JOURNAL ARTICLE

Climate Change Adaptation for Food Security and Gendered-Land Rights in Western Kenya.

  • Published In: Journal of Asian & African Studies (Sage Publications, Ltd.), 2024, v. 59, n. 1. P. 3 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Etale, Linda; Simatele, Mulala Danny 3 of 3

Abstract

This article examines the interplay between cultural values, legal frameworks, gender, land rights, and food security in rural Western Kenya, emphasizing that effective community transformation and climate change adaptation require integrating these factors into policy interventions. Using feminism as a methodological and analytical framework, the study employed qualitative methods with a sample of 384 participants, including men and women, to explore how patriarchal customs and statutory laws influence women's access to land—a critical capital for agricultural production and household food security. Despite progressive Kenyan laws promoting gender equality in land ownership, cultural norms and patriarchal practices continue to limit women's land rights, affecting their ability to adapt to climate change and secure food for their families. The authors argue that sustainable development initiatives must engage both men and women, respect local cultural contexts, and promote awareness to reconcile legal provisions with customary practices, thereby fostering equitable land access and enhancing food security.

Additional Information

  • Source:Journal of Asian & African Studies (Sage Publications, Ltd.). 2024/02, Vol. 59, Issue 1, p3
  • Document Type:Article
  • Subject Area:History
  • Publication Date:2024
  • ISSN:0021-9096
  • DOI:10.1177/0021909620988302
  • Accession Number:174911832
  • Copyright Statement:Copyright of Journal of Asian & African Studies (Sage Publications, Ltd.) is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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