Characteristics of sample producers
From the total sampled tomato producers, 69.17% were tomato market participants during the survey year. Average age of the sampled respondents was 40 years ranging from 21 to 72 years. The average family size of sample respondents was found to be 4 and 5 persons for participants and non-participants, respectively. The mean number of years that had been spent in formal school was 4.39 years. For the sample households, the average time to the nearest market in hours of walking time was 58.5 minutes. The survey result with respect to land holding of the respondents reveals that the land holding size of the sample households ranges from 0.25 to 5ha with a mean of 1.58 ha. The farming experience of tomato producer sample households ranges from 2 to 17 years with a mean of 7.68 years. The t-test result revealed that quantity of tomato produced by the market participants and non-participants showed variation at less than 1% probability level (Table 3). A farm household with larger quantity of tomato produced had higher marketed surplus than farm households with small quantity of tomato produced. This indicates that quantity of tomato produced can directly influence farmer’s market participation decision. The result also shows that tomato market participant households had more extension contact with extension agent than non-participant tomato producer. The t-test indicated that there is a significant mean difference between tomato market participants and non-participants at less than 5% probability level in terms of extension contact.
The survey results show that of the total sample households, 75.83% were male headed and 24.17% were female-headed households. The result also shows that from the sample households 48.33% of them replied that the price of tomato in the previous year was attractive. In terms of non-farm activity, the survey result shows that 45% of sample respondents were participating in non-farm income generating activity while 55% were not participating in the non-farm activity as their source of income. In study area petty trade, daily labor, a local brewery, firewood and charcoal sale, consumer good retailing, transportation of produces (from and to the market/farm) and temporary employment on others’ farm was found to be some of the non-farm income generating activities in which sampled farmers were participating. The chi-square test shows that there was no significant difference between those who participate in the market and those who didn’t participate regarding the sex of household head, perception of the previous year price of tomato, participation in non-farming activity, access to credit and irrigation.
Table 3: Characteristics of tomato market participants and non-participants
Continuous Variable
|
Mean
|
Total (120)
|
t-test
|
Participant (83)
|
Non-participant (37)
|
Age
|
41
|
38
|
40
|
1.56
|
Education
|
4.35
|
4.49
|
4.39
|
-0.20
|
Family
|
4
|
5
|
5
|
-0.87
|
Land
|
1.60
|
1.55
|
1.58
|
0.19
|
Experience
|
7.96
|
7.03
|
7.68
|
1.52
|
Extension
|
1.18
|
0.76
|
1.05
|
2.32**
|
Livestock
|
2.12
|
2.05
|
2.10
|
0.23
|
Quantity
|
377.10
|
53.11
|
277.2
|
5.04***
|
Distance
|
58.01
|
59.59
|
58.5
|
-0.41
|
Dummy variables
|
Percentage
|
Percentage
|
Percentage
|
X2-test
|
Sex
|
Male
|
79.52
|
67.57
|
75.83
|
1.9943
|
Female
|
20.48
|
32.43
|
24.17
|
Price
|
Attractive
|
51.81
|
40.54
|
48.33
|
1.3009
|
Not attractive
|
48.19
|
59.46
|
51.67
|
Non-farm
|
Yes
|
46.99
|
40.54
|
45
|
0.4298
|
No
|
53.01
|
59.46
|
55
|
Credit
|
Yes
|
57.83
|
54.05
|
56.67
|
0.1487
|
No
|
42.17
|
45.95
|
43.33
|
Irrigation
|
Yes
|
60.24
|
51.35
|
57.5
|
0.8276
|
No
|
39.76
|
48.65
|
42.5
|
*** and ** represents a significance level at 1% and 5% level
Source: Own survey, 2018
Value Chain Analysis
Major value chain actors and their functions
The value chain is a concept which can be described as the entire range of activities required to bring a product from the initial input-supply stage, through various phases of production, to its final market destination. Along with the farmers, a number of actors participated in the value chain of tomato from the production point to the consumer point. The major actors involved in tomato value chain, their roles and interrelationships are discussed in Table 4 below.
Table 4: Major value chain actors and their functions
Actors
|
Functions
|
Input Suppliers
|
Supplying inputs such as seeds, fertilizer, pesticides and farm implements. They include office of agriculture and irrigation, primary cooperatives, traders and informal farmers to farmer’s exchange.
|
Producers
|
They perform most of the value chain functions right from farm inputs preparation on their farms or procurement of the inputs from other sources to post-harvest handling and marketing.
|
Collectors
|
buying and assembling, repacking, sorting, transporting and reselling
|
Brokers
|
Facilitating transactions by bringing the buyers and sellers together.
|
Wholesalers
|
Purchasing and reselling to other traders and consumers.
|
Retailers
|
They are the last link between producers and consumers. They resell the produce to consumers
|
Consumers
|
Consumers are a final buyer of the product for consumption purpose.
|
Enablers and facilitators
|
They provide support services. They include office of agriculture, district irrigation and development authority, district trade and market development office, primary cooperatives, micro-finance institutions and NGO’s.
|
Source: Own survey, 2018
Value chain map of tomato in the study area
Value chain maps are the core of any value chain analysis. A value chain map illustrates the way the product flows from raw material to end markets and indicates how the industry functions [11]. Mapping of the value chain was carried out after principal actor identification. This mapping included all activities, starting from farm input supply through product delivery to final consumers.
