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- Title
Factors Influencing the Uptake of Agroforestry Practices among Rural Households: Empirical Evidence from the KwaZulu-Natal Province, South Africa.
- Authors
Zaca, Fortunate Nosisa; Ngidi, Mjabuliseni Simon Cloapas; Chipfupa, Unity; Ojo, Temitope Oluwaseun; Managa, Lavhelesani Rodney
- Abstract
Agroforestry is recognized as a significant element in climate-smart agriculture due to its high potential for addressing food insecurity, climate change challenges, and ecosystem management. However, despite the potential benefits of agroforestry practices, the adoption by rural households in Sub-Saharan Africa is low. Adopting agroforestry practices requires understanding rural households' socio-economic and socio-psychological factors. Hence, this study empirically examined the role of knowledge, attitudes, and perceptions in the uptake of agroforestry practices among rural households to better understand the adoption process. A sample of 305 households was obtained from three communities, namely, Swayimane, Umbumbulu, and Richmond, in KwaZulu-Natal province. Principal component analysis and a binary logistic regression model were employed to analyze the data. Knowledge, attitudes, and perceptions towards agroforestry were found to positively influence the adoption of agroforestry practices. The results also revealed that age, farming experience, education level, and land size were determinants of agroforestry adoption. Therefore, the study recommends that policymakers, extension officers, and climate change champions consider rural households' socio-economic characteristics, knowledge, attitudes, and perceptions when designing agroforestry projects. Implementing training programs with practical demonstration is also recommended to increase awareness of the benefits of agroforestry practices and encourage rural households to protect on-farm trees and shrubs.
- Subjects
KWAZULU-Natal (South Africa); RICHMOND (Va.); SUB-Saharan Africa; AGROFORESTRY; CLIMATE change; HOUSEHOLDS; PRINCIPAL components analysis; ECOSYSTEM management; LOGISTIC regression analysis; RURAL poor
- Publication
Forests (19994907), 2023, Vol 14, Issue 10, p2056
- ISSN
1999-4907
- Publication type
Article
- DOI
10.3390/f14102056