Download Evaluation of efficiency of orange marketing system in Tanzania

Sky Journal of Agricultural Research Vol. 1(2), pp. 12 - 27, December, 2012
Available online http://www.skyjournals.org/SJAR
©2012 Sky Journals
Full Length Research Paper
Evaluation of efficiency of orange marketing system in
Tanzania: Empirical evidence from Muheza district in
Tanga region
Makorere Robert* and Mbiha Emmanuel
Department of Agricultural Economics, Sokoine University of Agriculture, Tanzania.
Accepted 10 December, 2012
Orange is one of the most important crops in Muheza District of Tanga region in Tanzania, to improve farmers’
income from production and marketing of orange. Design and implementing proper marketing strategy is
necessity. In this study using analysis of market operating costs and pricing efficiency as well as profit margins
were used to study efficiency of orange market sub sector in Muheza district. This study aimed to evaluates the
efficiency of orange market sub sector using a sample of 152 farmers drawn from 13 villages in Muheza district,
Tanzania. It was hypothesized that smallholder farmers significantly connected to orange market inefficiency in
relation with other market participants. The result suggests that quantitatively smallholder farmers were
efficiently connected to orange markets. Overall farmer spent less marketing cost, which as TZS 1.68, followed
by wholesalers incurred TZS 143.32 and retailers incurred TZS. 193.8 per output. However, farmers earned net
profit margin of TZS 18.32, followed by Wholesalers TZS 44, and the last was retailers who earned TZS 40. In
general orange farmers found to have high market share of 43% in relation with other market actors. It is
therefore, recommended that the efficiency of orange market system for smallholder farmers could be
increased through the provision of subsides for inputs to reduce cost of production and enlightenment
campaigns to improve farmer’s knowledge and technical skills on how to reach lucrative markets.
Key words: Orange, orange marketing system, market efficiency, market operating costs, profitability.
INTRODUCTION
Since independence in 1961, agriculture is a leading
sector of Tanzanian economy and accounted for an
average of 26.5% of gross domestic product (GDP), 80%
of employment and about 35% of export earnings from
year 2005 to 2007 (URT, 2008, URT, 2008/9). For
instance over 80% of the poor people live in rural areas
and their livelihood depends on agriculture (URT, 2008).
The sector remains essential for food security and
poverty reduction as well as raw material for the industrial
sector. Despite this predominant role in the economy, the
performance of agriculture has not been satisfactory
(Danielson, 2002; Moshi, 2003). The sector grew at 3.3 in
1985 and went on increasing to a rate above 5.4 between
2001 and 2003 (Danielson, 2002); thereafter, it has been
growing at decreasing rate of 5.9, 4.3, 3.8, and 4% for the
*Corresponding author. E-mail: [email protected].
Tel: +255 (0) 232604380/ +255 784 235089. Fax: +255 (0) 23
2604382.
year 2004 to 2007 (URT, 2008/9). This is because
agriculture is almost completely rain dependent and
mainly low-technology with 70% of all farms cultivated by
hand hoe (Irish, 2008), cited by Tu (2008). Other causes
leading to poor agricultural performance including price of
inputs are high and poor access to support services
(credit facilities, extension services). Consequently,
poverty in Tanzania is still uncontrolled and per capital
income is less than USD 400 per year (ESRF, 2002;
TCCI, 2004). And that is why the agricultural sector is
considered to be unsatisfactory because it has failed to
improve livelihood of the rural people, whose major
occupation is agriculture (URT, 2005).
Despite the availability of markets for oranges in
Tanzania, still farmers’ livelihood is not improved. This
could be mainly because of market inefficiency and other
related factors. Market inefficiency is one of the problems
facing the Tanzanian orange industry. According to
Shapiro and Staal (1995), this could be a result of a
number of reasons. First, market inefficiency could result
from imperfect competition where a small number of
Robert and Emmanuel
buyers are able to influence aggregate demand and
therefore affect market prices. Secondly, inefficiency may
result from externalities whereby producers are unable to
capture the full benefits for the crops they produce.
Finally, “institutional” market inefficiency can be
experienced in a situation where markets do not function
efficiently because of inadequate development of
infrastructures (processing unit, storage facilities, roads
and communication) and institutions (financial services,
partnership arrangement). Market inefficiency influences
costs and revenues and prevents the realization of
potential income gains. Prices that a farmer faces are not
profitable and thus affects their income and welfare,
hence poverty status. Price incentives are captured
where transaction cost is low and market inefficiency can
be easily collected.
Model of marketing efficiency analysis
The study was aimed to evaluating efficiency of orange
marketing sub-sector in Tanzania especially Muheza
district. For the case of relevant analytical tools, the study
reviewed past studies which use related analytical tools.
Such as the study done by Emam (2011:22) shows that
marketing efficiency is measured as: Marketing efficiency
= (Gross marketing margin/ marketing cost) × 100. Also,
Sherpherd formula technique was used as follows:
marketing efficiency = (Consumer price/Total marketing
cost) – 1. While the study done by Mari (2009:15) shows
there are two approaches of evaluating marketing
efficiency of agricultural crops. One approach is to
evaluate the economic efficiency of marketing by using
marketing margins, prices received by the producers,
marketing costs and profit share; another was to evaluate
the operational efficiency of marketing by examining the
net returns on capital invested and the commission
received. A study on market efficiency done by Sindh
(Toaha, 1974), also support marketing efficiency could
evaluated by using cost of production, marketing
margins, net margin and breakdown the consumer’s
prices as well. Furthermore, Carl and Irwin (1997) did a
research on market efficiency and marketing to enhance
Income of Crop Producers using Grossman and
Stieglitz’s model of market efficiency to evaluate
marketing efficiency of agricultural crops in Ohio Stat,
USA. Delgado and Staatz (1980: 62) did study on beef
marketing efficiency. An evaluation of the efficiency of the
beef market in Alphabeta was carried out by examining
processing costs. According to Fama (1991) used
efficient market hypothesis model to evaluate marketing
efficiency. Moreover, the study done by Mari (2009:15)
on structure and efficiency analysis of vegetable
production, and marketing in Sindh, Pakistan, measured
marketing efficiency by looking on the relationships
across marketing chains involved in the selected
vegetable were studied by investigating marketing
13
margins, distribution of costs and net returns across the
functionaries. A study done by Toaha (1974) on market
efficiency in Sindh, used cost of production, marketing
margins, net margin and consumers’ price to measure
market efficiency in Sindh. However, the study by Haji
(2008) did a research on economic efficiency and
marketing performance of vegetable production in the
eastern and central Parts of Ethiopia; measured
marketing efficiency by assessing marketing performance
of vegetables in Ethiopia.
MATERIALS AND METHODS
The study area
The study was carried out in Muheza District of Tanzania
Mainland. Muheza district was purposively selected
because is the largest orange producer in Tanga region
(Makange, 2009; Mwanakatwe, 2006; Erick, 2008).
Muheza District lies south and west of Tanga district and
is bordered by Mkinga to the north, Pangani in the south
and Korogwe district in the west. Muheza district has a
2
2
total area of 1,974 km and arable land covers 1,145 km .
