Value Chain Analysis of Teff in East Wollega, Ethiopia

www.jard.edu.pl http://dx.doi.org/10.17306/J.JARD.2021.01313 Journal of Agribusiness and Rural Development pISSN 1899-5241 1(59) 2021, 17–28 eISSN 1899-5772 Accepted for print: 3.12.2020 VALUE CHAIN ANALYSIS OF TEFF IN EAST WOLLEGA, ETHIOPIA Temesgen Kabeta1, Jema Haji2, Rijalu Negash3 1Gambella University, Ethiopia 2Haramaya University, Ethiopia 3Jimma University, Ethiopia Abstract. This study attempted to analyze the teff value chain INTRODUCTION in the Jimma Arjo District of East Wollega Zone, Western Ethiopia. The multistage sampling technique was employed A value chain is crucial in enforcing standards, with to draw a sample of 123 teff producers, purposively selected each actor ensuring that the product originating from the 55 traders and 15 consumers. Both quantitative and qualita- previous stage meets the criteria. According to Bekabil tive data were collected from primary and secondary sources using pre-tested structured questionnaires and checklists. De- et al. (2011), the teff value chain program helps to dou- scriptive statistics and Kendall’s coefficient of concordance ble teff production. It ensures that farmers have access were applied to analyze data. Results showed that the main to sufficient markets to capture the highest value for teff value chain actors in the study area included input sup- their product. It also increases incomes and reduces the pliers, producers, local collectors, wholesalers, retailers, and price paid by consumers within five years. consumers. In the district, there were no proper upgrading In Ethiopia, land used for teff production during the practices and governance systems in the teff value chain. The 2017 production year was estimated at 3.02 million hec- predicted probability that teff producers choose local collec- tares, and 50.2 million quintals were produced with the tors, wholesalers, retailers, and consumer outlets amounted productivity of 16.64 quintals per hectare of land. In Oro- to 45%, 69.9%, 20.4%, and 74.6%, respectively. Kendall’s coefficient of concordance (W) analysis showed that 68.5% mia Regional State, 441,029.78 hectares of land were al- and 46.2% of farmers agreed with each other on the rank- located for teff production, and 24.74 million quintals of ing of constraints hindering teff production and marketing, teff were produced with productivity of 17.17 quintals respectively. Recommendations drawn from the study find- per hectares of land. In Eastern Wollega Zone, 77,455.03 ings include the need to improve the input supply system and hectares of land were used for teff production, and 1.4 governance, eliminate issues found in the chain, train farm- million quintals of teff were produced with productivity ers, enhance the quality of market information, boost teff pro- of 18.02 quintals per hectare of land (CSA, 2017). ductivity and volume sales, strengthen the links between teff In Jimma Arjo District, there are 11,995 farm house- value chain actors, and improve support institutions. holds, and among those, 7,512 (with 6,783 and 729 male and female household heads, respectively) are teff Keywords: constraints, governance, Jimma Arjo, Kendall’s producers. Land allocated for teff production during the coefficient of concordance, teff value chain, upgrading year (2017) was 4,630 hectares (16.54 percent of total land holdings) from 27,991 hectares of land. In the district, 56,717 quintals of teff were produced during the Rijalu Negash, Department of Agricultural Economics, Agribusiness Management, Jimma University, Ethiopia, P.O. BOX 307, e-mail: [email protected], https://orcid.org/0000-0002-1757-8584 © Copyright by Wydawnictwo Uniwersytetu Przyrodniczego w Poznaniu Kabeta, T., Haji, J., Negash, R. (2021). Value chain analysis of teff in East Wollega, Ethiopia. J. Agribus. Rural Dev., 1(59), 17–28. http://dx.doi.org/10.17306/J.JARD.2021.01313 current production year, and teff productivity was 12.25 taxation, insufficient infrastructure, capital shortage, in- quintals per hectare of land, which was below the na- adequate credit service, farmers’ reluctance to sell teff, tional standard (BoDANR, 2017). In light of the above lack of demand, scarcity of storage facilities and lack of information, this study focused on identifying actors government support. and their respective functions, governance of teff value The study area is known for teff production, mainly chain, identifying upgrading practices within the chain, for market and family consumption. The supply of teff and identifying and prioritizing teff production and mar- in the study area is subjected to a seasonal variation, keting constraints in the study area. with a surplus during harvest being its main feature. Yet, According to the World Bank (2013), because Afri- there is no such study that tries to look into the whole can countries have quickly developing economies, agri- spectrum of the teff value chain in the district, which culture is central to their development plans, and efforts encouraged the researcher to perform the necessary teff have