Factors affecting the market outlet choice of bamboo culm producers in Banja district, Awi zone, Ethiopia

Abstract

Bamboo meets a growing and various bamboo product demands and generates revenue. The study aimed to analyze the factors that affect the choice of alternative bamboo market outlets. By using two stages sampling procedures, 114 bamboo producers were randomly and proportionally selected. The determinants that affect the choice of bamboo market outlets were analyzed by multivariate probit (MVP) model. Based on the model result, the probability of bamboo producers to select wholesalers, retailers, processors and local traders outlet were 16.8%, 46.2%, 60.8% and 54.3%, respectively. The probability of success and failure to select four market outlets were 2.5% and 2.2%, respectively. The result of MVP revealed that family size, total land holding size, amount of culm production, farming experience, distance to the market and silviculture management practice affect the probability of farmers market outlet choice. Therefore, this study gives an emphasis to the analysis of bamboo market outlets for bamboo culm producers to increase the market return for producers.

Keywords:

• bamboo
• culm
• factors
• market outlet
• multivariate probit
• producers

PUBLIC INTEREST STATEMENT

Bamboo is a multipurpose plant with high economic and environmental value and which contributes to reforestation and climate change mitigation. Ethiopia has a large amount of bamboo resource in both highland and lowland areas. However, unlike other countries, the economic return obtained from bamboo product is very limited. Banja District is endowed by highland bamboo resource and it is the main source of income for the community. However, there are different problems in the study area. It include: low knowledge about the creation of value to the product, inconsistent market demand and weak markets linkage for bamboo and its products, and low access to infrastructure. The findings of this study enable to enhance the production and marketing of bamboo products at large scale to improve the livelihood of the producers and other actors to bring economic development for the study area as well as for the nation. Therefore, this study was conducted to analyze the factors that affect the market outlet choice of bamboo culm producers.

1. Introduction

The production to consumption system of non-timber forest products (NTFP) ranges from simple to complex chains in terms of the involvement of actors, transformations, transactions, nature of the product and destination of the product. Some NTFPs might be sold at local market directly for consumers and some products might be sold at international market through different intermediaries (Marshall et al., 2006). In Ethiopia, bamboo production and utilization are not integrated with bamboo market and it is not properly managed by the community (Aresema, 2008).

The commercial bamboo sector in Africa is inefficient due to a lack of skills regarding to the ways of doing business, poor infrastructure, and weak and inconsistent market demand (Ingram et al., 2010). Bamboo is most economically useful NTFP, with its renewable and accessibility to the rural poor people. It has also great potential for commercialization and can drive rural development. It can be used at all levels of industrial activity from small crafts based industries to modern integrated plants (Tefera et al., 2013).

The demand and supply of bamboo culms are minimal and the trading activity is limited regarding to product locations, distributions and final usage (Fayera et al., 2017). Bamboo producers sell their bamboo culms to local traders where local traders’ in turn sell the product to processors (Yalfal et al., 2015). The highland bamboo can grow between 2200 and 3200 m.a.s.l. and average yearly temperatures ranging from 10°C and 200°C${\mathrm{c}}^0$with annual rain fall of 1700–2200 mm (Tefera et al., 2013). In Amhara region, highland bamboo is found in Gagusa Shikudad, Fagita Lekoma, Ankesha, Banja, Sinan, Bibugni, Farta, Estie and other districts (Bureau of Agriculture (BoA) , 2012).

The choice of marketing channel is an important farm level decisions, which have a great impact on the income of households’. The choices of marketing outlets are mostly household specific decisions to sell their produce in different market outlets for generating high returns and it required the consideration of demographic, socio-economic and market related factors (Berhanu et al., 2013; Shewaye, 2016). Each market outlet is characterized different market return, risk, cost structure and other requirements, understanding these characteristics is beneficial to producers who aims to access market outlets (Soe et al., 2015).

Understanding the factors that affect the choice of marketing outlet selection strategies is imperative since the exploitation of such strategies have the potential to increase crop production, investment and farm income (Soe et al., 2015). Muricho et al. (2015) argue that understanding the relationship between the market outlets and the factors that affect the selection of each market outlet is fundamental in profiling the markets, as well as establishing policy interventions that are carefully designed to benefit farmers.