Approximately 70% of the arable land is utilized and the
rest unexploited. Tanga region is located in the northeastern side of the Tanzania mainland. It is bordered by
the republic of Kenya in the north, Kilimanjaro region in
the northwest, Manyara region in the west, Morogoro and
Coast region in the south and the Indian Ocean in the
east. Administratively, the region is divided into eight
districts, namely Handeni, Kilindi, Korogwe, Lushoto,
Muheza, Pangani, Tanga and recently Mkinga. The
2
region has an area of 26,770 Km or 3.0% of total land in
Tanzania, of which about 75% of the land (approximately
2 million hectares) is estimated for agriculture accounting
for 20% of the regional total land. Agriculture in this
region is the major economic activity of the people and
the major crop being oranges. Tanga is the largest
orange grower in the country (URT, 2007). Muheza
district is the largest orange producer within the Tanga
region. Figure 1 is a map of Tanga region shows the
existing districts.
Sampling and data collection
Data collected was purposively selected from 13 villages
based on the volume of orange production namely Kwabada, Mtindiro, Mkuzi, Mindu, Ngomeni, Bwembela,
Songa, Kwa-Mhamba, Kivindo, and Kwa-Lubuji,
Misozwe, Kicheba, and Mamboleo villages. In those
villages, respondents were randomly selected from
farmers’ meeting called by ward extension officers
(WEOs) because in some villages there was no village
extension officer. The WEOs were informed at least a
day prior to the visit and they were requested to call for
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Sky. J. Agric. Res.
Figure 1. A map of Tanzania showing Tanga region and Muheza district.
Source: Muheza District Council (2009)
smallholder orange farmers’ meeting on the day of the
visit. In total, 152 farmers were included in this study.
However, the proportion of women who showed up to the
meetings was relatively small. This phenomenon is not
uncommon for it has been well documented that the
gender division of labour which allocates all childcare,
household activities, and water and wood carrying to
women, constraints the capacity of women to participate
in market based production irrespective of opportunities
(Kaaria et al., 2007; World Bank, 2009). Secondly,
purposively sampling was used to select key informants
of the study. Key informants were selected basing on
their positions in the district, wards and villages. The key
informants were District Agriculture and Livestock
Development Officers (DALDO), Extension officers,
Village Chairperson, and Ward Executive Officer).
Purposive sampling is recommended when sample
elements and locations are chosen to fulfill criteria or
characteristics or have attributes relevant to the study.
A pilot survey to pre-test data collection instruments
and to gain familiarization with the study areas was
conducted in three villages namely Songa, Misozwe and
Mamboleo. Using a closed- and open-ended
administered questionnaire, data was then collected on
demo-graphic and socio-economic characteristics;
number and names of oranges varieties produced;
farmers’ preferences for certain varieties; main reasons
for selected orange varieties), production practice;
orange output; volume of orange sold; production cost
per output and average selling price per orange produced
in the agricultural season of 2010. Questionnaires were
administered by two trained enumerators together with
the researcher from May, 2010 to December, 2010 as
part of the research for PhD study.
Analytical techniques
The study employed various marketing efficiency
techniques in analyzing objective three, which aimed to
evaluate marketing efficiency of orange crops in
Tanzania especially Muheza district. The study done by
Emam (2011:22) measured marketing efficiency as:
Marketing Efficiency = (Gross Marketing Margin/
Marketing Cost) × 100. Alternatively used Sherpherd
Formula Technique to measure Market Efficiency as
follows: Marketing Efficiency = (Consumer Price/Total
Marketing Cost) – 1. The study also adopted the same
Robert and Emmanuel
Measurement techniques in evaluating marketing
efficiency of orange sub-sector in Tanzania specifically
Muheza district.
Marketing margin analysis
15
market. CI is the number of sellers and /or buyers in the
particular market (Pomeroy and Trinidad, 1995). As a rule
of thumb, CI ratio of over 1:2 is an indication of strong
monopolistic
industry,
1:3-1:2
indicates
weak
monopolistic industry and less than 1:3 is an indication of
un-concentrated industry.
Marketing margin analysis was used to estimate the
margin in terms of revenue and profit that accrue to the
orange marketers. Marketing margin can be estimated as
given by equation 1 below:
The CI is given by equation 1 below:
GMM= SP – BC
X 100
(4)
Where: CI= Seller index of concentration, X= total
revenue traded by a sampled big farmers/traders to the
market, Y= total revenue accrued traded by all sampled
farmers/traders in the study area.
(1)
Where;
GMM = Gross margin of a farmer/ wholesaler /retailer
(TZS/ orange)
SP = Selling price of a farmer/ wholesaler /retailer (TZS/
orange)
BP= Buying cost of oranges of a of farmer/ wholesaler
/retailer (TZS/ orange)
Farmer/ trader
Net marketing margin analysis
NMM = GMM) – MC
The interface pricing efficiency model (IPEM)
This was used to measure the degree of interface pricing
efficiency, which tested the hypothesis that whether or
not price changes had passed into the other market
chains (Schmidt, 1979) that is, the perfect market
transparency. It was done by running the simple
regression model as shown below:
(2)
Whereby,
NMM = Net Marketing Margin of a farmer/ wholesaler
/retailer (TZS/ Orange)
GMM = Gross Marketing Margin of a farmer/ wholesaler
/retailer (TZS/ orange)
MC = Marketing Costs of a farmer/ wholesaler /retailer
(TZS/ orange)
Farmer/ trader
(5)
Where:
= Market Margin,
= Buying price at a
specified market, = Error term
Market shares dispersion among orange market
actors
According to Emam (2011:22), the Marketing efficiency
(ME) is measured as:
This paper successful also evaluate individual market
share of a market actor (such as farmers, wholesalers
and retailers) was calculated using the equation given
below number (4). Market share was calculated using the
following relationship:
ME = (Gross Marketing Margin/ Marketing Cost) × 1 (3a)
A: Orange Producer’s Share (PS)
Marketing efficiency measurement techniques
Alternatively, Sherpherd Formula Technique was used to
measure marketing efficiency as:
ME = (Consumer Price/Total Marketing Cost) – 1
(3b)
(6)
Ps= Producer’s Share, Px= Producer Price, Rp= Retail
Price, Mm= Market Margin,
= Sum of average share (Throughout)
Market concentration index
The market concentration index (CI) of seller was used to
determine the degree of concentration of sellers in the
study area. In this paper, it was used to measure degree
of market efficiency through competition in the particular
B: Orange Wholesaler’s Share (WS)
(7)
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Sky. J. Agric. Res.
WS= Wholesaler’s Share, W p= Wholesaler Price, Rp=
Retail Price, Mm= Market Margin
= Sum of average share (Throughout)
C: Orange Retailer’s Share (RS)
(8)
RS= Retailer’s Share, Cx= Consumer Price, Rp= Retail
Price, Mm= Market Margin
= Sum of average share (Throughout)
RESULTS AND DISCUSSION
Orange production
In Tanzania, orange trees were planted in Muheza district
of Tanga Region in early 1900’s by Anglican missionaries
at Magila mission and then spread in the neighbourhood
with rapid expansion to other village such us Potwe
(Potwe ward), Semungano (Kilulu ward) and Tanga town.
