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An econometric analysis of the perception and adaptation response of smallholder farmers to climate change
Mini Dissertation (MSc Agric ( Agricultural Economics))--University of Pretoria, 2024.
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| Format: | Thesis |
| Language: | English |
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University of Pretoria
2025
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| _version_ | 1867613535393021952 |
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| access_status_str | Open Access |
| author2 | Ntuli, Herbert |
| author_browse | Ntuli, Herbert |
| author_facet | Ntuli, Herbert |
| collection | Thesis |
| dc_rights_str_mv | © 2023 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. |
| description | Mini Dissertation (MSc Agric ( Agricultural Economics))--University of Pretoria, 2024. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/101014 |
| institution | University of Pretoria (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:37:41.590Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | University of Pretoria |
| publisherStr | University of Pretoria |
| record_format | dspace |
| source_str | UPSpace — University of Pretoria Institutional Repository |
| spelling | oai:repository.up.ac.za:2263/101014 An econometric analysis of the perception and adaptation response of smallholder farmers to climate change Ntuli, Herbert u23947358@tuks.co.za Beyene, Daniel Abrha UCTD Sustainable Development Goals (SDGs) Climate-smart technologies Binomial logistic regression Multinomial logistic regression Preception Adaptation Strategies Climate Change Mini Dissertation (MSc Agric ( Agricultural Economics))--University of Pretoria, 2024. The study explores smallholder farmers' perceptions of climate change, adaptation responses, and the drivers that determine their decision-making in adopting climate-smart technologies in the agricultural sector. The research is based on a sample of 314 households from six randomly selected villages in Mendefera and Debarwa sub-zones in Debub-Eritrea. It identified the determinants affecting farmers’ perceptions of climate change and factors influencing their preferences for adaptation practices. A mixed-method approach was used, employing both descriptive statistics and econometric models. To address potential reverse causality bias between household income and adaptation strategies, the study employed non-farm income as an instrument variable for household income. A binary logistic regression was used to identify the determinants of farmers' perceptions of climate change (i.e. rainfall and temperature patterns), while a multinomial logistic regression model was used to examine factors influencing farmers' preferences for adopting climate-smart technologies. The binary logistic regression result indicated that factors such as age, experience, credit access, climate change information, ownership of communication tools, and training influenced farmers' perceptions positively. The multinomial logistic regression result indicated that experience, gender, education, income, family size, credit access, access to extension services, and farmers’ perceptions of rainfall positively influenced farmers' preference for adaptation strategies. However, farmers’ perceptions of temperature negatively influenced farmers' preferences for adaptation strategies. Based on these insights, the study suggests that improving farmers’ access to financial and technological resources and enhancing climate-related training through the digitalisation of extension services are crucial for promoting the adoption of climate-smart agricultural practices. Additionally, strengthening community-based initiatives can further support farmers’ resilience and knowledge-sharing efforts. By bridging scientific research with traditional knowledge, the study advocates for climate-compatible agriculture, incorporating indigenous practices to strengthen community resilience and sustainability. Ministry of Agriculture, Eritrea Agricultural Economics, Extension and Rural Development MSc Agric (Agricultural Economics) Unrestricted Faculty of Natural and Agricultural Sciences SDG-01: No poverty SDG-02: Zero hunger SDG-05: Gender equality SDG-13: Climate action 2025-02-18T11:07:47Z 2025-02-18T11:07:47Z 2025-04 2024-10-28 Mini Dissertation * A2025 http://hdl.handle.net/2263/101014 10.25403/UPresearchdata.27241188 en © 2023 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. application/pdf University of Pretoria |
| spellingShingle | UCTD Sustainable Development Goals (SDGs) Climate-smart technologies Binomial logistic regression Multinomial logistic regression Preception Adaptation Strategies Climate Change An econometric analysis of the perception and adaptation response of smallholder farmers to climate change |
| title | An econometric analysis of the perception and adaptation response of smallholder farmers to climate change |
| title_full | An econometric analysis of the perception and adaptation response of smallholder farmers to climate change |
| title_fullStr | An econometric analysis of the perception and adaptation response of smallholder farmers to climate change |
| title_full_unstemmed | An econometric analysis of the perception and adaptation response of smallholder farmers to climate change |
| title_short | An econometric analysis of the perception and adaptation response of smallholder farmers to climate change |
| title_sort | econometric analysis of the perception and adaptation response of smallholder farmers to climate change |
| topic | UCTD Sustainable Development Goals (SDGs) Climate-smart technologies Binomial logistic regression Multinomial logistic regression Preception Adaptation Strategies Climate Change |
| url | http://hdl.handle.net/2263/101014 |