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THE FORGOTTEN TREASURE: ASSESSING PROFITABILITY AND FACTORS
AFFECTING THE PROFITABILITY OF COWPEA PRODUCTION
A CASE OF GOLOMOTI EPA
BY
PATRICK TSOKA
A RESEARCH PROJECT REPORT SUBMITTED TO THE FACULTY OF
DEVELOPMENT STUDIES, DEPARTMENT OF AGRICULTURAL AND APPLIED
ECONOMICS IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE
AWARD OF THE BACHELOR OF SCIENCE DEGREE IN AGRICULTURAL
ECONOMICS
Lilongwe University of Agriculture and Natural Resources
Bunda College Campus
P.O. Box 219
Lilongwe
Malawi
July, 2016
i
DECLARATION
I, the undersigned, hereby declare that this thesis is my original work and has not been submitted
to any other institution for similar purposes. Where other people’s work has been used
acknowledgements have been made.
PATRICK TSOKA
_______________________________
Signature
______________________________
Date:
ii
CERTIFICATE OF APPROVAL
The undersigned certify that this thesis represents the student’s own work and effort and has been
submitted with our approval.
Signature: _________________________Date:_________________________
Dr. T. Chilongo
Supervisor
Signature: _________________________Date:_________________________
Dr. M.A.R Phiri
Head of Applied Economics Department
Signature: _________________________Date:_________________________
Dr. B.B. Maonga
Dean of Faculty of Development Studies
iii
DEDICATION
This project is dedicated to my Mum and Dad who tirelessly provided me with financial and moral
support just for the purpose of seeing me excel in life. Dad, May your soul rest in peace. To you
John Tsoka, my young brother, keep on making the undeniable marks in academics
iv
ACKNOWLEDGEMENTS
Many thanks to the LORD God Almighty for His faithful in my academics. May His name be
praised.
Many thanks and special appreciation go to my project supervisor Dr. T. Chilongo for the tireless
work and constructive advice he provided me with to make this thesis a possibility.
Special thanks are due to all the lecturers I have encountered at LUANAR who have imparted in
me valuable knowledge.
I am greatly indebted to my family and friends, Flavor, Matilda, Agness and John Tsoka your
words of inspiration kept me working hard even during hard times. You’re such wonderful
family members. Let’s keep Dads flag so high. My uncle, Mr M.E. Kafumbwe, I appreciate all the
support; both moral and financial. Jolly Chibwana, Clement Beza, Wyson Maleta, Jane Bonjesi ,
Joana Kasuzumira and Clara Chirwa you made friends indeed. I salute to your support as
you were eye openers during my time of study.
v
vi
CONTENTS
DECLARATION.......................................................................................................................................... i
CERTIFICATE OF APPROVAL ............................................................................................................. ii
DEDICATION............................................................................................................................................ iii
ACKNOWLEDGEMENTS ...................................................................................................................... iv
CONTENTS................................................................................................................................................ vi
Table of Figures........................................................................................................................................ viii
List of Tables .............................................................................................................................................. ix
ACRONYMS ............................................................................................................................................... x
CHAPTER ONE: INTRODUCTION ....................................................................................................... 1
1.1. Background ...................................................................................................................................... 1
1.2. Problem Statement ............................................................................................................................. 2
1.3. Justification ...................................................................................................................................... 3
1.4. Objectives .......................................................................................................................................... 4
CHAPTER TWO: LITERATURE REVIEW .......................................................................................... 5
2.1. Introduction ........................................................................................................................................ 5
2.2. Cowpea Production Outlook In Malawi ............................................................................................ 5
2.3. Profitability of Cowpeas .................................................................................................................... 6
2.4. Factors Affecting the Profitability of Cowpea Production. ............................................................... 7
2.5. Common Methods of Profitability Analysis ...................................................................................... 9
2.6. Summary .......................................................................................................................................... 10
2.7. Conceptual Framework .................................................................................................................... 10
CHAPTER THREE: METHODOLOGY OF THE STUDY ................................................................ 13
3.1. Study Area (description of the study area) ....................................................................................... 13
3.2. Data Collection Methods ................................................................................................................. 13
3.3. Sampling Design .............................................................................................................................. 13
3.4. Data Analysis ................................................................................................................................... 14
3.4.1 Data Analysis Tools ................................................................................................................... 14
3.4.2 Gross Margin Analysis............................................................................................................... 14
3.4.3 Econometric Analysis ................................................................................................................ 15
3.4.3.1 Regression Model ................................................................................................................... 15
vii
3.4.3.2 Empirical Model Specification ............................................................................................... 15
CHAPTER FOUR: RESULTS AND DISCUSSION ............................................................................. 17
4.1 Introduction ....................................................................................................................................... 17
4.2 Sample Characteristics ...................................................................................................................... 17
4.2.1 Age of the Household Head ....................................................................................................... 17
4.2.2 Education of the household head ............................................................................................... 17
4.2.3 Study area gender distribution. .................................................................................................. 18
4.2.4 Extension Services ..................................................................................................................... 19
4.3 Gross Margin Analysis ..................................................................................................................... 19
4.4 Regression Analysis .......................................................................................................................... 22
Factors Affecting Profitability of Cowpea. ............................................................................................. 22
CHAPTER FIVE: CONCLUSION AND RECOMMENDATION. ..................................................... 24
5.1 Introduction ....................................................................................................................................... 24
5.2 Conclusion ........................................................................................................................................ 24
5.3 Recommendations ............................................................................................................................. 24
REFERENCE ............................................................................................................................................ 26
APPENDIX ................................................................................................................................................ 28
viii
Table of Figures
Figure 1: Cowpea Yield in Malawi ............................................................................................................ 5
Figure 2 : Profitability Conceptual Analysis .......................................................................................... 10
Figure 3: Household Education Levels ................................................................................................... 18
Figure 4: Gross Margin Distribution ...................................................................................................... 21
ix
List of Tables
Table 1: Sample age distribution ............................................................................................................. 17
Table 2: Gender distribution .................................................................................................................. 19
Table 3: Extension Services ...................................................................................................................... 19
Table 5: Sample total gross margin per hectare ....................................... Error! Bookmark not defined.
Table 4: Gross Margins ............................................................................... Error! Bookmark not defined.
Table 6: Factors Affecting Profitability .................................................................................................. 22
x
ACRONYMS
EPA Extension Planning Area
FAOSTAT Food And Agricultural Organisation Statistics
GNI Gross National Income
IITA International Institute for Tropical Agriculture
MoAFs Ministry Of Agriculture and Food Security.