However, effective propagation of oranges has started
during the period 1930 to 1940, through the nursery run
by Mlingano Sisal research station. Plants of different
varieties and other citrus species produced by the station
by vegetative propagation were distributed to farmers
free of charge (Mbiha and Maerere, 2002).
Orange is one of the food-cash crops produced in
Tanzania. Regions producing oranges are Tanga, Coast,
Morogoro, Dar es Salaam, Mwanza, Lindi and Mtwara.
Among all regions, Tanga is the largest orange producer
in Tanzania (URT, 2003). It is mostly produced in
Muheza, Handeni and Korogwe districts. Muheza district
is the largest orange producer in Tanga region relatively.
The orange farmers are growing a mixture of the
following varieties: Jafa, Early Valencia, Washington,
Late Valencia, Zanzibar, Pemba and Nairobi (Mbiha et
al., 2004). Oranges produced are marketed both at local
and distant markets such as Nairobi in Kenya, Dar-esSalaam, Arusha, Kilimanjaro, Mwanza, Shinyanga and
Morogoro regions etc. Moreover, the major markets of
Tanga’s oranges are Dar es Salaam, Kilimanjaro and
Arusha regions as well as Nairobi province in Kenya.
Although, there is complains from Tanzanian
traders/farmers about the difficulty of penetrating the
Kenyan market (Nyange et al., 2004). Then for the case
of exports, there is no data showing the amount of
oranges that are exported abroad annually (URT, 2007).
Orange marketing system
No orange trade can be made without involvement of a
middleman. The middleman could be “Junior or Senior”
middleman based in the village or Muheza town.
Middleman plays a role of bridge the gaps between
orange farmers and buyers through facilitating exchange
process.
The “Junior Middleman” are famously called “Dalali
Mdogo” in Kiswahili, and “Senior Middleman” constitute a
relatively more established group of middleman locally
dubbed “Dalali Mkubwa”. The major difference between
“Junior Middleman” and the “Senior Middleman” is that
the latter have relatively large working capital and have
more experiences in trading than the former. Market
information tends to flow downwards from the main
Muheza market to the producers in the following manner:
After communicated with wholesalers who land at
Muheza town, the urban senior middleman send to the
rest of the middleman, the information about the quantity
required by a particular wholesaler. The rural based
middleman goes to negotiate with the producers about
the quantity to supply and buying price per fruits. When
an agreement is made (reached), the information is sent
back to the urban wholesaler. The next step is to
negotiate with pickers (harvesters) on picking and
collecting costs. Finally, the work of harvesting and
assembling begins and the wholesaler negotiates with
trackers on the transport charges.
Orange market concentration
Market concentration is an index of market power that
provides a statistical summary reflecting the distribution
of sellers in the study area. It is also reflects market share
in the industry (OPEC Co-operation, 2008). So, the study
analyzed
market
concentration
from
different
marketplaces such Muheza district, Dar-es-Salaam, and
Arusha regions for Tanzania; and Nairobi province in
Kenya, which aimed to evaluate level of market
concentration, competitiveness, and market power in the
orange industry. Table 1 shows statistical summary of
market concentration index in the study area.
Market concentration for orange traders
Table 1 show the degree of concentration differs among
market participants, for instance retailers have had
(27.8%) the highest concentration index overall, followed
by wholesalers (18.5%) and last producers (11.5%).
Different studies measured market concentration index
into various degree of measurement. According to Kohls
and Uhl (1985) defined that when a concentration index
is less than or equal to 33% (1:3), it shows there is
generally slight concentrated market; when a
concentration index is between 33% (1:3) to 50% (1:2), it
shows there is weak concentration and more than 50%
(1:2), it shows there is strong concentration. According to
Pomeroy and Trinidad (1995) a rule of thumb, when a
concentration ratio is 1:2 , it shows there is strong
Robert and Emmanuel
17
Table 1. Market concentration of orange traders such wholesalers and retailers.
Market Place
Producers:
Muheza District
Wholesalers:
Muheza
DSM
Arusha
Nairobi
Overall
Retailers:
Muheza
DSM
Arusha
Overall
Total amount
Traded (QTY
Total amount traded by
big buyers
Ratio
CI (x/y*100)
12,154,550
1,400,000
0.1152
11.5
5,000
465,000
260,000
515,000
1,485,000
60,000
60,000
70,000
85,000
275,000
0.2448
0.1290
0.2692
0.1650
0.1851
24.5
12.9
26.9
16.5
18.5
2,700
3,520
3,850
10,070
1,200
500
1,100
2800
0.4444
0.1420
0.2857
0.2780
44.4
14.2
28.6
27.8
Source: Surveyed Data (2010)
concentration; when a concentration ratio is between at
1:2 to 1:3, it shows there is weak concentration; when a
concentration ration is less than 1:3, it shows the industry
is un-concentrated (oligopoly industry). The study further
found Azzam and Rosenbaum (2001) defined
concentration index in quarter basis, where the first
quarter is shows 0 to 25%, the industry is considered
atomistic; second quarter is 25 to 50%, the industry is
considered as slightly concentrated; third quarter is 50 to
75%, the industry is considered as Moderately
concentrated; and fourth quarter is 75 to 100%, the
industry is considered as highly concentrated. In this
study, the research adopts a rule of thumb of Pomeroy
and Trinidad (1995) degree of measurement as stated
above.
Producers’ concentration index
The research was based from orange farmers located in
Muheza district. This study went further evaluated
farmers concentration index as the baseline for further
recommendations on policy formulation. Table 1 shows
that orange farmers have had concentration index of
11.5%; according to the rule of thumb, this value implies
that orange markets for orange farmers are not
concentrated. The degree of market competition is very
low. So, orange farmers were expected to do better but
this comes contrary to the research expectation.
Observations show they were not doing well because of
some impediment factors that caused market inefficiency
in orange production and marketing as well such as
externality factors where by orange farmers are unable to
capture the full benefits for the crops they produce due to
the fact that there were shortage of rainfall; bush fire;
diseases and pests attack; and being limited to the
available financial services. Physical locality of farm
found to be one of the externalities factors that affect
market efficiency. Some farms are located relative
remote from the rough roads as well as tarmac roads and
hence poor price received especially during rainfall.
Lastly, the study found that orange sub sector was
functioning inefficiency due to stopped its function of
MUWAMU Food Processing Unit and Tanga Best
Oranges Growers Organization (TABOGO), which
associated by limited of funds, lack storage facilities, lack
live and active district farmers’ association, and
inadequate market information.
Wholesalers’ concentration index
Table 1 depicts statistical summary on concentration
index shows that market concentration index in Arusha
was relative high by 26.9%, followed by Muheza, Nairobi
and Dar- es-Salaam by concentration index of 24.5, 16.5
and 12.9% respectively. While the overall market
concentration index was 18.51%. These values imply that
market concentration index differs from each region
sampled and surveyed respectively.
