| dc.contributor.author | SILESHI ADELLA MULUNEH | |
| dc.contributor.author | Solomon Tekalign (Ph.D) | |
| dc.contributor.author | Solomon Asefaw (Ph.D) | |
| dc.date.accessioned | 2023-05-10T06:27:57Z | |
| dc.date.available | 2023-05-10T06:27:57Z | |
| dc.date.issued | 2022-06 | |
| dc.identifier.uri | http://ir.haramaya.edu.et//hru/handle/123456789/5764 | |
| dc.description | 128 | en_US | 
| dc.description.abstract | The study was conducted in rural kebeles of Dire Dawa focusing on the impact of Climate Smart Agricultural practices on household food security together with the identification of Climate Smart Agricultural Practices being implemented and the factors affecting the practices in the study area. A two-stage sampling technique was employed to select sample from 5 kebeles to get a total of 377 households. A cross-sectional research design was used to collect and analyze data from the households. Both qualitative and quantitative methods were employed. Primary and secondary data were also used. The Qualitative data was analyzed through interpretation and conceptual generalization; and for the quantitative data both descriptive statistics binary logit model and Propensity Score Matching model were employed to analyze the relationship between the dependent and explanatory variables. Descriptive statistics was employed to describe background characteristics of sampled units; to analyze data related to attitude of the farmers towards the occurrence of climate variability and change. It was also used to describe the types of Climate Smart Agricultural practices implemented. The household food security was measured by using the different indicators like Caloric Intake, Household Food Consumption Score and Household Dietary Diversity Score. For the impact assessment, Binary Logit model and Propensity Score Matching approach were used to identify main factors affecting farmers‟ Climate Smart Agricultural practices and the impacts of the Climate Smart Agriculture practices on household food security, respectively. Statistical tests like t-test and chi-squared were also used to test differences in characteristics between practicing and non-practicing households of Climate Smart Agricultural practices. The Propensity Matching Model output confirmed that practicing Climate Smart Agriculture increased household income from crop and livestock production by more than 30% and 32%, respectively. Based on the empirical findings of this study, it is recommended that experienced household heads to share their experiences on the local CSA practices; it is also recommended that government to provide crop and livestock that are High Yielding Varieties and best suited to the climatic conditions; development agents to closely follow up the farming households and provide technical guidance on CSA practices at the farm level; food security programs need to consider the development of irrigation infrastructure to increase the households „access to irrigation water; and it needs to design a revolving fund in the interventions that help to provide agricultural inputs; and finance other income generating businesses of the farming households to diversify livelihood. | en_US | 
| dc.description.sponsorship | Haramaya University, Haramaya | en_US | 
| dc.language.iso | en | en_US | 
| dc.publisher | Haramaya University, Haramaya | en_US | 
| dc.subject | Climate Smart Agriculture, Food Security, Dire Dawa, Propensity Score Matching Binary Logit Model. | en_US | 
| dc.title | PRACTICES OF CLIMATE SMART AGRICULTURE AND IMPACTS ON THE HOUSEHOLD FOOD SECURITY IN RURAL KEBELES OF DIRE DAWA ADMINISTRATION, ETHIOPIA | en_US | 
| dc.type | Thesis | en_US |