Commercialization of Moringa: Evidence from Southern Ethiopia

Tafesse, Alula; Goshu, Degiye; Gelaw, Fekadu; Ademe, Alelign Article Commercialization of Moringa: Evidence from Southern Ethiopia Cogent Economics & Finance Provided in Cooperation with: Taylor & Francis Group Suggested Citation: Tafesse, Alula; Goshu, Degiye; Gelaw, Fekadu; Ademe, Alelign (2020) : Commercialization of Moringa: Evidence from Southern Ethiopia, Cogent Economics & Finance, ISSN 2332-2039, Taylor & Francis, Abingdon, Vol. 8, Iss. 1, pp. 1-15, https://doi.org/10.1080/23322039.2020.1783909 This Version is available at: https://hdl.handle.net/10419/269934 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. Published online: 16 Jul 2020. Submit your article to this journal Article views: 1553 View related articles View Crossmark data Citing articles: 1 View citing articles GENERAL & APPLIED ECONOMICS | RESEARCH ARTICLE Commercialization of Moringa: Evidence from Southern Ethiopia Alula Tafesse 1 *, Degiye Goshu 2 , Fekadu Gelaw 3 and Alelign Ademe 4 Abstract: The policies in Ethiopia to advance the commercial orientation of farmers need identification of challenges at farmer level and exhaustive actions to shift the farm sector. Further activities have to be done to change the country’s present subsistence-oriented farm production system of different crops. The research has aimed at investigating factors determining the Moringa commercialization in southern Ethiopia. The cross-sectional survey method was used to identify 232 Moringa producing smallholder farmers from Wolaita and Gamo zones. Heckman’s two-step sample selection model is adopted to find factors determining the probability of Moringa market participation and the intensity of participation. The study result revealed that the likelihood of the Moringa output market participation is influenced by the variables such as location, access to irrigation, and distance to market. On the other hand family size, per capita income, frequency of extension contact, access to irrigation, access to credit, and distance to market are among significantly influencing factors of the extent of Moringa marketing. Therefore, policy agents should mainly consider these variables on any development activities to improve Moringa marketing. Furthermore, it requires improving extension services and offering immediate practical training on techniques of market-oriented and value-added Moringa production and marketing systems. Alula Tafesse ABOUT THE AUTHORS Alula Tafesse is a Ph.D. candidate at Haramaya University, School of Agricultural Economics and Agribusiness. He is an Assistant Professor at the Department of Agricultural Economics, College of Agriculture, Wolaita Sodo University. His research interests include themes on natural resource and environmental economics, food and nutrition security, agricultural value chain analysis, impact analysis, agricultural productivity, and marketing. Degiye Goshu (Ph.D.) is an Associate Professor at the Department of Economics, Kotebe Metropolitan University. Fekadu Gelaw (Ph.D.) is Assistant Professor of Agricultural Economics, Institutional and Behavioral Economics, School of Agricultural Economics and Agribusiness, College of Agriculture and Environmental Sciences, Haramaya University. Alelign Ademe (Ph.D.) is Assistant Professor, Department of Agricultural Economics and Management, University of Swaziland. PUBLIC INTEREST STATEMENT It is known that agriculture is the mainstay for the Ethiopian economy. However, the sector is embedded with several constraints. It is perceived that advancing the commercial orientation of smallholder farmers will have higher implications in improving the country’s agricultural sector. The process should consider untapped, highly valuable, and indigenous tree potentials including Moringa. The recent activities to better utilize Moringa from different sides should be appreciated. However, it still requires more attention for the ample of benefits could be obtained from it. The research has observed the determinants of Moringa marketing in southern Ethiopia. It informs policymakers to act on the possible means and interventions which can be employed to improve marketing of the Moringa tree. Tafesse et al., Cogent Economics & Finance (2020), 8: 1783909 https://doi.org/10.1080/23322039.2020.1783909 © 2020 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. Received: 13 January 2020 Accepted: 14 June 2020 *Corresponding author: Alula Tafesse, Department of Agricultural Economics, Wolaita Sodo University, Wolaita Sodo, Ethiopia E-mail: [email protected] Reviewing editor: Goodness Aye, Agricultural Economics, University of Agriculture, makurdi Benue State, Nigeria Additional information is available at the end of the article Page 1 of 15 Subjects: Rural Development; Economics and