JOURNAL ARTICLE

Farmers' Attitudes Towards Conventional and Organic Farming in Indian Punjab: A Behavioural Analysis.

  • Published In: Journal of Asian & African Studies (Sage Publications, Ltd.), 2024, v. 59, n. 2. P. 512 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Singh, Paramjit; KAUR, JASPREET 3 of 3

Abstract

This article investigates the socioeconomic factors influencing farmers' decisions to adopt organic farming in Punjab, India, comparing 50 organic and 50 conventional farmers through economic and behavioral analyses. The study finds that while organic farming requires more human labor and yields lower wheat output, its net returns are not significantly different from conventional farming, making it an economically viable alternative that supports ecological sustainability. Behavioral analysis using the Theory of Planned Behaviour reveals that education, environmental concerns, and social benefits motivate organic farmers, whereas lack of government support in marketing and technical assistance are major barriers. Probit regression results indicate that younger, more educated farmers with smaller landholdings and diversified cropping patterns are more likely to adopt organic farming, while larger farm size and fewer direct consumer sales reduce this likelihood. The study concludes that policy support, improved market linkages, informational services, and eco-centric education are essential to promote organic farming as a sustainable alternative to conventional agriculture in Punjab.

Additional Information

  • Source:Journal of Asian & African Studies (Sage Publications, Ltd.). 2024/03, Vol. 59, Issue 2, p512
  • Document Type:Article
  • Subject Area:Agriculture and Agribusiness
  • Publication Date:2024
  • ISSN:0021-9096
  • DOI:10.1177/00219096221113582
  • Accession Number:175500838
  • 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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