Profitability and market performance of smallholder vegetable production: evidence from Ethiopia

Abstract

Regardless of the fact that the vegetable crop is crucial for Ethiopia's economy, public research on vegetable crops was negligible, and limited attention has been given to the sector. The main objective of the study was to analyze profitability of vegetable production, market performance and identify major constraints of smallholder vegetables production, specifically potato, tomato and cabbage. A multi-stage sampling procedure was employed to select 420 sample households randomly from nine kebeles, and 139 traders were selected purposively. Both qualitative and quantitative data were collected from primary and secondary sources by using structured questionnaires. Descriptive statistics, gross profit formula and structure-conduct-performance model was applied to analyze the data. The result of profitability analysis showed that potato, tomato and cabbage production in the study area was profitable. The result of structure analysis indicated that the market structure for potato, tomato and cabbage was characterized by weak oligopolistic market. The analysis of market conduct showed that the price of vegetables was determined by traders. Performance analysis demonstrated that the total gross marketing margin and profit margin was highest when producers sold vegetables to consumers and the lowest when they sold vegetables to collectors across the channel. Shortage of inputs, pests and diseases, lack of improved storage, post-harvest loss, brokers' interventions, low price of the product, poor transportation facility and information flow were the major constraints. Therefore, policy initiatives aiming at increasing farmers' access to quality vegetables inputs, strengthen vegetable extension services, improving infrastructure, disseminating reliable market information, reducing unfair profit distribution and increasing bargaining power of farmers to accelerate rural economic growth and poverty reduction. In addition, research institutes and universities should significantly contribute in releasing high yielding and disease resistant varieties to improve production and productivity of vegetables sector.

Ethiopia; Market performance; Profitability; Vegetables; Smallholder farmers.

1. Introduction

Agriculture is a key sector for Ethiopia's economy. The general economic growth of the country is highly dependent on the success of the agricultural sector. The entire sector offers employment to 72.7% of the population and contributes 43% to the GDP. The sector remains largely dominated by rain-fed subsistence farming by smallholders who cultivate an average landholding of less than one hectare (Amsalu, 2014). Despite these challenges, Ethiopia has favorable economic opportunities and prospects. The country has abundant natural resources, a low cost and trainable labor force, an emerging middle class, and a developmental state with an ambitious vision, commitment, and a strong sense of policy ownership (Getaneh and Sailaja, 2017).

Ethiopia exported 0.22 million tons of vegetables and generated USD 438 million in 2013 (Ethiopian Revenue and Customs Authority, 2013). Vegetable cultivation is a significant economic activity and play a central role in meeting food and nutrition security in Ethiopia, diversifying and increasing vegetable production can help to overcome malnutrition and poverty by augmenting household consumption and also create new income and employment opportunities in the production, trading, and processing sub-sectors (Ganry et al., 2010; Parrot et al., 2010; Virchow, 2015).

In spite of the fact that vegetable production is crucial for Ethiopia's economic growth and poverty reduction, limited attention has been given to the sector. Public research on vegetable crops was negligible and major public policies and attention of extension agents were mainly focused on staple crop production so far MoFED (2010). The majority of Ethiopian smallholders consider vegetable cultivation as supplementary to the production of main crops and the cultivation is fragmented and mostly managed by household labor. This low priority for vegetable crop cultivation was due to the traditional food consumption habits that are mostly dominated by grain crops in many parts of the country which leads to weak domestic market demand for vegetable production (Bezabih and Hadera, 2007).

Ethiopia has a comparative advantage in a number of horticultural commodities due to its favorable climate, proximity to European and Middle Eastern markets and cheap labor (EHDA, 2012). The area under vegetables was estimated to be 0.44 million hectares with a total production of 53 million tons which shares about 1.69 percent of the area under all crops at national level (Cochrane and Bekele, 2018).

Most poverty reduction strategies in developing countries are predicated on improving agricultural production and promoting market access and integration of smallholder producers in formal market exchange. Improved market access proves necessary for maintaining production incentives, permitting household specialization and enabling movement to high-value products and to value-added activities. However, small-scale producers in developing countries, struggle to gain market access because they lack knowledge of market requirements or the skills to meet them, inadequate information flow and other obstacles prevent them from entering into new markets, or reduce the benefits they obtained from entry, reducing poverty among small-scale producers, are often designed to overcome some of these obstacles (Steven et al., 2012). Ethiopia cannot be an exception from this challenges, the vegetable markets are characterized by inadequate transport network, poor market information system, and underdeveloped industrial sectors (Haji, 2008). The production-market linkage is very weak, smallholder farmers are not selling their produce in an organized manner, they are not getting the right share of consumer price. As a result, farmer's opportunity to diversify their livelihoods from vegetable production is very much limited (Mussema et al., 2013). Legesse et al. (2014) reveals that wholesalers are making the highest net margin as they relatively charge a higher price using their market power. The net margin for the smallholder farmers is highest only when vegetables are sold to individual consumers through unions via consumer cooperatives. Tegegn (2013) found out that the vegetable market chain is governed by wholesalers and exporters who have capital advantage over the other chain actors. Hence, farmers are forced to obtain a lower share of profit margin. Bezabih (2008) also identified that lack of markets to absorb the production, large number of middlemen in the marketing system, lack of coordination among producers, poor product handling and packaging, imperfect pricing system and lack of transparency in market information as major marketing challenges.

Southwest Ethiopia, particularly districts of Gomma, Dedo and Seka Chekorsa has a worthy potential in the production of vegetables. The main type of vegetables produced in the area is onion, potato, cabbage, green peppers, sweet potato, carrot, Garlic, etc (Kebebew et al., 2011). Among the vegetables produced in the area, the emphasis of this study lies on potato, tomato, and cabbage, they are widely grown by smallholders as a means of enhancing family income and achieving food security (Jimma zone irrigation authority office, 2019). However, there was no empirical data that validated the profitability of vegetable production and market performance of smallholder vegetable producers in southwest Ethiopia. Correspondingly, Haji (2008) argued that the detailed and systematic empirical studies on the vegetable marketing systems in southwest of Ethiopia are scarce or non-existent. Analysis of vegetable profitability and market performance is quite important for the decision making in agricultural marketing and policy analysis yet has not been conducted in the area. Therefore, this study was conducted to examine and fill the existing gap. The study answered the following research questions: 1) what are the major constraints that smallholder vegetable producers encounter in the area? 2) How profitable is vegetable production in the area? 3) Who gets more benefits along vegetable market channels? 4) What is the structure of vegetable market in the area? Generally, the main objective of the study was to analyze profitability and market performance of smallholder vegetables production in the area.

