Value Chain Analysis of Coffee: The Case of Amaro Woreda in Southern Ethiopia

Ethiopian coffee production and its productivity level are unsatisfactory to uphold the country’s comparative advantage. The study intends to examine possible reasons for low productive performance of coffee using cross sectional data gathered from Amaro Woreda and gathered data from 366 households by simple random sampling techniques. The study attempted to analyze different factors that hinder coffee value chains in the study area. Accordingly to the finding various factors that have approximately similar influence on coffee value chains. The study used 13 continuous variables from which all are highly statistically significant at 99% confidence interval and 19 discrete variables those are highly statistically significant at 99% confidence interval under descriptive statistic. The econometric model used for this study is MLR model and its result raveled on coffee value chains factors was also identified. The study included 13 continuous variables in the model from these about 6 variables are highly statistically significant at 95% and 99% confidence interval such as Distance from home of farmers, Average Number of coffee trees, Price of coffee in total Ethiopian Birr, Number of livestock in Tropical livestock Unit, Extension contact and family size have statistically significant that affecting coffee value chains of households. Family size and extension contact are factors negatively affecting coffee value chains while the remaining 4 variables enhance coffee value chains positively. About 4.43% of the variation in coffee value chains is explained by continuous variables in OLS technique but many factors were in discrete variables. There are many post-estimation tests used to check the satisfaction of the basic assumptions of multiple linear regression models. Based on the finding of the study, government and other related stockholders should target at development of rural infrastructure, work diversification cultures, empowering extension contacts for rural farmers, and family plans to improve both production and marketing of Coffee through awareness creation by training, follow up and creating the market linkages for participant in coffee production and marketing in Amaro Woreda.