In order to get maximum return, producers can select different marketoutlets. But, different factors affect the selection decision of households. Identifying the factors helps to pinpoint the possible area of interventions that may help bamboo producers to maximize benefits. The motivation to conduct this study was to provide information for intervention that would be important for bamboo producers, traders, government organizations, non-governmental organization and other stakeholders. Therefore, this study aimed to identify the factors that affect the market outlet choices for bamboo culm producers. Therefore, understanding different factors affecting the market outlet choice of bamboo culms helps to design sound policies related to the marketing of agricultural products and the overall contribution of bamboo sector for the development of the nation.

The paper is organized in to the following sections. The first section describes the introduction part which consists background information, problem and research objectives. The second section presents related literature review about the objectives of the study. The third section describes research methodology which consists sampling procedures, method of data collection and analysis and descriptions of variables. The fourth section describes the result and discussion part of the study. Finally, the last section summarizes the main finding of the study and draws conclusion and appropriate recommendations.

2. Literature review

2.1. Empirical literature review on the market channel choices

Asmamaw (2016) used multinomial logistic regression model to analyze the factors affecting the market outlet choices of NTFP north kordofan state. The result of the variable indicated that the quantity of Gum Arabic produced, access to market information, form of payment are the major factors affecting the probability of choosing market outlets. Jari and Fraser (2009) used multinomial logit model to assess the institutional and technical factors influencing agricultural marketing channel choices of green gram producers in Kat River valley South Africa. The model of the result indicated that access to market information, expertise on grades and standards, availability of contractual agreement, availability of good market infrastructure and group participation are positively related to the choice of market outlets.

Betelehem (2017) used logit model to analyze the determinants that affect market outlet choice for eucalyptus woodlots in Wogera district. The result of the model revealed that age of household head, distance from the main road, market information and access to credit are the main factors affecting the choice of market outlets. Similarly, Nasir (2016) used multinomial logit model to analyze the determinants that affect the market outlet choice of coffee producers in Seka Chokorsa district. The results of the model indicated that coffee farming experience, education level of household head, post-harvest value addition, age, livestock unit, access to market information and access to extension service are the major determinants affecting the choice of market channels. Kassa et al. (2017) used multivariate probit (MVP) model to analyze the factors affecting the market outlet choices of honey producers in Chena district. The result of the model revealed that quantity of honey sold, frequency of extension contact, experience, distance to the nearest market, market information; cooperative membership and trust in buyers had significant correlation with market outlet choices.

2.2. Conceptual framework

The theory of random Utility has been widely used to explain the producers’ behavior in the selection of market outlets (Kihoro et al., 2016; Maina et al., 2015; Muthini et al., 2017; Sigei et al., n.d.). MVP model considers the interdependent and simultaneous choice of various market outlets (Arinloye et al., 2015; Degye et al., 2013). Decision on the alternative market outlets is not an easy task for bamboo producers because their decision depends on different factors. The factors that affect the market outlet choice of bamboo culm producers were grouped into demographic, socio economic and institutional factors (Figure 1).

Figure 1. Conceptual framework of the study.

3. Methodology

3.1. Area description

Banja district was selected for this study (Figure 2). It has 25 rural and 1 urban kebeles. From the total kebeles, highland bamboo is produced in 20 kebeles. From North West of Addis Ababa and Bahirdar city, it is around 442 and 116 km, respectively. The total population of the Woreda is estimated at about 315,271; 5% in towns and 95% in rural areas. From the above population, 187,213 are females and 128,058 are males, respectively, and average family size is 7 people per HH (Awi Zone Natural and Agricultural Office (AWZNAO), 2017).

Figure 2. Study area.

