Adoption of Coffee Technologies: A Multivariate Probit Model

The sector of agriculture in Ethiopia is a source of livelihood for over 80% population residing in rural areas. It contributes about 50% to the national value of production. The country has huge potential to increase coffee production as it endowed with suitable elevation, temperature, and soil fertility, indigenous quality plantation materials, and sufficient rainfall in coffee growing belts of the country. Adoption of improved coffee varieties and recommended coffee management practice together have a significant effect on annual coffee production. The study was aimed to see the adoption rate and intensity of coffee technologies which are improved coffee Varity and coffee management practice, determinant of adoption of coffee technologies in Jima zone south western Ethiopia. A total of 196 sampled households from three woreda in the zone and 430 plots of 196 farmers household is considered in the survey. The study develops a multivariate probit model econometric model of farmers' choice of combination of coffee technologies. And two primary results were found. First, adoption rate and intensity of Improved coffee variety is greater than adoption of coffee management practice. Secondly adoption of coffee technologies determined by many institutional, resource and other related factor. This implies that policy makers and other stakeholders promoting a combination of technologies can enhance coffee yield through reducing production costs and decreasing coffee vulnerability to disease.

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Publication Details

Journal
International Journal of Theoretical and Applied Mathematics
Published
2024-08-15
DOI
https://doi.org/10.11648/j.ijtam.20241001.12
Citations
2
Primary Topic
Organic Food and Agriculture
Type
article
Field-Weighted Citation Impact
1.20
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article

Adoption of Coffee Technologies: A Multivariate Probit Model

Megdelawit Temesgen, Sisay Debeb
2 citations
International Journal of Theoretical and Applied Mathematics
Organic Food and Agriculture
1.20
article

Adoption of Coffee Technologies: A Multivariate Probit Model

Megdelawit Temesgen, Sisay Debeb
article en
2 citations

Abstract

The sector of agriculture in Ethiopia is a source of livelihood for over 80% population residing in rural areas. It contributes about 50% to the national value of production. The country has huge potential to increase coffee production as it endowed with suitable elevation, temperature, and soil fertility, indigenous quality plantation materials, and sufficient rainfall in coffee growing belts of the country. Adoption of improved coffee varieties and recommended coffee management practice together have a significant effect on annual coffee production. The study was aimed to see the adoption rate and intensity of coffee technologies which are improved coffee Varity and coffee management practice, determinant of adoption of coffee technologies in Jima zone south western Ethiopia. A total of 196 sampled households from three woreda in the zone and 430 plots of 196 farmers household is considered in the survey. The study develops a multivariate probit model econometric model of farmers' choice of combination of coffee technologies. And two primary results were found. First, adoption rate and intensity of Improved coffee variety is greater than adoption of coffee management practice. Secondly adoption of coffee technologies determined by many institutional, resource and other related factor. This implies that policy makers and other stakeholders promoting a combination of technologies can enhance coffee yield through reducing production costs and decreasing coffee vulnerability to disease.

International Journal of Theoretical and Applied MathematicsVol. 10(1)
Addis Ababa University (ET), Ethiopian Institute of Agricultural Research (ET)
Zero hunger
Openalex Percentile: Top 17%
Organic Food and Agriculture
1.20
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Adoption of Coffee Technologies: A Multivariate Probit Model — Megdelawit Temesgen, Sisay Debeb · International Journal of Theoretical and Applied Mathematics (2024) | TGRS Research Map | TGRS