Climate-Smart Agriculture Technologies and Determinants of Farmers’ Adoption Decisions in the Great Rift Valley of Ethiopia

Theodrose Sisay (), Kindie Tesfaye, Mengistu Ketema, Nigussie Dechassa and Mezegebu Getnet
Additional contact information
Theodrose Sisay: Africa Centre of Excellence for Climate Smart Agriculture and Biodiversity Conservation, Haramaya University, Dire Dawa P.O. Box 138, Ethiopia
Kindie Tesfaye: International Maize and Wheat Improvement Center (CIMMYT), Addis Ababa P.O. Box 5689, Ethiopia
Mengistu Ketema: School of Agricultureand Agribusiness, Haramaya University, Dire Dawa P.O. Box 138, Ethiopia
Nigussie Dechassa: School of Plant Science, College of Agriculture and Environmental Sciences, Haramaya University, Dire Dawa P.O. Box 138, Ethiopia
Mezegebu Getnet: Ethiopian Institute of Agricultural Research (EIAR), Addis Ababa P.O. Box 2003, Ethiopia
Authors registered in the RePEc Author Service: Mengistu Ketema Aredo

Sustainability, 2023, vol. 15, issue 4, 1-12

Abstract: 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.

Keywords: climate change; climate-smart agriculture; smallholder farmers; multivariate probit model (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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