain the investment 
viability of the production of black soldier fly larvae meal production and black soldier fly 
larvae based fish feed production.   
 
In assessing aggregate demand for a product, studies show that consumers make their own 
decisions to balance the marginal utility and marginal price of products. From the utility 
 
 
30 
 
theory, farmers derive satisfaction from the use of aquaculture inputs they use in fish farming 
to maximize output. This studies uses perception studies and willingness to pay studies to 
access the demand of BSFL based fish feed by fish farmers. 
 
3.3  Method of Data Analysis 
3.3.1 Determining the level of profitability of BSFL-based feed production 
 Scenario A: Production of BSFL meal 
BSFL meal is the larvae of the black soldier fly that has been grounded into powdered form. 
This serves as animal protein ingredient in the production of formulated fish feed. 
Costs and benefits analysis: 
One way of evaluating the viability of an enterprise is the use of cost benefit analysis 
(Gittinger, 1992). This usually involves matching the Cost: start-up cost and other operational 
cost, against revenue streams that accrue over a period of time, usually assumed to be the end 
of the production period. Various researchers have used cost benefit analysis to ascertain 
profitability Sogbesan & Ekundayo (2014) and Offei et al (2014). 
 
Preliminary results from experiment conducted by at BNARI sponsored by FiBL which 
started in 2014 and ended in 2016 helped to determine some of the cost of production. The 
experiment was conducted on the production of BSFL meal via the use of organic waste. 
Summary of logistics used for experiment was obtained from science researchers’ station 
experiment and cost extrapolations were made from it. Also, a summary of experimental 
results and was obtained, from these results, large scale projections were made.\ 
 
 
 
 
 
31 
 
The total cost for the production of the insect based feed was identified as follows: 
Fixed costs: Fixed costs are the costs that are paid regardless of production and usually 
accrue during the start of the project (Jolly and Clonts, 1993). In the short run, fixed cost 
remains constant regardless of variations in production.  
TFC= FC1 + CF2 +…….CF16                 (3.1) 
Where         
CF1 = Land           
CF2 = Building    
CF3 = Adult Cage: For Reproduction amongst adult larvae 
CF4 = Larval Container: For housing larvae 
CF5 = Drums: For storing organic waste for inoculation of larvae      
CF6 = Drums stand: For holding drums elevated above ground 
CF7 = Collection bins: For collecting of self-harvested larvae.   
CF8= PVC pipe: For connecting drums to collection bins  
CF9 = Industrial oven: For drying and killing of larvae 
CF10 = Grinder: For milling the BSFL into powder (BSFL meal)  
CF11 = Shovel: For turning organic waste  
CF12= Sieve, for sieving out waste from larvae after washing    
CF13= Weighing Scale: Mmeasuring of the weight of organic waste 
CF14= Gloves: For protection of hands while handling waste 
CF15= Nose mask: For protection against bad odour from waste  
CF16=Trucks: For moving of waste to project site  
  
Variable cost: Variable costs are the cost that change as the level of production changes 
TVCs= VC1 + VC2 +……….VC9                    (3.2) 
 
 
32 
 
Where  
VC1 = Layer feed (Larval Diet): For feeding larvae during the first 10 days after hatching 
VC2 = Organic Waste: Feed for larvae after 10 days 
VC3 = Electricity: For powering machinery and equipment and other general uses 
VC4= Water: For washing of larvae after self-harvesting and other general uses 
VC5 = Sacks: Fish feed is usually packed into 50kg sacks. The hessian bag, made from 
woven jute, is much used in tropical and sub-tropical countries. The relatively open fabric 
enables water vapour and heat to escape readily from the meal. 
VC6 = Labour: Human resource needed for operations which changes according to 
production level. (Minimum wage will be used for computation)  
VC7 = Fuel: For moving of organic waste to manufacturing site 
VC8 = Water: For washing of larvae before processing and other general use 
VC9 = Repairs and maintenance 
 
The assumptions for computations for cost and revenue were based on these premises: 
1. An average fresh larvae weighs 2 grams 
2. 300 larvae is fed on 10g of larvae meal daily for 10 days before inoculation on waste 
3. When dried up the weight of the larvae reduces to one-third of its original fresh weight 
4. The larvae has a mortality rate which is negligible and therefore not taken into account. 
5. The larval container can store up to 10,000 number of larvae 
6. The Pre-consumer waste is free 
7. 25 tonnes of waste is needed to produce 3 tonnes of larvae meal a day 
8. A single drum can store up to 30 kilos of waste 
9. Production is started at full capacity of 1000 tonnes and therefore remains constant over 
the 20 year period (20 years  based on asset with the longest lifespan) 
 
 
33 
 
10. The adult black soldier fly does not feed and therefore no funds will be spent on meal for 
adult. 
11. Casual workers are paid at minimum wage GHS8.80 per day 
 
Projections were made based on these premises and a cash flow statement was developed for 
20 years based on the assets with the longest useful life. Assets with shorter life were 
replaced within the 20 years of projected cash flow and subtracted from that year’s revenue.  
 
Stages of profitability analysis: 
BSFL meal is a relatively new product on the market. From Table 3.1, in computing the 
Revenue for the 1st price scenario, the market selling price of fishmeal was used. However, in 
order to encourage the adoption of the BSFL, and garner demand for the product, it should 
have an introductory price that is relatively lower than that of fishmeal (Agrawal et al, 2011). 
In addition, this study sought to provide a less costly option to fishmeal. As a result, from 
Table 3.1, a 2nd, 3rd and 4th price scenarios analysis was done on the profitability of the 
business by decreasing the market price by 20%, 15% and 10% respectively to ascertain the 
price below conventional fishmeal at which all the profitability indicators such as BCR, IRR 
and NPV are below 
Table 3.1: Summary of profitability analysis performed on price scenarios 
Analysis Description 
1 Using market prices of fishmeal(GHS117 per 20kg bag) 
2 Using 20% below market price of fishmeal (GHS94 per 20kg bag)  
3 Using 15% below market price  of fishmeal(GHS100 per 20kg bag) 
4 Using 10% below market price of fishmeal (GHS105 per 20kg bag) 
Source: Authors own computation (2017) 
 
 
 
 
 
34 
 
Sensitivity analysis: 
Sensitivity analysis was performed on analysis 4 (Using 10% below market price of fishmeal) 
since it was found to be the most viable projection below market price of fishmeal at which 
the investments profitability indicators such as BCR, NPV and IRR are still acceptable. The 
sensitivity analysis was performed by increasing cost by 10% and decreasing output by 10%. 
Since this is a new project, it is important to increase cost to make up for any contingencies 
that may arise. In addition, output was reduced because insects, like other animal production 
may not be able to meet the predicted output due to for example handling, diseases and 
mortality. 
 
Scenario B: Production of BSFL-based fish feed 
The BSFL-based fish feed is feed that has been formulated with BSFL meal as an animal 
protein ingredient. It is formulated by using BSFL meal and other ingredients such as wheat 
bran, soybean, fishmeal, common salt and maize. The research used BSFL meal as a 75% 
replacement of fishmeal as animal protein. 
 
Costs and benefits analysis: 
A research conducted at WRI provided some of the cost projected for the production of 
BSFL-based fish feed. This research converted the BSFL meal into BSFL-based fish feed by 
adding various ingredients such as fishmeal, wheat bran, soybean, maize, cassava flour and 
common salt. The experiment consisted of replacing fishmeal in fish feed with BSFL meal by 
75%, 50% and 25%. This BSFL feed projection is however based on 75% replacement of 
fishmeal by BSFL meal since the research showed that it had the highest profit index.  
 
 
 
 
35 
 
Total fixed cost was defined as: 
TFC= FC1+ FC2+……….FC10                   (3.3) 
Where: 
FC1 = Land           
FC2 = Building    
FC3 = Extruder: For turning the feed into a state that can float 
FC5 = Mixer: For mixing of various ingredients used for the meal      
FC6 = Grinder: For milling the various ingredients 
FC7 = Weighing Scale: For measuring of the weight of organic waste 
FC8 = Trucks: For moving of waste to project site 
FC9 = Conveyor belt 
FC10 = Pelleter 
 
The total variable cost is defined as:  
TVCs= VC1 + VC2 +……….VC9               (3.4) 
Where: 
VC1 = other ingredients for formulating the compound feed 
VC2 = BSFL meal 
VC3 = Electricity: For powering machinery and equipment and other general uses 
VC4= Water: For washing of larvae after self-harvesting and other general uses 
VC5 = Sacks: Fish feed is usually packed into 50kg sacks. The hessian bag, made from 
woven jute, is much used in tropical and sub-tropical countries. The relatively open fabric 
enables water vapour and heat to escape readily from the meal. 
VC6 = Labour: Human resource needed for operations which changes according to 
production level. (Minimum wage will be used for computation) 
 
 
36 
 
VC7 = Fuel: For moving of organic waste to manufacturing site 
VC8 = Water 
VC9 = Repairs and maintenance 
 
The assumptions for computations for cost and revenue were based on these premises: 
1. The feed is produced at 60% animal protein, 75% of this is BSFL meal and 25% is 
fishmeal. 
2. Production is started at full capacity of 1000 tonnes and therefore remains constant over 
the 20 year period 
3. Casual workers are paid at minimum wage GHS8.80 per day 
4. The BSFL meal used for production is purchased by the entrepreneur 
 
Stages of profitability analysis: 
BSFL feed is a relatively new product on the market. In the 1st analysis, revenue was 
computed by using the market price of conventional starter fish feed. However, in 2nd 
analysis, the average amount farmers are willing to pay for BSFL-based fish feed was also 
used as selling price to compute revenue for the cost benefit analysis. A summary of analysis 
performed is shown in Table 3.2. 
Table 3.2: Summary of analysis performed 
Price scenario Description 
1 Using market price (GHS200 per 20kg bag) 
2 Using MWTP amount (7%) below market price  (GHS186 per 20kg bag)  
Source: Authors secondary data (2017) 
 
 
 
 
 
 
37 
 
Sensitivity analysis: 
Sensitivity analysis was performed on analysis 2 (Using MWTP amount, 7% below market 
price) since that is the price at which farmers are willing to pay for the BSFL-based feed. The 
sensitivity analysis was performed by increasing cost by 10% and decreasing output by 10%. 
Since this is a new project, it is important to increase cost to make up for any contingencies 
that may arise. In addition, output was reduced because input supply (for example BSFL 
meal) for the feed production may not meet expectations and production may fall. 
 
The discounted measures of project worth for the two scenarios 
To evaluate the profitability or “Net Benefit” for investments which are expected to last for 
more than a year, discounted measures of project worth such as Net Present Value (NPV), 
Benefit Cost Ratio (BCR) and Internal Rate Return (IRR) are used. This is particularly 
important because of the premise that the value of a cedi today is worth more than the value 
of a cedi tomorrow. Discounting essentially is the technique through which future benefits 
and costs of BSFL meal and BSFL-based fish feed are brought down to their present value 
(Gittinger, 1982). The cash flow generated from the benefits and costs were discounted to 
their present value. A discount rate of 31% which is the average of Ghana Commercial Bank 
(31%) and Agricultural Development bank (31%) was used. The following indicators were 
computed from the discounted value: 
 
 
The Net Present Value (NPV) 
In calculating the Net Present Value, the sum of the discounted future streams of benefit were 
matched against the sum of discounted future streams of cost. The NPV is the difference 
between the two discounted figures. A positive NPV shows viability and profitability of the 
 
 
38 
 
project whereas a negative NPV indicates that the project is not viable. However, it is 
important to note that these computations are only as reliable as the data that is used in 
computing them. The researcher must therefore ensure that the data worked with is accurate.  
 
The NPV equation is specified as: 
(𝐵  𝐶 )
NPV=  ∑𝑡=𝑛
𝑡 − 𝑡
𝑡=1                    (3.5) (1+𝑟)𝑡
Where NPV= Net present value, t= number of years, r= discount rate, B=benefits, C=Cost 
The definitions for the alphabets is constant for the rest of the equations of the study. The 
decision rule for NPV is that the NPV should be greater than 1. 
 
Benefit-Cost Ratio (BCR) 
To compute the BCR, the discounted benefits were divided by the discounted cost in a period 
of time t. The benefit cost ratio is important because it shows the cedi made on every dollar 
invested in the venture. The decision rule for BCR is that the ratio of benefit to cost should be 
greater than 1. 
 
The BCR equation is specified as: 
∑𝑡=𝑛   𝐵 / (1+𝑟)𝑡𝑡=1 𝑡 
BCR=   
∑𝑡=𝑛
                (3.6) 
𝑡=1   𝐶 / (1+𝑟)
𝑡
𝑡 
 
 
Internal Rate of Return (IRR) 
To compute the IRR, the rate was found at which NPV is equal to zero. The IRR of a project 
should be higher than the interest rate on loan or return on capital to make the project 
desirable for investment. This means that the greater the IRR the better it is for the investor. It 
 
 
39 
 
can be computed with the trial and Error method by finding the rate at which NPV is equal to 
zero (Jolly and Clonts, 1993). 
 
The IRR model is specified as: 
(𝐵
∑𝑡=𝑛 𝑡 −
 𝐶𝑡)
IRR= 𝑡=1   𝑡 = 0                           (3.7) (1+𝑟)
 
3.3.2 Determining the perception of farmers toward BSFL-based feed 
As shown in Table 3.3, a Likert scale was used to collect information from respondents on 10 
different perception statements. The statements were grouped into five statements for 
perceived risk and five statement for perceived benefit. These perception statements were 
adapted from similar work done by Verbeke et al. (2015), on the perception of farmers on the 
use of insects in animal feed. The statements encompasses economic, health and 
environmental factors since these have been known to influence purchasing decisions. (Owen 
et al., 2000; Loureiro et al., 2001). The questionnaire had a scale ranging from disagree (1) to 
agree (3). Respondents were asked whether they agreed, were not sure or disagreed.  
 
Various scores were attached to their answers. The various means of the statements were then 
found using a weighted mean index.  
 
