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 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.

Bibliographic Details

Main Authors:	Sisay, T., Fantaye, K.T., Ketema, M., Nigussie Dechassa, Getnet, M.
Format:	Article biblioteca
Language:	English
Published:	MDPI 2023
Subjects:	AGRICULTURAL SCIENCES AND BIOTECHNOLOGY, Smallholder Farmers, Multivariate Probit Model, CLIMATE CHANGE, CLIMATE-SMART AGRICULTURE, PROBIT ANALYSIS, SMALLHOLDERS, Sustainable Agrifood Systems,
Online Access:	https://hdl.handle.net/10883/22547
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