2. Methodology

2.1. Description of the study areas

This study was conducted in three districts of the Jimma zone, including Gomma, Dedo, and Seka Chokersa (Figure 1). Jimma zone comprises 20 administrative districts with 2.5 million populations of which 94% are rural inhabitants (Population Census Commission, 2008). Jimma zone covers a total area of 15,569 km²and receives mean annual rainfall ranging between 1,200- and 2,800-mm. Subsistence farming is the dominant form of livelihood in Jimma zone for about 85% of the population. The area has suitable agro-ecological potential with the lowest drought risk rating in the country (Milas and Aynaoui, 2004). Cereals (maize, teff (eragrostis tef), sorghum, and barley), pulses (beans and peas), cash crops (coffee and chat (Catha edulis), fruits and vegetable crops (banana, mango, orange, avocado, potato, tomato, cabbage, sweet potato, garlic, paper, and onion) are widely grown by smallholders as a means of enhancing family income and achieving food security (Kebebew et al., 2011). Among the vegetables produced in the area, the emphasis of this study lies on potato, tomato, and cabbage. The choice of the crops intentionally based on their major production and marketability. They are the most important vegetable crops in the area because they contribute a significant benefit to the livelihood of smallholder farmers.

Figure 1.

Map of the study area.

2.2. Sampling procedure and sample size determination

2.2.1. Farmers sampling

In order to realize the objectives of this study, from Jimma zone, districts of Gomma, Dedo, and Seka Chokersa were selected purposively based on vegetable production potential. In the second step, three kebeles4 from each district and a total of nine kebeles were selected randomly from the three districts. Finally, 420 sampled households were selected using a random sampling method assisted by probability proportional to size (Table 1). The simplified formula provided by (Yamane, 1967) was used to determine the sample size. Accordingly, the required sample size at a 95% confidence level with a degree of variability of 5% and the level of precision equal to 5% are used to obtain a sample size required.

$$n_o=\frac{N}{1+N{(e)}^2}=\frac{9985}{1+{9985(0.05)}^2}=385$$ (1)

where $n_o$ is the sample size, N is total number of vegetable producer three districts of Jimma zone, e is the desired level of precision (e = 5%) Besides this, the sample size was increased by 10% to compensate for nonresponse or for persons that the researcher is unable to contact. In this way the sample size was increased to 420 (Table 1).

Table 1.

Number of households major from sample kebeles

Name of the district	Name of the Kebeles	Total number of vegetable producers	Proportion	Sample Size
Gomma	Ganji dalecho	1169	0.12	49
	Chami chego	1044	0.1	44
	Jimmate deru	947	0.09	40
Dedo	Waro Kolobo	1351	0.14	57
	Offole	1246	0.12	52
	Afaly Korti	1049	0.11	44
Seka Chekorsa	Dabbo Yaya	1175	0.12	49
	Dabbo Gibe	1051	0.11	44
	Ushane qacce	953	0.1	40
Grand total		9985	1	420

2.2.2. Sample of traders

The sample size of traders included in the study was determined based on information obtained from the district trade and market development office. Accordingly, from the total of 349 traders, 139 traders including collectors, retailers, and wholesalers were selected purposively based on the volume of the vegetable production they purchased and sold (Table 2).

Table 2.

Sample of actors other than farmers.

Name of the district	Actors	Number of actors	Number of sample
Gomma	Wholesaler	6	4
	Collector	44	14
	Retailer	56	19
Dedo	Wholesaler	9	7
	Collector	47	18
	Retailer	68	28
Seka Chokorsa	Wholesaler	8	6
	Collector	46	19
	Retailer	65	24
Grand total		349	139

2.2.3. Data types, sources, and methods of data collection

In this study, both quantitative and qualitative data types were collected from both primary and secondary sources. Primary data was collected from farmers and traders through an interview schedule containing close-ended and open-ended questions. Informal surveys such as key informant interviews and focus group discussions were held by using the checklist. Secondary data was gathered from the Central Statistical Agency (CSA), through reviewing journals and examination of reports as well as records of published and unpublished documents.

2.2.4. Method of data analysis

In this study different descriptive methods, gross profit formula and margin analysis were employed to analyze the data collected. Descriptive statistics such as mean and percentage were used in the process of analyzing socio-economic, demographic and institutional characteristics of households.

2.2.5. Analysis of profitability

Gross Margin (GM) by definition is simply the difference between the total revenue (TR) and the total variable cost (TVC) as expressed by Segun-Olasanmi and Bamire (2010); Modu et al. (2010); Girei et al. (2013) and presented by the equation below;

GM = TR – TVC; Л = GM – TFC (2)

where,

GM = Gross Margin, Л = profit, TR = Total Revenue, VC = Total Variable Cost, TFC = Total Fixed Cost.

The production efficiency (PE) per vegetable production was calculated as:

PE = ATR/ATC (3)

where,

ATR = Average Total Revenue, ATC = Average Total Cost

2.2.6. Structure, conduct and performance (S–C–P) model

The model was employed to examine the fundamental relationships between market structure, conduct and performance, and is usually referred to as the Structure, Conduct, and Performance (S–C–P) model. Amha (1994); Musema (2007) and Tadesse (2011) used this model to evaluate food grain, pepper and fruit market, respectively.