 
 
 
 
 
 
 
 
40 
 
Table 3.3: Perception statements of perceived risk and benefit 
Perception Statements                                                                   Disagree  Not Sure    Agree  
(1)             (2)           (3) 
    (Midpoint 
         Scale) 
   
The use of BSFL in fish feed can:   
Risk Statements   
1.  Decrease demand for fish    
2.  Cause release of noxious gases during BSFL production   
 3.  Introduce BSF which will spread diseases to human beings   
4.  Cause microbiological contamination to fish   
5.  Be detrimental to the health of fish consumers   
    
Benefit statements   
1.  Decrease the overexploitation of water bodies    
2.  Lower the cost of Fish feed    
3.  Lower our dependence on imported feed    
4.  Improve organic waste management in the country    
5.  Improve the sustainability of aquaculture production   
Source: Authors adaptation of Verbeke et al. (2015) 
 
A weighted mean index is the weighted average response of various respondents. It is 
calculated to find the mean response to various perception statements. 
The WMI model is specified as: 
∑𝑓𝑖∗𝑤𝑊𝑀𝐼 =  𝑖                                (3.8) 
𝑁
 
 Where: 𝑓𝑖 = Frequency of response to the i
th scale,   𝑤𝑖 = Weight of the i
th scale 
N =Number of Respondents 
 
The Cronbach’s alpha was used to test the internal consistency of the scale. This is a test 
performed to confirm that the various statements under risks and benefit are related and can 
therefore be aggregated into a composite score. This means that, the overall mean for risk and 
the overall mean for benefit can be computed. To show a high degree of internal reliability, 
the Cronbach’s alpha should be greater than 0.7. 
 
 
41 
 
 
 The paired sample t-test was used to test for the significance of the difference between the 
overall means computed and the midpoint scale. It was also used to test the significance of 
the difference between the overall risk mean and overall benefit mean. The paired sample T-
Test is usually used to compare two different means from the same sample. This was done to 
ascertain whether the difference between the two means is significant or not.  
 
3.3.3 Determining farmers’ willingness to pay (WTP) for BSFL-Based feed 
Contingent Valuation Method was used to determine maximum amount respondents are 
willing to pay for BSFL-based fish feed. WTP for a product is the maximum amount of 
money that may be contributed by an individual to equalize a utility change (Lusk and 
Hudson, 2004).  
 
The contingent valuation method was used because the product is not on the market yet in 
Ghana. Under the contingent valuation method, the bidding game was used to ascertain the 
maximum prices that farmers are willing to pay. Specifically, the dichotomous choice, double 
bounded contingent valuation method was used for the bidding game. This approach was 
used because it gives the farmer an easy choice of responding ‘yes’ or ‘no’ and it mimics a 
farmers purchasing decision in real life. In addition, it prevents outliers in the data since the 
responses are within a limited range. Before starting the bidding game, the product was 
described to respondents in detail, explaining the process of production and the components 
of the product, the current substitute of the product was also made known to the respondents. 
Each was then asked to choose whether they will be willing to pay for the product or not. 
 
 
 
42 
 
On the part of the bidding game, the various bids were set GHS220.00 maximum and 
GHS180.00 minimum. The bid began at GHS200, the price of conventional feed, if the 
respondent is not willing to pay, the price was reduced successfully by GHS10 until accepted 
or until it reached the minimum of GHS180. On the other hand, if the respondent is willing to 
pay, it is increased from GHS 200 by GHS10 upwards until the maximum of GHS 220 is 
reached. This is comparable to the bidding game conducted by Asenso-Okyere et al., (1997) 
on the willingness to pay for health insurance for health insurance in Ghana. The mean 
amount that farmers were willing to pay was computed from these responses.  
The formula used was: 
 
∑𝐸𝐵𝑉𝑖∗𝑓Mean Willingness to Pay (𝑀𝑊𝑇𝑃) =  𝑖              (3.9) 
𝑛
 
Where EBV = Elicited Bid Value of respondents (final bid), F = Frequency of respondents, n 
= total number of respondents 
 
Identifying the factors affecting the amount farmers are willing to pay: The Tobit 
regression model 
The Tobit regression model was used to determine the factors that influence the maximum 
amount farmers are willing to pay. The Tobit was chosen because the dependent variable is 
continuous. The model was specified as: 
 
Y* = β0+β1AGE+β2HSEH+ β3MS+β4ANINC+ β5EDUC+ β6AWA +β7MFBO + β8AEX + 
β9RSβ                  (3.10) 
Where Y*= is the latent variable for the maximum amount farmers are willing to pay for the 
product. 
 
 
43 
 
Explanation of the dependent variable 
The dependent variable represents the maximum amount each farmer was willing to pay (Y) 
during bidding. 
 
Explanation of the independent variables 
Annual income (ANINC): Income is measured in Ghana Cedi as a continuous variable. As a 
factor affecting demand, it is expected to have a positive relationship with MWTP which is in 
line with the economic theory of demand. Research conducted by Asenso-Okyere et al. 
(1997), showed that income is positively related to willingness to pay for health insurance. 
 
Education (EDUC): Education is measured in number of years as a continuous variable. It is 
expected to have a positive relationship with MWTP since studies have shown that the higher 
an individual’s educational level, the more the person is willing to pay for a product. This is 
because the individual is able to better understand benefits of the product and are willing to 
pay more. According to Owusu and Anifori (2013), education has a positive correlation with 
willingness to pay premium for organic fruits and vegetables.  
 
Marital status (MS): Marital status measured as a discrete variable, married or unmarried. 
Marital status is expected to have a positive relationship with willingness to pay for BSFL 
feed. This is because, married people are more likely to adopt innovations in order to make 
gains and support the family. In a research done by Engel (2008), marital status was found to 
positively influence willingness to pay premium for organic food.  
 
 
 
44 
 
Age: Age is measured in actual number of years as a continuous variable. It is expected to 
have a positive or a negative effect on MWTP. It is expected that young people are more 
likely to buy an innovative product than older people.  
 
Household size (HSEH): Household size is measured as a continuous variable. It is expected 
to have a positive or negative relationship. The rational here is that either the farmer will be 
willing to pay less since money left over with a large family size may be small, or the farmer 
may be willing to pay more in order to increase productivity and take care of the large family 
size. 
 
Membership with a farmer based organisation (MFBO): This was measured as a discrete 
variable with Yes as 1 and No as 0. The membership in an FBO is expected to have a positive 
relationship with MWTP. This is because members of an FBO are more likely to discuss the 
benefits and risks amongst themselves and gain better understanding to try the product 
(Asenso-Okyere et al., 1997). 
 
Access to extension service (AEX): This is another complimentary factor of demand. 
Access to extension was measured as a discrete variable with Yes as 1 and No as 0. It is 
expected to have a positive relationship with willingness to pay. Farmers with access to credit 
are expected to have better knowledge about feed and therefore be more willing to pay higher 
prices for BSFL-based fish feed 
 
Awareness (AWA): Awareness or knowledge of insects was measured as a discrete variable, 
with Yes as 1 and No as 0. Awareness of insects as feed is expected to affect and positively 
influence willingness to pay for BSFL-based fish feed. This is because people who are aware 
 
 
45 
 
of insects as feed will be more willing to pay for it since they are aware of its benefits. In a 
research conducted by Abbeam et al. (2014), awareness was found to be significant and 
positively related to the willingness of cocoa farmers to pay for farm insurance. 
 
Residential status (RS): The residential status of farmers was measured as a dummy 
variable. Residents is measured as 1 and migrant as 0. It is expected that residents will have a 
positive relationship with willingness to pay. This is because they are more willing to invest 
in new products since they expect to be involved in the production of fish for a long time. 
Migrants may leave the area for a different occupation at any point in time. A summary of the 
dependent variables is shown in Table 3.4. 
 
Table 3.4: Description of dependent and explanatory variables 
Variable Description Measurement Aprior 
Expectation 
AGE Age Years + / - 
MS Marital Status Dummy - 
Married=1, Otherwise=0 
EDUC Education Years + 
AEXT Access to extension Dummy + 
Yes=1, No=0 
ANINC Annual income Ghana Cedi + 
HSEH Household size Number +/ - 
MFBO Membership in FBO Dummy + 
Member=1, Non-member=0 
RS Residential status Dummy - 
Resident=1  
Migrant=0 
AWA Awareness Dummy  
Yes=1, No=0 
Source: Authors adaptation from Campiche et al. (2004) & Abbeam et al. (2014) 
 
 
 
46 
 
3.3.4 Estimating the gains in substituting the conventional feed for BSFL-based feed 
Partial budget analysis was used to estimate the gains of substituting the conventional feed 
with BSFL-based fish feed. The cost and income from the existing feed was matched against 
that of the new feed as shown in Table 3.5. Partial budget analysis was chosen as method of 
analysis because the new costs and income will not affect the whole enterprise but just a 
portion of it (Kay et al., 2008). With regard to introducing a new type of feed in production 
the two main cost that change are cost of feed and revenue from fish production. 
 
Table 3.5: Standard layout for partial budget 
Losses        Gains 
Income Lost        New Income 
New Cost       Old Cost 
Net Gain                             or    Net Loss 
Source: Kay et al. (2008) 
 
On the losses side, the average cost of existing feed was compared with the average cost of 
BSFL feed. On the revenue side, results were garnered from an experiment conducted by 
WRI with BSFL-based fish feed and conventional fish feed. The experiment was conducted 
to ascertain harvested biomass of fish after it is fed with BSFL-based fish feed and the 
harvested biomass when fish is fed with conventional feed. This was used to ascertain the 
revenue since the price of the fish is directly related to the size of the fish at the end of the 
production period (Cobbina, 2010). These were then matched against each other to ascertain 
the net gain or loss from substitution. 
 
 
 
 
 
 
47 
 
3.4  Method of Data Collection 
Secondary data 
Objective 1: To determine the profitability of BSFL meal and BSFL-based fish feed 
production, secondary data was collected from researchers at BNARI and Water Resources 
Institute who had cultivated the BSFL and turned it into meal and feed. The process was 
initiated under a three-year project that began in 2014 and ended in 2017. 
 
Primary data 
Objective 2: To determine the perception of farmers towards the use of BSFL in fish feed, 
survey data was collected from 150 fish farmers in two districts (Asuogyaman and Lower 
Manya Krobo) in the Eastern region. Structured questionnaires were employed in the data 
collection process. 
 
Objective 3: To determine the MWTP of farmers and factors affecting the amount they are 
willing to pay. Out the 150 fish farmers, survey data was collected from 68 of the farmers 
who operated hatcheries on the amount they were willing to pay for BSFL-based fish feed. 
The WTP price of BSFL feed was for fingerlings so only farmers who operated hatcheries 
were selected. 
 
Objective 4: To estimate the farmers’ gains losses in substituting conventional feed with 
BSFL-based fish feed. Primary data was collected from researchers at WRI. The data 
collected was on research performed on the growth performance (Yield) of fish when fed 
with conventional feed and the growth performance (Yield) when fed with BSFL feed. The 
2017 market price for fingerlings and fish feed ingredients were collected from input dealers 
in the region. 
 
 
48 
 
3.4.1 Questionnaire 
A structured questionnaire was used to conduct the survey of farmers to ascertain their 
perception of the use of BSFL in fish feed and their willingness to pay for the BSFL feed. 
Firstly the questionnaire was pre-tested with 6 farmers to ascertain whether additional 
questions should be added or removed from the questionnaire, the average time for answering 
the questionnaire and the clarity of the questionnaire. Changes were made from the results 
and the final questionnaire was printed and administered to 150 farmers. This was done 
through a face to face interview with the assistance of two enumerators. Farmers were drawn 
from a farmer based organisation where as some others were also interviewed that did not 
join the farmer based organisation.  
 
The questionnaire was divided into eight parts. The first and second part was on the 
demographic and socioeconomic factors such as age, sex and income. The third part was on 
the farm\culture characteristics to ascertain the economic standing of the farm. 
 
The forth part of the questionnaire was on the institutional characteristics, this is because for 
effective demand, institutions have to be in place. Questions such as access to credit and 
extension were asked. 
 
The fifth part contained perception statements and respondents were asked to scale their 
responses using a 3 point Likert scale. The seventh part contained biding prices where 
farmers where offered different prices to agree or disagree to buy. 
Data was summarised using descriptive statistics. 
 
 
 
49 
 
3.4.2 Sampling procedure 
The sampling was done in two stages (multistage sampling method), Firstly, the Asuogyaman 
district and Lower Manya Krobo districts were purposively selected from the Eastern Region 
because of the proliferation of fishing activities along the Volta River and government 
interventions in the districts in terms of aquaculture (Cobbina, 2010). An online calculator 
estimated a sample size of 230 for 5% confidence level, however, due to convenience, time 
constraint and availability, a sample of 150 fish farmers was used for the study. The farmers 
were selected proportionally from Asuogyaman district and Lower Manya Krobo district. 
Ninety of the respondents were selected from Asuogyaman district and 60 from Lower 
Manya Krobo district. The Asuogyaman fish farmers association provided a list of 38 farmers 
and all of them were interviewed. The rest were marked and selected through snowballing 
after identification by interviewed farmers. In Asuogyaman the communities visited were 
Atimpoku, Akosombo Marine, Senchi Ferry, and Old Akrade. In Lower Manya, the 
communities selected were Kpong and Akuse. These communities were selected due to their 
proximity to the Volta Lake and the proliferation of fish farmers in the area. 
 
3.5 Profile of Study Area 
The study was conducted in Asuogyaman and Lower Manya Krobo districts in the Eastern 
Region. 
 
3.5.1   Asuogyaman district 
According to GSS (2014a), the Asuogyaman district was created under government 
instrument LI 1431 of 1988. It has a population of 98,046, representing 3.7% of population of 
Eastern Region. Out of the total population there are more females (52.0%) than males 
(48.0%), the district is essentially a rural one with (70.6%) of the people living in rural areas 
 
 
50 
 
compared to urban areas (29.4%). The population of the district is largely youthful with more 
than half (64%) of the population below 30years and children (0-14 years) constitute 37.4% 
of the total population. 
 
Asuogyaman is located approximately between latitudes 6º 34º N and 6º 10º N and longitudes 
0º 1º W and 0º14E. It is about 120m above Mean Sea Level (MSL) and covers a total 
estimated surface area of 1,507 sq. km, constituting 5.7% of the total area of the Eastern 
Region. It is bordered to the north by Afram Plains South District borders and the Upper and 
Lower Manya districts to the south and west (Figure 3.1). The Volta River cuts through such 
ridges to create a gorge ideal for the construction of the Volta Dam at Akosombo.  The 
existence of the Volta Lake accounts for the proliferation of fish farms in the district. 
 