Development; Economics; Environmental Economics Keywords: Market participation; Heckman two-step sample selection model; Moringa; smallholder farmers; Ethiopia 1. Introduction According to Ebert (2014), the Moringaceae family includes 13 species that fitting into three broad life forms with different geographic origins. Four species fitting to the group of bottle trees with bloated water-storing trunks: Moringa drouhardii (Madagascar), Moringa hildebrandtii (Madagascar), Moringa ovalifolia (Namibia and southwest Angola), and Moringa stenopetala (Kenya and Ethiopia). Another three Moringa species are characterized by slender trees with a tuberous juvenile stage: Moringa concanensis (India), Moringa oleifera (India), and Moringa peregrina (Red Sea, Arabia, and the Horn of Africa). The other six tuberous Moringa species are originated in northeast Africa: Moringa arboreal (northeast Kenya), Moringa borazine (Kenya and Somalia), and Moringa longituba (Kenya, Ethiopia, and Somalia), and Moringa pygmaea (northern Somalia), and Moringa rivae (Kenya and Ethiopia), and Moringa ruspoliana (Kenya, Ethiopia, and Somalia). Most dominantly Moringa stenopetela and Moringa oleifera are becoming suppliers of multiple benefits in the South and the recently introduced other parts of the country (Ethiopia Public Health Institute (EPHI), 2014). Moringa tree, locally well known as Shiferaw, Halako, or Aleko in Ethiopia, is getting great popularity although little is studied to understand its various aspects. As Gonzalez and van der Maden (2015) pointed out in Bangladesh and other developing countries Moringa trees have great potential in terms of nutrition security and income generation, but often seem to be underutilized. In Ethiopia, it is indicated that there is high interest from the government side to increase the commercialization of high valued commodities (Ministery of Finance and Economic Development (MoFED), 2010). Following this interest and natural value of Moringa in the recent period’s various private projects involved in Moringa production (Ethiopia Public Health Institute (EPHI), 2014). Furthermore, Teshome et al. (2013) discussed the increasing marketing of Moringa in various parts of the country. It is also indicated that Moringa is the handled and traded commodity in the local and Addis Ababa markets. Moringa processing can be seen as one of the untapped potentials and highly valuable income-generating activities in developing countries, with its high rate of improvement and increasing local as well as international demand. Moreover, as Ethiopia Public Health Institute (EPHI) (2014) also indicated Moringa has become one of the much growing and traded commodities in different parts of the country for its benefits. The consumption of its leaves both in powder or dried form has been increasing; production is growing and new businesses are flourishing. Private businesses including small and informal businesses are dominating the emerging markets. Some investors are creating value chains for their production. The institute urged the proper development, efficiency, and competitiveness of the sector, as well as the marketing of its products, should assist in the quest for accelerated industrial growth and poverty alleviation in the country. However, Moringa leaf is sold at a very cheap price of about 10–20 Ethiopian Birr (ETB) per kilogram (Kg) in the local market as a cabbage in the research area (personal observation). That is about 0.75 USD in the current country’s exchange rate. The literature indicates that smallholder agricultural commercialization broadly related with institutional factors, infrastructural and market-related factors, household resource endowments, and household-specific characteristics (Abera, 2009; Bekele et al., 2010; Gebremedhin & Jaleta, 2010; Jaleta et al., 2009; Pender & Alemu, 2007). As to the knowledge of authors, there are very limited studies conducted in the analysis of Moringa commercialization in the country as well as in the world. Tafesse et al., Cogent Economics & Finance (2020), 8: 1783909 https://doi.org/10.1080/23322039.2020.1783909 Page 2 of 15 Solving the smallholder farmer’s Moringa commercialization challenges at the district and regional level through a well-designed policy will have a great implication in shifting the subsistent agriculture sector. Particularly the Moringa market in the country requires proper attention. If properly managed, it will have big implications for the national economy. Therefore, it is urged to identify the factors that determine the Moringa commercialization of smallholder farms at the district and regional level to use it as an input in policy designing. This study identified specific factors determining Moringa commercialization in the southern part of Ethiopia where no past empirical evidence addressed the issue