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Interdisciplinary Research in Medical Sciences Specialty

2024 Volume 4 Issue 1

Determinants of Green Production Adoption among Chinese Medicinal Herb Farmers: Evidence from a Binary Logistic Analysis


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  1. Department of Systems Biology, Faculty of Medicine, Ain Shams University, Cairo, Egypt.
Abstract

Encouraging farmers to engage in environmentally responsible production is essential for raising rural incomes, improving sectoral performance, and maintaining ecological stability. This research examines what shapes farmers’ green production behavior in Cheifeng, China, using a binary logistic model applied to 860 household-level observations. The analysis shows that only 54.5% of surveyed families practiced green production, indicating a clear gap that requires targeted support. The findings reveal that age, party membership, total earnings, cultivated area, irrigation conditions, market-related aspects, policy guidance, social influences, and the ability to transfer knowledge all play significant roles. Among these, age, party membership, total land area, irrigation conditions, and knowledge-diffusion ability reduce the likelihood of green behavior, while the remaining factors enhance it. To raise adoption levels, the study suggests expanding information channels, establishing training frameworks, improving transparency in quality and safety, creating traceability systems, and offering policy-based incentives. The research further emphasizes the importance of publicity efforts, technical assistance, and media outreach in promoting greener cultivation of Chinese medicinal materials.


How to cite this article
Vancouver
Abdelrahman Y, Salah M, Fathy T. Determinants of Green Production Adoption among Chinese Medicinal Herb Farmers: Evidence from a Binary Logistic Analysis. Interdiscip Res Med Sci Spec. 2024;4(1):154-69. https://doi.org/10.51847/uvt4NeftIW
APA
Abdelrahman, Y., Salah, M., & Fathy, T. (2024). Determinants of Green Production Adoption among Chinese Medicinal Herb Farmers: Evidence from a Binary Logistic Analysis. Interdisciplinary Research in Medical Sciences Specialty, 4(1), 154-169. https://doi.org/10.51847/uvt4NeftIW
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