An econometric analysis of the perception and adaptation response of smallholder farmers to climate change

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University of Pretoria

Abstract

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.

Description

Mini Dissertation (MSc Agric ( Agricultural Economics))--University of Pretoria, 2024.

Keywords

UCTD, Sustainable Development Goals (SDGs), Climate-smart technologies, Binomial logistic regression, Multinomial logistic regression, Preception, Adaptation Strategies, Climate Change

Sustainable Development Goals

SDG-01: No poverty
SDG-02: Zero hunger
SDG-05: Gender equality
SDG-13: Climate action

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