A synthesis of factors influencing sustainable agriculture practices adoption among rural farmers: A scoping review

Rural regions are significant food producers, yet there are concerns about adoption of sustainable agriculture practices among rural farmers. Empirical studies show that a wide range of factors influence adoption of sustainable practices. We reviewed 83 studies on this subject to analyze which climatic, economic, socio-cultural, and environmental factors significantly influence adoption of sustainable practices. Our analysis found that most of the selected studies (82 %) considered socio-cultural and economic characteristics of farmers but neglected climatic and environmental factors. The influence of these factors on farmers' adoption likelihood varied by type of sustainable practice and study context. The results indicated that drought events, rising temperatures, higher income and wealth, perceived environmental benefits, presence of disease, poor soil fertility, higher education, access to extension services, strong social networks, and positive psychological attitudes showed significant positive influence on farmers' uptake of most studied sustainable practices. In contrast, limited access to credit and markets consistently served as barriers to adoption. Characteristics such as gender and household size showed correlations to adoption of some studied practices, although these results were more mixed and affected by study context. While our findings are similar to the results of previous reviews, our specific focus on rural areas across the globe suggests that the positive effects of gender, education, and adequate access to credit on adoption may be stronger in more rural or less developed regions. Our analysis further indicates that most adoption studies’ models assumed a discrete binary approach to assessing adoption decisions, which may exclude the ability to capture discontinuation or re-adoption among farmers. Therefore, adoption literature would benefit from alternative approaches such as multivariate path-dependent analysis, which is particularly important for modeling factors with interactive relationships.