2.2.7. Market concentration

The concentration ratio is a way of measuring the concentration of market share held by particular suppliers in a market. "It is the percentage of total market sales accounted for by a given number of leading firms". Thus a four-firm concentration ratio is the total market share of the four firms with the largest market shares. Kohls and Uhl (2002) suggest that, as a rule of thumb, a four enterprise concentration ratios of 50 percent or more is indicative of strongly oligopolistic industry, 33–50 percent a weak oligopoly, and less than that, an un-concentrated industry. The usual measures of market concentration as:

$$C=\sum _i^r{S}_i$$ (4)

where C = concentration ratio, S_i =Percentage share of i^th firm, r = number of largest firms for which the ratio is going to be calculated.

$${\text{MS}}_i=\frac{V_i}{\sum V_i}$$ (5)

where, $V_i=$Amount of product handled by the buyer; ${\text{MS}}_i$ _{= Market} share of buyer I; $\sum V_i$ = Total amount of product handled by the r firms

Market conduct refers to the behavior of firms or the strategies used by the firms, for example, in their pricing, buying, selling, etc. The behavior of firms in setting their prices plays a vital role in the S–C–P paradigm (Scott, 1995).

2.2.8. Market performance

Market performance can be evaluated by analysis of costs and margins of marketing agents in different channels. A commonly used measure of performance is the marketing margin or price spread (Bashargo and Srinivasa, 2002). Marketing margin analysis deals with comparison of price at different levels of marketing chain over the same period of time. It measures the share of the final selling price that is captured by a particular agent in the marketing chain and always related to the final price or the price paid by the end consumer, expressed in percentage (Mendoza, 1995). Hence, marketing margins were employed for this study. According to Ghorbani (2008), marketing margin is important indices in the evaluation of market performance. Marketing margin refers to the difference between the price paid to the first seller (farm-gate price) and the price paid by the final buyer (retail price) (Abankwah et al., 2010). A systematically recording price at different levels of marketing chain during a two to three week period was sufficient to calculate quite accurately the relevant marketing margins (Pomeroy and Trinidad 1995). Thus, selling price by actor was utilized to estimate marketing margins. Marketing margins for the various vegetable traders were estimated using the following formulas.

$$\text{TGMM}=\frac{\text{Retailing}(\text{Consumer})\text{Price}-\text{Producer}\text{Sellingprice}}{\text{Retailing}(\text{Consumer})\text{price}}\ast 100$$ (6)

$${\text{GMM}}_{\text{C}}=\frac{\text{Collector}\text{Selling}\text{Price}-\text{Purchasing}\text{price}}{\text{Retailing}(\text{Consumer})\text{price}}\ast 100$$ (7)

$${\text{GMM}}_{\text{W}}=\frac{\text{Whole}\text{salers}\text{Selling}\text{Price}-\text{Purchasing}\text{price}}{\text{Retailing}(\text{Consumer})\text{price}}\ast 100$$ (8)

$${\text{GMM}}_{\text{R}}=\frac{\text{Retailing}\text{Price}-\text{Purchasing}\text{Price}}{\text{Retailing}(\text{Consumer})\text{price}}\ast 100$$ (9)

$${\text{GMM}}_{\text{P}}=100\%-\text{TGMM}$$ (10)

where;

TGMM is the total gross marketing margin;

${\text{GMM}}_{\text{C}}$ is the percentage of total gross marketing margin received by collectors;

${\text{GMM}}_{\text{W}}$ is the percentage of total gross marketing margin received by wholesalers

${\text{GMM}}_{\text{R}}$ is the percentage of total gross marketing margin received by Retailers

${\text{GMM}}_{\text{P}}$ is the producer gross marketing margin. It is the proportion of the price paid by the consumer that belongs to the producers.

3. Results and discussion

This section presents the major findings of the study. A detailed description of sample households’ demographic and socioeconomic characteristics is presented. Further, the result of profitability and market performance of the potato, tomato and cabbage output market are presented.

3.1. Demographic and socioeconomic characteristics of the farmers

Analysis of demographic and socio-economic characteristics of the sample unit is quite important to infer their close relationship with the vegetable marketing pattern of sample households. As indicated in Table 3, the mean age of sample households in the study area is 39 years with a standard deviation of 10 years. The mean family members of sample households is 7 in head count.

Table 3.

Characteristics of the farmers for continuous and dummy variables.

Continuous variable	Potato		Tomato		Cabbage
	Mean	SD	Mean	SD	Mean	SD
Age of household	39.30	10.39	39.30	10.39	39.30	10.39
Distance to Market(h0ur)	0.64	0.35	0.64	0.35	0.64	0.35
Family Size(number)	7.28	2.06	7.28	2.06	7.28	2.06
Education(years)	3.1	3.27	3.06	3.27	3.1	3.27
Total Livestock Unit (TLU)	6.31	3.69	6.31	3.69	6.31	3.69
Fertilizer used (Kg)	24.88	51.77	11.35	24.28	9.8	18.31
Farm Land (ha)	0.254	0.84	0.136	0.32	0.136	0.09
Farm Experience (year)	13.40	6.05	9.48	4.68	9.7	4.78
Non-Farm Income (birr or $)	3451	19242	3451	19242	3451	19242
Dummy variables	Frequency	%	Frequency	%	Frequency	%
SEX(Male)	385	91.7	385	91.7	80.7	80.7
Owing motor Pump	87	20.7	87	20.7	26.2	26.2
Member of cooperative	190	45.2	190	45.2	59.3	59.3
Market information	219	52.1	219	52.1	66.9	66.9
Use of credit	91	21.7	91	21.7	35.2	35.2
Access to extension contact	163	66.9	163	66.9	60.7	60.7
Owing transportation	385	38.8	385	38.8	80.7	80.7

The mean distance to the nearest market is 0.64 walking hour from vegetable production point to the nearest market. A long-distance market discourages the intensive participation of farmers as it needs a lot of time and effort to deliver to the market. The mean production experience of farmers for potato, tomato, and cabbage were 13, 9 and 10 respectively. The mean income obtained by sample households from non-farm income was 3451 birr ($93)1 and the mean average use of fertilizer per hectare for potato, tomato and cabbage were 24.88 kg, 11.35 kg, and 9.8 kg respectively. This implies that the use of fertilizer for vegetable production in the study area is very low, those households who use enough fertilizers per hectare produce optimum amount of vegetables. Therefore, to improve the production and productivity of vegetable, it is crucial to use fertilizer efficiently.

