Culture as a predictor of effective adoption of climate-smart agriculture in Mbeere North, Kenya

Author:

Gikunda RaphaelORCID,Lawver David,Magogo JumaORCID

Abstract

The research advances the existing extension education knowledge by illustrating the relationship between culture and adoption of Climate-Smart Agriculture (CSA). Using a sample of 127, the study adopted a descriptive correlational design to gather data that addressed the hypotheses. The sample was selected randomly through systematic sampling procedures covering all parts of the sub-county. A semi-structured questionnaire was utilized to gather data. Independent samples t-test and multiple regression analysis were applied in data analysis. The results indicated that farmers who received climate-smart information compared to farmers not receiving the information demonstrated significantly higher CSA practices adoption levels. A combination of cultural elements significantly predicted the adoption of climate-smart practices. The moderate effective adoption rates witnessed may have been contributed by limited access to extension services and cultural barriers. Among the cultural elements inability of extension agents to communicate in the local language was found to be the main inhibitor to effective dissemination and subsequent adoption. Hence, extension agents conversant with local language should be recruited to break the communication barrier to improve the diffusion of CSA practices. The county extension agents should be encouraged to use a mix of mass media extension education methods so as to expand the coverage.

Publisher

Advancements in Agricultural Development, Inc.

Reference35 articles.

1. Anuga, S. W., Gordon, C., Boon, E., & Surugu, J. M. (2019). Determinants of climate smart agriculture (CSA) adoption among smallholder food crop farmers in the Techiman Municipality, Ghana. Ghana Journal of Geography, 11(1), 124–139. https://www.ajol.info/index.php/gjg/article/view/186825

2. Asfaw, S., & Lipper, L. (2016). Managing climate risks using climate-smart agriculture. Food and Agriculture Organization. https://www.fao.org/3/i5402e/i5402e.pdf

3. Chimoita, E. L., Onyango, C. M., Kimenju, J. W., & Gweyi-Onyango, J. P. (2017). Agricultural Extension approaches influencing uptake of improved sorghum technologies in Embu County, Kenya. Universal Journal of Agricultural Research, 5(1), 39–45. https://doi.org/10.13189/ujar.2017.050106

4. Cohen, J. (1992). Statistical power analysis. Current Directions in Psychological Science, 1(3), 98–101. https://doi.org/10.1111/1467-8721.ep10768783

5. Davis, J. A. (1971). Elementary survey analysis. Prentice-Hall.

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