However, according to the rule of thumb, these results
show that concentration index of the wholesalers were
not market concentrated because observation depicted
that both markets shown concentration index less than
1:3 0r 0.33% (Pomeroy and Trinidad, 1995; Kohls and
Uhl, 1985). Also, it implies that there is low degree of
market competition for oranges especially during high
oranges production season and associated with high
supply during high season. Reference is made in four
regions interviewed.
Less concentration in market gives wholesalers market
power (ability to control market prices oranges) to
influence market price of oranges. However, during high
season there is high degree of competition relative to
short season. During peak season, orange traders got
market power of set a price because during that period
oranges are being abundantly. It was observed, however,
during short season, most of orange traders prefer to
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Sky. J. Agric. Res.
Table 2. Operational costs (production and marketing costs) of orange farmers.
Nature of variable cost
Harvesting/Picking
Labour charge
Transport Charge
Loading and Off-load
Pruning Cost
Weeding Cost
Village Levy
Overall variable costs per orange
Cost per quantity (TZS)
0.0074
0.13
0.07
0.03
0.46
0.98
0.0049
1.68
Percent
0.4%
7.7
4.2
1.8
27.4
58.2
0.3
100
Source: Surveyed Data (2010)
trade under contract arrangement system to be certain
with future oranges supply. Although, this research seen
contract farming not beneficial to some orange farmers
instead becomes bitter and burden. Contract farming,
itself, is not worse, but the way this study found to
operate obvious is worse. This is it might be resulted by
poor contract signed during “uwekezaji system”
(investment system).
This is because research
observation comes to found that most of contracts signed
during when oranges were at the flowering or prematured stage, simultaneously goes with price set up at
the same time contract was signed. This is believed to
influence market inefficiency in the study area.
Retailers’ concentration index
Retailer market concentration index is summarized in the
Table 1. As can be seen, the concentration index in
Muheza district was relative high by 44.4%, followed by
Arusha was 28.5% and Dar-es-Salaam was 14.2%
respectively. Generally, the overall concentration index
was 27.8%. These concentration indexes indicate that
the orange industry for retail market is not concentrated.
According to the Pomeroy and Trinidad (1995), a rule of
thumb, altogether Arusha and Dar-es-Salaam cities have
concentration index less than 1:3, which means that the
markets were not concentrated. This is, however,
contrary to the Muheza market, where concentration
index ranged between at 1:2 to 1:3, this implies that
Muheza market is weak concentrated if compared to
Arusha and Dar-es-Salaam cities. The research
observation, however, it shows retailers from all cities
interviewed found to charge almost the same selling
prices suck as100 TZS per piece of oranges. Retailer
markets are that they sell their oranges directly to
pedestrian, on board passengers, passengers just
waiting buses, and tax drivers.
Assessed production and marketing costs incurred by
each market participants along the market chains. The
paper used average costs to state degree of efficiency of
each market participants along the market chains.
Normally actors who have the lowest average operating
costs were considered to be more efficiency than others.
Table 2 shows statistical summary which shows
operational costs incurred during orange production as
well as marketing process.
Farmers’ costs analysis
The statistical evidence shows that farmers spend more
costs to pay weeding activities. It is shown in Table 2 that
58.2% of the total operating cost was used to pay
weeding activities, which followed by 27.4% pruning cost.
Furthermore, village levy had incurred the lowest cost of
0.3% in relation to other variables. Generally, in average,
farmers’ operating cost incurred was TZS 1.68/=, which is
too low in relation to other market actors.
Empirically, this value TZS 1.68/=, implies that orange
farmers relatively have better comparative advantage
relatively to other market actors. Orange farmers mostly
used to employ family labour and hire less labour if any.
Hired labour were used to prune or/and weed farm.
However, there is no neither fertilizer, pesticide nor
transport costs incurred by farmers. No transport cost
because they sell oranges normally at the farm gate.
There are important costs which link with harvest process
were paid by traders. Such costs are harvesting,
assembling, counting, and transporting to trader truck
along the road. Farmers who sell oranges direct to urban
or cities normally they incurred those marketing costs,
likewise.
Wholesalers’ costs analysis
Comparative marketing costs among market actors
along the market chains
The paper evaluated efficiency of orange marketing
system using operational efficiency assessment methods.
Despite the fact that farmers being more cost efficiency in
orange marketing sub sector, the paper went further
evaluated marketing cost of wholesalers in relation to
other actors. Table 3 shows different sources of
Robert and Emmanuel
19
Table 3. Wholesalers marketing costs incurred in different marketplaces.
Nature of variable cost
Harvesting/Picking
Counting
Loading
Off-loading
Village Levy
Market Levy
Transport Charges
General Charges
Customs (Tz: 0.50, Kny: 2.74)
Parking Materials
Road Levy
Buying Costs
Driver and Conductor
Overall variable Costs
Muheza (TZS)
1.59
2.0
0.92
2.10
0.86
0.80
0.87
0
0
0
0
12
0
29.43
DSM (TZS)
0.84
2.92
0.72
0.40
0.67
0.46
12.42
0.11
0
0
0
21.09
0
39.63
Arusha (TZS)
1.10
3.14
1.05
0
0.52
0.40
11.55
0.06
0
0
0
24.75
0
42.57
Nairobi (TZS)
0.78
2.63
2.63
1.71
0.47
0.31
9.16
0
3.24
.0.56
0.26
19
0.04
31.69
Overall cost
4.31
10.69
5.32
4.21
2.52
1.97
34.0
0.17
3.24
0.56
0.26
76.84
0.04
143.32
Sources: Surveyed Data (2011).
Table 4. Retail marketing costs incurred in different marketplaces.
Nature of variable cost
Buying Costs
Transport fees
Market Levy
General Charges
Overall variable costs
Muheza (TZS)
47.03
11.11
0.44
1.19
59.77
DSM (TZS)
73.96
0
3.67
0
77.63
Arusha
56.4
0
0
0
56.4
Overall costs (TZS)
177.39
11.11
4.11
1.19
193.8
Sources: Surveyed Data (2010)
marketing costs have been incurred in different
marketplaces.
Table 3 presents statistical summary for marketing
costs incurred in different marketplaces especially from
Muheza, Dar-es-Salaam, Arusha, and Nairobi markets.
Buying cost and transport charge are highly paid
relatively. Village levy was being the lowest cost incurred,
followed by the general charges. The overall expenses
incurred in four distance markets (cities/town) was TZS
143.32/=. In general, this paper further measured market
efficiency for each market aimed to discover which
market has a lowest marketing cost in relation to other
market surveyed. Apparent statistical observation shows
that in general wholesalers marketed in Muheza market
were incurred the lowest costs of TZS. 29.43/=, followed
by those marketed in Nairobi market of TZS. 31.69/=,
DSM market of TZS. 39.63/=, and Arusha market of TZS.