3.2. Sampling procedures

In order to select bamboo producers’ two stage sampling procedure was used.Initially, 20 potential kebeles were identified and four kebeles were selected randomly. The sampled kebeles include Kessa, Gashena, Ledeta and Surta. Then, populations were listed out and 114 sample producers were selected randomly. Yamane (1967) is used to determine the sample size. In the selected kebeles, highland bamboo is produced by 1449 farmers. The list of producers was found in the office of Banja district. Based on this formula, 114 sample respondents were selected from each kebele based on the proportional probability to the size of population (Table 1):

(1) $\mathrm{n}=\frac{\mathrm{N}}{1+\mathrm{N}{(\mathrm{e})}^2}$(1)

Table 1. Proportional sample size determination of sampled households

Kebele	Producers	Proportion	Sample size
Kessa	492	0.34	38
Gashena	350	0.24	27
Ledeta	325	0.22	25
Surta	282	0.2	24
Total	1449	1	114

where N = denoted the number of producers in selected kebeles; n = denoted the sample size; e = denoted the level of precision which is 9%.

A number of articles were published in indexed journals, similarly, in this study, 9% level of precision was used to calculate the sample size:

$\mathrm{n}=\frac{\mathrm{n}}{1+\mathrm{n}{(\mathrm{e})}^2}=\frac{1449}{1+1449{(0.09)}^2}=114\mathrm{H}\mathrm{H}\mathrm{S}$

3.3. Method of data collection

Primary data were collected through semi-structured interview schedule from bamboo producers, local traders, wholesalers and processors and secondary data were collected from different published articles and office reports. During data collection respondents were fully informed about the objectives of the study and consent was made with each respondent in the form of verbal agreements. During the household survey, the following issues were included: annual bamboo culms produced, bamboo market supply, price of bamboo culms and amount of income gained from different bamboo products.

3.4. Data analysis

Descriptive statistics was used to analyze the socioeconomic characteristics of bamboo producers and MVP were employed to analyze the determinant factors. Econometrics models such as multinomial probit/logit and MVP models are used for the analysis of categorical choice dependent variables. Multinomial probit/logit model assumes independence across the choices and it does not allow correlation between alternative choices. MVP considers interdependence and correlations among the outlets. It is an extension of probit model and used to estimate several correlated binary dependent variables jointly (Greene, 2003).

The decision whether to select the market outlet or not is considered in the profit maximization (Djalalou et al., 2015). It considered interdependence among the choice of alternative market outlets. Assume $i\mathrm{t}\mathrm{h}$ bamboo producer (i = 1, 2, 3, 4,…,N) facing a problem related to the selection of the alternatives market outlets. Assume $U_K^{\ast }$ denote profit of producer to select wholesalers (Y1), retailers (Y2), processors (Y3) and local traders (Y4). Bamboo producer decides to select Kth$K^{th}$market outlet if $Y_{ik}^{\ast }$ =$U_k^{\ast }-U_0>0.U_k^{\ast }$ represent the utility derived from the selected Kth$K^{th}$ market outlet if selected by ith$i^{th}$farmer and $U_0$ is utility if the market k is not selected. The net benefit ($Y_{ik}^{\ast }$) that producers obtained by selecting a market outlet which is latent variable depends on the observed independent variables (Xi) and error terms ($({\in }_i)$:

(2) $(Y\ast \mathrm{i}\mathrm{k}=X^{,}\mathrm{i}\beta \mathrm{k}+ϵ\mathrm{i}\mathrm{K}=(\mathrm{Y}1,\mathrm{Y}2,\mathrm{Y}3,\mathrm{Y}4),$(2)

Therefore, based on the indicator function, the above equation can be translated in to observed binary outcome equation:

(3) $Y_{(ik=)}\hspace{0.17em}\left\{\begin{array}{c}1if{Y}_{ik}^{\ast }\\ 0Otherwise\end{array}\right.>0(K=Y_1,Y_2,Y_3,Y_4)$(3)

where (Y_i1 = 1, if farmers choose wholesale market, 0 otherwise, Y_i2 = 1, if farmers choose retailers, 0 otherwise, Y_i3 = 1, if farmers choose processors, 0, otherwise, and Y_i4 = 1, if farmers choose local traders, 0 otherwise).