Background of the Study
Ethiopia is one of the poorest countries in the world in spite of its productive land, labor and natural resource. Agriculture is dominating means Ethiopian economy (CSA, 2010; MoA, 2014). The structural adjustment programme was introduced in 1991 with the aim of economic growth and poverty reduction following this the country has adopted agricultural development led industrialization (ADLI) strategy in 1993, Interim Poverty Reduction Strategy Paper (IPRSP) in 2000, Sustainable Development and Poverty Reduction Program (SDPRP) in 2002. All these strategies and programs were intended to bring about economic growth through Increase in agricultural productivity. These strategies and programs were primary focused up on the poverty reduction (Alemayehu, 2010). Ethiopia is the home and cradle of biodiversity of Arabica coffee seeds which has lead botanists and scientists to agree that Ethiopia is the centre for origin, diversification and dissemination of the coffee plant (Fernie, 1966;Bayetta, 2001).
Two main coffee varieties are traded internationally viz. Arabica (Coffea Arabica) and Robusta (Coffea. canephora var. robusta). Coffee Arabica is more favored by consumers in general than Robusta. Coffee Arabica is native to Ethiopia and it represents around 70% of world coffee production. On the world market, coffee arabica brings the highest prices. Arabica is more climate sensitive than Robusta and needs mild temperatures for optimum quality produce while Robusta is comparatively climate resistant and needs less agronomic attention. Robusta coffee contains about 50 to 60% more caffeine and has a unique taste compared to Arabica. Robusta coffee is mainly used for making instant coffee and for blending purposes (Tiwari and Bisht, 2010).
Coffee is the major agricultural export crop, providing currently 35% of Ethiopia"s foreign exchange earnings, down from 65% a decade ago because of the slump in coffee prices since the mid-1990"s. Coffee cultivation plays a vital role both in cultural and socio-economic life of the nation. About 25% (15 million) of the Ethiopian population depends, directly or indirectly on coffee production, processing and marketing or on value chain activities (Mekuria et al., 2004).
Coffee is the second-largest traded commodity in the world after oil and employs 25 million people in the developing world. Coffee landscapes are very important for the world"s market. Though an increase in market demand for coffee produced under biodiversity-friendly, sustainable production practices. By increasing market demand for certified coffee from all origins, providing market incentives through certification (UNDP, 2011).
Coffee is producing almost all in non-arid countries in the tropics. Over 50 countries produces coffee in significant amounts; in many of these, foreign exchange earnings from coffee exports are of vital importance to the balance of payments of payments and trades. International Coffee Organization (ICO) is the main intergovernmental organization for coffee, bringing together producing and consuming countries to tackle the challenges facing the world coffee sector through international cooperation (ICO, 2009). Coffee growers in developing countries receive a notoriously small share of the export price of green coffee, which often is explained with excessive government  (Baffes, 2003). Producer price shares vary substantially across countries, even when comparing countries with seemingly similar exporting systems (ITF, 2002b).
Ethiopia is Africa"s most important coffee producer until 1992 the Ethiopian Coffee Marketing Corporation (ECMC) fully controlled coffee marketing. Growers were committed to deliver annual quotas at a fixed price. After the switch in the country"s economic policy towards a market-based economy, ECMC was divided into two structures: the Ethiopian Coffee Purchasing Enterprise (ECPME), which purchases coffee and the Ethiopian Coffee Export Enterprise (ECEE), which handles exports. Both compete with the private sector. The reforms facilitated entry of new traders and exporters. Around 75 exporters were active and 240 hold an export license. Private traders account for 75% of exports, compared to only 10% prior to 1992. However, the sector remains somewhat regulated with Coffee Price Differential Setting Committee setting daily minimum export differentials (Krivonos, 2004).
According to Dominic (2011) Ethiopia is the largest coffee producer in Africa: Around 400,000 tons per annum all of it Arabica. However, Ethiopian coffee production and its productivity level are till unsatisfactory to uphold the country"s comparative advantage. Ethiopia and Brazil are the only coffee producing countries that consume a significant portion of their production; around 50% of the production for Ethiopia. Annual coffee export from Ethiopia is around 200,000 tons valued at around US$ 500 million. Coffee is Ethiopia"s number one source of foreign exchange. Ethiopia is one of the few countries where coffee sale is not liberalized (i.e. buyers must purchase through the commodity exchange only cooperatives and large scale growers are exempt but their coffee qualities are still checked by ECX laboratories). Coffee production is mainly in West and South Ethiopia, around 90% based on smallholders. An estimated 1.2 million smallholder farmers are engaged in coffee production. The quality of Coffee arabica from Ethiopia is generally good. Some regions (e.g. Sidama, Yirgacheffee, Amaro and Harar) receive very high prices from coffee value chain activities.
Global coffee consumption continues to grow at a steady pace, particularly in emerging markets. Consumption in 2010 is estimated at 135 million bags, an increase of 2.4% compared to2009. During the last ten years world consumption increased at an average rate of 2.5% per annum. Growth is fastest in emerging markets, such as those in Eastern Europe and Asia, and in the coffee producing countries themselves. In Europe and the United States, consumers are increasingly attentive to quality and origin, and show a growing interest in the economic, social and environmental aspects of coffee production (Lucia, 2012).

Statements of the problem
There are multiple factors that contributed to the current low level of coffee production and productivity with also inefficient marketing systems that impeded future revitalization and promotion of coffee production and marketing. There were many factors that hindered Coffee Value Chain activities (Belachew, 1997). Such as Low Net Incomes from Coffee Production, limited availability and Access to Appropriate Technology, Inefficient Extension Service, low farmer capacity to access and Use of technology but these constraints were not yet analyzed in depth to show the gaps. However, Coffee is one of the most important traded commodities in the world. The sector"s trade structure and performance have large development agenda and poverty implications, due to this reason given high concentration on the production and its marketing by small-holders in poor developing countries is needed. Coffee"s global value chains are quickly transforming because of shifts in demands and an increasing emphasis on product differentiation in importing countries farmers Jema (2008). This study has initiated to identifying different actors with their roles and determinant factors that hinders the, production, productivity, and processing, marketing and consuming coffee in detail. This study can also drop special recommendation based on the findings of the study.

1) To identify actors and their roles in Coffee value chain activities. 2) To analyze determinants that affecting Coffee Value
Chain in the study area.