42.57/= respectively. However, wholesalers from Arusha
market were incurred the highest marketing costs as
compared to the rest three markets, geographically,
Arusha market relative is located too far from Muheza
town. Problem of market being too far normally affect
traders especially on cost of transportation. Likewise,
wholesalers from Muheza market in general were
operating efficiently and effectively because they are not
relative far from the farmers, which warrant them to incur
less marketing costs relatively.
Retailer’s costs analysis
The paper also went further evaluated efficiency of
orange marketing sub sector though evaluating retail
marketing costs in the different marketplaces. Table 4
shows statistical summary of retailers marketing costs
established from various marketplaces.
As can be seen in Table 4, overall average retailers’
marketing costs is relevant high in relation to farmers’
and wholesalers’ marketing costs, the overall retail
marketing cost was TZS 193.8/=, which is relative too
high.
The study analyzed each retail marketplace in order to
unveil the most efficiency orange marketing chain within
the surveyed area. Retailers from Muheza were incurred
TZS 59.77/= average marketing costs, these marketing
costs are cost of buying oranges, transport fee, market
levy, and general charges as well. However, retailers
from Muheza also were incurred more variables costs
although Dar es-salaam retail markets had excessive
high marketing costs of TZS 77.63/=, whilst Arusha retail
market had the lowest marketing costs of TZS 56.4/=.
The study revealed that buying price has been
increasing value of marketing costs. Evidence from Table
4 shows that most of retailers of Arusha and DSM retail
markets were not incurred transport fees and general
charges as well. But retailers from DSM had seen to incur
20
Sky. J. Agric. Res.
Figure 2. Traders’ variable costs.
Source: Surveyed Data (2010)
only market fee apart of buying cost. In term of cost
conscious, it implies that in general retailers are not cost
conscious just because they were experienced high
marketing costs in all marketplaces as compared with
farmers and wholesalers. However, 'this is contrary from
Muheza where retailers were incurred buying price,
market fee, transport fee, and general charges. With all
these market costs incurred in Muheza but still marketing
costs relative were low as compared to marketing costs
incurred in DSM retail markets.
Generally, the overall statistical results show that
retailers relative are cost disadvantaged than other
market participants along the market chain. This is
because statistical evidence from the Table 2, 3 and 4
shows in general retailers spent TZS 193.8/= as
marketing costs, followed by wholesalers TZS 143.32/=
and the lowest marketing cost appeared from farmers
who spent TZS 1.68/=. As operational efficiency is
concerned, these statistical findings, therefore, show that
orange farmers have relative lowest efficiency in orange
market sub sector if compared with other market
participants along the market chain. This is because in
general orange farmers spent less amount of average
operating costs in relation to other market participants
along the market chain as could be seen in the Figure 2.
It is important to note that all operational costs were
presented in term of cost per unit.
Figure 2 illustrates total average variable costs
graphically. Retailers relative spent high costs
(TZS193.8/=), followed by wholesalers (TZS 143.32/=)
and farmers were the last costs driver (TZS1.68/=). Then,
this paper has declared that farmers are less cost driver,
followed by wholesalers, and last retailers. This implies
that retailers are cost inefficiency in relation with farmers
especially in term of cost reduction.
Market pricing mechanism
The analysis depicts that retailer paid high purchase price
as compared to wholesaler purchase price. So, here the
paper aimed to determine prices variation (average
market prices) prevailing among the three market
channels. This could be seen in the Table-5 which shows
the average market price paid in each market channel by
each market participants as follows:
Retailer sold orange at average price of TZS. 100/=,
followed by wholesaler TZS 65/= and farmers TZS 20/=.
In general farmers was experienced the least average
price of TZS 20/=. The different in selling price of a
farmer to wholesaler was TZS. 45/=, from retailer to
wholesaler was TZS 35/=. Consumer price was TZS 100.
The variations in selling prices were insignificant because
of a very big variation shown in one channel to another.
For instance, the different in selling price of a farmer from
wholesaler is more than a twice, earned by wholesaler
against a farmer. This variation is not economy or rational
to the farmers.
However, the differences in selling price are very big
among subsequent traders. These prices differences
from each chain participant are reflected by market cost
differentials that experienced into the markets. These
costs differentials include cost of weeding, pruning,
transportation, loading and off-loading, village levy,
general charges customs, road tax, packing materials,
cost of agents, buying costs, pesticides costs, labour
charges, and driver and conductor allowances in Kenya if
any.
Comparative of profitability among market actors
along the market channels
“Margins” are often used in the analysis of the efficiency
of marketing systems (Mahenga, 2008). Often people
who research marketing costs and margins start out with
the assumption that traders exploit farmers. When they
look at the margins they may think they have found the
proof. The average gross margins, market margins, and
average net profit margin for orange producers,
wholesalers, and retailers were presented in Table 5,
Robert and Emmanuel
21
Table 5. Market margins, gross margins and net margins for orange traders.
Traders’ level,
Muheza
DSM
Arusha
Nairobi
(Price/Qty)
(Price/Qty)
(Price/Qty)
(Price/Qty)
ASP
20
0
0
0
APP
0
0
0
0
AVC
1.68
0
0
0
AMM
20
0
0
0
AGM
18.32
0
0
0
ANP
18.32
0
0
0
ASP
65
62
64
62
APP
5
11
20
19
AVC
21
27
36
32
AMM
60
51
44
43
Costs and margins
PRODUCERS
WHOLESALERS:
AGM
44
35
28
30
ANP
43.86
22.37
22.43
21.25
ASP
100
100
100
0
APP
45
74
56
0
AVC
60
78
65
0
AMM
55
26
44
0
AGM
40
22
35
0
ANP
40.23
22.37
43.6
0
Consumer price
100
100
100
0
RETAILERS:
from their respective regions. Statistical evidence shows
the wholesalers earned average market margins ranging
from 43 to 60 TZS and average gross margins ranging
from 28 to 44 TZS. While the net profit margin ranging
from 18.32 to 43.86 TZS. It was observed that the
wholesalers from Muheza earned higher market margins
for about TZS 60/=, followed by DSM for about TZS.
51/=, Arusha for about TZS 44/= and, Nairobi for about
TZS 43/=, where the average gross margins earned by
wholesalers were TZS.44, followed by DSM about TZS
35/=, Nairobi about TZS 30/= and, Arusha about TZS.
28/=. These findings show marketing costs in Muheza
and DSM were below in relation to other cities surveyed.
Table 5 summarized statistical evidence for the retailers’
gross margins, net profit margins, and marketing
margins. The average market margins of the retailers
were higher in Muheza of TZS 55/=, followed by Arusha
region TZS 44/=, and DSM TZS 26/=. On the other side
of average gross margins, retailers were earned the
highest gross margin in Muheza of TZS. 40/=, followed by
Arusha TZS. 35/=, and DSM was the least earned an
average gross margins of TZS 22/= annually.
Meanwhile, wholesalers from Muheza district were the
higher overall average gross margins recipient; they
earned an overall average gross margin of TZS. 44/=.
However, wholesalers are the one earned more gross
margins if compared to retailers and farmers. This
observation implies that orange marketing system is too
efficiency to wholesalers if compared to farmers and
retailers as well.
