Purpose: This study aimed to establish a framework for testing the community capitals theory and assessing the empirical significance of financial, physical, human, and social capital in the adoption of climate-smart agriculture technologies. Materials and Methods: Data was collected through semi-structured questionnaires administered to 256 randomly selected household heads. An ordinal logistic regression model was employed to analyze the significance of community capitals in climate-smart agriculture adoption. The data is presented in tables. Findings: The results revealed several important findings. Access to finance has a significant positive association with Climate-Smart Agriculture adoption (P<0.001, OR=3.23). Input subsidies are also significantly positively associated with Climate-Smart Agriculture adoption (P=0.001, OR=3.66). Training shows a significant positive association with Climate-Smart Agriculture adoption (P<0.007, OR=2.03). Labor has a highly significant and positive relationship with Climate-Smart Agriculture adoption (P=0.001, OR=8.97). Interaction positively and significantly correlates with higher levels of Climate-Smart Agriculture adoption (P=0.021, OR=4.04). Additionally, empowerment demonstrates a significant positive association with Climate-Smart Agriculture adoption (P=0.006, OR=2.96). Notably, the model challenged the conventional view of finance and labor as independent determinants for climate-smart agriculture adoption, instead positioning them within a social context. Implications to Theory, Practice and Policy: The study suggests that climate action programs should prioritize social ties over investments in financial, physical, or human interventions to enhance climate-smart agriculture adoption and promote resilience. Lastly, combining the Community Capital Framework with Social Capital Theory offers a more detailed understanding of the factors influencing Climate-Smart Agriculture adoption, emphasizing the interaction between various types of capital and social dynamics.
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Jilani et al. (2024) studied this question.
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