Descriptions of the Study Area
This study was conducted in Amaro Woreda of Segen Area Peoples Zone, SNNPRS. Amaro is located in the southern part of the country at a distance of 468 kms from Addis Ababa and 207 kms south from regional city of Hawassa. It is bounded by NechSar National Park, Chamo Lake from the North, Guji Zones of Oromiya regional State from the east, Burji Woreda from the South and Konso Woreda from the west. Amaro Woreda comprises three agro-ecological zones namely, Highland (Dega) with altitude ranges from 2301-3601 m.a.s.l, middle altitude area (Weyena dega) its altitude ranges from 1501-2300 m.a.s.l and Lowland (Kola) which ranges from 1000-1500 m.a.s.l with 32%, 38%, and 30% of the area coverage, respectively. Which are very favorable for coffee and other crops production. The altitude of the Amaro Woreda ranges from 1000-3600 meters above sea level (m.a.s.l.) from Dulbe depression to Dello Mountain (AWADO, 2015). The study area is known by its bimodal rainfall distribution. The first small rainfall season is autumn (Belg), occurs from the beginning of March to the end of April and the second main rainfall season is summer (kiremt), occurs from the beginning of July to the end of November in normal years. The average annual rainfall of the study area ranges from 735-1200mms. The rainfall intensity and distribution of the study area in the cropping season was reported as decreasing over time, this causes crop failures and drought finally totally led to low productivity of crop productions. The Woreda is known by its chained mountains from north to south direction and with small stream flow on the sides of the mountains chain. Also the Woreda is known with use of long-time local irrigation from those streams.

Data Sources and Sampling Technique
The Sampling technique for this study was applied three stage sampling techniques. In the first stage, Amaro Woreda was selected purposely because the area was representative for Coffee production based on agro-ecological feature, soil type, farming system etc.; secondly 5 kebeles were selected by stratified sampling technique from 34 kebeles in the Woreda that are listed in the table 1 below based on similarity of their weather conditions and soil types. Finally, 366 respondents were selected by using simple random sampling technique according to Yemane (1967) formula as shown below in equation 1, Primary data were collected from sample households using semi-structured questioners, well prepared and pretested interview schedule that would be administered to the respondents by the trained enumerators and questionnaires and Seconder data were collected from Annual reports, different magazines, books articles, internets etc.
Where; n is the sample size from the population N is the total household heads in study area e is degree of precision at 95% confidence interval (Yemane, 1967). In this study i.e. e= 5%. The distributions of the total sample in sample kebeles were based on the probability of proportional to the number of population of Coffee producers in each kebele.

Descriptive and inferential statistics:
Descriptive and inferential statistics along with econometric models were used to analyze the collected data. Descriptive statistics such as mean, minimum, maximum, standard deviation and percentage were employed to analyze the data on socio-economic and institutional characteristics of the sample households while inferential statistics such as t-test and chi-square or tests were used to undertake statistical tests on different continuous and categorical or discrete variables respectively.

Econometric model specifications a) Analysis of Coffee Value Chain
To analyze Coffee value chain total amount of coffee production or efficiency of its marketing were see through a multiple linear regression model by regressing production against with different explanatory variables. Multiple liner regression was used to analyze factors that affected Coffee value chain activities in the study areas (Gujarati, 2003).
Where:  Y is total amount of coffee production in Kilo gram per Definition of Variables and Working Hypothesis a) Dependent variables  Coffee production: This is measured as a continuous variable taking a value from 0 to different amounts which produced in the production seasons. The status was determined on the basis of the kilo gram. This variable was included as a dependent variable in Multiple Linear Regression model to estimate coefficients of parameters to evaluate its influence on the production.  Participation in Coffee Value Chain Activities: The participation in Coffee Value Chain is measured as dummy variable taking a value of 1 if the household is coffee value chain actor and 0 otherwise.

Coffee Value chain Actors:
Measured as taking values 1 for Growers or producers, 2 for Processors, 3 for Wholesaler(s), 4 for Retailers, 5 for Consumers, 6 for Brokers and 7 for others Actors in Coffee value chain activities in the study area.
Explanatory or independent variables: These variables were highly likelihood with the study or these are determining variables in this study and there were identified and analysed in results and discussion part.