 
Figure 3.2: Map of Asuogyaman district 
Source: GSS, 2014 
 
 
51 
 
 
The Asuogyaman District lies within the Dry Equatorial Climate Zone, which experiences 
substantial amount of precipitation. It is characterized by a double maxima rainfall, which 
reaches its peak period in May - July, and the minor season occurs in the period of September 
- November. Annual rainfall usually starts in April with the peak month in June and ends in 
November (GSS, 2014a). 
 
Majority of the people in the district are engaged in agriculture and related trades. There are 
three (3) prominent types of agricultural activities in the district. These are livestock rearing, 
food cropping and cash cropping. However, most of the farming activities in the district are 
focused on the production of food crops. The major food crops produced are maize, cassava, 
plantain, yam and vegetable. A large number of these farmers have smallholdings. Most of 
the farmers engaged in crop farming are also involved in livestock rearing (GSS, 2014a). 
 
Fishing in the Volta Lake also constitutes an important segment of the agriculture sector. 
Fishing is done mainly in some communities along the 141km shoreline including parts of the 
Kpong headwaters. These communities include Dzidzokope, Atimpoku, Abume, Akosombo, 
Surveyline, Adomi, Dodi Asantekrom, Asikuma, Mpakadan and Senchi Ferry and old Akrade 
(GSS, 2014a).. 
  
3.5.2 Lower Manya Krobo district 
According to GSS (2014b), the Lower Manya Krobo district was elevated to a Municipality 
status in July 2012 by a Legislative Instrument (L.I.) 4026 with Odumase- Krobo as the 
capital. It has a total population of about 89,246 comprising of 46.5% male and 53.5% 
 
 
52 
 
female. This is mainly comprised of the young people with 35.1% of the population falling 
below 15 years. Only 8.3% of the population fall within 60 years and above. 
 
The Municipality is strategically located at the Eastern corner of the Eastern Region of Ghana 
and it lies between latitude 6.05N and 6.30N and longitude 0o08W and 0.20W with an 
altitude of 457.5m above sea level. The Municipality is bounded on the North-west by Upper 
Manya Krobo District, on the North-east by Asuogyaman district, on the South-east by North 
Tongu District and on the South by Yilo and Dangme West District (Figure 3.2). The LMKM 
covers an area of 304.4 square kilometres, with a population density of 293.2 persons per 
square kilometre (GSS, 2014b). 
 
 
Figure 3.3: Map of Lower Manya Krobo District 
Source: GSS (2014) 
 
 
53 
 
 
The topography of the Lower Manya Krobo Municipality is relatively flat with isolated hills 
partitioning the Municipality from the north-western point to the east. The Landscape of the 
northern part is generally undulating with several streams, most of which drain into the Volta 
Lake. Much of the eastern boundaries of the District constitute the shores of the Volta Lake. 
This has created proliferation of fish farming in the area. A section of the population 
especially the men folk earn their living through fishing on the Volta Lake which lies at the 
North-Eastern part of the Municipality (GSS, 2014b).  
 
The people of Lower Manya Krobo Municipal are mainly farmers with some of the 
population engaged in trading. Cereal (Maize) is the most common agricultural product found 
in the Municipality together with, cassava, pepper, pineapple, watermelon, sweet potatoes, 
plantain, yam, cocoyam, okra, tomatoes and others (GSS, 2014b). Some communities in the 
region are: Akuse, Agorman, Kpong and AsiteyMannwan. 
 
3.6  Scope and Limitations of the Study 
The study focuses on the profitability of production and farmers willingness to buy given 
certain factors. It does not cover the marketing and sales part of the product and it also 
assumes away the existence of inputs for production. 
 
Another limitation of the study is the fact that the actual feed was not available at the time of 
the research and farmers had to base their WTP on descriptions made to them. The WTP 
suggested may therefore not be a fair representative of what they are actually willing to pay. 
This is however a consistent limitation of most WTP studies since the product is usually not 
on the market. Finally, time and resource constraints limited the number of farmers that were 
 
 
54 
 
interviewed to 150, though the scientific online calculator suggested a number of 230 to get a 
confidence level of 5%. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
55 
 
CHAPTER FOUR 
RESULTS AND DISCUSSIONS 
Introduction 
This chapter deals with the presentation and discussion of results obtained from the study. 
First, the socio-economic characteristics of respondents are described and discussed. Then the 
results from the financial feasibility analysis, perception of farmers towards the use of BSFL 
in fish feed, the MWTP and the factors influencing the decision. Finally, results from the 
savings farmers will make from switching from conventional feed to BSFL feed is discussed 
 
4.1 Socio Economic Characteristics of Respondents 
The following profile was ascertained from the fish farmers surveyed in the district: 
Gender: 
The respondents interviewed were made up of 3% (4) female and 97 % (146) male. Very few 
women were involved in fish farming. This is in agreement with FAO (2012), which asserts 
that men are involved in the main fishing activities whereas women are involved in the post-
harvest activities like processing of the fish and trading. 
 
Female
3%
Males
97%  
Figure 4.1:     Gender profile of respondents 
Source:            Survey data (2017) 
 
 
56 
 
Age: 
Out of a total of 150 respondents interviewed, 24% (36) fell within the age range of 26-30 
and 24% (36) within 36- 40 years. This was followed by 21% (32) falling within the range of 
31- 35 years, ages 20-25 was represented by 14% (21) of respondents.  Age group 41-45 and 
46-50 contributed 7 %( 11) and 4 %( 6) respectively. Finally, age 51-55 and age 56-61 
contributed 4% (6) and 1% (2) respectively. This shows that majority of the respondents fell 
within the ages of 26- 40 years and only a few were above 55. This contradicts the research 
that reported that majority of fish farmers in the country are aging but agrees with GSS 
(2014) survey report of the districts that says that majority of the citizens in both districts are 
youthful. 
 
Education: 
Out of the respondents, JHS education was the highest at 39% (58) followed closely by SHS 
at 27% (41). Respondents that had no Education made up 13% (19) of the sample and those 
with tertiary education 14% (21). Respondents with primary education were lowest 7% (9). 
Most of the respondents fell within the level of JHS and Secondary school which means that 
most fish farmers in the region have some form of education.  
 
 
 
 
 
57 
 
50
40
30
20
10
0
No education Primary JHS SHS Tertiary
Educational Level
 
Figure 4.2: Educational profile of respondents 
Source:            Survey data (2017)  
 
 
Marital status: 
About 86% of the respondents were married whereas 14% indicated that they were single. 
This represents 129 and 21 of the respondents respectively. This to be expected since most of 
the population fell between 30-45 years. 
 
Single
14%
Married
86%
 
Figure 4.3:     Marital profile of respondents 
Source: Survey data (2017) 
 
 
 
 
Percentage number of 
Respondents
58 
 
Residential status: 
When farmers were asked about their residential status, out of the respondents, the highest 
was 74% (111) which represented migrants and 26% (39) which represents natives. Most of 
the respondents had migrated from the Volta region, Accra and other regions to farm in 
Eastern Region. 
Natives
26%
Migrants
74%
 
Figure 4.4: Residential profile of respondents 
Source: Survey data (2017) 
 
Religion: 
All 150 of the respondents interviewed were Christians. This shows that the whole 
community is predominantly Christian. Religion was therefore not used in the determinants 
of WTP since there were no variations amongst farmers 
 
 
 
59 
 
Christians
100%  
Figure 4.5:     Distribution of respondents by religion 
Source: Survey data (2017) 
 
 
Occupation: 
Out of the respondents interviewed, 87% (131) were full time farmers. A small percentage of 
13% (19) had jobs outside fish farming. Out of the 18 respondents who had other Jobs, (9) 
were into fish processing (turning fish into salted fish), (5) were maize farmers and (4) 
cultivated various vegetables. 
 
Business type: 
The field research showed that 45% representing 68 of the farmers interviewed owned 
hatcheries where as 55% representing 82 of the farmers operated grow-outs. Some of the 
hatcheries did not sell to the market but rather produced for the grow-outs as an integrated 
business enterprise. 
 
 
 
60 
 
Hatcheries
45%
Growout
55%
 
Figure 4.6: Profile of type of business operated by respondents 
Source: Survey data (2017) 
 
Annual revenue: 
Respondents have an average annual income of GHS 97,623.33. The highest respondent had 
an annual income of GHS330,000.00 and the lowest an annual income of GHS8,000.00. The 
revenue was higher for farmers depending on the number of cages owned by the respondents. 
Farmers asserted that each cage produces an average of 4000 fish and generates income of 
GHS10,000.00 
 
Land ownership: 
In fish farming, some of the farmers operate ponds and cages in dug outs on land whereas 
some put the cages out in the water. Land ownership refers to both types since ownership of 
the land automatically entitles the farmer to water rights. Out of the 150 farmers interviewed, 
34% representing 52 farmers owned the land they were fishing on and 66% representing 98 
farmers were renting the land. This is to be expected since ponds and cages which are kept on 
land need dug outs into the land at huge investment cost which will not be feasible for rented 
land. 
 
 
 
 
61 
 
Culture practice: 
All the farmers interviewed representing 100% of the sample practiced intensive fishing. All 
of the farmers fed their fish with feed at regular intervals and used hormones to make the sex 
of the tilapia homogenous so as to prevent proliferation of population growth. This reveals 
that, fish farmers are shifting from the semi intensive and extensive form of fish farming. 
 
Fish farming structure: 
Fish farmers that practice intensive farming do so in either a cage or a pond. Majority of the 
farmers at 56% representing 84 respondents farmed in Cage structures (average 5*5m) 
whereas 44% representing 66 of respondents farmed in ponds structures (average of 9m*9m). 
Most of the farmers owned cages which had been put in the Volta River for farming activity 
purposes, this makes it easier to farm the tilapia. This contradicts FD (2013), which purports 
that in terms of numbers, cages come after ponds.  
Tilapia monoculture: 
When farmers were asked if tilapia was the only fish farmed, 91% representing 137 of the 
sample responded yes, whereas 9% (13) responded no. The respondents that responded no to 
the question cultivated catfish on the side with the tilapia cultivation. This may be for 
economic purpose of catfish cultivation or to use the catfish to control tilapia prolific 
population growth. This confirms Cobbina (2010) statistics that 80% of the fish farmers in 
Ghana cultivate tilapia where as 20% cultivate catfish. 
 
Direct market sales: 
Fish farmers were asked whether they sold directly of the market to consumers. 90% of the 
respondents representing 135 farmers responded no. These people explained that middle men 
 
 
62 
 
buy from them and sell it to the market women. 10% of the respondents however mentioned 
that, they have workers who sell on their behalf directly on the market. 
 
Membership of FBO: 
35% (53) of the respondents interviewed were members of FBO. This figure may not be a 
true representation of the total population since farmers who join FBO’s were specifically 
targeted for the study. 65% (97) of the study did not join FBO’s however. These people were 
mainly the small scale uneducated farmers, the very large scale farmers and farms operated 
by foreigners. 
 
Access to extension: 
To ascertain respondent’s access to credit, farmers were asked if they had received extension 
services at any point. 22% representing 33 of the respondents had no access to extension, 
however 78% of the respondents had access to extension. These respondents claimed that 
they when a problem arises on the farm, they call in extension officers and they come in to 
investigate. 
 
Credit from financial institution: 
96% representing 144 of the population had never borrowed from any financial institution, 
however, around 4% representing 6 of the respondents had taken some form of loan from a 
financial institution.  
 
Cost of existing feed: 
Respondents were asked if they thought the existing feed on the market were expensive. 
100% of the respondents agreed that the existing feed is expensive. Researchers were shown 
 
 
63 
 
empty cages where farmers had abandoned farming due to high cost of feed. These farmers 
were unable to cover feed cost and had therefore closed shop.  
 
Use of farm made feed: 
Farmers were asked if they make their own feed. Out of the respondents interviewed, none of 
the farmers made their own feed although some have experimented with for example insects 
in the past. This is contradicts Obirikorang et al. (2015), which asserts that most farmers in 
Ghana produce supplementary fish feed at farm level. However, this shows that farmers are 
shifting to intensive farming and commercial formulated feed as a result. 
 
Knowledge of ingredients of existing feed: 
Respondents were asked if they knew the components of existing feed and was marked “yes” 
if the respondent was able to list at least one ingredient. 91% of the population did not know 
any of the ingredients used in making the current feed on the market. This is comparable to 
research done by 9% representing 13 respondents however, were able to mention at least one 
of the ingredients used to make current feed on the market. The ingredient that most of the 
respondents were able to mention was fishmeal.  
 
Deciding factor when purchasing feed: 
Respondents were asked what the deciding factor was when purchasing fish feed giving a 
choice between cost, brand and ingredient. Out of the 150 respondents, 92% (138) chose cost 
as the deciding factor when buying feed. 6% (9) of the respondents mentioned brand as the 
deciding factor. Only 2% (3) of the respondents mentioned ingredients as the deciding factor 
and this may be because most of the respondents did not know the ingredients of existing fish 
feed. 
 
 
64 
 
Knowledge on BSFL and insects as feed ingredient 
When asked if they knew about BSFL or the use of insects as feed 49% (73) of the 
respondents responded yes whereas 51%(77) responded no. The respondents that knew of 
insects as feed were relatively large because most of the farmers mentioned that insects are 
food for fish in their natural habitat. Also, FiBL and WRI had already organised workshops 
to educate the farmers in Asuogyaman district. In addition, a French company had also 
sensitized farmers in the region on BSFL since they wanted to start sale of a BSFL-based fish 
feed in the region. Some of the foreign owned fish farms, primarily the ones owned by 
Chinese farmers were also aware of the existence of BSFL as feed. 
 
Willingness to buy the BSFL-based fish feed 
All the respondents were willing to buy the BSFL-based fish feed. Farmers expressed their 
interest in the new feed and asserted that they were willing to pay for the BSFL-based fish 
feed. This was mainly due to the fact that farmers perceive the existing feed cost to be very 
high and are therefore looking for new alternatives.  
 
4.2 The Profitability of BSFL-based Feed Production 
 
4.2.1 Scenario A: Production of BSFL meal 
This scenario is under the assumption of an entrepreneur that invests in the manufacture of 
BSFL meal. 
 
The results from the financial analysis showed that, for a production of 1000 metric tonnes a 
land size of 3,078 metres square is needed (Appendix 2). For BSFL meal production, a start-
up cost of GHS4,230,192.98 with an initial investment in fixed asset of GHS2,061,896.69 
and an annual operating cost of GHS 2,168,296.30 (Appendix 3) was identified. The main 
 
 
65 
 
cost driver was the cost of layer meal used to feed the BSFL larvae which made up 60% of 
total cost of operations (Appendix 4). The cost of producing a kilo of BSFL larvae meal was 
GHS 2.2 (Appendix 5).  
 