in the area. 2. Methodology 2.1. Description of the study area Wolaita and Gamo Gofa are among the main growing zones of Moringa. South Omo, Gamo Gofa, Kaffa, Sheka, Bench Maji, Wolaita, Dawro, Bale, Borena, Sidama, Burji, Amaro, Konso and Darashe are the main Moringa cultivating zones and special districts of Ethiopia (Edwards et al., 2000). Wolaita and Gofa zones are two neighboring zones among more than 13 zones in the Southern Nations Nationalities and Peoples Regional (SNNPR) State of Ethiopia where Moringa is widely produced. Wolaita and Gamo Gofa zones are located in between 350–500 km south of Addis Ababa on the Sodo Gamo Gofa main road. Wolaita zone is subdivided into 12 districts. The zone has a total area of 4512 square kilometers, administratively divided into 12 districts (locally termed woredas). As to the 2007 census conducted by the Central Statistical Agency (CSA) of Ethiopia, the total population of the zone is nearly 2,473,190. Gamo Gofa zone has a total area of 18,010.99 square kilometers, administratively divided into 18 districts (locally termed woredas). Based on the 2007 census conducted by the CSA, the zone has a total population of 1,593,104. Figure 1 below shows the study area map. 2.2. Data sources and collection methods The study used both primary and secondary data. The primary data was collected from sample respondents and key informants using a household questionnaire and key informant discussion, respectively. Market and marketing data collected are such as market prices of Moringa output from different market agents (producer, broker, retailer, and consumer), amount of Moringa sold per household per year in market and pattern of Moringa selling, places, and challenges in selling Figure 1. Map of the study areas. Tafesse et al., Cogent Economics & Finance (2020), 8: 1783909 https://doi.org/10.1080/23322039.2020.1783909 Page 3 of 15 Moringa output. Data also collected on household and community characteristics such as age, income, marital status, sex, livestock and asset holdings, family size, land size, credit access, irrigation access, education, distance from infrastructural and social services, etc. Besides secondary data on Moringa production of zonal and district level, and price of Moringa output from different market agents (producer, broker, retailer, and consumer), etc. in the zones were collected from different sources, such as government institutions, the kebele administrations, trade offices and websites. Published and unpublished documents were also extensively consulted to secure relevant secondary information. 2.3. Sampling procedures First, the required information for the study was mainly obtained from cross-sectional primary data through a structured household questionnaire. The selection of Moringa growing smallholder farmers used a multi-stage sampling technique. That is in the first stage, two random zones selected from the many other Moringa producing zones in the southern region. Secondly, two Moringa growing districts Humbo and Mirab Abaya identified purposely based on their dominance to grow Moringa in Wolaita and Gamo zones, respectively. Thirdly, four Moringa growing kebeles from each district were selected randomly from among other Moringa growing kebeles (the smallest administrative unit in Ethiopia). Abala Faracho, Abala Kolshobo, Buke Dongola, and Abala Longena kebeles from Wolaita zone and Wanke Wajifo, Kola Barana, Yayike, and Delbo kebeles from Gamo zone were selected. In the fourth stage, based on proportional to the total sizes of Moringa growing households in each kebele, respondents were selected from respective kebeles. Finally, simple random sampling with replacement was used to obtain 232 Moring growing respondents in the sampled kebeles. 2.4. Methods of data analysis The Stata (version 14) statistical software package was used for data recording and analysis. The quantitative data collected on producer’s socio-economic, demographic, and community characteristics; the quantity of Moringa produced and marketed were analyzed by using descriptive and inferential statistics such as means, standard deviation, and mean difference (T and chi-square tests). The Heckman two-step sample selection Econometric model was used to examine factors affecting the smallholder Moringa producing household probability to participate in the Moringa market and degree of market participation. The model helps to identify the factors that affect smallholder farmer’s decision to participate in the Moringa market and evaluate the factors that affect the intensity of market participation, level of commercialization (HCI). This model adopted on the basis that it models the market participation decision as a two-step process that involves first the household deciding on whether or not to participate in the Moringa market and then