Abstract:
This study focused on identification of perception level and determinant factors that influence some selected
adaptation options to climate change on maize growing smallholder farmers in the South Eastern part of Ethiopia.
Analysis of the study was based on cross sectional data collected through household survey data. Representative
samples of 233 households were interviewed. Descriptive statistics and Multinomial logit equation model were
employed to evaluate the level of perception of households on climate change, to identify types of adaptation
options given priority by the local community, to examine determinant factors that influence the choice of
farmers to employ adaptation options to climate change and to provide suitable policy implications on adaptation
options to climate change. Results show that that about 86% of interviewed farm households perceived climate
change as rise and hot in temperature and changing of the rainfall in quantity and timing. Econometric analysis
result also reveals that; education level, age and gender of the household head, household size, land holding size
and access to information have significant and positive influences on households’ decision on employment of
various adaptation options to climate change. The study also identified the most prioritized adaptation options by
the households which include: soil conservation, off-farm works, fertilizers application, agro-forestry and use of
improved seeds. Based on the findings, policies and strategies that encourage participation of farmers in planning
and application of adaptation options with bottom-up approach is required for better climate change anticipation
instead of focusing on reacting the impacts. This can be achieved through increasing access to credit facilities,
comprising climate change in education policy, access to crop insurance schemes, improving agricultural
extension system in view of climate change, enhancing farmers’ organization for experiences sharing to strength
public adaptation capacity, improving institutional capacity to generate climate information at local level.
