Assessment of Determinants of Climate Smart Agriculture Practice Adoption in Gozamin District, North West Ethiopia
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Date
2021-09
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Addis Ababa University
Abstract
Climate change impacts on production are expected to translate into economic impacts at
various scales. At farm level, climate change will cause reduced income for households which
will limit the capacity to acquire assets. However, the existing empirical literature shows
different factors contribute for the adoption of climate smart agriculture practice. The purpose of
this study was to identify the factors of assessment of determinants of climate smart agriculture
practice adoption in Gozamin district, North West Ethiopia. This study used a cross sectional
survey research design. Structured interview questionnaire with 323 sample respondents were
held to collect primary data from the sample respondents, who were selected using probability
sampling technique supplemented by key informant interview. Descriptive statistics and binary
logistic regression model were used to identify demographic, socio-economic and extension
service determinants that determine the assessment of climate smart agricultural practices
adoption in the study area. The result show that 43.34% of the household heads adopted climate
smart agriculture. The resulting distribution show that, climate smart agriculture practices: crop
rotation (25.53%), inter-cropping (20.57%), soil and water conservation (16.31%), organic
fertilizer (15.6%), agroforestry (8.51%), mulching (4.96%), improved grazing (4.26%),
improved seed (2.13%) were adopted by the respondents in the study area. The study found that,
variables such as sex, educational status, access to extension service, credit and training are
significantly and positively affects the assessment of climate smart agriculture practice adoption
in the study area. Whereas, land size significantly and negatively affects the adoption of climate
smart agriculture practice in the study area. Thus, in the process of adoption of climate smart
agriculture, these variables should be considered by the agriculture sector decision makers,
donor agencies at different level and individual farm household heads
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Keywords
Adoption, Climate Smart Agriculture, Gozamin, Logit Regression Model