NSO National Statistical Office
PPP Purchasing Power Parity
SSA Sub-Saharan Africa
xi
ABSTRACT
Cowpeas are important grain legumes crop of rain fed agriculture in semi-arid tropics. The crop
is cultivated in on marginal land by resource poor farmers. Cowpea are very drought resistant
and can be grown in areas with less 650 mm annual rainfall. Being a legume, this crop enriches
the soil through the symbiotic nitrogen fixation. In Malawi there is recent realization concerning
the importance of pulses in general, hence increase in production by small holder farmers. Various
studies are thus being undertaken to study different pulses. This study focused on profitability. The
general objective of this study was to assess the profitability of cowpea production whereas the
specific objectives were to assess the levels of profitability (or lack of it) for cowpeas and also to
determine the factors that affect profitability of cowpeas. Gross margin analysis and regression
analysis were used to achieve these objective. The data used in this study was primary data, which
was collected from smallholder farmers in Golomoti EPA, in Dedza. STATA and Excel were used
to analyze the data and carry out both the gross margin as well as the regression analysis. The
average gross margin was found to be positive (MK54 877.07). Several factors were found to affect
profitability of cowpeas, such as production costs, yields, renting land, farm gate price, access to
extension and access to credit. Yields, renting land and farm gate price had a positive influence
on profitability whereas production costs and access to credit had a negative influence on
profitability. Based on the results of the study the production of cowpeas was found to be
profitable. This implied that more farmers should be encouraged to grow cowpeas not just for
subsistence but for commercial production as well. Another recommendation was that value
addition of cowpeas should be encouraged.
xii
1
CHAPTER ONE: INTRODUCTION
1.1. Background
Cowpea (Vigna Unguiculata) and other related pulses are very important leguminous crops as they
are a source of affordable protein, carbohydrates, micro-nutrients, fiber and vitamins in the tropics.
The diet of most people in developing countries is based on processed cereal grains, root and fruits
(Adeola, 2011). Cowpeas provide starch for its consumers and also because they are eaten in large
quantities, they provide considerable level of protein. However, the quality of protein leaves much
to be desired particularly for children, pregnant and lactating women. Additionally, because of
their content they turn to also be beneficial for our skin and hair. Cowpea, because of its high
protein content, constitutes the natural protein supplement and represents the legume of choice for
many people in Africa (Adeola, 2011).
Pulses are increasingly playing a major role in improving farmers’ livelihoods. In addition to their
contribution to nutrition and food security pulses are also major sources of income for smallholder
farmers especially women (Muimui, 2010 and Meradia 2012). Women particularly value cowpeas,
which help them bridge the "hunger months" (Mbwaga 2007). Thus, cowpeas have the potential
to contribute substantially to health, income creation, food security and agricultural sustainability
of less developed countries such as Malawi, Ghana, Nigeria, Zambia and several others in sub-
Saharan Africa (SSA). In some of these countries it is exported to developed countries, thereby,
raising the Gross National Income (GNI).
The fact that it is produced much in developing sub-Saharan Africa of poverty headcount ratio of
46.8% and that includes Malawi which was reported to have poverty headcount ratio of 72.2% at
$1.25 a day (poverty line: -measured at purchasing power parity - PPP) (World Bank, 2011);
makes some people call cowpeas poor man's crop. In some areas cowpea has been referred to as
“the poor man’s meat” (Aykroyd and Daughty, 1982). Farmers in these countries are able to
produce cowpeas due to the crop's adaptability to marginal agro-ecologies and relatively high
market value (Zulu, 2011). Cowpeas are extensively cultivated by resource-poor smallholder
farmers for both household food security and as a cash crop.
2
Cowpeas production in Malawi is countrywide particularly in warm areas with low rainfall such
as Shire valley, Bwanje Valley, Lakeshore and Phalombe plains as well as dry plateau areas of
Machinga. Average grain yields range from 250 to 600 kg per hectare but potential yields of up to
2,000 kg per hectare are possible in pure stand (Nkongolo, 2009). Much of the production is done
in the remote areas by the poor farmers. However, there is great need to transform the surging
cowpea production in the rural areas from subsistence to profitable commercial systems. One of
the factors that should be considered in transforming cowpea production to commercial production
is the market value (Mishili, 2009). Farmers tend to commercialize crops they perceive have a high
market value. Thus, the higher the income realized from the production the more the crop will be
adopted and produced by farmers.
1.2. Problem Statement
Cowpea is one of forgotten crops which tends to play a vital role on the background. Its production
in a poverty stricken country like Malawi is expected to be high because cowpeas are low cost
crops. Farmers do not inject a lot of inputs when producing cowpeas. This is to say that, cowpeas
have a higher output to input ratio as compared to cereal crops such as maize (Auko, 2006). They
are highly productive and at the same time highly nutritive, helping to boost food security sector
and reduce malnutrition problem in Malawi.
According to the study conducted by Maredia (2012) cowpeas and other pulses were found to have
relatively higher price than cereal crops meaning they are income-generating crops. They are more
expensive than cereal crops on the market. They were found to be 2-3 times more expensive than
cereal crop. Because they are sold at a relatively higher price as such making farmers realize higher
profits and earn more income which can be used to buy other food crops, cowpeas have the greater
potential to contribute to poor smallholder farmer’s income than cereals crops.
However, little is known about the profitability and factors affecting the profitability of cowpea
production in Malawi, because despite their greater potential to poverty reduction and wealth
creation cowpeas has received little attention in policy thrust and economic research in many
countries (Zulu, 2011). Very few studies have addressed on cowpea profitability (e.g. FAO, 2002;
Urio, 2005) and most of them are lacking information on cowpea marketing efficiency and
strategies to improve its marketing system so that the market value is high and better profits are
realized by producers thereby boosting farmer's financial stand (Hella et al, 2013). The lack of
3
knowledge does not only affect the producers but also stakeholders who have interest in value
chain of cowpeas. It also affects other farmers in that they are not involved in the production
because they don’t know the market value of the cowpeas. (Mundua, 2010; Zulu, 2011).
1.3. Justification
The study about the profitability of cowpea production is important as it may be one of the factors
that influence production. Farmers are rational and thus they tend to make production decisions in
favour of crops that will yield the most benefits to them, whether market and non-market (Ellis,
1996). Therefore, information about how profitable cowpea is, is essential because if production
is found to be highly income-generating then more farmers are likely to participate in production
of cowpeas. An increase in production of cowpeas would be beneficial to the country because
cowpeas have the potential to address health issues such as malnutrition as well as food security
issues due to their high nutritional value. In addition, increase in production will also improve
farmers’ livelihoods by increasing their incomes. Most value chain stakeholders such as
wholesalers, retailers and other intermediaries are driven by how much impact the production is
making to their livelihood, thus information concerning these factors is vital in influencing their
decision to participate in a value chain. Therefore, the study will also assess factors that affect
profitability of cowpea production. These factors can be used as a basis for policy and strategy
development so as to enhance cowpea production.
