Climate-Smart Agriculture Technologies and Determinants of Farmers’ Adoption Decisions in the Great Rift Valley of Ethiopia
Agriculture is a sector that is very vulnerable to the effects of climate change while contributing to anthropogenic greenhouse gas (GHG) emissions to the atmosphere. Therefore, applying Climate-Smart Agriculture (CSA) technologies and practices (referee hereafter as CSA technologies) that can susta...
| Main Authors: | , , , , |
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| Format: | Journal Article |
| Language: | Inglés |
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MDPI
2023
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/130046 |
| _version_ | 1855518241264238592 |
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| author | Sisay, Theodrose Tesfaye, Kindie Ketema, Mengistu Dechassa, Nigussie Getnet, Mezegebu |
| author_browse | Dechassa, Nigussie Getnet, Mezegebu Ketema, Mengistu Sisay, Theodrose Tesfaye, Kindie |
| author_facet | Sisay, Theodrose Tesfaye, Kindie Ketema, Mengistu Dechassa, Nigussie Getnet, Mezegebu |
| author_sort | Sisay, Theodrose |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Agriculture is a sector that is very vulnerable to the effects of climate change while contributing to anthropogenic greenhouse gas (GHG) emissions to the atmosphere. Therefore, applying Climate-Smart Agriculture (CSA) technologies and practices (referee hereafter as CSA technologies) that can sustainably boost productivity, improve resilience, and lower GHG emissions are crucial for a climate resilient agriculture. This study sought to identify the CSA technologies used by farmers and assess adoption levels and factors that influence them. A cross-sectional survey was carried out gather information from 384 smallholder farmers in the Great Rift Valley (GRV) of Ethiopia. Data were analyzed using percentage, chi-square test, t test, and the multivariate probit model. Results showed that crop diversification, agroforestry, and integrated soil fertility management were the most widely practiced technologies. The results of the chi-square and t tests showed that there are differences and significant and positive connections between adopters and non-adopters based on various attributes. The chi-square and t test results confirmed that households who were older and who had higher incomes, greater credit access, climate information access, better training, better education, larger farms, higher incomes, and more frequent interactions with extension specialists had positive and significant associations with CSA technology adopters. The model result showed that age, sex, and education of the head; farmland size; livestock ownership; income; access to credit; access to climate information; training; and extension contact influenced the adoption of CSA technologies. Therefore, considering barriers to the adoption of CSA technologies in policy and action is anticipated to support smallholder farmers in adapting to climate change while lowering GHG emissions. |
| format | Journal Article |
| id | CGSpace130046 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | MDPI |
| publisherStr | MDPI |
| record_format | dspace |
| spelling | CGSpace1300462025-12-08T10:29:22Z Climate-Smart Agriculture Technologies and Determinants of Farmers’ Adoption Decisions in the Great Rift Valley of Ethiopia Sisay, Theodrose Tesfaye, Kindie Ketema, Mengistu Dechassa, Nigussie Getnet, Mezegebu climate-smart agriculture technology farmers decision making adaptation Agriculture is a sector that is very vulnerable to the effects of climate change while contributing to anthropogenic greenhouse gas (GHG) emissions to the atmosphere. Therefore, applying Climate-Smart Agriculture (CSA) technologies and practices (referee hereafter as CSA technologies) that can sustainably boost productivity, improve resilience, and lower GHG emissions are crucial for a climate resilient agriculture. This study sought to identify the CSA technologies used by farmers and assess adoption levels and factors that influence them. A cross-sectional survey was carried out gather information from 384 smallholder farmers in the Great Rift Valley (GRV) of Ethiopia. Data were analyzed using percentage, chi-square test, t test, and the multivariate probit model. Results showed that crop diversification, agroforestry, and integrated soil fertility management were the most widely practiced technologies. The results of the chi-square and t tests showed that there are differences and significant and positive connections between adopters and non-adopters based on various attributes. The chi-square and t test results confirmed that households who were older and who had higher incomes, greater credit access, climate information access, better training, better education, larger farms, higher incomes, and more frequent interactions with extension specialists had positive and significant associations with CSA technology adopters. The model result showed that age, sex, and education of the head; farmland size; livestock ownership; income; access to credit; access to climate information; training; and extension contact influenced the adoption of CSA technologies. Therefore, considering barriers to the adoption of CSA technologies in policy and action is anticipated to support smallholder farmers in adapting to climate change while lowering GHG emissions. 2023-02-14 2023-04-19T13:47:19Z 2023-04-19T13:47:19Z Journal Article https://hdl.handle.net/10568/130046 en Open Access MDPI Sisay T, Tesfaye K, Ketema M, Dechassa N, Getnet M. 2023. Climate-Smart Agriculture Technologies and Determinants of Farmers’ Adoption Decisions in the Great Rift Valley of Ethiopia. Sustainability 15(4):3471. |
| spellingShingle | climate-smart agriculture technology farmers decision making adaptation Sisay, Theodrose Tesfaye, Kindie Ketema, Mengistu Dechassa, Nigussie Getnet, Mezegebu Climate-Smart Agriculture Technologies and Determinants of Farmers’ Adoption Decisions in the Great Rift Valley of Ethiopia |
| title | Climate-Smart Agriculture Technologies and Determinants of Farmers’ Adoption Decisions in the Great Rift Valley of Ethiopia |
| title_full | Climate-Smart Agriculture Technologies and Determinants of Farmers’ Adoption Decisions in the Great Rift Valley of Ethiopia |
| title_fullStr | Climate-Smart Agriculture Technologies and Determinants of Farmers’ Adoption Decisions in the Great Rift Valley of Ethiopia |
| title_full_unstemmed | Climate-Smart Agriculture Technologies and Determinants of Farmers’ Adoption Decisions in the Great Rift Valley of Ethiopia |
| title_short | Climate-Smart Agriculture Technologies and Determinants of Farmers’ Adoption Decisions in the Great Rift Valley of Ethiopia |
| title_sort | climate smart agriculture technologies and determinants of farmers adoption decisions in the great rift valley of ethiopia |
| topic | climate-smart agriculture technology farmers decision making adaptation |
| url | https://hdl.handle.net/10568/130046 |
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