The results from analysis 4, was the most favourable since the price used to compute revenue  
was below the market price of existing feed but all the profitability indicators such as NPV, 
IRR and BCR were still acceptable. This analysis revealed a positive NPV of 
GHS5,735,340.08, a BCR of 2.4 an IRR 32% and a PBP of 1 year, 4 months. This is in 
tandem with Kaliba et al. (2010), where research revealed that, at 1145 m.t. (15% discount 
rate) production an NPV of  USD3,084,341 and an IRR of 33% will be realised from 
producing fishmeal from the waste of channel catfish. The other analysis (1, 2, & 3) had 
favourable NPV, BCR and Payback period, but the IRR was lower than the 31% discount 
rate. The payback period for all the analysis were favourable, within the acceptable range 
(payback back period less than 5 years) for a risky agricultural investment.  The summary 
results of the discounted measures of the worth of the project with the various analysis given 
these costs and benefits is shown in table 4.1 and complete work is shown in Appendix 6, 7, 8 
& 9. 
Table 4.1: Summary results for discounted measures 
Indicator Amount(GHS) 
Analysis 1: Using market price (GHS117 per 20kg bag) 
NPV (31%) 7, 617,450.63 
BCR (31%) 2.7 
IRR 43% 
Payback Period 1 year, 2 months 
Analysis 2: Using 20% below market price (GHS94 per 20kg bag) 
Therefore NPV 3,853,229.54 
IRR 22% 
BCR 2.2 
Payback 1 year, 9 months 
Analysis 3: using 15% below market price (GHS100 per 20kg bag) 
NPV 4,794,284.81  
IRR 27% 
 
 
66 
 
BCR 2.3 
Payback 1 year, 6 months 
Analysis 4: Using 10% below market price (GHS105 per 20kg bag) 
NPV 5,735,340.08 
IRR 32% 
BCR 2.4 
Payback 1 year, 4 months 
Source: Cash flow projections in Appendix 6, 7, 8 & 9 
 
Analysis 1: Using market price (GHS 117 per 20kg bag) 
From table 4.1 the NPV of GHS7, 617,450.63 implies that the present value of all the future 
benefits exceeds the present value of the future costs projected over the 20 years of the 
project. The BCR of 2.7 shows that for every GHS1 invested in the production of BSFL, an 
earnings of GHS 2.7 is made on it. The IRR of 43% shows that the project has a high earning 
capacity and ability to cover the interest rate of 31%. This means that if the entrepreneur goes 
for a loan at a rate of 31% it can be paid back. Finally, upon investing in the project, it will 
take the Entrepreneur 1 year and 2 months to pay back. This means that the project is 
financially viable at market price. (See table 4.1 and appendix 6) 
  
Analysis 2: Using 20% below the market price (GHS 94 per 20kg bag) 
From Table 4.1 and Appendix 7, at the 20% reduction in the market price, the project is still 
viable at a positive NPV of GHS 3,853,229.54 and a BCR of 2.2.  However, the IRR is 21% 
which is less than the 31% average interest rate charged by banks (used as discount rate). In 
conclusion, though it is financially viable with a positive NPV, payment of loan at existing 
rate may be difficult since the projects’ internal rate of return (IRR) is lower than the interest 
rate (discount rate) 
 
 
 
 
67 
 
Analysis 3: Using 15% below the market price (GHS100 per 20kg bag) 
When the market price is reduced by 15%, the project is still viable at a positive NPV of 
GHS4,794,284.81, BCR of 2.3 and payback period of 1 year, 6 months. However, the IRR is 
27% which is less than the interest rate for borrowing loans at 31%. In conclusion, though it 
is financially viable with a positive NPV, covering at existing rate may be difficult since the 
projects’ internal rate of return (IRR) is lower than the banks’ lending interest rate (discount 
rate) 
 
Analysis 4: Using 10% below the market price (GHS105 per 20kg bag) 
From table 4.1, at a reduction in market price by 10%, the project is viable with a positive 
NPV of GHS 5,735,340, BCR of GHS 2.5 and payback period of 1 year, 4 months. The IRR 
of 31% is very acceptable since it is higher than the discount rate that is the commercial 
banks average lending rate of 31%. The lending rates in Ghana is very high, therefore 
projects must make enough returns to cover and pay back the high rates in the country. 
 
In conclusion, since BSFL is a relatively new product, and the study seeks to provide a less 
costly option to market price of conventional fish meal, the BSFL meal can be priced 10% 
below the price of fishmeal and still maintain acceptable rates in all the profitability 
indicators. 
 
Sensitivity analysis (10% cost overrun and 10% decrease in production) 
Sensitivity analysis was therefore performed on analysis 4 (using 10% below market price 
GHS107) since it is the price below market price at which all the profitability indicators is 
acceptable. It was done by increasing cost by 10% and reducing production by 10%. The 
results as shown in table 4.2 revealed that, at 10% reduction in cost and output, the 
 
 
68 
 
entrepreneur still makes a positive NPV and BCR. However, the IRR is below that of the 
discount rate which means that the internal rate of return may not be enough to cover the 
borrowing rate of 31%. This means that at a lower cost of capital (discount rate), the 
production of BSFL meal is still viable. 
Table 4.2: Summary of results for the sensitivity analysis 
Indicator  Amount 
NPV 3,398,740.46 
IRR 19% 
BCR 1.9 
Payback period 1 year, 11 months 
Source: Cash flow projections in Appendix 10 
 
4.2.2 Scenario B: Production of BSFL-based fish feed 
This scenario assumes Purchase of BSFL meal and the use of it as partial (75%) replacement 
of fishmeal to make fish feed. It is under the assumption of an entrepreneur that invests in use 
of BSFL in the production of fish feed. The results from the financial analysis showed that, 
for a production of 1000 metric tonnes an initial investment in fixed asset of 
GHS1,746,000.00 and an annual operating cost of GHS4,743,280 was identified. (See 
appendix 11 for breakdown of costs and benefits). The analysis 2, which used the WTP 
amount of fish farmers revealed favourable results of the discounted measures. This analysis, 
revealed a positive NPV of GHS8,130,254, a BCR of 1.95 and an IRR of 30%. The results 
form analysis 1 (Using market price) also produced similar results. The payback period for all 
the analysis were favourable, within the acceptable range (payback back period less than 5 
years) for a risky agricultural investment. These figures are especially promising because the 
fish feed industry is a high earning sector as shown by research. In a research conducted by 
Ruwangwa et al. (2015), at 60% margin of current feed prices, there was a profit (before 
 
 
69 
 
interest and tax) of USD 1,016,000 at 8000metric tonnes production. This shows a high 
degree of profit and earning in the feed industry.  The summary of the discounted worth of 
the project is shown in table 4.3 and appendix 12 & 13. 
 
Table 4.3: Summary of results for discount measures 
Indicator Amount (GHS) 
Analysis 1: Using market price (GHS200 per 20kg bag) 
NPV 10,378,126 
BCR 2.1 
IRR 38% 
Payback 1 years, 2 months 
Analysis 2: Using MWTP amount, 7% below market price (GHS186 per 20kg bag) 
NPV 8,130,254  
BCR 1.95 
IRR 30% 
Payback 1 years, 5 months 
Source: Cash flow projections in Appendix 12 & 13 
 
The results from the MWTP show that the farmers are willing to pay GHS186 (7%) less the 
price of existing feeds on the market. A financial analysis was therefore performed by 
reducing the selling price of BSFL-based feed by 7% to ascertain whether the project will be 
viable at that level. From table 4.3, when the Selling price is reduced by 7%, the project is 
still viable at a positive NPV of GHS8,130,254.00, a BCR of 1.95 and an IRR of 30%. 
GHS1.95 and payback period of 1 year, 5 months. However, the IRR is 30% which is slightly 
less than the interest rate for borrowing loans at 31%. In conclusion, production at the MWTP 
amount is financially viable. 
 
Sensitivity Analysis (10% cost overrun and 10% production decrease) 
Sensitivity analysis was performed by increasing cost by 10% and decreasing production by 
10% simultaneously. The results for the analysis as shown in Table 4.4 and Appendix 14 
revealed a positive NPV of GHS3,633,822.23, a BCR of 1.60, an IRR of 13% and a payback 
 
 
70 
 
of 2 years, 1 month. The NPV and BCR is favourable, however, the IRR is less than the cost 
of capital. The payback period is within the 5 year range of risky agricultural projects. 
Table 4.4 Results from sensitivity analysis 
Indicator Amount (GHS) 
NPV 3,633,822.23 
BCR 1.60 
IRR 13% 
Payback Period 2 years, 1 month 
Source: Cash flow projections in Appendix 14 
 
4.3 The Perception of Farmers towards the use of BSFL in Fish Feed 
Table 4.5 shows a summary of results of the means of various statements showing farmers’ 
perceived benefit and risk towards the use of BSFL in fish feed. A Likert scale was used to 
solicit response from farmers with a scale ranging from (1) disagree, (2) do not know to (3) 
agree.  
Table 4.5: Perception statements and various means 
Perception Statements  Mean Average 
Response 
Risk statements   
The use of BSFL in fish feed can:   
1.  Decrease demand for fish  1.053 Disagree 
2.  Cause release of noxious gases during BSFL production 1.593 Not sure 
3.  Introduce BSF which will spread diseases to human beings 1.513 Not sure 
4.  Cause microbiological contamination to fish 1.400 Disagree 
5.  Be detrimental to the health of fish consumers 1.406 Disagree 
    
Benefit statements   
The use of BSFL in fish feed can:   
1.  Decrease the overexploitation of water bodies  2.840 Agree 
2.  Lower the cost of Fish feed  2.753 Agree 
3.  Lower our dependence on imported feed  2.886 Agree 
4.  Improve organic waste management in the country  2.880 Agree 
5.  Improve the sustainability of aquaculture production 2.666 Agree 
Source: Survey data (2017) 
 
 
71 
 
 
From Table 4.5, on the side of the risk statements, results show that farmers disagree with 
most of the risk statements. For example, the 1st statement on the decrease in the demand for 
fish has a mean of 1.05, this means that most of the farmers disagreed with the statement that 
demand for fish will decrease if customers know that fish is currently being fed with BSFL-
based feed. This is not surprising since most of the farmers purported that consumers do not 
care and do not know the current feed that is used to feed the fish. A change to BSFL feed in 
their opinion will not affect demand. Also, according to TIU (2012), demand for fish may 
increase as environmentally conscious people may demand more of it. 
 
However the 2nd and 3rd statements that is on the production of noxious gases and the BSF 
spreading diseases, the farmers’ means were closer to the midpoint scale at 1.6 and 1.5 
respectively.  This means that on those issues most of the farmers did not know either way. 
Farmers mentioned that since they have not seen the production of BSFL they could not tell 
the smell and were not sure whether the BSFL will carry diseases. These risk statements are 
quite unfounded since BSFL is known to restrain bacterial growth and therefore reduce odour 
of waste (van Huis et al., 2013). In addition, BSFL do not spread diseases, on the contrary the 
reduce the oviposition of disease carrying houseflies (Graczyk et al., 2001) 
 
The 4th and 5th risk statements on the fact that BSFL may contaminate the fish and when 
eaten by consumers may be detrimental to the health of consumers also had means below the 
midpoint of the scale at 1.4. This means that most of the farmers disagreed with these 
statements. This may be because some of the farmers had experimented with feeding the fish 
with various insects before and had received varying successful results. They mentioned that 
no adverse effect had been noticed. Also, the farmers mentioned that insects are the diet of 
 
 
72 
 
fish in the wild so it is not harmful to fish or human beings. Since BSFL is an insect then they 
assumed the Black soldier fly larvae will also be the same 
 
The Cronbach’s alpha for risk statements was 0.800 showing that the scores for the various 
statements can be summed up into an overall score. The overall mean risk score computed 
was 1.4 and significantly lower than the average point of the scale (t=-6.576 , p<0.003) (See 
Table 4.5). This means that overall it can be concluded that farmers do not perceive the 
production of BSFL for fish feed will be harmful to demand, the environment or the health of 
the fish and consumers. 
 
From Table 4.5, on the side of the benefit statement, the farmers on average agreed with all 
the perceived benefit statements. This means that farmers vocally agreed with the benefit 
statements. After explaining BSFL technology used for turning it into feed, most of the 
farmers agreed with the various benefit statements. This may also be because some of the 
farmers interviewed joined FBO’s and had already been sensitized by workshops held by 
Water Research Institute (WRI) on the benefits of the BSFL. 
 
The Cronbach’s alpha for benefit statements was 0.719 showing that the scores for the 
various benefit statements can be summed up into an overall score. The overall mean benefit 
score computed was 2.8 and significantly higher than the average point of the scale (t=-
19.163, p<0.000). This means that overall it can be concluded that farmers perceive the 
production of BSFL for fish feed as beneficial to demand, the environment or the health of 
the fish and consumers. 
 
 
 
73 
 
Finally, a t-test performed showed that the overall mean benefit score of 2.8 was significantly 
higher than the overall mean risk score of 1.4 ((t=13.137, p<=0.000). In conclusion, the 
results show that farmers perceive the benefits of using BSFL in fish feed production to be 
significantly higher than they perceive the risk of using BSFL in fish feed.  This means that 
the use of BSFL in fish feed is perceived as highly beneficial in terms of economic 
significance, environmental, and social benefits. 
 
4.4 The Mean Amount Farmers are Willing to Pay for BSFL and the Factors 
Influencing the Decision 
4.4.1 The mean amount farmers are willing to pay for BSFL-based fish feed 
As shown in Figure 4.7, the mean amount farmers were willing to pay for a 20kg bag of 
BSFL-based fish feed was GHS186.00, 7% less than the price of current feed prices. Only 
few (15%) of the respondents (hatcheries) were willing to pay the market value of 
GHS200.00. None of the respondents were willing to pay GHS 220.00 that is a premium of 
GHS 20(10%) above current feed prices. About 3% (2) were willing to pay GHS 210.00 that 
is a premium of GHS10 (5%) premium above the existing feed price. These were the only 
farmers that were convinced that the benefits of the BSFL feed warranted premium to be paid 
on it. 12% (8) were willing to pay the GHS 200.00, existing feed price. Of the respondents 
who were not willing to pay the GHS200.00/20kg bag, 26% (18) were willing to pay 
GHS190.00 and 59% (40) were willing to pay GHS180.00.  
 