As depicted in Table 3, from the total of 420 sample households about 91.7% and 8.3% were male-headed and female-headed households, respectively. Among the sample farm households, about 45.2% of them were members of farmers’ cooperatives. Accordingly, about 52.1%,66.9%, 21.7%, 20.7%, and 38.8%, of sample households had access to market information, extension service, credit, motor pump ownership and has transportation facility, respectively.

3.2. Characteristics of sample traders

Sex, age, family size, trading experience, education status, access to credit, access to the storage facility, access to information, access to transportation facility and initial working capital were the socio-economic and demographic characteristics used to describe the traders (Table 4).

Table 4.

Demographic and socio-economic characteristics of sample traders.

Continuous Variable	Type of trader
	All sample Mean	Collectors (mean)	Wholesalers (mean)	Retailers (Mean)
Age (year)	38.9	37.9	40.04	38.7
Experience (year)	14.03	14.5	13.9	13.7
Education (school year)	5.43	4.98	5.78	4.73
Family size (number)	7.4	7.9	6.95	7.1
Initial capital in (Birr)	9760	4750	17052	7479
Net capital now in (Birr)	68280	44979	92,052.5	67809.5
Dummy variable (yes)	(%)	(%)	(%)	(%)
Sex (male)	55.5	48.55	65.2	51.6
Access to storage facility	21.6	18.9	24.5	21.45
Access to credit	47.6	12.2	73.3	53.6
Access to information	55.5	43.3	76.7	55.5
Transportation facility	56.7	12.2	56.7	62.7

The age of sample traders ranged from 25 to 78 years. The average age of all sample traders was 38.9 years and its standard deviation was 9.37 years. The family size of sample traders ranged from 1 to 12 people. The average family size of the total sample respondents of traders was found to be 7.4 person and its standard deviation was 2.55 person. The educational status of sample traders, on average was 5.43 schooling years and its standard deviation was 3.67 years.

Experience plays an important role in improving trading activities and marketing efficiency. The trading experience of sample traders ranges from 2 to 35 years and the average trading experience of sample traders’ respondents were 14.04 years.

The study result indicated that 55.5% of the sample traders were males while 44.5% of them were females. This implies that both women's and male's participate in vegetable trading. Likewise about 46.7% of the sample vegetable traders had access to credit but the majority of them (53.3%) did not use credit as working capital. On average, about 56.7%, 55.5% and 21.6% of sample traders had access to transportation facilities, access to information, and storage facility in the vegetable trading, respectively.

3.3. Profitability analysis of potato, tomato and cabbage production in the study area

Table 5 shows the profitability of potato, tomato, and cabbage production per hectare. The selected vegetables can be produced twice a year. The mean productivity of potato, tomato, and cabbage for the production year was 8.7, 6.3, 9.3 tons respectively. The costs of production and revenues were calculated to estimate the profitability of vegetable production.

Table 5.

Average cost of production and profitability of vegetable production (ETB/ha).

Cost item	Type of Vegetable
	Potato	Tomato	Cabbage
Variable cost
Costs of Seed	5028	3465	2250
Chemical fertilizer	2250	2516.3	1250
Manure cost	1040	923	967
Costs of land preparation	1914	1910.3	1763.3
Labor for crop management	1356	1733.7	1610.93
Labor for harvesting	1148	1123.4	898.73
Chemical cost	2701	2760.5	1700.94
Fuel cost	1650	1500	1120
Total variable cost (TVC)	17087	15932.2	11560.9
Fixed cost
Land rent	1500	1029	1029
Opportunity cost of land	2700	2471	2471
Land Tax	85	85	85
Rent of motor pump	1079	1115	700
Rent of pair oxen	1248	1200	1200
Total Fixed cost (TFC)	6612	5900	5485
Total cost of production/ha	23,699	21,832.2	17047.9
Cost of production/100kg	271.46	348.37	182.92
Yield (ton/ha)	8.73	6.27	9.32
Average selling price/100kg	451.2	520.71	287.53
Total Revenue/ha	39390	32633	26798
Gross Margin (TR-TVC)	22303	16701	15237
Profit (GM-TFC)/ha	15691	10801	9752
Input-output Ratio	1:1.66	1:1.5	1:1.57

Labor cost was estimated based on wage of labor per man/day. Costs of land preparation include: Cost for plowing (birr/ha) and animal power cost for plowing (birr/ha), crop management include: labor cost for weeding (birr/ha), and chemical spray (birr/ha), fertilizer cost, chemical costs, seed cost, fuel cost, compost preparation, and harvesting cost were reported by the sampled respondents. The overall average cost for production of potato, tomato, and cabbage were 23,699 birr ($641), 21,832 birr ($590), 17,048 ($461) per hectare respectively. The average price for potato, tomato, and cabbage at farm gate were 451 birr ($12), 521 birr ($14) and 288 Birr ($8) per 100kg, respectively.

Therefore, the total revenue of sample respondent from potato, tomato, and cabbage were 39,390 birr ($1065), 32,633 birr ($882) and 26,798 birr ($724) per hectare, respectively. The benefit-cost ratio of potato, tomato, and cabbage production were 1.7, 1.5, and 1.6 respectively. The analysis for selected vegetable crops shows that vegetable production are profitable in the area. The result is consistent with that of Addisu (2016) who concluded that both potato and onion production are profitable.