Furthermore, the paper analyzed profit margins of
orange farmers to uncover efficiency of orange marketing
system. Table 5 summarized statistical evidence shows
orange farmers were earned the market margins of TZS
20/= and gross margins of TZS 18.32/= as well as net
profit margin of TZS 18.32/= per orange being sold. The
evidence shows that orange farmers were earned less
market margins as compared to retailers and wholesalers
in Muheza district. Whilst in Muheza district, wholesalers
were earned average gross margin of TZS. 44/=, followed
by retailers earned TZS 40/=, and orange farmers were
22
Sky. J. Agric. Res.
earned average gross margin of TZS. 18.32/=. The same
to net profit margin that wholesalers were earned the
highest net profit margin of TZS.43.86/=, followed by
retailers were earned TZS 40.23/=, and the least net
profit margin farmers were earned TZS18.32/=. With this
results raised, it is obvious that markets of orange
farmers were not efficiency because of the market
system has failed to reduce operational costs, as mostly
have failed to improve themselves market price for their
orange produce and hence end up with low net profit
margin as compared to wholesalers and retailers earned.
Generally, the statistical evidence shows that the gross
margin of wholesalers and retailers were higher in
Muheza as compared to farmers and retailers as well. In
Muheza, gross margins of retailers, however, were higher
by TZS 40/=, followed by Arusha and DSM were earned
TZS 35/= and TZS. 22/= respectively. While the statistical
observation shows wholesalers in Muheza town earned
high gross margins approximately TZS 44/=, followed by
DSM earned TZS 53/=, Nairobi earned TZS. 30/=, the
lowest gross margin observed in Arusha city that was
TZS. 28/=. These findings suggest that retailers are
somehow disadvantaged specifically on gross margins
earned. Whilst TZS 44/= is the highest net profit margin
earned by wholesalers, followed by retailers earned TZS
40/=, and the lowest net profit margin earner (farmers)
earned TZS 18/=. However, this study further revealed
that retailers were earned lower gross margins as
compared to wholesalers. This is, however, contrary to
the Muheza, where orange farmers were observed to be
disadvantaged because they earned lower gross margin
in general as compared to wholesalers and retailers.
However, overall observation shown that the
wholesalers earned higher gross margins and net profit
margins as well as compared to farmers and retailers in
the case study research (Figure 3). Although the study
found retailers’ selling price was generally higher than the
wholesalers’ price as well as farmers’ price. On the other
side, it was further observed that retailers from all
markets surveyed are being paid the same retail selling
price. This is done in Muheza, DSM and Arusha market,
buyer normally pay retail price of TZS 100/= per one
orange being sold. The retail selling price of TZS 100
normally is common to most of consumers. Having
common retail selling price implies that the retail market
is perfect market in nature. Normally, retailers have been
charging similar consumer price despite the fact that they
are in different geographical location and operational
costs incurred as well.
Table 5 shows that generally wholesalers from Muheza
earned more net profit margin, followed by retailers and
retailers were the least earner net profit margin
respectively. Despite the wholesalers were associated
with a lot of marketing costs such as harvesting fee,
counting fee, transportation costs, market levy, village
levy and, loading and offloading, and brokerage fee, still
managed to earned highest net profit margin relatively. In
relation to operational efficiency, the statistical evidence
shown that the highest average marketing costs incurred
at Arusha was TZS. 42.57/=, followed by DSM was TZS.
39.63/=, Nairobi was TZS. 31.69/= and Muheza was TZS
29.43/= respectively. This is however, contrary to the
retailers’ side that costs of marketing were observed to
be relative high in DSM by TZS. 77.63/=, followed by TZS
59.77/= in Muheza, and the least costs was observed in
Arusha market such TZS 56.4/= respectively. On the
other side, reviewed findings show that retailers from
Arusha are cost advantaged in relation to retailers from
DSM and Muheza markets. However, these statistical
reviewed results, therefore, implies that net profit margins
of the market participants are inversely related to
distance to marketplace (cost of transportation from the
farmer to the marketplace). The reviewed findings shown
that transportation costs have been increasing
tremendously with time because of general increase in
fuel price and increasingly operational costs.
Market shares dispersion
Market share is the percentage of sales returns earned of
a particular product in a given region that are controlled
by a particular firm (Conjecture Corporation, 2012).
Market share accurately assess performance of orange
market participants through sale returns. On the other
side, “Margins” are often used in the analysis of the
efficiency of marketing systems. Often they are misused
even if they are correctly calculated. The presentation of
a trader's share of the final selling price in percentage
terms can give a totally misleading impression unless
there is an understanding of the costs involved. Often
people who study marketing costs and margins start out
with the assumption that traders exploit farmers.
Moreover, where farmers receive only a comparatively
small share of the selling price this does not necessarily
mean that they are being exploited. When they look at
the margins they may think they have found the proof.
The following share was determine in expansion of study
explore a snapshot picture regarding how well the market
efficiency for orange farmers in term of sales proportion.
PRODUCERS SHARE:
Ps = [Px / Rp] / ∑ = [1 –
(MM / Rp)] * 100
PERCENT
= [1 – (18/200] *
100 / 209.........................................43
WHOLESALER SHARE:
Ws = [Wx / Rp] /
∑ = [1 – (MM / Rp)] * 100
= [1 –
(49 / 63)]* 100 / 209.............................37
RETAILER SHARE:
Rs = [Cx / Rp] /
∑ = [1 – (MM / Rp)] * 100
= [1 – (41 / 100)]
* 100............................................
20
Note: Ps, Px, Rp, MM, Rs, Cx, Ws, Wx Stand for
Producer’s Share, Producer Price, Retail Price, Market
Robert and Emmanuel
23
Figure 3. Profits earned by Traders in the year 2010.
Source: Surveyed Data (2010)
Figure 4. Market share dispersion for producers, wholesalers and retailers
Source: Surveyed Data (2010).
Table 6. Average market share earned by various market participants.
Market
Participant
Producers
Wholesalers
Retailers
Overall
ATP/APP
ASP
AVC
MM
2
13
58
73
20
63
100
183
2
29
60
91
18
49
41
108
AV. MKT
SHARE
90
78
41
209
Percent
43
37
20
100
Source: Field Data (2010); Note: ATP/APP, ASP, AVC, MM, stand for Average Total Production
Cost, Average purchase Price, Average Selling Price, Average Variable Cost, and Market
Margins respectively.
Margins, Retail’s Share, Consumer Price, Wholesaler’s
Share, and Wholesale Price respectively.
The Figure 4 and Table 6 shows average market
shares percentage earned by each market participant in
orange market channels. Empirical evidence shows that
orange farmers earned an average market share of 43%
of the retail prices, while wholesalers earned an average
market share 37% of the retailer prices, and retailers
hardly earned about 20% of the consumer buying prices.
However, these variations imply that among all orange
24
Sky. J. Agric. Res.
Table 7. Summary of marketing margins, marketing costs and net marketing margin.