The probabilities that all alternative market outlets selected by a producer can be entered the likelihood function specified as follows:

(4) $\begin{array}{rl}& \mathrm{P}\mathrm{r}[\mathrm{y}1\mathrm{i}=1,\mathrm{y}2\mathrm{i},\mathrm{y}3\mathrm{i},\mathrm{y}4\mathrm{i}]=\hspace{0.17em}\mathrm{\Phi }\hspace{0.17em}({\beta }_1{\mathrm{x}}_{1\mathrm{i}},{\beta }_2{\mathrm{x}}_{2\mathrm{i}},{\beta }_3{\mathrm{x}}_{3\mathrm{i}},{\beta }_4{\mathrm{x}}_{4\mathrm{i}},\rho )\\ & =\mathrm{P}\mathrm{r}\hspace{0.17em}\left(\epsilon 1\mathrm{i}\hspace{0.17em}\le \hspace{0.17em}\beta 1\mathrm{x}1\mathrm{i},\hspace{0.17em}\epsilon 2\mathrm{i}\hspace{0.17em}\le \hspace{0.17em}\beta 2\mathrm{x}2\mathrm{i},\hspace{0.17em}\epsilon 3\mathrm{i}\hspace{0.17em}\le \beta 3\mathrm{x}3\mathrm{i},\hspace{0.17em}\epsilon 4\mathrm{i}\hspace{0.17em}\le \hspace{0.17em}\beta 4\mathrm{x}4\mathrm{i}\right)\end{array}$(4)

In MVP, the selection of combinations of the market outlets is possible and the error terms jointly follow normal distribution with zero mean and normalized to unity and symmetric to the covariance Ω is given as follows:

(5) $\mathrm{\Omega }\hspace{0.17em}=\left[\begin{array}{c}1\rho y1y2\rho y1y3\rho y1y4\\ \rho y2y11\rho y2y3\rho y2y4\\ \rho y3y1\rho y3y21\rho y3y4\\ \rho y4y1\rho y4y2\rho y4y31\end{array}\right]$(5)

The function of log-likelihood associated with the sample outcome is described as follows:

(6) $\mathrm{L}={\sum }_{\mathrm{i}=0}^{\mathrm{n}}\omega ln\phi i({\mu }_i,\mathrm{\Omega })$(6)

where ⍵ is an optional weight for observations i, …, N and Φi with arguments µi and Ω, is standard normal distribution,

where

(7) ${\mu }_i=Ki1\beta 1\mathrm{x}\mathrm{i}1,\mathrm{k}\mathrm{i}2\beta 2\mathrm{x}\mathrm{i}2,\mathrm{k}\mathrm{i}3\beta 3\mathrm{x}\mathrm{i}3,\mathrm{k}\mathrm{i}4\beta 4\mathrm{x}\mathrm{i}4)$(7)

(8) $\mathrm{\Omega }\mathrm{j}\mathrm{k}=\mathrm{\Omega }\mathrm{k}\mathrm{j}=\mathrm{K}\mathrm{i}\mathrm{j}\mathrm{K}\mathrm{i}\mathrm{k}\rho \mathrm{j}\mathrm{k},$(8)

(9) $\mathrm{W}\mathrm{i}\mathrm{t}\mathrm{h}\mathit{\text{Kik}}=2\mathit{\text{yik}}-1,$(9)

For each i and k = 1, …, 4. Matrix Ω constitutes Ω jk where Ω jk= for j≠k = 1.

3.5. Hypothesis and definition variables

3.5.1. Market outlet choices

These are categorical variables which represent the probability of producer’s choice among alternative market channels. The market outlet choices include four alternative channels which are denoted in the model as Y1 for producers who select wholesalers, Y2 who select retailers outlet, Y3 who select processors and Y4 represents households who choose local traders to sell bamboo culms. Each market outlet is a binary indicator which takes one if the producer chooses the given alternative outlet and zero otherwise.

4. Results and discussion

4.1. Characteristics of bamboo culm producers

The age of producers ranged from 26 to 75 years. In the study area, 53.11 year is the mean age of households. It indicated that most of the producers are categorized under the productive age of the population. The family size of respondents ranged from 2 to 9 members. Land is the main production factor which affects the production and supply bamboo culm. In Banja district, producers used their land for bamboo production. The minimum and maximum land size of the producer was 0.125 and 2.75 ha, respectively, and the mean land size was 1.21 ha of land. The average land size allocated for bamboo plantation was 0.15 ha (Table 3). It indicated that the land size allocated to bamboo production is limited in the study area. Table 2 describe and hypothesis the variables used in multivariate probit model.