3.4. Distribution of benefit along potato, tomato, and cabbage marketing channels

3.4.1. Potato marketing channels

Seven potato marketing channels were identified together with their total carrying capacity in the study areas. The study results indicates that around 709 tons of potato were delivered to the market by sample respondents. Wholesalers, collectors, consumers and retailers were the principal agents that obtained potato from the producers. With calculated amount of 54.06%, 14.3%, 20.16% and 11.47% respectively. In addition, channel comparison was performed based on volume of commodity flow. Accordingly, channel VII is the leading one in terms of carrying large amount of potato to the end users around the study area which accounts about 147 tons, followed by channel I and II with volume of 135 and 126 ton respectively. The study depicted the following marketing channels:

• Channel I: Producer------Consumer (135 ton)

• Channel II: Producer-------Retailer------consumer (130 ton)

• Channel III: producer-------collector-----wholesaler-----Retailer-----Consumer (126 ton)

• Channel IV: producer--------Wholesaler-------Retailer-----Consumer (72 ton)

• Channel V: Producer---------Wholesaler-------consumer (47 ton)

• Channel VI: producer-------Collector-------wholesaler-------consumer (52 ton)

• Channel VII: producer-------Collector------- Retailer -------consumer (147 ton)

3.4.2. Profit margin and share of profit along potato marketing channels

Cost: The major categories of costs along the chain are production, purchasing and marketing cost. Packing marital, labor for packing, loading and unloading, transport cost, post-harvest loss and overhead costs were the major marketing costs for vegetable producers and traders. Costs and marketing margins were used to estimate distribution of benefit in the market chain.

Selling price: The selling price of vegetable varies with time, market outlet and products quality. Thus, in order to calculate margins and profit share of actors, the average price for that particular channel was considered as selling price.

Profit margin: The profit margin by a given actor is calculated by dedicating total cost from selling price. The share of profit margin of actors was computed in a relative term as the ratio of actor's profit margin and total profit margin by all actors for that particular channel.

All figures in Table 6, are for 100 kg of potato product and shows marketing cost incurred by producer, collectors, wholesalers and retailers, and the share of profit margin for each actors. Profit margin for potato producers was the highest in channels IV and V (144 birr or $3.9)2 and the lowest in channel III, VI and VII (20 birr or $ 0.54) due to involvement of local collectors in this channel that purchase products at lower price. Wholesaler profit margin is the highest in channel V (202 birr or $5.5) and in channel IV (192 birr or $ 5) and the lowest in channel VI (73 birr or $2). Retailer profit margin is the highest in channel II (289 birr or $ 7.8) and in channel VI (241 birr or $ 6.5) and the lowest in channel III and VI (80 birr or $2.16). Collector profit margin is the highest in channels III and VI (212 birr or $ 5.7) and VII (158 birr or $4.3), in this channel collectors obtained the maximum profit margin because they purchase the potato relatively at lower price from producers while producers obtained the lowest profit margin in this channel. The results also shows that the maximum profit margin obtained by that of retailers, which accounts 289 birr ($7.8)/100kg and 241 birr ($6.5)/100kg in channel II and VII respectively, followed by collectors which accounts 212 birr ($5.7) in channel III and VI. But as we see from the channel distribution, collectors and retailers purchase and sell potatoes in smaller quantities than wholesalers, so that the overall profit is smaller than the wholesalers. The result is in line with that of Dawit Setegn H, 2016 who found that traders are more benefited when they are purchase directly from farmers and sold to consumers.

Table 6.

Profit margins and share of profit along Potato marketing channels.

Channel	Actors	Input/purchasing cost	Marketing cost	Total cost	Selling price	Profit margin	Share of profit (%)
Channel I	Producer	272	145	417	567	150	100
	Total					150	100
	Producer	272	95	367	503	136	32
Channel II	Retailer	503	115	618	907	289	68
	Total					425	100
	Producer	272	25	297	317	20	5
	Collector	317	115	432	644	212	51
Channel III	Wholesale	644	55	699	802	103	25
	Retailer	802	45	847	927	80	19
	Total					415	100
	Producer	272	85	357	501	144	35
	Wholesaler	501	99	600	802	192	46
Channel IV	Retailer	802	45	847	927	80	19
	Total					416	100
	Producer	272	85	357	501	144	42
Channel V	Wholesaler	501	99	600	802	202	58
	Total					346	100
	Producer	272	25	297	317	20	7
	Collector	317	115	432	644	212	70
Channel VI	Wholesaler	644	75	719	792	73	24
	Total					305	100
	Producer	272	25	297	317	20	5
	Collector	317	120	437	594	157	38
Channel VII	Retailer	594	105	699	940	241	58
	Total					418	100

The finding is also consistent with Dastagiri (2010) who concluded the highest percentage of benefit goes to potato wholesalers. The study result further indicated that the intermediaries play a crucial role in securing their profit margins.

3.4.3. Tomato marketing channels

To reach at consumers tomato passes through different channels. Around 7 marketing channels were identified together with total volume of commodity handled by the channels. Sample producers supplied about 457 tons of tomato. The vast majority of the products was received by wholesalers, retailers, collectors and consumers with an estimated percentage share of 58.8%, 15.12%, 14.86% and 11.22% respectively. Furthermore channel comparison was made based on amount of tomato passed through the channels. As result channel II is the most dominant one with 126 tons of commodity, followed by channel V with carrying capacity of 90 tons. The prominent market channels identified were:

• Channel I: Producer------Consumer (63 ton)

• Channel II: Producer-------Retailer------consumer (126 ton)

• Channel III: Producer---------Wholesaler-------consumer (36 ton)

• Channel IV: producer--------Wholesaler-------Retailer-----Consumer (52 ton)

• Channel V: -----wholesaler-----Retailer-----Consumer (90 ton)

• Channel VI: producer-------Collector-------wholesaler-------consumer (36 ton)

• Channel VII: producer-------- Collector -------Retailer-----Consumer (54 ton)

3.4.4. Profit margin and share of profit along tomato marketing channels

All figures in Table 7, are computed per 100 kg of tomato and indicates categories of marketing cost related to the transaction by producers, collectors, wholesalers and retailers, and the share of actor's profit margin. Excluding channel I, profit margin for tomato producers is the highest in channel III and IV (136 birr or $3.7) and relatively they obtain better profit margin in all channels. Accordingly, wholesaler's profit margin is the highest in channel III (184 birr or $5) and the lowest in channel VI (85 birr or $2.3). Similarly retailer's profit margin is the highest in channel II (220 birr or $ 6.5) and the lowest in channel VI (38 birr or $1). Collector profit margin is the highest in channel VII (129 birr or $ 3.5) and the lowest in channel V (68 birr or $1.8). The results also shows that the maximum profit margin among traders was generated by retailers, which accounts 240 birr ($ 6)/100kg in channel II, followed by wholesalers which accounts 184 birr ($5)/100kg in channel III.