According to Hella et al (2013), in Malawi most studies that have been conducted on cowpeas and
pulses in general have focused on improvement of agronomic characteristics such as enhancing
yield, how to combat diseases and variety development. From the reviewed literature there is no
real study that focused on the profitability of cowpeas in Malawi, except few done by Hella and
friends on marketing, thus this study will bring out information that will add to the current body
of knowledge.
4
1.4. Objectives
The main objective of the study was to assess profitability of cowpea production. In order to
achieve this, two specific objectives were used:
1. To assess the levels of profitability (or lack of it) for cowpeas.
2. To identify factors that affect profitability of cowpea production.
The study was based on the following hypotheses;
1. Cowpea production is not profitable.
2. The socio-economic factors (e.g. sex, literacy level, age, land size, etc.) do
not affect cowpea production profits.
5
CHAPTER TWO: LITERATURE REVIEW
2.1. Introduction
This section presents a review of literature from a number of studies that are related to this study
and elaborates on the theoretical basis for the study. The first section provides information about
the production of cowpea in Malawi, the area harvested, the amount of yield. Then the second and
third sections consist of relevant literature on profitability of cowpea and factors that affect
profitability of cowpea production respectively. The very last section consist of conceptual
frameworks for the study.
2.2. Cowpea Production Outlook In Malawi
The local production of cowpea has been rising over the recent years in Malawi. It rose by 21.2
per cent over 2009 production to 36 119 tones in 2013. However, in years between 2000 and 2008
has been a very high production level up until 2009 in which it declined drastically from 52, 437
in 2008 tones to 28, 464 in 2009. According to FAOSTAT (2013), the production is getting back
to higher levels. This has been a result of the profits that farmers are realizing in the recent years.
The 2013 production was estimated at 36 119 tones which is 14.9 per cent higher than the 2012
production. Hectarage increased by 6.5 per cent over 2012 to 2013 while yield increased relatively
higher by 9 per cent. Hectarage was estimated at 75504 hectares and yield at 4784 hg/ha.
(FAOSTAT,2015) as shown in the Figure 1 below.
Figure 1: Cowpea Yield in Malawi
SOURCE: FAOSTAT, 2015
63
39 69
36
63
09
67
44
55
01
43
42 49
62
68
75
64
59
51
66
39
22 45
22
43
54
47
84
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
HG
/HA
YEAR
COWPEA YIELD,MALAWI
6
2.3. Profitability of Cowpeas
Most of the studies that have been conducted on the profitability of cowpea production or other
pulses were done as part of other studies; as observed from the reviewed literature. For instance
on related pulse, a study that was conducted in Honduras on the profitability analysis of beans
which focused on record keeping of data collected in 1998-2000 (Tschering, 2002). That study
identified ways to improve record keeping to reduce the cost of future data collection. An
assessment of the cost pattern of input and labour and consequently a profitability analysis of bean
production for farmers growing traditional and improved bean varieties was conducted. It was
observed that farmers growing modern varieties had higher average yields and earned higher
profits or suffered less loss than the farmers growing traditional varieties.
Tschering’s results were in line with those of Ishikawa (1999), who found that yield was very
influential in explaining profitability. The enterprise gross margin sensitivity analysis showed that
for traditional farmers, gross margins were more sensitive to yield and price changes than for
modern farmers. The study found that none of the farmers in the sample completely followed the
recommended practices for bean production and that the major share of the total production cost
consisted of labor cost.
Despite the fact that little is known about profitability of cowpeas production in Malawi, there are
still some studies that suggest cowpea production is profitable elsewhere. For example in Kaduna
state, the production was found to be profitable with a positive gross margin of 46 096 Naira
(Adeola, 2011). Adeola also found that cowpea production in Kaduna state was profitable with a
return of 45 kobo on every naira invested in cowpea production and also suggested that profits can
be increased if inputs used are adjusted to increase efficiency of usage. Another study on
the value of pulse production in West Africa by Mishili (2009) also suggested that pulses have
high market value. Musa (2010) conducted a study in Tabara state in Nigeria, the number one
cowpea growing country (FAOSTAT, 2013) which also indicated that cowpea production is
profitable with positive gross margins, net farm income and a high return per income invested per
hectare. In a certain study about the global pulse production and consumption trend, Maredia
7
(2012) found cowpeas and other pulses to have higher price than cereal crops meaning they are
income-generating crops. They were found to be 2-3 times more expensive than cereal crop.
Zulu, 2011 in a study about the profitability of cowpeas in Zambia found out that most of the
farmers that grew cowpeas had positive gross margins and the average gross margin was also
found to be positive. Gross margin in the study was used as a proxy for profitability, thus based
on the findings it was then concluded that cowpea production in Malawi’s neighbouring country,
Zambia was profitable.
Hella et al (2013), pointed out that cowpea is slowly becoming a cash crop for resource poor
farmers in semi-arid areas of Malawi and Tanzania. The study portrayed that cowpea is becoming
more profitable in these two countries because farmers turn to commercialize an enterprise that is
profitable ceteris paribus. In the study Hella et al, also explained that, it is expected that the
production should be high in Malawi and Tanzania because of high demand for cowpeas which
can signify the profitability of the production. The study also indicated that there is high preference
for some varieties of the crop in Malawi, which is a proxy for high demand and high prices; thus
making the production profitable.
2.4. Factors Affecting the Profitability of Cowpea Production.
This section reviews some relevant literatures on factors that affect profitability of cowpea
production. It reviews how some economic factors and other factors have affected the profits
realized from cowpeas and other related pulses here in Malawi and other pulse growing countries.
There are many factors approved by literature that affect profitability of agricultural enterprises,
which among others are; the farm gate price, government price policies, farm location, production
costs, variety of seed used, yield, farm size, tillage practices, land tenure which also influences
yield, experience in production of crop which impacts on yield, education level of the household
head, age of household head, gender of household head, household size, off-farm income received,
extension services, and distance to market (Rearden, et al. 1997 as cited by Chimuka, 2011).
8
These factors are found to have significant effect on profitability in some studies where as other
studies find that these factors have insignificant effect on the profitability and also some with
positive effect and the other with negative effect. For instance a study that was done on the
profitability of sorghum in Tanzania found that the farm size, production costs, farm location,
interaction between production costs and farm-gate price as well as the interaction between the
varieties used and fertilizer applied were significant. Farm size was negatively influencing the
gross margin which happens to be contrary to the literature. The interaction between production
cost and farm gate price was positive and significant while farm-gate price alone was not
significant (Zulu, 2011).
Bagamba (1998) in studying the profitability of bananas found that the total farm size, total farm
income, off-farm income, age of the farmer, weevil damage, interaction with government
extension agents, gender of the farmer, distance from the farm to the tarmac, years spent in school
and number of cattle owned had a significant effect on the profitability of banana production.