Variable
Selling price/Consumer
price
Buying Price
Marketing Margin
Marketing Margin %
Marketing cost:
Transportation
&
Handling
Village levy
Market Levy
Labour charges
General Charges
Net Marketing Margin
Farmer
MHZ
20.00
MHZ
65.00
DSM
62.00
Wholesaler
ARUSHA
NAIROBI
64.00
62.00
0.00
20.00
0.00
1.68
0.00
12.00
53.00
18.46
9.14
0.87
21.09
40.91
34.02
18.54
12.42
24.75
39.25
38.67
17.82
11.55
0.00
0.00
1.68
0.00
18.32
0.86
0.80
6.61
0.00
43.86
0.67
0.46
4.88
0.11
22.37
0.52
0.40
5.29
0.06
21.43
MHZ
100.00
Retailer
DSM
RUSH
100.00
100.00
19.00
43.00
30.65
17.69
9.16
47.03
52.97
47.03
11.74
11.11
73.96
26.04
73.96
3.67
0.00
56.40
43.60
56.40
4.66
0.00
0.47
0.31
7.75
0.00
25.31
0.00
0.44
0.00
0.19
41.23
0.00
3.67
0.00
0.00
22.37
0.00
0.00
0.00
4.66
38.94
Source: Field data (2010).
marketing participants, only orange farmers observed to
enjoy a higher market margins for about 90 (43%) of the
ultimate consumer price; at the same times retailers
earned the least market share, which is 20% only. From
these reviewed results, one could interpret this as being a
threat to retailers. However, the reviewed empirical
findings show retailers were enjoying a business fairly
because they are selling oranges more frequently almost
throughout the year as compared to the rest market
participants. Normally retailers used to sell oranges to the
pedestrians, drivers and, on-board passengers who were
waiting buses to travel. Not only that, retailer relative
earned high net profit margin as compared to farmers
from Muheza because retailers are costs efficiency.
Generally, it was shown that orange farmers earned the
largest market share (43%) as compared to other market
participants along market channels. With due respect,
therefore, this paper narrates that market for farmers’
oranges were efficiency because farmers were managed
to produce at the lowest operating costs such TZS 1.68/=
cost per unit. This lowest cost is achieved because most
of farmers sold their oranges at the farm gate (so no
transport cost is incurred) and employ farm family labour
(they not hire farm labour). Empirically, increased in
market share might not be profitable if it has associated
with increasingly in marketing costs or a big price
decreases. In some cases, it might be advantage to
decrease market share, if the lower costs of lower market
share and improve profits hence increasingly efficiency of
orange marketing system. Managing market share,
therefore, is a very important aspect of managing a
business performance. With that it is hard hoc to
separate the word efficiency with performance. Efficiency
is a result of economic performance. As stated early,
efficiency is associated with lower operating costs and
increase profits whilst increase market share for market
participants. Observation shows participant with lower
operating costs normally increases chance of earn high
profit relatively.
Also, it was, however, observed that market share
could measures organization performance regardless of
the efficiency of the organization. The organization could
achieve its objective effectively but not efficiency, its
operational costs have been increased. The same as
farmers could increase sale revenues whilst marketing
costs increased tremendously. This tremendously
increases of marketing costs have negative impact in net
profit margins.
Thus this paper has taken care in drawing conclusions
in evaluating efficiency of orange marketing system
especially in measures the farmer's market share of the
consumer price and the farm-retail price spread. Indeed,
this is because these market share percentages, if
considered on their own, were likely to misleading when
used to evaluate the efficiency or fairness of a marketing
system. For instance, a given market share of the
retailers was relative low because provide few products
as well as marketing services (which together add little
value) or may be arithmetically low, as a percentage, but
achieve reasonable returns since the retail price of the
product is high. With that matter, the important thing here
is not the size of market share which received, but the
total return received by the orange farmers from the sale
of their products and marketing services.
STATISTICAL
SUMMARY
EFFICIENCY RESULTS
OF
MARKETING
Marketing margins analysis
The following Table 7, 8(a) and 8 (b) shows marketing
margins, marketing cost, and net marketing margins, and
marketing efficiencies respectively.
Robert and Emmanuel
25
Table 8a. Marketing efficiency of famers, wholesalers and retailers (Tshs.)
Variable
Gross Marketing Margin
Marketing cost
Marketing Efficiency %
Farmer
MHZ
20.00
1.68
11.90
MHZ
53.00
9.14
5.79
DSM
40.91
18.54
2.21
Wholesaler
ARUSHA
39.25
17.82
2.20
NAIROBI
43.00
17.69
2.43
MHZ
52.97
11.74
4.51
Retailer
DSM
RUSH
26.04
43.60
3.67
4.66
7.10
9.36
Source: Field Data (2010)
Table 8b. Estimation of marketing efficiency- sherpherd formula technique.
Variable
Consumer price
Marketing cost
Buying Price
Total marketing Cost
Marketing Efficiency
Farmer
MHZ
MHZ
20.00
65.00
1.68
9.14
0.00
12.00
1.68
21.14
10.98
2.07
DSM
62.00
18.54
21.09
39.63
0.56
Wholesaler
ARUSHA
NAIROBI
64.00
62.00
17.82
17.69
24.75
19.00
42.57
36.69
0.50
0.69
MHZ
100.00
11.74
47.03
58.77
0.70
Retailer
DSM
RUSH
100.00
100.00
3.67
4.66
73.96
56.40
77.63
61.06
0.29
0.64
Source: Field Data (2010)
Table 7 shows the net marketing margins for farmers,
wholesalers and retailers in Muheza, Dar-es-Salaam,
Arusha, and Nairobi. Muheza central market, wholesalers
generally got higher margins than farmers and retailers.
Whereby wholesalers got Tshs 43.86 per orange,
followed by retailers Tshs 41.23 and the least is farmer
got 18.32 respectively. At trader level, wholesalers
generally got higher marketing margins than retailers with
exception of Arusha market, where retailers got higher
marketing margins than wholesalers. This may be due
mainly to high transport and handling charges incurred by
wholesalers from Muheza to Arusha market, which
reflected to the fact that Arusha wholesalers were
received oranges product at high farm- gate price (24.75
Tshs/orange). At markets location, overall Muheza
wholesale market showed higher marketing margins
(43.86 Tshs/Orange) followed by Arusha Retail market
(38.94 Tshs/Orange), Nairobi wholesale market (25.31
Tshs/Orange), and lastly Dar-es-Salaam Wholesale and
Retail markets (22.37 Tshs/Orange) respectively. By the
same sequence of marketing margins, net marketing
margins followed. The lower net marketing margins of
Arusha wholesalers was reflected to the higher marketing
cost, which came as a results of higher transportation
and handling (11.55Tshs/Orange), village levy (0.52
Tshs/Orange), Market levy (0.40Tshs/Orange), Labour
charges (5.29Tshs/Orange), and general charges
(0.06Tshs/Orange). This result was agreed with study of
Altoum (2008), who found that the inadequate marketing
services such as transport, packing and handling
represent the main obstacles that facing marketing
activities. Arusha wholesale market is received Oranges
product from far distance area.