Table 7.

Profit margin and share of profit along tomato market chain/100kg of produce.

Channel	Actors	Input/purchasing cost	Marketing cost	Total cost	Selling price	Profit margin	Share of profit (%)
Channel I	Producer	348	80	428	604	176	100
	Total					176	100
	Producer	348	118	466	594	128	37
Channel II	Retailer	594	141	735	955	220	63
	Total					348	100
	Producer	348	105	453	589	136	43
Channel III	Wholesaler	589	115	704	888	184	58
	Total					284	100
	Producer	348	105	453	589	136	38
	Wholesaler	589	115	704	883	179	50
Channel IV	Retailer	883	45	928	971	43	12
	Total					358	100
	Producer	348	45	393	527	134	39
	Collector	527	91	618	686	68	20
Channel V	Wholesaler	686	97	783	888	105	30
	Retailer	888	45	933	971	38	11
	Total					345	100
	Producer	348	45	393	527	134	43
	Collector	527	91	618	713	95	30
Channel VI	Wholesaler	713	85	798	883	85	27
	Total					321	100
	Producer	348	45	393	527	134	35
	Collector	527	99	626	755	129	34
Channel VII	Retailer	755	95	850	971	121	32
	Total					384	100

3.4.5. Cabbage marketing channels

About seven cabbage marketing channels were identified, likewise total amount of products handled by the channels was also estimated in the study areas. The average amount of cabbage supplied was 781.2 tons. The main trade agents that receives cabbages from producers were wholesalers, collectors, retailers, and consumers with an estimated percentage share of 60.5%, 18.45%, 14% and 7.05% respectively. Moreover, comparison among channels was performed based on volume that passes through channels. As result, channel II is giant in terms of quantity of products that passes, which accounts 244.7 tons, followed by channel III with amount of 176.2 tons. The study revealed, the following major channels of cabbages.

• Channel I: Producer------Consumer (73.2 ton)

• Channel II: Producer-------Retailer------consumer (244.7 ton)

• Channel III: producer-------collector-----wholesaler-----Retailer-----Consumer (176.2 ton)

• Channel IV: producer--------Wholesaler-------Retailer-----Consumer (49.7 ton)

• Channel V: Producer---------Wholesaler-------consumer (51.6 ton)

• Channel VI: producer-------Collector-------wholesaler-------consumer (98.8 ton)

• Channel VII: producer-------- Collector -------Retailer-----Consumer (87 ton)

3.4.6. Profit margin and share of profit along cabbage marketing channels

All figures in Table 8, were calculated per 100 kg of cabbage and describes transaction costs incurred by producers, collectors, wholesalers and retailers, and the share of profit margin for each of marketing actors. As indicated on Table 8, profit margin for cabbage producers is the highest in channels II (232 birr or $6.27) and the lowest in channel VI (82 birr or $2.3) and also relatively they received better profit margin in channel IV,V and VII. Likewise, wholesaler's profit margin is the highest in channel VI (148 birr or $4) and the lowest in channel IV (106 birr or $2.9).

Table 8.

Profit margin and share of profit along cabbage marketing chain/100kg of produce.

Channel	Actors	Input/purchasing cost	Marketing cost	Total cost	Selling price	Profit margin	Share of profit (%)
Channel I	Producer	183	135	318	616	298	100
	Total				616	298	100
	Producer	183	125	308	540	232	67
Channel II	Retailer	540	95	635	750	115	33
	Total					347	100
	Producer	183	25	208	263	82	23
	Collector	263	75	338	392	54	15
Channel III	Wholesale	392	85	477	607	130	36
	Retailer	607	70	677	770	93	26
	Total					359	100
	Producer	183	90	273	411	138	41
	Wholesaler	411	90	501	607	106	31
Channel IV	Retailer	607	70	677	770	93	28
	Total					337	100
	Producer	183	90	273	411	138	52
Channel V	Wholesaler	411	90	501	625	124	48
	Total					262	100
	Producer	183	25	208	263	82	29
	Collector	263	75	338	392	54	19
Channel VI	Wholesaler	392	85	477	625	148	52
	Total					284	100
	Producer	183	25	208	263	183	37
	Collector	263	75	338	392	54	11
Channel VII	Retailer	392	96	488	750	262	53
	Total					499	100

Similarly, retailer profit margin is highest in channel VII (262 birr or $ 7) and the lowest in channel III and IV (93 birr or $ 2.5). Collector profit margin is the highest in channels VII (263 birr or $ 7) and lowest in channel II and VI (54 birr or $1.5). The results also shows that the maximum profit margin from traders was received by retailers, with about 262 birr ($ 7)/100kg in channel VII, followed by wholesalers which accounts 148 birr ($43)/100kg in channel VI.

3.5. Structure-conduct and performance of potato, tomato and cabbage

3.5.1. Market structure

To determine the market structure of potato, tomato and cabbage degree of market concentration were employed. Mostly the four firm's concentration ratio (CR4) are used for judging the market structure. The four firm's concentration ratio (CR4) greater than 50%, 33% up to 50%, and less than 33% is considered as strong oligopoly, weak oligopoly and un concentrated market respectively (Kohls, 1955). The levels of market concentrations (CR₄) for selected vegetables were computed by adding the four largest trader's market shares_. Market shares were estimated based on the amount of product handled by each unit, as a percentage of total volume handled in a market (Table 9). Accordingly, the levels of market concentrations (CR₄) for potato, tomato, and cabbage market are 43%, 38% and 39% respectively. Based on the result of the study the selected vegetables market in the study area displayed a weak oligopoly market structure, this indicated that the existence of a low concentration vegetable market in the study area. The result is in line with the finding of Yaregal (2018) who found that potato market was characterized by weak oligopolistic.

