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      Adoption of climate-smart agricultural practices by smallholder farmers in rural Ghana: An application of the theory of planned behavior

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      PLOS Climate
      Public Library of Science (PLoS)

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          Abstract

          Climate-Smart Agricultural (CSA) practices are crucial in managing climatic shocks faced by smallholder farmers in sub-Saharan Africa. However, evidence on the socio-psychological drivers of farmers’ adoption of CSA practices remains limited. This study employed the Theory of Planned Behavior framework to analyze smallholder farmers’ intention and adoption behavior toward CSA practices in rural Ghana. The study sampled 350 smallholder farmers from the Upper East and North-East Regions of Ghana and employed the Structural Equation Model to understand smallholder farmers’ intention and adoption behavior toward CSA practices. Results showed that farmers’ attitudes (notably their beneficial evaluation of CSA practices) had a significant impact (0.25) on their intention to adopt CSA practices. Social pressure exerted on farmers to use CSA practices (Subjective norm) also had a significant impact (0.52) on farmers’ adoption behavior. Perceived behavior control which measures the controllability and use of CSA practices also had a significant impact on both the intention (0.43) and adoption behavior (0.20) of smallholder farmers. Findings highlight the role socio-psychological factors play in explaining the adoption of CSA practices in rural Ghana. We recommend the need to create awareness of CSA practices by sharing relevant information more widely on CSA practices through community leaders, chief farmers, assembly members, and clan heads in order to exert influence on farmer’s adoption of CSA practices.

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          Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives

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            The theory of planned behavior

            Icek Ajzen (1991)
            Organizational Behavior and Human Decision Processes, 50(2), 179-211
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              Comparative fit indexes in structural models.

              Normed and nonnormed fit indexes are frequently used as adjuncts to chi-square statistics for evaluating the fit of a structural model. A drawback of existing indexes is that they estimate no known population parameters. A new coefficient is proposed to summarize the relative reduction in the noncentrality parameters of two nested models. Two estimators of the coefficient yield new normed (CFI) and nonnormed (FI) fit indexes. CFI avoids the underestimation of fit often noted in small samples for Bentler and Bonett's (1980) normed fit index (NFI). FI is a linear function of Bentler and Bonett's non-normed fit index (NNFI) that avoids the extreme underestimation and overestimation often found in NNFI. Asymptotically, CFI, FI, NFI, and a new index developed by Bollen are equivalent measures of comparative fit, whereas NNFI measures relative fit by comparing noncentrality per degree of freedom. All of the indexes are generalized to permit use of Wald and Lagrange multiplier statistics. An example illustrates the behavior of these indexes under conditions of correct specification and misspecification. The new fit indexes perform very well at all sample sizes.
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                Author and article information

                Contributors
                (View ORCID Profile)
                (View ORCID Profile)
                Journal
                PLOS Climate
                PLOS Clim
                Public Library of Science (PLoS)
                2767-3200
                October 10 2022
                October 10 2022
                : 1
                : 10
                : e0000082
                Article
                10.1371/journal.pclm.0000082
                101de5f2-09ff-4cbf-b779-c4c20dc4b7b9
                © 2022

                http://creativecommons.org/licenses/by/4.0/

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