In conclusion, farmers are not willing to pay premium for BSFL-based fish feed; farmers are 
willing to pay a price 7% lower than the current feed price. 
 
 
 
74 
 
50
40
40
30
18
20
8
10
2 0
0
180 190 200 210 220
Bid prices  
Figure 4.7: Frequencies and bidding amounts for willingness to pay survey 
Source: Author’s survey data (May 2017) 
4.4.2 Factors influencing the amount farmers are willing to pay for BSFL-based 
fish feed 
The results in table 4.6 show the Tobit regression results for ascertaining the significant 
factors influencing the amount farmers are willing to pay. The marginal changes and the 
direction of change in MWTP as the various variables change are shown in the table. 
Significant level is measured at 1% (***), 5% (**) and 10% (*) 
 
From table 4.6, the F value of  8.64 means the overall significance of the model that has been 
specified is significant at 1%. A significant level of 1% suggests that the defined variables 
affecting MWTP fits the model for the study. A Pseudo R2 of 0.249 signifies that variations 
in the dependent variable can be explained up to 24.9% by changes in the dependent variable.  
 
 
 
 
 
 
 
 
Percentage Frequency 
75 
 
Table 4.6: Tobit regression results on factors of willingness to pay 
Aprior 
Variables Coefficient P-value 
Expectation 
Age 0.255 0.104 +- 
Marital status -6.959* 0.060 + 
Household Size -0.069 0.917 +- 
Education 2.928 0.188 + 
Residential status 0.601 0.892 + 
Annual revenue 6.15E5*** 0.006 + 
Awareness 10.637*** 0.004 + 
Extension 6.138 0.392 + 
FBO 0.701 0.918 + 
Constant 142 0.000 
 
Number of obs = 68 
Pseudo R2 = 0.2490 
F(9,59) = 8.64 
Log likelihood = -108.053 
Probs>F=0.000 
Source: Survey data (May, 2017) 
 
Age, marital status, household size, education, access to extension, membership of FBO, 
residential status,  were expected to influence WTP, the Tobit regression (Table 4.6) revealed 
however that, only three of the nine variables defined were insignificant. These were marital 
Status, annual revenue and awareness of insects as feed. 
 
Annual revenue 
Annual Revenue met aprior expectation and had a positive relationship with MWTP. Annual 
revenue was significant at 5%. This means that annual income of people in the regions is able 
to influence MWTP. Income as mentioned had a positive relationship with MWTP, this 
 
 
76 
 
means that people of higher revenues are more willing to pay higher prices for BSFL-based 
fish feed than people with lower revenue. This is in tandem with research that revealed that 
income positively influenced WTP (Asenso-Okyere et al., 1996; Campiche et al., 2004) 
 
Awareness 
Awareness was measured as awareness of the use of insects as a source of feed. This was 
expected to have a positive relationship with WTP. The regression results revealed that 
awareness is significant at 1% and is positively related to WTP. This means that fish farmers 
that are aware of the use of insects as feed for fish are more willing to pay higher prices for 
BSFL-based feed than otherwise. This is in tandem with Abbeam et al. (2014), where 
awareness was found to be statistically significant in influencing farmers’ willingness to pay 
for farm insurance. 
 
Marital status 
Marital status had a negative relationship, which means that married fish farmers are more 
willing to pay lower amounts for BSFL-based fish feed than single fish farmers. This did not 
meet aprior expectation. In a research done by Engel (2008), marital status was found to 
positively influence willingness to pay premium for organic food. This research is in direct 
contrast with this findings. 
 
4.5 The Gains Made from Substituting Conventional Feed with BSFL-Based Feed 
The results revealed that, a fish farmer can make a gain of GHS6.30 by substituting 14.6kg of 
conventional feed with 14.6kg of BSFL-based fish feed. The stocked cages for the 
conventional feed and the BSFL-based fish feed yielded biomass of 7.73 and 7.67 
respectively. This means that the biomass (yield) from BSFL-based feed was approximately 
 
 
77 
 
0.8% less than that of conventional feed. Therefore the revenue gained from BSFL-based fish 
feed was less than that of the existing conventional feed using the market price of a kilogram 
of fish (GHS12.00). Revenue from BSFL-based feed was GHS 92.04 whereas that of existing 
conventional feed was 92.76. However, on the cost side, the existing conventional feed is 
more expensive than that of BSFL-based fish feed (when BSFL is priced at 7% less the cost 
of existing fish feed). When these costs and revenues were matched against each other a net 
gain of GHS 6.53 is realised (See Table 4.7 and Appendix 15). 
 
Table 4.7: Partial Budget analysis 
LOSSES GHS GAINS GHS 
Loss in yield (conventional feed) 92.76 Gain in Yield(BSFL-based 92.04 
feed) 
Cost of new feed 59.86 Cost of old feed 67.11 
Net Gain 6.53   
Total 159.15 Total 159.15 
Source: Survey data (May, 2017) 
 
In conclusion, when the fish farmer uses 14.6kg of BSFL-based fish feed in place of 14.6kg 
of conventional feed, there is a net gain in production of GHS6.53. Consequently, this means 
that per every kilogram of BSFL based fish feed substituted for conventional feed, the farmer 
makes a net gain of GHS 0.44. 
 
  
 
 
78 
 
CHAPTER FIVE 
SUMMARY, CONCLULSION AND RECOMMENDATIONS 
Introduction 
This chapter summarises the study and concludes on the various objectives set for the study. 
Based on these conclusions, recommendations are made to facilitate the production of BSFL 
meal and BSFL-based fish feed on an industrial scale.  
 
5.1 Summary 
The research sought to ascertain the feasibility of BSFL meal and BSFL-based feed 
production and to identify farmer’s acceptance of the product. The study revealed that the 
BSFL meal production is feasible at 10% below the Selling price of conventional fishmeal at 
NPV of GHS5,735,340.00 an IRR of 32% and a BCR of 2.4. The production of BSFL-based 
fish feed was also feasible at MWTP amount with NPV of GHS8,130,254.76, an IRR of 30% 
and a BCR of 1.95. However the production of BSFL meal and BSFL-based fish feed is 
capital intensive with an initial start-up cost (Fixed asset and operational cost) of 
GHS4,230,193.00 and GHS6,489,280.00  respectively.  
 
In ascertaining the perception and willingness to pay of farmers, 150 farmers were sampled 
from Asuogyaman and lower Manya krobo districts. Majority of the farmers were men and 
had some form of education. Under the perception analysis, the farmers overall mean score 
was 2.8 and 1.4 for benefit and risk statements respectively. When compared to the midpoint 
scale, the mean score for risk was significantly lower (t=-6.576, p<0.003) and for benefit 
score significantly higher (t=19.163, p<0.000) than the midpoint scale. Comparison of the 
overall mean benefit score with the overall mean score for risk showed that the score for 
benefit is significantly higher than that for risk (t=13.137, p<0.000).  
 
 
79 
 
The willingness to pay analysis revealed that farmers are willing to pay an average of 
GH186.00 for the BSFL-based feed. The factors that significantly affected WTP were annual 
income, marital status and awareness of insects as feed. Annual income and awareness 
affected WTP positively which means that higher income earners and people who are already 
aware that insects can be used as feed are more willing to pay higher amounts for the BSFL-
based fish feed. On the other, marital status affected WTP negatively, this means that married 
people are less willing to pay higher prices for BSFL-based fish feed. Finally the partial 
budget analysis showed that, fish farmers will have a net gain of GHS6.53 for every 14.6kg 
of BSFL- based feed used in place of 14.6kg of conventional fish feed. 
 
5.2 Conclusions 
Based on the discussed results the following conclusions arise: The production of BSFL meal 
and BSFL-based fish feed is financially feasible giving 1000 metric tonnes production level 
and a discount rate of 31%. The amount of capital outlay needed to begin operations is high. 
There is a high degree of acceptance of the BSFL-based fish feed amongst fish farmers; they 
have favourable perception towards the use of BSFL in fish feed and are willing to pay for it. 
They do not perceive the use of BSFL in fish feed to be harmful in terms of socioeconomic, 
health and environmental factors. The perceived benefit of the use of BSFL in fish feed is 
significantly higher than the perceived risk of the use of BSFL in fish feed.  
 
The farmers are willing to pay for and use the BSFL-based fish feed at price lower than the 
conventional feed. Being married and aware of insects as feed ingredient, as well as having 
higher annual income enhanced the amounts farmers were willing to pay for a unit of BSFL-
based feed. Finally, replacement of existing feed with BSFL-based fish feed, all other factors 
held constant, will bring about a net gain for the farmer. 
 
 
80 
 
5.3 Recommendations 
The use of BSFL to produce fish feed is a technique that will solve socio economic, health 
and sanitation issues. However, the initial cost of investment is quite high. The following are 
suggested: 
i. Entrepreneurs should form cooperatives and partnerships to produce the BSFL-fish 
due to the high initial investment required 
ii. Scientist-Entrepreneurs fora should be organised to share the results on the viability of 
BSFL production in order to encourage investments. 
iii. Potential BSFL meal manufacturers should be prepared to charge a price below that 
of the existing fishmeal to feed manufacturers. BSFL-based fish feed manufacturers 
should recognise the MWTP amount of farmers. This should be done in order to 
penetrate the market and to convince fish farmers to adopt the new product.  
iv. In making marketing and promotional decisions, the factors that significantly 
influence MWTP should be considered. Hence, higher income farmers and those with 
knowledge of insect as feed ingredient should be targeted by entrepreneurs. Ahead of 
introducing the product to the market, the entrepreneur should launch awareness of 
BSFL campaign to increase the number of people who get sensitised about the BSFL-
based feed. 
v. Farmers should be educated of the gains of switching to BSFL-based fish feed with 
the results attained from the study through workshops and seminars in order for them 
to make informed decisions on what feed to use for fish farming. 
  
 
 
81 
 
REFERENCES 
Abbeam D. G., Addai N.K & Ehiakpor D. (2014). Willingness to pay for farm insurance by 
smallholder cocoa farmers in Ghana. Journal of Social Science for Policy 
Implications. 2(1), 2334-2919. 
 
Agrawal, N., Chacko, M., & Ramachandran, M. M. T. (2011). Assessing the commercial 
viability of BSF as biodiesel & animal feed (Master’s thesis). University of 
California Berkeley, California. 
 
Alimi, T., & Alofe, C. O. (1992). Profitability response of improved open pollinated maize 
varieties to nitrogen fertilizer levels. Journal of Rural Development in Nigeria, 
5(1), 42-47. 
 
Asenso-Okyere, W. K., Osei-akoto, I., Anum, A., & Appiah, E. N. (1997). Willingness to pay 
for health insurance in a developing economy : A pilot study of the informal 
sector of Ghana using contingent valuation. Health Policy, 42, 223–237. 
 
Assis, K. and Mohd Ismail, H. A. (2011).Knowledge, attitude and practices of farmers 
towards organic farming. Int. J. Eco. Res. 2 (3), 1-6. 
 
Awity, L. (2005). National Aquaculture Sector Overview-Ghana. National Aquaculture 
Sector Overview Fact Sheets, 22. 
 
Barry, T. (2004). Evaluation of the Economic, Social, and Biological Feasibility of 
Bioconverting Food Wastes with the Black Soldier Fly (Hermetia 
illucens)(Master’s Thesis). University of North Texas, Texas. 
 
Bondari, K., & Sheppard, D. C. (1987). Soldier ßy, Hermetia illucens L., larvae as feed for 
channel catfish and blue tilapia, Oreochromis aureus (Steindachner). 
Aquaculture and Fisheries Mgt. 18, 209- 220. 
 
Brigham, E.F., Gapenski, L.C. & Daves, P.R. (1999), Intermediate Financial Management, 
6th ed., Dryden Press, Fort Worth, TX. 
 
Burtle, G., Newton, G. L., & Sheppard, D. C. (2008). Mass Production of Black Soldier Fly 
Prepupae for Aquaculture Diets (manual). A manucipt for aquaculture 
international, Georgia. 
 
Campiche, J., Holcomb, R. and Ward, C. (2004). Impacts of consumer characteristics and 
perceptions on willingness to pay for natural beef in the southern plains.” 
FoodTechnology Research Report, P-1006, Oklahoma Food and Agricultural 
Products Research and Technology Center, Oklahoma State University.  
 
Cobbina, R. (2010). Aquaculture in ghana: economic perspectives of ghanaian aquaculture 
for policy development (Master’s thesis). University of Iceland, Reykjavík.  
 
Cummings, R. G., Brookshire, D. S., Bishop, R. C., & Arrow, K. J. (1986). Valuing 
environmental goods: an assessment of the contingent valuation method. 
Rowman & Littlefield Pub Incorporated. 
 
 
82 
 
 
Diclaro, J. W., & Kaufman, P. E. (2009). Black soldier fly Hermetiaillucens Linnaeus 
Insecta: Diptera: Stratiomyidae). EENY,461, 1-3. 
 
Diener, S., Gutiérrez, F. R. O. A., Zurbrügg, C., & Tockner, K. (2009). Are larvae Of the 
black Soldier fly-hermetia illucens- A financially viable option for organic 
waste management in costa rica? In Twelfth International Waste Management 
and Landfill Symposium. Sardinia: CISA. 
 
Diener, S., StudtSolano, N.M., Roa Gutiérrez, F., Zurbrugg, C., & Tockner, K. (2011). 
Biological treatment of municipal Organic waste using Black Soldier Fly 
Larvae. Waste and Biomass Valorisation, 2(4), 357-363. 
 
Drees B, Jackman JA. (1998). A field guide to common Texas insects. Houston: Gulf 
Publishing Company. 
 
El-Sayed, A. F. M. (2013). On-farm feed management practices for Nile tilapia (Oreochromis 
niloticus) in Egypt. On-farm Feeding and Feed Management in Aquaculture, 
Hasan, MR and MB New (Eds.). FAO Fisheries and Aquaculture Department, 
Rome, Italy, 101-129. 
 
Engel. W. (2008). Determinants of consumer willingness to pay for organic food in South 
Africa (Master’s thesis).Extension and Rural Development. University of 
Pretoria, Pretoria. 
 
Erickson, M. C., Islam, M., Sheppard, C., Liao, J., & Doyle, M. P. (2004). Reduction of 
Escherichia coli O157 : H7 and Salmonella enterica Serovar Enteritidis in 
Chicken Manure by Larvae of the Black Soldier Fly. Journal of Food 
Protection, 67(4), 685–690.  
 