Table 9.

Market concentration of selected vegetable traders’

Number of Trader	Potato		Tomato		Cabbage
	Market volume (ton)	Market Share (%)	Market volume (ton)	Market Share (%)	Market volume (ton)	Market Share (%)
1	580.3	4.64	563.2	6.36	1445	10.05
1	1273.3	10.18	885	9.99	1376	9.57
1	1499	11.99	289.9	3.27	776	5.40
1	698	5.58	644.4	7.27	767.1	5.33
1	688.4	5.50	807.6	9.11	1245.1	8.66
1	560	4.48	777.8	8.78	1589	11.05
1	1424.5	11.39	564.4	6.37	967.4	6.73
1	1136.1	9.08	876.6	9.89	687.7	4.78
1	564.1	4.51	345.6	3.90	579	4.03
1	586.6	4.69	243.1	2.74	567.8	3.95
1	437	3.49	456.5	5.15	1035	7.20
1	574.5	4.59	542.8	6.13	987.6	6.87
1	786.5	6.29	642.8	7.25	548.2	3.81
1	678.6	5.43	323.4	3.65	367.5	2.56
1	564.3	4.51	395.5	4.46	862.4	6.00
1	455.2	3.64	502.4	5.67	578.2	4.02
Total	12506.4	100.00	8861	100.00	14379	100.00

3.5.2. Market conduct

In this section conduct of the vegetable market is analyzed in terms of producer and trader's price-setting strategies.

3.5.3. Producer's price setting strategy

According to the survey result, about 54% of sample farmer said that market price was set by buyers and 38% of respondents said that price was set by the supply and demand. The remaining 8% of farmer reported that the selling price was set by negotiation. This indicates that majority of producers are price taker.

3.5.4. Purchasing and selling strategy of trader

Study finding shows that the majority (50.8 %) of the respondents cited the price is determined by market (demand &supply) similarly about 28%, 12.2% and 9% of respondents reported that the price was set by negotiating with suppliers, discussing with other traders and by themselves respectively (Table 10).

Table 10.

Purchasing and selling strategy of trader.

Price setting mechanism	Traders
	Total (%)	Collectors (%)	Wholesalers (%)	Retailers (%)
Market (supply& demand)	50.8	52.3	45.8	54.4
Discussion with other traders	12.2	12.2	7	17.3
Negotiate with suppliers	28	24.5	40.5	19.2
Traders themselves	9	11.1	6.7	9.1

3.6. Analysis of market performance for potato, tomato and cabbage production

3.6.1. Marketing margins of potato at different channels

The study show that marketing margin among actors across channels of potato, tomato and cabbage (Table 11). Furthermore, the finding show that the highest and the lowest total gross marketing margin (TGMM) for potato production, were 66% and 37.5% in the channel III and channel V, respectively. Excluding the scenario of channel I producer's share (GMP) was the highest in channel V (62.5%) and the lowest in channel III (34%). The results also shows that the maximum gross marketing margin among traders was obtained by retailers, which accounts 44.4% of the consumer's price in channel II followed by collectors which was 41% in channel VI. Based on the finding we can conclude that, producers obtained higher percentage share of margin when they directly supplied their products to consumers.

Table 11.

Marketing margins of selected vegetable at different channels.

			Vegetables marketing channel
Vegetables	Actors	Cost item	I	II	III	IV	V	VI	VII
Marketing Margin of Potato	Producer	Purchase price
		Production cost	272	272	272	272	272	272	272
		Selling price	567	503	317	501	501	317	317
		GMMP (%)	100	55.5	34	49	62.5	40	35.5
	Collector	Purchase price	-	-	317	-	-	317	317
		Selling price	-	-	644	-	-	644	654
		GMMC (%)			35	-	-	41	36.6
	Wholesalers	Purchase price	-	-	644	501	501	644	-
		Selling price	-	-	802	802	802	792	-
		GMMW (%)		-	17	32.5	37.5	18.6	-
		Purchase price	-	503	802	802	-	-	654
	Retailer	Selling price		907	927	927	-	-	920
		GMMR (%)		44.5	13.5	13.5	-	-	30
	TGMM (%)		0	45.5	66	51	37.5	60	65.5
Marketing Margin of Tomato	Producer	Purchase price
		Production cost	348	348	348	348	348	348	348
		Selling price	604	594	589	589	527	527	527
		GMMP (%)	100	62	66	61	54	60	54
	Collector	Purchase price	-	-	-	-	527	527	527
		Selling price	-	-	-	-	686	713	755
		GMMC (%)					16	21	23.5
	Wholesalers	Purchase price	-	-	589	589	686	713	-
		Selling price	-	-	888	883	888	883	-
		GMMW (%)			34	33	21		-
	Retailer	Purchase price	-	594	-	883	888	-	755
		Selling price		955	-	971	971	-	971
		GMMR (%)		38	-	25	9
	TGMM (%)		0	38	34	39	46	40	46
Marketing Margin of Cabbage	Producer	Purchase price
		Production cost	183	183	183	183	183	183	183
		Selling price	616	540	263	411	411	263	263
		GMMP (%)	100	72	34	53.4	66	42	35
	Collector	Purchase price	-	-	263	-	-	263	263
		Selling price	-	-	392	-	-	392	392
		GMMC (%)			17	-	-	21	17
	Wholesalers	Purchase price	-	-	392	411	411	392	-
		Selling price	-	-	607	607	625	625	-
		GMMW (%)		-	28	25	34	37	-
	Retailer	Purchase price	-	540	607	607	-	-	392
		Selling price	-	750	770	770	-	-	750
		GMMR (%)	-	28	21	21	-	-	48
TGMM (%)			0	28	66	46.6	34	58	65

where: GMM_{P =} Gross Marketing Margin for producers, GMM_{C =} Gross Marketing Margin for collectors.