Marketing channels and margin analysis
Marketing channels
A marketing channel is a business structure of interdependent organizations that reach from the point of product origin to the consumer with the purpose of moving products to their final consumption destination [12]. The analysis of marketing channels is intended to provide a systematic knowledge of the flow of the goods and services from their origin (producer) to the final destination (consumer). Five main alternative marketing channels were identified tomato marketing in the study area. The main marketing channels identified from the point of production until the product reaches the final consumer are presented in Table below.
Table 5: Main alternative marketing channels of tomato marketing in the study area
Channel
|
Actors
|
I
|
Producer → Consumer
|
II
|
Producers → Retailer → Consumer
|
III
|
Producers → Collector → Retailer → Consumer
|
IV
|
Producers → Wholesaler → Retailer → Consumer
|
V
|
Producers → Wholesaler → Consumer
|
Source: survey result, 2018
Marketing margin analysis
Margin analysis can be conducted parallel to channel surveys and helps to determine how pro-poor a value chain is. It is determined based on the price received or selling price. A systematically recording of prices at different levels of the marketing chain during a two to three week period is sufficient to calculate quite accurately the relevant marketing margins [13]. The results of the study showed that tomato producer’s share is highest about 80% of the total consumer price in channel II and lowest in channel IV which was about 66.67% because of the involvement of the intermediaries in this channel. As the number of intermediaries increases, the producer’s share in consumer’s price decreases. Table 6 indicates the estimated marketing margin under various marketing channels of tomato.
Table 6: Estimated marketing margin of tomato value chain actors per kilogram
Actors
|
Item
|
Marketing channel
|
I
|
II
|
III
|
IV
|
V
|
Producer
|
Production cost
|
1.6
|
1.6
|
1.6
|
1.6
|
1.6
|
Marketing cost
|
0.7
|
0.6
|
0.5
|
0.4
|
0.4
|
Selling price
|
13
|
12
|
11
|
10
|
10
|
Gross profit
GMMp (%)
|
10.7
100
|
9.8
80
|
8.9
73.34
|
8
66.67
|
8
71.43
|
Collectors
|
Purchase price
|
|
|
11
|
|
|
Marketing cost
|
|
|
1
|
|
|
Selling price
|
|
|
13
|
|
|
Gross profit
GMMc (%)
|
|
|
1
13.33
|
|
|
Wholesaler
|
Purchase price
|
|
|
|
10
|
10
|
Marketing cost
|
|
|
|
1.5
|
2
|
Selling price
|
|
|
|
13
|
14
|
Gross profit
GMMw (%)
|
|
|
|
1.5
20
|
2
28.57
|
Retailer
|
Purchase price
|
|
12
|
13
|
13
|
|
Marketing cost
|
|
1
|
0.5
|
0.5
|
|
Selling price
|
|
15
|
15
|
15
|
|
Gross profit
GMMr (%)
|
|
2
20
|
1.5
13.33
|
1.5
13.33
|
|
TGMM (%)
|
0
|
20
|
21.43
|
33.33
|
28.57
|
Source: Own computation of survey result, 2018
Econometric Results
The Heckman two-step procedure was used to determine the determinants of tomato market participation decision of sample households and level of participation. The first step of the model predicted the probability of sample households to participate in the market and in the second step, it analyses the determinants of the level of market participation. The model result showed that out of fourteen explanatory variables hypothesized to affect the tomato market participation decision and level of participation, four variables were found to determine the probability of market participation and four variables including inverse mills ratio were found to be significantly affects the level of tomato market participation. The results of the model are depicted in (Table 7).
Table 7: Determinants of tomato market participation decision and level of participation
Variables
|
Probit regression
|
OLS regression
|
dy/dx
|
Coef.
|
Robust Std. Err.
|
P>z
|
Coef.
|
Std. Err.