Analysis of Descriptive statistics
Analysis of continuous descriptive statistics

Analysis of continuous data
Cross-section data consists of a sample of individuals, households, firms, cities, states, countries, or a variety of other units, taken at a given point in time. In a pure cross section analysis we would ignore any minor timing differences in collecting the data. An important feature of cross-sectional data is that we can often assume that they have been obtained by random sampling from the underlying population, which simplifies most of the analysis. There are various methods of analyzing cross-sectional data built in Stata. These basic methods with wider applications in economics and agribusiness are analyzed and reported here.  Based on the above findings; a multiple linear regression model analysis was carried out to determine the relationship between each variable associated with coffee value chain activities. The analysis result reveals that the variables had the most statistically significant relationship with the coffee value chain activities in the household level (endogenous factors). It does not necessarily mean that they did not have influence to community at national level, but it mean that probably of the majority features were lied on farmers in study area mainly take into account endogenous factors" characteristics to make coffee production and marketing decisions. This result may be related to one of two theories for determining the factors affecting individual"s coffee production and marketing decisions. "Subsistence theory", which states that farmers grow crops to satisfy principally their own consumption requirements, and therefore land allocation, is mainly associated with endogenous factors" characteristics.

Distance of Farm from Market of the sample household heads (DFH):
The distance of farm to the market of the sample household heads was statistically significant at less than 5% probability level and had positive relationship with either coffee value chain activities. A Km increases in the distance to the market increases the coffee production by 16.7 quintals. Thus, due to the long distance of coffee value chain activities encourages the participants to earn the highest price and profit margin due to high of coffee in cities and towns in the study area.

Age of coffee trees (ACT):
The average age of coffee tree was statistically significant at less than 5% probability level and had positive relationship with either coffee value chain activities. A year increases in the growth age of coffee tree it increases the coffee production by 7.6 quintals. Thus, due to the age coffee tree, the coffee value chain activities encourage the participants to earn the highest yield but its marginal production or productivity would be decreased in the study area. Biologically and practically after certain age the productivity will be decreases and the production will be stopped.

Price of Coffee in total Ethiopian Birr per Kg (PCIETEB):
The average price of coffee in the study area was statistically significant at less than 5% probability level and had positive relationship with Coffee Value Chain activities. One ETB increases in the coffee market prices, increases the coffee production by 25.12 quintals. Thus, since the prices increased, the producers of coffee and coffee marketing participants are interested to engage in coffee marketing and production by applying different newly introducing technologies to enhance the productivity of coffee in the study area.

Numbers of Livestock in Tropical Livestock unit (TLU):
The number of Livestock in the study area was highly statistically significant at less than 1% probability level and had negative relationship with Coffee Value Chain activities. Increases the number of Livestock by one TLU results the decreases in coffee production by 4.4 quintals and affect Coffee Value Chain activities. Because, the attention of the actors would be divided in to Livestock management and the care off Livestock is not a simple task for farmers and other actors. Therefore, participating in Livestock production is limited to engage fulltime in coffee Value chain in the study area.

Extension Contact (EXC):
The Development agents advise in Coffee Value Chain activities were highly statistically significant at 1% probability level and had positive relationship with the coffee production and marketing activities or Coffee Value chain activities. As a development agents decrease the contacting with farmers for agricultural activities consultancy, it decreases the coffee production by 7 quintals. This has a direct relationship with coffee Value chain activities.

Family size of the sample households (FAMSIZE):
The family sizes which attend in Coffee Value Chain activities were statistically significant at less than 5% probability level and had negative relationship with in either the coffee production or marketing. As a 1 person increase in the

Conclusions
The study was conducted in Amaro woreda of SNNPRS, Coffee is very important cash crop and it needs particular attention. Coffee is an export crops among African countries. However, Ethiopian has not yet exploited its comparative advantage in coffee production and marketing.