JOURNAL ARTICLE

Intersecting Knowledge With Landscape: Indigenous Agriculture, Sustainable Food Production and Response to Climate Change – A Case Study of Chuktia Bhunjia Tribe of Odisha, India.

  • Published In: Journal of Asian & African Studies (Sage Publications, Ltd.), 2024, v. 59, n. 1. P. 123 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Sabar, Bhubaneswar; Midya, Dipak K. 3 of 3

Abstract

This article documents the traditional agricultural practices of the Chuktia Bhunjia tribe, a Particularly Vulnerable Tribal Group (PVTG) residing in the Sunabeda Wildlife Sanctuary, Odisha, India, focusing on how their Indigenous knowledge, beliefs, rituals, and ecological understanding contribute to sustainable agriculture and livelihood. Their practices include slash-burning and rain-fed wet rice cultivation, intercropping, crop rotation, agroforestry, rainwater harvesting, and organic soil fertility management, all embedded within cultural rituals and climate-adaptive decision-making based on local environmental indicators. The study highlights that these knowledge-based practices promote soil conservation, biodiversity, carbon sequestration, and resilience to climate variability, although restrictions on shifting cultivation and external interventions pose threats to their traditional systems. The authors suggest integrating Chuktia Bhunjia's agricultural knowledge with scientific approaches to support ecological sustainability and food security amid socio-economic and environmental changes.

Additional Information

  • Source:Journal of Asian & African Studies (Sage Publications, Ltd.). 2024/02, Vol. 59, Issue 1, p123
  • Document Type:Article
  • Subject Area:Social Sciences and Humanities
  • Publication Date:2024
  • ISSN:0021-9096
  • DOI:10.1177/00219096221099634
  • Accession Number:174911838
  • 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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