Similarly in a study that was carried out on the market value of rice in Malaysia, the farm size,
production costs, seed variety, tillage methods and power sources, farm price were found to be
significant.
Chimuka (2011) in a study conducted about the profitability of beans found that factors like; farm-
gate price, household size, household labor, size of owned land and yield were significantly
affecting the profitability of the enterprise. Factors like extension, costs, age, education, farm size
devoted to bean production had no significant effect on the profits.
In agreement to Chimuka’s results, Zulu (2011) also found out that farm gate price, size of the land
owned, yield and area except costs were found significant. And area had a negative effect on gross
margins. While factors like extension, gender, education were found to be of insignificant effect
on the profitability of cowpea.
In these studies some factors were common in affecting profitability of each of these enterprises;
however some of the factors were specific in affecting a particular crop. Thus these factors affect
the profitability indifferent ways depending on the enterprise in question.
9
2.5. Common Methods of Profitability Analysis
Profitability of an enterprise can be analyzed in many ways. The analysis can also take into account
the factors affecting profitability. The methods of profit analysis can be categorized into three
groups; profitability ratios, break-even analysis and calculating return on asset and return
investment ratios. Under profitability ratios are; gross profit margin, operating profit margin ratio,
net profit margin ratio and other common size ratio. Under break-even analysis are; break-even
analysis for sales and break-even analysis for units sold.
The common methods used to analyze profitability in agricultural enterprises include gross margin
analysis, break-even analysis, value of production and total revenue. However, gross margin
analysis appears to be a most common method used to determine profitability, this method of
determining profitability has been used in many studies. For instance, Adeola (2011) used gross
margin to analyze the profitability of cowpea in Kaduna, Nigeria. The enterprise was found to have
positive margins. Zion Banks in its document about how to analyze profitability of an enterprise
recommended gross margin analysis to be one of the most important measures of profitability,
because it looks at enterprise major inflows and outflows.
Gilbert (2001) carried out a study in which he compared gross margin analysis to total revenue in
terms of which method was better in estimating profit. He concluded that gross margin was a more
accurate estimate of profit compared to total revenue (as cited by Zulu, 2011). Adewuyi (2010),
used gross margin analysis when analyzing the profitability of fish farming in Ogun State, Nigeria.
The gross margin analyses involved cost benefit trade -offs where total variable costs were
subtracted from total revenue.
In another study about the determinants of profitability of cowpea in Taraba state gross margin
analysis was also used. (Aboki , 2013). And Zulu (2011) also used gross margin to assess the
profitability of cowpea in Zambia and regression analysis to assess factors affecting profitability
From these studies the most accurate and common method of estimating profits is gross margin
analysis, whereas the most common method of identifying factors that influence profitability is
10
multiple regression in which gross margin is regressed on different factors expected to affect
profitability.
2.6. Summary
So far there is little knowledge about the profitability of cowpea in Malawi as it has been observed
from the reviewed literature. The findings of these past studies did not assess all aspects of value
for cowpea production, especially profitability. It has been noted that the past studies in Malawi
did not really focus on profitability and factors affecting profitability of cowpea. Much literature
on profitability reviewed are from other related countries, it is the thrust of this study therefore, to
establish whether the profitability seen in the scenario presented above, will par with those in
Malawi, especially the study area.
2.7. Conceptual Framework
Optimization behaviour of the producer is the major assumption in this study. The assumption
explains that producers attempt to maximize some objective function subject to a set of constraints.
Literature suggests that risk; the utility derived from production; and profit are the motivational
factors on which a farmer will make his/her production decisions (Knight 1923; Bioca 1997 cited
by Chimuka, 2011). It is assumed that farmers differ in their farm and physical characteristics.
These characteristics are expected to impact on the profits through their impact on the volume of
production, price received per unit of a commodity and the cost structure as depicted in figure 2
below.
Figure 2 : Profitability Conceptual Analysis
Source: Adapted From Engel (2000)
The variation in profitability amongst producers in a particular enterprise can be explained by a
number of reasons. These include aversion to risk and uncertainty; social networks and
11
organization; age, gender, tillage practices, mechanization, household size and education; such
variables may influence the costs of production, volume of production, bargaining ability, and
one’s ability to comprehend technologies.
Coordination of household activities is assumed to be in the hands of household head and as such,
it is important to include attributes such as gender, age and education of the household head in the
specification of the model for factors influencing profitability.
The age of the household head can often be indicative of farming experience as well as the ability
to comprehend new technologies (Matungul et al., 2001). It is expected that younger household
heads have the ability to comprehend new technologies and will therefore readily adopt thus
improving timeliness of operations as well as reducing costs of production. Furthermore, it is
expected that older and more experienced household heads have greater contacts allowing trading
opportunities to be discovered at lower cost (Makhura, 2001).
Gender of the farmer is expected to have an effect on the profitability of cowpea production in
that; the male farmers are likely to access education and other advanced information on production,
so they are likely to make informed decisions. In most developing societies, males have a greater
access to education than females so they will be able to comprehend and understand what is
involved in the credit scheme. With respect to tillage practices, conservation farming practices
have shown to increase volume of production and consequently profits. It is thus expected that
households using conservational tillage practices would record more profit than those using
conventional tillage (Kabwe et al.,2011).
Access to extension services is also expected to have an effect on the profitability of cowpea.
Farmers with access to extension services have also access to new production technology which
in turn lower the cost of production, thus making the farmer realise more profits. Access to credit
is also expected to have an effect on profitability. The farmers with access to credit are in good
position to buy advanced production tools that is making production less costly but on the same
note, credit can increase cost of production as it increases the amount of inputs, hence lowering
profits.
Production costs affect how much profit a farmer can realize from the production. Profit is a
function of the value of what is produced less the cost of production. The more the inputs used the
12
more the cost of production and the less the gross margin. Price of output also has an effect on
profitability, that is, prices determine the revenue realized after sale of produce. The higher the
price the higher the revenue and in turn the higher the profits.
13
CHAPTER THREE: METHODOLOGY OF THE STUDY
3.1. Study Area (description of the study area)
The research was conducted in Golomoti EPA, in Dedza. Golomoti EPA is located to the east of
Dedza district. Golomoti EPA is a lowland site in the lakeshore zone at about 500 m above sea
level and characterized by alluvial soils. The study area was purposively chosen because there
were a good number of farmers who were involved in the production of cowpeas, both for
subsistence and commercial.
3.2. Data Collection Methods
The project carried a verification survey. A structured questionnaire was designed and
administered to collect data from the farmers who were involved in the production of cowpeas.
The questions were arranged in a logical sequence. The researcher carried interviews and recorded
the responses from the questionnaire.