GMM_{W =} Gross Marketing Margin for wholesalers, GMM_{R =} Gross Marketing Margin for retailers.

3.6.2. Marketing margins of tomato at different channels

Tomatoes marketing margins for specific market participants was presented on Table 11. Accordingly, total gross marketing margin (TGMM) is highest (46%) in channel V and VII and lowest (34%)in channel III. Likewise, producer's share (GMMp) was highest (66%) in channel III and relatively lowest in channel V and VII. The variation in producer's share is consistent with theory, as the number of agent's increase's the amount of share received by the producer's will decreases. The results also indicates maximum (38%) of consumer's price goes to retailers in channel II, followed by wholesalers which was 34% in channel III. Generally, producers obtained higher percentage share of profit when they sold their products to consumers. The finding is in support of Aemro (2018) who indicated unfair distribution of consumers' price between tomato farmers and the market chain actors. Similarly, Kudzai (2017) explained that only few percent of consumer's price reaches tomato growers.

3.6.3. Marketing margins of cabbage at different channels

As presented on Table 11, marketing margins of cabbage for particular marketing agents was addressed. The result indicates that total gross marketing margin (TGMM) is highest (66%) in channel III and lowest (28%) in channel II. Producer's share (GMMp) was uppermost (66%) in channel II, in contrast it is lowest in channel III with share of 34 %. The great portion of gross marketing margin is obtained by retailers which accounts 48% of the consumer's price in channel VII, followed by wholesalers which was 37% in channel VI.

3.6.4. Major constraints hindering vegetable producers

A lots of constraints were cited during informal survey (focus group discussion and key informant interview) in addition to informal survey data were obtained on bottle necks of vegetables production and marketing. The prominent constraints of vegetable production and marketing were presented on Table 12.

Table 12.

Production and marketing constraints of vegetable producers.

Production constrain	Freq.	percentage	Rank
Problem of input supply	366	87.1	1
Pest and disease prevalence	363	86.4	2
Limited extension services	254	60.5	6
Inadequate improved storage facility & Post-harvest loss	328	78.1	3
High market price variability	267	63.6	5
Limited Agronomic and post-harvest handling	279	66.4	4
Market constraint
Price setting problem	251	59.8	6
Poor information flow	232	55.2	7
Poor transportation facility	297	70.7	4
Brokers problem or interference of intermediaries	366	87.1	1
Perishability of the product	324	77	3
Low price of the product	353	84	2
Poor linkage among actors	257	61.2	5

Source: Own Computation of Survey data, 2019

Input supply problem: The most important physical inputs for vegetable production are improved seeds, fertilizers, pesticides and herbicides. Regarding the input supply constraints (shortage of good quality seed, inadequate pesticides and herbicides, and high input price) was responded by the farmers. Sample respondents and informal survey participants presented the existence of limited supply of quality seed, statistically about 87.1% of sample respondents confirmed limited access and supply of improved seed as their major production problem (Table 12).

Limited extension services: Training plays paramount role in equipping farmers with skill, moreover it contributes a lot in achieving sustainable development. The vast majority (60.5%) reported that they have never attended training on agronomic practice and post-harvest handling. In addition, results from informal survey shows farmers are not getting adequate service regarding products marketing. The farmers also reported that, they suffer from poor post-harvest handling techniques which is leading to significant economic losses. Furthermore, farmers do not have good storage facilities available at the farm level, and this forces them to sell their product immediately after harvest. About 86.4% of producers reported disease and pest attacks - mainly fungal disease on potato as the major problems in the study area.

Broker's problem or market interference: Brokers problem is one of the major marketing constraints which affect vegetable producers (Table 12). About 87.1% of producers reported that brokers intervene the transactions of their products. The focus group discussion participants cited that, the brokers manipulate the prices, create market price information asymmetry which enforces the producers to sale their vegetables at the prices set by wholesalers. Sometimes, the farmers have no idea of the price paid by the wholesalers and only accept price offered by the brokers. In case, if the producers are not capable to sell through broker's network, they forced to sell to local collectors at the lower price due to perishable nature of the product.

4. Conclusion and recommendations

4.1. Conclusion

The benefit-cost ratio analysis has shown that potato, tomato and cabbage production is profitable. The producers supply their vegetable products to wholesalers, retailers, collectors and consumers. The retailers are generating maximum profit margin than other actors in vegetable market chain, while the producers are more profitable when they directly sell to the consumers. The market structure for potato, tomato and cabbage production is weak oligopolistic market structure. The marketing agents are the principal decision makers in setting vegetables price. Producers obtained higher percentage share of margin when they sold their product directly to consumers. The vegetables sector is hindered with inadequate supply of inputs, under developed vegetables post-harvest handling and poor market information flows.

4.2. Recommendations

Based on the finding the following core recommendations are forwarded to improve vegetables profitability and market performance. The overall profitability analysis shows that there is a positive gross profit margin and there is a room to enhance the profitability of vegetable production through increasing vegetable productivity and reasonable price. Government and non-governmental organizations should strengthen vegetable production extension services through using digital information dissemination. There was a need to reduce market intermediaries to minimize the marketing margins, reduce unfair profit distribution and thereby enhance the producers’ income. Concerning bodies should improve the input supply system so that farmers receive the right type of inputs, quantity and quality needed at the right time. The presence of vegetable diseases created frequent yield reduction and hampered market supply. Therefore, the Ethiopian agricultural research institutes and universities should play crucial role in releasing high yielding and disease resistant varieties to improve production and productivity of vegetables in the study area.

Declarations

Author contribution statement

Ibrahim Aliyi: Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data; Wrote the paper.

Abduselam Faris; Assefa Ayele; Alemayehu Oljirra; Mulubrihan Bayessa: Performed the experiments; Analyzed and interpreted the data; Wrote the paper.

Funding statement

This work was supported by Jimma University, Ethiopia.

Data availability statement

Data will be made available on request.

Declaration of interests statement

The authors declare no conflict of interest.

Additional information

No additional information is available for this paper.