been made to link production with agribusiness for value chain analysis. Since teff is an economically and better growth in agriculture. Nowadays, it earns an aver- socially crucial crop in the study area, this study is de- age of 24 percent of its annual growth from farmers, and signed to address the prevailing information gap on the their crops value chains reveal common and well-known proper understanding and identifying actors involved constraints, such as poor infrastructure; fragmented and in the chain and their respective roles, governance and risky markets; poorly functioning input markets; dif- upgrading practices of smallholder farmers, identifying ficulties accessing land, water, and finance; and inad- and prioritizing constraints on teff production and mar- equate skills and technology. More revealing, however, keting in the study area. are the significant differences across value chains. Efa et al. (2016) showed that teff value chain upgrad- METHODS ing practices employed by teff farmers included using improved seed and differentiating the product by color Description of the study area to meet the consumer demand. Teff traders entirely de- Jimma Arjo is located in East Wollega Zone of the Oro- termine teff price and standard. Teff farmers’ production mia Region, 379 km west of Finfinne/Addis Ababa. It is and marketing constraints included shortages of fertiliz- bordered on the southwest by the Didessa River that er and seed supply, price setting, and insufficient access separates it from the Bunno Beddele Zone, on the north- to credit, whereas those of teff traders included double west by Diga Lake, on the northeast by Guto Wayu, and Fig. 1. GIS map of the study area Source: www.arcgis.com 18 www.jard.edu.pl Kabeta, T., Haji, J., Negash, R. (2021). Value chain analysis of teff in East Wollega, Ethiopia. J. Agribus. Rural Dev., 1(59), 17–28. http://dx.doi.org/10.17306/J.JARD.2021.01313 southeast by Nunu Kumba District. The central part of district. From 20 rural village administrations, only 12 the study area can be described as having rolling and rural villages have teff producers. In the first stage, from undulating topography with a dendrite drainage pattern. 20 rural villages, 12 teff producing villages were select- The elevation of the study area ranges from 1500–2600 ed purposively. In the second stage, from 12 teff produc- m a.s.l. The common physiographic features include ing villages, three villages were chosen randomly. In the mountain ridges, plateaus, and basins. According to third stage, 122 farmers from three sample villages were the agro-climatic classification of Ethiopia, the relief/ randomly selected based on probability proportional to landform of the study area can be grouped into three size (PPS) sampling using the Yemane (1967) formula. major physiographic units based on their elevation. The N lowlands located <1500 m a.s.l., which is suitable for n = (1) 1 + N(e)2 maize, sorghum, sesame, noug, and Daguja production, mid-altitude of 1500–2300 m a.s.l., which is suitable for where: n = sample size, N= number of household heads all types of crops, and highlands located > 2300 m a.s.l., of 12 teff producer kebeles (7512), and e = level of pre- which is ideal for teff, wheat, bean, pea, with 30%, 58%, cision assumed 9%. Sultan (2016) and Addisu (2016) and 12% coverage, respectively. also used this level of precision. Accordingly, the re- The quantitative data were collected from the farm- quired sample size at 91% confidence level with a level ers via individual face-to-face interviews using a pre- of precision equal to 9% was used to obtain a sample tested interview schedule, while the qualitative data size required, representing an actual population. came from the focus group discussion and key informant interviews using checklists. The researcher has con- 2012 n = = 123 ducted the pilot study in administering the addition and 1 + 2012(0.09)2 deletion of the question in the interview schedule to maintain the research validity. Well-trained enumerators collected the data from the respondents. Table 1. Sample distribution of producer kebeles (PPS) Primary data sources included smallholder teff farmers interviewed randomly and purposively selected trad- Selected kebeles Total households Proportion Sample (n) ers and consumers. Secondary data sources included Hindhe 880 0.44 53 district agriculture and rural development offices, primary cooperatives, district trade and industry offices, Tibe caffe 506 0.25 31 data obtained from CSA, published and unpublished Hara 626 0.31 38 materials either from the internet or bulletins. Total 2012 1.00 122 Primary data: Data were collected formally by individual interviews using a pre-tested interview sched- Source: own data computation, 2018. ule, while data from focus group discussion and key informant interviews were collected using checklists. Before distributing the pre-tested interview schedule Data from traders and consumers were also col- among enumerators, the author trained enumerators on lected.

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