English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

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. Keywords: Amaro, Coffee, production, Value Chain and MLR model. DOI : 10.7176/DCS/9-11-04 Publication date: November 30 th 2019

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