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      Access to climate information services and climate-smart agriculture in Kenya: a gender-based analysis

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          Abstract

          Climate change is a significant threat to agriculture-related livelihoods, and its impacts amplify prevailing gender inequalities. Climate information services (CIS) are crucial enablers in adapting to climate change and managing climate-related risks by smallholder farmers. Even though various gender groups have distinct preferences, understandings, and uses of CIS, which affect adaptation decisions differently, there is little research on gender perspectives of CIS. This study employs a novel intra-household survey of 156 married couples to evaluate the gender-differentiated effects of CIS access on the adoption of climate-smart agriculture (CSA) technologies in Kenya. The findings reveal gender differences in access to CIS, with husbands having significantly more access to early warning systems and advisory services on adaptation. In contrast, wives had better access to weather forecasts. About 38% of wives perceived that CIS meets their needs, compared to 30% of husbands. As for CIS dissemination pathways, husbands preferred extension officers, print media, television, and local leaders, whereas wives preferred radio and social groups. Recursive bivariate probit analysis shows that trust in CIS, a bundle of CIS dissemination pathways, access to credit, and membership in a mixed-gender social group, affected access to CIS for both genders. Access to early warning systems and advisory services positively affected decisions to adopt CSA by both genders. Still, access to seasonal forecasts influenced husbands’ decisions to adopt CSA but not wives. Besides, there were gender differences in how CIS affected each CSA technology based on gendered access to resources and roles and responsibilities in a household. It is necessary to disseminate CIS through gender-sensitive channels that can satisfy the needs and preferences of different gender groups to encourage the adoption of climate-smart technologies.

          Supplementary Information

          The online version contains supplementary material available at 10.1007/s10584-022-03445-5.

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          Interrater reliability: the kappa statistic

          The kappa statistic is frequently used to test interrater reliability. The importance of rater reliability lies in the fact that it represents the extent to which the data collected in the study are correct representations of the variables measured. Measurement of the extent to which data collectors (raters) assign the same score to the same variable is called interrater reliability. While there have been a variety of methods to measure interrater reliability, traditionally it was measured as percent agreement, calculated as the number of agreement scores divided by the total number of scores. In 1960, Jacob Cohen critiqued use of percent agreement due to its inability to account for chance agreement. He introduced the Cohen’s kappa, developed to account for the possibility that raters actually guess on at least some variables due to uncertainty. Like most correlation statistics, the kappa can range from −1 to +1. While the kappa is one of the most commonly used statistics to test interrater reliability, it has limitations. Judgments about what level of kappa should be acceptable for health research are questioned. Cohen’s suggested interpretation may be too lenient for health related studies because it implies that a score as low as 0.41 might be acceptable. Kappa and percent agreement are compared, and levels for both kappa and percent agreement that should be demanded in healthcare studies are suggested.
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            Understanding interobserver agreement: the kappa statistic.

            Items such as physical exam findings, radiographic interpretations, or other diagnostic tests often rely on some degree of subjective interpretation by observers. Studies that measure the agreement between two or more observers should include a statistic that takes into account the fact that observers will sometimes agree or disagree simply by chance. The kappa statistic (or kappa coefficient) is the most commonly used statistic for this purpose. A kappa of 1 indicates perfect agreement, whereas a kappa of 0 indicates agreement equivalent to chance. A limitation of kappa is that it is affected by the prevalence of the finding under observation. Methods to overcome this limitation have been described.
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              Nonlinear principal components analysis with CATPCA: a tutorial.

              This article is set up as a tutorial for nonlinear principal components analysis (NLPCA), systematically guiding the reader through the process of analyzing actual data on personality assessment by the Rorschach Inkblot Test. NLPCA is a more flexible alternative to linear PCA that can handle the analysis of possibly nonlinearly related variables with different types of measurement level. The method is particularly suited to analyze nominal (qualitative) and ordinal (e.g., Likert-type) data, possibly combined with numeric data. The program CATPCA from the Categories module in SPSS is used in the analyses, but the method description can easily be generalized to other software packages.
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                Author and article information

                Contributors
                m_ngigi@mksu.ac.ke
                Journal
                Clim Change
                Clim Change
                Climatic Change
                Springer Netherlands (Dordrecht )
                0165-0009
                1573-1480
                12 October 2022
                2022
                : 174
                : 3-4
                : 21
                Affiliations
                GRID grid.493101.e, ISNI 0000 0004 4660 9348, Department of Agricultural Sciences, , Machakos University, ; P.O. Box 136-90100, Machakos, Kenya
                Author information
                http://orcid.org/0000-0003-3479-7938
                http://orcid.org/0000-0001-8137-5151
                Article
                3445
                10.1007/s10584-022-03445-5
                9554386
                36247717
                53dcd3f9-95ce-4051-a274-bc535cc1618f
                © The Author(s), under exclusive licence to Springer Nature B.V. 2022, Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

                This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.

                History
                : 25 March 2022
                : 2 October 2022
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100006456, Bundesministerium für Wirtschaftliche Zusammenarbeit und Entwicklung;
                Categories
                Article
                Custom metadata
                © Springer Nature B.V. 2022

                climate information services,climate-smart agriculture,adaptation,gender,intra-household,recursive bivariate probit

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