Table 3. Characteristics of bamboo producers

Variables	Measurement	Minimum	Maximum	Mean	SD
Age	Year	26	75	53.11	10.76
Family size	Man equivalent	2	9	5.33	1.55
Total land size	Hectar	0.125	2.75	1.21	0.49
Land size for bamboo	Hectar	0.013	0.75	0.15	0.10
Bamboo farming experience	Year	3	47	19.34	9.03

Source: Field survey result, 2020.

Table 2. Hypothesis of variables used in multivariate probit model

Variable	Measurement/code	Expected outcomes on the market outlets
		Wholesalers	Retailers	Processor	Local trader
Sex	Dummy (1 male, 0 otherwise)	+ve	-ve	+ve	-ve
Education	Categorical	+ve	-ve	-ve	-ve
Family size	Continuous (man equivalent)	+ve	-ve	-ve	-ve
Price	Continuous (in birr)	+ve	-ve	+ve	-ve
Total land size	Continuous (hectar)	+ve	+ve	+ve	-ve
Experience	Continuous (in year)	+ve	-ve	+ve	-ve
Distance to the market	Continuous (walking hour)	-ve	-ve	-ve	+ve
Extension contact	Continuous (number of contact	-ve	-ve	-ve	-ve
Silviculture management	Dummy (1=Yes, 0 otherwise)	+ve	+ve	+ve	-ve
Number of culm harvested	Continuous (in number)	+ve	+ve	+ve	-ve

4.2. Bamboo marketing outlets

In Banja district, bamboo producers used wholesalers, retailers, processors and local traders to sell bamboo culms. However, the selection of right market outlet is not easy because it is affected by different factors. The choice of alternative market outlet is not mutually exclusive, as producers could sell for more than one market outlet at the same time and the error terms of the market outlets may be correlated with each other (Arinloye et al., 2015). In this section, based on the result of MVP Model significant independent variables were discussed below.

The Wald $\chi$² (48) (102.52, p = 0.000) which indicated that the test is significant at 1%. It means the subsets of coefficients are jointly significant and explanatory variables explained the dependent variables. Therefore, MVP Model is highly significant. The simulated maximum likelihood ratio test (LR$\chi$² (6) = 29.68(prob > $\chi$² = 0.000) which is statistically significant at 1% (Table 4). It revealed the rejection of null hypothesis, i.e. all rho values equal to zero is rejected based on the test result. It also indicated the fitness of the model and decision to select alternative market outlets were interdependent with each other and it supports the use of MVP model.

Table 4. Overall fitness of the model, probabilities and correlation matrix of alternative market outlets

Variable	Wholesalers	Retailers	Processors	Local traders
Marginal probability	16.8	46.2	60.8	54.3
Joint prob (success)	2.5
Joint prob (failure)	2.2
Number of draws	5
Log likelihood	−219.18
Wald $\chi$^2 (48)	102.52***
Prob $\chi$^2	0.000
	ρ1	ρ2	ρ3	ρ4
ρ1	1.00
ρ2	0.0793271	1.00
ρ3	−0.549 ***	−0.174	1.00
ρ4	0.269	−0.388 ***	−0.533 ***	1.00
Log likelihood ratio :	ρ21 = ρ11=	ρ41 = ρ32=	ρ42 = ρ43 = 0:	Chi^2(6) = 29.68*** prob $\chi$^2 =0.000

Note: ***, ** and * indicates the statistical significance at 1%, 5% and 10%, respectively.

The ρ values (ρij) in Table 4 indicated the correlation between each pair of maket outlets. The value of ρ31 (the degree of correlation between processor and wholesaler) is negatively correlated and statistically significant at 1%. The value of ρ42 (degree of correlation between local traders and retailer) is negatively correlated and significant at 1% and the value of ρ43 (correlation between local traders and processor) indicated the existence of negative correlation and which is statistically significant at 1%. These correlations indicate each pair of market outlet competes with each other.