ESR International (2008). Bioconversion of Food Waste: Black Soldier Fly. ESR 
International.  06 Dec. 2015. 
 
FAO (1990). The definition of aquaculture and collection of statistics. FAO Aquaculture. 
Min., (7):4 
 
FAO (2010). Fisheries and Aquaculture. Food and agriculture organization of United 
Nations. Retrieved March 11, 2013 from FAO database on the World Wide 
Web:http://www.eoearth.org/article/Food and Agriculture Organization (FAO). 
 
FAO (2012). The State of World Fisheries and Aquaculture. FAO Fisheries and Aquaculture 
Department, Rome, Italy, 230. Retrieved December 20, 2013 from FAO 
database on the World Wide Web:http://www.eoearth.org/article/ Food and 
Agriculture Organization (FAO). 
 
FAO (2014). Moffitt, C. M., & Cajas-Cano, L. (2014). Blue growth: the 2014 FAO state of 
world fisheries and aquaculture. Fisheries, 39(11), 552-553. 
 
FAO (2016). The state of world fisheries and aquaculture 2016. Contributing to food security 
and nutrition for all. Rome, 2016. 
 
 
83 
 
 
FD (Fisheries Directorate). (2013). Reported Aquaculture Production in Ghana (2009- 
         2012). 
 
Gatlin, D. M., Barrows, F. T., Brown, P., Dabrowski, K., Gaylord, T. G., Hardy, R. W. 
&Overturf, K. (2007). Expanding the utilization of sustainable plant products in 
aquafeeds: a review. Aquaculture research, 38(6), 551-579. 
 
Ghosh, M. K. & Hasan, S. S (2013). Farmers’ attitude towards sustainable agricultural 
practices.Bangladesh Research Publications Journal. Vol 8, pp 227-234. Source: 
http://www.bdresearchpublications.com/admin/journal/upload/1308408/130840
8.pdf 
 
Gitman, L. J. (2006). Principles of managerial finance, 11th edn, Person. Addision Wesley, 
London. 
 
Gittinger, J.P.(1982), Economic Analysis of Agricultural Projects. The Economic  
Development Institute of the World Bank. The  John Hopkins University Press. 
Baltimore. 
Gittinger, J. P. (1992). Economic Analysis of Agricultural Projects. Economic Development 
Institute of the World Bank Series in Economic Development. 
 
Glencross, B.D., Booth, M. & Allan, G.L. (2007) A feed is only as good as its ingredients – a 
review of ingredient evaluation strategies for aquaculture feeds. Aquacult Nutr 
13(1),17-34. 
 
Graczyk, T.K., Knight, R., Gilman, R.H., Cranfield, M.R., (2001). The role of non-biting 
flies in the epidemiology of human infectious diseases. Microbes and Infection, 
3, 231-235. 
 
GSS (2014a). Population and Housing Census (2010): District analytical report-Asuogyaman 
district. website: www.statsghana.gov.gh 
 
GSS (2014b). Population and Housing Census (2010): District analytical report - Lower 
Manya Krobo district. website: www.statsghana.gov.gh 
 
GSS (2015). Statistics for development and progress - Annual gross domestic product. 
September edition 
 
Hardouin, J., & Mahoux, G. (2003). Zootechnics of insects-Livestock and use for the benefit 
of man and certain animals. 
 
Heidinger, R.C., (1971). Use of ultraviolet light to increase the availability of aerial insects to 
caged bluegill sunfish. Prog. Fish-Cult., 33, 187-192. 
 
Hickling, C.F., (1962). Fish Culture. Faber and Faber, London, 259 pp. byacano Jr., H.A., 
1974. Pupae of face fly as food for channel catfish. Proc. Annu. Conf. Southeast. 
Assoc. Game Fish Community, 28, 228-231. 
 
Hiheglo, P. K. (2008). Aquaculture in ghana; prospects, challenges, antidotes and future 
 
 
84 
 
perspectives(Masters Thesis). University of Tromso, Norway. 
 
Jolly C.M. and Clonts A.H. 1993. Economics of Aquaculture. Haworth Press Inc. 
Binghamton, N 
 
Jorgenson D. W. (1967).The Theory of investment behaviour. National Bureau of Economic 
Research. 126-175. Website: http://www.nber.org/chapters/c1235 
 
Kaliba, A. R., Engle, C. R., & Bouras, D. (2010). Economic Analysis of Producing Fishmeal 
and Fish Oil from Channel Catfish, Ictalurus punctatus, Processing Wastes. 
Journal of the World Aquaculture Society, 41(1), 49-60. 
 
Kay, D. R., Edwards, M.W., & Duffy, P.A. (2008). Farm Management. New York. McGraw 
Hill publication. 
 
King, D.M., Mazzotta, M.J. Markowits. K.J. (2000). “Ecosystem valuation.” 
(http://www.ecosystemvaluation.org/). 
 
Lalander, C., Diener, S., Magri, M.E., Zurbrugg, C., Lindstrom, A., & Vinneras, B. (2013). 
Faecal sludge management with the larvae of the black soldier fly (Hermetia 
illucens) - From a hygiene aspect. Sci Total Environ, 458-460C, 312-318. 
 
Lawi, M. B. (2014). Assessing consumers’ perceptions and willingness to pay for organically 
grown fresh tomato and pineapple in greater accra region of ghana (Masters 
hesis). University of Ghana, Legon, Ghana. 
 
Leclercq, M., (1997). About Hermetia illucens (Linnaeus, 1758) ("Black soldier fly") 
(Diptera Stratiomyidae: Hermetiinae). Bulletin and Annales of the Royal Society 
Belged 'Entomology,133, 275-282. 
 
Likert, R. (1932). A technique for the measurement of attitudes. Archives of psychology. 
 
Lim, C.E., Webster, C.D. & Lee, C.S. (2008). Alternative protein sources in aquaculture 
diets. The Haworth Press, Taylor and Francis Group. (Editors: Chorn  Lim, Carl 
D. Webster, Cheng-Sheng Lee) United State and Canada. 
 
Loureiro, M.L., McCluskey, J.J. & Mittelhammer, R.C. (2001). Assessing consumer 
preferences for organic, eco-Labeled, and regular apples.” Journal of 
agricultural and resource economics, 26 (2), 404-416.  
 
Lusk, J. L., & Hudson, D. (2004). Willingness-to-pay estimates and their relevance to 
agribusiness decision making. Applied Economic Perspectives and Policy, 
26(2), 152-169. 
 
Makkar, H. P. S., Tran, G., Heu, V., & Ankers, P. (2014). State-of-the-art on use of insects as 
animal feed. Animal Feed Science and Technology, 197, 1–33. 
https://doi.org/10.1016/j.anifeedsci.2014.07.008. 
 
Mbugua, H. M. (2007). A Comparative economic evaluation Of farming of three important 
aquaculture species in kenya (Master's thesis). 
 
 
85 
 
 
McGivern, Y. (2006). The practice of market and social research: an introduction London: 
Pearson Education. 
 
MOFAD. (2016), Fish production by sector. Retrieved from   
http//www.mofad.gov.gh/publications/statistics-and-reports/fish-production/ 5th 
May, 2017.  
 
MoFAD. (2011). Ministry of Fisheries and Aquaculture Development Ghana fisheries and 
aquaculture development plan. Accra. 
 
Newton L, Sheppard C, Watson DW, Burtle G, Dove, R. (June 2005). Using the black soldier 
fly, Hermetiaillucens, as a value-added tool for the management of swine 
manure. Waste Management Programs. North Carolina State University. 
http://www.cals.ncsu.edu/waste 
 
Neupane, R. P., Sharma, K. R. & Thapa, G. P. (2002). Adiotuib if agroforestry in the hills of 
Nepal: a logistic regression analysis. Agricultural Systems, 72, 177-196. 
 
Obirikorang, K. A., Amisah, S., Agbo, N. W., Adjei-boateng, D., Adjei, N. G., & Skov, P. V. 
(2015). Evaluation of Locally-available Agro- industrial Byproducts as Partial 
Replacements to Fishmeal in Diets for Nile Tilapia ( Oreochromis niloticus ) 
Production in Ghana. iMedPub Journals, 1(2), 1–9. 
 
Offei, M.K Egyir, I. . K. T.-M., & Olufunke, C. (2014). Financial feasibility of producing a 
urine-based fertiliser for vegetable farming in accra , ghana Article Info : 
Journal of Advances in Agricultural Science and Technology, 2(1), 1–9. 
 
Olsen, Rl, & Hasan MR. (2012). A Limited Supply of Fishmeal: Impact on Future Increases 
in Global Aquaculture Production. Trends in Food and Science Technology 27 
(2): 120–28. 
 
Owen, N., Leslie, E., Salmon, J. &  Fotheringham, M. J. (2000). Environmental Determinants 
of Physical Activity and Sedentary Behavior. Exercise and Sport Sciences 
Review, 28(4), 153-158.  
 
Owusu, V. and Anifori, M. (2013). Consumer Willingness to Pay a Premium for Organic 
Fruit and Vegetable in Ghana. International Food and Agribusiness 
Management Review, 16, (1), 67-86.  
 
Rurangwa E., Agyakwah S.K, Boon H & Bolman B.C (2015). Development of Aquaculture 
in Ghana, Analysis of the fish value chain and potential business cases. IMARES 
report. C021/15. 
 
Rumpold, B. A.,& Schluter, O.K. (2013). Potential and challenges of insects as an innovative 
source for food and feed production. Innovative Food Science & Emerging 
Technologies, 17, 1-11. 
 
Samuelson, P.A. (1948). Consumption theory in terms of revealed preference. Economica 
15:243–253. 
 
 
86 
 
 
Sheppard, D. C., Larry, G., Thompson, S. A., & Savage, S. (1994). A value added manure 
management system using the black soldier fly. Bioresource Technology, 50, 
275–279. 
 
Sheppard, D. C., Tomberlin, J. K., Joyce, J. A., Kiser, B. C., & Sumner, S. M. (2002). 
Rearing methods for the Black Soldier Fly ( Diptera : Stratiomyidae ), (1926), 
695–698. 
 
Sogbesan, A.O & Ekundayo, T. . (2014). Cost Benefits of Fermented Groundnut Shell Meal 
as Supplemented Feed in the Diets of Clarias gariepinus Fingerlings. Nigeria 
Journal of Fisheries and Aquaculture, 2, 30–41. 
 
Soha, M. E. D. (2014). The partial budget analysis for sorghum farm in Sinai Peninsula, 
Egypt. Annals of Agricultural Sciences, 59(1), 77-81. 
 
Stamer, A., Wesselss, S., Neidigk, R., & Hoerstgen-Schwark, G. (2014). Black Soldier Fly 
(Hermetia illucens) larvae-meal as an example for a new feed ingredients ’ class 
in aquaculture diets.  
 
Stamer, A., Neidig, R, Wessels, S., & Gabriele, H. (2007). Protein concentrates for animal 
feedstuff derived from fly hermetia meal (Abstract). In Utilitsation of diversity 
in land use systems: Sustainable and organice approaches to meet human needs 
(p.82166). 
 
St‐Hilaire, S., Cranfill, K., McGuire, M. A., Mosley, E. E., Tomberlin, J. K., Newton, L., ... 
& Irving, S. (2007). Fish Offal Recycling by the Black Soldier Fly Produces a 
Foodstuff High in Omega‐3 Fatty Acids. Journal of the World Aquaculture 
Society, 38(2), 309-313. 
 
St-Hilaire, S., Sheppard, C., Tomberlin, J. K., Irving, S., Newton, L., & McGuire, M. A. 
(2007). Fly prepupae as a feedstuff for rainbow trout, Oncorhynchusmykiss. 
Journal of the World Aquaculture Society, 38, 59–67. 
 
Tacon, A.G.J., Hasan, M.R. & Metian, M. (2011). Demand and supply of feed ingredients for 
farmed fish and crustaceans: trends and prospects. FAO Fisheries and 
Aquaculture Technical Paper No. 564. Rome, FAO. 87  
 
Tacon, A.G.J., Hasan, M.R., Allan, G., El-Sayed, A.-F., Jackson, A., Kaushik, S.J., Ng, W-
K., Suresh, V. & Viana, M.T. (2012). Aquaculture feeds: addressing the 
longterm sustainability of the sector. Proceedings of the Global Conference on 
Aquaculture 2010, Phuket, Thailand. 22–25 September 2010. FAO, Rome and 
NACA, Bangkok. 193–231. 
 
Tobin, J. (1969). A general equilibrium approach to monetary theory. Journal of money, 
credit and banking, 1(1), 15-29. 
 
Tiu, L.G., (2012). Enhancing sustainability of freshwater prawn production in Ohio. Ohio 
State University South Center Newsletter, 11: 4.   
 
 
 
87 
 
UN-HABITAT. 2010. Solid waste management in the world's cities. Earthscan, London and 
Washington, DC. 
 
van Huis, A., 2013. Potential of insects as food and feed in assuring food security. Annu. 
Rev. Entomol. 58, 563–583.  
 
van Huis, A., van Itterbeeck, J., Klunder, H., Mertens, E., Halloran, A., Muir, G., Vantomme, 
P., (2013). Edible Insects – Future Prospects for Food and Feed Security. FAO,  
171, Forestry Paper. 
 
Verbeke, W., Spranghers, T., De Clercq, P., De Smet, S., Sas, B., & Eeckhout, M. (2015). 
Insects in animal feed: Acceptance and its determinants among farmers, 
agriculture sector stakeholders and citizens. Animal Feed Science and 
Technology, 204, 72-87. 
 
Watanabe, T. (2002). Strategies for further development of aquatic feeds. Fisher Sci, 
68(2),242–252. 
 
Whittington, D. (1998). Administering contingent valuation surveys in developing countries. 
World development, 26(1), 21-30. 
 
Wilson, R.P. (2002). Amino acid and protein (Chapter 3). In: Halver J. E and Hardy 
R.W.Fish nutrition, (3rd version). Academic Press: Elsevier Secience Imprint, 
San Diego, USA, 43–179. 
 