the extent of participation, level of commercialization. The household commercialization index (HCI) of Moringa is implemented to capture the household level of Moringa commercialization, the extent of Moringa market participation. It is computed as the ratio of the gross value of Moringa sold to the gross value of Moringa produced that is taken by expecting to better explain Moringa commercialization than the commonly used gross amount sold. Here, the level of Moringa commercialization of Moringa producers was analyzed from the output side. Precisely, the HCI formula implemented here by following Von Braun et al. (1991) and Von Braun and Kennedy (1994) as expressed in equation (1) below: Household commercialization index HCIið Þ ¼ Gross Value of Moringa Sales by ith Household in year j Gross Value of Moringa Production by ithHousehold in year j �100 (1) Where: HCIi = Commercialization index of i th household in Moringa sales expressed in percentage. Tafesse et al., Cogent Economics & Finance (2020), 8: 1783909 https://doi.org/10.1080/23322039.2020.1783909 Page 4 of 15 The observed outcome of Moringa market participation can be modeled under the framework of a random utility function. Consider the i th Moringa producing household facing a decision on whether or not to market Moringa output. Let C* denote the difference between the benefit the smallholder farm household derives from marketing Moringa (E iA ) and the benefit from nonmarket participation (E iO ). Considering the axiom of rationality and profit maximization, the smallholder farm household will participate in Moringa market if C�¼EiA EiO>0 The net benefit C is unobservable and can be expressed as a function of observed characteristics (Z i ) and error term (ε i ) as follows: C� i¼ZiAβþεi;Ci¼1ifC� i>0andC� i¼0;otherwise (2) Where C is a dummy variable representing Moringa market participation decision; C = 1, if Moringa is marketed and C = 0, if otherwise. Z i is a vector denoting household characteristic, farm-specific, and other institutional or policy variables, β is a vector of parameters to be estimated, and ε i is an error term. It is expected that not all Moringa producing households will participate in the Moringa market. In such a situation, the fundamental econometric problem that is most likely to arise is the sample selection bias. The selection bias has aroused due to the existence of sales from a subset of households who participated in the Moringa markets. This is very necessary for the market participation variable but it is not observed for the sample as a whole. By excluding individuals who are non-market participants means the dependent variable is censored and the residuals may not satisfy the condition that the sum of residuals must be equal to zero (Maddala, 1977; Maddala, 1986). In this study, the problem of sample selection bias was resolved by the use of the Heckman two-step sample selection estimation procedure (Heckman, 1976). Thus, Moringa market participation involves a two-stage process: the first stage has to do with the probability of participating in Moringa marketing using the Probit maximum likelihood function. The second stage takes into consideration the extent (intensity) to which a Moringa farmer participates in Moringa marketing (level of Moringa commercialization) and this is done through Ordinary Least Square (OLS) estimator. Because the later decision largely depends on that taken in the former, likely, the procedure in the second stage is not random thereby creating selectivity bias. This is due to only those who are positively affected by the determinants of market participation will more participate in Moringa marketing. Thus, Heckman’s two-stage sample selection model used to correct for the sample selection bias (Heckman, 1976). The first step of Heckman’s model (selection equation) is given by: F� i¼β0þβ1Xiþεi(3) Where F* is an unobserved latent variable representing household market participation decision, X i is a vector of explanatory variables, β is a vector of parameters to be estimated, and ε i is an error term distributed with mean 0 and variance 1. The observed dummy variable can be expressed as: F¼1if F� i>0ðFor market participantsÞ(4) F¼0if F� i0ðFor non market participantsÞ(5) The substantive equation (the second step) which is usually estimated by an Ordinary Least Square (OLS) estimator is given as: Yi¼α0þα1Ziþμi(6) Tafesse et al., Cogent Economics & Finance (2020), 8: 1783909 https://doi.org/10.1080/23322039.2020.1783909 Page 5 of 15 It should be noted that equation (6) is a sub-sample of equation (2) and is only estimated for Moringa market participants. For the correction for self-selection biases in the substantive equation (8), and Inverse Mills Ratio (IMR) represented by the symbol λ as an extra explanatory variable is added. The computed IMR