Published April 15, 2026 | Version v1
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Technical efficiency analysis of coffee production

  • 1. Development Bank of Ethiopia
  • 2. Wollega University

Description

In the international economy, coffee is one of the most important cash crops. But, because of socioeconomic and technical restrictions, it is defined by poor productivity. Modern technology integration with more efficiency is becoming increasingly vital in resolving this challenge. To fill the gap, this study was conducted to measure the level of technical inefficiency and identify its determinants. We develop one dependent variable and two independent variables. The technical efficiency of coffee production in the study area was measured by considering the output obtained per household head as the dependent variable, as well as two independent variable sets. Those are:
1. The set of variables to measure the elasticity of coffee production in the study area.
2. The set of variables identifies factors that may contribute to the technical inefficiency of coffee producers in the study area. Cross-sectional data were acquired from 285 randomly chosen households as part of the study's basic random sample approach. The Cobb-Douglas production function with a stochastic frontier was used to estimate the coefficients.

The Cobb-Douglas functional form is highly efficient in terms of degrees of freedom and is suitable for interpreting the elasticity of production. Therefore, in this investigation, the Cobb-Douglas functional form was favored. As a result, the output elasticity of the explanatory variable coefficients used in this study to measure the elasticity of coffee production in the research region was greater than 1. This indicates that doubling the inputs more than doubles the output, demonstrating that returns to scale are boosted because it is greater than one.

Notes

Funding provided by: N/A
Crossref Funder Registry ID: 0
Award Number:

Methods

The study used both primary and secondary data as well as quantitative and qualitative data. The primary data was collected using a structured questionnaire. For this study, a structured questionnaire was designed and pre-tested. The feedback from the pretest was used to refine and modify the questionnaire. The process of primary data collection was held by the enumerator, the district's development agents, and the researcher. The enumerators were trained on data collection procedures. In the study, cross-sectional household data from the 2021 main harvest cropping season were used. Data for input (such as land, human labor, fertilizer, coffee plants, and herbicides) was used, and the output of coffee production was collected from the specified period of time. Data on input use and outputs were collected in local units and converted into standard units. In addition, primary data was collected by interviewing the selected coffee producers' farmers and variables that cause variation in production efficiency, like age, education, household size, extension contact, gender, and the like. In addition, socio-economic variables such as demographic data, credit access, livestock holdings, wealth indicators, and institutional data are collected. On the other hand, data related to coffee production trends, input supply, and extension services are gathered to clarify and support the analysis and interpretation of primary data. There was close supervision by the researcher during data collection so that errors, if any, could be corrected at the earliest possible time. Besides primary data, this study used secondary data from governmental and non-governmental institutions, published and unpublished documents, websites, and other relevant sources for analysis and descriptive purposes.

Files

Figure_1_Input_Oriented_measures_for_Technical_Allocative_and_Economic_Efficiency111.tiff

Additional details

Related works

Is derived from
10.5061/dryad.5mkkwh7fp (DOI)