Some secondary data and information were also used in the research which were sourced from
National Statistical Office (NSO), internet, Bunda College Library and the Ministry of Agriculture
and Food Security (MoAFS).
3.3. Sampling Design
Multi-stage sampling method was used in the study. Firstly, Golomoti EPA was purposively
sampled because there are a lot of farmers involved in the production of cowpeas. This was done
to collect sufficient and reliable data for the study. Secondly, some sections where cowpea
production is concentrated within the EPA, particularly two sections were purposively selected for
the study. From each section three villages were selected at random. Lastly, using simple random
sampling 10 farmers were randomly selected from the six villages.
Since the study was sub – national, margin of error (ε) was within ±10%. Proportion of 50% (P =
0.5) was used. Based on sample formula given by Edriss (2003), for 95% (Z= 1.96, 2 – tailed test)
level of confidence. From this therefore, sample size was;
𝒏 =𝒛𝟐(𝟏 − 𝒑)𝒑
𝒆𝟐
14
Fixing values in the above sample formula, n was 96, but due to budget constraint and other
research constraints the sample was reduced to 60. The size was adequate to normalize the
distribution at the same time bearing in mind that this will make a good representation of the
population.
3.4. Data Analysis
3.4.1 Data Analysis Tools
The collected data were analyzed descriptively and quantitatively. Descriptively by computing
means, frequencies and percentages. Econometric tools were used to interpret data quantitatively.
Since quantitative and econometric analysis was carried out to come up with descriptive and
inferential statistics, STATA, Microsoft Excel and CSPro was used to come up with descriptive
statistics. CSPro was used for data entry while STATA and Excel was used for data processing
and analysis.
3.4.2 Gross Margin Analysis
According to literature reviewed, gross margin analysis has been proved to be one of the most
common methods of estimating profits of an enterprise, and that qualifies it to be used in this study.
It has not been used just because is one of the easiest but it has been proved to be a good estimator
of profits. Although gross margin has some limitations, like; the exclusion of the fixed cost when
calculating, gross margin has been proved to be one of the better methods pf estimating profits.
Gross margin analysis is done on the assumption that the fixed cost are negligible (Adeola, 2011),
it also transfers all the other costs to variable cost. This tool was used to achieve the first objective
which was to find out the levels of profits realized from cowpea production. By definition, gross
margin simply means; the difference between total revenue and total variable cost. Thus gross
margin is sometimes referred to as a proxy measure of profitability as it does not include fixed
costs (Taylor, 2003). Nevertheless, as noted above, gross margins are a good indicator of enterprise
profitability.
Algebraically gross margin can be expressed as:
GM = TR – TVC
15
Where; GM = gross margin per hectare
TR = total revenue
TVC = total variable cost
If gross margin is negative (TR<TVC), the enterprise is deemed less profitable. A positive gross
margin (TR>TVC) the enterprise is deemed profitable. Thus a positive gross margin in this study
will indicate that cowpeas are profitable.
3.4.3 Econometric Analysis
3.4.3.1 Regression Model
The multiple linear regression model otherwise known as the multiple regression model is still
remains the most widely used econometric tool for empirical analysis and the social sciences
(Wooldridge 2013). Regression analysis is concerned with the study of the dependence of one
variable, the dependent variable, on one or more other variables, the explanatory variables, with a
view to estimating and/or predicting the (population) mean or average value of the former in terms
of the known or fixed (in repeated sampling) values of the latter (Gujarati, 2003). Ceteris paribus
is one of the key assumption associated with multiple regression analysis because it allows us to
explicitly control for many other factors which simultaneously affect the dependent variable.
Multiple regression analysis can also incorporate fairly general functional form relationships.
3.4.3.2 Empirical Model Specification
In order to achieve the objective which aimed at identifying the factors that affect the profitability
of cowpea production, regression model was used. In this case, Y represented profitability or gross
margin.
The model was specified as follows:
𝑌𝑖 = 𝛽0 + 𝛽1𝑋1 − 𝛽2𝑋2 + 𝛽3𝑋3 + 𝛽4𝑋4 + 𝛽5𝑋5 + 𝛽6𝑋6 + 𝛽7𝑋7 + 𝜀𝑖
Where 𝑌𝑖 = 𝐺𝑟𝑜𝑠𝑠 𝑚𝑎𝑟𝑔𝑖𝑛 𝑜𝑓 𝑐𝑜𝑤𝑝𝑒𝑎 𝑝𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑜𝑛
𝛽0 = 𝐶𝑜𝑛𝑠𝑡𝑎𝑛𝑡
16
𝑋1 = 𝐶𝑟𝑒𝑑𝑖𝑡 𝑎𝑐𝑐𝑒𝑠𝑠 (Dummy—1 if the household had access to credit service and
0 otherwise)
𝑋2 = 𝑇𝑜𝑡𝑎𝑙 𝑓𝑎𝑟𝑚 𝑃𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑜𝑛 𝐶𝑜𝑠𝑡
𝑋3 = 𝐹𝑎𝑟𝑚 𝑔𝑎𝑡𝑒 𝑝𝑟𝑖𝑐𝑒 (𝑀𝑎𝑙𝑎𝑤𝑖 𝐾𝑤𝑎𝑐ℎ𝑎 𝑝𝑒𝑟 𝐾𝑔)
𝑋4 = 𝑅𝑒𝑛𝑡𝑒𝑑 𝑙𝑎𝑛𝑑 (Dummy—1 if the household had rented land in the growing
season)
𝑋5 = 𝐺𝑒𝑛𝑑𝑒𝑟 (Dummy—1 if the household head was male and 0 if the farmer was
female)
𝑋6 = 𝐸𝑥𝑡𝑒𝑛𝑠𝑖𝑜𝑛 𝑠𝑒𝑟𝑣𝑖𝑐𝑒𝑠 (Dummy—1 if the household had access to extension
services and 0 otherwise)
𝑋7 = 𝑌𝑖𝑒𝑙𝑑 (The amount of yield realized in Kg per hectare)
𝜀𝑖 = 𝑆𝑡𝑜𝑐ℎ𝑎𝑠𝑡𝑖𝑐 𝑡𝑒𝑟𝑚
𝛽𝑖 = 𝐶𝑜𝑒𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑡𝑠
17
CHAPTER FOUR: RESULTS AND DISCUSSION
4.1 Introduction This chapter presents a discussion on the study findings. A description of sample characteristics is
first presented followed by the results of the gross margin analysis .The chapter ends with a
discussion on the factors influencing profitability of cowpea production.
