Climate change is primarily detrimental to the agriculture sector and the influence of climate change is decreased by using appropriate adaptation strategies. Studies on climate change adaptation recognize the importance of specific area-based research for designing policies to respond to climate change. This study, therefore, was applied at the district level to examine farmers' preference for climate change adaptation strategies and the factors determining their preference. The objective of this study is to identify and model factors that influence farmers' preference of adaptation strategies to counter the impacts of climate change in the case of Dera District, North Shoa, Oromia, Ethiopia. Cross-sectional study design was used with the questionnaire being administered on a multistage sample of 460 households from selected kebeles in the district. Descriptive statistics, multinomial logit, and count regression analysis were used to analyze the collected data. The study revealed that the farmers perceived that temperature had been increasing and rainfall had been decreasing over the last 10 years. The results also indicated that planting trees was the most preferred and frequently applied adaptation strategy to climate change while changing planting dates was the least. The results from the multinomial logit, Poisson regression, and negative binomial analysis showed that age, source of information, household size, education level of household head, distance to output market, distance to input market, agroecological locations of the farm, tropical livestock unit, size of the farm, tenure, grade of the farm, distance of the farm, formal extension service, farmer-to-farmer extension, credit service, rainfall expectation, and temperature expectations were significant factors in determining the adaptation strategies preferred by the farmers.
Belachew, TA; Ababu, DG
Statistical Modeling of Farmers' Preference for Adaptation Strategies for Climate Change: The Case of Dera District, Oromia, Ethiopia
Applied And Environmental Soil Science