The maximum likelihood estimation indicated the marginal probability of each market outlets. As indicated in Table 4, the likelihood of choosing wholesale market (16.8%) which was small as compared to the likelihood of selecting retailer (46.2%), processor (60.8%) and local trader (54.3%). The joint probability of success and failure was 2.5% and 2.2%, respectively. It indicated that bamboo producers are more likely to select four market outlets jointly (Table 4).

In the above table, three variables affect the wholesale market; two variables affect the retailer market, four variables affect the processor market and two variables significantly affected local traders’ market outlet at 1%, 5% and 10% levels of significance.

Family size was positively and negatively correlated and affected the probability of selecting wholesale market and processor market outlets at the significance level of 1% and 5%, respectively (Table 5). Household heads who have more active labor forces has high probability to sell bamboo culms to wholesale market because it helps the producer to harvest large number of bamboo culm and they can deliver to the wholesale market. This finding is supported by the findings of Takele et al. (2017) who found that active labour force in the household affected the probability of selecting wholesale market outlet positively. It is also consistent with Melkamu (2016) who reported that households who have large family size was positively related to the probability of choosing wholesaler outlet because having large family size helps to supply output to wholesale market rather than selling to local traders. On the other way, households who have more number of family members have less probability to choose processor outlet because they prefer to produce different value added product, in this case the probability of supplying to the processor become decreased.

Table 5. The results of multivariate probit model

Variable	Wholesaler	Retailers	Processors	Local traders
	Coef(se)	Coef(se)	Coef(se)	Coef(se)
Sex	0.726(0.744)	−0.131(0.506)	0.748(0.533)	0.193(0.426)
Read and write	−0.259(.369)	0.342(0.301)	−0.278(0.329)	−0.087(0.288)
Attend primary school	−0.226(.457)	0.607(0.380)	0.247(0.407)	0.134(0.354)
Attend secondary school	−4.314(103.9)	−0.777(0.845)	0.686(0.755)	−0.716(0.692)
Family size	0.328***(0.123)	0.119(0.089)	−0.231**(0.09)	0.115(0.083)
Price	0.030(0.034)	0.032(0.025)	0.027(0.025)	−0.007(0.023)
Total land size	0.737*(0.397)	0.470(0.320)	0.048(0.341)	−0.101(0.292)
Bamboo farming experience	−0.008(0.019)	−0.05***(0.024)	0.063***(0.023)	−0.037***(0.012)
Distance to the market	−0.001(0.004)	0.003(0.003)	−.02***(0.004)	0.009***(0.003)
Extension contact	−0.1003(0.112)	0.015(0.088)	−0.029(0.095)	−0.048(0.081)
Silviculture management	−0.061(0.370)	0.287(0.303)	0.664**(0.326)	−0.417(0.289)
Number of culm harvested Constant	0.001*(0.001) −4.769(1.604)	0.001*(0.001) −1.392(1.167)	.0003(0.0004) 0.326(1.169)	.0004(0.0004) −0.204(1.064)

Source: Field survey result, 2020.

Note: *** indicate the statistical significance of variables at 1%,

**indicates the statistical significance of variables at 5% and

*indicates the statistical significance of variables at 10%.

Total land size was positively correlated with wholesaler and it was significant at 10%. This finding revealed that households who have better land size are more likely to choose wholesaler outlet than households have small area of land because it enables to produce more number of bamboo culm and wholesalers need to buy a number of bamboo culms in bulk form (Tadesse et al., 2019). indicated that land size has positive relationship with wholesale market outlet because when producers produce and supply high amount of products they prefer to sell for wholesalers in bulk amount.

Bamboo farming experience was found to be negatively correlated with retailers and local traders and it was statistically significant at 1%. It indicated that longer experienced bamboo producers have less probability to sell bamboo culms to the retailors market outlets and local traders than less experienced bamboo producers because through time experienced producers make market linkage with processor. This finding is in line with Gizachew (2018) who found that experience affected negatively the probability of selecting retailer market outlet. Again, it is supported by the report of Bezabihe et al. (2015), who indicated farming experience is negatively correlated and it affected the probability of selecting collectors negatively. On the other way, it affected the probability of choosing the processor positively at 1% significance level. It means through time experienced bamboo producers form relationship with bamboo processors. A study conducted by Kiplangat and Kiprop (2015) indicated that pineapple farming experience has positive relation with the probability of selecting urban market.