 
  
 
 
88 
 
APPENDICES 
Appendix 1: Questionnaire for fish farmers 
Black Soldier Fly Larvae (BSFL) Based feed production: Profitability and Acceptability 
Analysis. 
This questionnaire is for academic purpose and will be treated as strictly confidential 
Thank you for granting me the opportunity to speak to you, I am a research student from the 
Department of Agricultural Economics and Agribusiness, College of Basic and Applied 
Sciences, University of Ghana, Legon.  In partial fulfilment of my programme, I am 
collecting data to support my Thesis. The major objective is to determine the feasibility of 
black soldier fly larvae (BSFL) meal and feed production and its acceptance amongst fish 
farmers. The specific objectives are to: 1.Determine the viability of BSFL meal and feed 
production, 2. Ascertain fish farmers’ perception towards the technique used for BSFL-based 
fish feed production 3. Determine the amount and factors that influence willingness of 
farmers to pay for BSFL meal and 4. Estimate the gains in substituting conventional feed 
with BSFL-based fish feed. Your assistance and input would be very much appreciated. 
Enumerator …………………………………………………Questionnaire No ……………… 
Field Worker‘s Code/No……………………………….   Date………………… 
District………………………… Mobile phone: …………….......................   
A. Demographic Characteristics 
1. Name of respondent: …………………………………………………………… 
2. Age of respondent (Years): ……………...  
3. Gender: 1= Male [  ]  0= Female [  ] 
4. Religion:  1= Christian [  ]  2= Muslim  3= Traditionalist [  ]  4= Others (specify)……….... 
5. Marital Status: 1= Single [  ] 2= Married [  ] 3= Divorced [  ] 4= Widowed [  ] 
5= Never Married [  ] 
 
 
89 
 
6. Household Size: …………..... 
7. Residential Status: 1= Native [  ]  2= Migrant [  ]  
B. Socioeconomic Characteristics  
8. Highest level of Education reached: 1= No Education [  ]  2= Primary [  ]  3= JHS [  ]  
4= SHS [  ]  5= Tertiary 
9. Are you a full time fish farmer? 1= Yes [   ]  0= No [  ] 
10. If no what other activity do you undertake besides fish farming?………………………….     
11. What type of business do you operate? 1= Hatchery [  ]  2= Grow out [  ] 
12. Land Ownership: 1= Land Owner [  ]  2= Tenant [  ] 
13. What is your annual income? GHS.......................................................................... 
C. Culture Characteristics 
14. Which culture practice are you engaged in? 1= Intensive [  ]  2= Extensive [  ]  3= Semi 
intensive [  ] 
15. What do you farm the fish in? 1= Cage [  ]  2= Pond [  ]  3= Tank [  ] 
15. How many cages/fishponds/fish tanks do you own? …………………………. 
16. Total size of cages/fishponds/fish tanks …………………………………….. 
17. How many years have you farmed on current land? ……………………years 
18. Do you farm tilapia only? 1= Yes [  ] 0= No [  ] 
 i. If “No” what else do you farm?…………………………….. 
20. How many times do you produce the tilapia in a given year? …………………… 
D. Institutional Characteristics 
21. Do you sell directly on the market? 1= Yes [  ]   2= No [  ]  
22. Do you sell through middlemen? 1= Yes [  ]   2= No [  ]  
23. If “No” who do you sell through? ………………………………………  
25. Are you under any contract or agreement to produce? 1= Yes [  ]  0= No [  ] 
 
 
90 
 
26. If “Yes”, with whom do you have such an agreement? 1= Individual(s) [  ]  
2= Restaurant/Hotel [  ]  3= School/Institution [  ]  4= Other(s)…………... 
24. Are you a member of any farmer based organization? 1= Yes [  ]  0= No [  ]  
27. Do you have access to extension service? 1= Yes [  ]  2= No [  ] 
27. How many visits per month? ............................... 
28. Have you borrowed from a financial institution before? 1= Yes [  ]   2= No [  ] 
E. Fish feed 
29. Do you make your own fish feed? 1= Yes [  ]   2= No [  ] 
30. If no then where do you get your feed? .................................................... 
31. Do you think the existing fish feed is expensive? 1= Yes [  ]  2= No [  ] 
32. Do you know the components of the existing feed? 1= Yes [  ]  2=No [  ] 
33. Which of these factors do you consider when buying commercial fish feeds? 
1= Ingredient [  ]  2= Cost [  ]  3= brand [  ]  4= Others [  ] 
F. Perception and awareness of BSFL-based fish feed 
Product Description 
This feed is an insect based feed that is made from the black soldier fly larvae (BSFL) meal 
and other ingredients. Unlike the conventional feeds where the main protein ingredient is 
fishmeal, the BSFL meal has replaced the fishmeal in this feed. This is due to high cost of 
fishmeal and its limited future supply. The BSFL is the larvae of the black soldier fly that can 
be found in tropical areas. It is easy to rear and it feeds on organic waste reducing the 
volume of the waste by up to 50% and producing a biomass that is rich in protein and fat. In 
formulating this feed, the BSFL is reared, harvested, dried and milled. Other ingredients such 
as fish oil, vitamins etc. are finally added to formulate the feed. 
Based on this description, and already existing knowledge on insect based feed (if any), 
please provide answers to these questions and statements. 
 
 
91 
 
34. Have you fed the tilapia with insect before? 1= Yes [  ]  0=No [  ] 
i. What was the result of the trial? 1= poor [  ]  2= Somewhat good [  ]  3= Good [  ] 
35. Have you heard of insect (Black soldier fly) based feed?  1= Yes [  ]  2= No [  ] 
36. If “yes” How did you get to know?  1=  institution/organization [  ]  2= workshop [  ]  3= 
Friends and family [  ]  4= Media [  ]  5= other Specify……… 
37. Please indicate the degree to which you agree or disagree with the following statements. 
Perception Statements                                                                   Disagree  Not Sure    Agree  
(1)             (2)           (3) 
    (Midpoint 
         Scale) 
   
The use of BSFL in fish feed can:   
Risk Statements   
1.  Decrease demand for fish    
2.  Cause release of noxious gases during BSFL production   
 3.  Introduce BSF which will spread diseases to human beings   
4.  Cause microbiological contamination to fish   
5.  Be detrimental to the health of fish consumers   
    
Benefit statements   
1.  Decrease the overexploitation of water bodies    
2.  Lower the cost of Fish feed    
3.  Lower our dependence on imported feed    
4.  Improve organic waste management in the country    
5.  Improve the sustainability of aquaculture production   
 
G. Farmers willingness to pay for the BSFL-based fish feed 
Based on the product description given above, please answer the following questions.  
39. Would you be willing to pay for BSFL-based fish feed? a) Yes…. b) No…. 
40. If “No” what is your reason(s)? 
…………………………………………………………………………………………………
…............................................................................................................................................... 
41. How much would you be willing to pay per KG?  
Elicited bidding Price (GHC)  Yes No 
 
 
92 
 
a. 180    
b. 190  If No (go to “1”) 
c. 200 (starting bid) If Yes (go to “4”) If No (go to “2”) 
d. 210 If Yes (go to “5”)  
e. 220   
 
H. Production Cost 
42. What is the average weight of the fingerlings harvested? ..........kg 
43. How many weeks does it take to grow fingerlings to harvest time? ………. Weeks 
44. How many tonnes of fingerlings do you produce per production cycle? ……… tonnes 
45. How much is the fingerlings produced sold for? …….kg ……GHS…… kg …….GHS 
46. What is your total revenue per production cycle? GHS............................ 
47. How many kilograms of feed do you use per production cycle? ....................................... 
Please provide information on the following operational cost items per production cycle 
Cost item Quantity/Number Cost per unit Total Cost 
Feed @ 1.1mm    
1.4mm 
1.9mm 
2.0mm 
2.5mm 
4.5mm 
 
Labour: Regular    
Casual 
Fertilizer    
Lime    
Transportation: Feed    
Others 
Harvesting cost    
 
Others? (a)Yes............. (b)No....................  
If yes, please specify............................................................................................................... 
................................................................................................................................................ 
................................................................................................................................................. 
................................................................................................................................................. 
 
 
93 
 
................................................................................................................................................. 
Enumerator Comments 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
 
 
94 
 
Appendix 2:  Land size required for BSFL production and cost of land 
Land size needed (metres square) 
Main Building  
office area 18 
Processing unit(oven, and mill) 100 
Room for housing of larvae up to 10 day old before inoculation 1000 
Room for larvae inoculated in waste stored in barrels 1500 
Storage room @ 85tonnes a month capacity 100 
room for housing adult larvae 50 
Sanitary facilities 10 
External Building  
tools and other equipment shed 10 
Vehicle parking 80 
Sorting and cutting of waste area @ 25 metric tonnes of waste a day 210 
Total area size 3078 
  
Average size of one plot in Ghana 900 
total area size in plot 3.5 
Cost of one plot of land at prampram (township where existing feed 16,000 
company Raanan feed is located).  
Cost of Three and a half plot (GHS)  56000 
 
 
 
 
 
 
 
  
 
 
95 
 
Appendix 3: Break down of initial start-up cost (Scenario A) 
Fixed Cost Cost per unit(GHS) Total cost (GHS) 
Land acquisition  16,000                    56,000  
EPA and other survey charges 20,000                    20,000 
Building construction 500,000                  500,000  
Building permit and other contingencies 10,000                    10,000  
Machinery and equipment   
Oven @ 5 mt per day capacity 40,000                    40,000  
mill @ 5 mt per day capacity 50,000                    50,000  
Truck (8*4) 3 mt capacity (2) 93,333                  186,667  
Conveyor system 30,000                    30,000  
Larval containers (0.3m *0.3m) 10                  208,000 
Drums 160                  624,900  
Drum stand 83                  291,620  
Adult cages (3m *3m) 70                      2,100  
Overall 78                      2,000  
Wellington boots 40                      1,200  
Shovels 20                      1,000  
Sieves 12                    13,788  
Wheelbarrows 186                      2,000  
Measuring scale  500                      2,000  
Sharp knifes 5                      1,000  
Cutlass 20                      1,000  
PVC pipes @ 0.7m per drum 18                      6,124  
Collection bins for BSFL 4                    12,498  
Total fixed cost                2,061,897  
Operational cost   
Cardboard 10                    12,000  
Masking tape 10                    12,000  
Labour 8.8/day                  145,200  
Transportation of waste (Fuel) 500 (1 truck)/day                  312,000  
Electricity  8                    84,000  
Water 4                    48,000  
Sacks  2                    50,000  
Maintenance and repairs  5400                      5,400  
Layer meal 70/45kg          1,296,296.30  
Nose mask (Rubber) 20                      2,400  
Gloves 5                      1,000  
Contingencies                   200,000  
Total Operational Cost                2,168,296  
Total start-up cost                4,230,193 
 
 
 
96 
 
 
Appendix 4: Breakdown of layer meal for feeding BSFL 
Months Larvae Cost of Quantity Number Total amount 
needed for meal/45kg of meal of days 
1000metric needed larvae 
tonnes BSFL (Kg)/day is fed 
meal 
January 208,333,333 70 6,944 10 108,025 
February 208,333,333 70 6,944 10 108,025 
March 208,333,333 70 6,944 10 108,025 
April 208,333,333 70 6,944 10 108,025 
May 208,333,333 70 6,944 10 108,025 
June 208,333,333 70 6,944 10 108,025 
July 208,333,333 70 6,944 10 108,025 
August 208,333,333 70 6,944 10 108,025 
September 208,333,333 70 6,944 10 108,025 
October 208,333,333 70 6,944 10 108,025 
November 208,333,333 70 6,944 10 108,025 
December 208,333,333 70 6,944 10 108,025 
Total 2,500,000,000 70 83,333 10 1,296,296 
 
 
 
Appendix 5: Breakdown of cost of producing a kilo of BSFL larvae meal 
Operational Cost GHS 
Cardboard 12,000 
Masking tape 12,000 
Labour 145,200 
Transportation of waste (Fuel) 312,000 
Electricity 84,000 
Water 48,000 
Sacks 50,000 
Maintenance and repairs @2% of cost 5,400 
Layer meal 1,296,296 
Nose mask (Rubber) 2,400 
Gloves 1,000 
Contingencies 200,000 
Total Operational Cost 2,168,296 
Costing by just operational cost ( Per kilo) 2.2 
Costing including fixed cost 'Depreciation of fixed assets' 2,269,181 
Cost of producing one kilo of BSFL meal 2.3 
 
 
 
 
 
97 
 
 
 
Appendix 6:    Scenario A (Breakdown of discounted costs and benefits) 
   Production at market price 
Production Discounted Present value Cumulative 
Discounted Cost 
year Benefit of net benefit Present value 
(4,230,192.98) (4,230,192.98) 
   
1 1,655,188.01 4,474,045.80 2,818,857.79 (1,411,335.19) 
2 1,263,502.30 3,415,302.14 2,151,799.84 740,464.65 
3 964,505.57 2,607,100.87 1,642,595.30 2,383,059.94 
4 736,263.80 1,990,153.34 1,253,889.54 3,636,949.48 
5 562,033.43 1,519,201.02 957,167.59 4,594,117.07 
6 429,033.15 1,159,695.43 722,231.19 5,316,348.26 
7 327,506.22 885,263.69 557,757.47 5,874,105.72 
8 250,004.75 675,773.81 425,769.06 6,299,874.78 
9 190,843.32 515,857.87 325,014.55 6,624,889.33 
10 145,681.92 393,784.63 248,102.71 6,872,992.04 
11 111,207.58 300,598.96 189,391.38 7,062,383.42 
12 84,891.28 229,464.85 142,905.34 7,205,288.76 
13 64,802.50 175,164.01 110,361.51 7,315,650.26 
14 49,467.56 133,712.98 84,245.42 7,399,895.69 
15 37,761.50 102,070.98 64,309.48 7,464,205.17 
16 28,825.57 77,916.78 49,091.21 7,513,296.38 
17 22,004.25 59,478.46 37,041.79 7,550,338.17 
18 16,797.14 45,403.40 28,606.26 7,578,944.44 
19 12,822.24 34,659.09 21,836.84 7,600,781.28 
20 9,787.97 26,457.32 16,669.35 7,617,450.63 
     
NPV 7,617,450.63 
   
IRR 43% 
   
BCR 2.70 
   
 
 
 
 
 
 
 
 
 
 