provides OLS selection corrected estimates (Greene, 2003). The IMR is estimated as the ratio of the ordinate of a standard normal to the tail area of the distribution (Greene, 2003). Declining to add the IMR will reduce the results from equation (6) bias (Heckman, 1976). Adding IMR translates Equation (4) into Equation (5) as: Yi¼α0þαiXiþδiλiþμi(7) Where; δ i is the coefficient of the IMR (λ i ). If lambda (λ) is statistically significant, sample selection bias is a problem and, therefore, Heckman’s two-stage sample selection model is appropriate for the estimation (Marchenko & Genton, 2012). The formulation process of IMR is given by: λi¼φðXiαÞ ϕðXiαÞ(8) Where; φ and ϕ are normal probability density function and cumulative density function, respectively of the standard normal distribution, and ϕ≡ (ω i χ). μ i is a two-sided error term with N(0,σ 2 v ): Equation (8) is obtained by an extrapolation process of Probit equation (2) with the substantive equation defined by OLS equation (6) and then integrate it into the equation defined by equation (7). In general, the model computes the inverse mills ratio from the Probit regression and uses it as a regressor with other explanatory variables to explain the outcome of the dependent variable. Table 1 explains and hypothesizes the relations of the dependent, outcome, and explanatory variables used in the study. 3. Results and discussion 3.1. Statistical summary of moringa market participants and non-participants This part briefly discusses and explains the results of the demographic and socio-economic characteristics of sampled Moringa producing households. The statistical summary provided in Table 2 below shows the proportion of Moringa producers who participated in the Moringa market (131) and non-participated (101). From sampled respondents, 57% participated in the market. The mean level of Moringa commercialization is 19.73% which varies across sample households with the highest 80% and the lowest zero. That is market participants on average sold 19.54% of their yearly Moringa product, leaf. It is indicated that (147) 63.36% of sample households are subsistent, (67) 28.87% in transition, and (18) 7.75% are commercialized farmers. The market participants in average received 391.70 ETB yearly incomes from selling Moringa leaf. About 43% have not participated in the Moringa market. The statistical summary result also shows the difference between market participants and non-participants in selected variables of demographic and socioeconomic characteristics. The age of household head, family size, household head level of education, livestock holding, farm experience, household per capita income, and land size of the selected Moringa producers are not significantly different. Moreover, the frequency of extension contact and access to irrigation there is no significant difference among market participants and non-participants. The numbers of market participants and non-participants living in Wolaita and Gamo zones are also not significantly different. Tafesse et al., Cogent Economics & Finance (2020), 8: 1783909 https://doi.org/10.1080/23322039.2020.1783909 Page 6 of 15 Table 1. Variables and proposed hypotheses Variables Description Expected effect, respectively Dependent and outcome variables Moringa market Participation The household decision to market Moringa (1 = yes, 0 = no) Level of commercialization The ratio of the gross value of Moringa sold to the gross value of Moringa produced Independent variables Age Age of household head (years) + and+ Age square Age of household head squared (years) + and+ Gender Sex of household head(1 = female, 0 = male) - and+ Family size Number of family members (in adult equivalent) - andEducation The education level of household head (Years of formal education) + and+ Livestock (TLU) Livestock holding (TLU) + and+ Per capita Income Per capita income (annual farm and nonfarm income divided into family Size) (ETB) + and+ Land size Total land size household-owned(ha) + and+ Cooperative membership Household membership in cooperatives (1 = yes, 0 otherwise) + Farm experience Number of years the household staid in the farming + Zone Household location (Wolaita zone = 1, 0 = Gamo gofa zone) Distance to the main road The average distance of household to reach the nearest allweather road (km) - andDistance to market The average distance of household to reach the nearest market (km) -andIrrigation Access of household to irrigation (1 = yes, 0 otherwise) + and+ Credit Access Access of household to credit (1 = yes, 0 otherwise) + and+ Extension contact Average Agricultural extension advice received (number of days) + and+ Tafesse et al., Cogent Economics & Finance (2020), 8: 1783909 https://doi.org/10.1080/23322039.2020.1783909 Page 7 of 15 of the EU. 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