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Determinants of Multiple Agricultural Technology Adoption: Evidence from Rural Amhara Region, Ethiopia

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The adoption of agricultural technology is an important path for raising agricultural productivity, and thereby for reducing food insecurity and poverty. Despite the efforts to promote adoption in most of the rural areas of Ethiopian farmers, the adoption rate has always been very low. So, it is essential to understand the barriers to adoption. As a result, this study examined the determinants of adoption of multiple agricultural technologies in rural Amhara region of Ethiopia. The study is based on Ethiopian socio-economic survey of 2015/16. A sample of 656 farm households was considered. The paper used multinomial logit model to assess the factors affecting adoption. The result shows that farmers with more educational level, family size, off-farm participation, livestock, extension contact, credit access, advisory service, and farmers closer to plot, all-weather road, zonal town, and farmers with lower remittance income are more likely to adopt new or improved agricultural technology. Therefore, the study recommends the need of policies and interventions on adoption of agricultural technology should pay attention and move along with those variables significantly influencing adoption of agricultural technology.
Title: Determinants of Multiple Agricultural Technology Adoption: Evidence from Rural Amhara Region, Ethiopia
Description:
The adoption of agricultural technology is an important path for raising agricultural productivity, and thereby for reducing food insecurity and poverty.
Despite the efforts to promote adoption in most of the rural areas of Ethiopian farmers, the adoption rate has always been very low.
So, it is essential to understand the barriers to adoption.
As a result, this study examined the determinants of adoption of multiple agricultural technologies in rural Amhara region of Ethiopia.
The study is based on Ethiopian socio-economic survey of 2015/16.
A sample of 656 farm households was considered.
The paper used multinomial logit model to assess the factors affecting adoption.
The result shows that farmers with more educational level, family size, off-farm participation, livestock, extension contact, credit access, advisory service, and farmers closer to plot, all-weather road, zonal town, and farmers with lower remittance income are more likely to adopt new or improved agricultural technology.
Therefore, the study recommends the need of policies and interventions on adoption of agricultural technology should pay attention and move along with those variables significantly influencing adoption of agricultural technology.

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