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      Impact of adoption of climate-smart agriculture on food security in the tropical moist montane ecosystem: The case of Geshy watershed, Southwest Ethiopia

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          Abstract

          The traditional rain-fed agriculture system of Ethiopia is suffering from climate change impacts and extremes. It must be improved to feed the growing population and create a resilient society. Climate-smart agriculture (CSA) is currently promoted as an approach intended to increase sustainable agricultural productivity, enhance household resilience, and reduce greenhouse gas emissions. This study was, therefore, undertaken to examine how food security can be improved by the adoption of multiple climate-smart agriculture (CSA) practices of smallholder farmers in a moist tropical montane ecosystem of Southwest Ethiopia. Data was collected from 384 purposively selected households through cross-sectional study design using a semi-structured questionnaire. Eight Focus group discussions and fifteen key informant interviews were also conducted to check the reliability of the survey data collected. In the study area, a total of eighteen CSA practices, adopted by farmers, were identified. Using principal component analysis, these practices were further grouped into five packages and a multinomial endogenous switching regression model was used to link these packages to the food security status. The findings revealed a great variation in the proportion of households using CSA practices where 92.3 % were using crop management practices whereas 11.2 % were using soil and water conservation practices. The study found that the maximum effect of CSA adoption on food security was by farmers who adopted all the five category CSA technologies. Households that adopted this package were more food secure by 41.2 % in terms of per capita annual food expenditure, 39.8% in terms of Household Food Insecurity Access Scale (HFIAS), and 12.1% in terms of Household Food Consumption Score (HFCS) than the non-adopters. The adoption of this group of practices was further influenced positively by farm size, gender, and productive farm asset values. Using CSA practices in combinations and to a relatively larger extent can potentially solve food security problems. Motivating farmers by providing income-generating activities and discouraging land fragmentation through public education is essential. This in turn improves CSA adoption and initiates production assets investment that can absorb climate change risks.

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          Large datasets are increasingly common and are often difficult to interpret. Principal component analysis (PCA) is a technique for reducing the dimensionality of such datasets, increasing interpretability but at the same time minimizing information loss. It does so by creating new uncorrelated variables that successively maximize variance. Finding such new variables, the principal components, reduces to solving an eigenvalue/eigenvector problem, and the new variables are defined by the dataset at hand, not a priori, hence making PCA an adaptive data analysis technique. It is adaptive in another sense too, since variants of the technique have been developed that are tailored to various different data types and structures. This article will begin by introducing the basic ideas of PCA, discussing what it can and cannot do. It will then describe some variants of PCA and their application.
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              Climate-smart agriculture for food security

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                Author and article information

                Contributors
                Journal
                Heliyon
                Heliyon
                Heliyon
                Elsevier
                2405-8440
                22 November 2023
                December 2023
                22 November 2023
                : 9
                : 12
                : e22620
                Affiliations
                [a ]Addis Ababa University, College of Development Studies, Center for Food Security Studies, Addis Ababa, Ethiopia
                [b ]Addis Ababa University, Resource Governance & Socioeconomic Research Division, Water and Land Resource Center, Addis Ababa, Ethiopia
                [c ]Bonga University, College of Agriculture and Natural Resources, Department of Natural Resources and Management, Bonga, Ethiopia
                Author notes
                []Corresponding author. P.O.Box: 329, Bonga, Ethiopia. girma2721@ 123456gmail.com
                Article
                S2405-8440(23)09828-6 e22620
                10.1016/j.heliyon.2023.e22620
                10724570
                38107277
                72baedd6-6df4-4d39-9dff-77eabfeb9aec
                © 2023 The Authors. Published by Elsevier Ltd.

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 20 October 2022
                : 7 November 2023
                : 15 November 2023
                Categories
                Research Article

                climate-smart agriculture,adoption,food security,smallholder farmers,principal component analysis,multinomial endogenous switching regression model,geshy watershed

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