98 
 
 
Appendix 7: Production at 20% below market price (Breakdown of discounted 
costs and benefits) 
Production Discounted Cost Discounted Present value Cumulative 
year Benefit of net benefit Present value 
   (4,230,192.98) (4,230,192.98) 
1 1,655,188.01 3,579,236.64 1,924,048.63 (2,306,144.35) 
2 1,263,502.30 2,732,241.71 1,468,739.41 (837,404.94) 
3 964,505.57 2,085,680.70 1,121,175.12 283,770.18 
4 736,263.80 1,592,122.67 855,858.87 1,139,629.05 
5 562,033.43 1,215,360.82 653,327.38 1,792,956.44 
6 429,033.15 927,756.35 490,292.10 2,283,248.54 
7 327,506.22 708,210.95 380,704.73 2,663,953.26 
8 250,004.75 540,619.05 290,614.30 2,954,567.56 
9 190,843.32 412,686.30 221,842.97 3,176,410.53 
10 145,681.92 315,027.71 169,345.78 3,345,756.32 
11 111,207.58 240,479.17 129,271.59 3,475,027.90 
12 84,891.28 183,571.88 97,012.37 3,572,040.28 
13 64,802.50 140,131.21 75,328.70 3,647,368.98 
14 49,467.56 106,970.39 57,502.83 3,704,871.81 
15 37,761.50 81,656.78 43,895.29 3,748,767.10 
16 28,825.57 62,333.42 33,507.85 3,782,274.95 
17 22,004.25 47,582.77 25,146.10 3,807,421.05 
18 16,797.14 36,322.72 19,525.58 3,826,946.63 
19 12,822.24 27,727.27 14,905.03 3,841,851.66 
20 9,787.97 21,165.85 11,377.88 3,853,229.54 
     
NPV   3,853,229.54  
IRR   22%  
BCR   2.16  
 
 
 
 
 
 
  
 
 
99 
 
Appendix 8: Production at 15% below the market price (Breakdown of 
discounted cost and benefit) 
Production year Discounted Cost Discounted Present value of Cumulative 
Benefit net benefit Present value 
   (4,230,192.98) (4,230,192.98) 
1 1,655,188.01 3,802,938.93 2,147,750.92 (2,082,442.06) 
2 1,263,502.30 2,903,006.82 1,639,504.52 (442,937.55) 
3 964,505.57 2,216,035.74 1,251,530.17 808,592.62 
4 736,263.80 1,691,630.33 955,366.54 1,763,959.16 
5 562,033.43 1,291,320.87 729,287.43 2,493,246.59 
6 429,033.15 985,741.12 548,276.87 3,041,523.47 
7 327,506.22 752,474.14 424,967.91 3,466,491.38 
8 250,004.75 574,407.74 324,402.99 3,790,894.36 
9 190,843.32 438,479.19 247,635.87 4,038,530.23 
10 145,681.92 334,716.94 189,035.01 4,227,565.25 
11 111,207.58 255,509.11 144,301.54 4,371,866.78 
12 84,891.28 195,045.12 108,485.61 4,480,352.40 
13 64,802.50 148,889.41 84,086.90 4,564,439.30 
14 49,467.56 113,656.04 64,188.48 4,628,627.78 
15 37,761.50 86,760.33 48,998.84 4,677,626.62 
16 28,825.57 66,229.26 37,403.69 4,715,030.31 
17 22,004.25 50,556.69 28,120.02 4,743,150.33 
18 16,797.14 38,592.89 21,795.75 4,764,946.08 
19 12,822.24 29,460.22 16,637.98 4,781,584.06 
20 9,787.97 22,488.72 12,700.75 4,794,284.81 
     
Therefore NPV   4,794,284.81  
IRR   27%  
BCR   2.30  
 
 
 
 
 
 
 
 
 
 
 
100 
 
Appendix 9: Production at 10% below market price (Breakdown of discounted 
costs and benefits) 
Production Discounted Cost Discounted Present value Cumulative 
year Benefit of net benefit Present value 
   (4,230,192.98) (4,230,192.98) 
1 1,655,188.01 4,026,641.22 2,371,453.21 (1,858,739.77) 
2 1,263,502.30 3,073,771.92 1,810,269.63 (48,470.15) 
3 964,505.57 2,346,390.78 1,381,885.21 1,333,415.06 
4 736,263.80 1,791,138.00 1,054,874.21 2,388,289.27 
5 562,033.43 1,367,280.92 805,247.49 3,193,536.75 
6 429,033.15 1,043,725.89 606,261.64 3,799,798.40 
7 327,506.22 796,737.32 469,231.10 4,269,029.49 
8 250,004.75 608,196.43 358,191.68 4,627,221.17 
9 190,843.32 464,272.08 273,428.76 4,900,649.93 
10 145,681.92 354,406.17 208,724.25 5,109,374.18 
11 111,207.58 270,539.06 159,331.48 5,268,705.66 
12 84,891.28 206,518.37 119,958.86 5,388,664.52 
13 64,802.50 157,647.61 92,845.11 5,481,509.62 
14 49,467.56 120,341.69 70,874.13 5,552,383.75 
15 37,761.50 91,863.88 54,102.39 5,606,486.13 
16 28,825.57 70,125.10 41,299.53 5,647,785.67 
17 22,004.25 53,530.61 31,093.95 5,678,879.61 
18 16,797.14 40,863.06 24,065.92 5,702,945.54 
19 12,822.24 31,193.18 18,370.93 5,721,316.47 
20 9,787.97 23,811.59 14,023.61 5,735,340.08 
     
NPV   5,735,340.08  
IRR   32%  
BCR   2.43  
 
 
 
 
 
 
 
 
  
 
 
101 
 
Appendix 10: Sensitivity analysis at 10% cost overrun and 10% production 
decrease (Breakdown of discounted costs and benefits) 
    Production at market price 
Production Discounted Cost Discounted Present value Cumulative 
year Benefit of net benefit Present value 
   (4,230,192.98) (4,230,192.98) 
1 1,820,706.81 3,623,977.10 1,803,270.29 (2,426,922.70) 
2 1,389,852.53 2,766,394.73 1,376,542.20 (1,050,380.49) 
3 1,060,956.13 2,111,751.70 1,050,795.57 415.08 
4 809,890.18 1,612,024.20 802,134.03 802,549.11 
5 618,236.77 1,230,552.83 612,316.05 1,414,865.16 
6 471,936.47 939,353.30 501,889.05 1,916,754.21 
7 360,256.85 717,063.59 356,806.74 2,273,560.95 
8 275,005.23 547,376.79 272,371.56 2,545,932.51 
9 209,927.65 417,844.87 207,917.22 2,753,849.73 
10 160,250.12 318,965.55 158,715.44 2,912,565.17 
11 122,328.33 243,485.16 121,156.82 3,033,721.99 
12 93,380.41 185,866.53 99,307.02 3,133,029.01 
13 71,282.75 141,882.85 70,600.09 3,203,629.10 
14 54,414.32 108,307.52 53,893.20 3,257,522.30 
15 41,537.65 82,677.49 41,139.85 3,298,662.15 
16 31,708.13 63,112.59 31,404.46 3,330,066.62 
17 24,204.68 48,177.55 25,740.88 3,355,807.50 
18 18,476.85 36,776.76 18,299.90 3,374,107.41 
19 14,104.47 28,073.86 13,969.39 3,388,076.80 
20 10,766.77 21,430.43 10,663.66 3,398,740.46 
     
NPV   3,398,740.46  
IRR   19%  
BCR   1.99  
 
 
 
 
 
 
 
 
 
 
 
102 
 
Appendix 11:      Break down of initial start-up cost (Scenario B) 
Cost Amount (GHS) 
Fixed assets ( fixed cost)  
Land 56,000 
EPA, surveyor fees and other charges 20,000 
Building 500,000 
Building permit and other charges 40,000 
Grinder @ 5 tonnes per day capacity 50,000 
Conveyer system 30,000 
Feed Containers 50,000 
Mixer 100,000 
Scale 5,000 
Trucks (2) at 3 tonnes capacity 300,000 
Extruder @ 5 tonnes per day capacity 400,000 
Pelleter 195,000 
Total fixed assets 1,746,000 
Operating expenses  
Electricity 252,000 
Water 144,000 
Cost of ingredients  
Fishmeal 870,000 
BSFL Meal 2,340,000 
Soybean 594,000 
Wheat bran 63,600 
Maize(white) 12,000 
Cassava flour 12,680 
Common Salt 20,000 
Maintenance 30,000 
Permanent skilled 90,000 
Transportation 90,000 
Sacks 125,000 
Contingencies 100,000 
Operating cost 4,743,280 
Total Start-up cost 6,489,280 
 
 
 
 
 
 
 
 
103 
 
Appendix 12: Scenario B (Breakdown of discounted costs and benefit) 
Production at market price 
Production year Discounted Discounted cost Present value Cumulative 
benefit Present value 
0   -6489280 -6489280 
1 7,633,587.79 3,620,824.43 4,012,763.36 (2,476,516.64) 
2 5,827,166.25 2,763,988.11 3,063,178.14 586,661.50 
3 4,448,218.51 2,109,914.59 2,338,303.92 2,924,965.42 
4 3,395,586.65 1,610,621.82 1,784,964.83 4,709,930.24 
5 2,592,050.88 1,230,228.82 1,361,822.06 6,071,752.30 
6 1,978,664.79 939,687.22 1,038,977.57 7,110,729.87 
7 1,510,431.14 717,771.07 792,660.07 7,903,389.94 
8 1,153,000.87 548,269.24 604,731.63 8,508,121.58 
9 880,153.34 418,800.51 461,352.82 8,969,474.40 
10 671,872.78 319,908.69 351,964.09 9,321,438.49 
11 512,879.98 244,371.45 268,508.53 9,589,947.02 
12 391,511.44 186,672.61 204,838.82 9,794,785.84 
13 298,863.69 142,599.02 156,264.67 9,951,050.51 
14 228,140.22 108,932.74 119,207.48 10,070,257.99 
15 174,152.84 83,215.90 90,936.94 10,161,194.93 
16 132,941.10 63,571.20 69,369.90 10,230,564.83 
17 101,481.76 48,564.70 52,917.06 10,283,481.89 
18 77,466.99 37,101.15 40,365.84 10,323,847.73 
19 59,135.11 28,343.96 30,791.15 10,354,638.88 
20 45,141.30 21,654.11 23,487.20 10,378,126.08 
NPV   10,378,126.08  
BCR   2.1  
IRR   38%  
 
 
 
 
 
 
 
 
 
 
 
104 
 
Appendix 13: Scenario B (Breakdown of discounted costs and benefits at MWTP 
amount-7% less the market price)  
Production year Discounted Discounted cost Present value Cumulative 
benefit Present value 
0   -6489280 -6489280 
1 7,099,236.641 3,620,824.427 3,478,412.21 (3,010,867.79) 
2 5,419,264.612 2,763,988.113 2,655,276.50 (355,591.29) 
3 4,136,843.215 2,109,914.59 2,026,928.63 1,671,337.34 
4 3,157,895.584 1,610,621.824 1,547,273.76 3,218,611.10 
5 2,410,607.316 1,230,228.819 1,180,378.50 4,398,989.59 
6 1,840,158.256 939,687.2215 900,471.04 5,299,460.63 
7 1,404,700.959 717,771.068 686,929.89 5,986,390.52 
8 1,072,290.809 548,269.235 524,021.57 6,510,412.09 
9 818,542.6019 418,800.513 399,742.09 6,910,154.18 
10 624,841.6809 319,908.687 304,932.99 7,215,087.18 
11 476,978.3823 244,371.4494 232,606.93 7,447,694.11 
12 364,105.6354 186,672.6116 177,433.02 7,625,127.13 
13 277,943.2331 142,599.0247 135,344.21 7,760,471.34 
14 212,170.4069 108,932.7401 103,237.67 7,863,709.01 
15 161,962.1427 83215.90338 78,746.24 7,942,455.25 
16 123,635.2234 63,571.19548 60,064.03 8,002,519.28 
17 94,378.03316 48,564.69672 45,813.34 8,048,332.61 
18 72,044.30012 37,101.14857 34,943.15 8,083,275.76 
19 54,995.64895 28,343.95939 26,651.69 8,109,927.45 
20 41,981.41141 21,654.10744 20,327.30 8,130,254.76 
NPV   8,130,254.76  
BCR   1.958969834  
IRR   30%  
 
 
 
 
  
 
 
 
 
 
 
 
105 
 
Appendix 14:  Scenario B: Sensitivity analysis at 10% reduction in cost and 
production  
Production year Discounted Discounted cost Present value Cumulative 
benefit Present value 
-   (6,489,280.00) (6,489,280.00) 
1 6,389,312.98 3,982,906.87 2,406,406.11 (4,082,873.89) 
2 4,877,338.15 3,040,386.92 1,836,951.23 (2,245,922.67) 
3 3,723,158.89 2,320,906.05 1,402,252.84 (843,669.82) 
4 2,842,106.03 1,771,684.01 1,070,422.02 226,752.20 
5 2,169,546.58 1,352,430.54 817,116.04 1,043,868.24 
6 1,656,142.43 1,032,389.72 623,752.71 1,667,620.95 
7 1,264,230.86 788,083.76 476,147.10 2,143,768.05 
8 965,061.73 601,590.66 363,471.07 2,507,239.12 
9 736,688.34 459,229.51 277,458.83 2,784,697.96 
10 562,357.51 350,556.88 211,800.64 2,996,498.59 
11 429,280.54 267,600.67 161,679.87 3,158,178.47 
12 327,695.07 204,275.32 123,419.75 3,281,598.22 
13 250,148.91 155,935.36 94,213.55 3,375,811.77 
14 190,953.37 119,034.63 71,918.74 3,447,730.51 
15 145,765.93 90,866.13 54,899.80 3,502,630.31 
16 111,271.70 69,363.45 41,908.25 3,544,538.56 
17 84,940.23 52,949.20 31,991.03 3,576,529.59 
18 64,839.87 40,419.24 24,420.63 3,600,950.22 
19 49,496.08 30,854.38 18,641.70 3,619,591.92 
20 37,783.27 23,552.96 14,230.31 3,633,822.23 
NPV   3,633,822.23  
BCR   1.60  
IRR   13%  
 
 
  
 
 
106 
 
Appendix 15: Breakdown of partial budget analysis 
Diet Feed Cost Harvested Cost of Estimated Profit 
input of biomass fish/kg value of 
(kg) feed (kg) biomass 
(GHS) (GHS) 
Conventional feed 14.6 67.11 7.73 12 92.76 25.65 
BSFL feed @75% 14.6 59.86 7.67 12 92.04 32.18 
replacement of 
fishmeal 
Difference  7.25   0.72 6.53