In spite of the fact that Muheza Wholesalers market got
a bit higher marketing costs (9.14 Tshs/Orange), they
showed higher net marketing margins than the other two.
This may be due the fact that wholesalers at Muheza
market got lower prices due to poor contract signed with
farmers as well as poor access on market information of
a farmer.
Marketing
efficiency
farmers/wholesalers/retailers
for
orange
A common means of measuring market efficiency is to
examine marketing margins (Delgado and Staatz, 1980).
Table 8a and 8b shows marketing efficiency of farmers
market with respect to traders markets in four markets in
Tanzania. The following Table 8 (a) shows statistical
summary indicating marketing efficiency of each market
agent and their ranks respectively.
Table 8 (a) shows orange farmers got relative higher
market efficiency (11.90 Tshs/Orange) followed by
Wholesalers
(5.79
Tshs/Orange),
and
retailer
(4.51Tshs/Orange). Also, the study extended estimation
of marketing Efficiency using Sherpherd formula
technique, still the results being the same to the first
method that Muheza farmers market still got higher
marketing efficiency. Such as Table 8(b) shows orange
farmers from Muheza district got relative higher
marketing efficiency relatively (10.90Tshs/Orange),
followed by wholesaler (2.07Tshs/Orange), and Retailer
(0.70Tshs/Orange). Moreover, both Table 8 (a) and 8 (b)
show orange farmers of Muheza District got relative
higher marketing efficiency than wholesalers and
retailers. This is a result of farmers to incur less
marketing operating costs compare to wholesalers and
retailers. During the field, the study observed oranges
were sold at the farm gate. Traders were going to collect
26
Sky. J. Agric. Res.
oranges at the farm gate. As the results, many marketing
costs like picking/harvesting costs, village levy, market
levy, loading and unloading, transport and handling
charges, brokerage fee, and other costs are paid by
traders.
However, despite the farmers appeared with high
market efficiency, but still they getting low farm gate
price. They receive low marketing price because many
farmers were not connected to market information
access. To support previous statement, Mari (2009)
argued in order to market to be efficiency, confirmed that
price information should be adequate flow. As well as
profit can be earned by using information (Grossman and
Stiglitz, 1980). Grossman and Stieglitz’s model of market
efficiency in which individuals who consistently earn
trading returns have superior access to information or
superior analytical ability. It implies that, the orange
producers who survive will be those who have the lowest
cost of production since efforts to improve revenue
through better marketing of the commodity produced will
meet with limited success over time. Second, qualitatively
the study observed farmers were experiencing high
transactional costs especially in monitoring contract.
Many orange farmers were selling oranges using contract
business. Despite the enforcement of contract is crucial
for efficient marketing and investment and economic
development (North, 1990; Gow and Swinnen, 2001;
Woodruff, 2002; Beckmann and Boger, 2004), but still
some studies have found evidence that contract farming
have been directly or indirectly harming producers (Glove
and Kusterer, 1990; Little and Watts, 1994). Likewise the
study observed most of contract were reducing farmers’
bargaining power as well as forces them to accept less
favorable contract terms. With this matter, farmers were
incurred a lot of qualitative costs in order to execute
his/her contract.
For the case of traders’ central markets: Nairobi
wholesale market got higher marketing efficiency
(2.43Tshs/Orange), followed by Dar-es-Salaam central
market (2.21Tshs/Orange) and Arusha central market
(2.20Tshs/Orange). This implies that Nairobi central
market gives traders more margins than other markets
respectively. This is because cost of operating this
market is relative lower and also receive better marketing
price relatively. Meanwhile, Arusha retail market got the
highest ranked market efficiency (9.36 Tshs/orange),
followed
by
Dar-es-Salaam
central
market
(7.10Tshs/Orange). Generally, the most profitable central
market is Arusha central market. This is because retailers
managed to operate under minimum marketing costs
relatively. Averagely retail price for both central markets
were 100Tsh/Orange.
Overall Muheza central market (11.90Tshs/Orange)
ranked at the first with respect to marketing efficiency
followed by Dar-sa-Salaam (2.21Tshs/Orange) and
Arusha (9.36Tshs/Orange) at farmers, wholesale and
retail, which agreed with the findings of Hamad (2000).
Hamad (2000) found that the tomato crop is profitable at
the producer level. Altoum (2008) recorded that tomato
marketing in Khartoum State is more profitable at the
producer level. The result of this study indicated that the
tomato product is efficient in the study area. Estimation of
market efficiency using Sherpherd formula technique,
data showed that the coefficients of marketing
efficiencies of wholesalers were 2.07, 0.56, 0.50 and 0.69
and retailers 0.70, 0.29, 0.64 in Muheza, Dar-es-Salaam,
Arusha, and Nairobi, respectively (Table 8a and 8b). The
wholesaler got higher coefficients of marketing
efficiencies. That means wholesalers run marketing
activities more efficient than retailers except in Arusha.
This result was assured with pervious study by Ugwumba
and Okoh (2010), who found that wholesalers slightly
higher efficiency than the retailers. At markets locations,
Muheza wholesale market ranked at the first followed by
Nairobi, Dar-es-Salaam, and Arusha, respectively. At
retail markets, Muheza market appeared higher
marketing efficiency than others ones.
Therefore, increasing marketing efficiency at farmers’
level in Muheza market through reducing marketing costs
(contract monitoring, information search, transportation,
handling, packing and other cost items if any) is
important.
CONCLUSION AND RECOMMENDATIONS
The study found that the most efficiency orange
marketing operator was farmers, followed by wholesalers
and last retailers. This is because it was shown that
farmers incurred less average operating cost of TZS 1.68
per output, followed by wholesalers was TZS 143.32, and
the highest cost drivers was retailers by TZS. 193.8 Per
orange. This lowest cost is achieved because most of
farmers sold their oranges at the farm gate (so no
transport cost is incurred) and employ farm family labour
(they not hire farm labour). To know whether margins are
reasonable it is necessary to understand the nature and
composition of marketing costs. However, farmers
earned net profit margin of TZS 18.32, followed by
Wholesalers TZS 44, and the last was retailers who
earned TZS 40. Therefore, increases in marketing
margins due to increases is marketing costs may not
mean increase in profits made by those doing the
marketing. Furthermore, the study looked market share
for every market trader to establish who have large
market shares along the chain. In general orange farmers
found to have high market share of 43% in relation with
other market actors. Moreover, if farmers would receive
only a comparatively small share of the selling price this
does not necessarily mean that they are being exploited.
It is therefore, recommended that the efficiency of orange
marketing system for smallholder farmers could be
increased through the provision of subsides for inputs to
reduce cost of production and enlightenment campaigns
Robert and Emmanuel
to improve farmer’s knowledge and technical skills on
how to reach lucrative markets.
ACKNOWLEDGEMENTS
The authors would like to thank the Mzumbe University
for funding this study. We, authors would like to thank Mr.
James Titiba Wanjara, Ms. Nahida Mbwana, and other
research enumerators for their assistance in the
collection of data.
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