|
P>t
|
Constant
|
-------
|
-1.350
|
2.280
|
0.554
|
51.059
|
48.931
|
0.299
|
Age
|
-0.002
|
-0.020
|
0.026
|
0.440
|
0.032
|
0.583
|
0.957
|
Sex
|
-0.016
|
-0.136
|
0.518
|
0.793
|
-0.539
|
11.244
|
0.962
|
Education
|
-0.001
|
-0.008
|
0.071
|
0.907
|
-0.019
|
1.580
|
0.990
|
Family
|
-0.064
|
-0.534***
|
0.171
|
0.002
|
-1.846
|
3.001
|
0.540
|
Land
|
-0.035
|
-0.293
|
0.232
|
0.206
|
1.076
|
5.307
|
0.840
|
Experience
|
0.024
|
0.201***
|
0.077
|
0.009
|
-0.785
|
1.761
|
0.657
|
Irrigation
|
0.039
|
0.324
|
0.417
|
0.438
|
-13.983
|
9.704
|
0.153
|
Extension
|
-0.057
|
-0.475
|
0.349
|
0.173
|
13.371**
|
6.615
|
0.046
|
Credit
|
0.073
|
0.614
|
0.494
|
0.214
|
3.325
|
9.838
|
0.736
|
Livestock
|
0.019
|
0.160
|
0.105
|
0.129
|
3.021
|
3.136
|
0.338
|
Non-farm
|
-0.082
|
-0.685*
|
0.409
|
0.094
|
-7.858
|
9.803
|
0.425
|
Quantity
|
0.006
|
0.054***
|
0.013
|
0.000
|
0.806***
|
0.019
|
0.000
|
Distance
|
0.000
|
0.001
|
0.009
|
0.884
|
-0.183
|
0.246
|
0.460
|
Price
|
-0.039
|
-0.326
|
0.467
|
0.485
|
-16.815*
|
9.455
|
0.078
|
IMR
|
|
|
|
|
-10.272*
|
5.634
|
0.071
|
Model summary
|
Number of obs = 120, Wald chi2 (14) = 27.07, Prob > chi2 = 0.0189, Pseudo R2 = 0.6571 and Log pseudo likelihood =
-25.421832
|
Number of obs = 120, F (15, 104)
= 293.99, Prob > F = 0.0000, R-squared = 0.9770, Adj R-squared = 0.9736 and Root MSE = 49.72
|
***, ** and * represents a significance level at 1%, 5% and 10%, respectively
|
Source: Own survey result, 2018
Family size: It was significant and negatively associated with the market participation decision at 1% level of significance. The marginal effect result also indicates that a unit increase in family size decreases the probability of participation in tomato market by 6.4%, keeping other factors constant. The implication is that households’ participation decision in tomato market could depend on family size or the per capita consumption requirement that could be satisfied from own production. Thus, the likelihood of being a seller in tomato market decreases for households with larger family size. The result is consistent with the result of [14] who found that household with large family sizes need to feed their family first and take the remaining small portion surplus to the market, especially if the crop is consumed at home.
Production experience: The result shows that tomato farming experience of households has positive and significant effect at the 1% level on the tomato market participation decision. Thus, the result implied that as farmer’s experience increase by one year, the probability of market participation increases by 2.4%, keeping other factors constant. This means that farmers with more experience in tomato production and marketing have higher ability to produce more and participate in the market.
Participation in non-farm activities (Non-farm): The result of the model depicts that participation in non-farming activities had a negative effect on decision to participate in the tomato market at 10% level of significance. If tomato producers participate in non-farm income generating activity, tomato market participation decision would decrease by 8.2%, keeping other factors constant. This implies that farmers who had non-farm income sources were not able to encouraged earning income from sale of tomato and also the income earned from this sector is not invested in farm improvement activities. The finding is consistent with the findings of [15] who found that households who earn income from non-farm activity participate less than those who did not have access.
Quantity produced: the result showed that the total amount of tomato produced in a year had a positive and significant impact both on the tomato market participation decision and level of participation at 1% significance level. The result also implied that a unit increase in the quantity of tomato produced would lead to increase the market participation decision and level of participation by 0.6% and 0.806 kilograms, respectively. The reason behind is that farmers can sell more from extra production/harvests which can meet and satisfy the demand of households. Unlike the other cereal crops, farmers cannot store tomato for a long time; since it is relatively perishable. So, they are forced to sell. Hence, as more is produced more will be supplied to the market. This is in line with that of [16] who found that the amount produced had a positive relationship with household market participation decision and level of market participation.
Extension contact: As expected, an increase in the number of extensions visits significantly and positively affected the level of market participation at less than 5% significance level. The result indicated that an extra extension visit would increase the level of market participation by 13.37 kilograms, keeping other factors constant. This could be attributed to the fact that an increase in the number of extension visits would avail up to date information and knowledge regarding agricultural technologies that might improve productivity and therefore increase the level of participation.
Perception of lagged price: The model result depicts that this variable had a negative relationship with the tomato level of market participation and it was found to be statistically significant at 10% probability level. The negative and significant relationship between the variables indicates as household’s perception on lagged market price of tomato goes from attractive to not-attractive (low), decreases the level of market participation of tomato by 16.815 kilograms, keeping other factors constant. This implies that when the perception of lagged market price of farmers is attractive, it motivates the farmers to produce more, they have surpluses to supply to the market and lagged price can act as a motivation to produce towards market participation. This is in line with the finding of [14] who found that output price is an incentive for farm households to participate more in the supply market. The study also confirms the study conducted by [17] who found that lagged market price affects the household’s decision to participate in the market.
Lambda (IMR): It was significantly and negatively related to the level of market participation at the 10% level of significance which implies that the error term in the selection and outcome equation is negatively correlated. It also indicates that there was a sample selection bias or the existence of unobserved factors that determine farmers’ likelihood to participate in the tomato market and thereby affecting the level of participation