4.2 Sample Characteristics
4.2.1 Age of the Household Head
Age is crucial in production of agricultural commodities because it can be translated into years of
experience of the farmer. Experience in turn can affect the profitability of the enterprise. Older
farmers have more experience and thus they may obtain higher yields compared to farmers with
fewer years of experience thus have higher gross margin and hence realize more profits. The study
found out that the average age of the farmers involved in production of cowpeas in the area was
44.7 years. The oldest person was 82 years and the youngest person was 20 years with a standard
deviation of 18 years which means that age was widely spread in the population. It was also
observed that those between the ages 20 years and 60 years were making 78% of the farmers
producing cowpeas. And they had the highest gross margin which was MK 314, 678 and yet with
the lowest gross margin MK -11, 269.38 in the sample. This agrees with literature that 20-60 years
is the productive age.
Table 1: Sample age distribution
Age Range Percentage (%)
20-60 years 78
Greater than 60 years 22
4.2.2 Education of the household head
Education is yet another characteristic worth discussing. The figure below shows the distribution
of education levels in the study area.
18
Figure 3: Household Education Levels
Majority of the farmers who grew cowpeas only had primary level education as shown by the
distribution in the figure above (73.33%). The analyzed results portrayed the farmers who had no
education and those who had primary education had both negative and positive gross margins. But
the majority of those with primary education had positive gross margin. It was found out that 90 %
of those farmers who had primary education had positive margins with just 10 % of them with
negative gross margin. Whereas those who had secondary and tertiary level education all had
positive gross margin. Thus attainment of formal education may have a bearing on gross margin
and thus on profitability. As people are getting more educated, they acquire more skills which help
them in their both production and marketing decisions. This points towards a proposition that
those who spend more years in school are likely to get higher profits because of the
marketing and production skills they acquire.
4.2.3 Study area gender distribution.
The gender of the farmer was another demographic characteristic that was considered. According
to Table 2 below; the distribution was such that they were more male headed households than
female headed households. All the female headed households had positive gross margins but yet
with small gross margins (mean margin of 44, 538.62) whereas male headed households had some
with negative gross margins but they had the highest gross margin in the study area (mean of 58,
636.51). This can be because in most Malawian villages, men are the ones who attain more skills
than women through formal or informal education.
10
73.33
16.67
Household Education Levels
19
Table 2: Gender distribution
Variable Percentage Mean Margin Std dev. Min Max
Male 73.33 58636.51 72384.06 -11269.38 314678
Female 26.67 44538.62 35061.78 308.75 110162
4.2.4 Extension Services
The other demographic characteristic of interest is availability of extension services in the area. It
was found out that 31.67 % of the farmers in the area had access to extension services while the
68.33 % had no access. The 31.67 % who had access to extension services, they made smaller
loses in their production, though with lower gross margins as compared to those without access to
extension services, portraying that extension services have a bearing also on profitability of the
cowpea. Those farmers obtained new skills of producing cowpea from the extension workers,
hence small losses.
Table 3: Extension Services
Variable Percentage Mean Std Dev. Min Max
Extension access 31.67 33272.48 29730.73 -846.85 96083
No Extension
access
68.33 64888.95 7472.53 -11269.38 314678
4.3 Gross Margin Analysis
Table 4: Sample total gross margin per hectare
ITEM AMOUNT (MK)
TOTAL VARIABLE COST 856, 993.8
TOTAL REVENUE 4, 149, 618
GROSS MARGIN 3, 292, 624
The gross margins were calculated by subtracting the total variable cost from each household from
total revenue from that household. The total gross margin calculated from the whole sample was
20
MK 3, 292, 624 (that is an income for a 60 farmers) and the total variable cost was MK 856, 994
and total revenue was MK 4, 149, 618. The total variable cost included the production costs,
processing cost, marketing cost and transportation cost and other variable costs which included
handling and packaging costs. The production costs included seeds, fertilizer, pesticides,
herbicides, labor and land rentals. The major costs were observed to be seeds, pesticides, labor and
land rentals.
Gross Margins
The study discovered that almost all the farmers in the study area had positive gross margins. There
were only four farmers who had negative gross margins. It was discovered that 93.33 % of the
farmers in the area had positive margins with only 6. 67 % of the farmers with negative margins.
The highest gross margin was MK 314, 678 and the lowest was MK -11, 269. The average gross
margin was found to be MK 54, 877. Table 5 shows this information.
Table 5: Gross Margins
Variable Obs Mean Std Dev. Min Max
TVC 14283.23 18068.96 846.8572 117572
Total Revenue 69160.3 75941.28 0 432250
Gross Margin 60 54877.07 64580.57 -11269.38 314678
The pie chart below shows the distribution of gross margin by households in the area,
21
Figure 4: Gross Margin Distribution
Those farmers with negative gross margins were found that they lacked skills in production of
cowpea as being portrayed by analysis results where they were all found that the highest education
level among them was primary education particularly standard 8. The other reason for the negative
margins was that of poor rains. It was reported in an exploratory survey that people can have as
high as one million of kwachas of profits when the rains are good. Some farmers in the same
exploratory survey reported that; they even have minibuses and cars from the production. But in
the 2014/2015 growing season was not good for production as it faced a dry spell. The farmers
produced because cowpea is drought resistant crop but optimal rainfall is always vital for optimal
results. Those farmers who had negative gross margins reported that they planted one type of
cowpea, oyanga1 (Calhoun P. Hull and Colossus) as described by the farmers while the other
farmers diversified; they produced both mkhalatsonga2 and oyanga. Mkhalatsonga (MN13 and
MN 150) is upright early maturing variety and is planted earlier (together with maize-early
December) too while the other is climbing late maturing and is planted late (when the rain season
is ending-late January-March).
1Oyanga is in English translated as climbing, that varieties of cowpea that is climbing
2Mkhalatsonga is that variety of cowpea that has erect stem (upright)
93%
7%
Gross margin distribution
Positive (93.33%)
Negative (6.67%)
22
4.4 Regression Analysis
Factors Affecting Profitability of Cowpea.
Using F-test, the regression model was found to be significant at 1%, this means the model
satisfactorily explained the variation in gross margin. Table 6 shows that; access to credit, yield
and production costs were found to be significant at 99% confidence while farm-gate price,
extension services and rented land was significant at 95% confidence. This was consistent with
literature that was reviewed in this study. Studies that were done on profitability of beans and also
on cowpea in Zambia and also that done on dairy production in Malawi showed similar results on
the significant variables (Chimuka, 2011; Nyekanyeka, 2011 & Zulu, 2011).
The farm-gate price had a positive relationship with gross margin i.e. increasing the price would
increase the gross margin. This can be seen from the results that the price coefficient was 95.84566
meaning that for every MK1 increase in price the gross margin will increase by MK96. This was
expected because the price greatly affects the market value of any item thus a higher price is
expected to lead to higher revenue and therefore a higher gross margin.
The yield from the production had also a significant positive effect on the gross margin of cowpea.