As hypothesized distance to the market was negatively and positively correlated with processors and local traders, respectively, at 1% significance level, respectively. Households who walked long distances are less likely to sell bamboo culms to processors because bamboo processors found near to towns. This result also supported by Yaregal (2018) who found that long market distance has negative relation with the probability of choosing processor market outlets. On the other way, producers who are distant to the district market are more likely to choose local traders to sell bamboo culms because the local traders collect the bamboo culm at the farm gate, in this case producers did not expend transportation cost. The result is consistent with Mmbando (2014) who revealed that producers far from the market incurred high transaction cost and they opt the brokers to sell the product at production place rather than selling to wholesale market. This finding is also consistent with the finding of Bezabihe et al. (2015) who indicated that distance from the nearest rural market is negatively related to the retail market because they prefer to sell their products for local traders. It is also supported by Berhanu et al. (2013) who confirmed that distance to the nearest market affected the processor market outlet negatively.

Households who practice good silviculture management are more likely to select processor outlet as compared to other bamboo producers cannot practice silviculture management in bamboo farming. It implies that when producers apply good silviculture management activities, they can produce a number of bamboo culms with the required quality. In this case, processors require a bamboo culm which has a required size, type, thickness with the required quality. Therefore, the probability of selling to the processor becomes increased.

Quantity or number of bamboo culm harvested was positively correlated with wholesalers and retailers and significantly affects the wholesalers and retailers at 10% level of significance. The result indicated that households who can produce large number of bamboo culm mostly preferred to select combinations of two or more market outlets that found in the district. It implies that when bamboo producers can produce enough bamboo culms they can sell culms to wholesalers and retailers. It is supported by Tadie et al. (2019), who reported the probability of selecting wholesale and retail markets were positively affected by the quantity of teff production. A study conducted by Yaregal (2018) also indicated that the probability of selecting wholesalers and retailers outlets were affected by the quantity of potato produced.

5. Conclusions and recommendations

The study was aimed at analysing the factors that affect the market outlet choices of bamboo producers. The joint probability of success and failure was 2.5% and 2.2%, respectively. The value of ρ31 (correlation between processor and wholesaler) is negatively and statistically significant at 1% significance level. The value of ρ42 (correlation between local traders and retailer) is negatively correlated and significant at 1% and the value of ρ43 (correlation between local traders and processor is negatively interdependent and significant at 1% significance level). These correlations indicated that each pair of market outlet competes with each other. The result of MVP model indicated that bamboo producers in the area made their market outlet choice based on the bamboo farming experience, area of total land holding size, number of bamboo culm harvested, family size, and distance to the market and silviculture management practice. To increase the benefit of bamboo producers, micro and small enterprise office and development agents of the district should link the processors with bamboo producers. If producers can link with bamboo processors the market margin for processors and producers become increased because unnecessary costs can be eliminated through market linkage. Awareness creation about ways of obtaining market information, ways of selecting appropriate market outlets and the way to get fair price for bamboo products should be provided by development agents and market experts for bamboo producers. The development agents and other concerned bodies should improve the producers’ knowledge and skill through capacity building and creating framers’ organization for collective action to increase the market supply of bamboo and their ability to choose appropriate market outlets to sell bamboo culms.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The authors received no direct funding for this study.

Notes on contributors

Mulat Mengstu

Mulat Mengstu is a Lecturer at Debre Markos University, Ethiopia in the Department of Agribusiness and Value Chain Management since February 2017. He thought different courses, such as micro economics, macroeconomics, agricultural project planning and analysis, farm management, agricultural marketing, business communication and other business courses for Agribusiness and Value Chain Management students and for other across department students. His areas of research include market chain analysis and value chain analysis of agricultural products, commercialization, determinants of market outlet choices and others. He has published articles in reputable journals.