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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 14 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) 16 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 18 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. 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