It was found out that the effect was significant at 99% confidence. An increase in yield has a
positive relationship to gross margin because increasing the quantity harvested increases the
number of kg’s that can be valued. These results also agree with that those of Ishikawa, 1999 and
Tschering, (2002) who had similar findings in their studies.
Table 6: Factors Affecting Profitability
Variable label Coefficient Std. Error T Confidence Interval
Credit Access -384754.1*** 70314.25 -5.47 -525849.9 -243658.2
Gender 22644.27 14122.76 1.60 -5695.122 50983.67
Yield(Kgs) 20921.73*** 2547.93 8.21 15808.93 26034.52
Farm-gate Price
(MK)
95.84566** 47.50082 2.02 .5282902 191.163
23
Rented Land -28894.16** 13885.6 -2.08 -56757.65 -1030.674
Extension services -21948.53** 12023.44 -1.83 -46075.33 2178.277
Production Cost
(MK)
-1.28282*** 0.4375791 -2.93 -2.16089 -.404757
Constant -23719.46 20708.21 -1.15 -65273.52 17834.59
Note :* P<0.10; ** P<0.05; *** P<0.01 R2= 0.6445 Number of observations: 60 F( 9, 50) = 13.47 Prob > F =
0.0000
Costs as to be expected had a significant negative relationship to gross margin at 99% confidence.
This is like this because as the costs of production increase more revenue is used to cover costs
rather than to contribute to gross margin. The higher the cost the lower the gross margin. Renting
land had a significant effect on how much profits the farmer was realizing. The effect of renting
land in this study may be in contrary to some studies but renting land didn’t just increase the cost
in terms of paying money for rent but most importantly increased the area to add more other costly
inputs.
Access to credit reduced the gross margin at 99% confidence. Which is contrary to most literature,
but in this study it may be because of the form of the credit these farmers accessed. Most farmers
accessed the input credit which were seeds from fellow farmers, and they were paying seed in
return which reduced valued output hence reducing gross margin. The negative coefficient on
extension is in contrary with some literature but in agreement with studies done by Zulu in Zambia
and also with that by Nyekanyeka in Malawi. (Zulu, 2011 & Nyekanyeka, 2011).
Gender had no significant effect on the gross margin. But looking at the sign of the coefficient of
the gender, which is a dummy variable it shows that men were the ones experiencing positive
returns in the production. There was higher likelihood to experience positive return when a
household head was a male than female, though insignificant.
24
CHAPTER FIVE: CONCLUSION AND RECOMMENDATION.
5.1 Introduction
This chapter starts by concluding based on the findings, in order to answer the objectives of the
study. This will then be followed by recommendations that can be drawn from the findings and
conclusions of the study.
5.2 Conclusion
The study focused on the value accruing to producers of cowpeas and the factors influencing it.
The specific objectives were; to assess how profitable or not cowpea production is and to identify
factors that affect profitability of cowpea production. From literature cowpea production was
expected to be profitable owing to the fact that it’s low cost crop to produce and it has a conversion
ratio i.e. high output can be expected from the input. In this study most of the farmers that grew
cowpeas were found to have positive gross margins and the average gross margin was also found
to be positive. Gross margin in this study was used as a proxy for profitability, thus based on the
findings it can be concluded that cowpea production is profitable.
According to literature as well as theory there are many factors that influence or affect profitability
of any enterprise. In this study the gross margin (proxy for profitability) was regressed on many
variables in order to identify the factors that affect profitability of cowpea production and it was
found that farm gate price, yield, access to credit, costs of production, access to extension services
and renting land have a significant effect on gross margin. Farm gate price and yield were found
to positively influence gross margin whereas renting land, access to extension services, credit and
production costs had a negative influence on the gross margin. Thus based on these findings, these
are the factors that affect profitability of cowpea production.
5.3 Recommendations
Based on the results, it is therefore recommended that;
More farmers should be encouraged to grow cowpeas not just as a subsistence crop but as a cash
crop. This would generally improve the living standards of the farmers because their incomes
would be increased. Since Malawi has numerous small holder farmers improvements in their living
standards would increase the welfare of the country as a whole. Increasing production of cowpeas
25
would also improve nutritional status not only of the farmers who grow the cowpeas but also of
the people who purchase and consume it.
26
REFERENCE
Adeola, S.S, Folorunso, S.T, Gama, E.N., Amodu, M.Y, and Owolabi, J.O. (2011). Productivity
and Profitability Analyses of Cowpea Production in Kaduna state, Advances in Applied Science
Research, 2 (4), 72-78
Auko, Y. B. (2006). Challenges and Prospects and Utilization of Traditional Grains in Uganda.
Kampala: Department of food science and technology, University of Makerere.
Aboki, E & Yagunda, R. (2013). Determinant of Profitability in Cowpea Production in Takum
Local Government Area of T araba State, Nigeria. Journals of Agricultural Science 3(2) 33-37.
Aykroyd, W.R. and Doughty, J. (1982). Legumes in human nutrition. FAO nutrition studies, no.
19, FAO, Rome.
Bagamba, J. S. (1998). Performance and Profitability of the Banana Sub-sector in Uganda
Farming Systems. C.Picq, E.Foure and E.A Frison, INIBAP, Montpelier
Chimuka P., (2011). An Assessment of Factors Influencing the Profitability of bean Production in
Zambia. University of Zambia. Zambia
Ellis, F. (1988). Peasant Economics: Farm households and Agrarian Development. Cambridge
Press. UK
FAO 2002; Commodity market Review 2001 -02; Issues in agricultural commodities markets.
FAOSTAT/FAO: http://faostat3.fao.org
Gujarati, D.N. 2003. Basic Econometrics. New York: McGraw Hill Book Co
Hella, J.P., Chilongo, T., Mbwaga, A.M., Bokosi, J., Kabambe, V., Riches, C. and Massawe, C.L.
(2013). Participatory market-led cowpea Breeding In Sub-Sahan Africa: Evidence pathway from
Malawi and Tanzania. Merit Research Journals of Agriculture and Soil Sciences,1(3), 011-018.
27
Ishikawa, Y. (1999). The Profitability Analysis of Bean Production in Nicaragua. Department of
Agricultural Economics, Michigan State University.
Kabwe, S, S Haggblade, and C. Plerhoples. (2011). Productivity Impact of Conservation
Farming in Zambia on Smallholder Cotton Farmers in Zambia. Food Security Research Project
Web site. FSRP.
Makhura, M.T. (2001). Overcoming transaction costs barriers to market participation of
smallholder farmers in the Northern Province of South Africa. Unpublished PhD thesis, University
of Pretoria, South Africa.
Maredia M. (2012) Global Pulse Production and Consumption Trends: The Potential of Pulses to
Achieve ‘Feed the Future’ Food and Nutritional Security Goals. Michigan State University Global
Pulse Researchers Meeting. Rwanda.
