Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/303859 
Year of Publication: 
2022
Citation: 
[Journal:] Cogent Economics & Finance [ISSN:] 2332-2039 [Volume:] 10 [Issue:] 1 [Article No.:] 2142313 [Year:] 2022 [Pages:] 1-21
Publisher: 
Taylor & Francis, Abingdon
Abstract: 
Suitable agro ecology and tremendous soybean potential of Ethiopia is the key to produce the crop in large-scale and maintaining a steady supply on the market. Although its demand rises quickly for export and local processing, the current production status is much below the market demand. The factors influencing the supply of soybeans in Northwestern Ethiopia were the main subject of this study. The sample households were chosen using a multistage sampling technique. In this study, data were gathered from 228 respondents that were randomly drawn. The data were obtained mainly from sampled soybean growers via structured interviews with key informant interviews and focus groups for triangulation. Descriptive statistics and multiple linear regression models were employed to analyze the data including one way ANOVA and t-tests. The findings showed that soybean producers' average productivity was 1.21 tons ha-1, much below the national average of 2.15 tons ha-1, due to the sparse usage of improved seed, fertilizer, and other recommended packages. According to the model results, productivity, lagged price, market information, prior experience with soybean farms, cultivated land, weekly extension contacts, education (preparatory school completion), and credit access all had a positive and significant impact on the quantity of soybean market supply. The results showed that soybeans are the most lucrative and important cash crop for producers in the study area. The availability of improved soybean technologies with full recommended packages boosts their productivity and enables them to guarantee sustainable market supply in order to meet the increased market demand.
Subjects: 
market supply
Multiple linear regression model
Pawe district
soybean
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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