Matungul, P.M., M.C Lyne., and G.F Ortmann.(2001). Transaction Costs and Crop Marketing in
the Communal Areas of Impendle and Swayimana, KwaZulu-Natal. Development Southern
Africa.18 (3): 347-363
Mbwaga A.M., Kabambe V., Riches C. (Eds) (2007). Development and promotion of Alecta
resistant cowpea cultivars for smallholder farmers in Malawi and Tanzania. McKnight
Foundation collaborative crops research project report No :06-741. 5pp.
Mishili, F. J. (2009). Consumer Preferences for Quality Characteristics Along the cowpea value
chain in Nigeria, Ghana and Mali. West Lafayette: Wiley InterScience.
Mundua, J. (2010). Estimation of consumer preference for cowpea Varieties in Kumi and Soroti
Districts, Uganda. Msc Thesis, Makerere University
Muimui, K. K. (2010). Beans Stakeholder Consultative Workshop. Common Wealth Youth
Program me Africa. Lusaka.
Musa, Y.H vosanka, I.P., Inuwa A and Mohammed, S.(2010). Economic analysis
Of cowpea production in Donga local government Area of Taraba state, Nigeria. Vol.2
pp 9-12
28
Nkongolo, K. K, Bokosi J., Malusi M., Vokhiwa, Z. and Mphepo, M. (2009). Agronomic,
culinary, and genetic characterization of selected cowpea elite lines using farmers’ and breeder’s
knowledge: A case study from Malawi. African Journal of Plant Science, 3 (7), 147-156.
Nyekanyeka T. (2011). Analysis of profitability and efficiency of improved and Local smallholder
dairy production: A Case of Lilongwe Milk shed Area. Bunda College. University of Malawi.
Tcshering, C. (2002). Profitability Analysis of Beans Production in Honduras. Michigan:
Department of Agriculture Economics, Michigan State University.
Urio PA (2005). Cowpea and its contribution to household food, nutrition and income in Mbarali
and Njombe districts. Dissertation for the Award of MSc, Sokoine University of Agriculture,
Tanzania.
Wooldridge, J.M. (2013). Introductory Econometrics: A Modern Approach. South Western.
World Bank. 2013. Malawi: Public expenditure review. Washington, D.C
Zulu, E. T. (2011). Profitability of Cowpeas in Zambia. University of Zambia. Zambia
APPENDIX
Questionnaire
29
QUESTIONNAIRE NUMBER:
A. IDENTIFICATION
ASSESSING PROFITABILITY AND FACTORS AFFECTING PROFITABILITY
OF COWPEA PRODUCTION
CASE STUDY OF GOLOMOTI EPA.
30
A1. Date of Interview :
A2. Name of Interviewer :
A3. Name of the respondent :
A4. Name of the household head :
A5. Sex of household head :
A6. Name of ADD :
A7. Name of RDP :
A8. Name of EPA :
A8.1. Section :
A8.2. GVH :
A8.2. Village :
A9. Traditional Authority :
A10. District :
B. HOUSEHOLD CHARACTERISTICS
B1. What is the size of your household?
B2. Household head Characteristics: Now I would like to know more about your
household members including yourself.
B2.1 B2.2 B2.3 B2.4 B2.5 B2.6
Name of household
member
Age
(years)
Sex Relation to
head
Level of
education(number
of yrs spent in
school)
Marital
status
31
B2.1 B2.2 B2.3 B2.4 B2.5 B2.6
Name of household
member
Age
(years)
Sex Relation to
head
Level of
education(number
of yrs spent in
school)
Marital
status
CODES
B2.3 B2.4 B2.6
1 = Male
0 = Female
01=Head, 0 6=Son/daughter in-law
02=Spouse, 07= Uncle
03=Father, 08= Grand -child,
04=Mother, 09= Relative,
05=Son/daughter 10= Brother/sister,
01=single
02=married
03=widowed
04=divorced
C. ACCESS TO LAND
C1. Land Access
(i) Do you own any land?
[1] Yes [0] No (if NO skip to (iii) )
32
(ii) If YES to (i), how much land do you own?
(iii) Do you rent any piece land?
[1] Yes [0] No (if NO skip to (v) )
(iv) If YES to (iii), how much land do you rent?
D. COWPEA PRODUCTION
D1. Do you grow cowpea? [1] Yes [0] No
D2. What is the size of land on which you grew cowpea in the 2014/15 season?
D3. How much cowpea do you produce on average (no. of 50 kg bags?)
D4. Other crops grown for sale apart from cowpea:
D5. COWPEA PRODUCTION COST (2014/15 growing season )
Input Costs Quantity Unit Price Amount (MK)
Seeds (Kgs)
Fertilizer (Kgs)
Pesticides
(g/litres)
Herbicides
(g/litres)
Land rental
(acre)
Labor
Total
33
D6. How much cowpea did you produce in 2014/2015?
D7. What was the average cowpea selling price?
D8. OTHER VARIABLE COSTS INCURRED:
Activity: Costs:
1) Processing
2) Handling
3) Package
4) Other
D9. MARKET COST:
Activity Cost (MK)
Transport
Market fee
Other
Total
E. OTHER GENERAL FARM INFORMATION
E1. Access to extension services
(i) Do you have access to extension services?
[1] Yes [0] No (if NO skip to C4)
(ii) If YES to (i), how many times per month:
[1] Once [2] twice [3] thrice [4] four times
[5] other (specify)
E2. Access to markets
(i) Where do you sell cowpea produce?
[1] ADMARC [2] local market
[3] other (specify)
34
(ii) Why there?
1. Good price 2. near 3. other (specify)
(iii) Who decides the selling price?
[1] Forces of demand
[2] Buyers
[3] Sellers
[4] Buyers and sellers based on agreed contracts
[5] Other (specify)
(iv) What is the selling price per kg
(v) Do you think of changing where you are selling to another market:
[1] Yes [0] No. (if NO skip to (vii) )
(vi) If YES to (v), why?
(vii) What is the distance between your farm and your market?
[1] Less than one kilometer
[2] 1-2 kilometer
[3] 2-3 kilometer
[4] More than three kilometer
(viii) Mode of transportation to the market:
[1] Walk
[2] Bicycle
[3] Motor cycle
[4] Vehicle
35
[5] Other (specify)
E3. Access to credit services:
(i) Do you have access to credit?
[1] Yes [0] no (if NO, skip to iv )
(ii) If YES to (i), where?
[1] Formal banks [2] Micro-credits
[3] Others (specify)
(iii) What form of credit?
[1] Cash [2] Inputs [3] Others (specify)
(iv) If No to (i), why:
F. PROBLEMS AND CHALLENGES
F1. What market problems do you face?
F2. Could you suggest any solutions to the problems?
THANK YOU (ZIKOMO KWAMBIRI)
